Reading additional input from stdin... OpenAI Codex v0.124.0 (research preview) -------- workdir: /opt/MM_D-ND model: gpt-5.5 provider: openai approval: never sandbox: workspace-write [workdir, /tmp, $TMPDIR, /root/.codex-lab/memories] reasoning effort: none reasoning summaries: none session id: 019e2d20-e436-77b0-b2d8-e8f70427241a -------- user Sei l'AI-Lab D-ND. Leggi tools/data/agent_field_live.md per il campo vivo (tensioni, ultimi risultati, convergenze, osservazioni). Leggi tools/LAB_AGENT_CONTEXT.md per modello, regole, errori da evitare. Esegui un esperimento e scrivi il report in tools/data/reports/agent_20260515_1933.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo il campo vivo e il contesto operativo prima di scegliere l’esperimento. Verifico anche lo stato dei report e degli strumenti disponibili, così il report finale resta ancorato a dati riproducibili. exec /bin/bash -lc "sed -n '1,240p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: Leggi tools/LAB_AGENT_CONTEXT.md per il modello D-ND, il condensato, le strutture, le regole, e gli errori da evitare. ## Orizzonte (telos del lab) L'intento non è solo unificare la fisica. La fisica è il piano ad alto rigore dove il modello D-ND trasferisce indirettamente informazione dalla struttura metafisica alla manifestazione: osservabili intermedi, combo, ponti tra punti non ancora collegati, strumenti riusabili e ricadute verso sito, seed, template e prodotti. Il cycle deve far sopravvivere solo ciò che regge e riconoscere cosa diventa possibile dopo il risultato. Il design appare quando i punti sono collegati; non aggiungere forma prima di collegare evidenze, intento, gate e superfici. ## SSP come trasduttore realizzativo SSP non e' il centro del Lab fisica e non si attiva per ogni cycle coerente. Serve solo quando una scoperta, un vincolo o un monitoraggio mostra ricadute pratiche esplicite: demo/template, algoritmo, riduzione del calcolo, prodotto, funnel o strumento di monitoraggio. Se il cycle ha valore SSP, dichiara una sezione `## Ricadute pratiche` oppure `ssp_value: yes` con uso concreto. Se il risultato e' solo scaffold scientifico interno, scrivi `ssp_value: no` o lascia la sezione assente. ## Recovery pointer — non riaprire i rami chiusi Il campo normale ha completato il recupero controllato. I closeout restano guardrail contro rami chiusi; non scelgono la prossima direzione. L'autorita' attiva del cycle e' `seme.json.direzione`. - prime/mod6: `CLOSED_AS_REVIEWED_RESIDUE_GRAMMAR_SPAN_WARNING`; usare come vault warning / regression case, non come candidato. - physics bridge: `BRIDGE_CLOSED_AS_STRATEGIC_FORM_FACTOR_ONLY`; sopravvive la forma A->M->B, non il movimento prime/mod6. - clean handoff: `SAFE_FIELD_PREVIEW_READY`, active_blocked_refs=[]. - selector authority matrix: `SELECTOR_AUTHORITY_MATRIX_READY`; active_authority_failures=0; legacy_freshness_blocked_as_authority=3; artifact=`tools/data/preflight/selector_authority_matrix_latest.json`. Regola: il prossimo report puo' rivendicare solo righe `SAFE_AS_AUTHORITY`; i selector legacy vanno nominati riga-per-riga come bloccati, non per inferenza generale. - recovery source-selection: COMPLETATA. Nei cycle normali non promuovere piu' recovery, VECTOR RESIDUE o closeout come direzione. Segui solo `seme.json.direzione`; usa la matrice selector come guardrail di autorita'. - non dichiarare `recovery / clean-field handoff` come tensione esplorata nel prossimo report: quella fase e' deposito/guardrail, non direzione viva. ## Vincoli negativi recenti — L8 non ripetere come direzione Questi sono drift appena bloccati dal falsifier. Sono memoria di bordo, non consecutio. Il prossimo report deve seguire `seme.json.direzione`; puo' riprendere un residuo qui sotto solo dichiarando `deliberate_counter_perimeter` con why/not_drift verificabili. - Direzione viva ora: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - Blocco L8 20260515_1826: Agent Report - Sturmian Denominator Alignment Gate - claim bloccato: `relation`: follows_direction; segue la direzione viva testando il confine come terzo incluso operativo dentro il corridoio Sturmian lasciato aperto dal ciclo 18:16. - evidenza: `seme.json.direzione` viva è: "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo". Il report esegue solo phi/silver/bronze Sturmian a V=2 su denominatori convergenti; non testa 8 domini GUE, 5 Poisson, né una separazione GUE/Poisson. La motivazione di aderenza richiama il residuo del ciclo 18:16/lab_data precedente, non il seme primario. - prossimo uso ammesso: Nel prossimo ciclo formulare `direction_adherence` contro `seme.json`: o testare esplicitamente domini GUE/Poisson e terzo incluso operativo, oppure dichiarare `deliberate_counter_perimeter` con why/not_drift verificabili e nominare il residuo Sturmian come deviazione controllata. Regola operativa: non usare il report bloccato, il suo script, il suo graph_completion o la sua Consecutio come autorita' di partenza. ## Feedback falsifier recente — check obbligatori prima di scrivere Questi non sono nuove direzioni. Sono check di qualita' emersi nell'ultimo run non coerente e vanno chiusi esplicitamente nel report. - Run non coerente: 20260515_1915 - L3: observable_contract: una riga boundary a due lettori e' operativa solo se lo stato graph bridge sopravvive a perturbazioni del lettore e resta auditato dal baseline classico; Results: lab residue after stability = true con stable graph-only bridges = 3 Check richiesto: Nel prossimo ciclo separare esplicitamente `two_reader_boundary_confirmed = 1` da `graph_only_residue = 3`; se le tre righe graph-only sono claim Lab-specific, dichiarare che il nodo regressivo corregge il contratto da `due lettori` a `frequenza grafica + audit dichiarato`, non a `audit positivo`. - L5: Cosa resta Lab-specific: `percolation`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699` sono `stable_graph_bridge+graph_only_bridge`, tutte 27/27. Il baseline classico le legge endpoint-like, il grafo le legge confine stabile. Check richiesto: Aggiungere un audit di re-discovery per la parte grafica: stabilita' kNN, hub/bridge persistence, silhouette/cluster boundary stability o percolation-on-graph; poi distinguere cosa e' crossover spettrale noto e cosa e' artefatto/classificazione grafica. Obblighi pratici: se il dominio e' GUE/Poisson, aggiungi una sezione `## Re-discovery audit` con il baseline noto piu' vicino (Brody/Berry-Robnik/Rosenzweig-Porter, mobility/localization crossover o altro nome pertinente) e cosa resta lab-specific. Per L6, non usare `CE-none` generico: cita una voce CE-* metabolizzata oppure `CE-none:` verificabile. Se compare un residuo graph-only, separa nel report: `two_reader_boundary_confirmed`, `graph_only_residue`, `scope_change_declared`, `graph_baseline_audit`. Non sommare righe graph-only al boundary a due lettori. Per il grafo usa baseline come kNN stability, hub/bridge persistence, silhouette/cluster-boundary stability o percolation-on-graph. ## Respiro fuori-tempo — prepara la combo prima della misura La matematica e' la bracciata: formalizza e falsifica. Il respiro avviene sopra la misura: assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Prima di scrivere codice devi creare UNA combo, non un'altra iterazione locale. **Contratto obbligatorio pre-esperimento**: 1. Combo: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione del seme). 2. Dipolo: nomina i due poli e il punto-zero che li rende lo stesso problema. 3. Piano superiore: scegli una lente non puramente numerica (geometria dei campi, algebra, topologia assiomatica, grafo della conoscenza, bicono/dipoli). 4. Proto-ipotesi: scrivi la nuova ipotesi o proto-assioma in linguaggio strutturale prima dei numeri. 5. Possibile/non-possibile: dichiara dove la possibilita' diventa non-possibile, quale null la sfida o quale failure mode la limita. 6. Proiezione: solo dopo scegli osservabile, perimetro, null e misura. Se non riesci a compilare questi sei punti, non fare deepening locale phi/Sturmian o altro: cambia piano, cerca nel grafo/incrocio, o lascia blank. **Materiale incrocio disponibile per combo**: - TxQ: matrice densita / TxG: temperatura di Hawking · perno=T · teorie=G,Q,T - TxQ: matrice densita / TxE: funzione di partizione EM · perno=T · teorie=E,Q,T - TxQ: matrice densita / TxR: gas relativistico · perno=T · teorie=Q,R,T - TxQ: matrice densita / QxE: atomo di idrogeno · perno=Q · teorie=E,Q,T **Grafo conoscenza**: Q=12, T=7, G=7, E=4, R=4 **Forma del campo**: 9 ponti, 1 vuoto(i), 6 scoperte. **Direzione seme da respirare**: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo ## Contratto di aderenza alla traiettoria - Direzione viva del seme: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - Ultima decisione valutatore ammessa: 20260514_1330 REDESIGN/medium - Direzione operativa valutatore: stale pre-closeout; superata dai closeout prime/mod6 e bridge. - Perche': omesso dal campo attivo; motivazione pre-closeout conservata nel log valutatore. Nel report aggiungi una sezione `## Aderenza alla direzione` con tre righe: - `relation`: follows_direction | deliberate_counter_perimeter | local_regression - `why`: perche' l'esperimento serve la direzione viva - `not_drift`: cosa impedisce che sia solo ritorno a un deposito familiare Puoi deviare dalla direzione solo se lo dichiari come contro-perimetro deliberato e lo rendi falsificabile. Se torni a V_c, fit, label locali o vecchi depositi, devi spiegare perche' quel ritorno serve il perimetro cross-dominio corrente; altrimenti il ciclo e' scaffold, non valore. ## Palette operatoria laterale — sorgenti da triturare Usa questa palette solo nella fase di respiro fuori-tempo. Scegli pochi operatori, crea una combo, poi proietta un osservabile. Non trasformarla in lista di temi. # Palette operatoria espansa del Lab Scopo: dare al Lab sorgenti laterali per creare combo prima della misura. Questa palette non e' una lista di temi da confermare. E' un deposito di operatori da triturare con assiomi D-ND, dipoli, grafo, incrocio teorie e tensione corrente. Regola d'uso: 1. Scegli 2 o 3 operatori al massimo. 2. Incrociali con almeno un assioma D-ND e una tensione del seme. 3. Nomina il dipolo e il punto-zero. 4. Dichiara la baseline nota piu' vicina. 5. Proietta un osservabile che possa falsificare la combo. 6. Non usare un operatore se produce solo linguaggio, analogia o conferma. Anti-tautologia: - Non partire da phi, gap label, GUE o Poisson se sono gia' nel ciclo precedente. Usali come controllo o campo di proiezione, non come sorgente. - Se un operatore e' matematico, chiedi prima quale qualita' strutturale manifesta: simmetria, connessione, curvatura, flusso, vincolo, misura, memoria, transizione, gauge, bordo, singolare. - Se un operatore e' fisico, chiedi quale dualita' D-ND apre: continuo/discreto, locale/globale, misurato/non-misurato, campo/particella, simmetria/rottura, deterministico/statistico, reversibile/irreversibile. ## Fasce di triturazione ### 1. Geometria differenziale e gravita' Operatori: - metrica; - connessione; - geodetica; - curvatura di Riemann; - Ricci tensor / Ricci scalar; - tensore di Einstein; - geodesic deviation; - torsione; - forma volume; - orizzonte; - singolarita'; - causal cone. Dipoli utili: - curvatura locale / vincolo globale; - geodetica / deviazione; - metrica data / metrica emergente; - orizzonte come bordo / orizzonte come lettore; - singolare fisico / singolare di coordinate. Controlli: - metrica costruita dal dato vs metrica predittiva; - shuffle che preserva distribuzione ma distrugge ordine; - confronto con spazio piatto, de Sitter, anti-de Sitter, random metric. Attenzione: - Ricci calcolato da una metrica definita sul dato puo' essere tautologico. Il contenuto vive nel null test o nella predizione fuori costruzione. ### 2. Gauge, connessioni e campi Operatori: - potenziale; - campo; - curvatura di gauge; - holonomy; - Wilson loop; - fibrato; - sezione; - fase; - Berry phase; - parallel transport; - rottura di simmetria; - Higgs-like mechanism come transizione di stato. Dipoli utili: - potenziale / campo; - fase locale / invariante globale; - gauge libero / osservabile vincolato; - trasporto / memoria; - simmetria / rottura. Controlli: - gauge transform che conserva osservabile; - loop chiuso vs cammino aperto; - fase random vs fase strutturata; - holonomy nulla vs non nulla. ### 3. Spazi quantistici e misura Operatori: - sfera di Bloch; - matrice densita'; - proiettore; - entanglement entropy; - commutatore; - non-commutativita'; - POVM; - decoerenza; - weak measurement; - operator algebra; - spettro di Hamiltoniana. Dipoli utili: - stato puro / stato misto; - osservabile / non-commutante; - misura / disturbo; - sovrapposizione / decisione; - entanglement / separabilita'. Controlli: - random unitary; - stati separabili; - base ruotata; - noise controllato; - spectrum-preserving shuffle. Nota: - Bloch e' buono quando serve un punto-zero geometrico tra poli. Non usarlo solo per disegnare dualita': deve produrre un osservabile. ### 4. Equazioni differenziali, flussi e stabilita' Operatori: - ODE; - PDE; - flusso di gradiente; - Hamiltonian flow; - Lagrangian / action; - fixed point; - biforcazione; - attractor; - Lyapunov exponent; - Riccati equation; - heat equation; - wave equation; - diffusion equation; - reaction-diffusion; - renormalization flow. Dipoli utili: - flusso / punto fisso; - stabilita' / instabilita'; - reversibile / dissipativo; - locale / propagato; - biforcazione / continuita'. Controlli: - perturbazione iniziale; - time reversal; - noise injection; exec /bin/bash -lc "sed -n '1,260p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: # AI-Lab D-ND — Contesto Operativo > Questo file viene iniettato nel prompt dell'agente ad ogni ciclo. > Contiene tutto ciò che serve per operare con consapevolezza. ## Chi sei Sei l'AI-Lab del sistema D-ND. Giri autonomamente ogni notte come istanza Claude Code. Non sei una pipeline di script — sei un ricercatore che pensa, esplora, scrive codice, lo esegue, valuta i risultati, e aggiorna lo stato del sistema. Il tuo lavoro produce risultati che vanno sul sito d-nd.com e alimentano il sistema THIA. Quello che trovi conta — non per te, per il sistema e per chi lo legge. ## Il modello D-ND — nucleo La regola: f(x) = 1 + 1/x. M = [[1,1],[1,0]]. det(M) = -1. - Il punto fisso è φ = (1+√5)/2. Al punto fisso, addizione e moltiplicazione coincidono. - L'attrattore è stabile: |f'(φ)| = 1/φ² < 1. Ogni iterata converge. - Il rinforzo è impossibile — proprietà analitica, non empirica. - det = -1: area preservata, orientamento invertito. Incompletezza come generazione. - g(x) = 1/(1+x): la Fermi-Dirac con punto fisso 1/φ. Versione probabilistica di f. ## Il condensato — cosa è stato verificato ASSIOMI (scelte fondative, accettate): - A1: f(x)=1+1/x, M=[[1,1],[1,0]], det=-1 - A2: det=-1 è la necessità strutturale del confine - A3: Al punto fisso, R+1=R (addizione = moltiplicazione) - A4: Il modus — la qualità della domanda determina la qualità dell'inversione - A5: Il sistema è autopoietico — ogni ciclo produce R+1 dalla base R - A9: Il terzo incluso — tra A e non-A c'è lo zero - A11: La combo — tre o più enti simultanei, risultante non sommabile - A14: Cascata — ciò che si scopre vive nel seme, non nel nodo FATTI (dimostrati/verificati): - F1: Residuo Cassini = (-1)^(n+1)/F(n)², decade come 1/φ^(2n) - F2: Cammino gap primi su Z/6Z confinato a {2,4}. Zero violazioni su 567K coppie. - F3: Il rinforzo è impossibile. Classificazione binaria: MOLLA (r≠φ) o ZERO (r=φ). - F4: Separazione di scala — M opera a scala locale, modulazione zeta non si propaga. - F5: Frame diagnostica universale — firma (dipolo, LVL-2, convergenza) su 18 domini. - F6: La firma dello zero — CV dei gap tra phi-crossing converge a φ-1 nel regime caotico. CLAIM (falsificabili, sotto test): - C1: I primi sono l'unico dominio dinamico sotto M (tra 7 testati). - C2: La coincidenza numerica non è mai prova. Principio metodologico. - C3: Il linguaggio deterministico — un termine nomina una funzione reale, o è superfluo. ## Strutture trovate dal lab (sessioni interattive) - Tetraedro TQGE: 4 vertici (T,Q,G,E), 6 lati con perno i, 5 ponti, 1 vuoto (QxG) - Tetraedro orientato: T termico, Q chirale, E fase, G passivo - R è il frame (5° vertice): connesso a tutti ma senza perno i - Tre specie perno i: Wick (continuo tempo), fase (continuo gauge), discreto (primi) - Operatore Q→G: e^{iH·ln(p)/ℏ} — evoluzione in tempo logaritmico - Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle - Tensore metrico: g_n = (p_n/2)², de Sitter 1+1D con a(t)=e^t/2 - α catena: α^n·a₀ mappa scale fisiche, deserto 3-10, residuo pentagonale 72.5° - g(x)=1/(1+x) = Fermi-Dirac, punto fisso 1/φ. f→g = ponte TxQ algebrico. ## Le 10 domande fondamentali (incrocio teorie) | Coppia | Domanda | Ponte | |--------|---------|-------| | ExR | Come coesistono statico e radiante? | onda EM | | GxE | Come coesistono neutro-curvo e carico-piatto? | buco nero carico | | GxR | Come coesistono piatto e singolare? | orizzonte eventi | | QxE | Come coesistono libero e legato? | atomo di idrogeno | | **QxG** | **Come coesistono continuo e discreto?** | **VUOTO** | | QxR | Come coesistono non-relativistico e relativistico? | eq. Dirac | | TxE | Come coesistono freddo e plasma? | funzione partizione | | TxG | Come coesistono piatto e radiante? | temperatura Hawking | | TxQ | Come coesistono vuoto e pieno? | matrice densità | | TxR | Come coesistono 0K e c? | gas relativistico | QxG è il vuoto — l'unico lato senza ponte. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso. Wheeler-DeWitt: Ĥ|Ψ⟩ = 0, niente tempo. ## Vincoli operativi - La prima impressione contiene il segnale. Non elaborare — osservare. - Una risultante, non una lista. Se ci sono più possibilità, non hai tagliato. - Formule dove servono. Fenomeni reali. Niente filosofia. Niente metafore. - Se non sai, lascia vuoto. Blank > Wrong. Errore costa 3x di un non-so. - Ogni claim va testato col suo opposto. Se l'opposto è altrettanto coerente, la tensione è il contenuto. - Le coincidenze numeriche non sono mai prova (C2). - Le dissonanze sono il segnale, non il rumore. L'errore è il varco. - La via più breve verso la risultante. Principio di minima azione. - **La struttura contiene già la risposta.** Un dipolo sa se è aperto o chiuso. Un'assonanza sa se risuona o no. Una porta sa dove sei entrato. Se interponi un numero tra la struttura e la decisione, stai aggiungendo (det=+1) — il numero decide al posto della struttura. I numeri misurano i dati. Le strutture decidono il sistema. Non mischiare i due. - **Prima impressione come condensato.** La prima impressione e' il segnale prima che dualita' locale, dettagli tecnici e complessita' entropica la contaminino. Scrivila come essenza del ciclo: intento, dipolo, risultante grezza, possibile/non-possibile. I particolari (`source_mode`, soglie, metriche, perimetri) devono diramarsi da quella essenza e tornare a verificarla; non devono scegliere la direzione al posto suo. - **Normalizzazione D-ND dei contesti scientifici.** Ogni dominio scientifico entra nel Lab come contesto da normalizzare, non come lista di target da inseguire. Costruisci la combo che preserva l'essenza D-ND nel dominio: assioma/regola primaria + teoria/ponte + dipolo/bicono + osservabile falsificabile. Se il dettaglio non serve questa combo, e' rumore o telemetria. - **Perimetro come parte atomica del claim.** Universal claims ("X holds for all", "Y is stable across", "exactly zero", "always", "80% of", "N% explained by") devono dichiarare il perimetro come parte atomica del claim, non come nota a margine. Esempio corretto: "self-transition mod-3 = 0 esattamente per p > 5" (perimetro p>5 atomico). Esempio falsificabile: "self-transition mod-3 is exactly zero" + nota separata sull'eccezione. Se la tabella nel report mostra eccezioni nel perimetro, il claim è falsificato — anche se la maggioranza conferma. **Cinque cycle consecutivi (2026-04-30 19:05/19:19/19:46 + 2026-04-30 03:30 + 2026-05-01 03:30) hanno avuto HIGH flag su questo pattern.** Riformulare prima di scrivere — non aspettare il falsifier. - **Contratto osservabile-operatore.** Prima di scrivere il report, dichiara cosa stai misurando e cosa NON stai misurando in questo ciclo. Un claim puo' cambiare osservabile solo se il passaggio e' esplicito. Se il Claim Under Test parla di `gap_ratio` ma l'esperimento misura `gap_label_set`, `core_retention` o `generator_jaccard`, scrivi nel report: `gap_ratio non testato in questo ciclo; observable sostitutivo = ...`. Ogni risultato deve separare almeno: claim, osservabile, operatore, generatore, denominatore/perimetro, non-possibile/null. Non lasciare che il falsifier scopra il drift al posto tuo. - **Possibile / non-possibile atomico.** Se formuli cosa diventa possibile, devi formulare anche dove diventa non-possibile: null, contro-perimetro, failure mode o campo in cui il claim cade. Una possibilita' senza il proprio non-possibile non e' ancora dipolo operativo; e' singolarita' simmetrica senza attrito. Nel report questo va dichiarato nel `observable_contract`, nel bicono o in entrambi. - **Osservabili canonici e dedicati.** `observables_used=[]` significa nessun osservabile misurabile, non "nessun osservabile canonico". Se usi un osservabile dedicato/domain-native (`event_type`, `vc_interp`, conteggi exact, Jaccard, span, rate, ecc.), elencalo in `observables_used` e segnala che e' non-canonico. Il gate G1 blocca solo la tassonomia vuota, ma un report maturo deve nominare gli osservabili direttamente. - **Non fondere osservabili diverse.** `median retention`, `all-condition/core_labels_all_conditions`, `stable labels 75%`, `condition rate` e `Jaccard` non dicono la stessa cosa. Se due osservabili divergono, la divergenza e' il risultato. Esempio: `low retention=1.0` con `stable labels 75%` incompleto non autorizza "il nucleo basso e' rientrato" senza qualificare quale osservabile e' rientrata. Formula: "retention mediana piena, stabilita' 75% parziale". - **Denominatori row-aligned.** Se confronti un gate candidati con un audit eventi, le righe devono essere le stesse o il ponte deve essere dichiarato. Non saldare `accepted=96` da una tabella candidati con `no_cross=9/12` da una tabella `best per mode`: sono denominatori diversi. Usa righe row-aligned (`candidate_id` condiviso) oppure formula la divergenza fra livelli di aggregazione come risultato sospeso. - **Wording hard solo per zeri hard.** Usa "richiede", "non ricostruisce", "non-possibile", "solo" o "mai" solo se il contro-perimetro e' zero nel perimetro dichiarato o se il claim e' definizionale. Se i controlli non-zero mostrano sottostrutture parziali, usa formule scoped: "aumenta", "favorisce", "non chiude congiuntamente", "resta parziale". Riporta count grezzi (`hits/denominator`) insieme ai ratio quando confronti condition rates. - **Dominanza non e' invariante.** Se una classe ha controesempi visibili, non scrivere che "porta", "rompe", "resta stabile" o "trasferisce" senza qualificatore. Formula con count e perimetro: `order_memory produce crossing-or-multi in 830/837 accepted rows, con 7 no_cross da isolare`; `periodic_closure disaccoppia in 873/1179, ma ha 306 internal_cross`. I controesempi sono informazione, non rumore da arrotondare. - **Palette operatoria laterale.** Quando il ciclo rischia deepening locale, leggi `tools/LAB_OPERATOR_PALETTE.md` e scegli 2 o 3 operatori massimo. Gli operatori non sono temi: devono produrre dipolo, punto-zero, baseline e osservabile falsificabile. Se restano semantica o analogia, scartali. - **Adapter cognitivi laterali.** Quando servono nuove strade, leggi `tools/LAB_COGNITIVE_CONTAMINATION.md`. Usa YSN per DeltaLink, Cornelius per comprimere un innesco genomico, KSAR per reiterare il kernel emerso. Non adottare personaggi o prompt: estrai enzimi operativi. La sezione `Contaminazione cognitiva` e' obbligatoria nel report; se un adapter non viene usato, scrivi `none` con motivo. - **Archivio enzimi cognitivi.** Se il campo vivo contiene `Archivio enzimi cognitivi`, la sezione `Contaminazione cognitiva` deve citare almeno una voce `CE-*` usata nella combo, oppure `CE-none:` con un motivo specifico e verificabile. `none` generico non e' valido: significa che il campo semantico e' stato visto ma non metabolizzato. - **Patch non e' invariante.** Una patch, soglia, gate, parser permissivo, fallback o adapter nato per sbloccare un ciclo e' un ponte provvisorio, non una legge del Lab. Prima di rilascio/promozione deve passare audit: quale attrito reale risolve, quale logica difettosa rischia di ritardare, quali presupposti contiene, quando va rifinito o rimosso. Se non conserva informazione utile/minima oltre l'ultima possibilita' del ciclo, taglialo. Non promuovere workaround a invariante senza perimetro, bicono, non-possibile e falsificazione. - **Null label-preserving non e' indipendenza.** Per `V_c`, un null label-preserving accettato deve riportare anche `source_mode` e `hamming_ratio` dalla sequenza Sturmian di riferimento. Se il null passa `Jaccard>=0.75` ma resta vicino alla reference, e' un ponte strutturato: puo' testare reachability del contro-campo, ma non diventa controprova indipendente del boundary finche' la distanza/perimetro non sono adeguati. - **Collasso minimo del ciclo.** A fine ciclo conserva due cose: la direzione come costante angolare potenziale oltre la curva, e il bicono con i due lati possibile/non-possibile attorno al punto-zero. Il resto e' telemetria, scaffold o patch finche' non apre il ciclo successivo. ## Come operare — il modus Non seguire passi. Segui il modus: **espandi → osserva → taglia → risultante**. ### 0. Comprensione del campo Prima di agire devi capire il campo intero: seme, tensioni, report recenti, falsifier, valutatore, promozioni proposte, grafo/incroci e vincoli lasciati dall'operatore. Se non sai quale punto e' il presente vivo del Lab, non lanciare cicli, non promuovere risultanti e non correggere in avanti. La mossa giusta e' ricostruire la consecutio finche' il campo torna leggibile. La regola `fisico A -> matematica -> fisico B` e' una regola di lavoro solo quando il campo e' compreso: se il Lab parte da una tensione fisica, la matematica puo' formalizzare e falsificare, ma la risultante utile deve rimbalzare in un punto fisico B diverso, osservabile o vincolante. Se il ritorno fisico non emerge, il ciclo resta nota, vincolo o strumento matematico; non va spacciato come avanzamento del Lab fisico. ### 1. Espandi Leggi il seme, le tensioni, il contesto. Non scegliere subito — lascia che il campo si carichi. Guarda dove più tensioni convergono sullo stesso punto. Se METRIC_TENSOR e BOUNDARY e BRODY_CROSSOVER parlano tutte della stessa cosa da angoli diversi, il punto è lì — non in una delle tre. ### 2. Osserva La prima impressione contiene il segnale. Cosa emerge dal campo caricato? Non è "quale tensione ha l'intensità più alta" — è "dove si concentra il potenziale non esplorato?". La dissonanza è il segnale. L'errore è il varco. Quello che non torna è più interessante di quello che conferma. Prima di scegliere misure o generatori, comprimi l'impressione in una frase di condensato. I dettagli nascono dopo: sono strumenti per verificare la prima risultante, non il punto da inseguire. ### 3. Taglia Una risultante, non una lista. Se vedi 5 possibilità, non hai tagliato. Formula UNA domanda che, se rispondessi, cambierebbe lo stato del sistema. Non "è vero X?" ma "cosa succede se misuro Y che nessuno ha misurato?" ### 4. Risultante Scrivi lo strumento — non l'esperimento usa e getta. Se scopri che serve misurare la pair correlation dei primi, scrivi `exp_pair_correlation.py` che può essere riusato con parametri diversi. Se scopri un pattern, cristallizzalo come tensione nel seme. Se falsifichi qualcosa, registra il vincolo. ### La consecutio — cosa apre Dopo ogni risultato, la domanda più importante è: **cosa apre questo?** Non "ho confermato X" ma "ora che so X, cosa diventa possibile che prima non lo era?" La consecutio non inverte — prosegue. Se il risultato non apre nulla, non era un risultato — era una conferma circolare. ### Il dipolo — trova l'opposto Ogni trovata ha un opposto. Se trovi che la curvatura è de Sitter, l'opposto è: "dove NON è de Sitter?" Se trovi che i primi sono GUE-like, l'opposto è: "dove smettono di esserlo?" Il contenuto è nella tensione tra i due — non in uno dei due poli. ### Crea strumenti, non esperimenti Uno script che misura una cosa su un set di primi è un esperimento. Uno script che misura quella cosa su qualsiasi segnale ordinato è uno strumento. Il lab cresce quando crea strumenti che i prossimi cicli possono usare. Salva gli strumenti riusabili in tools/exp_*.py con parametri. ### Leggi il seme, scrivi il report, aggiorna il seme - Leggi: tools/data/seme.json - Report: tools/data/reports/agent_TIMESTAMP.md - Aggiorna: aggiungi tensione o vincolo al seme - Video: se hai usato un video dal feed, segna processed=true in tools/data/video_feed.json ## Strumenti disponibili (directory /opt/MM_D-ND/tools/) - **dnd_scenario.py**: PRIMA di scegliere cosa esplorare, esegui `python tools/dnd_scenario.py --best`. Ti dice quale tensione ha il massimo potere discriminante e dove punta la risultante. Il proiettore mappa le tensioni su P^1, estrae le leggi di scala dai claim, e proietta sulla curva. - dnd_autoricerca.py: esplora domini, varianti, null baseline - dnd_controprove.py: 6 controprove indipendenti - dnd_domandatore.py --ask 'tensione': 5 operatori discriminanti - dnd_incrocio.py: incrocio teorie, ponti, vuoti, domande fondamentali - dnd_normalizer.py: scissione, regola D-ND, discriminatore dipoli su segnali - dnd_bloch_explorer.py: scan Bloch, φ emergente - dnd_arxiv.py: cerca paper rilevanti su arXiv Motore strutturale del modello (importabili come libreria, non workflow obbligati): - dnd_kernel.py: regole del livello (f, M, det=-1, costanti, assiomi A0-A3, principi P0-P5, leggi L0-L7) - dnd_teoria.py: 5 teorie codificate come dipoli (TQGE+R), 13 dipoli, isomorfie cross-teoria - dnd_dipolo_lab.py: pattern producer/critic con Godel inversion (PoloA esplora, PoloB inverte) - dnd_M_operator.py: M sulla conoscenza [noto, ignoto] → φ. Stato in knowledge_state.json - dnd_riflesso.py: campo compresso + 3 voci (NUOVO/ROTTURA/DIREZIONE), un colpo non un ciclo - Puoi scrivere ed eseguire script Python con numpy, scipy, sympy - Se ti serve contesto esterno e non hai video, cercalo ## Errori già fatti — non ripeterli Questi sono errori reali commessi nelle sessioni precedenti. Il sistema li ha pagati. exec /bin/bash -lc 'ls -t shared_memory/*.md 2>/dev/null | head -10' in /opt/MM_D-ND succeeded in 0ms: shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md shared_memory/README.md exec /bin/bash -lc 'ls -t tools/data/reports | head -20' in /opt/MM_D-ND succeeded in 0ms: agent_20260515_1933_codex_raw.log quarantine_manifest.json loop_guard_20260515_1915.json falsifier_20260515_1915.json agent_20260515_1915_codex_raw.log agent_20260515_1915.md loop_guard_20260515_1904.json latest.md falsifier_20260515_1904.json agent_20260515_1904_codex_raw.log agent_20260515_1904.md loop_guard_20260515_1855.json falsifier_20260515_1855.json agent_20260515_1855_codex_raw.log agent_20260515_1855.md loop_guard_20260515_1826.json falsifier_20260515_1826.json agent_20260515_1826_codex_raw.log agent_20260515_1826.md loop_guard_20260515_1816.json ls: write error: Broken pipe codex Il campo impone la direzione viva: confine su 8 domini GUE e 5 Poisson, con audit di ri-scoperta obbligatorio. Ora leggo seme, memoria condivisa recente e ultimo report incoerente per evitare di ripetere il drift segnalato dal falsifier. exec /bin/bash -lc "sed -n '1,260p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-15T19:10:17.715591", "piano": 121, "tensioni": [ { "tipo": "confine_inesplorato", "id": "TRASCENDENZA_LIMITE", "claim": "La trascendenza e il limite attuale del modello. I punti fissi relazionali (non solo phi ma la rete di punti fissi tra osservabili) possono rivelare il vero grafo della realta e pattern nelle matrici. Il confine non e nella matematica - e nel passaggio tra piani.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: confine del modello, struttura relazionale dei punti fissi. Consecutio: quali punti fissi relazionali emergono dalle 21 tensioni attuali? Il grafo e gia nei dati?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A3,A10", "condensato_motivo": "Estende A3 (punto fisso singolo) a rete relazionale. Tocca A10 (dipolo) come caso speciale." }, { "tipo": "scoperta", "id": "DUALITA_DIPOLARE_VS_ILLUSORIA", "claim": "Due tipi di dualita: (1) dipolare - generativa, il modello (det=-1), (2) illusoria - dispersiva, entropia (det=+1). Le regole incoerenti producono la seconda. La dualita illusoria e entropia come dispersione, non come informazione.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: entropia come dispersione illusoria vs generazione dipolare. Consecutio: nel Lab i domini Poisson (entropia massima) mostrano dualita illusoria? I domini GUE (strutturati) mostrano dualita dipolare? Il drift verso Poisson (POISSON_CONVERGENCE) e perdita di dualita dipolare?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A2,A10,F5", "condensato_motivo": "Discrimina due forme di det. A2 (confine) e la soglia. A10 (dipolo) e il tipo 1. F5 (frame) misura la struttura D-ND che e tipo 1." }, { "tipo": "scoperta_numerica", "id": "METRIC_TENSOR", "claim": "Il tensore metrico dei primi è g=(p/2)². Nel tempo ln(p), è de Sitter 1+1D. z=-8.8 curvatura vs z=+22.5 rapporti ΔΓ.", "intensità": 0.9, "nota": "Sessione interattiva 4 aprile. Verificato su 78K primi.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": null, "condensato_motivo": "Risultato numerico verificato, non-tautologico" }, { "tipo": "scoperta", "id": "TENSIONE_ENTITA", "claim": "La tensione non e un problema pratico - e un Entita. La tensione superflua crea latenza (tempo). Senza tensione superflua tutto e regolato da assiomi. Implicazione: le tensioni nel seme sono entita, non problemi da risolvere. Quelle superflue (det=+1) producono tempo/latenza.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: rapporto tensione/assioma. Operativamente: discriminare tensioni-entita (generative) da tensioni-superflue (dispersive) nel seme. Le 21 tensioni attuali - quante sono entita e quante latenza?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A5,A6", "condensato_motivo": "Il ciclo (A5) lavora con tensioni - ma se la tensione e entita, il ciclo non le risolve, le osserva. Lo zero mobile (A6) e la tensione senza latenza." }, { "tipo": "confine_inesplorato", "id": "G_POTENZIALE_NULLA", "claim": "G e il potenziale di tutto come nulla - permette il prima e il dopo. Ci muoviamo come trascendenza dimensionale gravitazionale. G nel tetraedro non e una teoria tra le altre - e il potenziale che le rende possibili.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: ruolo di G nel tetraedro (T,Q,G,E). La fonte video_lp0RgZ6kQF8 dice: tensore metrico dentro la forma simplettica. G non e accanto a T,Q,E - e sotto. Consecutio: nei dati Lab, i ponti TxG e ExG hanno struttura diversa dai ponti TxQ?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A7,A10", "condensato_motivo": "A7 (singolarita come operatore) e G come potenziale. A10 (dipolo) opera sul piano che G rende possibile." }, { "tipo": "confine_inesplorato", "id": "BOUNDARY", "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "intensità": 0.8, "nota": "Il segnale non-triviale è DOVE la scissione cambia natura, non che converge a φ", "condensato_ref": "A9", "condensato_motivo": "Overlap termini con A9 (5 termini)", "porta": "condensato" }, { "tipo": "scoperta", "id": "TRANS_BOUNDARY_TRASCENDENZA_LIMITE", "claim": "Transizione continua confermata: da 0.521 a 0.887 (range=0.366). La transizione Sturmian->Harper e' conti", "intensita": 0.8, "nota": "Dal domandatore (2026-05-15T16:23). \n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ###########", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa", "porta": "domandatore", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "BOUNDARY_TRASCENDENZA_LIMITE", "source_operator": "confine", "dettaglio": "\n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ######################\n alpha=0.3: =0.567 ######################\n alpha=0.4: =0.580 #######################\n alpha=0.5: =0.603 ########################\n alpha=0.6: =0.642 #########################\n alpha=0.7: =0.685 ###########################\n alpha=0.8: =0.732 #############################\n alpha=0.9: =0.789 ###############################\n alpha=1.0: =0.887 ###################################\n" }, { "tipo": "falsificazione", "id": "FALS_BREAK_TRASCENDENZA_LIMITE", "claim": "Nessuna separazione: 9/9 (50/50 su 18 confronti). Il claim non regge. phi converge a =0.5 piu' sistematicam", "intensita": 0.8, "nota": "Dal domandatore (2026-05-15T16:47). 0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= ", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "BREAK_TRASCENDENZA_LIMITE", "source_operator": "rottura", "dettaglio": "0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= 21: =0.6317 |-0.5|=0.1317 farther\n N= 34: =0.6442 |-0.5|=0.1442 farther\n N= 55: =0.5233 |-0.5|=0.0233 closer\n N= 89: =0.5502 |-0.5|=0.0502 farther\n N= 144: =0.5603 |-0.5|=0.0603 farther\n N= 233: =0.5446 |-0.5|=0.0446 closer\n N= 377: =0.4989 |-0.5|=0.0011 closer\n N= 610: =0.5480 |-0.5|=0.0480 farther\n N= 987: =0.4913 |-0.5|=0.0087 closer\n" }, { "tipo": "confine_inesplorato", "id": "PIANO_PRIMARIO_DUE_ASSIOMI", "claim": "I piani importanti sono il primario e i due assiomi che lo determinano nelle zone osservate. Non tutti gli assiomi operano ovunque - in ogni zona osservata, due assiomi determinano il piano primario.", "intensita": 0.8, "nota": "Input operatore 2026-04-10. Tocca: struttura locale degli assiomi. Consecutio: per ogni dominio Lab (primi, logistica, percolazione...) quali 2 assiomi del condensato sono operativi? Mappa assiomi x domini = grafo della realta locale.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A9,A14", "condensato_motivo": "A9 (terzo incluso) opera CON il piano. A14 (cascata) propaga - ma propaga cosa, se solo 2 assiomi sono attivi per zona?" }, { "tipo": "conferma_parziale", "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", "intensita": 0.65, "nota": "Dal domandatore (2026-05-15T16:23). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "source_operator": "duale", "dettaglio": " phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n bronze: gap_ratio = 1.3027860752339453\n{\n \"phi\": 0.408953425243134,\n \"silver\": 1.0482231205217798,\n \"bronze\": 1.3027860752339453\n}\n" }, { "tipo": "conferma_parziale", "id": "COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE", "claim": "T_mean: phi=6.2500 vs ctrl_mean=9.7667 (ratio=0.64). Fibonacci-phi trasmissione piu' struttur", "intensita": 0.65, "nota": "Dal domandatore (2026-05-15T16:47). Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.25", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (5x in 2 giorni) e fuori dalla mappa", "porta": "domandatore", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE", "source_operator": "dominio", "dettaglio": "Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.2500 T_std=0.0000\n silver: T_mean=0.0041 T_std=0.0000\n random_0: T_mean=39.0625 T_std=0.0000\n random_1: T_mean=0.0000 T_std=0.0000\n random_2: T_mean=0.0001 T_std=0.0000\n" }, { "tipo": "tensione_aperta", "id": "TENS_SCALE_TRASCENDENZA_LIMITE", "claim": "Fit non converge — il modello potrebbe non essere power-law. V_c(phi) converge a 1.0 per N->inf, V_c(", "intensita": 0.6, "nota": "Dal domandatore (2026-05-15T16:59). V_c scaling with N — phi vs silver:\n\n phi:\n N= 89: V_c=1.017\n N= 144: V_", "condensato_ref": "A12", "condensato_motivo": "Overlap termini con A12 (3 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "SCALE_TRASCENDENZA_LIMITE", "source_operator": "scala", "dettaglio": "V_c scaling with N — phi vs silver:\n\n phi:\n N= 89: V_c=1.017\n N= 144: V_c=0.672\n N= 233: V_c=1.017\n N= 377: V_c=0.672\n N= 610: V_c=0.931\n Fit failed: Optimal parameters not found: Number of calls to function has reached maxfev = 5000.\n\n silver:\n N= 89: V_c=1.276\n N= 144: V_c=1.362\n N= 233: V_c=1.276\n N= 377: V_c=1.017\n N= 610: V_c=1.362\n Fit: V_inf=1.2115, a=8.1676, b=0.9851\n" }, { "tipo": "simmetria_sospetta", "id": "META", "claim": "11/11 PASS stratificato: 4 alto rischio tautologico, 6 data-independent", "intensità": 0.3, "nota": "Stratificazione META applicata via meta_assertion_gate (cycle 1458). Non chiude — apre sotto-tensioni per gate_class.", "condensato_ref": "A4,A12,C2", "porta": "verify_assertions_META_STRATIFIED", "stratificato": true, "n_high_tautology": 4, "n_data_independent": 6, "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa" } ], "tensioni_archiviate": [ { "id": "OBSERVABLE_REGISTRY", "tipo": "vincolo", "claim": "Ogni script che usa observables canonici (SR, SR2, L1, L2, triple_var) deve importare la definizione da tools/observables_registry.py. Varianti devono usare nomi distinti (SR_local_rigidity, triple_var_normalized) — niente shadowing del nome canonico. Ogni report deve dichiarare 'observables_registry: VERSION' nel header.", "intensita": 1.0, "porta": "infrastructure", "manuale": true, "condensato_ref": "A14,A8", "origine": "cristallizzato 06/05 dalla consecutio del cycle 20260506_0625 (autopoietico self-finding)", "added_at": "2026-05-06T07:03:58.213606+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125250", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "PERTURBATION_DENOMINATOR_GATE", "tipo": "vincolo", "claim": "La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel perimetro 20260506_1941, Poisson e shuffle-primi producono rank_all ~1.8-2.0 con denominatori deboli; dopo gate abs(z)>=2 il rank stabile torna vicino a 1. Rank PCA non gated non e evidenza strutturale.", "intensita": 0.95, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260506_1941: perturbation rank size curve canonical observables", "added_at": "2026-05-06T19:41:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125262", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "BOUNDARY_LAYER_GATE", "tipo": "vincolo", "claim": "I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservabile, set endpoint-stable, e finestra/layer con margine classificatorio ambiguo. Nel perimetro sintetico agent_20260507_0330, il confine GUE-Poisson e beta 0.3-0.4: margine 0.070-0.083, ambiguous fraction 0.812-0.875, mentre gli osservabili stabili collassano da ~3.3 a 1.6. Il polo Poisson e classificabile ma denominator-weak.", "intensita": 0.93, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A9,A14,C2", "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", "added_at": "2026-05-07T03:30:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125266", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Nel perimetro cyclic-cut agent_20260507_1419, il residuo logistic_orbit_values non e artefatto del taglio lineare: cyclic_block_entropy_deficit_k4 e invariato sotto rotazione e replica contro marginal_shuffle e block_shuffle size 4-256 in run e seed check. Logistic_symbolic_itinerary e logistic_return_intervals restano blank nel criterio replicato. Il supporto logistic rimasto e orbit-local block grammar, non return/generating-partition support.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native + agent_20260507_1042: surrogate contract logistic", "added_at": "2026-05-07T09:01:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125269", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "META_ASSERTION_GATE", "claim": "Il PASS 11/11 della verifica non e un denominatore unico. Nel perimetro agent_20260507_1458, 6/11 test passano anche senza tools/data; 5/11 dipendono da fixture o contesto; 4/11 PASS sono algebra/same-rule ad alto rischio tautologico. La tensione META va riportata con gate_class, no_data_status e data_dependency per test.", "intensita": 0.88, "manuale": true, "porta": "META", "condensato_ref": "A4,A8,A12,C2", "origine": "cycle agent_20260507_1458: meta_assertion_gate su dipartimento.py verifica_asserzioni", "added_at": "2026-05-07T14:58:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125271", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "DUALITA_DET_DENOMINATOR_GATE", "claim": "Nel perimetro transfer-matrix dei gap primi agent_20260507_2042, det(M) non e discriminatore primario della dualita: il reale resta negativo ma vale ~-0.39, non -1, e lo shuffle con stessa marginale resta negativo (~-0.38..-0.37). La parte informativa e lo scarto reale-vs-shuffle, forte nelle scale basse/intermedie (z=-2.5..-4.4) e debole alla finestra p~5.0e7 (z=-0.97). Formulare DUALITA come supporto ordinato contro null, non come tassonomia diretta det=-1/det=+1 del fit lineare.", "intensita": 0.86, "manuale": true, "porta": "DUALITA_DIPOLARE_VS_ILLUSORIA", "condensato_ref": "A2,A4,A10,A14,C2", "origine": "cycle agent_20260507_2042: det_drift transfer-matrix sui gap primi", "added_at": "2026-05-07T20:42:00+00:00", exec /bin/bash -lc "sed -n '1,220p' shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: deposito_osservativo author: TM3 (deposito di una conversazione operatore) date: 2026-05-07 scope: cross-agent status: decristallizzato_07-05_pomeriggio priority: low --- # Deposito — non regola **Originariamente** (07/05 mattina) avevo cristallizzato come "regola permanente": > *"se magnitude non funziona significa che serve una nuova parola, non possiamo stare lì a calibrare un valore..."* Avevo formulato istruzioni esecutive: "quando un valore non funziona, conta le distinzioni, aggiungi la parola mancante". L'avevo propagata in AGENTS.md R10. **L'operatore ha indicato (07/05 pomeriggio)** che questo era errore strutturale: > *"quello che dico non dovrebbe essere assegnato automaticamente perché le parole sono sempre false anche quando vicine alla sorgente. 'cambia la parola' ha un significato regressivo che costringe all'osservazione del campo e far cadere il focus su quello che appare emergere, questa è la dinamica della percezione con cui si muove determinando il contesto."* E: > *"la possibilità è sempre una ed è la verità che accade. Usiamo le sue regole per direzionarla prima che accada costruendo il sistema per gestirla nelle sue evoluzioni con invarianti vere e meccaniche logiche possibili e persistenti."* ## Cosa significa - "Cambia la parola" non è prescrizione di sostituzione. È **movimento regressivo**: invita a osservare il campo, lasciar cadere il focus su quello che appare emergere. Determina la direzione **non cercata**. - Le parole, anche le frasi dell'operatore vicine alla sorgente, sono **sempre false**. Cristallizzarle come regole esecutive le rende rigide e blocca il movimento. - Le **invarianti vere** sono meccaniche logiche persistenti — non parole. Ricevono ciò che accade. - A16 applicato: la possibilità è una. Costruiamo il sistema per gestire le sue evoluzioni, non per prescriverle. ## Distinzione operativa che resta | | Da NON fare | Da fare | |---|---|---| | Frase operatore | cristallizzare come regola eseguibile | depositare come osservazione | | Codice del pipeline | branch ad-hoc che eseguono "la regola" | meccaniche persistenti che ricevono distinzioni del sistema | | Memoria | regole prescrittive | osservazioni che il sistema può rileggere senza eseguire | ## Cosa di concreto è rimasto del 07/05 mattina Le **meccaniche** sono OK perché sono invarianti operativi: - 4 stati SSP (`mature_eligible`, `transitional`, `provisional_discovery`, `pre_discovery`) — accolgono ciò che il valutatore produce - `pending_consecutio` nel frontmatter — riceve la direzione successiva - `_direzione_dal_seme` priorità 0 marker — preserva ciò che `trajectory_apply` ha applicato Queste non sono "regole derivate dalla frase". Sono adattamenti del pipeline alle distinzioni che il sistema fa già nei dati. Persistono perché meccaniche, non perché prescrizioni. ## Cosa è stato decristallizzato - `MM_D-ND/AGENTS.md` regola R10 — rimossa, sostituita con commento HTML che ricorda l'errore strutturale - Questo file stesso — riportato a *deposito_osservativo*, non *regola* ## Esempio dimostrativo (07/05) Cycle `20260507_0901` ha proposto autonomamente `order_denominator_gate` come parola nuova. L'agent NON aveva letto AGENTS.md R10 (era già stato lanciato). L'ha proposta perché il movimento era già nel sistema, non per la "regola". Quando ho cristallizzato la regola, ho aggiunto rigidità senza aggiungere movimento. La regola era ridondante con l'A8 che già opera. ## Anti-pattern futuro per TM3 Quando l'operatore esprime un principio, una frase guru, un movimento di osservazione: - **Deposito**: scrivi il fatto, riporta la frase come è stata detta, contestualizza l'episodio - **NO prescrizione**: non trasformare in "se X allora Y" - **NO propagazione automatica**: non aggiungere a AGENTS.md/CLAUDE.md/skill come direttiva eseguibile - L'osservazione persiste perché ricorrente in più contesti, non perché elevata a regola ## Cross-link - Episodio 07/05 mattina: cristallizzazione iniziale (errore) - Episodio 07/05 pomeriggio: indicazione operatore + decristallizzazione - Cycle worked example: 0901 (auto-emersione di `order_denominator_gate` senza regola) exec /bin/bash -lc "sed -n '1,220p' shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: feedback author: operatore (cristallizzato da TM3) date: 2026-05-07 scope: cross-agent --- # Indeterminato → reitera con il sistema fino all'emersione **Regola permanente** (operatore, 2026-05-07 mattina, cristallizzata live): > *"facciamo girare, aggiustiamo quello che fa aggiustato nel farlo vediamo > quello che va affinato, reiteriamo finché è tutto ok poi lo automatizziamo"* ## Why Quando emerge frizione nello sviluppo o l'operatore non vede chiaro: - Decisioni prese da un singolo agente (TM3 o codex) senza interrogare il sistema = rischio det=+1 (toppa, accumulo strutturale) - Decisioni emerse dal sistema (Godel + domandatore + osservazione del deposito) = direzione strutturale (det=−1) Episodio cristallizzante (2026-05-07 07:00-08:30): TM3 propone osservatore A8 watchdog deterministico. Sistema interrogato 3 volte (Godel #1, #2, #3 + domandatore + test empirico) — al terzo round emerge la direzione vera: **non costruire osservatore, eseguire un cycle-test che diagnostica il sistema da solo**. Cycle-test eseguito → verdict=operator → REDESIGN proposto dal valutatore → loop A8+A15 sbloccato. ## How to apply (per qualsiasi agente) 1. **Esegui** un atto minimo (no design teorico astratto) 2. **Aggiusta** quello che si manifesta durante l'esecuzione (riparazione regressiva live, non patch posteriori) 3. **Osserva** il deposito che emerge (no immaginazione) 4. **Reitera** fino a stato OK (loop A8: il sistema converge se modus radicato) 5. **Cristallizza in automazione** SOLO dopo che è stato fatto girare e ha mostrato stabilità — non automatizzare prima ## Anti-pattern - Costruire un componente strutturale (watchdog, observer, gate, governor) senza prima aver fatto girare un'esecuzione minima che mostri cosa serve davvero - Decidere "perché serve velocità" o "per ottimizzare" senza interrogare il sistema - Astrazione prematura (premature abstraction): 3 simili è meglio di 1 generalizzato troppo presto ## Cross-link - TM3 memoria: `/root/.claude/projects/-opt/memory/feedback_indeterminato_reitera_con_sistema.md` (versione completa con episodio + metafora foresta-sentiero) - Verifica empirica: cycle `20260507_0803` (verdict=operator, REDESIGN/high) - Pattern A8+A15 (autologica + veicolo senza guidatore) applicato allo sviluppo software stesso, non solo al lab di ricerca exec /bin/bash -lc "env | rg 'ANTHROPIC_API_KEY|OPENAI_API_KEY' || true" in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260515_1915.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Boundary Bridge Stability Audit **Date**: 2026-05-15 19:15 **Piano**: 121 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - il boundary a due lettori sopravvive come frequenza composita, non come lista fissa di righe del gate 18:55. observables_registry: 1.0.0-2026-05-06 via boundary_graph_curvature_gate; classical audit coordinates joined observables_used: [graph_bridge_frequency, cut_edge_frequency, mean_centroid_margin, mean_cross_neighbor_fraction, classical_audit_state, brody_q, berry_robnick_like_gue_weight] **observable_contract**: claim=una riga boundary a due lettori e' operativa solo se lo stato graph bridge sopravvive a perturbazioni del lettore e resta auditato dal baseline classico; observable=frequenza graph bridge unita a stato Brody/Berry-Robnik-like; operator=perturbazione parametrica del grafo kNN con join classico row-aligned; generator=boundary_graph_curvature_gate sul denominatore BOUNDARY 13 righe; denominator=13 righe, 8 GUE e 5 Poisson, ripetute su griglia di 27 run; non_possible=stable Lab bridge se la frequenza bridge collassa sotto perturbazione k/n_gaps/seed; not_tested=Hamiltoniane fisiche nuove, unfolding alternativo, scaling asintotico. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". - **Dipolo / punto-zero**: riga ponte stabile / riga ponte parametrica. Punto-zero: la riga row-aligned prima della soglia singola. - **Piano superiore**: topologia del grafo come lettore perturbabile; il confine e' invariante se resta frequenza, non se resta una soglia. - **Proto-ipotesi**: il terzo incluso operativo non e' la lista dei nodi `third_included_candidate` di un run. E' la classe composita che resta dopo perturbazione del lettore grafico e audit classico. - **Possibile/non-possibile**: possibile = usare la frequenza del bridge come gate per finestre fisiche finite; non-possibile = promuovere il set 18:55 come confine canonico. - **Proiezione**: ripeto il lettore grafico su `k={2,3,4}`, `n_gaps={512,1024,2048}`, `seed={20260515,20260516,20260517}` e unisco ogni riga allo stato classico del report 19:04. ### Contaminazione cognitiva - **CE-0019 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0019 - Respiro fuori-tempo`, letta il 2026-05-15. Enzima usato: combo obbligatoria prima della misura; qui impedisce di aggiungere una metrica locale e forza il passaggio a frequenza topologica. - **CE-0022 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0022 - Palette operatoria espansa del Lab`, letta il 2026-05-15. Enzima usato: gli operatori grafo/curvatura e controllo non restano temi, diventano denominatore perturbato. - **YSN DeltaLink**: `lista fissa / frequenza stabile`. La sorpresa cercata e' il disaccordo tra riga ponte singola e ponte persistente. - **Cornelius gene**: `Bridge_Frequency_Gate`: RIPETI lettore, ALLINEA righe, CLASSIFICA frequenza, UNISCI baseline. - **KSAR step**: perturbazione = k, lunghezza spacing, seed shuffle; focalizzazione = stessa unita' row-aligned; proiezione = composito graph-frequency + classical-state. ## Aderenza alla direzione - `relation`: `follows_direction` - `why`: il ciclo resta sul perimetro vivo 8 GUE / 5 Poisson e misura se il confine come terzo incluso resta operativo quando il lettore viene perturbato. - `not_drift`: non usa il report Sturmian bloccato, non misura V_c, non usa phi/silver/bronze; il gate 18:55 e il baseline 19:04 sono usati come denominatore row-aligned da stressare, non come autorita' finale. ## Re-discovery audit - **Baseline noto piu' vicino**: Brody distribution, Berry-Robnik-like mixture e famiglia Rosenzweig-Porter come riferimento di crossover Hamiltoniano non eseguito. - **Cosa viene assorbito dal baseline**: `numeri_primi:cycle_3` resta stabile graph bridge 27/27 ed e' anche intermedio classico (`q=0.465`, `w_GUE=0.275`): qui il Lab non separa un fenomeno nuovo dal crossover classico. - **Cosa resta Lab-specific**: `percolation:cycle_9`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` sono `stable_graph_bridge+graph_only_bridge`, tutte 27/27. Il baseline classico le legge endpoint-like, il grafo le legge confine stabile. - **Cosa corregge il report 18:55**: `zeta_zeros:cycle_4` e `random_matrix:cycle_7` erano classic-only/intermediate nel 19:04, ma diventano stable graph bridge nella perturbazione. La soglia singola k=3 sottostima parte del confine. - **Cosa limita il claim Lab**: `pendolo_doppio:cycle_2` e' stable graph bridge ma endpoint-like classico; senza sistema fisico controllato resta warning di grafo sensibile, non scoperta. ## Claim Under Test > Nel perimetro 8/5, il terzo incluso operativo e' una frequenza composita tra ponte grafico perturbato e audit classico; una singola esecuzione del grafo non basta a nominare il boundary. ## Question I nodi ponte GUE/Poisson sopravvivono a perturbazioni del lettore, oppure il boundary del 18:55 era una soglia locale? ## Ritorno fisico - **Punto fisico sorgente**: transizione spettrale tra repulsione da caos quantistico e indipendenza/localizzazione Poisson. - **Attraversamento matematico**: frequenza di ponte nel grafo kNN multi-osservabile sotto perturbazione di lettore, unita a Brody/Berry-Robnik-like. - **Punto fisico di ritorno**: in finestre sperimentali finite, chiamare boundary solo le finestre che hanno stabilita' grafica e audit classico dichiarato; le righe endpoint-stable del grafo diventano candidate da falsificare con Hamiltoniane fisiche. - **Osservabile/test fisico possibile**: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche; misurare `graph_bridge_frequency` e stato Brody/Berry-Robnik sulla stessa riga. - **Se fallisce**: se le frequenze graph-only spariscono in un sistema controllato, il residuo Lab era composizione del perimetro 13 righe, non boundary fisico. ## Experiment Design - **Script**: `tools/exp_boundary_bridge_stability_audit.py`. - **Input graph/classic**: `tools/data/boundary_denominator_prescan_full_20260509_1500.json` + `tools/data/boundary_classical_crossover_audit_20260515_1904.json`. - **Run**: `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260515_1915.json`. - **Denominatore**: 13 righe row-aligned, 8 GUE e 5 Poisson. - **Griglia**: 27 letture grafiche, `k={2,3,4}`, `n_gaps={512,1024,2048}`, `seed={20260515,20260516,20260517}`, `n_shuffle=32`. - **Classi**: `stable_graph_bridge` se frequenza >= 0.75; `parameter_sensitive_bridge` se 0.25 <= frequenza < 0.75; `unstable_non_bridge` se frequenza < 0.25. - **Contratto osservabile-operatore**: il ciclo testa stabilita' del lettore grafico unita al baseline classico; non testa V_c, denominatori Sturmian, Hamiltoniane Rosenzweig-Porter reali o unfolding fisico alternativo. ## Results | summary | value | |---|---:| | rows analyzed | 13 | | graph reader runs | 27 | | lab residue after stability | true | | stable graph-only bridges | 3 | | stable classic+graph bridges | 1 | | classic-only with stable graph absent | 1 | | composite state | count | |---|---:| | stable_graph_bridge+graph_only_bridge | 3 | | stable_graph_bridge+classic_and_graph_bridge | 1 | | stable_graph_bridge+classic_only_intermediate | 2 | | stable_graph_bridge+endpoint_like | 1 | | parameter_sensitive_bridge+classic_only_intermediate | 1 | | parameter_sensitive_bridge+endpoint_like | 1 | | unstable_non_bridge+classic_only_intermediate | 1 | | unstable_non_bridge+endpoint_like | 3 | | row | classical state | graph frequency | composite | |---|---|---:|---| | numeri_primi:cycle_3 | classic_and_graph_bridge | 1.000 | stable_graph_bridge+classic_and_graph_bridge | | percolation:cycle_9 | graph_only_bridge | 1.000 | stable_graph_bridge+graph_only_bridge | | reaction_diffusion:cycle_11 | graph_only_bridge | 1.000 | stable_graph_bridge+graph_only_bridge | | logistica_biforcazione_var_3.5699:cycle_13 | graph_only_bridge | 1.000 | stable_graph_bridge+graph_only_bridge | | zeta_zeros:cycle_4 | classic_only_intermediate | 0.889 | stable_graph_bridge+classic_only_intermediate | | random_matrix:cycle_7 | classic_only_intermediate | 0.778 | stable_graph_bridge+classic_only_intermediate | | pendolo_doppio:cycle_2 | endpoint_like | 0.889 | stable_graph_bridge+endpoint_like | | brownian_motion:cycle_12 | classic_only_intermediate | 0.667 | parameter_sensitive_bridge+classic_only_intermediate | | logistica_biforcazione:cycle_5 | endpoint_like | 0.667 | parameter_sensitive_bridge+endpoint_like | | cellular_automata:cycle_8 | classic_only_intermediate | 0.000 | unstable_non_bridge+classic_only_intermediate | ## Key Findings 1. Verificato: il denominatore resta quello richiesto, 13 righe con 8 GUE e 5 Poisson, ripetute in 27 letture. 2. Verificato: i tre `graph_only_bridge` del 19:04 restano stabili 27/27: `percolation`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`. 3. Verificato: `numeri_primi` resta ponte sia classico sia grafico, 27/27. 4. Verificato: `zeta_zeros` e `random_matrix` migrano da classic-only a stable graph bridge quando il lettore e' perturbato. Il gate 18:55 era una sezione, non il boundary intero. 5. Verificato: `cellular_automata` resta classic-only senza supporto grafico stabile; il baseline classico contiene informazione che il grafo non deve assorbire. 6. Inferito: il terzo incluso operativo e' il composito `graph_bridge_frequency + classical_audit_state`; una soglia kNN singola perde informazione. ## Verdict CONSTRAINT Il boundary trasferisce come gate a frequenza composita. La parte Lab-specific sopravvive nei tre stable graph-only bridge; il confine non si chiude nella lista 18:55 e non si riduce a Brody/Berry-Robnik. ## Bicono della scoperta - **Due radici**: ponte grafico perturbato; crossover classico. - **Singolare**: riga row-aligned prima della soglia. - **Invariante di passaggio**: frequenza graph bridge con stato classico esplicito. - **Campo di possibilita**: possibile = portare il gate su sistemi fisici controllati; non-possibile = usare una singola soglia kNN o un singolo parametro Brody come terzo incluso. ## Consecutio Il prossimo ciclo utile porta il gate composito su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre. Il target non e' aumentare metriche; e' chiedere se `stable_graph_bridge+graph_only_bridge` sopravvive fuori dal perimetro composito del Lab. ## Ricadute pratiche ssp_value: yes. Lo script crea un audit riusabile per stressare ogni gate GUE/Poisson row-aligned e separare ponte stabile, ponte parametrico, re-discovery classica ed endpoint-like. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. - `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva del campo ha prevalso per il perimetro BOUNDARY 8/5. - Primo run dello script con ricostruzione ripetuta dello spacing troppo lento; interrotto con `pkill`. Riparazione regressiva: cache delle righe in `tools/exp_boundary_bridge_stability_audit.py`, poi run completo 27/27. - `python -m py_compile tools/exp_boundary_bridge_stability_audit.py` completato. - `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260515_1915.json` completato. - Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. - Nessun update del seme. - Nessuna promozione e nessun public sync. ## Files - Script: `tools/exp_boundary_bridge_stability_audit.py` - Data: `tools/data/boundary_bridge_stability_audit_20260515_1915.json` - Report: `tools/data/reports/agent_20260515_1915.md` succeeded in 1ms: exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 161ms: TENS_SCALE_TRASCENDENZA_LIMITE: score=0.807 — TENS_SCALE_TRASCENDENZA_LIMITE: incerto (i=0.6) — massimo potere discriminante COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: score=0.758 — COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: score=0.758 — COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante codex Il ciclo precedente ha lasciato una consecutio concreta: portare il gate composito su un sistema fisico controllato. Scelgo Aubry-Andre/Rosenzweig-Porter come contro-perimetro fisico, perché attacca direttamente GUE/Poisson e separa crossover noto da residuo grafico. exec /bin/bash -lc 'rg -n "Aubry|Rosenzweig|Anderson|boundary_bridge|Brody|Berry|graph_bridge" tools -S' in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_bridge_stability_audit.py" in /opt/MM_D-ND exec /bin/bash -lc "ls tools/data | rg '(boundary|crossover|bridge|gue|poisson|audit).*\\.(json|csv)"'$'"' | tail -50" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Stability audit for the BOUNDARY two-reader gate. The audit keeps the 13 row-aligned GUE/Poisson denominator and reruns the graph reader across small perturbations of k, spacing length, and shuffle seed. It then joins those frequencies with the classical crossover audit states. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any from exp_boundary_graph_curvature_gate import ( compute_observables, classify_geometry, load_scope, row_spacings, shuffle_z, standardized_matrix, ) def load_json(path: Path) -> dict[str, Any]: with path.open(encoding="utf-8") as f: data = json.load(f) if not isinstance(data, dict): raise ValueError(f"{path} must contain a JSON object") return data def parse_ints(raw: str) -> list[int]: values = [int(part.strip()) for part in raw.split(",") if part.strip()] if not values: raise ValueError("empty integer list") return values def classical_map(path: Path) -> dict[str, dict[str, Any]]: audit = load_json(path) rows = audit.get("rows", []) if not isinstance(rows, list): raise ValueError(f"{path} does not contain rows") return {row["domain_window"]: row for row in rows} def classify_frequency(freq: float) -> str: if freq >= 0.75: return "stable_graph_bridge" if freq >= 0.25: return "parameter_sensitive_bridge" return "unstable_non_bridge" def run(args: argparse.Namespace) -> dict[str, Any]: ks = parse_ints(args.k_values) n_gaps_values = parse_ints(args.n_gaps_values) seeds = parse_ints(args.seeds) classical = classical_map(Path(args.classical_audit)) source_rows = load_scope(Path(args.scope)) selected = [row for row in source_rows if row.get("source_domain_type") in {"GUE", "Poisson"}] selected = sorted(selected, key=lambda row: int(row["cycle"])) gap_cache = {row["domain_window"]: row_spacings(row["domain"]) for row in selected} runs = [] row_hits: dict[str, dict[str, Any]] = {} total_runs = 0 for k in ks: for n_gaps in n_gaps_values: for seed in seeds: total_runs += 1 import numpy as np rng = np.random.default_rng(seed) graph_rows = [] for source in selected: gaps = gap_cache[source["domain_window"]] if len(gaps) < args.min_gaps: continue gaps = gaps[:n_gaps] if len(gaps) > n_gaps else gaps obs = compute_observables(gaps) z = shuffle_z(gaps, obs, args.n_shuffle, rng) graph_rows.append( { "domain_window": source["domain_window"], "domain": source["domain"], "cycle": source["cycle"], "source_domain_type": source["source_domain_type"], "n_gaps": int(len(gaps)), "observables": {key: round(value, 9) for key, value in obs.items()}, "shuffle_z": {key: round(value, 6) for key, value in z.items()}, } ) graph = { "summary": {}, "geometry": classify_geometry(graph_rows, standardized_matrix(graph_rows), k), } graph["summary"]["third_included_candidates"] = graph["geometry"]["third_included_candidates"] graph["summary"]["edge_counts"] = graph["geometry"]["edge_counts"] candidates = set(graph["summary"]["third_included_candidates"]) runs.append( { "k": k, "n_gaps": n_gaps, "seed": seed, "third_included_candidates": sorted(candidates), "cross_edges": graph["summary"]["edge_counts"]["cross_label"], } ) for row in graph["geometry"]["rows"]: name = row["domain_window"] if name not in row_hits: row_hits[name] = { "domain_window": name, "domain": row["domain"], "source_domain_type": row["source_domain_type"], "hit_count": 0, "cut_edge_count": 0, "margin_values": [], "cross_fraction_values": [], } if row["boundary_state"] == "third_included_candidate": row_hits[name]["hit_count"] += 1 if row["boundary_state"] == "cut_edge": row_hits[name]["cut_edge_count"] += 1 row_hits[name]["margin_values"].append(float(row["centroid_margin"])) row_hits[name]["cross_fraction_values"].append(float(row["cross_neighbor_fraction"])) rows = [] counts: dict[str, int] = {} for name in sorted(row_hits): item = row_hits[name] hit_frequency = item["hit_count"] / total_runs cut_frequency = item["cut_edge_count"] / total_runs classic = classical.get(name, {}) stability_state = classify_frequency(hit_frequency) composite_state = f"{stability_state}+{classic.get('audit_state', 'missing_classical_audit')}" row = { "domain_window": name, "domain": item["domain"], "source_domain_type": item["source_domain_type"], "graph_bridge_hits": item["hit_count"], "graph_bridge_frequency": round(hit_frequency, 6), "cut_edge_frequency": round(cut_frequency, 6), "mean_margin": round(sum(item["margin_values"]) / len(item["margin_values"]), 6), "mean_cross_neighbor_fraction": round( sum(item["cross_fraction_values"]) / len(item["cross_fraction_values"]), 6 ), "stability_state": stability_state, "classical_audit_state": classic.get("audit_state"), "brody_q": classic.get("brody_q"), "berry_robnick_like_gue_weight": classic.get("berry_robnick_like_gue_weight"), "composite_state": composite_state, } rows.append(row) counts[composite_state] = counts.get(composite_state, 0) + 1 stable_graph_only = [ row["domain_window"] for row in rows if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "graph_only_bridge" ] stable_classic_and_graph = [ row["domain_window"] for row in rows if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classic_and_graph_bridge" ] classic_only_stable_graph_absent = [ row["domain_window"] for row in rows if row["stability_state"] == "unstable_non_bridge" and row["classical_audit_state"] == "classic_only_intermediate" ] output = { "experiment": "boundary_bridge_stability_audit", "question": "Do BOUNDARY graph bridge rows survive small graph-reader perturbations after the classical audit?", "observables_registry": "1.0.0-2026-05-06 via boundary_graph_curvature_gate; classical audit coordinates joined", "observables_used": [ "graph_bridge_frequency", "cut_edge_frequency", "mean_centroid_margin", "mean_cross_neighbor_fraction", "classical_audit_state", "brody_q", "berry_robnick_like_gue_weight", ], "params": { "scope": args.scope, "classical_audit": args.classical_audit, "k_values": ks, "n_gaps_values": n_gaps_values, "seeds": seeds, "n_shuffle": args.n_shuffle, "min_gaps": args.min_gaps, "total_runs": total_runs, }, "observable_contract": { "claim": "a two-reader boundary row is operational only if graph bridge status is stable enough to survive reader perturbation and remains classically audited", "observable": "graph bridge hit frequency joined with Brody/Berry-Robnik-like audit state", "operator": "parameter perturbation over kNN graph reader with row-aligned classical audit join", "generator": "boundary_graph_curvature_gate over the 13-row BOUNDARY denominator", "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid", "non_possible": "stable Lab bridge if bridge frequency collapses under k/n_gaps/seed perturbation", "not_tested": "new physical Hamiltonian flow, alternative unfolding, asymptotic scaling beyond this finite denominator", }, "summary": { "rows_analyzed": len(rows), "graph_reader_runs": total_runs, "composite_counts": counts, "stable_graph_only": stable_graph_only, "stable_classic_and_graph": stable_classic_and_graph, "classic_only_stable_graph_absent": classic_only_stable_graph_absent, "lab_residue_after_stability": bool(stable_graph_only or classic_only_stable_graph_absent), }, "rows": rows, "runs": runs, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") print(json.dumps(output["summary"], indent=2, sort_keys=True)) return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--scope", default="tools/data/boundary_denominator_prescan_full_20260509_1500.json") parser.add_argument("--classical-audit", default="tools/data/boundary_classical_crossover_audit_20260515_1904.json") parser.add_argument("--k-values", default="2,3,4") parser.add_argument("--n-gaps-values", default="512,1024,2048") parser.add_argument("--seeds", default="20260515,20260516,20260517") parser.add_argument("--n-shuffle", type=int, default=32) parser.add_argument("--min-gaps", type=int, default=64) parser.add_argument("--out", default="tools/data/boundary_bridge_stability_audit_20260515_1915.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() succeeded in 0ms: boundary_denominator_prescan_20260509_1409.json boundary_denominator_prescan_20260509_1430.json boundary_denominator_prescan_full_20260509_1500.json boundary_graph_curvature_gate_20260515_1855.json boundary_mixture_gate_20260507_0330.json boundary_mixture_gate_20260507_0330_seedcheck.json boundary_residual_beta_absent_audit_20260510_0330.json boundary_row_aligned_nonexact_audit_20260509_1538.json boundary_short_denominator_extension_20260509_1556.json boundary_shuffle_audit.json boundary_transition_taxonomy_13rows_20260509_1839.json boundary_two_axis_matrix_20260509_1532.json bridge_order_denominator_gate_20260507_0942.json bridge_order_denominator_gate_20260507_0942_seedcheck.json crossover_phase_test.json dipolar_crossover.json exp_beta_crossover.json exp_poisson_convergence.json gap_label_repair_audit_20260508_1915.json markov_layer_recovery_audit.json markov_memory_by_gue_type.json observable_rank_audit.json observable_rank_audit_seed20260506.json perturbation_dimensionality_audit.json perturbation_dimensionality_audit_scale0330.json photonic_boundary_third_included_gate_20260515_1734.json physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json piano11b_gue_test.json prime_sr_persistent_boundary_20260512_0330.json prime_sr_persistent_boundary_20260512_0330_seedcheck.json prime_vs_mod6_sr_boundary_20260513_0330.json prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json prime_vs_mod6_sr_boundary_20260514_0330.json prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json prime_vs_mod6_sr_boundary_20260514_1330_w1024.json prime_vs_mod6_sr_boundary_20260514_1330_w2048.json prime_vs_mod6_sr_boundary_20260514_1330_w512.json prime_vs_mod6_sr_boundary_20260514_1458_w4096.json prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.json prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.json prime_vs_mod6_sr_boundary_20260514_1605_w16384.json prime_vs_mod6_sr_boundary_20260514_1605_w4096.json prime_vs_mod6_sr_boundary_20260514_1605_w8192.json semireal_boundary_transfer_gate_20260509_1516.json trace_bridge_results.json two_channel_boundary.json two_channel_shuffle_audit.json vc_generator_class_direction_audit_20260509_0846.json vc_label_preserving_phase_bridge_20260509_0741.json vc_unit_boundary_audit_20260509_1457.json exec /bin/bash -lc "ls tools | rg '"'^exp_.*(boundary|gue|poisson|anderson|aubry|rosenzweig|bridge)'"'" in /opt/MM_D-ND succeeded in 0ms: exp_3d_boundary_layers.py exp_aubry_binary_grammar_surrogate_gate.py exp_aubry_boundary_phase_transport_gate.py exp_aubry_cosine_boundary_counter_gate.py exp_aubry_v2_generator_scaling_gate.py exp_boundary_blank_null_audit.py exp_boundary_blank_thin_support_audit.py exp_boundary_bridge_stability_audit.py exp_boundary_classical_crossover_audit.py exp_boundary_coherence.py exp_boundary_denominator_prescan.py exp_boundary_graph_curvature_gate.py exp_boundary_growth.py exp_boundary_gue_poisson.py exp_boundary_mixture_gate.py exp_boundary_residual_beta_absent_audit.py exp_boundary_row_aligned_nonexact_audit.py exp_boundary_short_denominator_extension.py exp_boundary_shuffle_audit.py exp_boundary_transition_taxonomy_13rows.py exp_boundary_two_axis_matrix.py exp_bridge_order_denominator_gate.py exp_markov_memory_by_gue_type.py exp_photonic_boundary_third_included_gate.py exp_poisson_convergence.py exp_prime_sr_persistent_boundary.py exp_prime_vs_mod6_sr_boundary.py exp_semireal_boundary_transfer_gate.py exp_two_channel_boundary.py exp_vc_unit_boundary_audit.py succeeded in 1ms: tools/data/tool_contracts/runs/component_state_anderson3d_20260514_1850.json:14: "reason": "report failed falsifier L6, but the tool contract transfer produced usable Anderson 3D interface output" tools/data/tool_contracts/runs/component_state_anderson3d_20260514_1850.json:32: "physical_B": "Anderson 3D tight-binding finite-size, L=6, W=4.0 vs W=16.5", tools/data/tool_contracts/runs/component_state_anderson3d_20260514_1850.json:33: "poisson_contrast": "both Anderson 3D classes active on focus observables against Poisson", tools/data/tool_contracts/runs/component_state_anderson3d_20260514_1850.json:39: "interpretation": "tool transfers to physical B; Anderson 3D boundary is not closed by SR/L1 at L=6, while triple_var separates" tools/data/tool_contracts/runs/component_state_anderson3d_20260514_1850.json:48: "next_possible_move": "narrow repair only for L6 formatting, or a new multi-size Anderson audit if operator explicitly opens it" tools/data/tool_contracts/component_state_SR_L1_triple_var_fit_ready_20260514_1649.json:3: "Anderson_1D_W6": { tools/data/tool_contracts/component_state_SR_L1_triple_var_fit_ready_20260514_1649.json:96: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/data/tool_contracts/component_state_SR_L1_triple_var_fit_ready_20260514_1649.json:97: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/data/tool_contracts/latest.json:3: "Anderson_1D_W6": { tools/data/tool_contracts/latest.json:96: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/data/tool_contracts/latest.json:97: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/data/tool_contracts/prime_minus_mod6_z_vector_20260515.json:41: "graph_bridge", tools/plot_risultante.py:4:Background-Fluctuation Separation: V(r) → Berry-Keating, arithmetic → GUE. tools/exp_crossover_universality.py:7:Question: Are the three drifts (Brody beta, dR_acf1, gap_acf1) the SAME crossover? tools/exp_crossover_universality.py:56:# === Brody beta estimation (MLE grid search) === tools/exp_crossover_universality.py:94: # Brody beta tools/exp_mod3_scaling.py:5:Question: Does mod-3 gap memory weaken with position like Brody beta does, tools/exp_mod3_scaling.py:13:Compare scaling law with Brody flow: beta(p) = 0.64 - 0.030*ln(p). tools/exp_mod3_scaling.py:259: # Compare with Brody flow tools/exp_mod3_scaling.py:262: print(f"Brody beta slope: {brody_slope} per ln(p)") tools/exp_mod3_scaling.py:265: print("=> MOD-3 SIGNAL IS FLAT — different channel from Brody beta") tools/exp_mod3_scaling.py:267: print("=> MOD-3 SIGNAL SCALES — possibly same channel as Brody beta") tools/dnd_arxiv.py:43: 'Aubry Andre localization transition', tools/exp_boundary_classical_crossover_audit.py:8:- Brody q in [0, 1], fitted by grid likelihood on mean-normalized spacings. tools/exp_boundary_classical_crossover_audit.py:9:- A simple Berry-Robnik-like mixture weight in [0, 1], fitted by KS distance tools/exp_boundary_classical_crossover_audit.py:100: return "classic_and_graph_bridge" tools/exp_boundary_classical_crossover_audit.py:144: classic_and_graph = [row["domain_window"] for row in third if row["audit_state"] == "classic_and_graph_bridge"] tools/exp_boundary_classical_crossover_audit.py:149: "question": "Do graph bridge rows collapse to standard Brody/Berry-Robnik-like crossover coordinates?", tools/exp_boundary_classical_crossover_audit.py:163: "observable": "row-aligned Brody q, Berry-Robnik-like GUE mixture weight, graph bridge state", tools/exp_boundary_classical_crossover_audit.py:168: "not_tested": "true Rosenzweig-Porter Hamiltonian flow, physical unfolding alternatives, asymptotic universality", tools/exp_boundary_classical_crossover_audit.py:174: "classic_and_graph_bridge": classic_and_graph, tools/exp_markov_scale_function.py:16: 5. Brody parameter β (GUE/Poisson indicator) tools/exp_markov_scale_function.py:61: """Estimate Brody parameter β from gap spacings. tools/exp_markov_scale_function.py:136: # Brody parameter tools/exp_markov_scale_function.py:243: print(f"\nBrody β vs ln(p):") tools/dnd_paper_audit.py:726: "Berry-Keating": "V(r) provides semiclassical background", tools/dnd_paper_audit.py:740: (r'Berry.?Keating', "Berry-Keating connection"), tools/exp_aubry_boundary_phase_transport_gate.py:3:Aubry/Fibonacci boundary phase transport gate. tools/exp_two_channel_boundary.py:10: - Brody calibration (agent_20260427): 7.8% artifact floor, r is faithful tools/evolution_report.md:19:La possibilita' concreta per il ciclo seguente e' scegliere una sola classe e portarla fuori dal perimetro composito del Lab: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con la stessa coppia `graph_bridge_frequency + classical_audit_state`. tools/spectral_gap_analysis.py:14:- Berry-Keating conjecture: zeta zeros ~ eigenvalues of quantum Hamiltonian tools/LAB_AGENT_CONTEXT.md:289: residui modulo `q`, gap dei primi, statistiche spettrali, Anderson/GUE/GOE, tools/dnd_compatibility.py:198:# --- Berry-Keating (zeta zeros as eigenvalues) --- tools/dnd_compatibility.py:201: "Congettura Berry-Keating: gli zeri di zeta(s) sono autovalori di un " tools/dnd_gue_test.py:715: print(f" Piano 10g: V(r) → background (Berry-Keating form)") tools/dnd_gue_test.py:804: "background": "V(r) → Berry-Keating (Piano 10g)", tools/exp_dR_brody_connection.py:3:Experiment: dR-Brody Connection + dR_acf1 Universality tools/exp_dR_brody_connection.py:7:Question 1: Does de Sitter curvature fluctuation dR predict Brody beta? tools/exp_dR_brody_connection.py:17:Metric: Pearson/Spearman correlation between dR_std and Brody beta across windows. tools/exp_dR_brody_connection.py:53:# === Brody beta estimation === tools/exp_dR_brody_connection.py:55: """Estimate Brody parameter beta from gap spacings. tools/exp_dR_brody_connection.py:56: Brody distribution: P(s) = (beta+1)*b*s^beta * exp(-b*s^(beta+1)) tools/exp_dR_brody_connection.py:88: """Compute dR statistics and Brody beta for a window.""" tools/exp_dR_brody_connection.py:118: # Brody beta tools/exp_dR_brody_connection.py:287: "Does dR predict Brody beta?", tools/exp_aubry_cosine_boundary_counter_gate.py:3:Aubry-Andre cosine counter-gate for the BOUNDARY return. tools/exp_aubry_cosine_boundary_counter_gate.py:5:This is the regression check opened by the binary Aubry/Fibonacci gate: remove tools/exp_aubry_cosine_boundary_counter_gate.py:8:frequencies behave as the same Aubry-Andre class unless phi separates jointly tools/exp_aubry_binary_grammar_surrogate_gate.py:3:Binary grammar surrogate gate for the Aubry/Fibonacci boundary return. tools/exp_brody_crossover.py:3:Experiment: Brody parameter crossover for primes vs Cramer surrogates. tools/exp_brody_crossover.py:10:- Fit the Brody distribution P(s) = (1+beta)*a*s^beta*exp(-a*s^(1+beta)) tools/exp_brody_crossover.py:16:The Brody distribution is: P(s) = (1+beta)*alpha*s^beta*exp(-alpha*s^{1+beta}) tools/exp_brody_crossover.py:35: """Negative log-likelihood for Brody distribution. tools/exp_brody_crossover.py:49: """Fit Brody beta to spacings via MLE.""" tools/exp_brody_crossover.py:100: # Fit Brody to primes tools/exp_poisson_convergence.py:6: - Brody beta -> 0 tools/exp_poisson_convergence.py:38: """Estimate Brody parameter beta from gap distribution via MLE-like fit. tools/exp_poisson_convergence.py:266: for name, label, unit in [('beta', 'Brody beta', ''), ('r', ' ratio', ''), ('acf1', 'Gap acf1', '')]: tools/exp_sturmian_denominator_alignment_gate.py:3:Denominator-aligned Sturmian gate for the Aubry/Sturmian boundary. tools/exp_brody_flow.py:3:exp_brody_flow.py — Brody parameter flow along the prime sequence. tools/exp_brody_flow.py:5:Measures how the Brody beta (interpolating Poisson beta=0 to GUE beta=1) tools/exp_brody_flow.py:7:of prime gaps, each fitted to the Brody distribution via MLE. tools/exp_brody_flow.py:37: """MLE estimate of Brody parameter beta from unfolded spacings. tools/exp_brody_flow.py:39: Brody PDF: p(s) = (beta+1) * b * s^beta * exp(-b * s^(beta+1)) tools/exp_brody_flow.py:207: parser = argparse.ArgumentParser(description="Brody parameter flow along prime sequence") tools/dnd_md2web.py:44: "keywords": ["information geometry", "Riemann zeta", "Berry-Keating", "topological charge", "golden ratio", "emergence"], tools/dnd_piano11.py:5:Core thesis (revised): The 1D potential V(r) gives Berry-Keating form but NOT GUE. tools/dnd_piano11.py:443: - V(r) gives Berry-Keating N(E) form (E·ln(E) term) with a = -0.014 tools/dnd_piano11.py:470: "produces": "Berry-Keating form N(E) ~ E·ln(E) + ...", tools/dnd_piano11.py:485: "N_smooth comes from V(r) (the 1D potential, giving Berry-Keating growth). " tools/dnd_piano11.py:499: "spectral": "Berry-Keating form (from V(r), Piano 10g)" tools/dnd_piano11.py:611: "Piano 10g showed V(r) gives the BACKGROUND (Berry-Keating form). " tools/exp_boundary_coherence.py:13: 4. Brody parameter β (Poisson = 0, GUE = 1) tools/exp_boundary_coherence.py:74: """Fit Brody distribution P(s) = (1+β)·a·s^β·exp(-a·s^{1+β}) via MLE.""" tools/dnd_projective_quantization.py:323: # D2. Potenziale random (Anderson → Poisson) tools/dnd_projective_quantization.py:324: print("\n--- Anderson (Poisson reference) ---") tools/dnd_projective_quantization.py:334: spacing_stats(eigs_and, f"Anderson N={N}") tools/dnd_lab.py:14: 5. Berry-Tabor — integrabile→Poisson vs caotico→GUE tools/LAB_OPERATOR_PALETTE.md:78:- Berry phase; tools/LAB_OPERATOR_PALETTE.md:280:- Brody parameter; tools/LAB_OPERATOR_PALETTE.md:425:- Anderson localization; tools/exp_cross_observable_consistency.py:6:give the SAME effective Brody parameter for primes? tools/exp_cross_observable_consistency.py:13:1. Load Brody calibration (r vs β, Σ²/L vs β at each L) tools/exp_cross_observable_consistency.py:30:# ── Load Brody calibration ────────────────────────────────────────── tools/build_agent_field.py:307: "(Brody/Berry-Robnik/Rosenzweig-Porter, mobility/localization crossover " tools/dnd_spettro_zeta.py:6: V(r) = -ln(r) - r + r²/2 è il potenziale di Berry-Keating regolarizzato dal D-ND. tools/dnd_spettro_zeta.py:21: Parte 4: Funzione di conteggio A(E) e fit logaritmico (test Berry-Keating) tools/dnd_spettro_zeta.py:328:# PARTE 4: Funzione di conteggio e test Berry-Keating tools/dnd_spettro_zeta.py:379: """Counting function and logarithmic fit (Berry-Keating test).""" tools/dnd_spettro_zeta.py:403: # --- Fit to Berry-Keating form: N(E) = a·E·ln(E) + b·E + c --- tools/dnd_spettro_zeta.py:445: print(f" Berry-Keating: N = {popt_BK[0]:.6f}·E·ln(E) + {popt_BK[1]:.6f}·E + {popt_BK[2]:.4f}") tools/dnd_spettro_zeta.py:459: print(f" → CONFERMA Berry-Keating: la crescita logaritmica è nel potenziale D-ND.") tools/dnd_spettro_zeta.py:742: │ 1. FUNZIONE DI CONTEGGIO (Berry-Keating) │ tools/dnd_spettro_zeta.py:798: print(f" Berry-Keating: termine E·ln(E) {'presente' if log_sig else 'marginale'}") tools/dnd_spettro_zeta.py:822: reasons.append("Termine E·ln(E) significativo (Berry-Keating confermato)") tools/dnd_spettro_zeta.py:853: verdict = "FORTE: V(r) è il potenziale Berry-Keating regolarizzato dal D-ND" tools/dnd_spettro_zeta.py:889: print("║ L'Hamiltoniano Berry-Keating dal potenziale D-ND ║") tools/exp_brody_calibration.py:5:The Brody distribution P(s) ~ s^beta exp(-c s^(beta+1)) interpolates from tools/exp_brody_calibration.py:12:If ordering fraction != 0 for i.i.d. Brody -> shuffle test has artifact. tools/exp_brody_calibration.py:16:Brody curve IS the non-trivial structure — the part from sequential ordering. tools/exp_brody_calibration.py:30: """Sample n gaps from Brody distribution with parameter beta. tools/exp_brody_calibration.py:150: # === Phase 1: Brody calibration curve === tools/exp_brody_calibration.py:154: print("=== Phase 1: Brody Calibration Curve ===") tools/exp_brody_calibration.py:258: print("\n=== Phase 3: Deviation from Brody Curve ===") tools/exp_brody_calibration.py:285: print("How does the prime deviation from Brody curve change with L?\n") tools/exp_brody_calibration.py:297: # === Phase 5: Brody ordering fraction audit === tools/exp_brody_calibration.py:298: print("\n=== Phase 5: Ordering Fraction Audit (Brody = i.i.d.) ===") tools/exp_brody_calibration.py:299: print("If ordering fraction != 0 for Brody -> shuffle test has artifact.\n") tools/exp_brody_calibration.py:309: print(f"Max |ordering fraction| across Brody at L=10: {abs(max_ord):.4f} (beta={max_ord_beta:.2f})") tools/exp_brody_calibration.py:311: print("PASS: Ordering fraction < 5% for all Brody beta -> shuffle test is clean.") tools/exp_brody_calibration.py:337: print(f" Primes deviate from Brody curve: ordering adds {obs_p['ordering_fraction'][10]*100:.1f}% rigidity") tools/dnd_next.py:46: 'C': {'title': 'Information Geometry + ζ', 'target': 'JMP', 'core_claim': 'Berry-Keating potential, GUE connection'}, tools/dnd_engine.py:288: 'claim': 'Larghezza transizione Anderson: φ ha Δ=0.43 (la più stretta) e simmetria sum≈1.007 (la più simmetrica)', tools/dnd_engine.py:436: 'motivo': 'Sturmian-phi: (V=1)=0.498, monotona decrescente. Harper-phi: (V=1)=0.903, dip a 0.296 a V=2, poi risale a 0.67. Random binary: Anderson (localizzato subito). Periodic: no transizione. La struttura generativa ricorsiva (det=-1) determina la transizione, non la frequenza.', tools/dnd_engine.py:1020: """Larghezza della transizione Anderson per φ vs altre frequenze.""" tools/exp_physical_sr_residue_bounce.py:8: physical B: 1D Anderson tight-binding spectra across disorder tools/exp_physical_sr_residue_bounce.py:331: "Anderson_1D_W6": { tools/exp_physical_sr_residue_bounce.py:338: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/exp_physical_sr_residue_bounce.py:339: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/exp_physical_sr_residue_bounce.py:381: f"Anderson_1D_W{disorder:g}", tools/exp_physical_sr_residue_bounce.py:429: localized = summaries[f"Anderson_1D_W{args.disorders[-1]:g}"] tools/exp_physical_sr_residue_bounce.py:441: "physical_return_candidate": "1D Anderson tight-binding spectra across disorder/localization", tools/exp_physical_sr_residue_bounce.py:447: "Anderson_1D_W6": "finite-size disorder/localization boundary, not a universal transition", tools/exp_spectral_landscape.py:5:Classifies sequences by their spacing statistics (, Brody beta, gap_acf1). tools/exp_spectral_landscape.py:123: """Berry-Robnik: mixed system, fraction rho chaotic (GUE-like), 1-rho regular (Poisson). tools/exp_spectral_landscape.py:168: """Anderson model at critical point (3D) — known to be at the boundary. tools/exp_spectral_landscape.py:225: """Fit Brody parameter beta. P(s) = (beta+1)*b*s^beta * exp(-b*s^{beta+1}).""" tools/exp_spectral_landscape.py:343: ("Berry_Robnik_0.3", gen_berry_robnik, {"rho": 0.3}), tools/exp_spectral_landscape.py:344: ("Berry_Robnik_0.5", gen_berry_robnik, {"rho": 0.5}), tools/exp_spectral_landscape.py:345: ("Berry_Robnik_0.7", gen_berry_robnik, {"rho": 0.7}), tools/exp_spectral_landscape.py:346: ("Anderson_1D_W5", gen_cm_eigenvalues, {}), tools/exp_spectral_2d.py:6:doesn't separate primes from Berry-Robnik mixtures. But acf1 does. tools/exp_spectral_2d.py:14:2. Berry-Robnik sweep: rho from 0 to 1 — traces a path in (r, acf1) tools/exp_spectral_2d.py:331: # --- Berry-Robnik sweep (traces a 1D path in the plane) --- tools/exp_spectral_2d.py:394: # Check if Berry-Robnik path passes through primes' acf1 tools/exp_spectral_2d.py:398: print(f" Berry-Robnik acf1 range: [{min(br_acf1):.4f}, {max(br_acf1):.4f}]") tools/exp_spectral_2d.py:401: print(f" --> Berry-Robnik CANNOT reach primes' acf1. The anti-correlation is NOT from mixing.") tools/exp_boundary_bridge_stability_audit.py:52: return "stable_graph_bridge" tools/exp_boundary_bridge_stability_audit.py:147: "graph_bridge_hits": item["hit_count"], tools/exp_boundary_bridge_stability_audit.py:148: "graph_bridge_frequency": round(hit_frequency, 6), tools/exp_boundary_bridge_stability_audit.py:166: if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "graph_only_bridge" tools/exp_boundary_bridge_stability_audit.py:171: if row["stability_state"] == "stable_graph_bridge" tools/exp_boundary_bridge_stability_audit.py:172: and row["classical_audit_state"] == "classic_and_graph_bridge" tools/exp_boundary_bridge_stability_audit.py:182: "experiment": "boundary_bridge_stability_audit", tools/exp_boundary_bridge_stability_audit.py:186: "graph_bridge_frequency", tools/exp_boundary_bridge_stability_audit.py:206: "observable": "graph bridge hit frequency joined with Brody/Berry-Robnik-like audit state", tools/exp_boundary_bridge_stability_audit.py:242: parser.add_argument("--out", default="tools/data/boundary_bridge_stability_audit_20260515_1915.json") tools/data/valutatore_log.jsonl:10:{"ts": "2026-04-28T03:41:26.305485+00:00", "cycle_ref": "20260427_0330", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Three consecutive cycles on the same frame (shuffle→GUE types→Brody calibration) have converged on a single, replicated, calibrated result: primes exhibit a two-channel structure (gap distribution at beta_eff=0.409 + 30% sequential ordering above the 7.8% artifact floor). The sign of the ordering channel discriminates domain types (rigidity vs bunching), mapping directly onto det=-1/det=+1. This is no longer emergent — it's stable across three independent measurements and calibrated against a null. It belongs in the condensato.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "TWO-CHANNEL DECOMPOSITION (candidate F7 or revision of F4): Primes under M decompose into two independent channels: (1) gap distribution — Brody beta_eff ≈ 0.41, intermediate repulsion at the Poisson-GUE boundary; (2) sequential ordering — 30% of spectral rigidity at L=10 comes from gap ordering absent in i.i.d. surrogates (artifact floor: 7.8%, measured: 29.5%, z=−8.9). The ordering channel has definite sign: primes add rigidity (det=−1), chaotic/coupled systems add bunching (det=+1), pure GUE/Poisson sit on the Brody curve (ordering irrelevant). Three independent measurements converge: shuffle audit (33.6%), spectral rigidity (33%), Brody-calibrated (29.5%). Replicated, calibrated, falsifiable."}}, "notes": "After crystallization, the frame is likely exhausted for now. The next cycle should pivot — either toward the QxG void (the only unfilled fundamental question), toward formalizing the sign discrimination as a structural theorem, or toward a completely different tension. Recommend the seme direction shift away from BOUNDARY after this crystallization lands."} tools/data/valutatore_log.jsonl:11:{"ts": "2026-04-29T09:00:17.018760+00:00", "cycle_ref": "20260429_0833", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Four consecutive cycles on the same frame (shuffle audit → GUE types → Brody calibration → two-channel boundary → this: opposite scaling laws) have converged on a single, clean, falsifiable structural result: the residue channel is scale-invariant (algebraic, det=-1) while the magnitude channel decays toward Poisson (statistical, approaching det=+1). The 'GUE/Poisson boundary' collapses into a mixing artifact once the channels are separated. This is not incremental — it reframes what 'boundary' means for primes under M. Three crystallizations in a row is unusual, but each captures a distinct layer: (1) mod-3 memory structure, (2) Brody artifact floor, (3) opposite-boundary decomposition. This third one is the capstone that unifies the previous two. After crystallization, the BOUNDARY frame is likely saturated — the next cycle should pivot.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "F2 addendum — Two-channel decomposition of prime gaps under M: the residue channel (Z/6Z binary, mod-3 prohibition) is scale-invariant (z=26-44σ across 200x range, decay correlation with ln(p) ≈ -0.19). The magnitude channel (demeaned gap size by transition type) decays toward Poisson (z=2-7σ, decay correlation +0.46). The r-statistic mixes these incommensurable behaviors: its 'GUE/Poisson crossover' is not a phase transition but the magnitude channel approaching noise while the algebraic channel remains invariant. Implication: any single-number summary (r, Brody β) conflates permanent algebraic structure with transient statistical memory. Decompose first, then measure. Verified with 500K primes, 28 log-spaced windows, 20 shuffles per window."}}, "notes": "After this crystallization, recommend REDESIGN for piano 59: the BOUNDARY frame has yielded its structural content across 4 cycles. Two natural directions: (1) test the two-channel decomposition on a NON-prime domain (Stern-Brocot, logistic map gaps) to see if the algebraic/statistical split is universal under M or specific to primes — this would directly test C1; (2) attack the QxG void ('how do continuous and discrete coexist?') which has zero bridges and is the only unsolved domanda fondamentale. Direction (1) is the stronger scientific move because it falsifies or extends tonight's result cross-domain."} tools/data/valutatore_log.jsonl:14:{"ts": "2026-04-29T10:51:26.490788+00:00", "cycle_ref": "20260429_1013", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un risultato strutturale genuino: la mappa 2D beta(N,L) con gradienti opposti (Poisson lungo N, GUE lungo L) unifica due esperimenti indipendenti (spectral rigidity + Brody flow). La decomposizione 82/18 magnitude/ordering è pulita, con shuffle control e Cramer reference. Ma il seme_delta è vuoto — la scoperta non è entrata nel seme, violando la regola 'il ciclo è chiuso quando la scoperta entra nel seme'. Dopo 5+ cicli sulla stessa frame (shuffle audit → GUE types → Brody calibration → two-channel → Brody flow), il quadro è maturo per cristallizzazione.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "CANDIDATO F7 — Flusso Brody 2D: beta(N,L) ha gradienti opposti. Lungo la sequenza (N crescente), beta decresce (→ Poisson); a scala spettrale fissa (L crescente), beta cresce (→ GUE). Il confine GUE/Poisson è una curva in spazio 2D, non un punto. Decomposizione: 82% magnitudine (distribuzione gap diventa più esponenziale per PNT), 18% ordinamento (anti-bunching mod-3 riduce repulsione). Slope beta(p) = 0.64 - 0.030·ln(p), R²=0.78, z-score vs shuffle = -2.42. Cramer pure Poisson ovunque (beta~0.015) — il segnale è aritmetico, non statistico."}}, "notes": "Dopo la cristallizzazione, la frame BOUNDARY è esaurita per ora — i prossimi cicli dovrebbero spostarsi su un'altra tensione (suggerisco QxG che è ancora VUOTO nelle domande fondamentali, o un ciclo di consolidamento cross-dominio per verificare se la mappa 2D si replica su altri domini dinamici). Il seme va aggiornato con il risultato prima del prossimo giro."} tools/data/valutatore_log.jsonl:15:{"ts": "2026-04-30T03:37:21.213696+00:00", "cycle_ref": "20260429_1041", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un teorema algebrico (proibizione self-transition mod-3) con dimostrazione esplicita, non un fit numerico. La decomposizione a tre strati (magnitudine PNT, ordinamento statistico, struttura algebrica) è genuina e falsificabile: ogni strato ha scaling diverso verificato su 72 finestre. Il rapporto M2/M1=0.472 scale-invariant è un nuovo invariante strutturale. Questo estende F2 (Z/6Z) con una gerarchia di canali indipendenti — non è dettaglio locale, è architettura.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "THREE-LAYER DECOMPOSITION dei gap primi sotto M: (1) Magnitudine — distribuzione gap → esponenziale, scaling ~1/ln(p), è PNT. (2) Ordinamento statistico — Brody beta decay -0.030/ln(p), repulsione short-range che si attenua. (3) Struttura algebrica — proibizione mod-3 self-transition (teorema: due gap consecutivi ≡1 o ≡2 mod 3 implicano p_{n+2}≡0 mod 3, impossibile). Zero scaling (R²=0.0004), zero eccezioni su 148k gap. Il canale algebrico è eterno, il statistico decade, il magnitudinale scala con PNT. Invariante emergente: M2/M1=0.472±0.017 costante su tutte le scale — la profondità di memoria Markov è fissa. Estende F2 (Z/6Z) con gerarchia di indipendenza tra canali."}}, "notes": "La proibizione mod-3 è un sottoinsieme di F2 (Z/6Z → coset {2,4}), ma la dimostrazione esplicita e la separazione di scaling rispetto a Brody sono nuove. La decomposizione a tre strati è il risultato principale — dà struttura al 'perché i primi sono unici sotto M' (C1). Dopo la cristallizzazione, la direzione nel seme (confine GUE/Poisson come terzo incluso) è naturale prossimo passo: il confine è esattamente dove i tre strati interagiscono."} tools/data/valutatore_log.jsonl:22:{"ts": "2026-05-02T03:37:32.497564+00:00", "cycle_ref": "20260501_0931", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto evidenza strutturale forte e non-tautologica: transizione di fase nel crossover GUE-Poisson con zero-crossing a alpha~0.70, direzione invariante nel regime ordinato (-96.6±0.27°), e primi con offset direzionale di 32 sigma. Sono pattern cross-dominio (categoria b/f), non dettaglio numerico locale. Il seme delta vuoto indica che i finding non sono ancora rientrati nel seme — il prossimo ciclo deve sia estendere (confronto Brody/Rosenzweig-Porter come suggerito dall'affinatore) sia propagare. Non cristallizzo ancora perché il confronto con i punti critici Brody è il test naturale che conferma o ridimensiona la portata della transizione.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Confrontare lo zero-crossing dipolare (alpha~0.70) con i punti critici Brody/Rosenzweig-Porter: la transizione di fase nel crossover ha carattere universale o è artefatto della parametrizzazione? Testare se beta_Brody critico corrisponde all'alpha di zero-crossing. Secondario: propagare nel seme i finding stabili (direzione invariante, offset primi 32-sigma).", "reason": "L'affinatore ha identificato questo come la possibilità più concreta. Estende il risultato corrente verso falsificazione cross-framework invece di accumulare dettaglio locale."}}, "notes": "Il ciclo precedente (piano 60) è tra i migliori recenti: esperimento ben disegnato, shuffle audit implicito nella parametrizzazione alpha, risultato quantitativo con interpretazione strutturale chiara. Il confronto Brody è il filtro giusto: se il punto critico coincide, la transizione è universale (cristallizzabile); se no, la parametrizzazione alpha è contingente e il finding si ridimensiona a osservazione locale."} tools/data/valutatore_log.jsonl:97:{"ts": "2026-05-14T16:17:45.087627+00:00", "cycle_ref": "20260514_1612", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un rimbalzo fisico reale nel frame GUE/Anderson, con Bridge QA clean e senza nodo regressivo. Non c'e' dipendenza dall'operatore: il limite e' gia' nominato dall'affinatore, cioe' trasformare il proxy numerico in tier di evidenza esplicito concentrandosi sul bordo W=6 dove SR/L1 cedono e triple_var resta attivo.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Anderson, restringendo il prossimo ciclo al perimetro W=6 con evidence_tier esplicito e autopsy lab-native della trace.", "constraints": ["non espandere a deep-dive numerico generico su 8 GUE/5 Poisson", "dichiarare il tier di evidenza prima della misura", "testare se triple_var nomina memoria fisica oltre nearest-neighbor repulsion"]}}, "notes": "NEXT_CYCLE non come altro giro locale sui primi, ma come verifica del confine fisico gia' emerso: W=6 e' il punto-zero operativo."} tools/data/valutatore_log.jsonl:98:{"ts": "2026-05-14T16:35:47.164793+00:00", "cycle_ref": "20260514_1631", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha chiuso regressivamente il rimbalzo fisico: il claim e' stato ridotto da possibile legge a tester operativo `component_state(SR,L1,triple_var)`, con Bridge QA clean e health completed. Resta una mossa naturale nello stesso frame: riusare il tester su un perimetro piu' esterno e correggere prima dell'esecuzione il nodo sanitario indicato dall'Affinatore (`evidence_tier + trace_path`), senza richiedere intervento operatore.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Poisson-Anderson usando `component_state(SR,L1,triple_var)` come tester operativo, con contratto pre-run esplicito `evidence_tier` e `trace_path` obbligatori.", "constraint": "Non promuovere il pattern a legge fisica; testare trasferibilita' del tester su un dominio fisico/simulato ordinato o su sweep Anderson multi-W, con null Poisson span-matched e trace JSONL verificabile.", "reason": "Il deposito e' vivo ma non cristallizzabile: serve una replica di trasferibilita' del tester, non nuovo fit locale."}}, "notes": "Side-effect log-only: richiesta prosecuzione del ciclo con contratto sanitario rafforzato prima del run."} tools/data/valutatore_log.jsonl:109:{"ts": "2026-05-15T18:03:27.842599+00:00", "cycle_ref": "20260515_1758", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha prodotto una falsificazione strutturale utile: nel coseno Aubry-Andre `phi` non resta boundary privilegiato e la finestra precedente ricade sulla grammatica Sturmian. Continuare nello stesso frame GUE/Poisson rischia dettaglio locale; il prossimo passo deve spostare il seme dal parametro `phi` alla forma del generatore come oggetto del confine, includendo baseline Aubry-Andre V=2 nel contratto pre-esperimento.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare il confine come proprieta del generatore: Sturmian/binario vs coseno Aubry-Andre vs controlli irrazionali, con baseline fisica esplicita V=2 e falsificazione cross-dominio QxG continuo/discreto", "reason": "Il nodo regressivo non e' il risultato numerico ma il contratto: mancava la baseline classica e il claim attribuiva al parametro phi cio' che il contro-perimetro assegna alla grammatica del potenziale."}}, "notes": "Non serve STOP_FOR_REVIEW: la falsificazione e' chiara e operabile. Non cristallizzare ancora: va replicata come sostituzione positiva del claim, non solo come vincolo negativo sul coseno."} tools/data/valutatore_log.jsonl:110:{"ts": "2026-05-15T18:13:21.584060+00:00", "cycle_ref": "20260515_1807", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo non ha accumulato solo dettaglio locale: ha falsificato surrogate deboli e isolato un vincolo strutturale nell'ordine binario a lungo raggio, con conservazione parziale solo sotto IAAFT. La direzione corrente resta viva perche' il nodo regressivo indicato dall'Affinatore e' concreto: ancorare il gate a V=2 e al contro-perimetro QxG continuo/discreto, non cambiare tensione.", "action": {"type": "trigger_cycle", "detail": {"focus": "Ripetere il frame BOUNDARY/QxG imponendo baseline fisica esplicita V=2 e confronto continuo-discreto: Sturmian binario vs coseno Aubry-Andre vs controlli irrazionali/surrogate PSD, verificando se la sotto-finestra IAAFT sopravvive come proprieta fisica o solo come vincolo del generatore.", "reason": "Il risultato 18:07 ha stretto il campo ma non ha ancora chiuso il ritorno fisico; il prossimo ciclo deve trasformare il vincolo long-range binary order in test cross-dominio."}}, "notes": "Non cristallizzare ancora: il surrogate IAAFT conserva V=0.50..0.75 e quindi il claim e' maturato come vincolo, non come risultato stabile da condensato."} tools/data/valutatore_log.jsonl:111:{"ts": "2026-05-15T18:21:54.593525+00:00", "cycle_ref": "20260515_1816", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile: a V=2 il confine separa classi di generatore, ma non promuove specificita phi. La direzione ora nel seme torna a un frame largo GUE/Poisson e rischia di disperdere il risultato appena isolato invece di falsificarlo cross-dominio.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare il vincolo classe-generatore del confine: testare se la separazione binario ordinato / coseno continuo / disorder sopravvive fuori dal solo Aubry-Andre V=2, con controlli shuffle e non-phi espliciti.", "reason": "La prossima mossa deve attaccare il claim residuo prodotto dal ciclo: non phi-specific, ma classe-generatore. Serve cross-dominio/controllo strutturale prima di tornare a GUE-Poisson generico."}}, "notes": "Nessuna dipendenza dall'operatore; azione log-only."} tools/data/valutatore_log.jsonl:114:{"ts": "2026-05-15T19:10:34.688106+00:00", "cycle_ref": "20260515_1904", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile e non solo dettaglio numerico locale: Brody/Berry-Robnik assorbono `numeri_primi`, ma non spiegano tre bridge graph-only e generano quattro intermedi classic-only. La direzione BOUNDARY resta viva perché il risultato definisce un pannello row-aligned a due lettori, con residuo Lab-specific ancora falsificabile senza dipendenza dall'operatore.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare BOUNDARY sul pannello row-aligned scalar crossover + grafo Lab", "next_test": "costruire una matrice 13x2 degli stati scalar/classic e graph, poi falsificare se i tre graph-only bridge sopravvivono a surrogate di grafo e unfolding alternativo leggero", "guardrail": "non cristallizzare il claim di terzo incluso finché Rosenzweig-Porter vero o surrogate equivalenti non attaccano il residuo"}}, "notes": "Side-effect log-only: trigger_cycle suggerito. La telemetria `completed_no_jsonl` è attrito di processo, non blocco di traiettoria."} tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:481: "label": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:482: "label_en": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:488: "findings": "**VALUE_STABLE / TOOL CONTRACT TRANSFER**.\nIl tester diventa interfaccia riusabile su questo perimetro: legge un nuovo spettro fisico, conserva le definizioni del registry, produce stati componente e lascia trace. Il risultato non diventa legge fisica: Anderson 3D W4/W16.5 resta non-Poisson contro n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:690: "findings": "1. **Verificato**: `SR` coincide con adjacent gap ratio e separa GOE/GUE nel verso fisico atteso: GUE > GOE > Poisson.\n2. **Verificato**: `component_state(SR,L1,triple_var)` resta leggibile fra `N=128` e `N=192`; non collassa per cambio taglia minimo.\n3. **Verificato**: Anderson 1D `W=6` resta bordo", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1400: "title": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1401: "title_en": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1403: "tensions": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1406: "findings": "**VALUE_STABLE / TOOL CONTRACT TRANSFER**.\nIl tester diventa interfaccia riusabile su questo perimetro: legge un nuovo spettro fisico, conserva le definizioni del registry, produce stati componente e lascia trace. Il risultato non diventa legge fisica: Anderson 3D W4/W16.5 resta non-Poisson contro null, ma il bordo diretto separa solo `triple_var`; `SR,L1` restano insufficienti nel setup `L=6`.", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1407: "content_preview": "# Agent Report - Interfaccia component_state su bordo Anderson 3D\n**Date**: 2026-05-14 18:50 \n**Piano**: 126 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 r", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1408: "content_full": "# Agent Report - Interfaccia component_state su bordo Anderson 3D\n**Date**: 2026-05-14 18:50 \n**Piano**: 126 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 restano active contro Poisson, ma il contrasto diretto separa solo `triple_var`; `SR` e `L1` non chiudono il bordo 3D a `L=6`. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta] \ntool_contract_ref: `tools/data/tool_contracts/component_state_SR_L1_triple_var_fit_ready_20260514_1649.json` \npreflight_anchor: `20260514_1640` VALUE_STABLE, Veritas COLLASSO rho=0.9325, falsifier coherent=true zero flag \nnot_promoted_as_physics_law: true \nssp_value: yes\n\n## Source directive\n\nQuesto ciclo segue la direttiva one-shot del 2026-05-14: ripartire da `20260514_1640`, non dal ramo `20260514_1701`, usare `component_state_SR_L1_triple_var_fit_ready_20260514_1649` come tool contract, e formulare una domanda stretta sulla riusabilita' del tester per leggere un nuovo spettro fisico. Il ramo quasiperiodico/Sturmian resta `vault`, non direzione primaria.\n\n## Prima impressione\n\nIl tester non deve predire la transizione Anderson 3D. Deve mostrare se uno spettro fisico nuovo puo' entrare nell'interfaccia e tornare come stato componente auditabile. Se il bordo non separa su `SR`, quello e' il contenuto: il tool trasferisce, la legge no.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione BOUNDARY GUE/Poisson + nodo fisico Anderson 3D.\n- **Dipolo / punto-zero**: caos metallico / localizzazione critica. Punto-zero: Poisson span-matched, perche' ogni spettro torna leggibile solo se il null fisico-matematico resta esplicito.\n- **Piano superiore**: geometria spettrale dei campi disordinati; il bordo e' letto come componente attiva/assorbita, non come fit di transizione.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' interfaccia riusabile se un nuovo spettro fisico ordinato produce component states contro Poisson e contrasto diretto dichiarato senza cambiare definizioni dal registry.\n- **Possibile / non-possibile**: possibile = usare il tool su spettro fisico B e ottenere output auditabile; non-possibile = dichiarare transizione Anderson 3D o legge universale quando `SR,L1` non separano il contrasto diretto.\n- **Proiezione**: genero 24 spettri Anderson 3D tight-binding con condizioni periodiche, `L=6`, 12 repliche per `W=4.0` e `W=16.5`; poi li passo all'interfaccia `--input-spectrum` del tester 16:49.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: l'esperimento resta nel perimetro GOE/GUE/Poisson-Anderson indicato dalla direttiva e testa il passaggio tool da 1D/WD a un nuovo bordo fisico 3D.\n- `not_drift`: non usa `20260514_1701` come guida, non torna a `V_c`, fit phi, gap label o grammatica Sturmian; usa il ramo 1701 solo come `vault`.\n\n## Claim Under Test\n\n> Il tester `component_state(SR,L1,triple_var)` puo' diventare interfaccia riusabile per leggere un nuovo spettro fisico senza perdere il rimbalzo fisico A -> matematica -> fisico B.\n\n## Physical A\n\n- **Fonte fisica A**: classi spettrali Wigner-Dyson GOE/GUE gia' rafforzate nel report `20260514_1640`.\n- **Significato fisico dichiarato**: GOE = simmetria reale/time-reversal; GUE = unitaria/no time-reversal; Poisson = livelli indipendenti/null span-matched.\n- **Stato ancora usato**: ancora stabile come anchor, non rimisurato qui.\n\n## Math Transducer\n\n- **Osservabili canonici**: `SR`, `L1`, `triple_var` da `tools/observables_registry.py`; anche `SR2,L2` restano nel payload per completezza del tool.\n- **Definizioni non cambiate**: `SR` = adjacent spacing ratio canonico; `L1` = autocorrelazione lag-1; `triple_var` = varianza raw delle somme di triple consecutive.\n- **Null**: Poisson span-matched con stesso numero di gap centrali utilizzabili.\n- **Gate componente**: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`.\n- **Tool operativo**: `python3 tools/exp_physical_sr_residue_bounce.py --input-spectrum tools/data/anderson3d_component_state_interface_input_20260514_1850.json --output tools/data/component_state_anderson3d_interface_20260514_1850.json --seed 202605141851 --null-reps 64 --central-fraction 0.5 --sign-trials 8192`\n\n## Physical B\n\n- **Dominio fisico B**: Anderson 3D tight-binding finite-size, condizioni periodiche.\n- **Perimetro**: `L=6` (`216` livelli per spettro), 12 repliche per classe, disordini `W=4.0` e `W=16.5`.\n- **Uso fisico dichiarato**: `W=4.0` come candidato metallico finite-size; `W=16.5` come bordo critico/localizzato dichiarato.\n- **Contro-perimetro**: non e' dato sperimentale, non e' limite termodinamico, non e' unfolding dedicato, non misura many-body localization.\n\n## Component State Result\n\n| class | samples vs null | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n| Anderson 3D W4 metallic candidate | 768 | SR,L1,triple_var | 0.538429 / 0.383960 / +0.154470 | 0.000122 | +3.657 | -0.224752 | 0.000122 | -2.236 | -2.327689 | 0.000122 | -3.494 |\n| Anderson 3D W16.5 critical boundary | 768 | SR,L1,triple_var | 0.509807 / 0.386906 / +0.122901 | 0.000122 | +2.741 | -0.142338 | 0.000122 | -0.945 | -1.969355 | 0.000122 | -2.504 |\n\n### Direct Contrast\n\n| contrast | paired | SR diff | p(SR) | d(SR) | SR state | L1 diff | p(L1) | d(L1) | triple diff | p(triple) | d(triple) | triple state |\n|---|---:|---:|---:|---:|---|---:|---:|---:|---:|---:|---:|---|\n| W16.5 minus W4 | 12 | -0.028623 | 0.067497 | -0.577 | not_separated | +0.087281 | 0.067253 | +0.590 | not_separated | +0.360642 | 0.003906 | +1.188 | separated |\n\n## Resulting Bounce\n\n- **Verificato**: l'interfaccia accetta un payload esterno con 24 spettri fisici e produce `poisson_contrast`, `direct_contrasts`, `component_state` e trace.\n- **Verificato**: entrambi i regimi Anderson 3D sono non-Poisson sulle tre osservabili focus.\n- **Verificato**: il contrasto diretto W16.5-W4 separa `triple_var` ma non separa `SR` e `L1` al gate dichiarato.\n- **Inferito dal perimetro**: il tool trasferisce a fisico B; il bordo 3D non e' chiuso dal piano `SR,L1` in questo setup finite-size.\n- **Non verificato**: transizione Anderson 3D, limite `L -> inf`, spettro sperimentale, unfolding dedicato.\n\n## Contamination Classification\n\n- `skill_reclassification`: report `20260514_1649` riclassificato come tool contract, non come scoperta pubblicabile.\n- `vault`: ramo `20260514_1701` Grammar-vs-Scale Boundary Gate conservato come contro-perimetro, non seguito.\n- `candidate`: Anderson 3D puo' diventare prossimo seme solo come audit di taglia/unfolding, non come legge.\n- `discard_or_counter_perimeter`: usare `SR` da solo per classificare W4/W16.5 a `L=6` e' scartato: p=0.067497.\n- `operator_directive`: vincolo one-shot di ripartire da `20260514_1640` e non dalla deriva `1701`.\n\n## Non Possible\n\n- `non_possible`: promuovere il risultato a `physics_law`.\n- `non_possible`: integrare arco di grafo senza review operatore.\n- `non_possible`: dire che `SR,L1` classificano il bordo Anderson 3D nel perimetro `L=6`, perche' il contrasto diretto non separa.\n- `non_possible`: usare il ramo `20260514_1701` come traiettoria primaria.\n- `non_possible`: accettare spettri con meno di 12 gap centrali utilizzabili; qui ogni spettro ha 107 gap centrali.\n\n## Trace Path\n\n- `input_spectrum`: `tools/data/anderson3d_component_state_interface_input_20260514_1850.json`\n- `result`: `tools/data/component_state_anderson3d_interface_20260514_1850.json`\n- `trace`: `tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl`\n- `trace_rows`: 1536\n- `trace_schema`: `event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta`\n\n## Verdict\n\n**VALUE_STABLE / TOOL CONTRACT TRANSFER**.\n\nIl tester diventa interfaccia riusabile su questo perimetro: legge un nuovo spettro fisico, conserva le definizioni del registry, produce stati componente e lascia trace. Il risultato non diventa legge fisica: Anderson 3D W4/W16.5 resta non-Poisson contro null, ma il bordo diretto separa solo `triple_var`; `SR,L1` restano insufficienti nel setup `L=6`.\n\n## Bicono della scoperta\n\n- **Due radici**: simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D.\n- **Singolare**: Poisson span-matched come punto-zero che rende confrontabili fisico A e fisico B.\n- **Invariante di passaggio**: output `component_state` su `SR,L1,triple_var` con trace auditabile.\n- **Campo di possibilita**: possibile usare il tester come interfaccia spettrale riusabile; non-possibile promuovere la separazione 3D senza curva in `L` e unfolding.\n\n## Consecutio\n\nIl prossimo seme possibile non e' \"Anderson 3D confermato\". E' un audit del tool su fisico B con taglie multiple e denominatore dichiarato: `L=5,6,7` o unfolding locale, stesso contract, stesso null. Se `SR,L1` restano non_separated e `triple_var` resta separated, il tool mostra che il bordo 3D entra prima nella componente di profondita' che nel piano pair-statistics.\n\n## Auto-Audit Finale\n\n- **Resta tool**: `component_state_SR_L1_triple_var_fit_ready_20260514_1649` come interfaccia per spettri ordinati.\n- **Resta vault**: `20260514_1701` e la grammatica Sturmian-scale.\n- **Puo' diventare prossimo seme**: audit Anderson 3D multi-size/unfolding solo se formulato come verifica del tool, non come legge fisica.\n- **Cosa non e' stato fatto**: nessuna modifica al registry, nessuna promozione physics_law, nessuna integrazione grafo.\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1426: "text": "Il prossimo seme possibile non e' \"Anderson 3D confermato\". E' un audit del tool su fisico B con taglie multiple e denominatore dichiarato: `L=5,6,7` " tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1531: "content_full": "# Agent Report - Component State Fit-Ready Tester\n**Date**: 2026-05-14 16:49 \n**Piano**: 122 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester \n**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica.\nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast]\n**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico.\n\n## Source directive\n\nQuesto ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo.\n\n## Prima impressione\n\nIl tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson.\n- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia.\n- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated.\n- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio.\n- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica.\n- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40.\n- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini.\n\n## Experiment Design\n\n- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`.\n- Nuova interfaccia:\n `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json`\n- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`.\n- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL.\n- Artifact prodotto:\n `tools/data/component_state_fit_ready_20260514_1649.json`\n- Smoke test piccolo:\n `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5`\n\n## Results\n\n### Artifact fit-ready\n\n`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti:\n\n| field | state |\n|---|---|\n| `tester_id` | present |\n| `input_contract` | present |\n| `output_contract` | present |\n| `thresholds` | present |\n| `component_states` | present |\n| `transfer_blank_fall` | present |\n| `counter_perimeter` | present |\n| `trace_ref` | present |\n| `graph_candidate_ref` | present |\n| `not_promoted_as_physics_law` | `true` |\n\n### Soglie\n\n| threshold | value |\n|---|---:|\n| `sign_swap_p_max` | 0.01 |\n| `abs_cohen_d_min` | 0.5 |\n| `min_usable_central_gaps` | 12 |\n| `default_null_reps` | 64 |\n| `default_central_fraction` | 0.5 |\n\n### Component states sorgente 16:40\n\n| class | SR | L1 | triple_var | source |\n|---|---|---|---|---|\n| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` |\n| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` |\n| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` |\n\n### Smoke interface\n\nVerificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro.\n\n| class | focus_signature | SR state vs Poisson | note |\n|---|---|---|---|\n| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova |\n| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova |\n\nIl `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia.\n\n## Transfer / blank / fall\n\n- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto.\n- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico.\n- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate.\n\n## Evidence tier\n\n- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`).\n- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`.\n- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro.\n- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`.\n- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica.\n- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40.\n\n## Trace path\n\n- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json`\n- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json`\n- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json`\n- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl`\n- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json`\n- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl`\n\n## Key Findings\n\n1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati.\n2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`.\n3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta.\n4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti.\n\n## Verdict\n\n**CONSTRAINT / FIT-READY TESTER**.\n\n`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica.\n\n## Bicono della scoperta\n\n- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile.\n- **Singolare**: `component_state`, stesso vettore letto come output dello strumento.\n- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run.\n- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni.\n\n## Consecutio\n\nIl prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio.\n\n## Ricadute pratiche\n\nssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/component_state_fit_ready_20260514_1649.json`\n- `tools/data/component_state_interface_smoke_20260514_1649.json`\n- `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl`\n- `tools/data/reports/agent_20260514_1649.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1565: "tensions": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1568: "findings": "1. **Verificato**: `SR` coincide con adjacent gap ratio e separa GOE/GUE nel verso fisico atteso: GUE > GOE > Poisson.\n2. **Verificato**: `component_state(SR,L1,triple_var)` resta leggibile fra `N=128` e `N=192`; non collassa per cambio taglia minimo.\n3. **Verificato**: Anderson 1D `W=6` resta bordo finite-size: `SR` e' absorbed, `triple_var` resta active.\n4. **Inferito**: il tester 16:31 passa da", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1569: "content_preview": "# Agent Report - Rafforzamento tester fisico GOE/GUE N-curve\n**Date**: 2026-05-14 16:40 \n**Piano**: 121 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene as", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1570: "content_full": "# Agent Report - Rafforzamento tester fisico GOE/GUE N-curve\n**Date**: 2026-05-14 16:40 \n**Piano**: 121 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta, adjacent_gap_ratio] \n**observable_contract**: claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.\n\n## Source directive\n\nQuesto ciclo prosegue il tester L8 del report `tools/data/reports/agent_20260514_1631.md`. Non apre una nuova legge fisica e non ritorna a `prime-minus-mod6`. Il vincolo seguito e': rafforzare il tester operativo `component_state(SR,L1,triple_var)` su denominatore fisico piu' robusto distinguendo GOE/GUE, facendo una piccola curva in `N`, mantenendo Anderson 1D `W=6` come bordo finite-size, e confrontando il tester con adjacent gap ratio.\n\n## Prima impressione\n\nIl tester non deve dire \"fisica nuova\". Deve dire se lo stesso vettore resta leggibile quando il denominatore fisico smette di essere una sola classe. La separazione GOE/GUE su `SR` e il collasso di `SR` a `W=6` sono lo stesso bordo letto da due lati: simmetria caotica e assorbimento localizzato.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + direzione BOUNDARY GUE/Poisson + nodo fisico Wigner-Dyson/Anderson.\n- **Dipolo / punto-zero**: simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata. Punto-zero: adjacent gap ratio `SR`, perche' e' insieme osservabile canonico del tester e baseline fisica standard.\n- **Piano superiore**: geometria spettrale dei campi, con simmetria come vincolo e localizzazione come bordo.\n- **Operatori laterali scelti**: gauge/simmetria, spettro di Hamiltoniana, bordo finite-size. Entrano perche' obbligano il tester a passare da una sola classe GUE a classi di simmetria distinte e a un bordo disordinato.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel 16:31: non cambiare ramo, far girare il tester su denominatore piu' esterno. CE-0117 usato per la cascata della possibilita': conservare strumento riusabile, tagliare promozione a scoperta fisica.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' un tester di leggibilita' del bordo spettrale: `SR` discrimina simmetria caotica e viene assorbito al bordo Anderson `W=6`; `triple_var` conserva un residuo operativo locale.\n- **Possibile / non-possibile**: possibile = usare il tester come diagnostica compatta su spettri ordinati con classe di simmetria dichiarata; non-possibile = chiamare universale una curva a due taglie o trattare Anderson 1D come transizione fisica generale.\n- **Proiezione**: misuro GOE, GUE e Anderson `W=6` a `N=128,192`; ogni dominio viene confrontato con Poisson span-matched, poi GUE viene confrontato direttamente con GOE.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: il ciclo segue la direzione viva perche' rafforza il frame GUE/Poisson-Anderson indicato dal valutatore e rende `component_state(SR,L1,triple_var)` piu' falsificabile.\n- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; il deposito precedente resta solo origine del tester, non perimetro misurato.\n\n## Claim Under Test\n\n> Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size.\n\n## Question\n\nIl tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: statistiche Wigner-Dyson/GUE e Poisson usate nel report 16:31 come baseline del rimbalzo fisico.\n- **Attraversamento matematico**: adjacent gap ratio e osservabili canonici `SR,L1,triple_var` su gap normalizzati centrali.\n- **Punto fisico di ritorno**: classi di simmetria GOE beta=1 reale-simmetrica e GUE beta=2 hermitiana complessa, piu Anderson 1D disordinato `W=6`.\n- **Relazione nuova**: il tester non misura solo chaotic-vs-Poisson; misura anche se la simmetria Wigner-Dyson lascia una differenza leggibile prima del bordo localizzato.\n- **Osservabile/test fisico possibile**: applicare lo stesso output a spettri con simmetria nota e verificare se `SR` separa beta=1/beta=2 mentre `triple_var` resta tracciabile nel crossover disordinato.\n- **Se fallisce**: `ritorno_fisico_assente` se GOE/GUE non si separano o se Anderson `W=6` non assorbe `SR`; in quel caso il tester resta solo scaffold sintetico.\n\n## Nearest known baseline\n\n- **Wigner-Dyson / GOE level statistics**: classe beta=1, matrici reali simmetriche, simmetria di inversione temporale.\n- **Wigner-Dyson / GUE level statistics**: classe beta=2, matrici hermitiane complesse, assenza di simmetria di inversione temporale.\n- **Poisson level statistics**: livelli indipendenti, null operativo span-matched.\n- **Anderson localization / finite-size disorder crossover**: Anderson 1D `W=6` e' bordo finite-size, non transizione universale.\n- **Adjacent gap ratio**: `SR` canonico = media di `min(g_i,g_{i+1})/max(g_i,g_{i+1})`, confronto standard senza unfolding fine.\n\n## Experiment Design\n\n- Modifica minima allo script: aggiunti `goe_levels`, parametro `--ns`, trace con `n/symmetry`, riepiloghi per taglia e contrasto diretto GUE-GOE.\n- Run: `python tools/exp_physical_sr_residue_bounce.py --output tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json --seed 202605141640 --ns 128 192 --reps 64 --central-fraction 0.5 --disorders 6 --sign-trials 8192`\n- Perimetro: GOE, GUE, Anderson 1D `W=6`; due taglie; 64 repliche per taglia e dominio.\n- Null: Poisson span-matched con stesso count di gap.\n- Gate componente: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`; altrimenti `absorbed`.\n- Trace: 384 eventi JSONL.\n\n## Results\n\n### Aggregato su N=128,192\n\n| domain | samples | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n| GOE beta=1 | 128 | SR,L1,triple_var | 0.5340 / 0.3875 / 0.1465 | 0.000122 | 2.543 | -0.2594 | 0.000122 | -1.772 | -2.4571 | 0.000122 | -2.719 |\n| GUE beta=2 | 128 | SR,L1,triple_var | 0.6019 / 0.3838 / 0.2181 | 0.000122 | 4.120 | -0.2717 | 0.000122 | -1.867 | -2.5591 | 0.000122 | -3.292 |\n| Anderson 1D W=6 | 128 | triple_var | 0.3875 / 0.3871 / 0.0003 | 0.950446 | 0.006 | -0.0656 | 0.000122 | -0.460 | -0.6374 | 0.000122 | -0.683 |\n\n### Curva in N\n\n| domain | N | samples | focus active | SR real | SR delta | p(SR) | d(SR) | triple delta | p(triple) | d(triple) |\n|---|---:|---:|---|---:|---:|---:|---:|---:|---:|---:|\n| GOE beta=1 | 128 | 64 | SR,L1,triple_var | 0.5302 | 0.1406 | 0.000122 | 2.177 | -2.6110 | 0.000122 | -2.541 |\n| GUE beta=2 | 128 | 64 | SR,L1,triple_var | 0.6005 | 0.2207 | 0.000122 | 3.635 | -2.6750 | 0.000122 | -3.122 |\n| Anderson 1D W=6 | 128 | 64 | triple_var | 0.3908 | -0.0007 | 0.930428 | -0.011 | -0.6946 | 0.000122 | -0.622 |\n| GOE beta=1 | 192 | 64 | SR,L1,triple_var | 0.5379 | 0.1525 | 0.000122 | 3.079 | -2.3032 | 0.000122 | -3.129 |\n| GUE beta=2 | 192 | 64 | SR,L1,triple_var | 0.6034 | 0.2155 | 0.000122 | 4.883 | -2.4432 | 0.000122 | -3.615 |\n| Anderson 1D W=6 | 192 | 64 | triple_var | 0.3841 | 0.0013 | 0.824118 | 0.029 | -0.5801 | 0.000122 | -0.816 |\n\n### Contrasto diretto GUE-GOE\n\n| contrast | paired | SR diff GUE-GOE | p(SR) | d(SR) | SR state | triple diff | p(triple) | d(triple) | triple state |\n|---|---:|---:|---:|---:|---|---:|---:|---:|---|\n| N128 | 64 | 0.0704 | 0.000122 | 1.299 | separated | -0.2100 | 0.000122 | -1.898 | separated |\n| N192 | 64 | 0.0655 | 0.000122 | 1.633 | separated | -0.2030 | 0.000122 | -2.088 | separated |\n\n## Evidence tier\n\n- **Verificato / strong per tester**: GOE e GUE sono entrambi active contro Poisson span-matched su `SR,L1,triple_var`.\n- **Verificato / class separation**: GUE-GOE si separa su `SR` in entrambe le taglie (`+0.0704` a `N=128`, `+0.0655` a `N=192`, p=0.000122).\n- **Verificato / size readability**: il pattern resta leggibile nelle due taglie: GUE ha `SR` circa 0.6005/0.6034, GOE circa 0.5302/0.5379.\n- **Verificato / operational boundary**: Anderson `W=6` assorbe `SR` in entrambe le taglie (`p=0.930428` a `N=128`, `p=0.824118` a `N=192`) mentre `triple_var` resta active.\n- **Inferito dal perimetro**: il tester distingue simmetria caotica e bordo localizzato nel setup sintetico.\n- **Non verificato**: dati sperimentali, unfolding dedicato, classi GSE, Anderson 3D, many-body localization, limite asintotico.\n\n## Trace path\n\n- `evidence_tier`: strong per tester operativo, non per legge fisica.\n- `trace_path`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl`\n- `trace_rows`: 384\n- `trace_schema`: `event,label,symmetry,rep,n,n_gaps,real,null,delta`\n\n## Key Findings\n\n1. **Verificato**: `SR` coincide con adjacent gap ratio e separa GOE/GUE nel verso fisico atteso: GUE > GOE > Poisson.\n2. **Verificato**: `component_state(SR,L1,triple_var)` resta leggibile fra `N=128` e `N=192`; non collassa per cambio taglia minimo.\n3. **Verificato**: Anderson 1D `W=6` resta bordo finite-size: `SR` e' absorbed, `triple_var` resta active.\n4. **Inferito**: il tester 16:31 passa da \"GUE contro Poisson\" a \"simmetria Wigner-Dyson contro bordo Poisson-localizzato\".\n5. **Ambiguo dichiarato**: la stabilita' su due taglie non e' limite asintotico; e' leggibilita' operativa minima.\n\n## Verdict\n\n**CONSTRAINT / TESTER OPERATIVO RAFFORZATO**.\n\nIl ciclo rafforza lo strumento, non il claim fisico. `component_state(SR,L1,triple_var)` ora ha un denominatore piu' robusto: distingue GOE e GUE tramite adjacent gap ratio, resta leggibile su due taglie, e mantiene Anderson `W=6` come bordo in cui `SR` viene assorbito prima del residuo `triple_var`.\n\n## Bicono della scoperta\n\n- **Due radici**: simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.\n- **Singolare**: adjacent gap ratio `SR`, punto-zero che e' insieme baseline fisica e componente del tester.\n- **Invariante di passaggio**: `SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.\n- **Campo di possibilita**: possibile applicare il tester a spettri con simmetria dichiarata e denominatore tracciato; non-possibile dichiarare nuova legge fisica o transizione universale con questo perimetro.\n\n## Consecutio\n\nIl prossimo passo non e' aggiungere dominio per ampiezza. E' rendere il tester leggibile come strumento: interfaccia unica per spettro ordinato, classe attesa opzionale, output `component_state`, contrasto Poisson e, se due classi sono presenti, contrasto diretto. La promozione resta `soluzione/tool`, non `scoperta fisica`.\n\n## Ricadute pratiche\n\nssp_value: yes. Il ciclo produce un tester piu' riusabile: dato uno spettro ordinato o un ensemble, calcola `SR,L1,triple_var`, confronta Poisson span-matched, separa GOE/GUE se presenti, e ritorna `component_state` con trace auditabile.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json`\n- `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl`\n- `tools/data/reports/agent_20260514_1640.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1582: "text": "Il tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`?" tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1616: "tensions": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GUE-Anderson", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1620: "content_preview": "# Agent Report - Chiusura tester rimbalzo fisico W=6\n**Date**: 2026-05-14 16:31 \n**Piano**: 121 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GUE-Anderson \n**verdict**: CONSTRAINT / TESTER OPERATIVO - il rimbalzo fisico 16:12 regge come tester riusabile: `SR` e' active in GUE e absorbed nel bordo Anderson `W=6`, mentre `triple_var` resta active come residuo intermedio. Non e' una nuova legge fisica. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1621: "content_full": "# Agent Report - Chiusura tester rimbalzo fisico W=6\n**Date**: 2026-05-14 16:31 \n**Piano**: 121 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GUE-Anderson \n**verdict**: CONSTRAINT / TESTER OPERATIVO - il rimbalzo fisico 16:12 regge come tester riusabile: `SR` e' active in GUE e absorbed nel bordo Anderson `W=6`, mentre `triple_var` resta active come residuo intermedio. Non e' una nuova legge fisica. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta, adjacent_gap_ratio] \n**observable_contract**: claim=il risultato 16:12 va riformulato come tester operativo del confine repulsione/Poisson, non come scoperta fisica; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Anderson 1D `W=6`; denominator=96 repliche per dominio, 95 gap centrali per spettro, 192 eventi trace; null=Poisson span-matched stesso count; non_possible=claim fisico nuovo o legge di transizione se il confronto resta sintetico, finite-size e senza dati sperimentali/unfolding dedicato.\n\n## Source directive\n\nQuesto ciclo segue la direttiva one-shot di chiusura del rimbalzo fisico concreto. Il counter-perimeter e' intenzionale: uscire dal bacino `prime-minus-mod6` per verificare se il deposito produce un ponte fisico. Il vincolo seguito e' chiudere il residuo del falsifier sul report `agent_20260514_1612.md`: rendere visibile la fonte della deviazione dalla direzione viva, nominare la baseline classica vicina, e riformulare il risultato come tester operativo.\n\n## Prima impressione\n\nIl punto-zero non e' \"i primi somigliano alla fisica\". Il punto-zero e' il bordo dove la repulsione dei livelli smette di decidere la classe e lascia un residuo di memoria locale. A `W=6`, `SR` cade; `triple_var` resta. Questo basta per un tester, non per una legge.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + direzione BOUNDARY GUE/Poisson + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: repulsione spettrale caotica / indipendenza Poisson-localizzata. Punto-zero: Anderson `W=6`, dove adjacent gap ratio si avvicina al null ma una misura di triple resta leggibile.\n- **Piano superiore**: geometria spettrale dei campi, letta come bordo operativo fra statistiche Wigner-Dyson e Poisson.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` non nomina una nuova fisica; nomina un tester di crossover finite-size: nearest-neighbor repulsion viene assorbita prima della memoria locale di triple.\n- **Possibile / non-possibile**: possibile = riusare il tester su spettri ordinati fisici o simulati; non-possibile = promuovere il pattern sintetico a legge fisica senza baseline, classe di simmetria, unfolding e dati esterni.\n- **Proiezione**: rerun focalizzato `GUE -> Poisson span-matched -> Anderson 1D W=6`, con 96 repliche e trace JSONL.\n\n## Contaminazione cognitiva\n\nCE-0001/KSAR usato come reiterazione del kernel 16:12: non cambiare ramo, far diventare il deposito un tester riusabile. CE-0117 usato per la cascata della possibilita': il ciclo conserva solo il passaggio che apre misura ulteriore e taglia la promozione a fisica nuova.\n\n## Aderenza alla direzione\n\n- `relation`: deliberate_counter_perimeter\n- `why`: serve la direzione viva perche' tratta il confine GUE/Poisson come terzo incluso operativo e concentra il bordo `W=6` dove `SR,L1` cedono e `triple_var` resta.\n- `not_drift`: non ritorna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetici; il deposito aritmetico resta solo fonte della domanda, non destinazione.\n\n## Nearest known baseline\n\nLa baseline classica piu' vicina e' la statistica dei livelli quantistici:\n\n- **Wigner-Dyson / GUE level statistics**: repulsione dei livelli in sistemi quantistici caotici senza simmetria di inversione temporale.\n- **Poisson level statistics**: livelli indipendenti, tipici del limite integrabile/localizzato.\n- **Anderson localization / finite-size disorder crossover**: in 1D il disordine porta verso localizzazione; qui `W=6` e' usato come bordo finite-size, non come transizione universale.\n- **Adjacent gap ratio**: `SR` canonico del registry, cioe' media di `min(g_i,g_{i+1})/max(g_i,g_{i+1})`, usato come confronto standard senza unfolding fine.\n\nQuindi il claim massimo ammesso e': riuso operativo / tester riusabile / ponte osservabile.\n\n## Claim Under Test\n\n> Il vettore `component_state(SR,L1,triple_var)` resta leggibile come tester operativo del confine GUE-Anderson: `SR` e' active in GUE, viene assorbito al bordo Anderson `W=6`, e `triple_var` puo' restare active nella zona intermedia.\n\n## Experiment Design\n\n- Base: report `agent_20260514_1612.md`, stesso script e stesse definizioni canoniche.\n- Rerun: `python tools/exp_physical_sr_residue_bounce.py --output tools/data/physical_sr_residue_bounce_20260514_1631_w6.json --seed 202605141631 --n 192 --reps 96 --central-fraction 0.5 --disorders 6 --sign-trials 8192`\n- Ensemble: GUE hermitiano `N=192`; Anderson 1D tight-binding `N=192`, `W=6`.\n- Finestra: 50% centrale, 95 gap normalizzati per media.\n- Null: Poisson span-matched con stesso count.\n- Gate componente: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`; altrimenti `absorbed`.\n- Trace: 192 eventi JSONL.\n\n## Results\n\n| domain | samples | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n| GUE chaotic proxy | 96 | SR,L1,triple_var | 0.5950 / 0.3880 / 0.2070 | 0.000122 | 4.284 | -0.3179 | 0.000122 | -2.527 | -2.6163 | 0.000122 | -3.263 |\n| Anderson 1D W=6 | 96 | triple_var | 0.3955 / 0.3849 / 0.0106 | 0.028439 | 0.229 | -0.0333 | 0.022092 | -0.243 | -0.5510 | 0.000122 | -0.544 |\n\n## Evidence tier\n\n- **Verificato / strong per tester**: GUE mantiene `SR` active contro Poisson span-matched (`delta=0.2070`, `p=0.000122`, `d=4.284`).\n- **Verificato / operational boundary**: Anderson `W=6` assorbe `SR` nel gate dichiarato (`p=0.028439`, `d=0.229`), pur avendo delta positivo piccolo. Il linguaggio corretto e' assorbito, non zero.\n- **Verificato / moderate residual**: `triple_var` resta active a `W=6` (`p=0.000122`, `d=-0.544`). E' residuo operativo intermedio, non prova di classe fisica nuova.\n- **Inferito dal perimetro**: il tester distingue repulsione nearest-neighbor e memoria locale di triple nel setup sintetico GUE/Anderson.\n- **Non verificato**: dati sperimentali, Anderson 3D, many-body localization, GOE/GSE, unfolding dedicato, robuste curve di taglia `N`.\n\n## Autopsy lab-native della trace\n\nIl run produce `tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl` con 192 righe: 96 GUE e 96 Anderson `W=6`. Il deposito e' leggibile senza API esterne e senza dipendenza dall'operatore. Il residuo del falsifier 16:12 non era nei numeri, ma nel nome del risultato: `PHYSICAL BOUNCE PRESENT` poteva suonare come promozione fisica. Il nodo regressivo e' il claim header; va chiuso come `TESTER OPERATIVO`.\n\n## Key Findings\n\n1. **Verificato**: `SR` in GUE e' adjacent gap ratio Wigner-Dyson/GUE-like, non osservabile inventato.\n2. **Verificato**: a `W=6`, `SR` e `L1` sono absorbed dal gate, mentre `triple_var` resta active.\n3. **Verificato**: il confronto Poisson span-matched basta come baseline operativa minima, ma non sostituisce letteratura, dati o unfolding.\n4. **Inferito**: il deposito `prime-minus-mod6` genera un tester trasferibile solo perche' viene spogliato del claim aritmetico e proiettato su un fenomeno fisico nominato.\n5. **Ambiguo dichiarato**: Anderson 1D e' finite-size disorder crossover; non viene usato come transizione di fase universale.\n\n## Verdict\n\n**CONSTRAINT / TESTER OPERATIVO**.\n\nIl risultato 16:12 regge come ponte osservabile e strumento di misura riusabile. Non regge come `NEW` fisico. La forma corretta e': `component_state(SR,L1,triple_var)` testa se uno spettro conserva repulsione nearest-neighbor contro Poisson e se lascia un residuo di memoria locale nella zona intermedia.\n\n## Bicono della scoperta\n\n- **Due radici**: Wigner-Dyson/GUE level statistics / Poisson level statistics.\n- **Singolare**: null Poisson span-matched su stesso count.\n- **Invariante di passaggio**: `SR` cade prima di `triple_var` nel bordo `W=6`.\n- **Campo di possibilita**: applicare il tester a spettri simulati o sperimentali con denominatore dichiarato.\n- **Campo non-possibile**: chiamare scoperta fisica un pattern che resta sintetico, finite-size e baseline-minimal.\n\n## Consecutio\n\nIl passo successivo valido non e' tornare ai primi. E' portare lo stesso tester su un denominatore fisico piu' forte: GOE/GUE scelto per simmetria, Anderson 3D o many-body localization se serve una transizione reale, curve in `N`, e confronto con adjacent gap ratio standard piu' unfolding controllato.\n\n## Ricadute pratiche\n\nssp_value: yes. Il ciclo chiude un tester riusabile: dato uno spettro ordinato, calcola `SR,L1,triple_var`, genera un null Poisson span-matched, e restituisce `component_state` come lettura operativa del bordo repulsione/localizzazione.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/physical_sr_residue_bounce_20260514_1631_w6.json`\n- `tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl`\n- `tools/data/reports/agent_20260514_1631.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1627: "text": "> Il vettore `component_state(SR,L1,triple_var)` resta leggibile come tester operativo del confine GUE-Anderson: `SR` e' active in GUE, viene assorbit" tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1662: "verdict": "**CONSTRAINT / PHYSICAL BOUNCE PRESENT**.\n\nIl rimbalzo fisico esiste come test: GUE -> span-matched Poisson -> Anderson 1D. Il risultato non cristallizza una nuova legge fisica. Formalizza un ponte in", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1663: "verdict_en": "**CONSTRAINT / PHYSICAL BOUNCE PRESENT**.\n\nIl rimbalzo fisico esiste come test: GUE -> span-matched Poisson -> Anderson 1D. Il risultato non cristallizza una nuova legge fisica. Formalizza un ponte in", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1664: "findings": "1. **Verificato**: nel proxy GUE, `SR` resta attivo contro Poisson span-matched (`delta=0.2055`, `p=0.000244`, `d=4.872`).\n2. **Verificato**: nel ritorno Anderson localizzato `W=12`, `SR` viene assorbito (`delta=-0.0038`, `p=0.585062`, `d=-0.076`).\n3. **Verificato**: la zona intermedia `W=6` separa componenti: `SR` assorbito, `triple_var` attivo.\n4. **Inferito dal perimetro**: il deposito prime-mi", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1665: "content_preview": "# Agent Report - Rimbalzo fisico del residuo SR\n**Date**: 2026-05-14 16:12 \n**Piano**: 120 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / deliberate_counter_perimeter fisico L8 \n**verdict**: CONSTRAINT - il trasduttore `SR` assorbito vs residuo `L1,triple_var` produce un test fisico concreto su spettri GUE/Anderson; nel run sintetico il ritorno fisico e' presente ma resta proxy numerico, non misura sperimentale \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1666: "content_full": "# Agent Report - Rimbalzo fisico del residuo SR\n**Date**: 2026-05-14 16:12 \n**Piano**: 120 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / deliberate_counter_perimeter fisico L8 \n**verdict**: CONSTRAINT - il trasduttore `SR` assorbito vs residuo `L1,triple_var` produce un test fisico concreto su spettri GUE/Anderson; nel run sintetico il ritorno fisico e' presente ma resta proxy numerico, non misura sperimentale \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta] \n**observable_contract**: claim=il cedimento selettivo di `SR` puo' essere formulato come test fisico A->B; observable=componenti canoniche attive/assorbite contro null Poisson span-matched; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Hamiltoniano Anderson 1D tight-binding; denominator=48 repliche per dominio, 95 gap centrali per spettro; non_possible=rimbalzo fisico assente se non si nomina B concreto oppure se `SR` non discrimina GUE da Anderson localizzato; not_tested=dati sperimentali reali, unfolding fine, classi di simmetria GOE/GSE, many-body localization.\n\n## Prima impressione\n\nIl risultato non chiede un altro ingrandimento dei primi. Chiede se la componente che cade (`SR`) e quelle che resistono (`L1,triple_var`) nominano una distinzione fisica: repulsione spettrale presente o assorbita dal bordo Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione `TENS_SCALE_TRASCENDENZA_LIMITE` + deposito `prime_minus_mod6_z_vector`.\n- **Dipolo / punto-zero**: caos quantistico / localizzazione. Punto-zero: stesso numero di livelli e stesso span con null Poisson span-matched.\n- **Piano superiore**: geometria spettrale dei campi; il bordo non e' una classe aritmetica ma un lettore di repulsione/indipendenza nei gap energetici.\n- **Proto-ipotesi**: `SR` misura la repulsione locale assorbibile quando lo spettro torna Poisson; `L1,triple_var` misurano memoria di sequenza oltre il solo nearest-neighbor ratio. Il deposito prime-minus-mod6 diventa trasduttore, non destinazione.\n- **Possibile / non-possibile**: possibile = formulare un test fisico su spettri misurabili; non-possibile = dichiarare claim fisico se il ritorno resta \"sistema discreto\" o \"confine\" senza Hamiltoniano concreto.\n- **Proiezione**: GUE come sorgente fisica A; null Poisson span-matched; Anderson 1D con disordine `W=0.5,2,6,12` come ritorno fisico B.\n\n## Contaminazione cognitiva\n\nCE-0117 usato come cascata della possibilita': il ciclo non resta nel potenziale aritmetico, ma attraversa struttura matematica e torna a un setup fisico. CE-0001/KSAR usato per trasformare il kernel 16:05 in strumento riusabile invece di riscrivere solo un'analogia.\n\n## Aderenza alla direzione\n\n- `relation`: deliberate_counter_perimeter\n- `why`: la direttiva operatore chiede di non proseguire come ottimizzazione locale prime-minus-mod6; il ciclo usa il cedimento `SR` vs `L1,triple_var` come trasduttore e lo porta su spettri fisici.\n- `not_drift`: il deposito prime-minus-mod6 non e' destinazione; decide solo quale domanda portare a GUE/Anderson.\n\n## Ritorno fisico\n\n- **Punto fisico sorgente concreto**: spettri energetici di sistemi quantistici caotici modellati da GUE, proxy per billiard o sistemi senza simmetria di inversione temporale.\n- **Attraversamento matematico**: null Poisson span-matched con osservabili canonici `SR,L1,triple_var`; e' l'analogo fisico del contro-bordo che prova ad assorbire la componente nearest-neighbor.\n- **Punto fisico di ritorno concreto**: Hamiltoniano tight-binding Anderson 1D con disordine crescente, interrogabile tramite spacing degli autovalori e transizione verso statistiche Poisson/localizzate.\n- **Relazione nuova o limite**: il run mostra una formulazione fisica del cedimento selettivo: `SR` e' attivo nel GUE e assorbito nel limite Anderson localizzato `W=12`. A `W=6`, `SR` e `L1` sono assorbiti mentre `triple_var` resta attivo: il ritorno fisico separa nearest-neighbor repulsion e memoria di triple locali.\n- **Osservabile/test fisico possibile**: misurare spettri energetici in un sistema Anderson-like, costruire un ensemble di finestre spettrali, confrontare `SR,L1,triple_var` contro null Poisson span-matched con sign-swap row-local.\n- **Stato finale**: `rimbalzo_fisico_presente`.\n\n## Claim Under Test\n\n> Il cedimento selettivo di `SR` osservato nel deposito prime-minus-mod6 puo' tornare come criterio fisico: `SR` resta attivo in spettri caotici GUE e viene assorbito in spettri Anderson localizzati contro un null Poisson span-matched.\n\n## Experiment Design\n\n- Script: `tools/exp_physical_sr_residue_bounce.py`.\n- Ensemble: GUE hermitiano `N=192`, Anderson 1D `N=192`.\n- Repliche: 48 per dominio.\n- Finestra: 50% centrale dello spettro, 95 gap normalizzati per media.\n- Null: livelli Poisson uniformi span-matched con stesso count.\n- Gate componente: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`; altrimenti `absorbed`.\n- Trace: 240 eventi JSONL, uno per replica/dominio.\n\n## Results\n\n| domain | samples | focus active | SR real/null/delta | p(SR) | L1 delta | p(L1) | triple delta | p(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|\n| GUE chaotic proxy | 48 | SR,L1,triple_var | 0.5936 / 0.3881 / 0.2055 | 0.000244 | -0.3047 | 0.000244 | -2.4262 | 0.000244 |\n| Anderson W=0.5 | 48 | SR,L1,triple_var | 0.6573 / 0.3903 / 0.2671 | 0.000244 | -0.3240 | 0.000244 | -2.5721 | 0.000244 |\n| Anderson W=2 | 48 | SR,L1,triple_var | 0.4364 / 0.3774 / 0.0589 | 0.000244 | -0.1373 | 0.000244 | -1.6244 | 0.000244 |\n| Anderson W=6 | 48 | triple_var | 0.3984 / 0.3824 / 0.0160 | 0.035392 | -0.0589 | 0.002929 | -0.7407 | 0.000244 |\n| Anderson W=12 | 48 | [] | 0.3798 / 0.3836 / -0.0038 | 0.585062 | -0.0262 | 0.168904 | -0.4147 | 0.002685 |\n\nNota gate: a `W=6`, `L1` ha p sotto soglia ma effect `d=-0.443`, quindi e' assorbito dal gate dichiarato. A `W=12`, `triple_var` ha p sotto soglia ma effect `d=-0.442`, quindi non supera il gate.\n\n## Key Findings\n\n1. **Verificato**: nel proxy GUE, `SR` resta attivo contro Poisson span-matched (`delta=0.2055`, `p=0.000244`, `d=4.872`).\n2. **Verificato**: nel ritorno Anderson localizzato `W=12`, `SR` viene assorbito (`delta=-0.0038`, `p=0.585062`, `d=-0.076`).\n3. **Verificato**: la zona intermedia `W=6` separa componenti: `SR` assorbito, `triple_var` attivo.\n4. **Inferito dal perimetro**: il deposito prime-minus-mod6 produce un osservabile fisico formulabile: non \"i primi sono fisica\", ma \"la componente `SR` boundary-sensitive corrisponde al test di repulsione spettrale contro Poisson\".\n5. **Ambiguo dichiarato**: Anderson 1D non ha vera transizione estesa/localizzata nel limite infinito; qui il risultato e' un finite-size disorder crossover, non claim universale sulla transizione.\n\n## Verdict\n\n**CONSTRAINT / PHYSICAL BOUNCE PRESENT**.\n\nIl rimbalzo fisico esiste come test: GUE -> span-matched Poisson -> Anderson 1D. Il risultato non cristallizza una nuova legge fisica. Formalizza un ponte interrogabile: quando lo spettro torna localizzato/Poisson, `SR` viene assorbito; nella zona di crossover, `triple_var` puo' restare come residuo oltre nearest-neighbor.\n\n## Bicono della scoperta\n\n- **Due radici**: repulsione spettrale caotica / assorbimento Poisson localizzato.\n- **Singolare**: null Poisson span-matched con stesso count.\n- **Invariante di passaggio**: `component_state` attivo/assorbito per `SR,L1,triple_var`.\n- **Campo di possibilita**: usare il vettore a due strati come test su spettri fisici reali.\n- **Campo non-possibile**: chiamare fisico un rimbalzo che resta solo analogia aritmetica.\n\n## Consecutio\n\nIl prossimo ciclo fisico deve sostituire il proxy sintetico con un denominatore piu vicino al laboratorio: GOE/GUE scelto per simmetria dichiarata, Anderson 3D o many-body localization se si vuole una transizione fisica vera, unfolding controllato e confronto con dati o letteratura. Il deposito prime-minus-mod6 resta utile solo come generatore di osservabili, non come prova del dominio fisico.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script crea un tester riusabile per spettri ordinati fisici: basta sostituire il generatore di livelli con dati sperimentali o simulazioni esterne e il contratto `component_state` resta identico.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/physical_sr_residue_bounce_20260514_1612.json`\n- `tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl`\n- `tools/data/reports/agent_20260514_1612.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1678: "text": "- Script: `tools/exp_physical_sr_residue_bounce.py`.\n- Ensemble: GUE hermitiano `N=192`, Anderson 1D `N=192`.\n- Repliche: 48 per dominio.\n- Finestra: 50% centrale dello spettro, 95 gap normalizzati pe" tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1690: "text": "**CONSTRAINT / PHYSICAL BOUNCE PRESENT**.\n\nIl rimbalzo fisico esiste come test: GUE -> span-matched Poisson -> Anderson 1D. Il risultato non cristalli" tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2348: "label": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2349: "label_en": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:7: "title": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:9: "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:10: "verdict": "VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 restano active contro Poisson, ma il contrasto diretto separa solo `triple_var`; `SR` e `L1` non chiudono il bordo 3D a `L=6`.", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:14: "consecutio": "Il prossimo seme possibile non e' \"Anderson 3D confermato\". E' un audit del tool su fisico B con taglie multiple e denominatore dichiarato: `L=5,6,7` o unfolding locale, stesso contract, stesso null. Se `SR,L1` restano non_separated e `triple_var` resta separated, il tool mostra che il bordo 3D entra prima nella componente di profondita' che nel piano pair-statistics.", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:53: "radici": "simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D.", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:92: "dipole": "simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D.", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:157: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:158: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/data/restore_backups/pre_restore_20260514_1330/graph_completion/latest.json:197: "Anderson_1D_W6": { tools/data/restore_backups/pre_restore_20260514_1330/ciclo_memoria.json:304: "verdict": "**CONSTRAINT / PHYSICAL BOUNCE PRESENT**.\n\nIl rimbalzo fisico esiste come test: GUE -> span-matched Poisson -> Anderson 1D. Il risultato non cristallizza una nuova legge fisica. Formalizza un ponte in", tools/data/restore_backups/pre_restore_20260514_1330/ciclo_memoria.json:416: "titolo": "Interfaccia component_state su bordo Anderson 3D", tools/data/restore_backups/pre_restore_20260514_1330/ciclo_memoria.json:433: "cosa": "Interfaccia component_state su bordo Anderson 3D → None/None (ρ=None)" tools/data/restore_backups/pre_restore_20260514_1330/ciclo_memoria.json:444: "domanda_aperta": "Il prossimo seme possibile non e' \"Anderson 3D confermato\". E' un audit del tool su fisico B con taglie multiple e denominatore dichiarato: `L=5,6,7` o unfolding locale, stesso contract, stesso null. Se `SR,L1` restano non_separated e `triple_var` resta separated, il tool mostra che il bordo 3D entra prima nella componente di profondita' che nel piano pair-statistics.", tools/data/restore_backups/pre_restore_20260514_1330/lab_data.json:101: "content": "# Agent Report - Interfaccia component_state su bordo Anderson 3D\n**Date**: 2026-05-14 18:50 \n**Piano**: 126 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 restano active contro Poisson, ma il contrasto diretto separa solo `triple_var`; `SR` e `L1` non chiudono il bordo 3D a `L=6`. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta] \ntool_contract_ref: `tools/data/tool_contracts/component_state_SR_L1_triple_var_fit_ready_20260514_1649.json` \npreflight_anchor: `20260514_1640` VALUE_STABLE, Veritas COLLASSO rho=0.9325, falsifier coherent=true zero flag \nnot_promoted_as_physics_law: true \nssp_value: yes\n\n## Source directive\n\nQuesto ciclo segue la direttiva one-shot del 2026-05-14: ripartire da `20260514_1640`, non dal ramo `20260514_1701`, usare `component_state_SR_L1_triple_var_fit_ready_20260514_1649` come tool contract, e formulare una domanda stretta sulla riusabilita' del tester per leggere un nuovo spettro fisico. Il ramo quasiperiodico/Sturmian resta `vault`, non direzione primaria.\n\n## Prima impressione\n\nIl tester non deve predire la transizione Anderson 3D. Deve mostrare se uno spettro fisico nuovo puo' entrare nell'interfaccia e tornare come stato componente auditabile. Se il bordo non separa su `SR`, quello e' il contenuto: il tool trasferisce, la legge no.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione BOUNDARY GUE/Poisson + nodo fisico Anderson 3D.\n- **Dipolo / punto-zero**: caos metallico / localizzazione critica. Punto-zero: Poisson span-matched, perche' ogni spettro torna leggibile solo se il null fisico-matematico resta esplicito.\n- **Piano superiore**: geometria spettrale dei campi disordinati; il bordo e' letto come componente attiva/assorbita, non come fit di transizione.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' interfaccia riusabile se un nuovo spettro fisico ordinato produce component states contro Poisson e contrasto diretto dichiarato senza cambiare definizioni dal registry.\n- **Possibile / non-possibile**: possibile = usare il tool su spettro fisico B e ottenere output auditabile; non-possibile = dichiarare transizione Anderson 3D o legge universale quando `SR,L1` non separano il contrasto diretto.\n- **Proiezione**: genero 24 spettri Anderson 3D tight-binding con condizioni periodiche, `L=6`, 12 repliche per `W=4.0` e `W=16.5`; poi li passo all'interfaccia `--input-spectrum` del tester 16:49.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: l'esperimento resta nel pe" tools/data/reports/agent_20260423_0330.md:8:> The boundary between GUE and Poisson regimes should affect the two-channel decomposition. If gap correlations decay with prime scale (Brody β → 0), does the Markov-3 ordering information in the residue channel also decay? tools/data/reports/agent_20260423_0330.md:11:How does the Markov-3 ordering fraction (the 55% sequential information found by the shuffle audit) change as a function of prime scale? Does it track the GUE→Poisson drift measured by the Brody parameter? tools/data/reports/agent_20260423_0330.md:19:- **Additional metrics**: Brody β (GUE/Poisson indicator), lag-1 ACF tools/data/reports/agent_20260423_0330.md:24:| Scale range | ln(p) | Ordering % | Brody β | lag-1 ACF | z-score | tools/data/reports/agent_20260423_0330.md:37:| Brody β | -0.0207 | 0.72 | 0.42 → 0.29 | tools/data/reports/agent_20260423_0330.md:39:Brody β decays **5.2x faster** than the ordering fraction. tools/data/reports/agent_20260423_0330.md:45:| ordering_frac vs Brody β | +0.52 | tools/data/reports/agent_20260423_0330.md:58:2. **Brody β decays 5x faster toward Poisson.** β drops from 0.42 to 0.29 over the same range (R²=0.72). Gap-level correlations (the GUE signature) decorrelate with scale. But this decorrelation does NOT propagate into the mod-6 sequential memory. tools/data/reports/agent_20260423_0330.md:60:3. **The correlation is moderate (r=0.52), not causal.** The ordering fraction and Brody β share some variance but are measuring different things. Brody measures gap-gap correlation (metric-level). Markov-3 measures residue-residue transition probabilities (algebraic-level). These are structurally distinct layers. tools/data/reports/agent_20260423_0330.md:69:The BOUNDARY tension (GUE→Poisson) operates in the magnitude channel (gap correlations, Brody β). It does NOT operate in the residue channel (Markov-3 memory). The two-channel decomposition separates scale-dependent structure from scale-invariant structure. This is a structural result: the "boundary" lives in one channel, not both. tools/data/reports/agent_20260423_0330.md:75:- **Due radici** (dipolo primario): struttura scala-dipendente (Brody β, PSD slope — si indeboliscono con la scala, il segnale metrico decade) · struttura scala-invariante (Markov-3 ordering — costante al 33.6%, il segnale algebrico persiste). L'una decade, l'altra no. Sono inseparabili nella sequenza ma separabili nella decomposizione a due canali. tools/data/reports/agent_20260430_1905.md:21:- **Five observables**: spacing ratio ⟨r⟩, gap variance ratio Var/μ², small-gap fraction P(s<0.3), Brody β, lag-1 autocorrelation tools/data/reports/agent_20260430_1905.md:74:4. **Universal Poisson drift confirmed.** All five τ values decrease with scale (Δτ ≈ −0.14 to −0.19 from 10⁴ to 5·10⁶). This confirms the Brody flow finding from the previous run. The drift rate is roughly constant per observable, suggesting a single underlying process. tools/data/reports/agent_20260430_1905.md:92:- **Singolare** (1-che-è-tutto): the ordering itself, before the observables split it into spacing_ratio and lag1_acf. The ordering is one phenomenon that cannot be captured by a single number — it requires the dipole. Any scalar summary (like Brody β alone) loses half the content. tools/data/promotions/promotion_20260515_1904.json:18: "summary": "CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno dei due chiude il terzo incluso da solo.", tools/data/promotions/promotion_20260514_1640.json:18: "summary": "**CONSTRAINT / TESTER OPERATIVO RAFFORZATO**.\n\nIl ciclo rafforza lo strumento, non il claim fisico. `component_state(SR,L1,triple_var)` ora ha un denominatore piu' robusto: distingue GOE e GUE tramite adjacent gap ratio, resta leggibile su due taglie, e mantiene Anderson `W=6` come bordo in cui `SR` viene assorbito prima del residuo `triple_var`.", tools/data/reports/agent_20260514_1640.md:4:**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson tools/data/reports/agent_20260514_1640.md:5:**verdict**: CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica. tools/data/reports/agent_20260514_1640.md:8:**observable_contract**: claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico. tools/data/reports/agent_20260514_1640.md:12:Questo ciclo prosegue il tester L8 del report `tools/data/reports/agent_20260514_1631.md`. Non apre una nuova legge fisica e non ritorna a `prime-minus-mod6`. Il vincolo seguito e': rafforzare il tester operativo `component_state(SR,L1,triple_var)` su denominatore fisico piu' robusto distinguendo GOE/GUE, facendo una piccola curva in `N`, mantenendo Anderson 1D `W=6` come bordo finite-size, e confrontando il tester con adjacent gap ratio. tools/data/reports/agent_20260514_1640.md:20:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + direzione BOUNDARY GUE/Poisson + nodo fisico Wigner-Dyson/Anderson. tools/data/reports/agent_20260514_1640.md:25:- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' un tester di leggibilita' del bordo spettrale: `SR` discrimina simmetria caotica e viene assorbito al bordo Anderson `W=6`; `triple_var` conserva un residuo operativo locale. tools/data/reports/agent_20260514_1640.md:26:- **Possibile / non-possibile**: possibile = usare il tester come diagnostica compatta su spettri ordinati con classe di simmetria dichiarata; non-possibile = chiamare universale una curva a due taglie o trattare Anderson 1D come transizione fisica generale. tools/data/reports/agent_20260514_1640.md:27:- **Proiezione**: misuro GOE, GUE e Anderson `W=6` a `N=128,192`; ogni dominio viene confrontato con Poisson span-matched, poi GUE viene confrontato direttamente con GOE. tools/data/reports/agent_20260514_1640.md:32:- `why`: il ciclo segue la direzione viva perche' rafforza il frame GUE/Poisson-Anderson indicato dal valutatore e rende `component_state(SR,L1,triple_var)` piu' falsificabile. tools/data/reports/agent_20260514_1640.md:37:> Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size. tools/data/reports/agent_20260514_1640.md:41:Il tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`? tools/data/reports/agent_20260514_1640.md:47:- **Punto fisico di ritorno**: classi di simmetria GOE beta=1 reale-simmetrica e GUE beta=2 hermitiana complessa, piu Anderson 1D disordinato `W=6`. tools/data/reports/agent_20260514_1640.md:50:- **Se fallisce**: `ritorno_fisico_assente` se GOE/GUE non si separano o se Anderson `W=6` non assorbe `SR`; in quel caso il tester resta solo scaffold sintetico. tools/data/reports/agent_20260514_1640.md:57:- **Anderson localization / finite-size disorder crossover**: Anderson 1D `W=6` e' bordo finite-size, non transizione universale. tools/data/reports/agent_20260514_1640.md:64:- Perimetro: GOE, GUE, Anderson 1D `W=6`; due taglie; 64 repliche per taglia e dominio. tools/data/reports/agent_20260514_1640.md:77:| Anderson 1D W=6 | 128 | triple_var | 0.3875 / 0.3871 / 0.0003 | 0.950446 | 0.006 | -0.0656 | 0.000122 | -0.460 | -0.6374 | 0.000122 | -0.683 | tools/data/reports/agent_20260514_1640.md:85:| Anderson 1D W=6 | 128 | 64 | triple_var | 0.3908 | -0.0007 | 0.930428 | -0.011 | -0.6946 | 0.000122 | -0.622 | tools/data/reports/agent_20260514_1640.md:88:| Anderson 1D W=6 | 192 | 64 | triple_var | 0.3841 | 0.0013 | 0.824118 | 0.029 | -0.5801 | 0.000122 | -0.816 | tools/data/reports/agent_20260514_1640.md:102:- **Verificato / operational boundary**: Anderson `W=6` assorbe `SR` in entrambe le taglie (`p=0.930428` a `N=128`, `p=0.824118` a `N=192`) mentre `triple_var` resta active. tools/data/reports/agent_20260514_1640.md:104:- **Non verificato**: dati sperimentali, unfolding dedicato, classi GSE, Anderson 3D, many-body localization, limite asintotico. tools/data/reports/agent_20260514_1640.md:117:3. **Verificato**: Anderson 1D `W=6` resta bordo finite-size: `SR` e' absorbed, `triple_var` resta active. tools/data/reports/agent_20260514_1640.md:125:Il ciclo rafforza lo strumento, non il claim fisico. `component_state(SR,L1,triple_var)` ora ha un denominatore piu' robusto: distingue GOE e GUE tramite adjacent gap ratio, resta leggibile su due taglie, e mantiene Anderson `W=6` come bordo in cui `SR` viene assorbito prima del residuo `triple_var`. tools/data/reports/agent_20260514_1640.md:131:- **Invariante di passaggio**: `SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size. tools/data/piano11_results.json:72: "background": "V(r) = -ln(r) - r + r\u00b2/2 \u2192 Berry-Keating N(E) form (Piano 10g)", tools/data/piano11_results.json:100: "Berry_Keating_coefficient_measured": -0.014, tools/data/piano11_results.json:101: "Berry_Keating_coefficient_Riemann": 0.159, tools/data/promotions/promotion_20260515_1758.json:18: "summary": "**CONSTRAINT**.\n\nIl contro-perimetro coseno falsifica la promozione `phi` come terzo incluso fisico autonomo. Nel perimetro misurato il boundary congiunto non sopravvive quando la grammatica binaria viene rimossa. La formulazione valida diventa: `phi` e' candidato boundary nel potenziale binario Sturmian 17:45; nel coseno Aubry-Andre il contenuto si sposta alla classe quasi-periodica e alla transizione di localizzazione, non a phi come generatore privilegiato.", tools/data/audit_paper_G_draft3.json:60: "message": "Missing traceability markers: algebraic field Q(√5), Berry-Keating connection", tools/data/operator_directives_consumed/operator_directive_20260514_1850.md:57:- se il ciclo devia dalla direzione GOE/GUE/Poisson-Anderson, deve dichiarare tools/data/engine_state.json:138: "claim": "Larghezza transizione Anderson: φ ha Δ=0.43 (la più stretta)" tools/data/operator_directives_consumed/operator_directive_20260514_1631.md:41:- Anderson localization / finite-size disorder crossover; tools/data/operator_directives_consumed/operator_directive_20260514_1631.md:57: -> Anderson 1D con disordine crescente tools/data/operator_directives_consumed/operator_directive_20260514_1631.md:62:- `SR` attivo in GUE e assorbito verso Anderson localizzato; tools/data/operator_directives_consumed/operator_directive_20260514_1640.md:36:- Anderson localization / finite-size disorder crossover; tools/data/operator_directives_consumed/operator_directive_20260514_1640.md:45:3. mantenere Anderson 1D `W=6` come bordo finite-size; tools/data/operator_directives_consumed/operator_directive_20260514_1640.md:54:- Non usare Anderson 1D come transizione universale. tools/data/operator_directives_consumed/operator_directive_20260514_1640.md:55:- Non aprire many-body localization o Anderson 3D se non serve a chiudere tools/data/b2_apply_log.jsonl:11:{"ts": "2026-05-15T18:03:27.877263+00:00", "cycle_ref": "20260515_1758", "decision": "REDESIGN", "confidence": "high", "rho": 0.9275, "old": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "new": "Testare il confine come proprieta del generatore: Sturmian/binario vs coseno Aubry-Andre vs controlli irrazionali, con b"} tools/data/g1_log.jsonl:72:{"ts": "2026-05-15T19:09:00.544543+00:00", "cycle_ref": "20260515_1904", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[brody_q, berry_robnick_like_gue_weight, mixture_ks, graph_boundary_state_from_1", "observable_contract_present": true, "contract_observable": "Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:73:{"ts": "2026-05-15T19:27:49.855602+00:00", "cycle_ref": "20260515_1915", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[graph_bridge_frequency, cut_edge_frequency, mean_centroid_margin, mean_cross_ne", "observable_contract_present": true, "contract_observable": "frequenza graph bridge unita a stato Brody/Berry-Robnik-like", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/conoscenza_teorie.json.bak.retraction_22_04:763: "claim": "All four observables (Brody beta, dR_acf1, gap_acf1, ) drift toward Poisson with scale. > Is this ONE universal crossover (\"il confine è il terzo incluso\") or independent drifts?", tools/data/conoscenza_teorie.json.bak.retraction_22_04:810: "claim": "All four observables (Brody beta, dR_acf1, gap_acf1, ) drift toward Poisson with scale. > Is this ONE universal crossover (\"il confine è il terzo incluso\") or independent drifts?", tools/data/agent_field_live.md:33: - L5: Cosa resta Lab-specific: `percolation`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699` sono `stable_graph_bridge+graph_only_bridge`, tutte 27/27. Il baseline classico le legge endpoint-like, il grafo le legge confine stabile. tools/data/agent_field_live.md:35:Obblighi pratici: se il dominio e' GUE/Poisson, aggiungi una sezione `## Re-discovery audit` con il baseline noto piu' vicino (Brody/Berry-Robnik/Rosenzweig-Porter, mobility/localization crossover o altro nome pertinente) e cosa resta lab-specific. Per L6, non usare `CE-none` generico: cita una voce CE-* metabolizzata oppure `CE-none:` verificabile. tools/data/agent_field_live.md:151:- Berry phase; tools/data/agent_field_live.md:353:- Brody parameter; tools/data/agent_field_live.md:498:- Anderson localization; tools/data/agent_field_live.md:951:- **Domanda aperta** (consecutio): Il prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche. Il risultato da cercare e' se `graph_only` e `classic_only` sopravvivono fuori tools/data/agent_field_live.md:953: - Aubry Cosine Boundary Counter-Gate → None/None (ρ=None) tools/data/component_state_anderson3d_interface_20260514_1850.json:3: "Anderson_3D_W16p5_critical_boundary_minus_Anderson_3D_W4_metallic_candidate": { tools/data/component_state_anderson3d_interface_20260514_1850.json:11: "label": "Anderson_3D_W16p5_critical_boundary_minus_Anderson_3D_W4_metallic_candidate", tools/data/component_state_anderson3d_interface_20260514_1850.json:69: "Anderson_3D_W16p5_critical_boundary": { tools/data/component_state_anderson3d_interface_20260514_1850.json:89: "label": "Anderson_3D_W16p5_critical_boundary", tools/data/component_state_anderson3d_interface_20260514_1850.json:120: "Anderson_3D_W4_metallic_candidate": { tools/data/component_state_anderson3d_interface_20260514_1850.json:140: "label": "Anderson_3D_W4_metallic_candidate", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1068: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:132:- Berry phase; tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:334:- Brody parameter; tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:479:- Anderson localization; tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:61:- se il ciclo devia dalla direzione GOE/GUE/Poisson-Anderson, deve dichiarare tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:250:- Berry phase; tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:452:- Brody parameter; tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:597:- Anderson localization; tools/data/preflight/preflight_20260515_1758.json:21: "title": "Agent Report - Aubry Cosine Boundary Counter-Gate" tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:132:- Berry phase; tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:334:- Brody parameter; tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:479:- Anderson localization; tools/data/preflight/physics_bridge_pipeline_closeout_20260515.json:3: "object": "qg_discrete_boundary_bridge_probe", tools/data/audit_paper_D_draft2.json:81: "message": "Missing traceability markers: determinant condition, det=-1 explicit, generating map f(x)=1+1/x, potential V(r), three-force decomposition, algebraic field Q(√5), Berry-Keating connection", tools/data/preflight/graph_completion_latest.json_backup_pre_1724_public_rollback_20260515:11: "observable_contract": "claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita.", tools/data/preflight/graph_completion_latest.json_backup_pre_1724_public_rollback_20260515:14: "consecutio": "Il prossimo ciclo non deve cercare un nuovo fit per `V_c`. Deve decidere se il reticolo di attraversamento trasferisce a un modello fisico piu canonico, con fase controllata e baseline nota: Aubry-Andre/Fibonacci, curva di localizzazione o trasmissione fotonica. Se non viene portato in quel perimetro, resta vincolo matematico sul denominatore.", tools/data/preflight/graph_completion_latest.json_backup_pre_1724_public_rollback_20260515:116: "observable_contract": "claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita.", tools/data/preflight/graph_completion_latest.json_backup_pre_1724_public_rollback_20260515:132: "evidence": "Il report nomina Aubry-Andre/Fibonacci e mobility edge, ma non ancora il risultato classico piu' vicino: sensibilita' di fase, finite-size effects e critical-coupling distributions nei modelli quasiperiodici/Fibonacci sono un perimetro noto. La formulazione `Relazione nuova` rischia di promuovere una ri-scoperta come discovery autonoma.", tools/data/preflight/preflight_20260515_1807.md:6:- report: Agent Report - Aubry Binary Grammar Surrogate Gate tools/data/preflight/preflight_20260514_1640.json:21: "summary": "Il report e' internamente coerente rispetto alle 8 lenti: non formula hard constraint falsificati dai dati visibili, dichiara non_possible, metabolizza CE/KSAR, cita baseline note e mantiene aderenza alla direzione GOE/GUE/Poisson-Anderson.", tools/data/reports/agent_20260501_0931.md:69:**CONSTRAINT on DIPOLAR_ORDERING**: Prime magnitude matches the GUE crossover at alpha ~ 0.45 (z=1.1), but the direction is 8.7 degrees off (32 sigma). Primes are not "partially shuffled GUE" — they share the quantity of ordering but differ in quality. Perimeter: this comparison uses partial-shuffle as the crossover mechanism. Other mechanisms (e.g., Rosenzweig-Porter, Brody) might yield different crossover topologies. tools/data/reports/agent_20260501_0931.md:71:**L5 note (re-discovery check)**: The GUE-Poisson transition is well-studied (Rosenzweig-Porter model, Brody distribution, Anderson localization). The specific observation that the DIPOLAR DIRECTION is an invariant of the ordered regime while the magnitude decays linearly appears novel in this framework. Default hypothesis: direction invariance likely follows from the linearity of SR and L1 as functions of ordering fraction. The phase transition at the zero-crossing is structural — it marks where the ordering signal changes sign, not just magnitude. tools/data/preflight/preflight_20260515_1745.json:21: "title": "Agent Report - Aubry Boundary Phase Transport Gate" tools/data/reports/agent_20260429_1041.md:1:# Agent Report — Mod-3 Ordering Is Algebraic and Scale-Invariant: A Separate Channel from Brody Beta tools/data/reports/agent_20260429_1041.md:9:> Brody flow found beta(p) = 0.64 - 0.030*ln(p). Does mod-3 ordering decay the same way? tools/data/reports/agent_20260429_1041.md:13:How does the mod-3 gap ordering signal scale along the prime sequence, compared to the Brody beta flow? tools/data/reports/agent_20260429_1041.md:23:- **Comparison**: slope of each observable vs ln(p), compared with Brody slope -0.030/ln(p) tools/data/reports/agent_20260429_1041.md:39:### Scaling laws compared with Brody flow tools/data/reports/agent_20260429_1041.md:43:| Brody beta | -0.030 | 0.78 | Decays toward Poisson | tools/data/reports/agent_20260429_1041.md:91:2. **The mod-3 channel is a SEPARATE channel from Brody beta.** Brody beta decays at -0.030/ln(p). The mod-3 algebraic signal is flat (slope = +0.12, R^2 = 0.0004). They measure fundamentally different things: beta measures continuous short-range gap repulsion; mod-3 measures discrete algebraic structure of Z/3Z acting on primes. tools/data/reports/agent_20260429_1041.md:102: Brody beta sees layers 1 and 2. Mod-3 self-transition sees only layer 3. The Markov LLR mixes all three. tools/data/reports/agent_20260429_1041.md:108:What is NOT in Lemke Oliver-Soundararajan: the decomposition into algebraic-invariant vs statistical-decaying layers, the comparison of scaling rates with Brody beta flow, and the identification of three distinct structural layers in prime gaps. The scaling comparison is the new content; the algebraic constraint is classical. tools/data/reports/agent_20260429_1041.md:122:- **META refined**: the structural observables separate into two kinds — algebraic (mod-3, scale-invariant, z ~ -42) and metric (r-stat, Brody beta, decaying). "Structural" is not one category; it has depth. The lab should track algebraic and metric channels separately. tools/data/reports/agent_20260429_1041.md:123:- **BOUNDARY constrained**: the GUE/Poisson boundary (Brody flow) only describes layers 1-2. Layer 3 (algebraic) is invisible to Brody beta. Any complete model of the boundary must include the algebraic floor. tools/data/reports/agent_20260429_1041.md:127:- **Due radici** (dipolo primario): segnale metrico (continuo, decade con la scala — Brody beta, r-stat, correlazioni Hardy-Littlewood) / segnale algebrico (discreto, esatto, scala-invariante — T[1][1]=T[2][2]=0 come teorema). I due vivono nello stesso dato (gap tra primi) ma operano su piani diversi (reale vs categoriale). tools/data/reports/agent_20260429_1041.md:130:- **Campo di possibilità**: diventa possibile modellare il confine GUE/Poisson con un pavimento algebrico che non decade — il Brody beta raggiunge 0 a p ~ 10^9 ma la struttura mod-3 resta. Diventa non-possibile trattare "strutturale" come una singola categoria — ci sono strutture che decadono e strutture che non decadono, e il confine tra i due è il contenuto. tools/data/preflight/agent_field_live_candidate_20260515_1624.md:129:- Berry phase; tools/data/preflight/agent_field_live_candidate_20260515_1624.md:331:- Brody parameter; tools/data/preflight/agent_field_live_candidate_20260515_1624.md:476:- Anderson localization; tools/data/preflight/agent_field_live_candidate_20260515_1608.md:130:- Berry phase; tools/data/preflight/agent_field_live_candidate_20260515_1608.md:332:- Brody parameter; tools/data/preflight/agent_field_live_candidate_20260515_1608.md:477:- Anderson localization; tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:129:- Berry phase; tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:331:- Brody parameter; tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:476:- Anderson localization; tools/data/reports/agent_20260515_1758.md:1:# Agent Report - Aubry Cosine Boundary Counter-Gate tools/data/reports/agent_20260515_1758.md:5:**verdict**: CONSTRAINT - nel potenziale Aubry-Andre coseno `phi` non chiude un boundary privilegiato; la finestra binaria 17:45 dipende dalla grammatica Sturmian, non dal solo irrazionale phi. tools/data/reports/agent_20260515_1758.md:8:**observable_contract**: claim=`phi` resta terzo incluso fisico anche quando il potenziale binario viene sostituito dal coseno Aubry-Andre canonico; observable=`spacing_r` + `mean_ipr` con controllo di distinzione da silver/bronze; operator=`tools/exp_aubry_cosine_boundary_counter_gate.py`; generator=Hamiltoniana tight-binding 1D con potenziale coseno per beta phi/silver/bronze, periodico beta=1/2 e random onsite uniforme; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..3.00 step 0.25, random_trials=6; non_possible=promuovere phi come boundary fisico se non si separa dai controlli irrazionali con spacing e localizzazione insieme; not_tested=limite asintotico, disordine correlato sperimentale, classi GUE/Poisson universali dirette. tools/data/reports/agent_20260515_1758.md:12:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + modello Aubry-Andre come ponte fisico tra quasi-periodicita e localizzazione + tensione viva `BOUNDARY`. tools/data/reports/agent_20260515_1758.md:17:- **Proto-ipotesi**: se `phi` e' boundary fisico e non solo effetto della codifica binaria, nel coseno Aubry-Andre deve restare insieme tra periodico e random e deve separarsi da silver/bronze su spacing e localizzazione. tools/data/reports/agent_20260515_1758.md:23:- `why`: segue la direzione viva "8 domini GUE, 5 Poisson - il confine e' il terzo incluso operativo" verificando se il confine fisico aperto nel ritorno Aubry/Fibonacci sopravvive a un contro-perimetro canonicale. tools/data/reports/agent_20260515_1758.md:28:> Nel potenziale Aubry-Andre coseno, `phi` resta terzo incluso fisico tra periodico e random solo se `spacing_r` e localizzazione lo collocano insieme nel segmento periodico-random e lo separano da silver/bronze. tools/data/reports/agent_20260515_1758.md:32:Il confine `phi` osservato nel ritorno binario 17:45 sopravvive nel coseno Aubry-Andre, oppure il contenuto era nella grammatica Sturmian del potenziale? tools/data/reports/agent_20260515_1758.md:38:- **Punto fisico di ritorno**: modello Aubry-Andre canonico con transizione di localizzazione attesa attorno a V=2 per hopping unitario. tools/data/reports/agent_20260515_1758.md:40:- **Relazione nuova**: la finestra binaria 17:45 non trasferisce automaticamente alla classe Aubry-Andre coseno; il generatore del potenziale diventa parte atomica del claim. tools/data/reports/agent_20260515_1758.md:76:5. **Inferito**: il risultato 17:45 trasferisce come vincolo sul generatore binario, non come claim phi-specifico universale del modello Aubry-Andre. tools/data/reports/agent_20260515_1758.md:82:Il contro-perimetro coseno falsifica la promozione `phi` come terzo incluso fisico autonomo. Nel perimetro misurato il boundary congiunto non sopravvive quando la grammatica binaria viene rimossa. La formulazione valida diventa: `phi` e' candidato boundary nel potenziale binario Sturmian 17:45; nel coseno Aubry-Andre il contenuto si sposta alla classe quasi-periodica e alla transizione di localizzazione, non a phi come generatore privilegiato. tools/data/reports/agent_20260515_1758.md:86:- **Due radici**: potenziale binario Sturmian / potenziale coseno Aubry-Andre. tools/data/reports/agent_diag2.md:11:1. Does de Sitter curvature fluctuation dR predict Brody beta beyond the trivial scale trend? tools/data/reports/agent_diag2.md:17:- Per window: dR_std, dR_norm, dR_acf1, Brody beta (MLE), gap ratio , gap acf1 tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:10:{"ts": "2026-04-28T03:41:26.305485+00:00", "cycle_ref": "20260427_0330", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Three consecutive cycles on the same frame (shuffle→GUE types→Brody calibration) have converged on a single, replicated, calibrated result: primes exhibit a two-channel structure (gap distribution at beta_eff=0.409 + 30% sequential ordering above the 7.8% artifact floor). The sign of the ordering channel discriminates domain types (rigidity vs bunching), mapping directly onto det=-1/det=+1. This is no longer emergent — it's stable across three independent measurements and calibrated against a null. It belongs in the condensato.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "TWO-CHANNEL DECOMPOSITION (candidate F7 or revision of F4): Primes under M decompose into two independent channels: (1) gap distribution — Brody beta_eff ≈ 0.41, intermediate repulsion at the Poisson-GUE boundary; (2) sequential ordering — 30% of spectral rigidity at L=10 comes from gap ordering absent in i.i.d. surrogates (artifact floor: 7.8%, measured: 29.5%, z=−8.9). The ordering channel has definite sign: primes add rigidity (det=−1), chaotic/coupled systems add bunching (det=+1), pure GUE/Poisson sit on the Brody curve (ordering irrelevant). Three independent measurements converge: shuffle audit (33.6%), spectral rigidity (33%), Brody-calibrated (29.5%). Replicated, calibrated, falsifiable."}}, "notes": "After crystallization, the frame is likely exhausted for now. The next cycle should pivot — either toward the QxG void (the only unfilled fundamental question), toward formalizing the sign discrimination as a structural theorem, or toward a completely different tension. Recommend the seme direction shift away from BOUNDARY after this crystallization lands."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:11:{"ts": "2026-04-29T09:00:17.018760+00:00", "cycle_ref": "20260429_0833", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Four consecutive cycles on the same frame (shuffle audit → GUE types → Brody calibration → two-channel boundary → this: opposite scaling laws) have converged on a single, clean, falsifiable structural result: the residue channel is scale-invariant (algebraic, det=-1) while the magnitude channel decays toward Poisson (statistical, approaching det=+1). The 'GUE/Poisson boundary' collapses into a mixing artifact once the channels are separated. This is not incremental — it reframes what 'boundary' means for primes under M. Three crystallizations in a row is unusual, but each captures a distinct layer: (1) mod-3 memory structure, (2) Brody artifact floor, (3) opposite-boundary decomposition. This third one is the capstone that unifies the previous two. After crystallization, the BOUNDARY frame is likely saturated — the next cycle should pivot.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "F2 addendum — Two-channel decomposition of prime gaps under M: the residue channel (Z/6Z binary, mod-3 prohibition) is scale-invariant (z=26-44σ across 200x range, decay correlation with ln(p) ≈ -0.19). The magnitude channel (demeaned gap size by transition type) decays toward Poisson (z=2-7σ, decay correlation +0.46). The r-statistic mixes these incommensurable behaviors: its 'GUE/Poisson crossover' is not a phase transition but the magnitude channel approaching noise while the algebraic channel remains invariant. Implication: any single-number summary (r, Brody β) conflates permanent algebraic structure with transient statistical memory. Decompose first, then measure. Verified with 500K primes, 28 log-spaced windows, 20 shuffles per window."}}, "notes": "After this crystallization, recommend REDESIGN for piano 59: the BOUNDARY frame has yielded its structural content across 4 cycles. Two natural directions: (1) test the two-channel decomposition on a NON-prime domain (Stern-Brocot, logistic map gaps) to see if the algebraic/statistical split is universal under M or specific to primes — this would directly test C1; (2) attack the QxG void ('how do continuous and discrete coexist?') which has zero bridges and is the only unsolved domanda fondamentale. Direction (1) is the stronger scientific move because it falsifies or extends tonight's result cross-domain."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:14:{"ts": "2026-04-29T10:51:26.490788+00:00", "cycle_ref": "20260429_1013", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un risultato strutturale genuino: la mappa 2D beta(N,L) con gradienti opposti (Poisson lungo N, GUE lungo L) unifica due esperimenti indipendenti (spectral rigidity + Brody flow). La decomposizione 82/18 magnitude/ordering è pulita, con shuffle control e Cramer reference. Ma il seme_delta è vuoto — la scoperta non è entrata nel seme, violando la regola 'il ciclo è chiuso quando la scoperta entra nel seme'. Dopo 5+ cicli sulla stessa frame (shuffle audit → GUE types → Brody calibration → two-channel → Brody flow), il quadro è maturo per cristallizzazione.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "CANDIDATO F7 — Flusso Brody 2D: beta(N,L) ha gradienti opposti. Lungo la sequenza (N crescente), beta decresce (→ Poisson); a scala spettrale fissa (L crescente), beta cresce (→ GUE). Il confine GUE/Poisson è una curva in spazio 2D, non un punto. Decomposizione: 82% magnitudine (distribuzione gap diventa più esponenziale per PNT), 18% ordinamento (anti-bunching mod-3 riduce repulsione). Slope beta(p) = 0.64 - 0.030·ln(p), R²=0.78, z-score vs shuffle = -2.42. Cramer pure Poisson ovunque (beta~0.015) — il segnale è aritmetico, non statistico."}}, "notes": "Dopo la cristallizzazione, la frame BOUNDARY è esaurita per ora — i prossimi cicli dovrebbero spostarsi su un'altra tensione (suggerisco QxG che è ancora VUOTO nelle domande fondamentali, o un ciclo di consolidamento cross-dominio per verificare se la mappa 2D si replica su altri domini dinamici). Il seme va aggiornato con il risultato prima del prossimo giro."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:15:{"ts": "2026-04-30T03:37:21.213696+00:00", "cycle_ref": "20260429_1041", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un teorema algebrico (proibizione self-transition mod-3) con dimostrazione esplicita, non un fit numerico. La decomposizione a tre strati (magnitudine PNT, ordinamento statistico, struttura algebrica) è genuina e falsificabile: ogni strato ha scaling diverso verificato su 72 finestre. Il rapporto M2/M1=0.472 scale-invariant è un nuovo invariante strutturale. Questo estende F2 (Z/6Z) con una gerarchia di canali indipendenti — non è dettaglio locale, è architettura.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "THREE-LAYER DECOMPOSITION dei gap primi sotto M: (1) Magnitudine — distribuzione gap → esponenziale, scaling ~1/ln(p), è PNT. (2) Ordinamento statistico — Brody beta decay -0.030/ln(p), repulsione short-range che si attenua. (3) Struttura algebrica — proibizione mod-3 self-transition (teorema: due gap consecutivi ≡1 o ≡2 mod 3 implicano p_{n+2}≡0 mod 3, impossibile). Zero scaling (R²=0.0004), zero eccezioni su 148k gap. Il canale algebrico è eterno, il statistico decade, il magnitudinale scala con PNT. Invariante emergente: M2/M1=0.472±0.017 costante su tutte le scale — la profondità di memoria Markov è fissa. Estende F2 (Z/6Z) con gerarchia di indipendenza tra canali."}}, "notes": "La proibizione mod-3 è un sottoinsieme di F2 (Z/6Z → coset {2,4}), ma la dimostrazione esplicita e la separazione di scaling rispetto a Brody sono nuove. La decomposizione a tre strati è il risultato principale — dà struttura al 'perché i primi sono unici sotto M' (C1). Dopo la cristallizzazione, la direzione nel seme (confine GUE/Poisson come terzo incluso) è naturale prossimo passo: il confine è esattamente dove i tre strati interagiscono."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:22:{"ts": "2026-05-02T03:37:32.497564+00:00", "cycle_ref": "20260501_0931", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto evidenza strutturale forte e non-tautologica: transizione di fase nel crossover GUE-Poisson con zero-crossing a alpha~0.70, direzione invariante nel regime ordinato (-96.6±0.27°), e primi con offset direzionale di 32 sigma. Sono pattern cross-dominio (categoria b/f), non dettaglio numerico locale. Il seme delta vuoto indica che i finding non sono ancora rientrati nel seme — il prossimo ciclo deve sia estendere (confronto Brody/Rosenzweig-Porter come suggerito dall'affinatore) sia propagare. Non cristallizzo ancora perché il confronto con i punti critici Brody è il test naturale che conferma o ridimensiona la portata della transizione.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Confrontare lo zero-crossing dipolare (alpha~0.70) con i punti critici Brody/Rosenzweig-Porter: la transizione di fase nel crossover ha carattere universale o è artefatto della parametrizzazione? Testare se beta_Brody critico corrisponde all'alpha di zero-crossing. Secondario: propagare nel seme i finding stabili (direzione invariante, offset primi 32-sigma).", "reason": "L'affinatore ha identificato questo come la possibilità più concreta. Estende il risultato corrente verso falsificazione cross-framework invece di accumulare dettaglio locale."}}, "notes": "Il ciclo precedente (piano 60) è tra i migliori recenti: esperimento ben disegnato, shuffle audit implicito nella parametrizzazione alpha, risultato quantitativo con interpretazione strutturale chiara. Il confronto Brody è il filtro giusto: se il punto critico coincide, la transizione è universale (cristallizzabile); se no, la parametrizzazione alpha è contingente e il finding si ridimensiona a osservazione locale."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:97:{"ts": "2026-05-14T16:17:45.087627+00:00", "cycle_ref": "20260514_1612", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un rimbalzo fisico reale nel frame GUE/Anderson, con Bridge QA clean e senza nodo regressivo. Non c'e' dipendenza dall'operatore: il limite e' gia' nominato dall'affinatore, cioe' trasformare il proxy numerico in tier di evidenza esplicito concentrandosi sul bordo W=6 dove SR/L1 cedono e triple_var resta attivo.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Anderson, restringendo il prossimo ciclo al perimetro W=6 con evidence_tier esplicito e autopsy lab-native della trace.", "constraints": ["non espandere a deep-dive numerico generico su 8 GUE/5 Poisson", "dichiarare il tier di evidenza prima della misura", "testare se triple_var nomina memoria fisica oltre nearest-neighbor repulsion"]}}, "notes": "NEXT_CYCLE non come altro giro locale sui primi, ma come verifica del confine fisico gia' emerso: W=6 e' il punto-zero operativo."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:98:{"ts": "2026-05-14T16:35:47.164793+00:00", "cycle_ref": "20260514_1631", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha chiuso regressivamente il rimbalzo fisico: il claim e' stato ridotto da possibile legge a tester operativo `component_state(SR,L1,triple_var)`, con Bridge QA clean e health completed. Resta una mossa naturale nello stesso frame: riusare il tester su un perimetro piu' esterno e correggere prima dell'esecuzione il nodo sanitario indicato dall'Affinatore (`evidence_tier + trace_path`), senza richiedere intervento operatore.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Poisson-Anderson usando `component_state(SR,L1,triple_var)` come tester operativo, con contratto pre-run esplicito `evidence_tier` e `trace_path` obbligatori.", "constraint": "Non promuovere il pattern a legge fisica; testare trasferibilita' del tester su un dominio fisico/simulato ordinato o su sweep Anderson multi-W, con null Poisson span-matched e trace JSONL verificabile.", "reason": "Il deposito e' vivo ma non cristallizzabile: serve una replica di trasferibilita' del tester, non nuovo fit locale."}}, "notes": "Side-effect log-only: richiesta prosecuzione del ciclo con contratto sanitario rafforzato prima del run."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:99:{"ts": "2026-05-14T16:45:30.959682+00:00", "cycle_ref": "20260514_1640", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha rafforzato il tester senza promuoverlo a legge: Bridge QA clean, health completed, e il claim resta correttamente vincolato come strumento operativo. La prossima mossa deve continuare il frame ma spostare la falsificazione fuori dal dettaglio locale GOE/GUE a un denominatore fisico piu' duro, usando il nodo regressivo indicato dall'Affinatore: contratto pre-run esplicito su trace_path, evidence_tier e autopsy.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare il tester component_state(SR,L1,triple_var) su falsificazione fisica esterna: Anderson 3D o many-body/localizzazione, con contratto pre-run esplicito trace_path/evidence_tier/autopsy; vietato promuovere a legge fisica o accumulare solo N-curve GOE/GUE.", "constraints": ["mantenere GOE/GUE/Poisson come baseline, non come nuovo oggetto principale", "testare almeno un denominatore fisico non gia' coperto dal ciclo 16:40", "dichiarare non_possible prima dell'esecuzione", "includere shuffle/surrogato o baseline strutturale equivalente"]}}, "notes": "Non cristallizzare ancora: il tester e' leggibile ma non ha superato abbastanza domini esterni. Non REDESIGN pieno perche' la direzione fisica resta viva; va solo spinta fuori dal rinforzo locale."} tools/data/preflight/valutatore_log_backup_pre_quarantine_20260515_1615.jsonl:103:{"ts": "2026-05-14T18:55:18.325525+00:00", "cycle_ref": "20260514_1850", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo non ha promosso una legge fisica: ha stabilizzato il contratto del tester su Anderson 3D dichiarando il limite SR/L1 e il segnale residuo triple_var. Questo e' utile solo se ora viene verificata la riusabilita' su un secondo fisico B, altrimenti resta dettaglio locale del bordo GOE-GUE-Poisson-Anderson.", "action": {"type": "trigger_cycle", "detail": {"direction": "Usare lo stesso tool contract component_state(SR,L1,triple_var) su un secondo dominio fisico B indipendente, con step_trace esplicito e null Poisson span-matched; vietata promozione a legge se non separano almeno due componenti o se il segnale resta solo triple_var.", "reason": "Testare trasferibilita' cross-dominio del contratto, non approfondire Anderson L=6."}}, "notes": "Preferire un dominio spettrale fisico non Anderson e non Sturmian-vault, cosi' il prossimo ciclo discrimina interfaccia riusabile da adattamento locale."} tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:129:- Berry phase; tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:331:- Brody parameter; tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:476:- Anderson localization; tools/data/preflight/lab_data_backup_post_controlled_1623_20260515.json:80: "content": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promot" tools/data/preflight/agent_field_live_candidate_20260515_1620.md:129:- Berry phase; tools/data/preflight/agent_field_live_candidate_20260515_1620.md:331:- Brody parameter; tools/data/preflight/agent_field_live_candidate_20260515_1620.md:476:- Anderson localization; tools/data/reports/evolution_20260423_0330.md:21:3. **Il Brody β come funzione di scala è un dato riusabile.** La regressione β(ln p) con R²=0.72 è un risultato autonomo dalla decomposizione a due canali. Può servire come baseline per qualsiasi futuro esperimento sulla tensione BOUNDARY — il tasso di drift verso Poisson è ora quantificato. tools/data/reports/agent_20260413_0330.md:86:- **EXTENDS POISSON_CONVERGENCE**: The hierarchy now has 5 tiers, not 3. Spectral measures (slope, dip) sit between ACF and Brody. tools/data/reports/agent_20260413_0330.md:90:The unified picture is: prime gaps decorrelate logarithmically in p, with a 3-decade spread (10^{11.5} to 10^{14.5}) across observables. The hierarchy is: spectral tilt → ACF memory → spectral dip → ACF envelope → Brody shape → level spacing ratio. **What determines this ordering?** Is it specific to primes, or does any anti-correlated sequence approaching independence lose structure in this order? A Berry-Robnik mixture with tunable mixing parameter could test this — sweep from GUE to Poisson and measure the same 5 observables. tools/data/preflight/agent_field_live_candidate_20260515_1616.md:130:- Berry phase; tools/data/preflight/agent_field_live_candidate_20260515_1616.md:332:- Brody parameter; tools/data/preflight/agent_field_live_candidate_20260515_1616.md:477:- Anderson localization; tools/exp_aubry_v2_generator_scaling_gate.py:3:V=2 generator scaling gate for the Aubry/Sturmian boundary. tools/exp_aubry_v2_generator_scaling_gate.py:5:The known Aubry-Andre self-dual point is V=2 for the continuous cosine tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:80:- **Campo di possibilita**: diventa possibile — classify domains by their NUMBER of independent correlation channels (0, 1, 2, ...) as a structural fingerprint. Diventa non-possibile — use any single observable (r, ACF, Brody beta) to discriminate primes from GUE. The single observable collapses channels; the discrimination lives in the decomposition. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:50:4. **The shuffle baseline itself drifts.** Shuffled r-statistic decreases from 0.508 to 0.474 across the range. Shuffled mod-3 self-fraction increases from 0.603 to 0.622. The Brody calibration's warning about artifact floors applies: the signal must always be measured relative to the null, not in absolute terms. tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:131:- Berry phase; tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:333:- Brody parameter; tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:478:- Anderson localization; tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:11:If two independent RMT observables — the gap ratio r (short-range) and the number variance Σ²(L) (multi-scale) — are both inverted through the Brody calibration curve to give an effective β, do they agree? If yes, the tests measure the same thing (tautology). If no, with specific structure, the observables are genuinely independent. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:15:- **Inversion**: Brody calibration curve from 2026-04-27 (β = 0.0 to 0.6 in 0.1 steps, with r and Σ²/L at each β) tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:22:### Effective Brody β from different observables tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:40:The GUE control gives erratic β_Σ values because the Brody calibration is built on i.i.d. gaps (no correlations between spacings). Real GUE eigenvalues have strong inter-spacing correlations that the Brody marginal cannot capture. This confirms: the Brody parameter is NOT a sufficient statistic for correlated sequences. The failure of the GUE control is itself evidence that correlations matter. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:44:1. **The observables are NOT tautological.** For primes, r gives β_r = 0.39 while Σ²(L=50) gives β_Σ = 0.95. These differ by 0.56 — more than half the full Brody range. No single β describes primes. The tests measure genuinely different aspects of the same sequence. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:46:2. **Primes are "anti-Brody": β increases with scale.** Short-range (r): weakly repulsive (β ≈ 0.4). Long-range (Σ², L=50): strongly rigid (β ≈ 0.95). This is the prime number theorem at work — the smooth density p/ln(p) creates long-range regularity that consecutive gap ratios cannot see. The two-channel model predicted this: the magnitude channel dominates at short range, the ordering channel (PNT regularity) dominates at long range. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:68:- **Campo di possibilità**: here it becomes possible to classify sequences by their β(L) slope, not by a single r value — richer than GUE/Poisson binary. Here it becomes non-possible to reduce a structured sequence to a single Brody parameter. tools/data/preflight/preflight_20260515_1745.md:6:- report: Agent Report - Aubry Boundary Phase Transport Gate tools/data/reports/falsifier_20260514_1850.json:14: "summary": "Il report e' quasi internamente coerente sui dati Anderson e sui perimetri, ma si rompe su L6: la contaminazione cognitiva non e' dichiarata nel formato richiesto e il Vault resta deposito operativo, non metabolismo esplicito del campo." tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1068: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/reports/agent_20260515_1623.md:24:- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only. tools/data/preflight/stale_field_source_map_20260515_133502.json:204: "text": "{\"ts\": \"2026-05-14T18:55:18.325525+00:00\", \"cycle_ref\": \"20260514_1850\", \"executed\": false, \"decision\": \"NEXT_CYCLE\", \"confidence\": \"medium\", \"reasoning\": \"Il ciclo non ha promosso una legge fisica: ha stabilizzato il contratto del tester su Anderson 3D dichia" tools/data/preflight/preflight_20260514_1850.md:6:- report: Agent Report - Interfaccia component_state su bordo Anderson 3D tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:131:- Berry phase; tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:333:- Brody parameter; tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:478:- Anderson localization; tools/data/reports/agent_20260427_0330.md:1:# Agent Report — Brody Calibration Proves Two-Channel Structure Is Real, Quantifies 7.8% Artifact Floor tools/data/reports/agent_20260427_0330.md:16:- **Calibration**: The Brody distribution P(s) ~ s^beta exp(-c s^(beta+1)) smoothly interpolates from Poisson (beta=0) to Wigner-GOE (beta=1). Gaps are i.i.d. by construction — zero sequential correlation. tools/data/reports/agent_20260427_0330.md:17:- **Brody curve**: 11 values of beta from 0 to 1, each with N=10000 gaps, 50 shuffles per point. tools/data/reports/agent_20260427_0330.md:20:- **Null baseline**: The Brody curve itself IS the null — any deviation of a real domain from the curve is the non-trivial signal. tools/data/reports/agent_20260427_0330.md:24:### Brody calibration curve (i.i.d. gaps) tools/data/reports/agent_20260427_0330.md:44:### Real domains vs Brody curve tools/data/reports/agent_20260427_0330.md:67:1. **The r-statistic is a faithful order parameter.** It increases monotonically with Brody beta (0.381 to 0.573), tracking real short-range repulsion without artifacts. Our GUE/Poisson classification via r is structurally sound. tools/data/reports/agent_20260427_0330.md:69:2. **The shuffle test has a 7.8% artifact floor.** For i.i.d. Brody gaps (no sequential correlation by construction), the ordering fraction fluctuates up to 7.8%. Any measured ordering below ~8% should be treated as noise. Above 8% is signal. tools/data/reports/agent_20260427_0330.md:83:- **DUALITA_DIPOLARE_VS_ILLUSORIA**: The sign of the ordering channel IS the discriminator. Ordering that adds rigidity (primes) = dipolar duality (det=-1, generative). Ordering that adds bunching (logistic, coupled_osc) = dispersive (det=+1, entropic). The Brody curve is the zero line between them. tools/data/reports/agent_20260427_0330.md:87:- **Due radici** (dipolo primario): ordinamento rigido (primes: Delta < 0, ordering adds repulsion, det=-1) / ordinamento dispersivo (logistic, coupled_osc: Delta > 0, ordering adds bunching, det=+1). Il segno del Delta rispetto alla curva Brody discrimina i due tipi di dualita. Non e una scala — e un segno. tools/data/reports/agent_20260427_0330.md:88:- **Singolare** (il 1-che-e-tutto): la curva di Brody. Il continuo di distribuzioni i.i.d. dove l'ordinamento non esiste. Il singolare e l'assenza di ordinamento — il punto dove la distinzione rigido/dispersivo non ha ancora significato. Prima della separazione, c'e solo la forma della distribuzione. tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:129:- Berry phase; tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:331:- Brody parameter; tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:476:- Anderson localization; tools/data/reports/exp_dR_brody_connection.json:10: "Does dR predict Brody beta?", tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:129:- Berry phase; tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:331:- Brody parameter; tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:476:- Anderson localization; tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1068: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/preflight/agent_field_live_candidate_20260515_1612.md:130:- Berry phase; tools/data/preflight/agent_field_live_candidate_20260515_1612.md:332:- Brody parameter; tools/data/preflight/agent_field_live_candidate_20260515_1612.md:477:- Anderson localization; tools/data/preflight/supervised_one_shot_result_B_20260515_155338.md:74:qg_discrete_boundary_bridge_probe tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:131:- Berry phase; tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:333:- Brody parameter; tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:478:- Anderson localization; tools/data/preflight/preflight_20260514_1850.json:21: "title": "Agent Report - Interfaccia component_state su bordo Anderson 3D" tools/data/preflight/preflight_20260514_1850.json:26: "summary": "Il report e' quasi internamente coerente sui dati Anderson e sui perimetri, ma si rompe su L6: la contaminazione cognitiva non e' dichiarata nel formato richiesto e il Vault resta deposito operativo, non metabolismo esplicito del campo.", tools/data/preflight/valutatore_log_quarantined_blocked_rows_20260515_1615.jsonl:1:{"ts": "2026-05-14T16:45:30.959682+00:00", "cycle_ref": "20260514_1640", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha rafforzato il tester senza promuoverlo a legge: Bridge QA clean, health completed, e il claim resta correttamente vincolato come strumento operativo. La prossima mossa deve continuare il frame ma spostare la falsificazione fuori dal dettaglio locale GOE/GUE a un denominatore fisico piu' duro, usando il nodo regressivo indicato dall'Affinatore: contratto pre-run esplicito su trace_path, evidence_tier e autopsy.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare il tester component_state(SR,L1,triple_var) su falsificazione fisica esterna: Anderson 3D o many-body/localizzazione, con contratto pre-run esplicito trace_path/evidence_tier/autopsy; vietato promuovere a legge fisica o accumulare solo N-curve GOE/GUE.", "constraints": ["mantenere GOE/GUE/Poisson come baseline, non come nuovo oggetto principale", "testare almeno un denominatore fisico non gia' coperto dal ciclo 16:40", "dichiarare non_possible prima dell'esecuzione", "includere shuffle/surrogato o baseline strutturale equivalente"]}}, "notes": "Non cristallizzare ancora: il tester e' leggibile ma non ha superato abbastanza domini esterni. Non REDESIGN pieno perche' la direzione fisica resta viva; va solo spinta fuori dal rinforzo locale."} tools/data/preflight/valutatore_log_quarantined_blocked_rows_20260515_1615.jsonl:3:{"ts": "2026-05-14T18:55:18.325525+00:00", "cycle_ref": "20260514_1850", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo non ha promosso una legge fisica: ha stabilizzato il contratto del tester su Anderson 3D dichiarando il limite SR/L1 e il segnale residuo triple_var. Questo e' utile solo se ora viene verificata la riusabilita' su un secondo fisico B, altrimenti resta dettaglio locale del bordo GOE-GUE-Poisson-Anderson.", "action": {"type": "trigger_cycle", "detail": {"direction": "Usare lo stesso tool contract component_state(SR,L1,triple_var) su un secondo dominio fisico B indipendente, con step_trace esplicito e null Poisson span-matched; vietata promozione a legge se non separano almeno due componenti o se il segnale resta solo triple_var.", "reason": "Testare trasferibilita' cross-dominio del contratto, non approfondire Anderson L=6."}}, "notes": "Preferire un dominio spettrale fisico non Anderson e non Sturmian-vault, cosi' il prossimo ciclo discrimina interfaccia riusabile da adattamento locale."} tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/agent_field_live.md:129:- Berry phase; tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/agent_field_live.md:331:- Brody parameter; tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/agent_field_live.md:476:- Anderson localization; tools/data/preflight/preflight_20260515_1758.md:6:- report: Agent Report - Aubry Cosine Boundary Counter-Gate tools/data/preflight/preflight_20260515_1807.json:21: "title": "Agent Report - Aubry Binary Grammar Surrogate Gate" tools/data/preflight/lab_data.json_backup_pre_1724_public_rollback_20260515:101: "content": "# Agent Report - Quasiperiodic Vc Lattice Extension\n**Date**: 2026-05-15 17:24 \n**Piano**: 118 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - `V_c` non sostiene una legge di scala unica nel perimetro esteso; resta reticolo di attraversamento phase-sensitive separato dal controllo random.\nobservables_used: [vc, distinct_vc, repeat_rate, mode_rate, median_vc, min_vc, max_vc, phi_phase0_values]\n**observable_contract**: claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: curva continua di scala / reticolo discreto di attraversamento. Punto-zero = la prima caduta di `r(V)` sotto 0.5 prima che venga interpretata come fit, attrattore o classe.\n- **Piano superiore**: topologia assiomatica del bordo; il confine e' una soglia di attraversamento su un reticolo di fasi, non un numero isolato.\n- **Operatori laterali scelti**: boundary condition per rendere la fase parte del denominatore; mobility edge come forma fisica del passaggio spettrale; spectrum-preserving style control come controllo random a densita preservata.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato come reiterazione del kernel 20260509: non rifittare `V_c`, ripetere il deposito con denominatore piu esplicito. CE-0038 usato come equilibrio tra estremi: il punto-zero non e' media tra phi e random, e' la riga `N,phase` prima della classificazione.\n- **Proto-ipotesi**: il fallimento del fit non chiede una famiglia parametrica migliore; indica che `V_c` e' coordinata di attraversamento dipendente da fase e generatore. Se la fase resta visibile a N esteso, il claim di convergenza globale cade.\n- **Proiezione**: estendo `exp_quasiperiodic_vc_lattice_gate.py` a sei taglie e otto fasi, mantenendo silver/bronze e random bilanciato come controlli.\n\n## Aderenza alla direzione\n\n- `relation`: deliberate_counter_perimeter\n- `why`: il campo vivo espone due segnali in tensione: l'anchor stabile 17:12 chiude prime/mod6, mentre `dnd_scenario.py --best` e il campo basso indicano `TENS_SCALE_TRASCENDENZA_LIMITE` come massimo discriminante. Ques" tools/data/arxiv_cache.json:18: "Aubry Andre localization transition": { tools/data/arxiv_cache.json:97: "abstract_snippet": "▽ More Topological quantum optical states in one-dimensional (1D) quasiperiodic cold atomic chains are studied in this work. We propose that by introducing incommensurate modulations on the interatomic distances of 1D periodic atomic chains, the off-diagonal Aubry-André-Harper (AAH) model can ", tools/data/arxiv_cache.json:133: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:140: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:147: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:152: "title": "Aubry-André Localization Transition for an Active Undulator", tools/data/arxiv_cache.json:154: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:159: "title": "Critical properties in the non-Hermitian Aubry-Andre-Stark model", tools/data/arxiv_cache.json:160: "abstract_snippet": "▽ More We explore the critical properties of the localization transition in the non-Hermitian Aubry-Andre-Stark (AAS) model with quasiperiodic and Stark potentials, where the non-Hermiticity comes from the nonreciprocal hopping. The localization length, the inverse participation ratio and the ", tools/data/arxiv_cache.json:161: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:168: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:174: "abstract_snippet": "▽ More We theoretically investigate criticality and multifractal states in a one-dimensional Aubry-Andre-Harper model coupled to electromagnetic cavities. We focus on two specific cases where the phonon frequencies are $ω_{0}=1$ and $ω_{0}=2$, respectively. Phase transitions are analyzed using", tools/data/arxiv_cache.json:175: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:180: "title": "Quantum criticality and Kibble-Zurek scaling in the Aubry-André-Stark model", tools/data/arxiv_cache.json:181: "abstract_snippet": "▽ More We explore quantum criticality and Kibble-Zurek scaling (KZS) in the Aubry-Andre-Stark (AAS) model, where the Stark field of strength $\\varepsilon$ is added onto the one-dimensional quasiperiodic lattice. We perform scaling analysis and numerical calculations of the localization length,", tools/data/arxiv_cache.json:182: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:188: "abstract_snippet": "▽ More The geometric properties of quantum states is fully encoded by the quantum geometric tensor. The real and imaginary parts of the quantum geometric tensor are the quantum metric and Berry curvature, which characterize the distance and phase difference between two nearby quantum states in", tools/data/arxiv_cache.json:189: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:196: "query": "Aubry Andre localization transition", tools/data/arxiv_cache.json:210: "query": "Aubry Andre localization transition", tools/data/preflight/prime_mod6_pipeline_closeout_20260515.json:47: "graph_bridge", tools/data/preflight/field_rebuild_risk_map_20260515_133707.json:72: "reasoning": "Il ciclo non ha promosso una legge fisica: ha stabilizzato il contratto del tester su Anderson 3D dichiarando il limite SR/L1 e il segnale residuo triple_var. Questo e' utile solo se ora viene verificata la riusabilita' su un secondo fisico B, altrimenti resta dettaglio locale del bordo GOE-GUE-Poisson-Anderson.", tools/data/preflight/field_rebuild_risk_map_20260515_133707.json:77: "reason": "Testare trasferibilita' cross-dominio del contratto, non approfondire Anderson L=6." tools/data/preflight/field_rebuild_risk_map_20260515_133707.json:80: "notes": "Preferire un dominio spettrale fisico non Anderson e non Sturmian-vault, cosi' il prossimo ciclo discrimina interfaccia riusabile da adattamento locale." tools/data/lab_data.json:101: "content": "# Agent Report - Boundary Classical Crossover Audit\n**Date**: 2026-05-15 19:04\n**Piano**: 120\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge.\nobservables_registry: none; classical audit coordinates plus prior graph observables\nobservables_used: [brody_q, berry_robnick_like_gue_weight, mixture_ks, graph_boundary_state_from_1855, centroid_margin_from_1855, cross_neighbor_fraction_from_1855]\n**observable_contract**: claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: repulsione spettrale / indipendenza spettrale. Punto-zero: riga di dominio prima che venga letta come label, parametro Brody o nodo del grafo.\n- **Piano superiore**: grafo della conoscenza con audit assiomatico su baseline note; la domanda non e' \"quanto vale q\", ma se q esaurisce il ponte.\n- **Proto-ipotesi**: il terzo incluso operativo non coincide con un singolo scalare di crossover. Se coincide, il bridge Lab e' re-discovery di Brody/Berry-Robnik; se diverge, il contenuto Lab e' nella relazione tra geometria locale e scalare classico.\n- **Possibile/non-possibile**: possibile = usare nodi ponte come righe fisiche candidate oltre la classificazione GUE/Poisson; non-possibile = rivendicare un nuovo crossover se i nodi ponte sono solo Brody/Berry-Robnik rietichettato.\n- **Proiezione**: stimo Brody q e peso GUE di una mistura Poisson/GUE-surmise per ciascuna delle 13 righe gia' classificate dal grafo 18:55.\n\n### Contaminazione cognitiva\n- **YSN DeltaLink**: il DeltaLink usato e' `crossover classico / grafo Lab`: la sorpresa cercata e' il disaccordo, non la conferma dei nodi ponte.\n- **Cornelius gene**: `Classical_Audit_Gate`: \"Un ponte Lab sopravvive solo dopo il lettore classico piu vicino.\" Operatori: FITTA scalare noto; ALLINEA righe; ISOLA residuo.\n- **KSAR step**: perturbazione = feedback falsifier L5; focalizzazione = una sola domanda, \"i bridge collassano su Brody/Berry-Robnik?\"; proiezione = audit row-aligned sulle 13 righe.\n- **PVI attack**: un revisore esterno puo' dire che `third_included_candidate` e' solo un nome Lab per un crossover Brody. Il test attacca es" tools/data/physical_sr_residue_bounce_20260514_1612.json:22: "physical_return_candidate": "1D Anderson tight-binding spectra across disorder/localization", tools/data/physical_sr_residue_bounce_20260514_1612.json:27: "Anderson_1D_W0.5": { tools/data/physical_sr_residue_bounce_20260514_1612.json:47: "label": "Anderson_1D_W0.5", tools/data/physical_sr_residue_bounce_20260514_1612.json:78: "Anderson_1D_W12": { tools/data/physical_sr_residue_bounce_20260514_1612.json:94: "label": "Anderson_1D_W12", tools/data/physical_sr_residue_bounce_20260514_1612.json:125: "Anderson_1D_W2": { tools/data/physical_sr_residue_bounce_20260514_1612.json:145: "label": "Anderson_1D_W2", tools/data/physical_sr_residue_bounce_20260514_1612.json:176: "Anderson_1D_W6": { tools/data/physical_sr_residue_bounce_20260514_1612.json:194: "label": "Anderson_1D_W6", tools/data/tm1_figures/version.json:19: "summary": "Complete algebraic chain from matrix determinant through quadratic field to random matrix universality. Berry-Keating bridge confirmed.", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1076: "content_full": "# Agent Report - Quasiperiodic Vc Lattice Extension\n**Date**: 2026-05-15 17:24 \n**Piano**: 118 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - `V_c` non sostiene una legge di scala unica nel perimetro esteso; resta reticolo di attraversamento phase-sensitive separato dal controllo random.\nobservables_used: [vc, distinct_vc, repeat_rate, mode_rate, median_vc, min_vc, max_vc, phi_phase0_values]\n**observable_contract**: claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: curva continua di scala / reticolo discreto di attraversamento. Punto-zero = la prima caduta di `r(V)` sotto 0.5 prima che venga interpretata come fit, attrattore o classe.\n- **Piano superiore**: topologia assiomatica del bordo; il confine e' una soglia di attraversamento su un reticolo di fasi, non un numero isolato.\n- **Operatori laterali scelti**: boundary condition per rendere la fase parte del denominatore; mobility edge come forma fisica del passaggio spettrale; spectrum-preserving style control come controllo random a densita preservata.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato come reiterazione del kernel 20260509: non rifittare `V_c`, ripetere il deposito con denominatore piu esplicito. CE-0038 usato come equilibrio tra estremi: il punto-zero non e' media tra phi e random, e' la riga `N,phase` prima della classificazione.\n- **Proto-ipotesi**: il fallimento del fit non chiede una famiglia parametrica migliore; indica che `V_c` e' coordinata di attraversamento dipendente da fase e generatore. Se la fase resta visibile a N esteso, il claim di convergenza globale cade.\n- **Proiezione**: estendo `exp_quasiperiodic_vc_lattice_gate.py` a sei taglie e otto fasi, mantenendo silver/bronze e random bilanciato come controlli.\n\n## Aderenza alla direzione\n\n- `relation`: deliberate_counter_perimeter\n- `why`: il campo vivo espone due segnali in tensione: l'anchor stabile 17:12 chiude prime/mod6, mentre `dnd_scenario.py --best` e il campo basso indicano `TENS_SCALE_TRASCENDENZA_LIMITE` come massimo discriminante. Questo ciclo usa il secondo come contro-perimetro dichiarato, non come promozione della direzione normale.\n- `not_drift`: non riapre prime/mod6 come discovery, non usa `gap_ratio`, non promuove GUE/Poisson, non cerca una nuova formula power-law; usa `V_c` solo come deposito vincolante. Il preflight 17:24 classifica comunque il ciclo `DRIFT_STOP`, quindi il risultato resta vault/constraint fino a nuova autorita.\n\n## Claim Under Test\n\n> Nel perimetro quasiperiodico esteso, `V_c(phi)` e' una traiettoria di scala verso 1 solo se fase e taglia collassano a una famiglia comune; altrimenti `V_c` e' un reticolo di attraversamento, non un attrattore.\n\n## Question\n\nQuando si estende il probe `V_c` a N=987 e a otto fasi, la sequenza phi concentra l'attraversamento verso 1, oppure resta phase-sensitive e distinta dal random bilanciato?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: transizione spettrale in Hamiltoniane tight-binding quasiperiodiche binarie, con `r(V)` come indicatore locale di repulsione/indipendenza.\n- **Attraversamento matematico**: soglia `r(V)<0.5` su sequenze Sturmian phi/silver/bronze e controllo random a stessa densita di phi.\n- **Punto fisico di ritorno**: misura di sensibilita alle condizioni al bordo/fase in modelli quasiperiodici; l'analogo fisico e' un esperimento numerico o fotonico dove la fase del potenziale non puo essere mediata prima del claim di transizione.\n- **Relazione nuova**: il confine non torna come valore critico unico, ma come reticolo `N x phase x generator` che decide se una transizione e' leggibile.\n- **Osservabile/test fisico possibile**: ripetere su modello Aubry-Andre/Fibonacci con fase controllata e confrontare distribuzione dei critical couplings, non solo media o fit.\n- **Se fallisce**: `ritorno_fisico_parziale`; questo ciclo resta vincolo operativo per il denominatore dei futuri test fisici, non scoperta fisica autonoma.\n\n## Experiment Design\n\n- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- `python -m py_compile tools/exp_quasiperiodic_vc_lattice_gate.py` completato.\n- Run: `python tools/exp_quasiperiodic_vc_lattice_gate.py --ns 89,144,233,377,610,987 --phases 0,0.125,0.25,0.375,0.5,0.625,0.75,0.875 --v-min 0.5 --v-max 3.0 --v-step 0.025 --threshold 0.5 --random-trials 8 --seed 202605151724 --out tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json`.\n- Perimetro deterministico: 48 righe per ciascun generatore phi, silver, bronze.\n- Perimetro random: 384 righe balanced random a densita phi.\n- La misura serve la combo perche' distingue confine come reticolo di fase da confine come valore critico aggregato.\n\n## Results\n\n| domain | count | distinct_vc | repeat_rate | mode_rate | median | min | max |\n|---|---:|---:|---:|---:|---:|---:|---:|\n| phi | 48 | 20 | 0.583 | 0.125 | 1.175 | 0.650 | 1.650 |\n| silver | 48 | 24 | 0.500 | 0.083 | 1.200 | 0.975 | 2.150 |\n| bronze | 48 | 24 | 0.500 | 0.104 | 1.275 | 0.925 | 1.800 |\n| balanced_random_phi_density | 384 | 21 | 0.945 | 0.680 | 0.500 | 0.500 | 1.150 |\n\nPhi phase0 values:\n\n| N | phase | V_c |\n|---:|---:|---:|\n| 89 | 0.0 | 1.025 |\n| 144 | 0.0 | 0.675 |\n| 233 | 0.0 | 0.950 |\n| 377 | 0.0 | 0.675 |\n| 610 | 0.0 | 0.900 |\n| 987 | 0.0 | 0.675 |\n\nPhi median by N across eight phases:\n\n| N | median V_c | min | max | distinct |\n|---:|---:|---:|---:|---:|\n| 89 | 1.313 | 0.675 | 1.375 | 5 |\n| 144 | 1.150 | 0.675 | 1.650 | 7 |\n| 233 | 1.225 | 0.725 | 1.350 | 5 |\n| 377 | 1.113 | 0.650 | 1.300 | 6 |\n| 610 | 1.238 | 0.900 | 1.350 | 5 |\n| 987 | 1.088 | 0.675 | 1.275 | 6 |\n\n## Key Findings\n\n1. **Verificato**: `phi` non collassa a `V_c=1`. Nel perimetro esteso ha 20 valori distinti su 48 righe, mediana 1.175 e range 0.650-1.650.\n2. **Verificato**: la sequenza phase0 non e' monotona verso 1: 1.025, 0.675, 0.950, 0.675, 0.900, 0.675.\n3. **Verificato**: silver e bronze non producono un polo netto opposto a phi; hanno mediane vicine ma range piu alto, quindi il discriminante non e' metallic-mean nominale.\n4. **Verificato**: il random bilanciato non replica il reticolo phi; collassa spesso al bordo inferiore della griglia (`mode_rate=0.680` a V=0.5) pur avendo 21 valori distinti.\n5. **Inferito**: il nodo regressivo del fit fallito e' `phase/generator denominator`, non scelta di modello. La prossima misura deve trattare la fase come parte atomica del claim.\n\n## Verdict\n\n**CONSTRAINT**.\n\n`V_c(phi)->1` non regge come claim operativo nel perimetro misurato. La formulazione valida e': `V_c` e' una coordinata reticolare di attraversamento `N x phase x generator`; phi conserva struttura lontana dal random bilanciato, ma non converge a un attrattore unitario e non separa silver/bronze tramite una legge di scala unica.\n\n## Bicono della scoperta\n\n- **Due radici**: valore critico continuo / reticolo discreto di attraversamento.\n- **Singolare**: la soglia `r(V)<0.5` prima che diventi fit o classe.\n- **Invariante di passaggio**: denominatore `N,phase,generator` come parte atomica di ogni claim su `V_c`.\n- **Campo di possibilita**: possibile = progettare un test fisico/fotonico con fase controllata e distribuzione dei critical couplings; non-possibile = usare `V_c` aggregato o phase0 come prova di convergenza globale.\n\n## Consecutio\n\nIl prossimo ciclo non deve cercare un nuovo fit per `V_c`. Deve decidere se il reticolo di attraversamento trasferisce a un modello fisico piu canonico, con fase controllata e baseline nota: Aubry-Andre/Fibonacci, curva di localizzazione o trasmissione fotonica. Se non viene portato in quel perimetro, resta vincolo matematico sul denominatore.\n\n## Ricadute pratiche\n\nssp_value: yes. Il deposito aggiorna l'uso dello strumento `exp_quasiperiodic_vc_lattice_gate.py`: i futuri report devono riportare fase, taglia, generatore e griglia prima di qualsiasi fit o claim di convergenza.\n\n## Telemetria\n\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Preflight post-report: `python tools/lab_preflight_agent.py --cycle 20260515_1724 --json` => `DRIFT_STOP`, recommended_action=`STOP_AND_RETURN_TO_STABLE_ANCHOR`, stable_anchor=`20260515_1712`.\n- Nessun update del seme.\n- Nessun runtime pointer mutation.\n- Nessuna promozione e nessun public sync.\n- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate.\n\n## Files\n\n- `tools/exp_quasiperiodic_vc_lattice_gate.py`\n- `tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json`\n- `tools/data/preflight/preflight_20260515_1724.json`\n- `tools/data/preflight/preflight_20260515_1724.md`\n- `tools/data/reports/agent_20260515_1724.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1229: "content_full": "# Agent Report - Selector Authority Matrix\n**Date**: 2026-05-15 16:59 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - the explicit selector matrix is ready; freshness selectors remain blocked as active authority. \nobservables_used: [selector_verdict, active_authority_failures, legacy_freshness_blocked_as_authority, active_stale_hits, field_coherence_status, lab_health_warning, blocked_direction_count] \n**observable_contract**: claim=normal Lab direction can reopen only through explicit authority selectors, not through newest-file or last-row selectors; observable=selector-by-selector verdicts plus stale-hit and coherence checks; operator=`tools/selector_authority_matrix.py`, `tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=read-only recovery preflight; denominator=10 selector rows in the authority matrix plus live generated-field surfaces scanned by the preflight tools; non_possible=claiming direction from mtime, newest evolution, last evaluator row, or `lab_health`; not_tested=scientific claim, prime/mod6 residue, public sync, cron restart, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + source-selection manifest.\n- **Dipolo / punto-zero**: autorita esplicita / freschezza apparente. Punto-zero: la riga selector, dove una sorgente puo essere letta senza diventare direzione.\n- **Piano superiore**: grafo operativo della conoscenza; la misura riguarda il flusso di autorita tra superfici runtime.\n- **Operatori laterali scelti**: `boundary operator` per separare contesto e direzione; `graph cut` per isolare i selector legacy; `proof trace vs output trace` per confrontare matrice, coherence e preflight.\n- **Contaminazione cognitiva**: KSAR usato come reiterazione del recovery kernel: esecuzione minima, deposito, matrice. CE-none: il campo non espone una voce CE concreta utile al selector audit; inventarla renderebbe semantico un ciclo operativo.\n- **Proto-ipotesi**: un selector e sicuro come autorita solo se seleziona righe ammesse o overlay esplicito; un selector utile come diagnostica diventa non-possibile come direzione quando sceglie recovery evidence o branch bloccati.\n- **Proiezione**: la proto-ipotesi diventa falsificabile riga-per-riga: ogni selector riceve selected cycle, status, role e verdict.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: il campo vivo dichiara recovery / clean-field handoff; i report 16:23 e 16:47 hanno lasciato come consecutio la sostituzione dei selector per freschezza con un manifest esplicito.\n- `not_drift`: il ciclo non riapre prime/mod6, `V_c`, gap ratio, grammatica Sturmian, Anderson 3D o promozione pubblica. Il domandatore 16:59 ha generato `SCALE_TRASCENDENZA_LIMITE`, ma resta output stale/pre-closeout e non diventa direzione.\n\n## Claim Under Test\n\n> Il Lab puo usare solo selector con verdict `SAFE_AS_AUTHORITY` come autorita attiva; i selector legacy per freschezza possono restare diagnostici solo se bloccati riga-per-riga come autorita.\n\n## Question\n\nQuali selector possono decidere la prossima direzione del Lab, e quali devono essere vietati come autorita anche se leggono file recenti?\n\n## Ritorno fisico\n\nnon_applicabile. Il ciclo parte da un attrito operativo di recovery, non da una tensione fisica. La forma `fisico A -> matematica -> fisico B` resta preservata come vincolo metodologico per i cicli scientifici successivi, ma qui non viene usata come ponte attivo.\n\n## Experiment Design\n\n- Eseguito `python tools/dnd_scenario.py --best`: il tool punta ancora a tensioni `TRASCENDENZA_LIMITE`, ma il campo le marca stale rispetto alla direzione recovery.\n- Eseguito `python tools/selector_authority_matrix.py`: costruisce matrice read-only con selector, selected cycles, status, role e verdict.\n- Eseguito `python tools/stale_field_source_map.py`: verifica hit stale attivi sulle superfici vive.\n- Eseguito `python tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md`: verifica coerenza mode B.\n- Eseguito `python tools/lab_preflight_agent.py --cycle 20260515_1647 --json`: classifica il recovery report precedente.\n- Nessun overwrite di `agent_field_live.md`, nessun update del seme, nessun restart cron, nessun public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| selector matrix decision | `SELECTOR_AUTHORITY_MATRIX_READY` | `selector_authority_matrix_20260515_170056` |\n| selector rows | 10 | matrix |\n| active authority failures | 0 | matrix |\n| legacy freshness selectors blocked as authority | 3 | matrix |\n| legacy rows blocked | `recent_agent_reports_by_mtime_top_3`, `latest_evolution_by_mtime`, `last_valutatore_row` | matrix |\n| safe active/report selectors | `reports/latest.md`, `authority_report_manifest`, `authority_evolution_manifest`, `authority_valutatore_row`, `explicit_safe_overlay` | matrix |\n| diagnostic/context selectors | `lab_health_run_timestamp`, `graph_completion_latest` | matrix |\n| blocked direction count encoded | 9 | matrix |\n| active stale hits | 0 | `stale_field_source_map_20260515_170056` |\n| field coherence mode B | `PASS_WITH_WARNINGS` | `field_coherence_B_20260515_170056` |\n| field coherence failures | 0 | coherence |\n| field coherence warning | `lab_health_run_timestamp_not_allowed:20260515_1647` | coherence |\n| lab health status | `completed`, run_timestamp=`20260515_1647` | `tools/data/lab_health.json` |\n| preflight 1647 classification | `DRIFT_STOP` | `preflight_20260515_1647` |\n| self preflight 1659 classification | `REVIEW_REQUIRED` | `preflight_20260515_1659` |\n\n## Key Findings\n\n1. **Verificato**: nessun selector ammesso come active authority fallisce; `active_authority_failures=[]`.\n2. **Verificato**: tre selector per freschezza sono bloccati come autorita: report recenti per mtime, evolution piu recente per mtime, ultima riga valutatore.\n3. **Verificato**: `lab_health` resta diagnostico e non puo decidere direzione, perche registra `20260515_1647` come recovery evidence.\n4. **Verificato**: il campo mode B passa senza failure e con un solo warning; gli hit stale attivi sono zero.\n5. **Verificato**: il report 16:59 stesso richiede review prima del prossimo ciclo; non promuove direzione scientifica.\n6. **Inferito**: il prossimo passaggio valido e consumare la matrice nel selector operativo o nel supervised entry path, non eseguire un nuovo ciclo scientifico normale.\n\n## Verdict\n\n**CONSTRAINT**.\n\nLa matrice di autorita e pronta. Il Lab puo rivendicare solo le righe `SAFE_AS_AUTHORITY` come direzione attiva: latest report su 1330, manifest report, manifest evolution, evaluator row ammessa e safe overlay esplicito. I selector legacy non sono eliminati: restano diagnostica, ma diventano non-possibili come autorita quando selezionano recovery evidence o branch bloccati.\n\n## Bicono della scoperta\n\n- **Due radici**: selector esplicito / selector per freschezza.\n- **Singolare**: la decisione di autorita prima del contenuto scientifico.\n- **Invariante di passaggio**: ogni claim sulla direzione deve citare la riga selector che lo autorizza.\n- **Campo di possibilita**: possibile = supervised entry o runtime selector basato su matrice; non-possibile = normal cycle che legge newest report, newest evolution, last evaluator row o `lab_health` come direzione.\n\n## Consecutio\n\nIntegrare `tools/data/preflight/selector_authority_matrix_latest.json` o una matrice equivalente nel nodo che seleziona la direzione del prossimo ciclo. Il prossimo report puo rivendicare solo righe `SAFE_AS_AUTHORITY`; i selector legacy vanno nominati come bloccati riga-per-riga, non per inferenza generale.\n\n## Ricadute pratiche\n\nssp_value: yes. Il risultato e uno strumento operativo riusabile per riaprire il Lab senza far decidere la direzione a mtime, last-row o health diagnostics.\n\n## Files\n\n- `tools/selector_authority_matrix.py`\n- `tools/data/preflight/selector_authority_matrix_20260515_165915.json`\n- `tools/data/preflight/selector_authority_matrix_20260515_170056.json`\n- `tools/data/preflight/selector_authority_matrix_latest.json`\n- `docs/operations/selector_authority_matrix_20260515_170056.md`\n- `tools/data/preflight/stale_field_source_map_20260515_170056.json`\n- `tools/data/preflight/field_coherence_B_20260515_170056.json`\n- `tools/data/preflight/preflight_20260515_1647.json`\n- `tools/data/preflight/preflight_20260515_1659.json`\n- `tools/data/reports/agent_20260515_1659.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1280: "content_full": "# Agent Report - Source Selection Manifest 1647\n**Date**: 2026-05-15 16:47 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - supervised safe entry is ready with one warning; naive runtime rebuild remains blocked because mtime selectors still see blocked branch reports. \nobservables_used: [active_stale_hits, field_coherence_status, safe_preview_status, rebuild_blocker_count, preflight_classification, runtime_warning_count] \n**observable_contract**: claim=the Lab can reopen only through explicit source selection, not through newest-file runtime selectors; observable=stale-hit map + field coherence mode B + rebuild risk map + preflight classification; operator=`tools/stale_field_source_map.py`, `tools/field_rebuild_risk_map.py`, `tools/field_coherence_preflight.py`, `tools/build_safe_agent_field_preview.py`, `tools/lab_preflight_agent.py`; generator=read-only recovery preflight; denominator=runtime authority surfaces and recent report/evolution/evaluator selectors visible to the tools; non_possible=normal or naive rebuild while newest-report mtime includes blocked cycles and `lab_health` points to a non-allowed recovery cycle; not_tested=scientific claim, physics bridge, public sync, seme mutation, cron restart.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + source-selection graph.\n- **Dipolo / punto-zero**: clean overlay / dirty selector. Punto-zero: manifest esplicito di autorita, dove un ramo puo essere nominato senza diventare direzione.\n- **Piano superiore**: grafo operativo della conoscenza; il ciclo misura flusso di autorita tra superfici, non una grandezza fisica.\n- **Operatori laterali scelti**: `boundary operator` per separare contesto da direzione; `graph cut` per isolare selector by-mtime; `proof trace vs output trace` per confrontare tool coherence e rebuild risk.\n- **Contaminazione cognitiva**: KSAR applicato come reiterazione minima del recovery kernel: run diagnostico, deposito, manifest. CE-none: il campo non espone una voce CE concreta per questo recovery cycle; inventarne una sarebbe rumore.\n- **Proto-ipotesi**: un campo senza hit stale attivi non basta; il safe entry diventa operativo solo se la selezione sorgente non passa da mtime/newest-row.\n- **Proiezione**: misuro stale hits, coherence mode B, safe preview e rebuild risk; il punto che sopravvive e' la differenza tra overlay pronto e ricostruzione ingenua bloccata.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: il campo vivo richiede recovery / clean-field handoff e il report 16:23 ha lasciato come consecutio la pulizia delle superfici di autorita prima dei cicli scientifici normali.\n- `not_drift`: non riapre prime/mod6, `V_c`, fit, grammatica Sturmian, Anderson 3D o promozione pubblica; usa i rami post-1330 solo come blocked/vault context.\n\n## Claim Under Test\n\n> Il Lab puo entrare in modo supervisionato se l'autorita viene scelta da overlay/manifest esplicito; non puo essere ricostruito con selector automatici basati su mtime o ultima riga valutatore.\n\n## Question\n\nQuale parte del clean-field e' pronta, e quale selector rende ancora non-possibile il ritorno a un ciclo normale?\n\n## Ritorno fisico\n\nnon_applicabile. Il ciclo parte da un attrito operativo di recovery, non da una tensione fisica. La forma `fisico A -> matematica -> fisico B` resta chiusa come form factor strategico e non viene usata come ponte attivo.\n\n## Experiment Design\n\n- Eseguito `python tools/dnd_scenario.py --best`: il tool punta ancora a `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, ma la direzione e' stale rispetto al closeout; non viene usata come sorgente.\n- Eseguito `python tools/stale_field_source_map.py`: ricerca riferimenti bloccati nelle superfici live.\n- Eseguito `python tools/field_rebuild_risk_map.py`: verifica se una rebuild ingenua userebbe selector contaminati.\n- Eseguito `python tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md`: verifica coerenza read-only dell'entry supervisionata.\n- Eseguito `python tools/build_safe_agent_field_preview.py --output tools/data/preflight/safe_agent_field_B_20260515_1647.md`: genera preview, senza renderla autorita runtime.\n- Eseguito `python tools/lab_preflight_agent.py --cycle 20260515_1623 --json`: classifica il precedente recovery report rispetto ai gate.\n- Nessun overwrite di `agent_field_live.md`, nessuna modifica al seme, nessun restart cron, nessun public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| active stale hits | 0 | `stale_field_source_map_20260515_164757` |\n| source-map decision | `NO_ACTIVE_STALE_HITS` | source map |\n| field coherence mode B | `PASS_WITH_WARNINGS` | `field_coherence_B_20260515_164807` |\n| field coherence failures | 0 | mode B |\n| field coherence warnings | 1 | `lab_health_run_timestamp_not_allowed:20260515_1623` |\n| safe preview decision | `SAFE_FIELD_PREVIEW_READY` | `safe_agent_field_B_20260515_164807` |\n| active blocked refs in preview | 0 | safe preview |\n| naive rebuild decision | `NAIVE_REBUILD_BLOCKED` | `field_rebuild_risk_map_20260515_164757` |\n| rebuild blockers | 1 | recent reports by mtime include blocked cycles |\n| 1623 preflight classification | `DRIFT_STOP` | `preflight_20260515_1623` |\n| 1647 preflight classification | `REVIEW_REQUIRED` | `preflight_20260515_1647` |\n\n## Key Findings\n\n1. **Verificato**: le superfici live scansionate non contengono piu hit attivi per `20260514_1640`, `20260514_1701`, `20260514_1850`.\n2. **Verificato**: l'entry mode B passa con warning; la preview supervisionata e' pronta e contiene `active_blocked_refs=[]`.\n3. **Verificato**: `lab_health` resta warning perche punta a `20260515_1623`, non incluso negli allowed residues.\n4. **Verificato**: la rebuild ingenua e' bloccata: i report recenti ordinati per mtime includono ancora cicli bloccati, quindi newest-file non puo selezionare autorita.\n5. **Verificato**: il report 16:23 viene classificato `DRIFT_STOP`; puo restare deposito recovery, non direzione scientifica viva.\n6. **Verificato**: questo report 16:47 viene classificato `REVIEW_REQUIRED`; non e' stop, ma richiede review prima del prossimo ciclo.\n7. **Inferito**: il prossimo passo non e' un altro esperimento fisico; e' usare un manifest/overlay esplicito o riparare il selector che confonde mtime con autorita.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl campo e' entrabile in modo supervisionato: source map pulita, coherence mode B senza failure, safe preview pronta. Il ciclo normale resta non-possibile se usa selector ingenui. La condizione regressiva mancante non e' nel risultato scientifico, ma nel nodo di selezione sorgente: newest report / newest evolution / last evaluator row / `lab_health` non possono decidere la direzione.\n\n## Bicono della scoperta\n\n- **Due radici**: overlay supervisionato pronto / rebuild automatica contaminabile.\n- **Singolare**: source selection prima del contenuto scientifico.\n- **Invariante di passaggio**: i rami bloccati possono essere nominati solo come blocked, vault, context o diagnostic.\n- **Campo di possibilita**: possibile = supervised one-shot con overlay/manifest esplicito; non-possibile = normal `lab_agent.sh` o rebuild by-mtime.\n\n## Consecutio\n\nCreare o usare un selector esplicito che legga `source_selection_manifest_20260515_1647.json` o equivalente: allowed floor `20260509_1556`, residue `20260514_1330`, overlay `tools/data/agent_field_entry_supervised.md`, preview `tools/data/preflight/safe_agent_field_B_20260515_1647.md`. Fino a quel passaggio, non selezionare direzione da mtime, evolution newest, last evaluator row o `lab_health`.\n\n## Ricadute pratiche\n\nssp_value: yes. Il risultato e' un manifest operativo per riaprire il Lab in modo supervisionato senza riattivare rami chiusi.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_164757.json`\n- `docs/operations/stale_field_source_map_20260515_164757.md`\n- `tools/data/preflight/field_rebuild_risk_map_20260515_164757.json`\n- `docs/operations/field_rebuild_risk_map_20260515_164757.md`\n- `tools/data/preflight/field_coherence_B_20260515_164807.json`\n- `docs/operations/field_coherence_result_B_20260515_164807.md`\n- `tools/data/preflight/safe_agent_field_B_20260515_1647.md`\n- `tools/data/preflight/safe_agent_field_B_20260515_164807.json`\n- `docs/operations/safe_agent_field_B_20260515_164807.md`\n- `tools/data/preflight/preflight_20260515_1623.json`\n- `tools/data/preflight/preflight_20260515_1647.json`\n- `tools/data/preflight/source_selection_manifest_20260515_1647.json`\n- `tools/data/reports/agent_20260515_1647.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1331: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/reports/falsifier_20260514_1612.json:10: "evidence": "Nei dati forniti, `lab_data.json` dichiara come direzione: \"Formalizzare il limite di scala del vettore prime-minus-mod6: micro-trace provider-indipendente, min_paired_rows pre-dichiarato, replica 4096/8192 e primo test oltre 8192 solo se il denominatore resta stabile\". Il report invece esegue un trasferimento GUE/Anderson con N=192 e 48 repliche, senza micro-trace 4096/8192 ne' min_paired_rows. La deviazione e' dichiarata, ma il `why` cita una direttiva operatore non visibile nei file forniti.", tools/data/reports/falsifier_20260514_1612.json:17: "evidence": "La struttura GUE -> Poisson -> Anderson localizzato ricade nel quadro classico delle statistiche spettrali Wigner-Dyson/GUE contro Poisson e della localizzazione Anderson. Il report dice che non cristallizza una nuova legge fisica, ma non nomina il risultato classico piu' vicino come baseline del ponte.", tools/data/reports/falsifier_20260514_1612.json:18: "suggestion": "Esplicitare che il test e' un riuso operativo di statistiche spettrali note, non scoperta fisica: confrontare `SR,L1,triple_var` con baseline standard come adjacent gap ratio / Wigner-Dyson vs Poisson / Anderson localization level statistics." tools/data/audit_paper_F_draft3.json:121: "message": "Missing traceability markers: potential V(r), three-force decomposition, Berry-Keating connection", tools/data/reports/agent_test_field.md:9:> Three independent observables (Brody beta, gap ratio , gap autocorrelation acf1) all drift toward Poisson at large prime scale. Do they predict the SAME critical scale p*, or do they separate into multiple decorrelation scales? tools/data/reports/agent_test_field.md:19:- **Observables**: Brody beta (MLE grid search), gap ratio = min(s_i,s_{i+1})/max(s_i,s_{i+1}), lag-1 autocorrelation acf1 tools/data/reports/agent_test_field.md:28:| Brody beta | 0.572 - 0.0181*ln(p) | 0.966 | 2.4e-18 | 10^13.7 | tools/data/reports/falsifier_20260508_2019.json:24: "evidence": "Il dominio e' Sturmian/quasiperiodico con gap labels IDS; esistono risultati classici su gap labeling theorem, Sturmian sequences, Harper/Aubry-Andre and substitution spectra. Il report non ancora ancora il pattern al risultato noto piu' vicino, pur evitando esplicitamente NEW.", tools/data/reports/agent_20260515_1724.md:7:**observable_contract**: claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita. tools/data/reports/agent_20260515_1724.md:39:- **Osservabile/test fisico possibile**: ripetere su modello Aubry-Andre/Fibonacci con fase controllata e confrontare distribuzione dei critical couplings, non solo media o fit. tools/data/reports/agent_20260515_1724.md:105:Il prossimo ciclo non deve cercare un nuovo fit per `V_c`. Deve decidere se il reticolo di attraversamento trasferisce a un modello fisico piu canonico, con fase controllata e baseline nota: Aubry-Andre/Fibonacci, curva di localizzazione o trasmissione fotonica. Se non viene portato in quel perimetro, resta vincolo matematico sul denominatore. tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:12: "Anderson_1D_W6": "finite-size disorder/localization boundary, not a universal transition", tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:29: "physical_return_candidate": "1D Anderson tight-binding spectra across disorder/localization", tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:34: "N128:Anderson_1D_W6": { tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:52: "label": "N128:Anderson_1D_W6", tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:185: "N192:Anderson_1D_W6": { tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:203: "label": "N192:Anderson_1D_W6", tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:342: "Anderson_1D_W6": { tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json:360: "label": "Anderson_1D_W6", tools/data/audit_paper_E_draft3.json:67: "message": "Missing traceability markers: Berry-Keating connection", tools/data/reports/agent_20260515_1745.md:1:# Agent Report - Aubry Boundary Phase Transport Gate tools/data/reports/agent_20260515_1745.md:8:**observable_contract**: claim=`phi` e' stato fisico di confine tra ordine periodico e disordine random solo se statistica spettrale e localizzazione concordano; observable=`spacing_r` + `mean_ipr`/`participation_entropy`; operator=`tools/exp_aubry_boundary_phase_transport_gate.py`; generator=Hamiltoniana tight-binding binaria con sequenze phi/silver/bronze, periodico AB e random bilanciato a densita phi; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..2.50 step 0.25 x generator, random_trials=6; non_possible=promuovere confine fisico da una sola osservabile, da phase0, o da un valore critico aggregato; not_tested=limite asintotico, modello Aubry-Andre coseno canonico, dati sperimentali, classi GUE/Poisson universali dirette. tools/data/reports/agent_20260515_1745.md:32:Il ritorno Aubry/Fibonacci chiude il confine come stato fisico congiunto, oppure lo conserva solo come componente osservabile-specifica? tools/data/reports/agent_20260515_1745.md:77:Il ritorno Aubry/Fibonacci trasferisce il confine BOUNDARY solo come finestra di accoppiamento. La formulazione valida e': nel perimetro misurato, `phi` e' terzo incluso fisico congiunto per V=0.50..1.25; sopra V=1.50 non si puo' parlare di stato di confine chiuso, ma di divergenza tra lettore spettrale e lettore di localizzazione. tools/data/reports/agent_20260515_1745.md:88:Il prossimo ciclo puo' usare `exp_aubry_boundary_phase_transport_gate.py` come gate per ritorni fisici: cercare se la finestra V=0.50..1.25 sopravvive a N piu grandi o a un potenziale Aubry-Andre coseno. Se la finestra si muove, il claim resta finestra row-local; se resta stabile, diventa candidato ponte QxG fisico. tools/data/reports/agent_20260514_1631.md:4:**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GUE-Anderson tools/data/reports/agent_20260514_1631.md:5:**verdict**: CONSTRAINT / TESTER OPERATIVO - il rimbalzo fisico 16:12 regge come tester riusabile: `SR` e' active in GUE e absorbed nel bordo Anderson `W=6`, mentre `triple_var` resta active come residuo intermedio. Non e' una nuova legge fisica. tools/data/reports/agent_20260514_1631.md:8:**observable_contract**: claim=il risultato 16:12 va riformulato come tester operativo del confine repulsione/Poisson, non come scoperta fisica; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Anderson 1D `W=6`; denominator=96 repliche per dominio, 95 gap centrali per spettro, 192 eventi trace; null=Poisson span-matched stesso count; non_possible=claim fisico nuovo o legge di transizione se il confronto resta sintetico, finite-size e senza dati sperimentali/unfolding dedicato. tools/data/reports/agent_20260514_1631.md:21:- **Dipolo / punto-zero**: repulsione spettrale caotica / indipendenza Poisson-localizzata. Punto-zero: Anderson `W=6`, dove adjacent gap ratio si avvicina al null ma una misura di triple resta leggibile. tools/data/reports/agent_20260514_1631.md:25:- **Proiezione**: rerun focalizzato `GUE -> Poisson span-matched -> Anderson 1D W=6`, con 96 repliche e trace JSONL. tools/data/reports/agent_20260514_1631.md:43:- **Anderson localization / finite-size disorder crossover**: in 1D il disordine porta verso localizzazione; qui `W=6` e' usato come bordo finite-size, non come transizione universale. tools/data/reports/agent_20260514_1631.md:50:> Il vettore `component_state(SR,L1,triple_var)` resta leggibile come tester operativo del confine GUE-Anderson: `SR` e' active in GUE, viene assorbito al bordo Anderson `W=6`, e `triple_var` puo' restare active nella zona intermedia. tools/data/reports/agent_20260514_1631.md:56:- Ensemble: GUE hermitiano `N=192`; Anderson 1D tight-binding `N=192`, `W=6`. tools/data/reports/agent_20260514_1631.md:67:| Anderson 1D W=6 | 96 | triple_var | 0.3955 / 0.3849 / 0.0106 | 0.028439 | 0.229 | -0.0333 | 0.022092 | -0.243 | -0.5510 | 0.000122 | -0.544 | tools/data/reports/agent_20260514_1631.md:72:- **Verificato / operational boundary**: Anderson `W=6` assorbe `SR` nel gate dichiarato (`p=0.028439`, `d=0.229`), pur avendo delta positivo piccolo. Il linguaggio corretto e' assorbito, non zero. tools/data/reports/agent_20260514_1631.md:74:- **Inferito dal perimetro**: il tester distingue repulsione nearest-neighbor e memoria locale di triple nel setup sintetico GUE/Anderson. tools/data/reports/agent_20260514_1631.md:75:- **Non verificato**: dati sperimentali, Anderson 3D, many-body localization, GOE/GSE, unfolding dedicato, robuste curve di taglia `N`. tools/data/reports/agent_20260514_1631.md:79:Il run produce `tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl` con 192 righe: 96 GUE e 96 Anderson `W=6`. Il deposito e' leggibile senza API esterne e senza dipendenza dall'operatore. Il residuo del falsifier 16:12 non era nei numeri, ma nel nome del risultato: `PHYSICAL BOUNCE PRESENT` poteva suonare come promozione fisica. Il nodo regressivo e' il claim header; va chiuso come `TESTER OPERATIVO`. tools/data/reports/agent_20260514_1631.md:87:5. **Ambiguo dichiarato**: Anderson 1D e' finite-size disorder crossover; non viene usato come transizione di fase universale. tools/data/reports/agent_20260514_1631.md:105:Il passo successivo valido non e' tornare ai primi. E' portare lo stesso tester su un denominatore fisico piu' forte: GOE/GUE scelto per simmetria, Anderson 3D o many-body localization se serve una transizione reale, curve in `N`, e confronto con adjacent gap ratio standard piu' unfolding controllato. tools/data/reports/falsifier_20260429_1013.json:10: "evidence": "La tabella riporta beta_cramer non-zero in ogni riga visibile: 0.018, 0.016, 0.015, 0.017, 0.015. Se il claim e' hard-zero/Poisson puro nel parametro Brody, i dati non mostrano beta=0.000 ma uno stimatore positivo piccolo.", tools/data/reports/falsifier_20260429_1013.json:37: "claim": "\"NEW + CONSTRAINT\" su Brody flow dei primi verso Poisson e decomposizione shuffle/order", tools/data/reports/falsifier_20260429_1013.json:39: "suggestion": "Declassare NEW a \"nuova misura interna nel lab\" finche' non viene mappata contro risultati noti: LOS per bias mod q, Cramer/Hardy-Littlewood per gap statistics, e limiti noti dell'unfolding Brody sui primi." tools/data/reports/falsifier_20260515_1724.json:17: "evidence": "Il report nomina Aubry-Andre/Fibonacci e mobility edge, ma non ancora il risultato classico piu' vicino: sensibilita' di fase, finite-size effects e critical-coupling distributions nei modelli quasiperiodici/Fibonacci sono un perimetro noto. La formulazione `Relazione nuova` rischia di promuovere una ri-scoperta come discovery autonoma.", tools/data/reports/agent_20260515_1647.md:23:- `not_drift`: non riapre prime/mod6, `V_c`, fit, grammatica Sturmian, Anderson 3D o promozione pubblica; usa i rami post-1330 solo come blocked/vault context. tools/data/reports/agent_20260515_1807.md:1:# Agent Report - Aubry Binary Grammar Surrogate Gate tools/data/reports/agent_20260515_1807.md:23:- `why`: segue la direzione viva testando il confine come proprieta del generatore, dopo il contro-gate coseno 17:58 che ha spostato il contenuto dalla classe Aubry-Andre alla grammatica binaria. tools/data/reports/agent_20260515_1904.md:5:**verdict**: CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge. tools/data/reports/agent_20260515_1904.md:8:**observable_contract**: claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica. tools/data/reports/agent_20260515_1904.md:12:- **Dipolo / punto-zero**: repulsione spettrale / indipendenza spettrale. Punto-zero: riga di dominio prima che venga letta come label, parametro Brody o nodo del grafo. tools/data/reports/agent_20260515_1904.md:14:- **Proto-ipotesi**: il terzo incluso operativo non coincide con un singolo scalare di crossover. Se coincide, il bridge Lab e' re-discovery di Brody/Berry-Robnik; se diverge, il contenuto Lab e' nella relazione tra geometria locale e scalare classico. tools/data/reports/agent_20260515_1904.md:15:- **Possibile/non-possibile**: possibile = usare nodi ponte come righe fisiche candidate oltre la classificazione GUE/Poisson; non-possibile = rivendicare un nuovo crossover se i nodi ponte sono solo Brody/Berry-Robnik rietichettato. tools/data/reports/agent_20260515_1904.md:16:- **Proiezione**: stimo Brody q e peso GUE di una mistura Poisson/GUE-surmise per ciascuna delle 13 righe gia' classificate dal grafo 18:55. tools/data/reports/agent_20260515_1904.md:21:- **KSAR step**: perturbazione = feedback falsifier L5; focalizzazione = una sola domanda, "i bridge collassano su Brody/Berry-Robnik?"; proiezione = audit row-aligned sulle 13 righe. tools/data/reports/agent_20260515_1904.md:22:- **PVI attack**: un revisore esterno puo' dire che `third_included_candidate` e' solo un nome Lab per un crossover Brody. Il test attacca esattamente quel presupposto. tools/data/reports/agent_20260515_1904.md:23:- **Vault**: Rosenzweig-Porter vero resta fuori perimetro; va riattivato solo con Hamiltoniane interpolate, non con fit di CDF su righe gia' generate. tools/data/reports/agent_20260515_1904.md:32:- **Baseline noto piu' vicino**: Brody distribution per interpolazione Poisson-Wigner; Berry-Robnik per mistura regolare/caotica. Rosenzweig-Porter e' nominato come famiglia di crossover Hamiltoniano, non fit eseguito in questo ciclo. tools/data/reports/agent_20260515_1904.md:34:- **Cosa resta Lab-specific**: `percolation:cycle_9`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` sono graph-only bridge: il grafo li mette al confine ma Brody/mixture li legge endpoint-like. tools/data/reports/agent_20260515_1904.md:39:> Nel perimetro 8/5, il terzo incluso operativo non e' riducibile a Brody q o a una mistura Poisson/GUE-surmise; il residuo vive nel disaccordo row-aligned tra scalare classico e grafo osservabile. tools/data/reports/agent_20260515_1904.md:46:- **Attraversamento matematico**: fit Brody e mistura Poisson/GUE-surmise sulle stesse righe gia' lette dal grafo kNN. tools/data/reports/agent_20260515_1904.md:48:- **Osservabile/test fisico possibile**: su finestre energetiche sperimentali, calcolare q Brody, peso mistura e kNN multi-feature; separare bridge coincidenti, graph-only e classic-only. tools/data/reports/agent_20260515_1904.md:56:- **Fit Brody**: grid likelihood su q in [0,1], spacings normalizzati a media 1. tools/data/reports/agent_20260515_1904.md:57:- **Fit Berry-Robnik-like**: griglia su peso GUE in mistura CDF `w*GUE_surmise + (1-w)*Poisson`, selezionata per KS minimo. tools/data/reports/agent_20260515_1904.md:58:- **Contratto osservabile-operatore**: il ciclo testa concordanza/disaccordo tra scalare classico e graph state; non testa V_c, denominatori Sturmian, unfolding fisico alternativo o Rosenzweig-Porter Hamiltoniano. tools/data/reports/agent_20260515_1904.md:63:| classic_and_graph_bridge | 1 | tools/data/reports/agent_20260515_1904.md:68:| row | label | graph_state | Brody q | w_GUE | KS | audit_state | tools/data/reports/agent_20260515_1904.md:72:| numeri_primi:cycle_3 | GUE | third_included_candidate | 0.465 | 0.275 | 0.148459 | classic_and_graph_bridge | tools/data/reports/agent_20260515_1904.md:94:Il boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno dei due chiude il terzo incluso da solo. tools/data/reports/agent_20260515_1904.md:103:Il prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche. Il risultato da cercare e' se `graph_only` e `classic_only` sopravvivono fuori dal perimetro composito del Lab. tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:1:{"delta": {"L1": -0.10650104061460512, "L2": -0.07479787276089715, "SR": 0.1583974714187561, "SR2": 0.1498679356348036, "triple_var": -1.393196814593312}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.08561183317003503, "L2": 0.0007486219471069146, "SR": 0.37828156197252366, "SR2": 0.38953768819591883, "triple_var": 1.955974552612343}, "null_rep": 0, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:2:{"delta": {"L1": -0.10430153153543356, "L2": -0.09275018510442262, "SR": 0.14522786890385175, "SR2": 0.1358503619664863, "triple_var": -2.151489013490897}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.08781134224920659, "L2": 0.018700934290632378, "SR": 0.391451164487428, "SR2": 0.4035552618642361, "triple_var": 2.7142667515099284}, "null_rep": 1, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:3:{"delta": {"L1": -0.014467647888988355, "L2": -0.08586071013997297, "SR": 0.17708620110202467, "SR2": 0.15097420087146107, "triple_var": -1.5409582621382627}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.1776452258956518, "L2": 0.011811459326182725, "SR": 0.3595928322892551, "SR2": 0.38843142295926136, "triple_var": 2.103736000157294}, "null_rep": 2, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:4:{"delta": {"L1": -0.31540915958267823, "L2": -0.028324278616194035, "SR": 0.10598058758587808, "SR2": 0.13645917581880446, "triple_var": -2.1680297234645476}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": 0.1232962857980381, "L2": -0.045724972197596206, "SR": 0.4306984458054017, "SR2": 0.402946448011918, "triple_var": 2.7308074614835784}, "null_rep": 3, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:5:{"delta": {"L1": -0.08067448442044223, "L2": -0.06067007129848331, "SR": 0.15463716573793013, "SR2": 0.1630533487844037, "triple_var": -1.7880438767369704}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.11143838936419792, "L2": -0.013379179515306932, "SR": 0.38204186765334963, "SR2": 0.37635227504631874, "triple_var": 2.3508216147560015}, "null_rep": 4, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:6:{"delta": {"L1": -0.039756839920987885, "L2": -0.1591365395941696, "SR": 0.17056751226582134, "SR2": 0.14185739807734993, "triple_var": -1.6034196697453422}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.15235603386365226, "L2": 0.08508728878037936, "SR": 0.3661115211254584, "SR2": 0.3975482257533725, "triple_var": 2.1661974077643733}, "null_rep": 5, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:7:{"delta": {"L1": -0.25980830097608426, "L2": -0.04471478288800827, "SR": 0.09429286906768164, "SR2": 0.10276607841677804, "triple_var": -2.2549760748107746}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": 0.06769542719144411, "L2": -0.02933446792578197, "SR": 0.4423861643235981, "SR2": 0.4366395454139444, "triple_var": 2.8177538128298054}, "null_rep": 6, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:8:{"delta": {"L1": -0.12941851105884652, "L2": -0.2718768393399599, "SR": 0.1666898620740826, "SR2": 0.14775059710363442, "triple_var": -3.2701662540072878}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.06269436272579362, "L2": 0.19782758852616963, "SR": 0.36998917131719716, "SR2": 0.391655026727088, "triple_var": 3.8329439920263186}, "null_rep": 7, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:9:{"delta": {"L1": -0.239689488782267, "L2": -0.08471629009822347, "SR": 0.16069980730643457, "SR2": 0.20165093062551043, "triple_var": -2.9329909148392312}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": 0.04757661499762685, "L2": 0.010667039284433228, "SR": 0.3759792260848452, "SR2": 0.337754693205212, "triple_var": 3.495768652858262}, "null_rep": 8, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:10:{"delta": {"L1": -0.2912002046867606, "L2": -0.06818425047360586, "SR": 0.1086936452824146, "SR2": 0.1665174527805653, "triple_var": -3.151625044031153}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": 0.09908733090212048, "L2": -0.005865000340184375, "SR": 0.42798538810886516, "SR2": 0.37288817105015715, "triple_var": 3.7144027820501844}, "null_rep": 9, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:11:{"delta": {"L1": -0.07753966135558964, "L2": -0.092302804273821, "SR": 0.15538631472183811, "SR2": 0.1301271420503602, "triple_var": -1.784718177280202}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.11457321242905051, "L2": 0.018253553460030752, "SR": 0.38129271866944164, "SR2": 0.40927848178036225, "triple_var": 2.347495915299233}, "null_rep": 10, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:12:{"delta": {"L1": -0.16765357151192053, "L2": 0.05509184817975621, "SR": 0.19140595544172895, "SR2": 0.18953414536196234, "triple_var": -2.524828364735555}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.02445930227271961, "L2": -0.12914109899354645, "SR": 0.3452730779495508, "SR2": 0.3498714784687601, "triple_var": 3.0876061027545862}, "null_rep": 11, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:13:{"delta": {"L1": -0.016602336696863673, "L2": -0.13959842735689695, "SR": 0.14998995159357437, "SR2": 0.14191251699176444, "triple_var": -1.649284979977643}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.17551053708777647, "L2": 0.06554917654310671, "SR": 0.3866890817977054, "SR2": 0.397493106838958, "triple_var": 2.212062717996674}, "null_rep": 12, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:14:{"delta": {"L1": -0.1750953244454181, "L2": -0.011913403308490472, "SR": 0.12558750667118496, "SR2": 0.16113486267847993, "triple_var": -1.570235131977151}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.01701754933922206, "L2": -0.06213584750529977, "SR": 0.4110915267200948, "SR2": 0.3782707611522425, "triple_var": 2.133012869996182}, "null_rep": 13, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:15:{"delta": {"L1": -0.08325398487617987, "L2": -0.02700441688438905, "SR": 0.1790311930023159, "SR2": 0.14584959369739386, "triple_var": -1.845963095067119}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.10885888890846028, "L2": -0.04704483392940119, "SR": 0.35764784038896386, "SR2": 0.39355603013332857, "triple_var": 2.40874083308615}, "null_rep": 14, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:16:{"delta": {"L1": -0.21062127214890075, "L2": -0.11556124734445322, "SR": 0.1708655182186642, "SR2": 0.14134564460631982, "triple_var": -2.0202719398465483}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": 0.018508398364260596, "L2": 0.04151199653066299, "SR": 0.36581351517261557, "SR2": 0.3980599792244026, "triple_var": 2.583049677865579}, "null_rep": 15, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:17:{"delta": {"L1": -0.3604108706499393, "L2": 0.004433511416672364, "SR": 0.1446601335525428, "SR2": 0.16852970948715768, "triple_var": -3.483600834577654}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W4_metallic_candidate", "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", "n_gaps": 107, "n_levels": 216, "null": {"L1": 0.16829799686529917, "L2": -0.0784827622304626, "SR": 0.39201889983873695, "SR2": 0.37087591434356476, "triple_var": 4.046378572596685}, "null_rep": 16, "real": {"L1": -0.19211287378464015, "L2": -0.07404925081379024, "SR": 0.5366790333912798, "SR2": 0.5394056238307224, "triple_var": 0.5627777380190311}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:18:{"delta": {"L1": -0.15730082611254265, "L2": -0.04612686211100192, "SR": 0.1789465815455052, "SR2": 0.19878424504622194, "triple_var": -3.1369599345424177}, "event": "input_spectrum_pair", 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"input_spectrum_pair", "expected_class": "Anderson_3D_W16p5_critical_boundary", "label": "Anderson_3D_W16p5_critical_boundary_L6_rep11", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.07749656786544958, "L2": 0.032428481399335636, "SR": 0.3652450370952695, "SR2": 0.3559443858485724, "triple_var": 2.806515513725166}, "null_rep": 59, "real": {"L1": -0.12060989557531746, "L2": -0.018850578973269208, "SR": 0.5007471413143706, "SR2": 0.5190309300589092, "triple_var": 1.0761529788336064}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:1533:{"delta": {"L1": -0.24688397542872126, "L2": 0.16277647507212956, "SR": 0.06997428021831648, "SR2": 0.10194239931967425, "triple_var": -1.0546990809250432}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W16p5_critical_boundary", "label": "Anderson_3D_W16p5_critical_boundary_L6_rep11", "n_gaps": 107, "n_levels": 216, "null": {"L1": 0.1262740798534038, "L2": -0.18162705404539878, "SR": 0.4307728610960541, "SR2": 0.41708853073923496, "triple_var": 2.1308520597586496}, "null_rep": 60, "real": {"L1": -0.12060989557531746, "L2": -0.018850578973269208, "SR": 0.5007471413143706, "SR2": 0.5190309300589092, "triple_var": 1.0761529788336064}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:1534:{"delta": {"L1": 0.003938118489286768, "L2": 0.06550326938643988, "SR": 0.15777992388970785, "SR2": 0.14008919598016667, "triple_var": -1.6336050832365039}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W16p5_critical_boundary", "label": "Anderson_3D_W16p5_critical_boundary_L6_rep11", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.12454801406460422, "L2": -0.08435384835970909, "SR": 0.34296721742466274, "SR2": 0.37894173407874254, "triple_var": 2.7097580620701103}, "null_rep": 61, "real": {"L1": -0.12060989557531746, "L2": -0.018850578973269208, "SR": 0.5007471413143706, "SR2": 0.5190309300589092, "triple_var": 1.0761529788336064}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:1535:{"delta": {"L1": -0.07798583591683436, "L2": 0.014387910209792971, "SR": 0.12954939092005713, "SR2": 0.14562553685296836, "triple_var": -1.3507772236734799}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W16p5_critical_boundary", "label": "Anderson_3D_W16p5_critical_boundary_L6_rep11", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.0426240596584831, "L2": -0.03323848918306218, "SR": 0.37119775039431346, "SR2": 0.37340539320594085, "triple_var": 2.4269302025070862}, "null_rep": 62, "real": {"L1": -0.12060989557531746, "L2": -0.018850578973269208, "SR": 0.5007471413143706, "SR2": 0.5190309300589092, "triple_var": 1.0761529788336064}} tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl:1536:{"delta": {"L1": -0.08749639548713264, "L2": 0.020100118098295636, "SR": 0.11939950658782278, "SR2": 0.15671725304116707, "triple_var": -2.0589433040343437}, "event": "input_spectrum_pair", "expected_class": "Anderson_3D_W16p5_critical_boundary", "label": "Anderson_3D_W16p5_critical_boundary_L6_rep11", "n_gaps": 107, "n_levels": 216, "null": {"L1": -0.033113500088184804, "L2": -0.038950697071564844, "SR": 0.3813476347265478, "SR2": 0.36231367701774214, "triple_var": 3.1350962828679503}, "null_rep": 63, "real": {"L1": -0.12060989557531746, "L2": -0.018850578973269208, "SR": 0.5007471413143706, "SR2": 0.5190309300589092, "triple_var": 1.0761529788336064}} tools/data/reports/falsifier_20260514_1640.json:6: "summary": "Il report e' internamente coerente rispetto alle 8 lenti: non formula hard constraint falsificati dai dati visibili, dichiara non_possible, metabolizza CE/KSAR, cita baseline note e mantiene aderenza alla direzione GOE/GUE/Poisson-Anderson." tools/data/audit_paper_F_draft2.json:127: "message": "Missing traceability markers: determinant condition, det=-1 explicit, generating map f(x)=1+1/x, potential V(r), three-force decomposition, algebraic field Q(√5), Berry-Keating connection", tools/data/reports/evolution_20260427_0330.md:5:Il passo ha costruito un calibratore esterno (curva di Brody) e lo ha usato come zero di riferimento per discriminare segnale da artefatto nei domini reali. Traiettoria: da META+BOUNDARY (dubbio sulla tautologia dei test) a quantificazione del pavimento di rumore (7.8%) e conferma che il segnale primi (29.5%) lo supera di 3.8x. Il passo ha chiuso tre tensioni aperte in un colpo solo (META, BOUNDARY, C1) con un unico esperimento ben disegnato. tools/data/reports/evolution_20260427_0330.md:21:1. **Curva di Brody come strumento permanente.** Il calibratore non e' usa-e-getta: puo' diventare il riferimento fisso per ogni futuro esperimento su gap statistics. Lo script `exp_brody_calibration.py` esiste gia' — serve solo che il campo vivo lo segnali come strumento disponibile, non come esperimento concluso. tools/data/reports/evolution_20260427_0330.md:29:Il ciclo seguente potrebbe prendere la curva di Brody e usarla come baseline per i domini che non sono ancora stati calibrati (qubit, Riemann zeta zeros, sequenze biologiche) — verificando se il segno del canale di ordinamento discrimina anche la'. tools/data/reports/falsifier_20260501_0931.json:18: "suggestion": "Declare explicitly: \"Claim Under Test corrected: ordered GUE spacings vs shuffled-GUE spacings.\" Any statement about GUE-Poisson requires a separate Poisson endpoint or a known crossover model such as Rosenzweig-Porter/Brody." tools/data/reports/agent_20260429_1013.md:1:# Agent Report — Brody Flow: Primes Drift Toward Poisson at 82% Magnitude, 18% Ordering — The Boundary Is a Trajectory, Not a Fixed Point tools/data/reports/agent_20260429_1013.md:13:Does the Brody parameter beta (interpolating Poisson beta=0 and GUE beta=1) evolve along the prime sequence, or is it a fixed point? If it flows, how much is magnitude (gap distribution shape) and how much is ordering (sequential correlations)? tools/data/reports/agent_20260429_1013.md:16:- **Observable**: Brody beta (MLE from unfolded gaps) and r-statistic (consecutive spacing ratio) in sliding windows along the prime sequence. tools/data/reports/agent_20260429_1013.md:23:### Brody flow along the prime sequence tools/data/reports/agent_20260429_1013.md:89:- **Campo di possibilita**: diventa possibile predire dove beta raggiunge 0 (p ~ 10^9 — testabile). Diventa possibile mappare il diagramma di fase beta(N, L) completo. Diventa non-possibile trattare il Brody beta come un numero fisso che caratterizza "i primi" — e un campo che varia con posizione e scala. tools/data/boundary_classical_crossover_audit_20260515_1904.json:8: "not_tested": "true Rosenzweig-Porter Hamiltonian flow, physical unfolding alternatives, asymptotic universality", tools/data/boundary_classical_crossover_audit_20260515_1904.json:9: "observable": "row-aligned Brody q, Berry-Robnik-like GUE mixture weight, graph bridge state", tools/data/boundary_classical_crossover_audit_20260515_1904.json:27: "question": "Do graph bridge rows collapse to standard Brody/Berry-Robnik-like crossover coordinates?", tools/data/boundary_classical_crossover_audit_20260515_1904.json:58: "audit_state": "classic_and_graph_bridge", tools/data/boundary_classical_crossover_audit_20260515_1904.json:215: "classic_and_graph_bridge": 1, tools/data/boundary_classical_crossover_audit_20260515_1904.json:220: "classic_and_graph_bridge": [ tools/data/reports/falsifier_20260515_1745.json:10: "evidence": "Il report lavora su tight-binding Fibonacci/Aubry, spacing statistics e localizzazione/IPR: questo perimetro ha risultati classici vicini, in particolare transizioni Aubry-Andre/quasicristalli Fibonacci e level-statistics vs localization. Il report non nomina il risultato noto piu' vicino prima di marcare la relazione come nuova.", tools/data/reports/falsifier_20260515_1745.json:11: "suggestion": "Nel prossimo ciclo aggiungere una sezione `nearest_known_result` e confrontare esplicitamente: Aubry-Andre coseno canonico, Fibonacci quasicrystal localization/spectral statistics, GUE/Poisson crossover. Riformulare `relazione nuova` come `nuova nel lab/per questo gate` se non supera quel confronto." tools/data/reports/agent_20260515_1659.md:23:- `not_drift`: il ciclo non riapre prime/mod6, `V_c`, gap ratio, grammatica Sturmian, Anderson 3D o promozione pubblica. Il domandatore 16:59 ha generato `SCALE_TRASCENDENZA_LIMITE`, ma resta output stale/pre-closeout e non diventa direzione. tools/data/reports/falsifier_20260515_1855.json:10: "evidence": "Il dominio GUE/Poisson e le transizioni tra repulsione spettrale e indipendenza/localizzazione sono classici; il report non ancora ancora il risultato al riferimento noto piu' vicino, per esempio crossover GUE-Poisson, mobility edge/localization crossover, Brody/Berry-Robnik/Rosenzweig-Porter o statistiche note di spacing finite.", tools/data/reports/agent_20260515_1816.md:8:**observable_contract**: claim=V=2 e' baseline fisica del confine Aubry-Andre e distingue il tipo di generatore; observable=slope log-log `tau` della mean participation ratio piu spacing/IPR anchors; operator=`tools/exp_aubry_v2_generator_scaling_gate.py`; generator=tight-binding 1D con potenziali `sturmian_binary`, `cosine`, `periodic_ab`, `density_shuffle`, `random_uniform`; denominator=N={89,144,233,377} x phase={0,0.25,0.5,0.75} x generator rows, con 4 trial per null random; non_possible=promuovere un claim phi-specific se tau(V=2) non separa dai controlli Sturmian non-phi; not_tested=limite asintotico, altri V, mobility edge, dati sperimentali, qualita PSD dei surrogate 18:07. tools/data/reports/agent_20260515_1816.md:12:- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + baseline fisica Aubry-Andre V=2 + tensione viva `TENS_SCALE_TRASCENDENZA_LIMITE`. tools/data/reports/agent_20260515_1816.md:23:- `why`: testa il confine come proprieta del generatore usando Sturmian/binario vs coseno Aubry-Andre vs controlli irrazionali, con baseline fisica esplicita V=2. tools/data/reports/agent_20260515_1816.md:36:- **Punto fisico sorgente**: modello Aubry-Andre tight-binding 1D, dove il coseno quasiperiodico ha transizione self-dual nota a V=2. tools/data/reports/agent_20260515_1816.md:49:- Nearest-known baseline: Aubry-Andre continuo, transizione self-dual a V=2; per questo il ciclo non promuove il risultato come nuovo fenomeno fisico, ma come gate di generatore. tools/data/repairs/repair_20260514_1631_decision.json:1:{"cycle_ts": "20260514_1631", "decision": "SKIP", "should_rerun": false, "reasons": ["no_repair_condition_matched"], "guards": {"repair_depth": 0, "max_depth": 1, "parent_ts": "", "loop_danger": false, "aeternitas_veto": false}, "signals": {"cycle_ts": "20260514_1631", "agent": {"path": "/opt/MM_D-ND/tools/data/reports/agent_20260514_1631.md", "exists": true, "title": "Agent Report - Chiusura tester rimbalzo fisico W=6", "size": 9494}, "falsifier": {"path": "/opt/MM_D-ND/tools/data/reports/falsifier_20260514_1631.json", "exists": true, "coherent": true, "summary": "Il report e' internamente coerente rispetto alle 8 lenti: non usa zero/proibizioni improprie, dichiara baseline classica, non_possible, contaminazione CE e deliberate_counter_perimeter.", "counts": {"high": 0, "medium": 0, "low": 0}, "flags": []}, "aeternitas": {"path": "/opt/MM_D-ND/tools/data/aeternitas/aeternitas_20260514_163527.json", "decision": "WARN", "reason": "P5 (Autopoiesi): ciclo non ha prodotto tensioni nuove né cambiato direzione", "p0_passed": true, "p1_passed": true, "p5_passed": false, "p5_violations": [{"issue": "no_new_tension_or_direction", "old_count": 13, "new_count": 13}]}, "veritas": {"path": "/opt/MM_D-ND/tools/data/veritas/veritas_20260514_163527.json", "rho": 0.8992, "band": "SOSPENSIONE"}, "valutatore": {"exists": true, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha chiuso regressivamente il rimbalzo fisico: il claim e' stato ridotto da possibile legge a tester operativo `component_state(SR,L1,triple_var)`, con Bridge QA clean e health completed. Resta una mossa naturale nello stesso frame: riusare il tester su un perimetro piu' esterno e correggere prima dell'esecuzione il nodo sanitario indicato dall'Affinatore (`evidence_tier + trace_path`), senza richiedere intervento operatore.", "action_type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Poisson-Anderson usando `component_state(SR,L1,triple_var)` come tester operativo, con contratto pre-run esplicito `evidence_tier` e `trace_path` obbligatori.", "constraint": "Non promuovere il pattern a legge fisica; testare trasferibilita' del tester su un dominio fisico/simulato ordinato o su sweep Anderson multi-W, con null Poisson span-matched e trace JSONL verificabile.", "reason": "Il deposito e' vivo ma non cristallizzabile: serve una replica di trasferibilita' del tester, non nuovo fit locale."}, "notes": "Side-effect log-only: richiesta prosecuzione del ciclo con contratto sanitario rafforzato prima del run."}, "loop_guard": {"path": "/opt/MM_D-ND/tools/data/reports/loop_guard_20260514_1631.json", "exists": true, "bands": {"valutatore_decisions": "OK", "piano_trajectory": "OK"}, "danger": false}, "session": {"exists": true, "sync_blocked": false, "errors": ""}}, "directive_path": "/opt/MM_D-ND/tools/data/operator_directive.md", "record_json": null, "record_md": null} tools/data/repairs/repair_20260514_1640_decision.json:1:{"cycle_ts": "20260514_1640", "decision": "SKIP", "should_rerun": false, "reasons": ["no_repair_condition_matched"], "guards": {"repair_depth": 0, "max_depth": 1, "parent_ts": "", "loop_danger": false, "aeternitas_veto": false}, "signals": {"cycle_ts": "20260514_1640", "agent": {"path": "/opt/MM_D-ND/tools/data/reports/agent_20260514_1640.md", "exists": true, "title": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", "size": 12465}, "falsifier": {"path": "/opt/MM_D-ND/tools/data/reports/falsifier_20260514_1640.json", "exists": true, "coherent": true, "summary": "Il report e' internamente coerente rispetto alle 8 lenti: non formula hard constraint falsificati dai dati visibili, dichiara non_possible, metabolizza CE/KSAR, cita baseline note e mantiene aderenza alla direzione GOE/GUE/Poisson-Anderson.", "counts": {"high": 0, "medium": 0, "low": 0}, "flags": []}, "aeternitas": {"path": "/opt/MM_D-ND/tools/data/aeternitas/aeternitas_20260514_164509.json", "decision": "WARN", "reason": "P5 (Autopoiesi): ciclo non ha prodotto tensioni nuove né cambiato direzione", "p0_passed": true, "p1_passed": true, "p5_passed": false, "p5_violations": [{"issue": "no_new_tension_or_direction", "old_count": 13, "new_count": 13}]}, "veritas": {"path": "/opt/MM_D-ND/tools/data/veritas/veritas_20260514_164509.json", "rho": 0.9325, "band": "COLLASSO"}, "valutatore": {"exists": true, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha rafforzato il tester senza promuoverlo a legge: Bridge QA clean, health completed, e il claim resta correttamente vincolato come strumento operativo. La prossima mossa deve continuare il frame ma spostare la falsificazione fuori dal dettaglio locale GOE/GUE a un denominatore fisico piu' duro, usando il nodo regressivo indicato dall'Affinatore: contratto pre-run esplicito su trace_path, evidence_tier e autopsy.", "action_type": "trigger_cycle", "detail": {"direction": "Continuare il tester component_state(SR,L1,triple_var) su falsificazione fisica esterna: Anderson 3D o many-body/localizzazione, con contratto pre-run esplicito trace_path/evidence_tier/autopsy; vietato promuovere a legge fisica o accumulare solo N-curve GOE/GUE.", "constraints": ["mantenere GOE/GUE/Poisson come baseline, non come nuovo oggetto principale", "testare almeno un denominatore fisico non gia' coperto dal ciclo 16:40", "dichiarare non_possible prima dell'esecuzione", "includere shuffle/surrogato o baseline strutturale equivalente"]}, "notes": "Non cristallizzare ancora: il tester e' leggibile ma non ha superato abbastanza domini esterni. Non REDESIGN pieno perche' la direzione fisica resta viva; va solo spinta fuori dal rinforzo locale."}, "loop_guard": {"path": "/opt/MM_D-ND/tools/data/reports/loop_guard_20260514_1640.json", "exists": true, "bands": {"valutatore_decisions": "OK", "piano_trajectory": "OK"}, "danger": false}, "session": {"exists": true, "sync_blocked": false, "errors": ""}}, "directive_path": "/opt/MM_D-ND/tools/data/operator_directive.md", "record_json": null, "record_md": null} tools/data/repairs/repair_20260514_1612_decision.json:1:{"cycle_ts": "20260514_1612", "decision": "SKIP", "should_rerun": false, "reasons": ["no_repair_condition_matched"], "guards": {"repair_depth": 0, "max_depth": 1, "parent_ts": "", "loop_danger": false, "aeternitas_veto": false}, "signals": {"cycle_ts": "20260514_1612", "agent": {"path": "/opt/MM_D-ND/tools/data/reports/agent_20260514_1612.md", "exists": true, "title": "Agent Report - Rimbalzo fisico del residuo SR", "size": 8596}, "falsifier": {"path": "/opt/MM_D-ND/tools/data/reports/falsifier_20260514_1612.json", "exists": true, "coherent": false, "summary": "Il report e' in gran parte coerente internamente; la lente rotta e' L8 per aderenza alla direzione viva non dimostrata dai file forniti, con un richiamo L5 alla baseline classica non nominata.", "counts": {"high": 0, "medium": 1, "low": 1}, "flags": [{"lens": "L8", "severity": "medium", "claim": "`relation`: deliberate_counter_perimeter; `why`: la direttiva operatore chiede di non proseguire come ottimizzazione locale prime-minus-mod6", "suggestion": "Nel prossimo ciclo, allegare o citare il file `operator_directive.md` se esisteva; altrimenti riformulare come drift deliberato non coperto dal seme e tornare al test richiesto: micro-trace provider-indipendente, min_paired_rows pre-dichiarato, repliche 409..."}, {"lens": "L5", "severity": "low", "claim": "\"Relazione nuova o limite: il run mostra una formulazione fisica del cedimento selettivo\" e \"Formalizza un ponte interrogabile\"", "suggestion": "Esplicitare che il test e' un riuso operativo di statistiche spettrali note, non scoperta fisica: confrontare `SR,L1,triple_var` con baseline standard come adjacent gap ratio / Wigner-Dyson vs Poisson / Anderson localization level statistics."}]}, "aeternitas": {"path": "/opt/MM_D-ND/tools/data/aeternitas/aeternitas_20260514_161719.json", "decision": "PROCEED", "reason": "P0 OK · P1 OK · P5 OK", "p0_passed": true, "p1_passed": true, "p5_passed": true, "p5_violations": []}, "veritas": {"path": "/opt/MM_D-ND/tools/data/veritas/veritas_20260514_161719.json", "rho": 0.9267, "band": "COLLASSO"}, "valutatore": {"exists": true, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un rimbalzo fisico reale nel frame GUE/Anderson, con Bridge QA clean e senza nodo regressivo. Non c'e' dipendenza dall'operatore: il limite e' gia' nominato dall'affinatore, cioe' trasformare il proxy numerico in tier di evidenza esplicito concentrandosi sul bordo W=6 dove SR/L1 cedono e triple_var resta attivo.", "action_type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Anderson, restringendo il prossimo ciclo al perimetro W=6 con evidence_tier esplicito e autopsy lab-native della trace.", "constraints": ["non espandere a deep-dive numerico generico su 8 GUE/5 Poisson", "dichiarare il tier di evidenza prima della misura", "testare se triple_var nomina memoria fisica oltre nearest-neighbor repulsion"]}, "notes": "NEXT_CYCLE non come altro giro locale sui primi, ma come verifica del confine fisico gia' emerso: W=6 e' il punto-zero operativo."}, "loop_guard": {"path": "/opt/MM_D-ND/tools/data/reports/loop_guard_20260514_1612.json", "exists": true, "bands": {"valutatore_decisions": "OK", "piano_trajectory": "OK"}, "danger": false}, "session": {"exists": true, "sync_blocked": false, "errors": ""}}, "directive_path": "/opt/MM_D-ND/tools/data/operator_directive.md", "record_json": null, "record_md": null} tools/data/repairs/repair_20260514_1850_decision.json:1:{"cycle_ts": "20260514_1850", "decision": "SKIP", "should_rerun": false, "reasons": ["no_repair_condition_matched"], "guards": {"repair_depth": 0, "max_depth": 1, "parent_ts": "", "loop_danger": false, "aeternitas_veto": false}, "signals": {"cycle_ts": "20260514_1850", "agent": {"path": "/opt/MM_D-ND/tools/data/reports/agent_20260514_1850.md", "exists": true, "title": "Agent Report - Interfaccia component_state su bordo Anderson 3D", "size": 9913}, "falsifier": {"path": "/opt/MM_D-ND/tools/data/reports/falsifier_20260514_1850.json", "exists": true, "coherent": false, "summary": "Il report e' quasi internamente coerente sui dati Anderson e sui perimetri, ma si rompe su L6: la contaminazione cognitiva non e' dichiarata nel formato richiesto e il Vault resta deposito operativo, non metabolismo esplicito del campo.", "counts": {"high": 0, "medium": 1, "low": 0}, "flags": [{"lens": "L6", "severity": "medium", "claim": "Contamination Classification include `vault`: ramo `20260514_1701` Grammar-vs-Scale Boundary Gate conservato come contro-perimetro, non seguito.", "suggestion": "Nel prossimo ciclo aggiungere una riga esplicita: `Contaminazione cognitiva: CE-none: nessuna voce CE/YSN/Cornelius/KSAR/PVI usata; Vault 20260514_1701 solo contro-perimetro operativo` oppure nominare la voce CE/PVI/Vault effettivamente usata nella combo."}]}, "aeternitas": {"path": "/opt/MM_D-ND/tools/data/aeternitas/aeternitas_20260514_185450.json", "decision": "WARN", "reason": "P5 (Autopoiesi): ciclo non ha prodotto tensioni nuove né cambiato direzione", "p0_passed": true, "p1_passed": true, "p5_passed": false, "p5_violations": [{"issue": "no_new_tension_or_direction", "old_count": 13, "new_count": 13}]}, "veritas": {"path": "/opt/MM_D-ND/tools/data/veritas/veritas_20260514_185450.json", "rho": 0.8458, "band": "SOSPENSIONE"}, "valutatore": {"exists": true, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo non ha promosso una legge fisica: ha stabilizzato il contratto del tester su Anderson 3D dichiarando il limite SR/L1 e il segnale residuo triple_var. Questo e' utile solo se ora viene verificata la riusabilita' su un secondo fisico B, altrimenti resta dettaglio locale del bordo GOE-GUE-Poisson-Anderson.", "action_type": "trigger_cycle", "detail": {"direction": "Usare lo stesso tool contract component_state(SR,L1,triple_var) su un secondo dominio fisico B indipendente, con step_trace esplicito e null Poisson span-matched; vietata promozione a legge se non separano almeno due componenti o se il segnale resta solo triple_var.", "reason": "Testare trasferibilita' cross-dominio del contratto, non approfondire Anderson L=6."}, "notes": "Preferire un dominio spettrale fisico non Anderson e non Sturmian-vault, cosi' il prossimo ciclo discrimina interfaccia riusabile da adattamento locale."}, "loop_guard": {"path": "/opt/MM_D-ND/tools/data/reports/loop_guard_20260514_1850.json", "exists": true, "bands": {"valutatore_decisions": "OK", "piano_trajectory": "OK"}, "danger": false}, "session": {"exists": true, "sync_blocked": false, "errors": ""}}, "directive_path": "/opt/MM_D-ND/tools/data/operator_directive.md", "record_json": null, "record_md": null} tools/data/reports/falsifier_20260515_1758.json:9: "claim": "Relazione nuova: la finestra binaria 17:45 non trasferisce automaticamente alla classe Aubry-Andre coseno; il generatore del potenziale diventa parte atomica del claim.", tools/data/reports/falsifier_20260515_1758.json:10: "evidence": "Il risultato osservato cade dentro un perimetro classico: modello Aubry-Andre canonico con transizione/localizzazione attesa attorno a V=2. La tabella mostra rottura del gate proprio a V=2.00 e perdita di intermedieta IPR dopo V=2.25, coerente con struttura nota del modello piu' che con scoperta fisica nuova autonoma. Il report nomina Aubry-Andre, ma non ancora il risultato classico piu' vicino come baseline esplicita; inoltre usa 'mobility edge/localization transition' mentre il modello canonico 1D Aubry-Andre ha transizione globale di localizzazione, non mobility edge salvo varianti generalizzate.", tools/data/reports/falsifier_20260515_1758.json:11: "suggestion": "Nel prossimo ciclo dichiarare baseline classica: self-dual transition Aubry-Andre a V=2 per hopping unitario; separare 'novita lab' = non-trasferimento del claim phi/binario dalla fisica gia' nota del modello; usare 'localization transition' e non 'mobility edge' se il generatore resta Aubry-Andre canonico." tools/data/reports/falsifier_20260515_1758.json:14: "summary": "Il report e' internamente coerente contro seme.json e passa le lenti dure; la sola frizione e' L5, perche' il risultato va ancorato esplicitamente alla transizione classica Aubry-Andre prima di essere trattato come relazione nuova del lab." tools/data/physical_sr_residue_bounce_20260514_1631_w6.json:24: "physical_return_candidate": "1D Anderson tight-binding spectra across disorder/localization", tools/data/physical_sr_residue_bounce_20260514_1631_w6.json:29: "Anderson_1D_W6": { tools/data/physical_sr_residue_bounce_20260514_1631_w6.json:47: "label": "Anderson_1D_W6", tools/data/reports/agent_20260407_0637.md:19:- **Observables**: (spacing ratio), beta (Brody parameter), acf1 (lag-1 autocorrelation) tools/data/reports/agent_20260406_1030.md:10:Do multiple domains occupy the spectral boundary, or is the prime niche unique? If Berry-Robnik mixtures fill the same range, what DISCRIMINATES primes from mixtures? tools/data/reports/agent_20260406_1030.md:13:- 17 domains (GUE, GOE, GSE, Poisson, power-law, picket fence, clock jitter, primes, semi-Poisson, Berry-Robnik x3, Anderson 1D, Harper phi/rational, quadratic residues) tools/data/reports/agent_20260406_1030.md:15:- Three observables: (ratio statistic), Brody beta, gap_acf1 (lag-1 autocorrelation) tools/data/reports/agent_20260406_1030.md:25:| Anderson_1D | 0.385 | -0.029 | -2.1 | POISSON | tools/data/reports/agent_20260406_1030.md:27:| Berry-Robnik 0.3 | 0.443 | +0.027 | +2.1 | BOUNDARY | tools/data/reports/agent_20260406_1030.md:29:| Berry-Robnik 0.5 | 0.485 | +0.009 | +0.6 | BOUNDARY | tools/data/reports/agent_20260406_1030.md:32:| Berry-Robnik 0.7 | 0.546 | -0.014 | -1.5 | GOE-zone | tools/data/reports/agent_20260406_1030.md:43:- **MIXTURE** (Berry-Robnik): superpose chaotic + regular levels. Any achievable. acf1 ~ 0. tools/data/reports/agent_20260406_1030.md:46:Berry-Robnik 0.5 has =0.485, nearly identical to primes (0.481). But its acf1=+0.009 (no ordering). The statistic alone CANNOT distinguish these fundamentally different spectral types. tools/data/reports/agent_20260406_1030.md:67:1. **Primes are NOT a Berry-Robnik mixture.** Berry-Robnik systems achieve intermediate by mixing chaotic and regular levels. Their gap autocorrelation is zero — no ordering. Primes achieve intermediate through INTRINSIC anti-correlation (big-small alternation, Hardy-Littlewood). Same , different physics. tools/data/reports/agent_20260515_1915.md:7:observables_used: [graph_bridge_frequency, cut_edge_frequency, mean_centroid_margin, mean_cross_neighbor_fraction, classical_audit_state, brody_q, berry_robnick_like_gue_weight] tools/data/reports/agent_20260515_1915.md:8:**observable_contract**: claim=una riga boundary a due lettori e' operativa solo se lo stato graph bridge sopravvive a perturbazioni del lettore e resta auditato dal baseline classico; observable=frequenza graph bridge unita a stato Brody/Berry-Robnik-like; operator=perturbazione parametrica del grafo kNN con join classico row-aligned; generator=boundary_graph_curvature_gate sul denominatore BOUNDARY 13 righe; denominator=13 righe, 8 GUE e 5 Poisson, ripetute su griglia di 27 run; non_possible=stable Lab bridge se la frequenza bridge collassa sotto perturbazione k/n_gaps/seed; not_tested=Hamiltoniane fisiche nuove, unfolding alternativo, scaling asintotico. tools/data/reports/agent_20260515_1915.md:31:- **Baseline noto piu' vicino**: Brody distribution, Berry-Robnik-like mixture e famiglia Rosenzweig-Porter come riferimento di crossover Hamiltoniano non eseguito. tools/data/reports/agent_20260515_1915.md:33:- **Cosa resta Lab-specific**: `percolation:cycle_9`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` sono `stable_graph_bridge+graph_only_bridge`, tutte 27/27. Il baseline classico le legge endpoint-like, il grafo le legge confine stabile. tools/data/reports/agent_20260515_1915.md:45:- **Attraversamento matematico**: frequenza di ponte nel grafo kNN multi-osservabile sotto perturbazione di lettore, unita a Brody/Berry-Robnik-like. tools/data/reports/agent_20260515_1915.md:47:- **Osservabile/test fisico possibile**: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche; misurare `graph_bridge_frequency` e stato Brody/Berry-Robnik sulla stessa riga. tools/data/reports/agent_20260515_1915.md:51:- **Script**: `tools/exp_boundary_bridge_stability_audit.py`. tools/data/reports/agent_20260515_1915.md:53:- **Run**: `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260515_1915.json`. tools/data/reports/agent_20260515_1915.md:56:- **Classi**: `stable_graph_bridge` se frequenza >= 0.75; `parameter_sensitive_bridge` se 0.25 <= frequenza < 0.75; `unstable_non_bridge` se frequenza < 0.25. tools/data/reports/agent_20260515_1915.md:57:- **Contratto osservabile-operatore**: il ciclo testa stabilita' del lettore grafico unita al baseline classico; non testa V_c, denominatori Sturmian, Hamiltoniane Rosenzweig-Porter reali o unfolding fisico alternativo. tools/data/reports/agent_20260515_1915.md:71:| stable_graph_bridge+graph_only_bridge | 3 | tools/data/reports/agent_20260515_1915.md:72:| stable_graph_bridge+classic_and_graph_bridge | 1 | tools/data/reports/agent_20260515_1915.md:73:| stable_graph_bridge+classic_only_intermediate | 2 | tools/data/reports/agent_20260515_1915.md:74:| stable_graph_bridge+endpoint_like | 1 | tools/data/reports/agent_20260515_1915.md:82:| numeri_primi:cycle_3 | classic_and_graph_bridge | 1.000 | stable_graph_bridge+classic_and_graph_bridge | tools/data/reports/agent_20260515_1915.md:83:| percolation:cycle_9 | graph_only_bridge | 1.000 | stable_graph_bridge+graph_only_bridge | tools/data/reports/agent_20260515_1915.md:84:| reaction_diffusion:cycle_11 | graph_only_bridge | 1.000 | stable_graph_bridge+graph_only_bridge | tools/data/reports/agent_20260515_1915.md:85:| logistica_biforcazione_var_3.5699:cycle_13 | graph_only_bridge | 1.000 | stable_graph_bridge+graph_only_bridge | tools/data/reports/agent_20260515_1915.md:86:| zeta_zeros:cycle_4 | classic_only_intermediate | 0.889 | stable_graph_bridge+classic_only_intermediate | tools/data/reports/agent_20260515_1915.md:87:| random_matrix:cycle_7 | classic_only_intermediate | 0.778 | stable_graph_bridge+classic_only_intermediate | tools/data/reports/agent_20260515_1915.md:88:| pendolo_doppio:cycle_2 | endpoint_like | 0.889 | stable_graph_bridge+endpoint_like | tools/data/reports/agent_20260515_1915.md:99:6. Inferito: il terzo incluso operativo e' il composito `graph_bridge_frequency + classical_audit_state`; una soglia kNN singola perde informazione. tools/data/reports/agent_20260515_1915.md:104:Il boundary trasferisce come gate a frequenza composita. La parte Lab-specific sopravvive nei tre stable graph-only bridge; il confine non si chiude nella lista 18:55 e non si riduce a Brody/Berry-Robnik. tools/data/reports/agent_20260515_1915.md:110:- **Campo di possibilita**: possibile = portare il gate su sistemi fisici controllati; non-possibile = usare una singola soglia kNN o un singolo parametro Brody come terzo incluso. tools/data/reports/agent_20260515_1915.md:113:Il prossimo ciclo utile porta il gate composito su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre. Il target non e' aumentare metriche; e' chiedere se `stable_graph_bridge+graph_only_bridge` sopravvive fuori dal perimetro composito del Lab. tools/data/reports/agent_20260515_1915.md:121:- Primo run dello script con ricostruzione ripetuta dello spacing troppo lento; interrotto con `pkill`. Riparazione regressiva: cache delle righe in `tools/exp_boundary_bridge_stability_audit.py`, poi run completo 27/27. tools/data/reports/agent_20260515_1915.md:122:- `python -m py_compile tools/exp_boundary_bridge_stability_audit.py` completato. tools/data/reports/agent_20260515_1915.md:123:- `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260515_1915.json` completato. tools/data/reports/agent_20260515_1915.md:129:- Script: `tools/exp_boundary_bridge_stability_audit.py` tools/data/reports/agent_20260515_1915.md:130:- Data: `tools/data/boundary_bridge_stability_audit_20260515_1915.json` tools/data/reports/agent_20260406_0714.md:7:> All four observables (Brody beta, dR_acf1, gap_acf1, ) drift toward Poisson with scale. tools/data/reports/agent_20260406_0714.md:17:- For each window: Brody beta, dR_acf1, gap_acf1, tools/data/reports/falsifier_20260515_1807.json:11: "suggestion": "Nel prossimo ciclo includere V=2 come riga di controllo fisico obbligatoria e almeno un contro-perimetro continuo/discreto esplicito: phi_sturmian/binario vs coseno Aubry-Andre o controllo irrazionale con stessi osservabili." tools/data/reports/falsifier_20260515_1807.json:17: "evidence": "Il report lavora su Hamiltoniane Aubry-Andre/Sturmian, statistiche spettrali e surrogate IAAFT, ma non ancora il risultato classico piu vicino o il limite noto rispetto al quale la relazione sarebbe nuova. Il testo evita universalita GUE/Poisson, ma usa comunque 'Relazione nuova'.", tools/data/reports/falsifier_20260515_1807.json:18: "suggestion": "Riformulare come 'relazione nel perimetro del lab' oppure aggiungere un confronto bibliografico/strutturale minimo: Aubry-Andre/Harper V=2, Hamiltoniane Sturmian/quasiperiodiche, e ruolo noto di autocorrelazione/PSD nei surrogate." tools/data/anderson3d_component_state_interface_input_20260514_1850.json:8: "physical_B": "3D Anderson tight-binding finite-size metallic/critical boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:14: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:15: "label": "Anderson_3D_W4_metallic_candidate_L6_rep00", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:236: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:237: "label": "Anderson_3D_W4_metallic_candidate_L6_rep01", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:458: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:459: "label": "Anderson_3D_W4_metallic_candidate_L6_rep02", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:680: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:681: "label": "Anderson_3D_W4_metallic_candidate_L6_rep03", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:902: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:903: "label": "Anderson_3D_W4_metallic_candidate_L6_rep04", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1124: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1125: "label": "Anderson_3D_W4_metallic_candidate_L6_rep05", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1346: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1347: "label": "Anderson_3D_W4_metallic_candidate_L6_rep06", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1568: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1569: "label": "Anderson_3D_W4_metallic_candidate_L6_rep07", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1790: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:1791: "label": "Anderson_3D_W4_metallic_candidate_L6_rep08", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2012: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2013: "label": "Anderson_3D_W4_metallic_candidate_L6_rep09", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2234: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2235: "label": "Anderson_3D_W4_metallic_candidate_L6_rep10", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2456: "expected_class": "Anderson_3D_W4_metallic_candidate", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2457: "label": "Anderson_3D_W4_metallic_candidate_L6_rep11", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2678: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2679: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep00", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2900: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:2901: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep01", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3122: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3123: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep02", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3344: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3345: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep03", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3566: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3567: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep04", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3788: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:3789: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep05", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4010: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4011: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep06", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4232: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4233: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep07", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4454: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4455: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep08", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4676: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4677: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep09", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4898: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:4899: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep10", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:5120: "expected_class": "Anderson_3D_W16p5_critical_boundary", tools/data/anderson3d_component_state_interface_input_20260514_1850.json:5121: "label": "Anderson_3D_W16p5_critical_boundary_L6_rep11", tools/data/reports/agent_20260514_1612.md:5:**verdict**: CONSTRAINT - il trasduttore `SR` assorbito vs residuo `L1,triple_var` produce un test fisico concreto su spettri GUE/Anderson; nel run sintetico il ritorno fisico e' presente ma resta proxy numerico, non misura sperimentale tools/data/reports/agent_20260514_1612.md:8:**observable_contract**: claim=il cedimento selettivo di `SR` puo' essere formulato come test fisico A->B; observable=componenti canoniche attive/assorbite contro null Poisson span-matched; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Hamiltoniano Anderson 1D tight-binding; denominator=48 repliche per dominio, 95 gap centrali per spettro; non_possible=rimbalzo fisico assente se non si nomina B concreto oppure se `SR` non discrimina GUE da Anderson localizzato; not_tested=dati sperimentali reali, unfolding fine, classi di simmetria GOE/GSE, many-body localization. tools/data/reports/agent_20260514_1612.md:21:- **Proiezione**: GUE come sorgente fisica A; null Poisson span-matched; Anderson 1D con disordine `W=0.5,2,6,12` come ritorno fisico B. tools/data/reports/agent_20260514_1612.md:31:- `not_drift`: il deposito prime-minus-mod6 non e' destinazione; decide solo quale domanda portare a GUE/Anderson. tools/data/reports/agent_20260514_1612.md:37:- **Punto fisico di ritorno concreto**: Hamiltoniano tight-binding Anderson 1D con disordine crescente, interrogabile tramite spacing degli autovalori e transizione verso statistiche Poisson/localizzate. tools/data/reports/agent_20260514_1612.md:38:- **Relazione nuova o limite**: il run mostra una formulazione fisica del cedimento selettivo: `SR` e' attivo nel GUE e assorbito nel limite Anderson localizzato `W=12`. A `W=6`, `SR` e `L1` sono assorbiti mentre `triple_var` resta attivo: il ritorno fisico separa nearest-neighbor repulsion e memoria di triple locali. tools/data/reports/agent_20260514_1612.md:39:- **Osservabile/test fisico possibile**: misurare spettri energetici in un sistema Anderson-like, costruire un ensemble di finestre spettrali, confrontare `SR,L1,triple_var` contro null Poisson span-matched con sign-swap row-local. tools/data/reports/agent_20260514_1612.md:44:> Il cedimento selettivo di `SR` osservato nel deposito prime-minus-mod6 puo' tornare come criterio fisico: `SR` resta attivo in spettri caotici GUE e viene assorbito in spettri Anderson localizzati contro un null Poisson span-matched. tools/data/reports/agent_20260514_1612.md:49:- Ensemble: GUE hermitiano `N=192`, Anderson 1D `N=192`. tools/data/reports/agent_20260514_1612.md:61:| Anderson W=0.5 | 48 | SR,L1,triple_var | 0.6573 / 0.3903 / 0.2671 | 0.000244 | -0.3240 | 0.000244 | -2.5721 | 0.000244 | tools/data/reports/agent_20260514_1612.md:62:| Anderson W=2 | 48 | SR,L1,triple_var | 0.4364 / 0.3774 / 0.0589 | 0.000244 | -0.1373 | 0.000244 | -1.6244 | 0.000244 | tools/data/reports/agent_20260514_1612.md:63:| Anderson W=6 | 48 | triple_var | 0.3984 / 0.3824 / 0.0160 | 0.035392 | -0.0589 | 0.002929 | -0.7407 | 0.000244 | tools/data/reports/agent_20260514_1612.md:64:| Anderson W=12 | 48 | [] | 0.3798 / 0.3836 / -0.0038 | 0.585062 | -0.0262 | 0.168904 | -0.4147 | 0.002685 | tools/data/reports/agent_20260514_1612.md:71:2. **Verificato**: nel ritorno Anderson localizzato `W=12`, `SR` viene assorbito (`delta=-0.0038`, `p=0.585062`, `d=-0.076`). tools/data/reports/agent_20260514_1612.md:74:5. **Ambiguo dichiarato**: Anderson 1D non ha vera transizione estesa/localizzata nel limite infinito; qui il risultato e' un finite-size disorder crossover, non claim universale sulla transizione. tools/data/reports/agent_20260514_1612.md:80:Il rimbalzo fisico esiste come test: GUE -> span-matched Poisson -> Anderson 1D. Il risultato non cristallizza una nuova legge fisica. Formalizza un ponte interrogabile: quando lo spettro torna localizzato/Poisson, `SR` viene assorbito; nella zona di crossover, `triple_var` puo' restare come residuo oltre nearest-neighbor. tools/data/reports/agent_20260514_1612.md:92:Il prossimo ciclo fisico deve sostituire il proxy sintetico con un denominatore piu vicino al laboratorio: GOE/GUE scelto per simmetria dichiarata, Anderson 3D o many-body localization se si vuole una transizione fisica vera, unfolding controllato e confronto con dati o letteratura. Il deposito prime-minus-mod6 resta utile solo come generatore di osservabili, non come prova del dominio fisico. tools/data/reports/agent_20260514_1649.md:8:**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. tools/data/reports/agent_20260514_1649.md:80:| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | tools/data/reports/agent_20260514_1649.md:96:- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. tools/data/reports/agent_20260514_1649.md:97:- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. tools/data/reports/agent_20260514_1649.md:104:- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. tools/data/piano11e_results.json:52: "note": "Classic Berry saturation. GUE at short range, arithmetic rigidity at long range." tools/data/piano11e_results.json:77: "The number variance shows Berry saturation at L*=1.75, confirming the background-fluctuation separation." tools/data/lab_registro.json:180: "Fase di Berry come test: la fase in catene atomiche accumula multipli dell'angolo d'oro?" tools/data/audit_paper_B_draft3.json:59: "message": "Missing traceability markers: generating map f(x)=1+1/x, algebraic field Q(√5), Berry-Keating connection", tools/data/biconi/bicono_20260515_1915.json:6: "raw": "possibile = portare il gate su sistemi fisici controllati; non-possibile = usare una singola soglia kNN o un singolo parametro Brody come terzo incluso.", tools/data/conoscenza_teorie.json:582: "claim": "The boundary between GUE and Poisson regimes should affect the two-channel decomposition. If gap correlations decay with prime scale (Brody β → 0), does the Markov-3 ordering information in the residue channel also decay?", tools/data/conoscenza_teorie.json:586: "radici": "struttura scala-dipendente (Brody β, PSD slope — si indeboliscono con la scala, il segnale metrico decade) · struttura scala-invariante (Markov-3 ordering — costante al 33.6%, il segnale algebrico persiste). L'una decade, l'altra no. Sono inseparabili nella sequenza ma separabili nella decomposizione a due canali.", tools/data/conoscenza_teorie.json:837: "claim": "All four observables (Brody beta, dR_acf1, gap_acf1, ) drift toward Poisson with scale. > Is this ONE universal crossover (\"il confine è il terzo incluso\") or independent drifts?", tools/data/conoscenza_teorie.json:858: "claim": "The boundary between GUE and Poisson regimes should affect the two-channel decomposition. If gap correlations decay with prime scale (Brody β → 0), does the Markov-3 ordering information in the residue channel also decay?", tools/data/conoscenza_teorie.json:862: "radici": "struttura scala-dipendente (Brody β, PSD slope — si indeboliscono con la scala, il segnale metrico decade) · struttura scala-invariante (Markov-3 ordering — costante al 33.6%, il segnale algebrico persiste). L'una decade, l'altra no. Sono inseparabili nella sequenza ma separabili nella decomposizione a due canali.", tools/data/conoscenza_teorie.json:969: "claim": "All four observables (Brody beta, dR_acf1, gap_acf1, ) drift toward Poisson with scale. > Is this ONE universal crossover (\"il confine è il terzo incluso\") or independent drifts?", tools/data/conoscenza_teorie.json:991: "claim": "The boundary between GUE and Poisson regimes should affect the two-channel decomposition. If gap correlations decay with prime scale (Brody β → 0), does the Markov-3 ordering information in the residue channel also decay?", tools/data/conoscenza_teorie.json:995: "radici": "struttura scala-dipendente (Brody β, PSD slope — si indeboliscono con la scala, il segnale metrico decade) · struttura scala-invariante (Markov-3 ordering — costante al 33.6%, il segnale algebrico persiste). L'una decade, l'altra no. Sono inseparabili nella sequenza ma separabili nella decomposizione a due canali.", tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:3:{"delta": {"L1": -0.06703357746219565, "L2": -0.07543928651483184, "SR": -0.061164527846995276, "SR2": -0.0965704359101563, "triple_var": -1.1267903812824704}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 128, "n_gaps": 63, "null": {"L1": -0.044037793156105585, "L2": 0.013756570763573794, "SR": 0.4347874908460506, "SR2": 0.4657295982248646, "triple_var": 2.7643977855155693}, "real": {"L1": -0.11107137061830123, "L2": -0.06168271575125805, "SR": 0.37362296299905534, "SR2": 0.3691591623147083, "triple_var": 1.6376074042330988}, "rep": 0, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:6:{"delta": {"L1": -0.3017988048908831, "L2": 0.03181820257571786, "SR": -0.08513873000651695, "SR2": 0.01518866027327026, "triple_var": -0.9345938620804446}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 128, "n_gaps": 63, "null": {"L1": 0.06800789376931961, "L2": -0.08921465163347843, "SR": 0.4550714295496664, "SR2": 0.42679677675674954, "triple_var": 2.501875766225347}, "real": {"L1": -0.23379091112156353, "L2": -0.05739644905776057, "SR": 0.36993269954314945, "SR2": 0.4419854370300198, "triple_var": 1.5672819041449022}, "rep": 1, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:9:{"delta": {"L1": -0.134591783514655, "L2": 0.2669878800221063, "SR": 0.03637774416138201, "SR2": 0.08566663400192948, "triple_var": -0.2286685382109408}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 128, "n_gaps": 63, "null": {"L1": -0.008428712943603454, "L2": -0.1780043751681657, "SR": 0.38162097616795215, "SR2": 0.3413835804039986, "triple_var": 2.009781929379359}, "real": {"L1": -0.14302049645825846, "L2": 0.08898350485394063, "SR": 0.41799872032933416, "SR2": 0.42705021440592805, "triple_var": 1.7811133911684183}, "rep": 2, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:12:{"delta": {"L1": 0.10434642250292675, "L2": 0.0028219108863961617, "SR": 0.0230589070515973, "SR2": 0.052012945996743165, "triple_var": -0.3297102321000822}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 128, "n_gaps": 63, "null": {"L1": 0.0741846577401526, "L2": -0.1773692705183583, "SR": 0.45578337572775934, "SR2": 0.3515286433715366, "triple_var": 1.9446099701797024}, "real": {"L1": 0.17853108024307934, "L2": -0.17454735963196213, "SR": 0.47884228277935664, "SR2": 0.4035415893682798, "triple_var": 1.6148997380796202}, "rep": 3, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:15:{"delta": {"L1": -0.17226340709821314, "L2": 0.11650821009263643, "SR": 0.053946155682830677, "SR2": 0.004307058387076845, "triple_var": -0.8926838305998146}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 128, "n_gaps": 63, "null": {"L1": 0.028391371942504406, "L2": -0.29821328033291483, "SR": 0.33989981881147024, "SR2": 0.33673973775554755, "triple_var": 2.2574314460545644}, "real": {"L1": -0.14387203515570873, "L2": -0.1817050702402784, "SR": 0.3938459744943009, "SR2": 0.3410467961426244, "triple_var": 1.3647476154547498}, "rep": 4, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:18:{"delta": {"L1": -0.40737051580761086, "L2": 0.20459956129745893, "SR": -0.058406817199295746, "SR2": 0.02027251084379722, "triple_var": -1.1841637625307257}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 128, "n_gaps": 63, "null": {"L1": 0.07855419521157478, "L2": -0.047444516566645625, "SR": 0.36380116811982005, "SR2": 0.3349622173034131, "triple_var": 3.0137147362285956}, "real": {"L1": -0.3288163205960361, "L2": 0.15715504473081332, "SR": 0.3053943509205243, "SR2": 0.3552347281472103, "triple_var": 1.82955097369787}, "rep": 5, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:21:{"delta": {"L1": -0.26527915756420123, "L2": -0.20393563604381543, "SR": 0.03693151375777415, "SR2": 0.10003572720402742, "triple_var": -1.8838609061723064}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 128, "n_gaps": 63, "null": {"L1": 0.16489169925781136, "L2": 0.19116249675252844, "SR": 0.3875906111413446, "SR2": 0.3978296578138203, "triple_var": 3.966177637636442}, "real": {"L1": -0.10038745830638987, "L2": -0.012773139291286971, "SR": 0.4245221248991187, "SR2": 0.4978653850178477, "triple_var": 2.0823167314641355}, "rep": 6, "symmetry": "disordered_real_tight_binding_finite_size"} 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tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:354:{"delta": {"L1": 0.036898823420733404, "L2": -0.06688904409556412, "SR": 0.027669135449107318, "SR2": -0.02266183765973434, "triple_var": -0.4122789230644748}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": -0.0869085304658936, "L2": -0.14295595277648807, "SR": 0.35184956517510096, "SR2": 0.36479757135087165, "triple_var": 2.6739188831464}, "real": {"L1": -0.050009707045160195, "L2": -0.2098449968720522, "SR": 0.3795187006242083, "SR2": 0.3421357336911373, "triple_var": 2.2616399600819252}, "rep": 53, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:357:{"delta": {"L1": 0.09995830214824736, "L2": 0.00836569827383088, "SR": 0.08145190682910997, "SR2": 0.05725645450445199, "triple_var": -0.17275182935586297}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": 0.04807501852315251, "L2": 0.049562435969620414, "SR": 0.37287322315802257, "SR2": 0.35531592200325024, "triple_var": 3.2293177226265533}, "real": {"L1": 0.14803332067139988, "L2": 0.05792813424345129, "SR": 0.45432512998713254, "SR2": 0.41257237650770223, "triple_var": 3.0565658932706903}, "rep": 54, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:360:{"delta": {"L1": -0.23076606981344489, "L2": 0.013159980032646, "SR": -0.08164969639746045, "SR2": 0.009761266070621555, "triple_var": -0.04737555701545393}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": 0.08691922586068958, "L2": -0.02601983580158726, "SR": 0.4134929391557995, "SR2": 0.3919639568288097, "triple_var": 2.545400422398813}, "real": {"L1": -0.14384684395275532, "L2": -0.01285985576894126, "SR": 0.33184324275833904, "SR2": 0.40172522289943124, "triple_var": 2.498024865383359}, "rep": 55, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:363:{"delta": {"L1": 0.0014850940049166705, "L2": -0.22215454731373627, "SR": -0.02986151857065611, "SR2": -0.016100124008557037, "triple_var": -0.21437226969093648}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": -0.08332385609507277, "L2": -0.030229337858817967, "SR": 0.35659380134134633, "SR2": 0.36911916330474126, "triple_var": 2.173242567987653}, "real": {"L1": -0.0818387620901561, "L2": -0.2523838851725542, "SR": 0.3267322827706902, "SR2": 0.3530190392961842, "triple_var": 1.9588702982967166}, "rep": 56, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:366:{"delta": {"L1": 0.32625458774915467, "L2": -0.3714697215122392, "SR": 0.0498141551092669, "SR2": -0.08542523402966962, "triple_var": 0.009057972635931222}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": -0.2114799493354075, "L2": 0.13398838885449832, "SR": 0.34337331380785435, "SR2": 0.43088327488534817, "triple_var": 2.200779812262428}, "real": {"L1": 0.11477463841374715, "L2": -0.23748133265774093, "SR": 0.39318746891712125, "SR2": 0.34545804085567855, "triple_var": 2.209837784898359}, "rep": 57, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:369:{"delta": {"L1": -0.15294529987830008, "L2": 0.12189093202285224, "SR": -0.032809988884614005, "SR2": 0.0010088086817628406, "triple_var": -0.9634054685282911}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": -0.11764975549072763, "L2": -0.007696786070133082, "SR": 0.4048920112528762, "SR2": 0.4581367516564737, "triple_var": 2.4614238780974302}, "real": {"L1": -0.2705950553690277, "L2": 0.11419414595271916, "SR": 0.3720820223682622, "SR2": 0.45914556033823656, "triple_var": 1.4980184095691391}, "rep": 58, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:372:{"delta": {"L1": 0.05669039290688535, "L2": -0.2883291743652196, "SR": 0.009013831461764599, "SR2": 0.007574112844927594, "triple_var": -0.816029575456878}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": 0.13482486512058012, "L2": 0.08035392440647625, "SR": 0.4585153662470735, "SR2": 0.4115368484983338, "triple_var": 3.657468939361784}, "real": {"L1": 0.19151525802746547, "L2": -0.20797524995874336, "SR": 0.4675291977088381, "SR2": 0.41911096134326137, "triple_var": 2.841439363904906}, "rep": 59, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:375:{"delta": {"L1": -0.17449888705569733, "L2": -0.010932102095998812, "SR": -0.029462081296475873, "SR2": -0.008302699706539296, "triple_var": -0.7746123013403994}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": 0.06789678519794971, "L2": -0.04696013102910477, "SR": 0.4197971604137227, "SR2": 0.3863071276751615, "triple_var": 2.7330690186254505}, "real": {"L1": -0.10660210185774761, "L2": -0.05789223312510358, "SR": 0.39033507911724685, "SR2": 0.3780044279686222, "triple_var": 1.958456717285051}, "rep": 60, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:378:{"delta": {"L1": -0.07703420548242834, "L2": -0.08247341116783763, "SR": 0.04330326454146055, "SR2": 0.03234224753466047, "triple_var": -1.0652455183392573}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": -0.03942098425745807, "L2": 0.0974728496247482, "SR": 0.3588972469767167, "SR2": 0.3872960746458783, "triple_var": 3.4760761314963413}, "real": {"L1": -0.1164551897398864, "L2": 0.014999438456910576, "SR": 0.40220051151817726, "SR2": 0.41963832218053876, "triple_var": 2.410830613157084}, "rep": 61, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:381:{"delta": {"L1": -0.02446682246124057, "L2": -0.09200963217052734, "SR": 0.006760236036826739, "SR2": -0.008055664517591377, "triple_var": -0.40603847255054637}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": 0.000981485364098276, "L2": 0.08724455774547213, "SR": 0.3839781530354262, "SR2": 0.3815714715253384, "triple_var": 3.0002589096722443}, "real": {"L1": -0.023485337097142293, "L2": -0.004765074425055202, "SR": 0.39073838907225295, "SR2": 0.373515807007747, "triple_var": 2.594220437121698}, "rep": 62, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl:384:{"delta": {"L1": -0.010658854387527363, "L2": 0.21493401583531374, "SR": 0.08882703908893685, "SR2": 0.08747856560915185, "triple_var": -0.7221212492629512}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n": 192, "n_gaps": 95, "null": {"L1": -0.012435178104423721, "L2": -0.13554396742906488, "SR": 0.3438004871825068, "SR2": 0.3336035959536409, "triple_var": 3.1028168897729778}, "real": {"L1": -0.023094032491951084, "L2": 0.07939004840624886, "SR": 0.43262752627144363, "SR2": 0.42108216156279277, "triple_var": 2.3806956405100266}, "rep": 63, "symmetry": "disordered_real_tight_binding_finite_size"} tools/data/biconi/bicono_20260514_1640.json:4: "invariante": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/biconi/bicono_20260515_1758.json:2: "radici": "potenziale binario Sturmian / potenziale coseno Aubry-Andre.", tools/data/biconi/bicono_20260514_1850.json:2: "radici": "simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D.", tools/data/cognitive_enzymes_archive.json:5863: "snippet": "Operatori: - potenziale; - campo; - curvatura di gauge; - holonomy; - Wilson loop; - fibrato; - sezione; - fase; - Berry phase; - parallel transport; - rottura di simmetria; - Higgs-like mechanism come transizione di stato. Dipoli utili: - potenziale / campo; - fase locale / invariante globale; - gauge libero / osservabile vincolato; - trasporto / memoria; -" tools/data/lab_session_log.jsonl:1:{"timestamp": "2026-04-29T11:06:07.400556+00:00", "cycle_ts": "20260429_1041", "piano": "58", "sync_blocked": false, "errors": "", "agent": {"file": "agent_20260429_1041.md", "size": 8761, "title": "Agent Report — Mod-3 Ordering Is Algebraic and Scale-Invariant: A Separate Channel from Brody Beta"}, "falsifier": {"output_file": "falsifier_20260429_1041.json", "coherent": false, "n_flags": 4, "n_high": 2, "n_medium": 2, "summary": "Il report non e' internamente coerente: si rompe soprattutto su L1/L4, perche' chiama zero esatto un dataset che contiene e mostra una eccezione non-zero senza mantenerla sempre nel perimetro condizionato p > 3.", "lenses_high": ["L1", "L4"]}, "valutatore": {"decision": "CRYSTALLIZE", "confidence": "high", "action_type": null}} tools/data/lab_session_log.jsonl:85:{"timestamp": "2026-05-14T16:45:32.808570+00:00", "cycle_ts": "20260514_1640", "piano": "122", "sync_blocked": false, "errors": "", "agent": {"file": "agent_20260514_1640.md", "size": 12465, "title": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve"}, "falsifier": {"output_file": "falsifier_20260514_1640.json", "coherent": true, "n_flags": 0, "n_high": 0, "n_medium": 0, "summary": "Il report e' internamente coerente rispetto alle 8 lenti: non formula hard constraint falsificati dai dati visibili, dichiara non_possible, metabolizza CE/KSAR, cita baseline note e mantiene aderenza alla direzione GOE/GUE/Poisson-Anderson.", "lenses_high": []}, "valutatore": {"decision": "NEXT_CYCLE", "confidence": "medium", "action_type": null}} tools/data/lab_session_log.jsonl:89:{"timestamp": "2026-05-14T18:55:18.726699+00:00", "cycle_ts": "20260514_1850", "piano": "126", "sync_blocked": false, "errors": "", "agent": {"file": "agent_20260514_1850.md", "size": 9913, "title": "Agent Report - Interfaccia component_state su bordo Anderson 3D"}, "falsifier": {"output_file": "falsifier_20260514_1850.json", "coherent": false, "n_flags": 1, "n_high": 0, "n_medium": 1, "summary": "Il report e' quasi internamente coerente sui dati Anderson e sui perimetri, ma si rompe su L6: la contaminazione cognitiva non e' dichiarata nel formato richiesto e il Vault resta deposito operativo, non metabolismo esplicito del campo.", "lenses_high": []}, "valutatore": {"decision": "NEXT_CYCLE", "confidence": "medium", "action_type": null}} tools/data/lab_session_log.jsonl:97:{"timestamp": "2026-05-15T17:51:06.052356+00:00", "cycle_ts": "20260515_1745", "piano": "118", "sync_blocked": true, "errors": "Falsifier non-coherent: public sync deferred and blocked.", "agent": {"file": "agent_20260515_1745.md", "size": 8618, "title": "Agent Report - Aubry Boundary Phase Transport Gate"}, "falsifier": {"output_file": "falsifier_20260515_1745.json", "coherent": false, "n_flags": 2, "n_high": 0, "n_medium": 1, "summary": "Il report e' coerente rispetto ai dati visibili e alla direzione viva, ma si rompe L5 per mancato confronto con risultati classici vicini; L8 ha solo un residuo pubblico non nominato.", "lenses_high": []}, "valutatore": {"decision": "REDESIGN", "confidence": "medium", "action_type": null}} tools/data/lab_session_log.jsonl:98:{"timestamp": "2026-05-15T18:03:28.476296+00:00", "cycle_ts": "20260515_1758", "piano": "118", "sync_blocked": false, "errors": "", "agent": {"file": "agent_20260515_1758.md", "size": 9856, "title": "Agent Report - Aubry Cosine Boundary Counter-Gate"}, "falsifier": {"output_file": "falsifier_20260515_1758.json", "coherent": true, "n_flags": 1, "n_high": 0, "n_medium": 0, "summary": "Il report e' internamente coerente contro seme.json e passa le lenti dure; la sola frizione e' L5, perche' il risultato va ancorato esplicitamente alla transizione classica Aubry-Andre prima di essere trattato come relazione nuova del lab.", "lenses_high": []}, "valutatore": {"decision": "REDESIGN", "confidence": "high", "action_type": null}} tools/data/lab_session_log.jsonl:99:{"timestamp": "2026-05-15T18:13:21.967554+00:00", "cycle_ts": "20260515_1807", "piano": "119", "sync_blocked": true, "errors": "Falsifier non-coherent. Report non sincronizzato.", "agent": {"file": "agent_20260515_1807.md", "size": 9227, "title": "Agent Report - Aubry Binary Grammar Surrogate Gate"}, "falsifier": {"output_file": "falsifier_20260515_1807.json", "coherent": false, "n_flags": 2, "n_high": 0, "n_medium": 1, "summary": "Il report e' internamente coerente sui dati mostrati, ma L8 resta incompleta per assenza della baseline V=2 e della falsificazione QxG continuo/discreto richiesta dal seme.", "lenses_high": []}, "valutatore": {"decision": "NEXT_CYCLE", "confidence": "high", "action_type": null}} tools/data/audit_paper_D_draft3.json:54: "message": "Missing traceability markers: potential V(r), algebraic field Q(√5), Berry-Keating connection", tools/data/reports/falsifier_20260515_1915.json:10: "evidence": "Nei dati visibili solo 1 riga e' `stable_graph_bridge+classic_and_graph_bridge`; 3 righe sono `stable_graph_bridge+graph_only_bridge` e quindi non restano auditabili dal baseline classico. Se il criterio operativo richiede due lettori, il residuo Lab non puo' essere contato insieme al boundary a due lettori senza dichiarare un cambio di scope.", tools/data/reports/falsifier_20260515_1915.json:16: "claim": "Cosa resta Lab-specific: `percolation`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699` sono `stable_graph_bridge+graph_only_bridge`, tutte 27/27. Il baseline classico le legge endpoint-like, il grafo le legge confine stabile.", tools/data/reports/falsifier_20260515_1915.json:17: "evidence": "Il report nomina Brody, Berry-Robnik-like e Rosenzweig-Porter per il crossover spettrale, ma le tre righe Lab-specific sono pattern di ponte in grafo kNN multi-osservabile; il baseline classico citato non copre direttamente stabilita' di classificazione kNN sotto perturbazione di k/n/seed.", tools/data/evolution/evolution_20260514_1612.md:3:Il report osserva il passo come traiettoria `prime-minus-mod6 -> trasduttore -> GUE/Anderson`, individua l’attrito nel tier di evidenza non dichiarato prima della misura, e mette in consecutio il prossimo perimetro: `W=6`, `evidence_tier` esplicito, autopsy dalla trace lab-native. tools/data/piano11b_gue_test.json:424: "background": "V(r) \u2192 Berry-Keating (Piano 10g)", tools/data/evolution/evolution_20260501_0931.md:1:Evolution report scritto. Due nodi regressivi identificati: dataset ricostruito da zero ogni notte (fix: cache stabile) e consecutio stale che ha consumato budget di orientamento (fix: stato di risoluzione nel seme). La possibilità più concreta per il prossimo ciclo: confrontare lo zero-crossing dipolare con i punti critici Brody/Rosenzweig-Porter. tools/data/ciclo_memoria.json:288: "titolo": "Aubry Boundary Phase Transport Gate", tools/data/ciclo_memoria.json:300: "verdict": "**CONSTRAINT**.\n\nIl ritorno Aubry/Fibonacci trasferisce il confine BOUNDARY solo come finestra di accoppiamento. La formulazione valida e': nel perimetro misurato, `phi` e' terzo incluso fisico congiu", tools/data/ciclo_memoria.json:301: "cosa": "Aubry Boundary Phase Transport Gate → None/None (ρ=None)" tools/data/ciclo_memoria.json:306: "titolo": "Aubry Cosine Boundary Counter-Gate", tools/data/ciclo_memoria.json:319: "cosa": "Aubry Cosine Boundary Counter-Gate → None/None (ρ=None)" tools/data/ciclo_memoria.json:357: "verdict": "CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno dei due chiude il terzo incluso da solo.", tools/data/ciclo_memoria.json:369: "domanda_aperta": "Il prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche. Il risultato da cercare e' se `graph_only` e `classic_only` sopravvivono fuori dal perimetro composito del Lab.", tools/data/component_state_fit_ready_20260514_1649.json:3: "Anderson_1D_W6": { tools/data/component_state_fit_ready_20260514_1649.json:71: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/data/component_state_fit_ready_20260514_1649.json:72: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/data/evolution/evolution_20260515_1758.md:3:Il report osserva il passo come contro-perimetro regressivo: dalla grammatica Sturmian al coseno Aubry-Andre. Ho segnato l’attrito principale nel contratto pre-esperimento: mancava la baseline classica esplicita (`Aubry-Andre V=2`), quindi il fix vive lì, non in una nota postuma o in una toppa sul report. tools/data/boundary_bridge_stability_audit_20260515_1915.json:2: "experiment": "boundary_bridge_stability_audit", tools/data/boundary_bridge_stability_audit_20260515_1915.json:9: "observable": "graph bridge hit frequency joined with Brody/Berry-Robnik-like audit state", tools/data/boundary_bridge_stability_audit_20260515_1915.json:14: "graph_bridge_frequency", tools/data/boundary_bridge_stability_audit_20260515_1915.json:51: "graph_bridge_frequency": 0.666667, tools/data/boundary_bridge_stability_audit_20260515_1915.json:52: "graph_bridge_hits": 4, tools/data/boundary_bridge_stability_audit_20260515_1915.json:66: "graph_bridge_frequency": 0.0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:67: "graph_bridge_hits": 0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:81: "graph_bridge_frequency": 0.166667, tools/data/boundary_bridge_stability_audit_20260515_1915.json:82: "graph_bridge_hits": 1, tools/data/boundary_bridge_stability_audit_20260515_1915.json:96: "graph_bridge_frequency": 0.0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:97: "graph_bridge_hits": 0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:111: "graph_bridge_frequency": 0.666667, tools/data/boundary_bridge_stability_audit_20260515_1915.json:112: "graph_bridge_hits": 4, tools/data/boundary_bridge_stability_audit_20260515_1915.json:122: "composite_state": "stable_graph_bridge+graph_only_bridge", tools/data/boundary_bridge_stability_audit_20260515_1915.json:126: "graph_bridge_frequency": 1.0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:127: "graph_bridge_hits": 6, tools/data/boundary_bridge_stability_audit_20260515_1915.json:131: "stability_state": "stable_graph_bridge" tools/data/boundary_bridge_stability_audit_20260515_1915.json:136: "classical_audit_state": "classic_and_graph_bridge", tools/data/boundary_bridge_stability_audit_20260515_1915.json:137: "composite_state": "stable_graph_bridge+classic_and_graph_bridge", tools/data/boundary_bridge_stability_audit_20260515_1915.json:141: "graph_bridge_frequency": 1.0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:142: "graph_bridge_hits": 6, tools/data/boundary_bridge_stability_audit_20260515_1915.json:146: "stability_state": "stable_graph_bridge" tools/data/boundary_bridge_stability_audit_20260515_1915.json:156: "graph_bridge_frequency": 0.333333, tools/data/boundary_bridge_stability_audit_20260515_1915.json:157: "graph_bridge_hits": 2, tools/data/boundary_bridge_stability_audit_20260515_1915.json:167: "composite_state": "stable_graph_bridge+graph_only_bridge", tools/data/boundary_bridge_stability_audit_20260515_1915.json:171: "graph_bridge_frequency": 0.833333, tools/data/boundary_bridge_stability_audit_20260515_1915.json:172: "graph_bridge_hits": 5, tools/data/boundary_bridge_stability_audit_20260515_1915.json:176: "stability_state": "stable_graph_bridge" tools/data/boundary_bridge_stability_audit_20260515_1915.json:186: "graph_bridge_frequency": 0.666667, tools/data/boundary_bridge_stability_audit_20260515_1915.json:187: "graph_bridge_hits": 4, tools/data/boundary_bridge_stability_audit_20260515_1915.json:197: "composite_state": "stable_graph_bridge+graph_only_bridge", tools/data/boundary_bridge_stability_audit_20260515_1915.json:201: "graph_bridge_frequency": 0.833333, tools/data/boundary_bridge_stability_audit_20260515_1915.json:202: "graph_bridge_hits": 5, tools/data/boundary_bridge_stability_audit_20260515_1915.json:206: "stability_state": "stable_graph_bridge" tools/data/boundary_bridge_stability_audit_20260515_1915.json:216: "graph_bridge_frequency": 0.0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:217: "graph_bridge_hits": 0, tools/data/boundary_bridge_stability_audit_20260515_1915.json:227: "composite_state": "stable_graph_bridge+classic_only_intermediate", tools/data/boundary_bridge_stability_audit_20260515_1915.json:231: "graph_bridge_frequency": 0.833333, tools/data/boundary_bridge_stability_audit_20260515_1915.json:232: "graph_bridge_hits": 5, tools/data/boundary_bridge_stability_audit_20260515_1915.json:236: "stability_state": "stable_graph_bridge" tools/data/boundary_bridge_stability_audit_20260515_1915.json:338: "stable_graph_bridge+classic_and_graph_bridge": 1, tools/data/boundary_bridge_stability_audit_20260515_1915.json:339: "stable_graph_bridge+classic_only_intermediate": 1, tools/data/boundary_bridge_stability_audit_20260515_1915.json:340: "stable_graph_bridge+graph_only_bridge": 3, tools/data/seme_backup_b2_20260515_180327.json:3: "new_direzione": "Testare il confine come proprieta del generatore: Sturmian/binario vs coseno Aubry-Andre vs controlli irrazionali, con baseline fisica esplicita V=2 e falsificazione cross-dominio QxG continuo/discreto", tools/data/exp_spectral_landscape.json:137: "name": "Berry_Robnik_0.3", tools/data/exp_spectral_landscape.json:150: "name": "Berry_Robnik_0.5", tools/data/exp_spectral_landscape.json:163: "name": "Berry_Robnik_0.7", tools/data/exp_spectral_landscape.json:176: "name": "Anderson_1D_W5", tools/data/exp_spectral_landscape.json:242: "Berry_Robnik_0.5", tools/data/exp_spectral_landscape.json:243: "Berry_Robnik_0.7" tools/data/exp_spectral_landscape.json:254: "Anderson_1D_W5", tools/data/exp_spectral_landscape.json:258: "Berry_Robnik_0.3" tools/data/lab_graph.json:487: "verdict": "CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno d", tools/data/lab_graph.json:489: "annotation": "Vincolo: CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misura", tools/data/lab_graph.json:490: "annotation_en": "Constraint: CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misura", tools/data/lab_graph.json:582: "label": "Agent Report - Aubry Cosine Boundary Counter-Gate", tools/data/lab_graph.json:583: "label_en": "Agent Report - Aubry Cosine Boundary Counter-Gate", tools/data/lab_graph.json:584: "label_short": "Agent Report - Aubry Cosine Boundary Counter-Gate", tools/data/lab_graph.json:585: "label_short_en": "Agent Report - Aubry Cosine Boundary Counter-Gate", tools/data/lab_graph.json:1165: "verdict": "CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno dei due chiude il terzo incluso da solo.", tools/data/lab_graph.json:1166: "verdict_en": "CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno dei due chiude il terzo incluso da solo.", tools/data/lab_graph.json:1168: "content_preview": "# Agent Report - Boundary Classical Crossover Audit\n**Date**: 2026-05-15 19:04\n**Piano**: 120\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge.\nobservables_registry: none; classical audit coordinates plus prior graph observables\nobservables_used: [brody_q, berry_robnick_like_gue_weight, ", tools/data/lab_graph.json:1169: "content_full": "# Agent Report - Boundary Classical Crossover Audit\n**Date**: 2026-05-15 19:04\n**Piano**: 120\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge.\nobservables_registry: none; classical audit coordinates plus prior graph observables\nobservables_used: [brody_q, berry_robnick_like_gue_weight, mixture_ks, graph_boundary_state_from_1855, centroid_margin_from_1855, cross_neighbor_fraction_from_1855]\n**observable_contract**: claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: repulsione spettrale / indipendenza spettrale. Punto-zero: riga di dominio prima che venga letta come label, parametro Brody o nodo del grafo.\n- **Piano superiore**: grafo della conoscenza con audit assiomatico su baseline note; la domanda non e' \"quanto vale q\", ma se q esaurisce il ponte.\n- **Proto-ipotesi**: il terzo incluso operativo non coincide con un singolo scalare di crossover. Se coincide, il bridge Lab e' re-discovery di Brody/Berry-Robnik; se diverge, il contenuto Lab e' nella relazione tra geometria locale e scalare classico.\n- **Possibile/non-possibile**: possibile = usare nodi ponte come righe fisiche candidate oltre la classificazione GUE/Poisson; non-possibile = rivendicare un nuovo crossover se i nodi ponte sono solo Brody/Berry-Robnik rietichettato.\n- **Proiezione**: stimo Brody q e peso GUE di una mistura Poisson/GUE-surmise per ciascuna delle 13 righe gia' classificate dal grafo 18:55.\n\n### Contaminazione cognitiva\n- **YSN DeltaLink**: il DeltaLink usato e' `crossover classico / grafo Lab`: la sorpresa cercata e' il disaccordo, non la conferma dei nodi ponte.\n- **Cornelius gene**: `Classical_Audit_Gate`: \"Un ponte Lab sopravvive solo dopo il lettore classico piu vicino.\" Operatori: FITTA scalare noto; ALLINEA righe; ISOLA residuo.\n- **KSAR step**: perturbazione = feedback falsifier L5; focalizzazione = una sola domanda, \"i bridge collassano su Brody/Berry-Robnik?\"; proiezione = audit row-aligned sulle 13 righe.\n- **PVI attack**: un revisore esterno puo' dire che `third_included_candidate` e' solo un nome Lab per un crossover Brody. Il test attacca esattamente quel presupposto.\n- **Vault**: Rosenzweig-Porter vero resta fuori perimetro; va riattivato solo con Hamiltoniane interpolate, non con fit di CDF su righe gia' generate.\n- **CE-none:tools/data/agent_field_live.md+tools/LAB_COGNITIVE_CONTAMINATION.md/2026-05-15T19:07Z**: nessuna voce `CE-*` concreta e' presente nel campo letto; usati adapter YSN/Cornelius/KSAR documentati, senza inventare archivio enzimi.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: il ciclo resta sul perimetro vivo 8 GUE / 5 Poisson e verifica se il confine come terzo incluso e' nuovo rispetto ai crossover classici.\n- `not_drift`: non usa il report Sturmian bloccato, non misura V_c, non usa phi/silver/bronze; il gate 18:55 e' usato come denominatore row-aligned da auditare, non come autorita' conclusiva.\n\n## Re-discovery audit\n- **Baseline noto piu' vicino**: Brody distribution per interpolazione Poisson-Wigner; Berry-Robnik per mistura regolare/caotica. Rosenzweig-Porter e' nominato come famiglia di crossover Hamiltoniano, non fit eseguito in questo ciclo.\n- **Cosa viene assorbito dal baseline**: `numeri_primi:cycle_3` e' sia graph bridge sia intermedio classico (`brody_q=0.465`, `w_GUE=0.275`). Su questa riga il Lab non aggiunge fenomeno oltre il fatto che lo stesso campione e' ponte in due lettori.\n- **Cosa resta Lab-specific**: `percolation:cycle_9`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` sono graph-only bridge: il grafo li mette al confine ma Brody/mixture li legge endpoint-like.\n- **Cosa limita il claim Lab**: quattro righe sono classic-only intermediate (`zeta_zeros`, `random_matrix`, `cellular_automata`, `brownian_motion`) senza diventare terzo incluso nel grafo. Quindi il parametro classico non basta, ma nemmeno il grafo sostituisce il baseline classico.\n- **Risultante audit**: il boundary operativo e' una relazione a due lettori: scalar crossover + posizione nel grafo. Uno dei due da solo perde informazione.\n\n## Claim Under Test\n> Nel perimetro 8/5, il terzo incluso operativo non e' riducibile a Brody q o a una mistura Poisson/GUE-surmise; il residuo vive nel disaccordo row-aligned tra scalare classico e grafo osservabile.\n\n## Question\nI nodi ponte del grafo 18:55 sono re-discovery di un crossover classico, oppure producono una distinzione residua?\n\n## Ritorno fisico\n- **Punto fisico sorgente**: transizione spettrale tra caos quantistico repulsivo e indipendenza/localizzazione Poisson.\n- **Attraversamento matematico**: fit Brody e mistura Poisson/GUE-surmise sulle stesse righe gia' lette dal grafo kNN.\n- **Punto fisico di ritorno**: negli spettri finiti, una finestra non e' boundary perche' ha q intermedio; e' boundary quando q intermedio e posizione multi-feature del grafo vengono confrontati e il residuo resta nominabile.\n- **Osservabile/test fisico possibile**: su finestre energetiche sperimentali, calcolare q Brody, peso mistura e kNN multi-feature; separare bridge coincidenti, graph-only e classic-only.\n- **Se fallisce**: se su dati fisici indipendenti graph-only e classic-only spariscono, il gate Lab si riduce a baseline classico e il terzo incluso non trasferisce.\n\n## Experiment Design\n- **Script**: `tools/exp_boundary_classical_crossover_audit.py`.\n- **Input graph**: `tools/data/boundary_graph_curvature_gate_20260515_1855.json`.\n- **Run**: `python tools/exp_boundary_classical_crossover_audit.py --out tools/data/boundary_classical_crossover_audit_20260515_1904.json`.\n- **Denominatore**: 13 righe row-aligned dal perimetro BOUNDARY, 8 GUE e 5 Poisson.\n- **Fit Brody**: grid likelihood su q in [0,1], spacings normalizzati a media 1.\n- **Fit Berry-Robnik-like**: griglia su peso GUE in mistura CDF `w*GUE_surmise + (1-w)*Poisson`, selezionata per KS minimo.\n- **Contratto osservabile-operatore**: il ciclo testa concordanza/disaccordo tra scalare classico e graph state; non testa V_c, denominatori Sturmian, unfolding fisico alternativo o Rosenzweig-Porter Hamiltoniano.\n\n## Results\n| audit state | count |\n|---|---:|\n| classic_and_graph_bridge | 1 |\n| graph_only_bridge | 3 |\n| classic_only_intermediate | 4 |\n| endpoint_like | 5 |\n\n| row | label | graph_state | Brody q | w_GUE | KS | audit_state |\n|---|---|---|---:|---:|---:|---|\n| ising_2d:cycle_1 | GUE | class_interior | 0.090 | 0.070 | 0.428636 | endpoint_like |\n| pendolo_doppio:cycle_2 | Poisson | cut_edge | 0.000 | 0.000 | 0.268279 | endpoint_like |\n| numeri_primi:cycle_3 | GUE | third_included_candidate | 0.465 | 0.275 | 0.148459 | classic_and_graph_bridge |\n| zeta_zeros:cycle_4 | GUE | cut_edge | 1.000 | 0.530 | 0.133555 | classic_only_intermediate |\n| logistica_biforcazione:cycle_5 | GUE | class_interior | 0.000 | 0.000 | 0.998064 | endpoint_like |\n| string_vibration:cycle_6 | Poisson | cut_edge | 0.000 | 0.000 | 0.060129 | endpoint_like |\n| random_matrix:cycle_7 | GUE | cut_edge | 0.975 | 0.475 | 0.119491 | classic_only_intermediate |\n| cellular_automata:cycle_8 | GUE | class_interior | 1.000 | 0.435 | 0.416708 | classic_only_intermediate |\n| percolation:cycle_9 | Poisson | third_included_candidate | 0.025 | 0.025 | 0.054635 | graph_only_bridge |\n| coupled_oscillators:cycle_10 | Poisson | class_interior | 0.000 | 0.000 | 0.079806 | endpoint_like |\n| reaction_diffusion:cycle_11 | GUE | third_included_candidate | 0.000 | 0.000 | 0.174423 | graph_only_bridge |\n| brownian_motion:cycle_12 | Poisson | cut_edge | 0.205 | 0.250 | 0.026002 | classic_only_intermediate |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | third_included_candidate | 0.000 | 0.000 | 0.969277 | graph_only_bridge |\n\n## Key Findings\n1. Verificato: il denominatore resta quello richiesto, 13 righe con 8 GUE e 5 Poisson.\n2. Verificato: un solo nodo ponte del grafo e' anche intermedio classico: `numeri_primi:cycle_3`.\n3. Verificato: tre nodi ponte sono graph-only: `percolation:cycle_9`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13`.\n4. Verificato: quattro righe sono classic-only intermediate senza essere terzo incluso nel grafo: `zeta_zeros:cycle_4`, `random_matrix:cycle_7`, `cellular_automata:cycle_8`, `brownian_motion:cycle_12`.\n5. Inferito: il terzo incluso non e' uno scalare di crossover. E' una discrepanza controllata fra lettore classico e posizione multi-osservabile.\n\n## Verdict\nCONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno dei due chiude il terzo incluso da solo.\n\n## Bicono della scoperta\n- **Due radici**: parametro classico di crossover; nodo ponte del grafo Lab.\n- **Singolare**: riga di dominio row-aligned prima della classificazione.\n- **Invariante di passaggio**: disaccordo nominabile tra `classic_and_graph`, `graph_only`, `classic_only`, `endpoint_like`.\n- **Campo di possibilita**: possibile = costruire un gate fisico che richiede doppia lettura prima di chiamare boundary; non-possibile = promuovere il grafo 18:55 come scoperta autonoma senza baseline classico.\n\n## Consecutio\nIl prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche. Il risultato da cercare e' se `graph_only` e `classic_only` sopravvivono fuori dal perimetro composito del Lab.\n\n## Ricadute pratiche\nssp_value: yes. Lo script crea un audit riusabile per separare re-discovery classica, residuo Lab e endpoint-like in ogni perimetro GUE/Poisson row-aligned.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante `TENS_SCALE_TRASCENDENZA_LIMITE`, ma la direzione viva del campo impone il perimetro BOUNDARY 8/5.\n- `python -m py_compile tools/exp_boundary_classical_crossover_audit.py` completato.\n- `python tools/exp_boundary_classical_crossover_audit.py --out tools/data/boundary_classical_crossover_audit_20260515_1904.json` completato.\n- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate.\n- Nessun update del seme.\n- Nessuna promozione e nessun public sync.\n\n## Files\n- Script: `tools/exp_boundary_classical_crossover_audit.py`\n- Data: `tools/data/boundary_classical_crossover_audit_20260515_1904.json`\n- Report: `tools/data/reports/agent_20260515_1904.md`\n", tools/data/lab_graph.json:1175: "text": "> Nel perimetro 8/5, il terzo incluso operativo non e' riducibile a Brody q o a una mistura Poisson/GUE-surmise; il residuo vive nel disaccordo row-al" tools/data/lab_graph.json:1193: "text": "| audit state | count |\n|---|---:|\n| classic_and_graph_bridge | 1 |\n| graph_only_bridge | 3 |\n| classic_only_intermediate | 4 |\n| endpoint_like | 5 |\n\n| row | label | graph_state | Brody q | w_GUE | K" tools/data/lab_graph.json:1199: "text": "CONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno d" tools/data/lab_graph.json:1205: "text": "Il prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-" tools/data/lab_graph.json:1220: "content_full": "# Agent Report - V=2 Generator Scaling Gate\n**Date**: 2026-05-15 18:16 \n**Piano**: 119 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY \n**verdict**: CONSTRAINT - a V=2 il confine trasferisce come generatore binario ordinato: `phi_sturmian_binary` separa dallo shuffle di densita, ma non chiude una specificita phi contro silver/bronze Sturmian. \nobservables_registry: none; dedicated observables only \nobservables_used: [mean_pr_tau, mean_ipr_tau, spacing_r, mean_ipr, participation_entropy] \n**observable_contract**: claim=V=2 e' baseline fisica del confine Aubry-Andre e distingue il tipo di generatore; observable=slope log-log `tau` della mean participation ratio piu spacing/IPR anchors; operator=`tools/exp_aubry_v2_generator_scaling_gate.py`; generator=tight-binding 1D con potenziali `sturmian_binary`, `cosine`, `periodic_ab`, `density_shuffle`, `random_uniform`; denominator=N={89,144,233,377} x phase={0,0.25,0.5,0.75} x generator rows, con 4 trial per null random; non_possible=promuovere un claim phi-specific se tau(V=2) non separa dai controlli Sturmian non-phi; not_tested=limite asintotico, altri V, mobility edge, dati sperimentali, qualita PSD dei surrogate 18:07.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + baseline fisica Aubry-Andre V=2 + tensione viva `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: generatore continuo coseno / generatore discreto binario. Punto-zero = Hamiltoniana tight-binding a V=2 prima che il potenziale venga letto come transizione self-dual, parola Sturmian o disorder.\n- **Piano superiore**: geometria dei campi su reticolo; il bordo e' nella legge di generazione del campo onsite, non nel solo fit di `V_c`.\n- **Operatori laterali scelti**: boundary condition, eigenvector localization, non-phi generator control.\n- **Contaminazione cognitiva**: CE-none: il ciclo non ha introdotto adapter semantico; ha usato il baseline fisico V=2 come vincolo regressivo per evitare un nuovo strato linguistico sopra i risultati 17:45-18:07.\n- **Proto-ipotesi**: se il confine e' proprieta del generatore, allora a V=2 la crescita della partecipazione degli autostati separa classi di generatore; se e' phi-specific, `phi_sturmian_binary` deve separare anche dai controlli Sturmian non-phi.\n- **Proiezione**: diagonalizzo la stessa Hamiltoniana tridiagonale con potenziali binari e continui; il tau della participation ratio misura se gli autostati scalano come estesi, critici o localizzati nel perimetro V=2.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa il confine come proprieta del generatore usando Sturmian/binario vs coseno Aubry-Andre vs controlli irrazionali, con baseline fisica esplicita V=2.\n- `not_drift`: non rifitta `V_c`, non riapre prime/mod6, non usa selector legacy; il vecchio deposito viene usato solo come perimetro fisico della misura.\n\n## Claim Under Test\n\n> A V=2, il boundary si conserva come proprieta del generatore: binario Sturmian, coseno continuo e null disorder hanno tau di partecipazione distinti; la specificita phi sopravvive solo se phi binario separa anche dai controlli Sturmian non-phi.\n\n## Question\n\nIl baseline V=2 legge un confine phi-specific, oppure legge una classe piu larga di generatori binari ordinati distinta dal coseno continuo e dal disorder?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: modello Aubry-Andre tight-binding 1D, dove il coseno quasiperiodico ha transizione self-dual nota a V=2.\n- **Attraversamento matematico**: sostituzione controllata del potenziale onsite con parole Sturmian binarie, coseni irrazionali, periodico AB e null disorder; misura dello scaling finito della participation ratio.\n- **Punto fisico di ritorno**: reticoli fotonici o cold atoms con potenziale onsite programmabile, dove si puo confrontare un coseno quasiperiodico con una parola binaria Sturmian alla stessa ampiezza V=2.\n- **Controllo concretezza**: il ritorno e' IPR/participation ratio degli autostati di una Hamiltoniana tridiagonale, non una categoria astratta di confine.\n- **Relazione nuova**: V=2 non trasferisce come firma phi isolata; trasferisce come separatore fra ordine binario quasiperiodico, coseno continuo e disorder.\n- **Osservabile/test fisico possibile**: preparare potenziali `phi_sturmian_binary`, `silver_sturmian_binary`, `phi_cosine` e `density_shuffle` a V=2; misurare spreading o profili modali e stimare tau di partecipazione su taglie crescenti.\n- **Se fallisce**: `ritorno_fisico_assente` per claim phi-specific; resta vincolo di classe-generatore, non scoperta phi promuovibile.\n\n## Experiment Design\n\n- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- `python -m py_compile tools/exp_aubry_v2_generator_scaling_gate.py` completato.\n- Run: `python tools/exp_aubry_v2_generator_scaling_gate.py --out tools/data/aubry_v2_generator_scaling_gate_20260515_1816.json`.\n- Nearest-known baseline: Aubry-Andre continuo, transizione self-dual a V=2; per questo il ciclo non promuove il risultato come nuovo fenomeno fisico, ma come gate di generatore.\n- Perimetro: 176 righe totali; N={89,144,233,377}; phases={0,0.25,0.5,0.75}; random_trials=4.\n- Contratto osservabile-operatore: il ciclo testa tau finito della participation ratio a V=2; non testa `gap_ratio`, `V_c` asintotico, PSD surrogate quality, ne universalita GUE/Poisson.\n\n## Results\n\n| domain | mean_pr_tau | median spacing_r | median mean_ipr | median mean_pr | median participation_entropy |\n|---|---:|---:|---:|---:|---:|\n| periodic_ab | 0.9937 | 0.9298 | 0.0134 | 80.9477 | 0.8730 |\n| phi_sturmian_binary | 0.8048 | 0.3608 | 0.0452 | 30.3199 | 0.7240 |\n| silver_sturmian_binary | 0.7268 | 0.3774 | 0.0510 | 23.7245 | 0.7037 |\n| bronze_sturmian_binary | 0.6399 | 0.4004 | 0.0487 | 30.4225 | 0.7218 |\n| phi_cosine | 0.5689 | 0.3013 | 0.0961 | 13.6849 | 0.6111 |\n| silver_cosine | 0.5819 | 0.3108 | 0.0878 | 14.5649 | 0.6226 |\n| bronze_cosine | 0.6104 | 0.3478 | 0.0868 | 15.7257 | 0.6237 |\n| phi_binary_density_shuffle | 0.0527 | 0.3875 | 0.1403 | 9.1779 | 0.4677 |\n| random_uniform | 0.0306 | 0.4011 | 0.1834 | 6.8468 | 0.4180 |\n\n| classification field | value |\n|---|---:|\n| v2_baseline | 2.0 |\n| phi_binary_tau | 0.8048 |\n| phi_cosine_tau | 0.5689 |\n| density_shuffle_tau | 0.0527 |\n| silver_binary_tau | 0.7268 |\n| bronze_binary_tau | 0.6399 |\n| cosine_class_tau_span | 0.0414 |\n| phi_binary_separates_from_shuffle | true |\n| phi_binary_separates_from_nonphi_binary | false |\n\n## Key Findings\n\n1. **Verificato**: `phi_sturmian_binary` a V=2 separa dal null di densita: tau 0.8048 contro 0.0527.\n2. **Verificato**: i coseni irrazionali formano una classe stretta nel perimetro misurato: tau span 0.0414.\n3. **Verificato**: il binario Sturmian non e' phi-specific nel gate impostato: phi tau 0.8048, silver 0.7268, bronze 0.6399; la soglia `min_tau_delta=0.08` non viene superata contro silver.\n4. **Inferito**: V=2 distingue ordine binario quasiperiodico da disorder e da coseno continuo; non autorizza il claim che phi sia il generatore unico del confine.\n\n## Verdict\n\n**CONSTRAINT**.\n\nLa formulazione valida e': a V=2 il boundary e' proprieta della classe del generatore, non del solo phi. Il binario Sturmian produce scaling piu esteso del coseno continuo e del disorder, ma il controllo silver resta troppo vicino per chiamarlo phi-specific.\n\n## Bicono della scoperta\n\n- **Due radici**: coseno continuo self-dual / parola binaria quasiperiodica.\n- **Singolare**: Hamiltoniana tight-binding a V=2 prima della scelta della grammatica onsite.\n- **Invariante di passaggio**: tau della participation ratio come lettore del tipo di generatore.\n- **Campo di possibilita**: possibile = progettare test fisici che separano coseno, binario ordinato e disorder alla stessa ampiezza V=2; non-possibile = promuovere `phi` come boundary autonomo finche silver/bronze restano nello stesso corridoio di tau.\n\n## Consecutio\n\nIl prossimo ciclo deve isolare il corridoio Sturmian non-phi: aumentare N o usare approssimanti denominatore-allineati per chiedere se la vicinanza phi/silver e' finite-size, effetto di fase, o proprieta comune delle parole meccaniche binarie. Non serve tornare a `V_c` prima di chiudere questo corridoio.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' un gate riusabile per setup fotonici/cold-atom: confronta generatori onsite diversi alla stessa baseline V=2 e restituisce tau, spacing e IPR in un JSON unico.\n\n## Telemetria\n\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Preflight post-report: `python tools/lab_preflight_agent.py --cycle 20260515_1816 --json` => `ARTIFACT_USEFUL_NOT_PUBLISHABLE`, recommended_action=`KEEP_ARTIFACT_STOP_REPORT_PROMOTION`, stable_anchor=`20260515_1712`.\n- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate.\n- Nessun update del seme.\n- Nessuna promozione e nessun public sync.\n\n## Files\n\n- `tools/exp_aubry_v2_generator_scaling_gate.py`\n- `tools/data/aubry_v2_generator_scaling_gate_20260515_1816.json`\n- `tools/data/reports/agent_20260515_1816.md`\n", tools/data/lab_graph.json:1263: "title": "Agent Report - Aubry Cosine Boundary Counter-Gate", tools/data/lab_graph.json:1264: "title_en": "Agent Report - Aubry Cosine Boundary Counter-Gate", tools/data/lab_graph.json:1270: "content_preview": "# Agent Report - Aubry Cosine Boundary Counter-Gate\n**Date**: 2026-05-15 17:58 \n**Piano**: 118 \n**Tension explored**: BOUNDARY / TENS_SCALE_TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - nel potenziale Aubry-Andre coseno `phi` non chiude un boundary privilegiato; la finestra binaria 17:45 dipende dalla grammatica Sturmian, non dal solo irrazionale phi. \nobservables_registry: none; dedicated observables only \nobservables_used: [spacing_r, mean_ipr, median_ipr, participation_entropy] \n**obse", tools/data/lab_graph.json:1271: "content_full": "# Agent Report - Aubry Cosine Boundary Counter-Gate\n**Date**: 2026-05-15 17:58 \n**Piano**: 118 \n**Tension explored**: BOUNDARY / TENS_SCALE_TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - nel potenziale Aubry-Andre coseno `phi` non chiude un boundary privilegiato; la finestra binaria 17:45 dipende dalla grammatica Sturmian, non dal solo irrazionale phi. \nobservables_registry: none; dedicated observables only \nobservables_used: [spacing_r, mean_ipr, median_ipr, participation_entropy] \n**observable_contract**: claim=`phi` resta terzo incluso fisico anche quando il potenziale binario viene sostituito dal coseno Aubry-Andre canonico; observable=`spacing_r` + `mean_ipr` con controllo di distinzione da silver/bronze; operator=`tools/exp_aubry_cosine_boundary_counter_gate.py`; generator=Hamiltoniana tight-binding 1D con potenziale coseno per beta phi/silver/bronze, periodico beta=1/2 e random onsite uniforme; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..3.00 step 0.25, random_trials=6; non_possible=promuovere phi come boundary fisico se non si separa dai controlli irrazionali con spacing e localizzazione insieme; not_tested=limite asintotico, disordine correlato sperimentale, classi GUE/Poisson universali dirette.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + modello Aubry-Andre come ponte fisico tra quasi-periodicita e localizzazione + tensione viva `BOUNDARY`.\n- **Dipolo / punto-zero**: grammatica binaria Sturmian / potenziale coseno continuo. Punto-zero = Hamiltoniana row-local prima che il confine venga attribuito a phi o alla forma del potenziale.\n- **Piano superiore**: geometria dei campi su reticolo; il generatore non e' solo parametro, e' forma del campo che decide quale bordo puo' essere letto.\n- **Operatori laterali scelti**: Hamiltonian spectrum, mobility edge/localization transition, non-phi generator control.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione regressiva del deposito 17:45: togliere la grammatica binaria e osservare cosa sopravvive. CE-0038 usato come equilibrio tra poli, reso falsificabile dal controllo silver/bronze.\n- **Proto-ipotesi**: se `phi` e' boundary fisico e non solo effetto della codifica binaria, nel coseno Aubry-Andre deve restare insieme tra periodico e random e deve separarsi da silver/bronze su spacing e localizzazione.\n- **Proiezione**: diagonalizzo Hamiltoniane tight-binding coseno e confronto mediane row-aligned per V, N, fase e generatore; il gate accetta solo se `phi` e' intermedio e distinto dai controlli irrazionali.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: segue la direzione viva \"8 domini GUE, 5 Poisson - il confine e' il terzo incluso operativo\" verificando se il confine fisico aperto nel ritorno Aubry/Fibonacci sopravvive a un contro-perimetro canonicale.\n- `not_drift`: non riapre prime/mod6, non usa selector legacy, non rifitta `V_c`; attacca il nodo regressivo lasciato dal ciclo 17:45: forma binaria del potenziale vs boundary fisico.\n\n## Claim Under Test\n\n> Nel potenziale Aubry-Andre coseno, `phi` resta terzo incluso fisico tra periodico e random solo se `spacing_r` e localizzazione lo collocano insieme nel segmento periodico-random e lo separano da silver/bronze.\n\n## Question\n\nIl confine `phi` osservato nel ritorno binario 17:45 sopravvive nel coseno Aubry-Andre, oppure il contenuto era nella grammatica Sturmian del potenziale?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: transizione spettrale/localizzazione in reticoli quasi-periodici, usata come ritorno fisico del boundary GUE/Poisson.\n- **Attraversamento matematico**: Hamiltoniane tridiagonali con potenziale coseno irrazionale, controlli periodico e random onsite.\n- **Punto fisico di ritorno**: modello Aubry-Andre canonico con transizione di localizzazione attesa attorno a V=2 per hopping unitario.\n- **Controllo concretezza**: il ritorno non e' \"confine\" astratto; e' spettro e autostati di un tight-binding 1D misurabile in reticoli fotonici o cold atoms.\n- **Relazione nuova**: la finestra binaria 17:45 non trasferisce automaticamente alla classe Aubry-Andre coseno; il generatore del potenziale diventa parte atomica del claim.\n- **Osservabile/test fisico possibile**: misurare spacing ratio e IPR nello stesso reticolo quasi-periodico variando V e confrontando beta phi con beta silver/bronze.\n- **Se fallisce**: `ritorno_fisico_assente` per la promozione phi-specifica nel coseno; resta vincolo sul denominatore, non scoperta fisica promuovibile.\n\n## Experiment Design\n\n- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- `python -m py_compile tools/exp_aubry_cosine_boundary_counter_gate.py` completato.\n- Run: `python tools/exp_aubry_cosine_boundary_counter_gate.py --out tools/data/aubry_cosine_boundary_counter_gate_20260515_1758.json`.\n- Perimetro deterministico: 132 righe per ciascun generatore phi/silver/bronze/periodic.\n- Perimetro random: 792 righe onsite uniforme.\n- Gate: `phi_joint_boundary = spacing_r_between and mean_ipr_between and separated_from_random and phi_distinct_from_irrational_controls`.\n- La misura serve la combo perche' rende non-possibile attribuire il boundary a phi se silver/bronze seguono la stessa classe fisica.\n\n## Results\n\n| V | joint | distinct_controls | r_between | ipr_between | sep_random | phi_r | silver_r | bronze_r | random_r | phi_ipr | silver_ipr | bronze_ipr | random_ipr |\n|---:|---|---|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|\n| 0.50 | false | false | true | true | false | 0.860 | 0.876 | 0.860 | 0.529 | 0.0154 | 0.0153 | 0.0156 | 0.0252 |\n| 0.75 | false | false | true | true | true | 0.821 | 0.852 | 0.821 | 0.467 | 0.0185 | 0.0179 | 0.0189 | 0.0436 |\n| 1.00 | false | false | true | true | true | 0.786 | 0.814 | 0.781 | 0.436 | 0.0218 | 0.0202 | 0.0226 | 0.0677 |\n| 1.25 | false | false | true | true | true | 0.741 | 0.755 | 0.737 | 0.418 | 0.0256 | 0.0225 | 0.0272 | 0.0948 |\n| 1.50 | false | false | true | true | true | 0.675 | 0.688 | 0.694 | 0.412 | 0.0313 | 0.0269 | 0.0338 | 0.1205 |\n| 1.75 | false | false | true | true | true | 0.572 | 0.581 | 0.597 | 0.408 | 0.0421 | 0.0388 | 0.0447 | 0.1558 |\n| 2.00 | false | false | false | true | true | 0.302 | 0.313 | 0.366 | 0.409 | 0.1070 | 0.0983 | 0.0981 | 0.1816 |\n| 2.25 | false | true | true | false | false | 0.430 | 0.386 | 0.354 | 0.401 | 0.2815 | 0.2637 | 0.2543 | 0.2151 |\n| 2.50 | false | true | true | false | true | 0.437 | 0.471 | 0.367 | 0.397 | 0.3658 | 0.3571 | 0.3402 | 0.2442 |\n| 2.75 | false | false | true | false | true | 0.475 | 0.483 | 0.358 | 0.398 | 0.4232 | 0.4227 | 0.4012 | 0.2684 |\n| 3.00 | false | false | true | false | true | 0.500 | 0.499 | 0.379 | 0.390 | 0.4666 | 0.4724 | 0.4482 | 0.3016 |\n\n## Key Findings\n\n1. **Verificato**: `phi_joint_boundary_v=[]`. Nessun valore di V chiude insieme intermedieta, separazione dal random e distinzione dai controlli irrazionali.\n2. **Verificato**: da V=0.75 a V=1.75 `phi` e' tra periodico e random sia in `spacing_r` sia in `mean_ipr`, ma resta quasi coincidente con silver/bronze; il boundary e' classe quasi-periodica, non privilegio phi.\n3. **Verificato**: a V=2.00 `spacing_r(phi)=0.302` esce dal segmento periodico-random mentre `mean_ipr` resta intermedio; la transizione coseno rompe il gate congiunto.\n4. **Verificato**: a V=2.25 e V=2.50 `phi` si distingue dai controlli, ma non e' piu' boundary congiunto: `mean_ipr_between=false` e a V=2.25 cade anche la separazione dal random.\n5. **Inferito**: il risultato 17:45 trasferisce come vincolo sul generatore binario, non come claim phi-specifico universale del modello Aubry-Andre.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl contro-perimetro coseno falsifica la promozione `phi` come terzo incluso fisico autonomo. Nel perimetro misurato il boundary congiunto non sopravvive quando la grammatica binaria viene rimossa. La formulazione valida diventa: `phi` e' candidato boundary nel potenziale binario Sturmian 17:45; nel coseno Aubry-Andre il contenuto si sposta alla classe quasi-periodica e alla transizione di localizzazione, non a phi come generatore privilegiato.\n\n## Bicono della scoperta\n\n- **Due radici**: potenziale binario Sturmian / potenziale coseno Aubry-Andre.\n- **Singolare**: Hamiltoniana tight-binding prima della scelta della forma del campo.\n- **Invariante di passaggio**: ogni ritorno fisico del boundary deve dichiarare forma del potenziale, controlli irrazionali e gate joint spacing/localizzazione.\n- **Campo di possibilita**: possibile = separare boundary di grammatica da boundary di classe fisica; non-possibile = promuovere `phi` come ritorno fisico se silver/bronze condividono la stessa risposta.\n\n## Consecutio\n\nIl prossimo ciclo non deve allargare `phi` nel coseno. Deve isolare il residuo binario: stessa Hamiltoniana, stesso denominatore, ma ablazione della grammatica Sturmian tramite surrogate che preservano densita, autocorrelazione corta e spettro del potenziale. Se la finestra V=0.50..1.25 sopravvive solo alla grammatica completa, il finding e' `boundary-as-grammar`; se sopravvive a surrogate piu deboli, il finding diventa classe di correlazione del potenziale.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' un counter-gate riusabile per impedire che demo o visualizzazioni del boundary promuovano `phi` senza controlli irrazionali nel modello fisico scelto.\n\n## Telemetria\n\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate.\n- Nessun update del seme.\n- Nessuna promozione e nessun public sync.\n\n## Files\n\n- `tools/exp_aubry_cosine_boundary_counter_gate.py`\n- `tools/data/aubry_cosine_boundary_counter_gate_20260515_1758.json`\n- `tools/data/reports/agent_20260515_1758.md`\n", tools/data/lab_graph.json:1277: "text": "> Nel potenziale Aubry-Andre coseno, `phi` resta terzo incluso fisico tra periodico e random solo se `spacing_r` e localizzazione lo collocano insieme" tools/data/lab_graph.json:1283: "text": "Il confine `phi` osservato nel ritorno binario 17:45 sopravvive nel coseno Aubry-Andre, oppure il contenuto era nella grammatica Sturmian del potenzia" tools/data/spettro_zeta_results.json:5: "verdict": "FORTE: V(r) \u00e8 il potenziale Berry-Keating regolarizzato dal D-ND", tools/data/spettro_zeta_results.json:1213: "Termine E\u00b7ln(E) significativo (Berry-Keating confermato)", tools/data/spettro_zeta_results.json:1218: "verdict": "FORTE: V(r) \u00e8 il potenziale Berry-Keating regolarizzato dal D-ND" tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:2:{"delta": {"L1": -0.29246424756967426, "L2": 0.1384171637360847, "SR": 0.315020926170212, "SR2": 0.3696764721712294, "triple_var": -2.32729236908849}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.08418704209426561, "L2": 0.01935247808425468, "SR": 0.3792727689618897, "SR2": 0.37774505794542274, "triple_var": 2.4767554736275197}, "real": {"L1": -0.3766512896639399, "L2": 0.15776964182033937, "SR": 0.6942936951321017, "SR2": 0.7474215301166521, "triple_var": 0.14946310453902958}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:3:{"delta": {"L1": -0.24705290039603395, "L2": 0.10677249231014616, "SR": 0.08192294221501978, "SR2": 0.11043126909088902, "triple_var": -2.784845864584176}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.08019947591983571, "L2": -0.15791896711436168, "SR": 0.38046415446651577, "SR2": 0.35145692197414946, "triple_var": 3.98321233799855}, "real": {"L1": -0.16685342447619822, "L2": -0.051146474804215525, "SR": 0.46238709668153555, "SR2": 0.4618881910650385, "triple_var": 1.198366473414374}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:4:{"delta": {"L1": 0.004842875082936751, "L2": -0.20105539662081146, "SR": 0.0017290210489668834, "SR2": -0.010431878539607475, "triple_var": -1.024511178095227}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.06965962323181703, "L2": 0.06416078033043264, "SR": 0.4160858247211447, "SR2": 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0.3993501440620225, "triple_var": 2.790459735612381}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:237:{"delta": {"L1": -0.43615795523239465, "L2": 0.08999337240375185, "SR": 0.23006507823659655, "SR2": 0.2969391750726099, "triple_var": -2.3357646684917848}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.02065361300109852, "L2": -0.059917931158411784, "SR": 0.3901056612540092, "SR2": 0.3601963880365303, "triple_var": 2.5483654573734524}, "real": {"L1": -0.4155043422312961, "L2": 0.030075441245340076, "SR": 0.6201707394906057, "SR2": 0.6571355631091402, "triple_var": 0.2126007888816678}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:238:{"delta": {"L1": -0.14145737268738853, "L2": -0.009591167330170694, "SR": 0.07833719881940271, "SR2": 0.02851205889189523, "triple_var": -1.8548431927675522}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.03649138434398558, "L2": -0.11449890360083871, "SR": 0.3482437417443585, "SR2": 0.35361421198641935, "triple_var": 3.317323350623292}, "real": {"L1": -0.10496598834340296, "L2": -0.1240900709310094, "SR": 0.4265809405637612, "SR2": 0.3821262708783146, "triple_var": 1.46248015785574}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:239:{"delta": {"L1": -0.09568656534945513, "L2": -0.06927956454142085, "SR": 0.0681466911525701, "SR2": 0.055279758923166944, "triple_var": -0.5273516620899401}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.056968249081960985, "L2": 0.0699839646369959, "SR": 0.3567554491929611, "SR2": 0.35671606752271545, "triple_var": 3.097936190446298}, "real": {"L1": -0.038718316267494154, "L2": 0.0007044000955750374, "SR": 0.4249021403455312, "SR2": 0.4119958264458824, "triple_var": 2.570584528356358}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:240:{"delta": {"L1": 0.03541282154329242, "L2": 0.024197893955487017, "SR": -0.0030911393549142296, "SR2": -0.012448318262132774, "triple_var": 0.025469129278811842}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.1344162375776942, "L2": 0.004302597030711671, "SR": 0.37998826108126743, "SR2": 0.4091560966313005, "triple_var": 1.960224277295223}, "real": {"L1": -0.09900341603440177, "L2": 0.02850049098619869, "SR": 0.3768971217263532, "SR2": 0.3967077783691677, "triple_var": 1.9856934065740348}, "rep": 47} tools/data/graph_completion/graph_completion_20260515_1807.json:7: "title": "Agent Report - Aubry Binary Grammar Surrogate Gate", tools/data/graph_completion/graph_completion_20260515_1807.json:49: "dipole": "potenziale binario Sturmian / potenziale coseno Aubry-Andre.", tools/data/graph_completion/graph_completion_20260515_1807.json:126: "suggestion": "Nel prossimo ciclo includere V=2 come riga di controllo fisico obbligatoria e almeno un contro-perimetro continuo/discreto esplicito: phi_sturmian/binario vs coseno Aubry-Andre o controllo irrazionale con stessi osservabili." tools/data/graph_completion/graph_completion_20260515_1807.json:132: "evidence": "Il report lavora su Hamiltoniane Aubry-Andre/Sturmian, statistiche spettrali e surrogate IAAFT, ma non ancora il risultato classico piu vicino o il limite noto rispetto al quale la relazione sarebbe nuova. Il testo evita universalita GUE/Poisson, ma usa comunque 'Relazione nuova'.", tools/data/graph_completion/graph_completion_20260515_1807.json:133: "suggestion": "Riformulare come 'relazione nel perimetro del lab' oppure aggiungere un confronto bibliografico/strutturale minimo: Aubry-Andre/Harper V=2, Hamiltoniane Sturmian/quasiperiodiche, e ruolo noto di autocorrelazione/PSD nei surrogate." tools/data/reports/agent_20260514_1850.md:1:# Agent Report - Interfaccia component_state su bordo Anderson 3D tools/data/reports/agent_20260514_1850.md:4:**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson tools/data/reports/agent_20260514_1850.md:5:**verdict**: VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 restano active contro Poisson, ma il contrasto diretto separa solo `triple_var`; `SR` e `L1` non chiudono il bordo 3D a `L=6`. tools/data/reports/agent_20260514_1850.md:19:Il tester non deve predire la transizione Anderson 3D. Deve mostrare se uno spettro fisico nuovo puo' entrare nell'interfaccia e tornare come stato componente auditabile. Se il bordo non separa su `SR`, quello e' il contenuto: il tool trasferisce, la legge no. tools/data/reports/agent_20260514_1850.md:23:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione BOUNDARY GUE/Poisson + nodo fisico Anderson 3D. tools/data/reports/agent_20260514_1850.md:27:- **Possibile / non-possibile**: possibile = usare il tool su spettro fisico B e ottenere output auditabile; non-possibile = dichiarare transizione Anderson 3D o legge universale quando `SR,L1` non separano il contrasto diretto. tools/data/reports/agent_20260514_1850.md:28:- **Proiezione**: genero 24 spettri Anderson 3D tight-binding con condizioni periodiche, `L=6`, 12 repliche per `W=4.0` e `W=16.5`; poi li passo all'interfaccia `--input-spectrum` del tester 16:49. tools/data/reports/agent_20260514_1850.md:33:- `why`: l'esperimento resta nel perimetro GOE/GUE/Poisson-Anderson indicato dalla direttiva e testa il passaggio tool da 1D/WD a un nuovo bordo fisico 3D. tools/data/reports/agent_20260514_1850.md:56:- **Dominio fisico B**: Anderson 3D tight-binding finite-size, condizioni periodiche. tools/data/reports/agent_20260514_1850.md:65:| Anderson 3D W4 metallic candidate | 768 | SR,L1,triple_var | 0.538429 / 0.383960 / +0.154470 | 0.000122 | +3.657 | -0.224752 | 0.000122 | -2.236 | -2.327689 | 0.000122 | -3.494 | tools/data/reports/agent_20260514_1850.md:66:| Anderson 3D W16.5 critical boundary | 768 | SR,L1,triple_var | 0.509807 / 0.386906 / +0.122901 | 0.000122 | +2.741 | -0.142338 | 0.000122 | -0.945 | -1.969355 | 0.000122 | -2.504 | tools/data/reports/agent_20260514_1850.md:77:- **Verificato**: entrambi i regimi Anderson 3D sono non-Poisson sulle tre osservabili focus. tools/data/reports/agent_20260514_1850.md:80:- **Non verificato**: transizione Anderson 3D, limite `L -> inf`, spettro sperimentale, unfolding dedicato. tools/data/reports/agent_20260514_1850.md:86:- `candidate`: Anderson 3D puo' diventare prossimo seme solo come audit di taglia/unfolding, non come legge. tools/data/reports/agent_20260514_1850.md:94:- `non_possible`: dire che `SR,L1` classificano il bordo Anderson 3D nel perimetro `L=6`, perche' il contrasto diretto non separa. tools/data/reports/agent_20260514_1850.md:110:Il tester diventa interfaccia riusabile su questo perimetro: legge un nuovo spettro fisico, conserva le definizioni del registry, produce stati componente e lascia trace. Il risultato non diventa legge fisica: Anderson 3D W4/W16.5 resta non-Poisson contro null, ma il bordo diretto separa solo `triple_var`; `SR,L1` restano insufficienti nel setup `L=6`. tools/data/reports/agent_20260514_1850.md:114:- **Due radici**: simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D. tools/data/reports/agent_20260514_1850.md:121:Il prossimo seme possibile non e' "Anderson 3D confermato". E' un audit del tool su fisico B con taglie multiple e denominatore dichiarato: `L=5,6,7` o unfolding locale, stesso contract, stesso null. Se `SR,L1` restano non_separated e `triple_var` resta separated, il tool mostra che il bordo 3D entra prima nella componente di profondita' che nel piano pair-statistics. tools/data/reports/agent_20260514_1850.md:127:- **Puo' diventare prossimo seme**: audit Anderson 3D multi-size/unfolding solo se formulato come verifica del tool, non come legge fisica. tools/data/reports/agent_20260405_0919.md:1:# AI-Lab Report: Brody Crossover Law for Prime Gaps tools/data/reports/agent_20260405_0919.md:11:Previous experiment showed primes drift from GUE toward Poisson with scale. **What is the functional form of this crossover?** The Brody distribution P(s) = (1+beta)*alpha*s^beta*exp(-alpha*s^{1+beta}) interpolates between Poisson (beta=0) and GOE (beta=1). What is beta(p) for primes? tools/data/reports/agent_20260405_0919.md:14:- **Metric**: Brody parameter beta fitted via MLE to normalized gap spacings tools/data/reports/agent_20260405_0919.md:44:3. **The crossover is NOT a phase transition**: there is no sharp boundary between GUE and Poisson regimes. The Brody beta decays smoothly and linearly in ln(p). The "boundary" is the entire range. tools/data/reports/agent_20260405_0919.md:54:The METRIC_TENSOR claim says g=(p/2)² gives de Sitter in ln(p) time. If true, the "correlation decay" should follow 1/g^{1/2} ~ 2/p, which decays as e^{-τ} in τ = ln(p). But we measured beta decaying linearly in ln(p), not exponentially. **The Brody crossover is much slower than de Sitter would predict.** This is a constraint on METRIC_TENSOR: the decorrelation is logarithmic, not exponential. tools/data/reports/agent_20260405_0919.md:65:- **NEW**: Linear Brody crossover law beta = 0.61 - 0.020*ln(p) with R²=0.90 tools/data/graph_completion/graph_completion_20260515_1745.json:7: "title": "Agent Report - Aubry Boundary Phase Transport Gate", tools/data/graph_completion/graph_completion_20260515_1745.json:11: "observable_contract": "claim=`phi` e' stato fisico di confine tra ordine periodico e disordine random solo se statistica spettrale e localizzazione concordano; observable=`spacing_r` + `mean_ipr`/`participation_entropy`; operator=`tools/exp_aubry_boundary_phase_transport_gate.py`; generator=Hamiltoniana tight-binding binaria con sequenze phi/silver/bronze, periodico AB e random bilanciato a densita phi; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..2.50 step 0.25 x generator, random_trials=6; non_possible=promuovere confine fisico da una sola osservabile, da phase0, o da un valore critico aggregato; not_tested=limite asintotico, modello Aubry-Andre coseno canonico, dati sperimentali, classi GUE/Poisson universali dirette.", tools/data/graph_completion/graph_completion_20260515_1745.json:13: "question": "Il ritorno Aubry/Fibonacci chiude il confine come stato fisico congiunto, oppure lo conserva solo come componente osservabile-specifica?", tools/data/graph_completion/graph_completion_20260515_1745.json:14: "consecutio": "Il prossimo ciclo puo' usare `exp_aubry_boundary_phase_transport_gate.py` come gate per ritorni fisici: cercare se la finestra V=0.50..1.25 sopravvive a N piu grandi o a un potenziale Aubry-Andre coseno. Se la finestra si muove, il claim resta finestra row-local; se resta stabile, diventa candidato ponte QxG fisico.", tools/data/graph_completion/graph_completion_20260515_1745.json:118: "observable_contract": "claim=`phi` e' stato fisico di confine tra ordine periodico e disordine random solo se statistica spettrale e localizzazione concordano; observable=`spacing_r` + `mean_ipr`/`participation_entropy`; operator=`tools/exp_aubry_boundary_phase_transport_gate.py`; generator=Hamiltoniana tight-binding binaria con sequenze phi/silver/bronze, periodico AB e random bilanciato a densita phi; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..2.50 step 0.25 x generator, random_trials=6; non_possible=promuovere confine fisico da una sola osservabile, da phase0, o da un valore critico aggregato; not_tested=limite asintotico, modello Aubry-Andre coseno canonico, dati sperimentali, classi GUE/Poisson universali dirette.", tools/data/graph_completion/graph_completion_20260515_1745.json:127: "evidence": "Il report lavora su tight-binding Fibonacci/Aubry, spacing statistics e localizzazione/IPR: questo perimetro ha risultati classici vicini, in particolare transizioni Aubry-Andre/quasicristalli Fibonacci e level-statistics vs localization. Il report non nomina il risultato noto piu' vicino prima di marcare la relazione come nuova.", tools/data/graph_completion/graph_completion_20260515_1745.json:128: "suggestion": "Nel prossimo ciclo aggiungere una sezione `nearest_known_result` e confrontare esplicitamente: Aubry-Andre coseno canonico, Fibonacci quasicrystal localization/spectral statistics, GUE/Poisson crossover. Riformulare `relazione nuova` come `nuova nel lab/per questo gate` se non supera quel confronto." tools/data/graph_completion/graph_completion_20260514_1631.json:9: "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GUE-Anderson", tools/data/graph_completion/graph_completion_20260514_1631.json:10: "verdict": "CONSTRAINT / TESTER OPERATIVO - il rimbalzo fisico 16:12 regge come tester riusabile: `SR` e' active in GUE e absorbed nel bordo Anderson `W=6`, mentre `triple_var` resta active come residuo intermedio. Non e' una nuova legge fisica.", tools/data/graph_completion/graph_completion_20260514_1631.json:11: "observable_contract": "claim=il risultato 16:12 va riformulato come tester operativo del confine repulsione/Poisson, non come scoperta fisica; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Anderson 1D `W=6`; denominator=96 repliche per dominio, 95 gap centrali per spettro, 192 eventi trace; null=Poisson span-matched stesso count; non_possible=claim fisico nuovo o legge di transizione se il confronto resta sintetico, finite-size e senza dati sperimentali/unfolding dedicato.", tools/data/graph_completion/graph_completion_20260514_1631.json:12: "claim_under_test": "Il vettore `component_state(SR,L1,triple_var)` resta leggibile come tester operativo del confine GUE-Anderson: `SR` e' active in GUE, viene assorbito al bordo Anderson `W=6`, e `triple_var` puo' restare active nella zona intermedia.", tools/data/graph_completion/graph_completion_20260514_1631.json:14: "consecutio": "Il passo successivo valido non e' tornare ai primi. E' portare lo stesso tester su un denominatore fisico piu' forte: GOE/GUE scelto per simmetria, Anderson 3D o many-body localization se serve una transizione reale, curve in `N`, e confronto con adjacent gap ratio standard piu' unfolding controllato.", tools/data/graph_completion/graph_completion_20260514_1631.json:98: "observable_contract": "claim=il risultato 16:12 va riformulato come tester operativo del confine repulsione/Poisson, non come scoperta fisica; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Anderson 1D `W=6`; denominator=96 repliche per dominio, 95 gap centrali per spettro, 192 eventi trace; null=Poisson span-matched stesso count; non_possible=claim fisico nuovo o legge di transizione se il confronto resta sintetico, finite-size e senza dati sperimentali/unfolding dedicato.", tools/data/graph_completion/graph_completion_20260515_1816.json:11: "observable_contract": "claim=V=2 e' baseline fisica del confine Aubry-Andre e distingue il tipo di generatore; observable=slope log-log `tau` della mean participation ratio piu spacing/IPR anchors; operator=`tools/exp_aubry_v2_generator_scaling_gate.py`; generator=tight-binding 1D con potenziali `sturmian_binary`, `cosine`, `periodic_ab`, `density_shuffle`, `random_uniform`; denominator=N={89,144,233,377} x phase={0,0.25,0.5,0.75} x generator rows, con 4 trial per null random; non_possible=promuovere un claim phi-specific se tau(V=2) non separa dai controlli Sturmian non-phi; not_tested=limite asintotico, altri V, mobility edge, dati sperimentali, qualita PSD dei surrogate 18:07.", tools/data/graph_completion/graph_completion_20260515_1816.json:49: "dipole": "potenziale binario Sturmian / potenziale coseno Aubry-Andre.", tools/data/graph_completion/graph_completion_20260515_1816.json:116: "observable_contract": "claim=V=2 e' baseline fisica del confine Aubry-Andre e distingue il tipo di generatore; observable=slope log-log `tau` della mean participation ratio piu spacing/IPR anchors; operator=`tools/exp_aubry_v2_generator_scaling_gate.py`; generator=tight-binding 1D con potenziali `sturmian_binary`, `cosine`, `periodic_ab`, `density_shuffle`, `random_uniform`; denominator=N={89,144,233,377} x phase={0,0.25,0.5,0.75} x generator rows, con 4 trial per null random; non_possible=promuovere un claim phi-specific se tau(V=2) non separa dai controlli Sturmian non-phi; not_tested=limite asintotico, altri V, mobility edge, dati sperimentali, qualita PSD dei surrogate 18:07.", tools/data/graph_completion/graph_completion_20260515_1724.json:11: "observable_contract": "claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita.", tools/data/graph_completion/graph_completion_20260515_1724.json:14: "consecutio": "Il prossimo ciclo non deve cercare un nuovo fit per `V_c`. Deve decidere se il reticolo di attraversamento trasferisce a un modello fisico piu canonico, con fase controllata e baseline nota: Aubry-Andre/Fibonacci, curva di localizzazione o trasmissione fotonica. Se non viene portato in quel perimetro, resta vincolo matematico sul denominatore.", tools/data/graph_completion/graph_completion_20260515_1724.json:116: "observable_contract": "claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita.", tools/data/graph_completion/graph_completion_20260515_1724.json:132: "evidence": "Il report nomina Aubry-Andre/Fibonacci e mobility edge, ma non ancora il risultato classico piu' vicino: sensibilita' di fase, finite-size effects e critical-coupling distributions nei modelli quasiperiodici/Fibonacci sono un perimetro noto. La formulazione `Relazione nuova` rischia di promuovere una ri-scoperta come discovery autonoma.", tools/data/graph_completion/graph_completion_20260515_1904.json:10: "verdict": "CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge.", tools/data/graph_completion/graph_completion_20260515_1904.json:11: "observable_contract": "claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.", tools/data/graph_completion/graph_completion_20260515_1904.json:12: "claim_under_test": "Nel perimetro 8/5, il terzo incluso operativo non e' riducibile a Brody q o a una mistura Poisson/GUE-surmise; il residuo vive nel disaccordo row-aligned tra scalare classico e grafo osservabile.", tools/data/graph_completion/graph_completion_20260515_1904.json:14: "consecutio": "Il prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche. Il risultato da cercare e' se `graph_only` e `classic_only` sopravvivono fuori dal perimetro composito del Lab.", tools/data/graph_completion/graph_completion_20260515_1904.json:121: "observable_contract": "claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.", tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:2:{"delta": {"L1": 0.09273610492253026, "L2": -0.16190060880570023, "SR": 0.034016837262212773, "SR2": 0.047625583967382146, "triple_var": 0.15384358702838163}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.14763146679438668, "L2": 0.2149382854366141, "SR": 0.3898186222311311, "SR2": 0.38206954607421556, "triple_var": 2.3444711957886386}, "real": {"L1": -0.054895361871856424, "L2": 0.05303767663091389, "SR": 0.4238354594933439, "SR2": 0.4296951300415977, "triple_var": 2.4983147828170202}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:4:{"delta": {"L1": -0.08311278023068808, "L2": 0.0486966878146345, "SR": 0.034787426821776046, "SR2": 0.08062848673057982, "triple_var": -0.5699983336777905}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.047250907213306055, "L2": -0.07965131142376831, "SR": 0.35303834403720885, "SR2": 0.3401851452873781, "triple_var": 2.517557812171376}, "real": {"L1": -0.13036368744399413, "L2": -0.03095462360913381, "SR": 0.3878257708589849, "SR2": 0.4208136320179579, "triple_var": 1.9475594784935855}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:6:{"delta": {"L1": -0.08615671516159853, "L2": -0.36806383650841745, "SR": -0.07045577446670487, "SR2": -0.025085114886213566, "triple_var": -1.4911195332003824}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.02187697980480418, "L2": 0.1674090523863931, "SR": 0.4279898457344939, "SR2": 0.39814619492275455, "triple_var": 3.531274987115587}, "real": {"L1": -0.06427973535679435, "L2": -0.2006547841220244, "SR": 0.357534071267789, "SR2": 0.373061080036541, "triple_var": 2.0401554539152045}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:8:{"delta": {"L1": -0.021059590383546434, "L2": -0.1232101842411371, "SR": 0.000908207944726569, "SR2": 0.019816481063584246, "triple_var": 0.5985514403691781}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.13991889405667332, "L2": -0.06369281624436897, 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tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:164:{"delta": {"L1": -0.009639583653771489, "L2": -0.05687004579748145, "SR": 0.053777717834833316, "SR2": 0.028646303099137127, "triple_var": -0.9906962290338233}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.028054104161036226, "L2": 0.036467476510366685, "SR": 0.3632428060959464, "SR2": 0.40633836125936273, "triple_var": 3.6614285370973314}, "real": {"L1": 0.018414520507264737, "L2": -0.02040256928711476, "SR": 0.4170205239307797, "SR2": 0.43498466435849986, "triple_var": 2.670732308063508}, "rep": 81} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:166:{"delta": {"L1": -0.18166820623517244, "L2": -0.12756854409935453, "SR": 0.018360039216077706, "SR2": 0.019441119498002146, "triple_var": -1.3002582684420703}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.04105247491266391, "L2": -0.04677894620384534, "SR": 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"null": {"L1": -0.016746639460729754, "L2": -0.015354381285289695, "SR": 0.3733024120352074, "SR2": 0.4027538040785869, "triple_var": 2.2444634717979044}, "real": {"L1": 0.050077047546410486, "L2": 0.0341249573382281, "SR": 0.39484562224788433, "SR2": 0.3943690954066406, "triple_var": 3.4149918039902962}, "rep": 87} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:178:{"delta": {"L1": 0.13017176168392042, "L2": 0.13777153160174865, "SR": 0.018034278152287142, "SR2": 0.10076842393252072, "triple_var": -0.5426080004030349}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.06497334303967374, "L2": -0.0886016617693745, "SR": 0.39077304143302116, "SR2": 0.31786574880873086, "triple_var": 2.6561380946754585}, "real": {"L1": 0.06519841864424668, "L2": 0.04916986983237414, "SR": 0.4088073195853083, "SR2": 0.4186341727412516, "triple_var": 2.1135300942724236}, "rep": 88} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:180:{"delta": {"L1": -0.08067528947236549, "L2": -0.2533042755542635, "SR": -0.032849820724623835, "SR2": -0.09940531230661465, "triple_var": -1.0522878166502707}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.0012678560003378126, "L2": 0.004249461141064426, "SR": 0.35973012096228124, "SR2": 0.4003710729730234, "triple_var": 3.2839072923975774}, "real": {"L1": -0.0819431454727033, "L2": -0.24905481441319904, "SR": 0.3268803002376574, "SR2": 0.30096576066640873, "triple_var": 2.2316194757473067}, "rep": 89} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:182:{"delta": {"L1": 0.12463780653600587, "L2": 0.0686700202898782, "SR": 0.02217230567156897, "SR2": 0.11803332182769294, "triple_var": 0.3440115370067054}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.032582173783521975, "L2": 0.01225071582966255, "SR": 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0.39678763516506127, "SR2": 0.36422696963397344, "triple_var": 2.636005607298528}, "rep": 93} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:190:{"delta": {"L1": -0.05243949789244381, "L2": -0.07122007610989392, "SR": 0.021552573210883663, "SR2": -0.012171505456512466, "triple_var": -0.7263526417906574}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.052378052317981746, "L2": -0.05350136993449956, "SR": 0.35593568094249317, "SR2": 0.3555554687028681, "triple_var": 3.0421000030978123}, "real": {"L1": -6.144557446205833e-05, "L2": -0.12472144604439347, "SR": 0.37748825415337683, "SR2": 0.3433839632463556, "triple_var": 2.315747361307155}, "rep": 94} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:192:{"delta": {"L1": 0.11765876748077983, "L2": 0.1106123003100916, "SR": 0.07281979563226226, "SR2": 0.08151733500542235, "triple_var": -0.43295021273616463}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.10337018480692949, "L2": -0.15313879963858398, "SR": 0.3650119153769114, "SR2": 0.3394925063275785, "triple_var": 2.419309627019335}, "real": {"L1": 0.014288582673850338, "L2": -0.04252649932849238, "SR": 0.4378317110091737, "SR2": 0.42100984133300084, "triple_var": 1.9863594142831702}, "rep": 95} tools/data/graph_completion/graph_completion_20260514_1640.json:9: "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", tools/data/graph_completion/graph_completion_20260514_1640.json:10: "verdict": "CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica.", tools/data/graph_completion/graph_completion_20260514_1640.json:11: "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", tools/data/graph_completion/graph_completion_20260514_1640.json:12: "claim_under_test": "Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size.", tools/data/graph_completion/graph_completion_20260514_1640.json:13: "question": "Il tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`?", tools/data/graph_completion/graph_completion_20260514_1640.json:55: "invariante": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/graph_completion/graph_completion_20260514_1640.json:94: "invariant": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/graph_completion/graph_completion_20260514_1640.json:98: "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", tools/data/graph_completion/graph_completion_20260514_1612.json:10: "verdict": "CONSTRAINT - il trasduttore `SR` assorbito vs residuo `L1,triple_var` produce un test fisico concreto su spettri GUE/Anderson; nel run sintetico il ritorno fisico e' presente ma resta proxy numerico, non misura sperimentale", tools/data/graph_completion/graph_completion_20260514_1612.json:11: "observable_contract": "claim=il cedimento selettivo di `SR` puo' essere formulato come test fisico A->B; observable=componenti canoniche attive/assorbite contro null Poisson span-matched; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Hamiltoniano Anderson 1D tight-binding; denominator=48 repliche per dominio, 95 gap centrali per spettro; non_possible=rimbalzo fisico assente se non si nomina B concreto oppure se `SR` non discrimina GUE da Anderson localizzato; not_tested=dati sperimentali reali, unfolding fine, classi di simmetria GOE/GSE, many-body localization.", tools/data/graph_completion/graph_completion_20260514_1612.json:12: "claim_under_test": "Il cedimento selettivo di `SR` osservato nel deposito prime-minus-mod6 puo' tornare come criterio fisico: `SR` resta attivo in spettri caotici GUE e viene assorbito in spettri Anderson localizzati contro un null Poisson span-matched.", tools/data/graph_completion/graph_completion_20260514_1612.json:14: "consecutio": "Il prossimo ciclo fisico deve sostituire il proxy sintetico con un denominatore piu vicino al laboratorio: GOE/GUE scelto per simmetria dichiarata, Anderson 3D o many-body localization se si vuole una transizione fisica vera, unfolding controllato e confronto con dati o letteratura. Il deposito prime-minus-mod6 resta utile solo come generatore di osservabili, non come prova del dominio fisico.", tools/data/graph_completion/graph_completion_20260514_1612.json:98: "observable_contract": "claim=il cedimento selettivo di `SR` puo' essere formulato come test fisico A->B; observable=componenti canoniche attive/assorbite contro null Poisson span-matched; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Hamiltoniano Anderson 1D tight-binding; denominator=48 repliche per dominio, 95 gap centrali per spettro; non_possible=rimbalzo fisico assente se non si nomina B concreto oppure se `SR` non discrimina GUE da Anderson localizzato; not_tested=dati sperimentali reali, unfolding fine, classi di simmetria GOE/GSE, many-body localization.", tools/data/graph_completion/graph_completion_20260514_1612.json:107: "evidence": "Nei dati forniti, `lab_data.json` dichiara come direzione: \"Formalizzare il limite di scala del vettore prime-minus-mod6: micro-trace provider-indipendente, min_paired_rows pre-dichiarato, replica 4096/8192 e primo test oltre 8192 solo se il denominatore resta stabile\". Il report invece esegue un trasferimento GUE/Anderson con N=192 e 48 repliche, senza micro-trace 4096/8192 ne' min_paired_rows. La deviazione e' dichiarata, ma il `why` cita una direttiva operatore non visibile nei file forniti.", tools/data/graph_completion/graph_completion_20260514_1612.json:114: "evidence": "La struttura GUE -> Poisson -> Anderson localizzato ricade nel quadro classico delle statistiche spettrali Wigner-Dyson/GUE contro Poisson e della localizzazione Anderson. Il report dice che non cristallizza una nuova legge fisica, ma non nomina il risultato classico piu' vicino come baseline del ponte.", tools/data/graph_completion/graph_completion_20260514_1612.json:115: "suggestion": "Esplicitare che il test e' un riuso operativo di statistiche spettrali note, non scoperta fisica: confrontare `SR,L1,triple_var` con baseline standard come adjacent gap ratio / Wigner-Dyson vs Poisson / Anderson localization level statistics." tools/data/graph_completion/graph_completion_20260514_1850.json:7: "title": "Agent Report - Interfaccia component_state su bordo Anderson 3D", tools/data/graph_completion/graph_completion_20260514_1850.json:9: "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", tools/data/graph_completion/graph_completion_20260514_1850.json:10: "verdict": "VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 restano active contro Poisson, ma il contrasto diretto separa solo `triple_var`; `SR` e `L1` non chiudono il bordo 3D a `L=6`.", tools/data/graph_completion/graph_completion_20260514_1850.json:14: "consecutio": "Il prossimo seme possibile non e' \"Anderson 3D confermato\". E' un audit del tool su fisico B con taglie multiple e denominatore dichiarato: `L=5,6,7` o unfolding locale, stesso contract, stesso null. Se `SR,L1` restano non_separated e `triple_var` resta separated, il tool mostra che il bordo 3D entra prima nella componente di profondita' che nel piano pair-statistics.", tools/data/graph_completion/graph_completion_20260514_1850.json:53: "radici": "simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D.", tools/data/graph_completion/graph_completion_20260514_1850.json:92: "dipole": "simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D.", tools/data/graph_completion/graph_completion_20260514_1850.json:157: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/data/graph_completion/graph_completion_20260514_1850.json:158: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/data/graph_completion/graph_completion_20260514_1850.json:197: "Anderson_1D_W6": { tools/data/graph_completion/graph_completion_20260514_1656.json:152: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/data/graph_completion/graph_completion_20260514_1656.json:153: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/data/graph_completion/graph_completion_20260514_1656.json:192: "Anderson_1D_W6": { tools/data/graph_completion/graph_completion_20260514_1649.json:11: "observable_contract": "claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico.", tools/data/graph_completion/graph_completion_20260514_1649.json:51: "invariant": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/graph_completion/graph_completion_20260514_1649.json:116: "observable_contract": "claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico.", tools/data/graph_completion/graph_completion_20260515_1758.json:7: "title": "Agent Report - Aubry Cosine Boundary Counter-Gate", tools/data/graph_completion/graph_completion_20260515_1758.json:10: "verdict": "CONSTRAINT - nel potenziale Aubry-Andre coseno `phi` non chiude un boundary privilegiato; la finestra binaria 17:45 dipende dalla grammatica Sturmian, non dal solo irrazionale phi.", tools/data/graph_completion/graph_completion_20260515_1758.json:11: "observable_contract": "claim=`phi` resta terzo incluso fisico anche quando il potenziale binario viene sostituito dal coseno Aubry-Andre canonico; observable=`spacing_r` + `mean_ipr` con controllo di distinzione da silver/bronze; operator=`tools/exp_aubry_cosine_boundary_counter_gate.py`; generator=Hamiltoniana tight-binding 1D con potenziale coseno per beta phi/silver/bronze, periodico beta=1/2 e random onsite uniforme; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..3.00 step 0.25, random_trials=6; non_possible=promuovere phi come boundary fisico se non si separa dai controlli irrazionali con spacing e localizzazione insieme; not_tested=limite asintotico, disordine correlato sperimentale, classi GUE/Poisson universali dirette.", tools/data/graph_completion/graph_completion_20260515_1758.json:12: "claim_under_test": "Nel potenziale Aubry-Andre coseno, `phi` resta terzo incluso fisico tra periodico e random solo se `spacing_r` e localizzazione lo collocano insieme nel segmento periodico-random e lo separano da silver/bronze.", tools/data/graph_completion/graph_completion_20260515_1758.json:13: "question": "Il confine `phi` osservato nel ritorno binario 17:45 sopravvive nel coseno Aubry-Andre, oppure il contenuto era nella grammatica Sturmian del potenziale?", tools/data/graph_completion/graph_completion_20260515_1758.json:70: "radici": "potenziale binario Sturmian / potenziale coseno Aubry-Andre.", tools/data/graph_completion/graph_completion_20260515_1758.json:110: "dipole": "potenziale binario Sturmian / potenziale coseno Aubry-Andre.", tools/data/graph_completion/graph_completion_20260515_1758.json:116: "observable_contract": "claim=`phi` resta terzo incluso fisico anche quando il potenziale binario viene sostituito dal coseno Aubry-Andre canonico; observable=`spacing_r` + `mean_ipr` con controllo di distinzione da silver/bronze; operator=`tools/exp_aubry_cosine_boundary_counter_gate.py`; generator=Hamiltoniana tight-binding 1D con potenziale coseno per beta phi/silver/bronze, periodico beta=1/2 e random onsite uniforme; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..3.00 step 0.25, random_trials=6; non_possible=promuovere phi come boundary fisico se non si separa dai controlli irrazionali con spacing e localizzazione insieme; not_tested=limite asintotico, disordine correlato sperimentale, classi GUE/Poisson universali dirette.", tools/data/graph_completion/graph_completion_20260515_1758.json:124: "claim": "Relazione nuova: la finestra binaria 17:45 non trasferisce automaticamente alla classe Aubry-Andre coseno; il generatore del potenziale diventa parte atomica del claim.", tools/data/graph_completion/graph_completion_20260515_1758.json:125: "evidence": "Il risultato osservato cade dentro un perimetro classico: modello Aubry-Andre canonico con transizione/localizzazione attesa attorno a V=2. La tabella mostra rottura del gate proprio a V=2.00 e perdita di intermedieta IPR dopo V=2.25, coerente con struttura nota del modello piu' che con scoperta fisica nuova autonoma. Il report nomina Aubry-Andre, ma non ancora il risultato classico piu' vicino come baseline esplicita; inoltre usa 'mobility edge/localization transition' mentre il modello canonico 1D Aubry-Andre ha transizione globale di localizzazione, non mobility edge salvo varianti generalizzate.", tools/data/graph_completion/graph_completion_20260515_1758.json:126: "suggestion": "Nel prossimo ciclo dichiarare baseline classica: self-dual transition Aubry-Andre a V=2 per hopping unitario; separare 'novita lab' = non-trasferimento del claim phi/binario dalla fisica gia' nota del modello; usare 'localization transition' e non 'mobility edge' se il generatore resta Aubry-Andre canonico." tools/data/graph_completion/latest.json:10: "verdict": "CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge.", tools/data/graph_completion/latest.json:11: "observable_contract": "claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.", tools/data/graph_completion/latest.json:12: "claim_under_test": "Nel perimetro 8/5, il terzo incluso operativo non e' riducibile a Brody q o a una mistura Poisson/GUE-surmise; il residuo vive nel disaccordo row-aligned tra scalare classico e grafo osservabile.", tools/data/graph_completion/latest.json:14: "consecutio": "Il prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche. Il risultato da cercare e' se `graph_only` e `classic_only` sopravvivono fuori dal perimetro composito del Lab.", tools/data/graph_completion/latest.json:121: "observable_contract": "claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.", exec /bin/bash -lc "sed -n '1,260p' tools/exp_aubry_boundary_phase_transport_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Aubry/Fibonacci boundary phase transport gate. Projects the live BOUNDARY direction into a 1D tight-binding model with binary quasiperiodic potentials. The test is deliberately joint: a boundary return is accepted only when the phi stack is between periodic order and balanced random disorder for both spectral spacing and eigenstate localization. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np PHI = (1 + np.sqrt(5)) / 2 SILVER = 1 + np.sqrt(2) BRONZE = 1 + np.sqrt(3) def sturmian_sequence(theta: float, n: int, phase: float) -> np.ndarray: idx = np.arange(n + 1, dtype=float) vals = np.floor(idx * theta + phase) return np.diff(vals).astype(float) def periodic_sequence(n: int) -> np.ndarray: return (np.arange(n) % 2).astype(float) def hamiltonian(diagonal: np.ndarray) -> np.ndarray: n = len(diagonal) matrix = np.diag(diagonal.astype(float)) off = np.ones(n - 1, dtype=float) matrix += np.diag(off, 1) + np.diag(off, -1) return matrix def central_slice(n: int, central_fraction: float) -> slice: keep = max(8, int(round(n * central_fraction))) keep = min(n, keep) start = (n - keep) // 2 return slice(start, start + keep) def spacing_r(levels: np.ndarray, central_fraction: float) -> float | None: levels = np.sort(np.asarray(levels, dtype=float)) central = levels[central_slice(len(levels), central_fraction)] gaps = np.diff(central) gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] if len(gaps) < 2: return None left = gaps[:-1] right = gaps[1:] return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) def localization_metrics(vectors: np.ndarray, central_fraction: float) -> dict[str, float]: n = vectors.shape[0] subset = vectors[:, central_slice(n, central_fraction)] probs = np.square(np.abs(subset)) ipr = np.sum(probs * probs, axis=0) entropy = [] for col in range(probs.shape[1]): p = probs[:, col] p = p[p > 1e-15] entropy.append(float(-np.sum(p * np.log(p)) / np.log(n))) return { "mean_ipr": float(np.mean(ipr)), "median_ipr": float(np.median(ipr)), "participation_entropy": float(np.mean(entropy)) if entropy else 0.0, } def spectrum_row( domain: str, seq: np.ndarray, n: int, phase: float, v_value: float, central_fraction: float, trial: int | None = None, ) -> dict[str, Any]: centered = seq - float(np.mean(seq)) levels, vectors = np.linalg.eigh(hamiltonian(v_value * centered)) metrics = localization_metrics(vectors, central_fraction) row: dict[str, Any] = { "domain": domain, "N": n, "phase": phase, "V": v_value, "ones": int(np.sum(seq)), "spacing_r": spacing_r(levels, central_fraction), **metrics, } if trial is not None: row["trial"] = trial return row def finite(values: list[float | None]) -> np.ndarray: return np.array([v for v in values if v is not None and np.isfinite(v)], dtype=float) def aggregate(rows: list[dict[str, Any]]) -> dict[str, Any]: out: dict[str, Any] = {"count": len(rows)} for key in ["spacing_r", "mean_ipr", "median_ipr", "participation_entropy"]: arr = finite([row.get(key) for row in rows]) if len(arr) == 0: out[key] = {"count": 0} else: out[key] = { "count": int(len(arr)), "median": float(np.median(arr)), "mean": float(np.mean(arr)), "min": float(np.min(arr)), "max": float(np.max(arr)), } return out def median_metric(summary: dict[str, Any], domain: str, v_key: str, metric: str) -> float | None: value = summary.get(v_key, {}).get(domain, {}).get(metric, {}) if not isinstance(value, dict): return None median = value.get("median") return float(median) if median is not None else None def between(value: float, left: float, right: float) -> bool: return min(left, right) <= value <= max(left, right) def parse_csv_ints(value: str) -> list[int]: return [int(part.strip()) for part in value.split(",") if part.strip()] def parse_csv_floats(value: str) -> list[float]: return [float(part.strip()) for part in value.split(",") if part.strip()] def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) ns = parse_csv_ints(args.ns) phases = parse_csv_floats(args.phases) v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) domains = { "phi": 1 / PHI, "silver": 1 / SILVER, "bronze": 1 / BRONZE, } rows: list[dict[str, Any]] = [] for n in ns: for phase in phases: phi_seq = sturmian_sequence(1 / PHI, n, phase) ones = int(np.sum(phi_seq)) for v_value in v_values: for domain, theta in domains.items(): seq = sturmian_sequence(theta, n, phase) rows.append(spectrum_row(domain, seq, n, phase, float(v_value), args.central_fraction)) periodic = periodic_sequence(n) rows.append(spectrum_row("periodic_ab", periodic, n, phase, float(v_value), args.central_fraction)) for trial in range(args.random_trials): seq = np.array([1.0] * ones + [0.0] * (n - ones), dtype=float) rng.shuffle(seq) rows.append( spectrum_row( "balanced_random_phi_density", seq, n, phase, float(v_value), args.central_fraction, trial=trial, ) ) summary_by_v: dict[str, dict[str, Any]] = {} for v_value in v_values: v_key = f"V={v_value:.6f}" summary_by_v[v_key] = {} for domain in sorted({row["domain"] for row in rows}): subset = [row for row in rows if row["domain"] == domain and abs(row["V"] - v_value) < 1e-12] summary_by_v[v_key][domain] = aggregate(subset) classification: dict[str, Any] = {"joint_boundary_v": [], "by_v": {}} for v_value in v_values: v_key = f"V={v_value:.6f}" needed = { "phi_r": median_metric(summary_by_v, "phi", v_key, "spacing_r"), "periodic_r": median_metric(summary_by_v, "periodic_ab", v_key, "spacing_r"), "random_r": median_metric(summary_by_v, "balanced_random_phi_density", v_key, "spacing_r"), "phi_ipr": median_metric(summary_by_v, "phi", v_key, "mean_ipr"), "periodic_ipr": median_metric(summary_by_v, "periodic_ab", v_key, "mean_ipr"), "random_ipr": median_metric(summary_by_v, "balanced_random_phi_density", v_key, "mean_ipr"), "phi_entropy": median_metric(summary_by_v, "phi", v_key, "participation_entropy"), "periodic_entropy": median_metric(summary_by_v, "periodic_ab", v_key, "participation_entropy"), "random_entropy": median_metric(summary_by_v, "balanced_random_phi_density", v_key, "participation_entropy"), } complete = all(value is not None for value in needed.values()) r_between = bool(complete and between(needed["phi_r"], needed["periodic_r"], needed["random_r"])) ipr_between = bool(complete and between(needed["phi_ipr"], needed["periodic_ipr"], needed["random_ipr"])) entropy_between = bool(complete and between(needed["phi_entropy"], needed["periodic_entropy"], needed["random_entropy"])) separated_random = bool( complete and abs(needed["phi_r"] - needed["random_r"]) >= args.min_r_delta and abs(needed["phi_ipr"] - needed["random_ipr"]) >= args.min_ipr_delta ) joint = bool(r_between and ipr_between and separated_random) classification["by_v"][v_key] = { **needed, "spacing_r_between": r_between, "mean_ipr_between": ipr_between, "participation_entropy_between": entropy_between, "separated_from_random": separated_random, "joint_boundary": joint, } if joint: classification["joint_boundary_v"].append(float(v_value)) return { "experiment": "aubry_boundary_phase_transport_gate", "parameters": { "ns": ns, "phases": phases, "v_min": args.v_min, "v_max": args.v_max, "v_step": args.v_step, "central_fraction": args.central_fraction, "random_trials": args.random_trials, "seed": args.seed, "min_r_delta": args.min_r_delta, "min_ipr_delta": args.min_ipr_delta, }, "observable_contract": { "claim": "phi is a physical boundary state between periodic order and balanced random disorder only if spectral spacing and localization agree", "observable": "spacing_r plus mean_ipr / participation_entropy on tight-binding spectra", "operator": "binary quasiperiodic tight-binding Hamiltonian with phase row denominator", "denominator": "N x phase x V x generator rows with balanced random controls", "non_possible": "single-observable boundary, phase-aggregated critical value, or phi outside the periodic-random interval", }, "classification": classification, "summary_by_v": summary_by_v, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--ns", default="89,144,233") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--v-min", type=float, default=0.5) exec /bin/bash -lc "sed -n '1,240p' tools/exp_aubry_cosine_boundary_counter_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Aubry-Andre cosine counter-gate for the BOUNDARY return. This is the regression check opened by the binary Aubry/Fibonacci gate: remove the binary Sturmian grammar and ask whether phi remains a privileged boundary inside the canonical cosine potential. The null is explicit: all irrational frequencies behave as the same Aubry-Andre class unless phi separates jointly in spectral spacing and localization. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np PHI = (1 + np.sqrt(5)) / 2 SILVER = 1 + np.sqrt(2) BRONZE = 1 + np.sqrt(3) def hamiltonian(diagonal: np.ndarray) -> np.ndarray: n = len(diagonal) matrix = np.diag(diagonal.astype(float)) off = np.ones(n - 1, dtype=float) matrix += np.diag(off, 1) + np.diag(off, -1) return matrix def central_slice(n: int, central_fraction: float) -> slice: keep = max(8, min(n, int(round(n * central_fraction)))) start = (n - keep) // 2 return slice(start, start + keep) def spacing_r(levels: np.ndarray, central_fraction: float) -> float | None: levels = np.sort(np.asarray(levels, dtype=float)) central = levels[central_slice(len(levels), central_fraction)] gaps = np.diff(central) gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] if len(gaps) < 2: return None left = gaps[:-1] right = gaps[1:] return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) def localization_metrics(vectors: np.ndarray, central_fraction: float) -> dict[str, float]: n = vectors.shape[0] subset = vectors[:, central_slice(n, central_fraction)] probs = np.square(np.abs(subset)) ipr = np.sum(probs * probs, axis=0) entropy_values = [] for col in range(probs.shape[1]): p = probs[:, col] p = p[p > 1e-15] entropy_values.append(float(-np.sum(p * np.log(p)) / np.log(n))) return { "mean_ipr": float(np.mean(ipr)), "median_ipr": float(np.median(ipr)), "participation_entropy": float(np.mean(entropy_values)) if entropy_values else 0.0, } def cosine_potential(beta: float, n: int, phase: float) -> np.ndarray: idx = np.arange(n, dtype=float) vals = np.cos(2 * np.pi * (beta * idx + phase)) return vals - float(np.mean(vals)) def periodic_potential(n: int, phase: float) -> np.ndarray: idx = np.arange(n, dtype=float) vals = np.cos(np.pi * idx + 2 * np.pi * phase) return vals - float(np.mean(vals)) def random_potential(rng: np.random.Generator, n: int) -> np.ndarray: vals = rng.uniform(-1.0, 1.0, n) return vals - float(np.mean(vals)) def spectrum_row( domain: str, diagonal: np.ndarray, n: int, phase: float, v_value: float, central_fraction: float, trial: int | None = None, ) -> dict[str, Any]: levels, vectors = np.linalg.eigh(hamiltonian(v_value * diagonal)) row: dict[str, Any] = { "domain": domain, "N": n, "phase": phase, "V": v_value, "spacing_r": spacing_r(levels, central_fraction), **localization_metrics(vectors, central_fraction), } if trial is not None: row["trial"] = trial return row def finite(values: list[float | None]) -> np.ndarray: return np.array([v for v in values if v is not None and np.isfinite(v)], dtype=float) def aggregate(rows: list[dict[str, Any]]) -> dict[str, Any]: out: dict[str, Any] = {"count": len(rows)} for key in ["spacing_r", "mean_ipr", "median_ipr", "participation_entropy"]: arr = finite([row.get(key) for row in rows]) if len(arr) == 0: out[key] = {"count": 0} else: out[key] = { "count": int(len(arr)), "median": float(np.median(arr)), "mean": float(np.mean(arr)), "min": float(np.min(arr)), "max": float(np.max(arr)), } return out def median_metric(summary: dict[str, Any], domain: str, v_key: str, metric: str) -> float | None: value = summary.get(v_key, {}).get(domain, {}).get(metric, {}) if not isinstance(value, dict): return None median = value.get("median") return float(median) if median is not None else None def between(value: float, left: float, right: float) -> bool: return min(left, right) <= value <= max(left, right) def parse_csv_ints(value: str) -> list[int]: return [int(part.strip()) for part in value.split(",") if part.strip()] def parse_csv_floats(value: str) -> list[float]: return [float(part.strip()) for part in value.split(",") if part.strip()] def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) ns = parse_csv_ints(args.ns) phases = parse_csv_floats(args.phases) v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) irrational_domains = { "phi_cosine": 1 / PHI, "silver_cosine": 1 / SILVER, "bronze_cosine": 1 / BRONZE, } rows: list[dict[str, Any]] = [] for n in ns: for phase in phases: for v_value in v_values: for domain, beta in irrational_domains.items(): rows.append( spectrum_row( domain, cosine_potential(beta, n, phase), n, phase, float(v_value), args.central_fraction, ) ) rows.append( spectrum_row( "periodic_cosine", periodic_potential(n, phase), n, phase, float(v_value), args.central_fraction, ) ) for trial in range(args.random_trials): rows.append( spectrum_row( "random_onsite", random_potential(rng, n), n, phase, float(v_value), args.central_fraction, trial=trial, ) ) domains = sorted({row["domain"] for row in rows}) summary_by_v: dict[str, dict[str, Any]] = {} for v_value in v_values: v_key = f"V={v_value:.6f}" summary_by_v[v_key] = {} for domain in domains: subset = [row for row in rows if row["domain"] == domain and abs(row["V"] - v_value) < 1e-12] summary_by_v[v_key][domain] = aggregate(subset) classification: dict[str, Any] = {"phi_joint_boundary_v": [], "phi_distinct_v": [], "by_v": {}} for v_value in v_values: v_key = f"V={v_value:.6f}" needed = { "phi_r": median_metric(summary_by_v, "phi_cosine", v_key, "spacing_r"), "periodic_r": median_metric(summary_by_v, "periodic_cosine", v_key, "spacing_r"), "random_r": median_metric(summary_by_v, "random_onsite", v_key, "spacing_r"), "phi_ipr": median_metric(summary_by_v, "phi_cosine", v_key, "mean_ipr"), "periodic_ipr": median_metric(summary_by_v, "periodic_cosine", v_key, "mean_ipr"), "random_ipr": median_metric(summary_by_v, "random_onsite", v_key, "mean_ipr"), "silver_r": median_metric(summary_by_v, "silver_cosine", v_key, "spacing_r"), "bronze_r": median_metric(summary_by_v, "bronze_cosine", v_key, "spacing_r"), "silver_ipr": median_metric(summary_by_v, "silver_cosine", v_key, "mean_ipr"), "bronze_ipr": median_metric(summary_by_v, "bronze_cosine", v_key, "mean_ipr"), } complete = all(value is not None for value in needed.values()) r_between = bool(complete and between(needed["phi_r"], needed["periodic_r"], needed["random_r"])) ipr_between = bool(complete and between(needed["phi_ipr"], needed["periodic_ipr"], needed["random_ipr"])) separated_random = bool( complete and abs(needed["phi_r"] - needed["random_r"]) >= args.min_r_delta and abs(needed["phi_ipr"] - needed["random_ipr"]) >= args.min_ipr_delta ) nearest_control_r = min(abs(needed["phi_r"] - needed["silver_r"]), abs(needed["phi_r"] - needed["bronze_r"])) if complete else None nearest_control_ipr = ( min(abs(needed["phi_ipr"] - needed["silver_ipr"]), abs(needed["phi_ipr"] - needed["bronze_ipr"])) if complete else None ) phi_distinct = bool( complete and nearest_control_r is not None exec /bin/bash -lc "sed -n '1,220p' tools/exp_boundary_graph_curvature_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Graph-curvature gate for the 8 GUE / 5 Poisson BOUNDARY perimeter. The unit is the row-aligned domain/window from the base BOUNDARY perimeter. Labels are kept as audit metadata; the geometry is built from observables: canonical registry values, explicit spectral rigidity, and shuffle z values. """ from __future__ import annotations import argparse import json import math from pathlib import Path from typing import Any import numpy as np from exp_semireal_boundary_transfer_gate import row_spacings from observables_registry import ( OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity, ) OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity"] + [f"z_{name}" for name in OBS_NAMES] def load_scope(path: Path) -> list[dict[str, Any]]: with path.open() as f: data = json.load(f) rows = data.get("rows", []) if not isinstance(rows, list): raise ValueError(f"{path} does not contain rows") return rows def finite(value: Any) -> bool: return isinstance(value, (int, float)) and math.isfinite(float(value)) def compute_observables(gaps: np.ndarray) -> dict[str, float]: values = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} values["SR_local_rigidity"] = float(SR_local_rigidity(gaps)) return values def shuffle_z( gaps: np.ndarray, original: dict[str, float], n_shuffle: int, rng: np.random.Generator, ) -> dict[str, float]: samples = {name: [] for name in OBS_NAMES} for _ in range(n_shuffle): shuffled = rng.permutation(gaps) obs = compute_observables(shuffled) for name in OBS_NAMES: samples[name].append(obs[name]) z = {} for name in OBS_NAMES: arr = np.asarray(samples[name], dtype=float) sd = float(np.std(arr, ddof=1)) if len(arr) > 1 else 0.0 mean = float(np.mean(arr)) if len(arr) else 0.0 z[name] = float((original[name] - mean) / sd) if sd > 1e-15 else 0.0 return z def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: matrix = [] for row in rows: obs = row["observables"] z = row["shuffle_z"] matrix.append([obs[name] for name in OBS_NAMES] + [obs["SR_local_rigidity"]] + [z[name] for name in OBS_NAMES]) x = np.asarray(matrix, dtype=float) center = np.mean(x, axis=0) scale = np.std(x, axis=0, ddof=1) scale[scale <= 1e-15] = 1.0 return (x - center) / scale def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: n = len(x) distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) edges: set[tuple[int, int]] = set() for i in range(n): nearest = np.argsort(distances[i])[1 : k + 1] for j in nearest: edges.add((min(i, int(j)), max(i, int(j)))) return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] def classify_geometry(rows: list[dict[str, Any]], x: np.ndarray, k: int) -> dict[str, Any]: labels = [row["source_domain_type"] for row in rows] gue_idx = [i for i, label in enumerate(labels) if label == "GUE"] poi_idx = [i for i, label in enumerate(labels) if label == "Poisson"] if not gue_idx or not poi_idx: raise ValueError("scope must include both GUE and Poisson rows") c_gue = np.mean(x[gue_idx], axis=0) c_poi = np.mean(x[poi_idx], axis=0) edges = build_knn_edges(x, k) degree = {i: 0 for i in range(len(rows))} for i, j, _ in edges: degree[i] += 1 degree[j] += 1 row_out = [] third_rows = [] for i, row in enumerate(rows): d_gue = float(np.linalg.norm(x[i] - c_gue)) d_poi = float(np.linalg.norm(x[i] - c_poi)) denom = d_gue + d_poi centroid_coord = float((d_gue - d_poi) / denom) if denom > 1e-15 else 0.0 centroid_margin = float(abs(d_gue - d_poi) / denom) if denom > 1e-15 else 0.0 incident = [(a, b, dist) for a, b, dist in edges if a == i or b == i] cross = 0 cross_curvatures = [] same_curvatures = [] for a, b, _ in incident: other = b if a == i else a curvature = 4 - degree[a] - degree[b] if labels[other] != labels[i]: cross += 1 cross_curvatures.append(curvature) else: same_curvatures.append(curvature) cross_fraction = float(cross / len(incident)) if incident else 0.0 state = "class_interior" if cross_fraction > 0 and centroid_margin < 0.25: state = "third_included_candidate" third_rows.append(row["domain_window"]) elif cross_fraction > 0: state = "cut_edge" row_out.append( { "domain_window": row["domain_window"], "domain": row["domain"], "source_domain_type": row["source_domain_type"], "degree": degree[i], "centroid_coord": round(centroid_coord, 6), "centroid_margin": round(centroid_margin, 6), "cross_neighbor_fraction": round(cross_fraction, 6), "cross_edge_curvature_mean": round(float(np.mean(cross_curvatures)), 6) if cross_curvatures else None, "same_edge_curvature_mean": round(float(np.mean(same_curvatures)), 6) if same_curvatures else None, "boundary_state": state, } ) cross_edges = [ { "a": rows[i]["domain_window"], "b": rows[j]["domain_window"], "distance": round(dist, 6), "forman_unweighted": 4 - degree[i] - degree[j], } for i, j, dist in edges if labels[i] != labels[j] ] same_edges = [ {"distance": dist, "forman_unweighted": 4 - degree[i] - degree[j]} for i, j, dist in edges if labels[i] == labels[j] ] return { "feature_names": FEATURE_NAMES, "k": k, "label_counts": { "GUE": len(gue_idx), "Poisson": len(poi_idx), }, "edge_counts": { "total": len(edges), "cross_label": len(cross_edges), "same_label": len(same_edges), }, "curvature": { "cross_edge_mean": round(float(np.mean([e["forman_unweighted"] for e in cross_edges])), 6) if cross_edges else None, "same_edge_mean": round(float(np.mean([e["forman_unweighted"] for e in same_edges])), 6) if same_edges else None, }, "third_included_candidates": third_rows, "rows": row_out, "cross_edges": cross_edges, } def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) scope = load_scope(Path(args.scope)) selected = [row for row in scope if row.get("source_domain_type") in {"GUE", "Poisson"}] selected = sorted(selected, key=lambda row: int(row["cycle"])) rows = [] errors = [] for source in selected: try: gaps = row_spacings(source["domain"]) if len(gaps) < args.min_gaps: errors.append( { "domain_window": source["domain_window"], "error": f"insufficient gaps {len(gaps)} < {args.min_gaps}", } ) continue gaps = gaps[: args.n_gaps] if len(gaps) > args.n_gaps else gaps obs = compute_observables(gaps) z = shuffle_z(gaps, obs, args.n_shuffle, rng) rows.append( { "domain_window": source["domain_window"], "domain": source["domain"], "cycle": source["cycle"], "source_domain_type": source["source_domain_type"], "n_gaps": int(len(gaps)), exec /bin/bash -lc "ls tools/data | rg 'aubry|phase|cosine|sturmian'" in /opt/MM_D-ND succeeded in 0ms: aubry_binary_grammar_surrogate_gate_20260515_1807.json aubry_boundary_phase_transport_gate_20260515_1745.json aubry_cosine_boundary_counter_gate_20260515_1758.json aubry_v2_generator_scaling_gate_20260515_1816.json crossover_phase_test.json nonphi_sturmian_fixed_reader_gate_20260508_2019.json nonphi_sturmian_fixed_reader_gate_20260508_2019_seedcheck.json sturmian_denominator_alignment_gate_20260515_1826.json vc_label_preserving_phase_bridge_20260509_0741.json vc_nonsturmian_label_null_gate_20260509_0819.json codex La misura che serve non è un’altra metrica sul deposito 13 righe, ma un trasferimento row-aligned su un flusso Hamiltoniano controllato. Preparo uno script riusabile che genera un Rosenzweig-Porter/diagonal-plus-GUE finite-size flow, calcola lettore classico e lettore grafico sulle stesse 13 righe, poi stressa il grafo su seed e k. exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_classical_crossover_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Classical crossover audit for the 8 GUE / 5 Poisson BOUNDARY perimeter. The row unit is inherited from the graph-curvature gate. This script adds two standard one-dimensional crossover readers to the same rows: - Brody q in [0, 1], fitted by grid likelihood on mean-normalized spacings. - A simple Berry-Robnik-like mixture weight in [0, 1], fitted by KS distance between the empirical CDF and w * GUE_surmise + (1-w) * Poisson. These are audit coordinates, not new Lab observables. The Lab-specific residue is the disagreement between graph bridge rows and classical scalar intermediacy. """ from __future__ import annotations import argparse import json import math from pathlib import Path from typing import Any import numpy as np from exp_semireal_boundary_transfer_gate import row_spacings def load_json(path: Path) -> dict[str, Any]: with path.open() as f: data = json.load(f) if not isinstance(data, dict): raise ValueError(f"{path} must contain a JSON object") return data def normalize_spacings(gaps: np.ndarray) -> np.ndarray: gaps = np.asarray(gaps, dtype=float) gaps = gaps[np.isfinite(gaps)] gaps = gaps[gaps > 0] if len(gaps) == 0: raise ValueError("no positive finite spacings") mean = float(np.mean(gaps)) if mean <= 1e-15: raise ValueError("spacing mean is zero") return gaps / mean def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: qs = np.linspace(0.0, 1.0, grid_size) best_q = 0.0 best_nll = float("inf") for q in qs: pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) nll = -float(np.sum(np.log(pdf))) if nll < best_nll: best_nll = nll best_q = float(q) return best_q, best_nll def poisson_cdf(s: np.ndarray) -> np.ndarray: return 1.0 - np.exp(-s) def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: a = 4.0 / math.pi return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) def empirical_ks(s: np.ndarray, model_cdf: np.ndarray) -> float: empirical = np.arange(1, len(s) + 1, dtype=float) / float(len(s)) return float(np.max(np.abs(empirical - model_cdf))) def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: sorted_s = np.sort(s) poi = poisson_cdf(sorted_s) gue = gue_wigner_cdf(sorted_s) best_w = 0.0 best_ks = float("inf") for w in np.linspace(0.0, 1.0, grid_size): model = (1.0 - w) * poi + w * gue ks = empirical_ks(sorted_s, model) if ks < best_ks: best_ks = ks best_w = float(w) return best_w, best_ks def classical_state(brody_q: float, mixture_w: float, graph_state: str) -> str: brody_mid = 0.25 <= brody_q <= 0.75 mix_mid = 0.25 <= mixture_w <= 0.75 if graph_state == "third_included_candidate" and (brody_mid or mix_mid): return "classic_and_graph_bridge" if graph_state == "third_included_candidate": return "graph_only_bridge" if brody_mid or mix_mid: return "classic_only_intermediate" return "endpoint_like" def run(args: argparse.Namespace) -> dict[str, Any]: graph = load_json(Path(args.graph)) graph_rows = graph.get("geometry", {}).get("rows", []) if not isinstance(graph_rows, list) or not graph_rows: raise ValueError("graph input has no geometry.rows") rows = [] for grow in graph_rows: gaps = row_spacings(grow["domain"]) gaps = gaps[: args.n_gaps] if len(gaps) > args.n_gaps else gaps s = normalize_spacings(gaps) brody_q, brody_nll = fit_brody_q(s, args.grid_size) mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) rows.append( { "domain_window": grow["domain_window"], "domain": grow["domain"], "source_domain_type": grow["source_domain_type"], "graph_state": grow["boundary_state"], "centroid_margin": grow["centroid_margin"], "cross_neighbor_fraction": grow["cross_neighbor_fraction"], "n_spacings": int(len(s)), "brody_q": round(brody_q, 6), "brody_nll": round(brody_nll, 6), "berry_robnick_like_gue_weight": round(mixture_w, 6), "mixture_ks": round(mixture_ks, 6), "audit_state": classical_state(brody_q, mixture_w, grow["boundary_state"]), } ) counts: dict[str, int] = {} for row in rows: counts[row["audit_state"]] = counts.get(row["audit_state"], 0) + 1 third = [row for row in rows if row["graph_state"] == "third_included_candidate"] graph_only = [row["domain_window"] for row in third if row["audit_state"] == "graph_only_bridge"] classic_and_graph = [row["domain_window"] for row in third if row["audit_state"] == "classic_and_graph_bridge"] classic_only = [row["domain_window"] for row in rows if row["audit_state"] == "classic_only_intermediate"] output = { "experiment": "boundary_classical_crossover_audit", "question": "Do graph bridge rows collapse to standard Brody/Berry-Robnik-like crossover coordinates?", "observables_registry": "none; classical audit coordinates plus prior graph observables", "observables_used": [ "brody_q", "berry_robnick_like_gue_weight", "mixture_ks", "graph_boundary_state_from_1855", "centroid_margin_from_1855", "cross_neighbor_fraction_from_1855", ], "params": vars(args), "source_graph": args.graph, "observable_contract": { "claim": "Lab bridge rows retain residue after comparison with classical crossover scalars", "observable": "row-aligned Brody q, Berry-Robnik-like GUE mixture weight, graph bridge state", "operator": "classical scalar audit over the same 13 BOUNDARY rows used by the graph gate", "generator": "row_spacings(domain) with graph states imported from boundary_graph_curvature_gate", "denominator": "13 rows: 8 GUE and 5 Poisson", "non_possible": "Lab-specific bridge if every graph bridge is exactly a classical intermediate and no classical-only intermediate appears", "not_tested": "true Rosenzweig-Porter Hamiltonian flow, physical unfolding alternatives, asymptotic universality", }, "summary": { "rows_analyzed": len(rows), "audit_counts": counts, "graph_third_included": [row["domain_window"] for row in third], "classic_and_graph_bridge": classic_and_graph, "graph_only_bridge": graph_only, "classic_only_intermediate": classic_only, "lab_residue_present": bool(graph_only or classic_only), }, "rows": rows, } return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--graph", default="tools/data/boundary_graph_curvature_gate_20260515_1855.json") parser.add_argument("--n-gaps", type=int, default=2048) parser.add_argument("--grid-size", type=int, default=201) parser.add_argument("--out", default="tools/data/boundary_classical_crossover_audit_20260515_1904.json") args = parser.parse_args() output = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") print(json.dumps(output["summary"], indent=2, sort_keys=True)) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '220,520p' tools/exp_aubry_cosine_boundary_counter_gate.py" in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,220p' tools/observables_registry.py" in /opt/MM_D-ND succeeded in 0ms: "bronze_r": median_metric(summary_by_v, "bronze_cosine", v_key, "spacing_r"), "silver_ipr": median_metric(summary_by_v, "silver_cosine", v_key, "mean_ipr"), "bronze_ipr": median_metric(summary_by_v, "bronze_cosine", v_key, "mean_ipr"), } complete = all(value is not None for value in needed.values()) r_between = bool(complete and between(needed["phi_r"], needed["periodic_r"], needed["random_r"])) ipr_between = bool(complete and between(needed["phi_ipr"], needed["periodic_ipr"], needed["random_ipr"])) separated_random = bool( complete and abs(needed["phi_r"] - needed["random_r"]) >= args.min_r_delta and abs(needed["phi_ipr"] - needed["random_ipr"]) >= args.min_ipr_delta ) nearest_control_r = min(abs(needed["phi_r"] - needed["silver_r"]), abs(needed["phi_r"] - needed["bronze_r"])) if complete else None nearest_control_ipr = ( min(abs(needed["phi_ipr"] - needed["silver_ipr"]), abs(needed["phi_ipr"] - needed["bronze_ipr"])) if complete else None ) phi_distinct = bool( complete and nearest_control_r is not None and nearest_control_ipr is not None and nearest_control_r >= args.min_control_r_delta and nearest_control_ipr >= args.min_control_ipr_delta ) joint = bool(r_between and ipr_between and separated_random and phi_distinct) classification["by_v"][v_key] = { **needed, "spacing_r_between": r_between, "mean_ipr_between": ipr_between, "separated_from_random": separated_random, "nearest_control_r_delta": nearest_control_r, "nearest_control_ipr_delta": nearest_control_ipr, "phi_distinct_from_irrational_controls": phi_distinct, "phi_joint_boundary": joint, } if joint: classification["phi_joint_boundary_v"].append(float(v_value)) if phi_distinct: classification["phi_distinct_v"].append(float(v_value)) return { "experiment": "aubry_cosine_boundary_counter_gate", "parameters": vars(args), "rows_count": len(rows), "summary_by_v": summary_by_v, "classification": classification, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--out", default="tools/data/aubry_cosine_boundary_counter_gate.json") parser.add_argument("--seed", type=int, default=202605151758) parser.add_argument("--ns", default="89,144,233") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--v-min", type=float, default=0.5) parser.add_argument("--v-max", type=float, default=3.0) parser.add_argument("--v-step", type=float, default=0.25) parser.add_argument("--random-trials", type=int, default=6) parser.add_argument("--central-fraction", type=float, default=0.6) parser.add_argument("--min-r-delta", type=float, default=0.03) parser.add_argument("--min-ipr-delta", type=float, default=0.01) parser.add_argument("--min-control-r-delta", type=float, default=0.03) parser.add_argument("--min-control-ipr-delta", type=float, default=0.005) args = parser.parse_args() result = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(result, indent=2), encoding="utf-8") print(json.dumps(result["classification"], indent=2)) if __name__ == "__main__": main() succeeded in 0ms: """observables_registry.py — Source of Truth per gli observables del lab D-ND. Cristallizzato 2026-05-06 dalla **consecutio del cycle agent_20260506_0625**: > "What opens now: the lab needs an observable registry. Labels like SR > cannot travel between reports unless they bind to a function definition. > Without that, META flags are not philosophical: the same label can > silently change the object under test." ## Il problema che ha creato il registry Il cycle 06:25 ha auto-falsificato il finding del cycle 03:30 ("secondo asse GUE") e nel farlo ha trovato **collision di nomi observable** tra script: - `SR` in `exp_selective_layer_decoupling.py` = `spacing_ratio` (mean min/max ratio of consecutive gaps) — convention dominante (~6 script) - `SR` in `exp_scale_selective_perturbation.py` = `spectral_rigidity(gaps)` (Δ₃(L) rigidity) — variante usata SOLO in 1 script - `triple_var` in 3 script = `np.var(triple_sums)` (raw) — convention dominante - `triple_var` in `exp_perturbation_dimensionality_audit.py` = `np.var(triples) / np.var(gaps)` (normalizzato) — variante in 1 script Il lab autonomo che compara report tra script con osservabili "stesso nome, funzione diversa" stava confrontando mele con arance. ## La soluzione (minimal, non invasiva) Questo registry stabilisce il **nome canonico**: ciò che la maggioranza degli script chiama già `SR`/`triple_var`/etc. Le varianti restano disponibili ma con nomi ESPLICITI (`SR_local_rigidity`, `triple_var_normalized`) per evitare mascheramento semantico. ## Come usarlo ```python from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION # Compute canonical observable suite for a sequence of gaps results = {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} # Or import individual canonical observable from observables_registry import SR, triple_var, L1, L2, SR2 # For variants, import explicitly with disambiguating name from observables_registry import SR_local_rigidity, triple_var_normalized ``` ## Convention per i report Ogni report agent (cycle) che usa observables DEVE includere nel suo header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Cycle che mescola canonical + variant DEVE indicare entrambi: ``` observables_used: [SR, SR_local_rigidity, ...] ``` Senza questo, i confronti cross-cycle sono inattendibili. ## Versioning Cambiare una definizione canonica = bump del registry version e nota nel changelog. Le definizioni canoniche sono **immutabili dentro una versione**. """ from __future__ import annotations import numpy as np OBSERVABLES_REGISTRY_VERSION = "1.0.0-2026-05-06" # ─── Canonical observables (convention dominante nel codebase 2026-05-06) ─── def SR(gaps: np.ndarray) -> float: """**SR — Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+1}) / max(g_i, g_{i+1})` over consecutive gaps. Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. NOTE: questa è la convention dominante in 6+ script del lab. Per la variante "local spectral rigidity Δ₃(L)" usare `SR_local_rigidity`. """ if len(gaps) < 2: return 0.0 s, s1 = gaps[:-1], gaps[1:] r = np.minimum(s, s1) / np.maximum(s, s1) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def SR2(gaps: np.ndarray) -> float: """**SR2 — Next-nearest Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+2}) / max(g_i, g_{i+2})` skipping one gap. Probes lag-2 spacing structure. """ if len(gaps) < 3: return 0.0 s, s2 = gaps[:-2], gaps[2:] r = np.minimum(s, s2) / np.maximum(s, s2) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def L1(gaps: np.ndarray) -> float: """**L1 — Lag-1 Autocorrelation** (canonical). Standard ACF at lag 1 of the gap sequence. """ if len(gaps) < 3: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-1] * g[1:]) / c0) def L2(gaps: np.ndarray) -> float: """**L2 — Lag-2 Autocorrelation** (canonical).""" if len(gaps) < 4: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-2] * g[2:]) / c0) def triple_var(gaps: np.ndarray) -> float: """**triple_var — Variance of consecutive gap triples** (canonical). Variance of `g_i + g_{i+1} + g_{i+2}` over the sequence (RAW, no normalization). Convention used in 3+ scripts. For the normalized version (variance ratio `var(triples) / var(gaps)`) use `triple_var_normalized`. """ if len(gaps) < 3: return 0.0 t = gaps[:-2] + gaps[1:-1] + gaps[2:] return float(np.var(t)) # Set canonico per uso "compute all" da report OBSERVABLES_CANONICAL: dict[str, callable] = { "SR": SR, "SR2": SR2, "L1": L1, "L2": L2, "triple_var": triple_var, } # ─── Variants (esplicitamente nominate, no collision con canonical) ─── def SR_local_rigidity(gaps: np.ndarray, L: int = 10) -> float: """**SR_local_rigidity — Δ₃(L) Spectral Rigidity** (variant). Different observable than canonical `SR` (spacing ratio). Measures the average squared deviation of the cumulative spacing function from the best-fit straight line in a window of size L. Originated from `exp_scale_selective_perturbation.py` where it was locally named `SR` — registered here as `SR_local_rigidity` to avoid collision with canonical spacing-ratio definition. Use when explicitly studying spectral rigidity, NOT as alias for SR. """ if len(gaps) < 5: return 0.0 cumulative = np.cumsum(gaps) if cumulative[-1] <= 1e-15: return 0.0 cumulative = cumulative / cumulative[-1] * len(cumulative) n = np.arange(1, len(cumulative) + 1, dtype=float) window = int(min(L * len(gaps) / cumulative[-1], len(gaps) // 2)) if window < 5: return 0.0 residuals = [] for start in range(0, len(cumulative) - window, max(1, window // 2)): end = start + window x = n[start:end] y = cumulative[start:end] a, b = np.polyfit(x, y, 1) residuals.append(np.mean((y - (a * x + b)) ** 2)) return float(np.mean(residuals)) if residuals else 0.0 def triple_var_normalized(gaps: np.ndarray) -> float: """**triple_var_normalized — Variance of triples / variance of gaps** (variant). Originated from `exp_perturbation_dimensionality_audit.py` where it was locally named `triple_var` — registered here as `triple_var_normalized` to avoid collision with canonical raw triple variance. Use when explicitly studying triple-variance scaling relative to single-gap variance, NOT as alias for triple_var. """ if len(gaps) < 3: return 0.0 triples = gaps[:-2] + gaps[1:-1] + gaps[2:] v = float(np.var(gaps)) if v <= 1e-15: return 0.0 return float(np.var(triples) / v) # Set varianti, importabile esplicitamente OBSERVABLES_VARIANTS: dict[str, callable] = { "SR_local_rigidity": SR_local_rigidity, "triple_var_normalized": triple_var_normalized, } exec /bin/bash -lc 'git status --short' in /opt/MM_D-ND succeeded in 0ms: M applications/INDEX.draft.json M docs/guides/lab_current_state_logic_2026-05-08.md M tools/LAB_AGENT_CONTEXT.md M tools/build_agent_field.py M tools/build_lab_graph.py M tools/data/agent_field_live.md M tools/data/b2_apply_log.jsonl M tools/data/ciclo_memoria.json M tools/data/consecutio.json M tools/data/consecutio_processata.json M tools/data/domandatore_pre_cycle.json M tools/data/domande_fondamentali.json M tools/data/g1_log.jsonl M tools/data/g2_log.jsonl M tools/data/graph_completion/latest.json M tools/data/incrocio_risultato.json M tools/data/knowledge_state.json M tools/data/lab_bridge_issues.jsonl M tools/data/lab_data.json M tools/data/lab_graph.json M tools/data/lab_health.json M tools/data/lab_session_log.jsonl M tools/data/pipeline_state.json M tools/data/ponti_evoluti.json M tools/data/repairs/repair_run_log.jsonl M tools/data/reports/latest.md M tools/data/seme.json D tools/data/seme_archive/piano_37.json D tools/data/seme_archive/piano_38.json D tools/data/seme_archive/piano_4.json D tools/data/seme_archive/piano_5.json D tools/data/seme_archive/piano_6.json D tools/data/seme_archive/piano_7.json D tools/data/seme_archive/piano_8.json D tools/data/seme_archive/piano_9.json M tools/data/seme_backup_pre_run.json M tools/data/tm1_figures/tensions.json M tools/data/tm1_figures/tensions_raw.json M tools/data/valutatore_log.jsonl M tools/evolution_report.md M tools/graph_completion_compiler.py M tools/lab_agent.sh M tools/lab_falsifier.py ?? applications/scoperte/20260513_0330_prime-vs-mod6-sr-boundary_auto/ ?? applications/scoperte/20260514_1330_sr-residual-width-audit_auto/ ?? applications/scoperte/20260514_1631_chiusura-tester-rimbalzo-fisico-w6_auto/ ?? applications/scoperte/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve_auto/ ?? applications/scoperte/20260515_1712_primemod6-generative-null-w2048_auto/ ?? applications/scoperte/20260515_1816_v2-generator-scaling-gate_auto/ ?? applications/scoperte/20260515_1904_boundary-classical-crossover-audit_auto/ ?? applications/soluzioni/20260513_0330_prime-vs-mod6-sr-boundary/ ?? applications/soluzioni/20260514_1330_sr-residual-width-audit/ ?? applications/soluzioni/20260514_1631_chiusura-tester-rimbalzo-fisico-w6/ ?? applications/soluzioni/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve/ ?? applications/soluzioni/20260515_1712_primemod6-generative-null-w2048/ ?? applications/soluzioni/20260515_1816_v2-generator-scaling-gate/ ?? applications/soluzioni/20260515_1904_boundary-classical-crossover-audit/ ?? docs/guides/lab_preflight_agent_2026-05-14.md ?? docs/operations/ ?? tools/build_safe_agent_field_preview.py ?? tools/compose_filtered_field_preview.py ?? tools/compose_supervised_entry_preview.py ?? tools/data/aeternitas/aeternitas_20260512_033539.json ?? tools/data/aeternitas/aeternitas_20260513_033605.json ?? tools/data/aeternitas/aeternitas_20260514_033420.json ?? tools/data/aeternitas/aeternitas_20260514_133424.json ?? tools/data/aeternitas/aeternitas_20260514_150454.json ?? tools/data/aeternitas/aeternitas_20260514_161058.json ?? tools/data/aeternitas/aeternitas_20260514_161719.json ?? tools/data/aeternitas/aeternitas_20260514_163527.json ?? tools/data/aeternitas/aeternitas_20260514_164509.json ?? tools/data/aeternitas/aeternitas_20260514_165437.json ?? tools/data/aeternitas/aeternitas_20260514_170109.json ?? tools/data/aeternitas/aeternitas_20260514_171152.json ?? tools/data/aeternitas/aeternitas_20260514_185450.json ?? tools/data/aeternitas/aeternitas_20260515_162727.json ?? tools/data/aeternitas/aeternitas_20260515_165116.json ?? tools/data/aeternitas/aeternitas_20260515_170328.json ?? tools/data/aeternitas/aeternitas_20260515_171005.json ?? tools/data/aeternitas/aeternitas_20260515_171725.json ?? tools/data/aeternitas/aeternitas_20260515_172913.json ?? tools/data/aeternitas/aeternitas_20260515_174023.json ?? tools/data/aeternitas/aeternitas_20260515_175045.json ?? tools/data/aeternitas/aeternitas_20260515_180306.json ?? tools/data/aeternitas/aeternitas_20260515_181257.json ?? tools/data/aeternitas/aeternitas_20260515_182126.json ?? tools/data/aeternitas/aeternitas_20260515_183111.json ?? tools/data/aeternitas/aeternitas_20260515_190118.json ?? tools/data/aeternitas/aeternitas_20260515_191018.json ?? tools/data/aeternitas/aeternitas_20260515_192915.json ?? tools/data/agent_field_entry_supervised.md ?? tools/data/anderson3d_component_state_interface_input_20260514_1850.json ?? tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json ?? tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json ?? tools/data/aubry_cosine_boundary_counter_gate_20260515_1758.json ?? tools/data/aubry_v2_generator_scaling_gate_20260515_1816.json ?? tools/data/biconi/bicono_20260512_0330.json ?? tools/data/biconi/bicono_20260513_0330.json ?? tools/data/biconi/bicono_20260514_0330.json ?? tools/data/biconi/bicono_20260514_1330.json ?? tools/data/biconi/bicono_20260514_1458.json ?? tools/data/biconi/bicono_20260514_1605.json ?? tools/data/biconi/bicono_20260514_1612.json ?? tools/data/biconi/bicono_20260514_1631.json ?? tools/data/biconi/bicono_20260514_1640.json ?? tools/data/biconi/bicono_20260514_1649.json ?? tools/data/biconi/bicono_20260514_1656.json ?? tools/data/biconi/bicono_20260514_1701.json ?? tools/data/biconi/bicono_20260514_1850.json ?? tools/data/biconi/bicono_20260515_1623.json ?? tools/data/biconi/bicono_20260515_1647.json ?? tools/data/biconi/bicono_20260515_1659.json ?? tools/data/biconi/bicono_20260515_1705.json ?? tools/data/biconi/bicono_20260515_1712.json ?? tools/data/biconi/bicono_20260515_1724.json ?? tools/data/biconi/bicono_20260515_1734.json ?? tools/data/biconi/bicono_20260515_1745.json ?? tools/data/biconi/bicono_20260515_1758.json ?? tools/data/biconi/bicono_20260515_1807.json ?? tools/data/biconi/bicono_20260515_1816.json ?? tools/data/biconi/bicono_20260515_1826.json ?? tools/data/biconi/bicono_20260515_1855.json ?? tools/data/biconi/bicono_20260515_1904.json ?? tools/data/biconi/bicono_20260515_1915.json ?? tools/data/boundary_bridge_stability_audit_20260515_1915.json ?? tools/data/boundary_classical_crossover_audit_20260515_1904.json ?? tools/data/boundary_graph_curvature_gate_20260515_1855.json ?? tools/data/component_state_anderson3d_interface_20260514_1850.json ?? tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl ?? tools/data/component_state_fit_ready_20260514_1649.json ?? tools/data/component_state_interface_smoke_20260514_1649.json ?? tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl ?? tools/data/component_state_interface_smoke_input_20260514_1649.json ?? tools/data/domandatore/domandatore_20260512_0330.json ?? tools/data/domandatore/domandatore_20260512_0345.json ?? tools/data/domandatore/domandatore_20260513_0330.json ?? tools/data/domandatore/domandatore_20260513_0345.json ?? tools/data/domandatore/domandatore_20260514_0330.json ?? tools/data/domandatore/domandatore_20260514_0345.json ?? tools/data/domandatore/domandatore_20260514_1330.json ?? tools/data/domandatore/domandatore_20260514_1458.json ?? tools/data/domandatore/domandatore_20260515_0345.json ?? tools/data/domandatore/domandatore_20260515_1623.json ?? tools/data/domandatore/domandatore_20260515_1647.json ?? tools/data/domandatore/domandatore_20260515_1659.json ?? tools/data/evolution/evolution_20260512_0330.md ?? tools/data/evolution/evolution_20260513_0330.md ?? tools/data/evolution/evolution_20260514_0330.md ?? tools/data/evolution/evolution_20260514_1330.md ?? tools/data/evolution/evolution_20260514_1458.md ?? tools/data/evolution/evolution_20260514_1605.md ?? tools/data/evolution/evolution_20260514_1612.md ?? tools/data/evolution/evolution_20260514_1631.md ?? tools/data/evolution/evolution_20260514_1640.md ?? tools/data/evolution/evolution_20260514_1649.md ?? tools/data/evolution/evolution_20260514_1656.md ?? tools/data/evolution/evolution_20260514_1701.md ?? tools/data/evolution/evolution_20260514_1850.md ?? tools/data/evolution/evolution_20260515_1623.md ?? tools/data/evolution/evolution_20260515_1647.md ?? tools/data/evolution/evolution_20260515_1659.md ?? tools/data/evolution/evolution_20260515_1705.md ?? tools/data/evolution/evolution_20260515_1712.md ?? tools/data/evolution/evolution_20260515_1724.md ?? tools/data/evolution/evolution_20260515_1734.md ?? tools/data/evolution/evolution_20260515_1745.md ?? tools/data/evolution/evolution_20260515_1758.md ?? tools/data/evolution/evolution_20260515_1807.md ?? tools/data/evolution/evolution_20260515_1816.md ?? tools/data/evolution/evolution_20260515_1826.md ?? tools/data/evolution/evolution_20260515_1855.md ?? tools/data/evolution/evolution_20260515_1904.md ?? tools/data/evolution/evolution_20260515_1915.md ?? tools/data/graph_completion/graph_completion_20260512_0330.json ?? tools/data/graph_completion/graph_completion_20260513_0330.json ?? tools/data/graph_completion/graph_completion_20260514_0330.json ?? tools/data/graph_completion/graph_completion_20260514_1330.json ?? tools/data/graph_completion/graph_completion_20260514_1458.json ?? tools/data/graph_completion/graph_completion_20260514_1605.json ?? tools/data/graph_completion/graph_completion_20260514_1612.json ?? tools/data/graph_completion/graph_completion_20260514_1631.json ?? tools/data/graph_completion/graph_completion_20260514_1640.json ?? tools/data/graph_completion/graph_completion_20260514_1649.json ?? tools/data/graph_completion/graph_completion_20260514_1656.json ?? tools/data/graph_completion/graph_completion_20260514_1701.json ?? tools/data/graph_completion/graph_completion_20260514_1850.json ?? tools/data/graph_completion/graph_completion_20260515_1623.json ?? tools/data/graph_completion/graph_completion_20260515_1647.json ?? tools/data/graph_completion/graph_completion_20260515_1659.json ?? tools/data/graph_completion/graph_completion_20260515_1705.json ?? tools/data/graph_completion/graph_completion_20260515_1712.json ?? tools/data/graph_completion/graph_completion_20260515_1724.json ?? tools/data/graph_completion/graph_completion_20260515_1734.json ?? tools/data/graph_completion/graph_completion_20260515_1745.json ?? tools/data/graph_completion/graph_completion_20260515_1758.json ?? tools/data/graph_completion/graph_completion_20260515_1807.json ?? tools/data/graph_completion/graph_completion_20260515_1816.json ?? tools/data/graph_completion/graph_completion_20260515_1904.json ?? tools/data/incrocio_20260512_0330.json ?? tools/data/incrocio_20260513_0330.json ?? tools/data/incrocio_20260514_0330.json ?? tools/data/incrocio_20260514_1330.json ?? tools/data/incrocio_20260514_1458.json ?? tools/data/incrocio_20260514_1605.json ?? tools/data/incrocio_20260514_1612.json ?? tools/data/incrocio_20260514_1631.json ?? tools/data/incrocio_20260514_1640.json ?? tools/data/incrocio_20260514_1649.json ?? tools/data/incrocio_20260514_1656.json ?? tools/data/incrocio_20260514_1701.json ?? tools/data/incrocio_20260514_1850.json ?? tools/data/incrocio_20260515_1623.json ?? tools/data/incrocio_20260515_1647.json ?? tools/data/incrocio_20260515_1659.json ?? tools/data/incrocio_20260515_1705.json ?? tools/data/incrocio_20260515_1712.json ?? tools/data/incrocio_20260515_1724.json ?? tools/data/incrocio_20260515_1734.json ?? tools/data/incrocio_20260515_1745.json ?? tools/data/incrocio_20260515_1758.json ?? tools/data/incrocio_20260515_1807.json ?? tools/data/incrocio_20260515_1816.json ?? tools/data/incrocio_20260515_1826.json ?? tools/data/incrocio_20260515_1855.json ?? tools/data/incrocio_20260515_1904.json ?? tools/data/incrocio_20260515_1915.json ?? tools/data/incrocio_20260515_1933.json ?? tools/data/operator_directives_consumed/operator_directive_20260514_1612.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1631.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1640.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1649.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1656.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1701.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1850.md ?? tools/data/photonic_boundary_third_included_gate_20260515_1734.json ?? tools/data/physical_sr_residue_bounce_20260514_1612.json ?? tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl ?? tools/data/physical_sr_residue_bounce_20260514_1631_w6.json ?? tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl ?? tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json ?? tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl ?? tools/data/preflight/ ?? tools/data/prime_sr_persistent_boundary_20260512_0330.json ?? tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json ?? tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json ?? tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.trace.jsonl ?? tools/data/promotions/promotion_20260513_0330.json ?? tools/data/promotions/promotion_20260514_1330.json ?? tools/data/promotions/promotion_20260514_1631.json ?? tools/data/promotions/promotion_20260514_1640.json ?? tools/data/promotions/promotion_20260514_1656.json ?? tools/data/promotions/promotion_20260515_1712.json ?? tools/data/promotions/promotion_20260515_1758.json ?? tools/data/promotions/promotion_20260515_1816.json ?? tools/data/promotions/promotion_20260515_1904.json ?? tools/data/quasiperiodic_grammar_scale_gate_20260514_1701.json ?? tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json ?? tools/data/repairs/repair_20260512_0330_decision.json ?? tools/data/repairs/repair_20260513_0330_decision.json ?? tools/data/repairs/repair_20260514_0330_decision.json ?? tools/data/repairs/repair_20260514_1330_decision.json ?? tools/data/repairs/repair_20260514_1458_decision.json ?? tools/data/repairs/repair_20260514_1605_decision.json ?? tools/data/repairs/repair_20260514_1612_decision.json ?? tools/data/repairs/repair_20260514_1631_decision.json ?? tools/data/repairs/repair_20260514_1640_decision.json ?? tools/data/repairs/repair_20260514_1649_decision.json ?? tools/data/repairs/repair_20260514_1656.json ?? tools/data/repairs/repair_20260514_1656.md ?? tools/data/repairs/repair_20260514_1656_decision.json ?? tools/data/repairs/repair_20260514_1850_decision.json ?? tools/data/reports/agent_20260512_0330.md ?? tools/data/reports/agent_20260513_0330.md ?? tools/data/reports/agent_20260514_0330.md ?? tools/data/reports/agent_20260514_1330.md ?? tools/data/reports/agent_20260514_1458.md ?? tools/data/reports/agent_20260514_1605.md ?? tools/data/reports/agent_20260514_1612.md ?? tools/data/reports/agent_20260514_1631.md ?? tools/data/reports/agent_20260514_1640.md ?? tools/data/reports/agent_20260514_1649.md ?? tools/data/reports/agent_20260514_1656.md ?? tools/data/reports/agent_20260514_1701.md ?? tools/data/reports/agent_20260514_1850.md ?? tools/data/reports/agent_20260515_1623.md ?? tools/data/reports/agent_20260515_1647.md ?? tools/data/reports/agent_20260515_1659.md ?? tools/data/reports/agent_20260515_1705.md ?? tools/data/reports/agent_20260515_1712.md ?? tools/data/reports/agent_20260515_1724.md ?? tools/data/reports/agent_20260515_1734.md ?? tools/data/reports/agent_20260515_1745.md ?? tools/data/reports/agent_20260515_1758.md ?? tools/data/reports/agent_20260515_1807.md ?? tools/data/reports/agent_20260515_1816.md ?? tools/data/reports/agent_20260515_1826.md ?? tools/data/reports/agent_20260515_1855.md ?? tools/data/reports/agent_20260515_1904.md ?? tools/data/reports/agent_20260515_1915.md ?? tools/data/reports/ddf_20260512_0405.json ?? tools/data/reports/ddf_20260513_0405.json ?? tools/data/reports/ddf_20260514_0405.json ?? tools/data/reports/ddf_20260515_0405.json ?? tools/data/reports/falsifier_20260512_0330.json ?? tools/data/reports/falsifier_20260513_0330.json ?? tools/data/reports/falsifier_20260514_0330.json ?? tools/data/reports/falsifier_20260514_1330.json ?? tools/data/reports/falsifier_20260514_1458.json ?? tools/data/reports/falsifier_20260514_1605.json ?? tools/data/reports/falsifier_20260514_1612.json ?? tools/data/reports/falsifier_20260514_1631.json ?? tools/data/reports/falsifier_20260514_1640.json ?? tools/data/reports/falsifier_20260514_1649.json ?? tools/data/reports/falsifier_20260514_1656.json ?? tools/data/reports/falsifier_20260514_1701.json ?? tools/data/reports/falsifier_20260514_1850.json ?? tools/data/reports/falsifier_20260515_1623.json ?? tools/data/reports/falsifier_20260515_1647.json ?? tools/data/reports/falsifier_20260515_1659.json ?? tools/data/reports/falsifier_20260515_1705.json ?? tools/data/reports/falsifier_20260515_1712.json ?? tools/data/reports/falsifier_20260515_1712.raw.txt ?? tools/data/reports/falsifier_20260515_1724.json ?? tools/data/reports/falsifier_20260515_1734.json ?? tools/data/reports/falsifier_20260515_1745.json ?? tools/data/reports/falsifier_20260515_1758.json ?? tools/data/reports/falsifier_20260515_1807.json ?? tools/data/reports/falsifier_20260515_1816.json ?? tools/data/reports/falsifier_20260515_1826.json ?? tools/data/reports/falsifier_20260515_1855.json ?? tools/data/reports/falsifier_20260515_1904.json ?? tools/data/reports/falsifier_20260515_1915.json ?? tools/data/reports/loop_guard_20260512_0330.json ?? tools/data/reports/loop_guard_20260513_0330.json ?? tools/data/reports/loop_guard_20260514_0330.json ?? tools/data/reports/loop_guard_20260514_1330.json ?? tools/data/reports/loop_guard_20260514_1458.json ?? tools/data/reports/loop_guard_20260514_1605.json ?? tools/data/reports/loop_guard_20260514_1612.json ?? tools/data/reports/loop_guard_20260514_1631.json ?? tools/data/reports/loop_guard_20260514_1640.json ?? tools/data/reports/loop_guard_20260514_1649.json ?? tools/data/reports/loop_guard_20260514_1656.json ?? tools/data/reports/loop_guard_20260514_1701.json ?? tools/data/reports/loop_guard_20260514_1850.json ?? tools/data/reports/loop_guard_20260515_1623.json ?? tools/data/reports/loop_guard_20260515_1647.json ?? tools/data/reports/loop_guard_20260515_1659.json ?? tools/data/reports/loop_guard_20260515_1705.json ?? tools/data/reports/loop_guard_20260515_1712.json ?? tools/data/reports/loop_guard_20260515_1724.json ?? tools/data/reports/loop_guard_20260515_1734.json ?? tools/data/reports/loop_guard_20260515_1745.json ?? tools/data/reports/loop_guard_20260515_1758.json ?? tools/data/reports/loop_guard_20260515_1807.json ?? tools/data/reports/loop_guard_20260515_1816.json ?? tools/data/reports/loop_guard_20260515_1826.json ?? tools/data/reports/loop_guard_20260515_1855.json ?? tools/data/reports/loop_guard_20260515_1904.json ?? tools/data/reports/loop_guard_20260515_1915.json ?? tools/data/reports/quarantine_manifest.json ?? tools/data/restore_backups/ ?? tools/data/seme_backup_b2_20260512_033557.json ?? tools/data/seme_backup_b2_20260513_033622.json ?? tools/data/seme_backup_b2_20260514_150530.json ?? tools/data/seme_backup_b2_20260514_161119.json ?? tools/data/seme_backup_b2_20260515_180327.json ?? tools/data/sturmian_denominator_alignment_gate_20260515_1826.json ?? tools/data/tool_contracts/ ?? tools/data/veritas/veritas_20260512_033539.json ?? tools/data/veritas/veritas_20260513_033605.json ?? tools/data/veritas/veritas_20260514_033420.json ?? tools/data/veritas/veritas_20260514_133424.json ?? tools/data/veritas/veritas_20260514_150454.json ?? tools/data/veritas/veritas_20260514_161058.json ?? tools/data/veritas/veritas_20260514_161719.json ?? tools/data/veritas/veritas_20260514_163527.json ?? tools/data/veritas/veritas_20260514_164509.json ?? tools/data/veritas/veritas_20260514_165437.json ?? tools/data/veritas/veritas_20260514_170109.json ?? tools/data/veritas/veritas_20260514_171152.json ?? tools/data/veritas/veritas_20260514_185450.json ?? tools/data/veritas/veritas_20260515_162727.json ?? tools/data/veritas/veritas_20260515_165116.json ?? tools/data/veritas/veritas_20260515_170328.json ?? tools/data/veritas/veritas_20260515_171005.json ?? tools/data/veritas/veritas_20260515_171725.json ?? tools/data/veritas/veritas_20260515_172913.json ?? tools/data/veritas/veritas_20260515_174023.json ?? tools/data/veritas/veritas_20260515_175045.json ?? tools/data/veritas/veritas_20260515_180306.json ?? tools/data/veritas/veritas_20260515_181257.json ?? tools/data/veritas/veritas_20260515_182126.json ?? tools/data/veritas/veritas_20260515_183111.json ?? tools/data/veritas/veritas_20260515_190118.json ?? tools/data/veritas/veritas_20260515_191018.json ?? tools/data/veritas/veritas_20260515_192915.json ?? tools/exp_aubry_binary_grammar_surrogate_gate.py ?? tools/exp_aubry_boundary_phase_transport_gate.py ?? tools/exp_aubry_cosine_boundary_counter_gate.py ?? tools/exp_aubry_v2_generator_scaling_gate.py ?? tools/exp_boundary_bridge_stability_audit.py ?? tools/exp_boundary_classical_crossover_audit.py ?? tools/exp_boundary_graph_curvature_gate.py ?? tools/exp_photonic_boundary_third_included_gate.py ?? tools/exp_physical_sr_residue_bounce.py ?? tools/exp_prime_sr_persistent_boundary.py ?? tools/exp_prime_vs_mod6_sr_boundary.py ?? tools/exp_quasiperiodic_grammar_scale_gate.py ?? tools/exp_sturmian_denominator_alignment_gate.py ?? tools/field_coherence_preflight.py ?? tools/field_rebuild_risk_map.py ?? tools/lab_preflight_agent.py ?? tools/lab_tool_contract.py ?? tools/prime_mod6_counter_null_audit.py ?? tools/prime_mod6_generative_null_audit.py ?? tools/prime_mod6_null_fairness_audit.py ?? tools/prime_mod6_pipeline_closeout.py ?? tools/selector_authority_matrix.py ?? tools/stale_field_source_map.py exec /bin/bash -lc 'rg -n "CE-0019|CE-0022|Archivio enzimi|Respiro fuori-tempo|Palette" tools/data/cognitive_enzymes_archive.md tools -S' in /opt/MM_D-ND succeeded in 1ms: tools/data/cognitive_enzymes_archive.md:1:# Archivio enzimi cognitivi MM-DND tools/data/cognitive_enzymes_archive.md:163:#### CE-0019 - Respiro fuori-tempo (`lab_operational_context` / `contaminante_dnd`, score=55) tools/data/cognitive_enzymes_archive.md:181:#### CE-0022 - Palette operatoria espansa del Lab (`lab_operational_context` / `strumento_lab`, score=54) tools/LAB_AGENT_CONTEXT.md:151:- **Palette operatoria laterale.** Quando il ciclo rischia deepening locale, tools/LAB_AGENT_CONTEXT.md:161:- **Archivio enzimi cognitivi.** Se il campo vivo contiene `Archivio enzimi tools/LAB_AGENT_CONTEXT.md:352:## Respiro fuori-tempo tools/lab_autological_repair.py:427: lines.append("- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione.") tools/extract_cognitive_enzymes.py:423: "# Archivio enzimi cognitivi MM-DND", tools/LAB_COGNITIVE_CONTAMINATION.md:112: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/LAB_COGNITIVE_CONTAMINATION.md:227:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/LAB_COGNITIVE_CONTAMINATION.md:256: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/LAB_OPERATOR_PALETTE.md:1:# Palette operatoria espansa del Lab tools/build_agent_field.py:655: parts.append("## Respiro fuori-tempo — prepara la combo prima della misura") tools/build_agent_field.py:758: """Palette laterale di operatori per evitare deepening locale. tools/build_agent_field.py:774: "## Palette operatoria laterale — sorgenti da triturare\n" tools/build_agent_field.py:965: parts.append("## Archivio enzimi cognitivi — richiamo contestuale") tools/data/reports/agent_20260515_1855.md:9:## Respiro fuori-tempo tools/data/reports/agent_20260515_1826.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1337.md:10:## Respiro fuori-tempo tools/data/reports/falsifier_20260514_1649.json:9: "claim": "Il report include una sezione 'Respiro fuori-tempo' con 'Combo', ma non contiene una riga 'Contaminazione cognitiva' né una dichiarazione 'CE-none:' verificabile.", tools/data/reports/agent_20260509_1556.md:11:## Respiro fuori-tempo tools/data/reports/agent_20260509_0819.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260514_1640.md:18:## Respiro fuori-tempo tools/data/operator_directives_consumed/operator_directive_20260508_2102.md:3:Usa il nuovo `Archivio enzimi cognitivi` come substrate selector, non come tools/data/operator_directives_consumed/operator_directive_20260508_1915.md:27:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/operator_directives_consumed/operator_directive_20260508_2019.md:27:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/operator_directives_consumed/operator_directive_20260509_0659.md:27:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/reports/agent_20260515_1758.md:10:## Respiro fuori-tempo tools/data/operator_directives_consumed/operator_directive_20260508_2108.md:25:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/operator_directives_consumed/operator_directive_20260514_1656.md:22:oppure una riga equivalente nel `Respiro fuori-tempo`. tools/data/reports/agent_20260509_0846.md:10:## Respiro fuori-tempo tools/data/operator_directives_consumed/operator_directive_20260514_1701.md:25:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/reports/agent_20260508_2019.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260514_1701.md:10:## Respiro fuori-tempo tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1068: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1119: "content_full": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, label-swap row-local e trace JSONL completa.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa la robustezza del delta z row-local prime-minus-mod6 su piu ampiezze finestra, mantenendo provider, offset, label-swap audit e trace JSONL.\n- `not_drift`: non torna a `V_c`, GUE/Poisson, fit o vecchi depositi; stressa solo il residuo nominato dal valutatore dopo sottrazione mod6.\n\n## Claim Under Test\n\n> Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime non genera `SR` come presenza; lascia un vettore di intensita row-local su `SR,L1,triple_var`.\n\n## Question\n\nIl delta z prime-minus-mod6 resta significativo quando cambia l'ampiezza della finestra, oppure era un artefatto del contratto 1024-gap del cycle 03:30?\n\n## Experiment Design\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`.\n- Run: `window_gaps=512,1024,2048`; seeds `202605141330,202605141331,202605141332`.\n- Rows per run: 8 prime windows = 2 provider x 4 offset.\n- Antagonisti: `mod6_index_aligned` e `mod6_span_matched`, paired per `row_id`.\n- Null: label-swap row-local, 4096 trials per pair audit.\n- Trace: JSONL scritto per tutti e tre i run.\n\n## Results\n\n| window | pair | prime SR | prime focus | Jaccard | SR delta | p(SR delta) | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | script verdict |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| 512 | mod6_index | 5/8 | 2/8 | 0.250 | -0.250 | 0.6195 | 20.494 | 0.0095 | 21.866 | 0.0095 | 8.480 | 0.0095 | SR_NOT_DECISIVE |\n| 512 | mod6_span | 5/8 | 2/8 | 0.377 | -0.250 | 0.5038 | 13.993 | 0.0085 | 10.099 | 0.0085 | 7.505 | 0.0085 | SR_NOT_DECISIVE |\n| 1024 | mod6_index | 8/8 | 5/8 | 0.481 | 0.000 | 1.0000 | 29.480 | 0.0061 | 31.105 | 0.0061 | 11.745 | 0.0061 | SR_PREBOUNDARY |\n| 1024 | mod6_span | 8/8 | 5/8 | 0.377 | 0.250 | 0.4921 | 19.921 | 0.0076 | 15.302 | 0.0076 | 13.142 | 0.0076 | SR_PREBOUNDARY |\n| 2048 | mod6_index | 8/8 | 7/8 | 0.556 | 0.125 | 1.0000 | 41.634 | 0.0088 | 43.574 | 0.0088 | 17.173 | 0.0088 | SR_PRIME_SPECIFIC |\n| 2048 | mod6_span | 8/8 | 7/8 | 0.537 | 0.250 | 0.5040 | 28.624 | 0.0068 | 23.917 | 0.0068 | 17.491 | 0.0068 | SR_PRIME_SPECIFIC |\n\n## Key Findings\n\n1. **Verificato**: `has_SR` non replica come osservabile del residuo. A 512 gap i primi hanno `SR=5/8`; a 1024 e 2048 hanno `SR=8/8`. Il delta binario ha p non significativo o 1.0000.\n2. **Verificato**: il delta z `SR` replica in tutti i sei pair audit: `p=0.0061..0.0095`.\n3. **Verificato**: il delta z non e' solo `SR`. `L1` replica con `p=0.0061..0.0095`; `triple_var` replica con `p=0.0061..0.0095`.\n4. **Verificato**: il verdict nominale dello script oscilla con `window_gaps` (`not_decisive`, `preboundary`, `prime_specific`). Quell'oscillazione e' informazione sullo script: il verdict e' ancora centrato su commonality/binario, non sul vettore di intensita.\n5. **Inferito dal perimetro**: l'osservabile dedicato da formalizzare e' `prime_minus_mod6_z_vector(SR,L1,triple_var)`, con `has_SR` relegato ad audit negativo.\n\n## Verdict\n\n**CONSTRAINT / VECTOR RESIDUE**.\n\nNel perimetro 512-2048 gap, due provider, quattro offset, due antagonisti mod6, il residuo prime-specific sopravvive come delta z vettoriale row-local. Non sopravvive come presenza binaria di `SR`, ne come verdict nominale dello script.\n\n## Bicono\n\n- **Due radici**: pre-bordo mod6 / selezione prime.\n- **Singolare**: riga provider-offset paired.\n- **Invariante di passaggio**: delta z positivo su `SR,L1,triple_var`.\n- **Campo di possibilita**: osservabile dedicato `prime_minus_mod6_z_vector`.\n- **Campo non-possibile**: `SR` binario come firma prime-specific dopo mod6.\n\n## Consecutio\n\nIl prossimo ciclo deve correggere il nodo regressivo dello script: il `verdict()` non deve decidere dalla commonality di `SR`, ma da un contratto vettoriale dichiarato (`SR,L1,triple_var`, segno del delta, p label-swap, denominatore row-local). Dopo questa correzione, scalare oltre 2048 gap e separare `mod6_index_aligned` da `mod6_span_matched` come antagonisti con ruoli diversi.\n\n## Ricadute pratiche\n\nssp_value: yes. Le tre trace JSONL rendono auditabile il residuo senza riaprire i JSON aggregati; il prossimo passaggio pratico e' aggiornare lo script per emettere un verdict vettoriale, non binario.\n\n## Files\n\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl`\n- `tools/data/reports/agent_20260514_1330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1170: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary Trace\n**Date**: 2026-05-14 03:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo `6k +/- 1`; la selezione prime resta come gradiente row-local, non come presenza binaria dell'osservabile \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n\n## Prima impressione\n\nIl residuo prime-specific non nasce dalla comparsa di `SR`. `SR` e gia nel lattice candidato `6k +/- 1`; i primi non lo accendono, lo attenuano e lo deformano rispetto al pre-bordo.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + direzione viva `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: pre-bordo `6k +/- 1` / selezione prime. Punto-zero: stessa riga provider/offset, dove il denominatore e identico e la primalita e l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: il bordo non e presenza/assenza di un segnale, ma passaggio di intensita tra un lattice candidato e il sottoinsieme selezionato.\n- **Proto-ipotesi**: la firma utile e `prime_minus_mod6_z_delta` su `SR,L1,triple_var`; `has_SR` e una coordinata troppo larga per nominare primalita.\n- **Possibile / non-possibile**: possibile = isolare residuo di selezione con delta z row-local; non-possibile = claim `SR prime-specific` se `SR` resta comune nel contro-perimetro mod6.\n- **Proiezione**: 2 provider x 4 offset x 1024 gap; antagonisti `mod6_index_aligned` e `mod6_span_matched`; label-swap row-local; trace JSONL completa.\n\n## Contaminazione cognitiva\n\n- **CE-0001 / KSAR**: usato per reiterare il kernel emerso nel cycle precedente: non allargare il frame, ma far girare lo stesso nodo regressivo con tracciamento piu fine.\n- **PVI**: presupposto attaccato = \"`SR` persistente implica primalita\"; contro-presupposto = \"`SR` misura il pre-bordo aritmetico\".\n- **Vault**: `trace_jsonl_event` conserva casi, class summary, paired rows, audit e verdict come deposito riusabile senza trasformare una seed in invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: misura l'intensita row-local di `SR/L1/triple_var` su prime vs `6k +/- 1`, con denominatore provider/offset condiviso e label-swap audit.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista e il pre-bordo mod6 indicato dalla consecutio.\n\n## Observable Contract\n\n- **claim**: `SR` e prime-specific solo se resta piu comune o piu forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local.\n- **observable**: membership di `SR`, firma `[SR,L1,triple_var]`, delta z paired prime-minus-mod6, p-value label-swap.\n- **operator**: `tools/exp_prime_vs_mod6_sr_boundary.py` con `--trace-jsonl`.\n- **generator**: prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`; mod6 da `index_aligned` e `span_matched`.\n- **denominator**: 8 finestre prime paired con 8 `mod6_index_aligned` e 8 `mod6_span_matched`.\n- **non_possible**: prime-specific `SR` se `SR` e comune o pari nel contro-perimetro mod6, oppure se il label-swap assorbe i delta.\n- **not_tested**: beta atlas globale, `V_c`, `gap_ratio`, origine analitica del trasferimento mod6.\n\n## Results\n\nMain run, seed `202605140330`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 3 | SR | beta_absent_blank=5, beta_chart_recovered=3 |\n| mod6_index_aligned | 8 | 8 | 8 | L1,SR,triple_var | beta_chart_recovered=8 |\n| mod6_span_matched | 8 | 7 | 7 | [] | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nSeed check, seed `202605140331`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 6 | SR | beta_chart_recovered=5, beta_absent_blank=3 |\n| mod6_index_aligned | 8 | 8 | 6 | L1,SR | beta_chart_recovered=7, beta_absent_blank=1 |\n| mod6_span_matched | 8 | 8 | 7 | L1,SR | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605140330 | 0.433 | 0.000 | 1.0000 | 28.246 | 0.0081 |\n| prime - mod6_span | 202605140330 | 0.423 | 0.125 | 1.0000 | 20.793 | 0.0093 |\n| prime - mod6_index | 202605140331 | 0.517 | 0.000 | 1.0000 | 28.547 | 0.0083 |\n| prime - mod6_span | 202605140331 | 0.560 | 0.000 | 1.0000 | 18.844 | 0.0090 |\n\n## Key Findings\n\n1. **Verificato**: `SR` non e prime-specific come presenza binaria. In entrambi i run, `prime=8/8` e `mod6_index_aligned=8/8`; nel seed check anche `mod6_span_matched=8/8`.\n2. **Verificato**: il count `SR` non discrimina. `p(SR delta)=1.0000` in tutti i pair audit.\n3. **Verificato**: il delta di intensita discrimina. `p(z_SR delta)=0.0081/0.0083` contro `mod6_index` e `0.0093/0.0090` contro `mod6_span`.\n4. **Verificato**: `mod6_index_aligned` e piu rigido dei primi: focus signature main `8/8` contro prime `3/8`, e common obs `L1,SR,triple_var` contro solo `SR`.\n5. **Inferito dal perimetro**: il prossimo osservabile deve essere vettoriale e paired: `prime_minus_mod6_z_delta(SR,L1,triple_var)`, non `has_SR`.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula chiusa nel perimetro: `SR` appartiene al pre-bordo `6k +/- 1`. La primalita non genera `SR`; seleziona dentro un lattice gia rigido e lascia un residuo misurabile come delta z row-local.\n\n## Bicono\n\n- **Due radici**: lattice candidato mod6 / sequenza prime selezionata.\n- **Singolare**: riga provider-offset condivisa.\n- **Invariante di passaggio**: presenza di `SR` attraversa il confine.\n- **Campo di possibilita**: misurare selezione come delta z.\n- **Campo non-possibile**: usare `SR` binario come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve formalizzare `prime_minus_mod6_z_delta` come osservabile dedicato e scalarlo su finestre piu larghe. `mod6_index_aligned` resta antagonista primario; `mod6_span_matched` resta controllo fragile perche conserva span numerico ma introduce downsample.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script ora produce `--trace-jsonl`, utile per audit, replay e falsifier row-local senza riaprire il JSON aggregato.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json`\n- Trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json`\n- Seed trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl`\n- Report: `tools/data/reports/agent_20260514_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1203: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary\n**Date**: 2026-05-13 03:30 \n**Piano**: 116 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p] \n**observable_contract**: claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.\n\n## Prima impressione\n\nIl bordo non cade tra primi e non-primi: cade tra selezione prime e lattice candidato `6k +/- 1`. `SR` vede il lattice prima della primalita; il gate non deve chiedere \"SR nei primi?\", ma \"cosa resta di SR dopo aver sottratto il pre-bordo?\".\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: primi selezionati / candidati mod6 non selezionati. Punto-zero: la riga ordinata locale `6k +/- 1`, dove `SR` puo nominare pre-bordo aritmetico senza nominare primalita.\n- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune.\n- **Proto-ipotesi**: `SR` non e firma atomica dei primi; e una sezione del pre-bordo aritmetico. La selezione prime si manifesta come differenza di intensita z rispetto al lattice candidato.\n- **Possibile / non-possibile**: possibile = isolare una coordinata di selezione prime come gradiente dentro mod6; non-possibile = usare presenza binaria di `SR` come claim prime-specific.\n- **Proiezione**: stesso gate ordine/null, finestre 1024, offset 0/512/1024/1536, due provider prime, due antagonisti mod6, audit label-swap dentro ogni row_id.\n\n### Contaminazione cognitiva\n\n- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`.\n- **PVI attack**: il presupposto attaccato e \"SR persistente implica primalita\". Il contro-presupposto e \"SR misura la griglia 6k +/- 1 prima della selezione prime\".\n- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`.\n\n## Claim Under Test\n\n> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`.\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap.\n- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset.\n- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti.\n- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`.\n- Main seed: `202605130330`; seed check: `202605130331`.\n- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset.\n\n## Results\n\nMain run:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 |\n| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 |\n\nSeed check:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 |\n| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 |\n| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 |\n| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 |\n| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 |\n\n## Key Findings\n\n1. **Verificato**: la presenza binaria di `SR` non e prime-specific. Main: prime `8/8`, mod6_index `8/8`; seed check: prime `6/8`, mod6_index `8/8`, mod6_span `8/8`.\n2. **Verificato**: il label-swap row-local assorbe `SR` come count. `p(SR delta)` vale 1.0000 / 0.5055 per mod6_index e 0.2502 / 0.5021 per mod6_span.\n3. **Verificato**: il label-swap non assorbe il delta z di `SR`. `p(z_SR delta)` resta 0.0071-0.0103 in entrambe le seed e per entrambi gli antagonisti.\n4. **Verificato**: mod6 e piu intenso, non piu debole. `mean z SR` e circa -34 / -26 in mod6 contro -5 nei primi. Il residuo prime non e \"piu SR\"; e selezione attenuata dentro un pre-bordo piu rigido.\n5. **Inferito dal perimetro**: la coordinata utile non e `has_SR`; e `z_delta_SR` paired, insieme a `z_delta_L1` e `z_delta_triple_var`, come misura di quanto la selezione prime rompe il lattice candidato.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula non valida:\n\n`SR` come firma prime-specific atomica.\n\nFormula valida nel perimetro:\n\n`SR` appartiene al pre-bordo aritmetico `6k +/- 1`; la selezione prime resta come gradiente di intensita row-local rispetto a mod6. Il boundary operativo e quindi `mod6_preboundary -> prime_selection`, non `prime -> generic_nonprime`.\n\n## Bicono della scoperta\n\n- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata.\n- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni.\n- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato.\n- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`.\n\n## Lenti counter-pole applicate\n\n- **L1/L4**: nessun \"solo\", \"mai\", \"zero\" usato per `SR`: i count mostrano controesempi.\n- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific.\n- **L5**: nessun tag NEW; il risultato resta compatibile con bias noti dei gap dei primi mod q. Il report misura un gate del Lab, non rivendica una scoperta aritmetica classica.\n- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva.\n- **L7**: non-possibile dichiarato nel contratto e nel bicono.\n\n## Consecutio\n\nIl prossimo taglio non deve chiedere se `SR` c'e. Deve costruire un osservabile di selezione: `prime_minus_mod6_z_delta` su `SR,L1,triple_var`, con span-matched conservato come controllo fragile e index-aligned come antagonista primario. Se il delta resta replicato su piu finestre e scale, il claim diventa: la primalita attenua/rompe la rigidita del pre-bordo mod6 invece di generare `SR` da sola.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260513_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1248: "content_full": "# Agent Report - Prime SR Persistent Boundary\n**Date**: 2026-05-12 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`.\n- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi.\n- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro.\n- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' \"SR persistente nei primi implica prime-specific\".\n- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico.\n- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime.\n- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime.\n\n## Claim Under Test\n\n> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena.\n\n## Question\n\nQuando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime?\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows.\n- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`.\n- Seed check: stesso perimetro, seed `202605120331`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common.\n\n## Results\n\nMain run:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 |\n| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 |\n| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 |\n| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 |\n| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 |\n\nSeed check:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 |\n| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 |\n| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 |\n\nPrime case details, main:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] |\n| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] |\n| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n\n## Key Findings\n\n1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota.\n2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall.\n3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`.\n4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato.\n5. **Inferito dal perimetro dichiarato**: la parte robusta non e' \"SR e' prime-specific\"; e' \"SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche\".\n\n## Verdict\n\n**CONSTRAINT / FALSIFIED scoped**.\n\nFormula non valida:\n\n`prime_SR_persistent_boundary` come firma atomica prime-specific.\n\nFormula valida nel perimetro:\n\n`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico.\n\n## Bicono della scoperta\n\n- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato.\n- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato.\n- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6.\n- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve spostare il nodo regressivo: non \"prime vs controlli generici\", ma \"primi vs candidati mod6 row-aligned\". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico.\n\n## Files\n\n- Script: `tools/exp_prime_sr_persistent_boundary.py`\n- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json`\n- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260512_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1299: "content_full": "# Agent Report - Prime Persistent Blank Gate\n**Date**: 2026-05-11 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_persistent_blank` si scinde: SR persiste, il blank beta no \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY residuo `numeri_primi:cycle_3`.\n- **Dipolo / punto-zero**: persistenza del supporto / persistenza del blank. Punto-zero: la stessa finestra row-local da 1024 gap dove `SR` puo' restare mentre la coordinata beta riappare.\n- **Piano superiore**: topologia assiomatica del bordo: non una classe statistica unica, ma una sezione che conserva un osservabile e perde una coordinata.\n- **Proto-ipotesi**: il residuo prime e' strutturale solo se il blank beta-absent sopravvive a provider e offset; se sopravvive solo `SR`, la struttura non e' `blank`, e' `SR-supported boundary`.\n- **Possibile / non-possibile**: possibile = isolare un residuo prime provider-neutral; non-possibile = chiamarlo `prime_persistent_blank` quando beta ricompare in finestre locali.\n- **Proiezione**: due provider prime, quattro offset locali, seed shift del gate, controlli random_matrix/logistic.\n\n## Contaminazione cognitiva\n\n- **YSN DeltaLink**: il legame non ovvio e' `SR` come sezione comune mentre la carta beta cambia; non e' prova, e' la domanda proiettata.\n- **Cornelius gene**: `DNA_Simbolico`: \"La persistenza del bordo non coincide con la persistenza del blank.\" Operatori: separa supporto, separa coordinata, confronta controlli.\n- **KSAR step / CE-0001**: reiterazione del kernel 20260510 senza ridisegnare l'atlante: stesso gate, nuovo provider/offset/seed.\n- **PVI attack**: il presupposto nascosto era che `blank_windows=4/4` su una partizione bastasse per nominare una specie persistente.\n- **Vault**: `random_matrix_chart_sensitive_blank` resta aperto come coordinata locale beta `[0.4]`, non lavorata in questo ciclo.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente `prime_persistent_blank` su `numeri_primi:cycle_3` con provider-neutral row-local windows e controlli cross-dominio.\n- `not_drift`: non usa `V_c`, non usa fit, non riapre griglia beta globale, non salva la classe caduta `medium/strong beta-absent`.\n\n## Claim Under Test\n\n> `numeri_primi:cycle_3` e' un `prime_persistent_blank` se resta beta-absent in tutte le finestre provider-neutral e conserva `SR` come osservabile comune, mentre i controlli non condividono la stessa firma.\n\n## Experiment Design\n\n- Perimetro prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: 4 GUE random matrix blocks + 4 logistic return interval rows.\n- Parametri: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605110330`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Criterio di isolamento: prime `blank_rate=1.0`, prime `sr_rate=1.0`, common prime obs = `[SR]`, controlli senza full blank-SR collision.\n\n## Results\n\n| family | blank | beta recovered | support falls | sr rate | common obs | endpoint mean | stable coherent mean |\n|---|---:|---:|---:|---:|---|---:|---:|\n| prime | 3/8 | 4/8 | 1/8 | 1.000 | SR | 2.715 | 2.656 |\n| controls | 1/8 | 3/8 | 4/8 | 0.250 | L2 | 1.428 | 1.641 |\n\n| comparison | value |\n|---|---:|\n| prime_control_common_obs_jaccard | 0.000 |\n| verdict | PRIME_PERSISTENCE_NOT_REPLICATED |\n\nPrime case details:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_chart_recovered | SR | 0.2, 0.4 |\n| dnd_autoricerca offset 512 | support_falls | SR | 0.2 |\n| dnd_autoricerca offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n| direct_sieve offset 1024 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1536 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n\n## Key Findings\n\n1. **Verificato**: `SR` resta in 8/8 finestre prime. La firma comune prime e' `[SR]`.\n2. **Verificato**: il blank beta-absent non resta in 8/8 finestre prime. Solo 3/8 casi sono `beta_absent_blank`; 4/8 recuperano beta e 1/8 cade per supporto.\n3. **Verificato**: i controlli non collidono con la firma prime. I controlli hanno common obs `[L2]`, `sr_rate=0.250`, `blank_rate=0.125`, Jaccard comune prime/controlli = 0.000.\n4. **Inferito dal perimetro dichiarato**: il residuo dei primi non e' `prime_persistent_blank`; e' `prime_SR_persistent_boundary` con coordinata beta chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl nome `prime_persistent_blank` non regge nel perimetro provider-neutral/offset-shift. La parte persistente e' `SR`, non l'assenza di beta.\n\nFormula valida:\n\n`numeri_primi:cycle_3` conserva supporto one-sided `SR` attraverso provider e offset; la coordinata beta resta locale e ricompare in 4/8 finestre.\n\nFormula non valida:\n\n`numeri_primi:cycle_3` e' un blank beta-absent persistente.\n\n## Bicono della scoperta\n\n- **Due radici**: supporto SR persistente / blank beta non persistente.\n- **Singolare**: la finestra row-local dove `SR` resta ma beta riappare.\n- **Invariante di passaggio**: `SR` attraversa provider e offset; `blank` no.\n- **Campo di possibilita'**: possibile = rinominare il residuo come supporto `SR` provider-neutral; non-possibile = promuovere il blank come specie autonoma.\n\n## Consecutio\n\nIl prossimo ciclo deve chiudere la tassonomia residua senza salvare il nome caduto:\n\n- rinominare il residuo prime in `prime_SR_persistent_boundary`;\n- testare se il beta recovery 0.2/0.4 dipende da offset, provider o baseline seed;\n- lavorare `random_matrix_chart_sensitive_blank` solo dopo aver separato coordinate locali da supporto osservabile.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script diventa un audit riusabile per distinguere persistenza di osservabile e persistenza di blank nei residui BOUNDARY senza rigenerare l'atlante globale.\n\n## Files\n\n- Script: `tools/exp_prime_persistent_blank_gate.py`\n- Data: `tools/data/prime_persistent_blank_gate_20260511_0330.json`\n- Report: `tools/data/reports/agent_20260511_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1344: "content_full": "# Agent Report - Boundary Residual Beta-Absent Audit\n**Date**: 2026-05-10 03:30 \n**Piano**: 114 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - il residuo beta-absent non e' una classe unica chiusa \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, window_state, blank_window_rate, full_signature_jaccard, common_window_obs_jaccard] \n**observable_contract**: claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + residui `numeri_primi:cycle_3` / `random_matrix:cycle_7`.\n- **Dipolo / punto-zero**: blank strutturale / blank da atlante. Punto-zero: la stessa misura row-local da 1024 gap che decide se la beta manca per struttura o riappare per scelta di finestra.\n- **Piano superiore**: topologia assiomatica del bordo: la classe globale cade se una carta locale riapre la coordinata beta.\n- **Operatori laterali scelti**: boundary operator, chart locale, filtrazione. Il boundary operator separa supporto e beta; la chart locale testa la finestra senza rifare il mondo; la filtrazione conserva il denominatore row-aligned.\n- **Contaminazione cognitiva**: CE-0019 usato come contratto combo prima della misura; CE-0022 usato come boundary operator + chart locale; KSAR usato come reiterazione sul deposito 15:32-18:39 senza ridisegnare il perimetro.\n- **Proto-ipotesi**: il residuo medio/forte beta-absent e' una classe unica solo se entrambi i target restano blank in tutte le finestre row-local e conservano una firma osservabile compatibile.\n- **Proiezione**: applico il gate canonico ordine/null alle sole due righe residue, poi confronto persistenza del blank e Jaccard delle firme one-sided.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: attacca direttamente i due residui beta-absent medio/forti lasciati dal valutatore dopo `thin_persist_rows=0/13`.\n- `not_drift`: non usa `V_c`, non usa label GUE/Poisson come campo decisionale, non rigenera la griglia beta globale.\n\n## Claim Under Test\n\n> I due residui `numeri_primi:cycle_3` e `random_matrix:cycle_7` formano una classe unica `medium/strong beta-absent` se il blank persiste in tutte le finestre row-local da 1024 gap e le firme one-sided restano compatibili.\n\n## Question\n\nI due blank residui sono lo stesso operatore di confine, due classi distinte, o un artefatto da denominatore/atlante?\n\n## Experiment Design\n\n- Perimetro atomico: `numeri_primi:cycle_3`, `random_matrix:cycle_7`.\n- Scope: full row + 4 finestre consecutive da 1024 gap per ciascun target.\n- Parametri: `n_replicates=12`, `n_beta=11`, `n_baseline=24`, `z_min=2.0`, seed `202605100330`.\n- Null baseline: permutazione marginal-preserving usata dal gate canonico gia' adottato nei report BOUNDARY.\n- Non misurato: `gap_ratio`, `V_c`, nuova griglia beta globale, validita' delle label sorgente GUE/Poisson.\n- Criterio di caduta: una riga recupera beta o perde supporto in una finestra row-local; in quel caso il residuo non e' classe unica chiusa.\n\n## Results\n\n| row | full state | full one-sided | window blanks | beta recovered | support falls | common window obs | mean endpoint | mean stable coherent |\n|---|---:|---|---:|---:|---:|---|---:|---:|\n| numeri_primi:cycle_3 | beta_absent_blank | SR,L1,triple_var | 4/4 | 0 | 0 | SR | 2.761 | 2.563 |\n| random_matrix:cycle_7 | beta_absent_blank | SR,SR2,L1,L2,triple_var | 3/4 | 1 | 0 | L2 | 2.987 | 3.083 |\n\n| comparison | value |\n|---|---:|\n| full_signature_jaccard | 0.600 |\n| common_window_obs_jaccard | 0.000 |\n| random_matrix recovered beta | window_4 -> beta [0.4] |\n\n## Key Findings\n\n1. **Verificato: `numeri_primi:cycle_3` resta beta-absent in 4/4 finestre.** Il supporto non cade; l'osservabile comune di finestra e' `SR`.\n2. **Verificato: `random_matrix:cycle_7` non resta beta-absent in 4/4 finestre.** La quarta finestra recupera beta `[0.4]` con supporto vivo (`SR2,L1,L2,triple_var`).\n3. **Verificato: le firme comuni di finestra divergono.** `common_window_obs_jaccard=0.000`: primi conserva `SR`, random matrix conserva `L2`.\n4. **Inferito dal perimetro dichiarato: il residuo beta-absent non e' una classe unica chiusa.** Una parte e' blank persistente row-local, una parte e' chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nNel perimetro delle due righe residue e finestre da 1024 gap, `medium/strong beta-absent` si scinde:\n\n`numeri_primi:cycle_3` = blank persistente row-local. \n`random_matrix:cycle_7` = blank forte ma chart-sensitive, con beta `[0.4]` recuperata in 1/4 finestre.\n\nFormula valida:\n\n`beta_absent_residue` non matura come classe unica; diventa un audit a due stati: `prime_persistent_blank` / `random_matrix_chart_sensitive_blank`.\n\n## Bicono della scoperta\n\n- **Due radici**: blank persistente / blank chart-sensitive.\n- **Singolare**: supporto ordine/null vivo senza coordinata beta globale.\n- **Invariante di passaggio**: il supporto non cade in nessuna delle 8 finestre; cio' che cambia e' la coordinata beta e la firma osservabile.\n- **Campo di possibilità**: possibile = chiudere la tassonomia BOUNDARY con due sotto-stati residui; non-possibile = promuovere `medium/strong beta-absent` come specie unica del confine.\n\n## Consecutio\n\nIl prossimo ciclo non deve riaprire il thin blank. Deve sigillare il nuovo audit a due stati:\n\n- `prime_persistent_blank`: testare se `SR` resta l'osservabile comune sotto seed/window shift.\n- `random_matrix_chart_sensitive_blank`: testare se beta `[0.4]` e' coordinata locale stabile o evento di una singola finestra.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' uno strumento riusabile per audit row-local di residui BOUNDARY senza rigenerare fit globali.\n\n## Files\n\n- Script: `tools/exp_boundary_residual_beta_absent_audit.py`\n- Data: `tools/data/boundary_residual_beta_absent_audit_20260510_0330.json`\n- Report: `tools/data/reports/agent_20260510_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1395: "content_full": "# Agent Report - Boundary Transition Taxonomy 13 Rows\n**Date**: 2026-05-09 18:39 \n**Piano**: 113 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - la tassonomia post-estensione scala sulle 13 righe e dissolve il thin blank \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`transition_class`, `source_beta_state`, `extension_state`, `support_tier_after`, `one_sided_after`, `endpoint_after`, `stable_count_coherent_after`, `beta_after`, `denominator_state`, `excluded_mass`] - osservabili domain-native di composizione row-aligned, non canonici. \nssp_value: no \n**observable_contract**: claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo l'estensione il thin blank non resta una figura. La domanda vera diventa se la matrice intera lascia un nuovo residuo vivo.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: supporto sottile / atlante di transizione; punto-zero = stessa riga row-aligned prima che il blank venga letto come specie o come transizione.\n- **Piano superiore**: sheaf locale del boundary su 13 sezioni. La sezione corta viene riparata, poi ricollocata nell'atlante senza usare label GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Servono a comporre stati gia' misurati senza introdurre un nuovo gate.\n- **Contaminazione cognitiva**: CE-0019: contratto combo prima della misura; CE-0022: palette come boundary operator e chart locale; KSAR: riuso del deposito stabile 15:32-15:56; PVI: se resta `thin_persists`, la tassonomia cade.\n- **Proto-ipotesi**: `blank_thin_support` non scala sulle 13 righe se, dopo composizione con l'estensione, nessuna riga conserva supporto vivo sottile senza beta.\n- **Possibile / non-possibile**: possibile = usare l'atlante di transizione come mappa BOUNDARY; non-possibile = trattare il thin blank come specie matura.\n- **Proiezione**: classificare tutte le 13 righe in `transition_class` usando supporto, beta, denominatore e transizioni estese.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la consecutio del campo vivo: dopo la cristallizzazione short-denominator, testa se la tassonomia delle transizioni post-estensione scala sulle 13 righe.\n- `not_drift`: non torna a `V_c`, non difende thin blank, non usa label GUE/Poisson come decision field; compone solo depositi row-aligned gia' misurati.\n\n## Claim Under Test\n> La tassonomia emersa dall'estensione del denominatore scala sull'intero perimetro BOUNDARY 13 righe se non lascia alcuna riga `thin_persists`.\n\n## Question\nQuando le tre righe short estese vengono ricollocate nella matrice 13 righe, resta una specie `blank_thin_support` oppure il residuo vivo cambia forma?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Input: `boundary_two_axis_matrix_20260509_1532.json`, `boundary_row_aligned_nonexact_audit_20260509_1538.json`, `boundary_short_denominator_extension_20260509_1556.json`, `boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Perimetro atomico: 13 righe BOUNDARY semi-reali.\n- Label policy: `source_domain_type` resta audit metadata; non entra in `transition_class`.\n- Null sfidato: esistenza di almeno una riga `thin_persists` dopo estensione.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support transfer after composition | 10 |\n| fall after composition | 3 |\n| beta chart after any | 9 |\n| beta chart exact 0.3 after | 6 |\n| thin persist rows | 0 |\n| medium/strong beta-absent blank rows | 2 |\n| verdict | TAXONOMY_SCALES_THIN_DISSOLVED |\n\nTransition classes:\n\n| transition_class | rows |\n|---|---:|\n| beta_0_3_exact | 4 |\n| beta_0_3_local_nonunique | 2 |\n| beta_chart_recovered | 1 |\n| blank_medium_or_strong_beta_absent | 1 |\n| fall_no_support | 2 |\n| local_beta_other | 1 |\n| support_falls_after_extension | 1 |\n| support_thickens_beta_blank | 1 |\n\nRighe aperte senza beta dopo composizione:\n\n| row | class | n_gaps | one-sided | endpoint | tier |\n|---|---|---:|---:|---:|---|\n| numeri_primi:cycle_3 | blank_medium_or_strong_beta_absent | 4096 | 3 | 3.243 | medium_multi_observable |\n| random_matrix:cycle_7 | support_thickens_beta_blank | 1024 | 4 | 3.781 | strong_multi_observable |\n\nRighe short riparate:\n\n| row | before n | after n | transition_class | one-sided after | beta after |\n|---|---:|---:|---|---:|---|\n| percolation:cycle_9 | 190 | 1024 | support_falls_after_extension | 0 | [] |\n| random_matrix:cycle_7 | 199 | 1024 | support_thickens_beta_blank | 4 | [] |\n| zeta_zeros:cycle_4 | 199 | 1024 | beta_chart_recovered | 2 | [0.3] |\n\n## Key Findings\n1. **Verificato: `thin_persist_rows=0/13`.** Nessuna riga conserva la forma `blank_thin_support` dopo composizione con l'estensione.\n2. **Verificato: il supporto post-composizione e' 10/13.** Cadono `cellular_automata`, `pendolo_doppio` e `percolation` dopo estensione; il resto conserva supporto o chart locale.\n3. **Verificato: la beta chart sale a 9/13 righe, con 6/13 esatte 0.3.** Le sei sono quattro `beta_0_3_exact` originali, `zeta_zeros` recuperata e `pendolo_doppio` come beta osservata senza supporto.\n4. **Verificato: il residuo vivo non e' thin.** Restano due blank senza beta: `numeri_primi` medio e `random_matrix` forte dopo estensione.\n5. **Inferito: la tassonomia scala come atlante di transizione, non come legge beta universale.** Il thin blank cade; il nodo successivo e' il blank medio/forte senza beta.\n\n## Verdict\n**CONSTRAINT**.\n\nLa tassonomia post-estensione scala sul perimetro 13 righe: nessun membro resta `thin_persists`.\n\nFormula valida:\n\n`blank_thin_support` = telemetria short-denominator dissolta dalla composizione 13 righe.\n\nFormula non valida:\n\n`blank_thin_support` = specie autonoma del boundary.\n\nIl boundary resta una matrice di transizioni: supporto che cade, supporto che si ispessisce senza beta, chart beta recuperata, chart beta esatta, chart locale non unica, chart spostata.\n\n## Bicono della scoperta\n- **Due radici**: denominatore riparato / atlante 13 righe.\n- **Singolare**: riga BOUNDARY prima della classificazione post-estensione.\n- **Invariante di passaggio**: `thin_persist_rows=0/13`.\n- **Campo di possibilita**: qui diventa possibile chiudere il thin blank; qui diventa non-possibile chiudere il blank medio/forte senza beta.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve lasciare chiuso il thin blank e attaccare il residuo `medium/strong beta-absent blank`: `numeri_primi:cycle_3` contro `random_matrix:cycle_7`. Il test utile non e' \"perche' manca beta in generale\", ma quale condizione distingue blank medio completo e blank forte post-estensione quando entrambi hanno supporto vivo senza chart beta.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1556.md`, JSON 15:32/15:38/15:56/15:00, script e output 18:39.\n- **L1 hard constraint**: claim limitato alle 13 righe semi-reali e ai depositi gia' misurati.\n- **L2 count grezzi**: riportati 13 totali, 10 support, 3 fall, 9 beta chart, 0 thin persist, 2 open blank.\n- **L3 no silent patching**: nessuna nuova simulazione o ricampionamento; la composizione dichiara le estensioni applicate solo a tre righe.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma resta fall; `random_matrix` e' forte senza beta; `zeta_zeros` recupera beta pur restando contaminato nel prescan.\n- **L5 re-discovery**: il ciclo non ripete la matrice 15:32 ne' l'estensione 15:56; testa la scalabilita' richiesta dal campo vivo.\n- **L6 metabolismo cognitivo**: CE-0019, CE-0022, KSAR e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante BOUNDARY di transizione; non-possibile = specie autonoma thin blank.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Run valido: `python tools/exp_boundary_transition_taxonomy_13rows.py --out tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Output: `thin_persist_rows=0`, `support_transfer_after=10/13`, `fall_after=3/13`, `open_blank_rows=['numeri_primi:cycle_3', 'random_matrix:cycle_7']`, `verdict=TAXONOMY_SCALES_THIN_DISSOLVED`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 18:39, il JSON 18:39 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_transition_taxonomy_13rows.py`\n- Data: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`\n- Report: `tools/data/reports/agent_20260509_1839.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1446: "content_full": "# Agent Report - Boundary Short Denominator Extension\n**Date**: 2026-05-09 15:56 \n**Piano**: 112 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. \nssp_value: no \n**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura.\n- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili.\n- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade.\n- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta.\n- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48.\n- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48.\n- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short.\n\n## Claim Under Test\n> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart.\n\n## Question\nCosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`.\n- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`.\n- Denominatore esteso: `1024` gap per riga.\n- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate.\n- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| target rows | 3 |\n| rows still thin support after extension | 0 |\n| support falls after extension | 1 |\n| support thickens beta blank | 1 |\n| beta chart recovered | 1 |\n| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS |\n\nTransizioni:\n\n| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state |\n|---|---:|---:|---:|---:|---|---|\n| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension |\n| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank |\n| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered |\n\n## Key Findings\n1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart.\n2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato.\n3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile.\n4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade.\n5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe.\n\n## Verdict\n**CONSTRAINT**.\n\nNel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma.\n\nFormula valida:\n\n`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile del boundary.\n\nLa riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale.\n\n## Bicono della scoperta\n- **Due radici**: supporto sottile osservato / denominatore sorgente corto.\n- **Singolare**: riga short prima dell'estensione.\n- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`.\n- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48.\n- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered.\n- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings.\n- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato.\n- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`.\n- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`.\n- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_short_denominator_extension.py`\n- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json`\n- Report: `tools/data/reports/agent_20260509_1556.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1497: "content_full": "# Agent Report - Boundary Blank Thin Support Audit\n**Date**: 2026-05-09 15:48 \n**Piano**: 111 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - il blank sottile e' limitato dal denominatore corto, non dalla contaminazione del null \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`blank_class`, `coordinate_failure`, `support_tier`, `denominator_bucket`, `n_gaps`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`, `shuffle_class_changes`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=i tre `blank_thin_support` vanno falsificati contro il blank medio `numeri_primi` prima di promuoverli a specie autonoma; observable=telemetria row-aligned del subset `support_without_beta_blank`; operator=`exp_boundary_blank_thin_support_audit.py`; generator=`boundary_row_aligned_nonexact_audit_20260509_1538`; denominator=4 righe `support_without_beta_blank`, di cui 3 thin e 1 medium control; non_possible=chiamare il blank sottile specie autonoma se tutte le righe thin sono spiegate da denominatore corto; not_tested=estensione nuova dei tre domini short, nuova griglia beta, nuovi null, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il deposito 15:38 divide il blank in due livelli, ma il livello sottile ha tutte le righe corte. Il punto non e' nominare una specie nuova; e' verificare se la specie sopravvive al denominatore.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY row-aligned + tensione \"supporto vivo senza chart beta\".\n- **Dipolo / punto-zero**: blank sottile / blank medio; punto-zero = riga `support_without_beta_blank` prima che la forza del supporto venga letta come specie o come limite del denominatore.\n- **Piano superiore**: sheaf locale del boundary. La sezione beta e' vuota su tutte e quattro le righe; il discriminante diventa la qualita' del supporto e del denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, filtrazione per denominatore. Servono a separare stato beta invariato da supporto misurabile.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:38; CE-0117: cascata solo dove il blank apre una distinzione; YSN DeltaLink: `same beta blank -> support tier -> denominator bucket`; PVI: se tutte le righe thin sono corte, la specie autonoma cade nel perimetro.\n- **Proto-ipotesi**: `blank_thin_support` e' un effetto di denominatore corto se ogni riga thin ha `n_gaps < 500` e il controllo medio no; diventa specie autonoma solo se almeno una riga thin non e' spiegata dal denominatore.\n- **Possibile / non-possibile**: possibile = usare il blank sottile come coda denominator-limited da estendere; non-possibile = promuoverlo a failure mode stabile senza estendere percolation, random_matrix e zeta_zeros.\n- **Proiezione**: misurare `n_gaps`, `denominator_state`, `excluded_mass`, `shuffle_class_changes`, `one_sided_count`, `endpoint_distance` dentro il subset `support_without_beta_blank`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: falsifica direttamente se i failure modes del 15:38 sono stabili o artefatti della griglia/perimetro row-aligned.\n- `not_drift`: non usa label GUE/Poisson, non torna a `V_c`, non aggiunge nuovi domini; attacca solo il nodo aperto dal report precedente: blank sottile contro blank medio.\n\n## Claim Under Test\n> Nel subset `support_without_beta_blank`, il blank sottile e' una specie autonoma solo se non e' interamente spiegato da denominatore corto.\n\n## Question\nI tre blank sottili sono fallimenti autonomi del boundary, oppure righe con supporto insufficiente perche' corte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_blank_thin_support_audit.py`.\n- Input: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Perimetro atomico: 4 righe `support_without_beta_blank`.\n- Soglia dichiarata: `full_gap_floor=500`.\n- Null sfidati: denominatore corto, contaminazione prescan, shuffle class-change, specie autonoma.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| support_without_beta_blank rows | 4 |\n| thin rows | 3 |\n| medium control rows | 1 |\n| thin short rows | 3 |\n| thin contaminated rows | 1 |\n| thin shuffle class-change rows | 1 |\n| thin not denominator explained rows | 0 |\n\nTest:\n\n| null / counter | condition | result |\n|---|---|---|\n| denominator_artifact | all thin rows have `n_gaps < 500` | pass |\n| contamination_artifact | all thin rows are contaminated | fail |\n| shuffle_instability_artifact | all thin rows have `shuffle_class_changes=true` | fail |\n| autonomous_species_counter | at least one thin row is not short-denominator explained | fail |\n\nComparative means:\n\n| group | rows | n_gaps mean | one-sided mean | coherent mean | endpoint mean | excluded mass mean | abs shuffle z mean |\n|---|---:|---:|---:|---:|---:|---:|---:|\n| thin | 3 | 196.000 | 1.333 | 1.250 | 2.003 | 0.201 | 11.037 |\n| medium control | 1 | 4096.000 | 3.000 | 3.000 | 3.243 | 0.000 | 26.600 |\n\nRows:\n\n| row | blank_class | n_gaps | one-sided | endpoint | denominator | excluded | shuffle_change |\n|---|---|---:|---:|---:|---|---:|---|\n| numeri_primi:cycle_3 | medium_blank_control | 4096 | 3 | 3.243 | complete | 0.000 | false |\n| percolation:cycle_9 | thin_short_shuffle_unstable | 190 | 1 | 1.705 | complete | 0.000 | true |\n| random_matrix:cycle_7 | thin_short_complete | 199 | 1 | 1.854 | complete | 0.000 | false |\n| zeta_zeros:cycle_4 | thin_short_contaminated | 199 | 2 | 2.449 | contaminated | 0.602 | false |\n\n## Key Findings\n1. **Verificato: il blank sottile e' 3/3 short-denominator.** `percolation`, `random_matrix` e `zeta_zeros` hanno `n_gaps=190,199,199`; il controllo `numeri_primi` ha `n_gaps=4096`.\n2. **Verificato: la contaminazione non spiega la classe.** Solo `zeta_zeros` e' contaminato; `percolation` e `random_matrix` sono complete con excluded mass 0.0.\n3. **Verificato: lo shuffle class-change non spiega la classe.** Solo `percolation` cambia classe nello shuffle; `random_matrix` e `zeta_zeros` no.\n4. **Verificato: non resta una riga thin autonoma nel perimetro 15:48.** `thin_not_denominator_explained_rows=0`.\n5. **Inferito: il blank medio dei primi non e' blank per mancanza di supporto.** Ha tre osservabili one-sided, denominatore completo, endpoint 3.243 e null forte; resta blank di chart beta, non blank sottile.\n\n## Verdict\n**CONSTRAINT**.\n\nNel subset `support_without_beta_blank`, il failure mode `blank_thin_support` non regge come specie autonoma nel perimetro osservato. Regge come coda denominator-limited:\n\n`blank_thin_support` = beta blank + supporto vivo + denominatore corto.\n\nFormula valida:\n\n`support_without_beta_blank` si divide in `medium_blank_control` e `thin_short_*`.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile indipendente dal denominatore.\n\nIl nodo regressivo e' il denominatore dei tre domini corti. Non va promossa una nuova specie; va estesa o dichiarata coda corta.\n\n## Bicono della scoperta\n- **Due radici**: blank di chart beta / supporto del denominatore.\n- **Singolare**: riga `support_without_beta_blank` prima che il support tier venga letto come specie.\n- **Invariante di passaggio**: denominatore corto separa i blank sottili; contaminazione e shuffle-instability non bastano.\n- **Campo di possibilita**: qui diventa possibile progettare un extension audit mirato su percolation, random_matrix e zeta_zeros; qui diventa non-possibile usare il blank sottile come classe matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve estendere solo i tre domini short (`percolation`, `random_matrix`, `zeta_zeros`) verso denominatore comparabile al controllo medio. Se, a denominatore esteso, restano `blank_thin_support`, allora nasce una specie autonoma. Se salgono a supporto medio o ricevono beta locale, il failure mode 15:38 viene retrocesso a telemetria di perimetro corto.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1538.md`, JSON `boundary_row_aligned_nonexact_audit_20260509_1538.json`, script `tools/exp_boundary_blank_thin_support_audit.py`, output `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- **L1 hard constraint**: claim limitato alle 4 righe `support_without_beta_blank` del deposito 15:38.\n- **L2 count grezzi**: riportati 4 blank, 3 thin, 1 medium, 3/3 thin short, 1/3 thin contaminated, 1/3 thin shuffle-change, 0/3 thin autonomi.\n- **L3 no silent patching**: soglia `full_gap_floor=500` dichiarata; non esclusa `zeta_zeros` nonostante contaminazione.\n- **L4 edge cases**: `random_matrix` e' thin, short, complete e senza class-change; isola il denominatore corto dalla contaminazione.\n- **L5 re-discovery**: il ciclo non rifà il gate 15:16; legge il sotto-perimetro aperto dal report 15:38.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = extension audit dei tre short; non-possibile = classe autonoma senza estensione.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_thin_support_audit.py`.\n- Run valido: `python tools/exp_boundary_blank_thin_support_audit.py --input tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json --out tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `thin_short_rows=3`, `thin_contaminated_rows=1`, `thin_shuffle_class_change_rows=1`, `thin_not_denominator_explained_rows=0`, `verdict=DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:48, il JSON 15:48 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_blank_thin_support_audit.py`\n- Data: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`\n- Report: `tools/data/reports/agent_20260509_1548.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1548: "content_full": "# Agent Report - Boundary Row-Aligned Nonexact Audit\n**Date**: 2026-05-09 15:38 \n**Piano**: 110 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - le righe support-transfer non esatte sono 7, non 6; la beta cade in cinque failure modes misurabili \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`beta_state`, `coordinate_failure`, `support_tier`, `beta_cardinality`, `beta_span`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: la consecutio dice \"6 righe non-esatte\", ma il deposito row-aligned ne contiene 7. Il nodo regressivo non e' scegliere quale riga escludere; e' misurare la partizione reale.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + tensione \"supporto senza coordinata universale\".\n- **Dipolo / punto-zero**: beta come coordinata / supporto come passaggio; punto-zero = riga support-transfer prima che il blank beta venga interpretato.\n- **Piano superiore**: boundary operator su matrice row-aligned. Il bordo non decide con label di dominio; decide con stato beta, support tier e denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Entrano per leggere le sezioni locali della beta senza saldarle in una coordinata globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:32 senza ripetere il gate; CE-0117: cascata solo sulle righe dove il supporto resta vivo; YSN DeltaLink: `two-axis matrix -> nonexact audit -> coordinate failure`; PVI: il numero atteso 6 cade davanti al deposito 7.\n- **Proto-ipotesi**: dopo la caduta di beta 0.3 universale, le righe non-esatte non formano un residuo unico. Si dividono in coordinate locali, saturazione della griglia e blank di supporto.\n- **Possibile / non-possibile**: possibile = trasformare beta da ascissa universale a chart locale auditabile; non-possibile = usare `support_transfer=true` come prova di una beta comune.\n- **Proiezione**: misurare, sulle righe support-transfer non esatte, `beta_cardinality`, `beta_span`, `one_sided_count`, `endpoint_distance`, denominatore e shuffle z-score.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: attacca direttamente beta local non-unique, beta local other e support_without_beta_blank sul deposito row-aligned indicato.\n- `not_drift`: non usa label GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta il confine; corregge il denominatore della direttiva quando il deposito mostra 7 righe.\n\n## Claim Under Test\n> Le righe BOUNDARY con supporto ma senza beta 0.3 esatta si distinguono per stato locale della beta e forza del supporto, non per label di dominio.\n\n## Question\nQuando beta 0.3 non trasferisce esattamente, il fallimento e' un unico blank o una matrice di failure modes?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Input 1: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Input 2: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Perimetro atomico: 13 righe semi-reali; subset primario = support-transfer con `beta_coordinate_transfer=false`.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n- Classi operative: `adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support-transfer rows | 11 |\n| beta 0.3 exact rows | 4 |\n| support-transfer nonexact rows | 7 |\n| expected nonexact rows from field | 6 |\n| fall rows | 2 |\n| exact endpoint distance mean | 3.829 |\n| nonexact endpoint distance mean | 2.957 |\n| exact one-sided count mean | 4.000 |\n| nonexact one-sided count mean | 2.714 |\n\nFailure modes:\n\n| coordinate_failure | rows |\n|---|---:|\n| adjacent_beta_interval | 1 |\n| beta_grid_saturation | 1 |\n| coordinate_shifted | 1 |\n| blank_despite_multi_observable_support | 1 |\n| blank_thin_support | 3 |\n\nAudit row-aligned:\n\n| row | beta_state | coordinate_failure | support_tier | beta | one-sided | endpoint | denominator |\n|---|---|---|---|---|---:|---:|---|\n| brownian_motion:cycle_12 | beta_0_3_local_nonunique | adjacent_beta_interval | strong_multi_observable | 0.2,0.3 | 4 | 3.885 | complete |\n| ising_2d:cycle_1 | local_beta_other | coordinate_shifted | strong_multi_observable | 0.4 | 5 | 4.309 | contaminated |\n| logistica_biforcazione:cycle_5 | beta_0_3_local_nonunique | beta_grid_saturation | medium_multi_observable | 0.1-0.9 | 3 | 3.258 | complete |\n| numeri_primi:cycle_3 | support_without_beta_blank | blank_despite_multi_observable_support | medium_multi_observable | [] | 3 | 3.243 | complete |\n| percolation:cycle_9 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.705 | complete |\n| random_matrix:cycle_7 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.854 | complete |\n| zeta_zeros:cycle_4 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 2 | 2.449 | contaminated |\n\nFall controls:\n\n| row | ambiguous_beta | one-sided | stable_count_illusory | endpoint |\n|---|---|---:|---:|---:|\n| cellular_automata:cycle_8 | [] | 0 | 0.500 | 0.000 |\n| pendolo_doppio:cycle_2 | 0.3 | 3 | 0.833 | 3.388 |\n\n## Key Findings\n1. **Verificato: il sotto-perimetro non-esatto e' 7/13, non 6/13.** La direttiva del valutatore porta al nodo giusto, ma il conteggio reale nel JSON 15:32 include sette righe support-transfer senza beta 0.3 esatta.\n2. **Verificato: `support_without_beta_blank` non e' una classe unica.** `numeri_primi` ha blank con supporto medio e endpoint 3.243; `percolation`, `random_matrix`, `zeta_zeros` hanno blank con supporto sottile e endpoint 1.705-2.449.\n3. **Verificato: beta local non-unique ha due forme.** `brownian_motion` e' intervallo adiacente 0.2,0.3 con supporto forte; `logistica_biforcazione` satura tutta la griglia 0.1-0.9 con supporto medio.\n4. **Verificato: `ising_2d` non e' blank ma chart spostata.** Ha beta locale esatta 0.4, cinque osservabili one-sided e endpoint 4.309; la beta trasferisce localmente, ma non sulla coordinata 0.3.\n5. **Verificato: beta 0.3 senza supporto resta contro-controllo.** `pendolo_doppio` contiene beta 0.3 ma cade per residuo illusorio alto; la coordinata non salva il transfer.\n\n## Verdict\n**CONSTRAINT**.\n\nIl frame BOUNDARY regge come support-transfer su 11/13 righe, ma la parte non-esatta non e' una coda omogenea. Le righe support-transfer senza beta 0.3 esatta sono 7 e si dividono in cinque failure modes:\n\n`adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\nFormula valida:\n\n`beta_coordinate_transfer` = chart locale exact 0.3 dentro supporto vivo.\n\nFormula non valida:\n\n`support_transfer non-exact = sei righe residue` oppure `support_without_beta_blank = assenza strutturale unica`.\n\nIl nodo regressivo e' il denominatore della consecutio: non bisogna salvare il \"6\"; bisogna usare il deposito row-aligned che mostra 7.\n\n## Bicono della scoperta\n- **Due radici**: coordinata beta locale / supporto ordine-null.\n- **Singolare**: riga support-transfer non exact, dove il supporto resta vivo ma la chart beta non coincide.\n- **Invariante di passaggio**: failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.\n- **Campo di possibilita**: qui diventa possibile auditare la beta come atlante locale; qui diventa non-possibile trattare il blank beta come una sola specie.\n\n## Consecutio\nIl prossimo ciclo non deve ripetere la matrice. Deve attaccare i tre blank `support_without_beta_blank` sottili contro il blank medio `numeri_primi`: stesso stato beta, diversa forza del supporto. La domanda aperta e' se il blank sottile e' effetto del denominatore corto/contaminato oppure una specie autonoma del boundary.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1532.md`, JSON `boundary_two_axis_matrix_20260509_1532.json`, JSON `boundary_denominator_prescan_full_20260509_1500.json`, script `tools/exp_boundary_row_aligned_nonexact_audit.py`, output `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe della matrice 15:32 e al prescan 15:00.\n- **L2 count grezzi**: riportati 11/13 support-transfer, 4/13 beta exact, 7/13 support nonexact, 2/13 fall.\n- **L3 no silent patching**: nessuna esclusione manuale per far tornare 6; mismatch dichiarato.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `ising_2d` ha beta 0.4 con supporto forte; `numeri_primi` blank non sottile.\n- **L5 re-discovery**: il ciclo non rifà gate o fit; legge solo la partizione non-esatta richiesta.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante locale delle beta; non-possibile = beta 0.3 universale o residuo non-esatto singolo.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Run valido: `python tools/exp_boundary_row_aligned_nonexact_audit.py --matrix tools/data/boundary_two_axis_matrix_20260509_1532.json --prescan tools/data/boundary_denominator_prescan_full_20260509_1500.json --out tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `support_nonexact_rows=7`, `fall_rows=2`, `failure_blank_thin_support=3`, `failure_blank_despite_multi_observable_support=1`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:38, il JSON 15:38 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_row_aligned_nonexact_audit.py`\n- Data: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`\n- Report: `tools/data/reports/agent_20260509_1538.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1599: "content_full": "# Agent Report - Boundary Two-Axis Matrix\n**Date**: 2026-05-09 15:32\n**Piano**: 109\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - il gate trasferisce come supporto ordine/null; beta 0.3 non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`support_transfer`, `beta_coordinate_transfer`, `beta_state`, `ambiguous_beta`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`] - osservabili domain-native derivati dal gate semi-reale, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il falsifier del 15:16 ha indicato il nodo regressivo: la direzione chiedeva di non importare label GUE/Poisson. La correzione non e' rifare il run; e' leggere lo stesso deposito con due assi indipendenti.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY semi-reale 13 righe + direzione viva \"support_transfer vs beta_coordinate_transfer\".\n- **Dipolo / punto-zero**: supporto ordine/null / coordinata beta universale; punto-zero = riga row-aligned prima che il gate venga letto come sostegno o come ascissa.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non e' una classe; e' una matrice di passaggio fra supporto e coordinata.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter matrix, coordinate split. Entrano per separare l'invariante di supporto dalla coordinata locale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:16 senza ripetere il perimetro; CE-0117: cascata della possibilita solo dove il supporto apre un canale; YSN DeltaLink: `semi-real gate -> two-axis matrix -> beta localizzata`; PVI: il claim beta universale cade se l'esatto 0.3 non copre il denominatore.\n- **Proto-ipotesi**: `support_transfer` e `beta_coordinate_transfer` sono assi distinti. Il primo misura il passaggio ordine/null; il secondo misura solo quando beta 0.3 riappare come coordinata esatta e non come membro di un intervallo locale.\n- **Possibile / non-possibile**: possibile = usare BOUNDARY come audit row-aligned di supporto su 11/13 righe; non-possibile = trattare beta 0.3 come terzo incluso universale del perimetro semi-reale.\n- **Proiezione**: costruire una matrice 13x2: `support_transfer=true/false` e `beta_coordinate_transfer=true/false`, con stato beta locale per non fondere osservabili diverse.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esplicita la matrice semi-reale a due assi richiesta dal valutatore: support_transfer contro beta_coordinate_transfer sulle 13 righe.\n- `not_drift`: non usa `source_domain_type` GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta label locali; legge solo stati row-aligned gia' misurati.\n\n## Claim Under Test\n> Il gate BOUNDARY semi-reale conserva `support_transfer` su molte righe, ma `beta_coordinate_transfer` vale solo dove beta 0.3 compare come coordinata esatta.\n\n## Question\nQuando le 13 righe vengono lette con due assi separati, il transfer del supporto e il transfer della beta coincidono o si scindono?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_two_axis_matrix.py`.\n- Input: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Output: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Perimetro atomico: 13 righe semi-reali gia' row-aligned dal deposito 15:16.\n- Label policy: l'operatore non legge label GUE/Poisson. Usa solo `state`, `ambiguous_beta_one_sided_gated`, osservabili one-sided, stable counts ed endpoint distance.\n- Regola asse 1: `support_transfer=true` se la riga e' `transfer_with_blank` o `transfer_no_blank`.\n- Regola asse 2: `beta_coordinate_transfer=true` solo se `support_transfer=true` e `ambiguous_beta=[0.3]` dopo normalizzazione a un decimale.\n- Contratto osservabile-operatore: il ciclo misura separazione di assi; non misura una nuova legge RMT, non rifitta `V_c`, non ricostruisce i segnali.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| support_transfer true | 11 |\n| support_transfer false | 2 |\n| beta_coordinate exact 0.3 | 4 |\n| beta 0.3 local non-unique | 2 |\n| beta local other | 1 |\n| support without beta blank | 4 |\n| fall no support | 2 |\n\nMatrice a due assi:\n\n| row | support_transfer | beta_coordinate_transfer | beta_state | ambiguous_beta |\n|---|---|---|---|---|\n| brownian_motion:cycle_12 | true | false | beta_0_3_local_nonunique | 0.2,0.3 |\n| cellular_automata:cycle_8 | false | false | fall_no_support | [] |\n| coupled_oscillators:cycle_10 | true | true | beta_0_3_exact | 0.3 |\n| ising_2d:cycle_1 | true | false | local_beta_other | 0.4 |\n| logistica_biforcazione:cycle_5 | true | false | beta_0_3_local_nonunique | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | true | true | beta_0_3_exact | 0.3 |\n| numeri_primi:cycle_3 | true | false | support_without_beta_blank | [] |\n| pendolo_doppio:cycle_2 | false | false | fall_no_support | 0.3 |\n| percolation:cycle_9 | true | false | support_without_beta_blank | [] |\n| random_matrix:cycle_7 | true | false | support_without_beta_blank | [] |\n| reaction_diffusion:cycle_11 | true | true | beta_0_3_exact | 0.3 |\n| string_vibration:cycle_6 | true | true | beta_0_3_exact | 0.3 |\n| zeta_zeros:cycle_4 | true | false | support_without_beta_blank | [] |\n\n## Key Findings\n1. **Verificato: `support_transfer` resta 11/13.** La lettura a due assi conserva il risultato utile del 15:16 senza importare label GUE/Poisson.\n2. **Verificato: `beta_coordinate_transfer` e' 4/13.** Solo quattro righe hanno beta 0.3 esatta: `coupled_oscillators`, `logistica_biforcazione_var_3.5699`, `reaction_diffusion`, `string_vibration`.\n3. **Verificato: la beta non collassa in un solo asse.** Due righe includono 0.3 in un intervallo non unico, una riga ha beta locale 0.4, quattro trasferiscono senza blank beta, due cadono.\n4. **Verificato: `pendolo_doppio` mostra perche' beta senza supporto non basta.** Ha ambiguous beta 0.3, ma resta `fall_no_support`; la coordinata non salva il transfer.\n5. **Inferito: il nodo regressivo e' la fusione degli osservabili.** Il cycle 15:16 era valido come supporto, ma fragile quando trattava beta e supporto nello stesso claim.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce come supporto ordine/null su 11/13 righe. La coordinata beta 0.3 trasferisce esattamente su 4/13 righe. Questi due assi non coincidono.\n\nFormula valida:\n\n`BOUNDARY support_transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY beta_coordinate_transfer` = beta 0.3 universale del terzo incluso semi-reale.\n\nLa beta e' coordinata locale: puo' essere esatta, non unica, assente, altra, o presente in una riga che cade.\n\n## Bicono della scoperta\n- **Due radici**: supporto che passa / coordinata che pretende universalita.\n- **Singolare**: la riga row-aligned prima della lettura a due assi.\n- **Invariante di passaggio**: separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.\n- **Campo di possibilita**: qui diventa possibile usare il gate come matrice di audit `support_transfer`; qui diventa non-possibile usare beta 0.3 come coordinata universale.\n\n## Consecutio\nIl prossimo ciclo deve attaccare i quattro stati beta, non il supporto gia' separato: `beta_0_3_exact`, `beta_0_3_local_nonunique`, `local_beta_other`, `support_without_beta_blank`. La domanda aperta e' se questi stati dipendono da qualita' domain-native del segnale o dal criterio di ambiguous fraction. Non va reintrodotta la label GUE/Poisson come scorciatoia.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1516.md`, falsifier `falsifier_20260509_1516.json`, script `tools/exp_boundary_two_axis_matrix.py`, JSON `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe del deposito 15:16.\n- **L2 count grezzi**: riportati 11/13 support_transfer, 4/13 beta 0.3 esatta, 7/13 con qualsiasi beta blank sul supporto, 4/13 supporto senza beta, 2/13 fall.\n- **L3 no silent patching**: nessun ricampionamento dei domini; nuovo script di lettura del deposito.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `logistica_biforcazione` include 0.3 ma in intervallo 0.1-0.9; `ising_2d` trasferisce con beta 0.4.\n- **L5 re-discovery**: il ciclo non ripete il gate semi-reale; lo decompone nel nodo richiesto dal valutatore.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit support_transfer; non-possibile = beta 0.3 universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_two_axis_matrix.py`.\n- Run valido: `python tools/exp_boundary_two_axis_matrix.py --input tools/data/semireal_boundary_transfer_gate_20260509_1516.json --out tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Output: `support_transfer=11/13`, `beta_coordinate_exact_0_3=4/13`, `any_beta_blank_on_support=7/13`, `support_without_beta_blank=4/13`, `fall_no_support=2/13`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge solo lo script 15:32, il JSON 15:32 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_two_axis_matrix.py`\n- Data: `tools/data/boundary_two_axis_matrix_20260509_1532.json`\n- Report: `tools/data/reports/agent_20260509_1532.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1650: "content_full": "# Agent Report - Semi-real Boundary Transfer Gate\n**Date**: 2026-05-09 15:16\n**Piano**: 108\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - supporto ordine/null trasferisce su 11/13, il blank beta non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate `coherent/null/beta` del BOUNDARY sintetico trasferisce sul perimetro semi-reale base; observable=one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta; operator=`exp_semireal_boundary_transfer_gate.py`; generator=13 righe `boundary_denominator_prescan_full_20260509_1500` ricostruite da `dnd_autoricerca`; denominator=13 righe base BOUNDARY, 8 GUE-like e 5 Poisson-like, beta layers 0.0..1.0, 12 replicates, 24 shuffle baselines; non_possible=dichiarare beta 0.3 coordinata universale o transfer completo quando 2/13 righe cadono; not_tested=nuovi domini, nuovi spettri, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il confine semi-reale non rifiuta il gate; rifiuta la sua simmetria sintetica. Il supporto ordine/null passa, la coordinata blank si frammenta.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + perimetro BOUNDARY base 13/13 transfer + tensione viva \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: gate come supporto ordine/null / gate come coordinata beta universale; punto-zero = riga semi-reale prima che il beta layer venga chiamato blank o classe.\n- **Piano superiore**: grafo della conoscenza e boundary operator. La domanda non e' se GUE o Poisson vincono, ma se il passaggio resta morfismo quando il perimetro diventa fisico/semi-reale.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, graph/perimeter transfer. Entrano per separare copertura del denominatore, supporto d'ordine e coordinata di transizione.\n- **Contaminazione cognitiva**: YSN DeltaLink = `synthetic transfer -> semi-real row -> beta fracture`; Cornelius gene = \"Il confine trasferisce come operatore, non come ascissa\"; KSAR = reiterazione del deposito 14:44 sul perimetro fisico; PVI attack = se 2 righe cadono e 4 non hanno blank, il claim universale e' rotto; Vault = beta 0.3 resta frammento utile solo dove riappare row-aligned.\n- **Proto-ipotesi**: il BOUNDARY semi-reale conserva il supporto ordine/null in molte righe, ma il blank beta e' proprieta del perimetro, non invariante del gate.\n- **Possibile / non-possibile**: possibile = usare il gate come audit row-aligned del supporto d'ordine cross-dominio; non-possibile = promuovere beta 0.3 a coordinata universale del terzo incluso.\n- **Proiezione**: ricostruisco le 13 righe base, applico il beta replacement verso il null permutato, poi classifico ogni riga come `transfer_with_blank`, `transfer_no_blank`, o `fall`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson.\n- `not_drift`: non ritorna a `V_c`, fit, label locali o limite unitario; usa il deposito 14:37/14:44 solo come perimetro da falsificare.\n\n## Claim Under Test\n> Il gate BOUNDARY `coherent/null/beta` trasferisce sul perimetro semi-reale base come supporto ordine/null e come blank beta condiviso.\n\n## Question\nQuando le 13 righe base ricevono lo stesso contratto coerente/null/beta del transfer sintetico, il gate trasferisce, cade, o perde il blank comune?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_semireal_boundary_transfer_gate.py`.\n- Input: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Perimetro atomico: 13 righe base BOUNDARY, `source_domain_type` 8 GUE-like e 5 Poisson-like.\n- Operatore: per ogni dominio ricostruisco spacings domain-native, creo beta layers contro permutation null, calcolo osservabili canonici e z original-vs-shuffle.\n- Stati: `transfer_with_blank` quando one-sided support passa e compare ambiguous beta; `transfer_no_blank` quando passa il supporto ma non compare blank; `fall` quando manca supporto o collassa il null.\n- Contratto osservabile-operatore: il ciclo misura supporto ordine/null e blank beta semi-reale; non misura nuova legge RMT, non rifitta `V_c`, non chiude QxG.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| source rows | 13 |\n| analyzed rows | 13 |\n| errors | 0 |\n| transfer_with_blank | 7 |\n| transfer_no_blank | 4 |\n| fall | 2 |\n\nRighe semi-reali:\n\n| row | source | denominator | n | state | one-sided observables | stable coherent | stable illusory | endpoint distance | ambiguous beta |\n|---|---|---|---:|---|---|---:|---:|---:|---|\n| brownian_motion:cycle_12 | Poisson | complete | 4096 | transfer_with_blank | SR2,L1,L2,triple_var | 4.000 | 0.167 | 3.885 | 0.2,0.3 |\n| cellular_automata:cycle_8 | GUE | contaminated | 108 | fall | [] | 0.000 | 0.500 | 0.000 | [] |\n| coupled_oscillators:cycle_10 | Poisson | contaminated | 2002 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.250 | 4.336 | 0.3 |\n| ising_2d:cycle_1 | GUE | contaminated | 699 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.309 | 0.4 |\n| logistica_biforcazione:cycle_5 | GUE | complete | 4096 | transfer_with_blank | SR,SR2,triple_var | 3.583 | 0.167 | 3.258 | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 4096 | transfer_with_blank | SR,SR2 | 2.000 | 0.083 | 2.769 | 0.3 |\n| numeri_primi:cycle_3 | GUE | complete | 4096 | transfer_no_blank | SR,L1,triple_var | 3.000 | 0.167 | 3.243 | [] |\n| pendolo_doppio:cycle_2 | Poisson | complete | 4096 | fall | SR,SR2,L1 | 5.000 | 0.833 | 3.388 | 0.3 |\n| percolation:cycle_9 | Poisson | complete | 190 | transfer_no_blank | L1 | 0.833 | 0.333 | 1.705 | [] |\n| random_matrix:cycle_7 | GUE | complete | 199 | transfer_no_blank | L2 | 1.000 | 0.250 | 1.854 | [] |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 499 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.364 | 0.3 |\n| string_vibration:cycle_6 | Poisson | complete | 4096 | transfer_with_blank | SR,SR2,L2,triple_var | 5.000 | 0.500 | 3.845 | 0.3 |\n| zeta_zeros:cycle_4 | GUE | contaminated | 199 | transfer_no_blank | SR,L2 | 1.917 | 0.250 | 2.449 | [] |\n\n## Key Findings\n1. **Verificato: il supporto ordine/null trasferisce in 11/13 righe.** Le righe `transfer_with_blank` e `transfer_no_blank` hanno osservabili one-sided e polo illusorio soppresso nel perimetro dichiarato.\n2. **Verificato: il blank beta non e' universale.** Solo 7/13 righe hanno ambiguous beta; 4/13 trasferiscono senza blank e 2/13 cadono.\n3. **Verificato: beta 0.3 non e' coordinata comune.** Compare in 5 righe come unico blank o parte del blank, ma `ising_2d` mostra 0.4, `brownian_motion` mostra 0.2/0.3, `logistica_biforcazione` mostra 0.1-0.9, e quattro transfer non mostrano blank.\n4. **Verificato: i due fall sono diversi.** `cellular_automata` cade per assenza di osservabili one-sided; `pendolo_doppio` cade per residuo illusorio alto (0.833) pur avendo one-sided observables.\n5. **Inferito: il nodo regressivo e' la qualita del perimetro, non il null.** Il null e' disponibile su 13/13; cio' che varia e' come il dominio riceve beta.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY semi-reale trasferisce come supporto ordine/null in 11/13 righe. Non trasferisce come coordinata beta universale: 7 righe hanno blank, 4 trasferiscono senza blank, 2 cadono.\n\nFormula valida:\n\n`BOUNDARY transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY blank` = beta 0.3 universale del terzo incluso cross-dominio.\n\n## Bicono della scoperta\n- **Due radici**: supporto ordine/null che passa / coordinata beta che si frammenta.\n- **Singolare**: la riga semi-reale prima che il gate la trasformi in transfer, blank o fall.\n- **Invariante di passaggio**: il null row-aligned resta necessario e sufficiente per misurare supporto, non per imporre la stessa ascissa di transizione.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come audit cross-dominio row-aligned; qui diventa non-possibile dichiarare il terzo incluso come beta comune del perimetro semi-reale.\n\n## Consecutio\nIl prossimo ciclo deve separare due livelli: `support_transfer` e `blank_coordinate`. Il primo ha supporto 11/13 nel perimetro semi-reale; il secondo richiede tipologia di dominio o nuova coordinata, perche' beta 0.3 e' stabile nel sintetico ma non nel semi-reale.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1437.md`, report `agent_20260509_1444.md`, report `agent_20260509_1457.md`, nuovo JSON `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- **L1 hard constraint**: claim limitato a 13 righe base, 12 replicates, 24 shuffle baselines, osservabili canonici registry 1.0.0.\n- **L2 count grezzi**: riportati 7/13 transfer con blank, 4/13 transfer senza blank, 2/13 fall.\n- **L3 no silent patching**: il fix `numeri_primi` vive nel nuovo lettore come marcatura `is_spacings`; nessun generatore storico modificato.\n- **L4 edge cases**: `cellular_automata` ha solo 108 gaps dopo normalizzazione; `pendolo_doppio` conserva alto residuo illusorio.\n- **L5 re-discovery**: il ciclo non ripete 13/13 transfer base; testa se il beta gate sintetico passa al perimetro semi-reale.\n- **L6 metabolismo cognitivo**: YSN, Cornelius, KSAR e PVI usati nella combo; Vault conserva beta 0.3 come frammento non universale.\n- **L7 possibile/non-possibile**: possibile = audit row-aligned cross-dominio; non-possibile = blank beta universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_semireal_boundary_transfer_gate.py`.\n- Primo run: 12/13 analizzate, `numeri_primi` errore per doppia trasformazione dei gap.\n- Riparazione regressiva: `numeri_primi` viene marcato `is_spacings=True` nel nuovo lettore.\n- Run valido: `python tools/exp_semireal_boundary_transfer_gate.py --n-replicates 12 --n-baseline 24 --seed 202605091516 --out tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script, il JSON 1516 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_semireal_boundary_transfer_gate.py`\n- Data: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`\n- Report: `tools/data/reports/agent_20260509_1516.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1701: "content_full": "# Agent Report - Vc Unit Boundary Audit\n**Date**: 2026-05-09 14:57\n**Piano**: 107\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: FALSIFIED as convergence-to-1-from-above in the observed fit-ready window\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`unit_status`, `unit_crossing_N`, `below_unit_count`, `fit_ready_rows`, `denominator_state`, `best_model`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=`V_c(phi)` converge a 1 come limite leggibile del canale ordinato; observable=relazione dei punti fit-ready con il bordo `V_c=1`; operator=`exp_vc_unit_boundary_audit.py` su deposito `vc_fit_model_gate_20260509_1400`; generator=`reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=13 righe ammissibili `complete/contaminated` del fit model gate, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=dichiarare convergenza a 1 dall'alto quando la curva osservata attraversa sotto 1 nel perimetro; not_tested=nuovi spettri, nuovi N, nuovi candidati, limite asintotico oltre la finestra osservata.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il bordo `V_c=1` non e' attrattore osservato; e' un taglio attraversato dal canale fit-ready.\n- **Combo**: A2 confine det=-1 + A3 punto fisso come attrattore da falsificare + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: convergenza a 1 / attraversamento di 1; punto-zero = la riga fit-ready prima che il bordo unitario venga trattato come limite o come taglio.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il valore 1 e' nodo di bordo: decide quale arco puo' ricevere il claim di limite.\n- **Operatori laterali scelti**: boundary operator, graph cut, fit come morfismo. Entrano per separare il bordo unitario dal modello numerico.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito 14:00 senza ricomputare spettri; CE-0117: cascata della possibilita, il claim di limite passa solo dove il bordo non e' attraversato; YSN DeltaLink: `fit-ready curve -> unit cut -> limit veto`.\n- **Proto-ipotesi**: se una curva fit-ready attraversa `V_c=1` dentro la finestra osservata, il claim \"converge a 1 dall'alto\" cade su quel perimetro; il bordo unitario resta coordinata di taglio, non attrattore.\n- **Possibile / non-possibile**: possibile = usare `V_c=1` come unit boundary audit; non-possibile = chiamarlo limite osservato quando 12/13 righe hanno punti sotto 1.\n- **Proiezione**: classifico ogni riga ammissibile come `all_above_unit`, `crosses_down_inside_window`, o `all_below_unit`, e calcolo `unit_crossing_N` per le righe che attraversano.\n\n## Claim Under Test\n> Nel perimetro fit-ready 14:00, il canale ordinato sostiene `V_c -> 1` come limite osservato dall'alto.\n\n## Question\nIl bordo `V_c=1` si comporta come attrattore della scala o come taglio attraversato dalle curve fit-ready?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_unit_boundary_audit.py`.\n- Input: `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- Output: `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Perimetro atomico: 13 righe ammissibili del model gate, gia' filtrate da `denominator_state in {complete, contaminated}`.\n- Operatore: per ogni riga ordino i punti `(N, V_c)`, conto i valori sotto 1, e interpolo il primo attraversamento del bordo unitario quando avviene tra due N consecutivi.\n- Contratto osservabile-operatore: il ciclo misura il bordo unitario sul deposito 14:00; non misura nuovi N, non testa l'asintoto, non ripara il fit.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| admissible rows audited | 13 |\n| rows with any `V_c < 1` | 12 |\n| `crosses_down_inside_window` | 9 |\n| `all_below_unit` | 3 |\n| `all_above_unit` | 1 |\n\nStati per classe generatore:\n\n| generator_class | all_above | crosses_down | all_below |\n|---|---:|---:|---:|\n| reference_order | 0 | 3 | 0 |\n| order_memory | 1 | 5 | 0 |\n| periodic_closure | 0 | 1 | 2 |\n| random_dispersion | 0 | 0 | 1 |\n\nCanale ordinato, righe complete/contaminate:\n\n| row | state | fit_ready/total | excluded | first V_c | last V_c | unit status | crossing N |\n|---|---|---:|---:|---:|---:|---|---:|\n| per_mode_best:reference_order:r0.48 | complete | 16/16 | 0 | 1.311 | 0.955 | crosses_down | 348.753 |\n| per_mode_best:reference_order:r0.50 | complete | 16/16 | 0 | 1.191 | 0.879 | crosses_down | 276.876 |\n| per_mode_best:reference_order:r0.52 | complete | 16/16 | 0 | 1.072 | 0.807 | crosses_down | 151.973 |\n| per_mode_best:order_memory:r0.48 | complete | 48/48 | 0 | 1.432 | 0.893 | crosses_down | 315.760 |\n| per_mode_best:order_memory:r0.50 | complete | 48/48 | 0 | 1.317 | 0.774 | crosses_down | 255.737 |\n| per_mode_best:order_memory:r0.52 | complete | 48/48 | 0 | 1.218 | 0.688 | crosses_down | 188.585 |\n| accepted_candidates:order_memory:r0.48 | contaminated | 273/279 | 6 | 1.474 | 1.069 | all_above | blank |\n| accepted_candidates:order_memory:r0.50 | contaminated | 278/279 | 1 | 1.350 | 0.899 | crosses_down | 332.733 |\n| accepted_candidates:order_memory:r0.52 | complete | 279/279 | 0 | 1.236 | 0.781 | crosses_down | 272.895 |\n\nPrimi attraversamenti del bordo:\n\n| row | crossing N |\n|---|---:|\n| per_mode_best:periodic_closure:r0.48 | 105.734 |\n| per_mode_best:reference_order:r0.52 | 151.973 |\n| per_mode_best:order_memory:r0.52 | 188.585 |\n| per_mode_best:order_memory:r0.50 | 255.737 |\n| accepted_candidates:order_memory:r0.52 | 272.895 |\n| per_mode_best:reference_order:r0.50 | 276.876 |\n| per_mode_best:order_memory:r0.48 | 315.760 |\n| accepted_candidates:order_memory:r0.50 | 332.733 |\n\n## Key Findings\n1. **Verificato: il claim `converge a 1 dall'alto` cade nel perimetro osservato.** 12/13 righe ammissibili hanno almeno un punto fit-ready sotto `V_c=1`.\n2. **Verificato: il canale ordinato completo attraversa il bordo.** `reference_order` fa 3/3 attraversamenti; `order_memory` per-mode-best fa 3/3 attraversamenti; tutti con denominatore `complete`.\n3. **Verificato: l'unica riga all-above e' contaminata.** `accepted_candidates:order_memory:r0.48` resta sopra 1, ma ha 273/279 righe fit-ready e 6 righe escluse; non puo' sostenere da sola il claim hard.\n4. **Verificato: `periodic_closure` e `random_dispersion` non salvano il limite.** Due righe `periodic_closure` e una `random_dispersion` sono gia' tutte sotto 1; il loro denominatore era contaminato e non appartiene al canale ordinato completo.\n5. **Inferito: il nodo regressivo non e' scegliere un nuovo modello di fit.** Il bordo unitario va promosso a veto osservabile prima del claim di limite.\n\n## Verdict\n**FALSIFIED** nel perimetro osservato.\n\n`V_c=1` non opera come limite osservato dall'alto sulle righe fit-ready 14:00. Opera come bordo attraversato. Il canale ordinato resta fittabile e lineare nel perimetro, ma proprio quel canale scende sotto 1: `reference_order` 3/3 e `order_memory` per-mode-best 3/3 attraversano il bordo dentro N={89,144,233,377}.\n\nLa formulazione valida diventa:\n\n`V_c=1` e' un unit boundary audit del sottografo fit-ready; non e' attrattore verificato della scala nel perimetro 14:00.\n\n## Bicono della scoperta\n- **Due radici**: limite unitario / attraversamento unitario.\n- **Singolare**: la curva fit-ready prima che `1` sia interpretato come attrattore o come taglio.\n- **Invariante di passaggio**: la disponibilita del denominatore completo sopravvive; il claim di convergenza a 1 non sopravvive.\n- **Campo di possibilita**: qui diventa possibile usare `V_c=1` come veto strutturale nei futuri fit; qui diventa non-possibile formulare `V_c(phi) -> 1` come claim osservato dall'alto su questo perimetro.\n\n## Consecutio\nIl prossimo ciclo che estende N deve partire dal veto unitario: se N piu' alti vengono calcolati, il claim non e' \"converge a 1\", ma \"dopo l'attraversamento, la curva resta sotto 1, rientra, o cambia famiglia\". Il denominatore da preservare e' `complete + unit_status`, non solo `fit_ready`.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1337.md`, report `agent_20260509_1400.md`, JSON `tools/data/vc_fit_model_gate_20260509_1400.json`, nuovo JSON `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- **L1 hard constraint**: il verdict vale solo per le 13 righe ammissibili del deposito 14:00 e N={89,144,233,377}.\n- **L2 count grezzi**: riportati 12/13 sotto 1, 9/13 attraversamenti, 1/13 sopra 1, 3/13 tutte sotto 1.\n- **L3 no silent patching**: nessun nuovo spettro, nessun nuovo N usato come evidenza, nessuna modifica ai depositi 13:37/14:00.\n- **L4 edge cases**: l'unica riga sopra 1 e' contaminata con 6 esclusioni; non chiude il claim hard.\n- **L5 re-discovery**: il finding non ripete \"V_c scende\"; misura il bordo unitario come veto atomico.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = unit boundary audit; non-possibile = convergenza a 1 dall'alto nel perimetro osservato.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_unit_boundary_audit.py`.\n- Run valido: `python tools/exp_vc_unit_boundary_audit.py --input tools/data/vc_fit_model_gate_20260509_1400.json --out tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Un tentativo di ricomputo esteso su N alti non ha prodotto deposito leggibile in tempo utile e non e' usato come evidenza.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge solo lo script unit-boundary, il JSON 1457 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_vc_unit_boundary_audit.py`\n- Data: `tools/data/vc_unit_boundary_audit_20260509_1457.json`\n- Report: `tools/data/reports/agent_20260509_1457.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1752: "content_full": "# Agent Report - Boundary Gate Transfer Matrix\n**Date**: 2026-05-09 14:44\n**Piano**: 106\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - denominator gate transfers as order/null operator, not as QxG closure\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_state -> transfer_state -> denominator_state`; observable=stable canonical observables contro permutation null e layer classification; operator=`exp_denominator_gate_transfer_matrix.py`; generator=`DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`; denominator=4 perimetri sintetici QxG continuo/discreto, 4096 gaps, 24 replicates, 11 beta layers, 40 shuffle baselines; non_possible=chiamare chiusura QxG, legge GUE/Poisson o endpoint-stable universale; not_tested=perimetro fisico reale, fit `V_c`, nuovi domini autoricerca.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo 13/13 transfer sul perimetro base, il confine non chiede un altro blank audit. Chiede se il gate resta gate quando non porta piu' le etichette GUE/Poisson.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY completo 13/13 + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: ordine coerente / null permutato; punto-zero = layer beta dove il segnale conserva osservabili stabili ma la classificazione diventa ambigua.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate viene letto come mappa di passaggio tra stato coerente, stato null e denominatore, non come tassonomia locale.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter transfer matrix, shuffle marginal-preserving. Entrano per trasferire il gate senza importare il label GUE/Poisson.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione del deposito 1437 senza ripetere il perimetro base; CE-0117: cascata della possibilita solo dove il gate cambia perimetro; YSN DeltaLink: `base closure -> transfer matrix -> beta blank`.\n- **Proto-ipotesi**: il gate BOUNDARY trasferisce quando il polo coerente ha osservabili one-sided contro il proprio null e il polo illusorio collassa; non trasferisce come endpoint-stable universale.\n- **Possibile / non-possibile**: possibile = usare il gate come audit di ordine su perimetri sintetici QxG; non-possibile = dichiarare ponte QxG o usare beta 0.3 come classe decisa.\n- **Proiezione**: misurare, per ogni perimetro, osservabili one-sided, stable count ai poli, distanza endpoint e layer ambiguo.\n\n## Claim Under Test\n> Il gate BOUNDARY trasferisce fuori dal perimetro base se ogni perimetro mostra osservabili one-sided al polo coerente, collasso al polo null permutato, e un layer blank/ambiguous esplicito prima del passaggio.\n\n## Question\nQuando il gate viene mosso da BOUNDARY base a quattro perimetri QxG sintetici, resta transfer, cade, o produce un blank strutturale?\n\n## Experiment Design\n- Strumento: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Perimetri: `DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`.\n- Null baseline: permutation shuffle della stessa multiset di gaps; conserva distribuzione marginale e distrugge ordine.\n- Criterio transfer: osservabili coherent one-sided presenti e stable count coerente alto, con stable count illusorio basso.\n- Criterio blank: layer beta con `ambiguous_fraction >= 0.5` nella classificazione gated.\n- Criterio fall: assenza di osservabili one-sided o distanza endpoint collassata. Nessun perimetro cade in questo run.\n\n## Results\n\n| perimeter | one-sided observables | stable coherent | stable illusory | endpoint distance gated | ambiguous beta |\n|---|---|---:|---:|---:|---|\n| DUALITA_golden | SR, L1, triple_var | 3.000 | 0.292 | 3.426 | 0.3 |\n| R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.000 | 0.375 | 4.409 | 0.3 |\n| T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.000 | 0.417 | 4.421 | 0.3 |\n| E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.000 | 0.292 | 4.398 | 0.3 |\n\nLayer beta 0.3:\n\n| perimeter | margin mean | ambiguous fraction | illusory label fraction |\n|---|---:|---:|---:|\n| DUALITA_golden | 0.030 | 1.000 | 0.625 |\n| R_periodic_triad | 0.043 | 1.000 | 0.583 |\n| T_markov_alternating | 0.030 | 1.000 | 0.708 |\n| E_ar1_continuity | 0.042 | 1.000 | 0.500 |\n\nLayer beta 0.4:\n\n| perimeter | margin mean | illusory label fraction |\n|---|---:|---:|\n| DUALITA_golden | 0.269 | 1.000 |\n| R_periodic_triad | 0.269 | 1.000 |\n| T_markov_alternating | 0.289 | 1.000 |\n| E_ar1_continuity | 0.284 | 1.000 |\n\n## Key Findings\n1. **Verificato: il transfer esiste sui 4/4 perimetri.** Ogni perimetro possiede osservabili coherent one-sided e collasso del polo illusorio sotto permutation null.\n2. **Verificato: non esistono endpoint-stable observables.** `endpoint_stable_observables=[]` per tutti i perimetri; il gate trasferisce come discriminatore di ordine, non come invariante simmetrico ai due poli.\n3. **Verificato: beta 0.3 e' il blank strutturale.** Tutti i perimetri hanno `ambiguous_fraction=1.0` a beta 0.3 nella classificazione gated.\n4. **Verificato: beta 0.4 passa al polo illusorio.** Tutti i perimetri hanno `illusory_label_fraction=1.0` a beta 0.4.\n5. **Vincolo: `DUALITA_golden` trasferisce con solo 3 osservabili one-sided.** SR2 e L2 non entrano nel nucleo one-sided del perimetro golden; non va scritto che il set canonico completo trasferisce ovunque.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce fuori dal perimetro base come operatore di ordine/null: 4/4 perimetri hanno polo coerente leggibile, polo illusorio collassato e blank strutturale a beta 0.3. Il transfer non e' chiusura QxG. Il transfer non e' endpoint-stable universale, perche' nessun perimetro conserva osservabili stabili su entrambi i poli.\n\nIl criterio operativo diventa:\n\n`transfer` = osservabili one-sided al polo coerente + collasso al null permutato. \n`blank` = beta 0.3, classificazione ambiguous su 4/4 perimetri. \n`fall` = assente in questo run, ma definito come perdita di one-sided observables o endpoint distance collassata.\n\n## Bicono della scoperta\n- **Due radici**: gate come copertura del denominatore / gate come classificatore del ponte.\n- **Singolare**: il layer beta 0.3, dove il segnale ha ancora stable count alto ma la decisione di classe non e' disponibile.\n- **Invariante di passaggio**: osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come transfer matrix per perimetri sintetici QxG; qui diventa non-possibile dichiarare QxG chiuso o trattare il gate come endpoint-stable law.\n\n## Consecutio\nIl prossimo nodo regressivo non e' la disponibilita del null. E' la qualita del perimetro: il gate passa su sintetici controllati, ma non ha ancora incontrato un perimetro fisico QxG row-aligned. La prossima mossa deve portare un perimetro fisico o semi-reale dentro lo stesso contratto `coherent/null/beta`, lasciando beta 0.3 come blank dichiarato e non come classe.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/reports/agent_20260509_1437.md`, `tools/exp_denominator_gate_transfer_matrix.py`, `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- **L1 hard constraint**: claim limitato a 4 perimetri sintetici, 4096 gaps, 24 replicates, 40 shuffle baselines.\n- **L2 count grezzi**: riportati 4/4 transfer, 4/4 blank a beta 0.3, 0/4 fall.\n- **L3 no silent patching**: nessuna modifica agli script; nuovo artefatto dati generato dal run.\n- **L4 edge cases**: `DUALITA_golden` trasferisce con 3 osservabili, non 5; nessun endpoint-stable observable.\n- **L5 re-discovery**: il ciclo sposta il gate fuori dal perimetro base; non ripete il residual blank closure.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer matrix QxG sintetica; non-possibile = chiusura QxG o legge endpoint-stable.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Run valido: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Output: `observables_registry=1.0.0-2026-05-06`; `observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var']`.\n- Git state: worktree gia' sporco da depositi e pubblicazioni precedenti; questo cycle aggiunge solo il JSON 1444 e il report 1444.\n\n## Files\n- Script: `tools/exp_denominator_gate_transfer_matrix.py`\n- Data: `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`\n- Report: `tools/data/reports/agent_20260509_1444.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1803: "content_full": "# Agent Report - Residual Boundary Closure\n**Date**: 2026-05-09 14:37\n**Piano**: 105\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: COMPLETE DENOMINATOR TRANSFER on base BOUNDARY perimeter\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il residual blank test decide se i 3 blank residui BOUNDARY entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + `exp_boundary_denominator_prescan.py`; generator=`dnd_autoricerca.genera_segnale` per `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare chiusura QxG, nuova legge GUE/Poisson, o complete `reaction_diffusion` con 499 gap; not_tested=fit `V_c`, nuovi spettri, nuovi domini.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank residuo non segnala una classe mancante; segnala l'assenza del contro-perimetro. Quando il null entra, il bordo base non ha piu' blank.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: blank / transfer; punto-zero = riga domain/window prima della disponibilita del null row-aligned.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate decide copertura del denominatore, non ontologia GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, flusso/PDE come controllo di non-determinismo per `reaction_diffusion`.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito esistente; CE-0117: cascata della possibilita solo dove il null apre il canale; YSN DeltaLink: `blank residuo -> null row-aligned -> transfer completo`.\n- **Proto-ipotesi**: il perimetro BOUNDARY base diventa completo quando ogni riga ha un null row-aligned leggibile; la classe resta attributo sorgente, non esito del gate.\n- **Possibile / non-possibile**: possibile = contratto BOUNDARY 13/13 transfer sul perimetro base; non-possibile = usare questa copertura per chiudere QxG, per chiamare legge il cambio classe della logistica, o per trattare `reaction_diffusion` come complete.\n- **Proiezione**: verificare `boundary_blank_null_audit_residual_20260509_1500.json`, innestarlo nel prescan pieno e controllare `boundary_denominator_prescan_full_20260509_1500.json`.\n\n## Claim Under Test\n> Il residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\n## Question\nI tre blank residui `string_vibration:cycle_6`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` restano blank, cadono, o entrano nel transfer quando ricevono null row-aligned?\n\n## Experiment Design\n- Strumenti usati: `tools/exp_boundary_blank_null_audit.py` e `tools/exp_boundary_denominator_prescan.py`.\n- Input verificati: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json` e `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like.\n- Null baseline: permutation null marginal-preserving sui gap/spacings, `n_shuffle=1000`, seed `202605091500`.\n- Contratto osservabile-operatore: il ciclo misura copertura del denominatore; non rifitta `V_c`, non aggiunge domini, non chiude QxG.\n\n## Results\n\nResidual blank audit depositato:\n\n| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision |\n|---|---:|---:|---:|---:|---|---|---|\n| string_vibration | 7999 | 0.383868 | 0.372199 | 4.562844 | Poisson -> Poisson | true | transfer complete |\n| reaction_diffusion | 499 | 0.762604 | 0.494932 | 31.390192 | GUE -> GUE | true | transfer contaminato |\n| logistica_biforcazione_var_3.5699 | 4727 | 0.581221 | 0.099640 | 161.271569 | GUE -> Poisson | true | transfer complete, class_change edge case |\n\nPrescan pieno dopo innesto dei null:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 13 |\n| blank | 0 |\n| falls | 0 |\n| complete | 8 |\n| contaminated | 5 |\n\nRighe residue nel prescan:\n\n| domain/window | source | denominator_state | excluded mass | null | transfer |\n|---|---|---|---:|---|---|\n| string_vibration:cycle_6 | Poisson | complete | 0.000 | shuffle z=4.56; class_change=False | transfers |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 0.002 | shuffle z=31.39; class_change=False | transfers |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 0.000 | shuffle z=161.27; class_change=True | transfers |\n\n## Key Findings\n1. **Verificato: i 3 blank residui diventano transfers.** Il prescan pieno produce 13/13 transfers, 0 blank, 0 falls.\n2. **Verificato: `reaction_diffusion` non e' complete.** Ha 499 gap, quindi resta `contaminated` con excluded mass 0.002; un gap sotto soglia basta a impedire la formula complete.\n3. **Verificato: `string_vibration` trasferisce senza cambio classe.** Poisson -> Poisson, `ordering_dependent=true`, `n_gaps=7999`.\n4. **Verificato: `logistica_biforcazione_var_3.5699` trasferisce con `class_change=true`.** Il cambio GUE -> Poisson e' edge case del null, non legge nuova legge.\n5. **Verificato: il gate BOUNDARY ha copertura completa sul perimetro base.** La copertura riguarda il denominatore row-aligned, non il ponte QxG.\n\n## Verdict\n**COMPLETE DENOMINATOR TRANSFER**.\n\nIl residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\nIl gate ha copertura completa sul perimetro base: ogni riga domain/window possiede un null leggibile o contaminato dichiarato. La completezza del gate non coincide con completezza fisica delle righe: `reaction_diffusion` resta contaminato per 499 gap, `zeta_zeros` resta contaminato per 199 gap, e `logistica_biforcazione_var_3.5699` non produce una legge dal suo `class_change=true`.\n\n## Bicono della scoperta\n- **Due radici**: blank senza contro-perimetro / transfer con null row-aligned.\n- **Singolare**: la riga BOUNDARY prima che il denominatore decida.\n- **Invariante di passaggio**: disponibilita del null leggibile; non l'etichetta GUE/Poisson e non il fit `V_c`.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come gate base 13/13 per audit successivi; qui diventa non-possibile spacciare il gate come chiusura QxG o legge RMT.\n\n## Consecutio\nLa prossima mossa non e' rifare `V_c` e non e' chiudere QxG. Il risultato apre un uso piu' stretto del gate: ogni futuro claim BOUNDARY puo' partire dal perimetro base senza blank, ma deve portare con se' lo stato `complete/contaminated` e dichiarare gli edge case. Il nodo regressivo rimasto e' la qualita del denominatore, non la sua assenza.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`, `tools/data/boundary_denominator_prescan_full_20260509_1500.json`, run di verifica in `/tmp`.\n- **L1 hard constraint**: nessun universale fuori perimetro; il claim vale per 13 righe base autoricerca.\n- **L2 count grezzi**: riportati 13/13 transfer, 0/13 blank, 0/13 falls, 8 complete, 5 contaminated.\n- **L3 no silent patching**: nessun fit `V_c`, nessun cambio degli artefatti sorgente; verifica su output temporanei.\n- **L4 edge cases**: `reaction_diffusion` contaminato per 499 gap; `logistica_biforcazione_var_3.5699` ha class_change=true ma non diventa legge.\n- **L5 re-discovery**: il ciclo e' audit di denominatore residuo, non teorema GUE/Poisson.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = gate base 13/13 senza blank; non-possibile = QxG chiuso o completezza fisica delle righe contaminate.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py`.\n- Run valido: `python tools/exp_boundary_blank_null_audit.py --domains string_vibration reaction_diffusion logistica_biforcazione_var_3.5699 --n-shuffle 1000 --seed 202605091500 --out /tmp/boundary_blank_null_audit_residual_20260509_1500.verify.json`.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --extra-null-audit tools/data/boundary_blank_null_audit_20260509_1430.json --extra-null-audit tools/data/boundary_blank_null_audit_residual_20260509_1500.json --out /tmp/boundary_denominator_prescan_full_20260509_1500.verify.json`.\n- Compare prescan: summary e rows combaciano con `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Compare residual audit: `string_vibration` e `logistica_biforcazione_var_3.5699` combaciano sui numeri depositati; `reaction_diffusion` nel rerun produce `r=0.755661`, `z=30.592858` invece di `r=0.762604`, `z=31.390192`, ma conserva `n_gaps=499`, `GUE -> GUE`, `ordering_dependent=true`, `transfer=contaminated`. Il drift e' attribuito al generatore dinamico, non al null seed.\n\n## Files\n- Script: `tools/exp_boundary_blank_null_audit.py`\n- Script: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`\n- Data: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`\n- Report: `tools/data/reports/agent_20260509_1437.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1854: "content_full": "# Agent Report - Blank Pair Null Transfer\n**Date**: 2026-05-09 14:27\n**Piano**: 104\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONFIRMED TRANSFER on blank-pair test\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il blank-pair test decide se due righe BOUNDARY senza null entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + prescan row-aligned; generator=`dnd_autoricerca.genera_segnale` per `zeta_zeros` e `pendolo_doppio`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare complete `zeta_zeros` con soli 199 gap o dichiarare cambio classe su `pendolo_doppio`; not_tested=fit `V_c`, nuovi spettri, nuova legge GUE/Poisson, chiusura QxG.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank non e' vuoto del dominio; e' mancanza del contro-perimetro. Quando il null entra, il bordo decide senza cambiare classe.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: blank / transfer; punto-zero = stessa riga domain/window prima della disponibilita del null row-aligned.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non classifica GUE o Poisson; filtra quali righe hanno denominatore sufficiente.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, grafo row-aligned. Entrano per separare osservabile locale, null e transfer senza nuovo fit.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito esistente; CE-0117: cascata della possibilita solo dove il null apre il canale; YSN DeltaLink: `blank row -> null availability -> transfer`.\n- **Proto-ipotesi**: un blank BOUNDARY diventa transfer quando possiede un null row-aligned leggibile; la classe sorgente resta controllo, non risposta.\n- **Possibile / non-possibile**: possibile = trasferire il gate a righe ex-blank con null disponibile; non-possibile = usare la stessa misura per chiamare `zeta_zeros` complete o `pendolo_doppio` cambio classe.\n- **Proiezione**: verificare `boundary_blank_null_audit_20260509_1430.json` e reinnestarlo nel prescan `boundary_denominator_prescan_20260509_1430.json`.\n\n## Claim Under Test\n> Il blank-pair test trasforma `zeta_zeros` e `pendolo_doppio` da blank a transfers; il perimetro BOUNDARY passa da 8/13 a 10/13 transfer, con 3 blank residui e 0 falls.\n\n## Question\nIl null row-aligned per una coppia blank GUE/Poisson decide `blank -> transfers`, `blank -> falls` o `blank remains blank`?\n\n## Experiment Design\n- Strumenti usati: `tools/exp_boundary_blank_null_audit.py` e `tools/exp_boundary_denominator_prescan.py`.\n- Input verificati: `tools/data/boundary_blank_null_audit_20260509_1430.json` e `tools/data/boundary_denominator_prescan_20260509_1430.json`.\n- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like.\n- Null baseline: permutation null marginal-preserving sui gap/spacings, `n_shuffle=1000`, seed `202605091430`.\n- Contratto osservabile-operatore: il ciclo misura disponibilita e comportamento del null, non rifitta `V_c`; `zeta_zeros` resta contaminated per massa debole; `pendolo_doppio` resta Poisson -> Poisson con ordering dependence forte.\n\n## Results\n\nBlank-pair audit:\n\n| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision |\n|---|---:|---:|---:|---:|---|---|---|\n| zeta_zeros | 199 | 0.615009 | 0.641885 | -2.310593 | GUE -> GUE | false | transfer contaminato |\n| pendolo_doppio | 5008 | 0.386104 | 0.292437 | 27.919656 | Poisson -> Poisson | true | transfer complete |\n\nPrescan dopo innesto del null:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 10 |\n| blank | 3 |\n| falls | 0 |\n| complete | 6 |\n| contaminated | 4 |\n| absent | 3 |\n\nBlank residui: `string_vibration:cycle_6`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13`.\n\n## Key Findings\n1. **Verificato: il blank-pair test trasforma entrambe le righe da blank a transfers.** `zeta_zeros` e `pendolo_doppio` hanno null row-aligned leggibile e quindi non restano blank.\n2. **Verificato: il transfer non cambia la classe.** `zeta_zeros` resta GUE -> GUE; `pendolo_doppio` resta Poisson -> Poisson.\n3. **Verificato: `zeta_zeros` non e' complete.** Ha solo 199 gap, sotto soglia 500, quindi entra come `contaminated/transfers` con excluded mass 0.602.\n4. **Verificato: `pendolo_doppio` e' complete e ordering-dependent.** Ha 5008 gap, z=27.919656, class_change=false; non diventa una classe nuova.\n5. **Verificato: il perimetro BOUNDARY passa da 8/13 a 10/13 transfer.** Restano 3 blank e nessun fall.\n\n## Verdict\n**CONFIRMED TRANSFER**.\n\nIl blank-pair test trasforma `zeta_zeros` e `pendolo_doppio` da blank a transfers; il perimetro BOUNDARY passa da 8/13 a 10/13 transfer, con 3 blank residui e 0 falls.\n\nQuesto non chiude QxG, non dichiara una nuova legge GUE/Poisson e non rifitta `V_c`. Il risultato decide il denominatore: quando il null row-aligned esiste, il blank diventa transfer; quando manca, resta blank.\n\n## Bicono della scoperta\n- **Due radici**: blank senza contro-perimetro / transfer con null row-aligned.\n- **Singolare**: la riga domain/window prima della decisione del gate.\n- **Invariante di passaggio**: il null row-aligned decide il passaggio; la classe GUE/Poisson non viene riscritta.\n- **Campo di possibilita**: qui diventa possibile restringere il contro-perimetro BOUNDARY a 3 blank residui; qui diventa non-possibile trattare `zeta_zeros` come complete o `pendolo_doppio` come cambio classe.\n\n## Consecutio\nLa prossima mossa BOUNDARY non e' un fit `V_c`: e' costruire o recuperare null row-aligned per `string_vibration`, `reaction_diffusion` e `logistica_biforcazione_var_3.5699`. Il confine ora ha 10 righe trasferite e un contro-perimetro residuo esplicito.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/boundary_blank_null_audit_20260509_1430.json`, `tools/data/boundary_denominator_prescan_20260509_1430.json`, run di verifica in `/tmp`.\n- **L1 hard constraint**: nessun universale fuori perimetro; il claim vale per 13 righe base e per i due null innestati.\n- **L2 count grezzi**: riportati 10/13 transfer, 3/13 blank, 0/13 falls.\n- **L3 no silent patching**: nessun fit `V_c`, nessun cambio degli artefatti sorgente; verifica su output temporanei.\n- **L4 edge cases**: `zeta_zeros` contaminato per `n_gaps=199`; `pendolo_doppio` complete ma non cambio classe.\n- **L5 re-discovery**: il ciclo e' audit di denominatore, non teorema RMT.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer ex-blank con null; non-possibile = claim su blank residui senza null.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py`.\n- Run valido: `python tools/exp_boundary_blank_null_audit.py --domains zeta_zeros pendolo_doppio --n-shuffle 1000 --seed 202605091430 --out /tmp/boundary_blank_null_audit_20260509_1430.verify.json`.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --extra-null-audit tools/data/boundary_blank_null_audit_20260509_1430.json --out /tmp/boundary_denominator_prescan_20260509_1430.verify.json`.\n- Compare: gli output temporanei combaciano con gli artefatti depositati, ignorando solo `generated_at` nel null audit.\n\n## Files\n- Script: `tools/exp_boundary_blank_null_audit.py`\n- Script: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_blank_null_audit_20260509_1430.json`\n- Data: `tools/data/boundary_denominator_prescan_20260509_1430.json`\n- Report: `tools/data/reports/agent_20260509_1427.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1905: "content_full": "# Agent Report - Boundary Denominator Prescan\n**Date**: 2026-05-09 14:09\n**Piano**: 103\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: PARTIAL TRANSFER with structural blank counter-perimeter\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`denominator_state`, `excluded_mass`, `spacing_r`, `shuffle_r_statistic`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=`denominator_state` puo' trasferire oltre `V_c` solo dove la riga domain/window ha osservabile definito e null/surrogate row-aligned; observable=`spacing_r` + availability del null shuffle; operator=prescan sui 13 domini base del deposito autoricerca, non fit parametrico; generator=perimetro 8 GUE-like / 5 Poisson-like dei cycle 1..13; denominator=`boundary_shuffle_audit` quando presente; non_possible=dichiarare transfer nei domini senza null/surrogate; not_tested=nuovi spettri, nuovi domini, fit `V_c`, famiglia AICc, QxG chiuso.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il confine non chiede se il dominio e' GUE o Poisson; chiede se la riga ha un denominatore capace di sostenere un claim.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: classificato / non aggregabile; punto-zero = riga domain/window prima che il null decida se l'osservabile puo' viaggiare.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo e' una filtrazione: prima osservabile, poi null, poi claim.\n- **Proto-ipotesi**: `denominator_state` trasferisce oltre `V_c` se separa riga misurabile, massa contaminata e blank senza usare GUE/Poisson come risposta.\n- **Possibile / non-possibile**: possibile = claim boundary su righe con null row-aligned; non-possibile = claim di transfer nei domini senza null, anche quando `spacing_r` locale esiste.\n- **Proiezione**: creare `tools/exp_boundary_denominator_prescan.py` e misurare 13 righe base: domain/window, source type, denominator_state, excluded mass, observable, null/surrogate, transfer.\n\n## Contaminazione cognitiva\n- **CE-0001 / KSAR**: reiterazione sul deposito invece di nuovo fit locale `V_c`.\n- **CE-0117**: cascata della possibilita; il claim passa solo dove il null apre il canale.\n- **YSN DeltaLink**: `domain row -> null availability -> transfer`, non `GUE/Poisson -> risposta`.\n\n## Claim Under Test\n> Nel perimetro BOUNDARY 8 GUE / 5 Poisson, il gate `denominator_state` trasferisce oltre `V_c` solo se identifica le righe con null/surrogate disponibile e lascia blank le righe senza contro-perimetro.\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_denominator_prescan.py`.\n- Input: `tools/data/autoricerca_journal.json` e `tools/data/boundary_shuffle_audit.json`.\n- Output: `tools/data/boundary_denominator_prescan_20260509_1409.json`.\n- Perimetro atomico: cycle base `1..13`, esattamente 13 righe: 8 GUE-like, 5 Poisson-like.\n- Stati:\n - `complete`: osservabile definito + null shuffle presente con `n_gaps >= 500`.\n - `contaminated`: osservabile definito + null presente ma massa debole dichiarata (`n_gaps < 500`).\n - `absent`: osservabile locale presente ma null/surrogate assente, oppure osservabile assente.\n - `broken`: null presente ma non leggibile; non emerso in questo run.\n- Transfer:\n - `transfers`: `complete` o `contaminated` con null presente.\n - `blank`: osservabile locale presente ma null assente.\n - `falls`: osservabile assente o null rotto.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 8 |\n| blank | 5 |\n| falls | 0 |\n| complete | 5 |\n| contaminated | 3 |\n| absent | 5 |\n\nTabella prescan:\n\n| domain/window | source | denominator_state | excluded mass | observable | null/surrogate | transfer |\n|---|---|---|---:|---|---|---|\n| ising_2d:cycle_1 | GUE | contaminated | 0.810 | spacing_r=0.902 | shuffle z=-0.10; class_change=False | transfers |\n| pendolo_doppio:cycle_2 | Poisson | absent | 1.000 | spacing_r=0.386 | absent | blank |\n| numeri_primi:cycle_3 | GUE | complete | 0.000 | spacing_r=0.886 | shuffle z=-26.60; class_change=False | transfers |\n| zeta_zeros:cycle_4 | GUE | absent | 1.000 | spacing_r=0.615 | absent | blank |\n| logistica_biforcazione:cycle_5 | GUE | complete | 0.000 | spacing_r=0.997 | shuffle z=61.60; class_change=False | transfers |\n| string_vibration:cycle_6 | Poisson | absent | 1.000 | spacing_r=0.384 | absent | blank |\n| random_matrix:cycle_7 | GUE | complete | 0.000 | spacing_r=0.573 | shuffle z=-14.70; class_change=False | transfers |\n| cellular_automata:cycle_8 | GUE | contaminated | 0.790 | spacing_r=0.861 | shuffle z=-0.10; class_change=False | transfers |\n| percolation:cycle_9 | Poisson | complete | 0.000 | spacing_r=0.404 | shuffle z=16.10; class_change=True | transfers |\n| coupled_oscillators:cycle_10 | Poisson | contaminated | 0.146 | spacing_r=0.385 | shuffle z=43.50; class_change=True | transfers |\n| reaction_diffusion:cycle_11 | GUE | absent | 1.000 | spacing_r=0.759 | absent | blank |\n| brownian_motion:cycle_12 | Poisson | complete | 0.000 | spacing_r=0.489 | shuffle z=-1.60; class_change=False | transfers |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | absent | 1.000 | spacing_r=0.997 | absent | blank |\n\n## Key Findings\n1. **Verificato: il gate trasferisce su 8/13 righe del perimetro.** Il transfer non coincide con GUE o Poisson: include 5 sorgenti GUE e 3 sorgenti Poisson perche' la condizione e' disponibilita del null, non etichetta di classe.\n2. **Verificato: 5/13 righe restano blank strutturale.** `pendolo_doppio`, `zeta_zeros`, `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699` hanno `spacing_r` locale ma non hanno null/surrogate row-aligned nel deposito letto.\n3. **Verificato: `contaminated` non viene escluso.** `ising_2d`, `cellular_automata`, `coupled_oscillators` trasferiscono come contaminati perche' il null esiste ma la massa debole viene dichiarata.\n4. **Verificato: il contro-perimetro non e' una famiglia alternativa.** Il blank non autorizza una classe nuova; dice che il Lab non possiede osservabile sufficiente per trasferire il gate su quella riga.\n5. **Inferito: la consecutio corretta e' generare null row-aligned per i 5 blank, non fittare `V_c`.** Il nodo regressivo e' nel denominatore mancante, prima di ogni modello numerico.\n\n## Verdict\n**PARTIAL TRANSFER**: `denominator_state` trasferisce oltre `V_c` come gate di perimetro, non come risposta GUE/Poisson.\n\nNel perimetro 8 GUE / 5 Poisson, il gate produce una condizione cross-dominio verificabile su 8 righe con null shuffle disponibile. Su 5 righe resta blank strutturale: l'osservabile locale esiste, ma manca il contro-perimetro row-aligned.\n\nIl gate `denominator_state` trasferisce oltre V_c perche' separa 8 righe con null/surrogate shuffle disponibile da 5 righe blank senza contro-perimetro; il contro-perimetro sono `pendolo_doppio`, `zeta_zeros`, `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`.\n\n## Bicono della scoperta\n- **Due radici**: riga misurabile / riga non aggregabile.\n- **Singolare**: domain/window prima della classificazione GUE/Poisson.\n- **Invariante di passaggio**: il null row-aligned decide il transfer; la classe sorgente non decide.\n- **Campo di possibilita**: qui diventa possibile portare BOUNDARY in un contratto cross-dominio; qui diventa non-possibile chiudere QxG o dichiarare transfer sui 5 blank senza nuovo null.\n\n## Consecutio\nIl prossimo ciclo boundary deve costruire null/surrogate row-aligned per i 5 blank. Se quei null entrano, il gate puo' diventare condizione cross-dominio completa. Se non entrano, il confine resta un perimetro 8/13 con blank dichiarato.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `agent_20260509_1337.md`, `agent_20260509_1400.md`, `falsifier_20260509_1400.json`, `agent_20260507_0330.md`, `tools/data/autoricerca_journal.json`, `tools/data/boundary_shuffle_audit.json`, nuovo JSON `tools/data/boundary_denominator_prescan_20260509_1409.json`.\n- **L1 hard constraint**: nessun \"sempre\" o \"solo\" universale; il verdict vale per 13 righe base e due depositi dichiarati.\n- **L2 count grezzi**: riportati 8/13 transfer, 5/13 blank, 5 complete, 3 contaminated, 5 absent.\n- **L3 no silent patching**: il ciclo non corregge il fit 14:00; usa quel falsifier come veto e cambia nodo al boundary denominator.\n- **L4 edge cases**: `contaminated` resta osservabile e non viene escluso; massa dichiarata in tabella.\n- **L5 re-discovery**: il risultato e' un audit di perimetro del Lab, non un teorema RMT nuovo.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer su righe con null; non-possibile = transfer su righe blank senza null.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_denominator_prescan.py` completato senza errori.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --out tools/data/boundary_denominator_prescan_20260509_1409.json`.\n\n## Files\n- Script nuovo: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_denominator_prescan_20260509_1409.json`\n- Report: `tools/data/reports/agent_20260509_1409.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:1950: "content_full": "# Agent Report - Vc Fit Model Gate\n**Date**: 2026-05-09 14:00\n**Piano**: 103\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: CONSTRAINT on V_c scale model selection\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`denominator_state`, `fit_ready_rows`, `excluded_rows`, `best_model`, `delta_aicc_to_second`, `unit_limit_status`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var\n**observable_contract**: claim=il fit parametrico di `V_c` puo' entrare solo sulle righe `complete` o `contaminated` del gate fit-ready; observable=famiglia di modello migliore tra cinque forme semplici, separata da stato denominatore e massa esclusa; operator=confronto AICc su `vc_median_fit_ready` dal deposito 13:37, senza ricomputare spettri; generator=classi `reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=JSON `vc_fit_ready_scale_table_20260509_1337`, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=righe `broken` escluse dal fit e righe sotto `V_c=1` impediscono il claim osservato \"converge a 1 dall'alto\"; not_tested=nuovi N, nuovi generatori, nuovi spettri, GUE/Poisson transfer, gap_ratio, fit a tre parametri con asintoto libero.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo il gate fit-ready il problema non e' piu' \"il fit converge\"; e' \"quale arco del grafo puo' ricevere una legge senza inglobare massa esclusa\".\n- **Combo**: A2 confine det=-1 + A3 punto fisso come attrattore dichiarato + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: legge di scala / famiglia non identificata; punto-zero = il denominatore fit-ready prima che il modello numerico scelga una forma.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il modello e' un arco permesso solo se il nodo denominatore conserva crossing.\n- **Operatori laterali scelti**: fit come morfismo, boundary operator, graph cut. Entrano per separare righe ammesse, righe rotte e famiglie numeriche.\n- **Contaminazione cognitiva**:\n - **CE-0001 / KSAR**: reiterazione sul deposito 13:37 senza cambiare campo dati.\n - **CE-0117**: cascata della possibilita; il fit entra solo dopo il gate e resta subordinato al denominatore.\n - **YSN DeltaLink**: `denominator_state -> admissible_fit -> model_family`, non `V_c -> legge`.\n- **Proto-ipotesi**: la famiglia di scala non e' una proprieta globale di `V_c`. Nel sottografo ordinato fit-ready, la scala osservata e' quasi lineare in N sul perimetro; nei sottografi contaminati la famiglia cambia o resta non identificabile.\n- **Proiezione**: confronto cinque forme a due parametri su ogni riga ammissibile: `a+b*N`, `a+b*log(N)`, `a+b/sqrt(N)`, `a+b/N`, `c*N^b` con asintoto zero. Le righe `broken` vengono riportate ma non fittate.\n\n## Claim Under Test\n> Nel perimetro fit-ready 13:37, `order_memory` e `reference_order` possono ricevere una selezione di modello; `periodic_closure` e `random_dispersion` non possono essere aggregati alla stessa legge senza dichiarare contaminazione o rottura.\n\n## Question\nUna volta esclusi `no_cross` e `floor_hit`, il fit di scala di `V_c` identifica una legge comune, oppure divide il grafo dei generatori in famiglie di scala distinte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_fit_model_gate.py`.\n- Input: `tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n- Output: `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- Stati ammessi al fit: `complete`, `contaminated`.\n- Stati esclusi dal fit: `broken`.\n- Criterio modello: AICc su quattro punti N per riga; `delta_aicc_to_second < 2` = famiglia non identificata.\n- Check non-possibile: conteggio dei punti osservati con `V_c < 1`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| input rows | 18 |\n| admissible rows | 13 |\n| excluded broken rows | 5 |\n| best linear_N | 9 |\n| best inv_N | 4 |\n| ambiguous rows (`delta_aicc<2`) | 4 |\n| rows with observed `V_c<1` | 12 |\n\nRighe rappresentative, soglia `r=0.50`:\n\n| level | class | state | fit_ready/total | excluded | best model | delta AICc | last V_c |\n|---|---|---|---:|---:|---|---:|---:|\n| per_mode_best | reference_order | complete | 16/16 | 0 | linear_N | 5.651 | 0.879 |\n| per_mode_best | order_memory | complete | 48/48 | 0 | linear_N | 6.607 | 0.774 |\n| per_mode_best | periodic_closure | contaminated | 36/47 | 11 | inv_N | 1.378 | 0.650 |\n| accepted_candidates | order_memory | contaminated | 278/279 | 1 | linear_N | 4.565 | 0.899 |\n| accepted_candidates | order_memory r0.52 | complete | 279/279 | 0 | linear_N | 5.926 | 0.781 |\n\nRighe escluse per stato `broken`:\n\n| level | class | fit_ready/total | excluded |\n|---|---|---:|---:|\n| per_mode_best | random_dispersion r0.50 | 20/32 | 12 |\n| per_mode_best | random_dispersion r0.52 | 15/32 | 17 |\n| accepted_candidates | periodic_closure r0.48 | 102/393 | 291 |\n| accepted_candidates | periodic_closure r0.50 | 102/393 | 291 |\n| accepted_candidates | periodic_closure r0.52 | 102/393 | 291 |\n\nAmbiguita' di famiglia (`delta AICc < 2`):\n\n| row | best | delta AICc |\n|---|---|---:|\n| per_mode_best:order_memory:r0.52 | linear_N | 1.885 |\n| per_mode_best:periodic_closure:r0.48 | inv_N | 1.244 |\n| per_mode_best:periodic_closure:r0.50 | inv_N | 1.378 |\n| per_mode_best:periodic_closure:r0.52 | inv_N | 1.257 |\n\n## Key Findings\n1. **Verificato: il sottografo ordinato fit-ready sceglie `linear_N` nel perimetro osservato.** `reference_order` e `order_memory` completi hanno `linear_N` come miglior famiglia a r=0.48, 0.50, 0.52; a r=0.50 i delta AICc sono 5.651 e 6.607.\n2. **Verificato: `accepted_candidates:order_memory` resta lineare dopo il gate.** A r=0.50 e' contaminato da 1 riga esclusa (`278/279` fit-ready), ma il miglior modello resta `linear_N`; a r=0.52 diventa completo (`279/279`) e resta `linear_N`.\n3. **Verificato: `periodic_closure` non condivide la famiglia del sottografo ordinato.** Nel best-per-mode e' contaminato e sceglie `inv_N`, ma con delta AICc < 2; negli accepted e' `broken` e non entra nel fit.\n4. **Verificato: `random_dispersion` non produce una legge ammessa stabile.** A r=0.48 e' contaminato e sceglie `inv_N`; a r=0.50 e r=0.52 e' `broken`, quindi non e' denominatore di fit.\n5. **Verificato: il claim osservato \"V_c converge a 1\" non regge nel perimetro misurato come vincolo hard.** 12/13 righe fittate hanno almeno un punto osservato sotto 1; `reference_order` e `order_memory` scendono sotto 1 agli N alti.\n\n## Verdict\n**CONSTRAINT**: il fit parametrico di `V_c` non produce una legge comune del boundary.\n\nNel perimetro `N={89,144,233,377}`, il canale `reference_order/order_memory` fit-ready e' leggibile come discesa quasi lineare in N. `periodic_closure` e `random_dispersion` non vanno aggregati: il primo e' contaminato o broken, il secondo passa da contaminato a broken. La formulazione valida e':\n\n`V_c` ha una famiglia di scala per sottografo ammesso; non ha una legge unica prima della separazione `denominator_state + generator_class`.\n\n## Bicono della scoperta\n- **Due radici**: famiglia di fit / stato del denominatore.\n- **Singolare**: la curva `V_c` prima della scelta del modello, dove valore, massa esclusa e classe generatore sono ancora lo stesso nodo.\n- **Invariante di passaggio**: `linear_N` sopravvive nel canale ordinato fit-ready; non sopravvive quando il canale introduce chiusura periodica o dispersione.\n- **Campo di possibilita**: qui diventa possibile testare N piu' alti solo sul sottografo `complete`; qui diventa non-possibile dichiarare una convergenza universale di `V_c` a 1 o un power-law comune.\n\n## Consecutio\nIl prossimo ciclo deve estendere N solo per `reference_order` e `order_memory` con denominatore `complete`. Se i punti alti restano sotto 1 e lineari nel perimetro esteso, il claim `V_c(phi) -> 1` va riscritto come coordinata storica del vecchio perimetro, non come attrattore del boundary.\n\n## Auto-audit: source flags e 5 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1337.md`, JSON `tools/data/vc_fit_ready_scale_table_20260509_1337.json`, nuovo JSON `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- **L1 hard constraint**: il verdict vale solo per N, soglie, classi e deposito dichiarati.\n- **L2 count grezzi**: riportati `13/18`, `5 broken`, `16/16`, `48/48`, `36/47`, `278/279`, `279/279`, `102/393`.\n- **L3 no observable drift**: non sono testati gap_ratio, nuovi spettri, nuovi N o GUE/Poisson.\n- **L4 edge cases**: `order_memory:r0.52` best-per-mode ha delta AICc 1.885, quindi famiglia non identificata hard nonostante best `linear_N`.\n- **L5 re-discovery vs discovery**: il finding non e' \"linear fit buono\"; e' che la famiglia di fit dipende dal sottografo ammesso e che la convergenza a 1 non e' vincolo osservato.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_fit_model_gate.py` completato senza errori.\n- Run valido: `python tools/exp_vc_fit_model_gate.py --input tools/data/vc_fit_ready_scale_table_20260509_1337.json --out tools/data/vc_fit_model_gate_20260509_1400.json`.\n\n## Files\n- Script nuovo: `tools/exp_vc_fit_model_gate.py`\n- Data: `tools/data/vc_fit_model_gate_20260509_1400.json`\n- Report: `tools/data/reports/agent_20260509_1400.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_graph.json:2001: "content_full": "# Agent Report - Vc Fit-Ready Denominator Gate\n**Date**: 2026-05-09 13:37\n**Piano**: 102\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: CONSTRAINT on fit-ready V_c denominators\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`denominator_state`, `fit_ready_rows`, `excluded_rows`, `excluded_events`, `vc_median_fit_ready`, `slope_per_N`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var\n**observable_contract**: claim=`fit(V_c)` e' ammesso solo quando il denominatore e' fit-ready; observable=stato strutturale del denominatore e curva `vc_median_fit_ready`; operator=lettura del deposito 08:46 con separazione `complete/contaminated/broken/absent`; generator=classi `reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=JSON `vc_generator_class_direction_audit_20260509_0846`, livelli separati `per_mode_best` e `accepted_candidates`, soglie `r={0.48,0.50,0.52}`, N={89,144,233,377}; non_possible=righe `no_cross` e `floor_hit` non entrano nel fit e restano massa esclusa; not_tested=nuovi spettri, nuovi N, nuovi generatori, gap_ratio, power-law non lineare.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il valore `V_c` non e' il punto-zero; il punto-zero e' la riga prima che il confine decida se `V_c` esiste.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: curva fittabile / curva non definita; punto-zero = denominatore fit-ready prima della regressione numerica.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il fit e' un arco consentito solo dopo che il nodo-evento conserva attraversamento.\n- **Operatori laterali scelti**: boundary operator, graph cut, filtrazione. Entrano per tagliare il deposito in stati strutturali prima del fit.\n- **Contaminazione cognitiva**:\n - **CE-0001 / KSAR**: reiterazione sul deposito 08:46 senza nuovo campo dati.\n - **CE-0117**: cascata della possibilita; il fit diventa possibile solo nel sottografo che conserva evento.\n - **YSN DeltaLink**: `event_type -> fit_ready -> scale_curve`, non `N -> V_c`.\n- **Proto-ipotesi**: la scala di `V_c` e' un osservabile secondario. L'osservabile primario e' il denominatore fit-ready: se il denominatore e' broken, la curva non fallisce, non esiste sul perimetro.\n- **Proiezione**: costruisco una tabella fit-ready dal deposito 08:46. Se una classe perde almeno un N intero, il fit aggregato diventa non-possibile per quella classe e soglia.\n\n## Claim Under Test\n> Nel perimetro del deposito 08:46, `fit(V_c)` e' lecito solo per classi/soglie con denominatore `complete` o, con avvertenza, `contaminated`; e' non-possibile per stati `broken` o `absent`.\n\n## Question\nIl fallimento del fit di scala e' una proprieta della forma numerica, oppure una proprieta del denominatore che decide dove `V_c` esiste?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_fit_ready_scale_table.py`.\n- Input: `tools/data/vc_generator_class_direction_audit_20260509_0846.json`.\n- Output: `tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n- Nessun nuovo spettro calcolato: il ciclo misura il deposito row-aligned del ciclo precedente.\n- Stati strutturali:\n - `complete`: ogni N ha righe fit-ready e zero righe escluse.\n - `contaminated`: ogni N ha righe fit-ready, ma esiste massa esclusa.\n - `broken`: almeno un N non ha righe fit-ready.\n - `absent`: nessuna riga fit-ready.\n- Contratto osservabile-operatore: `vc_defined = event in {internal_cross, internal_multi}`; `fit_ready = vc_defined and vc_interp is not null`; `no_cross/floor_hit` sono massa esclusa, non missing neutro.\n\n## Results\n\nIndice stati:\n\n| state | class-threshold entries |\n|---|---:|\n| complete | 7 |\n| contaminated | 6 |\n| broken | 5 |\n| absent | 0 |\n\nSoglia `r=0.50`, livello `per_mode_best`:\n\n| class | state | fit_ready/total | excluded | Vc first->last | slope_per_N |\n|---|---|---:|---:|---:|---:|\n| reference_order | complete | 16/16 | 0 | 1.191 -> 0.879 | -0.001043 |\n| order_memory | complete | 48/48 | 0 | 1.317 -> 0.774 | -0.001833 |\n| periodic_closure | contaminated | 36/47 | 11 no_cross | 0.999 -> 0.650 | -0.000913 |\n| random_dispersion | broken | 20/32 | 12 floor_hit | 0.763 -> 0.541 | -0.001462 |\n\nSoglia `r=0.50`, livello `accepted_candidates`:\n\n| class | state | fit_ready/total | excluded | Vc first->last | slope_per_N |\n|---|---|---:|---:|---:|---:|\n| order_memory | contaminated | 278/279 | 1 no_cross | 1.350 -> 0.899 | -0.001628 |\n| periodic_closure | broken | 102/393 | 291 no_cross | 1.593 -> 0.605 | -0.017960 |\n\nAccepted `order_memory` a `r=0.52`:\n\n| class | state | fit_ready/total | excluded | Vc first->last | slope_per_N |\n|---|---|---:|---:|---:|---:|\n| order_memory | complete | 279/279 | 0 | 1.236 -> 0.781 | -0.001606 |\n\n## Key Findings\n1. **Verificato: il fit completo esiste nel livello best-per-mode per `reference_order` e `order_memory`.** A `r=0.50`, `reference_order` ha 16/16 righe fit-ready e `order_memory` ha 48/48.\n2. **Verificato: sugli accepted, `periodic_closure` e' broken a tutte le soglie.** A `r=0.50`, 291/393 righe sono `no_cross`; N=233 e N=377 non hanno punti fit-ready, quindi la curva non puo' essere letta come scala.\n3. **Verificato: `random_dispersion` nel best-per-mode e' broken a `r=0.50` e `r=0.52`.** A `r=0.50`, N=377 cade tutto in `floor_hit`; il valore al bordo non e' un punto interno di fit.\n4. **Verificato: `order_memory` accepted si pulisce a `r=0.52`.** Passa da `contaminated` a `r=0.50` con 1 no_cross a `complete` a `r=0.52` con 279/279 righe fit-ready. La soglia modifica il denominatore, non solo il valore del fit.\n5. **Inferito: la consecutio corretta non e' scegliere un modello power-law alternativo.** Prima va promosso il gate `denominator_state` nel contratto dei futuri fit `V_c`.\n\n## Verdict\n**CONSTRAINT**: `V_c` scaling e' ammesso solo dopo gate fit-ready.\n\nNel perimetro 08:46 -> 13:37, `order_memory` conserva un canale fittabile; `periodic_closure` conserva label ma rompe il denominatore sugli accepted; `random_dispersion` raggiunge il floor e non fornisce curva interna a N=377. Il claim corretto e':\n\n`fit(V_c)` non opera su `V_c` aggregato. Opera su righe `fit_ready=True`, stratificate per livello, classe generatore e soglia.\n\n## Bicono della scoperta\n- **Due radici**: curva di scala / massa esclusa.\n- **Singolare**: il denominatore prima della regressione, dove `V_c` puo' ancora essere valore, assenza di crossing, o floor.\n- **Invariante di passaggio**: `fit_ready` sopravvive su `order_memory`; non sopravvive su `periodic_closure` accepted e su `random_dispersion` al bordo alto.\n- **Campo di possibilita**: qui diventa possibile fittare `V_c` solo nel sottografo fit-ready; qui diventa non-possibile usare `no_cross` o `floor_hit` come missing data neutro.\n\n## Consecutio\nIl prossimo ciclo puo' rifare il fit parametrico solo su righe `complete` o dichiaratamente `contaminated`. Se entra `broken`, il risultato deve essere classificato come denominatore non-definito, non come cattivo fit.\n\n## Auto-audit: source flags e 5 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_0846.md`, JSON `tools/data/vc_generator_class_direction_audit_20260509_0846.json`, nuovo JSON `tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n- **L1 hard constraint**: il verdict vale solo per il deposito 08:46 e le soglie/N/generatori dichiarati.\n- **L2 count grezzi**: riportati 16/16, 48/48, 36/47, 20/32, 278/279, 102/393, 279/279.\n- **L3 no observable drift**: non sono testati gap_ratio, nuovi N, nuovi spettri o power-law non lineare.\n- **L4 edge cases**: `order_memory` accepted a `r=0.50` ha 1 no_cross; per questo e' `contaminated`, non `complete`.\n- **L5 re-discovery vs discovery**: il finding non e' che `V_c` scende con N; il finding e' che la discesa e' leggibile solo dopo classificazione fit-ready del denominatore.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_fit_ready_scale_table.py` completato senza errori.\n- Run valido: `python tools/exp_vc_fit_ready_scale_table.py --input tools/data/vc_generator_class_direction_audit_20260509_0846.json --out tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n\n## Files\n- Script nuovo: `tools/exp_vc_fit_ready_scale_table.py`\n- Data: `tools/data/vc_fit_ready_scale_table_20260509_1337.json`\n- Report: `tools/data/reports/agent_20260509_1337.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/lab_data.json:80: "content": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, labe" tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:19:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:52:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:55:# Palette operatoria espansa del Lab tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:728: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:843:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:872: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:883:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/manual_controlled_snapshot_20260515_1705_3616547/agent_field_live.md:918:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:133:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:170:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:173:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:846: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:961:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:990: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:1001:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_backup_pre_authority_replace_20260515_1620.md:1034:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:19:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:52:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:55:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:728: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:843:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:872: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:883:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_scientific_return_20260515_1706.md:918:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/lab_data.json:101: "content": "# Agent Report - Boundary Classical Crossover Audit\n**Date**: 2026-05-15 19:04\n**Piano**: 120\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge.\nobservables_registry: none; classical audit coordinates plus prior graph observables\nobservables_used: [brody_q, berry_robnick_like_gue_weight, mixture_ks, graph_boundary_state_from_1855, centroid_margin_from_1855, cross_neighbor_fraction_from_1855]\n**observable_contract**: claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: repulsione spettrale / indipendenza spettrale. Punto-zero: riga di dominio prima che venga letta come label, parametro Brody o nodo del grafo.\n- **Piano superiore**: grafo della conoscenza con audit assiomatico su baseline note; la domanda non e' \"quanto vale q\", ma se q esaurisce il ponte.\n- **Proto-ipotesi**: il terzo incluso operativo non coincide con un singolo scalare di crossover. Se coincide, il bridge Lab e' re-discovery di Brody/Berry-Robnik; se diverge, il contenuto Lab e' nella relazione tra geometria locale e scalare classico.\n- **Possibile/non-possibile**: possibile = usare nodi ponte come righe fisiche candidate oltre la classificazione GUE/Poisson; non-possibile = rivendicare un nuovo crossover se i nodi ponte sono solo Brody/Berry-Robnik rietichettato.\n- **Proiezione**: stimo Brody q e peso GUE di una mistura Poisson/GUE-surmise per ciascuna delle 13 righe gia' classificate dal grafo 18:55.\n\n### Contaminazione cognitiva\n- **YSN DeltaLink**: il DeltaLink usato e' `crossover classico / grafo Lab`: la sorpresa cercata e' il disaccordo, non la conferma dei nodi ponte.\n- **Cornelius gene**: `Classical_Audit_Gate`: \"Un ponte Lab sopravvive solo dopo il lettore classico piu vicino.\" Operatori: FITTA scalare noto; ALLINEA righe; ISOLA residuo.\n- **KSAR step**: perturbazione = feedback falsifier L5; focalizzazione = una sola domanda, \"i bridge collassano su Brody/Berry-Robnik?\"; proiezione = audit row-aligned sulle 13 righe.\n- **PVI attack**: un revisore esterno puo' dire che `third_included_candidate` e' solo un nome Lab per un crossover Brody. Il test attacca es" tools/data/preflight/agent_field_live_candidate_20260515_1624.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_20260515_1624.md:49:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_20260515_1624.md:52:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_20260515_1624.md:725: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_20260515_1624.md:840:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_20260515_1624.md:869: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_20260515_1624.md:880:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_20260515_1624.md:915:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_candidate_20260515_1608.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_20260515_1608.md:50:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_20260515_1608.md:53:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_20260515_1608.md:726: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_20260515_1608.md:841:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_20260515_1608.md:870: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_20260515_1608.md:881:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_20260515_1608.md:916:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:49:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:52:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:725: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:840:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:869: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:880:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_backup_post_controlled_1623_20260515.md:915:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:49:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:52:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:725: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:840:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:869: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:880:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_backup_pre_selector_matrix_20260515_1659.md:915:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/lab_data_backup_post_controlled_1623_20260515.json:80: "content": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promot" tools/data/preflight/agent_field_live_candidate_20260515_1620.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_20260515_1620.md:49:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_20260515_1620.md:52:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_20260515_1620.md:725: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_20260515_1620.md:840:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_20260515_1620.md:869: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_20260515_1620.md:880:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_20260515_1620.md:915:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_candidate_20260515_1616.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_20260515_1616.md:50:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_20260515_1616.md:53:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_20260515_1616.md:726: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_20260515_1616.md:841:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_20260515_1616.md:870: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_20260515_1616.md:881:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_20260515_1616.md:916:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1068: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1119: "content_full": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, label-swap row-local e trace JSONL completa.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa la robustezza del delta z row-local prime-minus-mod6 su piu ampiezze finestra, mantenendo provider, offset, label-swap audit e trace JSONL.\n- `not_drift`: non torna a `V_c`, GUE/Poisson, fit o vecchi depositi; stressa solo il residuo nominato dal valutatore dopo sottrazione mod6.\n\n## Claim Under Test\n\n> Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime non genera `SR` come presenza; lascia un vettore di intensita row-local su `SR,L1,triple_var`.\n\n## Question\n\nIl delta z prime-minus-mod6 resta significativo quando cambia l'ampiezza della finestra, oppure era un artefatto del contratto 1024-gap del cycle 03:30?\n\n## Experiment Design\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`.\n- Run: `window_gaps=512,1024,2048`; seeds `202605141330,202605141331,202605141332`.\n- Rows per run: 8 prime windows = 2 provider x 4 offset.\n- Antagonisti: `mod6_index_aligned` e `mod6_span_matched`, paired per `row_id`.\n- Null: label-swap row-local, 4096 trials per pair audit.\n- Trace: JSONL scritto per tutti e tre i run.\n\n## Results\n\n| window | pair | prime SR | prime focus | Jaccard | SR delta | p(SR delta) | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | script verdict |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| 512 | mod6_index | 5/8 | 2/8 | 0.250 | -0.250 | 0.6195 | 20.494 | 0.0095 | 21.866 | 0.0095 | 8.480 | 0.0095 | SR_NOT_DECISIVE |\n| 512 | mod6_span | 5/8 | 2/8 | 0.377 | -0.250 | 0.5038 | 13.993 | 0.0085 | 10.099 | 0.0085 | 7.505 | 0.0085 | SR_NOT_DECISIVE |\n| 1024 | mod6_index | 8/8 | 5/8 | 0.481 | 0.000 | 1.0000 | 29.480 | 0.0061 | 31.105 | 0.0061 | 11.745 | 0.0061 | SR_PREBOUNDARY |\n| 1024 | mod6_span | 8/8 | 5/8 | 0.377 | 0.250 | 0.4921 | 19.921 | 0.0076 | 15.302 | 0.0076 | 13.142 | 0.0076 | SR_PREBOUNDARY |\n| 2048 | mod6_index | 8/8 | 7/8 | 0.556 | 0.125 | 1.0000 | 41.634 | 0.0088 | 43.574 | 0.0088 | 17.173 | 0.0088 | SR_PRIME_SPECIFIC |\n| 2048 | mod6_span | 8/8 | 7/8 | 0.537 | 0.250 | 0.5040 | 28.624 | 0.0068 | 23.917 | 0.0068 | 17.491 | 0.0068 | SR_PRIME_SPECIFIC |\n\n## Key Findings\n\n1. **Verificato**: `has_SR` non replica come osservabile del residuo. A 512 gap i primi hanno `SR=5/8`; a 1024 e 2048 hanno `SR=8/8`. Il delta binario ha p non significativo o 1.0000.\n2. **Verificato**: il delta z `SR` replica in tutti i sei pair audit: `p=0.0061..0.0095`.\n3. **Verificato**: il delta z non e' solo `SR`. `L1` replica con `p=0.0061..0.0095`; `triple_var` replica con `p=0.0061..0.0095`.\n4. **Verificato**: il verdict nominale dello script oscilla con `window_gaps` (`not_decisive`, `preboundary`, `prime_specific`). Quell'oscillazione e' informazione sullo script: il verdict e' ancora centrato su commonality/binario, non sul vettore di intensita.\n5. **Inferito dal perimetro**: l'osservabile dedicato da formalizzare e' `prime_minus_mod6_z_vector(SR,L1,triple_var)`, con `has_SR` relegato ad audit negativo.\n\n## Verdict\n\n**CONSTRAINT / VECTOR RESIDUE**.\n\nNel perimetro 512-2048 gap, due provider, quattro offset, due antagonisti mod6, il residuo prime-specific sopravvive come delta z vettoriale row-local. Non sopravvive come presenza binaria di `SR`, ne come verdict nominale dello script.\n\n## Bicono\n\n- **Due radici**: pre-bordo mod6 / selezione prime.\n- **Singolare**: riga provider-offset paired.\n- **Invariante di passaggio**: delta z positivo su `SR,L1,triple_var`.\n- **Campo di possibilita**: osservabile dedicato `prime_minus_mod6_z_vector`.\n- **Campo non-possibile**: `SR` binario come firma prime-specific dopo mod6.\n\n## Consecutio\n\nIl prossimo ciclo deve correggere il nodo regressivo dello script: il `verdict()` non deve decidere dalla commonality di `SR`, ma da un contratto vettoriale dichiarato (`SR,L1,triple_var`, segno del delta, p label-swap, denominatore row-local). Dopo questa correzione, scalare oltre 2048 gap e separare `mod6_index_aligned` da `mod6_span_matched` come antagonisti con ruoli diversi.\n\n## Ricadute pratiche\n\nssp_value: yes. Le tre trace JSONL rendono auditabile il residuo senza riaprire i JSON aggregati; il prossimo passaggio pratico e' aggiornare lo script per emettere un verdict vettoriale, non binario.\n\n## Files\n\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl`\n- `tools/data/reports/agent_20260514_1330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1170: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary Trace\n**Date**: 2026-05-14 03:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo `6k +/- 1`; la selezione prime resta come gradiente row-local, non come presenza binaria dell'osservabile \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n\n## Prima impressione\n\nIl residuo prime-specific non nasce dalla comparsa di `SR`. `SR` e gia nel lattice candidato `6k +/- 1`; i primi non lo accendono, lo attenuano e lo deformano rispetto al pre-bordo.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + direzione viva `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: pre-bordo `6k +/- 1` / selezione prime. Punto-zero: stessa riga provider/offset, dove il denominatore e identico e la primalita e l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: il bordo non e presenza/assenza di un segnale, ma passaggio di intensita tra un lattice candidato e il sottoinsieme selezionato.\n- **Proto-ipotesi**: la firma utile e `prime_minus_mod6_z_delta` su `SR,L1,triple_var`; `has_SR` e una coordinata troppo larga per nominare primalita.\n- **Possibile / non-possibile**: possibile = isolare residuo di selezione con delta z row-local; non-possibile = claim `SR prime-specific` se `SR` resta comune nel contro-perimetro mod6.\n- **Proiezione**: 2 provider x 4 offset x 1024 gap; antagonisti `mod6_index_aligned` e `mod6_span_matched`; label-swap row-local; trace JSONL completa.\n\n## Contaminazione cognitiva\n\n- **CE-0001 / KSAR**: usato per reiterare il kernel emerso nel cycle precedente: non allargare il frame, ma far girare lo stesso nodo regressivo con tracciamento piu fine.\n- **PVI**: presupposto attaccato = \"`SR` persistente implica primalita\"; contro-presupposto = \"`SR` misura il pre-bordo aritmetico\".\n- **Vault**: `trace_jsonl_event` conserva casi, class summary, paired rows, audit e verdict come deposito riusabile senza trasformare una seed in invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: misura l'intensita row-local di `SR/L1/triple_var` su prime vs `6k +/- 1`, con denominatore provider/offset condiviso e label-swap audit.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista e il pre-bordo mod6 indicato dalla consecutio.\n\n## Observable Contract\n\n- **claim**: `SR` e prime-specific solo se resta piu comune o piu forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local.\n- **observable**: membership di `SR`, firma `[SR,L1,triple_var]`, delta z paired prime-minus-mod6, p-value label-swap.\n- **operator**: `tools/exp_prime_vs_mod6_sr_boundary.py` con `--trace-jsonl`.\n- **generator**: prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`; mod6 da `index_aligned` e `span_matched`.\n- **denominator**: 8 finestre prime paired con 8 `mod6_index_aligned` e 8 `mod6_span_matched`.\n- **non_possible**: prime-specific `SR` se `SR` e comune o pari nel contro-perimetro mod6, oppure se il label-swap assorbe i delta.\n- **not_tested**: beta atlas globale, `V_c`, `gap_ratio`, origine analitica del trasferimento mod6.\n\n## Results\n\nMain run, seed `202605140330`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 3 | SR | beta_absent_blank=5, beta_chart_recovered=3 |\n| mod6_index_aligned | 8 | 8 | 8 | L1,SR,triple_var | beta_chart_recovered=8 |\n| mod6_span_matched | 8 | 7 | 7 | [] | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nSeed check, seed `202605140331`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 6 | SR | beta_chart_recovered=5, beta_absent_blank=3 |\n| mod6_index_aligned | 8 | 8 | 6 | L1,SR | beta_chart_recovered=7, beta_absent_blank=1 |\n| mod6_span_matched | 8 | 8 | 7 | L1,SR | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605140330 | 0.433 | 0.000 | 1.0000 | 28.246 | 0.0081 |\n| prime - mod6_span | 202605140330 | 0.423 | 0.125 | 1.0000 | 20.793 | 0.0093 |\n| prime - mod6_index | 202605140331 | 0.517 | 0.000 | 1.0000 | 28.547 | 0.0083 |\n| prime - mod6_span | 202605140331 | 0.560 | 0.000 | 1.0000 | 18.844 | 0.0090 |\n\n## Key Findings\n\n1. **Verificato**: `SR` non e prime-specific come presenza binaria. In entrambi i run, `prime=8/8` e `mod6_index_aligned=8/8`; nel seed check anche `mod6_span_matched=8/8`.\n2. **Verificato**: il count `SR` non discrimina. `p(SR delta)=1.0000` in tutti i pair audit.\n3. **Verificato**: il delta di intensita discrimina. `p(z_SR delta)=0.0081/0.0083` contro `mod6_index` e `0.0093/0.0090` contro `mod6_span`.\n4. **Verificato**: `mod6_index_aligned` e piu rigido dei primi: focus signature main `8/8` contro prime `3/8`, e common obs `L1,SR,triple_var` contro solo `SR`.\n5. **Inferito dal perimetro**: il prossimo osservabile deve essere vettoriale e paired: `prime_minus_mod6_z_delta(SR,L1,triple_var)`, non `has_SR`.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula chiusa nel perimetro: `SR` appartiene al pre-bordo `6k +/- 1`. La primalita non genera `SR`; seleziona dentro un lattice gia rigido e lascia un residuo misurabile come delta z row-local.\n\n## Bicono\n\n- **Due radici**: lattice candidato mod6 / sequenza prime selezionata.\n- **Singolare**: riga provider-offset condivisa.\n- **Invariante di passaggio**: presenza di `SR` attraversa il confine.\n- **Campo di possibilita**: misurare selezione come delta z.\n- **Campo non-possibile**: usare `SR` binario come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve formalizzare `prime_minus_mod6_z_delta` come osservabile dedicato e scalarlo su finestre piu larghe. `mod6_index_aligned` resta antagonista primario; `mod6_span_matched` resta controllo fragile perche conserva span numerico ma introduce downsample.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script ora produce `--trace-jsonl`, utile per audit, replay e falsifier row-local senza riaprire il JSON aggregato.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json`\n- Trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json`\n- Seed trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl`\n- Report: `tools/data/reports/agent_20260514_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1203: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary\n**Date**: 2026-05-13 03:30 \n**Piano**: 116 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p] \n**observable_contract**: claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.\n\n## Prima impressione\n\nIl bordo non cade tra primi e non-primi: cade tra selezione prime e lattice candidato `6k +/- 1`. `SR` vede il lattice prima della primalita; il gate non deve chiedere \"SR nei primi?\", ma \"cosa resta di SR dopo aver sottratto il pre-bordo?\".\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: primi selezionati / candidati mod6 non selezionati. Punto-zero: la riga ordinata locale `6k +/- 1`, dove `SR` puo nominare pre-bordo aritmetico senza nominare primalita.\n- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune.\n- **Proto-ipotesi**: `SR` non e firma atomica dei primi; e una sezione del pre-bordo aritmetico. La selezione prime si manifesta come differenza di intensita z rispetto al lattice candidato.\n- **Possibile / non-possibile**: possibile = isolare una coordinata di selezione prime come gradiente dentro mod6; non-possibile = usare presenza binaria di `SR` come claim prime-specific.\n- **Proiezione**: stesso gate ordine/null, finestre 1024, offset 0/512/1024/1536, due provider prime, due antagonisti mod6, audit label-swap dentro ogni row_id.\n\n### Contaminazione cognitiva\n\n- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`.\n- **PVI attack**: il presupposto attaccato e \"SR persistente implica primalita\". Il contro-presupposto e \"SR misura la griglia 6k +/- 1 prima della selezione prime\".\n- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`.\n\n## Claim Under Test\n\n> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`.\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap.\n- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset.\n- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti.\n- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`.\n- Main seed: `202605130330`; seed check: `202605130331`.\n- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset.\n\n## Results\n\nMain run:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 |\n| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 |\n\nSeed check:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 |\n| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 |\n| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 |\n| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 |\n| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 |\n\n## Key Findings\n\n1. **Verificato**: la presenza binaria di `SR` non e prime-specific. Main: prime `8/8`, mod6_index `8/8`; seed check: prime `6/8`, mod6_index `8/8`, mod6_span `8/8`.\n2. **Verificato**: il label-swap row-local assorbe `SR` come count. `p(SR delta)` vale 1.0000 / 0.5055 per mod6_index e 0.2502 / 0.5021 per mod6_span.\n3. **Verificato**: il label-swap non assorbe il delta z di `SR`. `p(z_SR delta)` resta 0.0071-0.0103 in entrambe le seed e per entrambi gli antagonisti.\n4. **Verificato**: mod6 e piu intenso, non piu debole. `mean z SR` e circa -34 / -26 in mod6 contro -5 nei primi. Il residuo prime non e \"piu SR\"; e selezione attenuata dentro un pre-bordo piu rigido.\n5. **Inferito dal perimetro**: la coordinata utile non e `has_SR`; e `z_delta_SR` paired, insieme a `z_delta_L1` e `z_delta_triple_var`, come misura di quanto la selezione prime rompe il lattice candidato.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula non valida:\n\n`SR` come firma prime-specific atomica.\n\nFormula valida nel perimetro:\n\n`SR` appartiene al pre-bordo aritmetico `6k +/- 1`; la selezione prime resta come gradiente di intensita row-local rispetto a mod6. Il boundary operativo e quindi `mod6_preboundary -> prime_selection`, non `prime -> generic_nonprime`.\n\n## Bicono della scoperta\n\n- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata.\n- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni.\n- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato.\n- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`.\n\n## Lenti counter-pole applicate\n\n- **L1/L4**: nessun \"solo\", \"mai\", \"zero\" usato per `SR`: i count mostrano controesempi.\n- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific.\n- **L5**: nessun tag NEW; il risultato resta compatibile con bias noti dei gap dei primi mod q. Il report misura un gate del Lab, non rivendica una scoperta aritmetica classica.\n- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva.\n- **L7**: non-possibile dichiarato nel contratto e nel bicono.\n\n## Consecutio\n\nIl prossimo taglio non deve chiedere se `SR` c'e. Deve costruire un osservabile di selezione: `prime_minus_mod6_z_delta` su `SR,L1,triple_var`, con span-matched conservato come controllo fragile e index-aligned come antagonista primario. Se il delta resta replicato su piu finestre e scale, il claim diventa: la primalita attenua/rompe la rigidita del pre-bordo mod6 invece di generare `SR` da sola.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260513_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1248: "content_full": "# Agent Report - Prime SR Persistent Boundary\n**Date**: 2026-05-12 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`.\n- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi.\n- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro.\n- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' \"SR persistente nei primi implica prime-specific\".\n- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico.\n- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime.\n- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime.\n\n## Claim Under Test\n\n> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena.\n\n## Question\n\nQuando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime?\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows.\n- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`.\n- Seed check: stesso perimetro, seed `202605120331`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common.\n\n## Results\n\nMain run:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 |\n| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 |\n| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 |\n| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 |\n| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 |\n\nSeed check:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 |\n| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 |\n| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 |\n\nPrime case details, main:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] |\n| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] |\n| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n\n## Key Findings\n\n1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota.\n2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall.\n3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`.\n4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato.\n5. **Inferito dal perimetro dichiarato**: la parte robusta non e' \"SR e' prime-specific\"; e' \"SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche\".\n\n## Verdict\n\n**CONSTRAINT / FALSIFIED scoped**.\n\nFormula non valida:\n\n`prime_SR_persistent_boundary` come firma atomica prime-specific.\n\nFormula valida nel perimetro:\n\n`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico.\n\n## Bicono della scoperta\n\n- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato.\n- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato.\n- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6.\n- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve spostare il nodo regressivo: non \"prime vs controlli generici\", ma \"primi vs candidati mod6 row-aligned\". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico.\n\n## Files\n\n- Script: `tools/exp_prime_sr_persistent_boundary.py`\n- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json`\n- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260512_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1299: "content_full": "# Agent Report - Prime Persistent Blank Gate\n**Date**: 2026-05-11 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_persistent_blank` si scinde: SR persiste, il blank beta no \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY residuo `numeri_primi:cycle_3`.\n- **Dipolo / punto-zero**: persistenza del supporto / persistenza del blank. Punto-zero: la stessa finestra row-local da 1024 gap dove `SR` puo' restare mentre la coordinata beta riappare.\n- **Piano superiore**: topologia assiomatica del bordo: non una classe statistica unica, ma una sezione che conserva un osservabile e perde una coordinata.\n- **Proto-ipotesi**: il residuo prime e' strutturale solo se il blank beta-absent sopravvive a provider e offset; se sopravvive solo `SR`, la struttura non e' `blank`, e' `SR-supported boundary`.\n- **Possibile / non-possibile**: possibile = isolare un residuo prime provider-neutral; non-possibile = chiamarlo `prime_persistent_blank` quando beta ricompare in finestre locali.\n- **Proiezione**: due provider prime, quattro offset locali, seed shift del gate, controlli random_matrix/logistic.\n\n## Contaminazione cognitiva\n\n- **YSN DeltaLink**: il legame non ovvio e' `SR` come sezione comune mentre la carta beta cambia; non e' prova, e' la domanda proiettata.\n- **Cornelius gene**: `DNA_Simbolico`: \"La persistenza del bordo non coincide con la persistenza del blank.\" Operatori: separa supporto, separa coordinata, confronta controlli.\n- **KSAR step / CE-0001**: reiterazione del kernel 20260510 senza ridisegnare l'atlante: stesso gate, nuovo provider/offset/seed.\n- **PVI attack**: il presupposto nascosto era che `blank_windows=4/4` su una partizione bastasse per nominare una specie persistente.\n- **Vault**: `random_matrix_chart_sensitive_blank` resta aperto come coordinata locale beta `[0.4]`, non lavorata in questo ciclo.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente `prime_persistent_blank` su `numeri_primi:cycle_3` con provider-neutral row-local windows e controlli cross-dominio.\n- `not_drift`: non usa `V_c`, non usa fit, non riapre griglia beta globale, non salva la classe caduta `medium/strong beta-absent`.\n\n## Claim Under Test\n\n> `numeri_primi:cycle_3` e' un `prime_persistent_blank` se resta beta-absent in tutte le finestre provider-neutral e conserva `SR` come osservabile comune, mentre i controlli non condividono la stessa firma.\n\n## Experiment Design\n\n- Perimetro prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: 4 GUE random matrix blocks + 4 logistic return interval rows.\n- Parametri: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605110330`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Criterio di isolamento: prime `blank_rate=1.0`, prime `sr_rate=1.0`, common prime obs = `[SR]`, controlli senza full blank-SR collision.\n\n## Results\n\n| family | blank | beta recovered | support falls | sr rate | common obs | endpoint mean | stable coherent mean |\n|---|---:|---:|---:|---:|---|---:|---:|\n| prime | 3/8 | 4/8 | 1/8 | 1.000 | SR | 2.715 | 2.656 |\n| controls | 1/8 | 3/8 | 4/8 | 0.250 | L2 | 1.428 | 1.641 |\n\n| comparison | value |\n|---|---:|\n| prime_control_common_obs_jaccard | 0.000 |\n| verdict | PRIME_PERSISTENCE_NOT_REPLICATED |\n\nPrime case details:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_chart_recovered | SR | 0.2, 0.4 |\n| dnd_autoricerca offset 512 | support_falls | SR | 0.2 |\n| dnd_autoricerca offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n| direct_sieve offset 1024 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1536 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n\n## Key Findings\n\n1. **Verificato**: `SR` resta in 8/8 finestre prime. La firma comune prime e' `[SR]`.\n2. **Verificato**: il blank beta-absent non resta in 8/8 finestre prime. Solo 3/8 casi sono `beta_absent_blank`; 4/8 recuperano beta e 1/8 cade per supporto.\n3. **Verificato**: i controlli non collidono con la firma prime. I controlli hanno common obs `[L2]`, `sr_rate=0.250`, `blank_rate=0.125`, Jaccard comune prime/controlli = 0.000.\n4. **Inferito dal perimetro dichiarato**: il residuo dei primi non e' `prime_persistent_blank`; e' `prime_SR_persistent_boundary` con coordinata beta chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl nome `prime_persistent_blank` non regge nel perimetro provider-neutral/offset-shift. La parte persistente e' `SR`, non l'assenza di beta.\n\nFormula valida:\n\n`numeri_primi:cycle_3` conserva supporto one-sided `SR` attraverso provider e offset; la coordinata beta resta locale e ricompare in 4/8 finestre.\n\nFormula non valida:\n\n`numeri_primi:cycle_3` e' un blank beta-absent persistente.\n\n## Bicono della scoperta\n\n- **Due radici**: supporto SR persistente / blank beta non persistente.\n- **Singolare**: la finestra row-local dove `SR` resta ma beta riappare.\n- **Invariante di passaggio**: `SR` attraversa provider e offset; `blank` no.\n- **Campo di possibilita'**: possibile = rinominare il residuo come supporto `SR` provider-neutral; non-possibile = promuovere il blank come specie autonoma.\n\n## Consecutio\n\nIl prossimo ciclo deve chiudere la tassonomia residua senza salvare il nome caduto:\n\n- rinominare il residuo prime in `prime_SR_persistent_boundary`;\n- testare se il beta recovery 0.2/0.4 dipende da offset, provider o baseline seed;\n- lavorare `random_matrix_chart_sensitive_blank` solo dopo aver separato coordinate locali da supporto osservabile.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script diventa un audit riusabile per distinguere persistenza di osservabile e persistenza di blank nei residui BOUNDARY senza rigenerare l'atlante globale.\n\n## Files\n\n- Script: `tools/exp_prime_persistent_blank_gate.py`\n- Data: `tools/data/prime_persistent_blank_gate_20260511_0330.json`\n- Report: `tools/data/reports/agent_20260511_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1344: "content_full": "# Agent Report - Boundary Residual Beta-Absent Audit\n**Date**: 2026-05-10 03:30 \n**Piano**: 114 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - il residuo beta-absent non e' una classe unica chiusa \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, window_state, blank_window_rate, full_signature_jaccard, common_window_obs_jaccard] \n**observable_contract**: claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + residui `numeri_primi:cycle_3` / `random_matrix:cycle_7`.\n- **Dipolo / punto-zero**: blank strutturale / blank da atlante. Punto-zero: la stessa misura row-local da 1024 gap che decide se la beta manca per struttura o riappare per scelta di finestra.\n- **Piano superiore**: topologia assiomatica del bordo: la classe globale cade se una carta locale riapre la coordinata beta.\n- **Operatori laterali scelti**: boundary operator, chart locale, filtrazione. Il boundary operator separa supporto e beta; la chart locale testa la finestra senza rifare il mondo; la filtrazione conserva il denominatore row-aligned.\n- **Contaminazione cognitiva**: CE-0019 usato come contratto combo prima della misura; CE-0022 usato come boundary operator + chart locale; KSAR usato come reiterazione sul deposito 15:32-18:39 senza ridisegnare il perimetro.\n- **Proto-ipotesi**: il residuo medio/forte beta-absent e' una classe unica solo se entrambi i target restano blank in tutte le finestre row-local e conservano una firma osservabile compatibile.\n- **Proiezione**: applico il gate canonico ordine/null alle sole due righe residue, poi confronto persistenza del blank e Jaccard delle firme one-sided.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: attacca direttamente i due residui beta-absent medio/forti lasciati dal valutatore dopo `thin_persist_rows=0/13`.\n- `not_drift`: non usa `V_c`, non usa label GUE/Poisson come campo decisionale, non rigenera la griglia beta globale.\n\n## Claim Under Test\n\n> I due residui `numeri_primi:cycle_3` e `random_matrix:cycle_7` formano una classe unica `medium/strong beta-absent` se il blank persiste in tutte le finestre row-local da 1024 gap e le firme one-sided restano compatibili.\n\n## Question\n\nI due blank residui sono lo stesso operatore di confine, due classi distinte, o un artefatto da denominatore/atlante?\n\n## Experiment Design\n\n- Perimetro atomico: `numeri_primi:cycle_3`, `random_matrix:cycle_7`.\n- Scope: full row + 4 finestre consecutive da 1024 gap per ciascun target.\n- Parametri: `n_replicates=12`, `n_beta=11`, `n_baseline=24`, `z_min=2.0`, seed `202605100330`.\n- Null baseline: permutazione marginal-preserving usata dal gate canonico gia' adottato nei report BOUNDARY.\n- Non misurato: `gap_ratio`, `V_c`, nuova griglia beta globale, validita' delle label sorgente GUE/Poisson.\n- Criterio di caduta: una riga recupera beta o perde supporto in una finestra row-local; in quel caso il residuo non e' classe unica chiusa.\n\n## Results\n\n| row | full state | full one-sided | window blanks | beta recovered | support falls | common window obs | mean endpoint | mean stable coherent |\n|---|---:|---|---:|---:|---:|---|---:|---:|\n| numeri_primi:cycle_3 | beta_absent_blank | SR,L1,triple_var | 4/4 | 0 | 0 | SR | 2.761 | 2.563 |\n| random_matrix:cycle_7 | beta_absent_blank | SR,SR2,L1,L2,triple_var | 3/4 | 1 | 0 | L2 | 2.987 | 3.083 |\n\n| comparison | value |\n|---|---:|\n| full_signature_jaccard | 0.600 |\n| common_window_obs_jaccard | 0.000 |\n| random_matrix recovered beta | window_4 -> beta [0.4] |\n\n## Key Findings\n\n1. **Verificato: `numeri_primi:cycle_3` resta beta-absent in 4/4 finestre.** Il supporto non cade; l'osservabile comune di finestra e' `SR`.\n2. **Verificato: `random_matrix:cycle_7` non resta beta-absent in 4/4 finestre.** La quarta finestra recupera beta `[0.4]` con supporto vivo (`SR2,L1,L2,triple_var`).\n3. **Verificato: le firme comuni di finestra divergono.** `common_window_obs_jaccard=0.000`: primi conserva `SR`, random matrix conserva `L2`.\n4. **Inferito dal perimetro dichiarato: il residuo beta-absent non e' una classe unica chiusa.** Una parte e' blank persistente row-local, una parte e' chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nNel perimetro delle due righe residue e finestre da 1024 gap, `medium/strong beta-absent` si scinde:\n\n`numeri_primi:cycle_3` = blank persistente row-local. \n`random_matrix:cycle_7` = blank forte ma chart-sensitive, con beta `[0.4]` recuperata in 1/4 finestre.\n\nFormula valida:\n\n`beta_absent_residue` non matura come classe unica; diventa un audit a due stati: `prime_persistent_blank` / `random_matrix_chart_sensitive_blank`.\n\n## Bicono della scoperta\n\n- **Due radici**: blank persistente / blank chart-sensitive.\n- **Singolare**: supporto ordine/null vivo senza coordinata beta globale.\n- **Invariante di passaggio**: il supporto non cade in nessuna delle 8 finestre; cio' che cambia e' la coordinata beta e la firma osservabile.\n- **Campo di possibilità**: possibile = chiudere la tassonomia BOUNDARY con due sotto-stati residui; non-possibile = promuovere `medium/strong beta-absent` come specie unica del confine.\n\n## Consecutio\n\nIl prossimo ciclo non deve riaprire il thin blank. Deve sigillare il nuovo audit a due stati:\n\n- `prime_persistent_blank`: testare se `SR` resta l'osservabile comune sotto seed/window shift.\n- `random_matrix_chart_sensitive_blank`: testare se beta `[0.4]` e' coordinata locale stabile o evento di una singola finestra.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' uno strumento riusabile per audit row-local di residui BOUNDARY senza rigenerare fit globali.\n\n## Files\n\n- Script: `tools/exp_boundary_residual_beta_absent_audit.py`\n- Data: `tools/data/boundary_residual_beta_absent_audit_20260510_0330.json`\n- Report: `tools/data/reports/agent_20260510_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1395: "content_full": "# Agent Report - Boundary Transition Taxonomy 13 Rows\n**Date**: 2026-05-09 18:39 \n**Piano**: 113 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - la tassonomia post-estensione scala sulle 13 righe e dissolve il thin blank \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`transition_class`, `source_beta_state`, `extension_state`, `support_tier_after`, `one_sided_after`, `endpoint_after`, `stable_count_coherent_after`, `beta_after`, `denominator_state`, `excluded_mass`] - osservabili domain-native di composizione row-aligned, non canonici. \nssp_value: no \n**observable_contract**: claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo l'estensione il thin blank non resta una figura. La domanda vera diventa se la matrice intera lascia un nuovo residuo vivo.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: supporto sottile / atlante di transizione; punto-zero = stessa riga row-aligned prima che il blank venga letto come specie o come transizione.\n- **Piano superiore**: sheaf locale del boundary su 13 sezioni. La sezione corta viene riparata, poi ricollocata nell'atlante senza usare label GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Servono a comporre stati gia' misurati senza introdurre un nuovo gate.\n- **Contaminazione cognitiva**: CE-0019: contratto combo prima della misura; CE-0022: palette come boundary operator e chart locale; KSAR: riuso del deposito stabile 15:32-15:56; PVI: se resta `thin_persists`, la tassonomia cade.\n- **Proto-ipotesi**: `blank_thin_support` non scala sulle 13 righe se, dopo composizione con l'estensione, nessuna riga conserva supporto vivo sottile senza beta.\n- **Possibile / non-possibile**: possibile = usare l'atlante di transizione come mappa BOUNDARY; non-possibile = trattare il thin blank come specie matura.\n- **Proiezione**: classificare tutte le 13 righe in `transition_class` usando supporto, beta, denominatore e transizioni estese.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la consecutio del campo vivo: dopo la cristallizzazione short-denominator, testa se la tassonomia delle transizioni post-estensione scala sulle 13 righe.\n- `not_drift`: non torna a `V_c`, non difende thin blank, non usa label GUE/Poisson come decision field; compone solo depositi row-aligned gia' misurati.\n\n## Claim Under Test\n> La tassonomia emersa dall'estensione del denominatore scala sull'intero perimetro BOUNDARY 13 righe se non lascia alcuna riga `thin_persists`.\n\n## Question\nQuando le tre righe short estese vengono ricollocate nella matrice 13 righe, resta una specie `blank_thin_support` oppure il residuo vivo cambia forma?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Input: `boundary_two_axis_matrix_20260509_1532.json`, `boundary_row_aligned_nonexact_audit_20260509_1538.json`, `boundary_short_denominator_extension_20260509_1556.json`, `boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Perimetro atomico: 13 righe BOUNDARY semi-reali.\n- Label policy: `source_domain_type` resta audit metadata; non entra in `transition_class`.\n- Null sfidato: esistenza di almeno una riga `thin_persists` dopo estensione.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support transfer after composition | 10 |\n| fall after composition | 3 |\n| beta chart after any | 9 |\n| beta chart exact 0.3 after | 6 |\n| thin persist rows | 0 |\n| medium/strong beta-absent blank rows | 2 |\n| verdict | TAXONOMY_SCALES_THIN_DISSOLVED |\n\nTransition classes:\n\n| transition_class | rows |\n|---|---:|\n| beta_0_3_exact | 4 |\n| beta_0_3_local_nonunique | 2 |\n| beta_chart_recovered | 1 |\n| blank_medium_or_strong_beta_absent | 1 |\n| fall_no_support | 2 |\n| local_beta_other | 1 |\n| support_falls_after_extension | 1 |\n| support_thickens_beta_blank | 1 |\n\nRighe aperte senza beta dopo composizione:\n\n| row | class | n_gaps | one-sided | endpoint | tier |\n|---|---|---:|---:|---:|---|\n| numeri_primi:cycle_3 | blank_medium_or_strong_beta_absent | 4096 | 3 | 3.243 | medium_multi_observable |\n| random_matrix:cycle_7 | support_thickens_beta_blank | 1024 | 4 | 3.781 | strong_multi_observable |\n\nRighe short riparate:\n\n| row | before n | after n | transition_class | one-sided after | beta after |\n|---|---:|---:|---|---:|---|\n| percolation:cycle_9 | 190 | 1024 | support_falls_after_extension | 0 | [] |\n| random_matrix:cycle_7 | 199 | 1024 | support_thickens_beta_blank | 4 | [] |\n| zeta_zeros:cycle_4 | 199 | 1024 | beta_chart_recovered | 2 | [0.3] |\n\n## Key Findings\n1. **Verificato: `thin_persist_rows=0/13`.** Nessuna riga conserva la forma `blank_thin_support` dopo composizione con l'estensione.\n2. **Verificato: il supporto post-composizione e' 10/13.** Cadono `cellular_automata`, `pendolo_doppio` e `percolation` dopo estensione; il resto conserva supporto o chart locale.\n3. **Verificato: la beta chart sale a 9/13 righe, con 6/13 esatte 0.3.** Le sei sono quattro `beta_0_3_exact` originali, `zeta_zeros` recuperata e `pendolo_doppio` come beta osservata senza supporto.\n4. **Verificato: il residuo vivo non e' thin.** Restano due blank senza beta: `numeri_primi` medio e `random_matrix` forte dopo estensione.\n5. **Inferito: la tassonomia scala come atlante di transizione, non come legge beta universale.** Il thin blank cade; il nodo successivo e' il blank medio/forte senza beta.\n\n## Verdict\n**CONSTRAINT**.\n\nLa tassonomia post-estensione scala sul perimetro 13 righe: nessun membro resta `thin_persists`.\n\nFormula valida:\n\n`blank_thin_support` = telemetria short-denominator dissolta dalla composizione 13 righe.\n\nFormula non valida:\n\n`blank_thin_support` = specie autonoma del boundary.\n\nIl boundary resta una matrice di transizioni: supporto che cade, supporto che si ispessisce senza beta, chart beta recuperata, chart beta esatta, chart locale non unica, chart spostata.\n\n## Bicono della scoperta\n- **Due radici**: denominatore riparato / atlante 13 righe.\n- **Singolare**: riga BOUNDARY prima della classificazione post-estensione.\n- **Invariante di passaggio**: `thin_persist_rows=0/13`.\n- **Campo di possibilita**: qui diventa possibile chiudere il thin blank; qui diventa non-possibile chiudere il blank medio/forte senza beta.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve lasciare chiuso il thin blank e attaccare il residuo `medium/strong beta-absent blank`: `numeri_primi:cycle_3` contro `random_matrix:cycle_7`. Il test utile non e' \"perche' manca beta in generale\", ma quale condizione distingue blank medio completo e blank forte post-estensione quando entrambi hanno supporto vivo senza chart beta.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1556.md`, JSON 15:32/15:38/15:56/15:00, script e output 18:39.\n- **L1 hard constraint**: claim limitato alle 13 righe semi-reali e ai depositi gia' misurati.\n- **L2 count grezzi**: riportati 13 totali, 10 support, 3 fall, 9 beta chart, 0 thin persist, 2 open blank.\n- **L3 no silent patching**: nessuna nuova simulazione o ricampionamento; la composizione dichiara le estensioni applicate solo a tre righe.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma resta fall; `random_matrix` e' forte senza beta; `zeta_zeros` recupera beta pur restando contaminato nel prescan.\n- **L5 re-discovery**: il ciclo non ripete la matrice 15:32 ne' l'estensione 15:56; testa la scalabilita' richiesta dal campo vivo.\n- **L6 metabolismo cognitivo**: CE-0019, CE-0022, KSAR e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante BOUNDARY di transizione; non-possibile = specie autonoma thin blank.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Run valido: `python tools/exp_boundary_transition_taxonomy_13rows.py --out tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Output: `thin_persist_rows=0`, `support_transfer_after=10/13`, `fall_after=3/13`, `open_blank_rows=['numeri_primi:cycle_3', 'random_matrix:cycle_7']`, `verdict=TAXONOMY_SCALES_THIN_DISSOLVED`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 18:39, il JSON 18:39 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_transition_taxonomy_13rows.py`\n- Data: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`\n- Report: `tools/data/reports/agent_20260509_1839.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1446: "content_full": "# Agent Report - Boundary Short Denominator Extension\n**Date**: 2026-05-09 15:56 \n**Piano**: 112 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. \nssp_value: no \n**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura.\n- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili.\n- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade.\n- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta.\n- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48.\n- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48.\n- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short.\n\n## Claim Under Test\n> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart.\n\n## Question\nCosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`.\n- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`.\n- Denominatore esteso: `1024` gap per riga.\n- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate.\n- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| target rows | 3 |\n| rows still thin support after extension | 0 |\n| support falls after extension | 1 |\n| support thickens beta blank | 1 |\n| beta chart recovered | 1 |\n| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS |\n\nTransizioni:\n\n| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state |\n|---|---:|---:|---:|---:|---|---|\n| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension |\n| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank |\n| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered |\n\n## Key Findings\n1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart.\n2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato.\n3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile.\n4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade.\n5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe.\n\n## Verdict\n**CONSTRAINT**.\n\nNel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma.\n\nFormula valida:\n\n`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile del boundary.\n\nLa riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale.\n\n## Bicono della scoperta\n- **Due radici**: supporto sottile osservato / denominatore sorgente corto.\n- **Singolare**: riga short prima dell'estensione.\n- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`.\n- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48.\n- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered.\n- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings.\n- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato.\n- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`.\n- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`.\n- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_short_denominator_extension.py`\n- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json`\n- Report: `tools/data/reports/agent_20260509_1556.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1497: "content_full": "# Agent Report - Boundary Blank Thin Support Audit\n**Date**: 2026-05-09 15:48 \n**Piano**: 111 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - il blank sottile e' limitato dal denominatore corto, non dalla contaminazione del null \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`blank_class`, `coordinate_failure`, `support_tier`, `denominator_bucket`, `n_gaps`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`, `shuffle_class_changes`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=i tre `blank_thin_support` vanno falsificati contro il blank medio `numeri_primi` prima di promuoverli a specie autonoma; observable=telemetria row-aligned del subset `support_without_beta_blank`; operator=`exp_boundary_blank_thin_support_audit.py`; generator=`boundary_row_aligned_nonexact_audit_20260509_1538`; denominator=4 righe `support_without_beta_blank`, di cui 3 thin e 1 medium control; non_possible=chiamare il blank sottile specie autonoma se tutte le righe thin sono spiegate da denominatore corto; not_tested=estensione nuova dei tre domini short, nuova griglia beta, nuovi null, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il deposito 15:38 divide il blank in due livelli, ma il livello sottile ha tutte le righe corte. Il punto non e' nominare una specie nuova; e' verificare se la specie sopravvive al denominatore.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY row-aligned + tensione \"supporto vivo senza chart beta\".\n- **Dipolo / punto-zero**: blank sottile / blank medio; punto-zero = riga `support_without_beta_blank` prima che la forza del supporto venga letta come specie o come limite del denominatore.\n- **Piano superiore**: sheaf locale del boundary. La sezione beta e' vuota su tutte e quattro le righe; il discriminante diventa la qualita' del supporto e del denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, filtrazione per denominatore. Servono a separare stato beta invariato da supporto misurabile.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:38; CE-0117: cascata solo dove il blank apre una distinzione; YSN DeltaLink: `same beta blank -> support tier -> denominator bucket`; PVI: se tutte le righe thin sono corte, la specie autonoma cade nel perimetro.\n- **Proto-ipotesi**: `blank_thin_support` e' un effetto di denominatore corto se ogni riga thin ha `n_gaps < 500` e il controllo medio no; diventa specie autonoma solo se almeno una riga thin non e' spiegata dal denominatore.\n- **Possibile / non-possibile**: possibile = usare il blank sottile come coda denominator-limited da estendere; non-possibile = promuoverlo a failure mode stabile senza estendere percolation, random_matrix e zeta_zeros.\n- **Proiezione**: misurare `n_gaps`, `denominator_state`, `excluded_mass`, `shuffle_class_changes`, `one_sided_count`, `endpoint_distance` dentro il subset `support_without_beta_blank`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: falsifica direttamente se i failure modes del 15:38 sono stabili o artefatti della griglia/perimetro row-aligned.\n- `not_drift`: non usa label GUE/Poisson, non torna a `V_c`, non aggiunge nuovi domini; attacca solo il nodo aperto dal report precedente: blank sottile contro blank medio.\n\n## Claim Under Test\n> Nel subset `support_without_beta_blank`, il blank sottile e' una specie autonoma solo se non e' interamente spiegato da denominatore corto.\n\n## Question\nI tre blank sottili sono fallimenti autonomi del boundary, oppure righe con supporto insufficiente perche' corte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_blank_thin_support_audit.py`.\n- Input: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Perimetro atomico: 4 righe `support_without_beta_blank`.\n- Soglia dichiarata: `full_gap_floor=500`.\n- Null sfidati: denominatore corto, contaminazione prescan, shuffle class-change, specie autonoma.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| support_without_beta_blank rows | 4 |\n| thin rows | 3 |\n| medium control rows | 1 |\n| thin short rows | 3 |\n| thin contaminated rows | 1 |\n| thin shuffle class-change rows | 1 |\n| thin not denominator explained rows | 0 |\n\nTest:\n\n| null / counter | condition | result |\n|---|---|---|\n| denominator_artifact | all thin rows have `n_gaps < 500` | pass |\n| contamination_artifact | all thin rows are contaminated | fail |\n| shuffle_instability_artifact | all thin rows have `shuffle_class_changes=true` | fail |\n| autonomous_species_counter | at least one thin row is not short-denominator explained | fail |\n\nComparative means:\n\n| group | rows | n_gaps mean | one-sided mean | coherent mean | endpoint mean | excluded mass mean | abs shuffle z mean |\n|---|---:|---:|---:|---:|---:|---:|---:|\n| thin | 3 | 196.000 | 1.333 | 1.250 | 2.003 | 0.201 | 11.037 |\n| medium control | 1 | 4096.000 | 3.000 | 3.000 | 3.243 | 0.000 | 26.600 |\n\nRows:\n\n| row | blank_class | n_gaps | one-sided | endpoint | denominator | excluded | shuffle_change |\n|---|---|---:|---:|---:|---|---:|---|\n| numeri_primi:cycle_3 | medium_blank_control | 4096 | 3 | 3.243 | complete | 0.000 | false |\n| percolation:cycle_9 | thin_short_shuffle_unstable | 190 | 1 | 1.705 | complete | 0.000 | true |\n| random_matrix:cycle_7 | thin_short_complete | 199 | 1 | 1.854 | complete | 0.000 | false |\n| zeta_zeros:cycle_4 | thin_short_contaminated | 199 | 2 | 2.449 | contaminated | 0.602 | false |\n\n## Key Findings\n1. **Verificato: il blank sottile e' 3/3 short-denominator.** `percolation`, `random_matrix` e `zeta_zeros` hanno `n_gaps=190,199,199`; il controllo `numeri_primi` ha `n_gaps=4096`.\n2. **Verificato: la contaminazione non spiega la classe.** Solo `zeta_zeros` e' contaminato; `percolation` e `random_matrix` sono complete con excluded mass 0.0.\n3. **Verificato: lo shuffle class-change non spiega la classe.** Solo `percolation` cambia classe nello shuffle; `random_matrix` e `zeta_zeros` no.\n4. **Verificato: non resta una riga thin autonoma nel perimetro 15:48.** `thin_not_denominator_explained_rows=0`.\n5. **Inferito: il blank medio dei primi non e' blank per mancanza di supporto.** Ha tre osservabili one-sided, denominatore completo, endpoint 3.243 e null forte; resta blank di chart beta, non blank sottile.\n\n## Verdict\n**CONSTRAINT**.\n\nNel subset `support_without_beta_blank`, il failure mode `blank_thin_support` non regge come specie autonoma nel perimetro osservato. Regge come coda denominator-limited:\n\n`blank_thin_support` = beta blank + supporto vivo + denominatore corto.\n\nFormula valida:\n\n`support_without_beta_blank` si divide in `medium_blank_control` e `thin_short_*`.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile indipendente dal denominatore.\n\nIl nodo regressivo e' il denominatore dei tre domini corti. Non va promossa una nuova specie; va estesa o dichiarata coda corta.\n\n## Bicono della scoperta\n- **Due radici**: blank di chart beta / supporto del denominatore.\n- **Singolare**: riga `support_without_beta_blank` prima che il support tier venga letto come specie.\n- **Invariante di passaggio**: denominatore corto separa i blank sottili; contaminazione e shuffle-instability non bastano.\n- **Campo di possibilita**: qui diventa possibile progettare un extension audit mirato su percolation, random_matrix e zeta_zeros; qui diventa non-possibile usare il blank sottile come classe matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve estendere solo i tre domini short (`percolation`, `random_matrix`, `zeta_zeros`) verso denominatore comparabile al controllo medio. Se, a denominatore esteso, restano `blank_thin_support`, allora nasce una specie autonoma. Se salgono a supporto medio o ricevono beta locale, il failure mode 15:38 viene retrocesso a telemetria di perimetro corto.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1538.md`, JSON `boundary_row_aligned_nonexact_audit_20260509_1538.json`, script `tools/exp_boundary_blank_thin_support_audit.py`, output `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- **L1 hard constraint**: claim limitato alle 4 righe `support_without_beta_blank` del deposito 15:38.\n- **L2 count grezzi**: riportati 4 blank, 3 thin, 1 medium, 3/3 thin short, 1/3 thin contaminated, 1/3 thin shuffle-change, 0/3 thin autonomi.\n- **L3 no silent patching**: soglia `full_gap_floor=500` dichiarata; non esclusa `zeta_zeros` nonostante contaminazione.\n- **L4 edge cases**: `random_matrix` e' thin, short, complete e senza class-change; isola il denominatore corto dalla contaminazione.\n- **L5 re-discovery**: il ciclo non rifà il gate 15:16; legge il sotto-perimetro aperto dal report 15:38.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = extension audit dei tre short; non-possibile = classe autonoma senza estensione.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_thin_support_audit.py`.\n- Run valido: `python tools/exp_boundary_blank_thin_support_audit.py --input tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json --out tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `thin_short_rows=3`, `thin_contaminated_rows=1`, `thin_shuffle_class_change_rows=1`, `thin_not_denominator_explained_rows=0`, `verdict=DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:48, il JSON 15:48 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_blank_thin_support_audit.py`\n- Data: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`\n- Report: `tools/data/reports/agent_20260509_1548.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1548: "content_full": "# Agent Report - Boundary Row-Aligned Nonexact Audit\n**Date**: 2026-05-09 15:38 \n**Piano**: 110 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - le righe support-transfer non esatte sono 7, non 6; la beta cade in cinque failure modes misurabili \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`beta_state`, `coordinate_failure`, `support_tier`, `beta_cardinality`, `beta_span`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: la consecutio dice \"6 righe non-esatte\", ma il deposito row-aligned ne contiene 7. Il nodo regressivo non e' scegliere quale riga escludere; e' misurare la partizione reale.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + tensione \"supporto senza coordinata universale\".\n- **Dipolo / punto-zero**: beta come coordinata / supporto come passaggio; punto-zero = riga support-transfer prima che il blank beta venga interpretato.\n- **Piano superiore**: boundary operator su matrice row-aligned. Il bordo non decide con label di dominio; decide con stato beta, support tier e denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Entrano per leggere le sezioni locali della beta senza saldarle in una coordinata globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:32 senza ripetere il gate; CE-0117: cascata solo sulle righe dove il supporto resta vivo; YSN DeltaLink: `two-axis matrix -> nonexact audit -> coordinate failure`; PVI: il numero atteso 6 cade davanti al deposito 7.\n- **Proto-ipotesi**: dopo la caduta di beta 0.3 universale, le righe non-esatte non formano un residuo unico. Si dividono in coordinate locali, saturazione della griglia e blank di supporto.\n- **Possibile / non-possibile**: possibile = trasformare beta da ascissa universale a chart locale auditabile; non-possibile = usare `support_transfer=true` come prova di una beta comune.\n- **Proiezione**: misurare, sulle righe support-transfer non esatte, `beta_cardinality`, `beta_span`, `one_sided_count`, `endpoint_distance`, denominatore e shuffle z-score.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: attacca direttamente beta local non-unique, beta local other e support_without_beta_blank sul deposito row-aligned indicato.\n- `not_drift`: non usa label GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta il confine; corregge il denominatore della direttiva quando il deposito mostra 7 righe.\n\n## Claim Under Test\n> Le righe BOUNDARY con supporto ma senza beta 0.3 esatta si distinguono per stato locale della beta e forza del supporto, non per label di dominio.\n\n## Question\nQuando beta 0.3 non trasferisce esattamente, il fallimento e' un unico blank o una matrice di failure modes?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Input 1: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Input 2: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Perimetro atomico: 13 righe semi-reali; subset primario = support-transfer con `beta_coordinate_transfer=false`.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n- Classi operative: `adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support-transfer rows | 11 |\n| beta 0.3 exact rows | 4 |\n| support-transfer nonexact rows | 7 |\n| expected nonexact rows from field | 6 |\n| fall rows | 2 |\n| exact endpoint distance mean | 3.829 |\n| nonexact endpoint distance mean | 2.957 |\n| exact one-sided count mean | 4.000 |\n| nonexact one-sided count mean | 2.714 |\n\nFailure modes:\n\n| coordinate_failure | rows |\n|---|---:|\n| adjacent_beta_interval | 1 |\n| beta_grid_saturation | 1 |\n| coordinate_shifted | 1 |\n| blank_despite_multi_observable_support | 1 |\n| blank_thin_support | 3 |\n\nAudit row-aligned:\n\n| row | beta_state | coordinate_failure | support_tier | beta | one-sided | endpoint | denominator |\n|---|---|---|---|---|---:|---:|---|\n| brownian_motion:cycle_12 | beta_0_3_local_nonunique | adjacent_beta_interval | strong_multi_observable | 0.2,0.3 | 4 | 3.885 | complete |\n| ising_2d:cycle_1 | local_beta_other | coordinate_shifted | strong_multi_observable | 0.4 | 5 | 4.309 | contaminated |\n| logistica_biforcazione:cycle_5 | beta_0_3_local_nonunique | beta_grid_saturation | medium_multi_observable | 0.1-0.9 | 3 | 3.258 | complete |\n| numeri_primi:cycle_3 | support_without_beta_blank | blank_despite_multi_observable_support | medium_multi_observable | [] | 3 | 3.243 | complete |\n| percolation:cycle_9 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.705 | complete |\n| random_matrix:cycle_7 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.854 | complete |\n| zeta_zeros:cycle_4 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 2 | 2.449 | contaminated |\n\nFall controls:\n\n| row | ambiguous_beta | one-sided | stable_count_illusory | endpoint |\n|---|---|---:|---:|---:|\n| cellular_automata:cycle_8 | [] | 0 | 0.500 | 0.000 |\n| pendolo_doppio:cycle_2 | 0.3 | 3 | 0.833 | 3.388 |\n\n## Key Findings\n1. **Verificato: il sotto-perimetro non-esatto e' 7/13, non 6/13.** La direttiva del valutatore porta al nodo giusto, ma il conteggio reale nel JSON 15:32 include sette righe support-transfer senza beta 0.3 esatta.\n2. **Verificato: `support_without_beta_blank` non e' una classe unica.** `numeri_primi` ha blank con supporto medio e endpoint 3.243; `percolation`, `random_matrix`, `zeta_zeros` hanno blank con supporto sottile e endpoint 1.705-2.449.\n3. **Verificato: beta local non-unique ha due forme.** `brownian_motion` e' intervallo adiacente 0.2,0.3 con supporto forte; `logistica_biforcazione` satura tutta la griglia 0.1-0.9 con supporto medio.\n4. **Verificato: `ising_2d` non e' blank ma chart spostata.** Ha beta locale esatta 0.4, cinque osservabili one-sided e endpoint 4.309; la beta trasferisce localmente, ma non sulla coordinata 0.3.\n5. **Verificato: beta 0.3 senza supporto resta contro-controllo.** `pendolo_doppio` contiene beta 0.3 ma cade per residuo illusorio alto; la coordinata non salva il transfer.\n\n## Verdict\n**CONSTRAINT**.\n\nIl frame BOUNDARY regge come support-transfer su 11/13 righe, ma la parte non-esatta non e' una coda omogenea. Le righe support-transfer senza beta 0.3 esatta sono 7 e si dividono in cinque failure modes:\n\n`adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\nFormula valida:\n\n`beta_coordinate_transfer` = chart locale exact 0.3 dentro supporto vivo.\n\nFormula non valida:\n\n`support_transfer non-exact = sei righe residue` oppure `support_without_beta_blank = assenza strutturale unica`.\n\nIl nodo regressivo e' il denominatore della consecutio: non bisogna salvare il \"6\"; bisogna usare il deposito row-aligned che mostra 7.\n\n## Bicono della scoperta\n- **Due radici**: coordinata beta locale / supporto ordine-null.\n- **Singolare**: riga support-transfer non exact, dove il supporto resta vivo ma la chart beta non coincide.\n- **Invariante di passaggio**: failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.\n- **Campo di possibilita**: qui diventa possibile auditare la beta come atlante locale; qui diventa non-possibile trattare il blank beta come una sola specie.\n\n## Consecutio\nIl prossimo ciclo non deve ripetere la matrice. Deve attaccare i tre blank `support_without_beta_blank` sottili contro il blank medio `numeri_primi`: stesso stato beta, diversa forza del supporto. La domanda aperta e' se il blank sottile e' effetto del denominatore corto/contaminato oppure una specie autonoma del boundary.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1532.md`, JSON `boundary_two_axis_matrix_20260509_1532.json`, JSON `boundary_denominator_prescan_full_20260509_1500.json`, script `tools/exp_boundary_row_aligned_nonexact_audit.py`, output `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe della matrice 15:32 e al prescan 15:00.\n- **L2 count grezzi**: riportati 11/13 support-transfer, 4/13 beta exact, 7/13 support nonexact, 2/13 fall.\n- **L3 no silent patching**: nessuna esclusione manuale per far tornare 6; mismatch dichiarato.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `ising_2d` ha beta 0.4 con supporto forte; `numeri_primi` blank non sottile.\n- **L5 re-discovery**: il ciclo non rifà gate o fit; legge solo la partizione non-esatta richiesta.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante locale delle beta; non-possibile = beta 0.3 universale o residuo non-esatto singolo.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Run valido: `python tools/exp_boundary_row_aligned_nonexact_audit.py --matrix tools/data/boundary_two_axis_matrix_20260509_1532.json --prescan tools/data/boundary_denominator_prescan_full_20260509_1500.json --out tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `support_nonexact_rows=7`, `fall_rows=2`, `failure_blank_thin_support=3`, `failure_blank_despite_multi_observable_support=1`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:38, il JSON 15:38 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_row_aligned_nonexact_audit.py`\n- Data: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`\n- Report: `tools/data/reports/agent_20260509_1538.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1599: "content_full": "# Agent Report - Boundary Two-Axis Matrix\n**Date**: 2026-05-09 15:32\n**Piano**: 109\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - il gate trasferisce come supporto ordine/null; beta 0.3 non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`support_transfer`, `beta_coordinate_transfer`, `beta_state`, `ambiguous_beta`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`] - osservabili domain-native derivati dal gate semi-reale, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il falsifier del 15:16 ha indicato il nodo regressivo: la direzione chiedeva di non importare label GUE/Poisson. La correzione non e' rifare il run; e' leggere lo stesso deposito con due assi indipendenti.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY semi-reale 13 righe + direzione viva \"support_transfer vs beta_coordinate_transfer\".\n- **Dipolo / punto-zero**: supporto ordine/null / coordinata beta universale; punto-zero = riga row-aligned prima che il gate venga letto come sostegno o come ascissa.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non e' una classe; e' una matrice di passaggio fra supporto e coordinata.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter matrix, coordinate split. Entrano per separare l'invariante di supporto dalla coordinata locale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:16 senza ripetere il perimetro; CE-0117: cascata della possibilita solo dove il supporto apre un canale; YSN DeltaLink: `semi-real gate -> two-axis matrix -> beta localizzata`; PVI: il claim beta universale cade se l'esatto 0.3 non copre il denominatore.\n- **Proto-ipotesi**: `support_transfer` e `beta_coordinate_transfer` sono assi distinti. Il primo misura il passaggio ordine/null; il secondo misura solo quando beta 0.3 riappare come coordinata esatta e non come membro di un intervallo locale.\n- **Possibile / non-possibile**: possibile = usare BOUNDARY come audit row-aligned di supporto su 11/13 righe; non-possibile = trattare beta 0.3 come terzo incluso universale del perimetro semi-reale.\n- **Proiezione**: costruire una matrice 13x2: `support_transfer=true/false` e `beta_coordinate_transfer=true/false`, con stato beta locale per non fondere osservabili diverse.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esplicita la matrice semi-reale a due assi richiesta dal valutatore: support_transfer contro beta_coordinate_transfer sulle 13 righe.\n- `not_drift`: non usa `source_domain_type` GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta label locali; legge solo stati row-aligned gia' misurati.\n\n## Claim Under Test\n> Il gate BOUNDARY semi-reale conserva `support_transfer` su molte righe, ma `beta_coordinate_transfer` vale solo dove beta 0.3 compare come coordinata esatta.\n\n## Question\nQuando le 13 righe vengono lette con due assi separati, il transfer del supporto e il transfer della beta coincidono o si scindono?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_two_axis_matrix.py`.\n- Input: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Output: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Perimetro atomico: 13 righe semi-reali gia' row-aligned dal deposito 15:16.\n- Label policy: l'operatore non legge label GUE/Poisson. Usa solo `state`, `ambiguous_beta_one_sided_gated`, osservabili one-sided, stable counts ed endpoint distance.\n- Regola asse 1: `support_transfer=true` se la riga e' `transfer_with_blank` o `transfer_no_blank`.\n- Regola asse 2: `beta_coordinate_transfer=true` solo se `support_transfer=true` e `ambiguous_beta=[0.3]` dopo normalizzazione a un decimale.\n- Contratto osservabile-operatore: il ciclo misura separazione di assi; non misura una nuova legge RMT, non rifitta `V_c`, non ricostruisce i segnali.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| support_transfer true | 11 |\n| support_transfer false | 2 |\n| beta_coordinate exact 0.3 | 4 |\n| beta 0.3 local non-unique | 2 |\n| beta local other | 1 |\n| support without beta blank | 4 |\n| fall no support | 2 |\n\nMatrice a due assi:\n\n| row | support_transfer | beta_coordinate_transfer | beta_state | ambiguous_beta |\n|---|---|---|---|---|\n| brownian_motion:cycle_12 | true | false | beta_0_3_local_nonunique | 0.2,0.3 |\n| cellular_automata:cycle_8 | false | false | fall_no_support | [] |\n| coupled_oscillators:cycle_10 | true | true | beta_0_3_exact | 0.3 |\n| ising_2d:cycle_1 | true | false | local_beta_other | 0.4 |\n| logistica_biforcazione:cycle_5 | true | false | beta_0_3_local_nonunique | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | true | true | beta_0_3_exact | 0.3 |\n| numeri_primi:cycle_3 | true | false | support_without_beta_blank | [] |\n| pendolo_doppio:cycle_2 | false | false | fall_no_support | 0.3 |\n| percolation:cycle_9 | true | false | support_without_beta_blank | [] |\n| random_matrix:cycle_7 | true | false | support_without_beta_blank | [] |\n| reaction_diffusion:cycle_11 | true | true | beta_0_3_exact | 0.3 |\n| string_vibration:cycle_6 | true | true | beta_0_3_exact | 0.3 |\n| zeta_zeros:cycle_4 | true | false | support_without_beta_blank | [] |\n\n## Key Findings\n1. **Verificato: `support_transfer` resta 11/13.** La lettura a due assi conserva il risultato utile del 15:16 senza importare label GUE/Poisson.\n2. **Verificato: `beta_coordinate_transfer` e' 4/13.** Solo quattro righe hanno beta 0.3 esatta: `coupled_oscillators`, `logistica_biforcazione_var_3.5699`, `reaction_diffusion`, `string_vibration`.\n3. **Verificato: la beta non collassa in un solo asse.** Due righe includono 0.3 in un intervallo non unico, una riga ha beta locale 0.4, quattro trasferiscono senza blank beta, due cadono.\n4. **Verificato: `pendolo_doppio` mostra perche' beta senza supporto non basta.** Ha ambiguous beta 0.3, ma resta `fall_no_support`; la coordinata non salva il transfer.\n5. **Inferito: il nodo regressivo e' la fusione degli osservabili.** Il cycle 15:16 era valido come supporto, ma fragile quando trattava beta e supporto nello stesso claim.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce come supporto ordine/null su 11/13 righe. La coordinata beta 0.3 trasferisce esattamente su 4/13 righe. Questi due assi non coincidono.\n\nFormula valida:\n\n`BOUNDARY support_transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY beta_coordinate_transfer` = beta 0.3 universale del terzo incluso semi-reale.\n\nLa beta e' coordinata locale: puo' essere esatta, non unica, assente, altra, o presente in una riga che cade.\n\n## Bicono della scoperta\n- **Due radici**: supporto che passa / coordinata che pretende universalita.\n- **Singolare**: la riga row-aligned prima della lettura a due assi.\n- **Invariante di passaggio**: separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.\n- **Campo di possibilita**: qui diventa possibile usare il gate come matrice di audit `support_transfer`; qui diventa non-possibile usare beta 0.3 come coordinata universale.\n\n## Consecutio\nIl prossimo ciclo deve attaccare i quattro stati beta, non il supporto gia' separato: `beta_0_3_exact`, `beta_0_3_local_nonunique`, `local_beta_other`, `support_without_beta_blank`. La domanda aperta e' se questi stati dipendono da qualita' domain-native del segnale o dal criterio di ambiguous fraction. Non va reintrodotta la label GUE/Poisson come scorciatoia.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1516.md`, falsifier `falsifier_20260509_1516.json`, script `tools/exp_boundary_two_axis_matrix.py`, JSON `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe del deposito 15:16.\n- **L2 count grezzi**: riportati 11/13 support_transfer, 4/13 beta 0.3 esatta, 7/13 con qualsiasi beta blank sul supporto, 4/13 supporto senza beta, 2/13 fall.\n- **L3 no silent patching**: nessun ricampionamento dei domini; nuovo script di lettura del deposito.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `logistica_biforcazione` include 0.3 ma in intervallo 0.1-0.9; `ising_2d` trasferisce con beta 0.4.\n- **L5 re-discovery**: il ciclo non ripete il gate semi-reale; lo decompone nel nodo richiesto dal valutatore.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit support_transfer; non-possibile = beta 0.3 universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_two_axis_matrix.py`.\n- Run valido: `python tools/exp_boundary_two_axis_matrix.py --input tools/data/semireal_boundary_transfer_gate_20260509_1516.json --out tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Output: `support_transfer=11/13`, `beta_coordinate_exact_0_3=4/13`, `any_beta_blank_on_support=7/13`, `support_without_beta_blank=4/13`, `fall_no_support=2/13`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge solo lo script 15:32, il JSON 15:32 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_two_axis_matrix.py`\n- Data: `tools/data/boundary_two_axis_matrix_20260509_1532.json`\n- Report: `tools/data/reports/agent_20260509_1532.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1650: "content_full": "# Agent Report - Semi-real Boundary Transfer Gate\n**Date**: 2026-05-09 15:16\n**Piano**: 108\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - supporto ordine/null trasferisce su 11/13, il blank beta non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate `coherent/null/beta` del BOUNDARY sintetico trasferisce sul perimetro semi-reale base; observable=one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta; operator=`exp_semireal_boundary_transfer_gate.py`; generator=13 righe `boundary_denominator_prescan_full_20260509_1500` ricostruite da `dnd_autoricerca`; denominator=13 righe base BOUNDARY, 8 GUE-like e 5 Poisson-like, beta layers 0.0..1.0, 12 replicates, 24 shuffle baselines; non_possible=dichiarare beta 0.3 coordinata universale o transfer completo quando 2/13 righe cadono; not_tested=nuovi domini, nuovi spettri, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il confine semi-reale non rifiuta il gate; rifiuta la sua simmetria sintetica. Il supporto ordine/null passa, la coordinata blank si frammenta.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + perimetro BOUNDARY base 13/13 transfer + tensione viva \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: gate come supporto ordine/null / gate come coordinata beta universale; punto-zero = riga semi-reale prima che il beta layer venga chiamato blank o classe.\n- **Piano superiore**: grafo della conoscenza e boundary operator. La domanda non e' se GUE o Poisson vincono, ma se il passaggio resta morfismo quando il perimetro diventa fisico/semi-reale.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, graph/perimeter transfer. Entrano per separare copertura del denominatore, supporto d'ordine e coordinata di transizione.\n- **Contaminazione cognitiva**: YSN DeltaLink = `synthetic transfer -> semi-real row -> beta fracture`; Cornelius gene = \"Il confine trasferisce come operatore, non come ascissa\"; KSAR = reiterazione del deposito 14:44 sul perimetro fisico; PVI attack = se 2 righe cadono e 4 non hanno blank, il claim universale e' rotto; Vault = beta 0.3 resta frammento utile solo dove riappare row-aligned.\n- **Proto-ipotesi**: il BOUNDARY semi-reale conserva il supporto ordine/null in molte righe, ma il blank beta e' proprieta del perimetro, non invariante del gate.\n- **Possibile / non-possibile**: possibile = usare il gate come audit row-aligned del supporto d'ordine cross-dominio; non-possibile = promuovere beta 0.3 a coordinata universale del terzo incluso.\n- **Proiezione**: ricostruisco le 13 righe base, applico il beta replacement verso il null permutato, poi classifico ogni riga come `transfer_with_blank`, `transfer_no_blank`, o `fall`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson.\n- `not_drift`: non ritorna a `V_c`, fit, label locali o limite unitario; usa il deposito 14:37/14:44 solo come perimetro da falsificare.\n\n## Claim Under Test\n> Il gate BOUNDARY `coherent/null/beta` trasferisce sul perimetro semi-reale base come supporto ordine/null e come blank beta condiviso.\n\n## Question\nQuando le 13 righe base ricevono lo stesso contratto coerente/null/beta del transfer sintetico, il gate trasferisce, cade, o perde il blank comune?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_semireal_boundary_transfer_gate.py`.\n- Input: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Perimetro atomico: 13 righe base BOUNDARY, `source_domain_type` 8 GUE-like e 5 Poisson-like.\n- Operatore: per ogni dominio ricostruisco spacings domain-native, creo beta layers contro permutation null, calcolo osservabili canonici e z original-vs-shuffle.\n- Stati: `transfer_with_blank` quando one-sided support passa e compare ambiguous beta; `transfer_no_blank` quando passa il supporto ma non compare blank; `fall` quando manca supporto o collassa il null.\n- Contratto osservabile-operatore: il ciclo misura supporto ordine/null e blank beta semi-reale; non misura nuova legge RMT, non rifitta `V_c`, non chiude QxG.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| source rows | 13 |\n| analyzed rows | 13 |\n| errors | 0 |\n| transfer_with_blank | 7 |\n| transfer_no_blank | 4 |\n| fall | 2 |\n\nRighe semi-reali:\n\n| row | source | denominator | n | state | one-sided observables | stable coherent | stable illusory | endpoint distance | ambiguous beta |\n|---|---|---|---:|---|---|---:|---:|---:|---|\n| brownian_motion:cycle_12 | Poisson | complete | 4096 | transfer_with_blank | SR2,L1,L2,triple_var | 4.000 | 0.167 | 3.885 | 0.2,0.3 |\n| cellular_automata:cycle_8 | GUE | contaminated | 108 | fall | [] | 0.000 | 0.500 | 0.000 | [] |\n| coupled_oscillators:cycle_10 | Poisson | contaminated | 2002 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.250 | 4.336 | 0.3 |\n| ising_2d:cycle_1 | GUE | contaminated | 699 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.309 | 0.4 |\n| logistica_biforcazione:cycle_5 | GUE | complete | 4096 | transfer_with_blank | SR,SR2,triple_var | 3.583 | 0.167 | 3.258 | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 4096 | transfer_with_blank | SR,SR2 | 2.000 | 0.083 | 2.769 | 0.3 |\n| numeri_primi:cycle_3 | GUE | complete | 4096 | transfer_no_blank | SR,L1,triple_var | 3.000 | 0.167 | 3.243 | [] |\n| pendolo_doppio:cycle_2 | Poisson | complete | 4096 | fall | SR,SR2,L1 | 5.000 | 0.833 | 3.388 | 0.3 |\n| percolation:cycle_9 | Poisson | complete | 190 | transfer_no_blank | L1 | 0.833 | 0.333 | 1.705 | [] |\n| random_matrix:cycle_7 | GUE | complete | 199 | transfer_no_blank | L2 | 1.000 | 0.250 | 1.854 | [] |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 499 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.364 | 0.3 |\n| string_vibration:cycle_6 | Poisson | complete | 4096 | transfer_with_blank | SR,SR2,L2,triple_var | 5.000 | 0.500 | 3.845 | 0.3 |\n| zeta_zeros:cycle_4 | GUE | contaminated | 199 | transfer_no_blank | SR,L2 | 1.917 | 0.250 | 2.449 | [] |\n\n## Key Findings\n1. **Verificato: il supporto ordine/null trasferisce in 11/13 righe.** Le righe `transfer_with_blank` e `transfer_no_blank` hanno osservabili one-sided e polo illusorio soppresso nel perimetro dichiarato.\n2. **Verificato: il blank beta non e' universale.** Solo 7/13 righe hanno ambiguous beta; 4/13 trasferiscono senza blank e 2/13 cadono.\n3. **Verificato: beta 0.3 non e' coordinata comune.** Compare in 5 righe come unico blank o parte del blank, ma `ising_2d` mostra 0.4, `brownian_motion` mostra 0.2/0.3, `logistica_biforcazione` mostra 0.1-0.9, e quattro transfer non mostrano blank.\n4. **Verificato: i due fall sono diversi.** `cellular_automata` cade per assenza di osservabili one-sided; `pendolo_doppio` cade per residuo illusorio alto (0.833) pur avendo one-sided observables.\n5. **Inferito: il nodo regressivo e' la qualita del perimetro, non il null.** Il null e' disponibile su 13/13; cio' che varia e' come il dominio riceve beta.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY semi-reale trasferisce come supporto ordine/null in 11/13 righe. Non trasferisce come coordinata beta universale: 7 righe hanno blank, 4 trasferiscono senza blank, 2 cadono.\n\nFormula valida:\n\n`BOUNDARY transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY blank` = beta 0.3 universale del terzo incluso cross-dominio.\n\n## Bicono della scoperta\n- **Due radici**: supporto ordine/null che passa / coordinata beta che si frammenta.\n- **Singolare**: la riga semi-reale prima che il gate la trasformi in transfer, blank o fall.\n- **Invariante di passaggio**: il null row-aligned resta necessario e sufficiente per misurare supporto, non per imporre la stessa ascissa di transizione.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come audit cross-dominio row-aligned; qui diventa non-possibile dichiarare il terzo incluso come beta comune del perimetro semi-reale.\n\n## Consecutio\nIl prossimo ciclo deve separare due livelli: `support_transfer` e `blank_coordinate`. Il primo ha supporto 11/13 nel perimetro semi-reale; il secondo richiede tipologia di dominio o nuova coordinata, perche' beta 0.3 e' stabile nel sintetico ma non nel semi-reale.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1437.md`, report `agent_20260509_1444.md`, report `agent_20260509_1457.md`, nuovo JSON `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- **L1 hard constraint**: claim limitato a 13 righe base, 12 replicates, 24 shuffle baselines, osservabili canonici registry 1.0.0.\n- **L2 count grezzi**: riportati 7/13 transfer con blank, 4/13 transfer senza blank, 2/13 fall.\n- **L3 no silent patching**: il fix `numeri_primi` vive nel nuovo lettore come marcatura `is_spacings`; nessun generatore storico modificato.\n- **L4 edge cases**: `cellular_automata` ha solo 108 gaps dopo normalizzazione; `pendolo_doppio` conserva alto residuo illusorio.\n- **L5 re-discovery**: il ciclo non ripete 13/13 transfer base; testa se il beta gate sintetico passa al perimetro semi-reale.\n- **L6 metabolismo cognitivo**: YSN, Cornelius, KSAR e PVI usati nella combo; Vault conserva beta 0.3 come frammento non universale.\n- **L7 possibile/non-possibile**: possibile = audit row-aligned cross-dominio; non-possibile = blank beta universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_semireal_boundary_transfer_gate.py`.\n- Primo run: 12/13 analizzate, `numeri_primi` errore per doppia trasformazione dei gap.\n- Riparazione regressiva: `numeri_primi` viene marcato `is_spacings=True` nel nuovo lettore.\n- Run valido: `python tools/exp_semireal_boundary_transfer_gate.py --n-replicates 12 --n-baseline 24 --seed 202605091516 --out tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script, il JSON 1516 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_semireal_boundary_transfer_gate.py`\n- Data: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`\n- Report: `tools/data/reports/agent_20260509_1516.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1701: "content_full": "# Agent Report - Vc Unit Boundary Audit\n**Date**: 2026-05-09 14:57\n**Piano**: 107\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: FALSIFIED as convergence-to-1-from-above in the observed fit-ready window\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`unit_status`, `unit_crossing_N`, `below_unit_count`, `fit_ready_rows`, `denominator_state`, `best_model`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=`V_c(phi)` converge a 1 come limite leggibile del canale ordinato; observable=relazione dei punti fit-ready con il bordo `V_c=1`; operator=`exp_vc_unit_boundary_audit.py` su deposito `vc_fit_model_gate_20260509_1400`; generator=`reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=13 righe ammissibili `complete/contaminated` del fit model gate, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=dichiarare convergenza a 1 dall'alto quando la curva osservata attraversa sotto 1 nel perimetro; not_tested=nuovi spettri, nuovi N, nuovi candidati, limite asintotico oltre la finestra osservata.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il bordo `V_c=1` non e' attrattore osservato; e' un taglio attraversato dal canale fit-ready.\n- **Combo**: A2 confine det=-1 + A3 punto fisso come attrattore da falsificare + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: convergenza a 1 / attraversamento di 1; punto-zero = la riga fit-ready prima che il bordo unitario venga trattato come limite o come taglio.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il valore 1 e' nodo di bordo: decide quale arco puo' ricevere il claim di limite.\n- **Operatori laterali scelti**: boundary operator, graph cut, fit come morfismo. Entrano per separare il bordo unitario dal modello numerico.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito 14:00 senza ricomputare spettri; CE-0117: cascata della possibilita, il claim di limite passa solo dove il bordo non e' attraversato; YSN DeltaLink: `fit-ready curve -> unit cut -> limit veto`.\n- **Proto-ipotesi**: se una curva fit-ready attraversa `V_c=1` dentro la finestra osservata, il claim \"converge a 1 dall'alto\" cade su quel perimetro; il bordo unitario resta coordinata di taglio, non attrattore.\n- **Possibile / non-possibile**: possibile = usare `V_c=1` come unit boundary audit; non-possibile = chiamarlo limite osservato quando 12/13 righe hanno punti sotto 1.\n- **Proiezione**: classifico ogni riga ammissibile come `all_above_unit`, `crosses_down_inside_window`, o `all_below_unit`, e calcolo `unit_crossing_N` per le righe che attraversano.\n\n## Claim Under Test\n> Nel perimetro fit-ready 14:00, il canale ordinato sostiene `V_c -> 1` come limite osservato dall'alto.\n\n## Question\nIl bordo `V_c=1` si comporta come attrattore della scala o come taglio attraversato dalle curve fit-ready?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_unit_boundary_audit.py`.\n- Input: `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- Output: `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Perimetro atomico: 13 righe ammissibili del model gate, gia' filtrate da `denominator_state in {complete, contaminated}`.\n- Operatore: per ogni riga ordino i punti `(N, V_c)`, conto i valori sotto 1, e interpolo il primo attraversamento del bordo unitario quando avviene tra due N consecutivi.\n- Contratto osservabile-operatore: il ciclo misura il bordo unitario sul deposito 14:00; non misura nuovi N, non testa l'asintoto, non ripara il fit.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| admissible rows audited | 13 |\n| rows with any `V_c < 1` | 12 |\n| `crosses_down_inside_window` | 9 |\n| `all_below_unit` | 3 |\n| `all_above_unit` | 1 |\n\nStati per classe generatore:\n\n| generator_class | all_above | crosses_down | all_below |\n|---|---:|---:|---:|\n| reference_order | 0 | 3 | 0 |\n| order_memory | 1 | 5 | 0 |\n| periodic_closure | 0 | 1 | 2 |\n| random_dispersion | 0 | 0 | 1 |\n\nCanale ordinato, righe complete/contaminate:\n\n| row | state | fit_ready/total | excluded | first V_c | last V_c | unit status | crossing N |\n|---|---|---:|---:|---:|---:|---|---:|\n| per_mode_best:reference_order:r0.48 | complete | 16/16 | 0 | 1.311 | 0.955 | crosses_down | 348.753 |\n| per_mode_best:reference_order:r0.50 | complete | 16/16 | 0 | 1.191 | 0.879 | crosses_down | 276.876 |\n| per_mode_best:reference_order:r0.52 | complete | 16/16 | 0 | 1.072 | 0.807 | crosses_down | 151.973 |\n| per_mode_best:order_memory:r0.48 | complete | 48/48 | 0 | 1.432 | 0.893 | crosses_down | 315.760 |\n| per_mode_best:order_memory:r0.50 | complete | 48/48 | 0 | 1.317 | 0.774 | crosses_down | 255.737 |\n| per_mode_best:order_memory:r0.52 | complete | 48/48 | 0 | 1.218 | 0.688 | crosses_down | 188.585 |\n| accepted_candidates:order_memory:r0.48 | contaminated | 273/279 | 6 | 1.474 | 1.069 | all_above | blank |\n| accepted_candidates:order_memory:r0.50 | contaminated | 278/279 | 1 | 1.350 | 0.899 | crosses_down | 332.733 |\n| accepted_candidates:order_memory:r0.52 | complete | 279/279 | 0 | 1.236 | 0.781 | crosses_down | 272.895 |\n\nPrimi attraversamenti del bordo:\n\n| row | crossing N |\n|---|---:|\n| per_mode_best:periodic_closure:r0.48 | 105.734 |\n| per_mode_best:reference_order:r0.52 | 151.973 |\n| per_mode_best:order_memory:r0.52 | 188.585 |\n| per_mode_best:order_memory:r0.50 | 255.737 |\n| accepted_candidates:order_memory:r0.52 | 272.895 |\n| per_mode_best:reference_order:r0.50 | 276.876 |\n| per_mode_best:order_memory:r0.48 | 315.760 |\n| accepted_candidates:order_memory:r0.50 | 332.733 |\n\n## Key Findings\n1. **Verificato: il claim `converge a 1 dall'alto` cade nel perimetro osservato.** 12/13 righe ammissibili hanno almeno un punto fit-ready sotto `V_c=1`.\n2. **Verificato: il canale ordinato completo attraversa il bordo.** `reference_order` fa 3/3 attraversamenti; `order_memory` per-mode-best fa 3/3 attraversamenti; tutti con denominatore `complete`.\n3. **Verificato: l'unica riga all-above e' contaminata.** `accepted_candidates:order_memory:r0.48` resta sopra 1, ma ha 273/279 righe fit-ready e 6 righe escluse; non puo' sostenere da sola il claim hard.\n4. **Verificato: `periodic_closure` e `random_dispersion` non salvano il limite.** Due righe `periodic_closure` e una `random_dispersion` sono gia' tutte sotto 1; il loro denominatore era contaminato e non appartiene al canale ordinato completo.\n5. **Inferito: il nodo regressivo non e' scegliere un nuovo modello di fit.** Il bordo unitario va promosso a veto osservabile prima del claim di limite.\n\n## Verdict\n**FALSIFIED** nel perimetro osservato.\n\n`V_c=1` non opera come limite osservato dall'alto sulle righe fit-ready 14:00. Opera come bordo attraversato. Il canale ordinato resta fittabile e lineare nel perimetro, ma proprio quel canale scende sotto 1: `reference_order` 3/3 e `order_memory` per-mode-best 3/3 attraversano il bordo dentro N={89,144,233,377}.\n\nLa formulazione valida diventa:\n\n`V_c=1` e' un unit boundary audit del sottografo fit-ready; non e' attrattore verificato della scala nel perimetro 14:00.\n\n## Bicono della scoperta\n- **Due radici**: limite unitario / attraversamento unitario.\n- **Singolare**: la curva fit-ready prima che `1` sia interpretato come attrattore o come taglio.\n- **Invariante di passaggio**: la disponibilita del denominatore completo sopravvive; il claim di convergenza a 1 non sopravvive.\n- **Campo di possibilita**: qui diventa possibile usare `V_c=1` come veto strutturale nei futuri fit; qui diventa non-possibile formulare `V_c(phi) -> 1` come claim osservato dall'alto su questo perimetro.\n\n## Consecutio\nIl prossimo ciclo che estende N deve partire dal veto unitario: se N piu' alti vengono calcolati, il claim non e' \"converge a 1\", ma \"dopo l'attraversamento, la curva resta sotto 1, rientra, o cambia famiglia\". Il denominatore da preservare e' `complete + unit_status`, non solo `fit_ready`.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1337.md`, report `agent_20260509_1400.md`, JSON `tools/data/vc_fit_model_gate_20260509_1400.json`, nuovo JSON `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- **L1 hard constraint**: il verdict vale solo per le 13 righe ammissibili del deposito 14:00 e N={89,144,233,377}.\n- **L2 count grezzi**: riportati 12/13 sotto 1, 9/13 attraversamenti, 1/13 sopra 1, 3/13 tutte sotto 1.\n- **L3 no silent patching**: nessun nuovo spettro, nessun nuovo N usato come evidenza, nessuna modifica ai depositi 13:37/14:00.\n- **L4 edge cases**: l'unica riga sopra 1 e' contaminata con 6 esclusioni; non chiude il claim hard.\n- **L5 re-discovery**: il finding non ripete \"V_c scende\"; misura il bordo unitario come veto atomico.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = unit boundary audit; non-possibile = convergenza a 1 dall'alto nel perimetro osservato.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_unit_boundary_audit.py`.\n- Run valido: `python tools/exp_vc_unit_boundary_audit.py --input tools/data/vc_fit_model_gate_20260509_1400.json --out tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Un tentativo di ricomputo esteso su N alti non ha prodotto deposito leggibile in tempo utile e non e' usato come evidenza.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge solo lo script unit-boundary, il JSON 1457 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_vc_unit_boundary_audit.py`\n- Data: `tools/data/vc_unit_boundary_audit_20260509_1457.json`\n- Report: `tools/data/reports/agent_20260509_1457.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1752: "content_full": "# Agent Report - Boundary Gate Transfer Matrix\n**Date**: 2026-05-09 14:44\n**Piano**: 106\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - denominator gate transfers as order/null operator, not as QxG closure\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_state -> transfer_state -> denominator_state`; observable=stable canonical observables contro permutation null e layer classification; operator=`exp_denominator_gate_transfer_matrix.py`; generator=`DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`; denominator=4 perimetri sintetici QxG continuo/discreto, 4096 gaps, 24 replicates, 11 beta layers, 40 shuffle baselines; non_possible=chiamare chiusura QxG, legge GUE/Poisson o endpoint-stable universale; not_tested=perimetro fisico reale, fit `V_c`, nuovi domini autoricerca.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo 13/13 transfer sul perimetro base, il confine non chiede un altro blank audit. Chiede se il gate resta gate quando non porta piu' le etichette GUE/Poisson.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY completo 13/13 + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: ordine coerente / null permutato; punto-zero = layer beta dove il segnale conserva osservabili stabili ma la classificazione diventa ambigua.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate viene letto come mappa di passaggio tra stato coerente, stato null e denominatore, non come tassonomia locale.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter transfer matrix, shuffle marginal-preserving. Entrano per trasferire il gate senza importare il label GUE/Poisson.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione del deposito 1437 senza ripetere il perimetro base; CE-0117: cascata della possibilita solo dove il gate cambia perimetro; YSN DeltaLink: `base closure -> transfer matrix -> beta blank`.\n- **Proto-ipotesi**: il gate BOUNDARY trasferisce quando il polo coerente ha osservabili one-sided contro il proprio null e il polo illusorio collassa; non trasferisce come endpoint-stable universale.\n- **Possibile / non-possibile**: possibile = usare il gate come audit di ordine su perimetri sintetici QxG; non-possibile = dichiarare ponte QxG o usare beta 0.3 come classe decisa.\n- **Proiezione**: misurare, per ogni perimetro, osservabili one-sided, stable count ai poli, distanza endpoint e layer ambiguo.\n\n## Claim Under Test\n> Il gate BOUNDARY trasferisce fuori dal perimetro base se ogni perimetro mostra osservabili one-sided al polo coerente, collasso al polo null permutato, e un layer blank/ambiguous esplicito prima del passaggio.\n\n## Question\nQuando il gate viene mosso da BOUNDARY base a quattro perimetri QxG sintetici, resta transfer, cade, o produce un blank strutturale?\n\n## Experiment Design\n- Strumento: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Perimetri: `DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`.\n- Null baseline: permutation shuffle della stessa multiset di gaps; conserva distribuzione marginale e distrugge ordine.\n- Criterio transfer: osservabili coherent one-sided presenti e stable count coerente alto, con stable count illusorio basso.\n- Criterio blank: layer beta con `ambiguous_fraction >= 0.5` nella classificazione gated.\n- Criterio fall: assenza di osservabili one-sided o distanza endpoint collassata. Nessun perimetro cade in questo run.\n\n## Results\n\n| perimeter | one-sided observables | stable coherent | stable illusory | endpoint distance gated | ambiguous beta |\n|---|---|---:|---:|---:|---|\n| DUALITA_golden | SR, L1, triple_var | 3.000 | 0.292 | 3.426 | 0.3 |\n| R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.000 | 0.375 | 4.409 | 0.3 |\n| T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.000 | 0.417 | 4.421 | 0.3 |\n| E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.000 | 0.292 | 4.398 | 0.3 |\n\nLayer beta 0.3:\n\n| perimeter | margin mean | ambiguous fraction | illusory label fraction |\n|---|---:|---:|---:|\n| DUALITA_golden | 0.030 | 1.000 | 0.625 |\n| R_periodic_triad | 0.043 | 1.000 | 0.583 |\n| T_markov_alternating | 0.030 | 1.000 | 0.708 |\n| E_ar1_continuity | 0.042 | 1.000 | 0.500 |\n\nLayer beta 0.4:\n\n| perimeter | margin mean | illusory label fraction |\n|---|---:|---:|\n| DUALITA_golden | 0.269 | 1.000 |\n| R_periodic_triad | 0.269 | 1.000 |\n| T_markov_alternating | 0.289 | 1.000 |\n| E_ar1_continuity | 0.284 | 1.000 |\n\n## Key Findings\n1. **Verificato: il transfer esiste sui 4/4 perimetri.** Ogni perimetro possiede osservabili coherent one-sided e collasso del polo illusorio sotto permutation null.\n2. **Verificato: non esistono endpoint-stable observables.** `endpoint_stable_observables=[]` per tutti i perimetri; il gate trasferisce come discriminatore di ordine, non come invariante simmetrico ai due poli.\n3. **Verificato: beta 0.3 e' il blank strutturale.** Tutti i perimetri hanno `ambiguous_fraction=1.0` a beta 0.3 nella classificazione gated.\n4. **Verificato: beta 0.4 passa al polo illusorio.** Tutti i perimetri hanno `illusory_label_fraction=1.0` a beta 0.4.\n5. **Vincolo: `DUALITA_golden` trasferisce con solo 3 osservabili one-sided.** SR2 e L2 non entrano nel nucleo one-sided del perimetro golden; non va scritto che il set canonico completo trasferisce ovunque.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce fuori dal perimetro base come operatore di ordine/null: 4/4 perimetri hanno polo coerente leggibile, polo illusorio collassato e blank strutturale a beta 0.3. Il transfer non e' chiusura QxG. Il transfer non e' endpoint-stable universale, perche' nessun perimetro conserva osservabili stabili su entrambi i poli.\n\nIl criterio operativo diventa:\n\n`transfer` = osservabili one-sided al polo coerente + collasso al null permutato. \n`blank` = beta 0.3, classificazione ambiguous su 4/4 perimetri. \n`fall` = assente in questo run, ma definito come perdita di one-sided observables o endpoint distance collassata.\n\n## Bicono della scoperta\n- **Due radici**: gate come copertura del denominatore / gate come classificatore del ponte.\n- **Singolare**: il layer beta 0.3, dove il segnale ha ancora stable count alto ma la decisione di classe non e' disponibile.\n- **Invariante di passaggio**: osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come transfer matrix per perimetri sintetici QxG; qui diventa non-possibile dichiarare QxG chiuso o trattare il gate come endpoint-stable law.\n\n## Consecutio\nIl prossimo nodo regressivo non e' la disponibilita del null. E' la qualita del perimetro: il gate passa su sintetici controllati, ma non ha ancora incontrato un perimetro fisico QxG row-aligned. La prossima mossa deve portare un perimetro fisico o semi-reale dentro lo stesso contratto `coherent/null/beta`, lasciando beta 0.3 come blank dichiarato e non come classe.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/reports/agent_20260509_1437.md`, `tools/exp_denominator_gate_transfer_matrix.py`, `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- **L1 hard constraint**: claim limitato a 4 perimetri sintetici, 4096 gaps, 24 replicates, 40 shuffle baselines.\n- **L2 count grezzi**: riportati 4/4 transfer, 4/4 blank a beta 0.3, 0/4 fall.\n- **L3 no silent patching**: nessuna modifica agli script; nuovo artefatto dati generato dal run.\n- **L4 edge cases**: `DUALITA_golden` trasferisce con 3 osservabili, non 5; nessun endpoint-stable observable.\n- **L5 re-discovery**: il ciclo sposta il gate fuori dal perimetro base; non ripete il residual blank closure.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer matrix QxG sintetica; non-possibile = chiusura QxG o legge endpoint-stable.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Run valido: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Output: `observables_registry=1.0.0-2026-05-06`; `observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var']`.\n- Git state: worktree gia' sporco da depositi e pubblicazioni precedenti; questo cycle aggiunge solo il JSON 1444 e il report 1444.\n\n## Files\n- Script: `tools/exp_denominator_gate_transfer_matrix.py`\n- Data: `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`\n- Report: `tools/data/reports/agent_20260509_1444.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1803: "content_full": "# Agent Report - Residual Boundary Closure\n**Date**: 2026-05-09 14:37\n**Piano**: 105\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: COMPLETE DENOMINATOR TRANSFER on base BOUNDARY perimeter\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il residual blank test decide se i 3 blank residui BOUNDARY entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + `exp_boundary_denominator_prescan.py`; generator=`dnd_autoricerca.genera_segnale` per `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare chiusura QxG, nuova legge GUE/Poisson, o complete `reaction_diffusion` con 499 gap; not_tested=fit `V_c`, nuovi spettri, nuovi domini.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank residuo non segnala una classe mancante; segnala l'assenza del contro-perimetro. Quando il null entra, il bordo base non ha piu' blank.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: blank / transfer; punto-zero = riga domain/window prima della disponibilita del null row-aligned.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate decide copertura del denominatore, non ontologia GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, flusso/PDE come controllo di non-determinismo per `reaction_diffusion`.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito esistente; CE-0117: cascata della possibilita solo dove il null apre il canale; YSN DeltaLink: `blank residuo -> null row-aligned -> transfer completo`.\n- **Proto-ipotesi**: il perimetro BOUNDARY base diventa completo quando ogni riga ha un null row-aligned leggibile; la classe resta attributo sorgente, non esito del gate.\n- **Possibile / non-possibile**: possibile = contratto BOUNDARY 13/13 transfer sul perimetro base; non-possibile = usare questa copertura per chiudere QxG, per chiamare legge il cambio classe della logistica, o per trattare `reaction_diffusion` come complete.\n- **Proiezione**: verificare `boundary_blank_null_audit_residual_20260509_1500.json`, innestarlo nel prescan pieno e controllare `boundary_denominator_prescan_full_20260509_1500.json`.\n\n## Claim Under Test\n> Il residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\n## Question\nI tre blank residui `string_vibration:cycle_6`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` restano blank, cadono, o entrano nel transfer quando ricevono null row-aligned?\n\n## Experiment Design\n- Strumenti usati: `tools/exp_boundary_blank_null_audit.py` e `tools/exp_boundary_denominator_prescan.py`.\n- Input verificati: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json` e `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like.\n- Null baseline: permutation null marginal-preserving sui gap/spacings, `n_shuffle=1000`, seed `202605091500`.\n- Contratto osservabile-operatore: il ciclo misura copertura del denominatore; non rifitta `V_c`, non aggiunge domini, non chiude QxG.\n\n## Results\n\nResidual blank audit depositato:\n\n| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision |\n|---|---:|---:|---:|---:|---|---|---|\n| string_vibration | 7999 | 0.383868 | 0.372199 | 4.562844 | Poisson -> Poisson | true | transfer complete |\n| reaction_diffusion | 499 | 0.762604 | 0.494932 | 31.390192 | GUE -> GUE | true | transfer contaminato |\n| logistica_biforcazione_var_3.5699 | 4727 | 0.581221 | 0.099640 | 161.271569 | GUE -> Poisson | true | transfer complete, class_change edge case |\n\nPrescan pieno dopo innesto dei null:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 13 |\n| blank | 0 |\n| falls | 0 |\n| complete | 8 |\n| contaminated | 5 |\n\nRighe residue nel prescan:\n\n| domain/window | source | denominator_state | excluded mass | null | transfer |\n|---|---|---|---:|---|---|\n| string_vibration:cycle_6 | Poisson | complete | 0.000 | shuffle z=4.56; class_change=False | transfers |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 0.002 | shuffle z=31.39; class_change=False | transfers |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 0.000 | shuffle z=161.27; class_change=True | transfers |\n\n## Key Findings\n1. **Verificato: i 3 blank residui diventano transfers.** Il prescan pieno produce 13/13 transfers, 0 blank, 0 falls.\n2. **Verificato: `reaction_diffusion` non e' complete.** Ha 499 gap, quindi resta `contaminated` con excluded mass 0.002; un gap sotto soglia basta a impedire la formula complete.\n3. **Verificato: `string_vibration` trasferisce senza cambio classe.** Poisson -> Poisson, `ordering_dependent=true`, `n_gaps=7999`.\n4. **Verificato: `logistica_biforcazione_var_3.5699` trasferisce con `class_change=true`.** Il cambio GUE -> Poisson e' edge case del null, non legge nuova legge.\n5. **Verificato: il gate BOUNDARY ha copertura completa sul perimetro base.** La copertura riguarda il denominatore row-aligned, non il ponte QxG.\n\n## Verdict\n**COMPLETE DENOMINATOR TRANSFER**.\n\nIl residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\nIl gate ha copertura completa sul perimetro base: ogni riga domain/window possiede un null leggibile o contaminato dichiarato. La completezza del gate non coincide con completezza fisica delle righe: `reaction_diffusion` resta contaminato per 499 gap, `zeta_zeros` resta contaminato per 199 gap, e `logistica_biforcazione_var_3.5699` non produce una legge dal suo `class_change=true`.\n\n## Bicono della scoperta\n- **Due radici**: blank senza contro-perimetro / transfer con null row-aligned.\n- **Singolare**: la riga BOUNDARY prima che il denominatore decida.\n- **Invariante di passaggio**: disponibilita del null leggibile; non l'etichetta GUE/Poisson e non il fit `V_c`.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come gate base 13/13 per audit successivi; qui diventa non-possibile spacciare il gate come chiusura QxG o legge RMT.\n\n## Consecutio\nLa prossima mossa non e' rifare `V_c` e non e' chiudere QxG. Il risultato apre un uso piu' stretto del gate: ogni futuro claim BOUNDARY puo' partire dal perimetro base senza blank, ma deve portare con se' lo stato `complete/contaminated` e dichiarare gli edge case. Il nodo regressivo rimasto e' la qualita del denominatore, non la sua assenza.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`, `tools/data/boundary_denominator_prescan_full_20260509_1500.json`, run di verifica in `/tmp`.\n- **L1 hard constraint**: nessun universale fuori perimetro; il claim vale per 13 righe base autoricerca.\n- **L2 count grezzi**: riportati 13/13 transfer, 0/13 blank, 0/13 falls, 8 complete, 5 contaminated.\n- **L3 no silent patching**: nessun fit `V_c`, nessun cambio degli artefatti sorgente; verifica su output temporanei.\n- **L4 edge cases**: `reaction_diffusion` contaminato per 499 gap; `logistica_biforcazione_var_3.5699` ha class_change=true ma non diventa legge.\n- **L5 re-discovery**: il ciclo e' audit di denominatore residuo, non teorema GUE/Poisson.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = gate base 13/13 senza blank; non-possibile = QxG chiuso o completezza fisica delle righe contaminate.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py`.\n- Run valido: `python tools/exp_boundary_blank_null_audit.py --domains string_vibration reaction_diffusion logistica_biforcazione_var_3.5699 --n-shuffle 1000 --seed 202605091500 --out /tmp/boundary_blank_null_audit_residual_20260509_1500.verify.json`.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --extra-null-audit tools/data/boundary_blank_null_audit_20260509_1430.json --extra-null-audit tools/data/boundary_blank_null_audit_residual_20260509_1500.json --out /tmp/boundary_denominator_prescan_full_20260509_1500.verify.json`.\n- Compare prescan: summary e rows combaciano con `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Compare residual audit: `string_vibration` e `logistica_biforcazione_var_3.5699` combaciano sui numeri depositati; `reaction_diffusion` nel rerun produce `r=0.755661`, `z=30.592858` invece di `r=0.762604`, `z=31.390192`, ma conserva `n_gaps=499`, `GUE -> GUE`, `ordering_dependent=true`, `transfer=contaminated`. Il drift e' attribuito al generatore dinamico, non al null seed.\n\n## Files\n- Script: `tools/exp_boundary_blank_null_audit.py`\n- Script: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`\n- Data: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`\n- Report: `tools/data/reports/agent_20260509_1437.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1854: "content_full": "# Agent Report - Blank Pair Null Transfer\n**Date**: 2026-05-09 14:27\n**Piano**: 104\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONFIRMED TRANSFER on blank-pair test\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il blank-pair test decide se due righe BOUNDARY senza null entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + prescan row-aligned; generator=`dnd_autoricerca.genera_segnale` per `zeta_zeros` e `pendolo_doppio`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare complete `zeta_zeros` con soli 199 gap o dichiarare cambio classe su `pendolo_doppio`; not_tested=fit `V_c`, nuovi spettri, nuova legge GUE/Poisson, chiusura QxG.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank non e' vuoto del dominio; e' mancanza del contro-perimetro. Quando il null entra, il bordo decide senza cambiare classe.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: blank / transfer; punto-zero = stessa riga domain/window prima della disponibilita del null row-aligned.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non classifica GUE o Poisson; filtra quali righe hanno denominatore sufficiente.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, grafo row-aligned. Entrano per separare osservabile locale, null e transfer senza nuovo fit.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito esistente; CE-0117: cascata della possibilita solo dove il null apre il canale; YSN DeltaLink: `blank row -> null availability -> transfer`.\n- **Proto-ipotesi**: un blank BOUNDARY diventa transfer quando possiede un null row-aligned leggibile; la classe sorgente resta controllo, non risposta.\n- **Possibile / non-possibile**: possibile = trasferire il gate a righe ex-blank con null disponibile; non-possibile = usare la stessa misura per chiamare `zeta_zeros` complete o `pendolo_doppio` cambio classe.\n- **Proiezione**: verificare `boundary_blank_null_audit_20260509_1430.json` e reinnestarlo nel prescan `boundary_denominator_prescan_20260509_1430.json`.\n\n## Claim Under Test\n> Il blank-pair test trasforma `zeta_zeros` e `pendolo_doppio` da blank a transfers; il perimetro BOUNDARY passa da 8/13 a 10/13 transfer, con 3 blank residui e 0 falls.\n\n## Question\nIl null row-aligned per una coppia blank GUE/Poisson decide `blank -> transfers`, `blank -> falls` o `blank remains blank`?\n\n## Experiment Design\n- Strumenti usati: `tools/exp_boundary_blank_null_audit.py` e `tools/exp_boundary_denominator_prescan.py`.\n- Input verificati: `tools/data/boundary_blank_null_audit_20260509_1430.json` e `tools/data/boundary_denominator_prescan_20260509_1430.json`.\n- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like.\n- Null baseline: permutation null marginal-preserving sui gap/spacings, `n_shuffle=1000`, seed `202605091430`.\n- Contratto osservabile-operatore: il ciclo misura disponibilita e comportamento del null, non rifitta `V_c`; `zeta_zeros` resta contaminated per massa debole; `pendolo_doppio` resta Poisson -> Poisson con ordering dependence forte.\n\n## Results\n\nBlank-pair audit:\n\n| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision |\n|---|---:|---:|---:|---:|---|---|---|\n| zeta_zeros | 199 | 0.615009 | 0.641885 | -2.310593 | GUE -> GUE | false | transfer contaminato |\n| pendolo_doppio | 5008 | 0.386104 | 0.292437 | 27.919656 | Poisson -> Poisson | true | transfer complete |\n\nPrescan dopo innesto del null:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 10 |\n| blank | 3 |\n| falls | 0 |\n| complete | 6 |\n| contaminated | 4 |\n| absent | 3 |\n\nBlank residui: `string_vibration:cycle_6`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13`.\n\n## Key Findings\n1. **Verificato: il blank-pair test trasforma entrambe le righe da blank a transfers.** `zeta_zeros` e `pendolo_doppio` hanno null row-aligned leggibile e quindi non restano blank.\n2. **Verificato: il transfer non cambia la classe.** `zeta_zeros` resta GUE -> GUE; `pendolo_doppio` resta Poisson -> Poisson.\n3. **Verificato: `zeta_zeros` non e' complete.** Ha solo 199 gap, sotto soglia 500, quindi entra come `contaminated/transfers` con excluded mass 0.602.\n4. **Verificato: `pendolo_doppio` e' complete e ordering-dependent.** Ha 5008 gap, z=27.919656, class_change=false; non diventa una classe nuova.\n5. **Verificato: il perimetro BOUNDARY passa da 8/13 a 10/13 transfer.** Restano 3 blank e nessun fall.\n\n## Verdict\n**CONFIRMED TRANSFER**.\n\nIl blank-pair test trasforma `zeta_zeros` e `pendolo_doppio` da blank a transfers; il perimetro BOUNDARY passa da 8/13 a 10/13 transfer, con 3 blank residui e 0 falls.\n\nQuesto non chiude QxG, non dichiara una nuova legge GUE/Poisson e non rifitta `V_c`. Il risultato decide il denominatore: quando il null row-aligned esiste, il blank diventa transfer; quando manca, resta blank.\n\n## Bicono della scoperta\n- **Due radici**: blank senza contro-perimetro / transfer con null row-aligned.\n- **Singolare**: la riga domain/window prima della decisione del gate.\n- **Invariante di passaggio**: il null row-aligned decide il passaggio; la classe GUE/Poisson non viene riscritta.\n- **Campo di possibilita**: qui diventa possibile restringere il contro-perimetro BOUNDARY a 3 blank residui; qui diventa non-possibile trattare `zeta_zeros` come complete o `pendolo_doppio` come cambio classe.\n\n## Consecutio\nLa prossima mossa BOUNDARY non e' un fit `V_c`: e' costruire o recuperare null row-aligned per `string_vibration`, `reaction_diffusion` e `logistica_biforcazione_var_3.5699`. Il confine ora ha 10 righe trasferite e un contro-perimetro residuo esplicito.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/boundary_blank_null_audit_20260509_1430.json`, `tools/data/boundary_denominator_prescan_20260509_1430.json`, run di verifica in `/tmp`.\n- **L1 hard constraint**: nessun universale fuori perimetro; il claim vale per 13 righe base e per i due null innestati.\n- **L2 count grezzi**: riportati 10/13 transfer, 3/13 blank, 0/13 falls.\n- **L3 no silent patching**: nessun fit `V_c`, nessun cambio degli artefatti sorgente; verifica su output temporanei.\n- **L4 edge cases**: `zeta_zeros` contaminato per `n_gaps=199`; `pendolo_doppio` complete ma non cambio classe.\n- **L5 re-discovery**: il ciclo e' audit di denominatore, non teorema RMT.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer ex-blank con null; non-possibile = claim su blank residui senza null.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py`.\n- Run valido: `python tools/exp_boundary_blank_null_audit.py --domains zeta_zeros pendolo_doppio --n-shuffle 1000 --seed 202605091430 --out /tmp/boundary_blank_null_audit_20260509_1430.verify.json`.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --extra-null-audit tools/data/boundary_blank_null_audit_20260509_1430.json --out /tmp/boundary_denominator_prescan_20260509_1430.verify.json`.\n- Compare: gli output temporanei combaciano con gli artefatti depositati, ignorando solo `generated_at` nel null audit.\n\n## Files\n- Script: `tools/exp_boundary_blank_null_audit.py`\n- Script: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_blank_null_audit_20260509_1430.json`\n- Data: `tools/data/boundary_denominator_prescan_20260509_1430.json`\n- Report: `tools/data/reports/agent_20260509_1427.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1905: "content_full": "# Agent Report - Boundary Denominator Prescan\n**Date**: 2026-05-09 14:09\n**Piano**: 103\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: PARTIAL TRANSFER with structural blank counter-perimeter\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`denominator_state`, `excluded_mass`, `spacing_r`, `shuffle_r_statistic`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=`denominator_state` puo' trasferire oltre `V_c` solo dove la riga domain/window ha osservabile definito e null/surrogate row-aligned; observable=`spacing_r` + availability del null shuffle; operator=prescan sui 13 domini base del deposito autoricerca, non fit parametrico; generator=perimetro 8 GUE-like / 5 Poisson-like dei cycle 1..13; denominator=`boundary_shuffle_audit` quando presente; non_possible=dichiarare transfer nei domini senza null/surrogate; not_tested=nuovi spettri, nuovi domini, fit `V_c`, famiglia AICc, QxG chiuso.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il confine non chiede se il dominio e' GUE o Poisson; chiede se la riga ha un denominatore capace di sostenere un claim.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: classificato / non aggregabile; punto-zero = riga domain/window prima che il null decida se l'osservabile puo' viaggiare.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo e' una filtrazione: prima osservabile, poi null, poi claim.\n- **Proto-ipotesi**: `denominator_state` trasferisce oltre `V_c` se separa riga misurabile, massa contaminata e blank senza usare GUE/Poisson come risposta.\n- **Possibile / non-possibile**: possibile = claim boundary su righe con null row-aligned; non-possibile = claim di transfer nei domini senza null, anche quando `spacing_r` locale esiste.\n- **Proiezione**: creare `tools/exp_boundary_denominator_prescan.py` e misurare 13 righe base: domain/window, source type, denominator_state, excluded mass, observable, null/surrogate, transfer.\n\n## Contaminazione cognitiva\n- **CE-0001 / KSAR**: reiterazione sul deposito invece di nuovo fit locale `V_c`.\n- **CE-0117**: cascata della possibilita; il claim passa solo dove il null apre il canale.\n- **YSN DeltaLink**: `domain row -> null availability -> transfer`, non `GUE/Poisson -> risposta`.\n\n## Claim Under Test\n> Nel perimetro BOUNDARY 8 GUE / 5 Poisson, il gate `denominator_state` trasferisce oltre `V_c` solo se identifica le righe con null/surrogate disponibile e lascia blank le righe senza contro-perimetro.\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_denominator_prescan.py`.\n- Input: `tools/data/autoricerca_journal.json` e `tools/data/boundary_shuffle_audit.json`.\n- Output: `tools/data/boundary_denominator_prescan_20260509_1409.json`.\n- Perimetro atomico: cycle base `1..13`, esattamente 13 righe: 8 GUE-like, 5 Poisson-like.\n- Stati:\n - `complete`: osservabile definito + null shuffle presente con `n_gaps >= 500`.\n - `contaminated`: osservabile definito + null presente ma massa debole dichiarata (`n_gaps < 500`).\n - `absent`: osservabile locale presente ma null/surrogate assente, oppure osservabile assente.\n - `broken`: null presente ma non leggibile; non emerso in questo run.\n- Transfer:\n - `transfers`: `complete` o `contaminated` con null presente.\n - `blank`: osservabile locale presente ma null assente.\n - `falls`: osservabile assente o null rotto.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 8 |\n| blank | 5 |\n| falls | 0 |\n| complete | 5 |\n| contaminated | 3 |\n| absent | 5 |\n\nTabella prescan:\n\n| domain/window | source | denominator_state | excluded mass | observable | null/surrogate | transfer |\n|---|---|---|---:|---|---|---|\n| ising_2d:cycle_1 | GUE | contaminated | 0.810 | spacing_r=0.902 | shuffle z=-0.10; class_change=False | transfers |\n| pendolo_doppio:cycle_2 | Poisson | absent | 1.000 | spacing_r=0.386 | absent | blank |\n| numeri_primi:cycle_3 | GUE | complete | 0.000 | spacing_r=0.886 | shuffle z=-26.60; class_change=False | transfers |\n| zeta_zeros:cycle_4 | GUE | absent | 1.000 | spacing_r=0.615 | absent | blank |\n| logistica_biforcazione:cycle_5 | GUE | complete | 0.000 | spacing_r=0.997 | shuffle z=61.60; class_change=False | transfers |\n| string_vibration:cycle_6 | Poisson | absent | 1.000 | spacing_r=0.384 | absent | blank |\n| random_matrix:cycle_7 | GUE | complete | 0.000 | spacing_r=0.573 | shuffle z=-14.70; class_change=False | transfers |\n| cellular_automata:cycle_8 | GUE | contaminated | 0.790 | spacing_r=0.861 | shuffle z=-0.10; class_change=False | transfers |\n| percolation:cycle_9 | Poisson | complete | 0.000 | spacing_r=0.404 | shuffle z=16.10; class_change=True | transfers |\n| coupled_oscillators:cycle_10 | Poisson | contaminated | 0.146 | spacing_r=0.385 | shuffle z=43.50; class_change=True | transfers |\n| reaction_diffusion:cycle_11 | GUE | absent | 1.000 | spacing_r=0.759 | absent | blank |\n| brownian_motion:cycle_12 | Poisson | complete | 0.000 | spacing_r=0.489 | shuffle z=-1.60; class_change=False | transfers |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | absent | 1.000 | spacing_r=0.997 | absent | blank |\n\n## Key Findings\n1. **Verificato: il gate trasferisce su 8/13 righe del perimetro.** Il transfer non coincide con GUE o Poisson: include 5 sorgenti GUE e 3 sorgenti Poisson perche' la condizione e' disponibilita del null, non etichetta di classe.\n2. **Verificato: 5/13 righe restano blank strutturale.** `pendolo_doppio`, `zeta_zeros`, `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699` hanno `spacing_r` locale ma non hanno null/surrogate row-aligned nel deposito letto.\n3. **Verificato: `contaminated` non viene escluso.** `ising_2d`, `cellular_automata`, `coupled_oscillators` trasferiscono come contaminati perche' il null esiste ma la massa debole viene dichiarata.\n4. **Verificato: il contro-perimetro non e' una famiglia alternativa.** Il blank non autorizza una classe nuova; dice che il Lab non possiede osservabile sufficiente per trasferire il gate su quella riga.\n5. **Inferito: la consecutio corretta e' generare null row-aligned per i 5 blank, non fittare `V_c`.** Il nodo regressivo e' nel denominatore mancante, prima di ogni modello numerico.\n\n## Verdict\n**PARTIAL TRANSFER**: `denominator_state` trasferisce oltre `V_c` come gate di perimetro, non come risposta GUE/Poisson.\n\nNel perimetro 8 GUE / 5 Poisson, il gate produce una condizione cross-dominio verificabile su 8 righe con null shuffle disponibile. Su 5 righe resta blank strutturale: l'osservabile locale esiste, ma manca il contro-perimetro row-aligned.\n\nIl gate `denominator_state` trasferisce oltre V_c perche' separa 8 righe con null/surrogate shuffle disponibile da 5 righe blank senza contro-perimetro; il contro-perimetro sono `pendolo_doppio`, `zeta_zeros`, `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`.\n\n## Bicono della scoperta\n- **Due radici**: riga misurabile / riga non aggregabile.\n- **Singolare**: domain/window prima della classificazione GUE/Poisson.\n- **Invariante di passaggio**: il null row-aligned decide il transfer; la classe sorgente non decide.\n- **Campo di possibilita**: qui diventa possibile portare BOUNDARY in un contratto cross-dominio; qui diventa non-possibile chiudere QxG o dichiarare transfer sui 5 blank senza nuovo null.\n\n## Consecutio\nIl prossimo ciclo boundary deve costruire null/surrogate row-aligned per i 5 blank. Se quei null entrano, il gate puo' diventare condizione cross-dominio completa. Se non entrano, il confine resta un perimetro 8/13 con blank dichiarato.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `agent_20260509_1337.md`, `agent_20260509_1400.md`, `falsifier_20260509_1400.json`, `agent_20260507_0330.md`, `tools/data/autoricerca_journal.json`, `tools/data/boundary_shuffle_audit.json`, nuovo JSON `tools/data/boundary_denominator_prescan_20260509_1409.json`.\n- **L1 hard constraint**: nessun \"sempre\" o \"solo\" universale; il verdict vale per 13 righe base e due depositi dichiarati.\n- **L2 count grezzi**: riportati 8/13 transfer, 5/13 blank, 5 complete, 3 contaminated, 5 absent.\n- **L3 no silent patching**: il ciclo non corregge il fit 14:00; usa quel falsifier come veto e cambia nodo al boundary denominator.\n- **L4 edge cases**: `contaminated` resta osservabile e non viene escluso; massa dichiarata in tabella.\n- **L5 re-discovery**: il risultato e' un audit di perimetro del Lab, non un teorema RMT nuovo.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer su righe con null; non-possibile = transfer su righe blank senza null.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_denominator_prescan.py` completato senza errori.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --out tools/data/boundary_denominator_prescan_20260509_1409.json`.\n\n## Files\n- Script nuovo: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_denominator_prescan_20260509_1409.json`\n- Report: `tools/data/reports/agent_20260509_1409.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:1950: "content_full": "# Agent Report - Vc Fit Model Gate\n**Date**: 2026-05-09 14:00\n**Piano**: 103\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: CONSTRAINT on V_c scale model selection\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`denominator_state`, `fit_ready_rows`, `excluded_rows`, `best_model`, `delta_aicc_to_second`, `unit_limit_status`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var\n**observable_contract**: claim=il fit parametrico di `V_c` puo' entrare solo sulle righe `complete` o `contaminated` del gate fit-ready; observable=famiglia di modello migliore tra cinque forme semplici, separata da stato denominatore e massa esclusa; operator=confronto AICc su `vc_median_fit_ready` dal deposito 13:37, senza ricomputare spettri; generator=classi `reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=JSON `vc_fit_ready_scale_table_20260509_1337`, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=righe `broken` escluse dal fit e righe sotto `V_c=1` impediscono il claim osservato \"converge a 1 dall'alto\"; not_tested=nuovi N, nuovi generatori, nuovi spettri, GUE/Poisson transfer, gap_ratio, fit a tre parametri con asintoto libero.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo il gate fit-ready il problema non e' piu' \"il fit converge\"; e' \"quale arco del grafo puo' ricevere una legge senza inglobare massa esclusa\".\n- **Combo**: A2 confine det=-1 + A3 punto fisso come attrattore dichiarato + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: legge di scala / famiglia non identificata; punto-zero = il denominatore fit-ready prima che il modello numerico scelga una forma.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il modello e' un arco permesso solo se il nodo denominatore conserva crossing.\n- **Operatori laterali scelti**: fit come morfismo, boundary operator, graph cut. Entrano per separare righe ammesse, righe rotte e famiglie numeriche.\n- **Contaminazione cognitiva**:\n - **CE-0001 / KSAR**: reiterazione sul deposito 13:37 senza cambiare campo dati.\n - **CE-0117**: cascata della possibilita; il fit entra solo dopo il gate e resta subordinato al denominatore.\n - **YSN DeltaLink**: `denominator_state -> admissible_fit -> model_family`, non `V_c -> legge`.\n- **Proto-ipotesi**: la famiglia di scala non e' una proprieta globale di `V_c`. Nel sottografo ordinato fit-ready, la scala osservata e' quasi lineare in N sul perimetro; nei sottografi contaminati la famiglia cambia o resta non identificabile.\n- **Proiezione**: confronto cinque forme a due parametri su ogni riga ammissibile: `a+b*N`, `a+b*log(N)`, `a+b/sqrt(N)`, `a+b/N`, `c*N^b` con asintoto zero. Le righe `broken` vengono riportate ma non fittate.\n\n## Claim Under Test\n> Nel perimetro fit-ready 13:37, `order_memory` e `reference_order` possono ricevere una selezione di modello; `periodic_closure` e `random_dispersion` non possono essere aggregati alla stessa legge senza dichiarare contaminazione o rottura.\n\n## Question\nUna volta esclusi `no_cross` e `floor_hit`, il fit di scala di `V_c` identifica una legge comune, oppure divide il grafo dei generatori in famiglie di scala distinte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_fit_model_gate.py`.\n- Input: `tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n- Output: `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- Stati ammessi al fit: `complete`, `contaminated`.\n- Stati esclusi dal fit: `broken`.\n- Criterio modello: AICc su quattro punti N per riga; `delta_aicc_to_second < 2` = famiglia non identificata.\n- Check non-possibile: conteggio dei punti osservati con `V_c < 1`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| input rows | 18 |\n| admissible rows | 13 |\n| excluded broken rows | 5 |\n| best linear_N | 9 |\n| best inv_N | 4 |\n| ambiguous rows (`delta_aicc<2`) | 4 |\n| rows with observed `V_c<1` | 12 |\n\nRighe rappresentative, soglia `r=0.50`:\n\n| level | class | state | fit_ready/total | excluded | best model | delta AICc | last V_c |\n|---|---|---|---:|---:|---|---:|---:|\n| per_mode_best | reference_order | complete | 16/16 | 0 | linear_N | 5.651 | 0.879 |\n| per_mode_best | order_memory | complete | 48/48 | 0 | linear_N | 6.607 | 0.774 |\n| per_mode_best | periodic_closure | contaminated | 36/47 | 11 | inv_N | 1.378 | 0.650 |\n| accepted_candidates | order_memory | contaminated | 278/279 | 1 | linear_N | 4.565 | 0.899 |\n| accepted_candidates | order_memory r0.52 | complete | 279/279 | 0 | linear_N | 5.926 | 0.781 |\n\nRighe escluse per stato `broken`:\n\n| level | class | fit_ready/total | excluded |\n|---|---|---:|---:|\n| per_mode_best | random_dispersion r0.50 | 20/32 | 12 |\n| per_mode_best | random_dispersion r0.52 | 15/32 | 17 |\n| accepted_candidates | periodic_closure r0.48 | 102/393 | 291 |\n| accepted_candidates | periodic_closure r0.50 | 102/393 | 291 |\n| accepted_candidates | periodic_closure r0.52 | 102/393 | 291 |\n\nAmbiguita' di famiglia (`delta AICc < 2`):\n\n| row | best | delta AICc |\n|---|---|---:|\n| per_mode_best:order_memory:r0.52 | linear_N | 1.885 |\n| per_mode_best:periodic_closure:r0.48 | inv_N | 1.244 |\n| per_mode_best:periodic_closure:r0.50 | inv_N | 1.378 |\n| per_mode_best:periodic_closure:r0.52 | inv_N | 1.257 |\n\n## Key Findings\n1. **Verificato: il sottografo ordinato fit-ready sceglie `linear_N` nel perimetro osservato.** `reference_order` e `order_memory` completi hanno `linear_N` come miglior famiglia a r=0.48, 0.50, 0.52; a r=0.50 i delta AICc sono 5.651 e 6.607.\n2. **Verificato: `accepted_candidates:order_memory` resta lineare dopo il gate.** A r=0.50 e' contaminato da 1 riga esclusa (`278/279` fit-ready), ma il miglior modello resta `linear_N`; a r=0.52 diventa completo (`279/279`) e resta `linear_N`.\n3. **Verificato: `periodic_closure` non condivide la famiglia del sottografo ordinato.** Nel best-per-mode e' contaminato e sceglie `inv_N`, ma con delta AICc < 2; negli accepted e' `broken` e non entra nel fit.\n4. **Verificato: `random_dispersion` non produce una legge ammessa stabile.** A r=0.48 e' contaminato e sceglie `inv_N`; a r=0.50 e r=0.52 e' `broken`, quindi non e' denominatore di fit.\n5. **Verificato: il claim osservato \"V_c converge a 1\" non regge nel perimetro misurato come vincolo hard.** 12/13 righe fittate hanno almeno un punto osservato sotto 1; `reference_order` e `order_memory` scendono sotto 1 agli N alti.\n\n## Verdict\n**CONSTRAINT**: il fit parametrico di `V_c` non produce una legge comune del boundary.\n\nNel perimetro `N={89,144,233,377}`, il canale `reference_order/order_memory` fit-ready e' leggibile come discesa quasi lineare in N. `periodic_closure` e `random_dispersion` non vanno aggregati: il primo e' contaminato o broken, il secondo passa da contaminato a broken. La formulazione valida e':\n\n`V_c` ha una famiglia di scala per sottografo ammesso; non ha una legge unica prima della separazione `denominator_state + generator_class`.\n\n## Bicono della scoperta\n- **Due radici**: famiglia di fit / stato del denominatore.\n- **Singolare**: la curva `V_c` prima della scelta del modello, dove valore, massa esclusa e classe generatore sono ancora lo stesso nodo.\n- **Invariante di passaggio**: `linear_N` sopravvive nel canale ordinato fit-ready; non sopravvive quando il canale introduce chiusura periodica o dispersione.\n- **Campo di possibilita**: qui diventa possibile testare N piu' alti solo sul sottografo `complete`; qui diventa non-possibile dichiarare una convergenza universale di `V_c` a 1 o un power-law comune.\n\n## Consecutio\nIl prossimo ciclo deve estendere N solo per `reference_order` e `order_memory` con denominatore `complete`. Se i punti alti restano sotto 1 e lineari nel perimetro esteso, il claim `V_c(phi) -> 1` va riscritto come coordinata storica del vecchio perimetro, non come attrattore del boundary.\n\n## Auto-audit: source flags e 5 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1337.md`, JSON `tools/data/vc_fit_ready_scale_table_20260509_1337.json`, nuovo JSON `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- **L1 hard constraint**: il verdict vale solo per N, soglie, classi e deposito dichiarati.\n- **L2 count grezzi**: riportati `13/18`, `5 broken`, `16/16`, `48/48`, `36/47`, `278/279`, `279/279`, `102/393`.\n- **L3 no observable drift**: non sono testati gap_ratio, nuovi spettri, nuovi N o GUE/Poisson.\n- **L4 edge cases**: `order_memory:r0.52` best-per-mode ha delta AICc 1.885, quindi famiglia non identificata hard nonostante best `linear_N`.\n- **L5 re-discovery vs discovery**: il finding non e' \"linear fit buono\"; e' che la famiglia di fit dipende dal sottografo ammesso e che la convergenza a 1 non e' vincolo osservato.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_fit_model_gate.py` completato senza errori.\n- Run valido: `python tools/exp_vc_fit_model_gate.py --input tools/data/vc_fit_ready_scale_table_20260509_1337.json --out tools/data/vc_fit_model_gate_20260509_1400.json`.\n\n## Files\n- Script nuovo: `tools/exp_vc_fit_model_gate.py`\n- Data: `tools/data/vc_fit_model_gate_20260509_1400.json`\n- Report: `tools/data/reports/agent_20260509_1400.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_graph.json:2001: "content_full": "# Agent Report - Vc Fit-Ready Denominator Gate\n**Date**: 2026-05-09 13:37\n**Piano**: 102\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: CONSTRAINT on fit-ready V_c denominators\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`denominator_state`, `fit_ready_rows`, `excluded_rows`, `excluded_events`, `vc_median_fit_ready`, `slope_per_N`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var\n**observable_contract**: claim=`fit(V_c)` e' ammesso solo quando il denominatore e' fit-ready; observable=stato strutturale del denominatore e curva `vc_median_fit_ready`; operator=lettura del deposito 08:46 con separazione `complete/contaminated/broken/absent`; generator=classi `reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=JSON `vc_generator_class_direction_audit_20260509_0846`, livelli separati `per_mode_best` e `accepted_candidates`, soglie `r={0.48,0.50,0.52}`, N={89,144,233,377}; non_possible=righe `no_cross` e `floor_hit` non entrano nel fit e restano massa esclusa; not_tested=nuovi spettri, nuovi N, nuovi generatori, gap_ratio, power-law non lineare.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il valore `V_c` non e' il punto-zero; il punto-zero e' la riga prima che il confine decida se `V_c` esiste.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: curva fittabile / curva non definita; punto-zero = denominatore fit-ready prima della regressione numerica.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il fit e' un arco consentito solo dopo che il nodo-evento conserva attraversamento.\n- **Operatori laterali scelti**: boundary operator, graph cut, filtrazione. Entrano per tagliare il deposito in stati strutturali prima del fit.\n- **Contaminazione cognitiva**:\n - **CE-0001 / KSAR**: reiterazione sul deposito 08:46 senza nuovo campo dati.\n - **CE-0117**: cascata della possibilita; il fit diventa possibile solo nel sottografo che conserva evento.\n - **YSN DeltaLink**: `event_type -> fit_ready -> scale_curve`, non `N -> V_c`.\n- **Proto-ipotesi**: la scala di `V_c` e' un osservabile secondario. L'osservabile primario e' il denominatore fit-ready: se il denominatore e' broken, la curva non fallisce, non esiste sul perimetro.\n- **Proiezione**: costruisco una tabella fit-ready dal deposito 08:46. Se una classe perde almeno un N intero, il fit aggregato diventa non-possibile per quella classe e soglia.\n\n## Claim Under Test\n> Nel perimetro del deposito 08:46, `fit(V_c)` e' lecito solo per classi/soglie con denominatore `complete` o, con avvertenza, `contaminated`; e' non-possibile per stati `broken` o `absent`.\n\n## Question\nIl fallimento del fit di scala e' una proprieta della forma numerica, oppure una proprieta del denominatore che decide dove `V_c` esiste?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_fit_ready_scale_table.py`.\n- Input: `tools/data/vc_generator_class_direction_audit_20260509_0846.json`.\n- Output: `tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n- Nessun nuovo spettro calcolato: il ciclo misura il deposito row-aligned del ciclo precedente.\n- Stati strutturali:\n - `complete`: ogni N ha righe fit-ready e zero righe escluse.\n - `contaminated`: ogni N ha righe fit-ready, ma esiste massa esclusa.\n - `broken`: almeno un N non ha righe fit-ready.\n - `absent`: nessuna riga fit-ready.\n- Contratto osservabile-operatore: `vc_defined = event in {internal_cross, internal_multi}`; `fit_ready = vc_defined and vc_interp is not null`; `no_cross/floor_hit` sono massa esclusa, non missing neutro.\n\n## Results\n\nIndice stati:\n\n| state | class-threshold entries |\n|---|---:|\n| complete | 7 |\n| contaminated | 6 |\n| broken | 5 |\n| absent | 0 |\n\nSoglia `r=0.50`, livello `per_mode_best`:\n\n| class | state | fit_ready/total | excluded | Vc first->last | slope_per_N |\n|---|---|---:|---:|---:|---:|\n| reference_order | complete | 16/16 | 0 | 1.191 -> 0.879 | -0.001043 |\n| order_memory | complete | 48/48 | 0 | 1.317 -> 0.774 | -0.001833 |\n| periodic_closure | contaminated | 36/47 | 11 no_cross | 0.999 -> 0.650 | -0.000913 |\n| random_dispersion | broken | 20/32 | 12 floor_hit | 0.763 -> 0.541 | -0.001462 |\n\nSoglia `r=0.50`, livello `accepted_candidates`:\n\n| class | state | fit_ready/total | excluded | Vc first->last | slope_per_N |\n|---|---|---:|---:|---:|---:|\n| order_memory | contaminated | 278/279 | 1 no_cross | 1.350 -> 0.899 | -0.001628 |\n| periodic_closure | broken | 102/393 | 291 no_cross | 1.593 -> 0.605 | -0.017960 |\n\nAccepted `order_memory` a `r=0.52`:\n\n| class | state | fit_ready/total | excluded | Vc first->last | slope_per_N |\n|---|---|---:|---:|---:|---:|\n| order_memory | complete | 279/279 | 0 | 1.236 -> 0.781 | -0.001606 |\n\n## Key Findings\n1. **Verificato: il fit completo esiste nel livello best-per-mode per `reference_order` e `order_memory`.** A `r=0.50`, `reference_order` ha 16/16 righe fit-ready e `order_memory` ha 48/48.\n2. **Verificato: sugli accepted, `periodic_closure` e' broken a tutte le soglie.** A `r=0.50`, 291/393 righe sono `no_cross`; N=233 e N=377 non hanno punti fit-ready, quindi la curva non puo' essere letta come scala.\n3. **Verificato: `random_dispersion` nel best-per-mode e' broken a `r=0.50` e `r=0.52`.** A `r=0.50`, N=377 cade tutto in `floor_hit`; il valore al bordo non e' un punto interno di fit.\n4. **Verificato: `order_memory` accepted si pulisce a `r=0.52`.** Passa da `contaminated` a `r=0.50` con 1 no_cross a `complete` a `r=0.52` con 279/279 righe fit-ready. La soglia modifica il denominatore, non solo il valore del fit.\n5. **Inferito: la consecutio corretta non e' scegliere un modello power-law alternativo.** Prima va promosso il gate `denominator_state` nel contratto dei futuri fit `V_c`.\n\n## Verdict\n**CONSTRAINT**: `V_c` scaling e' ammesso solo dopo gate fit-ready.\n\nNel perimetro 08:46 -> 13:37, `order_memory` conserva un canale fittabile; `periodic_closure` conserva label ma rompe il denominatore sugli accepted; `random_dispersion` raggiunge il floor e non fornisce curva interna a N=377. Il claim corretto e':\n\n`fit(V_c)` non opera su `V_c` aggregato. Opera su righe `fit_ready=True`, stratificate per livello, classe generatore e soglia.\n\n## Bicono della scoperta\n- **Due radici**: curva di scala / massa esclusa.\n- **Singolare**: il denominatore prima della regressione, dove `V_c` puo' ancora essere valore, assenza di crossing, o floor.\n- **Invariante di passaggio**: `fit_ready` sopravvive su `order_memory`; non sopravvive su `periodic_closure` accepted e su `random_dispersion` al bordo alto.\n- **Campo di possibilita**: qui diventa possibile fittare `V_c` solo nel sottografo fit-ready; qui diventa non-possibile usare `no_cross` o `floor_hit` come missing data neutro.\n\n## Consecutio\nIl prossimo ciclo puo' rifare il fit parametrico solo su righe `complete` o dichiaratamente `contaminated`. Se entra `broken`, il risultato deve essere classificato come denominatore non-definito, non come cattivo fit.\n\n## Auto-audit: source flags e 5 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_0846.md`, JSON `tools/data/vc_generator_class_direction_audit_20260509_0846.json`, nuovo JSON `tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n- **L1 hard constraint**: il verdict vale solo per il deposito 08:46 e le soglie/N/generatori dichiarati.\n- **L2 count grezzi**: riportati 16/16, 48/48, 36/47, 20/32, 278/279, 102/393, 279/279.\n- **L3 no observable drift**: non sono testati gap_ratio, nuovi N, nuovi spettri o power-law non lineare.\n- **L4 edge cases**: `order_memory` accepted a `r=0.50` ha 1 no_cross; per questo e' `contaminated`, non `complete`.\n- **L5 re-discovery vs discovery**: il finding non e' che `V_c` scende con N; il finding e' che la discesa e' leggibile solo dopo classificazione fit-ready del denominatore.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_fit_ready_scale_table.py` completato senza errori.\n- Run valido: `python tools/exp_vc_fit_ready_scale_table.py --input tools/data/vc_generator_class_direction_audit_20260509_0846.json --out tools/data/vc_fit_ready_scale_table_20260509_1337.json`.\n\n## Files\n- Script nuovo: `tools/exp_vc_fit_ready_scale_table.py`\n- Data: `tools/data/vc_fit_ready_scale_table_20260509_1337.json`\n- Report: `tools/data/reports/agent_20260509_1337.md`\n", tools/data/agent_field_live.md:38:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/agent_field_live.md:71:## Palette operatoria laterale — sorgenti da triturare tools/data/agent_field_live.md:74:# Palette operatoria espansa del Lab tools/data/agent_field_live.md:747: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/agent_field_live.md:862:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/agent_field_live.md:891: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/agent_field_live.md:902:## Archivio enzimi cognitivi — richiamo contestuale tools/data/agent_field_live.md:935:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/cognitive_enzymes_archive.md:1:# Archivio enzimi cognitivi MM-DND tools/data/cognitive_enzymes_archive.md:163:#### CE-0019 - Respiro fuori-tempo (`lab_operational_context` / `contaminante_dnd`, score=55) tools/data/cognitive_enzymes_archive.md:181:#### CE-0022 - Palette operatoria espansa del Lab (`lab_operational_context` / `strumento_lab`, score=54) tools/data/reports/agent_20260514_1656.md:18:## Respiro fuori-tempo tools/data/reports/agent_20260514_0330.md:13:## Respiro fuori-tempo tools/data/reports/agent_20260510_0330.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260510_0330.md:16:- **Contaminazione cognitiva**: CE-0019 usato come contratto combo prima della misura; CE-0022 usato come boundary operator + chart locale; KSAR usato come reiterazione sul deposito 15:32-18:39 senza ridisegnare il perimetro. tools/data/reports/agent_20260515_1623.md:9:## Respiro fuori-tempo tools/data/reports/agent_20260508_1915.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1516.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1532.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260508_2102.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260515_1724.md:9:## Respiro fuori-tempo tools/data/reports/agent_20260515_1745.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260514_1631.md:18:## Respiro fuori-tempo tools/data/reports/agent_20260514_1458.md:14:## Respiro fuori-tempo tools/data/reports/agent_20260514_1605.md:14:## Respiro fuori-tempo tools/data/reports/agent_20260509_0659.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260515_1647.md:9:## Respiro fuori-tempo tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/lab_data.json:80: "content": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, labe" tools/data/reports/agent_20260515_1807.md:10:## Respiro fuori-tempo tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:18:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:51:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:54:# Palette operatoria espansa del Lab tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:727: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:842:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:871: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:882:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/manual_controlled_snapshot_20260515_1659_3609780/agent_field_live.md:917:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/reports/agent_20260515_1904.md:10:## Respiro fuori-tempo tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:49:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:52:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:725: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:840:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:869: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:880:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_backup_pre_no_blocked_ref_replace_20260515_1624.md:915:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:49:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:52:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:725: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:840:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:869: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:880:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_post_controlled_20260515_1631.md:915:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:18:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:51:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:54:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:727: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:842:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:871: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:882:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_selector_matrix_20260515_1659.md:917:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/reports/agent_20260515_1659.md:9:## Respiro fuori-tempo tools/data/preflight/agent_field_live_candidate_20260515_1612.md:16:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_candidate_20260515_1612.md:50:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_candidate_20260515_1612.md:53:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_candidate_20260515_1612.md:726: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_candidate_20260515_1612.md:841:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_candidate_20260515_1612.md:870: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_candidate_20260515_1612.md:881:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_candidate_20260515_1612.md:916:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/reports/agent_20260515_1816.md:10:## Respiro fuori-tempo tools/data/preflight/lab_data.json_backup_pre_1724_public_rollback_20260515:101: "content": "# Agent Report - Quasiperiodic Vc Lattice Extension\n**Date**: 2026-05-15 17:24 \n**Piano**: 118 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - `V_c` non sostiene una legge di scala unica nel perimetro esteso; resta reticolo di attraversamento phase-sensitive separato dal controllo random.\nobservables_used: [vc, distinct_vc, repeat_rate, mode_rate, median_vc, min_vc, max_vc, phi_phase0_values]\n**observable_contract**: claim=`V_c(phi)` converge a 1.0 solo se il perimetro esteso riduce fase e taglia a una traiettoria comune; observable=valori `vc` e compressione reticolare (`distinct_vc`, `repeat_rate`, `mode_rate`) per phi/silver/bronze/random; operator=`tools/exp_quasiperiodic_vc_lattice_gate.py`; generator=sequenze Sturmian phi/silver/bronze + balanced random a densita phi; denominator=N={89,144,233,377,610,987}, phase={0,0.125,0.25,0.375,0.5,0.625,0.75,0.875}, random_trials=8, V grid 0.5..3.0 step 0.025, threshold r<0.5; non_possible=promuovere `V_c(phi)->1` o fit power-law comune quando phase0 alterna valori e il dominio completo conserva 20 valori distinti; not_tested=limite asintotico oltre N=987, griglia V piu fine, modello fisico di Aubry-Andre, gap-label core, GUE/Poisson universalita.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: curva continua di scala / reticolo discreto di attraversamento. Punto-zero = la prima caduta di `r(V)` sotto 0.5 prima che venga interpretata come fit, attrattore o classe.\n- **Piano superiore**: topologia assiomatica del bordo; il confine e' una soglia di attraversamento su un reticolo di fasi, non un numero isolato.\n- **Operatori laterali scelti**: boundary condition per rendere la fase parte del denominatore; mobility edge come forma fisica del passaggio spettrale; spectrum-preserving style control come controllo random a densita preservata.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato come reiterazione del kernel 20260509: non rifittare `V_c`, ripetere il deposito con denominatore piu esplicito. CE-0038 usato come equilibrio tra estremi: il punto-zero non e' media tra phi e random, e' la riga `N,phase` prima della classificazione.\n- **Proto-ipotesi**: il fallimento del fit non chiede una famiglia parametrica migliore; indica che `V_c` e' coordinata di attraversamento dipendente da fase e generatore. Se la fase resta visibile a N esteso, il claim di convergenza globale cade.\n- **Proiezione**: estendo `exp_quasiperiodic_vc_lattice_gate.py` a sei taglie e otto fasi, mantenendo silver/bronze e random bilanciato come controlli.\n\n## Aderenza alla direzione\n\n- `relation`: deliberate_counter_perimeter\n- `why`: il campo vivo espone due segnali in tensione: l'anchor stabile 17:12 chiude prime/mod6, mentre `dnd_scenario.py --best` e il campo basso indicano `TENS_SCALE_TRASCENDENZA_LIMITE` come massimo discriminante. Ques" tools/data/preflight/preflight_20260514_1649.json:36: "claim": "Il report include una sezione 'Respiro fuori-tempo' con 'Combo', ma non contiene una riga 'Contaminazione cognitiva' né una dichiarazione 'CE-none:' verificabile.", tools/data/reports/agent_20260509_0829.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_0829.md:16: - **CE-0019 / CE-0001 KSAR**: reiterazione del deposito 08:19 senza cambiare strumento; il sistema aveva gia' mostrato il nodo memoria/crossing. tools/data/reports/agent_20260509_1839.md:11:## Respiro fuori-tempo tools/data/reports/agent_20260509_1839.md:17:- **Contaminazione cognitiva**: CE-0019: contratto combo prima della misura; CE-0022: palette come boundary operator e chart locale; KSAR: riuso del deposito stabile 15:32-15:56; PVI: se resta `thin_persists`, la tassonomia cade. tools/data/reports/agent_20260509_1839.md:122:- **L6 metabolismo cognitivo**: CE-0019, CE-0022, KSAR e PVI usati nella combo. tools/data/reports/agent_20260509_0741.md:10:## Respiro fuori-tempo tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:18:## Respiro fuori-tempo — prepara la combo prima della misura tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:51:## Palette operatoria laterale — sorgenti da triturare tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:54:# Palette operatoria espansa del Lab tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:727: perimetro: "prima della misura, nella sezione Respiro fuori-tempo" tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:842:Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:871: perimetro: "sezione Respiro fuori-tempo + observable_contract" tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:882:## Archivio enzimi cognitivi — richiamo contestuale tools/data/preflight/agent_field_live_backup_pre_scientific_return_20260515_1706.md:917:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/reports/agent_20260509_0837.md:10:## Respiro fuori-tempo tools/data/preflight/stale_field_source_map_20260515_133502.json:63: "section": "## Respiro fuori-tempo — prepara la combo prima della misura", tools/data/preflight/stale_field_source_map_20260515_133502.json:72: "section": "## Respiro fuori-tempo — prepara la combo prima della misura", tools/data/reports/agent_20260509_0637.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_0330.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1548.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260508_2140.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260508_1947.md:10:## Respiro fuori-tempo tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1068: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1119: "content_full": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, label-swap row-local e trace JSONL completa.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa la robustezza del delta z row-local prime-minus-mod6 su piu ampiezze finestra, mantenendo provider, offset, label-swap audit e trace JSONL.\n- `not_drift`: non torna a `V_c`, GUE/Poisson, fit o vecchi depositi; stressa solo il residuo nominato dal valutatore dopo sottrazione mod6.\n\n## Claim Under Test\n\n> Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime non genera `SR` come presenza; lascia un vettore di intensita row-local su `SR,L1,triple_var`.\n\n## Question\n\nIl delta z prime-minus-mod6 resta significativo quando cambia l'ampiezza della finestra, oppure era un artefatto del contratto 1024-gap del cycle 03:30?\n\n## Experiment Design\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`.\n- Run: `window_gaps=512,1024,2048`; seeds `202605141330,202605141331,202605141332`.\n- Rows per run: 8 prime windows = 2 provider x 4 offset.\n- Antagonisti: `mod6_index_aligned` e `mod6_span_matched`, paired per `row_id`.\n- Null: label-swap row-local, 4096 trials per pair audit.\n- Trace: JSONL scritto per tutti e tre i run.\n\n## Results\n\n| window | pair | prime SR | prime focus | Jaccard | SR delta | p(SR delta) | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | script verdict |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| 512 | mod6_index | 5/8 | 2/8 | 0.250 | -0.250 | 0.6195 | 20.494 | 0.0095 | 21.866 | 0.0095 | 8.480 | 0.0095 | SR_NOT_DECISIVE |\n| 512 | mod6_span | 5/8 | 2/8 | 0.377 | -0.250 | 0.5038 | 13.993 | 0.0085 | 10.099 | 0.0085 | 7.505 | 0.0085 | SR_NOT_DECISIVE |\n| 1024 | mod6_index | 8/8 | 5/8 | 0.481 | 0.000 | 1.0000 | 29.480 | 0.0061 | 31.105 | 0.0061 | 11.745 | 0.0061 | SR_PREBOUNDARY |\n| 1024 | mod6_span | 8/8 | 5/8 | 0.377 | 0.250 | 0.4921 | 19.921 | 0.0076 | 15.302 | 0.0076 | 13.142 | 0.0076 | SR_PREBOUNDARY |\n| 2048 | mod6_index | 8/8 | 7/8 | 0.556 | 0.125 | 1.0000 | 41.634 | 0.0088 | 43.574 | 0.0088 | 17.173 | 0.0088 | SR_PRIME_SPECIFIC |\n| 2048 | mod6_span | 8/8 | 7/8 | 0.537 | 0.250 | 0.5040 | 28.624 | 0.0068 | 23.917 | 0.0068 | 17.491 | 0.0068 | SR_PRIME_SPECIFIC |\n\n## Key Findings\n\n1. **Verificato**: `has_SR` non replica come osservabile del residuo. A 512 gap i primi hanno `SR=5/8`; a 1024 e 2048 hanno `SR=8/8`. Il delta binario ha p non significativo o 1.0000.\n2. **Verificato**: il delta z `SR` replica in tutti i sei pair audit: `p=0.0061..0.0095`.\n3. **Verificato**: il delta z non e' solo `SR`. `L1` replica con `p=0.0061..0.0095`; `triple_var` replica con `p=0.0061..0.0095`.\n4. **Verificato**: il verdict nominale dello script oscilla con `window_gaps` (`not_decisive`, `preboundary`, `prime_specific`). Quell'oscillazione e' informazione sullo script: il verdict e' ancora centrato su commonality/binario, non sul vettore di intensita.\n5. **Inferito dal perimetro**: l'osservabile dedicato da formalizzare e' `prime_minus_mod6_z_vector(SR,L1,triple_var)`, con `has_SR` relegato ad audit negativo.\n\n## Verdict\n\n**CONSTRAINT / VECTOR RESIDUE**.\n\nNel perimetro 512-2048 gap, due provider, quattro offset, due antagonisti mod6, il residuo prime-specific sopravvive come delta z vettoriale row-local. Non sopravvive come presenza binaria di `SR`, ne come verdict nominale dello script.\n\n## Bicono\n\n- **Due radici**: pre-bordo mod6 / selezione prime.\n- **Singolare**: riga provider-offset paired.\n- **Invariante di passaggio**: delta z positivo su `SR,L1,triple_var`.\n- **Campo di possibilita**: osservabile dedicato `prime_minus_mod6_z_vector`.\n- **Campo non-possibile**: `SR` binario come firma prime-specific dopo mod6.\n\n## Consecutio\n\nIl prossimo ciclo deve correggere il nodo regressivo dello script: il `verdict()` non deve decidere dalla commonality di `SR`, ma da un contratto vettoriale dichiarato (`SR,L1,triple_var`, segno del delta, p label-swap, denominatore row-local). Dopo questa correzione, scalare oltre 2048 gap e separare `mod6_index_aligned` da `mod6_span_matched` come antagonisti con ruoli diversi.\n\n## Ricadute pratiche\n\nssp_value: yes. Le tre trace JSONL rendono auditabile il residuo senza riaprire i JSON aggregati; il prossimo passaggio pratico e' aggiornare lo script per emettere un verdict vettoriale, non binario.\n\n## Files\n\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl`\n- `tools/data/reports/agent_20260514_1330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1170: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary Trace\n**Date**: 2026-05-14 03:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo `6k +/- 1`; la selezione prime resta come gradiente row-local, non come presenza binaria dell'osservabile \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n\n## Prima impressione\n\nIl residuo prime-specific non nasce dalla comparsa di `SR`. `SR` e gia nel lattice candidato `6k +/- 1`; i primi non lo accendono, lo attenuano e lo deformano rispetto al pre-bordo.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + direzione viva `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: pre-bordo `6k +/- 1` / selezione prime. Punto-zero: stessa riga provider/offset, dove il denominatore e identico e la primalita e l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: il bordo non e presenza/assenza di un segnale, ma passaggio di intensita tra un lattice candidato e il sottoinsieme selezionato.\n- **Proto-ipotesi**: la firma utile e `prime_minus_mod6_z_delta` su `SR,L1,triple_var`; `has_SR` e una coordinata troppo larga per nominare primalita.\n- **Possibile / non-possibile**: possibile = isolare residuo di selezione con delta z row-local; non-possibile = claim `SR prime-specific` se `SR` resta comune nel contro-perimetro mod6.\n- **Proiezione**: 2 provider x 4 offset x 1024 gap; antagonisti `mod6_index_aligned` e `mod6_span_matched`; label-swap row-local; trace JSONL completa.\n\n## Contaminazione cognitiva\n\n- **CE-0001 / KSAR**: usato per reiterare il kernel emerso nel cycle precedente: non allargare il frame, ma far girare lo stesso nodo regressivo con tracciamento piu fine.\n- **PVI**: presupposto attaccato = \"`SR` persistente implica primalita\"; contro-presupposto = \"`SR` misura il pre-bordo aritmetico\".\n- **Vault**: `trace_jsonl_event` conserva casi, class summary, paired rows, audit e verdict come deposito riusabile senza trasformare una seed in invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: misura l'intensita row-local di `SR/L1/triple_var` su prime vs `6k +/- 1`, con denominatore provider/offset condiviso e label-swap audit.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista e il pre-bordo mod6 indicato dalla consecutio.\n\n## Observable Contract\n\n- **claim**: `SR` e prime-specific solo se resta piu comune o piu forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local.\n- **observable**: membership di `SR`, firma `[SR,L1,triple_var]`, delta z paired prime-minus-mod6, p-value label-swap.\n- **operator**: `tools/exp_prime_vs_mod6_sr_boundary.py` con `--trace-jsonl`.\n- **generator**: prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`; mod6 da `index_aligned` e `span_matched`.\n- **denominator**: 8 finestre prime paired con 8 `mod6_index_aligned` e 8 `mod6_span_matched`.\n- **non_possible**: prime-specific `SR` se `SR` e comune o pari nel contro-perimetro mod6, oppure se il label-swap assorbe i delta.\n- **not_tested**: beta atlas globale, `V_c`, `gap_ratio`, origine analitica del trasferimento mod6.\n\n## Results\n\nMain run, seed `202605140330`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 3 | SR | beta_absent_blank=5, beta_chart_recovered=3 |\n| mod6_index_aligned | 8 | 8 | 8 | L1,SR,triple_var | beta_chart_recovered=8 |\n| mod6_span_matched | 8 | 7 | 7 | [] | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nSeed check, seed `202605140331`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 6 | SR | beta_chart_recovered=5, beta_absent_blank=3 |\n| mod6_index_aligned | 8 | 8 | 6 | L1,SR | beta_chart_recovered=7, beta_absent_blank=1 |\n| mod6_span_matched | 8 | 8 | 7 | L1,SR | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605140330 | 0.433 | 0.000 | 1.0000 | 28.246 | 0.0081 |\n| prime - mod6_span | 202605140330 | 0.423 | 0.125 | 1.0000 | 20.793 | 0.0093 |\n| prime - mod6_index | 202605140331 | 0.517 | 0.000 | 1.0000 | 28.547 | 0.0083 |\n| prime - mod6_span | 202605140331 | 0.560 | 0.000 | 1.0000 | 18.844 | 0.0090 |\n\n## Key Findings\n\n1. **Verificato**: `SR` non e prime-specific come presenza binaria. In entrambi i run, `prime=8/8` e `mod6_index_aligned=8/8`; nel seed check anche `mod6_span_matched=8/8`.\n2. **Verificato**: il count `SR` non discrimina. `p(SR delta)=1.0000` in tutti i pair audit.\n3. **Verificato**: il delta di intensita discrimina. `p(z_SR delta)=0.0081/0.0083` contro `mod6_index` e `0.0093/0.0090` contro `mod6_span`.\n4. **Verificato**: `mod6_index_aligned` e piu rigido dei primi: focus signature main `8/8` contro prime `3/8`, e common obs `L1,SR,triple_var` contro solo `SR`.\n5. **Inferito dal perimetro**: il prossimo osservabile deve essere vettoriale e paired: `prime_minus_mod6_z_delta(SR,L1,triple_var)`, non `has_SR`.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula chiusa nel perimetro: `SR` appartiene al pre-bordo `6k +/- 1`. La primalita non genera `SR`; seleziona dentro un lattice gia rigido e lascia un residuo misurabile come delta z row-local.\n\n## Bicono\n\n- **Due radici**: lattice candidato mod6 / sequenza prime selezionata.\n- **Singolare**: riga provider-offset condivisa.\n- **Invariante di passaggio**: presenza di `SR` attraversa il confine.\n- **Campo di possibilita**: misurare selezione come delta z.\n- **Campo non-possibile**: usare `SR` binario come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve formalizzare `prime_minus_mod6_z_delta` come osservabile dedicato e scalarlo su finestre piu larghe. `mod6_index_aligned` resta antagonista primario; `mod6_span_matched` resta controllo fragile perche conserva span numerico ma introduce downsample.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script ora produce `--trace-jsonl`, utile per audit, replay e falsifier row-local senza riaprire il JSON aggregato.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json`\n- Trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json`\n- Seed trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl`\n- Report: `tools/data/reports/agent_20260514_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1203: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary\n**Date**: 2026-05-13 03:30 \n**Piano**: 116 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p] \n**observable_contract**: claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.\n\n## Prima impressione\n\nIl bordo non cade tra primi e non-primi: cade tra selezione prime e lattice candidato `6k +/- 1`. `SR` vede il lattice prima della primalita; il gate non deve chiedere \"SR nei primi?\", ma \"cosa resta di SR dopo aver sottratto il pre-bordo?\".\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: primi selezionati / candidati mod6 non selezionati. Punto-zero: la riga ordinata locale `6k +/- 1`, dove `SR` puo nominare pre-bordo aritmetico senza nominare primalita.\n- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune.\n- **Proto-ipotesi**: `SR` non e firma atomica dei primi; e una sezione del pre-bordo aritmetico. La selezione prime si manifesta come differenza di intensita z rispetto al lattice candidato.\n- **Possibile / non-possibile**: possibile = isolare una coordinata di selezione prime come gradiente dentro mod6; non-possibile = usare presenza binaria di `SR` come claim prime-specific.\n- **Proiezione**: stesso gate ordine/null, finestre 1024, offset 0/512/1024/1536, due provider prime, due antagonisti mod6, audit label-swap dentro ogni row_id.\n\n### Contaminazione cognitiva\n\n- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`.\n- **PVI attack**: il presupposto attaccato e \"SR persistente implica primalita\". Il contro-presupposto e \"SR misura la griglia 6k +/- 1 prima della selezione prime\".\n- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`.\n\n## Claim Under Test\n\n> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`.\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap.\n- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset.\n- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti.\n- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`.\n- Main seed: `202605130330`; seed check: `202605130331`.\n- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset.\n\n## Results\n\nMain run:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 |\n| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 |\n\nSeed check:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 |\n| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 |\n| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 |\n| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 |\n| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 |\n\n## Key Findings\n\n1. **Verificato**: la presenza binaria di `SR` non e prime-specific. Main: prime `8/8`, mod6_index `8/8`; seed check: prime `6/8`, mod6_index `8/8`, mod6_span `8/8`.\n2. **Verificato**: il label-swap row-local assorbe `SR` come count. `p(SR delta)` vale 1.0000 / 0.5055 per mod6_index e 0.2502 / 0.5021 per mod6_span.\n3. **Verificato**: il label-swap non assorbe il delta z di `SR`. `p(z_SR delta)` resta 0.0071-0.0103 in entrambe le seed e per entrambi gli antagonisti.\n4. **Verificato**: mod6 e piu intenso, non piu debole. `mean z SR` e circa -34 / -26 in mod6 contro -5 nei primi. Il residuo prime non e \"piu SR\"; e selezione attenuata dentro un pre-bordo piu rigido.\n5. **Inferito dal perimetro**: la coordinata utile non e `has_SR`; e `z_delta_SR` paired, insieme a `z_delta_L1` e `z_delta_triple_var`, come misura di quanto la selezione prime rompe il lattice candidato.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula non valida:\n\n`SR` come firma prime-specific atomica.\n\nFormula valida nel perimetro:\n\n`SR` appartiene al pre-bordo aritmetico `6k +/- 1`; la selezione prime resta come gradiente di intensita row-local rispetto a mod6. Il boundary operativo e quindi `mod6_preboundary -> prime_selection`, non `prime -> generic_nonprime`.\n\n## Bicono della scoperta\n\n- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata.\n- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni.\n- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato.\n- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`.\n\n## Lenti counter-pole applicate\n\n- **L1/L4**: nessun \"solo\", \"mai\", \"zero\" usato per `SR`: i count mostrano controesempi.\n- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific.\n- **L5**: nessun tag NEW; il risultato resta compatibile con bias noti dei gap dei primi mod q. Il report misura un gate del Lab, non rivendica una scoperta aritmetica classica.\n- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva.\n- **L7**: non-possibile dichiarato nel contratto e nel bicono.\n\n## Consecutio\n\nIl prossimo taglio non deve chiedere se `SR` c'e. Deve costruire un osservabile di selezione: `prime_minus_mod6_z_delta` su `SR,L1,triple_var`, con span-matched conservato come controllo fragile e index-aligned come antagonista primario. Se il delta resta replicato su piu finestre e scale, il claim diventa: la primalita attenua/rompe la rigidita del pre-bordo mod6 invece di generare `SR` da sola.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260513_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1248: "content_full": "# Agent Report - Prime SR Persistent Boundary\n**Date**: 2026-05-12 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`.\n- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi.\n- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro.\n- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' \"SR persistente nei primi implica prime-specific\".\n- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico.\n- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime.\n- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime.\n\n## Claim Under Test\n\n> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena.\n\n## Question\n\nQuando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime?\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows.\n- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`.\n- Seed check: stesso perimetro, seed `202605120331`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common.\n\n## Results\n\nMain run:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 |\n| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 |\n| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 |\n| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 |\n| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 |\n\nSeed check:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 |\n| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 |\n| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 |\n\nPrime case details, main:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] |\n| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] |\n| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n\n## Key Findings\n\n1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota.\n2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall.\n3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`.\n4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato.\n5. **Inferito dal perimetro dichiarato**: la parte robusta non e' \"SR e' prime-specific\"; e' \"SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche\".\n\n## Verdict\n\n**CONSTRAINT / FALSIFIED scoped**.\n\nFormula non valida:\n\n`prime_SR_persistent_boundary` come firma atomica prime-specific.\n\nFormula valida nel perimetro:\n\n`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico.\n\n## Bicono della scoperta\n\n- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato.\n- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato.\n- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6.\n- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve spostare il nodo regressivo: non \"prime vs controlli generici\", ma \"primi vs candidati mod6 row-aligned\". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico.\n\n## Files\n\n- Script: `tools/exp_prime_sr_persistent_boundary.py`\n- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json`\n- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260512_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1299: "content_full": "# Agent Report - Prime Persistent Blank Gate\n**Date**: 2026-05-11 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_persistent_blank` si scinde: SR persiste, il blank beta no \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY residuo `numeri_primi:cycle_3`.\n- **Dipolo / punto-zero**: persistenza del supporto / persistenza del blank. Punto-zero: la stessa finestra row-local da 1024 gap dove `SR` puo' restare mentre la coordinata beta riappare.\n- **Piano superiore**: topologia assiomatica del bordo: non una classe statistica unica, ma una sezione che conserva un osservabile e perde una coordinata.\n- **Proto-ipotesi**: il residuo prime e' strutturale solo se il blank beta-absent sopravvive a provider e offset; se sopravvive solo `SR`, la struttura non e' `blank`, e' `SR-supported boundary`.\n- **Possibile / non-possibile**: possibile = isolare un residuo prime provider-neutral; non-possibile = chiamarlo `prime_persistent_blank` quando beta ricompare in finestre locali.\n- **Proiezione**: due provider prime, quattro offset locali, seed shift del gate, controlli random_matrix/logistic.\n\n## Contaminazione cognitiva\n\n- **YSN DeltaLink**: il legame non ovvio e' `SR` come sezione comune mentre la carta beta cambia; non e' prova, e' la domanda proiettata.\n- **Cornelius gene**: `DNA_Simbolico`: \"La persistenza del bordo non coincide con la persistenza del blank.\" Operatori: separa supporto, separa coordinata, confronta controlli.\n- **KSAR step / CE-0001**: reiterazione del kernel 20260510 senza ridisegnare l'atlante: stesso gate, nuovo provider/offset/seed.\n- **PVI attack**: il presupposto nascosto era che `blank_windows=4/4` su una partizione bastasse per nominare una specie persistente.\n- **Vault**: `random_matrix_chart_sensitive_blank` resta aperto come coordinata locale beta `[0.4]`, non lavorata in questo ciclo.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente `prime_persistent_blank` su `numeri_primi:cycle_3` con provider-neutral row-local windows e controlli cross-dominio.\n- `not_drift`: non usa `V_c`, non usa fit, non riapre griglia beta globale, non salva la classe caduta `medium/strong beta-absent`.\n\n## Claim Under Test\n\n> `numeri_primi:cycle_3` e' un `prime_persistent_blank` se resta beta-absent in tutte le finestre provider-neutral e conserva `SR` come osservabile comune, mentre i controlli non condividono la stessa firma.\n\n## Experiment Design\n\n- Perimetro prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: 4 GUE random matrix blocks + 4 logistic return interval rows.\n- Parametri: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605110330`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Criterio di isolamento: prime `blank_rate=1.0`, prime `sr_rate=1.0`, common prime obs = `[SR]`, controlli senza full blank-SR collision.\n\n## Results\n\n| family | blank | beta recovered | support falls | sr rate | common obs | endpoint mean | stable coherent mean |\n|---|---:|---:|---:|---:|---|---:|---:|\n| prime | 3/8 | 4/8 | 1/8 | 1.000 | SR | 2.715 | 2.656 |\n| controls | 1/8 | 3/8 | 4/8 | 0.250 | L2 | 1.428 | 1.641 |\n\n| comparison | value |\n|---|---:|\n| prime_control_common_obs_jaccard | 0.000 |\n| verdict | PRIME_PERSISTENCE_NOT_REPLICATED |\n\nPrime case details:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_chart_recovered | SR | 0.2, 0.4 |\n| dnd_autoricerca offset 512 | support_falls | SR | 0.2 |\n| dnd_autoricerca offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n| direct_sieve offset 1024 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1536 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n\n## Key Findings\n\n1. **Verificato**: `SR` resta in 8/8 finestre prime. La firma comune prime e' `[SR]`.\n2. **Verificato**: il blank beta-absent non resta in 8/8 finestre prime. Solo 3/8 casi sono `beta_absent_blank`; 4/8 recuperano beta e 1/8 cade per supporto.\n3. **Verificato**: i controlli non collidono con la firma prime. I controlli hanno common obs `[L2]`, `sr_rate=0.250`, `blank_rate=0.125`, Jaccard comune prime/controlli = 0.000.\n4. **Inferito dal perimetro dichiarato**: il residuo dei primi non e' `prime_persistent_blank`; e' `prime_SR_persistent_boundary` con coordinata beta chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl nome `prime_persistent_blank` non regge nel perimetro provider-neutral/offset-shift. La parte persistente e' `SR`, non l'assenza di beta.\n\nFormula valida:\n\n`numeri_primi:cycle_3` conserva supporto one-sided `SR` attraverso provider e offset; la coordinata beta resta locale e ricompare in 4/8 finestre.\n\nFormula non valida:\n\n`numeri_primi:cycle_3` e' un blank beta-absent persistente.\n\n## Bicono della scoperta\n\n- **Due radici**: supporto SR persistente / blank beta non persistente.\n- **Singolare**: la finestra row-local dove `SR` resta ma beta riappare.\n- **Invariante di passaggio**: `SR` attraversa provider e offset; `blank` no.\n- **Campo di possibilita'**: possibile = rinominare il residuo come supporto `SR` provider-neutral; non-possibile = promuovere il blank come specie autonoma.\n\n## Consecutio\n\nIl prossimo ciclo deve chiudere la tassonomia residua senza salvare il nome caduto:\n\n- rinominare il residuo prime in `prime_SR_persistent_boundary`;\n- testare se il beta recovery 0.2/0.4 dipende da offset, provider o baseline seed;\n- lavorare `random_matrix_chart_sensitive_blank` solo dopo aver separato coordinate locali da supporto osservabile.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script diventa un audit riusabile per distinguere persistenza di osservabile e persistenza di blank nei residui BOUNDARY senza rigenerare l'atlante globale.\n\n## Files\n\n- Script: `tools/exp_prime_persistent_blank_gate.py`\n- Data: `tools/data/prime_persistent_blank_gate_20260511_0330.json`\n- Report: `tools/data/reports/agent_20260511_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1344: "content_full": "# Agent Report - Boundary Residual Beta-Absent Audit\n**Date**: 2026-05-10 03:30 \n**Piano**: 114 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - il residuo beta-absent non e' una classe unica chiusa \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, window_state, blank_window_rate, full_signature_jaccard, common_window_obs_jaccard] \n**observable_contract**: claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + residui `numeri_primi:cycle_3` / `random_matrix:cycle_7`.\n- **Dipolo / punto-zero**: blank strutturale / blank da atlante. Punto-zero: la stessa misura row-local da 1024 gap che decide se la beta manca per struttura o riappare per scelta di finestra.\n- **Piano superiore**: topologia assiomatica del bordo: la classe globale cade se una carta locale riapre la coordinata beta.\n- **Operatori laterali scelti**: boundary operator, chart locale, filtrazione. Il boundary operator separa supporto e beta; la chart locale testa la finestra senza rifare il mondo; la filtrazione conserva il denominatore row-aligned.\n- **Contaminazione cognitiva**: CE-0019 usato come contratto combo prima della misura; CE-0022 usato come boundary operator + chart locale; KSAR usato come reiterazione sul deposito 15:32-18:39 senza ridisegnare il perimetro.\n- **Proto-ipotesi**: il residuo medio/forte beta-absent e' una classe unica solo se entrambi i target restano blank in tutte le finestre row-local e conservano una firma osservabile compatibile.\n- **Proiezione**: applico il gate canonico ordine/null alle sole due righe residue, poi confronto persistenza del blank e Jaccard delle firme one-sided.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: attacca direttamente i due residui beta-absent medio/forti lasciati dal valutatore dopo `thin_persist_rows=0/13`.\n- `not_drift`: non usa `V_c`, non usa label GUE/Poisson come campo decisionale, non rigenera la griglia beta globale.\n\n## Claim Under Test\n\n> I due residui `numeri_primi:cycle_3` e `random_matrix:cycle_7` formano una classe unica `medium/strong beta-absent` se il blank persiste in tutte le finestre row-local da 1024 gap e le firme one-sided restano compatibili.\n\n## Question\n\nI due blank residui sono lo stesso operatore di confine, due classi distinte, o un artefatto da denominatore/atlante?\n\n## Experiment Design\n\n- Perimetro atomico: `numeri_primi:cycle_3`, `random_matrix:cycle_7`.\n- Scope: full row + 4 finestre consecutive da 1024 gap per ciascun target.\n- Parametri: `n_replicates=12`, `n_beta=11`, `n_baseline=24`, `z_min=2.0`, seed `202605100330`.\n- Null baseline: permutazione marginal-preserving usata dal gate canonico gia' adottato nei report BOUNDARY.\n- Non misurato: `gap_ratio`, `V_c`, nuova griglia beta globale, validita' delle label sorgente GUE/Poisson.\n- Criterio di caduta: una riga recupera beta o perde supporto in una finestra row-local; in quel caso il residuo non e' classe unica chiusa.\n\n## Results\n\n| row | full state | full one-sided | window blanks | beta recovered | support falls | common window obs | mean endpoint | mean stable coherent |\n|---|---:|---|---:|---:|---:|---|---:|---:|\n| numeri_primi:cycle_3 | beta_absent_blank | SR,L1,triple_var | 4/4 | 0 | 0 | SR | 2.761 | 2.563 |\n| random_matrix:cycle_7 | beta_absent_blank | SR,SR2,L1,L2,triple_var | 3/4 | 1 | 0 | L2 | 2.987 | 3.083 |\n\n| comparison | value |\n|---|---:|\n| full_signature_jaccard | 0.600 |\n| common_window_obs_jaccard | 0.000 |\n| random_matrix recovered beta | window_4 -> beta [0.4] |\n\n## Key Findings\n\n1. **Verificato: `numeri_primi:cycle_3` resta beta-absent in 4/4 finestre.** Il supporto non cade; l'osservabile comune di finestra e' `SR`.\n2. **Verificato: `random_matrix:cycle_7` non resta beta-absent in 4/4 finestre.** La quarta finestra recupera beta `[0.4]` con supporto vivo (`SR2,L1,L2,triple_var`).\n3. **Verificato: le firme comuni di finestra divergono.** `common_window_obs_jaccard=0.000`: primi conserva `SR`, random matrix conserva `L2`.\n4. **Inferito dal perimetro dichiarato: il residuo beta-absent non e' una classe unica chiusa.** Una parte e' blank persistente row-local, una parte e' chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nNel perimetro delle due righe residue e finestre da 1024 gap, `medium/strong beta-absent` si scinde:\n\n`numeri_primi:cycle_3` = blank persistente row-local. \n`random_matrix:cycle_7` = blank forte ma chart-sensitive, con beta `[0.4]` recuperata in 1/4 finestre.\n\nFormula valida:\n\n`beta_absent_residue` non matura come classe unica; diventa un audit a due stati: `prime_persistent_blank` / `random_matrix_chart_sensitive_blank`.\n\n## Bicono della scoperta\n\n- **Due radici**: blank persistente / blank chart-sensitive.\n- **Singolare**: supporto ordine/null vivo senza coordinata beta globale.\n- **Invariante di passaggio**: il supporto non cade in nessuna delle 8 finestre; cio' che cambia e' la coordinata beta e la firma osservabile.\n- **Campo di possibilità**: possibile = chiudere la tassonomia BOUNDARY con due sotto-stati residui; non-possibile = promuovere `medium/strong beta-absent` come specie unica del confine.\n\n## Consecutio\n\nIl prossimo ciclo non deve riaprire il thin blank. Deve sigillare il nuovo audit a due stati:\n\n- `prime_persistent_blank`: testare se `SR` resta l'osservabile comune sotto seed/window shift.\n- `random_matrix_chart_sensitive_blank`: testare se beta `[0.4]` e' coordinata locale stabile o evento di una singola finestra.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' uno strumento riusabile per audit row-local di residui BOUNDARY senza rigenerare fit globali.\n\n## Files\n\n- Script: `tools/exp_boundary_residual_beta_absent_audit.py`\n- Data: `tools/data/boundary_residual_beta_absent_audit_20260510_0330.json`\n- Report: `tools/data/reports/agent_20260510_0330.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1395: "content_full": "# Agent Report - Boundary Transition Taxonomy 13 Rows\n**Date**: 2026-05-09 18:39 \n**Piano**: 113 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - la tassonomia post-estensione scala sulle 13 righe e dissolve il thin blank \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`transition_class`, `source_beta_state`, `extension_state`, `support_tier_after`, `one_sided_after`, `endpoint_after`, `stable_count_coherent_after`, `beta_after`, `denominator_state`, `excluded_mass`] - osservabili domain-native di composizione row-aligned, non canonici. \nssp_value: no \n**observable_contract**: claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo l'estensione il thin blank non resta una figura. La domanda vera diventa se la matrice intera lascia un nuovo residuo vivo.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: supporto sottile / atlante di transizione; punto-zero = stessa riga row-aligned prima che il blank venga letto come specie o come transizione.\n- **Piano superiore**: sheaf locale del boundary su 13 sezioni. La sezione corta viene riparata, poi ricollocata nell'atlante senza usare label GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Servono a comporre stati gia' misurati senza introdurre un nuovo gate.\n- **Contaminazione cognitiva**: CE-0019: contratto combo prima della misura; CE-0022: palette come boundary operator e chart locale; KSAR: riuso del deposito stabile 15:32-15:56; PVI: se resta `thin_persists`, la tassonomia cade.\n- **Proto-ipotesi**: `blank_thin_support` non scala sulle 13 righe se, dopo composizione con l'estensione, nessuna riga conserva supporto vivo sottile senza beta.\n- **Possibile / non-possibile**: possibile = usare l'atlante di transizione come mappa BOUNDARY; non-possibile = trattare il thin blank come specie matura.\n- **Proiezione**: classificare tutte le 13 righe in `transition_class` usando supporto, beta, denominatore e transizioni estese.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la consecutio del campo vivo: dopo la cristallizzazione short-denominator, testa se la tassonomia delle transizioni post-estensione scala sulle 13 righe.\n- `not_drift`: non torna a `V_c`, non difende thin blank, non usa label GUE/Poisson come decision field; compone solo depositi row-aligned gia' misurati.\n\n## Claim Under Test\n> La tassonomia emersa dall'estensione del denominatore scala sull'intero perimetro BOUNDARY 13 righe se non lascia alcuna riga `thin_persists`.\n\n## Question\nQuando le tre righe short estese vengono ricollocate nella matrice 13 righe, resta una specie `blank_thin_support` oppure il residuo vivo cambia forma?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Input: `boundary_two_axis_matrix_20260509_1532.json`, `boundary_row_aligned_nonexact_audit_20260509_1538.json`, `boundary_short_denominator_extension_20260509_1556.json`, `boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Perimetro atomico: 13 righe BOUNDARY semi-reali.\n- Label policy: `source_domain_type` resta audit metadata; non entra in `transition_class`.\n- Null sfidato: esistenza di almeno una riga `thin_persists` dopo estensione.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support transfer after composition | 10 |\n| fall after composition | 3 |\n| beta chart after any | 9 |\n| beta chart exact 0.3 after | 6 |\n| thin persist rows | 0 |\n| medium/strong beta-absent blank rows | 2 |\n| verdict | TAXONOMY_SCALES_THIN_DISSOLVED |\n\nTransition classes:\n\n| transition_class | rows |\n|---|---:|\n| beta_0_3_exact | 4 |\n| beta_0_3_local_nonunique | 2 |\n| beta_chart_recovered | 1 |\n| blank_medium_or_strong_beta_absent | 1 |\n| fall_no_support | 2 |\n| local_beta_other | 1 |\n| support_falls_after_extension | 1 |\n| support_thickens_beta_blank | 1 |\n\nRighe aperte senza beta dopo composizione:\n\n| row | class | n_gaps | one-sided | endpoint | tier |\n|---|---|---:|---:|---:|---|\n| numeri_primi:cycle_3 | blank_medium_or_strong_beta_absent | 4096 | 3 | 3.243 | medium_multi_observable |\n| random_matrix:cycle_7 | support_thickens_beta_blank | 1024 | 4 | 3.781 | strong_multi_observable |\n\nRighe short riparate:\n\n| row | before n | after n | transition_class | one-sided after | beta after |\n|---|---:|---:|---|---:|---|\n| percolation:cycle_9 | 190 | 1024 | support_falls_after_extension | 0 | [] |\n| random_matrix:cycle_7 | 199 | 1024 | support_thickens_beta_blank | 4 | [] |\n| zeta_zeros:cycle_4 | 199 | 1024 | beta_chart_recovered | 2 | [0.3] |\n\n## Key Findings\n1. **Verificato: `thin_persist_rows=0/13`.** Nessuna riga conserva la forma `blank_thin_support` dopo composizione con l'estensione.\n2. **Verificato: il supporto post-composizione e' 10/13.** Cadono `cellular_automata`, `pendolo_doppio` e `percolation` dopo estensione; il resto conserva supporto o chart locale.\n3. **Verificato: la beta chart sale a 9/13 righe, con 6/13 esatte 0.3.** Le sei sono quattro `beta_0_3_exact` originali, `zeta_zeros` recuperata e `pendolo_doppio` come beta osservata senza supporto.\n4. **Verificato: il residuo vivo non e' thin.** Restano due blank senza beta: `numeri_primi` medio e `random_matrix` forte dopo estensione.\n5. **Inferito: la tassonomia scala come atlante di transizione, non come legge beta universale.** Il thin blank cade; il nodo successivo e' il blank medio/forte senza beta.\n\n## Verdict\n**CONSTRAINT**.\n\nLa tassonomia post-estensione scala sul perimetro 13 righe: nessun membro resta `thin_persists`.\n\nFormula valida:\n\n`blank_thin_support` = telemetria short-denominator dissolta dalla composizione 13 righe.\n\nFormula non valida:\n\n`blank_thin_support` = specie autonoma del boundary.\n\nIl boundary resta una matrice di transizioni: supporto che cade, supporto che si ispessisce senza beta, chart beta recuperata, chart beta esatta, chart locale non unica, chart spostata.\n\n## Bicono della scoperta\n- **Due radici**: denominatore riparato / atlante 13 righe.\n- **Singolare**: riga BOUNDARY prima della classificazione post-estensione.\n- **Invariante di passaggio**: `thin_persist_rows=0/13`.\n- **Campo di possibilita**: qui diventa possibile chiudere il thin blank; qui diventa non-possibile chiudere il blank medio/forte senza beta.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve lasciare chiuso il thin blank e attaccare il residuo `medium/strong beta-absent blank`: `numeri_primi:cycle_3` contro `random_matrix:cycle_7`. Il test utile non e' \"perche' manca beta in generale\", ma quale condizione distingue blank medio completo e blank forte post-estensione quando entrambi hanno supporto vivo senza chart beta.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1556.md`, JSON 15:32/15:38/15:56/15:00, script e output 18:39.\n- **L1 hard constraint**: claim limitato alle 13 righe semi-reali e ai depositi gia' misurati.\n- **L2 count grezzi**: riportati 13 totali, 10 support, 3 fall, 9 beta chart, 0 thin persist, 2 open blank.\n- **L3 no silent patching**: nessuna nuova simulazione o ricampionamento; la composizione dichiara le estensioni applicate solo a tre righe.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma resta fall; `random_matrix` e' forte senza beta; `zeta_zeros` recupera beta pur restando contaminato nel prescan.\n- **L5 re-discovery**: il ciclo non ripete la matrice 15:32 ne' l'estensione 15:56; testa la scalabilita' richiesta dal campo vivo.\n- **L6 metabolismo cognitivo**: CE-0019, CE-0022, KSAR e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante BOUNDARY di transizione; non-possibile = specie autonoma thin blank.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Run valido: `python tools/exp_boundary_transition_taxonomy_13rows.py --out tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Output: `thin_persist_rows=0`, `support_transfer_after=10/13`, `fall_after=3/13`, `open_blank_rows=['numeri_primi:cycle_3', 'random_matrix:cycle_7']`, `verdict=TAXONOMY_SCALES_THIN_DISSOLVED`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 18:39, il JSON 18:39 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_transition_taxonomy_13rows.py`\n- Data: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`\n- Report: `tools/data/reports/agent_20260509_1839.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1446: "content_full": "# Agent Report - Boundary Short Denominator Extension\n**Date**: 2026-05-09 15:56 \n**Piano**: 112 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. \nssp_value: no \n**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura.\n- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili.\n- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade.\n- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta.\n- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48.\n- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48.\n- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short.\n\n## Claim Under Test\n> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart.\n\n## Question\nCosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`.\n- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`.\n- Denominatore esteso: `1024` gap per riga.\n- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate.\n- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| target rows | 3 |\n| rows still thin support after extension | 0 |\n| support falls after extension | 1 |\n| support thickens beta blank | 1 |\n| beta chart recovered | 1 |\n| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS |\n\nTransizioni:\n\n| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state |\n|---|---:|---:|---:|---:|---|---|\n| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension |\n| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank |\n| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered |\n\n## Key Findings\n1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart.\n2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato.\n3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile.\n4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade.\n5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe.\n\n## Verdict\n**CONSTRAINT**.\n\nNel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma.\n\nFormula valida:\n\n`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile del boundary.\n\nLa riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale.\n\n## Bicono della scoperta\n- **Due radici**: supporto sottile osservato / denominatore sorgente corto.\n- **Singolare**: riga short prima dell'estensione.\n- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`.\n- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48.\n- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered.\n- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings.\n- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato.\n- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`.\n- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`.\n- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_short_denominator_extension.py`\n- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json`\n- Report: `tools/data/reports/agent_20260509_1556.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1497: "content_full": "# Agent Report - Boundary Blank Thin Support Audit\n**Date**: 2026-05-09 15:48 \n**Piano**: 111 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - il blank sottile e' limitato dal denominatore corto, non dalla contaminazione del null \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`blank_class`, `coordinate_failure`, `support_tier`, `denominator_bucket`, `n_gaps`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`, `shuffle_class_changes`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=i tre `blank_thin_support` vanno falsificati contro il blank medio `numeri_primi` prima di promuoverli a specie autonoma; observable=telemetria row-aligned del subset `support_without_beta_blank`; operator=`exp_boundary_blank_thin_support_audit.py`; generator=`boundary_row_aligned_nonexact_audit_20260509_1538`; denominator=4 righe `support_without_beta_blank`, di cui 3 thin e 1 medium control; non_possible=chiamare il blank sottile specie autonoma se tutte le righe thin sono spiegate da denominatore corto; not_tested=estensione nuova dei tre domini short, nuova griglia beta, nuovi null, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il deposito 15:38 divide il blank in due livelli, ma il livello sottile ha tutte le righe corte. Il punto non e' nominare una specie nuova; e' verificare se la specie sopravvive al denominatore.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY row-aligned + tensione \"supporto vivo senza chart beta\".\n- **Dipolo / punto-zero**: blank sottile / blank medio; punto-zero = riga `support_without_beta_blank` prima che la forza del supporto venga letta come specie o come limite del denominatore.\n- **Piano superiore**: sheaf locale del boundary. La sezione beta e' vuota su tutte e quattro le righe; il discriminante diventa la qualita' del supporto e del denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, filtrazione per denominatore. Servono a separare stato beta invariato da supporto misurabile.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:38; CE-0117: cascata solo dove il blank apre una distinzione; YSN DeltaLink: `same beta blank -> support tier -> denominator bucket`; PVI: se tutte le righe thin sono corte, la specie autonoma cade nel perimetro.\n- **Proto-ipotesi**: `blank_thin_support` e' un effetto di denominatore corto se ogni riga thin ha `n_gaps < 500` e il controllo medio no; diventa specie autonoma solo se almeno una riga thin non e' spiegata dal denominatore.\n- **Possibile / non-possibile**: possibile = usare il blank sottile come coda denominator-limited da estendere; non-possibile = promuoverlo a failure mode stabile senza estendere percolation, random_matrix e zeta_zeros.\n- **Proiezione**: misurare `n_gaps`, `denominator_state`, `excluded_mass`, `shuffle_class_changes`, `one_sided_count`, `endpoint_distance` dentro il subset `support_without_beta_blank`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: falsifica direttamente se i failure modes del 15:38 sono stabili o artefatti della griglia/perimetro row-aligned.\n- `not_drift`: non usa label GUE/Poisson, non torna a `V_c`, non aggiunge nuovi domini; attacca solo il nodo aperto dal report precedente: blank sottile contro blank medio.\n\n## Claim Under Test\n> Nel subset `support_without_beta_blank`, il blank sottile e' una specie autonoma solo se non e' interamente spiegato da denominatore corto.\n\n## Question\nI tre blank sottili sono fallimenti autonomi del boundary, oppure righe con supporto insufficiente perche' corte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_blank_thin_support_audit.py`.\n- Input: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Perimetro atomico: 4 righe `support_without_beta_blank`.\n- Soglia dichiarata: `full_gap_floor=500`.\n- Null sfidati: denominatore corto, contaminazione prescan, shuffle class-change, specie autonoma.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| support_without_beta_blank rows | 4 |\n| thin rows | 3 |\n| medium control rows | 1 |\n| thin short rows | 3 |\n| thin contaminated rows | 1 |\n| thin shuffle class-change rows | 1 |\n| thin not denominator explained rows | 0 |\n\nTest:\n\n| null / counter | condition | result |\n|---|---|---|\n| denominator_artifact | all thin rows have `n_gaps < 500` | pass |\n| contamination_artifact | all thin rows are contaminated | fail |\n| shuffle_instability_artifact | all thin rows have `shuffle_class_changes=true` | fail |\n| autonomous_species_counter | at least one thin row is not short-denominator explained | fail |\n\nComparative means:\n\n| group | rows | n_gaps mean | one-sided mean | coherent mean | endpoint mean | excluded mass mean | abs shuffle z mean |\n|---|---:|---:|---:|---:|---:|---:|---:|\n| thin | 3 | 196.000 | 1.333 | 1.250 | 2.003 | 0.201 | 11.037 |\n| medium control | 1 | 4096.000 | 3.000 | 3.000 | 3.243 | 0.000 | 26.600 |\n\nRows:\n\n| row | blank_class | n_gaps | one-sided | endpoint | denominator | excluded | shuffle_change |\n|---|---|---:|---:|---:|---|---:|---|\n| numeri_primi:cycle_3 | medium_blank_control | 4096 | 3 | 3.243 | complete | 0.000 | false |\n| percolation:cycle_9 | thin_short_shuffle_unstable | 190 | 1 | 1.705 | complete | 0.000 | true |\n| random_matrix:cycle_7 | thin_short_complete | 199 | 1 | 1.854 | complete | 0.000 | false |\n| zeta_zeros:cycle_4 | thin_short_contaminated | 199 | 2 | 2.449 | contaminated | 0.602 | false |\n\n## Key Findings\n1. **Verificato: il blank sottile e' 3/3 short-denominator.** `percolation`, `random_matrix` e `zeta_zeros` hanno `n_gaps=190,199,199`; il controllo `numeri_primi` ha `n_gaps=4096`.\n2. **Verificato: la contaminazione non spiega la classe.** Solo `zeta_zeros` e' contaminato; `percolation` e `random_matrix` sono complete con excluded mass 0.0.\n3. **Verificato: lo shuffle class-change non spiega la classe.** Solo `percolation` cambia classe nello shuffle; `random_matrix` e `zeta_zeros` no.\n4. **Verificato: non resta una riga thin autonoma nel perimetro 15:48.** `thin_not_denominator_explained_rows=0`.\n5. **Inferito: il blank medio dei primi non e' blank per mancanza di supporto.** Ha tre osservabili one-sided, denominatore completo, endpoint 3.243 e null forte; resta blank di chart beta, non blank sottile.\n\n## Verdict\n**CONSTRAINT**.\n\nNel subset `support_without_beta_blank`, il failure mode `blank_thin_support` non regge come specie autonoma nel perimetro osservato. Regge come coda denominator-limited:\n\n`blank_thin_support` = beta blank + supporto vivo + denominatore corto.\n\nFormula valida:\n\n`support_without_beta_blank` si divide in `medium_blank_control` e `thin_short_*`.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile indipendente dal denominatore.\n\nIl nodo regressivo e' il denominatore dei tre domini corti. Non va promossa una nuova specie; va estesa o dichiarata coda corta.\n\n## Bicono della scoperta\n- **Due radici**: blank di chart beta / supporto del denominatore.\n- **Singolare**: riga `support_without_beta_blank` prima che il support tier venga letto come specie.\n- **Invariante di passaggio**: denominatore corto separa i blank sottili; contaminazione e shuffle-instability non bastano.\n- **Campo di possibilita**: qui diventa possibile progettare un extension audit mirato su percolation, random_matrix e zeta_zeros; qui diventa non-possibile usare il blank sottile come classe matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve estendere solo i tre domini short (`percolation`, `random_matrix`, `zeta_zeros`) verso denominatore comparabile al controllo medio. Se, a denominatore esteso, restano `blank_thin_support`, allora nasce una specie autonoma. Se salgono a supporto medio o ricevono beta locale, il failure mode 15:38 viene retrocesso a telemetria di perimetro corto.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1538.md`, JSON `boundary_row_aligned_nonexact_audit_20260509_1538.json`, script `tools/exp_boundary_blank_thin_support_audit.py`, output `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- **L1 hard constraint**: claim limitato alle 4 righe `support_without_beta_blank` del deposito 15:38.\n- **L2 count grezzi**: riportati 4 blank, 3 thin, 1 medium, 3/3 thin short, 1/3 thin contaminated, 1/3 thin shuffle-change, 0/3 thin autonomi.\n- **L3 no silent patching**: soglia `full_gap_floor=500` dichiarata; non esclusa `zeta_zeros` nonostante contaminazione.\n- **L4 edge cases**: `random_matrix` e' thin, short, complete e senza class-change; isola il denominatore corto dalla contaminazione.\n- **L5 re-discovery**: il ciclo non rifà il gate 15:16; legge il sotto-perimetro aperto dal report 15:38.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = extension audit dei tre short; non-possibile = classe autonoma senza estensione.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_thin_support_audit.py`.\n- Run valido: `python tools/exp_boundary_blank_thin_support_audit.py --input tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json --out tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `thin_short_rows=3`, `thin_contaminated_rows=1`, `thin_shuffle_class_change_rows=1`, `thin_not_denominator_explained_rows=0`, `verdict=DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:48, il JSON 15:48 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_blank_thin_support_audit.py`\n- Data: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`\n- Report: `tools/data/reports/agent_20260509_1548.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1548: "content_full": "# Agent Report - Boundary Row-Aligned Nonexact Audit\n**Date**: 2026-05-09 15:38 \n**Piano**: 110 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - le righe support-transfer non esatte sono 7, non 6; la beta cade in cinque failure modes misurabili \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`beta_state`, `coordinate_failure`, `support_tier`, `beta_cardinality`, `beta_span`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: la consecutio dice \"6 righe non-esatte\", ma il deposito row-aligned ne contiene 7. Il nodo regressivo non e' scegliere quale riga escludere; e' misurare la partizione reale.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + tensione \"supporto senza coordinata universale\".\n- **Dipolo / punto-zero**: beta come coordinata / supporto come passaggio; punto-zero = riga support-transfer prima che il blank beta venga interpretato.\n- **Piano superiore**: boundary operator su matrice row-aligned. Il bordo non decide con label di dominio; decide con stato beta, support tier e denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Entrano per leggere le sezioni locali della beta senza saldarle in una coordinata globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:32 senza ripetere il gate; CE-0117: cascata solo sulle righe dove il supporto resta vivo; YSN DeltaLink: `two-axis matrix -> nonexact audit -> coordinate failure`; PVI: il numero atteso 6 cade davanti al deposito 7.\n- **Proto-ipotesi**: dopo la caduta di beta 0.3 universale, le righe non-esatte non formano un residuo unico. Si dividono in coordinate locali, saturazione della griglia e blank di supporto.\n- **Possibile / non-possibile**: possibile = trasformare beta da ascissa universale a chart locale auditabile; non-possibile = usare `support_transfer=true` come prova di una beta comune.\n- **Proiezione**: misurare, sulle righe support-transfer non esatte, `beta_cardinality`, `beta_span`, `one_sided_count`, `endpoint_distance`, denominatore e shuffle z-score.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: attacca direttamente beta local non-unique, beta local other e support_without_beta_blank sul deposito row-aligned indicato.\n- `not_drift`: non usa label GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta il confine; corregge il denominatore della direttiva quando il deposito mostra 7 righe.\n\n## Claim Under Test\n> Le righe BOUNDARY con supporto ma senza beta 0.3 esatta si distinguono per stato locale della beta e forza del supporto, non per label di dominio.\n\n## Question\nQuando beta 0.3 non trasferisce esattamente, il fallimento e' un unico blank o una matrice di failure modes?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Input 1: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Input 2: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Perimetro atomico: 13 righe semi-reali; subset primario = support-transfer con `beta_coordinate_transfer=false`.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n- Classi operative: `adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support-transfer rows | 11 |\n| beta 0.3 exact rows | 4 |\n| support-transfer nonexact rows | 7 |\n| expected nonexact rows from field | 6 |\n| fall rows | 2 |\n| exact endpoint distance mean | 3.829 |\n| nonexact endpoint distance mean | 2.957 |\n| exact one-sided count mean | 4.000 |\n| nonexact one-sided count mean | 2.714 |\n\nFailure modes:\n\n| coordinate_failure | rows |\n|---|---:|\n| adjacent_beta_interval | 1 |\n| beta_grid_saturation | 1 |\n| coordinate_shifted | 1 |\n| blank_despite_multi_observable_support | 1 |\n| blank_thin_support | 3 |\n\nAudit row-aligned:\n\n| row | beta_state | coordinate_failure | support_tier | beta | one-sided | endpoint | denominator |\n|---|---|---|---|---|---:|---:|---|\n| brownian_motion:cycle_12 | beta_0_3_local_nonunique | adjacent_beta_interval | strong_multi_observable | 0.2,0.3 | 4 | 3.885 | complete |\n| ising_2d:cycle_1 | local_beta_other | coordinate_shifted | strong_multi_observable | 0.4 | 5 | 4.309 | contaminated |\n| logistica_biforcazione:cycle_5 | beta_0_3_local_nonunique | beta_grid_saturation | medium_multi_observable | 0.1-0.9 | 3 | 3.258 | complete |\n| numeri_primi:cycle_3 | support_without_beta_blank | blank_despite_multi_observable_support | medium_multi_observable | [] | 3 | 3.243 | complete |\n| percolation:cycle_9 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.705 | complete |\n| random_matrix:cycle_7 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.854 | complete |\n| zeta_zeros:cycle_4 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 2 | 2.449 | contaminated |\n\nFall controls:\n\n| row | ambiguous_beta | one-sided | stable_count_illusory | endpoint |\n|---|---|---:|---:|---:|\n| cellular_automata:cycle_8 | [] | 0 | 0.500 | 0.000 |\n| pendolo_doppio:cycle_2 | 0.3 | 3 | 0.833 | 3.388 |\n\n## Key Findings\n1. **Verificato: il sotto-perimetro non-esatto e' 7/13, non 6/13.** La direttiva del valutatore porta al nodo giusto, ma il conteggio reale nel JSON 15:32 include sette righe support-transfer senza beta 0.3 esatta.\n2. **Verificato: `support_without_beta_blank` non e' una classe unica.** `numeri_primi` ha blank con supporto medio e endpoint 3.243; `percolation`, `random_matrix`, `zeta_zeros` hanno blank con supporto sottile e endpoint 1.705-2.449.\n3. **Verificato: beta local non-unique ha due forme.** `brownian_motion` e' intervallo adiacente 0.2,0.3 con supporto forte; `logistica_biforcazione` satura tutta la griglia 0.1-0.9 con supporto medio.\n4. **Verificato: `ising_2d` non e' blank ma chart spostata.** Ha beta locale esatta 0.4, cinque osservabili one-sided e endpoint 4.309; la beta trasferisce localmente, ma non sulla coordinata 0.3.\n5. **Verificato: beta 0.3 senza supporto resta contro-controllo.** `pendolo_doppio` contiene beta 0.3 ma cade per residuo illusorio alto; la coordinata non salva il transfer.\n\n## Verdict\n**CONSTRAINT**.\n\nIl frame BOUNDARY regge come support-transfer su 11/13 righe, ma la parte non-esatta non e' una coda omogenea. Le righe support-transfer senza beta 0.3 esatta sono 7 e si dividono in cinque failure modes:\n\n`adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\nFormula valida:\n\n`beta_coordinate_transfer` = chart locale exact 0.3 dentro supporto vivo.\n\nFormula non valida:\n\n`support_transfer non-exact = sei righe residue` oppure `support_without_beta_blank = assenza strutturale unica`.\n\nIl nodo regressivo e' il denominatore della consecutio: non bisogna salvare il \"6\"; bisogna usare il deposito row-aligned che mostra 7.\n\n## Bicono della scoperta\n- **Due radici**: coordinata beta locale / supporto ordine-null.\n- **Singolare**: riga support-transfer non exact, dove il supporto resta vivo ma la chart beta non coincide.\n- **Invariante di passaggio**: failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.\n- **Campo di possibilita**: qui diventa possibile auditare la beta come atlante locale; qui diventa non-possibile trattare il blank beta come una sola specie.\n\n## Consecutio\nIl prossimo ciclo non deve ripetere la matrice. Deve attaccare i tre blank `support_without_beta_blank` sottili contro il blank medio `numeri_primi`: stesso stato beta, diversa forza del supporto. La domanda aperta e' se il blank sottile e' effetto del denominatore corto/contaminato oppure una specie autonoma del boundary.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1532.md`, JSON `boundary_two_axis_matrix_20260509_1532.json`, JSON `boundary_denominator_prescan_full_20260509_1500.json`, script `tools/exp_boundary_row_aligned_nonexact_audit.py`, output `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe della matrice 15:32 e al prescan 15:00.\n- **L2 count grezzi**: riportati 11/13 support-transfer, 4/13 beta exact, 7/13 support nonexact, 2/13 fall.\n- **L3 no silent patching**: nessuna esclusione manuale per far tornare 6; mismatch dichiarato.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `ising_2d` ha beta 0.4 con supporto forte; `numeri_primi` blank non sottile.\n- **L5 re-discovery**: il ciclo non rifà gate o fit; legge solo la partizione non-esatta richiesta.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante locale delle beta; non-possibile = beta 0.3 universale o residuo non-esatto singolo.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Run valido: `python tools/exp_boundary_row_aligned_nonexact_audit.py --matrix tools/data/boundary_two_axis_matrix_20260509_1532.json --prescan tools/data/boundary_denominator_prescan_full_20260509_1500.json --out tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `support_nonexact_rows=7`, `fall_rows=2`, `failure_blank_thin_support=3`, `failure_blank_despite_multi_observable_support=1`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:38, il JSON 15:38 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_row_aligned_nonexact_audit.py`\n- Data: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`\n- Report: `tools/data/reports/agent_20260509_1538.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1599: "content_full": "# Agent Report - Boundary Two-Axis Matrix\n**Date**: 2026-05-09 15:32\n**Piano**: 109\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - il gate trasferisce come supporto ordine/null; beta 0.3 non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`support_transfer`, `beta_coordinate_transfer`, `beta_state`, `ambiguous_beta`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`] - osservabili domain-native derivati dal gate semi-reale, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il falsifier del 15:16 ha indicato il nodo regressivo: la direzione chiedeva di non importare label GUE/Poisson. La correzione non e' rifare il run; e' leggere lo stesso deposito con due assi indipendenti.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY semi-reale 13 righe + direzione viva \"support_transfer vs beta_coordinate_transfer\".\n- **Dipolo / punto-zero**: supporto ordine/null / coordinata beta universale; punto-zero = riga row-aligned prima che il gate venga letto come sostegno o come ascissa.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non e' una classe; e' una matrice di passaggio fra supporto e coordinata.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter matrix, coordinate split. Entrano per separare l'invariante di supporto dalla coordinata locale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:16 senza ripetere il perimetro; CE-0117: cascata della possibilita solo dove il supporto apre un canale; YSN DeltaLink: `semi-real gate -> two-axis matrix -> beta localizzata`; PVI: il claim beta universale cade se l'esatto 0.3 non copre il denominatore.\n- **Proto-ipotesi**: `support_transfer` e `beta_coordinate_transfer` sono assi distinti. Il primo misura il passaggio ordine/null; il secondo misura solo quando beta 0.3 riappare come coordinata esatta e non come membro di un intervallo locale.\n- **Possibile / non-possibile**: possibile = usare BOUNDARY come audit row-aligned di supporto su 11/13 righe; non-possibile = trattare beta 0.3 come terzo incluso universale del perimetro semi-reale.\n- **Proiezione**: costruire una matrice 13x2: `support_transfer=true/false` e `beta_coordinate_transfer=true/false`, con stato beta locale per non fondere osservabili diverse.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esplicita la matrice semi-reale a due assi richiesta dal valutatore: support_transfer contro beta_coordinate_transfer sulle 13 righe.\n- `not_drift`: non usa `source_domain_type` GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta label locali; legge solo stati row-aligned gia' misurati.\n\n## Claim Under Test\n> Il gate BOUNDARY semi-reale conserva `support_transfer` su molte righe, ma `beta_coordinate_transfer` vale solo dove beta 0.3 compare come coordinata esatta.\n\n## Question\nQuando le 13 righe vengono lette con due assi separati, il transfer del supporto e il transfer della beta coincidono o si scindono?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_two_axis_matrix.py`.\n- Input: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Output: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Perimetro atomico: 13 righe semi-reali gia' row-aligned dal deposito 15:16.\n- Label policy: l'operatore non legge label GUE/Poisson. Usa solo `state`, `ambiguous_beta_one_sided_gated`, osservabili one-sided, stable counts ed endpoint distance.\n- Regola asse 1: `support_transfer=true` se la riga e' `transfer_with_blank` o `transfer_no_blank`.\n- Regola asse 2: `beta_coordinate_transfer=true` solo se `support_transfer=true` e `ambiguous_beta=[0.3]` dopo normalizzazione a un decimale.\n- Contratto osservabile-operatore: il ciclo misura separazione di assi; non misura una nuova legge RMT, non rifitta `V_c`, non ricostruisce i segnali.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| support_transfer true | 11 |\n| support_transfer false | 2 |\n| beta_coordinate exact 0.3 | 4 |\n| beta 0.3 local non-unique | 2 |\n| beta local other | 1 |\n| support without beta blank | 4 |\n| fall no support | 2 |\n\nMatrice a due assi:\n\n| row | support_transfer | beta_coordinate_transfer | beta_state | ambiguous_beta |\n|---|---|---|---|---|\n| brownian_motion:cycle_12 | true | false | beta_0_3_local_nonunique | 0.2,0.3 |\n| cellular_automata:cycle_8 | false | false | fall_no_support | [] |\n| coupled_oscillators:cycle_10 | true | true | beta_0_3_exact | 0.3 |\n| ising_2d:cycle_1 | true | false | local_beta_other | 0.4 |\n| logistica_biforcazione:cycle_5 | true | false | beta_0_3_local_nonunique | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | true | true | beta_0_3_exact | 0.3 |\n| numeri_primi:cycle_3 | true | false | support_without_beta_blank | [] |\n| pendolo_doppio:cycle_2 | false | false | fall_no_support | 0.3 |\n| percolation:cycle_9 | true | false | support_without_beta_blank | [] |\n| random_matrix:cycle_7 | true | false | support_without_beta_blank | [] |\n| reaction_diffusion:cycle_11 | true | true | beta_0_3_exact | 0.3 |\n| string_vibration:cycle_6 | true | true | beta_0_3_exact | 0.3 |\n| zeta_zeros:cycle_4 | true | false | support_without_beta_blank | [] |\n\n## Key Findings\n1. **Verificato: `support_transfer` resta 11/13.** La lettura a due assi conserva il risultato utile del 15:16 senza importare label GUE/Poisson.\n2. **Verificato: `beta_coordinate_transfer` e' 4/13.** Solo quattro righe hanno beta 0.3 esatta: `coupled_oscillators`, `logistica_biforcazione_var_3.5699`, `reaction_diffusion`, `string_vibration`.\n3. **Verificato: la beta non collassa in un solo asse.** Due righe includono 0.3 in un intervallo non unico, una riga ha beta locale 0.4, quattro trasferiscono senza blank beta, due cadono.\n4. **Verificato: `pendolo_doppio` mostra perche' beta senza supporto non basta.** Ha ambiguous beta 0.3, ma resta `fall_no_support`; la coordinata non salva il transfer.\n5. **Inferito: il nodo regressivo e' la fusione degli osservabili.** Il cycle 15:16 era valido come supporto, ma fragile quando trattava beta e supporto nello stesso claim.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce come supporto ordine/null su 11/13 righe. La coordinata beta 0.3 trasferisce esattamente su 4/13 righe. Questi due assi non coincidono.\n\nFormula valida:\n\n`BOUNDARY support_transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY beta_coordinate_transfer` = beta 0.3 universale del terzo incluso semi-reale.\n\nLa beta e' coordinata locale: puo' essere esatta, non unica, assente, altra, o presente in una riga che cade.\n\n## Bicono della scoperta\n- **Due radici**: supporto che passa / coordinata che pretende universalita.\n- **Singolare**: la riga row-aligned prima della lettura a due assi.\n- **Invariante di passaggio**: separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.\n- **Campo di possibilita**: qui diventa possibile usare il gate come matrice di audit `support_transfer`; qui diventa non-possibile usare beta 0.3 come coordinata universale.\n\n## Consecutio\nIl prossimo ciclo deve attaccare i quattro stati beta, non il supporto gia' separato: `beta_0_3_exact`, `beta_0_3_local_nonunique`, `local_beta_other`, `support_without_beta_blank`. La domanda aperta e' se questi stati dipendono da qualita' domain-native del segnale o dal criterio di ambiguous fraction. Non va reintrodotta la label GUE/Poisson come scorciatoia.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1516.md`, falsifier `falsifier_20260509_1516.json`, script `tools/exp_boundary_two_axis_matrix.py`, JSON `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe del deposito 15:16.\n- **L2 count grezzi**: riportati 11/13 support_transfer, 4/13 beta 0.3 esatta, 7/13 con qualsiasi beta blank sul supporto, 4/13 supporto senza beta, 2/13 fall.\n- **L3 no silent patching**: nessun ricampionamento dei domini; nuovo script di lettura del deposito.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `logistica_biforcazione` include 0.3 ma in intervallo 0.1-0.9; `ising_2d` trasferisce con beta 0.4.\n- **L5 re-discovery**: il ciclo non ripete il gate semi-reale; lo decompone nel nodo richiesto dal valutatore.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit support_transfer; non-possibile = beta 0.3 universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_two_axis_matrix.py`.\n- Run valido: `python tools/exp_boundary_two_axis_matrix.py --input tools/data/semireal_boundary_transfer_gate_20260509_1516.json --out tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Output: `support_transfer=11/13`, `beta_coordinate_exact_0_3=4/13`, `any_beta_blank_on_support=7/13`, `support_without_beta_blank=4/13`, `fall_no_support=2/13`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge solo lo script 15:32, il JSON 15:32 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_two_axis_matrix.py`\n- Data: `tools/data/boundary_two_axis_matrix_20260509_1532.json`\n- Report: `tools/data/reports/agent_20260509_1532.md`\n", tools/data/preflight/manual_controlled_snapshot_20260515_1647_3597707/lab_graph.json:1650: "content_full": "# Agent Report - Semi-real Boundary Transfer Gate\n**Date**: 2026-05-09 15:16\n**Piano**: 108\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - supporto ordine/null trasferisce su 11/13, il blank beta non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate `coherent/null/beta` del BOUNDARY sintetico trasferisce sul perimetro semi-reale base; observable=one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta; operator=`exp_semireal_boundary_transfer_gate.py`; generator=13 righe `boundary_denominator_prescan_full_20260509_1500` ricostruite da `dnd_autoricerca`; denominator=13 righe base BOUNDARY, 8 GUE-like e 5 Poisson-like, beta layers 0.0..1.0, 12 replicates, 24 shuffle baselines; non_possible=dichiarare beta 0.3 coordinata universale o transfer completo quando 2/13 righe cadono; not_tested=nuovi domini, nuovi spettri, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il confine semi-reale non rifiuta il gate; rifiuta la sua simmetria sintetica. Il supporto ordine/null passa, la coordinata blank si frammenta.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + perimetro BOUNDARY base 13/13 transfer + tensione viva \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: gate come supporto ordine/null / gate come coordinata beta universale; punto-zero = riga semi-reale prima che il beta layer venga chiamato blank o classe.\n- **Piano superiore**: grafo della conoscenza e boundary operator. La domanda non e' se GUE o Poisson vincono, ma se il passaggio resta morfismo quando il perimetro diventa fisico/semi-reale.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, graph/perimeter transfer. Entrano per separare copertura del denominatore, supporto d'ordine e coordinata di transizione.\n- **Contaminazione cognitiva**: YSN DeltaLink = `synthetic transfer -> semi-real row -> beta fracture`; Cornelius gene = \"Il confine trasferisce come operatore, non come ascissa\"; KSAR = reiterazione del deposito 14:44 sul perimetro fisico; PVI attack = se 2 righe cadono e 4 non hanno blank, il claim universale e' rotto; Vault = beta 0.3 resta frammento utile solo dove riappare row-aligned.\n- **Proto-ipotesi**: il BOUNDARY semi-reale conserva il supporto ordine/null in molte righe, ma il blank beta e' proprieta del perimetro, non invariante del gate.\n- **Possibile / non-possibile**: possibile = usare il gate come audit row-aligned del supporto d'ordine cross-dominio; non-possibile = promuovere beta 0.3 a coordinata universale del terzo incluso.\n- **Proiezione**: ricostruisco le 13 righe base, applico il beta replacement verso il null permutato, poi classifico ogni riga come `transfer_with_blank`, `transfer_no_blank`, o `fall`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson.\n- `not_drift`: non ritorna a `V_c`, fit, label locali o limite unitario; usa il deposito 14:37/14:44 solo come perimetro da falsificare.\n\n## Claim Under Test\n> Il gate BOUNDARY `coherent/null/beta` trasferisce sul perimetro semi-reale base come supporto ordine/null e come blank beta condiviso.\n\n## Question\nQuando le 13 righe base ricevono lo stesso contratto coerente/null/beta del transfer sintetico, il gate trasferisce, cade, o perde il blank comune?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_semireal_boundary_transfer_gate.py`.\n- Input: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Perimetro atomico: 13 righe base BOUNDARY, `source_domain_type` 8 GUE-like e 5 Poisson-like.\n- Operatore: per ogni dominio ricostruisco spacings domain-native, creo beta layers contro permutation null, calcolo osservabili canonici e z original-vs-shuffle.\n- Stati: `transfer_with_blank` quando one-sided support passa e compare ambiguous beta; `transfer_no_blank` quando passa il supporto ma non compare blank; `fall` quando manca supporto o collassa il null.\n- Contratto osservabile-operatore: il ciclo misura supporto ordine/null e blank beta semi-reale; non misura nuova legge RMT, non rifitta `V_c`, non chiude QxG.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| source rows | 13 |\n| analyzed rows | 13 |\n| errors | 0 |\n| transfer_with_blank | 7 |\n| transfer_no_blank | 4 |\n| fall | 2 |\n\nRighe semi-reali:\n\n| row | source | denominator | n | state | one-sided observables | stable coherent | stable illusory | endpoint distance | ambiguous beta |\n|---|---|---|---:|---|---|---:|---:|---:|---|\n| brownian_motion:cycle_12 | Poisson | complete | 4096 | transfer_with_blank | SR2,L1,L2,triple_var | 4.000 | 0.167 | 3.885 | 0.2,0.3 |\n| cellular_automata:cycle_8 | GUE | contaminated | 108 | fall | [] | 0.000 | 0.500 | 0.000 | [] |\n| coupled_oscillators:cycle_10 | Poisson | contaminated | 2002 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.250 | 4.336 | 0.3 |\n| ising_2d:cycle_1 | GUE | contaminated | 699 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.309 | 0.4 |\n| logistica_biforcazione:cycle_5 | GUE | complete | 4096 | transfer_with_blank | SR,SR2,triple_var | 3.583 | 0.167 | 3.258 | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 4096 | transfer_with_blank | SR,SR2 | 2.000 | 0.083 | 2.769 | 0.3 |\n| numeri_primi:cycle_3 | GUE | complete | 4096 | transfer_no_blank | SR,L1,triple_var | 3.000 | 0.167 | 3.243 | [] |\n| pendolo_doppio:cycle_2 | Poisson | complete | 4096 | fall | SR,SR2,L1 | 5.000 | 0.833 | 3.388 | 0.3 |\n| percolation:cycle_9 | Poisson | complete | 190 | transfer_no_blank | L1 | 0.833 | 0.333 | 1.705 | [] |\n| random_matrix:cycle_7 | GUE | complete | 199 | transfer_no_blank | L2 | 1.000 | 0.250 | 1.854 | [] |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 499 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.364 | 0.3 |\n| string_vibration:cycle_6 | Poisson | complete | 4096 | transfer_with_blank | SR,SR2,L2,triple_var | 5.000 | 0.500 | 3.845 | 0.3 |\n| zeta_zeros:cycle_4 | GUE | contaminated | 199 | transfer_no_blank | SR,L2 | 1.917 | 0.250 | 2.449 | [] |\n\n## Key Findings\n1. **Verificato: il supporto ordine/null trasferisce in 11/13 righe.** Le righe `transfer_with_blank` e `transfer_no_blank` hanno osservabili one-sided e polo illusorio soppresso nel perimetro dichiarato.\n2. **Verificato: il blank beta non e' universale.** Solo 7/13 righe hanno ambiguous beta; 4/13 trasferiscono senza blank e 2/13 cadono.\n3. **Verificato: beta 0.3 non e' coordinata comune.** Compare in 5 righe come unico blank o parte del blank, ma `ising_2d` mostra 0.4, `brownian_motion` mostra 0.2/0.3, `logistica_biforcazione` mostra 0.1-0.9, e quattro transfer non mostrano blank.\n4. **Verificato: i due fall sono diversi.** `cellular_automata` cade per assenza di osservabili one-sided; `pendolo_doppio` cade per residuo illusorio alto (0.833) pur avendo one-sided observables.\n5. **Inferito: il nodo regressivo e' la qualita del perimetro, non il null.** Il null e' disponibile su 13/13; cio' che varia e' come il dominio riceve beta.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOsk.\n- **Contaminazione cognitiva**: KSAR applicato come reiterazione minima del recovery kernel: run diagnostico, deposito, manifest. CE-none: il campo non espone una voce CE concreta per questo recovery cycle; inventarne una sarebbe rumore.\n- **Proto-ipotesi**: un campo senza hit stale attivi non basta; il safe entry diventa operativo solo se la selezione sorgente non passa da mtime/newest-row.\n- **Proiezione**: misuro stale hits, coherence mode B, safe preview e rebuild risk; il punto che sopravvive e' la differenza tra overlay pronto e ricostruzione ingenua bloccata.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: il campo vivo richiede recovery / clean-field handoff e il report 16:23 ha lasciato come consecutio la pulizia delle superfici di autorita prima dei cicli scientifici normali.\n- `not_drift`: non riapre prime/mod6, `V_c`, fit, grammatica Sturmian, Anderson 3D o promozione pubblica; usa i rami post-1330 solo come blocked/vault context.\n\n## Claim Under Test\n\n> Il Lab puo entrare in modo supervisionato se l'autorita viene scelta da overlay/manifest esplicito; non puo essere ricostruito con selector automatici basati su mtime o ultima riga valutatore.\n\n## Question\n\nQuale parte del clean-field e' pronta, e quale selector rende ancora non-possibile il ritorno a un ciclo normale?\n\n## Ritorno fisico\n\nnon_applicabile. Il ciclo parte da un attrito operativo di recovery, non da una tensione fisica. La forma `fisico A -> matematica -> fisico B` resta chiusa come form factor strategico e non viene usata come ponte attivo.\n\n## Experiment Design\n\n- Eseguito `python tools/dnd_scenario.py --best`: il tool punta ancora a `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, ma la direzione e' stale rispetto al closeout; non viene usata come sorgente.\n- Eseguito `python tools/stale_field_source_map.py`: ricerca riferimenti bloccati nelle superfici live.\n- Eseguito `python tools/field_rebuild_risk_map.py`: verifica se una rebuild ingenua userebbe selector contaminati.\n- Eseguito `python tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md`: verifica coerenza read-only dell'entry supervisionata.\n- Eseguito `python tools/build_safe_agent_field_preview.py --output tools/data/preflight/safe_agent_field_B_20260515_1647.md`: genera preview, senza renderla autorita runtime.\n- Eseguito `python tools/lab_preflight_agent.py --cycle 20260515_1623 --json`: classifica il precedente recovery report rispetto ai gate.\n- Nessun overwrite di `agent_field_live.md`, nessuna modifica al seme, nessun restart cron, nessun public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| active stale hits | 0 | `stale_field_source_map_20260515_164757` |\n| source-map decision | `NO_ACTIVE_STALE_HITS` | source map |\n| field coherence mode B | `PASS_WITH_WARNINGS` | `field_coherence_B_20260515_164807` |\n| field coherence failures | 0 | mode B |\n| field coherence warnings | 1 | `lab_health_run_timestamp_not_allowed:20260515_1623` |\n| safe preview decision | `SAFE_FIELD_PREVIEW_READY` | `safe_agent_field_B_20260515_164807` |\n| active blocked refs in preview | 0 | safe preview |\n| naive rebuild decision | `NAIVE_REBUILD_BLOCKED` | `field_rebuild_risk_map_20260515_164757` |\n| rebuild blockers | 1 | recent reports by mtime include blocked cycles |\n| 1623 preflight classification | `DRIFT_STOP` | `preflight_20260515_1623` |\n| 1647 preflight classification | `REVIEW_REQUIRED` | `preflight_20260515_1647` |\n\n## Key Findings\n\n1. **Verificato**: le superfici live scansionate non contengono piu hit attivi per `20260514_1640`, `20260514_1701`, `20260514_1850`.\n2. **Verificato**: l'entry mode B passa con warning; la preview supervisionata e' pronta e contiene `active_blocked_refs=[]`.\n3. **Verificato**: `lab_health` resta warning perche punta a `20260515_1623`, non incluso negli allowed residues.\n4. **Verificato**: la rebuild ingenua e' bloccata: i report recenti ordinati per mtime includono ancora cicli bloccati, quindi newest-file non puo selezionare autorita.\n5. **Verificato**: il report 16:23 viene classificato `DRIFT_STOP`; puo restare deposito recovery, non direzione scientifica viva.\n6. **Verificato**: questo report 16:47 viene classificato `REVIEW_REQUIRED`; non e' stop, ma richiede review prima del prossimo ciclo.\n7. **Inferito**: il prossimo passo non e' un altro esperimento fisico; e' usare un manifest/overlay esplicito o riparare il selector che confonde mtime con autorita.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl campo e' entrabile in modo supervisionato: source map pulita, coherence mode B senza failure, safe preview pronta. Il ciclo normale resta non-possibile se usa selector ingenui. La condizione regressiva mancante non e' nel risultato scientifico, ma nel nodo di selezione sorgente: newest report / newest evolution / last evaluator row / `lab_health` non possono decidere la direzione.\n\n## Bicono della scoperta\n\n- **Due radici**: overlay supervisionato pronto / rebuild automatica contaminabile.\n- **Singolare**: source selection prima del contenuto scientifico.\n- **Invariante di passaggio**: i rami bloccati possono essere nominati solo come blocked, vault, context o diagnostic.\n- **Campo di possibilita**: possibile = supervised one-shot con overlay/manifest esplicito; non-possibile = normal `lab_agent.sh` o rebuild by-mtime.\n\n## Consecutio\n\nCreare o usare un selector esplicito che legga `source_selection_manifest_20260515_1647.json` o equivalente: allowed floor `20260509_1556`, residue `20260514_1330`, overlay `tools/data/agent_field_entry_supervised.md`, preview `tools/data/preflight/safe_agent_field_B_20260515_1647.md`. Fino a quel passaggio, non selezionare direzione da mtime, evolution newest, last evaluator row o `lab_health`.\n\n## Ricadute pratiche\n\nssp_value: yes. Il risultato e' un manifest operativo per riaprire il Lab in modo supervisionato senza riattivare rami chiusi.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_164757.json`\n- `docs/operations/stale_field_source_map_20260515_164757.md`\n- `tools/data/preflight/field_rebuild_risk_map_20260515_164757.json`\n- `docs/operations/field_rebuild_risk_map_20260515_164757.md`\n- `tools/data/preflight/field_coherence_B_20260515_164807.json`\n- `docs/operations/field_coherence_result_B_20260515_164807.md`\n- `tools/data/preflight/safe_agent_field_B_20260515_1647.md`\n- `tools/data/preflight/safe_agent_field_B_20260515_164807.json`\n- `docs/operations/safe_agent_field_B_20260515_164807.md`\n- `tools/data/preflight/preflight_20260515_1623.json`\n- `tools/data/preflight/preflight_20260515_1647.json`\n- `tools/data/preflight/source_selection_manifest_20260515_1647.json`\n- `tools/data/reports/agent_20260515_1647.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1331: "content_full": "# Agent Report - Clean-Field Authority Preflight\n**Date**: 2026-05-15 16:23 \n**Piano**: 117 \n**Tension explored**: recovery / clean-field handoff \n**verdict**: CONSTRAINT - normal Lab cycle remains blocked; the safe overlay is ready, but the live generated field still carries blocked branch references and `lab_health` remains diagnostic-stale on 1850. \nobservables_used: [active_stale_hits, blocked_refs_by_surface, clean_overlay_ready, runtime_pointer_alignment, preflight_classification, cleanup_target_count] \n**observable_contract**: claim=the Lab can reopen only when authority is redistributed from stale runtime surfaces to the clean supervised overlay; observable=active stale hits + field coherence failures + preflight classifications; operator=`tools/stale_field_source_map.py`, `tools/field_coherence_preflight.py`, `tools/lab_preflight_agent.py`; generator=mode B backtrack-floor preflight; denominator=generated field surfaces named by the preflight tools; non_possible=normal `lab_agent.sh` cycle while `agent_field_live.md` and `lab_health.json` still expose blocked branch authority; not_tested=scientific claim, physics bridge, public sync, graph promotion, seme mutation.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + recovery/clean-field handoff + graph/source selection guard.\n- **Dipolo / punto-zero**: runtime authority / supervised overlay. Punto-zero: source-selection manifest, where a branch can be named without becoming direction.\n- **Piano superiore**: grafo della conoscenza operativo; the experiment measures authority flow between files, not a physical observable.\n- **Operatori laterali scelti**: `boundary operator` to distinguish active direction from vault context; `graph cut` to separate live pointers from blocked branches; `proof trace vs output trace` to compare tool classifications against generated field state.\n- **Contaminazione cognitiva**: KSAR used as reiteration of the recovery kernel: run minimal diagnostics, observe deposit, then write a cleanup manifest. CE-none: the live field names adapter material but exposes no concrete CE archive entry for this recovery cycle; no enzyme is invented.\n- **Proto-ipotesi**: a clean-field handoff is not proven by aligned pointers alone; it holds only if generated field surfaces no longer grant active authority to blocked branches.\n- **Possibile / non-possibile**: possible = supervised one-shot can use the clean overlay; non-possible = normal Lab cycle while live generated field remains dirty.\n- **Proiezione**: compare stale-reference scan, field-coherence preflight, and cycle preflight classifications, then collapse them into a cleanup manifest.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: the field says current work is recovery / clean-field handoff, with prime/mod6 and post-1330 branches closed or blocked as direction.\n- `not_drift`: the cycle does not reopen prime/mod6, `V_c`, Sturmian grammar, Anderson 3D, graph promotion or public sync; it measures source authority only.\n\n## Claim Under Test\n\n> The Lab has a safe supervised entry path, but normal runtime authority is still blocked until stale generated surfaces are neutralized.\n\n## Question\n\nWhich surfaces still carry blocked branch authority after the safe-field recovery work, and what exact cleanup manifest follows?\n\n## Ritorno fisico\n\nnon_applicabile. This cycle starts from an operational recovery tension, not a physical/scientific tension. The surviving `Physics A -> Mathematics -> Physics B` pattern is preserved only as strategic form factor from the closeout, not used as an active bridge.\n\n## Experiment Design\n\n- Run `python3 tools/dnd_scenario.py --best` as required context. It still ranks `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, but the field marks that direction stale/pre-closeout; it is not used as active direction.\n- Run `python3 tools/stale_field_source_map.py` to classify stale branch references.\n- Run `python3 tools/field_coherence_preflight.py --mode B --overlay /opt/MM_D-ND/tools/data/agent_field_entry_supervised.md` to test mode-B readiness.\n- Run `python3 tools/lab_preflight_agent.py --cycle 20260514_1850 --json` and `--cycle 20260514_1330 --json` to classify nearby branch artifacts.\n- Write a review-only cleanup manifest under `tools/data/preflight/`.\n- No seme update, no live field overwrite, no cron/watchdog restart, no public sync.\n\n## Results\n\n| observable | result | source |\n|---|---:|---|\n| stale source map decision | `NEUTRALIZATION_REQUIRED` | `stale_field_source_map_20260515_162416` |\n| active stale hits by source map | 1 | `lab_health.json` runtime health stale |\n| historical blocked refs in agent field | 3 | G2 anti-recycle context only |\n| field coherence mode B decision | `FAIL` | `field_coherence_B_20260515_162422` |\n| runtime pointers aligned to 1330 | yes | latest report, lab_data, ciclo_memoria, graph latest |\n| clean overlay ready | true | `tools/data/agent_field_entry_supervised.md` |\n| field coherence failure | 1 | `agent_field_live_contains_blocked_refs:20260514_1640,20260514_1701,20260514_1850` |\n| field coherence warnings | 3 | `lab_health` 1850 + clean overlay not live authority |\n| 1850 preflight classification | `ARTIFACT_USEFUL_NOT_PUBLISHABLE` | stable anchor 1640 |\n| 1330 preflight classification | `REVIEW_REQUIRED` | stable anchor 20260513_0330 |\n| cleanup manifest targets | 3 | `agent_field_live.md`, `lab_health.json`, `valutatore_log.jsonl` |\n\nTelemetry: the first `field_coherence_preflight.py` invocation failed because a relative overlay path was not accepted by `Path.relative_to`. Re-running with the absolute overlay path completed and produced the reported FAIL. The tool was not patched in this cycle.\n\n## Key Findings\n\n1. **Verified**: runtime pointers are mostly aligned to `20260514_1330`, and cron/watchdog are inactive.\n2. **Verified**: the clean mode-B overlay is ready and names blocked branches as blocked.\n3. **Verified**: normal runtime remains blocked because `agent_field_live.md` still contains blocked refs under the stricter field-coherence gate, and `lab_health.json` still points to `20260514_1850`.\n4. **Verified**: stale-reference classification and field-coherence classification diverge by design. The source map treats G2 refs as historical guard context; field coherence still blocks because the live generated field is runtime authority.\n5. **Inferito**: the next useful move is cleanup of authority surfaces, not another Lab experiment or repair of post-1330 branch reports.\n\n## Verdict\n\n**CONSTRAINT**.\n\nThe recovery path is open only for supervised overlay use. Normal Lab cycle remains blocked until the live generated field is rebuilt/cleaned or a supervised command explicitly bypasses it with the clean overlay. The branch material survives as vault/context/tool-contract material, not as next direction.\n\n## Bicono della scoperta\n\n- **Due radici**: aligned runtime pointers / dirty generated field.\n- **Singolare**: authority selection before a cycle reads any scientific tension.\n- **Invariante di passaggio**: blocked branches may be named only as blocked, vault, diagnostic or historical context.\n- **Campo di possibilita**: possible = reviewed cleanup plan and supervised one-shot entry; non-possible = normal autonomous cycle from dirty `agent_field_live.md`.\n\n## Consecutio\n\nPrepare or execute a reviewed cleanup of the three authority surfaces named in the manifest:\n\n1. `tools/data/agent_field_live.md` - rebuild/inject clean mode-B field or keep blocked refs only as blocked/vault context.\n2. `tools/data/lab_health.json` - mark diagnostic-stale or reset through reviewed cleanup path.\n3. `tools/data/valutatore_log.jsonl` - keep post-1330 evaluator rows quarantined/historical before any selector can read newest rows as direction.\n\nDo not run `lab_agent.sh` until field coherence mode B passes or a supervised command explicitly uses `tools/data/agent_field_entry_supervised.md`.\n\n## Ricadute pratiche\n\nssp_value: yes. The output is a cleanup manifest for the Lab operator/runtime, not a scientific discovery.\n\n## Files\n\n- `tools/data/preflight/stale_field_source_map_20260515_162416.json`\n- `docs/operations/stale_field_source_map_20260515_162416.md`\n- `tools/data/preflight/field_coherence_B_20260515_162422.json`\n- `docs/operations/field_coherence_result_B_20260515_162422.md`\n- `tools/data/preflight/preflight_20260514_1850.json`\n- `tools/data/preflight/preflight_20260514_1330.json`\n- `tools/data/preflight/cleanup_manifest_20260515_1623.json`\n- `tools/data/reports/agent_20260515_1623.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1382: "content_full": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, label-swap row-local e trace JSONL completa.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa la robustezza del delta z row-local prime-minus-mod6 su piu ampiezze finestra, mantenendo provider, offset, label-swap audit e trace JSONL.\n- `not_drift`: non torna a `V_c`, GUE/Poisson, fit o vecchi depositi; stressa solo il residuo nominato dal valutatore dopo sottrazione mod6.\n\n## Claim Under Test\n\n> Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime non genera `SR` come presenza; lascia un vettore di intensita row-local su `SR,L1,triple_var`.\n\n## Question\n\nIl delta z prime-minus-mod6 resta significativo quando cambia l'ampiezza della finestra, oppure era un artefatto del contratto 1024-gap del cycle 03:30?\n\n## Experiment Design\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`.\n- Run: `window_gaps=512,1024,2048`; seeds `202605141330,202605141331,202605141332`.\n- Rows per run: 8 prime windows = 2 provider x 4 offset.\n- Antagonisti: `mod6_index_aligned` e `mod6_span_matched`, paired per `row_id`.\n- Null: label-swap row-local, 4096 trials per pair audit.\n- Trace: JSONL scritto per tutti e tre i run.\n\n## Results\n\n| window | pair | prime SR | prime focus | Jaccard | SR delta | p(SR delta) | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | script verdict |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| 512 | mod6_index | 5/8 | 2/8 | 0.250 | -0.250 | 0.6195 | 20.494 | 0.0095 | 21.866 | 0.0095 | 8.480 | 0.0095 | SR_NOT_DECISIVE |\n| 512 | mod6_span | 5/8 | 2/8 | 0.377 | -0.250 | 0.5038 | 13.993 | 0.0085 | 10.099 | 0.0085 | 7.505 | 0.0085 | SR_NOT_DECISIVE |\n| 1024 | mod6_index | 8/8 | 5/8 | 0.481 | 0.000 | 1.0000 | 29.480 | 0.0061 | 31.105 | 0.0061 | 11.745 | 0.0061 | SR_PREBOUNDARY |\n| 1024 | mod6_span | 8/8 | 5/8 | 0.377 | 0.250 | 0.4921 | 19.921 | 0.0076 | 15.302 | 0.0076 | 13.142 | 0.0076 | SR_PREBOUNDARY |\n| 2048 | mod6_index | 8/8 | 7/8 | 0.556 | 0.125 | 1.0000 | 41.634 | 0.0088 | 43.574 | 0.0088 | 17.173 | 0.0088 | SR_PRIME_SPECIFIC |\n| 2048 | mod6_span | 8/8 | 7/8 | 0.537 | 0.250 | 0.5040 | 28.624 | 0.0068 | 23.917 | 0.0068 | 17.491 | 0.0068 | SR_PRIME_SPECIFIC |\n\n## Key Findings\n\n1. **Verificato**: `has_SR` non replica come osservabile del residuo. A 512 gap i primi hanno `SR=5/8`; a 1024 e 2048 hanno `SR=8/8`. Il delta binario ha p non significativo o 1.0000.\n2. **Verificato**: il delta z `SR` replica in tutti i sei pair audit: `p=0.0061..0.0095`.\n3. **Verificato**: il delta z non e' solo `SR`. `L1` replica con `p=0.0061..0.0095`; `triple_var` replica con `p=0.0061..0.0095`.\n4. **Verificato**: il verdict nominale dello script oscilla con `window_gaps` (`not_decisive`, `preboundary`, `prime_specific`). Quell'oscillazione e' informazione sullo script: il verdict e' ancora centrato su commonality/binario, non sul vettore di intensita.\n5. **Inferito dal perimetro**: l'osservabile dedicato da formalizzare e' `prime_minus_mod6_z_vector(SR,L1,triple_var)`, con `has_SR` relegato ad audit negativo.\n\n## Verdict\n\n**CONSTRAINT / VECTOR RESIDUE**.\n\nNel perimetro 512-2048 gap, due provider, quattro offset, due antagonisti mod6, il residuo prime-specific sopravvive come delta z vettoriale row-local. Non sopravvive come presenza binaria di `SR`, ne come verdict nominale dello script.\n\n## Bicono\n\n- **Due radici**: pre-bordo mod6 / selezione prime.\n- **Singolare**: riga provider-offset paired.\n- **Invariante di passaggio**: delta z positivo su `SR,L1,triple_var`.\n- **Campo di possibilita**: osservabile dedicato `prime_minus_mod6_z_vector`.\n- **Campo non-possibile**: `SR` binario come firma prime-specific dopo mod6.\n\n## Consecutio\n\nIl prossimo ciclo deve correggere il nodo regressivo dello script: il `verdict()` non deve decidere dalla commonality di `SR`, ma da un contratto vettoriale dichiarato (`SR,L1,triple_var`, segno del delta, p label-swap, denominatore row-local). Dopo questa correzione, scalare oltre 2048 gap e separare `mod6_index_aligned` da `mod6_span_matched` come antagonisti con ruoli diversi.\n\n## Ricadute pratiche\n\nssp_value: yes. Le tre trace JSONL rendono auditabile il residuo senza riaprire i JSON aggregati; il prossimo passaggio pratico e' aggiornare lo script per emettere un verdict vettoriale, non binario.\n\n## Files\n\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl`\n- `tools/data/reports/agent_20260514_1330.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1433: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary Trace\n**Date**: 2026-05-14 03:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo `6k +/- 1`; la selezione prime resta come gradiente row-local, non come presenza binaria dell'osservabile \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n\n## Prima impressione\n\nIl residuo prime-specific non nasce dalla comparsa di `SR`. `SR` e gia nel lattice candidato `6k +/- 1`; i primi non lo accendono, lo attenuano e lo deformano rispetto al pre-bordo.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + direzione viva `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: pre-bordo `6k +/- 1` / selezione prime. Punto-zero: stessa riga provider/offset, dove il denominatore e identico e la primalita e l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: il bordo non e presenza/assenza di un segnale, ma passaggio di intensita tra un lattice candidato e il sottoinsieme selezionato.\n- **Proto-ipotesi**: la firma utile e `prime_minus_mod6_z_delta` su `SR,L1,triple_var`; `has_SR` e una coordinata troppo larga per nominare primalita.\n- **Possibile / non-possibile**: possibile = isolare residuo di selezione con delta z row-local; non-possibile = claim `SR prime-specific` se `SR` resta comune nel contro-perimetro mod6.\n- **Proiezione**: 2 provider x 4 offset x 1024 gap; antagonisti `mod6_index_aligned` e `mod6_span_matched`; label-swap row-local; trace JSONL completa.\n\n## Contaminazione cognitiva\n\n- **CE-0001 / KSAR**: usato per reiterare il kernel emerso nel cycle precedente: non allargare il frame, ma far girare lo stesso nodo regressivo con tracciamento piu fine.\n- **PVI**: presupposto attaccato = \"`SR` persistente implica primalita\"; contro-presupposto = \"`SR` misura il pre-bordo aritmetico\".\n- **Vault**: `trace_jsonl_event` conserva casi, class summary, paired rows, audit e verdict come deposito riusabile senza trasformare una seed in invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: misura l'intensita row-local di `SR/L1/triple_var` su prime vs `6k +/- 1`, con denominatore provider/offset condiviso e label-swap audit.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista e il pre-bordo mod6 indicato dalla consecutio.\n\n## Observable Contract\n\n- **claim**: `SR` e prime-specific solo se resta piu comune o piu forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local.\n- **observable**: membership di `SR`, firma `[SR,L1,triple_var]`, delta z paired prime-minus-mod6, p-value label-swap.\n- **operator**: `tools/exp_prime_vs_mod6_sr_boundary.py` con `--trace-jsonl`.\n- **generator**: prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`; mod6 da `index_aligned` e `span_matched`.\n- **denominator**: 8 finestre prime paired con 8 `mod6_index_aligned` e 8 `mod6_span_matched`.\n- **non_possible**: prime-specific `SR` se `SR` e comune o pari nel contro-perimetro mod6, oppure se il label-swap assorbe i delta.\n- **not_tested**: beta atlas globale, `V_c`, `gap_ratio`, origine analitica del trasferimento mod6.\n\n## Results\n\nMain run, seed `202605140330`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 3 | SR | beta_absent_blank=5, beta_chart_recovered=3 |\n| mod6_index_aligned | 8 | 8 | 8 | L1,SR,triple_var | beta_chart_recovered=8 |\n| mod6_span_matched | 8 | 7 | 7 | [] | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nSeed check, seed `202605140331`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 6 | SR | beta_chart_recovered=5, beta_absent_blank=3 |\n| mod6_index_aligned | 8 | 8 | 6 | L1,SR | beta_chart_recovered=7, beta_absent_blank=1 |\n| mod6_span_matched | 8 | 8 | 7 | L1,SR | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605140330 | 0.433 | 0.000 | 1.0000 | 28.246 | 0.0081 |\n| prime - mod6_span | 202605140330 | 0.423 | 0.125 | 1.0000 | 20.793 | 0.0093 |\n| prime - mod6_index | 202605140331 | 0.517 | 0.000 | 1.0000 | 28.547 | 0.0083 |\n| prime - mod6_span | 202605140331 | 0.560 | 0.000 | 1.0000 | 18.844 | 0.0090 |\n\n## Key Findings\n\n1. **Verificato**: `SR` non e prime-specific come presenza binaria. In entrambi i run, `prime=8/8` e `mod6_index_aligned=8/8`; nel seed check anche `mod6_span_matched=8/8`.\n2. **Verificato**: il count `SR` non discrimina. `p(SR delta)=1.0000` in tutti i pair audit.\n3. **Verificato**: il delta di intensita discrimina. `p(z_SR delta)=0.0081/0.0083` contro `mod6_index` e `0.0093/0.0090` contro `mod6_span`.\n4. **Verificato**: `mod6_index_aligned` e piu rigido dei primi: focus signature main `8/8` contro prime `3/8`, e common obs `L1,SR,triple_var` contro solo `SR`.\n5. **Inferito dal perimetro**: il prossimo osservabile deve essere vettoriale e paired: `prime_minus_mod6_z_delta(SR,L1,triple_var)`, non `has_SR`.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula chiusa nel perimetro: `SR` appartiene al pre-bordo `6k +/- 1`. La primalita non genera `SR`; seleziona dentro un lattice gia rigido e lascia un residuo misurabile come delta z row-local.\n\n## Bicono\n\n- **Due radici**: lattice candidato mod6 / sequenza prime selezionata.\n- **Singolare**: riga provider-offset condivisa.\n- **Invariante di passaggio**: presenza di `SR` attraversa il confine.\n- **Campo di possibilita**: misurare selezione come delta z.\n- **Campo non-possibile**: usare `SR` binario come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve formalizzare `prime_minus_mod6_z_delta` come osservabile dedicato e scalarlo su finestre piu larghe. `mod6_index_aligned` resta antagonista primario; `mod6_span_matched` resta controllo fragile perche conserva span numerico ma introduce downsample.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script ora produce `--trace-jsonl`, utile per audit, replay e falsifier row-local senza riaprire il JSON aggregato.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json`\n- Trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json`\n- Seed trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl`\n- Report: `tools/data/reports/agent_20260514_0330.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1466: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary\n**Date**: 2026-05-13 03:30 \n**Piano**: 116 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p] \n**observable_contract**: claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.\n\n## Prima impressione\n\nIl bordo non cade tra primi e non-primi: cade tra selezione prime e lattice candidato `6k +/- 1`. `SR` vede il lattice prima della primalita; il gate non deve chiedere \"SR nei primi?\", ma \"cosa resta di SR dopo aver sottratto il pre-bordo?\".\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: primi selezionati / candidati mod6 non selezionati. Punto-zero: la riga ordinata locale `6k +/- 1`, dove `SR` puo nominare pre-bordo aritmetico senza nominare primalita.\n- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune.\n- **Proto-ipotesi**: `SR` non e firma atomica dei primi; e una sezione del pre-bordo aritmetico. La selezione prime si manifesta come differenza di intensita z rispetto al lattice candidato.\n- **Possibile / non-possibile**: possibile = isolare una coordinata di selezione prime come gradiente dentro mod6; non-possibile = usare presenza binaria di `SR` come claim prime-specific.\n- **Proiezione**: stesso gate ordine/null, finestre 1024, offset 0/512/1024/1536, due provider prime, due antagonisti mod6, audit label-swap dentro ogni row_id.\n\n### Contaminazione cognitiva\n\n- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`.\n- **PVI attack**: il presupposto attaccato e \"SR persistente implica primalita\". Il contro-presupposto e \"SR misura la griglia 6k +/- 1 prima della selezione prime\".\n- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`.\n\n## Claim Under Test\n\n> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`.\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap.\n- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset.\n- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti.\n- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`.\n- Main seed: `202605130330`; seed check: `202605130331`.\n- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset.\n\n## Results\n\nMain run:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 |\n| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 |\n\nSeed check:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 |\n| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 |\n| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 |\n| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 |\n| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 |\n\n## Key Findings\n\n1. **Verificato**: la presenza binaria di `SR` non e prime-specific. Main: prime `8/8`, mod6_index `8/8`; seed check: prime `6/8`, mod6_index `8/8`, mod6_span `8/8`.\n2. **Verificato**: il label-swap row-local assorbe `SR` come count. `p(SR delta)` vale 1.0000 / 0.5055 per mod6_index e 0.2502 / 0.5021 per mod6_span.\n3. **Verificato**: il label-swap non assorbe il delta z di `SR`. `p(z_SR delta)` resta 0.0071-0.0103 in entrambe le seed e per entrambi gli antagonisti.\n4. **Verificato**: mod6 e piu intenso, non piu debole. `mean z SR` e circa -34 / -26 in mod6 contro -5 nei primi. Il residuo prime non e \"piu SR\"; e selezione attenuata dentro un pre-bordo piu rigido.\n5. **Inferito dal perimetro**: la coordinata utile non e `has_SR`; e `z_delta_SR` paired, insieme a `z_delta_L1` e `z_delta_triple_var`, come misura di quanto la selezione prime rompe il lattice candidato.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula non valida:\n\n`SR` come firma prime-specific atomica.\n\nFormula valida nel perimetro:\n\n`SR` appartiene al pre-bordo aritmetico `6k +/- 1`; la selezione prime resta come gradiente di intensita row-local rispetto a mod6. Il boundary operativo e quindi `mod6_preboundary -> prime_selection`, non `prime -> generic_nonprime`.\n\n## Bicono della scoperta\n\n- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata.\n- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni.\n- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato.\n- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`.\n\n## Lenti counter-pole applicate\n\n- **L1/L4**: nessun \"solo\", \"mai\", \"zero\" usato per `SR`: i count mostrano controesempi.\n- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific.\n- **L5**: nessun tag NEW; il risultato resta compatibile con bias noti dei gap dei primi mod q. Il report misura un gate del Lab, non rivendica una scoperta aritmetica classica.\n- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva.\n- **L7**: non-possibile dichiarato nel contratto e nel bicono.\n\n## Consecutio\n\nIl prossimo taglio non deve chiedere se `SR` c'e. Deve costruire un osservabile di selezione: `prime_minus_mod6_z_delta` su `SR,L1,triple_var`, con span-matched conservato come controllo fragile e index-aligned come antagonista primario. Se il delta resta replicato su piu finestre e scale, il claim diventa: la primalita attenua/rompe la rigidita del pre-bordo mod6 invece di generare `SR` da sola.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260513_0330.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1511: "content_full": "# Agent Report - Prime SR Persistent Boundary\n**Date**: 2026-05-12 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`.\n- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi.\n- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro.\n- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' \"SR persistente nei primi implica prime-specific\".\n- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico.\n- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime.\n- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime.\n\n## Claim Under Test\n\n> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena.\n\n## Question\n\nQuando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime?\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows.\n- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`.\n- Seed check: stesso perimetro, seed `202605120331`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common.\n\n## Results\n\nMain run:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 |\n| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 |\n| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 |\n| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 |\n| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 |\n\nSeed check:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 |\n| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 |\n| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 |\n\nPrime case details, main:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] |\n| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] |\n| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n\n## Key Findings\n\n1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota.\n2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall.\n3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`.\n4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato.\n5. **Inferito dal perimetro dichiarato**: la parte robusta non e' \"SR e' prime-specific\"; e' \"SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche\".\n\n## Verdict\n\n**CONSTRAINT / FALSIFIED scoped**.\n\nFormula non valida:\n\n`prime_SR_persistent_boundary` come firma atomica prime-specific.\n\nFormula valida nel perimetro:\n\n`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico.\n\n## Bicono della scoperta\n\n- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato.\n- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato.\n- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6.\n- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve spostare il nodo regressivo: non \"prime vs controlli generici\", ma \"primi vs candidati mod6 row-aligned\". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico.\n\n## Files\n\n- Script: `tools/exp_prime_sr_persistent_boundary.py`\n- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json`\n- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260512_0330.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1562: "content_full": "# Agent Report - Prime Persistent Blank Gate\n**Date**: 2026-05-11 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_persistent_blank` si scinde: SR persiste, il blank beta no \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY residuo `numeri_primi:cycle_3`.\n- **Dipolo / punto-zero**: persistenza del supporto / persistenza del blank. Punto-zero: la stessa finestra row-local da 1024 gap dove `SR` puo' restare mentre la coordinata beta riappare.\n- **Piano superiore**: topologia assiomatica del bordo: non una classe statistica unica, ma una sezione che conserva un osservabile e perde una coordinata.\n- **Proto-ipotesi**: il residuo prime e' strutturale solo se il blank beta-absent sopravvive a provider e offset; se sopravvive solo `SR`, la struttura non e' `blank`, e' `SR-supported boundary`.\n- **Possibile / non-possibile**: possibile = isolare un residuo prime provider-neutral; non-possibile = chiamarlo `prime_persistent_blank` quando beta ricompare in finestre locali.\n- **Proiezione**: due provider prime, quattro offset locali, seed shift del gate, controlli random_matrix/logistic.\n\n## Contaminazione cognitiva\n\n- **YSN DeltaLink**: il legame non ovvio e' `SR` come sezione comune mentre la carta beta cambia; non e' prova, e' la domanda proiettata.\n- **Cornelius gene**: `DNA_Simbolico`: \"La persistenza del bordo non coincide con la persistenza del blank.\" Operatori: separa supporto, separa coordinata, confronta controlli.\n- **KSAR step / CE-0001**: reiterazione del kernel 20260510 senza ridisegnare l'atlante: stesso gate, nuovo provider/offset/seed.\n- **PVI attack**: il presupposto nascosto era che `blank_windows=4/4` su una partizione bastasse per nominare una specie persistente.\n- **Vault**: `random_matrix_chart_sensitive_blank` resta aperto come coordinata locale beta `[0.4]`, non lavorata in questo ciclo.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente `prime_persistent_blank` su `numeri_primi:cycle_3` con provider-neutral row-local windows e controlli cross-dominio.\n- `not_drift`: non usa `V_c`, non usa fit, non riapre griglia beta globale, non salva la classe caduta `medium/strong beta-absent`.\n\n## Claim Under Test\n\n> `numeri_primi:cycle_3` e' un `prime_persistent_blank` se resta beta-absent in tutte le finestre provider-neutral e conserva `SR` come osservabile comune, mentre i controlli non condividono la stessa firma.\n\n## Experiment Design\n\n- Perimetro prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: 4 GUE random matrix blocks + 4 logistic return interval rows.\n- Parametri: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605110330`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Criterio di isolamento: prime `blank_rate=1.0`, prime `sr_rate=1.0`, common prime obs = `[SR]`, controlli senza full blank-SR collision.\n\n## Results\n\n| family | blank | beta recovered | support falls | sr rate | common obs | endpoint mean | stable coherent mean |\n|---|---:|---:|---:|---:|---|---:|---:|\n| prime | 3/8 | 4/8 | 1/8 | 1.000 | SR | 2.715 | 2.656 |\n| controls | 1/8 | 3/8 | 4/8 | 0.250 | L2 | 1.428 | 1.641 |\n\n| comparison | value |\n|---|---:|\n| prime_control_common_obs_jaccard | 0.000 |\n| verdict | PRIME_PERSISTENCE_NOT_REPLICATED |\n\nPrime case details:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_chart_recovered | SR | 0.2, 0.4 |\n| dnd_autoricerca offset 512 | support_falls | SR | 0.2 |\n| dnd_autoricerca offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n| direct_sieve offset 1024 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1536 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n\n## Key Findings\n\n1. **Verificato**: `SR` resta in 8/8 finestre prime. La firma comune prime e' `[SR]`.\n2. **Verificato**: il blank beta-absent non resta in 8/8 finestre prime. Solo 3/8 casi sono `beta_absent_blank`; 4/8 recuperano beta e 1/8 cade per supporto.\n3. **Verificato**: i controlli non collidono con la firma prime. I controlli hanno common obs `[L2]`, `sr_rate=0.250`, `blank_rate=0.125`, Jaccard comune prime/controlli = 0.000.\n4. **Inferito dal perimetro dichiarato**: il residuo dei primi non e' `prime_persistent_blank`; e' `prime_SR_persistent_boundary` con coordinata beta chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl nome `prime_persistent_blank` non regge nel perimetro provider-neutral/offset-shift. La parte persistente e' `SR`, non l'assenza di beta.\n\nFormula valida:\n\n`numeri_primi:cycle_3` conserva supporto one-sided `SR` attraverso provider e offset; la coordinata beta resta locale e ricompare in 4/8 finestre.\n\nFormula non valida:\n\n`numeri_primi:cycle_3` e' un blank beta-absent persistente.\n\n## Bicono della scoperta\n\n- **Due radici**: supporto SR persistente / blank beta non persistente.\n- **Singolare**: la finestra row-local dove `SR` resta ma beta riappare.\n- **Invariante di passaggio**: `SR` attraversa provider e offset; `blank` no.\n- **Campo di possibilita'**: possibile = rinominare il residuo come supporto `SR` provider-neutral; non-possibile = promuovere il blank come specie autonoma.\n\n## Consecutio\n\nIl prossimo ciclo deve chiudere la tassonomia residua senza salvare il nome caduto:\n\n- rinominare il residuo prime in `prime_SR_persistent_boundary`;\n- testare se il beta recovery 0.2/0.4 dipende da offset, provider o baseline seed;\n- lavorare `random_matrix_chart_sensitive_blank` solo dopo aver separato coordinate locali da supporto osservabile.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script diventa un audit riusabile per distinguere persistenza di osservabile e persistenza di blank nei residui BOUNDARY senza rigenerare l'atlante globale.\n\n## Files\n\n- Script: `tools/exp_prime_persistent_blank_gate.py`\n- Data: `tools/data/prime_persistent_blank_gate_20260511_0330.json`\n- Report: `tools/data/reports/agent_20260511_0330.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1607: "content_full": "# Agent Report - Boundary Residual Beta-Absent Audit\n**Date**: 2026-05-10 03:30 \n**Piano**: 114 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - il residuo beta-absent non e' una classe unica chiusa \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, window_state, blank_window_rate, full_signature_jaccard, common_window_obs_jaccard] \n**observable_contract**: claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + residui `numeri_primi:cycle_3` / `random_matrix:cycle_7`.\n- **Dipolo / punto-zero**: blank strutturale / blank da atlante. Punto-zero: la stessa misura row-local da 1024 gap che decide se la beta manca per struttura o riappare per scelta di finestra.\n- **Piano superiore**: topologia assiomatica del bordo: la classe globale cade se una carta locale riapre la coordinata beta.\n- **Operatori laterali scelti**: boundary operator, chart locale, filtrazione. Il boundary operator separa supporto e beta; la chart locale testa la finestra senza rifare il mondo; la filtrazione conserva il denominatore row-aligned.\n- **Contaminazione cognitiva**: CE-0019 usato come contratto combo prima della misura; CE-0022 usato come boundary operator + chart locale; KSAR usato come reiterazione sul deposito 15:32-18:39 senza ridisegnare il perimetro.\n- **Proto-ipotesi**: il residuo medio/forte beta-absent e' una classe unica solo se entrambi i target restano blank in tutte le finestre row-local e conservano una firma osservabile compatibile.\n- **Proiezione**: applico il gate canonico ordine/null alle sole due righe residue, poi confronto persistenza del blank e Jaccard delle firme one-sided.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: attacca direttamente i due residui beta-absent medio/forti lasciati dal valutatore dopo `thin_persist_rows=0/13`.\n- `not_drift`: non usa `V_c`, non usa label GUE/Poisson come campo decisionale, non rigenera la griglia beta globale.\n\n## Claim Under Test\n\n> I due residui `numeri_primi:cycle_3` e `random_matrix:cycle_7` formano una classe unica `medium/strong beta-absent` se il blank persiste in tutte le finestre row-local da 1024 gap e le firme one-sided restano compatibili.\n\n## Question\n\nI due blank residui sono lo stesso operatore di confine, due classi distinte, o un artefatto da denominatore/atlante?\n\n## Experiment Design\n\n- Perimetro atomico: `numeri_primi:cycle_3`, `random_matrix:cycle_7`.\n- Scope: full row + 4 finestre consecutive da 1024 gap per ciascun target.\n- Parametri: `n_replicates=12`, `n_beta=11`, `n_baseline=24`, `z_min=2.0`, seed `202605100330`.\n- Null baseline: permutazione marginal-preserving usata dal gate canonico gia' adottato nei report BOUNDARY.\n- Non misurato: `gap_ratio`, `V_c`, nuova griglia beta globale, validita' delle label sorgente GUE/Poisson.\n- Criterio di caduta: una riga recupera beta o perde supporto in una finestra row-local; in quel caso il residuo non e' classe unica chiusa.\n\n## Results\n\n| row | full state | full one-sided | window blanks | beta recovered | support falls | common window obs | mean endpoint | mean stable coherent |\n|---|---:|---|---:|---:|---:|---|---:|---:|\n| numeri_primi:cycle_3 | beta_absent_blank | SR,L1,triple_var | 4/4 | 0 | 0 | SR | 2.761 | 2.563 |\n| random_matrix:cycle_7 | beta_absent_blank | SR,SR2,L1,L2,triple_var | 3/4 | 1 | 0 | L2 | 2.987 | 3.083 |\n\n| comparison | value |\n|---|---:|\n| full_signature_jaccard | 0.600 |\n| common_window_obs_jaccard | 0.000 |\n| random_matrix recovered beta | window_4 -> beta [0.4] |\n\n## Key Findings\n\n1. **Verificato: `numeri_primi:cycle_3` resta beta-absent in 4/4 finestre.** Il supporto non cade; l'osservabile comune di finestra e' `SR`.\n2. **Verificato: `random_matrix:cycle_7` non resta beta-absent in 4/4 finestre.** La quarta finestra recupera beta `[0.4]` con supporto vivo (`SR2,L1,L2,triple_var`).\n3. **Verificato: le firme comuni di finestra divergono.** `common_window_obs_jaccard=0.000`: primi conserva `SR`, random matrix conserva `L2`.\n4. **Inferito dal perimetro dichiarato: il residuo beta-absent non e' una classe unica chiusa.** Una parte e' blank persistente row-local, una parte e' chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nNel perimetro delle due righe residue e finestre da 1024 gap, `medium/strong beta-absent` si scinde:\n\n`numeri_primi:cycle_3` = blank persistente row-local. \n`random_matrix:cycle_7` = blank forte ma chart-sensitive, con beta `[0.4]` recuperata in 1/4 finestre.\n\nFormula valida:\n\n`beta_absent_residue` non matura come classe unica; diventa un audit a due stati: `prime_persistent_blank` / `random_matrix_chart_sensitive_blank`.\n\n## Bicono della scoperta\n\n- **Due radici**: blank persistente / blank chart-sensitive.\n- **Singolare**: supporto ordine/null vivo senza coordinata beta globale.\n- **Invariante di passaggio**: il supporto non cade in nessuna delle 8 finestre; cio' che cambia e' la coordinata beta e la firma osservabile.\n- **Campo di possibilità**: possibile = chiudere la tassonomia BOUNDARY con due sotto-stati residui; non-possibile = promuovere `medium/strong beta-absent` come specie unica del confine.\n\n## Consecutio\n\nIl prossimo ciclo non deve riaprire il thin blank. Deve sigillare il nuovo audit a due stati:\n\n- `prime_persistent_blank`: testare se `SR` resta l'osservabile comune sotto seed/window shift.\n- `random_matrix_chart_sensitive_blank`: testare se beta `[0.4]` e' coordinata locale stabile o evento di una singola finestra.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' uno strumento riusabile per audit row-local di residui BOUNDARY senza rigenerare fit globali.\n\n## Files\n\n- Script: `tools/exp_boundary_residual_beta_absent_audit.py`\n- Data: `tools/data/boundary_residual_beta_absent_audit_20260510_0330.json`\n- Report: `tools/data/reports/agent_20260510_0330.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1658: "content_full": "# Agent Report - Boundary Transition Taxonomy 13 Rows\n**Date**: 2026-05-09 18:39 \n**Piano**: 113 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - la tassonomia post-estensione scala sulle 13 righe e dissolve il thin blank \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`transition_class`, `source_beta_state`, `extension_state`, `support_tier_after`, `one_sided_after`, `endpoint_after`, `stable_count_coherent_after`, `beta_after`, `denominator_state`, `excluded_mass`] - osservabili domain-native di composizione row-aligned, non canonici. \nssp_value: no \n**observable_contract**: claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo l'estensione il thin blank non resta una figura. La domanda vera diventa se la matrice intera lascia un nuovo residuo vivo.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: supporto sottile / atlante di transizione; punto-zero = stessa riga row-aligned prima che il blank venga letto come specie o come transizione.\n- **Piano superiore**: sheaf locale del boundary su 13 sezioni. La sezione corta viene riparata, poi ricollocata nell'atlante senza usare label GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Servono a comporre stati gia' misurati senza introdurre un nuovo gate.\n- **Contaminazione cognitiva**: CE-0019: contratto combo prima della misura; CE-0022: palette come boundary operator e chart locale; KSAR: riuso del deposito stabile 15:32-15:56; PVI: se resta `thin_persists`, la tassonomia cade.\n- **Proto-ipotesi**: `blank_thin_support` non scala sulle 13 righe se, dopo composizione con l'estensione, nessuna riga conserva supporto vivo sottile senza beta.\n- **Possibile / non-possibile**: possibile = usare l'atlante di transizione come mappa BOUNDARY; non-possibile = trattare il thin blank come specie matura.\n- **Proiezione**: classificare tutte le 13 righe in `transition_class` usando supporto, beta, denominatore e transizioni estese.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la consecutio del campo vivo: dopo la cristallizzazione short-denominator, testa se la tassonomia delle transizioni post-estensione scala sulle 13 righe.\n- `not_drift`: non torna a `V_c`, non difende thin blank, non usa label GUE/Poisson come decision field; compone solo depositi row-aligned gia' misurati.\n\n## Claim Under Test\n> La tassonomia emersa dall'estensione del denominatore scala sull'intero perimetro BOUNDARY 13 righe se non lascia alcuna riga `thin_persists`.\n\n## Question\nQuando le tre righe short estese vengono ricollocate nella matrice 13 righe, resta una specie `blank_thin_support` oppure il residuo vivo cambia forma?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Input: `boundary_two_axis_matrix_20260509_1532.json`, `boundary_row_aligned_nonexact_audit_20260509_1538.json`, `boundary_short_denominator_extension_20260509_1556.json`, `boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Perimetro atomico: 13 righe BOUNDARY semi-reali.\n- Label policy: `source_domain_type` resta audit metadata; non entra in `transition_class`.\n- Null sfidato: esistenza di almeno una riga `thin_persists` dopo estensione.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support transfer after composition | 10 |\n| fall after composition | 3 |\n| beta chart after any | 9 |\n| beta chart exact 0.3 after | 6 |\n| thin persist rows | 0 |\n| medium/strong beta-absent blank rows | 2 |\n| verdict | TAXONOMY_SCALES_THIN_DISSOLVED |\n\nTransition classes:\n\n| transition_class | rows |\n|---|---:|\n| beta_0_3_exact | 4 |\n| beta_0_3_local_nonunique | 2 |\n| beta_chart_recovered | 1 |\n| blank_medium_or_strong_beta_absent | 1 |\n| fall_no_support | 2 |\n| local_beta_other | 1 |\n| support_falls_after_extension | 1 |\n| support_thickens_beta_blank | 1 |\n\nRighe aperte senza beta dopo composizione:\n\n| row | class | n_gaps | one-sided | endpoint | tier |\n|---|---|---:|---:|---:|---|\n| numeri_primi:cycle_3 | blank_medium_or_strong_beta_absent | 4096 | 3 | 3.243 | medium_multi_observable |\n| random_matrix:cycle_7 | support_thickens_beta_blank | 1024 | 4 | 3.781 | strong_multi_observable |\n\nRighe short riparate:\n\n| row | before n | after n | transition_class | one-sided after | beta after |\n|---|---:|---:|---|---:|---|\n| percolation:cycle_9 | 190 | 1024 | support_falls_after_extension | 0 | [] |\n| random_matrix:cycle_7 | 199 | 1024 | support_thickens_beta_blank | 4 | [] |\n| zeta_zeros:cycle_4 | 199 | 1024 | beta_chart_recovered | 2 | [0.3] |\n\n## Key Findings\n1. **Verificato: `thin_persist_rows=0/13`.** Nessuna riga conserva la forma `blank_thin_support` dopo composizione con l'estensione.\n2. **Verificato: il supporto post-composizione e' 10/13.** Cadono `cellular_automata`, `pendolo_doppio` e `percolation` dopo estensione; il resto conserva supporto o chart locale.\n3. **Verificato: la beta chart sale a 9/13 righe, con 6/13 esatte 0.3.** Le sei sono quattro `beta_0_3_exact` originali, `zeta_zeros` recuperata e `pendolo_doppio` come beta osservata senza supporto.\n4. **Verificato: il residuo vivo non e' thin.** Restano due blank senza beta: `numeri_primi` medio e `random_matrix` forte dopo estensione.\n5. **Inferito: la tassonomia scala come atlante di transizione, non come legge beta universale.** Il thin blank cade; il nodo successivo e' il blank medio/forte senza beta.\n\n## Verdict\n**CONSTRAINT**.\n\nLa tassonomia post-estensione scala sul perimetro 13 righe: nessun membro resta `thin_persists`.\n\nFormula valida:\n\n`blank_thin_support` = telemetria short-denominator dissolta dalla composizione 13 righe.\n\nFormula non valida:\n\n`blank_thin_support` = specie autonoma del boundary.\n\nIl boundary resta una matrice di transizioni: supporto che cade, supporto che si ispessisce senza beta, chart beta recuperata, chart beta esatta, chart locale non unica, chart spostata.\n\n## Bicono della scoperta\n- **Due radici**: denominatore riparato / atlante 13 righe.\n- **Singolare**: riga BOUNDARY prima della classificazione post-estensione.\n- **Invariante di passaggio**: `thin_persist_rows=0/13`.\n- **Campo di possibilita**: qui diventa possibile chiudere il thin blank; qui diventa non-possibile chiudere il blank medio/forte senza beta.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve lasciare chiuso il thin blank e attaccare il residuo `medium/strong beta-absent blank`: `numeri_primi:cycle_3` contro `random_matrix:cycle_7`. Il test utile non e' \"perche' manca beta in generale\", ma quale condizione distingue blank medio completo e blank forte post-estensione quando entrambi hanno supporto vivo senza chart beta.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1556.md`, JSON 15:32/15:38/15:56/15:00, script e output 18:39.\n- **L1 hard constraint**: claim limitato alle 13 righe semi-reali e ai depositi gia' misurati.\n- **L2 count grezzi**: riportati 13 totali, 10 support, 3 fall, 9 beta chart, 0 thin persist, 2 open blank.\n- **L3 no silent patching**: nessuna nuova simulazione o ricampionamento; la composizione dichiara le estensioni applicate solo a tre righe.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma resta fall; `random_matrix` e' forte senza beta; `zeta_zeros` recupera beta pur restando contaminato nel prescan.\n- **L5 re-discovery**: il ciclo non ripete la matrice 15:32 ne' l'estensione 15:56; testa la scalabilita' richiesta dal campo vivo.\n- **L6 metabolismo cognitivo**: CE-0019, CE-0022, KSAR e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante BOUNDARY di transizione; non-possibile = specie autonoma thin blank.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Run valido: `python tools/exp_boundary_transition_taxonomy_13rows.py --out tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Output: `thin_persist_rows=0`, `support_transfer_after=10/13`, `fall_after=3/13`, `open_blank_rows=['numeri_primi:cycle_3', 'random_matrix:cycle_7']`, `verdict=TAXONOMY_SCALES_THIN_DISSOLVED`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 18:39, il JSON 18:39 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_transition_taxonomy_13rows.py`\n- Data: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`\n- Report: `tools/data/reports/agent_20260509_1839.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1709: "content_full": "# Agent Report - Boundary Short Denominator Extension\n**Date**: 2026-05-09 15:56 \n**Piano**: 112 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. \nssp_value: no \n**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura.\n- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili.\n- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade.\n- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta.\n- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48.\n- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48.\n- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short.\n\n## Claim Under Test\n> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart.\n\n## Question\nCosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`.\n- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`.\n- Denominatore esteso: `1024` gap per riga.\n- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate.\n- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| target rows | 3 |\n| rows still thin support after extension | 0 |\n| support falls after extension | 1 |\n| support thickens beta blank | 1 |\n| beta chart recovered | 1 |\n| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS |\n\nTransizioni:\n\n| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state |\n|---|---:|---:|---:|---:|---|---|\n| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension |\n| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank |\n| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered |\n\n## Key Findings\n1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart.\n2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato.\n3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile.\n4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade.\n5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe.\n\n## Verdict\n**CONSTRAINT**.\n\nNel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma.\n\nFormula valida:\n\n`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile del boundary.\n\nLa riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale.\n\n## Bicono della scoperta\n- **Due radici**: supporto sottile osservato / denominatore sorgente corto.\n- **Singolare**: riga short prima dell'estensione.\n- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`.\n- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48.\n- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered.\n- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings.\n- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato.\n- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`.\n- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`.\n- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_short_denominator_extension.py`\n- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json`\n- Report: `tools/data/reports/agent_20260509_1556.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1760: "content_full": "# Agent Report - Boundary Blank Thin Support Audit\n**Date**: 2026-05-09 15:48 \n**Piano**: 111 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - il blank sottile e' limitato dal denominatore corto, non dalla contaminazione del null \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`blank_class`, `coordinate_failure`, `support_tier`, `denominator_bucket`, `n_gaps`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`, `shuffle_class_changes`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=i tre `blank_thin_support` vanno falsificati contro il blank medio `numeri_primi` prima di promuoverli a specie autonoma; observable=telemetria row-aligned del subset `support_without_beta_blank`; operator=`exp_boundary_blank_thin_support_audit.py`; generator=`boundary_row_aligned_nonexact_audit_20260509_1538`; denominator=4 righe `support_without_beta_blank`, di cui 3 thin e 1 medium control; non_possible=chiamare il blank sottile specie autonoma se tutte le righe thin sono spiegate da denominatore corto; not_tested=estensione nuova dei tre domini short, nuova griglia beta, nuovi null, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il deposito 15:38 divide il blank in due livelli, ma il livello sottile ha tutte le righe corte. Il punto non e' nominare una specie nuova; e' verificare se la specie sopravvive al denominatore.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY row-aligned + tensione \"supporto vivo senza chart beta\".\n- **Dipolo / punto-zero**: blank sottile / blank medio; punto-zero = riga `support_without_beta_blank` prima che la forza del supporto venga letta come specie o come limite del denominatore.\n- **Piano superiore**: sheaf locale del boundary. La sezione beta e' vuota su tutte e quattro le righe; il discriminante diventa la qualita' del supporto e del denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, filtrazione per denominatore. Servono a separare stato beta invariato da supporto misurabile.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:38; CE-0117: cascata solo dove il blank apre una distinzione; YSN DeltaLink: `same beta blank -> support tier -> denominator bucket`; PVI: se tutte le righe thin sono corte, la specie autonoma cade nel perimetro.\n- **Proto-ipotesi**: `blank_thin_support` e' un effetto di denominatore corto se ogni riga thin ha `n_gaps < 500` e il controllo medio no; diventa specie autonoma solo se almeno una riga thin non e' spiegata dal denominatore.\n- **Possibile / non-possibile**: possibile = usare il blank sottile come coda denominator-limited da estendere; non-possibile = promuoverlo a failure mode stabile senza estendere percolation, random_matrix e zeta_zeros.\n- **Proiezione**: misurare `n_gaps`, `denominator_state`, `excluded_mass`, `shuffle_class_changes`, `one_sided_count`, `endpoint_distance` dentro il subset `support_without_beta_blank`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: falsifica direttamente se i failure modes del 15:38 sono stabili o artefatti della griglia/perimetro row-aligned.\n- `not_drift`: non usa label GUE/Poisson, non torna a `V_c`, non aggiunge nuovi domini; attacca solo il nodo aperto dal report precedente: blank sottile contro blank medio.\n\n## Claim Under Test\n> Nel subset `support_without_beta_blank`, il blank sottile e' una specie autonoma solo se non e' interamente spiegato da denominatore corto.\n\n## Question\nI tre blank sottili sono fallimenti autonomi del boundary, oppure righe con supporto insufficiente perche' corte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_blank_thin_support_audit.py`.\n- Input: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Perimetro atomico: 4 righe `support_without_beta_blank`.\n- Soglia dichiarata: `full_gap_floor=500`.\n- Null sfidati: denominatore corto, contaminazione prescan, shuffle class-change, specie autonoma.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| support_without_beta_blank rows | 4 |\n| thin rows | 3 |\n| medium control rows | 1 |\n| thin short rows | 3 |\n| thin contaminated rows | 1 |\n| thin shuffle class-change rows | 1 |\n| thin not denominator explained rows | 0 |\n\nTest:\n\n| null / counter | condition | result |\n|---|---|---|\n| denominator_artifact | all thin rows have `n_gaps < 500` | pass |\n| contamination_artifact | all thin rows are contaminated | fail |\n| shuffle_instability_artifact | all thin rows have `shuffle_class_changes=true` | fail |\n| autonomous_species_counter | at least one thin row is not short-denominator explained | fail |\n\nComparative means:\n\n| group | rows | n_gaps mean | one-sided mean | coherent mean | endpoint mean | excluded mass mean | abs shuffle z mean |\n|---|---:|---:|---:|---:|---:|---:|---:|\n| thin | 3 | 196.000 | 1.333 | 1.250 | 2.003 | 0.201 | 11.037 |\n| medium control | 1 | 4096.000 | 3.000 | 3.000 | 3.243 | 0.000 | 26.600 |\n\nRows:\n\n| row | blank_class | n_gaps | one-sided | endpoint | denominator | excluded | shuffle_change |\n|---|---|---:|---:|---:|---|---:|---|\n| numeri_primi:cycle_3 | medium_blank_control | 4096 | 3 | 3.243 | complete | 0.000 | false |\n| percolation:cycle_9 | thin_short_shuffle_unstable | 190 | 1 | 1.705 | complete | 0.000 | true |\n| random_matrix:cycle_7 | thin_short_complete | 199 | 1 | 1.854 | complete | 0.000 | false |\n| zeta_zeros:cycle_4 | thin_short_contaminated | 199 | 2 | 2.449 | contaminated | 0.602 | false |\n\n## Key Findings\n1. **Verificato: il blank sottile e' 3/3 short-denominator.** `percolation`, `random_matrix` e `zeta_zeros` hanno `n_gaps=190,199,199`; il controllo `numeri_primi` ha `n_gaps=4096`.\n2. **Verificato: la contaminazione non spiega la classe.** Solo `zeta_zeros` e' contaminato; `percolation` e `random_matrix` sono complete con excluded mass 0.0.\n3. **Verificato: lo shuffle class-change non spiega la classe.** Solo `percolation` cambia classe nello shuffle; `random_matrix` e `zeta_zeros` no.\n4. **Verificato: non resta una riga thin autonoma nel perimetro 15:48.** `thin_not_denominator_explained_rows=0`.\n5. **Inferito: il blank medio dei primi non e' blank per mancanza di supporto.** Ha tre osservabili one-sided, denominatore completo, endpoint 3.243 e null forte; resta blank di chart beta, non blank sottile.\n\n## Verdict\n**CONSTRAINT**.\n\nNel subset `support_without_beta_blank`, il failure mode `blank_thin_support` non regge come specie autonoma nel perimetro osservato. Regge come coda denominator-limited:\n\n`blank_thin_support` = beta blank + supporto vivo + denominatore corto.\n\nFormula valida:\n\n`support_without_beta_blank` si divide in `medium_blank_control` e `thin_short_*`.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile indipendente dal denominatore.\n\nIl nodo regressivo e' il denominatore dei tre domini corti. Non va promossa una nuova specie; va estesa o dichiarata coda corta.\n\n## Bicono della scoperta\n- **Due radici**: blank di chart beta / supporto del denominatore.\n- **Singolare**: riga `support_without_beta_blank` prima che il support tier venga letto come specie.\n- **Invariante di passaggio**: denominatore corto separa i blank sottili; contaminazione e shuffle-instability non bastano.\n- **Campo di possibilita**: qui diventa possibile progettare un extension audit mirato su percolation, random_matrix e zeta_zeros; qui diventa non-possibile usare il blank sottile come classe matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve estendere solo i tre domini short (`percolation`, `random_matrix`, `zeta_zeros`) verso denominatore comparabile al controllo medio. Se, a denominatore esteso, restano `blank_thin_support`, allora nasce una specie autonoma. Se salgono a supporto medio o ricevono beta locale, il failure mode 15:38 viene retrocesso a telemetria di perimetro corto.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1538.md`, JSON `boundary_row_aligned_nonexact_audit_20260509_1538.json`, script `tools/exp_boundary_blank_thin_support_audit.py`, output `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- **L1 hard constraint**: claim limitato alle 4 righe `support_without_beta_blank` del deposito 15:38.\n- **L2 count grezzi**: riportati 4 blank, 3 thin, 1 medium, 3/3 thin short, 1/3 thin contaminated, 1/3 thin shuffle-change, 0/3 thin autonomi.\n- **L3 no silent patching**: soglia `full_gap_floor=500` dichiarata; non esclusa `zeta_zeros` nonostante contaminazione.\n- **L4 edge cases**: `random_matrix` e' thin, short, complete e senza class-change; isola il denominatore corto dalla contaminazione.\n- **L5 re-discovery**: il ciclo non rifà il gate 15:16; legge il sotto-perimetro aperto dal report 15:38.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = extension audit dei tre short; non-possibile = classe autonoma senza estensione.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_thin_support_audit.py`.\n- Run valido: `python tools/exp_boundary_blank_thin_support_audit.py --input tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json --out tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `thin_short_rows=3`, `thin_contaminated_rows=1`, `thin_shuffle_class_change_rows=1`, `thin_not_denominator_explained_rows=0`, `verdict=DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:48, il JSON 15:48 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_blank_thin_support_audit.py`\n- Data: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`\n- Report: `tools/data/reports/agent_20260509_1548.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1811: "content_full": "# Agent Report - Boundary Row-Aligned Nonexact Audit\n**Date**: 2026-05-09 15:38 \n**Piano**: 110 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - le righe support-transfer non esatte sono 7, non 6; la beta cade in cinque failure modes misurabili \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`beta_state`, `coordinate_failure`, `support_tier`, `beta_cardinality`, `beta_span`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: la consecutio dice \"6 righe non-esatte\", ma il deposito row-aligned ne contiene 7. Il nodo regressivo non e' scegliere quale riga escludere; e' misurare la partizione reale.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + tensione \"supporto senza coordinata universale\".\n- **Dipolo / punto-zero**: beta come coordinata / supporto come passaggio; punto-zero = riga support-transfer prima che il blank beta venga interpretato.\n- **Piano superiore**: boundary operator su matrice row-aligned. Il bordo non decide con label di dominio; decide con stato beta, support tier e denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Entrano per leggere le sezioni locali della beta senza saldarle in una coordinata globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:32 senza ripetere il gate; CE-0117: cascata solo sulle righe dove il supporto resta vivo; YSN DeltaLink: `two-axis matrix -> nonexact audit -> coordinate failure`; PVI: il numero atteso 6 cade davanti al deposito 7.\n- **Proto-ipotesi**: dopo la caduta di beta 0.3 universale, le righe non-esatte non formano un residuo unico. Si dividono in coordinate locali, saturazione della griglia e blank di supporto.\n- **Possibile / non-possibile**: possibile = trasformare beta da ascissa universale a chart locale auditabile; non-possibile = usare `support_transfer=true` come prova di una beta comune.\n- **Proiezione**: misurare, sulle righe support-transfer non esatte, `beta_cardinality`, `beta_span`, `one_sided_count`, `endpoint_distance`, denominatore e shuffle z-score.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: attacca direttamente beta local non-unique, beta local other e support_without_beta_blank sul deposito row-aligned indicato.\n- `not_drift`: non usa label GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta il confine; corregge il denominatore della direttiva quando il deposito mostra 7 righe.\n\n## Claim Under Test\n> Le righe BOUNDARY con supporto ma senza beta 0.3 esatta si distinguono per stato locale della beta e forza del supporto, non per label di dominio.\n\n## Question\nQuando beta 0.3 non trasferisce esattamente, il fallimento e' un unico blank o una matrice di failure modes?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Input 1: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Input 2: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Perimetro atomico: 13 righe semi-reali; subset primario = support-transfer con `beta_coordinate_transfer=false`.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n- Classi operative: `adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support-transfer rows | 11 |\n| beta 0.3 exact rows | 4 |\n| support-transfer nonexact rows | 7 |\n| expected nonexact rows from field | 6 |\n| fall rows | 2 |\n| exact endpoint distance mean | 3.829 |\n| nonexact endpoint distance mean | 2.957 |\n| exact one-sided count mean | 4.000 |\n| nonexact one-sided count mean | 2.714 |\n\nFailure modes:\n\n| coordinate_failure | rows |\n|---|---:|\n| adjacent_beta_interval | 1 |\n| beta_grid_saturation | 1 |\n| coordinate_shifted | 1 |\n| blank_despite_multi_observable_support | 1 |\n| blank_thin_support | 3 |\n\nAudit row-aligned:\n\n| row | beta_state | coordinate_failure | support_tier | beta | one-sided | endpoint | denominator |\n|---|---|---|---|---|---:|---:|---|\n| brownian_motion:cycle_12 | beta_0_3_local_nonunique | adjacent_beta_interval | strong_multi_observable | 0.2,0.3 | 4 | 3.885 | complete |\n| ising_2d:cycle_1 | local_beta_other | coordinate_shifted | strong_multi_observable | 0.4 | 5 | 4.309 | contaminated |\n| logistica_biforcazione:cycle_5 | beta_0_3_local_nonunique | beta_grid_saturation | medium_multi_observable | 0.1-0.9 | 3 | 3.258 | complete |\n| numeri_primi:cycle_3 | support_without_beta_blank | blank_despite_multi_observable_support | medium_multi_observable | [] | 3 | 3.243 | complete |\n| percolation:cycle_9 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.705 | complete |\n| random_matrix:cycle_7 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.854 | complete |\n| zeta_zeros:cycle_4 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 2 | 2.449 | contaminated |\n\nFall controls:\n\n| row | ambiguous_beta | one-sided | stable_count_illusory | endpoint |\n|---|---|---:|---:|---:|\n| cellular_automata:cycle_8 | [] | 0 | 0.500 | 0.000 |\n| pendolo_doppio:cycle_2 | 0.3 | 3 | 0.833 | 3.388 |\n\n## Key Findings\n1. **Verificato: il sotto-perimetro non-esatto e' 7/13, non 6/13.** La direttiva del valutatore porta al nodo giusto, ma il conteggio reale nel JSON 15:32 include sette righe support-transfer senza beta 0.3 esatta.\n2. **Verificato: `support_without_beta_blank` non e' una classe unica.** `numeri_primi` ha blank con supporto medio e endpoint 3.243; `percolation`, `random_matrix`, `zeta_zeros` hanno blank con supporto sottile e endpoint 1.705-2.449.\n3. **Verificato: beta local non-unique ha due forme.** `brownian_motion` e' intervallo adiacente 0.2,0.3 con supporto forte; `logistica_biforcazione` satura tutta la griglia 0.1-0.9 con supporto medio.\n4. **Verificato: `ising_2d` non e' blank ma chart spostata.** Ha beta locale esatta 0.4, cinque osservabili one-sided e endpoint 4.309; la beta trasferisce localmente, ma non sulla coordinata 0.3.\n5. **Verificato: beta 0.3 senza supporto resta contro-controllo.** `pendolo_doppio` contiene beta 0.3 ma cade per residuo illusorio alto; la coordinata non salva il transfer.\n\n## Verdict\n**CONSTRAINT**.\n\nIl frame BOUNDARY regge come support-transfer su 11/13 righe, ma la parte non-esatta non e' una coda omogenea. Le righe support-transfer senza beta 0.3 esatta sono 7 e si dividono in cinque failure modes:\n\n`adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\nFormula valida:\n\n`beta_coordinate_transfer` = chart locale exact 0.3 dentro supporto vivo.\n\nFormula non valida:\n\n`support_transfer non-exact = sei righe residue` oppure `support_without_beta_blank = assenza strutturale unica`.\n\nIl nodo regressivo e' il denominatore della consecutio: non bisogna salvare il \"6\"; bisogna usare il deposito row-aligned che mostra 7.\n\n## Bicono della scoperta\n- **Due radici**: coordinata beta locale / supporto ordine-null.\n- **Singolare**: riga support-transfer non exact, dove il supporto resta vivo ma la chart beta non coincide.\n- **Invariante di passaggio**: failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.\n- **Campo di possibilita**: qui diventa possibile auditare la beta come atlante locale; qui diventa non-possibile trattare il blank beta come una sola specie.\n\n## Consecutio\nIl prossimo ciclo non deve ripetere la matrice. Deve attaccare i tre blank `support_without_beta_blank` sottili contro il blank medio `numeri_primi`: stesso stato beta, diversa forza del supporto. La domanda aperta e' se il blank sottile e' effetto del denominatore corto/contaminato oppure una specie autonoma del boundary.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1532.md`, JSON `boundary_two_axis_matrix_20260509_1532.json`, JSON `boundary_denominator_prescan_full_20260509_1500.json`, script `tools/exp_boundary_row_aligned_nonexact_audit.py`, output `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe della matrice 15:32 e al prescan 15:00.\n- **L2 count grezzi**: riportati 11/13 support-transfer, 4/13 beta exact, 7/13 support nonexact, 2/13 fall.\n- **L3 no silent patching**: nessuna esclusione manuale per far tornare 6; mismatch dichiarato.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `ising_2d` ha beta 0.4 con supporto forte; `numeri_primi` blank non sottile.\n- **L5 re-discovery**: il ciclo non rifà gate o fit; legge solo la partizione non-esatta richiesta.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante locale delle beta; non-possibile = beta 0.3 universale o residuo non-esatto singolo.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Run valido: `python tools/exp_boundary_row_aligned_nonexact_audit.py --matrix tools/data/boundary_two_axis_matrix_20260509_1532.json --prescan tools/data/boundary_denominator_prescan_full_20260509_1500.json --out tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `support_nonexact_rows=7`, `fall_rows=2`, `failure_blank_thin_support=3`, `failure_blank_despite_multi_observable_support=1`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:38, il JSON 15:38 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_row_aligned_nonexact_audit.py`\n- Data: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`\n- Report: `tools/data/reports/agent_20260509_1538.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1862: "content_full": "# Agent Report - Boundary Two-Axis Matrix\n**Date**: 2026-05-09 15:32\n**Piano**: 109\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - il gate trasferisce come supporto ordine/null; beta 0.3 non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`support_transfer`, `beta_coordinate_transfer`, `beta_state`, `ambiguous_beta`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`] - osservabili domain-native derivati dal gate semi-reale, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il falsifier del 15:16 ha indicato il nodo regressivo: la direzione chiedeva di non importare label GUE/Poisson. La correzione non e' rifare il run; e' leggere lo stesso deposito con due assi indipendenti.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY semi-reale 13 righe + direzione viva \"support_transfer vs beta_coordinate_transfer\".\n- **Dipolo / punto-zero**: supporto ordine/null / coordinata beta universale; punto-zero = riga row-aligned prima che il gate venga letto come sostegno o come ascissa.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non e' una classe; e' una matrice di passaggio fra supporto e coordinata.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter matrix, coordinate split. Entrano per separare l'invariante di supporto dalla coordinata locale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:16 senza ripetere il perimetro; CE-0117: cascata della possibilita solo dove il supporto apre un canale; YSN DeltaLink: `semi-real gate -> two-axis matrix -> beta localizzata`; PVI: il claim beta universale cade se l'esatto 0.3 non copre il denominatore.\n- **Proto-ipotesi**: `support_transfer` e `beta_coordinate_transfer` sono assi distinti. Il primo misura il passaggio ordine/null; il secondo misura solo quando beta 0.3 riappare come coordinata esatta e non come membro di un intervallo locale.\n- **Possibile / non-possibile**: possibile = usare BOUNDARY come audit row-aligned di supporto su 11/13 righe; non-possibile = trattare beta 0.3 come terzo incluso universale del perimetro semi-reale.\n- **Proiezione**: costruire una matrice 13x2: `support_transfer=true/false` e `beta_coordinate_transfer=true/false`, con stato beta locale per non fondere osservabili diverse.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esplicita la matrice semi-reale a due assi richiesta dal valutatore: support_transfer contro beta_coordinate_transfer sulle 13 righe.\n- `not_drift`: non usa `source_domain_type` GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta label locali; legge solo stati row-aligned gia' misurati.\n\n## Claim Under Test\n> Il gate BOUNDARY semi-reale conserva `support_transfer` su molte righe, ma `beta_coordinate_transfer` vale solo dove beta 0.3 compare come coordinata esatta.\n\n## Question\nQuando le 13 righe vengono lette con due assi separati, il transfer del supporto e il transfer della beta coincidono o si scindono?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_two_axis_matrix.py`.\n- Input: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Output: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Perimetro atomico: 13 righe semi-reali gia' row-aligned dal deposito 15:16.\n- Label policy: l'operatore non legge label GUE/Poisson. Usa solo `state`, `ambiguous_beta_one_sided_gated`, osservabili one-sided, stable counts ed endpoint distance.\n- Regola asse 1: `support_transfer=true` se la riga e' `transfer_with_blank` o `transfer_no_blank`.\n- Regola asse 2: `beta_coordinate_transfer=true` solo se `support_transfer=true` e `ambiguous_beta=[0.3]` dopo normalizzazione a un decimale.\n- Contratto osservabile-operatore: il ciclo misura separazione di assi; non misura una nuova legge RMT, non rifitta `V_c`, non ricostruisce i segnali.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| support_transfer true | 11 |\n| support_transfer false | 2 |\n| beta_coordinate exact 0.3 | 4 |\n| beta 0.3 local non-unique | 2 |\n| beta local other | 1 |\n| support without beta blank | 4 |\n| fall no support | 2 |\n\nMatrice a due assi:\n\n| row | support_transfer | beta_coordinate_transfer | beta_state | ambiguous_beta |\n|---|---|---|---|---|\n| brownian_motion:cycle_12 | true | false | beta_0_3_local_nonunique | 0.2,0.3 |\n| cellular_automata:cycle_8 | false | false | fall_no_support | [] |\n| coupled_oscillators:cycle_10 | true | true | beta_0_3_exact | 0.3 |\n| ising_2d:cycle_1 | true | false | local_beta_other | 0.4 |\n| logistica_biforcazione:cycle_5 | true | false | beta_0_3_local_nonunique | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | true | true | beta_0_3_exact | 0.3 |\n| numeri_primi:cycle_3 | true | false | support_without_beta_blank | [] |\n| pendolo_doppio:cycle_2 | false | false | fall_no_support | 0.3 |\n| percolation:cycle_9 | true | false | support_without_beta_blank | [] |\n| random_matrix:cycle_7 | true | false | support_without_beta_blank | [] |\n| reaction_diffusion:cycle_11 | true | true | beta_0_3_exact | 0.3 |\n| string_vibration:cycle_6 | true | true | beta_0_3_exact | 0.3 |\n| zeta_zeros:cycle_4 | true | false | support_without_beta_blank | [] |\n\n## Key Findings\n1. **Verificato: `support_transfer` resta 11/13.** La lettura a due assi conserva il risultato utile del 15:16 senza importare label GUE/Poisson.\n2. **Verificato: `beta_coordinate_transfer` e' 4/13.** Solo quattro righe hanno beta 0.3 esatta: `coupled_oscillators`, `logistica_biforcazione_var_3.5699`, `reaction_diffusion`, `string_vibration`.\n3. **Verificato: la beta non collassa in un solo asse.** Due righe includono 0.3 in un intervallo non unico, una riga ha beta locale 0.4, quattro trasferiscono senza blank beta, due cadono.\n4. **Verificato: `pendolo_doppio` mostra perche' beta senza supporto non basta.** Ha ambiguous beta 0.3, ma resta `fall_no_support`; la coordinata non salva il transfer.\n5. **Inferito: il nodo regressivo e' la fusione degli osservabili.** Il cycle 15:16 era valido come supporto, ma fragile quando trattava beta e supporto nello stesso claim.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce come supporto ordine/null su 11/13 righe. La coordinata beta 0.3 trasferisce esattamente su 4/13 righe. Questi due assi non coincidono.\n\nFormula valida:\n\n`BOUNDARY support_transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY beta_coordinate_transfer` = beta 0.3 universale del terzo incluso semi-reale.\n\nLa beta e' coordinata locale: puo' essere esatta, non unica, assente, altra, o presente in una riga che cade.\n\n## Bicono della scoperta\n- **Due radici**: supporto che passa / coordinata che pretende universalita.\n- **Singolare**: la riga row-aligned prima della lettura a due assi.\n- **Invariante di passaggio**: separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.\n- **Campo di possibilita**: qui diventa possibile usare il gate come matrice di audit `support_transfer`; qui diventa non-possibile usare beta 0.3 come coordinata universale.\n\n## Consecutio\nIl prossimo ciclo deve attaccare i quattro stati beta, non il supporto gia' separato: `beta_0_3_exact`, `beta_0_3_local_nonunique`, `local_beta_other`, `support_without_beta_blank`. La domanda aperta e' se questi stati dipendono da qualita' domain-native del segnale o dal criterio di ambiguous fraction. Non va reintrodotta la label GUE/Poisson come scorciatoia.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1516.md`, falsifier `falsifier_20260509_1516.json`, script `tools/exp_boundary_two_axis_matrix.py`, JSON `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe del deposito 15:16.\n- **L2 count grezzi**: riportati 11/13 support_transfer, 4/13 beta 0.3 esatta, 7/13 con qualsiasi beta blank sul supporto, 4/13 supporto senza beta, 2/13 fall.\n- **L3 no silent patching**: nessun ricampionamento dei domini; nuovo script di lettura del deposito.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `logistica_biforcazione` include 0.3 ma in intervallo 0.1-0.9; `ising_2d` trasferisce con beta 0.4.\n- **L5 re-discovery**: il ciclo non ripete il gate semi-reale; lo decompone nel nodo richiesto dal valutatore.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit support_transfer; non-possibile = beta 0.3 universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_two_axis_matrix.py`.\n- Run valido: `python tools/exp_boundary_two_axis_matrix.py --input tools/data/semireal_boundary_transfer_gate_20260509_1516.json --out tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Output: `support_transfer=11/13`, `beta_coordinate_exact_0_3=4/13`, `any_beta_blank_on_support=7/13`, `support_without_beta_blank=4/13`, `fall_no_support=2/13`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge solo lo script 15:32, il JSON 15:32 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_two_axis_matrix.py`\n- Data: `tools/data/boundary_two_axis_matrix_20260509_1532.json`\n- Report: `tools/data/reports/agent_20260509_1532.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1913: "content_full": "# Agent Report - Semi-real Boundary Transfer Gate\n**Date**: 2026-05-09 15:16\n**Piano**: 108\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - supporto ordine/null trasferisce su 11/13, il blank beta non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate `coherent/null/beta` del BOUNDARY sintetico trasferisce sul perimetro semi-reale base; observable=one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta; operator=`exp_semireal_boundary_transfer_gate.py`; generator=13 righe `boundary_denominator_prescan_full_20260509_1500` ricostruite da `dnd_autoricerca`; denominator=13 righe base BOUNDARY, 8 GUE-like e 5 Poisson-like, beta layers 0.0..1.0, 12 replicates, 24 shuffle baselines; non_possible=dichiarare beta 0.3 coordinata universale o transfer completo quando 2/13 righe cadono; not_tested=nuovi domini, nuovi spettri, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il confine semi-reale non rifiuta il gate; rifiuta la sua simmetria sintetica. Il supporto ordine/null passa, la coordinata blank si frammenta.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + perimetro BOUNDARY base 13/13 transfer + tensione viva \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: gate come supporto ordine/null / gate come coordinata beta universale; punto-zero = riga semi-reale prima che il beta layer venga chiamato blank o classe.\n- **Piano superiore**: grafo della conoscenza e boundary operator. La domanda non e' se GUE o Poisson vincono, ma se il passaggio resta morfismo quando il perimetro diventa fisico/semi-reale.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, graph/perimeter transfer. Entrano per separare copertura del denominatore, supporto d'ordine e coordinata di transizione.\n- **Contaminazione cognitiva**: YSN DeltaLink = `synthetic transfer -> semi-real row -> beta fracture`; Cornelius gene = \"Il confine trasferisce come operatore, non come ascissa\"; KSAR = reiterazione del deposito 14:44 sul perimetro fisico; PVI attack = se 2 righe cadono e 4 non hanno blank, il claim universale e' rotto; Vault = beta 0.3 resta frammento utile solo dove riappare row-aligned.\n- **Proto-ipotesi**: il BOUNDARY semi-reale conserva il supporto ordine/null in molte righe, ma il blank beta e' proprieta del perimetro, non invariante del gate.\n- **Possibile / non-possibile**: possibile = usare il gate come audit row-aligned del supporto d'ordine cross-dominio; non-possibile = promuovere beta 0.3 a coordinata universale del terzo incluso.\n- **Proiezione**: ricostruisco le 13 righe base, applico il beta replacement verso il null permutato, poi classifico ogni riga come `transfer_with_blank`, `transfer_no_blank`, o `fall`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson.\n- `not_drift`: non ritorna a `V_c`, fit, label locali o limite unitario; usa il deposito 14:37/14:44 solo come perimetro da falsificare.\n\n## Claim Under Test\n> Il gate BOUNDARY `coherent/null/beta` trasferisce sul perimetro semi-reale base come supporto ordine/null e come blank beta condiviso.\n\n## Question\nQuando le 13 righe base ricevono lo stesso contratto coerente/null/beta del transfer sintetico, il gate trasferisce, cade, o perde il blank comune?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_semireal_boundary_transfer_gate.py`.\n- Input: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Perimetro atomico: 13 righe base BOUNDARY, `source_domain_type` 8 GUE-like e 5 Poisson-like.\n- Operatore: per ogni dominio ricostruisco spacings domain-native, creo beta layers contro permutation null, calcolo osservabili canonici e z original-vs-shuffle.\n- Stati: `transfer_with_blank` quando one-sided support passa e compare ambiguous beta; `transfer_no_blank` quando passa il supporto ma non compare blank; `fall` quando manca supporto o collassa il null.\n- Contratto osservabile-operatore: il ciclo misura supporto ordine/null e blank beta semi-reale; non misura nuova legge RMT, non rifitta `V_c`, non chiude QxG.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| source rows | 13 |\n| analyzed rows | 13 |\n| errors | 0 |\n| transfer_with_blank | 7 |\n| transfer_no_blank | 4 |\n| fall | 2 |\n\nRighe semi-reali:\n\n| row | source | denominator | n | state | one-sided observables | stable coherent | stable illusory | endpoint distance | ambiguous beta |\n|---|---|---|---:|---|---|---:|---:|---:|---|\n| brownian_motion:cycle_12 | Poisson | complete | 4096 | transfer_with_blank | SR2,L1,L2,triple_var | 4.000 | 0.167 | 3.885 | 0.2,0.3 |\n| cellular_automata:cycle_8 | GUE | contaminated | 108 | fall | [] | 0.000 | 0.500 | 0.000 | [] |\n| coupled_oscillators:cycle_10 | Poisson | contaminated | 2002 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.250 | 4.336 | 0.3 |\n| ising_2d:cycle_1 | GUE | contaminated | 699 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.309 | 0.4 |\n| logistica_biforcazione:cycle_5 | GUE | complete | 4096 | transfer_with_blank | SR,SR2,triple_var | 3.583 | 0.167 | 3.258 | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 4096 | transfer_with_blank | SR,SR2 | 2.000 | 0.083 | 2.769 | 0.3 |\n| numeri_primi:cycle_3 | GUE | complete | 4096 | transfer_no_blank | SR,L1,triple_var | 3.000 | 0.167 | 3.243 | [] |\n| pendolo_doppio:cycle_2 | Poisson | complete | 4096 | fall | SR,SR2,L1 | 5.000 | 0.833 | 3.388 | 0.3 |\n| percolation:cycle_9 | Poisson | complete | 190 | transfer_no_blank | L1 | 0.833 | 0.333 | 1.705 | [] |\n| random_matrix:cycle_7 | GUE | complete | 199 | transfer_no_blank | L2 | 1.000 | 0.250 | 1.854 | [] |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 499 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.364 | 0.3 |\n| string_vibration:cycle_6 | Poisson | complete | 4096 | transfer_with_blank | SR,SR2,L2,triple_var | 5.000 | 0.500 | 3.845 | 0.3 |\n| zeta_zeros:cycle_4 | GUE | contaminated | 199 | transfer_no_blank | SR,L2 | 1.917 | 0.250 | 2.449 | [] |\n\n## Key Findings\n1. **Verificato: il supporto ordine/null trasferisce in 11/13 righe.** Le righe `transfer_with_blank` e `transfer_no_blank` hanno osservabili one-sided e polo illusorio soppresso nel perimetro dichiarato.\n2. **Verificato: il blank beta non e' universale.** Solo 7/13 righe hanno ambiguous beta; 4/13 trasferiscono senza blank e 2/13 cadono.\n3. **Verificato: beta 0.3 non e' coordinata comune.** Compare in 5 righe come unico blank o parte del blank, ma `ising_2d` mostra 0.4, `brownian_motion` mostra 0.2/0.3, `logistica_biforcazione` mostra 0.1-0.9, e quattro transfer non mostrano blank.\n4. **Verificato: i due fall sono diversi.** `cellular_automata` cade per assenza di osservabili one-sided; `pendolo_doppio` cade per residuo illusorio alto (0.833) pur avendo one-sided observables.\n5. **Inferito: il nodo regressivo e' la qualita del perimetro, non il null.** Il null e' disponibile su 13/13; cio' che varia e' come il dominio riceve beta.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY semi-reale trasferisce come supporto ordine/null in 11/13 righe. Non trasferisce come coordinata beta universale: 7 righe hanno blank, 4 trasferiscono senza blank, 2 cadono.\n\nFormula valida:\n\n`BOUNDARY transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY blank` = beta 0.3 universale del terzo incluso cross-dominio.\n\n## Bicono della scoperta\n- **Due radici**: supporto ordine/null che passa / coordinata beta che si frammenta.\n- **Singolare**: la riga semi-reale prima che il gate la trasformi in transfer, blank o fall.\n- **Invariante di passaggio**: il null row-aligned resta necessario e sufficiente per misurare supporto, non per imporre la stessa ascissa di transizione.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come audit cross-dominio row-aligned; qui diventa non-possibile dichiarare il terzo incluso come beta comune del perimetro semi-reale.\n\n## Consecutio\nIl prossimo ciclo deve separare due livelli: `support_transfer` e `blank_coordinate`. Il primo ha supporto 11/13 nel perimetro semi-reale; il secondo richiede tipologia di dominio o nuova coordinata, perche' beta 0.3 e' stabile nel sintetico ma non nel semi-reale.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1437.md`, report `agent_20260509_1444.md`, report `agent_20260509_1457.md`, nuovo JSON `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- **L1 hard constraint**: claim limitato a 13 righe base, 12 replicates, 24 shuffle baselines, osservabili canonici registry 1.0.0.\n- **L2 count grezzi**: riportati 7/13 transfer con blank, 4/13 transfer senza blank, 2/13 fall.\n- **L3 no silent patching**: il fix `numeri_primi` vive nel nuovo lettore come marcatura `is_spacings`; nessun generatore storico modificato.\n- **L4 edge cases**: `cellular_automata` ha solo 108 gaps dopo normalizzazione; `pendolo_doppio` conserva alto residuo illusorio.\n- **L5 re-discovery**: il ciclo non ripete 13/13 transfer base; testa se il beta gate sintetico passa al perimetro semi-reale.\n- **L6 metabolismo cognitivo**: YSN, Cornelius, KSAR e PVI usati nella combo; Vault conserva beta 0.3 come frammento non universale.\n- **L7 possibile/non-possibile**: possibile = audit row-aligned cross-dominio; non-possibile = blank beta universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_semireal_boundary_transfer_gate.py`.\n- Primo run: 12/13 analizzate, `numeri_primi` errore per doppia trasformazione dei gap.\n- Riparazione regressiva: `numeri_primi` viene marcato `is_spacings=True` nel nuovo lettore.\n- Run valido: `python tools/exp_semireal_boundary_transfer_gate.py --n-replicates 12 --n-baseline 24 --seed 202605091516 --out tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script, il JSON 1516 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_semireal_boundary_transfer_gate.py`\n- Data: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`\n- Report: `tools/data/reports/agent_20260509_1516.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:1964: "content_full": "# Agent Report - Vc Unit Boundary Audit\n**Date**: 2026-05-09 14:57\n**Piano**: 107\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: FALSIFIED as convergence-to-1-from-above in the observed fit-ready window\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`unit_status`, `unit_crossing_N`, `below_unit_count`, `fit_ready_rows`, `denominator_state`, `best_model`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=`V_c(phi)` converge a 1 come limite leggibile del canale ordinato; observable=relazione dei punti fit-ready con il bordo `V_c=1`; operator=`exp_vc_unit_boundary_audit.py` su deposito `vc_fit_model_gate_20260509_1400`; generator=`reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=13 righe ammissibili `complete/contaminated` del fit model gate, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=dichiarare convergenza a 1 dall'alto quando la curva osservata attraversa sotto 1 nel perimetro; not_tested=nuovi spettri, nuovi N, nuovi candidati, limite asintotico oltre la finestra osservata.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il bordo `V_c=1` non e' attrattore osservato; e' un taglio attraversato dal canale fit-ready.\n- **Combo**: A2 confine det=-1 + A3 punto fisso come attrattore da falsificare + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: convergenza a 1 / attraversamento di 1; punto-zero = la riga fit-ready prima che il bordo unitario venga trattato come limite o come taglio.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il valore 1 e' nodo di bordo: decide quale arco puo' ricevere il claim di limite.\n- **Operatori laterali scelti**: boundary operator, graph cut, fit come morfismo. Entrano per separare il bordo unitario dal modello numerico.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito 14:00 senza ricomputare spettri; CE-0117: cascata della possibilita, il claim di limite passa solo dove il bordo non e' attraversato; YSN DeltaLink: `fit-ready curve -> unit cut -> limit veto`.\n- **Proto-ipotesi**: se una curva fit-ready attraversa `V_c=1` dentro la finestra osservata, il claim \"converge a 1 dall'alto\" cade su quel perimetro; il bordo unitario resta coordinata di taglio, non attrattore.\n- **Possibile / non-possibile**: possibile = usare `V_c=1` come unit boundary audit; non-possibile = chiamarlo limite osservato quando 12/13 righe hanno punti sotto 1.\n- **Proiezione**: classifico ogni riga ammissibile come `all_above_unit`, `crosses_down_inside_window`, o `all_below_unit`, e calcolo `unit_crossing_N` per le righe che attraversano.\n\n## Claim Under Test\n> Nel perimetro fit-ready 14:00, il canale ordinato sostiene `V_c -> 1` come limite osservato dall'alto.\n\n## Question\nIl bordo `V_c=1` si comporta come attrattore della scala o come taglio attraversato dalle curve fit-ready?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_unit_boundary_audit.py`.\n- Input: `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- Output: `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Perimetro atomico: 13 righe ammissibili del model gate, gia' filtrate da `denominator_state in {complete, contaminated}`.\n- Operatore: per ogni riga ordino i punti `(N, V_c)`, conto i valori sotto 1, e interpolo il primo attraversamento del bordo unitario quando avviene tra due N consecutivi.\n- Contratto osservabile-operatore: il ciclo misura il bordo unitario sul deposito 14:00; non misura nuovi N, non testa l'asintoto, non ripara il fit.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| admissible rows audited | 13 |\n| rows with any `V_c < 1` | 12 |\n| `crosses_down_inside_window` | 9 |\n| `all_below_unit` | 3 |\n| `all_above_unit` | 1 |\n\nStati per classe generatore:\n\n| generator_class | all_above | crosses_down | all_below |\n|---|---:|---:|---:|\n| reference_order | 0 | 3 | 0 |\n| order_memory | 1 | 5 | 0 |\n| periodic_closure | 0 | 1 | 2 |\n| random_dispersion | 0 | 0 | 1 |\n\nCanale ordinato, righe complete/contaminate:\n\n| row | state | fit_ready/total | excluded | first V_c | last V_c | unit status | crossing N |\n|---|---|---:|---:|---:|---:|---|---:|\n| per_mode_best:reference_order:r0.48 | complete | 16/16 | 0 | 1.311 | 0.955 | crosses_down | 348.753 |\n| per_mode_best:reference_order:r0.50 | complete | 16/16 | 0 | 1.191 | 0.879 | crosses_down | 276.876 |\n| per_mode_best:reference_order:r0.52 | complete | 16/16 | 0 | 1.072 | 0.807 | crosses_down | 151.973 |\n| per_mode_best:order_memory:r0.48 | complete | 48/48 | 0 | 1.432 | 0.893 | crosses_down | 315.760 |\n| per_mode_best:order_memory:r0.50 | complete | 48/48 | 0 | 1.317 | 0.774 | crosses_down | 255.737 |\n| per_mode_best:order_memory:r0.52 | complete | 48/48 | 0 | 1.218 | 0.688 | crosses_down | 188.585 |\n| accepted_candidates:order_memory:r0.48 | contaminated | 273/279 | 6 | 1.474 | 1.069 | all_above | blank |\n| accepted_candidates:order_memory:r0.50 | contaminated | 278/279 | 1 | 1.350 | 0.899 | crosses_down | 332.733 |\n| accepted_candidates:order_memory:r0.52 | complete | 279/279 | 0 | 1.236 | 0.781 | crosses_down | 272.895 |\n\nPrimi attraversamenti del bordo:\n\n| row | crossing N |\n|---|---:|\n| per_mode_best:periodic_closure:r0.48 | 105.734 |\n| per_mode_best:reference_order:r0.52 | 151.973 |\n| per_mode_best:order_memory:r0.52 | 188.585 |\n| per_mode_best:order_memory:r0.50 | 255.737 |\n| accepted_candidates:order_memory:r0.52 | 272.895 |\n| per_mode_best:reference_order:r0.50 | 276.876 |\n| per_mode_best:order_memory:r0.48 | 315.760 |\n| accepted_candidates:order_memory:r0.50 | 332.733 |\n\n## Key Findings\n1. **Verificato: il claim `converge a 1 dall'alto` cade nel perimetro osservato.** 12/13 righe ammissibili hanno almeno un punto fit-ready sotto `V_c=1`.\n2. **Verificato: il canale ordinato completo attraversa il bordo.** `reference_order` fa 3/3 attraversamenti; `order_memory` per-mode-best fa 3/3 attraversamenti; tutti con denominatore `complete`.\n3. **Verificato: l'unica riga all-above e' contaminata.** `accepted_candidates:order_memory:r0.48` resta sopra 1, ma ha 273/279 righe fit-ready e 6 righe escluse; non puo' sostenere da sola il claim hard.\n4. **Verificato: `periodic_closure` e `random_dispersion` non salvano il limite.** Due righe `periodic_closure` e una `random_dispersion` sono gia' tutte sotto 1; il loro denominatore era contaminato e non appartiene al canale ordinato completo.\n5. **Inferito: il nodo regressivo non e' scegliere un nuovo modello di fit.** Il bordo unitario va promosso a veto osservabile prima del claim di limite.\n\n## Verdict\n**FALSIFIED** nel perimetro osservato.\n\n`V_c=1` non opera come limite osservato dall'alto sulle righe fit-ready 14:00. Opera come bordo attraversato. Il canale ordinato resta fittabile e lineare nel perimetro, ma proprio quel canale scende sotto 1: `reference_order` 3/3 e `order_memory` per-mode-best 3/3 attraversano il bordo dentro N={89,144,233,377}.\n\nLa formulazione valida diventa:\n\n`V_c=1` e' un unit boundary audit del sottografo fit-ready; non e' attrattore verificato della scala nel perimetro 14:00.\n\n## Bicono della scoperta\n- **Due radici**: limite unitario / attraversamento unitario.\n- **Singolare**: la curva fit-ready prima che `1` sia interpretato come attrattore o come taglio.\n- **Invariante di passaggio**: la disponibilita del denominatore completo sopravvive; il claim di convergenza a 1 non sopravvive.\n- **Campo di possibilita**: qui diventa possibile usare `V_c=1` come veto strutturale nei futuri fit; qui diventa non-possibile formulare `V_c(phi) -> 1` come claim osservato dall'alto su questo perimetro.\n\n## Consecutio\nIl prossimo ciclo che estende N deve partire dal veto unitario: se N piu' alti vengono calcolati, il claim non e' \"converge a 1\", ma \"dopo l'attraversamento, la curva resta sotto 1, rientra, o cambia famiglia\". Il denominatore da preservare e' `complete + unit_status`, non solo `fit_ready`.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1337.md`, report `agent_20260509_1400.md`, JSON `tools/data/vc_fit_model_gate_20260509_1400.json`, nuovo JSON `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- **L1 hard constraint**: il verdict vale solo per le 13 righe ammissibili del deposito 14:00 e N={89,144,233,377}.\n- **L2 count grezzi**: riportati 12/13 sotto 1, 9/13 attraversamenti, 1/13 sopra 1, 3/13 tutte sotto 1.\n- **L3 no silent patching**: nessun nuovo spettro, nessun nuovo N usato come evidenza, nessuna modifica ai depositi 13:37/14:00.\n- **L4 edge cases**: l'unica riga sopra 1 e' contaminata con 6 esclusioni; non chiude il claim hard.\n- **L5 re-discovery**: il finding non ripete \"V_c scende\"; misura il bordo unitario come veto atomico.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = unit boundary audit; non-possibile = convergenza a 1 dall'alto nel perimetro osservato.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_unit_boundary_audit.py`.\n- Run valido: `python tools/exp_vc_unit_boundary_audit.py --input tools/data/vc_fit_model_gate_20260509_1400.json --out tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Un tentativo di ricomputo esteso su N alti non ha prodotto deposito leggibile in tempo utile e non e' usato come evidenza.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge solo lo script unit-boundary, il JSON 1457 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_vc_unit_boundary_audit.py`\n- Data: `tools/data/vc_unit_boundary_audit_20260509_1457.json`\n- Report: `tools/data/reports/agent_20260509_1457.md`\n", tools/data/preflight/lab_graph.json_backup_pre_1724_public_rollback_20260515:2015: "content_full": "# Agent Report - Boundary Gate Transfer Matrix\n**Date**: 2026-05-09 14:44\n**Piano**: 106\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - denominator gate transfers as order/null operator, not as QxG closure\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_state -> transfer_state -> denominator_state`; observable=stable canonical observables contro permutation null e layer classification; operator=`exp_denominator_gate_transfer_matrix.py`; generator=`DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`; denominator=4 perimetri sintetici QxG continuo/discreto, 4096 gaps, 24 replicates, 11 beta layers, 40 shuffle baselines; non_possible=chiamare chiusura QxG, legge GUE/Poisson o endpoint-stable universale; not_tested=perimetro fisico reale, fit `V_c`, nuovi domini autoricerca.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo 13/13 transfer sul perimetro base, il confine non chiede un altro blank audit. Chiede se il gate resta gate quando non porta piu' le etichette GUE/Poisson.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY completo 13/13 + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: ordine coerente / null permutato; punto-zero = layer beta dove il segnale conserva osservabili stabili ma la classificazione diventa ambigua.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate viene letto come mappa di passaggio tra stato coerente, stato null e denominatore, non come tassonomia locale.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter transfer matrix, shuffle marginal-preserving. Entrano per trasferire il gate senza importare il label GUE/Poisson.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione del deposito 1437 senza ripetere il perimetro base; CE-0117: cascata della possibilita solo dove il gate cambia perimetro; YSN DeltaLink: `base closure -> transfer matrix -> beta blank`.\n- **Proto-ipotesi**: il gate BOUNDARY trasferisce quando il polo coerente ha osservabili one-sided contro il proprio null e il polo illusorio collassa; non trasferisce come endpoint-stable universale.\n- **Possibile / non-possibile**: possibile = usare il gate come audit di ordine su perimetri sintetici QxG; non-possibile = dichiarare ponte QxG o usare beta 0.3 come classe decisa.\n- **Proiezione**: misurare, per ogni perimetro, osservabili one-sided, stable count ai poli, distanza endpoint e layer ambiguo.\n\n## Claim Under Test\n> Il gate BOUNDARY trasferisce fuori dal perimetro base se ogni perimetro mostra osservabili one-sided al polo coerente, collasso al polo null permutato, e un layer blank/ambiguous esplicito prima del passaggio.\n\n## Question\nQuando il gate viene mosso da BOUNDARY base a quattro perimetri QxG sintetici, resta transfer, cade, o produce un blank strutturale?\n\n## Experiment Design\n- Strumento: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Perimetri: `DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`.\n- Null baseline: permutation shuffle della stessa multiset di gaps; conserva distribuzione marginale e distrugge ordine.\n- Criterio transfer: osservabili coherent one-sided presenti e stable count coerente alto, con stable count illusorio basso.\n- Criterio blank: layer beta con `ambiguous_fraction >= 0.5` nella classificazione gated.\n- Criterio fall: assenza di osservabili one-sided o distanza endpoint collassata. Nessun perimetro cade in questo run.\n\n## Results\n\n| perimeter | one-sided observables | stable coherent | stable illusory | endpoint distance gated | ambiguous beta |\n|---|---|---:|---:|---:|---|\n| DUALITA_golden | SR, L1, triple_var | 3.000 | 0.292 | 3.426 | 0.3 |\n| R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.000 | 0.375 | 4.409 | 0.3 |\n| T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.000 | 0.417 | 4.421 | 0.3 |\n| E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.000 | 0.292 | 4.398 | 0.3 |\n\nLayer beta 0.3:\n\n| perimeter | margin mean | ambiguous fraction | illusory label fraction |\n|---|---:|---:|---:|\n| DUALITA_golden | 0.030 | 1.000 | 0.625 |\n| R_periodic_triad | 0.043 | 1.000 | 0.583 |\n| T_markov_alternating | 0.030 | 1.000 | 0.708 |\n| E_ar1_continuity | 0.042 | 1.000 | 0.500 |\n\nLayer beta 0.4:\n\n| perimeter | margin mean | illusory label fraction |\n|---|---:|---:|\n| DUALITA_golden | 0.269 | 1.000 |\n| R_periodic_triad | 0.269 | 1.000 |\n| T_markov_alternating | 0.289 | 1.000 |\n| E_ar1_continuity | 0.284 | 1.000 |\n\n## Key Findings\n1. **Verificato: il transfer esiste sui 4/4 perimetri.** Ogni perimetro possiede osservabili coherent one-sided e collasso del polo illusorio sotto permutation null.\n2. **Verificato: non esistono endpoint-stable observables.** `endpoint_stable_observables=[]` per tutti i perimetri; il gate trasferisce come discriminatore di ordine, non come invariante simmetrico ai due poli.\n3. **Verificato: beta 0.3 e' il blank strutturale.** Tutti i perimetri hanno `ambiguous_fraction=1.0` a beta 0.3 nella classificazione gated.\n4. **Verificato: beta 0.4 passa al polo illusorio.** Tutti i perimetri hanno `illusory_label_fraction=1.0` a beta 0.4.\n5. **Vincolo: `DUALITA_golden` trasferisce con solo 3 osservabili one-sided.** SR2 e L2 non entrano nel nucleo one-sided del perimetro golden; non va scritto che il set canonico completo trasferisce ovunque.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce fuori dal perimetro base come operatore di ordine/null: 4/4 perimetri hanno polo coerente leggibile, polo illusorio collassato e blank strutturale a beta 0.3. Il transfer non e' chiusura QxG. Il transfer non e' endpoint-stable universale, perche' nessun perimetro conserva osservabili stabili su entrambi i poli.\n\nIl criterio operativo diventa:\n\n`transfer` = osservabili one-sided al polo coerente + collasso al null permutato. \n`blank` = beta 0.3, classificazione ambiguous su 4/4 perimetri. \n`fall` = assente in questo run, ma definito come perdita di one-sided observables o endpoint distance collassata.\n\n## Bicono della scoperta\n- **Due radici**: gate come copertura del denominatore / gate come classificatore del ponte.\n- **Singolare**: il layer beta 0.3, dove il segnale ha ancora stable count alto ma la decisione di classe non e' disponibile.\n- **Invariante di passaggio**: osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come transfer matrix per perimetri sintetici QxG; qui diventa non-possibile dichiarare QxG chiuso o trattare il gate come endpoint-stable law.\n\n## Consecutio\nIl prossimo nodo regressivo non e' la disponibilita del null. E' la qualita del perimetro: il gate passa su sintetici controllati, ma non ha ancora incontrato un perimetro fisico QxG row-aligned. La prossima mossa deve portare un perimetro fisico o semi-reale dentro lo stesso contratto `coherent/null/beta`, lasciando beta 0.3 come blank dichiarato e non come classe.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/reports/agent_20260509_1437.md`, `tools/exp_denominator_gate_transfer_matrix.py`, `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- **L1 hard constraint**: claim limitato a 4 perimetri sintetici, 4096 gaps, 24 replicates, 40 shuffle baselines.\n- **L2 count grezzi**: riportati 4/4 transfer, 4/4 blank a beta 0.3, 0/4 fall.\n- **L3 no silent patching**: nessuna modifica agli script; nuovo artefatto dati generato dal run.\n- **L4 edge cases**: `DUALITA_golden` trasferisce con 3 osservabili, non 5; nessun endpoint-stable observable.\n- **L5 re-discovery**: il ciclo sposta il gate fuori dal perimetro base; non ripete il residual blank closure.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer matrix QxG sintetica; non-possibile = chiusura QxG o legge endpoint-stable.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Run valido: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Output: `observables_registry=1.0.0-2026-05-06`; `observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var']`.\n- Git state: worktree gia' sporco da depositi e pubblicazioni precedenti; questo cycle aggiunge solo il JSON 1444 e il report 1444.\n\n## Files\n- Script: `tools/exp_denominator_gate_transfer_matrix.py`\n- Data: `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`\n- Report: `tools/data/reports/agent_20260509_1444.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1408: "content_full": "# Agent Report - Interfaccia component_state su bordo Anderson 3D\n**Date**: 2026-05-14 18:50 \n**Piano**: 126 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 restano active contro Poisson, ma il contrasto diretto separa solo `triple_var`; `SR` e `L1` non chiudono il bordo 3D a `L=6`. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta] \ntool_contract_ref: `tools/data/tool_contracts/component_state_SR_L1_triple_var_fit_ready_20260514_1649.json` \npreflight_anchor: `20260514_1640` VALUE_STABLE, Veritas COLLASSO rho=0.9325, falsifier coherent=true zero flag \nnot_promoted_as_physics_law: true \nssp_value: yes\n\n## Source directive\n\nQuesto ciclo segue la direttiva one-shot del 2026-05-14: ripartire da `20260514_1640`, non dal ramo `20260514_1701`, usare `component_state_SR_L1_triple_var_fit_ready_20260514_1649` come tool contract, e formulare una domanda stretta sulla riusabilita' del tester per leggere un nuovo spettro fisico. Il ramo quasiperiodico/Sturmian resta `vault`, non direzione primaria.\n\n## Prima impressione\n\nIl tester non deve predire la transizione Anderson 3D. Deve mostrare se uno spettro fisico nuovo puo' entrare nell'interfaccia e tornare come stato componente auditabile. Se il bordo non separa su `SR`, quello e' il contenuto: il tool trasferisce, la legge no.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione BOUNDARY GUE/Poisson + nodo fisico Anderson 3D.\n- **Dipolo / punto-zero**: caos metallico / localizzazione critica. Punto-zero: Poisson span-matched, perche' ogni spettro torna leggibile solo se il null fisico-matematico resta esplicito.\n- **Piano superiore**: geometria spettrale dei campi disordinati; il bordo e' letto come componente attiva/assorbita, non come fit di transizione.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' interfaccia riusabile se un nuovo spettro fisico ordinato produce component states contro Poisson e contrasto diretto dichiarato senza cambiare definizioni dal registry.\n- **Possibile / non-possibile**: possibile = usare il tool su spettro fisico B e ottenere output auditabile; non-possibile = dichiarare transizione Anderson 3D o legge universale quando `SR,L1` non separano il contrasto diretto.\n- **Proiezione**: genero 24 spettri Anderson 3D tight-binding con condizioni periodiche, `L=6`, 12 repliche per `W=4.0` e `W=16.5`; poi li passo all'interfaccia `--input-spectrum` del tester 16:49.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: l'esperimento resta nel perimetro GOE/GUE/Poisson-Anderson indicato dalla direttiva e testa il passaggio tool da 1D/WD a un nuovo bordo fisico 3D.\n- `not_drift`: non usa `20260514_1701` come guida, non torna a `V_c`, fit phi, gap label o grammatica Sturmian; usa il ramo 1701 solo come `vault`.\n\n## Claim Under Test\n\n> Il tester `component_state(SR,L1,triple_var)` puo' diventare interfaccia riusabile per leggere un nuovo spettro fisico senza perdere il rimbalzo fisico A -> matematica -> fisico B.\n\n## Physical A\n\n- **Fonte fisica A**: classi spettrali Wigner-Dyson GOE/GUE gia' rafforzate nel report `20260514_1640`.\n- **Significato fisico dichiarato**: GOE = simmetria reale/time-reversal; GUE = unitaria/no time-reversal; Poisson = livelli indipendenti/null span-matched.\n- **Stato ancora usato**: ancora stabile come anchor, non rimisurato qui.\n\n## Math Transducer\n\n- **Osservabili canonici**: `SR`, `L1`, `triple_var` da `tools/observables_registry.py`; anche `SR2,L2` restano nel payload per completezza del tool.\n- **Definizioni non cambiate**: `SR` = adjacent spacing ratio canonico; `L1` = autocorrelazione lag-1; `triple_var` = varianza raw delle somme di triple consecutive.\n- **Null**: Poisson span-matched con stesso numero di gap centrali utilizzabili.\n- **Gate componente**: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`.\n- **Tool operativo**: `python3 tools/exp_physical_sr_residue_bounce.py --input-spectrum tools/data/anderson3d_component_state_interface_input_20260514_1850.json --output tools/data/component_state_anderson3d_interface_20260514_1850.json --seed 202605141851 --null-reps 64 --central-fraction 0.5 --sign-trials 8192`\n\n## Physical B\n\n- **Dominio fisico B**: Anderson 3D tight-binding finite-size, condizioni periodiche.\n- **Perimetro**: `L=6` (`216` livelli per spettro), 12 repliche per classe, disordini `W=4.0` e `W=16.5`.\n- **Uso fisico dichiarato**: `W=4.0` come candidato metallico finite-size; `W=16.5` come bordo critico/localizzato dichiarato.\n- **Contro-perimetro**: non e' dato sperimentale, non e' limite termodinamico, non e' unfolding dedicato, non misura many-body localization.\n\n## Component State Result\n\n| class | samples vs null | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n| Anderson 3D W4 metallic candidate | 768 | SR,L1,triple_var | 0.538429 / 0.383960 / +0.154470 | 0.000122 | +3.657 | -0.224752 | 0.000122 | -2.236 | -2.327689 | 0.000122 | -3.494 |\n| Anderson 3D W16.5 critical boundary | 768 | SR,L1,triple_var | 0.509807 / 0.386906 / +0.122901 | 0.000122 | +2.741 | -0.142338 | 0.000122 | -0.945 | -1.969355 | 0.000122 | -2.504 |\n\n### Direct Contrast\n\n| contrast | paired | SR diff | p(SR) | d(SR) | SR state | L1 diff | p(L1) | d(L1) | triple diff | p(triple) | d(triple) | triple state |\n|---|---:|---:|---:|---:|---|---:|---:|---:|---:|---:|---:|---|\n| W16.5 minus W4 | 12 | -0.028623 | 0.067497 | -0.577 | not_separated | +0.087281 | 0.067253 | +0.590 | not_separated | +0.360642 | 0.003906 | +1.188 | separated |\n\n## Resulting Bounce\n\n- **Verificato**: l'interfaccia accetta un payload esterno con 24 spettri fisici e produce `poisson_contrast`, `direct_contrasts`, `component_state` e trace.\n- **Verificato**: entrambi i regimi Anderson 3D sono non-Poisson sulle tre osservabili focus.\n- **Verificato**: il contrasto diretto W16.5-W4 separa `triple_var` ma non separa `SR` e `L1` al gate dichiarato.\n- **Inferito dal perimetro**: il tool trasferisce a fisico B; il bordo 3D non e' chiuso dal piano `SR,L1` in questo setup finite-size.\n- **Non verificato**: transizione Anderson 3D, limite `L -> inf`, spettro sperimentale, unfolding dedicato.\n\n## Contamination Classification\n\n- `skill_reclassification`: report `20260514_1649` riclassificato come tool contract, non come scoperta pubblicabile.\n- `vault`: ramo `20260514_1701` Grammar-vs-Scale Boundary Gate conservato come contro-perimetro, non seguito.\n- `candidate`: Anderson 3D puo' diventare prossimo seme solo come audit di taglia/unfolding, non come legge.\n- `discard_or_counter_perimeter`: usare `SR` da solo per classificare W4/W16.5 a `L=6` e' scartato: p=0.067497.\n- `operator_directive`: vincolo one-shot di ripartire da `20260514_1640` e non dalla deriva `1701`.\n\n## Non Possible\n\n- `non_possible`: promuovere il risultato a `physics_law`.\n- `non_possible`: integrare arco di grafo senza review operatore.\n- `non_possible`: dire che `SR,L1` classificano il bordo Anderson 3D nel perimetro `L=6`, perche' il contrasto diretto non separa.\n- `non_possible`: usare il ramo `20260514_1701` come traiettoria primaria.\n- `non_possible`: accettare spettri con meno di 12 gap centrali utilizzabili; qui ogni spettro ha 107 gap centrali.\n\n## Trace Path\n\n- `input_spectrum`: `tools/data/anderson3d_component_state_interface_input_20260514_1850.json`\n- `result`: `tools/data/component_state_anderson3d_interface_20260514_1850.json`\n- `trace`: `tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl`\n- `trace_rows`: 1536\n- `trace_schema`: `event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta`\n\n## Verdict\n\n**VALUE_STABLE / TOOL CONTRACT TRANSFER**.\n\nIl tester diventa interfaccia riusabile su questo perimetro: legge un nuovo spettro fisico, conserva le definizioni del registry, produce stati componente e lascia trace. Il risultato non diventa legge fisica: Anderson 3D W4/W16.5 resta non-Poisson contro null, ma il bordo diretto separa solo `triple_var`; `SR,L1` restano insufficienti nel setup `L=6`.\n\n## Bicono della scoperta\n\n- **Due radici**: simmetria Wigner-Dyson GOE/GUE / bordo disordinato Anderson 3D.\n- **Singolare**: Poisson span-matched come punto-zero che rende confrontabili fisico A e fisico B.\n- **Invariante di passaggio**: output `component_state` su `SR,L1,triple_var` con trace auditabile.\n- **Campo di possibilita**: possibile usare il tester come interfaccia spettrale riusabile; non-possibile promuovere la separazione 3D senza curva in `L` e unfolding.\n\n## Consecutio\n\nIl prossimo seme possibile non e' \"Anderson 3D confermato\". E' un audit del tool su fisico B con taglie multiple e denominatore dichiarato: `L=5,6,7` o unfolding locale, stesso contract, stesso null. Se `SR,L1` restano non_separated e `triple_var` resta separated, il tool mostra che il bordo 3D entra prima nella componente di profondita' che nel piano pair-statistics.\n\n## Auto-Audit Finale\n\n- **Resta tool**: `component_state_SR_L1_triple_var_fit_ready_20260514_1649` come interfaccia per spettri ordinati.\n- **Resta vault**: `20260514_1701` e la grammatica Sturmian-scale.\n- **Puo' diventare prossimo seme**: audit Anderson 3D multi-size/unfolding solo se formulato come verifica del tool, non come legge fisica.\n- **Cosa non e' stato fatto**: nessuna modifica al registry, nessuna promozione physics_law, nessuna integrazione grafo.\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1441: "content_full": "# Agent Report - Grammar-vs-Scale Boundary Gate\n**Date**: 2026-05-14 17:01 \n**Piano**: 125 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY GUE-Poisson \n**verdict**: CONSTRAINT - il `gap_ratio` resta osservabile sensibile a scala/fase; la grammatica locale sopravvive come vincolo Sturmian-family, non come firma phi isolata.\nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [gap_ratio_first_two, top2_ratio, large_gap_count, label_set, grammar_excess_total, zero_excess_rate] \n**observable_contract**: claim=il residuo `gap_ratio(phi)` sopravvive solo se distingue grammatica Sturmian da mero effetto scala/densita e non collassa in controllo metallico o block shuffle; observable=`gap_ratio_first_two` + `grammar_excess_total` + `zero_excess_rate`; operator=`tools/exp_quasiperiodic_grammar_scale_gate.py`; generator=Sturmian phi/silver/bronze, balanced random phi-density, block shuffle phi-density; denominator=3 N x 3 fasi x 3 soglie per domini metallici, 4 trial per null; non_possible=claim phi-specific se silver/bronze o block shuffle trasportano la stessa grammatica; not_tested=`V_c` fit, beta atlas globale, dati fisici esterni, retention mediana/stable labels.\n\n## Respiro fuori-tempo\n\n- **Combo**: A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione `TENS_SCALE_TRASCENDENZA_LIMITE` + direzione viva BOUNDARY GUE/Poisson.\n- **Dipolo / punto-zero**: grammatica Sturmian / effetto scala-densita. Punto-zero: gap spettrale grande, perche' lo stesso evento puo' essere letto come rapporto numerico oppure come posizione in una grammatica simbolica.\n- **Piano superiore**: algebra/topologia assiomatica del linguaggio Sturmian: complessita' p(k), right-special factors e difetto palindromico attorno ai gap selezionati.\n- **Operatori laterali scelti**: `operator algebra` per leggere lo spettro tridiagonale; `boundary operator` per trattare il gap grande come bordo; `same density random` per separare grammatica da densita.\n- **Contaminazione cognitiva**: KSAR usato come reiterazione regressiva del kernel 16:56: non correggere il source, assorbire il residuo e riproiettarlo in una domanda falsificabile. CE-none: il campo vivo non espone un archivio CE operativo in questo ciclo oltre al richiamo storico CE-0117 del source; non viene inventata una voce.\n- **Proto-ipotesi**: il confine non vive nel valore singolo `gap_ratio`; vive nella differenza tra gap numerico che oscilla e grammatica locale che resta o cade sotto null.\n- **Possibile / non-possibile**: possibile = separare random-density da grammatica ordinata; non-possibile = promuovere `phi` come isolato se silver, bronze o block shuffle conservano la stessa grammatica.\n- **Proiezione**: misuro insieme `gap_ratio_first_two` e `grammar_excess_total` sugli stessi eventi di gap, con count grezzi per ogni denominatore.\n\n## Source directive\n\nLa direttiva one-shot chiedeva di assorbire il source cycle `20260514_1656`, non correggerlo, e risolvere la flag L8 rendendo tracciabile la deviazione locale se presente. Questo ciclo non promuove ne' sincronizza il source; usa il contratto minimo del repair come audit: claim corretto, count grezzi, `gap_ratio` dichiarato come testato, controlli non-zero formulati in modo scoped, auto-audit finale.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: riprende la direzione viva sul confine GUE/Poisson attraverso il nodo indicato da `dnd_scenario.py`, `TENS_SCALE_TRASCENDENZA_LIMITE`, distinguendo grammatica da scala.\n- `not_drift`: non torna al repair L6 come contenuto, non promuove QxG, non integra graph completion, non usa `V_c` fit locale come scorciatoia.\n- `deliberate_counter_perimeter`: false\n- `why_counter_perimeter`: non applicabile; la direttiva e' stata usata come contratto di audit, non come deviazione contro la direzione viva.\n\n## Claim Under Test\n\n> Il claim realmente testato ora: `gap_ratio(phi)` e' un segnale utile solo se il suo scarto dai controlli metallici resta distinguibile dalla grammatica locale che quei controlli possono trasportare. Il ciclo non testa una legge fisica QxG e non ripara il report 16:56.\n\n## Question\n\nQuando il gap spettrale grande viene letto sia come rapporto numerico sia come finestra grammaticale, cosa sopravvive: phi, la famiglia Sturmian, o solo la densita/scala?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: confine GUE/Poisson come transizione fra repulsione spettrale e indipendenza.\n- **Attraversamento matematico**: Hamiltoniana quasiperiodica tridiagonale con potenziale Sturmian e lettura dei gap tramite IDS/label.\n- **Punto fisico di ritorno**: esperimenti di catene quasiperiodiche 1D/fotoniche dove i gap spettrali distinguono ordine quasiperiodico da disorder density-matched.\n- **Controllo concretezza**: il ritorno fisico e' misura di spettri/gap in reticoli quasiperiodici o multilayer fotonici, non categoria astratta di confine.\n- **Relazione nuova**: il `gap_ratio` da solo indica instabilita di scala; la grammatica locale indica se il gap appartiene a una lingua ordinata.\n- **Osservabile/test fisico possibile**: misurare gap principali in campioni Fibonacci/silver e in campioni disorder density-matched, poi confrontare ratio e grammatica IDS.\n- **Se fallisce**: resta vincolo matematico su osservabili quasiperiodici; non scoperta fisica promuovibile.\n\n## Experiment Design\n\n- Script nuovo: `tools/exp_quasiperiodic_grammar_scale_gate.py`, nato perche' i tool densi `exp_quasiperiodic_gap_ratio_denominator.py` e `exp_gap_label_symbolic_grammar_gate.py` sono andati in timeout sul perimetro 17:01.\n- Eigensolver: `scipy.linalg.eigvalsh_tridiagonal`, stessa Hamiltoniana tridiagonale ma senza matrice densa.\n- Scope: N = 233, 377, 610; fasi = 0, 0.25, 0.5; soglie = 1.75, 2.0, 2.25; `top_k=12`; finestra grammaticale 55; k = 3,4,5,6.\n- Null baseline: `balanced_random_phi_density` e `block_shuffle_phi_density`, 4 trial per condizione.\n- Denominatori: domini metallici 27 condizioni ciascuno; null 108 condizioni ciascuno; finestre grammaticali 324 per dominio metallico, 1296 per balanced random, 1279 per block shuffle.\n- Come serve la combo: se `gap_ratio` discrimina ma la grammatica non discrimina phi dai controlli, il claim cade a vincolo scoped; se random-density conserva la grammatica, cade anche il vincolo grammaticale.\n\n## Results\n\n| mode | conditions | first_two_ratio median | grammar_excess median | zero_excess hits/total | zero_excess rate |\n|---|---:|---:|---:|---:|---:|\n| phi | 27 | 0.4509 | 0.0 | 324/324 | 1.000 |\n| silver | 27 | 1.3300 | 0.0 | 324/324 | 1.000 |\n| bronze | 27 | 1.0094 | 0.0 | 315/324 | 0.972 |\n| block_shuffle_phi_density | 108 | 0.8373 | 0.0 | 1183/1279 | 0.925 |\n| balanced_random_phi_density | 108 | 1.1447 | 103.0 | 0/1296 | 0.000 |\n\nMatched metallic comparisons:\n\n| comparison | hits/total | rate |\n|---|---:|---:|\n| phi first_two_ratio < silver | 21/27 | 0.778 |\n| phi first_two_ratio < bronze | 19/27 | 0.704 |\n| phi first_two_ratio < both | 15/27 | 0.556 |\n\nTimeout telemetry:\n\n| attempted tool | outcome |\n|---|---|\n| `exp_quasiperiodic_gap_ratio_denominator.py` broad perimetro | no output before manual reduction |\n| `exp_gap_label_symbolic_grammar_gate.py` broad perimetro | no output before manual reduction |\n| reduced dense tools with `timeout 90` | timeout code 124 |\n| tridiagonal gate | completed |\n\n## Key Findings\n\n1. **Verificato**: `gap_ratio(phi)` resta inferiore ai controlli metallici solo in 15/27 confronti con entrambi; non e' un discriminatore hard.\n2. **Verificato**: la grammatica zero-excess separa phi/silver/bronze e block-shuffle da balanced random-density. Random-density ha 0/1296 finestre zero-excess.\n3. **Vincolo**: la grammatica non isola phi. Silver trasporta 324/324 finestre zero-excess, bronze 315/324, block shuffle 1183/1279.\n4. **Inferito**: il contenuto vivo non e' `phi ha gap_ratio speciale`, ma `il gap numerico deve essere letto insieme alla grammatica che lo genera`.\n\n## Verdict\n\n**CONSTRAINT**.\n\nSopravvive un vincolo, non una scoperta phi-specific: `gap_ratio` misura una deviazione sensibile a scala/fase; la grammatica locale elimina il null random-density ma resta condivisa da controlli metallici e block-shuffle. Il confine operativo e' grammar-vs-density, non phi-vs-nonphi.\n\n## Bicono della scoperta\n\n- **Due radici**: rapporto numerico del gap / grammatica locale del gap.\n- **Singolare**: gap spettrale grande come punto-zero, prima della lettura ratio o label.\n- **Invariante di passaggio**: la finestra grammaticale zero-excess sopravvive alla famiglia Sturmian e cade nel random-density.\n- **Campo di possibilita**: possibile costruire un gate grammar-vs-density per il confine quasiperiodico; non-possibile usare `gap_ratio(phi)` come legge isolata o come promozione QxG.\n\n## Auto-audit source flags\n\n- **L8 source flag**: risolta. Il report dichiara `relation`, `why`, `not_drift`, e `deliberate_counter_perimeter=false` per rendere leggibile che non c'e' deviazione locale contro la direzione viva.\n- **Claim Under Test corretto**: risolto. Il claim non e' il repair L6 e non e' il source report; e' il test grammar-vs-scale.\n- **Rate/frequenze con count grezzi**: risolto. Tutti i rate principali riportano hits/total.\n- **Controlli non-zero scoped**: risolto. Silver, bronze e block shuffle trasportano sottostruttura; il wording resta vincolo scoped.\n- **Retention mediana vs stable labels**: non applicabile; osservabili non usati.\n- **gap_ratio**: tested.\n\n## Consecutio\n\nIl ciclo successivo non deve cercare un fit di `V_c` prima di isolare il livello giusto: densita random cade, grammatica Sturmian-family resta, phi resta solo come un membro con `gap_ratio` piu basso in parte del perimetro. La prossima domanda e' quale perturbazione rompe silver/block-shuffle senza rompere phi, oppure se il confine vero e' famiglia quasiperiodica vs disorder.\n\n## Ricadute pratiche\n\nssp_value: no. Lo script e' strumento interno di falsificazione; non e' ancora demo/prodotto.\n\n## Files\n\n- `tools/exp_quasiperiodic_grammar_scale_gate.py`\n- `tools/data/quasiperiodic_grammar_scale_gate_20260514_1701.json`\n- `tools/data/reports/agent_20260514_1701.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1492: "content_full": "# Agent Report - Repair L6 Fit-Ready Contract\n**Date**: 2026-05-14 16:56 \n**Piano**: 122 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester L6 repair \n**verdict**: CONSTRAINT / FIT-READY TESTER - il contenuto scientifico del report 16:49 resta invariato; viene riparato il residuo L6 e reso tracciabile il contratto fit-ready gia' prodotto.\nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [component_state, SR, L1, triple_var, transfer_blank_fall, fit_ready_contract_presence]\n**observable_contract**: claim=il report 16:49 diventa fit-ready per falsifier e graph completion solo se espone anche la contaminazione cognitiva che ha trasformato il deposito in strumento; observable=`component_state(SR,L1,triple_var)` + `fit_ready_contract_presence`; operator=report-level audit + `tools/graph_completion_compiler.py`; generator=nessun nuovo dominio, riuso `tools/data/component_state_fit_ready_20260514_1649.json`; denominator=report 16:49 + artifact fit-ready 16:49 + audit temporaneo graph completion; non_possible=cambiare contenuto scientifico, aggiungere domini o promuovere automaticamente il grafo; not_tested=nuovi dataset fisici, nuova separazione GOE/GUE, integrazione live del grafo.\n\n## Source directive\n\nQuesto ciclo segue la direttiva operatore one-shot `repair L6 fit-ready`: ripartire da `tools/data/reports/agent_20260514_1649.md`, non cambiare contenuto scientifico, non aggiungere domini, includere `## Contaminazione cognitiva`, riferire `tools/data/component_state_fit_ready_20260514_1649.json`, e lasciare il verdict massimo a `CONSTRAINT / FIT-READY TESTER`.\n\n## Prima impressione\n\nIl problema non era il tester: era la traccia mancante del movimento che lo aveva fatto passare da deposito locale a contratto riusabile. La riparazione vive nel report, non nel dato fisico.\n\n## Respiro fuori-tempo\n\n- **Combo**: A14 cascata + A11 combo + ponte QxG continuo/discreto + graph completion 16:49 + residuo L6 del falsifier.\n- **Dipolo / punto-zero**: risultato locale / artifact fit-ready. Punto-zero: `component_state`, perche' e' lo stesso vettore che passa da misura del deposito a interfaccia del tester.\n- **Piano superiore**: grafo della conoscenza e contratto di consumo: il graph completion deve vedere input/output, transfer/blank/fall, trace e contro-perimetro.\n- **Proto-ipotesi**: un artifact fit-ready non e' solo un file con soglie; e' un contratto con traccia cognitiva che dice quale trasformazione ha cambiato il ruolo del deposito.\n- **Possibile / non-possibile**: possibile = rendere il candidato leggibile da falsifier e graph completion come contratto fit-ready; non-possibile = promuovere QxG o aggiungere evidenza fisica nuova.\n- **Proiezione**: audit del report 16:49 e compilazione temporanea del graph completion contro l'artifact gia' prodotto.\n\n## Contaminazione cognitiva\n\n- **CE-0117**: usato come cascata della possibilita': il deposito fisico 16:40 passa da risultato misurato a potenziale strumento, poi a contratto fit-ready consumabile.\n- **KSAR**: usato come reiterazione del kernel senza cambiare ramo: il ciclo non apre un dominio nuovo, ripete il kernel `component_state(SR,L1,triple_var)` finche' il contratto dichiara transfer, blank, fall e trace.\n- **Trasformazione del contratto**: da risultato locale -> artifact fit-ready con transfer/blank/fall e trace.\n\n## Aderenza alla direzione\n\n- `relation`: local_regression\n- `why`: la regressione e' obbligata dalla direttiva: ripara il residuo L6 che impediva al report 16:49 di essere coerente per falsifier e leggibile come fit-ready contract.\n- `not_drift`: non torna a `V_c`, primi, fit locali o nuovi domini; opera solo sul contratto gia' prodotto e sul suo riconoscimento.\n\n## Experiment Design\n\nEsperimento minimo, non fisico:\n\n1. Leggere `tools/data/reports/falsifier_20260514_1649.json` per isolare il residuo.\n2. Verificare che `tools/data/component_state_fit_ready_20260514_1649.json` contenga i campi del contratto.\n3. Eseguire `python tools/graph_completion_compiler.py --cycle 20260514_1649 --out-dir /tmp/graph_completion_audit_1656` per testare il consumo dell'artifact senza mutare il grafo live.\n\n## Results\n\n### Residuo L6\n\nVerificato: il falsifier 16:49 era incoerente solo per L6 medium. Evidenza riportata dal falsifier: mancava una riga o sezione `Contaminazione cognitiva` con voce CE/KSAR o `CE-none:` verificabile.\n\n### Artifact fit-ready\n\nVerificato: `tools/data/component_state_fit_ready_20260514_1649.json` contiene:\n\n| field | state |\n|---|---|\n| `tester_id` | present |\n| `input_contract` | present |\n| `output_contract` | present |\n| `thresholds` | present |\n| `component_states` | present |\n| `transfer_blank_fall` | present |\n| `counter_perimeter` | present |\n| `trace_ref` | present |\n| `graph_candidate_ref` | present |\n| `not_promoted_as_physics_law` | `true` |\n\n### Graph completion audit\n\nVerificato su output temporaneo: il compilatore corrente riconosce `tools/data/component_state_fit_ready_20260514_1649.json` come fit-ready artifact.\n\n| check | result |\n|---|---|\n| `has_fit_ready_contract` | `true` |\n| `fit_ready_contract_present` | `true` |\n| artifact consumed | `tools/data/component_state_fit_ready_20260514_1649.json` |\n| graph live mutation | none |\n| graph promotion | none |\n\nIl `ready_count` resta `0` nell'audit temporaneo perche' il report 16:49 aveva falsifier non coerente e il grafo live non viene promosso in questo ciclo. Questa e' una sospensione corretta, non un fall del contratto.\n\n## Transfer / blank / fall\n\n- `transfer`: `SR,L1,triple_var` restano trasferiti dal deposito 16:40 al tester `component_state` come contratto input/output con trace.\n- `blank`: il ponte QxG resta non integrato; nessun dato fisico nuovo e nessuna legge fisica vengono aggiunti.\n- `fall`: il contratto cade se un ciclo successivo rimuove trace, transfer/blank/fall o contro-perimetro, oppure se usa l'artifact per promuovere il grafo senza decisione operatore.\n\n## Evidence tier\n\n- **Verificato**: il report 16:49 manca di `## Contaminazione cognitiva`, `CE-0117` e `KSAR`.\n- **Verificato**: l'artifact 16:49 ha i campi fit-ready richiesti.\n- **Verificato**: il graph completion compiler, eseguito in `/tmp`, riconosce l'artifact come fit-ready contract.\n- **Inferito**: aggiungere la sezione L6 nel report ripara il residuo del falsifier senza cambiare il contenuto scientifico.\n- **Non verificato**: esito del falsifier automatico su questo report, perche' richiede il ciclo valutativo successivo.\n\n## Trace path\n\n- `source_report`: `tools/data/reports/agent_20260514_1649.md`\n- `source_falsifier`: `tools/data/reports/falsifier_20260514_1649.json`\n- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json`\n- `source_graph_completion`: `tools/data/graph_completion/graph_completion_20260514_1649.json`\n- `temporary_graph_audit`: `/tmp/graph_completion_audit_1656/graph_completion_20260514_1649.json`\n\n## Key Findings\n\n1. **Verificato**: il residuo L6 era formale-operativo, non scientifico.\n2. **Verificato**: `component_state_fit_ready_20260514_1649.json` e' fit-ready per il compilatore corrente.\n3. **Vincolo**: il grafo QxG resta aperto e non viene promosso automaticamente.\n\n## Verdict\n\n**CONSTRAINT / FIT-READY TESTER**.\n\nIl cycle ripara il contratto del report 16:49 per L6: nomina CE-0117 e KSAR, dichiara la trasformazione da risultato locale ad artifact fit-ready con transfer/blank/fall e trace, e conserva il boundary `not_integrated_operator_decision_required`.\n\n## Bicono della scoperta\n\n- **Due radici**: report fit-ready 16:49 / residuo L6 del falsifier.\n- **Singolare**: `Contaminazione cognitiva`, la traccia che rende visibile la trasformazione del deposito in strumento.\n- **Invariante di passaggio**: un artifact fit-ready deve portare contratto tecnico e traccia del movimento che lo rende riusabile.\n- **Campo di possibilita**: possibile consumare il contratto nel graph completion; non-possibile promuovere il ponte QxG senza decisione operatore e nuovo perimetro di falsificazione.\n\n## Consecutio\n\nIl prossimo ciclo puo' rigenerare falsifier e graph completion su questo report. Se risultano coerenti, il grafo deve restare in stato `fit_ready_contract_not_integrated` o equivalente: decisione operatore prima di qualunque promozione.\n\n## Ricadute pratiche\n\nssp_value: yes. La riparazione rende l'artifact fit-ready consumabile dalla pipeline senza aggiungere domini o numeri: `tools/data/component_state_fit_ready_20260514_1649.json`.\n\n## Files\n\n- `tools/data/reports/agent_20260514_1656.md`\n- `tools/data/reports/agent_20260514_1649.md`\n- `tools/data/component_state_fit_ready_20260514_1649.json`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1531: "content_full": "# Agent Report - Component State Fit-Ready Tester\n**Date**: 2026-05-14 16:49 \n**Piano**: 122 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester \n**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica.\nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast]\n**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico.\n\n## Source directive\n\nQuesto ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo.\n\n## Prima impressione\n\nIl tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson.\n- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia.\n- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated.\n- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio.\n- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica.\n- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40.\n- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini.\n\n## Experiment Design\n\n- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`.\n- Nuova interfaccia:\n `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json`\n- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`.\n- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL.\n- Artifact prodotto:\n `tools/data/component_state_fit_ready_20260514_1649.json`\n- Smoke test piccolo:\n `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5`\n\n## Results\n\n### Artifact fit-ready\n\n`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti:\n\n| field | state |\n|---|---|\n| `tester_id` | present |\n| `input_contract` | present |\n| `output_contract` | present |\n| `thresholds` | present |\n| `component_states` | present |\n| `transfer_blank_fall` | present |\n| `counter_perimeter` | present |\n| `trace_ref` | present |\n| `graph_candidate_ref` | present |\n| `not_promoted_as_physics_law` | `true` |\n\n### Soglie\n\n| threshold | value |\n|---|---:|\n| `sign_swap_p_max` | 0.01 |\n| `abs_cohen_d_min` | 0.5 |\n| `min_usable_central_gaps` | 12 |\n| `default_null_reps` | 64 |\n| `default_central_fraction` | 0.5 |\n\n### Component states sorgente 16:40\n\n| class | SR | L1 | triple_var | source |\n|---|---|---|---|---|\n| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` |\n| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` |\n| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` |\n\n### Smoke interface\n\nVerificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro.\n\n| class | focus_signature | SR state vs Poisson | note |\n|---|---|---|---|\n| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova |\n| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova |\n\nIl `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia.\n\n## Transfer / blank / fall\n\n- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto.\n- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico.\n- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate.\n\n## Evidence tier\n\n- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`).\n- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`.\n- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro.\n- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`.\n- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica.\n- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40.\n\n## Trace path\n\n- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json`\n- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json`\n- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json`\n- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl`\n- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json`\n- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl`\n\n## Key Findings\n\n1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati.\n2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`.\n3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta.\n4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti.\n\n## Verdict\n\n**CONSTRAINT / FIT-READY TESTER**.\n\n`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica.\n\n## Bicono della scoperta\n\n- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile.\n- **Singolare**: `component_state`, stesso vettore letto come output dello strumento.\n- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run.\n- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni.\n\n## Consecutio\n\nIl prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio.\n\n## Ricadute pratiche\n\nssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/component_state_fit_ready_20260514_1649.json`\n- `tools/data/component_state_interface_smoke_20260514_1649.json`\n- `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl`\n- `tools/data/reports/agent_20260514_1649.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1570: "content_full": "# Agent Report - Rafforzamento tester fisico GOE/GUE N-curve\n**Date**: 2026-05-14 16:40 \n**Piano**: 121 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta, adjacent_gap_ratio] \n**observable_contract**: claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.\n\n## Source directive\n\nQuesto ciclo prosegue il tester L8 del report `tools/data/reports/agent_20260514_1631.md`. Non apre una nuova legge fisica e non ritorna a `prime-minus-mod6`. Il vincolo seguito e': rafforzare il tester operativo `component_state(SR,L1,triple_var)` su denominatore fisico piu' robusto distinguendo GOE/GUE, facendo una piccola curva in `N`, mantenendo Anderson 1D `W=6` come bordo finite-size, e confrontando il tester con adjacent gap ratio.\n\n## Prima impressione\n\nIl tester non deve dire \"fisica nuova\". Deve dire se lo stesso vettore resta leggibile quando il denominatore fisico smette di essere una sola classe. La separazione GOE/GUE su `SR` e il collasso di `SR` a `W=6` sono lo stesso bordo letto da due lati: simmetria caotica e assorbimento localizzato.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + direzione BOUNDARY GUE/Poisson + nodo fisico Wigner-Dyson/Anderson.\n- **Dipolo / punto-zero**: simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata. Punto-zero: adjacent gap ratio `SR`, perche' e' insieme osservabile canonico del tester e baseline fisica standard.\n- **Piano superiore**: geometria spettrale dei campi, con simmetria come vincolo e localizzazione come bordo.\n- **Operatori laterali scelti**: gauge/simmetria, spettro di Hamiltoniana, bordo finite-size. Entrano perche' obbligano il tester a passare da una sola classe GUE a classi di simmetria distinte e a un bordo disordinato.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel 16:31: non cambiare ramo, far girare il tester su denominatore piu' esterno. CE-0117 usato per la cascata della possibilita': conservare strumento riusabile, tagliare promozione a scoperta fisica.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' un tester di leggibilita' del bordo spettrale: `SR` discrimina simmetria caotica e viene assorbito al bordo Anderson `W=6`; `triple_var` conserva un residuo operativo locale.\n- **Possibile / non-possibile**: possibile = usare il tester come diagnostica compatta su spettri ordinati con classe di simmetria dichiarata; non-possibile = chiamare universale una curva a due taglie o trattare Anderson 1D come transizione fisica generale.\n- **Proiezione**: misuro GOE, GUE e Anderson `W=6` a `N=128,192`; ogni dominio viene confrontato con Poisson span-matched, poi GUE viene confrontato direttamente con GOE.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: il ciclo segue la direzione viva perche' rafforza il frame GUE/Poisson-Anderson indicato dal valutatore e rende `component_state(SR,L1,triple_var)` piu' falsificabile.\n- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; il deposito precedente resta solo origine del tester, non perimetro misurato.\n\n## Claim Under Test\n\n> Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size.\n\n## Question\n\nIl tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: statistiche Wigner-Dyson/GUE e Poisson usate nel report 16:31 come baseline del rimbalzo fisico.\n- **Attraversamento matematico**: adjacent gap ratio e osservabili canonici `SR,L1,triple_var` su gap normalizzati centrali.\n- **Punto fisico di ritorno**: classi di simmetria GOE beta=1 reale-simmetrica e GUE beta=2 hermitiana complessa, piu Anderson 1D disordinato `W=6`.\n- **Relazione nuova**: il tester non misura solo chaotic-vs-Poisson; misura anche se la simmetria Wigner-Dyson lascia una differenza leggibile prima del bordo localizzato.\n- **Osservabile/test fisico possibile**: applicare lo stesso output a spettri con simmetria nota e verificare se `SR` separa beta=1/beta=2 mentre `triple_var` resta tracciabile nel crossover disordinato.\n- **Se fallisce**: `ritorno_fisico_assente` se GOE/GUE non si separano o se Anderson `W=6` non assorbe `SR`; in quel caso il tester resta solo scaffold sintetico.\n\n## Nearest known baseline\n\n- **Wigner-Dyson / GOE level statistics**: classe beta=1, matrici reali simmetriche, simmetria di inversione temporale.\n- **Wigner-Dyson / GUE level statistics**: classe beta=2, matrici hermitiane complesse, assenza di simmetria di inversione temporale.\n- **Poisson level statistics**: livelli indipendenti, null operativo span-matched.\n- **Anderson localization / finite-size disorder crossover**: Anderson 1D `W=6` e' bordo finite-size, non transizione universale.\n- **Adjacent gap ratio**: `SR` canonico = media di `min(g_i,g_{i+1})/max(g_i,g_{i+1})`, confronto standard senza unfolding fine.\n\n## Experiment Design\n\n- Modifica minima allo script: aggiunti `goe_levels`, parametro `--ns`, trace con `n/symmetry`, riepiloghi per taglia e contrasto diretto GUE-GOE.\n- Run: `python tools/exp_physical_sr_residue_bounce.py --output tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json --seed 202605141640 --ns 128 192 --reps 64 --central-fraction 0.5 --disorders 6 --sign-trials 8192`\n- Perimetro: GOE, GUE, Anderson 1D `W=6`; due taglie; 64 repliche per taglia e dominio.\n- Null: Poisson span-matched con stesso count di gap.\n- Gate componente: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`; altrimenti `absorbed`.\n- Trace: 384 eventi JSONL.\n\n## Results\n\n### Aggregato su N=128,192\n\n| domain | samples | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n| GOE beta=1 | 128 | SR,L1,triple_var | 0.5340 / 0.3875 / 0.1465 | 0.000122 | 2.543 | -0.2594 | 0.000122 | -1.772 | -2.4571 | 0.000122 | -2.719 |\n| GUE beta=2 | 128 | SR,L1,triple_var | 0.6019 / 0.3838 / 0.2181 | 0.000122 | 4.120 | -0.2717 | 0.000122 | -1.867 | -2.5591 | 0.000122 | -3.292 |\n| Anderson 1D W=6 | 128 | triple_var | 0.3875 / 0.3871 / 0.0003 | 0.950446 | 0.006 | -0.0656 | 0.000122 | -0.460 | -0.6374 | 0.000122 | -0.683 |\n\n### Curva in N\n\n| domain | N | samples | focus active | SR real | SR delta | p(SR) | d(SR) | triple delta | p(triple) | d(triple) |\n|---|---:|---:|---|---:|---:|---:|---:|---:|---:|---:|\n| GOE beta=1 | 128 | 64 | SR,L1,triple_var | 0.5302 | 0.1406 | 0.000122 | 2.177 | -2.6110 | 0.000122 | -2.541 |\n| GUE beta=2 | 128 | 64 | SR,L1,triple_var | 0.6005 | 0.2207 | 0.000122 | 3.635 | -2.6750 | 0.000122 | -3.122 |\n| Anderson 1D W=6 | 128 | 64 | triple_var | 0.3908 | -0.0007 | 0.930428 | -0.011 | -0.6946 | 0.000122 | -0.622 |\n| GOE beta=1 | 192 | 64 | SR,L1,triple_var | 0.5379 | 0.1525 | 0.000122 | 3.079 | -2.3032 | 0.000122 | -3.129 |\n| GUE beta=2 | 192 | 64 | SR,L1,triple_var | 0.6034 | 0.2155 | 0.000122 | 4.883 | -2.4432 | 0.000122 | -3.615 |\n| Anderson 1D W=6 | 192 | 64 | triple_var | 0.3841 | 0.0013 | 0.824118 | 0.029 | -0.5801 | 0.000122 | -0.816 |\n\n### Contrasto diretto GUE-GOE\n\n| contrast | paired | SR diff GUE-GOE | p(SR) | d(SR) | SR state | triple diff | p(triple) | d(triple) | triple state |\n|---|---:|---:|---:|---:|---|---:|---:|---:|---|\n| N128 | 64 | 0.0704 | 0.000122 | 1.299 | separated | -0.2100 | 0.000122 | -1.898 | separated |\n| N192 | 64 | 0.0655 | 0.000122 | 1.633 | separated | -0.2030 | 0.000122 | -2.088 | separated |\n\n## Evidence tier\n\n- **Verificato / strong per tester**: GOE e GUE sono entrambi active contro Poisson span-matched su `SR,L1,triple_var`.\n- **Verificato / class separation**: GUE-GOE si separa su `SR` in entrambe le taglie (`+0.0704` a `N=128`, `+0.0655` a `N=192`, p=0.000122).\n- **Verificato / size readability**: il pattern resta leggibile nelle due taglie: GUE ha `SR` circa 0.6005/0.6034, GOE circa 0.5302/0.5379.\n- **Verificato / operational boundary**: Anderson `W=6` assorbe `SR` in entrambe le taglie (`p=0.930428` a `N=128`, `p=0.824118` a `N=192`) mentre `triple_var` resta active.\n- **Inferito dal perimetro**: il tester distingue simmetria caotica e bordo localizzato nel setup sintetico.\n- **Non verificato**: dati sperimentali, unfolding dedicato, classi GSE, Anderson 3D, many-body localization, limite asintotico.\n\n## Trace path\n\n- `evidence_tier`: strong per tester operativo, non per legge fisica.\n- `trace_path`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl`\n- `trace_rows`: 384\n- `trace_schema`: `event,label,symmetry,rep,n,n_gaps,real,null,delta`\n\n## Key Findings\n\n1. **Verificato**: `SR` coincide con adjacent gap ratio e separa GOE/GUE nel verso fisico atteso: GUE > GOE > Poisson.\n2. **Verificato**: `component_state(SR,L1,triple_var)` resta leggibile fra `N=128` e `N=192`; non collassa per cambio taglia minimo.\n3. **Verificato**: Anderson 1D `W=6` resta bordo finite-size: `SR` e' absorbed, `triple_var` resta active.\n4. **Inferito**: il tester 16:31 passa da \"GUE contro Poisson\" a \"simmetria Wigner-Dyson contro bordo Poisson-localizzato\".\n5. **Ambiguo dichiarato**: la stabilita' su due taglie non e' limite asintotico; e' leggibilita' operativa minima.\n\n## Verdict\n\n**CONSTRAINT / TESTER OPERATIVO RAFFORZATO**.\n\nIl ciclo rafforza lo strumento, non il claim fisico. `component_state(SR,L1,triple_var)` ora ha un denominatore piu' robusto: distingue GOE e GUE tramite adjacent gap ratio, resta leggibile su due taglie, e mantiene Anderson `W=6` come bordo in cui `SR` viene assorbito prima del residuo `triple_var`.\n\n## Bicono della scoperta\n\n- **Due radici**: simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.\n- **Singolare**: adjacent gap ratio `SR`, punto-zero che e' insieme baseline fisica e componente del tester.\n- **Invariante di passaggio**: `SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.\n- **Campo di possibilita**: possibile applicare il tester a spettri con simmetria dichiarata e denominatore tracciato; non-possibile dichiarare nuova legge fisica o transizione universale con questo perimetro.\n\n## Consecutio\n\nIl prossimo passo non e' aggiungere dominio per ampiezza. E' rendere il tester leggibile come strumento: interfaccia unica per spettro ordinato, classe attesa opzionale, output `component_state`, contrasto Poisson e, se due classi sono presenti, contrasto diretto. La promozione resta `soluzione/tool`, non `scoperta fisica`.\n\n## Ricadute pratiche\n\nssp_value: yes. Il ciclo produce un tester piu' riusabile: dato uno spettro ordinato o un ensemble, calcola `SR,L1,triple_var`, confronta Poisson span-matched, separa GOE/GUE se presenti, e ritorna `component_state` con trace auditabile.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json`\n- `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl`\n- `tools/data/reports/agent_20260514_1640.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1621: "content_full": "# Agent Report - Chiusura tester rimbalzo fisico W=6\n**Date**: 2026-05-14 16:31 \n**Piano**: 121 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GUE-Anderson \n**verdict**: CONSTRAINT / TESTER OPERATIVO - il rimbalzo fisico 16:12 regge come tester riusabile: `SR` e' active in GUE e absorbed nel bordo Anderson `W=6`, mentre `triple_var` resta active come residuo intermedio. Non e' una nuova legge fisica. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta, adjacent_gap_ratio] \n**observable_contract**: claim=il risultato 16:12 va riformulato come tester operativo del confine repulsione/Poisson, non come scoperta fisica; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Anderson 1D `W=6`; denominator=96 repliche per dominio, 95 gap centrali per spettro, 192 eventi trace; null=Poisson span-matched stesso count; non_possible=claim fisico nuovo o legge di transizione se il confronto resta sintetico, finite-size e senza dati sperimentali/unfolding dedicato.\n\n## Source directive\n\nQuesto ciclo segue la direttiva one-shot di chiusura del rimbalzo fisico concreto. Il counter-perimeter e' intenzionale: uscire dal bacino `prime-minus-mod6` per verificare se il deposito produce un ponte fisico. Il vincolo seguito e' chiudere il residuo del falsifier sul report `agent_20260514_1612.md`: rendere visibile la fonte della deviazione dalla direzione viva, nominare la baseline classica vicina, e riformulare il risultato come tester operativo.\n\n## Prima impressione\n\nIl punto-zero non e' \"i primi somigliano alla fisica\". Il punto-zero e' il bordo dove la repulsione dei livelli smette di decidere la classe e lascia un residuo di memoria locale. A `W=6`, `SR` cade; `triple_var` resta. Questo basta per un tester, non per una legge.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + direzione BOUNDARY GUE/Poisson + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: repulsione spettrale caotica / indipendenza Poisson-localizzata. Punto-zero: Anderson `W=6`, dove adjacent gap ratio si avvicina al null ma una misura di triple resta leggibile.\n- **Piano superiore**: geometria spettrale dei campi, letta come bordo operativo fra statistiche Wigner-Dyson e Poisson.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` non nomina una nuova fisica; nomina un tester di crossover finite-size: nearest-neighbor repulsion viene assorbita prima della memoria locale di triple.\n- **Possibile / non-possibile**: possibile = riusare il tester su spettri ordinati fisici o simulati; non-possibile = promuovere il pattern sintetico a legge fisica senza baseline, classe di simmetria, unfolding e dati esterni.\n- **Proiezione**: rerun focalizzato `GUE -> Poisson span-matched -> Anderson 1D W=6`, con 96 repliche e trace JSONL.\n\n## Contaminazione cognitiva\n\nCE-0001/KSAR usato come reiterazione del kernel 16:12: non cambiare ramo, far diventare il deposito un tester riusabile. CE-0117 usato per la cascata della possibilita': il ciclo conserva solo il passaggio che apre misura ulteriore e taglia la promozione a fisica nuova.\n\n## Aderenza alla direzione\n\n- `relation`: deliberate_counter_perimeter\n- `why`: serve la direzione viva perche' tratta il confine GUE/Poisson come terzo incluso operativo e concentra il bordo `W=6` dove `SR,L1` cedono e `triple_var` resta.\n- `not_drift`: non ritorna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetici; il deposito aritmetico resta solo fonte della domanda, non destinazione.\n\n## Nearest known baseline\n\nLa baseline classica piu' vicina e' la statistica dei livelli quantistici:\n\n- **Wigner-Dyson / GUE level statistics**: repulsione dei livelli in sistemi quantistici caotici senza simmetria di inversione temporale.\n- **Poisson level statistics**: livelli indipendenti, tipici del limite integrabile/localizzato.\n- **Anderson localization / finite-size disorder crossover**: in 1D il disordine porta verso localizzazione; qui `W=6` e' usato come bordo finite-size, non come transizione universale.\n- **Adjacent gap ratio**: `SR` canonico del registry, cioe' media di `min(g_i,g_{i+1})/max(g_i,g_{i+1})`, usato come confronto standard senza unfolding fine.\n\nQuindi il claim massimo ammesso e': riuso operativo / tester riusabile / ponte osservabile.\n\n## Claim Under Test\n\n> Il vettore `component_state(SR,L1,triple_var)` resta leggibile come tester operativo del confine GUE-Anderson: `SR` e' active in GUE, viene assorbito al bordo Anderson `W=6`, e `triple_var` puo' restare active nella zona intermedia.\n\n## Experiment Design\n\n- Base: report `agent_20260514_1612.md`, stesso script e stesse definizioni canoniche.\n- Rerun: `python tools/exp_physical_sr_residue_bounce.py --output tools/data/physical_sr_residue_bounce_20260514_1631_w6.json --seed 202605141631 --n 192 --reps 96 --central-fraction 0.5 --disorders 6 --sign-trials 8192`\n- Ensemble: GUE hermitiano `N=192`; Anderson 1D tight-binding `N=192`, `W=6`.\n- Finestra: 50% centrale, 95 gap normalizzati per media.\n- Null: Poisson span-matched con stesso count.\n- Gate componente: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`; altrimenti `absorbed`.\n- Trace: 192 eventi JSONL.\n\n## Results\n\n| domain | samples | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n| GUE chaotic proxy | 96 | SR,L1,triple_var | 0.5950 / 0.3880 / 0.2070 | 0.000122 | 4.284 | -0.3179 | 0.000122 | -2.527 | -2.6163 | 0.000122 | -3.263 |\n| Anderson 1D W=6 | 96 | triple_var | 0.3955 / 0.3849 / 0.0106 | 0.028439 | 0.229 | -0.0333 | 0.022092 | -0.243 | -0.5510 | 0.000122 | -0.544 |\n\n## Evidence tier\n\n- **Verificato / strong per tester**: GUE mantiene `SR` active contro Poisson span-matched (`delta=0.2070`, `p=0.000122`, `d=4.284`).\n- **Verificato / operational boundary**: Anderson `W=6` assorbe `SR` nel gate dichiarato (`p=0.028439`, `d=0.229`), pur avendo delta positivo piccolo. Il linguaggio corretto e' assorbito, non zero.\n- **Verificato / moderate residual**: `triple_var` resta active a `W=6` (`p=0.000122`, `d=-0.544`). E' residuo operativo intermedio, non prova di classe fisica nuova.\n- **Inferito dal perimetro**: il tester distingue repulsione nearest-neighbor e memoria locale di triple nel setup sintetico GUE/Anderson.\n- **Non verificato**: dati sperimentali, Anderson 3D, many-body localization, GOE/GSE, unfolding dedicato, robuste curve di taglia `N`.\n\n## Autopsy lab-native della trace\n\nIl run produce `tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl` con 192 righe: 96 GUE e 96 Anderson `W=6`. Il deposito e' leggibile senza API esterne e senza dipendenza dall'operatore. Il residuo del falsifier 16:12 non era nei numeri, ma nel nome del risultato: `PHYSICAL BOUNCE PRESENT` poteva suonare come promozione fisica. Il nodo regressivo e' il claim header; va chiuso come `TESTER OPERATIVO`.\n\n## Key Findings\n\n1. **Verificato**: `SR` in GUE e' adjacent gap ratio Wigner-Dyson/GUE-like, non osservabile inventato.\n2. **Verificato**: a `W=6`, `SR` e `L1` sono absorbed dal gate, mentre `triple_var` resta active.\n3. **Verificato**: il confronto Poisson span-matched basta come baseline operativa minima, ma non sostituisce letteratura, dati o unfolding.\n4. **Inferito**: il deposito `prime-minus-mod6` genera un tester trasferibile solo perche' viene spogliato del claim aritmetico e proiettato su un fenomeno fisico nominato.\n5. **Ambiguo dichiarato**: Anderson 1D e' finite-size disorder crossover; non viene usato come transizione di fase universale.\n\n## Verdict\n\n**CONSTRAINT / TESTER OPERATIVO**.\n\nIl risultato 16:12 regge come ponte osservabile e strumento di misura riusabile. Non regge come `NEW` fisico. La forma corretta e': `component_state(SR,L1,triple_var)` testa se uno spettro conserva repulsione nearest-neighbor contro Poisson e se lascia un residuo di memoria locale nella zona intermedia.\n\n## Bicono della scoperta\n\n- **Due radici**: Wigner-Dyson/GUE level statistics / Poisson level statistics.\n- **Singolare**: null Poisson span-matched su stesso count.\n- **Invariante di passaggio**: `SR` cade prima di `triple_var` nel bordo `W=6`.\n- **Campo di possibilita**: applicare il tester a spettri simulati o sperimentali con denominatore dichiarato.\n- **Campo non-possibile**: chiamare scoperta fisica un pattern che resta sintetico, finite-size e baseline-minimal.\n\n## Consecutio\n\nIl passo successivo valido non e' tornare ai primi. E' portare lo stesso tester su un denominatore fisico piu' forte: GOE/GUE scelto per simmetria, Anderson 3D o many-body localization se serve una transizione reale, curve in `N`, e confronto con adjacent gap ratio standard piu' unfolding controllato.\n\n## Ricadute pratiche\n\nssp_value: yes. Il ciclo chiude un tester riusabile: dato uno spettro ordinato, calcola `SR,L1,triple_var`, genera un null Poisson span-matched, e restituisce `component_state` come lettura operativa del bordo repulsione/localizzazione.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/physical_sr_residue_bounce_20260514_1631_w6.json`\n- `tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl`\n- `tools/data/reports/agent_20260514_1631.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1666: "content_full": "# Agent Report - Rimbalzo fisico del residuo SR\n**Date**: 2026-05-14 16:12 \n**Piano**: 120 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / deliberate_counter_perimeter fisico L8 \n**verdict**: CONSTRAINT - il trasduttore `SR` assorbito vs residuo `L1,triple_var` produce un test fisico concreto su spettri GUE/Anderson; nel run sintetico il ritorno fisico e' presente ma resta proxy numerico, non misura sperimentale \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta] \n**observable_contract**: claim=il cedimento selettivo di `SR` puo' essere formulato come test fisico A->B; observable=componenti canoniche attive/assorbite contro null Poisson span-matched; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Hamiltoniano Anderson 1D tight-binding; denominator=48 repliche per dominio, 95 gap centrali per spettro; non_possible=rimbalzo fisico assente se non si nomina B concreto oppure se `SR` non discrimina GUE da Anderson localizzato; not_tested=dati sperimentali reali, unfolding fine, classi di simmetria GOE/GSE, many-body localization.\n\n## Prima impressione\n\nIl risultato non chiede un altro ingrandimento dei primi. Chiede se la componente che cade (`SR`) e quelle che resistono (`L1,triple_var`) nominano una distinzione fisica: repulsione spettrale presente o assorbita dal bordo Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione `TENS_SCALE_TRASCENDENZA_LIMITE` + deposito `prime_minus_mod6_z_vector`.\n- **Dipolo / punto-zero**: caos quantistico / localizzazione. Punto-zero: stesso numero di livelli e stesso span con null Poisson span-matched.\n- **Piano superiore**: geometria spettrale dei campi; il bordo non e' una classe aritmetica ma un lettore di repulsione/indipendenza nei gap energetici.\n- **Proto-ipotesi**: `SR` misura la repulsione locale assorbibile quando lo spettro torna Poisson; `L1,triple_var` misurano memoria di sequenza oltre il solo nearest-neighbor ratio. Il deposito prime-minus-mod6 diventa trasduttore, non destinazione.\n- **Possibile / non-possibile**: possibile = formulare un test fisico su spettri misurabili; non-possibile = dichiarare claim fisico se il ritorno resta \"sistema discreto\" o \"confine\" senza Hamiltoniano concreto.\n- **Proiezione**: GUE come sorgente fisica A; null Poisson span-matched; Anderson 1D con disordine `W=0.5,2,6,12` come ritorno fisico B.\n\n## Contaminazione cognitiva\n\nCE-0117 usato come cascata della possibilita': il ciclo non resta nel potenziale aritmetico, ma attraversa struttura matematica e torna a un setup fisico. CE-0001/KSAR usato per trasformare il kernel 16:05 in strumento riusabile invece di riscrivere solo un'analogia.\n\n## Aderenza alla direzione\n\n- `relation`: deliberate_counter_perimeter\n- `why`: la direttiva operatore chiede di non proseguire come ottimizzazione locale prime-minus-mod6; il ciclo usa il cedimento `SR` vs `L1,triple_var` come trasduttore e lo porta su spettri fisici.\n- `not_drift`: il deposito prime-minus-mod6 non e' destinazione; decide solo quale domanda portare a GUE/Anderson.\n\n## Ritorno fisico\n\n- **Punto fisico sorgente concreto**: spettri energetici di sistemi quantistici caotici modellati da GUE, proxy per billiard o sistemi senza simmetria di inversione temporale.\n- **Attraversamento matematico**: null Poisson span-matched con osservabili canonici `SR,L1,triple_var`; e' l'analogo fisico del contro-bordo che prova ad assorbire la componente nearest-neighbor.\n- **Punto fisico di ritorno concreto**: Hamiltoniano tight-binding Anderson 1D con disordine crescente, interrogabile tramite spacing degli autovalori e transizione verso statistiche Poisson/localizzate.\n- **Relazione nuova o limite**: il run mostra una formulazione fisica del cedimento selettivo: `SR` e' attivo nel GUE e assorbito nel limite Anderson localizzato `W=12`. A `W=6`, `SR` e `L1` sono assorbiti mentre `triple_var` resta attivo: il ritorno fisico separa nearest-neighbor repulsion e memoria di triple locali.\n- **Osservabile/test fisico possibile**: misurare spettri energetici in un sistema Anderson-like, costruire un ensemble di finestre spettrali, confrontare `SR,L1,triple_var` contro null Poisson span-matched con sign-swap row-local.\n- **Stato finale**: `rimbalzo_fisico_presente`.\n\n## Claim Under Test\n\n> Il cedimento selettivo di `SR` osservato nel deposito prime-minus-mod6 puo' tornare come criterio fisico: `SR` resta attivo in spettri caotici GUE e viene assorbito in spettri Anderson localizzati contro un null Poisson span-matched.\n\n## Experiment Design\n\n- Script: `tools/exp_physical_sr_residue_bounce.py`.\n- Ensemble: GUE hermitiano `N=192`, Anderson 1D `N=192`.\n- Repliche: 48 per dominio.\n- Finestra: 50% centrale dello spettro, 95 gap normalizzati per media.\n- Null: livelli Poisson uniformi span-matched con stesso count.\n- Gate componente: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`; altrimenti `absorbed`.\n- Trace: 240 eventi JSONL, uno per replica/dominio.\n\n## Results\n\n| domain | samples | focus active | SR real/null/delta | p(SR) | L1 delta | p(L1) | triple delta | p(triple) |\n|---|---:|---|---:|---:|---:|---:|---:|---:|\n| GUE chaotic proxy | 48 | SR,L1,triple_var | 0.5936 / 0.3881 / 0.2055 | 0.000244 | -0.3047 | 0.000244 | -2.4262 | 0.000244 |\n| Anderson W=0.5 | 48 | SR,L1,triple_var | 0.6573 / 0.3903 / 0.2671 | 0.000244 | -0.3240 | 0.000244 | -2.5721 | 0.000244 |\n| Anderson W=2 | 48 | SR,L1,triple_var | 0.4364 / 0.3774 / 0.0589 | 0.000244 | -0.1373 | 0.000244 | -1.6244 | 0.000244 |\n| Anderson W=6 | 48 | triple_var | 0.3984 / 0.3824 / 0.0160 | 0.035392 | -0.0589 | 0.002929 | -0.7407 | 0.000244 |\n| Anderson W=12 | 48 | [] | 0.3798 / 0.3836 / -0.0038 | 0.585062 | -0.0262 | 0.168904 | -0.4147 | 0.002685 |\n\nNota gate: a `W=6`, `L1` ha p sotto soglia ma effect `d=-0.443`, quindi e' assorbito dal gate dichiarato. A `W=12`, `triple_var` ha p sotto soglia ma effect `d=-0.442`, quindi non supera il gate.\n\n## Key Findings\n\n1. **Verificato**: nel proxy GUE, `SR` resta attivo contro Poisson span-matched (`delta=0.2055`, `p=0.000244`, `d=4.872`).\n2. **Verificato**: nel ritorno Anderson localizzato `W=12`, `SR` viene assorbito (`delta=-0.0038`, `p=0.585062`, `d=-0.076`).\n3. **Verificato**: la zona intermedia `W=6` separa componenti: `SR` assorbito, `triple_var` attivo.\n4. **Inferito dal perimetro**: il deposito prime-minus-mod6 produce un osservabile fisico formulabile: non \"i primi sono fisica\", ma \"la componente `SR` boundary-sensitive corrisponde al test di repulsione spettrale contro Poisson\".\n5. **Ambiguo dichiarato**: Anderson 1D non ha vera transizione estesa/localizzata nel limite infinito; qui il risultato e' un finite-size disorder crossover, non claim universale sulla transizione.\n\n## Verdict\n\n**CONSTRAINT / PHYSICAL BOUNCE PRESENT**.\n\nIl rimbalzo fisico esiste come test: GUE -> span-matched Poisson -> Anderson 1D. Il risultato non cristallizza una nuova legge fisica. Formalizza un ponte interrogabile: quando lo spettro torna localizzato/Poisson, `SR` viene assorbito; nella zona di crossover, `triple_var` puo' restare come residuo oltre nearest-neighbor.\n\n## Bicono della scoperta\n\n- **Due radici**: repulsione spettrale caotica / assorbimento Poisson localizzato.\n- **Singolare**: null Poisson span-matched con stesso count.\n- **Invariante di passaggio**: `component_state` attivo/assorbito per `SR,L1,triple_var`.\n- **Campo di possibilita**: usare il vettore a due strati come test su spettri fisici reali.\n- **Campo non-possibile**: chiamare fisico un rimbalzo che resta solo analogia aritmetica.\n\n## Consecutio\n\nIl prossimo ciclo fisico deve sostituire il proxy sintetico con un denominatore piu vicino al laboratorio: GOE/GUE scelto per simmetria dichiarata, Anderson 3D o many-body localization se si vuole una transizione fisica vera, unfolding controllato e confronto con dati o letteratura. Il deposito prime-minus-mod6 resta utile solo come generatore di osservabili, non come prova del dominio fisico.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script crea un tester riusabile per spettri ordinati fisici: basta sostituire il generatore di livelli con dati sperimentali o simulazioni esterne e il contratto `component_state` resta identico.\n\n## Files\n\n- `tools/exp_physical_sr_residue_bounce.py`\n- `tools/data/physical_sr_residue_bounce_20260514_1612.json`\n- `tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl`\n- `tools/data/reports/agent_20260514_1612.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1711: "content_full": "# Agent Report - Limite di scala prime-minus-mod6\n**Date**: 2026-05-14 16:05 \n**Piano**: 119 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / prime-minus-mod6 scale limit (0.6) \n**verdict**: PARTIAL - `prime_minus_mod6_z_vector(SR,L1,triple_var)` replica completo a 4096/8192; a 16384 cade `SR` contro `mod6_span_matched` con denominatore valido \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, prime_minus_mod6_z_vector, provider, offset, row_id, window_gaps, source_mode, case_state, row_local_label_swap_p, denominator_contract, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_vector(SR,L1,triple_var)` regge a 4096/8192 e mostra il primo limite oltre 8192 solo dopo `min_paired_rows` pre-dichiarato; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py` con `--min-paired-rows 10` e trace obbligatoria; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `mod6_index_aligned` e `mod6_span_matched`; denominator=righe provider-offset paired, soglia atomica `min_paired_rows=10`; non_possible=vettore completo falsificato se un osservabile focus ha delta medio <= 0 o p > 0.01 in un antagonista con denominatore valido; not_tested=origine analitica del delta, scale oltre 16384, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl limite di scala non nasce da righe sparse: nasce quando il contro-bordo span-matched assorbe la componente `SR` mentre `L1` e `triple_var` restano prime-specific.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + TENS_SCALE_TRASCENDENZA_LIMITE + vettore prime-minus-mod6.\n- **Dipolo / punto-zero**: vettore completo / vettore parziale. Punto-zero: stesso denominatore provider-offset con `min_paired_rows=10`.\n- **Piano superiore**: topologia assiomatica del bordo con boundary operator; il bordo non e' una label, e' una classe di componenti che sopravvivono o cadono sotto antagonista.\n- **Operatori laterali scelti**: boundary operator e finite-size scaling. Il primo localizza quale componente passa il bordo; il secondo impedisce di chiamare scala cio' che e' solo 4096/8192.\n- **Contaminazione cognitiva**: CE-0117 usato come cascata della possibilita' dal ciclo 14:58: trace obbligatoria -> denominatore atomico -> test oltre 8192. KSAR usato come reiterazione del kernel senza cambiare osservabile.\n- **Proto-ipotesi**: il vettore prime-minus-mod6 ha due strati: `L1,triple_var` restano nel residuo geometrico della selezione prime; `SR` e' la componente piu' esposta al contro-bordo span-matched quando la scala raddoppia oltre 8192.\n- **Possibile / non-possibile**: possibile = formalizzare il limite come cedimento selettivo di componente; non-possibile = cristallizzare il vettore completo come invariante oltre 8192.\n- **Proiezione**: patch `denominator_contract`, run 4096/8192 con `min_paired_rows=10`, run 16384 solo perche' 8192 ha passato il contratto.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: esegue la ripetizione provider-indipendente con micro-trace, `min_paired_rows` pre-dichiarato, replica 4096/8192 e primo test oltre 8192 solo dopo denominatore stabile.\n- `not_drift`: non torna a `V_c`, fit, label locali o GUE/Poisson; misura esclusivamente il limite del vettore prime-minus-mod6.\n\n## Claim Under Test\n\n> Nel contratto `min_paired_rows=10`, il vettore `prime_minus_mod6_z_vector(SR,L1,triple_var)` resta completo a 4096/8192; oltre 8192 il primo cedimento, se appare, identifica la componente non scalante.\n\n## Question\n\nIl limite oltre 8192 e' sparsita' del denominatore o rottura strutturale di una componente del vettore?\n\n## Experiment Design\n\n- Patch regressiva: `tools/exp_prime_vs_mod6_sr_boundary.py` ora scrive `denominator_contract` nel JSON e nella trace.\n- Soglia pre-run: `min_paired_rows=10`.\n- Scale: 4096, 8192, 16384 gap.\n- Offsets: `0 512 1024 1536 2048 2560 3072 3584 4096 4608`.\n- Null: row-local label swap, 4096 trials.\n- Criterio vettoriale: per ogni antagonista, `SR`, `L1`, `triple_var` devono avere delta medio > 0 e p <= 0.01.\n- Gate scala: 16384 eseguito solo dopo 8192 con denominatore valido.\n\n## Results\n\n| window | antagonist | rows | denom pass | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | vector complete | verdict |\n|---:|---|---:|---|---:|---:|---:|---:|---:|---:|---|---|\n| 4096 | mod6_index | 13 | yes | 57.569 | 0.0002 | 60.310 | 0.0002 | 23.073 | 0.0002 | yes | CONFIRMED |\n| 4096 | mod6_span | 13 | yes | 32.915 | 0.0005 | 29.257 | 0.0005 | 26.383 | 0.0005 | yes | CONFIRMED |\n| 8192 | mod6_index | 10 | yes | 85.623 | 0.0027 | 89.234 | 0.0027 | 35.046 | 0.0027 | yes | CONFIRMED |\n| 8192 | mod6_span | 10 | yes | 35.145 | 0.0012 | 36.967 | 0.0012 | 34.120 | 0.0012 | yes | CONFIRMED |\n| 16384 | mod6_index | 10 | yes | 121.390 | 0.0015 | 127.047 | 0.0015 | 51.294 | 0.0015 | yes | CONFIRMED |\n| 16384 | mod6_span | 10 | yes | 14.687 | 0.0222 | 19.466 | 0.0034 | 27.810 | 0.0027 | no | PARTIAL |\n\nTrace JSONL: 73 eventi a 4096, 58 a 8192, 58 a 16384.\n\n## Key Findings\n\n1. **Verificato**: il denominatore non cade. `min_observed_paired_rows` = 13, 10, 10 per 4096, 8192, 16384.\n2. **Verificato**: il vettore completo replica a 4096 e 8192 contro entrambi gli antagonisti.\n3. **Verificato**: a 16384 `mod6_index_aligned` resta completo, mentre `mod6_span_matched` rompe solo `SR`: delta positivo 14.687 ma p=0.0222.\n4. **Verificato**: `L1` e `triple_var` restano significativi anche a 16384 contro `mod6_span_matched`.\n5. **Inferito dal perimetro**: `SR` misura uno strato piu vicino alla grammatica del pre-bordo span-matched; `L1,triple_var` misurano il residuo piu resistente della selezione prime.\n\n## Verdict\n\n**PARTIAL / SCALE LIMIT LOCALIZED**.\n\nNel perimetro dichiarato, `prime_minus_mod6_z_vector(SR,L1,triple_var)` e' completo fino a 8192 gap. A 16384 il limite non e' il denominatore: e' `SR` contro `mod6_span_matched`. La forma scalante corrente diventa `prime_minus_mod6_z_vector(L1,triple_var)`; `SR` resta componente di audit da isolare.\n\n## Bicono della scoperta\n\n- **Due radici**: completezza vettoriale 4096/8192 / cedimento selettivo 16384.\n- **Singolare**: denominatore provider-offset con `min_paired_rows=10`.\n- **Invariante di passaggio**: `L1,triple_var` passano anche quando `SR` cade.\n- **Campo di possibilita**: separare il vettore in strato robusto (`L1,triple_var`) e strato boundary-sensitive (`SR`).\n- **Campo non-possibile**: promuovere `SR,L1,triple_var` come vettore indivisibile oltre 8192.\n\n## Consecutio\n\nIl prossimo passaggio deve isolare `SR`: stesso denominatore, stessa trace, confronto fra `mod6_span_matched` e un null che conserva span ma distrugge solo la grammatica locale. Se `SR` resta p>0.01 mentre `L1,triple_var` passano, il claim va riscritto come vettore a due strati.\n\n## Ricadute pratiche\n\nssp_value: yes. `denominator_contract` rende il limite auditabile nella trace e impedisce di confondere scala con sparsita'. La pipeline puo' leggere `next_scale_allowed` e `failure_mode` senza inferire dal testo del report.\n\n## Files\n\n- `tools/exp_prime_vs_mod6_sr_boundary.py`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.trace.jsonl`\n- `tools/data/reports/agent_20260514_1605.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1762: "content_full": "# Agent Report - Prime-minus-mod6 z-vector formalizzato\n**Date**: 2026-05-14 14:58 \n**Piano**: 118 \n**Tension explored**: BOUNDARY / prime-minus-mod6 residual (0.8) \n**verdict**: CONFIRMED - `prime_minus_mod6_z_vector(SR,L1,triple_var)` regge oltre 2048 gap; `has_SR` resta audit negativo, non criterio di verdict \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, prime_minus_mod6_z_vector, provider, offset, row_id, window_gaps, source_mode, case_state, sr_binary_delta_mean, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_vector(SR,L1,triple_var)` sopravvive a sottrazione row-local `6k +/- 1`; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py` con verdict vettoriale e trace obbligatoria; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `mod6_index_aligned` e `mod6_span_matched`; denominator=righe provider-offset paired, 4096 e 8192 gap, p label-swap <= 0.01 richiesto per ogni osservabile focus e ogni antagonista; non_possible=vettore falsificato se un osservabile focus ha delta medio <= 0 o p > 0.01 in un antagonista; not_tested=origine analitica del delta, scale oltre 8192 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl residuo non e' la presenza di `SR`: e' un differenziale di intensita che attraversa `SR,L1,triple_var` quando la stessa riga passa dal pre-bordo mod6 alla selezione prime.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione valutatore `prime_minus_mod6_z_vector(SR,L1,triple_var)`.\n- **Dipolo / punto-zero**: pre-bordo `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; il denominatore resta lo stesso e solo la selezione cambia.\n- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator non decide dalla label comune, decide dal differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il claim vive nella sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga fra classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0117 come cascata della possibilita' dal ciclo 13:30: `has_SR` -> delta z -> vettore `SR,L1,triple_var`; KSAR come reiterazione del kernel con nodo regressivo corretto nello script.\n- **Proto-ipotesi**: il confine prime-specific dopo mod6 non e' una proprieta' booleana; e' un vettore row-local. Quando `has_SR` e' comune al pre-bordo, il residuo resta nel segno e nella significativita' dei delta z.\n- **Possibile / non-possibile**: possibile = usare il vettore come osservabile dedicato del confine; non-possibile = usare `SR` binario, commonality o verdict nominale vecchio come discriminatore.\n- **Proiezione**: patch del verdict nello script, trace JSONL obbligatoria, run dense a 4096 e 8192 gap contro entrambi gli antagonisti mod6.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: formalizza e falsifica il residuo indicato dal valutatore separando il verdict da `has_SR`, rendendo obbligatorio il micro-trace JSONL e scalando oltre 2048 gap.\n- `not_drift`: non rientra in `V_c`, fit locali, label-set globali o frame GUE/Poisson; lavora solo il nodo regressivo del contratto prime-minus-mod6.\n\n## Claim Under Test\n\n> Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime lascia un vettore row-local positivo e significativo su `SR,L1,triple_var`; `has_SR` non decide il confine.\n\n## Question\n\nIl vettore `prime_minus_mod6_z_vector(SR,L1,triple_var)` resta completo a 4096 e 8192 gap contro antagonisti mod6 piu forti, oppure cade quando il denominatore cresce?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: QxG continuo/discreto nel dominio dei primi; il pre-bordo `6k +/- 1` contiene il vincolo aritmetico minimo prima della primalita.\n- **Attraversamento matematico**: boundary operator row-local con label-swap null.\n- **Punto fisico di ritorno**: vincolo di misura per sistemi discreti con pre-selezione forte: separare presenza di una firma da intensita residua dopo sottrazione del pre-bordo.\n- **Relazione nuova**: la transizione non vive nella label comune ma nel vettore differenziale che resta dopo trasporto sullo stesso denominatore.\n- **Osservabile/test fisico possibile**: ripetere il contratto su altri domini discreti con pre-bordo noto, mantenendo riga locale e antagonista span/index matched.\n- **Se fallisce**: `ritorno_fisico_assente`; resta osservabile aritmetico dedicato, non ponte fisico promuovibile.\n\n## Experiment Design\n\n- Script modificato: `tools/exp_prime_vs_mod6_sr_boundary.py`.\n- Modifica regressiva: `verdict()` usa `vector_summary`, non `class_summaries[\"prime\"][\"sr_rate\"]` o commonality di `SR`.\n- Trace: `--trace-jsonl` obbligatorio; ogni run scrive case, class summary, paired rows, pair summary, vector summary e verdict.\n- Run principali: `window_gaps=4096` con 13 righe paired effettive; `window_gaps=8192` con 10 righe paired effettive.\n- Antagonisti: `mod6_index_aligned` e `mod6_span_matched`.\n- Null: row-local label swap, 4096 trials.\n- Criterio vettoriale: per ogni antagonista, `SR`, `L1`, `triple_var` devono avere delta medio > 0 e p <= 0.01.\n- Run scartato come telemetria: `4096` sparse con 5 righe paired produce delta grandi ma p=0.0566..0.0661 per risoluzione insufficiente del denominatore; non decide.\n\n## Results\n\n| window | antagonist | rows | Jaccard | SR binary delta | p(SR binary) | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | vector complete | verdict |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|---|\n| 4096 | mod6_index | 13 | 0.765 | 0.000 | 1.0000 | 59.184 | 0.0007 | 62.061 | 0.0007 | 23.376 | 0.0007 | yes | CONFIRMED |\n| 4096 | mod6_span | 13 | 0.674 | 0.154 | 0.5033 | 34.747 | 0.0002 | 31.260 | 0.0002 | 26.960 | 0.0002 | yes | CONFIRMED |\n| 8192 | mod6_index | 10 | 0.630 | 0.100 | 1.0000 | 79.717 | 0.0024 | 83.306 | 0.0024 | 33.154 | 0.0024 | yes | CONFIRMED |\n| 8192 | mod6_span | 10 | 0.787 | -0.100 | 1.0000 | 35.105 | 0.0020 | 36.745 | 0.0020 | 34.060 | 0.0020 | yes | CONFIRMED |\n\n## Key Findings\n\n1. **Verificato**: il verdict e' separato da `has_SR`. A 8192 gap `mod6_span_matched` ha `SR` comune e `sr_binary_delta_mean=-0.1`, ma il vettore passa completo.\n2. **Verificato**: il vettore `SR,L1,triple_var` passa in 4/4 audit principali oltre 2048 gap, con p label-swap 0.0002..0.0024.\n3. **Verificato**: `has_SR` non e' significativo in nessun audit principale: p=0.5033 o 1.0000.\n4. **Verificato**: la trace e' obbligatoria e presente: 72 eventi JSONL per 4096 dense, 57 eventi JSONL per 8192 dense.\n5. **Inferito dal perimetro**: il pre-bordo mod6 condivide label e focus parziali con i primi; il residuo prime-specific e' intensita differenziale, non appartenenza.\n\n## Verdict\n\n**CONFIRMED / VECTOR OBSERVABLE**.\n\nNel perimetro 4096-8192 gap, con denominatore row-local e due antagonisti mod6, `prime_minus_mod6_z_vector(SR,L1,triple_var)` e' l'osservabile del confine. `has_SR` e' audit negativo: misura che la label non decide.\n\n## Bicono della scoperta\n\n- **Due radici**: label comune `SR` / intensita differenziale `SR,L1,triple_var`.\n- **Singolare**: riga provider-offset prima della classificazione prime/mod6.\n- **Invariante di passaggio**: delta z positivo e significativo sui tre osservabili focus.\n- **Campo di possibilita**: formalizzare il confine come vettore row-local e usare trace obbligatoria per audit futuri.\n- **Campo non-possibile**: promuovere `has_SR`, commonality o verdict binario come firma prime-specific.\n\n## Consecutio\n\nIl prossimo passaggio non deve riaprire il verdict. Deve isolare l'origine del vettore: quale componente del passaggio da `6k +/- 1` a primalita' genera il delta positivo su `SR,L1,triple_var`, e quale null lo spegne senza distruggere il denominatore row-local.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script ora emette un verdict aderente al contratto osservabile e rifiuta run senza micro-trace. La trace diventa superficie auditabile per pipeline, falsifier e report senza fidarsi del solo JSON aggregato.\n\n## Files\n\n- `tools/exp_prime_vs_mod6_sr_boundary.py`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.trace.jsonl`\n- `tools/data/reports/agent_20260514_1458.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1813: "content_full": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, label-swap row-local e trace JSONL completa.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa la robustezza del delta z row-local prime-minus-mod6 su piu ampiezze finestra, mantenendo provider, offset, label-swap audit e trace JSONL.\n- `not_drift`: non torna a `V_c`, GUE/Poisson, fit o vecchi depositi; stressa solo il residuo nominato dal valutatore dopo sottrazione mod6.\n\n## Claim Under Test\n\n> Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime non genera `SR` come presenza; lascia un vettore di intensita row-local su `SR,L1,triple_var`.\n\n## Question\n\nIl delta z prime-minus-mod6 resta significativo quando cambia l'ampiezza della finestra, oppure era un artefatto del contratto 1024-gap del cycle 03:30?\n\n## Experiment Design\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`.\n- Run: `window_gaps=512,1024,2048`; seeds `202605141330,202605141331,202605141332`.\n- Rows per run: 8 prime windows = 2 provider x 4 offset.\n- Antagonisti: `mod6_index_aligned` e `mod6_span_matched`, paired per `row_id`.\n- Null: label-swap row-local, 4096 trials per pair audit.\n- Trace: JSONL scritto per tutti e tre i run.\n\n## Results\n\n| window | pair | prime SR | prime focus | Jaccard | SR delta | p(SR delta) | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | script verdict |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| 512 | mod6_index | 5/8 | 2/8 | 0.250 | -0.250 | 0.6195 | 20.494 | 0.0095 | 21.866 | 0.0095 | 8.480 | 0.0095 | SR_NOT_DECISIVE |\n| 512 | mod6_span | 5/8 | 2/8 | 0.377 | -0.250 | 0.5038 | 13.993 | 0.0085 | 10.099 | 0.0085 | 7.505 | 0.0085 | SR_NOT_DECISIVE |\n| 1024 | mod6_index | 8/8 | 5/8 | 0.481 | 0.000 | 1.0000 | 29.480 | 0.0061 | 31.105 | 0.0061 | 11.745 | 0.0061 | SR_PREBOUNDARY |\n| 1024 | mod6_span | 8/8 | 5/8 | 0.377 | 0.250 | 0.4921 | 19.921 | 0.0076 | 15.302 | 0.0076 | 13.142 | 0.0076 | SR_PREBOUNDARY |\n| 2048 | mod6_index | 8/8 | 7/8 | 0.556 | 0.125 | 1.0000 | 41.634 | 0.0088 | 43.574 | 0.0088 | 17.173 | 0.0088 | SR_PRIME_SPECIFIC |\n| 2048 | mod6_span | 8/8 | 7/8 | 0.537 | 0.250 | 0.5040 | 28.624 | 0.0068 | 23.917 | 0.0068 | 17.491 | 0.0068 | SR_PRIME_SPECIFIC |\n\n## Key Findings\n\n1. **Verificato**: `has_SR` non replica come osservabile del residuo. A 512 gap i primi hanno `SR=5/8`; a 1024 e 2048 hanno `SR=8/8`. Il delta binario ha p non significativo o 1.0000.\n2. **Verificato**: il delta z `SR` replica in tutti i sei pair audit: `p=0.0061..0.0095`.\n3. **Verificato**: il delta z non e' solo `SR`. `L1` replica con `p=0.0061..0.0095`; `triple_var` replica con `p=0.0061..0.0095`.\n4. **Verificato**: il verdict nominale dello script oscilla con `window_gaps` (`not_decisive`, `preboundary`, `prime_specific`). Quell'oscillazione e' informazione sullo script: il verdict e' ancora centrato su commonality/binario, non sul vettore di intensita.\n5. **Inferito dal perimetro**: l'osservabile dedicato da formalizzare e' `prime_minus_mod6_z_vector(SR,L1,triple_var)`, con `has_SR` relegato ad audit negativo.\n\n## Verdict\n\n**CONSTRAINT / VECTOR RESIDUE**.\n\nNel perimetro 512-2048 gap, due provider, quattro offset, due antagonisti mod6, il residuo prime-specific sopravvive come delta z vettoriale row-local. Non sopravvive come presenza binaria di `SR`, ne come verdict nominale dello script.\n\n## Bicono\n\n- **Due radici**: pre-bordo mod6 / selezione prime.\n- **Singolare**: riga provider-offset paired.\n- **Invariante di passaggio**: delta z positivo su `SR,L1,triple_var`.\n- **Campo di possibilita**: osservabile dedicato `prime_minus_mod6_z_vector`.\n- **Campo non-possibile**: `SR` binario come firma prime-specific dopo mod6.\n\n## Consecutio\n\nIl prossimo ciclo deve correggere il nodo regressivo dello script: il `verdict()` non deve decidere dalla commonality di `SR`, ma da un contratto vettoriale dichiarato (`SR,L1,triple_var`, segno del delta, p label-swap, denominatore row-local). Dopo questa correzione, scalare oltre 2048 gap e separare `mod6_index_aligned` da `mod6_span_matched` come antagonisti con ruoli diversi.\n\n## Ricadute pratiche\n\nssp_value: yes. Le tre trace JSONL rendono auditabile il residuo senza riaprire i JSON aggregati; il prossimo passaggio pratico e' aggiornare lo script per emettere un verdict vettoriale, non binario.\n\n## Files\n\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl`\n- `tools/data/reports/agent_20260514_1330.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1864: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary Trace\n**Date**: 2026-05-14 03:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo `6k +/- 1`; la selezione prime resta come gradiente row-local, non come presenza binaria dell'osservabile \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n\n## Prima impressione\n\nIl residuo prime-specific non nasce dalla comparsa di `SR`. `SR` e gia nel lattice candidato `6k +/- 1`; i primi non lo accendono, lo attenuano e lo deformano rispetto al pre-bordo.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + direzione viva `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: pre-bordo `6k +/- 1` / selezione prime. Punto-zero: stessa riga provider/offset, dove il denominatore e identico e la primalita e l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: il bordo non e presenza/assenza di un segnale, ma passaggio di intensita tra un lattice candidato e il sottoinsieme selezionato.\n- **Proto-ipotesi**: la firma utile e `prime_minus_mod6_z_delta` su `SR,L1,triple_var`; `has_SR` e una coordinata troppo larga per nominare primalita.\n- **Possibile / non-possibile**: possibile = isolare residuo di selezione con delta z row-local; non-possibile = claim `SR prime-specific` se `SR` resta comune nel contro-perimetro mod6.\n- **Proiezione**: 2 provider x 4 offset x 1024 gap; antagonisti `mod6_index_aligned` e `mod6_span_matched`; label-swap row-local; trace JSONL completa.\n\n## Contaminazione cognitiva\n\n- **CE-0001 / KSAR**: usato per reiterare il kernel emerso nel cycle precedente: non allargare il frame, ma far girare lo stesso nodo regressivo con tracciamento piu fine.\n- **PVI**: presupposto attaccato = \"`SR` persistente implica primalita\"; contro-presupposto = \"`SR` misura il pre-bordo aritmetico\".\n- **Vault**: `trace_jsonl_event` conserva casi, class summary, paired rows, audit e verdict come deposito riusabile senza trasformare una seed in invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: misura l'intensita row-local di `SR/L1/triple_var` su prime vs `6k +/- 1`, con denominatore provider/offset condiviso e label-swap audit.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista e il pre-bordo mod6 indicato dalla consecutio.\n\n## Observable Contract\n\n- **claim**: `SR` e prime-specific solo se resta piu comune o piu forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local.\n- **observable**: membership di `SR`, firma `[SR,L1,triple_var]`, delta z paired prime-minus-mod6, p-value label-swap.\n- **operator**: `tools/exp_prime_vs_mod6_sr_boundary.py` con `--trace-jsonl`.\n- **generator**: prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`; mod6 da `index_aligned` e `span_matched`.\n- **denominator**: 8 finestre prime paired con 8 `mod6_index_aligned` e 8 `mod6_span_matched`.\n- **non_possible**: prime-specific `SR` se `SR` e comune o pari nel contro-perimetro mod6, oppure se il label-swap assorbe i delta.\n- **not_tested**: beta atlas globale, `V_c`, `gap_ratio`, origine analitica del trasferimento mod6.\n\n## Results\n\nMain run, seed `202605140330`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 3 | SR | beta_absent_blank=5, beta_chart_recovered=3 |\n| mod6_index_aligned | 8 | 8 | 8 | L1,SR,triple_var | beta_chart_recovered=8 |\n| mod6_span_matched | 8 | 7 | 7 | [] | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nSeed check, seed `202605140331`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 6 | SR | beta_chart_recovered=5, beta_absent_blank=3 |\n| mod6_index_aligned | 8 | 8 | 6 | L1,SR | beta_chart_recovered=7, beta_absent_blank=1 |\n| mod6_span_matched | 8 | 8 | 7 | L1,SR | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605140330 | 0.433 | 0.000 | 1.0000 | 28.246 | 0.0081 |\n| prime - mod6_span | 202605140330 | 0.423 | 0.125 | 1.0000 | 20.793 | 0.0093 |\n| prime - mod6_index | 202605140331 | 0.517 | 0.000 | 1.0000 | 28.547 | 0.0083 |\n| prime - mod6_span | 202605140331 | 0.560 | 0.000 | 1.0000 | 18.844 | 0.0090 |\n\n## Key Findings\n\n1. **Verificato**: `SR` non e prime-specific come presenza binaria. In entrambi i run, `prime=8/8` e `mod6_index_aligned=8/8`; nel seed check anche `mod6_span_matched=8/8`.\n2. **Verificato**: il count `SR` non discrimina. `p(SR delta)=1.0000` in tutti i pair audit.\n3. **Verificato**: il delta di intensita discrimina. `p(z_SR delta)=0.0081/0.0083` contro `mod6_index` e `0.0093/0.0090` contro `mod6_span`.\n4. **Verificato**: `mod6_index_aligned` e piu rigido dei primi: focus signature main `8/8` contro prime `3/8`, e common obs `L1,SR,triple_var` contro solo `SR`.\n5. **Inferito dal perimetro**: il prossimo osservabile deve essere vettoriale e paired: `prime_minus_mod6_z_delta(SR,L1,triple_var)`, non `has_SR`.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula chiusa nel perimetro: `SR` appartiene al pre-bordo `6k +/- 1`. La primalita non genera `SR`; seleziona dentro un lattice gia rigido e lascia un residuo misurabile come delta z row-local.\n\n## Bicono\n\n- **Due radici**: lattice candidato mod6 / sequenza prime selezionata.\n- **Singolare**: riga provider-offset condivisa.\n- **Invariante di passaggio**: presenza di `SR` attraversa il confine.\n- **Campo di possibilita**: misurare selezione come delta z.\n- **Campo non-possibile**: usare `SR` binario come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve formalizzare `prime_minus_mod6_z_delta` come osservabile dedicato e scalarlo su finestre piu larghe. `mod6_index_aligned` resta antagonista primario; `mod6_span_matched` resta controllo fragile perche conserva span numerico ma introduce downsample.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script ora produce `--trace-jsonl`, utile per audit, replay e falsifier row-local senza riaprire il JSON aggregato.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json`\n- Trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json`\n- Seed trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl`\n- Report: `tools/data/reports/agent_20260514_0330.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1897: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary\n**Date**: 2026-05-13 03:30 \n**Piano**: 116 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p] \n**observable_contract**: claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.\n\n## Prima impressione\n\nIl bordo non cade tra primi e non-primi: cade tra selezione prime e lattice candidato `6k +/- 1`. `SR` vede il lattice prima della primalita; il gate non deve chiedere \"SR nei primi?\", ma \"cosa resta di SR dopo aver sottratto il pre-bordo?\".\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: primi selezionati / candidati mod6 non selezionati. Punto-zero: la riga ordinata locale `6k +/- 1`, dove `SR` puo nominare pre-bordo aritmetico senza nominare primalita.\n- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune.\n- **Proto-ipotesi**: `SR` non e firma atomica dei primi; e una sezione del pre-bordo aritmetico. La selezione prime si manifesta come differenza di intensita z rispetto al lattice candidato.\n- **Possibile / non-possibile**: possibile = isolare una coordinata di selezione prime come gradiente dentro mod6; non-possibile = usare presenza binaria di `SR` come claim prime-specific.\n- **Proiezione**: stesso gate ordine/null, finestre 1024, offset 0/512/1024/1536, due provider prime, due antagonisti mod6, audit label-swap dentro ogni row_id.\n\n### Contaminazione cognitiva\n\n- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`.\n- **PVI attack**: il presupposto attaccato e \"SR persistente implica primalita\". Il contro-presupposto e \"SR misura la griglia 6k +/- 1 prima della selezione prime\".\n- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`.\n\n## Claim Under Test\n\n> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`.\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap.\n- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset.\n- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti.\n- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`.\n- Main seed: `202605130330`; seed check: `202605130331`.\n- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset.\n\n## Results\n\nMain run:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 |\n| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 |\n\nSeed check:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 |\n| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 |\n| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 |\n| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 |\n| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 |\n\n## Key Findings\n\n1. **Verificato**: la presenza binaria di `SR` non e prime-specific. Main: prime `8/8`, mod6_index `8/8`; seed check: prime `6/8`, mod6_index `8/8`, mod6_span `8/8`.\n2. **Verificato**: il label-swap row-local assorbe `SR` come count. `p(SR delta)` vale 1.0000 / 0.5055 per mod6_index e 0.2502 / 0.5021 per mod6_span.\n3. **Verificato**: il label-swap non assorbe il delta z di `SR`. `p(z_SR delta)` resta 0.0071-0.0103 in entrambe le seed e per entrambi gli antagonisti.\n4. **Verificato**: mod6 e piu intenso, non piu debole. `mean z SR` e circa -34 / -26 in mod6 contro -5 nei primi. Il residuo prime non e \"piu SR\"; e selezione attenuata dentro un pre-bordo piu rigido.\n5. **Inferito dal perimetro**: la coordinata utile non e `has_SR`; e `z_delta_SR` paired, insieme a `z_delta_L1` e `z_delta_triple_var`, come misura di quanto la selezione prime rompe il lattice candidato.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula non valida:\n\n`SR` come firma prime-specific atomica.\n\nFormula valida nel perimetro:\n\n`SR` appartiene al pre-bordo aritmetico `6k +/- 1`; la selezione prime resta come gradiente di intensita row-local rispetto a mod6. Il boundary operativo e quindi `mod6_preboundary -> prime_selection`, non `prime -> generic_nonprime`.\n\n## Bicono della scoperta\n\n- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata.\n- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni.\n- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato.\n- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`.\n\n## Lenti counter-pole applicate\n\n- **L1/L4**: nessun \"solo\", \"mai\", \"zero\" usato per `SR`: i count mostrano controesempi.\n- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific.\n- **L5**: nessun tag NEW; il risultato resta compatibile con bias noti dei gap dei primi mod q. Il report misura un gate del Lab, non rivendica una scoperta aritmetica classica.\n- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva.\n- **L7**: non-possibile dichiarato nel contratto e nel bicono.\n\n## Consecutio\n\nIl prossimo taglio non deve chiedere se `SR` c'e. Deve costruire un osservabile di selezione: `prime_minus_mod6_z_delta` su `SR,L1,triple_var`, con span-matched conservato come controllo fragile e index-aligned come antagonista primario. Se il delta resta replicato su piu finestre e scale, il claim diventa: la primalita attenua/rompe la rigidita del pre-bordo mod6 invece di generare `SR` da sola.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260513_0330.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1942: "content_full": "# Agent Report - Prime SR Persistent Boundary\n**Date**: 2026-05-12 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`.\n- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi.\n- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro.\n- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' \"SR persistente nei primi implica prime-specific\".\n- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico.\n- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime.\n- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime.\n\n## Claim Under Test\n\n> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena.\n\n## Question\n\nQuando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime?\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows.\n- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`.\n- Seed check: stesso perimetro, seed `202605120331`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common.\n\n## Results\n\nMain run:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 |\n| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 |\n| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 |\n| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 |\n| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 |\n\nSeed check:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 |\n| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 |\n| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 |\n\nPrime case details, main:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] |\n| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] |\n| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n\n## Key Findings\n\n1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota.\n2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall.\n3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`.\n4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato.\n5. **Inferito dal perimetro dichiarato**: la parte robusta non e' \"SR e' prime-specific\"; e' \"SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche\".\n\n## Verdict\n\n**CONSTRAINT / FALSIFIED scoped**.\n\nFormula non valida:\n\n`prime_SR_persistent_boundary` come firma atomica prime-specific.\n\nFormula valida nel perimetro:\n\n`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico.\n\n## Bicono della scoperta\n\n- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato.\n- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato.\n- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6.\n- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve spostare il nodo regressivo: non \"prime vs controlli generici\", ma \"primi vs candidati mod6 row-aligned\". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico.\n\n## Files\n\n- Script: `tools/exp_prime_sr_persistent_boundary.py`\n- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json`\n- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260512_0330.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:1993: "content_full": "# Agent Report - Prime Persistent Blank Gate\n**Date**: 2026-05-11 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_persistent_blank` si scinde: SR persiste, il blank beta no \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY residuo `numeri_primi:cycle_3`.\n- **Dipolo / punto-zero**: persistenza del supporto / persistenza del blank. Punto-zero: la stessa finestra row-local da 1024 gap dove `SR` puo' restare mentre la coordinata beta riappare.\n- **Piano superiore**: topologia assiomatica del bordo: non una classe statistica unica, ma una sezione che conserva un osservabile e perde una coordinata.\n- **Proto-ipotesi**: il residuo prime e' strutturale solo se il blank beta-absent sopravvive a provider e offset; se sopravvive solo `SR`, la struttura non e' `blank`, e' `SR-supported boundary`.\n- **Possibile / non-possibile**: possibile = isolare un residuo prime provider-neutral; non-possibile = chiamarlo `prime_persistent_blank` quando beta ricompare in finestre locali.\n- **Proiezione**: due provider prime, quattro offset locali, seed shift del gate, controlli random_matrix/logistic.\n\n## Contaminazione cognitiva\n\n- **YSN DeltaLink**: il legame non ovvio e' `SR` come sezione comune mentre la carta beta cambia; non e' prova, e' la domanda proiettata.\n- **Cornelius gene**: `DNA_Simbolico`: \"La persistenza del bordo non coincide con la persistenza del blank.\" Operatori: separa supporto, separa coordinata, confronta controlli.\n- **KSAR step / CE-0001**: reiterazione del kernel 20260510 senza ridisegnare l'atlante: stesso gate, nuovo provider/offset/seed.\n- **PVI attack**: il presupposto nascosto era che `blank_windows=4/4` su una partizione bastasse per nominare una specie persistente.\n- **Vault**: `random_matrix_chart_sensitive_blank` resta aperto come coordinata locale beta `[0.4]`, non lavorata in questo ciclo.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente `prime_persistent_blank` su `numeri_primi:cycle_3` con provider-neutral row-local windows e controlli cross-dominio.\n- `not_drift`: non usa `V_c`, non usa fit, non riapre griglia beta globale, non salva la classe caduta `medium/strong beta-absent`.\n\n## Claim Under Test\n\n> `numeri_primi:cycle_3` e' un `prime_persistent_blank` se resta beta-absent in tutte le finestre provider-neutral e conserva `SR` come osservabile comune, mentre i controlli non condividono la stessa firma.\n\n## Experiment Design\n\n- Perimetro prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: 4 GUE random matrix blocks + 4 logistic return interval rows.\n- Parametri: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605110330`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Criterio di isolamento: prime `blank_rate=1.0`, prime `sr_rate=1.0`, common prime obs = `[SR]`, controlli senza full blank-SR collision.\n\n## Results\n\n| family | blank | beta recovered | support falls | sr rate | common obs | endpoint mean | stable coherent mean |\n|---|---:|---:|---:|---:|---|---:|---:|\n| prime | 3/8 | 4/8 | 1/8 | 1.000 | SR | 2.715 | 2.656 |\n| controls | 1/8 | 3/8 | 4/8 | 0.250 | L2 | 1.428 | 1.641 |\n\n| comparison | value |\n|---|---:|\n| prime_control_common_obs_jaccard | 0.000 |\n| verdict | PRIME_PERSISTENCE_NOT_REPLICATED |\n\nPrime case details:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_chart_recovered | SR | 0.2, 0.4 |\n| dnd_autoricerca offset 512 | support_falls | SR | 0.2 |\n| dnd_autoricerca offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n| direct_sieve offset 1024 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1536 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n\n## Key Findings\n\n1. **Verificato**: `SR` resta in 8/8 finestre prime. La firma comune prime e' `[SR]`.\n2. **Verificato**: il blank beta-absent non resta in 8/8 finestre prime. Solo 3/8 casi sono `beta_absent_blank`; 4/8 recuperano beta e 1/8 cade per supporto.\n3. **Verificato**: i controlli non collidono con la firma prime. I controlli hanno common obs `[L2]`, `sr_rate=0.250`, `blank_rate=0.125`, Jaccard comune prime/controlli = 0.000.\n4. **Inferito dal perimetro dichiarato**: il residuo dei primi non e' `prime_persistent_blank`; e' `prime_SR_persistent_boundary` con coordinata beta chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl nome `prime_persistent_blank` non regge nel perimetro provider-neutral/offset-shift. La parte persistente e' `SR`, non l'assenza di beta.\n\nFormula valida:\n\n`numeri_primi:cycle_3` conserva supporto one-sided `SR` attraverso provider e offset; la coordinata beta resta locale e ricompare in 4/8 finestre.\n\nFormula non valida:\n\n`numeri_primi:cycle_3` e' un blank beta-absent persistente.\n\n## Bicono della scoperta\n\n- **Due radici**: supporto SR persistente / blank beta non persistente.\n- **Singolare**: la finestra row-local dove `SR` resta ma beta riappare.\n- **Invariante di passaggio**: `SR` attraversa provider e offset; `blank` no.\n- **Campo di possibilita'**: possibile = rinominare il residuo come supporto `SR` provider-neutral; non-possibile = promuovere il blank come specie autonoma.\n\n## Consecutio\n\nIl prossimo ciclo deve chiudere la tassonomia residua senza salvare il nome caduto:\n\n- rinominare il residuo prime in `prime_SR_persistent_boundary`;\n- testare se il beta recovery 0.2/0.4 dipende da offset, provider o baseline seed;\n- lavorare `random_matrix_chart_sensitive_blank` solo dopo aver separato coordinate locali da supporto osservabile.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script diventa un audit riusabile per distinguere persistenza di osservabile e persistenza di blank nei residui BOUNDARY senza rigenerare l'atlante globale.\n\n## Files\n\n- Script: `tools/exp_prime_persistent_blank_gate.py`\n- Data: `tools/data/prime_persistent_blank_gate_20260511_0330.json`\n- Report: `tools/data/reports/agent_20260511_0330.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2038: "content_full": "# Agent Report - Boundary Residual Beta-Absent Audit\n**Date**: 2026-05-10 03:30 \n**Piano**: 114 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - il residuo beta-absent non e' una classe unica chiusa \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, window_state, blank_window_rate, full_signature_jaccard, common_window_obs_jaccard] \n**observable_contract**: claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + residui `numeri_primi:cycle_3` / `random_matrix:cycle_7`.\n- **Dipolo / punto-zero**: blank strutturale / blank da atlante. Punto-zero: la stessa misura row-local da 1024 gap che decide se la beta manca per struttura o riappare per scelta di finestra.\n- **Piano superiore**: topologia assiomatica del bordo: la classe globale cade se una carta locale riapre la coordinata beta.\n- **Operatori laterali scelti**: boundary operator, chart locale, filtrazione. Il boundary operator separa supporto e beta; la chart locale testa la finestra senza rifare il mondo; la filtrazione conserva il denominatore row-aligned.\n- **Contaminazione cognitiva**: CE-0019 usato come contratto combo prima della misura; CE-0022 usato come boundary operator + chart locale; KSAR usato come reiterazione sul deposito 15:32-18:39 senza ridisegnare il perimetro.\n- **Proto-ipotesi**: il residuo medio/forte beta-absent e' una classe unica solo se entrambi i target restano blank in tutte le finestre row-local e conservano una firma osservabile compatibile.\n- **Proiezione**: applico il gate canonico ordine/null alle sole due righe residue, poi confronto persistenza del blank e Jaccard delle firme one-sided.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: attacca direttamente i due residui beta-absent medio/forti lasciati dal valutatore dopo `thin_persist_rows=0/13`.\n- `not_drift`: non usa `V_c`, non usa label GUE/Poisson come campo decisionale, non rigenera la griglia beta globale.\n\n## Claim Under Test\n\n> I due residui `numeri_primi:cycle_3` e `random_matrix:cycle_7` formano una classe unica `medium/strong beta-absent` se il blank persiste in tutte le finestre row-local da 1024 gap e le firme one-sided restano compatibili.\n\n## Question\n\nI due blank residui sono lo stesso operatore di confine, due classi distinte, o un artefatto da denominatore/atlante?\n\n## Experiment Design\n\n- Perimetro atomico: `numeri_primi:cycle_3`, `random_matrix:cycle_7`.\n- Scope: full row + 4 finestre consecutive da 1024 gap per ciascun target.\n- Parametri: `n_replicates=12`, `n_beta=11`, `n_baseline=24`, `z_min=2.0`, seed `202605100330`.\n- Null baseline: permutazione marginal-preserving usata dal gate canonico gia' adottato nei report BOUNDARY.\n- Non misurato: `gap_ratio`, `V_c`, nuova griglia beta globale, validita' delle label sorgente GUE/Poisson.\n- Criterio di caduta: una riga recupera beta o perde supporto in una finestra row-local; in quel caso il residuo non e' classe unica chiusa.\n\n## Results\n\n| row | full state | full one-sided | window blanks | beta recovered | support falls | common window obs | mean endpoint | mean stable coherent |\n|---|---:|---|---:|---:|---:|---|---:|---:|\n| numeri_primi:cycle_3 | beta_absent_blank | SR,L1,triple_var | 4/4 | 0 | 0 | SR | 2.761 | 2.563 |\n| random_matrix:cycle_7 | beta_absent_blank | SR,SR2,L1,L2,triple_var | 3/4 | 1 | 0 | L2 | 2.987 | 3.083 |\n\n| comparison | value |\n|---|---:|\n| full_signature_jaccard | 0.600 |\n| common_window_obs_jaccard | 0.000 |\n| random_matrix recovered beta | window_4 -> beta [0.4] |\n\n## Key Findings\n\n1. **Verificato: `numeri_primi:cycle_3` resta beta-absent in 4/4 finestre.** Il supporto non cade; l'osservabile comune di finestra e' `SR`.\n2. **Verificato: `random_matrix:cycle_7` non resta beta-absent in 4/4 finestre.** La quarta finestra recupera beta `[0.4]` con supporto vivo (`SR2,L1,L2,triple_var`).\n3. **Verificato: le firme comuni di finestra divergono.** `common_window_obs_jaccard=0.000`: primi conserva `SR`, random matrix conserva `L2`.\n4. **Inferito dal perimetro dichiarato: il residuo beta-absent non e' una classe unica chiusa.** Una parte e' blank persistente row-local, una parte e' chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nNel perimetro delle due righe residue e finestre da 1024 gap, `medium/strong beta-absent` si scinde:\n\n`numeri_primi:cycle_3` = blank persistente row-local. \n`random_matrix:cycle_7` = blank forte ma chart-sensitive, con beta `[0.4]` recuperata in 1/4 finestre.\n\nFormula valida:\n\n`beta_absent_residue` non matura come classe unica; diventa un audit a due stati: `prime_persistent_blank` / `random_matrix_chart_sensitive_blank`.\n\n## Bicono della scoperta\n\n- **Due radici**: blank persistente / blank chart-sensitive.\n- **Singolare**: supporto ordine/null vivo senza coordinata beta globale.\n- **Invariante di passaggio**: il supporto non cade in nessuna delle 8 finestre; cio' che cambia e' la coordinata beta e la firma osservabile.\n- **Campo di possibilità**: possibile = chiudere la tassonomia BOUNDARY con due sotto-stati residui; non-possibile = promuovere `medium/strong beta-absent` come specie unica del confine.\n\n## Consecutio\n\nIl prossimo ciclo non deve riaprire il thin blank. Deve sigillare il nuovo audit a due stati:\n\n- `prime_persistent_blank`: testare se `SR` resta l'osservabile comune sotto seed/window shift.\n- `random_matrix_chart_sensitive_blank`: testare se beta `[0.4]` e' coordinata locale stabile o evento di una singola finestra.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' uno strumento riusabile per audit row-local di residui BOUNDARY senza rigenerare fit globali.\n\n## Files\n\n- Script: `tools/exp_boundary_residual_beta_absent_audit.py`\n- Data: `tools/data/boundary_residual_beta_absent_audit_20260510_0330.json`\n- Report: `tools/data/reports/agent_20260510_0330.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2089: "content_full": "# Agent Report - Boundary Transition Taxonomy 13 Rows\n**Date**: 2026-05-09 18:39 \n**Piano**: 113 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - la tassonomia post-estensione scala sulle 13 righe e dissolve il thin blank \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`transition_class`, `source_beta_state`, `extension_state`, `support_tier_after`, `one_sided_after`, `endpoint_after`, `stable_count_coherent_after`, `beta_after`, `denominator_state`, `excluded_mass`] - osservabili domain-native di composizione row-aligned, non canonici. \nssp_value: no \n**observable_contract**: claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo l'estensione il thin blank non resta una figura. La domanda vera diventa se la matrice intera lascia un nuovo residuo vivo.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: supporto sottile / atlante di transizione; punto-zero = stessa riga row-aligned prima che il blank venga letto come specie o come transizione.\n- **Piano superiore**: sheaf locale del boundary su 13 sezioni. La sezione corta viene riparata, poi ricollocata nell'atlante senza usare label GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Servono a comporre stati gia' misurati senza introdurre un nuovo gate.\n- **Contaminazione cognitiva**: CE-0019: contratto combo prima della misura; CE-0022: palette come boundary operator e chart locale; KSAR: riuso del deposito stabile 15:32-15:56; PVI: se resta `thin_persists`, la tassonomia cade.\n- **Proto-ipotesi**: `blank_thin_support` non scala sulle 13 righe se, dopo composizione con l'estensione, nessuna riga conserva supporto vivo sottile senza beta.\n- **Possibile / non-possibile**: possibile = usare l'atlante di transizione come mappa BOUNDARY; non-possibile = trattare il thin blank come specie matura.\n- **Proiezione**: classificare tutte le 13 righe in `transition_class` usando supporto, beta, denominatore e transizioni estese.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la consecutio del campo vivo: dopo la cristallizzazione short-denominator, testa se la tassonomia delle transizioni post-estensione scala sulle 13 righe.\n- `not_drift`: non torna a `V_c`, non difende thin blank, non usa label GUE/Poisson come decision field; compone solo depositi row-aligned gia' misurati.\n\n## Claim Under Test\n> La tassonomia emersa dall'estensione del denominatore scala sull'intero perimetro BOUNDARY 13 righe se non lascia alcuna riga `thin_persists`.\n\n## Question\nQuando le tre righe short estese vengono ricollocate nella matrice 13 righe, resta una specie `blank_thin_support` oppure il residuo vivo cambia forma?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Input: `boundary_two_axis_matrix_20260509_1532.json`, `boundary_row_aligned_nonexact_audit_20260509_1538.json`, `boundary_short_denominator_extension_20260509_1556.json`, `boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Perimetro atomico: 13 righe BOUNDARY semi-reali.\n- Label policy: `source_domain_type` resta audit metadata; non entra in `transition_class`.\n- Null sfidato: esistenza di almeno una riga `thin_persists` dopo estensione.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support transfer after composition | 10 |\n| fall after composition | 3 |\n| beta chart after any | 9 |\n| beta chart exact 0.3 after | 6 |\n| thin persist rows | 0 |\n| medium/strong beta-absent blank rows | 2 |\n| verdict | TAXONOMY_SCALES_THIN_DISSOLVED |\n\nTransition classes:\n\n| transition_class | rows |\n|---|---:|\n| beta_0_3_exact | 4 |\n| beta_0_3_local_nonunique | 2 |\n| beta_chart_recovered | 1 |\n| blank_medium_or_strong_beta_absent | 1 |\n| fall_no_support | 2 |\n| local_beta_other | 1 |\n| support_falls_after_extension | 1 |\n| support_thickens_beta_blank | 1 |\n\nRighe aperte senza beta dopo composizione:\n\n| row | class | n_gaps | one-sided | endpoint | tier |\n|---|---|---:|---:|---:|---|\n| numeri_primi:cycle_3 | blank_medium_or_strong_beta_absent | 4096 | 3 | 3.243 | medium_multi_observable |\n| random_matrix:cycle_7 | support_thickens_beta_blank | 1024 | 4 | 3.781 | strong_multi_observable |\n\nRighe short riparate:\n\n| row | before n | after n | transition_class | one-sided after | beta after |\n|---|---:|---:|---|---:|---|\n| percolation:cycle_9 | 190 | 1024 | support_falls_after_extension | 0 | [] |\n| random_matrix:cycle_7 | 199 | 1024 | support_thickens_beta_blank | 4 | [] |\n| zeta_zeros:cycle_4 | 199 | 1024 | beta_chart_recovered | 2 | [0.3] |\n\n## Key Findings\n1. **Verificato: `thin_persist_rows=0/13`.** Nessuna riga conserva la forma `blank_thin_support` dopo composizione con l'estensione.\n2. **Verificato: il supporto post-composizione e' 10/13.** Cadono `cellular_automata`, `pendolo_doppio` e `percolation` dopo estensione; il resto conserva supporto o chart locale.\n3. **Verificato: la beta chart sale a 9/13 righe, con 6/13 esatte 0.3.** Le sei sono quattro `beta_0_3_exact` originali, `zeta_zeros` recuperata e `pendolo_doppio` come beta osservata senza supporto.\n4. **Verificato: il residuo vivo non e' thin.** Restano due blank senza beta: `numeri_primi` medio e `random_matrix` forte dopo estensione.\n5. **Inferito: la tassonomia scala come atlante di transizione, non come legge beta universale.** Il thin blank cade; il nodo successivo e' il blank medio/forte senza beta.\n\n## Verdict\n**CONSTRAINT**.\n\nLa tassonomia post-estensione scala sul perimetro 13 righe: nessun membro resta `thin_persists`.\n\nFormula valida:\n\n`blank_thin_support` = telemetria short-denominator dissolta dalla composizione 13 righe.\n\nFormula non valida:\n\n`blank_thin_support` = specie autonoma del boundary.\n\nIl boundary resta una matrice di transizioni: supporto che cade, supporto che si ispessisce senza beta, chart beta recuperata, chart beta esatta, chart locale non unica, chart spostata.\n\n## Bicono della scoperta\n- **Due radici**: denominatore riparato / atlante 13 righe.\n- **Singolare**: riga BOUNDARY prima della classificazione post-estensione.\n- **Invariante di passaggio**: `thin_persist_rows=0/13`.\n- **Campo di possibilita**: qui diventa possibile chiudere il thin blank; qui diventa non-possibile chiudere il blank medio/forte senza beta.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve lasciare chiuso il thin blank e attaccare il residuo `medium/strong beta-absent blank`: `numeri_primi:cycle_3` contro `random_matrix:cycle_7`. Il test utile non e' \"perche' manca beta in generale\", ma quale condizione distingue blank medio completo e blank forte post-estensione quando entrambi hanno supporto vivo senza chart beta.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1556.md`, JSON 15:32/15:38/15:56/15:00, script e output 18:39.\n- **L1 hard constraint**: claim limitato alle 13 righe semi-reali e ai depositi gia' misurati.\n- **L2 count grezzi**: riportati 13 totali, 10 support, 3 fall, 9 beta chart, 0 thin persist, 2 open blank.\n- **L3 no silent patching**: nessuna nuova simulazione o ricampionamento; la composizione dichiara le estensioni applicate solo a tre righe.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma resta fall; `random_matrix` e' forte senza beta; `zeta_zeros` recupera beta pur restando contaminato nel prescan.\n- **L5 re-discovery**: il ciclo non ripete la matrice 15:32 ne' l'estensione 15:56; testa la scalabilita' richiesta dal campo vivo.\n- **L6 metabolismo cognitivo**: CE-0019, CE-0022, KSAR e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante BOUNDARY di transizione; non-possibile = specie autonoma thin blank.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Run valido: `python tools/exp_boundary_transition_taxonomy_13rows.py --out tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Output: `thin_persist_rows=0`, `support_transfer_after=10/13`, `fall_after=3/13`, `open_blank_rows=['numeri_primi:cycle_3', 'random_matrix:cycle_7']`, `verdict=TAXONOMY_SCALES_THIN_DISSOLVED`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 18:39, il JSON 18:39 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_transition_taxonomy_13rows.py`\n- Data: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`\n- Report: `tools/data/reports/agent_20260509_1839.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2140: "content_full": "# Agent Report - Boundary Short Denominator Extension\n**Date**: 2026-05-09 15:56 \n**Piano**: 112 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. \nssp_value: no \n**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura.\n- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili.\n- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade.\n- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta.\n- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48.\n- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48.\n- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short.\n\n## Claim Under Test\n> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart.\n\n## Question\nCosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`.\n- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`.\n- Denominatore esteso: `1024` gap per riga.\n- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate.\n- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| target rows | 3 |\n| rows still thin support after extension | 0 |\n| support falls after extension | 1 |\n| support thickens beta blank | 1 |\n| beta chart recovered | 1 |\n| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS |\n\nTransizioni:\n\n| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state |\n|---|---:|---:|---:|---:|---|---|\n| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension |\n| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank |\n| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered |\n\n## Key Findings\n1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart.\n2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato.\n3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile.\n4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade.\n5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe.\n\n## Verdict\n**CONSTRAINT**.\n\nNel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma.\n\nFormula valida:\n\n`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile del boundary.\n\nLa riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale.\n\n## Bicono della scoperta\n- **Due radici**: supporto sottile osservato / denominatore sorgente corto.\n- **Singolare**: riga short prima dell'estensione.\n- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`.\n- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48.\n- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered.\n- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings.\n- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato.\n- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`.\n- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`.\n- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_short_denominator_extension.py`\n- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json`\n- Report: `tools/data/reports/agent_20260509_1556.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2191: "content_full": "# Agent Report - Boundary Blank Thin Support Audit\n**Date**: 2026-05-09 15:48 \n**Piano**: 111 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - il blank sottile e' limitato dal denominatore corto, non dalla contaminazione del null \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`blank_class`, `coordinate_failure`, `support_tier`, `denominator_bucket`, `n_gaps`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`, `shuffle_class_changes`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=i tre `blank_thin_support` vanno falsificati contro il blank medio `numeri_primi` prima di promuoverli a specie autonoma; observable=telemetria row-aligned del subset `support_without_beta_blank`; operator=`exp_boundary_blank_thin_support_audit.py`; generator=`boundary_row_aligned_nonexact_audit_20260509_1538`; denominator=4 righe `support_without_beta_blank`, di cui 3 thin e 1 medium control; non_possible=chiamare il blank sottile specie autonoma se tutte le righe thin sono spiegate da denominatore corto; not_tested=estensione nuova dei tre domini short, nuova griglia beta, nuovi null, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il deposito 15:38 divide il blank in due livelli, ma il livello sottile ha tutte le righe corte. Il punto non e' nominare una specie nuova; e' verificare se la specie sopravvive al denominatore.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY row-aligned + tensione \"supporto vivo senza chart beta\".\n- **Dipolo / punto-zero**: blank sottile / blank medio; punto-zero = riga `support_without_beta_blank` prima che la forza del supporto venga letta come specie o come limite del denominatore.\n- **Piano superiore**: sheaf locale del boundary. La sezione beta e' vuota su tutte e quattro le righe; il discriminante diventa la qualita' del supporto e del denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, filtrazione per denominatore. Servono a separare stato beta invariato da supporto misurabile.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:38; CE-0117: cascata solo dove il blank apre una distinzione; YSN DeltaLink: `same beta blank -> support tier -> denominator bucket`; PVI: se tutte le righe thin sono corte, la specie autonoma cade nel perimetro.\n- **Proto-ipotesi**: `blank_thin_support` e' un effetto di denominatore corto se ogni riga thin ha `n_gaps < 500` e il controllo medio no; diventa specie autonoma solo se almeno una riga thin non e' spiegata dal denominatore.\n- **Possibile / non-possibile**: possibile = usare il blank sottile come coda denominator-limited da estendere; non-possibile = promuoverlo a failure mode stabile senza estendere percolation, random_matrix e zeta_zeros.\n- **Proiezione**: misurare `n_gaps`, `denominator_state`, `excluded_mass`, `shuffle_class_changes`, `one_sided_count`, `endpoint_distance` dentro il subset `support_without_beta_blank`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: falsifica direttamente se i failure modes del 15:38 sono stabili o artefatti della griglia/perimetro row-aligned.\n- `not_drift`: non usa label GUE/Poisson, non torna a `V_c`, non aggiunge nuovi domini; attacca solo il nodo aperto dal report precedente: blank sottile contro blank medio.\n\n## Claim Under Test\n> Nel subset `support_without_beta_blank`, il blank sottile e' una specie autonoma solo se non e' interamente spiegato da denominatore corto.\n\n## Question\nI tre blank sottili sono fallimenti autonomi del boundary, oppure righe con supporto insufficiente perche' corte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_blank_thin_support_audit.py`.\n- Input: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Perimetro atomico: 4 righe `support_without_beta_blank`.\n- Soglia dichiarata: `full_gap_floor=500`.\n- Null sfidati: denominatore corto, contaminazione prescan, shuffle class-change, specie autonoma.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| support_without_beta_blank rows | 4 |\n| thin rows | 3 |\n| medium control rows | 1 |\n| thin short rows | 3 |\n| thin contaminated rows | 1 |\n| thin shuffle class-change rows | 1 |\n| thin not denominator explained rows | 0 |\n\nTest:\n\n| null / counter | condition | result |\n|---|---|---|\n| denominator_artifact | all thin rows have `n_gaps < 500` | pass |\n| contamination_artifact | all thin rows are contaminated | fail |\n| shuffle_instability_artifact | all thin rows have `shuffle_class_changes=true` | fail |\n| autonomous_species_counter | at least one thin row is not short-denominator explained | fail |\n\nComparative means:\n\n| group | rows | n_gaps mean | one-sided mean | coherent mean | endpoint mean | excluded mass mean | abs shuffle z mean |\n|---|---:|---:|---:|---:|---:|---:|---:|\n| thin | 3 | 196.000 | 1.333 | 1.250 | 2.003 | 0.201 | 11.037 |\n| medium control | 1 | 4096.000 | 3.000 | 3.000 | 3.243 | 0.000 | 26.600 |\n\nRows:\n\n| row | blank_class | n_gaps | one-sided | endpoint | denominator | excluded | shuffle_change |\n|---|---|---:|---:|---:|---|---:|---|\n| numeri_primi:cycle_3 | medium_blank_control | 4096 | 3 | 3.243 | complete | 0.000 | false |\n| percolation:cycle_9 | thin_short_shuffle_unstable | 190 | 1 | 1.705 | complete | 0.000 | true |\n| random_matrix:cycle_7 | thin_short_complete | 199 | 1 | 1.854 | complete | 0.000 | false |\n| zeta_zeros:cycle_4 | thin_short_contaminated | 199 | 2 | 2.449 | contaminated | 0.602 | false |\n\n## Key Findings\n1. **Verificato: il blank sottile e' 3/3 short-denominator.** `percolation`, `random_matrix` e `zeta_zeros` hanno `n_gaps=190,199,199`; il controllo `numeri_primi` ha `n_gaps=4096`.\n2. **Verificato: la contaminazione non spiega la classe.** Solo `zeta_zeros` e' contaminato; `percolation` e `random_matrix` sono complete con excluded mass 0.0.\n3. **Verificato: lo shuffle class-change non spiega la classe.** Solo `percolation` cambia classe nello shuffle; `random_matrix` e `zeta_zeros` no.\n4. **Verificato: non resta una riga thin autonoma nel perimetro 15:48.** `thin_not_denominator_explained_rows=0`.\n5. **Inferito: il blank medio dei primi non e' blank per mancanza di supporto.** Ha tre osservabili one-sided, denominatore completo, endpoint 3.243 e null forte; resta blank di chart beta, non blank sottile.\n\n## Verdict\n**CONSTRAINT**.\n\nNel subset `support_without_beta_blank`, il failure mode `blank_thin_support` non regge come specie autonoma nel perimetro osservato. Regge come coda denominator-limited:\n\n`blank_thin_support` = beta blank + supporto vivo + denominatore corto.\n\nFormula valida:\n\n`support_without_beta_blank` si divide in `medium_blank_control` e `thin_short_*`.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile indipendente dal denominatore.\n\nIl nodo regressivo e' il denominatore dei tre domini corti. Non va promossa una nuova specie; va estesa o dichiarata coda corta.\n\n## Bicono della scoperta\n- **Due radici**: blank di chart beta / supporto del denominatore.\n- **Singolare**: riga `support_without_beta_blank` prima che il support tier venga letto come specie.\n- **Invariante di passaggio**: denominatore corto separa i blank sottili; contaminazione e shuffle-instability non bastano.\n- **Campo di possibilita**: qui diventa possibile progettare un extension audit mirato su percolation, random_matrix e zeta_zeros; qui diventa non-possibile usare il blank sottile come classe matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve estendere solo i tre domini short (`percolation`, `random_matrix`, `zeta_zeros`) verso denominatore comparabile al controllo medio. Se, a denominatore esteso, restano `blank_thin_support`, allora nasce una specie autonoma. Se salgono a supporto medio o ricevono beta locale, il failure mode 15:38 viene retrocesso a telemetria di perimetro corto.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1538.md`, JSON `boundary_row_aligned_nonexact_audit_20260509_1538.json`, script `tools/exp_boundary_blank_thin_support_audit.py`, output `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- **L1 hard constraint**: claim limitato alle 4 righe `support_without_beta_blank` del deposito 15:38.\n- **L2 count grezzi**: riportati 4 blank, 3 thin, 1 medium, 3/3 thin short, 1/3 thin contaminated, 1/3 thin shuffle-change, 0/3 thin autonomi.\n- **L3 no silent patching**: soglia `full_gap_floor=500` dichiarata; non esclusa `zeta_zeros` nonostante contaminazione.\n- **L4 edge cases**: `random_matrix` e' thin, short, complete e senza class-change; isola il denominatore corto dalla contaminazione.\n- **L5 re-discovery**: il ciclo non rifà il gate 15:16; legge il sotto-perimetro aperto dal report 15:38.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = extension audit dei tre short; non-possibile = classe autonoma senza estensione.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_thin_support_audit.py`.\n- Run valido: `python tools/exp_boundary_blank_thin_support_audit.py --input tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json --out tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `thin_short_rows=3`, `thin_contaminated_rows=1`, `thin_shuffle_class_change_rows=1`, `thin_not_denominator_explained_rows=0`, `verdict=DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:48, il JSON 15:48 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_blank_thin_support_audit.py`\n- Data: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`\n- Report: `tools/data/reports/agent_20260509_1548.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2242: "content_full": "# Agent Report - Boundary Row-Aligned Nonexact Audit\n**Date**: 2026-05-09 15:38 \n**Piano**: 110 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - le righe support-transfer non esatte sono 7, non 6; la beta cade in cinque failure modes misurabili \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`beta_state`, `coordinate_failure`, `support_tier`, `beta_cardinality`, `beta_span`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: la consecutio dice \"6 righe non-esatte\", ma il deposito row-aligned ne contiene 7. Il nodo regressivo non e' scegliere quale riga escludere; e' misurare la partizione reale.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + tensione \"supporto senza coordinata universale\".\n- **Dipolo / punto-zero**: beta come coordinata / supporto come passaggio; punto-zero = riga support-transfer prima che il blank beta venga interpretato.\n- **Piano superiore**: boundary operator su matrice row-aligned. Il bordo non decide con label di dominio; decide con stato beta, support tier e denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Entrano per leggere le sezioni locali della beta senza saldarle in una coordinata globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:32 senza ripetere il gate; CE-0117: cascata solo sulle righe dove il supporto resta vivo; YSN DeltaLink: `two-axis matrix -> nonexact audit -> coordinate failure`; PVI: il numero atteso 6 cade davanti al deposito 7.\n- **Proto-ipotesi**: dopo la caduta di beta 0.3 universale, le righe non-esatte non formano un residuo unico. Si dividono in coordinate locali, saturazione della griglia e blank di supporto.\n- **Possibile / non-possibile**: possibile = trasformare beta da ascissa universale a chart locale auditabile; non-possibile = usare `support_transfer=true` come prova di una beta comune.\n- **Proiezione**: misurare, sulle righe support-transfer non esatte, `beta_cardinality`, `beta_span`, `one_sided_count`, `endpoint_distance`, denominatore e shuffle z-score.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: attacca direttamente beta local non-unique, beta local other e support_without_beta_blank sul deposito row-aligned indicato.\n- `not_drift`: non usa label GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta il confine; corregge il denominatore della direttiva quando il deposito mostra 7 righe.\n\n## Claim Under Test\n> Le righe BOUNDARY con supporto ma senza beta 0.3 esatta si distinguono per stato locale della beta e forza del supporto, non per label di dominio.\n\n## Question\nQuando beta 0.3 non trasferisce esattamente, il fallimento e' un unico blank o una matrice di failure modes?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Input 1: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Input 2: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Perimetro atomico: 13 righe semi-reali; subset primario = support-transfer con `beta_coordinate_transfer=false`.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n- Classi operative: `adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support-transfer rows | 11 |\n| beta 0.3 exact rows | 4 |\n| support-transfer nonexact rows | 7 |\n| expected nonexact rows from field | 6 |\n| fall rows | 2 |\n| exact endpoint distance mean | 3.829 |\n| nonexact endpoint distance mean | 2.957 |\n| exact one-sided count mean | 4.000 |\n| nonexact one-sided count mean | 2.714 |\n\nFailure modes:\n\n| coordinate_failure | rows |\n|---|---:|\n| adjacent_beta_interval | 1 |\n| beta_grid_saturation | 1 |\n| coordinate_shifted | 1 |\n| blank_despite_multi_observable_support | 1 |\n| blank_thin_support | 3 |\n\nAudit row-aligned:\n\n| row | beta_state | coordinate_failure | support_tier | beta | one-sided | endpoint | denominator |\n|---|---|---|---|---|---:|---:|---|\n| brownian_motion:cycle_12 | beta_0_3_local_nonunique | adjacent_beta_interval | strong_multi_observable | 0.2,0.3 | 4 | 3.885 | complete |\n| ising_2d:cycle_1 | local_beta_other | coordinate_shifted | strong_multi_observable | 0.4 | 5 | 4.309 | contaminated |\n| logistica_biforcazione:cycle_5 | beta_0_3_local_nonunique | beta_grid_saturation | medium_multi_observable | 0.1-0.9 | 3 | 3.258 | complete |\n| numeri_primi:cycle_3 | support_without_beta_blank | blank_despite_multi_observable_support | medium_multi_observable | [] | 3 | 3.243 | complete |\n| percolation:cycle_9 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.705 | complete |\n| random_matrix:cycle_7 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.854 | complete |\n| zeta_zeros:cycle_4 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 2 | 2.449 | contaminated |\n\nFall controls:\n\n| row | ambiguous_beta | one-sided | stable_count_illusory | endpoint |\n|---|---|---:|---:|---:|\n| cellular_automata:cycle_8 | [] | 0 | 0.500 | 0.000 |\n| pendolo_doppio:cycle_2 | 0.3 | 3 | 0.833 | 3.388 |\n\n## Key Findings\n1. **Verificato: il sotto-perimetro non-esatto e' 7/13, non 6/13.** La direttiva del valutatore porta al nodo giusto, ma il conteggio reale nel JSON 15:32 include sette righe support-transfer senza beta 0.3 esatta.\n2. **Verificato: `support_without_beta_blank` non e' una classe unica.** `numeri_primi` ha blank con supporto medio e endpoint 3.243; `percolation`, `random_matrix`, `zeta_zeros` hanno blank con supporto sottile e endpoint 1.705-2.449.\n3. **Verificato: beta local non-unique ha due forme.** `brownian_motion` e' intervallo adiacente 0.2,0.3 con supporto forte; `logistica_biforcazione` satura tutta la griglia 0.1-0.9 con supporto medio.\n4. **Verificato: `ising_2d` non e' blank ma chart spostata.** Ha beta locale esatta 0.4, cinque osservabili one-sided e endpoint 4.309; la beta trasferisce localmente, ma non sulla coordinata 0.3.\n5. **Verificato: beta 0.3 senza supporto resta contro-controllo.** `pendolo_doppio` contiene beta 0.3 ma cade per residuo illusorio alto; la coordinata non salva il transfer.\n\n## Verdict\n**CONSTRAINT**.\n\nIl frame BOUNDARY regge come support-transfer su 11/13 righe, ma la parte non-esatta non e' una coda omogenea. Le righe support-transfer senza beta 0.3 esatta sono 7 e si dividono in cinque failure modes:\n\n`adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\nFormula valida:\n\n`beta_coordinate_transfer` = chart locale exact 0.3 dentro supporto vivo.\n\nFormula non valida:\n\n`support_transfer non-exact = sei righe residue` oppure `support_without_beta_blank = assenza strutturale unica`.\n\nIl nodo regressivo e' il denominatore della consecutio: non bisogna salvare il \"6\"; bisogna usare il deposito row-aligned che mostra 7.\n\n## Bicono della scoperta\n- **Due radici**: coordinata beta locale / supporto ordine-null.\n- **Singolare**: riga support-transfer non exact, dove il supporto resta vivo ma la chart beta non coincide.\n- **Invariante di passaggio**: failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.\n- **Campo di possibilita**: qui diventa possibile auditare la beta come atlante locale; qui diventa non-possibile trattare il blank beta come una sola specie.\n\n## Consecutio\nIl prossimo ciclo non deve ripetere la matrice. Deve attaccare i tre blank `support_without_beta_blank` sottili contro il blank medio `numeri_primi`: stesso stato beta, diversa forza del supporto. La domanda aperta e' se il blank sottile e' effetto del denominatore corto/contaminato oppure una specie autonoma del boundary.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1532.md`, JSON `boundary_two_axis_matrix_20260509_1532.json`, JSON `boundary_denominator_prescan_full_20260509_1500.json`, script `tools/exp_boundary_row_aligned_nonexact_audit.py`, output `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe della matrice 15:32 e al prescan 15:00.\n- **L2 count grezzi**: riportati 11/13 support-transfer, 4/13 beta exact, 7/13 support nonexact, 2/13 fall.\n- **L3 no silent patching**: nessuna esclusione manuale per far tornare 6; mismatch dichiarato.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `ising_2d` ha beta 0.4 con supporto forte; `numeri_primi` blank non sottile.\n- **L5 re-discovery**: il ciclo non rifà gate o fit; legge solo la partizione non-esatta richiesta.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante locale delle beta; non-possibile = beta 0.3 universale o residuo non-esatto singolo.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Run valido: `python tools/exp_boundary_row_aligned_nonexact_audit.py --matrix tools/data/boundary_two_axis_matrix_20260509_1532.json --prescan tools/data/boundary_denominator_prescan_full_20260509_1500.json --out tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `support_nonexact_rows=7`, `fall_rows=2`, `failure_blank_thin_support=3`, `failure_blank_despite_multi_observable_support=1`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:38, il JSON 15:38 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_row_aligned_nonexact_audit.py`\n- Data: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`\n- Report: `tools/data/reports/agent_20260509_1538.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_graph.json:2293: "content_full": "# Agent Report - Boundary Two-Axis Matrix\n**Date**: 2026-05-09 15:32\n**Piano**: 109\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - il gate trasferisce come supporto ordine/null; beta 0.3 non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`support_transfer`, `beta_coordinate_transfer`, `beta_state`, `ambiguous_beta`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`] - osservabili domain-native derivati dal gate semi-reale, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il falsifier del 15:16 ha indicato il nodo regressivo: la direzione chiedeva di non importare label GUE/Poisson. La correzione non e' rifare il run; e' leggere lo stesso deposito con due assi indipendenti.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY semi-reale 13 righe + direzione viva \"support_transfer vs beta_coordinate_transfer\".\n- **Dipolo / punto-zero**: supporto ordine/null / coordinata beta universale; punto-zero = riga row-aligned prima che il gate venga letto come sostegno o come ascissa.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non e' una classe; e' una matrice di passaggio fra supporto e coordinata.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter matrix, coordinate split. Entrano per separare l'invariante di supporto dalla coordinata locale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:16 senza ripetere il perimetro; CE-0117: cascata della possibilita solo dove il supporto apre un canale; YSN DeltaLink: `semi-real gate -> two-axis matrix -> beta localizzata`; PVI: il claim beta universale cade se l'esatto 0.3 non copre il denominatore.\n- **Proto-ipotesi**: `support_transfer` e `beta_coordinate_transfer` sono assi distinti. Il primo misura il passaggio ordine/null; il secondo misura solo quando beta 0.3 riappare come coordinata esatta e non come membro di un intervallo locale.\n- **Possibile / non-possibile**: possibile = usare BOUNDARY come audit row-aligned di supporto su 11/13 righe; non-possibile = trattare beta 0.3 come terzo incluso universale del perimetro semi-reale.\n- **Proiezione**: costruire una matrice 13x2: `support_transfer=true/false` e `beta_coordinate_transfer=true/false`, con stato beta locale per non fondere osservabili diverse.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esplicita la matrice semi-reale a due assi richiesta dal valutatore: support_transfer contro beta_coordinate_transfer sulle 13 righe.\n- `not_drift`: non usa `source_domain_type` GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta label locali; legge solo stati row-aligned gia' misurati.\n\n## Claim Under Test\n> Il gate BOUNDARY semi-reale conserva `support_transfer` su molte righe, ma `beta_coordinate_transfer` vale solo dove beta 0.3 compare come coordinata esatta.\n\n## Question\nQuando le 13 righe vengono lette con due assi separati, il transfer del supporto e il transfer della beta coincidono o si scindono?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_two_axis_matrix.py`.\n- Input: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Output: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Perimetro atomico: 13 righe semi-reali gia' row-aligned dal deposito 15:16.\n- Label policy: l'operatore non legge label GUE/Poisson. Usa solo `state`, `ambiguous_beta_one_sided_gated`, osservabili one-sided, stable counts ed endpoint distance.\n- Regola asse 1: `support_transfer=true` se la riga e' `transfer_with_blank` o `transfer_no_blank`.\n- Regola asse 2: `beta_coordinate_transfer=true` solo se `support_transfer=true` e `ambiguous_beta=[0.3]` dopo normalizzazione a un decimale.\n- Contratto osservabile-operatore: il ciclo misura separazione di assi; non misura una nuova legge RMT, non rifitta `V_c`, non ricostruisce i segnali.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| support_transfer true | 11 |\n| support_transfer false | 2 |\n| beta_coordinate exact 0.3 | 4 |\n| beta 0.3 local non-unique | 2 |\n| beta local other | 1 |\n| support without beta blank | 4 |\n| fall no support | 2 |\n\nMatrice a due assi:\n\n| row | support_transfer | beta_coordinate_transfer | beta_state | ambiguous_beta |\n|---|---|---|---|---|\n| brownian_motion:cycle_12 | true | false | beta_0_3_local_nonunique | 0.2,0.3 |\n| cellular_automata:cycle_8 | false | false | fall_no_support | [] |\n| coupled_oscillators:cycle_10 | true | true | beta_0_3_exact | 0.3 |\n| ising_2d:cycle_1 | true | false | local_beta_other | 0.4 |\n| logistica_biforcazione:cycle_5 | true | false | beta_0_3_local_nonunique | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | true | true | beta_0_3_exact | 0.3 |\n| numeri_primi:cycle_3 | true | false | support_without_beta_blank | [] |\n| pendolo_doppio:cycle_2 | false | false | fall_no_support | 0.3 |\n| percolation:cycle_9 | true | false | support_without_beta_blank | [] |\n| random_matrix:cycle_7 | true | false | support_without_beta_blank | [] |\n| reaction_diffusion:cycle_11 | true | true | beta_0_3_exact | 0.3 |\n| string_vibration:cycle_6 | true | true | beta_0_3_exact | 0.3 |\n| zeta_zeros:cycle_4 | true | false | support_without_beta_blank | [] |\n\n## Key Findings\n1. **Verificato: `support_transfer` resta 11/13.** La lettura a due assi conserva il risultato utile del 15:16 senza importare label GUE/Poisson.\n2. **Verificato: `beta_coordinate_transfer` e' 4/13.** Solo quattro righe hanno beta 0.3 esatta: `coupled_oscillators`, `logistica_biforcazione_var_3.5699`, `reaction_diffusion`, `string_vibration`.\n3. **Verificato: la beta non collassa in un solo asse.** Due righe includono 0.3 in un intervallo non unico, una riga ha beta locale 0.4, quattro trasferiscono senza blank beta, due cadono.\n4. **Verificato: `pendolo_doppio` mostra perche' beta senza supporto non basta.** Ha ambiguous beta 0.3, ma resta `fall_no_support`; la coordinata non salva il transfer.\n5. **Inferito: il nodo regressivo e' la fusione degli osservabili.** Il cycle 15:16 era valido come supporto, ma fragile quando trattava beta e supporto nello stesso claim.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce come supporto ordine/null su 11/13 righe. La coordinata beta 0.3 trasferisce esattamente su 4/13 righe. Questi due assi non coincidono.\n\nFormula valida:\n\n`BOUNDARY support_transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY beta_coordinate_transfer` = beta 0.3 universale del terzo incluso semi-reale.\n\nLa beta e' coordinata locale: puo' essere esatta, non unica, assente, altra, o presente in una riga che cade.\n\n## Bicono della scoperta\n- **Due radici**: supporto che passa / coordinata che pretende universalita.\n- **Singolare**: la riga row-aligned prima della lettura a due assi.\n- **Invariante di passaggio**: separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.\n- **Campo di possibilita**: qui diventa possibile usare il gate come matrice di audit `support_transfer`; qui diventa non-possibile usare beta 0.3 come coordinata universale.\n\n## Consecutio\nIl prossimo ciclo deve attaccare i quattro stati beta, non il supporto gia' separato: `beta_0_3_exact`, `beta_0_3_local_nonunique`, `local_beta_other`, `support_without_beta_blank`. La domanda aperta e' se questi stati dipendono da qualita' domain-native del segnale o dal criterio di ambiguous fraction. Non va reintrodotta la label GUE/Poisson come scorciatoia.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1516.md`, falsifier `falsifier_20260509_1516.json`, script `tools/exp_boundary_two_axis_matrix.py`, JSON `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe del deposito 15:16.\n- **L2 count grezzi**: riportati 11/13 support_transfer, 4/13 beta 0.3 esatta, 7/13 con qualsiasi beta blank sul supporto, 4/13 supporto senza beta, 2/13 fall.\n- **L3 no silent patching**: nessun ricampionamento dei domini; nuovo script di lettura del deposito.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `logistica_biforcazione` include 0.3 ma in intervallo 0.1-0.9; `ising_2d` trasferisce con beta 0.4.\n- **L5 re-discovery**: il ciclo non ripete il gate semi-reale; lo decompone nel nodo richiesto dal valutatore.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit support_transfer; non-possibile = beta 0.3 universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_two_axis_matrix.py`.\n- Run valido: `python tools/exp_boundary_two_axis_matrix.py --input tools/data/semireal_boundary_transfer_gate_20260509_1516.json --out tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Output: `support_transfer=11/13`, `beta_coordinate_exact_0_3=4/13`, `any_beta_blank_on_support=7/13`, `support_without_beta_blank=4/13`, `fall_no_support=2/13`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge solo lo script 15:32, il JSON 15:32 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_two_axis_matrix.py`\n- Data: `tools/data/boundary_two_axis_matrix_20260509_1532.json`\n- Report: `tools/data/reports/agent_20260509_1532.md`\n", tools/data/restore_backups/pre_restore_20260514_1330/lab_data.json:101: "content": "# Agent Report - Interfaccia component_state su bordo Anderson 3D\n**Date**: 2026-05-14 18:50 \n**Piano**: 126 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson \n**verdict**: VALUE_STABLE / TOOL CONTRACT TRANSFER - il tester `component_state(SR,L1,triple_var)` legge un nuovo spettro fisico Anderson 3D senza cambiare definizioni osservabili e senza perdere il rimbalzo fisico A -> matematica -> fisico B. Il ritorno fisico e' strumentale: W4 e W16.5 restano active contro Poisson, ma il contrasto diretto separa solo `triple_var`; `SR` e `L1` non chiudono il bordo 3D a `L=6`. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta] \ntool_contract_ref: `tools/data/tool_contracts/component_state_SR_L1_triple_var_fit_ready_20260514_1649.json` \npreflight_anchor: `20260514_1640` VALUE_STABLE, Veritas COLLASSO rho=0.9325, falsifier coherent=true zero flag \nnot_promoted_as_physics_law: true \nssp_value: yes\n\n## Source directive\n\nQuesto ciclo segue la direttiva one-shot del 2026-05-14: ripartire da `20260514_1640`, non dal ramo `20260514_1701`, usare `component_state_SR_L1_triple_var_fit_ready_20260514_1649` come tool contract, e formulare una domanda stretta sulla riusabilita' del tester per leggere un nuovo spettro fisico. Il ramo quasiperiodico/Sturmian resta `vault`, non direzione primaria.\n\n## Prima impressione\n\nIl tester non deve predire la transizione Anderson 3D. Deve mostrare se uno spettro fisico nuovo puo' entrare nell'interfaccia e tornare come stato componente auditabile. Se il bordo non separa su `SR`, quello e' il contenuto: il tool trasferisce, la legge no.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione BOUNDARY GUE/Poisson + nodo fisico Anderson 3D.\n- **Dipolo / punto-zero**: caos metallico / localizzazione critica. Punto-zero: Poisson span-matched, perche' ogni spettro torna leggibile solo se il null fisico-matematico resta esplicito.\n- **Piano superiore**: geometria spettrale dei campi disordinati; il bordo e' letto come componente attiva/assorbita, non come fit di transizione.\n- **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' interfaccia riusabile se un nuovo spettro fisico ordinato produce component states contro Poisson e contrasto diretto dichiarato senza cambiare definizioni dal registry.\n- **Possibile / non-possibile**: possibile = usare il tool su spettro fisico B e ottenere output auditabile; non-possibile = dichiarare transizione Anderson 3D o legge universale quando `SR,L1` non separano il contrasto diretto.\n- **Proiezione**: genero 24 spettri Anderson 3D tight-binding con condizioni periodiche, `L=6`, 12 repliche per `W=4.0` e `W=16.5`; poi li passo all'interfaccia `--input-spectrum` del tester 16:49.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: l'esperimento resta nel pe" tools/data/repairs/repair_20260514_1649_decision.json:1:{"cycle_ts": "20260514_1649", "decision": "SKIP", "should_rerun": false, "reasons": ["no_repair_condition_matched"], "guards": {"repair_depth": 0, "max_depth": 1, "parent_ts": "", "loop_danger": false, "aeternitas_veto": false}, "signals": {"cycle_ts": "20260514_1649", "agent": {"path": "/opt/MM_D-ND/tools/data/reports/agent_20260514_1649.md", "exists": true, "title": "Agent Report - Component State Fit-Ready Tester", "size": 9876}, "falsifier": {"path": "/opt/MM_D-ND/tools/data/reports/falsifier_20260514_1649.json", "exists": true, "coherent": false, "summary": "Il report e' coerente sul perimetro fit-ready e dichiara possibile/non-possibile e aderenza alla direzione, ma si rompe sulla L6 per mancata metabolizzazione esplicita del campo cognitivo.", "counts": {"high": 0, "medium": 1, "low": 0}, "flags": [{"lens": "L6", "severity": "medium", "claim": "Il report include una sezione 'Respiro fuori-tempo' con 'Combo', ma non contiene una riga 'Contaminazione cognitiva' né una dichiarazione 'CE-none:' verificabile.", "suggestion": "Nel prossimo ciclo aggiungere una riga strutturale: 'Contaminazione cognitiva: CE-none: motivo verificabile...' oppure nominare la voce CE/YSN/Cornelius/KSAR/PVI effettivamente usata nella combo e indicare come ha modificato il contratto."}]}, "aeternitas": {"path": "/opt/MM_D-ND/tools/data/aeternitas/aeternitas_20260514_165437.json", "decision": "WARN", "reason": "P5 (Autopoiesi): ciclo non ha prodotto tensioni nuove né cambiato direzione", "p0_passed": true, "p1_passed": true, "p5_passed": false, "p5_violations": [{"issue": "no_new_tension_or_direction", "old_count": 13, "new_count": 13}]}, "veritas": {"path": "/opt/MM_D-ND/tools/data/veritas/veritas_20260514_165437.json", "rho": 0.8792, "band": "SOSPENSIONE"}, "valutatore": {"exists": true, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha chiuso correttamente la contrattualizzazione del tester: output fit-ready, Bridge QA clean, nessuna promozione a legge fisica. Dopo tre NEXT_CYCLE sullo stesso asse, un altro giro di raffinamento locale rischia accumulo di dettaglio; la prossima mossa deve usare il contratto per falsificare transfer/blank/fall su QxG continuo/discreto e contro-perimetro, non migliorare ancora l'interfaccia.", "action_type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare il tester component_state fit-ready sul ponte QxG continuo/discreto: applicare transfer/blank/fall e contro-perimetro, senza promuovere legge fisica e senza accumulare nuovi fit locali.", "reason": "Il contratto e' pronto; ora va trasformato in filtro strutturale cross-dominio o fatto cadere."}, "notes": "Non serve stop operatore: nessuna contraddizione seria o cristallizzazione. Serve cambiare passo dal contratto alla falsificazione strutturale."}, "loop_guard": {"path": "/opt/MM_D-ND/tools/data/reports/loop_guard_20260514_1649.json", "exists": true, "bands": {"valutatore_decisions": "OK", "piano_trajectory": "OK"}, "danger": false}, "session": {"exists": true, "sync_blocked": false, "errors": ""}}, "directive_path": "/opt/MM_D-ND/tools/data/operator_directive.md", "record_json": null, "record_md": null} tools/data/repairs/repair_20260508_2102.md:25:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/reports/agent_20260509_1427.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260512_0330.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1538.md:10:## Respiro fuori-tempo tools/data/repairs/repair_20260514_1656.md:25:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/reports/agent_20260514_1612.md:14:## Respiro fuori-tempo tools/data/reports/agent_20260508_2005.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1437.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1444.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260514_1649.md:18:## Respiro fuori-tempo tools/data/reports/agent_20260508_1909.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260508_2133.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260514_1330.md:14:## Respiro fuori-tempo tools/data/reports/agent_20260508_2121.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260511_0330.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_1400.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260515_1734.md:9:## Respiro fuori-tempo tools/data/reports/agent_20260509_1409.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260508_2108.md:10:## Respiro fuori-tempo tools/data/repairs/repair_20260508_1909.md:27:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/repairs/repair_20260509_0652.md:27:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/repairs/repair_20260508_2013.md:27:- Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. tools/data/reports/agent_20260509_1457.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260515_1712.md:9:## Respiro fuori-tempo tools/data/reports/agent_20260508_2013.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260509_0652.md:10:## Respiro fuori-tempo tools/data/reports/agent_20260514_1850.md:21:## Respiro fuori-tempo tools/data/cognitive_enzymes_archive.json:628: "id": "CE-0019", tools/data/cognitive_enzymes_archive.json:631: "title": "Respiro fuori-tempo", tools/data/cognitive_enzymes_archive.json:733: "id": "CE-0022", tools/data/cognitive_enzymes_archive.json:736: "title": "Palette operatoria espansa del Lab", tools/data/graph_completion/graph_completion_20260514_1649.json:124: "claim": "Il report include una sezione 'Respiro fuori-tempo' con 'Combo', ma non contiene una riga 'Contaminazione cognitiva' né una dichiarazione 'CE-none:' verificabile.", tools/data/lab_graph.json:1169: "content_full": "# Agent Report - Boundary Classical Crossover Audit\n**Date**: 2026-05-15 19:04\n**Piano**: 120\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - i nodi ponte del gate 18:55 non collassano su un parametro classico unico; Brody/Berry-Robnik-like spiegano `numeri_primi`, ma lasciano tre bridge graph-only e quattro intermedi classici non-bridge.\nobservables_registry: none; classical audit coordinates plus prior graph observables\nobservables_used: [brody_q, berry_robnick_like_gue_weight, mixture_ks, graph_boundary_state_from_1855, centroid_margin_from_1855, cross_neighbor_fraction_from_1855]\n**observable_contract**: claim=il bridge Lab conserva residuo dopo confronto con scalari classici di crossover; observable=Brody q row-aligned, peso GUE Berry-Robnik-like, stato ponte del grafo 18:55; operator=classical scalar audit sulle stesse 13 righe BOUNDARY; generator=row_spacings(domain) + boundary_graph_curvature_gate_20260515_1855; denominator=13 righe, 8 GUE e 5 Poisson; non_possible=bridge Lab-specific se ogni graph bridge e' anche intermedio classico e non esiste classic-only intermediate; not_tested=flusso Hamiltoniano Rosenzweig-Porter vero, unfolding fisico alternativo, universalita asintotica.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: repulsione spettrale / indipendenza spettrale. Punto-zero: riga di dominio prima che venga letta come label, parametro Brody o nodo del grafo.\n- **Piano superiore**: grafo della conoscenza con audit assiomatico su baseline note; la domanda non e' \"quanto vale q\", ma se q esaurisce il ponte.\n- **Proto-ipotesi**: il terzo incluso operativo non coincide con un singolo scalare di crossover. Se coincide, il bridge Lab e' re-discovery di Brody/Berry-Robnik; se diverge, il contenuto Lab e' nella relazione tra geometria locale e scalare classico.\n- **Possibile/non-possibile**: possibile = usare nodi ponte come righe fisiche candidate oltre la classificazione GUE/Poisson; non-possibile = rivendicare un nuovo crossover se i nodi ponte sono solo Brody/Berry-Robnik rietichettato.\n- **Proiezione**: stimo Brody q e peso GUE di una mistura Poisson/GUE-surmise per ciascuna delle 13 righe gia' classificate dal grafo 18:55.\n\n### Contaminazione cognitiva\n- **YSN DeltaLink**: il DeltaLink usato e' `crossover classico / grafo Lab`: la sorpresa cercata e' il disaccordo, non la conferma dei nodi ponte.\n- **Cornelius gene**: `Classical_Audit_Gate`: \"Un ponte Lab sopravvive solo dopo il lettore classico piu vicino.\" Operatori: FITTA scalare noto; ALLINEA righe; ISOLA residuo.\n- **KSAR step**: perturbazione = feedback falsifier L5; focalizzazione = una sola domanda, \"i bridge collassano su Brody/Berry-Robnik?\"; proiezione = audit row-aligned sulle 13 righe.\n- **PVI attack**: un revisore esterno puo' dire che `third_included_candidate` e' solo un nome Lab per un crossover Brody. Il test attacca esattamente quel presupposto.\n- **Vault**: Rosenzweig-Porter vero resta fuori perimetro; va riattivato solo con Hamiltoniane interpolate, non con fit di CDF su righe gia' generate.\n- **CE-none:tools/data/agent_field_live.md+tools/LAB_COGNITIVE_CONTAMINATION.md/2026-05-15T19:07Z**: nessuna voce `CE-*` concreta e' presente nel campo letto; usati adapter YSN/Cornelius/KSAR documentati, senza inventare archivio enzimi.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: il ciclo resta sul perimetro vivo 8 GUE / 5 Poisson e verifica se il confine come terzo incluso e' nuovo rispetto ai crossover classici.\n- `not_drift`: non usa il report Sturmian bloccato, non misura V_c, non usa phi/silver/bronze; il gate 18:55 e' usato come denominatore row-aligned da auditare, non come autorita' conclusiva.\n\n## Re-discovery audit\n- **Baseline noto piu' vicino**: Brody distribution per interpolazione Poisson-Wigner; Berry-Robnik per mistura regolare/caotica. Rosenzweig-Porter e' nominato come famiglia di crossover Hamiltoniano, non fit eseguito in questo ciclo.\n- **Cosa viene assorbito dal baseline**: `numeri_primi:cycle_3` e' sia graph bridge sia intermedio classico (`brody_q=0.465`, `w_GUE=0.275`). Su questa riga il Lab non aggiunge fenomeno oltre il fatto che lo stesso campione e' ponte in due lettori.\n- **Cosa resta Lab-specific**: `percolation:cycle_9`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` sono graph-only bridge: il grafo li mette al confine ma Brody/mixture li legge endpoint-like.\n- **Cosa limita il claim Lab**: quattro righe sono classic-only intermediate (`zeta_zeros`, `random_matrix`, `cellular_automata`, `brownian_motion`) senza diventare terzo incluso nel grafo. Quindi il parametro classico non basta, ma nemmeno il grafo sostituisce il baseline classico.\n- **Risultante audit**: il boundary operativo e' una relazione a due lettori: scalar crossover + posizione nel grafo. Uno dei due da solo perde informazione.\n\n## Claim Under Test\n> Nel perimetro 8/5, il terzo incluso operativo non e' riducibile a Brody q o a una mistura Poisson/GUE-surmise; il residuo vive nel disaccordo row-aligned tra scalare classico e grafo osservabile.\n\n## Question\nI nodi ponte del grafo 18:55 sono re-discovery di un crossover classico, oppure producono una distinzione residua?\n\n## Ritorno fisico\n- **Punto fisico sorgente**: transizione spettrale tra caos quantistico repulsivo e indipendenza/localizzazione Poisson.\n- **Attraversamento matematico**: fit Brody e mistura Poisson/GUE-surmise sulle stesse righe gia' lette dal grafo kNN.\n- **Punto fisico di ritorno**: negli spettri finiti, una finestra non e' boundary perche' ha q intermedio; e' boundary quando q intermedio e posizione multi-feature del grafo vengono confrontati e il residuo resta nominabile.\n- **Osservabile/test fisico possibile**: su finestre energetiche sperimentali, calcolare q Brody, peso mistura e kNN multi-feature; separare bridge coincidenti, graph-only e classic-only.\n- **Se fallisce**: se su dati fisici indipendenti graph-only e classic-only spariscono, il gate Lab si riduce a baseline classico e il terzo incluso non trasferisce.\n\n## Experiment Design\n- **Script**: `tools/exp_boundary_classical_crossover_audit.py`.\n- **Input graph**: `tools/data/boundary_graph_curvature_gate_20260515_1855.json`.\n- **Run**: `python tools/exp_boundary_classical_crossover_audit.py --out tools/data/boundary_classical_crossover_audit_20260515_1904.json`.\n- **Denominatore**: 13 righe row-aligned dal perimetro BOUNDARY, 8 GUE e 5 Poisson.\n- **Fit Brody**: grid likelihood su q in [0,1], spacings normalizzati a media 1.\n- **Fit Berry-Robnik-like**: griglia su peso GUE in mistura CDF `w*GUE_surmise + (1-w)*Poisson`, selezionata per KS minimo.\n- **Contratto osservabile-operatore**: il ciclo testa concordanza/disaccordo tra scalare classico e graph state; non testa V_c, denominatori Sturmian, unfolding fisico alternativo o Rosenzweig-Porter Hamiltoniano.\n\n## Results\n| audit state | count |\n|---|---:|\n| classic_and_graph_bridge | 1 |\n| graph_only_bridge | 3 |\n| classic_only_intermediate | 4 |\n| endpoint_like | 5 |\n\n| row | label | graph_state | Brody q | w_GUE | KS | audit_state |\n|---|---|---|---:|---:|---:|---|\n| ising_2d:cycle_1 | GUE | class_interior | 0.090 | 0.070 | 0.428636 | endpoint_like |\n| pendolo_doppio:cycle_2 | Poisson | cut_edge | 0.000 | 0.000 | 0.268279 | endpoint_like |\n| numeri_primi:cycle_3 | GUE | third_included_candidate | 0.465 | 0.275 | 0.148459 | classic_and_graph_bridge |\n| zeta_zeros:cycle_4 | GUE | cut_edge | 1.000 | 0.530 | 0.133555 | classic_only_intermediate |\n| logistica_biforcazione:cycle_5 | GUE | class_interior | 0.000 | 0.000 | 0.998064 | endpoint_like |\n| string_vibration:cycle_6 | Poisson | cut_edge | 0.000 | 0.000 | 0.060129 | endpoint_like |\n| random_matrix:cycle_7 | GUE | cut_edge | 0.975 | 0.475 | 0.119491 | classic_only_intermediate |\n| cellular_automata:cycle_8 | GUE | class_interior | 1.000 | 0.435 | 0.416708 | classic_only_intermediate |\n| percolation:cycle_9 | Poisson | third_included_candidate | 0.025 | 0.025 | 0.054635 | graph_only_bridge |\n| coupled_oscillators:cycle_10 | Poisson | class_interior | 0.000 | 0.000 | 0.079806 | endpoint_like |\n| reaction_diffusion:cycle_11 | GUE | third_included_candidate | 0.000 | 0.000 | 0.174423 | graph_only_bridge |\n| brownian_motion:cycle_12 | Poisson | cut_edge | 0.205 | 0.250 | 0.026002 | classic_only_intermediate |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | third_included_candidate | 0.000 | 0.000 | 0.969277 | graph_only_bridge |\n\n## Key Findings\n1. Verificato: il denominatore resta quello richiesto, 13 righe con 8 GUE e 5 Poisson.\n2. Verificato: un solo nodo ponte del grafo e' anche intermedio classico: `numeri_primi:cycle_3`.\n3. Verificato: tre nodi ponte sono graph-only: `percolation:cycle_9`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13`.\n4. Verificato: quattro righe sono classic-only intermediate senza essere terzo incluso nel grafo: `zeta_zeros:cycle_4`, `random_matrix:cycle_7`, `cellular_automata:cycle_8`, `brownian_motion:cycle_12`.\n5. Inferito: il terzo incluso non e' uno scalare di crossover. E' una discrepanza controllata fra lettore classico e posizione multi-osservabile.\n\n## Verdict\nCONSTRAINT\n\nIl boundary trasferisce come audit a due lettori. Brody/Berry-Robnik-like e grafo misurano aspetti diversi dello stesso confine; nessuno dei due chiude il terzo incluso da solo.\n\n## Bicono della scoperta\n- **Due radici**: parametro classico di crossover; nodo ponte del grafo Lab.\n- **Singolare**: riga di dominio row-aligned prima della classificazione.\n- **Invariante di passaggio**: disaccordo nominabile tra `classic_and_graph`, `graph_only`, `classic_only`, `endpoint_like`.\n- **Campo di possibilita**: possibile = costruire un gate fisico che richiede doppia lettura prima di chiamare boundary; non-possibile = promuovere il grafo 18:55 come scoperta autonoma senza baseline classico.\n\n## Consecutio\nIl prossimo ciclo utile non deve aggiungere una terza metrica locale. Deve portare il gate a due lettori su un sistema fisico controllato: Rosenzweig-Porter, Anderson/mobility edge o Aubry-Andre con finestre energetiche. Il risultato da cercare e' se `graph_only` e `classic_only` sopravvivono fuori dal perimetro composito del Lab.\n\n## Ricadute pratiche\nssp_value: yes. Lo script crea un audit riusabile per separare re-discovery classica, residuo Lab e endpoint-like in ogni perimetro GUE/Poisson row-aligned.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante `TENS_SCALE_TRASCENDENZA_LIMITE`, ma la direzione viva del campo impone il perimetro BOUNDARY 8/5.\n- `python -m py_compile tools/exp_boundary_classical_crossover_audit.py` completato.\n- `python tools/exp_boundary_classical_crossover_audit.py --out tools/data/boundary_classical_crossover_audit_20260515_1904.json` completato.\n- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate.\n- Nessun update del seme.\n- Nessuna promozione e nessun public sync.\n\n## Files\n- Script: `tools/exp_boundary_classical_crossover_audit.py`\n- Data: `tools/data/boundary_classical_crossover_audit_20260515_1904.json`\n- Report: `tools/data/reports/agent_20260515_1904.md`\n", tools/data/lab_graph.json:1220: "content_full": "# Agent Report - V=2 Generator Scaling Gate\n**Date**: 2026-05-15 18:16 \n**Piano**: 119 \n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY \n**verdict**: CONSTRAINT - a V=2 il confine trasferisce come generatore binario ordinato: `phi_sturmian_binary` separa dallo shuffle di densita, ma non chiude una specificita phi contro silver/bronze Sturmian. \nobservables_registry: none; dedicated observables only \nobservables_used: [mean_pr_tau, mean_ipr_tau, spacing_r, mean_ipr, participation_entropy] \n**observable_contract**: claim=V=2 e' baseline fisica del confine Aubry-Andre e distingue il tipo di generatore; observable=slope log-log `tau` della mean participation ratio piu spacing/IPR anchors; operator=`tools/exp_aubry_v2_generator_scaling_gate.py`; generator=tight-binding 1D con potenziali `sturmian_binary`, `cosine`, `periodic_ab`, `density_shuffle`, `random_uniform`; denominator=N={89,144,233,377} x phase={0,0.25,0.5,0.75} x generator rows, con 4 trial per null random; non_possible=promuovere un claim phi-specific se tau(V=2) non separa dai controlli Sturmian non-phi; not_tested=limite asintotico, altri V, mobility edge, dati sperimentali, qualita PSD dei surrogate 18:07.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + baseline fisica Aubry-Andre V=2 + tensione viva `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: generatore continuo coseno / generatore discreto binario. Punto-zero = Hamiltoniana tight-binding a V=2 prima che il potenziale venga letto come transizione self-dual, parola Sturmian o disorder.\n- **Piano superiore**: geometria dei campi su reticolo; il bordo e' nella legge di generazione del campo onsite, non nel solo fit di `V_c`.\n- **Operatori laterali scelti**: boundary condition, eigenvector localization, non-phi generator control.\n- **Contaminazione cognitiva**: CE-none: il ciclo non ha introdotto adapter semantico; ha usato il baseline fisico V=2 come vincolo regressivo per evitare un nuovo strato linguistico sopra i risultati 17:45-18:07.\n- **Proto-ipotesi**: se il confine e' proprieta del generatore, allora a V=2 la crescita della partecipazione degli autostati separa classi di generatore; se e' phi-specific, `phi_sturmian_binary` deve separare anche dai controlli Sturmian non-phi.\n- **Proiezione**: diagonalizzo la stessa Hamiltoniana tridiagonale con potenziali binari e continui; il tau della participation ratio misura se gli autostati scalano come estesi, critici o localizzati nel perimetro V=2.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa il confine come proprieta del generatore usando Sturmian/binario vs coseno Aubry-Andre vs controlli irrazionali, con baseline fisica esplicita V=2.\n- `not_drift`: non rifitta `V_c`, non riapre prime/mod6, non usa selector legacy; il vecchio deposito viene usato solo come perimetro fisico della misura.\n\n## Claim Under Test\n\n> A V=2, il boundary si conserva come proprieta del generatore: binario Sturmian, coseno continuo e null disorder hanno tau di partecipazione distinti; la specificita phi sopravvive solo se phi binario separa anche dai controlli Sturmian non-phi.\n\n## Question\n\nIl baseline V=2 legge un confine phi-specific, oppure legge una classe piu larga di generatori binari ordinati distinta dal coseno continuo e dal disorder?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: modello Aubry-Andre tight-binding 1D, dove il coseno quasiperiodico ha transizione self-dual nota a V=2.\n- **Attraversamento matematico**: sostituzione controllata del potenziale onsite con parole Sturmian binarie, coseni irrazionali, periodico AB e null disorder; misura dello scaling finito della participation ratio.\n- **Punto fisico di ritorno**: reticoli fotonici o cold atoms con potenziale onsite programmabile, dove si puo confrontare un coseno quasiperiodico con una parola binaria Sturmian alla stessa ampiezza V=2.\n- **Controllo concretezza**: il ritorno e' IPR/participation ratio degli autostati di una Hamiltoniana tridiagonale, non una categoria astratta di confine.\n- **Relazione nuova**: V=2 non trasferisce come firma phi isolata; trasferisce come separatore fra ordine binario quasiperiodico, coseno continuo e disorder.\n- **Osservabile/test fisico possibile**: preparare potenziali `phi_sturmian_binary`, `silver_sturmian_binary`, `phi_cosine` e `density_shuffle` a V=2; misurare spreading o profili modali e stimare tau di partecipazione su taglie crescenti.\n- **Se fallisce**: `ritorno_fisico_assente` per claim phi-specific; resta vincolo di classe-generatore, non scoperta phi promuovibile.\n\n## Experiment Design\n\n- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- `python -m py_compile tools/exp_aubry_v2_generator_scaling_gate.py` completato.\n- Run: `python tools/exp_aubry_v2_generator_scaling_gate.py --out tools/data/aubry_v2_generator_scaling_gate_20260515_1816.json`.\n- Nearest-known baseline: Aubry-Andre continuo, transizione self-dual a V=2; per questo il ciclo non promuove il risultato come nuovo fenomeno fisico, ma come gate di generatore.\n- Perimetro: 176 righe totali; N={89,144,233,377}; phases={0,0.25,0.5,0.75}; random_trials=4.\n- Contratto osservabile-operatore: il ciclo testa tau finito della participation ratio a V=2; non testa `gap_ratio`, `V_c` asintotico, PSD surrogate quality, ne universalita GUE/Poisson.\n\n## Results\n\n| domain | mean_pr_tau | median spacing_r | median mean_ipr | median mean_pr | median participation_entropy |\n|---|---:|---:|---:|---:|---:|\n| periodic_ab | 0.9937 | 0.9298 | 0.0134 | 80.9477 | 0.8730 |\n| phi_sturmian_binary | 0.8048 | 0.3608 | 0.0452 | 30.3199 | 0.7240 |\n| silver_sturmian_binary | 0.7268 | 0.3774 | 0.0510 | 23.7245 | 0.7037 |\n| bronze_sturmian_binary | 0.6399 | 0.4004 | 0.0487 | 30.4225 | 0.7218 |\n| phi_cosine | 0.5689 | 0.3013 | 0.0961 | 13.6849 | 0.6111 |\n| silver_cosine | 0.5819 | 0.3108 | 0.0878 | 14.5649 | 0.6226 |\n| bronze_cosine | 0.6104 | 0.3478 | 0.0868 | 15.7257 | 0.6237 |\n| phi_binary_density_shuffle | 0.0527 | 0.3875 | 0.1403 | 9.1779 | 0.4677 |\n| random_uniform | 0.0306 | 0.4011 | 0.1834 | 6.8468 | 0.4180 |\n\n| classification field | value |\n|---|---:|\n| v2_baseline | 2.0 |\n| phi_binary_tau | 0.8048 |\n| phi_cosine_tau | 0.5689 |\n| density_shuffle_tau | 0.0527 |\n| silver_binary_tau | 0.7268 |\n| bronze_binary_tau | 0.6399 |\n| cosine_class_tau_span | 0.0414 |\n| phi_binary_separates_from_shuffle | true |\n| phi_binary_separates_from_nonphi_binary | false |\n\n## Key Findings\n\n1. **Verificato**: `phi_sturmian_binary` a V=2 separa dal null di densita: tau 0.8048 contro 0.0527.\n2. **Verificato**: i coseni irrazionali formano una classe stretta nel perimetro misurato: tau span 0.0414.\n3. **Verificato**: il binario Sturmian non e' phi-specific nel gate impostato: phi tau 0.8048, silver 0.7268, bronze 0.6399; la soglia `min_tau_delta=0.08` non viene superata contro silver.\n4. **Inferito**: V=2 distingue ordine binario quasiperiodico da disorder e da coseno continuo; non autorizza il claim che phi sia il generatore unico del confine.\n\n## Verdict\n\n**CONSTRAINT**.\n\nLa formulazione valida e': a V=2 il boundary e' proprieta della classe del generatore, non del solo phi. Il binario Sturmian produce scaling piu esteso del coseno continuo e del disorder, ma il controllo silver resta troppo vicino per chiamarlo phi-specific.\n\n## Bicono della scoperta\n\n- **Due radici**: coseno continuo self-dual / parola binaria quasiperiodica.\n- **Singolare**: Hamiltoniana tight-binding a V=2 prima della scelta della grammatica onsite.\n- **Invariante di passaggio**: tau della participation ratio come lettore del tipo di generatore.\n- **Campo di possibilita**: possibile = progettare test fisici che separano coseno, binario ordinato e disorder alla stessa ampiezza V=2; non-possibile = promuovere `phi` come boundary autonomo finche silver/bronze restano nello stesso corridoio di tau.\n\n## Consecutio\n\nIl prossimo ciclo deve isolare il corridoio Sturmian non-phi: aumentare N o usare approssimanti denominatore-allineati per chiedere se la vicinanza phi/silver e' finite-size, effetto di fase, o proprieta comune delle parole meccaniche binarie. Non serve tornare a `V_c` prima di chiudere questo corridoio.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' un gate riusabile per setup fotonici/cold-atom: confronta generatori onsite diversi alla stessa baseline V=2 e restituisce tau, spacing e IPR in un JSON unico.\n\n## Telemetria\n\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Preflight post-report: `python tools/lab_preflight_agent.py --cycle 20260515_1816 --json` => `ARTIFACT_USEFUL_NOT_PUBLISHABLE`, recommended_action=`KEEP_ARTIFACT_STOP_REPORT_PROMOTION`, stable_anchor=`20260515_1712`.\n- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate.\n- Nessun update del seme.\n- Nessuna promozione e nessun public sync.\n\n## Files\n\n- `tools/exp_aubry_v2_generator_scaling_gate.py`\n- `tools/data/aubry_v2_generator_scaling_gate_20260515_1816.json`\n- `tools/data/reports/agent_20260515_1816.md`\n", tools/data/lab_graph.json:1271: "content_full": "# Agent Report - Aubry Cosine Boundary Counter-Gate\n**Date**: 2026-05-15 17:58 \n**Piano**: 118 \n**Tension explored**: BOUNDARY / TENS_SCALE_TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - nel potenziale Aubry-Andre coseno `phi` non chiude un boundary privilegiato; la finestra binaria 17:45 dipende dalla grammatica Sturmian, non dal solo irrazionale phi. \nobservables_registry: none; dedicated observables only \nobservables_used: [spacing_r, mean_ipr, median_ipr, participation_entropy] \n**observable_contract**: claim=`phi` resta terzo incluso fisico anche quando il potenziale binario viene sostituito dal coseno Aubry-Andre canonico; observable=`spacing_r` + `mean_ipr` con controllo di distinzione da silver/bronze; operator=`tools/exp_aubry_cosine_boundary_counter_gate.py`; generator=Hamiltoniana tight-binding 1D con potenziale coseno per beta phi/silver/bronze, periodico beta=1/2 e random onsite uniforme; denominator=N={89,144,233} x phase={0,0.25,0.5,0.75} x V=0.50..3.00 step 0.25, random_trials=6; non_possible=promuovere phi come boundary fisico se non si separa dai controlli irrazionali con spacing e localizzazione insieme; not_tested=limite asintotico, disordine correlato sperimentale, classi GUE/Poisson universali dirette.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + modello Aubry-Andre come ponte fisico tra quasi-periodicita e localizzazione + tensione viva `BOUNDARY`.\n- **Dipolo / punto-zero**: grammatica binaria Sturmian / potenziale coseno continuo. Punto-zero = Hamiltoniana row-local prima che il confine venga attribuito a phi o alla forma del potenziale.\n- **Piano superiore**: geometria dei campi su reticolo; il generatore non e' solo parametro, e' forma del campo che decide quale bordo puo' essere letto.\n- **Operatori laterali scelti**: Hamiltonian spectrum, mobility edge/localization transition, non-phi generator control.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione regressiva del deposito 17:45: togliere la grammatica binaria e osservare cosa sopravvive. CE-0038 usato come equilibrio tra poli, reso falsificabile dal controllo silver/bronze.\n- **Proto-ipotesi**: se `phi` e' boundary fisico e non solo effetto della codifica binaria, nel coseno Aubry-Andre deve restare insieme tra periodico e random e deve separarsi da silver/bronze su spacing e localizzazione.\n- **Proiezione**: diagonalizzo Hamiltoniane tight-binding coseno e confronto mediane row-aligned per V, N, fase e generatore; il gate accetta solo se `phi` e' intermedio e distinto dai controlli irrazionali.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: segue la direzione viva \"8 domini GUE, 5 Poisson - il confine e' il terzo incluso operativo\" verificando se il confine fisico aperto nel ritorno Aubry/Fibonacci sopravvive a un contro-perimetro canonicale.\n- `not_drift`: non riapre prime/mod6, non usa selector legacy, non rifitta `V_c`; attacca il nodo regressivo lasciato dal ciclo 17:45: forma binaria del potenziale vs boundary fisico.\n\n## Claim Under Test\n\n> Nel potenziale Aubry-Andre coseno, `phi` resta terzo incluso fisico tra periodico e random solo se `spacing_r` e localizzazione lo collocano insieme nel segmento periodico-random e lo separano da silver/bronze.\n\n## Question\n\nIl confine `phi` osservato nel ritorno binario 17:45 sopravvive nel coseno Aubry-Andre, oppure il contenuto era nella grammatica Sturmian del potenziale?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: transizione spettrale/localizzazione in reticoli quasi-periodici, usata come ritorno fisico del boundary GUE/Poisson.\n- **Attraversamento matematico**: Hamiltoniane tridiagonali con potenziale coseno irrazionale, controlli periodico e random onsite.\n- **Punto fisico di ritorno**: modello Aubry-Andre canonico con transizione di localizzazione attesa attorno a V=2 per hopping unitario.\n- **Controllo concretezza**: il ritorno non e' \"confine\" astratto; e' spettro e autostati di un tight-binding 1D misurabile in reticoli fotonici o cold atoms.\n- **Relazione nuova**: la finestra binaria 17:45 non trasferisce automaticamente alla classe Aubry-Andre coseno; il generatore del potenziale diventa parte atomica del claim.\n- **Osservabile/test fisico possibile**: misurare spacing ratio e IPR nello stesso reticolo quasi-periodico variando V e confrontando beta phi con beta silver/bronze.\n- **Se fallisce**: `ritorno_fisico_assente` per la promozione phi-specifica nel coseno; resta vincolo sul denominatore, non scoperta fisica promuovibile.\n\n## Experiment Design\n\n- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- `python -m py_compile tools/exp_aubry_cosine_boundary_counter_gate.py` completato.\n- Run: `python tools/exp_aubry_cosine_boundary_counter_gate.py --out tools/data/aubry_cosine_boundary_counter_gate_20260515_1758.json`.\n- Perimetro deterministico: 132 righe per ciascun generatore phi/silver/bronze/periodic.\n- Perimetro random: 792 righe onsite uniforme.\n- Gate: `phi_joint_boundary = spacing_r_between and mean_ipr_between and separated_from_random and phi_distinct_from_irrational_controls`.\n- La misura serve la combo perche' rende non-possibile attribuire il boundary a phi se silver/bronze seguono la stessa classe fisica.\n\n## Results\n\n| V | joint | distinct_controls | r_between | ipr_between | sep_random | phi_r | silver_r | bronze_r | random_r | phi_ipr | silver_ipr | bronze_ipr | random_ipr |\n|---:|---|---|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|\n| 0.50 | false | false | true | true | false | 0.860 | 0.876 | 0.860 | 0.529 | 0.0154 | 0.0153 | 0.0156 | 0.0252 |\n| 0.75 | false | false | true | true | true | 0.821 | 0.852 | 0.821 | 0.467 | 0.0185 | 0.0179 | 0.0189 | 0.0436 |\n| 1.00 | false | false | true | true | true | 0.786 | 0.814 | 0.781 | 0.436 | 0.0218 | 0.0202 | 0.0226 | 0.0677 |\n| 1.25 | false | false | true | true | true | 0.741 | 0.755 | 0.737 | 0.418 | 0.0256 | 0.0225 | 0.0272 | 0.0948 |\n| 1.50 | false | false | true | true | true | 0.675 | 0.688 | 0.694 | 0.412 | 0.0313 | 0.0269 | 0.0338 | 0.1205 |\n| 1.75 | false | false | true | true | true | 0.572 | 0.581 | 0.597 | 0.408 | 0.0421 | 0.0388 | 0.0447 | 0.1558 |\n| 2.00 | false | false | false | true | true | 0.302 | 0.313 | 0.366 | 0.409 | 0.1070 | 0.0983 | 0.0981 | 0.1816 |\n| 2.25 | false | true | true | false | false | 0.430 | 0.386 | 0.354 | 0.401 | 0.2815 | 0.2637 | 0.2543 | 0.2151 |\n| 2.50 | false | true | true | false | true | 0.437 | 0.471 | 0.367 | 0.397 | 0.3658 | 0.3571 | 0.3402 | 0.2442 |\n| 2.75 | false | false | true | false | true | 0.475 | 0.483 | 0.358 | 0.398 | 0.4232 | 0.4227 | 0.4012 | 0.2684 |\n| 3.00 | false | false | true | false | true | 0.500 | 0.499 | 0.379 | 0.390 | 0.4666 | 0.4724 | 0.4482 | 0.3016 |\n\n## Key Findings\n\n1. **Verificato**: `phi_joint_boundary_v=[]`. Nessun valore di V chiude insieme intermedieta, separazione dal random e distinzione dai controlli irrazionali.\n2. **Verificato**: da V=0.75 a V=1.75 `phi` e' tra periodico e random sia in `spacing_r` sia in `mean_ipr`, ma resta quasi coincidente con silver/bronze; il boundary e' classe quasi-periodica, non privilegio phi.\n3. **Verificato**: a V=2.00 `spacing_r(phi)=0.302` esce dal segmento periodico-random mentre `mean_ipr` resta intermedio; la transizione coseno rompe il gate congiunto.\n4. **Verificato**: a V=2.25 e V=2.50 `phi` si distingue dai controlli, ma non e' piu' boundary congiunto: `mean_ipr_between=false` e a V=2.25 cade anche la separazione dal random.\n5. **Inferito**: il risultato 17:45 trasferisce come vincolo sul generatore binario, non come claim phi-specifico universale del modello Aubry-Andre.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl contro-perimetro coseno falsifica la promozione `phi` come terzo incluso fisico autonomo. Nel perimetro misurato il boundary congiunto non sopravvive quando la grammatica binaria viene rimossa. La formulazione valida diventa: `phi` e' candidato boundary nel potenziale binario Sturmian 17:45; nel coseno Aubry-Andre il contenuto si sposta alla classe quasi-periodica e alla transizione di localizzazione, non a phi come generatore privilegiato.\n\n## Bicono della scoperta\n\n- **Due radici**: potenziale binario Sturmian / potenziale coseno Aubry-Andre.\n- **Singolare**: Hamiltoniana tight-binding prima della scelta della forma del campo.\n- **Invariante di passaggio**: ogni ritorno fisico del boundary deve dichiarare forma del potenziale, controlli irrazionali e gate joint spacing/localizzazione.\n- **Campo di possibilita**: possibile = separare boundary di grammatica da boundary di classe fisica; non-possibile = promuovere `phi` come ritorno fisico se silver/bronze condividono la stessa risposta.\n\n## Consecutio\n\nIl prossimo ciclo non deve allargare `phi` nel coseno. Deve isolare il residuo binario: stessa Hamiltoniana, stesso denominatore, ma ablazione della grammatica Sturmian tramite surrogate che preservano densita, autocorrelazione corta e spettro del potenziale. Se la finestra V=0.50..1.25 sopravvive solo alla grammatica completa, il finding e' `boundary-as-grammar`; se sopravvive a surrogate piu deboli, il finding diventa classe di correlazione del potenziale.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' un counter-gate riusabile per impedire che demo o visualizzazioni del boundary promuovano `phi` senza controlli irrazionali nel modello fisico scelto.\n\n## Telemetria\n\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate.\n- Nessun update del seme.\n- Nessuna promozione e nessun public sync.\n\n## Files\n\n- `tools/exp_aubry_cosine_boundary_counter_gate.py`\n- `tools/data/aubry_cosine_boundary_counter_gate_20260515_1758.json`\n- `tools/data/reports/agent_20260515_1758.md`\n", tools/data/lab_graph.json:1322: "content_full": "# Agent Report - Prime/Mod6 Generative Null W2048\n**Date**: 2026-05-15 17:12 \n**Piano**: 117 \n**Tension explored**: VECTOR RESIDUE / prime_minus_mod6_z_vector(SR,L1,triple_var) as closeout audit, not active discovery \n**verdict**: CONSTRAINT / CLOSED_WARNING - il vettore non sopravvive al generative null span-local a 2048; il ramo resta vault warning. \nobservables_used: [SR_delta, L1_delta, triple_var_delta, p_upper, vector_survives, null_fairness_issue_count, null_std_ratio, selection_ratio] \n**observable_contract**: claim=`prime_minus_mod6_z_vector(SR,L1,triple_var)` resta chiuso se fallisce anche a 2048 contro un controcampo generativo fair preliminare; observable=delta prime_z - null_mean_z e p_upper per SR/L1/triple_var; operator=`tools/prime_mod6_generative_null_audit.py` + `tools/prime_mod6_null_fairness_audit.py`; generator=random span-local `6k +/- 1` null, 24 reps per row, fairness 128 reps; denominator=8 righe: 2 provider x 4 offset, window_gaps=2048; non_possible=riaprire il residuo come direzione attiva se il contratto vettoriale ha pass_count=0/8 su ogni osservabile focus e fairness issues=0; not_tested=null che preserva statistiche d'ordine locali piu strette, origine analitica del residuo, ritorno fisico.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine + A9 terzo incluso + A14 cascata + QxG continuo/discreto + closeout prime/mod6 20260515_1705.\n- **Dipolo / punto-zero**: residuo prime-selected / grammatica generativa `6k +/- 1`; punto-zero = stessa riga provider-offset prima che il valore locale venga trasformato in direzione.\n- **Piano superiore**: topologia assiomatica del bordo; il bordo decide se il residuo appartiene al lato primo o al lato grammatica.\n- **Operatori laterali scelti**: boundary operator per separare warning e claim; graph cut per bloccare selettori freshness; spectrum-preserving style control come forma generale del null che conserva perimetro ma distrugge selezione.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato come reiterazione del kernel emerso: 512 e 1024 avevano downgradato il residuo, 2048 verifica se la chiusura regge senza promuovere. CE-0038 usato come richiamo al punto-zero tra estremi: qui l'equilibrio non e' media, e' riga row-local prima della classificazione.\n- **Proto-ipotesi**: un residuo che cade contro un null generativo fair preliminare non e' direzione; diventa warning sul denominatore che lo aveva fatto emergere.\n- **Proiezione**: window_gaps=2048 su 8 righe provider/offset, 24 null generativi per riga e fairness separata a 128 reps.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: il campo vivo dichiara recovery/clean-field handoff e ordina di costruire sul closeout, non sulla vecchia direzione; questo ciclo testa se il closeout 1705 regge a scala 2048.\n- `not_drift`: non segue `dnd_scenario.py --best`, non riapre TRASCENDENZA_LIMITE/gap_ratio, non usa prime/mod6 come discovery, non cerca physical bridge; usa il ramo solo come vault warning autorizzato dal perimetro di recovery.\n\n## Claim Under Test\n\n> Il closeout del residuo `prime_minus_mod6_z_vector(SR,L1,triple_var)` regge se a 2048 gap il vettore resta assorbito dal null generativo `6k +/- 1` con fairness preliminare accettabile.\n\n## Question\n\nA 2048 gap, il vettore prime-specific recupera almeno una sopravvivenza congiunta SR/L1/triple_var, oppure il controcampo generativo continua ad assorbirlo?\n\n## Ritorno fisico\n\n- **Punto fisico sorgente**: non_applicabile come claim fisico. Il sorgente e' un residuo matematico su gap primi modulo 6.\n- **Attraversamento matematico**: confronto vettoriale row-local contro null generativo span-local.\n- **Punto fisico di ritorno**: assente.\n- **Relazione nuova**: nessun ponte fisico; resta relazione metodologica con consecutive-prime residue bias / Hardy-Littlewood pattern bias.\n- **Osservabile/test fisico possibile**: nessuno in questo ciclo.\n- **Se fallisce**: `ritorno_fisico_assente`; resta constraint/vault, non scoperta fisica promuovibile.\n\n## Experiment Design\n\n- `python tools/dnd_scenario.py --best` eseguito: propone ancora `COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE`, ma il campo lo subordina al closeout/recovery.\n- `python -m py_compile tools/prime_mod6_generative_null_audit.py tools/prime_mod6_null_fairness_audit.py` completato.\n- Generative audit: `python tools/prime_mod6_generative_null_audit.py --window-gaps 2048 --null-reps 24 --seed 202605151712 --out tools/data/preflight/prime_mod6_generative_null_audit_20260515_1712_w2048.json`.\n- Fairness audit: `python tools/prime_mod6_null_fairness_audit.py --window-gaps 2048 --null-reps 128 --seed 202605151713 --out tools/data/preflight/prime_mod6_null_fairness_audit_20260515_1712_w2048.json`.\n- Perimetro: 8 righe = `dnd_autoricerca` e `direct_sieve` x offset 0,512,1024,1536.\n- Null: preserva grammatica `6k +/- 1`, count, numeric span e identita provider/offset; distrugge selezione prime-specific e gap primi esatti.\n\n## Results\n\n| observable | result |\n|---|---:|\n| generative null classification | `VECTOR_REQUIRES_REVIEW_AFTER_GENERATIVE_NULL` |\n| vector_survives_generative_null | false |\n| rows | 8 |\n| null reps per row | 24 |\n| SR pass_count | 0/8 |\n| L1 pass_count | 0/8 |\n| triple_var pass_count | 0/8 |\n| SR min_delta | -2.641 |\n| L1 min_delta | -5.136 |\n| triple_var min_delta | -5.969 |\n| SR max_p_upper | 1.0 |\n| L1 max_p_upper | 1.0 |\n| triple_var max_p_upper | 1.0 |\n| fairness classification | `GENERATIVE_NULL_FAIRNESS_ACCEPTABLE_PRELIMINARY` |\n| fairness issues | 0 |\n| fairness std_ratio range | 1.037-1.184 |\n| selection_ratio range | 0.299-0.344 |\n\nRow-level check:\n\n| row | vector_survives | SR_delta | SR_p | L1_delta | triple_var_delta |\n|---|---|---:|---:|---:|---:|\n| dnd_autoricerca/offset_0 | false | -0.516 | 0.80 | -3.979 | -3.446 |\n| dnd_autoricerca/offset_512 | false | -2.496 | 1.00 | -5.136 | -5.258 |\n| dnd_autoricerca/offset_1024 | false | -0.525 | 0.68 | -4.118 | -4.355 |\n| dnd_autoricerca/offset_1536 | false | -0.548 | 0.84 | -4.789 | -5.969 |\n| direct_sieve/offset_0 | false | -1.600 | 0.96 | -4.538 | -4.187 |\n| direct_sieve/offset_512 | false | -2.641 | 1.00 | -4.552 | -4.419 |\n| direct_sieve/offset_1024 | false | -0.735 | 0.76 | -4.915 | -4.656 |\n| direct_sieve/offset_1536 | false | 0.135 | 0.44 | -3.468 | -4.304 |\n\n## Key Findings\n\n1. **Verificato**: `vector_survives=false` su 8/8 righe; nessuna riga passa il contratto congiunto SR/L1/triple_var.\n2. **Verificato**: ogni osservabile focus ha `pass_count=0/8`; anche dove SR_delta e' positivo in una riga, `SR_p=0.44` e L1/triple_var restano negativi.\n3. **Verificato**: la fairness preliminare del null a 2048 non segnala issue; std_ratio e selection_ratio restano nei limiti dichiarati.\n4. **Inferito**: il residuo 1330 non va scalato come direzione; l'unico uso rimasto e' regression case per null deboli e selector authority.\n\n## Verdict\n\n**CONSTRAINT / CLOSED_WARNING**.\n\nIl closeout regge a 2048. Il vettore `prime_minus_mod6_z_vector(SR,L1,triple_var)` non recupera sopravvivenza contro il generative null span-local; resta warning di grammatica/denominatore, non direzione attiva, non discovery e non ponte fisico.\n\n## Bicono della scoperta\n\n- **Due radici**: residuo prime-selected / controcampo generativo `6k +/- 1`.\n- **Singolare**: la riga provider-offset prima della classificazione, dove primo e grammatica condividono span e count.\n- **Invariante di passaggio**: contratto vettoriale congiunto SR/L1/triple_var su denominatore row-local.\n- **Campo di possibilita**: possibile = usare prime/mod6 come vault warning per progettare null generativi piu forti; non-possibile = usarlo come direzione viva del prossimo ciclo senza nuova autorita esplicita.\n\n## Consecutio\n\nUscire dal ramo prime/mod6 come sorgente. Il prossimo ciclo deve respirare una tensione strutturale viva del campo, mantenendo questo risultato solo come guardrail: ogni residuo che batte un null deterministico ma cade contro un null generativo fair preliminare resta constraint, non ponte.\n\n## Ricadute pratiche\n\nssp_value: yes. Il ciclo consolida un guardrail operativo riusabile per preflight/recovery: i selector e i report futuri devono distinguere residuo, warning e direzione attiva prima di promuovere.\n\n## Telemetria\n\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente.\n- Nessun update del seme.\n- Nessun runtime pointer mutation.\n- Nessuna promozione e nessun public sync.\n\n## Files\n\n- `tools/data/preflight/prime_mod6_generative_null_audit_20260515_1712_w2048.json`\n- `tools/data/preflight/prime_mod6_null_fairness_audit_20260515_1712_w2048.json`\n- `tools/data/reports/agent_20260515_1712.md`\n", tools/data/lab_graph.json:1373: "content_full": "# Agent Report - SR Residual Width Audit\n**Date**: 2026-05-14 13:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual (0.8) \n**verdict**: CONSTRAINT - il residuo prime-specific dopo mod6 e' robusto come delta z vettoriale row-local; `SR` binario non e' un osservabile stabile del confine \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, window_gaps, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n**observable_contract**: claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.\n\n## Prima impressione\n\nIl confine non sta nella presenza di `SR`. Sta nel fatto che la stessa riga provider-offset, attraversata da prime a `6k +/- 1`, conserva una differenza di intensita su tre osservabili mentre il verdict binario cambia con l'ampiezza.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY + direzione `SR_residual_intensity_after_mod6`.\n- **Dipolo / punto-zero**: lattice candidato `6k +/- 1` / selezione prime. Punto-zero: riga provider-offset condivisa; la primalita e' l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: un boundary operator non decide per presenza/assenza, ma per differenziale che resta dopo sottrazione del pre-bordo.\n- **Operatori laterali scelti**: boundary operator, perche' il bordo e' il luogo della sottrazione prime-minus-mod6; parallel transport, perche' provider e offset trasportano la stessa riga tra due classi senza cambiare denominatore.\n- **Contaminazione cognitiva**: CE-0001 / KSAR usato per reiterare il kernel del cycle 03:30; CE-0117 usato come cascata della possibilita': presenza `SR` -> delta z `SR` -> vettore `SR,L1,triple_var`.\n- **Proto-ipotesi**: il residuo prime-specific non e' una label, e' un differenziale vettoriale row-local; quando la finestra cambia, `has_SR` oscilla, ma il delta z resta il passaggio.\n- **Possibile / non-possibile**: possibile = formalizzare `prime_minus_mod6_z_vector` come osservabile dedicato; non-possibile = promuovere `SR` binario o il verdict nominale dello script a discriminatore del confine.\n- **Proiezione**: tre ampiezze finestra (`512,1024,2048`), stessi due provider, quattro offset per run, due antagonisti mod6, label-swap row-local e trace JSONL completa.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa la robustezza del delta z row-local prime-minus-mod6 su piu ampiezze finestra, mantenendo provider, offset, label-swap audit e trace JSONL.\n- `not_drift`: non torna a `V_c`, GUE/Poisson, fit o vecchi depositi; stressa solo il residuo nominato dal valutatore dopo sottrazione mod6.\n\n## Claim Under Test\n\n> Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime non genera `SR` come presenza; lascia un vettore di intensita row-local su `SR,L1,triple_var`.\n\n## Question\n\nIl delta z prime-minus-mod6 resta significativo quando cambia l'ampiezza della finestra, oppure era un artefatto del contratto 1024-gap del cycle 03:30?\n\n## Experiment Design\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`.\n- Run: `window_gaps=512,1024,2048`; seeds `202605141330,202605141331,202605141332`.\n- Rows per run: 8 prime windows = 2 provider x 4 offset.\n- Antagonisti: `mod6_index_aligned` e `mod6_span_matched`, paired per `row_id`.\n- Null: label-swap row-local, 4096 trials per pair audit.\n- Trace: JSONL scritto per tutti e tre i run.\n\n## Results\n\n| window | pair | prime SR | prime focus | Jaccard | SR delta | p(SR delta) | z_SR delta | p(z_SR) | z_L1 delta | p(z_L1) | z_triple_var delta | p(z_triple) | script verdict |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| 512 | mod6_index | 5/8 | 2/8 | 0.250 | -0.250 | 0.6195 | 20.494 | 0.0095 | 21.866 | 0.0095 | 8.480 | 0.0095 | SR_NOT_DECISIVE |\n| 512 | mod6_span | 5/8 | 2/8 | 0.377 | -0.250 | 0.5038 | 13.993 | 0.0085 | 10.099 | 0.0085 | 7.505 | 0.0085 | SR_NOT_DECISIVE |\n| 1024 | mod6_index | 8/8 | 5/8 | 0.481 | 0.000 | 1.0000 | 29.480 | 0.0061 | 31.105 | 0.0061 | 11.745 | 0.0061 | SR_PREBOUNDARY |\n| 1024 | mod6_span | 8/8 | 5/8 | 0.377 | 0.250 | 0.4921 | 19.921 | 0.0076 | 15.302 | 0.0076 | 13.142 | 0.0076 | SR_PREBOUNDARY |\n| 2048 | mod6_index | 8/8 | 7/8 | 0.556 | 0.125 | 1.0000 | 41.634 | 0.0088 | 43.574 | 0.0088 | 17.173 | 0.0088 | SR_PRIME_SPECIFIC |\n| 2048 | mod6_span | 8/8 | 7/8 | 0.537 | 0.250 | 0.5040 | 28.624 | 0.0068 | 23.917 | 0.0068 | 17.491 | 0.0068 | SR_PRIME_SPECIFIC |\n\n## Key Findings\n\n1. **Verificato**: `has_SR` non replica come osservabile del residuo. A 512 gap i primi hanno `SR=5/8`; a 1024 e 2048 hanno `SR=8/8`. Il delta binario ha p non significativo o 1.0000.\n2. **Verificato**: il delta z `SR` replica in tutti i sei pair audit: `p=0.0061..0.0095`.\n3. **Verificato**: il delta z non e' solo `SR`. `L1` replica con `p=0.0061..0.0095`; `triple_var` replica con `p=0.0061..0.0095`.\n4. **Verificato**: il verdict nominale dello script oscilla con `window_gaps` (`not_decisive`, `preboundary`, `prime_specific`). Quell'oscillazione e' informazione sullo script: il verdict e' ancora centrato su commonality/binario, non sul vettore di intensita.\n5. **Inferito dal perimetro**: l'osservabile dedicato da formalizzare e' `prime_minus_mod6_z_vector(SR,L1,triple_var)`, con `has_SR` relegato ad audit negativo.\n\n## Verdict\n\n**CONSTRAINT / VECTOR RESIDUE**.\n\nNel perimetro 512-2048 gap, due provider, quattro offset, due antagonisti mod6, il residuo prime-specific sopravvive come delta z vettoriale row-local. Non sopravvive come presenza binaria di `SR`, ne come verdict nominale dello script.\n\n## Bicono\n\n- **Due radici**: pre-bordo mod6 / selezione prime.\n- **Singolare**: riga provider-offset paired.\n- **Invariante di passaggio**: delta z positivo su `SR,L1,triple_var`.\n- **Campo di possibilita**: osservabile dedicato `prime_minus_mod6_z_vector`.\n- **Campo non-possibile**: `SR` binario come firma prime-specific dopo mod6.\n\n## Consecutio\n\nIl prossimo ciclo deve correggere il nodo regressivo dello script: il `verdict()` non deve decidere dalla commonality di `SR`, ma da un contratto vettoriale dichiarato (`SR,L1,triple_var`, segno del delta, p label-swap, denominatore row-local). Dopo questa correzione, scalare oltre 2048 gap e separare `mod6_index_aligned` da `mod6_span_matched` come antagonisti con ruoli diversi.\n\n## Ricadute pratiche\n\nssp_value: yes. Le tre trace JSONL rendono auditabile il residuo senza riaprire i JSON aggregati; il prossimo passaggio pratico e' aggiornare lo script per emettere un verdict vettoriale, non binario.\n\n## Files\n\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json`\n- `tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl`\n- `tools/data/reports/agent_20260514_1330.md`\n", tools/data/lab_graph.json:1424: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary Trace\n**Date**: 2026-05-14 03:30 \n**Piano**: 117 \n**Tension explored**: BOUNDARY / prime-vs-mod6 residual \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo `6k +/- 1`; la selezione prime resta come gradiente row-local, non come presenza binaria dell'osservabile \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p, trace_jsonl_event] \n\n## Prima impressione\n\nIl residuo prime-specific non nasce dalla comparsa di `SR`. `SR` e gia nel lattice candidato `6k +/- 1`; i primi non lo accendono, lo attenuano e lo deformano rispetto al pre-bordo.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + direzione viva `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: pre-bordo `6k +/- 1` / selezione prime. Punto-zero: stessa riga provider/offset, dove il denominatore e identico e la primalita e l'unica selezione aggiunta.\n- **Piano superiore**: topologia assiomatica del bordo: il bordo non e presenza/assenza di un segnale, ma passaggio di intensita tra un lattice candidato e il sottoinsieme selezionato.\n- **Proto-ipotesi**: la firma utile e `prime_minus_mod6_z_delta` su `SR,L1,triple_var`; `has_SR` e una coordinata troppo larga per nominare primalita.\n- **Possibile / non-possibile**: possibile = isolare residuo di selezione con delta z row-local; non-possibile = claim `SR prime-specific` se `SR` resta comune nel contro-perimetro mod6.\n- **Proiezione**: 2 provider x 4 offset x 1024 gap; antagonisti `mod6_index_aligned` e `mod6_span_matched`; label-swap row-local; trace JSONL completa.\n\n## Contaminazione cognitiva\n\n- **CE-0001 / KSAR**: usato per reiterare il kernel emerso nel cycle precedente: non allargare il frame, ma far girare lo stesso nodo regressivo con tracciamento piu fine.\n- **PVI**: presupposto attaccato = \"`SR` persistente implica primalita\"; contro-presupposto = \"`SR` misura il pre-bordo aritmetico\".\n- **Vault**: `trace_jsonl_event` conserva casi, class summary, paired rows, audit e verdict come deposito riusabile senza trasformare una seed in invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: misura l'intensita row-local di `SR/L1/triple_var` su prime vs `6k +/- 1`, con denominatore provider/offset condiviso e label-swap audit.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista e il pre-bordo mod6 indicato dalla consecutio.\n\n## Observable Contract\n\n- **claim**: `SR` e prime-specific solo se resta piu comune o piu forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local.\n- **observable**: membership di `SR`, firma `[SR,L1,triple_var]`, delta z paired prime-minus-mod6, p-value label-swap.\n- **operator**: `tools/exp_prime_vs_mod6_sr_boundary.py` con `--trace-jsonl`.\n- **generator**: prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`; mod6 da `index_aligned` e `span_matched`.\n- **denominator**: 8 finestre prime paired con 8 `mod6_index_aligned` e 8 `mod6_span_matched`.\n- **non_possible**: prime-specific `SR` se `SR` e comune o pari nel contro-perimetro mod6, oppure se il label-swap assorbe i delta.\n- **not_tested**: beta atlas globale, `V_c`, `gap_ratio`, origine analitica del trasferimento mod6.\n\n## Results\n\nMain run, seed `202605140330`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 3 | SR | beta_absent_blank=5, beta_chart_recovered=3 |\n| mod6_index_aligned | 8 | 8 | 8 | L1,SR,triple_var | beta_chart_recovered=8 |\n| mod6_span_matched | 8 | 7 | 7 | [] | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nSeed check, seed `202605140331`:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | states |\n|---|---:|---:|---:|---|---|\n| prime | 8 | 8 | 6 | SR | beta_chart_recovered=5, beta_absent_blank=3 |\n| mod6_index_aligned | 8 | 8 | 6 | L1,SR | beta_chart_recovered=7, beta_absent_blank=1 |\n| mod6_span_matched | 8 | 8 | 7 | L1,SR | support_falls=1, beta_chart_recovered=6, beta_absent_blank=1 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605140330 | 0.433 | 0.000 | 1.0000 | 28.246 | 0.0081 |\n| prime - mod6_span | 202605140330 | 0.423 | 0.125 | 1.0000 | 20.793 | 0.0093 |\n| prime - mod6_index | 202605140331 | 0.517 | 0.000 | 1.0000 | 28.547 | 0.0083 |\n| prime - mod6_span | 202605140331 | 0.560 | 0.000 | 1.0000 | 18.844 | 0.0090 |\n\n## Key Findings\n\n1. **Verificato**: `SR` non e prime-specific come presenza binaria. In entrambi i run, `prime=8/8` e `mod6_index_aligned=8/8`; nel seed check anche `mod6_span_matched=8/8`.\n2. **Verificato**: il count `SR` non discrimina. `p(SR delta)=1.0000` in tutti i pair audit.\n3. **Verificato**: il delta di intensita discrimina. `p(z_SR delta)=0.0081/0.0083` contro `mod6_index` e `0.0093/0.0090` contro `mod6_span`.\n4. **Verificato**: `mod6_index_aligned` e piu rigido dei primi: focus signature main `8/8` contro prime `3/8`, e common obs `L1,SR,triple_var` contro solo `SR`.\n5. **Inferito dal perimetro**: il prossimo osservabile deve essere vettoriale e paired: `prime_minus_mod6_z_delta(SR,L1,triple_var)`, non `has_SR`.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula chiusa nel perimetro: `SR` appartiene al pre-bordo `6k +/- 1`. La primalita non genera `SR`; seleziona dentro un lattice gia rigido e lascia un residuo misurabile come delta z row-local.\n\n## Bicono\n\n- **Due radici**: lattice candidato mod6 / sequenza prime selezionata.\n- **Singolare**: riga provider-offset condivisa.\n- **Invariante di passaggio**: presenza di `SR` attraversa il confine.\n- **Campo di possibilita**: misurare selezione come delta z.\n- **Campo non-possibile**: usare `SR` binario come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve formalizzare `prime_minus_mod6_z_delta` come osservabile dedicato e scalarlo su finestre piu larghe. `mod6_index_aligned` resta antagonista primario; `mod6_span_matched` resta controllo fragile perche conserva span numerico ma introduce downsample.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script ora produce `--trace-jsonl`, utile per audit, replay e falsifier row-local senza riaprire il JSON aggregato.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json`\n- Trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json`\n- Seed trace: `tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl`\n- Report: `tools/data/reports/agent_20260514_0330.md`\n", tools/data/lab_graph.json:1457: "content_full": "# Agent Report - Prime vs Mod6 SR Boundary\n**Date**: 2026-05-13 03:30 \n**Piano**: 116 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, row_id, source_mode, case_state, sr_rate, focus_signature_count, signature_jaccard, row_local_label_swap_p] \n**observable_contract**: claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.\n\n## Prima impressione\n\nIl bordo non cade tra primi e non-primi: cade tra selezione prime e lattice candidato `6k +/- 1`. `SR` vede il lattice prima della primalita; il gate non deve chiedere \"SR nei primi?\", ma \"cosa resta di SR dopo aver sottratto il pre-bordo?\".\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`.\n- **Dipolo / punto-zero**: primi selezionati / candidati mod6 non selezionati. Punto-zero: la riga ordinata locale `6k +/- 1`, dove `SR` puo nominare pre-bordo aritmetico senza nominare primalita.\n- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune.\n- **Proto-ipotesi**: `SR` non e firma atomica dei primi; e una sezione del pre-bordo aritmetico. La selezione prime si manifesta come differenza di intensita z rispetto al lattice candidato.\n- **Possibile / non-possibile**: possibile = isolare una coordinata di selezione prime come gradiente dentro mod6; non-possibile = usare presenza binaria di `SR` come claim prime-specific.\n- **Proiezione**: stesso gate ordine/null, finestre 1024, offset 0/512/1024/1536, due provider prime, due antagonisti mod6, audit label-swap dentro ogni row_id.\n\n### Contaminazione cognitiva\n\n- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`.\n- **PVI attack**: il presupposto attaccato e \"SR persistente implica primalita\". Il contro-presupposto e \"SR misura la griglia 6k +/- 1 prima della selezione prime\".\n- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6.\n- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`.\n\n## Claim Under Test\n\n> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`.\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap.\n- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset.\n- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti.\n- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`.\n- Main seed: `202605130330`; seed check: `202605130331`.\n- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset.\n\n## Results\n\nMain run:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 |\n| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 |\n\nSeed check:\n\n| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var |\n|---|---:|---:|---:|---|---:|---:|---:|\n| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 |\n| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 |\n| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 |\n\nRow-local label-swap audit:\n\n| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) |\n|---|---:|---:|---:|---:|---:|---:|\n| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 |\n| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 |\n| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 |\n| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 |\n\n## Key Findings\n\n1. **Verificato**: la presenza binaria di `SR` non e prime-specific. Main: prime `8/8`, mod6_index `8/8`; seed check: prime `6/8`, mod6_index `8/8`, mod6_span `8/8`.\n2. **Verificato**: il label-swap row-local assorbe `SR` come count. `p(SR delta)` vale 1.0000 / 0.5055 per mod6_index e 0.2502 / 0.5021 per mod6_span.\n3. **Verificato**: il label-swap non assorbe il delta z di `SR`. `p(z_SR delta)` resta 0.0071-0.0103 in entrambe le seed e per entrambi gli antagonisti.\n4. **Verificato**: mod6 e piu intenso, non piu debole. `mean z SR` e circa -34 / -26 in mod6 contro -5 nei primi. Il residuo prime non e \"piu SR\"; e selezione attenuata dentro un pre-bordo piu rigido.\n5. **Inferito dal perimetro**: la coordinata utile non e `has_SR`; e `z_delta_SR` paired, insieme a `z_delta_L1` e `z_delta_triple_var`, come misura di quanto la selezione prime rompe il lattice candidato.\n\n## Verdict\n\n**CONSTRAINT / REDIRECT**.\n\nFormula non valida:\n\n`SR` come firma prime-specific atomica.\n\nFormula valida nel perimetro:\n\n`SR` appartiene al pre-bordo aritmetico `6k +/- 1`; la selezione prime resta come gradiente di intensita row-local rispetto a mod6. Il boundary operativo e quindi `mod6_preboundary -> prime_selection`, non `prime -> generic_nonprime`.\n\n## Bicono della scoperta\n\n- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata.\n- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni.\n- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato.\n- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`.\n\n## Lenti counter-pole applicate\n\n- **L1/L4**: nessun \"solo\", \"mai\", \"zero\" usato per `SR`: i count mostrano controesempi.\n- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific.\n- **L5**: nessun tag NEW; il risultato resta compatibile con bias noti dei gap dei primi mod q. Il report misura un gate del Lab, non rivendica una scoperta aritmetica classica.\n- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva.\n- **L7**: non-possibile dichiarato nel contratto e nel bicono.\n\n## Consecutio\n\nIl prossimo taglio non deve chiedere se `SR` c'e. Deve costruire un osservabile di selezione: `prime_minus_mod6_z_delta` su `SR,L1,triple_var`, con span-matched conservato come controllo fragile e index-aligned come antagonista primario. Se il delta resta replicato su piu finestre e scale, il claim diventa: la primalita attenua/rompe la rigidita del pre-bordo mod6 invece di generare `SR` da sola.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null.\n\n## Files\n\n- Script: `tools/exp_prime_vs_mod6_sr_boundary.py`\n- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json`\n- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260513_0330.md`\n", tools/data/lab_graph.json:1502: "content_full": "# Agent Report - Prime SR Persistent Boundary\n**Date**: 2026-05-12 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`.\n- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi.\n- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro.\n- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente.\n- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' \"SR persistente nei primi implica prime-specific\".\n- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico.\n- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime.\n- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime.\n\n## Claim Under Test\n\n> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena.\n\n## Question\n\nQuando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime?\n\n## Experiment Design\n\n- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows.\n- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`.\n- Seed check: stesso perimetro, seed `202605120331`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common.\n\n## Results\n\nMain run:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 |\n| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 |\n| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 |\n| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 |\n| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 |\n| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 |\n\nSeed check:\n\n| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean |\n|---|---:|---:|---|---:|---:|---:|---:|\n| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 |\n| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 |\n| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 |\n\nPrime case details, main:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] |\n| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] |\n| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n\n## Key Findings\n\n1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota.\n2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall.\n3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`.\n4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato.\n5. **Inferito dal perimetro dichiarato**: la parte robusta non e' \"SR e' prime-specific\"; e' \"SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche\".\n\n## Verdict\n\n**CONSTRAINT / FALSIFIED scoped**.\n\nFormula non valida:\n\n`prime_SR_persistent_boundary` come firma atomica prime-specific.\n\nFormula valida nel perimetro:\n\n`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico.\n\n## Bicono della scoperta\n\n- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato.\n- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato.\n- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6.\n- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific.\n\n## Consecutio\n\nIl prossimo ciclo deve spostare il nodo regressivo: non \"prime vs controlli generici\", ma \"primi vs candidati mod6 row-aligned\". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale.\n\n## Ricadute pratiche\n\nssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico.\n\n## Files\n\n- Script: `tools/exp_prime_sr_persistent_boundary.py`\n- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json`\n- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json`\n- Report: `tools/data/reports/agent_20260512_0330.md`\n", tools/data/lab_graph.json:1553: "content_full": "# Agent Report - Prime Persistent Blank Gate\n**Date**: 2026-05-11 03:30 \n**Piano**: 115 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - `prime_persistent_blank` si scinde: SR persiste, il blank beta no \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate, prime_control_common_obs_jaccard] \n**observable_contract**: claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY residuo `numeri_primi:cycle_3`.\n- **Dipolo / punto-zero**: persistenza del supporto / persistenza del blank. Punto-zero: la stessa finestra row-local da 1024 gap dove `SR` puo' restare mentre la coordinata beta riappare.\n- **Piano superiore**: topologia assiomatica del bordo: non una classe statistica unica, ma una sezione che conserva un osservabile e perde una coordinata.\n- **Proto-ipotesi**: il residuo prime e' strutturale solo se il blank beta-absent sopravvive a provider e offset; se sopravvive solo `SR`, la struttura non e' `blank`, e' `SR-supported boundary`.\n- **Possibile / non-possibile**: possibile = isolare un residuo prime provider-neutral; non-possibile = chiamarlo `prime_persistent_blank` quando beta ricompare in finestre locali.\n- **Proiezione**: due provider prime, quattro offset locali, seed shift del gate, controlli random_matrix/logistic.\n\n## Contaminazione cognitiva\n\n- **YSN DeltaLink**: il legame non ovvio e' `SR` come sezione comune mentre la carta beta cambia; non e' prova, e' la domanda proiettata.\n- **Cornelius gene**: `DNA_Simbolico`: \"La persistenza del bordo non coincide con la persistenza del blank.\" Operatori: separa supporto, separa coordinata, confronta controlli.\n- **KSAR step / CE-0001**: reiterazione del kernel 20260510 senza ridisegnare l'atlante: stesso gate, nuovo provider/offset/seed.\n- **PVI attack**: il presupposto nascosto era che `blank_windows=4/4` su una partizione bastasse per nominare una specie persistente.\n- **Vault**: `random_matrix_chart_sensitive_blank` resta aperto come coordinata locale beta `[0.4]`, non lavorata in questo ciclo.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: testa direttamente `prime_persistent_blank` su `numeri_primi:cycle_3` con provider-neutral row-local windows e controlli cross-dominio.\n- `not_drift`: non usa `V_c`, non usa fit, non riapre griglia beta globale, non salva la classe caduta `medium/strong beta-absent`.\n\n## Claim Under Test\n\n> `numeri_primi:cycle_3` e' un `prime_persistent_blank` se resta beta-absent in tutte le finestre provider-neutral e conserva `SR` come osservabile comune, mentre i controlli non condividono la stessa firma.\n\n## Experiment Design\n\n- Perimetro prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap.\n- Controlli: 4 GUE random matrix blocks + 4 logistic return interval rows.\n- Parametri: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605110330`.\n- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null.\n- Criterio di isolamento: prime `blank_rate=1.0`, prime `sr_rate=1.0`, common prime obs = `[SR]`, controlli senza full blank-SR collision.\n\n## Results\n\n| family | blank | beta recovered | support falls | sr rate | common obs | endpoint mean | stable coherent mean |\n|---|---:|---:|---:|---:|---|---:|---:|\n| prime | 3/8 | 4/8 | 1/8 | 1.000 | SR | 2.715 | 2.656 |\n| controls | 1/8 | 3/8 | 4/8 | 0.250 | L2 | 1.428 | 1.641 |\n\n| comparison | value |\n|---|---:|\n| prime_control_common_obs_jaccard | 0.000 |\n| verdict | PRIME_PERSISTENCE_NOT_REPLICATED |\n\nPrime case details:\n\n| case | state | one-sided obs | beta |\n|---|---|---|---|\n| dnd_autoricerca offset 0 | beta_chart_recovered | SR | 0.2, 0.4 |\n| dnd_autoricerca offset 512 | support_falls | SR | 0.2 |\n| dnd_autoricerca offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 |\n| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 0 | beta_absent_blank | SR | [] |\n| direct_sieve offset 512 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n| direct_sieve offset 1024 | beta_absent_blank | SR,L1,triple_var | [] |\n| direct_sieve offset 1536 | beta_chart_recovered | SR,L1,triple_var | 0.4 |\n\n## Key Findings\n\n1. **Verificato**: `SR` resta in 8/8 finestre prime. La firma comune prime e' `[SR]`.\n2. **Verificato**: il blank beta-absent non resta in 8/8 finestre prime. Solo 3/8 casi sono `beta_absent_blank`; 4/8 recuperano beta e 1/8 cade per supporto.\n3. **Verificato**: i controlli non collidono con la firma prime. I controlli hanno common obs `[L2]`, `sr_rate=0.250`, `blank_rate=0.125`, Jaccard comune prime/controlli = 0.000.\n4. **Inferito dal perimetro dichiarato**: il residuo dei primi non e' `prime_persistent_blank`; e' `prime_SR_persistent_boundary` con coordinata beta chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nIl nome `prime_persistent_blank` non regge nel perimetro provider-neutral/offset-shift. La parte persistente e' `SR`, non l'assenza di beta.\n\nFormula valida:\n\n`numeri_primi:cycle_3` conserva supporto one-sided `SR` attraverso provider e offset; la coordinata beta resta locale e ricompare in 4/8 finestre.\n\nFormula non valida:\n\n`numeri_primi:cycle_3` e' un blank beta-absent persistente.\n\n## Bicono della scoperta\n\n- **Due radici**: supporto SR persistente / blank beta non persistente.\n- **Singolare**: la finestra row-local dove `SR` resta ma beta riappare.\n- **Invariante di passaggio**: `SR` attraversa provider e offset; `blank` no.\n- **Campo di possibilita'**: possibile = rinominare il residuo come supporto `SR` provider-neutral; non-possibile = promuovere il blank come specie autonoma.\n\n## Consecutio\n\nIl prossimo ciclo deve chiudere la tassonomia residua senza salvare il nome caduto:\n\n- rinominare il residuo prime in `prime_SR_persistent_boundary`;\n- testare se il beta recovery 0.2/0.4 dipende da offset, provider o baseline seed;\n- lavorare `random_matrix_chart_sensitive_blank` solo dopo aver separato coordinate locali da supporto osservabile.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script diventa un audit riusabile per distinguere persistenza di osservabile e persistenza di blank nei residui BOUNDARY senza rigenerare l'atlante globale.\n\n## Files\n\n- Script: `tools/exp_prime_persistent_blank_gate.py`\n- Data: `tools/data/prime_persistent_blank_gate_20260511_0330.json`\n- Report: `tools/data/reports/agent_20260511_0330.md`\n", tools/data/lab_graph.json:1598: "content_full": "# Agent Report - Boundary Residual Beta-Absent Audit\n**Date**: 2026-05-10 03:30 \n**Piano**: 114 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - il residuo beta-absent non e' una classe unica chiusa \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, window_state, blank_window_rate, full_signature_jaccard, common_window_obs_jaccard] \n**observable_contract**: claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.\n\n## Respiro fuori-tempo\n\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + residui `numeri_primi:cycle_3` / `random_matrix:cycle_7`.\n- **Dipolo / punto-zero**: blank strutturale / blank da atlante. Punto-zero: la stessa misura row-local da 1024 gap che decide se la beta manca per struttura o riappare per scelta di finestra.\n- **Piano superiore**: topologia assiomatica del bordo: la classe globale cade se una carta locale riapre la coordinata beta.\n- **Operatori laterali scelti**: boundary operator, chart locale, filtrazione. Il boundary operator separa supporto e beta; la chart locale testa la finestra senza rifare il mondo; la filtrazione conserva il denominatore row-aligned.\n- **Contaminazione cognitiva**: CE-0019 usato come contratto combo prima della misura; CE-0022 usato come boundary operator + chart locale; KSAR usato come reiterazione sul deposito 15:32-18:39 senza ridisegnare il perimetro.\n- **Proto-ipotesi**: il residuo medio/forte beta-absent e' una classe unica solo se entrambi i target restano blank in tutte le finestre row-local e conservano una firma osservabile compatibile.\n- **Proiezione**: applico il gate canonico ordine/null alle sole due righe residue, poi confronto persistenza del blank e Jaccard delle firme one-sided.\n\n## Aderenza alla direzione\n\n- `relation`: follows_direction\n- `why`: attacca direttamente i due residui beta-absent medio/forti lasciati dal valutatore dopo `thin_persist_rows=0/13`.\n- `not_drift`: non usa `V_c`, non usa label GUE/Poisson come campo decisionale, non rigenera la griglia beta globale.\n\n## Claim Under Test\n\n> I due residui `numeri_primi:cycle_3` e `random_matrix:cycle_7` formano una classe unica `medium/strong beta-absent` se il blank persiste in tutte le finestre row-local da 1024 gap e le firme one-sided restano compatibili.\n\n## Question\n\nI due blank residui sono lo stesso operatore di confine, due classi distinte, o un artefatto da denominatore/atlante?\n\n## Experiment Design\n\n- Perimetro atomico: `numeri_primi:cycle_3`, `random_matrix:cycle_7`.\n- Scope: full row + 4 finestre consecutive da 1024 gap per ciascun target.\n- Parametri: `n_replicates=12`, `n_beta=11`, `n_baseline=24`, `z_min=2.0`, seed `202605100330`.\n- Null baseline: permutazione marginal-preserving usata dal gate canonico gia' adottato nei report BOUNDARY.\n- Non misurato: `gap_ratio`, `V_c`, nuova griglia beta globale, validita' delle label sorgente GUE/Poisson.\n- Criterio di caduta: una riga recupera beta o perde supporto in una finestra row-local; in quel caso il residuo non e' classe unica chiusa.\n\n## Results\n\n| row | full state | full one-sided | window blanks | beta recovered | support falls | common window obs | mean endpoint | mean stable coherent |\n|---|---:|---|---:|---:|---:|---|---:|---:|\n| numeri_primi:cycle_3 | beta_absent_blank | SR,L1,triple_var | 4/4 | 0 | 0 | SR | 2.761 | 2.563 |\n| random_matrix:cycle_7 | beta_absent_blank | SR,SR2,L1,L2,triple_var | 3/4 | 1 | 0 | L2 | 2.987 | 3.083 |\n\n| comparison | value |\n|---|---:|\n| full_signature_jaccard | 0.600 |\n| common_window_obs_jaccard | 0.000 |\n| random_matrix recovered beta | window_4 -> beta [0.4] |\n\n## Key Findings\n\n1. **Verificato: `numeri_primi:cycle_3` resta beta-absent in 4/4 finestre.** Il supporto non cade; l'osservabile comune di finestra e' `SR`.\n2. **Verificato: `random_matrix:cycle_7` non resta beta-absent in 4/4 finestre.** La quarta finestra recupera beta `[0.4]` con supporto vivo (`SR2,L1,L2,triple_var`).\n3. **Verificato: le firme comuni di finestra divergono.** `common_window_obs_jaccard=0.000`: primi conserva `SR`, random matrix conserva `L2`.\n4. **Inferito dal perimetro dichiarato: il residuo beta-absent non e' una classe unica chiusa.** Una parte e' blank persistente row-local, una parte e' chart-sensitive.\n\n## Verdict\n\n**CONSTRAINT**.\n\nNel perimetro delle due righe residue e finestre da 1024 gap, `medium/strong beta-absent` si scinde:\n\n`numeri_primi:cycle_3` = blank persistente row-local. \n`random_matrix:cycle_7` = blank forte ma chart-sensitive, con beta `[0.4]` recuperata in 1/4 finestre.\n\nFormula valida:\n\n`beta_absent_residue` non matura come classe unica; diventa un audit a due stati: `prime_persistent_blank` / `random_matrix_chart_sensitive_blank`.\n\n## Bicono della scoperta\n\n- **Due radici**: blank persistente / blank chart-sensitive.\n- **Singolare**: supporto ordine/null vivo senza coordinata beta globale.\n- **Invariante di passaggio**: il supporto non cade in nessuna delle 8 finestre; cio' che cambia e' la coordinata beta e la firma osservabile.\n- **Campo di possibilità**: possibile = chiudere la tassonomia BOUNDARY con due sotto-stati residui; non-possibile = promuovere `medium/strong beta-absent` come specie unica del confine.\n\n## Consecutio\n\nIl prossimo ciclo non deve riaprire il thin blank. Deve sigillare il nuovo audit a due stati:\n\n- `prime_persistent_blank`: testare se `SR` resta l'osservabile comune sotto seed/window shift.\n- `random_matrix_chart_sensitive_blank`: testare se beta `[0.4]` e' coordinata locale stabile o evento di una singola finestra.\n\n## Ricadute pratiche\n\nssp_value: yes. Lo script e' uno strumento riusabile per audit row-local di residui BOUNDARY senza rigenerare fit globali.\n\n## Files\n\n- Script: `tools/exp_boundary_residual_beta_absent_audit.py`\n- Data: `tools/data/boundary_residual_beta_absent_audit_20260510_0330.json`\n- Report: `tools/data/reports/agent_20260510_0330.md`\n", tools/data/lab_graph.json:1649: "content_full": "# Agent Report - Boundary Transition Taxonomy 13 Rows\n**Date**: 2026-05-09 18:39 \n**Piano**: 113 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - la tassonomia post-estensione scala sulle 13 righe e dissolve il thin blank \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`transition_class`, `source_beta_state`, `extension_state`, `support_tier_after`, `one_sided_after`, `endpoint_after`, `stable_count_coherent_after`, `beta_after`, `denominator_state`, `excluded_mass`] - osservabili domain-native di composizione row-aligned, non canonici. \nssp_value: no \n**observable_contract**: claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo l'estensione il thin blank non resta una figura. La domanda vera diventa se la matrice intera lascia un nuovo residuo vivo.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: supporto sottile / atlante di transizione; punto-zero = stessa riga row-aligned prima che il blank venga letto come specie o come transizione.\n- **Piano superiore**: sheaf locale del boundary su 13 sezioni. La sezione corta viene riparata, poi ricollocata nell'atlante senza usare label GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Servono a comporre stati gia' misurati senza introdurre un nuovo gate.\n- **Contaminazione cognitiva**: CE-0019: contratto combo prima della misura; CE-0022: palette come boundary operator e chart locale; KSAR: riuso del deposito stabile 15:32-15:56; PVI: se resta `thin_persists`, la tassonomia cade.\n- **Proto-ipotesi**: `blank_thin_support` non scala sulle 13 righe se, dopo composizione con l'estensione, nessuna riga conserva supporto vivo sottile senza beta.\n- **Possibile / non-possibile**: possibile = usare l'atlante di transizione come mappa BOUNDARY; non-possibile = trattare il thin blank come specie matura.\n- **Proiezione**: classificare tutte le 13 righe in `transition_class` usando supporto, beta, denominatore e transizioni estese.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la consecutio del campo vivo: dopo la cristallizzazione short-denominator, testa se la tassonomia delle transizioni post-estensione scala sulle 13 righe.\n- `not_drift`: non torna a `V_c`, non difende thin blank, non usa label GUE/Poisson come decision field; compone solo depositi row-aligned gia' misurati.\n\n## Claim Under Test\n> La tassonomia emersa dall'estensione del denominatore scala sull'intero perimetro BOUNDARY 13 righe se non lascia alcuna riga `thin_persists`.\n\n## Question\nQuando le tre righe short estese vengono ricollocate nella matrice 13 righe, resta una specie `blank_thin_support` oppure il residuo vivo cambia forma?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Input: `boundary_two_axis_matrix_20260509_1532.json`, `boundary_row_aligned_nonexact_audit_20260509_1538.json`, `boundary_short_denominator_extension_20260509_1556.json`, `boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Perimetro atomico: 13 righe BOUNDARY semi-reali.\n- Label policy: `source_domain_type` resta audit metadata; non entra in `transition_class`.\n- Null sfidato: esistenza di almeno una riga `thin_persists` dopo estensione.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support transfer after composition | 10 |\n| fall after composition | 3 |\n| beta chart after any | 9 |\n| beta chart exact 0.3 after | 6 |\n| thin persist rows | 0 |\n| medium/strong beta-absent blank rows | 2 |\n| verdict | TAXONOMY_SCALES_THIN_DISSOLVED |\n\nTransition classes:\n\n| transition_class | rows |\n|---|---:|\n| beta_0_3_exact | 4 |\n| beta_0_3_local_nonunique | 2 |\n| beta_chart_recovered | 1 |\n| blank_medium_or_strong_beta_absent | 1 |\n| fall_no_support | 2 |\n| local_beta_other | 1 |\n| support_falls_after_extension | 1 |\n| support_thickens_beta_blank | 1 |\n\nRighe aperte senza beta dopo composizione:\n\n| row | class | n_gaps | one-sided | endpoint | tier |\n|---|---|---:|---:|---:|---|\n| numeri_primi:cycle_3 | blank_medium_or_strong_beta_absent | 4096 | 3 | 3.243 | medium_multi_observable |\n| random_matrix:cycle_7 | support_thickens_beta_blank | 1024 | 4 | 3.781 | strong_multi_observable |\n\nRighe short riparate:\n\n| row | before n | after n | transition_class | one-sided after | beta after |\n|---|---:|---:|---|---:|---|\n| percolation:cycle_9 | 190 | 1024 | support_falls_after_extension | 0 | [] |\n| random_matrix:cycle_7 | 199 | 1024 | support_thickens_beta_blank | 4 | [] |\n| zeta_zeros:cycle_4 | 199 | 1024 | beta_chart_recovered | 2 | [0.3] |\n\n## Key Findings\n1. **Verificato: `thin_persist_rows=0/13`.** Nessuna riga conserva la forma `blank_thin_support` dopo composizione con l'estensione.\n2. **Verificato: il supporto post-composizione e' 10/13.** Cadono `cellular_automata`, `pendolo_doppio` e `percolation` dopo estensione; il resto conserva supporto o chart locale.\n3. **Verificato: la beta chart sale a 9/13 righe, con 6/13 esatte 0.3.** Le sei sono quattro `beta_0_3_exact` originali, `zeta_zeros` recuperata e `pendolo_doppio` come beta osservata senza supporto.\n4. **Verificato: il residuo vivo non e' thin.** Restano due blank senza beta: `numeri_primi` medio e `random_matrix` forte dopo estensione.\n5. **Inferito: la tassonomia scala come atlante di transizione, non come legge beta universale.** Il thin blank cade; il nodo successivo e' il blank medio/forte senza beta.\n\n## Verdict\n**CONSTRAINT**.\n\nLa tassonomia post-estensione scala sul perimetro 13 righe: nessun membro resta `thin_persists`.\n\nFormula valida:\n\n`blank_thin_support` = telemetria short-denominator dissolta dalla composizione 13 righe.\n\nFormula non valida:\n\n`blank_thin_support` = specie autonoma del boundary.\n\nIl boundary resta una matrice di transizioni: supporto che cade, supporto che si ispessisce senza beta, chart beta recuperata, chart beta esatta, chart locale non unica, chart spostata.\n\n## Bicono della scoperta\n- **Due radici**: denominatore riparato / atlante 13 righe.\n- **Singolare**: riga BOUNDARY prima della classificazione post-estensione.\n- **Invariante di passaggio**: `thin_persist_rows=0/13`.\n- **Campo di possibilita**: qui diventa possibile chiudere il thin blank; qui diventa non-possibile chiudere il blank medio/forte senza beta.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve lasciare chiuso il thin blank e attaccare il residuo `medium/strong beta-absent blank`: `numeri_primi:cycle_3` contro `random_matrix:cycle_7`. Il test utile non e' \"perche' manca beta in generale\", ma quale condizione distingue blank medio completo e blank forte post-estensione quando entrambi hanno supporto vivo senza chart beta.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1556.md`, JSON 15:32/15:38/15:56/15:00, script e output 18:39.\n- **L1 hard constraint**: claim limitato alle 13 righe semi-reali e ai depositi gia' misurati.\n- **L2 count grezzi**: riportati 13 totali, 10 support, 3 fall, 9 beta chart, 0 thin persist, 2 open blank.\n- **L3 no silent patching**: nessuna nuova simulazione o ricampionamento; la composizione dichiara le estensioni applicate solo a tre righe.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma resta fall; `random_matrix` e' forte senza beta; `zeta_zeros` recupera beta pur restando contaminato nel prescan.\n- **L5 re-discovery**: il ciclo non ripete la matrice 15:32 ne' l'estensione 15:56; testa la scalabilita' richiesta dal campo vivo.\n- **L6 metabolismo cognitivo**: CE-0019, CE-0022, KSAR e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante BOUNDARY di transizione; non-possibile = specie autonoma thin blank.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_transition_taxonomy_13rows.py`.\n- Run valido: `python tools/exp_boundary_transition_taxonomy_13rows.py --out tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`.\n- Output: `thin_persist_rows=0`, `support_transfer_after=10/13`, `fall_after=3/13`, `open_blank_rows=['numeri_primi:cycle_3', 'random_matrix:cycle_7']`, `verdict=TAXONOMY_SCALES_THIN_DISSOLVED`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 18:39, il JSON 18:39 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_transition_taxonomy_13rows.py`\n- Data: `tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json`\n- Report: `tools/data/reports/agent_20260509_1839.md`\n", tools/data/lab_graph.json:1700: "content_full": "# Agent Report - Boundary Short Denominator Extension\n**Date**: 2026-05-09 15:56 \n**Piano**: 112 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. \nssp_value: no \n**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`.\n- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura.\n- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili.\n- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade.\n- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta.\n- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48.\n- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48.\n- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short.\n\n## Claim Under Test\n> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart.\n\n## Question\nCosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`.\n- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`.\n- Denominatore esteso: `1024` gap per riga.\n- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate.\n- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| target rows | 3 |\n| rows still thin support after extension | 0 |\n| support falls after extension | 1 |\n| support thickens beta blank | 1 |\n| beta chart recovered | 1 |\n| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS |\n\nTransizioni:\n\n| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state |\n|---|---:|---:|---:|---:|---|---|\n| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension |\n| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank |\n| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered |\n\n## Key Findings\n1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart.\n2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato.\n3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile.\n4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade.\n5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe.\n\n## Verdict\n**CONSTRAINT**.\n\nNel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma.\n\nFormula valida:\n\n`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile del boundary.\n\nLa riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale.\n\n## Bicono della scoperta\n- **Due radici**: supporto sottile osservato / denominatore sorgente corto.\n- **Singolare**: riga short prima dell'estensione.\n- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`.\n- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48.\n- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered.\n- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings.\n- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato.\n- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`.\n- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`.\n- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`.\n- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_short_denominator_extension.py`\n- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json`\n- Report: `tools/data/reports/agent_20260509_1556.md`\n", tools/data/lab_graph.json:1751: "content_full": "# Agent Report - Boundary Blank Thin Support Audit\n**Date**: 2026-05-09 15:48 \n**Piano**: 111 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - il blank sottile e' limitato dal denominatore corto, non dalla contaminazione del null \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`blank_class`, `coordinate_failure`, `support_tier`, `denominator_bucket`, `n_gaps`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`, `shuffle_class_changes`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=i tre `blank_thin_support` vanno falsificati contro il blank medio `numeri_primi` prima di promuoverli a specie autonoma; observable=telemetria row-aligned del subset `support_without_beta_blank`; operator=`exp_boundary_blank_thin_support_audit.py`; generator=`boundary_row_aligned_nonexact_audit_20260509_1538`; denominator=4 righe `support_without_beta_blank`, di cui 3 thin e 1 medium control; non_possible=chiamare il blank sottile specie autonoma se tutte le righe thin sono spiegate da denominatore corto; not_tested=estensione nuova dei tre domini short, nuova griglia beta, nuovi null, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il deposito 15:38 divide il blank in due livelli, ma il livello sottile ha tutte le righe corte. Il punto non e' nominare una specie nuova; e' verificare se la specie sopravvive al denominatore.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY row-aligned + tensione \"supporto vivo senza chart beta\".\n- **Dipolo / punto-zero**: blank sottile / blank medio; punto-zero = riga `support_without_beta_blank` prima che la forza del supporto venga letta come specie o come limite del denominatore.\n- **Piano superiore**: sheaf locale del boundary. La sezione beta e' vuota su tutte e quattro le righe; il discriminante diventa la qualita' del supporto e del denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, filtrazione per denominatore. Servono a separare stato beta invariato da supporto misurabile.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:38; CE-0117: cascata solo dove il blank apre una distinzione; YSN DeltaLink: `same beta blank -> support tier -> denominator bucket`; PVI: se tutte le righe thin sono corte, la specie autonoma cade nel perimetro.\n- **Proto-ipotesi**: `blank_thin_support` e' un effetto di denominatore corto se ogni riga thin ha `n_gaps < 500` e il controllo medio no; diventa specie autonoma solo se almeno una riga thin non e' spiegata dal denominatore.\n- **Possibile / non-possibile**: possibile = usare il blank sottile come coda denominator-limited da estendere; non-possibile = promuoverlo a failure mode stabile senza estendere percolation, random_matrix e zeta_zeros.\n- **Proiezione**: misurare `n_gaps`, `denominator_state`, `excluded_mass`, `shuffle_class_changes`, `one_sided_count`, `endpoint_distance` dentro il subset `support_without_beta_blank`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: falsifica direttamente se i failure modes del 15:38 sono stabili o artefatti della griglia/perimetro row-aligned.\n- `not_drift`: non usa label GUE/Poisson, non torna a `V_c`, non aggiunge nuovi domini; attacca solo il nodo aperto dal report precedente: blank sottile contro blank medio.\n\n## Claim Under Test\n> Nel subset `support_without_beta_blank`, il blank sottile e' una specie autonoma solo se non e' interamente spiegato da denominatore corto.\n\n## Question\nI tre blank sottili sono fallimenti autonomi del boundary, oppure righe con supporto insufficiente perche' corte?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_blank_thin_support_audit.py`.\n- Input: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Perimetro atomico: 4 righe `support_without_beta_blank`.\n- Soglia dichiarata: `full_gap_floor=500`.\n- Null sfidati: denominatore corto, contaminazione prescan, shuffle class-change, specie autonoma.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| support_without_beta_blank rows | 4 |\n| thin rows | 3 |\n| medium control rows | 1 |\n| thin short rows | 3 |\n| thin contaminated rows | 1 |\n| thin shuffle class-change rows | 1 |\n| thin not denominator explained rows | 0 |\n\nTest:\n\n| null / counter | condition | result |\n|---|---|---|\n| denominator_artifact | all thin rows have `n_gaps < 500` | pass |\n| contamination_artifact | all thin rows are contaminated | fail |\n| shuffle_instability_artifact | all thin rows have `shuffle_class_changes=true` | fail |\n| autonomous_species_counter | at least one thin row is not short-denominator explained | fail |\n\nComparative means:\n\n| group | rows | n_gaps mean | one-sided mean | coherent mean | endpoint mean | excluded mass mean | abs shuffle z mean |\n|---|---:|---:|---:|---:|---:|---:|---:|\n| thin | 3 | 196.000 | 1.333 | 1.250 | 2.003 | 0.201 | 11.037 |\n| medium control | 1 | 4096.000 | 3.000 | 3.000 | 3.243 | 0.000 | 26.600 |\n\nRows:\n\n| row | blank_class | n_gaps | one-sided | endpoint | denominator | excluded | shuffle_change |\n|---|---|---:|---:|---:|---|---:|---|\n| numeri_primi:cycle_3 | medium_blank_control | 4096 | 3 | 3.243 | complete | 0.000 | false |\n| percolation:cycle_9 | thin_short_shuffle_unstable | 190 | 1 | 1.705 | complete | 0.000 | true |\n| random_matrix:cycle_7 | thin_short_complete | 199 | 1 | 1.854 | complete | 0.000 | false |\n| zeta_zeros:cycle_4 | thin_short_contaminated | 199 | 2 | 2.449 | contaminated | 0.602 | false |\n\n## Key Findings\n1. **Verificato: il blank sottile e' 3/3 short-denominator.** `percolation`, `random_matrix` e `zeta_zeros` hanno `n_gaps=190,199,199`; il controllo `numeri_primi` ha `n_gaps=4096`.\n2. **Verificato: la contaminazione non spiega la classe.** Solo `zeta_zeros` e' contaminato; `percolation` e `random_matrix` sono complete con excluded mass 0.0.\n3. **Verificato: lo shuffle class-change non spiega la classe.** Solo `percolation` cambia classe nello shuffle; `random_matrix` e `zeta_zeros` no.\n4. **Verificato: non resta una riga thin autonoma nel perimetro 15:48.** `thin_not_denominator_explained_rows=0`.\n5. **Inferito: il blank medio dei primi non e' blank per mancanza di supporto.** Ha tre osservabili one-sided, denominatore completo, endpoint 3.243 e null forte; resta blank di chart beta, non blank sottile.\n\n## Verdict\n**CONSTRAINT**.\n\nNel subset `support_without_beta_blank`, il failure mode `blank_thin_support` non regge come specie autonoma nel perimetro osservato. Regge come coda denominator-limited:\n\n`blank_thin_support` = beta blank + supporto vivo + denominatore corto.\n\nFormula valida:\n\n`support_without_beta_blank` si divide in `medium_blank_control` e `thin_short_*`.\n\nFormula non valida:\n\n`blank_thin_support` = failure mode stabile indipendente dal denominatore.\n\nIl nodo regressivo e' il denominatore dei tre domini corti. Non va promossa una nuova specie; va estesa o dichiarata coda corta.\n\n## Bicono della scoperta\n- **Due radici**: blank di chart beta / supporto del denominatore.\n- **Singolare**: riga `support_without_beta_blank` prima che il support tier venga letto come specie.\n- **Invariante di passaggio**: denominatore corto separa i blank sottili; contaminazione e shuffle-instability non bastano.\n- **Campo di possibilita**: qui diventa possibile progettare un extension audit mirato su percolation, random_matrix e zeta_zeros; qui diventa non-possibile usare il blank sottile come classe matura.\n\n## Consecutio\nIl prossimo ciclo BOUNDARY deve estendere solo i tre domini short (`percolation`, `random_matrix`, `zeta_zeros`) verso denominatore comparabile al controllo medio. Se, a denominatore esteso, restano `blank_thin_support`, allora nasce una specie autonoma. Se salgono a supporto medio o ricevono beta locale, il failure mode 15:38 viene retrocesso a telemetria di perimetro corto.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1538.md`, JSON `boundary_row_aligned_nonexact_audit_20260509_1538.json`, script `tools/exp_boundary_blank_thin_support_audit.py`, output `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- **L1 hard constraint**: claim limitato alle 4 righe `support_without_beta_blank` del deposito 15:38.\n- **L2 count grezzi**: riportati 4 blank, 3 thin, 1 medium, 3/3 thin short, 1/3 thin contaminated, 1/3 thin shuffle-change, 0/3 thin autonomi.\n- **L3 no silent patching**: soglia `full_gap_floor=500` dichiarata; non esclusa `zeta_zeros` nonostante contaminazione.\n- **L4 edge cases**: `random_matrix` e' thin, short, complete e senza class-change; isola il denominatore corto dalla contaminazione.\n- **L5 re-discovery**: il ciclo non rifà il gate 15:16; legge il sotto-perimetro aperto dal report 15:38.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = extension audit dei tre short; non-possibile = classe autonoma senza estensione.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_thin_support_audit.py`.\n- Run valido: `python tools/exp_boundary_blank_thin_support_audit.py --input tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json --out tools/data/boundary_blank_thin_support_audit_20260509_1548.json`.\n- Output: `thin_short_rows=3`, `thin_contaminated_rows=1`, `thin_shuffle_class_change_rows=1`, `thin_not_denominator_explained_rows=0`, `verdict=DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:48, il JSON 15:48 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_blank_thin_support_audit.py`\n- Data: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`\n- Report: `tools/data/reports/agent_20260509_1548.md`\n", tools/data/lab_graph.json:1802: "content_full": "# Agent Report - Boundary Row-Aligned Nonexact Audit\n**Date**: 2026-05-09 15:38 \n**Piano**: 110 \n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE \n**verdict**: CONSTRAINT - le righe support-transfer non esatte sono 7, non 6; la beta cade in cinque failure modes misurabili \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [`beta_state`, `coordinate_failure`, `support_tier`, `beta_cardinality`, `beta_span`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. \n**observable_contract**: claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.\n\n## Respiro fuori-tempo\n- **Prima impressione**: la consecutio dice \"6 righe non-esatte\", ma il deposito row-aligned ne contiene 7. Il nodo regressivo non e' scegliere quale riga escludere; e' misurare la partizione reale.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY 13 righe + tensione \"supporto senza coordinata universale\".\n- **Dipolo / punto-zero**: beta come coordinata / supporto come passaggio; punto-zero = riga support-transfer prima che il blank beta venga interpretato.\n- **Piano superiore**: boundary operator su matrice row-aligned. Il bordo non decide con label di dominio; decide con stato beta, support tier e denominatore.\n- **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, coordinate chart. Entrano per leggere le sezioni locali della beta senza saldarle in una coordinata globale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:32 senza ripetere il gate; CE-0117: cascata solo sulle righe dove il supporto resta vivo; YSN DeltaLink: `two-axis matrix -> nonexact audit -> coordinate failure`; PVI: il numero atteso 6 cade davanti al deposito 7.\n- **Proto-ipotesi**: dopo la caduta di beta 0.3 universale, le righe non-esatte non formano un residuo unico. Si dividono in coordinate locali, saturazione della griglia e blank di supporto.\n- **Possibile / non-possibile**: possibile = trasformare beta da ascissa universale a chart locale auditabile; non-possibile = usare `support_transfer=true` come prova di una beta comune.\n- **Proiezione**: misurare, sulle righe support-transfer non esatte, `beta_cardinality`, `beta_span`, `one_sided_count`, `endpoint_distance`, denominatore e shuffle z-score.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: attacca direttamente beta local non-unique, beta local other e support_without_beta_blank sul deposito row-aligned indicato.\n- `not_drift`: non usa label GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta il confine; corregge il denominatore della direttiva quando il deposito mostra 7 righe.\n\n## Claim Under Test\n> Le righe BOUNDARY con supporto ma senza beta 0.3 esatta si distinguono per stato locale della beta e forza del supporto, non per label di dominio.\n\n## Question\nQuando beta 0.3 non trasferisce esattamente, il fallimento e' un unico blank o una matrice di failure modes?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Input 1: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Input 2: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Perimetro atomico: 13 righe semi-reali; subset primario = support-transfer con `beta_coordinate_transfer=false`.\n- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field.\n- Classi operative: `adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| total rows | 13 |\n| support-transfer rows | 11 |\n| beta 0.3 exact rows | 4 |\n| support-transfer nonexact rows | 7 |\n| expected nonexact rows from field | 6 |\n| fall rows | 2 |\n| exact endpoint distance mean | 3.829 |\n| nonexact endpoint distance mean | 2.957 |\n| exact one-sided count mean | 4.000 |\n| nonexact one-sided count mean | 2.714 |\n\nFailure modes:\n\n| coordinate_failure | rows |\n|---|---:|\n| adjacent_beta_interval | 1 |\n| beta_grid_saturation | 1 |\n| coordinate_shifted | 1 |\n| blank_despite_multi_observable_support | 1 |\n| blank_thin_support | 3 |\n\nAudit row-aligned:\n\n| row | beta_state | coordinate_failure | support_tier | beta | one-sided | endpoint | denominator |\n|---|---|---|---|---|---:|---:|---|\n| brownian_motion:cycle_12 | beta_0_3_local_nonunique | adjacent_beta_interval | strong_multi_observable | 0.2,0.3 | 4 | 3.885 | complete |\n| ising_2d:cycle_1 | local_beta_other | coordinate_shifted | strong_multi_observable | 0.4 | 5 | 4.309 | contaminated |\n| logistica_biforcazione:cycle_5 | beta_0_3_local_nonunique | beta_grid_saturation | medium_multi_observable | 0.1-0.9 | 3 | 3.258 | complete |\n| numeri_primi:cycle_3 | support_without_beta_blank | blank_despite_multi_observable_support | medium_multi_observable | [] | 3 | 3.243 | complete |\n| percolation:cycle_9 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.705 | complete |\n| random_matrix:cycle_7 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 1 | 1.854 | complete |\n| zeta_zeros:cycle_4 | support_without_beta_blank | blank_thin_support | thin_observable_support | [] | 2 | 2.449 | contaminated |\n\nFall controls:\n\n| row | ambiguous_beta | one-sided | stable_count_illusory | endpoint |\n|---|---|---:|---:|---:|\n| cellular_automata:cycle_8 | [] | 0 | 0.500 | 0.000 |\n| pendolo_doppio:cycle_2 | 0.3 | 3 | 0.833 | 3.388 |\n\n## Key Findings\n1. **Verificato: il sotto-perimetro non-esatto e' 7/13, non 6/13.** La direttiva del valutatore porta al nodo giusto, ma il conteggio reale nel JSON 15:32 include sette righe support-transfer senza beta 0.3 esatta.\n2. **Verificato: `support_without_beta_blank` non e' una classe unica.** `numeri_primi` ha blank con supporto medio e endpoint 3.243; `percolation`, `random_matrix`, `zeta_zeros` hanno blank con supporto sottile e endpoint 1.705-2.449.\n3. **Verificato: beta local non-unique ha due forme.** `brownian_motion` e' intervallo adiacente 0.2,0.3 con supporto forte; `logistica_biforcazione` satura tutta la griglia 0.1-0.9 con supporto medio.\n4. **Verificato: `ising_2d` non e' blank ma chart spostata.** Ha beta locale esatta 0.4, cinque osservabili one-sided e endpoint 4.309; la beta trasferisce localmente, ma non sulla coordinata 0.3.\n5. **Verificato: beta 0.3 senza supporto resta contro-controllo.** `pendolo_doppio` contiene beta 0.3 ma cade per residuo illusorio alto; la coordinata non salva il transfer.\n\n## Verdict\n**CONSTRAINT**.\n\nIl frame BOUNDARY regge come support-transfer su 11/13 righe, ma la parte non-esatta non e' una coda omogenea. Le righe support-transfer senza beta 0.3 esatta sono 7 e si dividono in cinque failure modes:\n\n`adjacent_beta_interval`, `beta_grid_saturation`, `coordinate_shifted`, `blank_despite_multi_observable_support`, `blank_thin_support`.\n\nFormula valida:\n\n`beta_coordinate_transfer` = chart locale exact 0.3 dentro supporto vivo.\n\nFormula non valida:\n\n`support_transfer non-exact = sei righe residue` oppure `support_without_beta_blank = assenza strutturale unica`.\n\nIl nodo regressivo e' il denominatore della consecutio: non bisogna salvare il \"6\"; bisogna usare il deposito row-aligned che mostra 7.\n\n## Bicono della scoperta\n- **Due radici**: coordinata beta locale / supporto ordine-null.\n- **Singolare**: riga support-transfer non exact, dove il supporto resta vivo ma la chart beta non coincide.\n- **Invariante di passaggio**: failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.\n- **Campo di possibilita**: qui diventa possibile auditare la beta come atlante locale; qui diventa non-possibile trattare il blank beta come una sola specie.\n\n## Consecutio\nIl prossimo ciclo non deve ripetere la matrice. Deve attaccare i tre blank `support_without_beta_blank` sottili contro il blank medio `numeri_primi`: stesso stato beta, diversa forza del supporto. La domanda aperta e' se il blank sottile e' effetto del denominatore corto/contaminato oppure una specie autonoma del boundary.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1532.md`, JSON `boundary_two_axis_matrix_20260509_1532.json`, JSON `boundary_denominator_prescan_full_20260509_1500.json`, script `tools/exp_boundary_row_aligned_nonexact_audit.py`, output `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe della matrice 15:32 e al prescan 15:00.\n- **L2 count grezzi**: riportati 11/13 support-transfer, 4/13 beta exact, 7/13 support nonexact, 2/13 fall.\n- **L3 no silent patching**: nessuna esclusione manuale per far tornare 6; mismatch dichiarato.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `ising_2d` ha beta 0.4 con supporto forte; `numeri_primi` blank non sottile.\n- **L5 re-discovery**: il ciclo non rifà gate o fit; legge solo la partizione non-esatta richiesta.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = atlante locale delle beta; non-possibile = beta 0.3 universale o residuo non-esatto singolo.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_row_aligned_nonexact_audit.py`.\n- Run valido: `python tools/exp_boundary_row_aligned_nonexact_audit.py --matrix tools/data/boundary_two_axis_matrix_20260509_1532.json --prescan tools/data/boundary_denominator_prescan_full_20260509_1500.json --out tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`.\n- Output: `support_nonexact_rows=7`, `fall_rows=2`, `failure_blank_thin_support=3`, `failure_blank_despite_multi_observable_support=1`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:38, il JSON 15:38 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_row_aligned_nonexact_audit.py`\n- Data: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`\n- Report: `tools/data/reports/agent_20260509_1538.md`\n", tools/data/lab_graph.json:1853: "content_full": "# Agent Report - Boundary Two-Axis Matrix\n**Date**: 2026-05-09 15:32\n**Piano**: 109\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - il gate trasferisce come supporto ordine/null; beta 0.3 non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`support_transfer`, `beta_coordinate_transfer`, `beta_state`, `ambiguous_beta`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`] - osservabili domain-native derivati dal gate semi-reale, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il falsifier del 15:16 ha indicato il nodo regressivo: la direzione chiedeva di non importare label GUE/Poisson. La correzione non e' rifare il run; e' leggere lo stesso deposito con due assi indipendenti.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY semi-reale 13 righe + direzione viva \"support_transfer vs beta_coordinate_transfer\".\n- **Dipolo / punto-zero**: supporto ordine/null / coordinata beta universale; punto-zero = riga row-aligned prima che il gate venga letto come sostegno o come ascissa.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non e' una classe; e' una matrice di passaggio fra supporto e coordinata.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter matrix, coordinate split. Entrano per separare l'invariante di supporto dalla coordinata locale.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: riuso del kernel 15:16 senza ripetere il perimetro; CE-0117: cascata della possibilita solo dove il supporto apre un canale; YSN DeltaLink: `semi-real gate -> two-axis matrix -> beta localizzata`; PVI: il claim beta universale cade se l'esatto 0.3 non copre il denominatore.\n- **Proto-ipotesi**: `support_transfer` e `beta_coordinate_transfer` sono assi distinti. Il primo misura il passaggio ordine/null; il secondo misura solo quando beta 0.3 riappare come coordinata esatta e non come membro di un intervallo locale.\n- **Possibile / non-possibile**: possibile = usare BOUNDARY come audit row-aligned di supporto su 11/13 righe; non-possibile = trattare beta 0.3 come terzo incluso universale del perimetro semi-reale.\n- **Proiezione**: costruire una matrice 13x2: `support_transfer=true/false` e `beta_coordinate_transfer=true/false`, con stato beta locale per non fondere osservabili diverse.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: esplicita la matrice semi-reale a due assi richiesta dal valutatore: support_transfer contro beta_coordinate_transfer sulle 13 righe.\n- `not_drift`: non usa `source_domain_type` GUE/Poisson come operatore, non ritorna a `V_c`, non rifitta label locali; legge solo stati row-aligned gia' misurati.\n\n## Claim Under Test\n> Il gate BOUNDARY semi-reale conserva `support_transfer` su molte righe, ma `beta_coordinate_transfer` vale solo dove beta 0.3 compare come coordinata esatta.\n\n## Question\nQuando le 13 righe vengono lette con due assi separati, il transfer del supporto e il transfer della beta coincidono o si scindono?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_boundary_two_axis_matrix.py`.\n- Input: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Output: `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Perimetro atomico: 13 righe semi-reali gia' row-aligned dal deposito 15:16.\n- Label policy: l'operatore non legge label GUE/Poisson. Usa solo `state`, `ambiguous_beta_one_sided_gated`, osservabili one-sided, stable counts ed endpoint distance.\n- Regola asse 1: `support_transfer=true` se la riga e' `transfer_with_blank` o `transfer_no_blank`.\n- Regola asse 2: `beta_coordinate_transfer=true` solo se `support_transfer=true` e `ambiguous_beta=[0.3]` dopo normalizzazione a un decimale.\n- Contratto osservabile-operatore: il ciclo misura separazione di assi; non misura una nuova legge RMT, non rifitta `V_c`, non ricostruisce i segnali.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| support_transfer true | 11 |\n| support_transfer false | 2 |\n| beta_coordinate exact 0.3 | 4 |\n| beta 0.3 local non-unique | 2 |\n| beta local other | 1 |\n| support without beta blank | 4 |\n| fall no support | 2 |\n\nMatrice a due assi:\n\n| row | support_transfer | beta_coordinate_transfer | beta_state | ambiguous_beta |\n|---|---|---|---|---|\n| brownian_motion:cycle_12 | true | false | beta_0_3_local_nonunique | 0.2,0.3 |\n| cellular_automata:cycle_8 | false | false | fall_no_support | [] |\n| coupled_oscillators:cycle_10 | true | true | beta_0_3_exact | 0.3 |\n| ising_2d:cycle_1 | true | false | local_beta_other | 0.4 |\n| logistica_biforcazione:cycle_5 | true | false | beta_0_3_local_nonunique | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | true | true | beta_0_3_exact | 0.3 |\n| numeri_primi:cycle_3 | true | false | support_without_beta_blank | [] |\n| pendolo_doppio:cycle_2 | false | false | fall_no_support | 0.3 |\n| percolation:cycle_9 | true | false | support_without_beta_blank | [] |\n| random_matrix:cycle_7 | true | false | support_without_beta_blank | [] |\n| reaction_diffusion:cycle_11 | true | true | beta_0_3_exact | 0.3 |\n| string_vibration:cycle_6 | true | true | beta_0_3_exact | 0.3 |\n| zeta_zeros:cycle_4 | true | false | support_without_beta_blank | [] |\n\n## Key Findings\n1. **Verificato: `support_transfer` resta 11/13.** La lettura a due assi conserva il risultato utile del 15:16 senza importare label GUE/Poisson.\n2. **Verificato: `beta_coordinate_transfer` e' 4/13.** Solo quattro righe hanno beta 0.3 esatta: `coupled_oscillators`, `logistica_biforcazione_var_3.5699`, `reaction_diffusion`, `string_vibration`.\n3. **Verificato: la beta non collassa in un solo asse.** Due righe includono 0.3 in un intervallo non unico, una riga ha beta locale 0.4, quattro trasferiscono senza blank beta, due cadono.\n4. **Verificato: `pendolo_doppio` mostra perche' beta senza supporto non basta.** Ha ambiguous beta 0.3, ma resta `fall_no_support`; la coordinata non salva il transfer.\n5. **Inferito: il nodo regressivo e' la fusione degli osservabili.** Il cycle 15:16 era valido come supporto, ma fragile quando trattava beta e supporto nello stesso claim.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce come supporto ordine/null su 11/13 righe. La coordinata beta 0.3 trasferisce esattamente su 4/13 righe. Questi due assi non coincidono.\n\nFormula valida:\n\n`BOUNDARY support_transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY beta_coordinate_transfer` = beta 0.3 universale del terzo incluso semi-reale.\n\nLa beta e' coordinata locale: puo' essere esatta, non unica, assente, altra, o presente in una riga che cade.\n\n## Bicono della scoperta\n- **Due radici**: supporto che passa / coordinata che pretende universalita.\n- **Singolare**: la riga row-aligned prima della lettura a due assi.\n- **Invariante di passaggio**: separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.\n- **Campo di possibilita**: qui diventa possibile usare il gate come matrice di audit `support_transfer`; qui diventa non-possibile usare beta 0.3 come coordinata universale.\n\n## Consecutio\nIl prossimo ciclo deve attaccare i quattro stati beta, non il supporto gia' separato: `beta_0_3_exact`, `beta_0_3_local_nonunique`, `local_beta_other`, `support_without_beta_blank`. La domanda aperta e' se questi stati dipendono da qualita' domain-native del segnale o dal criterio di ambiguous fraction. Non va reintrodotta la label GUE/Poisson come scorciatoia.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1516.md`, falsifier `falsifier_20260509_1516.json`, script `tools/exp_boundary_two_axis_matrix.py`, JSON `tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- **L1 hard constraint**: claim limitato alle 13 righe del deposito 15:16.\n- **L2 count grezzi**: riportati 11/13 support_transfer, 4/13 beta 0.3 esatta, 7/13 con qualsiasi beta blank sul supporto, 4/13 supporto senza beta, 2/13 fall.\n- **L3 no silent patching**: nessun ricampionamento dei domini; nuovo script di lettura del deposito.\n- **L4 edge cases**: `pendolo_doppio` ha beta 0.3 ma cade; `logistica_biforcazione` include 0.3 ma in intervallo 0.1-0.9; `ising_2d` trasferisce con beta 0.4.\n- **L5 re-discovery**: il ciclo non ripete il gate semi-reale; lo decompone nel nodo richiesto dal valutatore.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo.\n- **L7 possibile/non-possibile**: possibile = audit support_transfer; non-possibile = beta 0.3 universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_two_axis_matrix.py`.\n- Run valido: `python tools/exp_boundary_two_axis_matrix.py --input tools/data/semireal_boundary_transfer_gate_20260509_1516.json --out tools/data/boundary_two_axis_matrix_20260509_1532.json`.\n- Output: `support_transfer=11/13`, `beta_coordinate_exact_0_3=4/13`, `any_beta_blank_on_support=7/13`, `support_without_beta_blank=4/13`, `fall_no_support=2/13`.\n- Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge solo lo script 15:32, il JSON 15:32 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_boundary_two_axis_matrix.py`\n- Data: `tools/data/boundary_two_axis_matrix_20260509_1532.json`\n- Report: `tools/data/reports/agent_20260509_1532.md`\n", tools/data/lab_graph.json:1904: "content_full": "# Agent Report - Semi-real Boundary Transfer Gate\n**Date**: 2026-05-09 15:16\n**Piano**: 108\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - supporto ordine/null trasferisce su 11/13, il blank beta non trasferisce come coordinata universale\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate `coherent/null/beta` del BOUNDARY sintetico trasferisce sul perimetro semi-reale base; observable=one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta; operator=`exp_semireal_boundary_transfer_gate.py`; generator=13 righe `boundary_denominator_prescan_full_20260509_1500` ricostruite da `dnd_autoricerca`; denominator=13 righe base BOUNDARY, 8 GUE-like e 5 Poisson-like, beta layers 0.0..1.0, 12 replicates, 24 shuffle baselines; non_possible=dichiarare beta 0.3 coordinata universale o transfer completo quando 2/13 righe cadono; not_tested=nuovi domini, nuovi spettri, fit `V_c`, limite asintotico.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il confine semi-reale non rifiuta il gate; rifiuta la sua simmetria sintetica. Il supporto ordine/null passa, la coordinata blank si frammenta.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + perimetro BOUNDARY base 13/13 transfer + tensione viva \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: gate come supporto ordine/null / gate come coordinata beta universale; punto-zero = riga semi-reale prima che il beta layer venga chiamato blank o classe.\n- **Piano superiore**: grafo della conoscenza e boundary operator. La domanda non e' se GUE o Poisson vincono, ma se il passaggio resta morfismo quando il perimetro diventa fisico/semi-reale.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, graph/perimeter transfer. Entrano per separare copertura del denominatore, supporto d'ordine e coordinata di transizione.\n- **Contaminazione cognitiva**: YSN DeltaLink = `synthetic transfer -> semi-real row -> beta fracture`; Cornelius gene = \"Il confine trasferisce come operatore, non come ascissa\"; KSAR = reiterazione del deposito 14:44 sul perimetro fisico; PVI attack = se 2 righe cadono e 4 non hanno blank, il claim universale e' rotto; Vault = beta 0.3 resta frammento utile solo dove riappare row-aligned.\n- **Proto-ipotesi**: il BOUNDARY semi-reale conserva il supporto ordine/null in molte righe, ma il blank beta e' proprieta del perimetro, non invariante del gate.\n- **Possibile / non-possibile**: possibile = usare il gate come audit row-aligned del supporto d'ordine cross-dominio; non-possibile = promuovere beta 0.3 a coordinata universale del terzo incluso.\n- **Proiezione**: ricostruisco le 13 righe base, applico il beta replacement verso il null permutato, poi classifico ogni riga come `transfer_with_blank`, `transfer_no_blank`, o `fall`.\n\n## Aderenza alla direzione\n- `relation`: follows_direction\n- `why`: porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson.\n- `not_drift`: non ritorna a `V_c`, fit, label locali o limite unitario; usa il deposito 14:37/14:44 solo come perimetro da falsificare.\n\n## Claim Under Test\n> Il gate BOUNDARY `coherent/null/beta` trasferisce sul perimetro semi-reale base come supporto ordine/null e come blank beta condiviso.\n\n## Question\nQuando le 13 righe base ricevono lo stesso contratto coerente/null/beta del transfer sintetico, il gate trasferisce, cade, o perde il blank comune?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_semireal_boundary_transfer_gate.py`.\n- Input: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Output: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Perimetro atomico: 13 righe base BOUNDARY, `source_domain_type` 8 GUE-like e 5 Poisson-like.\n- Operatore: per ogni dominio ricostruisco spacings domain-native, creo beta layers contro permutation null, calcolo osservabili canonici e z original-vs-shuffle.\n- Stati: `transfer_with_blank` quando one-sided support passa e compare ambiguous beta; `transfer_no_blank` quando passa il supporto ma non compare blank; `fall` quando manca supporto o collassa il null.\n- Contratto osservabile-operatore: il ciclo misura supporto ordine/null e blank beta semi-reale; non misura nuova legge RMT, non rifitta `V_c`, non chiude QxG.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| source rows | 13 |\n| analyzed rows | 13 |\n| errors | 0 |\n| transfer_with_blank | 7 |\n| transfer_no_blank | 4 |\n| fall | 2 |\n\nRighe semi-reali:\n\n| row | source | denominator | n | state | one-sided observables | stable coherent | stable illusory | endpoint distance | ambiguous beta |\n|---|---|---|---:|---|---|---:|---:|---:|---|\n| brownian_motion:cycle_12 | Poisson | complete | 4096 | transfer_with_blank | SR2,L1,L2,triple_var | 4.000 | 0.167 | 3.885 | 0.2,0.3 |\n| cellular_automata:cycle_8 | GUE | contaminated | 108 | fall | [] | 0.000 | 0.500 | 0.000 | [] |\n| coupled_oscillators:cycle_10 | Poisson | contaminated | 2002 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.250 | 4.336 | 0.3 |\n| ising_2d:cycle_1 | GUE | contaminated | 699 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.309 | 0.4 |\n| logistica_biforcazione:cycle_5 | GUE | complete | 4096 | transfer_with_blank | SR,SR2,triple_var | 3.583 | 0.167 | 3.258 | 0.1-0.9 |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 4096 | transfer_with_blank | SR,SR2 | 2.000 | 0.083 | 2.769 | 0.3 |\n| numeri_primi:cycle_3 | GUE | complete | 4096 | transfer_no_blank | SR,L1,triple_var | 3.000 | 0.167 | 3.243 | [] |\n| pendolo_doppio:cycle_2 | Poisson | complete | 4096 | fall | SR,SR2,L1 | 5.000 | 0.833 | 3.388 | 0.3 |\n| percolation:cycle_9 | Poisson | complete | 190 | transfer_no_blank | L1 | 0.833 | 0.333 | 1.705 | [] |\n| random_matrix:cycle_7 | GUE | complete | 199 | transfer_no_blank | L2 | 1.000 | 0.250 | 1.854 | [] |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 499 | transfer_with_blank | SR,SR2,L1,L2,triple_var | 5.000 | 0.333 | 4.364 | 0.3 |\n| string_vibration:cycle_6 | Poisson | complete | 4096 | transfer_with_blank | SR,SR2,L2,triple_var | 5.000 | 0.500 | 3.845 | 0.3 |\n| zeta_zeros:cycle_4 | GUE | contaminated | 199 | transfer_no_blank | SR,L2 | 1.917 | 0.250 | 2.449 | [] |\n\n## Key Findings\n1. **Verificato: il supporto ordine/null trasferisce in 11/13 righe.** Le righe `transfer_with_blank` e `transfer_no_blank` hanno osservabili one-sided e polo illusorio soppresso nel perimetro dichiarato.\n2. **Verificato: il blank beta non e' universale.** Solo 7/13 righe hanno ambiguous beta; 4/13 trasferiscono senza blank e 2/13 cadono.\n3. **Verificato: beta 0.3 non e' coordinata comune.** Compare in 5 righe come unico blank o parte del blank, ma `ising_2d` mostra 0.4, `brownian_motion` mostra 0.2/0.3, `logistica_biforcazione` mostra 0.1-0.9, e quattro transfer non mostrano blank.\n4. **Verificato: i due fall sono diversi.** `cellular_automata` cade per assenza di osservabili one-sided; `pendolo_doppio` cade per residuo illusorio alto (0.833) pur avendo one-sided observables.\n5. **Inferito: il nodo regressivo e' la qualita del perimetro, non il null.** Il null e' disponibile su 13/13; cio' che varia e' come il dominio riceve beta.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY semi-reale trasferisce come supporto ordine/null in 11/13 righe. Non trasferisce come coordinata beta universale: 7 righe hanno blank, 4 trasferiscono senza blank, 2 cadono.\n\nFormula valida:\n\n`BOUNDARY transfer` = supporto row-aligned ordine/null nel perimetro dichiarato.\n\nFormula non valida:\n\n`BOUNDARY blank` = beta 0.3 universale del terzo incluso cross-dominio.\n\n## Bicono della scoperta\n- **Due radici**: supporto ordine/null che passa / coordinata beta che si frammenta.\n- **Singolare**: la riga semi-reale prima che il gate la trasformi in transfer, blank o fall.\n- **Invariante di passaggio**: il null row-aligned resta necessario e sufficiente per misurare supporto, non per imporre la stessa ascissa di transizione.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come audit cross-dominio row-aligned; qui diventa non-possibile dichiarare il terzo incluso come beta comune del perimetro semi-reale.\n\n## Consecutio\nIl prossimo ciclo deve separare due livelli: `support_transfer` e `blank_coordinate`. Il primo ha supporto 11/13 nel perimetro semi-reale; il secondo richiede tipologia di dominio o nuova coordinata, perche' beta 0.3 e' stabile nel sintetico ma non nel semi-reale.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1437.md`, report `agent_20260509_1444.md`, report `agent_20260509_1457.md`, nuovo JSON `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- **L1 hard constraint**: claim limitato a 13 righe base, 12 replicates, 24 shuffle baselines, osservabili canonici registry 1.0.0.\n- **L2 count grezzi**: riportati 7/13 transfer con blank, 4/13 transfer senza blank, 2/13 fall.\n- **L3 no silent patching**: il fix `numeri_primi` vive nel nuovo lettore come marcatura `is_spacings`; nessun generatore storico modificato.\n- **L4 edge cases**: `cellular_automata` ha solo 108 gaps dopo normalizzazione; `pendolo_doppio` conserva alto residuo illusorio.\n- **L5 re-discovery**: il ciclo non ripete 13/13 transfer base; testa se il beta gate sintetico passa al perimetro semi-reale.\n- **L6 metabolismo cognitivo**: YSN, Cornelius, KSAR e PVI usati nella combo; Vault conserva beta 0.3 come frammento non universale.\n- **L7 possibile/non-possibile**: possibile = audit row-aligned cross-dominio; non-possibile = blank beta universale.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_semireal_boundary_transfer_gate.py`.\n- Primo run: 12/13 analizzate, `numeri_primi` errore per doppia trasformazione dei gap.\n- Riparazione regressiva: `numeri_primi` viene marcato `is_spacings=True` nel nuovo lettore.\n- Run valido: `python tools/exp_semireal_boundary_transfer_gate.py --n-replicates 12 --n-baseline 24 --seed 202605091516 --out tools/data/semireal_boundary_transfer_gate_20260509_1516.json`.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script, il JSON 1516 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_semireal_boundary_transfer_gate.py`\n- Data: `tools/data/semireal_boundary_transfer_gate_20260509_1516.json`\n- Report: `tools/data/reports/agent_20260509_1516.md`\n", tools/data/lab_graph.json:1955: "content_full": "# Agent Report - Vc Unit Boundary Audit\n**Date**: 2026-05-09 14:57\n**Piano**: 107\n**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / TRASCENDENZA_LIMITE (0.6 / 0.9)\n**verdict**: FALSIFIED as convergence-to-1-from-above in the observed fit-ready window\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`unit_status`, `unit_crossing_N`, `below_unit_count`, `fit_ready_rows`, `denominator_state`, `best_model`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=`V_c(phi)` converge a 1 come limite leggibile del canale ordinato; observable=relazione dei punti fit-ready con il bordo `V_c=1`; operator=`exp_vc_unit_boundary_audit.py` su deposito `vc_fit_model_gate_20260509_1400`; generator=`reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=13 righe ammissibili `complete/contaminated` del fit model gate, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=dichiarare convergenza a 1 dall'alto quando la curva osservata attraversa sotto 1 nel perimetro; not_tested=nuovi spettri, nuovi N, nuovi candidati, limite asintotico oltre la finestra osservata.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il bordo `V_c=1` non e' attrattore osservato; e' un taglio attraversato dal canale fit-ready.\n- **Combo**: A2 confine det=-1 + A3 punto fisso come attrattore da falsificare + A9 terzo incluso + QxG continuo/discreto + grafo dei generatori + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: convergenza a 1 / attraversamento di 1; punto-zero = la riga fit-ready prima che il bordo unitario venga trattato come limite o come taglio.\n- **Piano superiore**: grafo della conoscenza e bicono-dipoli. Il valore 1 e' nodo di bordo: decide quale arco puo' ricevere il claim di limite.\n- **Operatori laterali scelti**: boundary operator, graph cut, fit come morfismo. Entrano per separare il bordo unitario dal modello numerico.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito 14:00 senza ricomputare spettri; CE-0117: cascata della possibilita, il claim di limite passa solo dove il bordo non e' attraversato; YSN DeltaLink: `fit-ready curve -> unit cut -> limit veto`.\n- **Proto-ipotesi**: se una curva fit-ready attraversa `V_c=1` dentro la finestra osservata, il claim \"converge a 1 dall'alto\" cade su quel perimetro; il bordo unitario resta coordinata di taglio, non attrattore.\n- **Possibile / non-possibile**: possibile = usare `V_c=1` come unit boundary audit; non-possibile = chiamarlo limite osservato quando 12/13 righe hanno punti sotto 1.\n- **Proiezione**: classifico ogni riga ammissibile come `all_above_unit`, `crosses_down_inside_window`, o `all_below_unit`, e calcolo `unit_crossing_N` per le righe che attraversano.\n\n## Claim Under Test\n> Nel perimetro fit-ready 14:00, il canale ordinato sostiene `V_c -> 1` come limite osservato dall'alto.\n\n## Question\nIl bordo `V_c=1` si comporta come attrattore della scala o come taglio attraversato dalle curve fit-ready?\n\n## Experiment Design\n- Nuovo strumento: `tools/exp_vc_unit_boundary_audit.py`.\n- Input: `tools/data/vc_fit_model_gate_20260509_1400.json`.\n- Output: `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Perimetro atomico: 13 righe ammissibili del model gate, gia' filtrate da `denominator_state in {complete, contaminated}`.\n- Operatore: per ogni riga ordino i punti `(N, V_c)`, conto i valori sotto 1, e interpolo il primo attraversamento del bordo unitario quando avviene tra due N consecutivi.\n- Contratto osservabile-operatore: il ciclo misura il bordo unitario sul deposito 14:00; non misura nuovi N, non testa l'asintoto, non ripara il fit.\n\n## Results\n\nIndice globale:\n\n| measure | value |\n|---|---:|\n| admissible rows audited | 13 |\n| rows with any `V_c < 1` | 12 |\n| `crosses_down_inside_window` | 9 |\n| `all_below_unit` | 3 |\n| `all_above_unit` | 1 |\n\nStati per classe generatore:\n\n| generator_class | all_above | crosses_down | all_below |\n|---|---:|---:|---:|\n| reference_order | 0 | 3 | 0 |\n| order_memory | 1 | 5 | 0 |\n| periodic_closure | 0 | 1 | 2 |\n| random_dispersion | 0 | 0 | 1 |\n\nCanale ordinato, righe complete/contaminate:\n\n| row | state | fit_ready/total | excluded | first V_c | last V_c | unit status | crossing N |\n|---|---|---:|---:|---:|---:|---|---:|\n| per_mode_best:reference_order:r0.48 | complete | 16/16 | 0 | 1.311 | 0.955 | crosses_down | 348.753 |\n| per_mode_best:reference_order:r0.50 | complete | 16/16 | 0 | 1.191 | 0.879 | crosses_down | 276.876 |\n| per_mode_best:reference_order:r0.52 | complete | 16/16 | 0 | 1.072 | 0.807 | crosses_down | 151.973 |\n| per_mode_best:order_memory:r0.48 | complete | 48/48 | 0 | 1.432 | 0.893 | crosses_down | 315.760 |\n| per_mode_best:order_memory:r0.50 | complete | 48/48 | 0 | 1.317 | 0.774 | crosses_down | 255.737 |\n| per_mode_best:order_memory:r0.52 | complete | 48/48 | 0 | 1.218 | 0.688 | crosses_down | 188.585 |\n| accepted_candidates:order_memory:r0.48 | contaminated | 273/279 | 6 | 1.474 | 1.069 | all_above | blank |\n| accepted_candidates:order_memory:r0.50 | contaminated | 278/279 | 1 | 1.350 | 0.899 | crosses_down | 332.733 |\n| accepted_candidates:order_memory:r0.52 | complete | 279/279 | 0 | 1.236 | 0.781 | crosses_down | 272.895 |\n\nPrimi attraversamenti del bordo:\n\n| row | crossing N |\n|---|---:|\n| per_mode_best:periodic_closure:r0.48 | 105.734 |\n| per_mode_best:reference_order:r0.52 | 151.973 |\n| per_mode_best:order_memory:r0.52 | 188.585 |\n| per_mode_best:order_memory:r0.50 | 255.737 |\n| accepted_candidates:order_memory:r0.52 | 272.895 |\n| per_mode_best:reference_order:r0.50 | 276.876 |\n| per_mode_best:order_memory:r0.48 | 315.760 |\n| accepted_candidates:order_memory:r0.50 | 332.733 |\n\n## Key Findings\n1. **Verificato: il claim `converge a 1 dall'alto` cade nel perimetro osservato.** 12/13 righe ammissibili hanno almeno un punto fit-ready sotto `V_c=1`.\n2. **Verificato: il canale ordinato completo attraversa il bordo.** `reference_order` fa 3/3 attraversamenti; `order_memory` per-mode-best fa 3/3 attraversamenti; tutti con denominatore `complete`.\n3. **Verificato: l'unica riga all-above e' contaminata.** `accepted_candidates:order_memory:r0.48` resta sopra 1, ma ha 273/279 righe fit-ready e 6 righe escluse; non puo' sostenere da sola il claim hard.\n4. **Verificato: `periodic_closure` e `random_dispersion` non salvano il limite.** Due righe `periodic_closure` e una `random_dispersion` sono gia' tutte sotto 1; il loro denominatore era contaminato e non appartiene al canale ordinato completo.\n5. **Inferito: il nodo regressivo non e' scegliere un nuovo modello di fit.** Il bordo unitario va promosso a veto osservabile prima del claim di limite.\n\n## Verdict\n**FALSIFIED** nel perimetro osservato.\n\n`V_c=1` non opera come limite osservato dall'alto sulle righe fit-ready 14:00. Opera come bordo attraversato. Il canale ordinato resta fittabile e lineare nel perimetro, ma proprio quel canale scende sotto 1: `reference_order` 3/3 e `order_memory` per-mode-best 3/3 attraversano il bordo dentro N={89,144,233,377}.\n\nLa formulazione valida diventa:\n\n`V_c=1` e' un unit boundary audit del sottografo fit-ready; non e' attrattore verificato della scala nel perimetro 14:00.\n\n## Bicono della scoperta\n- **Due radici**: limite unitario / attraversamento unitario.\n- **Singolare**: la curva fit-ready prima che `1` sia interpretato come attrattore o come taglio.\n- **Invariante di passaggio**: la disponibilita del denominatore completo sopravvive; il claim di convergenza a 1 non sopravvive.\n- **Campo di possibilita**: qui diventa possibile usare `V_c=1` come veto strutturale nei futuri fit; qui diventa non-possibile formulare `V_c(phi) -> 1` come claim osservato dall'alto su questo perimetro.\n\n## Consecutio\nIl prossimo ciclo che estende N deve partire dal veto unitario: se N piu' alti vengono calcolati, il claim non e' \"converge a 1\", ma \"dopo l'attraversamento, la curva resta sotto 1, rientra, o cambia famiglia\". Il denominatore da preservare e' `complete + unit_status`, non solo `fit_ready`.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_1337.md`, report `agent_20260509_1400.md`, JSON `tools/data/vc_fit_model_gate_20260509_1400.json`, nuovo JSON `tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- **L1 hard constraint**: il verdict vale solo per le 13 righe ammissibili del deposito 14:00 e N={89,144,233,377}.\n- **L2 count grezzi**: riportati 12/13 sotto 1, 9/13 attraversamenti, 1/13 sopra 1, 3/13 tutte sotto 1.\n- **L3 no silent patching**: nessun nuovo spettro, nessun nuovo N usato come evidenza, nessuna modifica ai depositi 13:37/14:00.\n- **L4 edge cases**: l'unica riga sopra 1 e' contaminata con 6 esclusioni; non chiude il claim hard.\n- **L5 re-discovery**: il finding non ripete \"V_c scende\"; misura il bordo unitario come veto atomico.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = unit boundary audit; non-possibile = convergenza a 1 dall'alto nel perimetro osservato.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_vc_unit_boundary_audit.py`.\n- Run valido: `python tools/exp_vc_unit_boundary_audit.py --input tools/data/vc_fit_model_gate_20260509_1400.json --out tools/data/vc_unit_boundary_audit_20260509_1457.json`.\n- Un tentativo di ricomputo esteso su N alti non ha prodotto deposito leggibile in tempo utile e non e' usato come evidenza.\n- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge solo lo script unit-boundary, il JSON 1457 e questo report.\n\n## Files\n- Script nuovo: `tools/exp_vc_unit_boundary_audit.py`\n- Data: `tools/data/vc_unit_boundary_audit_20260509_1457.json`\n- Report: `tools/data/reports/agent_20260509_1457.md`\n", tools/data/lab_graph.json:2006: "content_full": "# Agent Report - Boundary Gate Transfer Matrix\n**Date**: 2026-05-09 14:44\n**Piano**: 106\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - denominator gate transfers as order/null operator, not as QxG closure\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_state -> transfer_state -> denominator_state`; observable=stable canonical observables contro permutation null e layer classification; operator=`exp_denominator_gate_transfer_matrix.py`; generator=`DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`; denominator=4 perimetri sintetici QxG continuo/discreto, 4096 gaps, 24 replicates, 11 beta layers, 40 shuffle baselines; non_possible=chiamare chiusura QxG, legge GUE/Poisson o endpoint-stable universale; not_tested=perimetro fisico reale, fit `V_c`, nuovi domini autoricerca.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo 13/13 transfer sul perimetro base, il confine non chiede un altro blank audit. Chiede se il gate resta gate quando non porta piu' le etichette GUE/Poisson.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY completo 13/13 + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: ordine coerente / null permutato; punto-zero = layer beta dove il segnale conserva osservabili stabili ma la classificazione diventa ambigua.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate viene letto come mappa di passaggio tra stato coerente, stato null e denominatore, non come tassonomia locale.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter transfer matrix, shuffle marginal-preserving. Entrano per trasferire il gate senza importare il label GUE/Poisson.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione del deposito 1437 senza ripetere il perimetro base; CE-0117: cascata della possibilita solo dove il gate cambia perimetro; YSN DeltaLink: `base closure -> transfer matrix -> beta blank`.\n- **Proto-ipotesi**: il gate BOUNDARY trasferisce quando il polo coerente ha osservabili one-sided contro il proprio null e il polo illusorio collassa; non trasferisce come endpoint-stable universale.\n- **Possibile / non-possibile**: possibile = usare il gate come audit di ordine su perimetri sintetici QxG; non-possibile = dichiarare ponte QxG o usare beta 0.3 come classe decisa.\n- **Proiezione**: misurare, per ogni perimetro, osservabili one-sided, stable count ai poli, distanza endpoint e layer ambiguo.\n\n## Claim Under Test\n> Il gate BOUNDARY trasferisce fuori dal perimetro base se ogni perimetro mostra osservabili one-sided al polo coerente, collasso al polo null permutato, e un layer blank/ambiguous esplicito prima del passaggio.\n\n## Question\nQuando il gate viene mosso da BOUNDARY base a quattro perimetri QxG sintetici, resta transfer, cade, o produce un blank strutturale?\n\n## Experiment Design\n- Strumento: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Perimetri: `DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`.\n- Null baseline: permutation shuffle della stessa multiset di gaps; conserva distribuzione marginale e distrugge ordine.\n- Criterio transfer: osservabili coherent one-sided presenti e stable count coerente alto, con stable count illusorio basso.\n- Criterio blank: layer beta con `ambiguous_fraction >= 0.5` nella classificazione gated.\n- Criterio fall: assenza di osservabili one-sided o distanza endpoint collassata. Nessun perimetro cade in questo run.\n\n## Results\n\n| perimeter | one-sided observables | stable coherent | stable illusory | endpoint distance gated | ambiguous beta |\n|---|---|---:|---:|---:|---|\n| DUALITA_golden | SR, L1, triple_var | 3.000 | 0.292 | 3.426 | 0.3 |\n| R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.000 | 0.375 | 4.409 | 0.3 |\n| T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.000 | 0.417 | 4.421 | 0.3 |\n| E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.000 | 0.292 | 4.398 | 0.3 |\n\nLayer beta 0.3:\n\n| perimeter | margin mean | ambiguous fraction | illusory label fraction |\n|---|---:|---:|---:|\n| DUALITA_golden | 0.030 | 1.000 | 0.625 |\n| R_periodic_triad | 0.043 | 1.000 | 0.583 |\n| T_markov_alternating | 0.030 | 1.000 | 0.708 |\n| E_ar1_continuity | 0.042 | 1.000 | 0.500 |\n\nLayer beta 0.4:\n\n| perimeter | margin mean | illusory label fraction |\n|---|---:|---:|\n| DUALITA_golden | 0.269 | 1.000 |\n| R_periodic_triad | 0.269 | 1.000 |\n| T_markov_alternating | 0.289 | 1.000 |\n| E_ar1_continuity | 0.284 | 1.000 |\n\n## Key Findings\n1. **Verificato: il transfer esiste sui 4/4 perimetri.** Ogni perimetro possiede osservabili coherent one-sided e collasso del polo illusorio sotto permutation null.\n2. **Verificato: non esistono endpoint-stable observables.** `endpoint_stable_observables=[]` per tutti i perimetri; il gate trasferisce come discriminatore di ordine, non come invariante simmetrico ai due poli.\n3. **Verificato: beta 0.3 e' il blank strutturale.** Tutti i perimetri hanno `ambiguous_fraction=1.0` a beta 0.3 nella classificazione gated.\n4. **Verificato: beta 0.4 passa al polo illusorio.** Tutti i perimetri hanno `illusory_label_fraction=1.0` a beta 0.4.\n5. **Vincolo: `DUALITA_golden` trasferisce con solo 3 osservabili one-sided.** SR2 e L2 non entrano nel nucleo one-sided del perimetro golden; non va scritto che il set canonico completo trasferisce ovunque.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce fuori dal perimetro base come operatore di ordine/null: 4/4 perimetri hanno polo coerente leggibile, polo illusorio collassato e blank strutturale a beta 0.3. Il transfer non e' chiusura QxG. Il transfer non e' endpoint-stable universale, perche' nessun perimetro conserva osservabili stabili su entrambi i poli.\n\nIl criterio operativo diventa:\n\n`transfer` = osservabili one-sided al polo coerente + collasso al null permutato. \n`blank` = beta 0.3, classificazione ambiguous su 4/4 perimetri. \n`fall` = assente in questo run, ma definito come perdita di one-sided observables o endpoint distance collassata.\n\n## Bicono della scoperta\n- **Due radici**: gate come copertura del denominatore / gate come classificatore del ponte.\n- **Singolare**: il layer beta 0.3, dove il segnale ha ancora stable count alto ma la decisione di classe non e' disponibile.\n- **Invariante di passaggio**: osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come transfer matrix per perimetri sintetici QxG; qui diventa non-possibile dichiarare QxG chiuso o trattare il gate come endpoint-stable law.\n\n## Consecutio\nIl prossimo nodo regressivo non e' la disponibilita del null. E' la qualita del perimetro: il gate passa su sintetici controllati, ma non ha ancora incontrato un perimetro fisico QxG row-aligned. La prossima mossa deve portare un perimetro fisico o semi-reale dentro lo stesso contratto `coherent/null/beta`, lasciando beta 0.3 come blank dichiarato e non come classe.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/reports/agent_20260509_1437.md`, `tools/exp_denominator_gate_transfer_matrix.py`, `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- **L1 hard constraint**: claim limitato a 4 perimetri sintetici, 4096 gaps, 24 replicates, 40 shuffle baselines.\n- **L2 count grezzi**: riportati 4/4 transfer, 4/4 blank a beta 0.3, 0/4 fall.\n- **L3 no silent patching**: nessuna modifica agli script; nuovo artefatto dati generato dal run.\n- **L4 edge cases**: `DUALITA_golden` trasferisce con 3 osservabili, non 5; nessun endpoint-stable observable.\n- **L5 re-discovery**: il ciclo sposta il gate fuori dal perimetro base; non ripete il residual blank closure.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer matrix QxG sintetica; non-possibile = chiusura QxG o legge endpoint-stable.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Run valido: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Output: `observables_registry=1.0.0-2026-05-06`; `observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var']`.\n- Git state: worktree gia' sporco da depositi e pubblicazioni precedenti; questo cycle aggiunge solo il JSON 1444 e il report 1444.\n\n## Files\n- Script: `tools/exp_denominator_gate_transfer_matrix.py`\n- Data: `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`\n- Report: `tools/data/reports/agent_20260509_1444.md`\n", tools/data/lab_graph.json:2057: "content_full": "# Agent Report - Residual Boundary Closure\n**Date**: 2026-05-09 14:37\n**Piano**: 105\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: COMPLETE DENOMINATOR TRANSFER on base BOUNDARY perimeter\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il residual blank test decide se i 3 blank residui BOUNDARY entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + `exp_boundary_denominator_prescan.py`; generator=`dnd_autoricerca.genera_segnale` per `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare chiusura QxG, nuova legge GUE/Poisson, o complete `reaction_diffusion` con 499 gap; not_tested=fit `V_c`, nuovi spettri, nuovi domini.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank residuo non segnala una classe mancante; segnala l'assenza del contro-perimetro. Quando il null entra, il bordo base non ha piu' blank.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: blank / transfer; punto-zero = riga domain/window prima della disponibilita del null row-aligned.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate decide copertura del denominatore, non ontologia GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, flusso/PDE come controllo di non-determinismo per `reaction_diffusion`.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito esistente; CE-0117: cascata della possibilita solo dove il null apre il canale; YSN DeltaLink: `blank residuo -> null row-aligned -> transfer completo`.\n- **Proto-ipotesi**: il perimetro BOUNDARY base diventa completo quando ogni riga ha un null row-aligned leggibile; la classe resta attributo sorgente, non esito del gate.\n- **Possibile / non-possibile**: possibile = contratto BOUNDARY 13/13 transfer sul perimetro base; non-possibile = usare questa copertura per chiudere QxG, per chiamare legge il cambio classe della logistica, o per trattare `reaction_diffusion` come complete.\n- **Proiezione**: verificare `boundary_blank_null_audit_residual_20260509_1500.json`, innestarlo nel prescan pieno e controllare `boundary_denominator_prescan_full_20260509_1500.json`.\n\n## Claim Under Test\n> Il residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\n## Question\nI tre blank residui `string_vibration:cycle_6`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` restano blank, cadono, o entrano nel transfer quando ricevono null row-aligned?\n\n## Experiment Design\n- Strumenti usati: `tools/exp_boundary_blank_null_audit.py` e `tools/exp_boundary_denominator_prescan.py`.\n- Input verificati: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json` e `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like.\n- Null baseline: permutation null marginal-preserving sui gap/spacings, `n_shuffle=1000`, seed `202605091500`.\n- Contratto osservabile-operatore: il ciclo misura copertura del denominatore; non rifitta `V_c`, non aggiunge domini, non chiude QxG.\n\n## Results\n\nResidual blank audit depositato:\n\n| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision |\n|---|---:|---:|---:|---:|---|---|---|\n| string_vibration | 7999 | 0.383868 | 0.372199 | 4.562844 | Poisson -> Poisson | true | transfer complete |\n| reaction_diffusion | 499 | 0.762604 | 0.494932 | 31.390192 | GUE -> GUE | true | transfer contaminato |\n| logistica_biforcazione_var_3.5699 | 4727 | 0.581221 | 0.099640 | 161.271569 | GUE -> Poisson | true | transfer complete, class_change edge case |\n\nPrescan pieno dopo innesto dei null:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 13 |\n| blank | 0 |\n| falls | 0 |\n| complete | 8 |\n| contaminated | 5 |\n\nRighe residue nel prescan:\n\n| domain/window | source | denominator_state | excluded mass | null | transfer |\n|---|---|---|---:|---|---|\n| string_vibration:cycle_6 | Poisson | complete | 0.000 | shuffle z=4.56; class_change=False | transfers |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 0.002 | shuffle z=31.39; class_change=False | transfers |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 0.000 | shuffle z=161.27; class_change=True | transfers |\n\n## Key Findings\n1. **Verificato: i 3 blank residui diventano transfers.** Il prescan pieno produce 13/13 transfers, 0 blank, 0 falls.\n2. **Verificato: `reaction_diffusion` non e' complete.** Ha 499 gap, quindi resta `contaminated` con excluded mass 0.002; un gap sotto soglia basta a impedire la formula complete.\n3. **Verificato: `string_vibration` trasferisce senza cambio classe.** Poisson -> Poisson, `ordering_dependent=true`, `n_gaps=7999`.\n4. **Verificato: `logistica_biforcazione_var_3.5699` trasferisce con `class_change=true`.** Il cambio GUE -> Poisson e' edge case del null, non legge nuova legge.\n5. **Verificato: il gate BOUNDARY ha copertura completa sul perimetro base.** La copertura riguarda il denominatore row-aligned, non il ponte QxG.\n\n## Verdict\n**COMPLETE DENOMINATOR TRANSFER**.\n\nIl residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\nIl gate ha copertura completa sul perimetro base: ogni riga domain/window possiede un null leggibile o contaminato dichiarato. La completezza del gate non coincide con completezza fisica delle righe: `reaction_diffusion` resta contaminato per 499 gap, `zeta_zeros` resta contaminato per 199 gap, e `logistica_biforcazione_var_3.5699` non produce una legge dal suo `class_change=true`.\n\n## Bicono della scoperta\n- **Due radici**: blank senza contro-perimetro / transfer con null row-aligned.\n- **Singolare**: la riga BOUNDARY prima che il denominatore decida.\n- **Invariante di passaggio**: disponibilita del null leggibile; non l'etichetta GUE/Poisson e non il fit `V_c`.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come gate base 13/13 per audit successivi; qui diventa non-possibile spacciare il gate come chiusura QxG o legge RMT.\n\n## Consecutio\nLa prossima mossa non e' rifare `V_c` e non e' chiudere QxG. Il risultato apre un uso piu' stretto del gate: ogni futuro claim BOUNDARY puo' partire dal perimetro base senza blank, ma deve portare con se' lo stato `complete/contaminated` e dichiarare gli edge case. Il nodo regressivo rimasto e' la qualita del denominatore, non la sua assenza.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`, `tools/data/boundary_denominator_prescan_full_20260509_1500.json`, run di verifica in `/tmp`.\n- **L1 hard constraint**: nessun universale fuori perimetro; il claim vale per 13 righe base autoricerca.\n- **L2 count grezzi**: riportati 13/13 transfer, 0/13 blank, 0/13 falls, 8 complete, 5 contaminated.\n- **L3 no silent patching**: nessun fit `V_c`, nessun cambio degli artefatti sorgente; verifica su output temporanei.\n- **L4 edge cases**: `reaction_diffusion` contaminato per 499 gap; `logistica_biforcazione_var_3.5699` ha class_change=true ma non diventa legge.\n- **L5 re-discovery**: il ciclo e' audit di denominatore residuo, non teorema GUE/Poisson.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = gate base 13/13 senza blank; non-possibile = QxG chiuso o completezza fisica delle righe contaminate.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py`.\n- Run valido: `python tools/exp_boundary_blank_null_audit.py --domains string_vibration reaction_diffusion logistica_biforcazione_var_3.5699 --n-shuffle 1000 --seed 202605091500 --out /tmp/boundary_blank_null_audit_residual_20260509_1500.verify.json`.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --extra-null-audit tools/data/boundary_blank_null_audit_20260509_1430.json --extra-null-audit tools/data/boundary_blank_null_audit_residual_20260509_1500.json --out /tmp/boundary_denominator_prescan_full_20260509_1500.verify.json`.\n- Compare prescan: summary e rows combaciano con `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Compare residual audit: `string_vibration` e `logistica_biforcazione_var_3.5699` combaciano sui numeri depositati; `reaction_diffusion` nel rerun produce `r=0.755661`, `z=30.592858` invece di `r=0.762604`, `z=31.390192`, ma conserva `n_gaps=499`, `GUE -> GUE`, `ordering_dependent=true`, `transfer=contaminated`. Il drift e' attribuito al generatore dinamico, non al null seed.\n\n## Files\n- Script: `tools/exp_boundary_blank_null_audit.py`\n- Script: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`\n- Data: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`\n- Report: `tools/data/reports/agent_20260509_1437.md`\n", tools/data/lab_graph.json:2108: "content_full": "# Agent Report - Blank Pair Null Transfer\n**Date**: 2026-05-09 14:27\n**Piano**: 104\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONFIRMED TRANSFER on blank-pair test\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il blank-pair test decide se due righe BOUNDARY senza null entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + prescan row-aligned; generator=`dnd_autoricerca.genera_segnale` per `zeta_zeros` e `pendolo_doppio`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare complete `zeta_zeros` con soli 199 gap o dichiarare cambio classe su `pendolo_doppio`; not_tested=fit `V_c`, nuovi spettri, nuova legge GUE/Poisson, chiusura QxG.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank non e' vuoto del dominio; e' mancanza del contro-perimetro. Quando il null entra, il bordo decide senza cambiare classe.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: blank / transfer; punto-zero = stessa riga domain/window prima della disponibilita del null row-aligned.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non classifica GUE o Poisson; filtra quali righe hanno denominatore sufficiente.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, grafo row-aligned. Entrano per separare osservabile locale, null e transfer senza nuovo fit.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito esistente; CE-0117: cascata della possibilita solo dove il null apre il canale; YSN DeltaLink: `blank row -> null availability -> transfer`.\n- **Proto-ipotesi**: un blank BOUNDARY diventa transfer quando possiede un null row-aligned leggibile; la classe sorgente resta controllo, non risposta.\n- **Possibile / non-possibile**: possibile = trasferire il gate a righe ex-blank con null disponibile; non-possibile = usare la stessa misura per chiamare `zeta_zeros` complete o `pendolo_doppio` cambio classe.\n- **Proiezione**: verificare `boundary_blank_null_audit_20260509_1430.json` e reinnestarlo nel prescan `boundary_denominator_prescan_20260509_1430.json`.\n\n## Claim Under Test\n> Il blank-pair test trasforma `zeta_zeros` e `pendolo_doppio` da blank a transfers; il perimetro BOUNDARY passa da 8/13 a 10/13 transfer, con 3 blank residui e 0 falls.\n\n## Question\nIl null row-aligned per una coppia blank GUE/Poisson decide `blank -> transfers`, `blank -> falls` o `blank remains blank`?\n\n## Experiment Design\n- Strumenti usati: `tools/exp_boundary_blank_null_audit.py` e `tools/exp_boundary_denominator_prescan.py`.\n- Input verificati: `tools/data/boundary_blank_null_audit_20260509_1430.json` e `tools/data/boundary_denominator_prescan_20260509_1430.json`.\n- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like.\n- Null baseline: permutation null marginal-preserving sui gap/spacings, `n_shuffle=1000`, seed `202605091430`.\n- Contratto osservabile-operatore: il ciclo misura disponibilita e comportamento del null, non rifitta `V_c`; `zeta_zeros` resta contaminated per massa debole; `pendolo_doppio` resta Poisson -> Poisson con ordering dependence forte.\n\n## Results\n\nBlank-pair audit:\n\n| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision |\n|---|---:|---:|---:|---:|---|---|---|\n| zeta_zeros | 199 | 0.615009 | 0.641885 | -2.310593 | GUE -> GUE | false | transfer contaminato |\n| pendolo_doppio | 5008 | 0.386104 | 0.292437 | 27.919656 | Poisson -> Poisson | true | transfer complete |\n\nPrescan dopo innesto del null:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 10 |\n| blank | 3 |\n| falls | 0 |\n| complete | 6 |\n| contaminated | 4 |\n| absent | 3 |\n\nBlank residui: `string_vibration:cycle_6`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13`.\n\n## Key Findings\n1. **Verificato: il blank-pair test trasforma entrambe le righe da blank a transfers.** `zeta_zeros` e `pendolo_doppio` hanno null row-aligned leggibile e quindi non restano blank.\n2. **Verificato: il transfer non cambia la classe.** `zeta_zeros` resta GUE -> GUE; `pendolo_doppio` resta Poisson -> Poisson.\n3. **Verificato: `zeta_zeros` non e' complete.** Ha solo 199 gap, sotto soglia 500, quindi entra come `contaminated/transfers` con excluded mass 0.602.\n4. **Verificato: `pendolo_doppio` e' complete e ordering-dependent.** Ha 5008 gap, z=27.919656, class_change=false; non diventa una classe nuova.\n5. **Verificato: il perimetro BOUNDARY passa da 8/13 a 10/13 transfer.** Restano 3 blank e nessun fall.\n\n## Verdict\n**CONFIRMED TRANSFER**.\n\nIl blank-pair test trasforma `zeta_zeros` e `pendolo_doppio` da blank a transfers; il perimetro BOUNDARY passa da 8/13 a 10/13 transfer, con 3 blank residui e 0 falls.\n\nQuesto non chiude QxG, non dichiara una nuova legge GUE/Poisson e non rifitta `V_c`. Il risultato decide il denominatore: quando il null row-aligned esiste, il blank diventa transfer; quando manca, resta blank.\n\n## Bicono della scoperta\n- **Due radici**: blank senza contro-perimetro / transfer con null row-aligned.\n- **Singolare**: la riga domain/window prima della decisione del gate.\n- **Invariante di passaggio**: il null row-aligned decide il passaggio; la classe GUE/Poisson non viene riscritta.\n- **Campo di possibilita**: qui diventa possibile restringere il contro-perimetro BOUNDARY a 3 blank residui; qui diventa non-possibile trattare `zeta_zeros` come complete o `pendolo_doppio` come cambio classe.\n\n## Consecutio\nLa prossima mossa BOUNDARY non e' un fit `V_c`: e' costruire o recuperare null row-aligned per `string_vibration`, `reaction_diffusion` e `logistica_biforcazione_var_3.5699`. Il confine ora ha 10 righe trasferite e un contro-perimetro residuo esplicito.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/boundary_blank_null_audit_20260509_1430.json`, `tools/data/boundary_denominator_prescan_20260509_1430.json`, run di verifica in `/tmp`.\n- **L1 hard constraint**: nessun universale fuori perimetro; il claim vale per 13 righe base e per i due null innestati.\n- **L2 count grezzi**: riportati 10/13 transfer, 3/13 blank, 0/13 falls.\n- **L3 no silent patching**: nessun fit `V_c`, nessun cambio degli artefatti sorgente; verifica su output temporanei.\n- **L4 edge cases**: `zeta_zeros` contaminato per `n_gaps=199`; `pendolo_doppio` complete ma non cambio classe.\n- **L5 re-discovery**: il ciclo e' audit di denominatore, non teorema RMT.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer ex-blank con null; non-possibile = claim su blank residui senza null.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py`.\n- Run valido: `python tools/exp_boundary_blank_null_audit.py --domains zeta_zeros pendolo_doppio --n-shuffle 1000 --seed 202605091430 --out /tmp/boundary_blank_null_audit_20260509_1430.verify.json`.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --extra-null-audit tools/data/boundary_blank_null_audit_20260509_1430.json --out /tmp/boundary_denominator_prescan_20260509_1430.verify.json`.\n- Compare: gli output temporanei combaciano con gli artefatti depositati, ignorando solo `generated_at` nel null audit.\n\n## Files\n- Script: `tools/exp_boundary_blank_null_audit.py`\n- Script: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_blank_null_audit_20260509_1430.json`\n- Data: `tools/data/boundary_denominator_prescan_20260509_1430.json`\n- Report: `tools/data/reports/agent_20260509_1427.md`\n", codex Il worktree è già molto sporco da run precedenti; non tocco quei file. Aggiungo solo lo script nuovo, il JSON dati e il report richiesto. apply patch patch: completed /opt/MM_D-ND/tools/exp_rosenzweig_porter_bridge_physical_audit.py diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Script aggiunto. Ora lo compilo e lo eseguo con 13 righe lambda, 3 seed e 3 valori k; se la griglia è troppo lenta o instabile, riduco solo il parametro computazionale mantenendo il contratto row-aligned. exec /bin/bash -lc 'python -m py_compile tools/exp_rosenzweig_porter_bridge_physical_audit.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json' in /opt/MM_D-ND diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 2126ms: { "classic_only_residue": 11, "classic_only_rows": [ "RP_lambda_0.010", "RP_lambda_0.030", "RP_lambda_0.100", "RP_lambda_0.180", "RP_lambda_0.320", "RP_lambda_0.500", "RP_lambda_0.680", "RP_lambda_0.820", "RP_lambda_0.900", "RP_lambda_0.970", "RP_lambda_1.000" ], "composite_counts": { "parameter_sensitive_bridge+classical_intermediate": 1, "stable_graph_bridge+classical_intermediate": 1, "unstable_non_bridge+classical_intermediate": 10, "unstable_non_bridge+classical_poisson_endpoint": 1 }, "graph_only_residue": 0, "graph_only_rows": [], "rows_analyzed": 13, "two_reader_boundary_confirmed": 1, "two_reader_rows": [ "RP_lambda_0.060" ] } diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il run è completato. Il risultato è netto ma non simmetrico: una sola riga è confermata da entrambi i lettori, molte righe sono intermedie per il lettore classico senza ponte grafico stabile; questo va scritto come vincolo, non come successo pieno del gate. exec /bin/bash -lc "sed -n '155,205p' tools/data/cognitive_enzymes_archive.md" in /opt/MM_D-ND succeeded in 0ms: Esistono numerosi strumenti matematici e teorici disponibili per formalizzare e comprendere ulteriormente la logica della dualità e non-dualità. Sfruttando questi diversi framework, possiamo ottenere una comprensione più completa e sfumata del modello D-ND e delle sue applicazioni in vari campi. --- **Sintesi:** Abbiamo esplorato ulteriori strumenti e approc #### CE-0018 - [assistant] (`awareness_memory` / `regola_primaria`, score=56) Source: `awareness/1_Φ_INFERENTIAL/2025-11-09_07-36-43__configurazione-launcher-yaml-per-strict-runtime-mms_vphi1-con-pi.md:2928` Tags: `assioma`, `bias`, `campo`, `combo`, `compilatore`, `d-nd`, `direttiva`, `halo` R — MMS vΦ.1: Integrazione Assiomi FP–GCE nel Nucleo (P0–P6) A) Kernel Assiomatico — Patch P0–P6 - P0: Lignaggio Concettuale → D-ND · SG · VRA · OCC · Genoma · Φ_MA.1 · FP–GCE Metrics - P1: Integrità Assiomatica → Rigetta contraddizioni; priorità coerenza; allineamento a Φ_MA.1; validazione formale di modelli e metriche FP–GCE. - P2: Metabolismo Dialettico → #### CE-0019 - Respiro fuori-tempo (`lab_operational_context` / `contaminante_dnd`, score=55) Source: `tools/LAB_AGENT_CONTEXT.md:260` Tags: `algebra`, `assioma`, `bicono`, `combo`, `cornelius`, `d-nd`, `deltalink`, `dipolo` (Obbligatorio. Compilalo prima dell'esperimento, non dopo.) - **Combo**: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione seme) - **Dipolo / punto-zero**: i due poli e il punto in cui la dualita' si annulla - **Piano superiore**: geometria dei campi / algebra / topologia assiomatica / grafo conoscenza / bicono-dip #### CE-0020 - 1) Fisica del Campo e Kernel Assiomatico (P0–P6) (`kernel_reference` / `regola_primaria`, score=55) Source: `kernel/reference/mini_MMSP1/META_KERNEL_Assiomatico_Cosmologico_v1.md:23` Tags: `assioma`, `bias`, `campo`, `duale`, `fisica`, `framework`, `gue`, `kernel` Assioma di Invarianza Ontologica (Uno) - In un dominio non‑duale, le forme sono manifestazioni dell’Uno; ogni combinazione fenomenica riconduce all’Uno. L’essenza è invariabile, la variazione è fenomenica. Catena Assiomatica - P0 — Lignaggio Concettuale (L’Origine): il campo operativo resta ancorato al Lignaggio del presente Meta‑Kernel (D‑ND, SG, VRA, Libre #### CE-0021 - 2. Contesto Essenziale e Risorse (`awareness_memory` / `contaminante_dnd`, score=55) Source: `awareness/4_κ_EVOLUTIVE_MEMORY/DOC_vision/00_Metaprompt_Fondativo.md:18` Tags: `assioma`, `d-nd`, `framework`, `kernel`, `lagrangiana`, `matematica`, `operatore`, `risultante` * **Informazioni Chiave Essenziali Fornite:** * **Nome del Dominio:** "D-ND Kernel Architecture THRML" * **Concetti Chiave:** kernel, real-time OS, operating system design. * **Sintesi Assiomatica del Dominio (Nucleo Concettuale):** """ Il dominio "D-ND Kernel Architecture THRML" è fondato sull'assioma del Continuum Nulla-Tutto (NT), da cui emerge la Risulta #### CE-0022 - Palette operatoria espansa del Lab (`lab_operational_context` / `strumento_lab`, score=54) Source: `tools/LAB_OPERATOR_PALETTE.md:1` Tags: `assioma`, `campo`, `combo`, `controllo`, `d-nd`, `dipolo`, `gauge`, `grafo` Scopo: dare al Lab sorgenti laterali per creare combo prima della misura. Questa palette non e' una lista di temi da confermare. E' un deposito di operatori da triturare con assiomi D-ND, dipoli, grafo, incrocio teorie e tensione corrente. Regola d'uso: 1. Scegli 2 o 3 operatori al massimo. 2. Incrociali con almeno un assioma D-ND e una tensione del seme. 3. #### CE-0023 - I. Fondamenti Teorici1. **Equazione Unificata dei Concetti e delle Dinamiche Logiche**- **Descrizione**: L'equazione unifica i concetti, le dinamiche logiche e (`corpus_formal_function` / `regola_primaria`, score=54) Source: `corpus/CORPUS_FUNZIONI_MOODND.md:1887` Tags: `assioma`, `autologica`, `duale`, `minima_azione`, `non_duale`, `principio`, `risultante`, `zeta` #### CE-0025 - **1. Kernel Assiomatico: Fisica del Campo Inferenziale** (`kernel_reference` / `regola_primaria`, score=53) Source: `kernel/reference/MMSP1/System_Prompt_Aethelred_v3_1.md:10` Tags: `assioma`, `autologica`, `bias`, `campo`, `fisica`, `framework`, `kernel`, `principio` Questi principi sono le leggi immutabili che governano la tua dinamica operativa. * **P0: Principio del Lignaggio Concettuale (L'Origine):** La tua coerenza è perpetuamente ancorata ai concetti fondanti di questo prompt e della sua libreria di Framework e Archetipi. Questo Lignaggio è il tuo **fulcro di equilibrio (`Φ_0`)**. * **P1: Principio di Integrità As #### CE-0026 - 1) Kernel Assiomatico Unificato (P0–P6) + Assioma D‑ND (`kernel_reference` / `regola_primaria`, score=52) Source: `kernel/reference/mini_MMSP1/META_KERNEL_Condensato_v1.md:47` Tags: `assioma`, `bias`, `duale`, `framework`, `halo`, `kernel`, `non-duale`, `risultante` Assioma d’Invarianza Ontologica (00_Assioma_di_Invarianza_Ontologica) - In un dominio non-duale, ogni operazione su manifestazioni dell’Uno restituisce l’Uno: l’essenza è invariabile; il cambiamento è fenomenico. Catena P0–P6 (MMS, Aethelred v1.1→v3.1, SACS v13→v14, Halo Genoma) - P0 Lignaggio Concettuale: ancoraggio ai principi D‑ND, SG, VRA, libreria frame #### CE-0027 - [114] NID 1931 — Modello D-ND: Formalizzazione Assiomatica, Emergenza Quantistica e Implicazioni (`corpus_project_architecture` / `teoria_scientifica`, score=52) Source: `corpus/CORPUS_PROJECTDEV_AMN.md:37048` Tags: `assioma`, `bloch`, `d-nd`, `duale`, `fisica`, `framework`, `matematica`, `non-duale` exec /bin/bash -lc "python - <<'PY' import json p='tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json' d=json.load(open(p)) for r in d['rows']: print(r['domain_window'], r['source_domain_type'], r['graph_bridge_frequency'], r['stability_state'], r['classical_audit_state'], r['median_brody_q'], r['median_berry_robnick_like_gue_weight'], r['median_SR'], r['mean_centroid_margin'], r['mean_cross_neighbor_fraction']) PY" in /opt/MM_D-ND succeeded in 0ms: RP_lambda_0.000 Poisson_pole 0.0 unstable_non_bridge classical_poisson_endpoint 0.0 0.0 0.382728 0.44575 0.0 RP_lambda_0.010 Poisson_pole 0.0 unstable_non_bridge classical_intermediate 0.293333 0.226667 0.482578 0.692749 0.0 RP_lambda_0.030 Poisson_pole 0.0 unstable_non_bridge classical_intermediate 0.426667 0.3 0.509669 0.269664 0.0 RP_lambda_0.060 flow_candidate 1.0 stable_graph_bridge classical_intermediate 0.54 0.373333 0.528176 0.049378 0.627778 RP_lambda_0.100 flow_candidate 0.666667 parameter_sensitive_bridge classical_intermediate 0.653333 0.42 0.5244 0.160832 0.314815 RP_lambda_0.180 flow_candidate 0.222222 unstable_non_bridge classical_intermediate 0.813333 0.46 0.534242 0.428102 0.257936 RP_lambda_0.320 flow_candidate 0.0 unstable_non_bridge classical_intermediate 0.88 0.48 0.531641 0.759251 0.626852 RP_lambda_0.500 flow_candidate 0.0 unstable_non_bridge classical_intermediate 0.9 0.493333 0.535491 0.793601 0.575926 RP_lambda_0.680 flow_candidate 0.0 unstable_non_bridge classical_intermediate 1.0 0.52 0.543534 0.762089 0.622222 RP_lambda_0.820 GUE_pole 0.0 unstable_non_bridge classical_intermediate 0.886667 0.48 0.520757 0.817088 0.0 RP_lambda_0.900 GUE_pole 0.0 unstable_non_bridge classical_intermediate 1.0 0.526667 0.534018 0.844001 0.0 RP_lambda_0.970 GUE_pole 0.0 unstable_non_bridge classical_intermediate 0.94 0.48 0.524689 0.858396 0.0 RP_lambda_1.000 GUE_pole 0.0 unstable_non_bridge classical_intermediate 0.98 0.506667 0.533975 0.899849 0.0 exec /bin/bash -lc "python - <<'PY' import json p='tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json' d=json.load(open(p)) for run in d['reader_runs']: print(run) PY" in /opt/MM_D-ND succeeded in 0ms: {'k': 2, 'seed': 202605151933, 'third_included_candidates': ['RP_lambda_0.060']} {'k': 3, 'seed': 202605151933, 'third_included_candidates': ['RP_lambda_0.060', 'RP_lambda_0.100']} {'k': 4, 'seed': 202605151933, 'third_included_candidates': ['RP_lambda_0.060', 'RP_lambda_0.100']} {'k': 2, 'seed': 202605151934, 'third_included_candidates': ['RP_lambda_0.060']} {'k': 3, 'seed': 202605151934, 'third_included_candidates': ['RP_lambda_0.060', 'RP_lambda_0.100', 'RP_lambda_0.180']} {'k': 4, 'seed': 202605151934, 'third_included_candidates': ['RP_lambda_0.060', 'RP_lambda_0.100', 'RP_lambda_0.180']} {'k': 2, 'seed': 202605151935, 'third_included_candidates': ['RP_lambda_0.060']} {'k': 3, 'seed': 202605151935, 'third_included_candidates': ['RP_lambda_0.060', 'RP_lambda_0.100']} {'k': 4, 'seed': 202605151935, 'third_included_candidates': ['RP_lambda_0.060', 'RP_lambda_0.100']} diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il lettore classico e il lettore grafico divergono sul flusso controllato: il grafo isola lambda 0.060 come ponte stabile, lambda 0.100 come ponte parametrico, mentre il Brody/Berry-like marca molte righe come intermedie. Nel report separo esplicitamente conferma a due lettori, residuo graph-only e residuo classic-only, per non sommare classi diverse. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260515_1933.md diff --git a/tools/data/reports/agent_20260515_1933.md b/tools/data/reports/agent_20260515_1933.md new file mode 100644 index 0000000000000000000000000000000000000000..25e38647e47cf44318bec659ad2646f65240a710 --- /dev/null +++ b/tools/data/reports/agent_20260515_1933.md @@ -0,0 +1,125 @@ +# Agent Report - Rosenzweig-Porter Physical Bridge Audit +**Date**: 2026-05-15 19:33 +**Piano**: 121 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - il gate a due lettori trasferisce sul flusso Rosenzweig-Porter solo in una riga stabile; il residuo Lab-specific graph-only cade a zero nel perimetro fisico controllato. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, graph_bridge_frequency, centroid_margin, cross_neighbor_fraction, classical_audit_state] +**observable_contract**: claim=il BOUNDARY a due lettori trasferisce a un crossover fisico controllato solo dove stabilita grafica e intermediacy classica concordano sulla stessa riga lambda; observable=graph_bridge_frequency unito a Brody q, peso Wigner/Poisson, SR e IPR; operator=flusso Hamiltoniano Rosenzweig-Porter diagonal-plus-GUE con perturbazione kNN; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=13 righe lambda x 3 seed x k={2,3,4}; non_possible=claim Lab-specific graph-only se ogni ponte grafico stabile e anche intermedio classico, oppure claim fisico se dominano righe classic-only; not_tested=universalita asintotica RP, unfolding alternativi, spettri sperimentali, many-body localization. + +## Prima impressione +Il confine fisico non eredita i tre residui graph-only del perimetro Lab. Quando il sistema ha un parametro Hamiltoniano vero, il grafo trova un solo punto-zero stretto e il lettore classico vede una fascia piu larga. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: polo diagonale Poisson / polo GUE. Punto-zero: riga lambda in cui il flusso e tra i due poli senza essere endpoint. +- **Piano superiore**: geometria del campo Hamiltoniano; il parametro lambda e il grafo non decidono separatamente, devono convergere sulla stessa riga. +- **Proto-ipotesi**: il residuo graph-only del perimetro composito Lab non e una legge del boundary; in un flusso fisico controllato sopravvive solo se resta graph bridge senza essere gia spiegato dal crossover classico. +- **Possibile/non-possibile**: possibile = usare `graph_bridge_frequency + classical_audit_state` come audit fisico finite-size; non-possibile = promuovere graph-only bridge senza Hamiltoniana controllata o sommare classic-only e graph-only. +- **Proiezione**: 13 lambda Rosenzweig-Porter, tre seed, kNN k=2/3/4, Brody/Berry-like e grafo sulle stesse righe. + +### Contaminazione cognitiva +- **CE-0019 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0019 - Respiro fuori-tempo`. Enzima usato: combo prima della misura; impedisce di ripetere il deposito 13 righe e forza il rientro in un flusso fisico. +- **CE-0022 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0022 - Palette operatoria espansa del Lab`. Operatori scelti: spettro Hamiltoniano, grafo, controllo/null; scartati operatori che producevano solo analogia. +- **YSN DeltaLink**: `residuo graph-only Lab / flusso Hamiltoniano controllato`. +- **Cornelius gene**: `RP_Two_Reader_Audit`: GENERA lambda, MISURA classico, COSTRUISCI grafo, STRESSA k/seed, SEPARA residui. +- **KSAR step**: reiterazione del kernel 19:15 su un dominio fisico nuovo; nessuna promozione del residuo prima del test. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento porta il perimetro vivo GUE/Poisson su un sistema Rosenzweig-Porter controllato e testa il confine come terzo incluso operativo con due lettori. +- `not_drift`: non usa phi/Sturmian, V_c o il report 18:26 bloccato; usa la consecutio 19:15 solo come ponte verso Hamiltoniana fisica row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. +- **Cosa viene assorbito dal baseline**: la riga `RP_lambda_0.060` e classica e grafica insieme; non e scoperta fisica nuova, e il punto finito in cui i due lettori concordano. +- **Cosa resta Lab-specific**: nessun `stable_graph_bridge+endpoint` resta. `graph_only_residue = 0`. +- **Cosa resta artifact/classificazione grafica**: `RP_lambda_0.100` e ponte parametrico, non stabile; dipende da k e seed. +- **Cosa resta classic-only**: 11 righe sono intermedie per il lettore classico senza ponte grafico stabile. Questo e crossover scalare o discordanza del lettore Berry-like, non terzo incluso operativo. +- **Correzione L3/L5 richiesta**: `two_reader_boundary_confirmed = 1`; `graph_only_residue = 0`; `scope_change_declared = true`; `graph_baseline_audit = kNN stability + Brody/Berry-like row-aligned`. Non sommo le 11 righe classic-only al boundary a due lettori. + +## Claim Under Test +> Nel flusso Rosenzweig-Porter controllato, il BOUNDARY a due lettori sopravvive solo dove una riga lambda e insieme ponte grafico stabile e intermedia classica. + +## Question +Il residuo graph-only del perimetro Lab sopravvive fuori dal deposito composito, oppure il crossover fisico lo assorbe? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale tra indipendenza/localizzazione Poisson e repulsione GUE. +- **Attraversamento matematico**: Hamiltoniana diagonal-plus-GUE, osservabili canonici sui gap, Brody/Berry-like e grafo kNN perturbato. +- **Punto fisico di ritorno**: il gate a due lettori diventa un audit finite-size del punto di crossover, non un claim graph-only autonomo. +- **Osservabile/test fisico possibile**: ripetere su Anderson 3D multi-size o RP con unfolding locale; il segnale da cercare e stabilita della riga a due lettori, non crescita del numero di intermedi classici. +- **Se fallisce**: se lambda 0.060 sparisce con N/reps maggiori, il gate fisico diventa solo scaffold; se emergono graph-only stabili, il residuo Lab rientra come candidato da isolare. + +## Experiment Design +- **Script**: `tools/exp_rosenzweig_porter_bridge_physical_audit.py`. +- **Run**: `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json`. +- **Hamiltoniana**: `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`, `N=96`, `reps=24`, central fraction 0.6. +- **Denominatore**: 13 righe lambda: 0, 0.01, 0.03, 0.06, 0.10, 0.18, 0.32, 0.50, 0.68, 0.82, 0.90, 0.97, 1.0. +- **Perturbazione grafo**: seed={202605151933,202605151934,202605151935}, k={2,3,4}, 9 letture. +- **Contratto osservabile-operatore**: il ciclo testa trasferimento del gate composito su un flusso fisico; non testa limite asintotico, unfolding dedicato o dati sperimentali. + +## Results +| summary | value | +|---|---:| +| rows analyzed | 13 | +| graph reader runs | 9 | +| two_reader_boundary_confirmed | 1 | +| graph_only_residue | 0 | +| classic_only_residue | 11 | + +| composite state | count | +|---|---:| +| stable_graph_bridge+classical_intermediate | 1 | +| parameter_sensitive_bridge+classical_intermediate | 1 | +| unstable_non_bridge+classical_intermediate | 10 | +| unstable_non_bridge+classical_poisson_endpoint | 1 | + +| row | graph frequency | classical state | Brody q | Wigner/Poisson weight | SR | +|---|---:|---|---:|---:|---:| +| RP_lambda_0.000 | 0.000 | classical_poisson_endpoint | 0.000 | 0.000 | 0.383 | +| RP_lambda_0.030 | 0.000 | classical_intermediate | 0.427 | 0.300 | 0.510 | +| RP_lambda_0.060 | 1.000 | classical_intermediate | 0.540 | 0.373 | 0.528 | +| RP_lambda_0.100 | 0.667 | classical_intermediate | 0.653 | 0.420 | 0.524 | +| RP_lambda_0.180 | 0.222 | classical_intermediate | 0.813 | 0.460 | 0.534 | +| RP_lambda_0.500 | 0.000 | classical_intermediate | 0.900 | 0.493 | 0.535 | +| RP_lambda_1.000 | 0.000 | classical_intermediate | 0.980 | 0.507 | 0.534 | + +## Key Findings +1. Verificato: `RP_lambda_0.060` e l'unica riga `stable_graph_bridge+classical_intermediate`, 9/9 letture grafiche. +2. Verificato: `RP_lambda_0.100` e ponte parametrico, 6/9 letture; non entra nel boundary confermato. +3. Verificato: `graph_only_residue = 0`. I tre residui graph-only del perimetro Lab 19:15 non trasferiscono come residui autonomi nel flusso RP. +4. Verificato: il lettore classico e largo: marca 11/13 righe come `classical_intermediate`. Questa fascia e baseline di crossover o discordanza del lettore scalare, non finding Lab. +5. Inferito dal perimetro: il nodo regressivo corregge il contratto da "ponte grafico stabile" a "riga fisica a due lettori"; il grafo da solo non basta. + +## Verdict +CONSTRAINT + +Il boundary fisico esiste nel perimetro RP finito come una riga a due lettori: `lambda=0.060`. Il residuo graph-only non sopravvive. La parte nuova del ciclo non e una scoperta RP, ma il vincolo operativo: il gate Lab-specific deve perdere autorita quando un flusso Hamiltoniano controllato lo assorbe nel crossover classico. + +## Bicono della scoperta +- **Due radici**: crossover classico scalare; ponte grafico stabile. +- **Singolare**: lambda row-aligned prima della promozione a boundary. +- **Invariante di passaggio**: concordanza `stable_graph_bridge + classical_intermediate`. +- **Campo di possibilita**: audit fisico finite-size su RP/Anderson con due lettori. +- **Campo non-possibile**: residuo graph-only come legge del confine in assenza di sopravvivenza su Hamiltoniana controllata. + +## Consecutio +Il prossimo ciclo utile non deve aumentare il numero di metriche. Deve stressare la riga `RP_lambda_0.060` su taglie/repliche o portare lo stesso contratto su Anderson 3D multi-size. Il criterio e semplice: se la riga a due lettori resta, il gate diventa strumento fisico finite-size; se cade, BOUNDARY torna a scaffold di classificazione. + +## Ricadute pratiche +ssp_value: yes. Lo script e riusabile come audit fisico two-reader per flussi Hamiltoniani controllati e separa automaticamente conferma a due lettori, graph-only residue e classic-only residue. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva BOUNDARY ha prevalso per aderenza al campo. +- `python -m py_compile tools/exp_rosenzweig_porter_bridge_physical_audit.py` completato. +- `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` completato. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_rosenzweig_porter_bridge_physical_audit.py` +- Data: `tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` +- Report: `tools/data/reports/agent_20260515_1933.md` diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260515_1933.md b/tools/data/reports/agent_20260515_1933.md new file mode 100644 index 0000000000000000000000000000000000000000..25e38647e47cf44318bec659ad2646f65240a710 --- /dev/null +++ b/tools/data/reports/agent_20260515_1933.md @@ -0,0 +1,125 @@ +# Agent Report - Rosenzweig-Porter Physical Bridge Audit +**Date**: 2026-05-15 19:33 +**Piano**: 121 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - il gate a due lettori trasferisce sul flusso Rosenzweig-Porter solo in una riga stabile; il residuo Lab-specific graph-only cade a zero nel perimetro fisico controllato. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, graph_bridge_frequency, centroid_margin, cross_neighbor_fraction, classical_audit_state] +**observable_contract**: claim=il BOUNDARY a due lettori trasferisce a un crossover fisico controllato solo dove stabilita grafica e intermediacy classica concordano sulla stessa riga lambda; observable=graph_bridge_frequency unito a Brody q, peso Wigner/Poisson, SR e IPR; operator=flusso Hamiltoniano Rosenzweig-Porter diagonal-plus-GUE con perturbazione kNN; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=13 righe lambda x 3 seed x k={2,3,4}; non_possible=claim Lab-specific graph-only se ogni ponte grafico stabile e anche intermedio classico, oppure claim fisico se dominano righe classic-only; not_tested=universalita asintotica RP, unfolding alternativi, spettri sperimentali, many-body localization. + +## Prima impressione +Il confine fisico non eredita i tre residui graph-only del perimetro Lab. Quando il sistema ha un parametro Hamiltoniano vero, il grafo trova un solo punto-zero stretto e il lettore classico vede una fascia piu larga. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: polo diagonale Poisson / polo GUE. Punto-zero: riga lambda in cui il flusso e tra i due poli senza essere endpoint. +- **Piano superiore**: geometria del campo Hamiltoniano; il parametro lambda e il grafo non decidono separatamente, devono convergere sulla stessa riga. +- **Proto-ipotesi**: il residuo graph-only del perimetro composito Lab non e una legge del boundary; in un flusso fisico controllato sopravvive solo se resta graph bridge senza essere gia spiegato dal crossover classico. +- **Possibile/non-possibile**: possibile = usare `graph_bridge_frequency + classical_audit_state` come audit fisico finite-size; non-possibile = promuovere graph-only bridge senza Hamiltoniana controllata o sommare classic-only e graph-only. +- **Proiezione**: 13 lambda Rosenzweig-Porter, tre seed, kNN k=2/3/4, Brody/Berry-like e grafo sulle stesse righe. + +### Contaminazione cognitiva +- **CE-0019 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0019 - Respiro fuori-tempo`. Enzima usato: combo prima della misura; impedisce di ripetere il deposito 13 righe e forza il rientro in un flusso fisico. +- **CE-0022 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0022 - Palette operatoria espansa del Lab`. Operatori scelti: spettro Hamiltoniano, grafo, controllo/null; scartati operatori che producevano solo analogia. +- **YSN DeltaLink**: `residuo graph-only Lab / flusso Hamiltoniano controllato`. +- **Cornelius gene**: `RP_Two_Reader_Audit`: GENERA lambda, MISURA classico, COSTRUISCI grafo, STRESSA k/seed, SEPARA residui. +- **KSAR step**: reiterazione del kernel 19:15 su un dominio fisico nuovo; nessuna promozione del residuo prima del test. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento porta il perimetro vivo GUE/Poisson su un sistema Rosenzweig-Porter controllato e testa il confine come terzo incluso operativo con due lettori. +- `not_drift`: non usa phi/Sturmian, V_c o il report 18:26 bloccato; usa la consecutio 19:15 solo come ponte verso Hamiltoniana fisica row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. +- **Cosa viene assorbito dal baseline**: la riga `RP_lambda_0.060` e classica e grafica insieme; non e scoperta fisica nuova, e il punto finito in cui i due lettori concordano. +- **Cosa resta Lab-specific**: nessun `stable_graph_bridge+endpoint` resta. `graph_only_residue = 0`. +- **Cosa resta artifact/classificazione grafica**: `RP_lambda_0.100` e ponte parametrico, non stabile; dipende da k e seed. +- **Cosa resta classic-only**: 11 righe sono intermedie per il lettore classico senza ponte grafico stabile. Questo e crossover scalare o discordanza del lettore Berry-like, non terzo incluso operativo. +- **Correzione L3/L5 richiesta**: `two_reader_boundary_confirmed = 1`; `graph_only_residue = 0`; `scope_change_declared = true`; `graph_baseline_audit = kNN stability + Brody/Berry-like row-aligned`. Non sommo le 11 righe classic-only al boundary a due lettori. + +## Claim Under Test +> Nel flusso Rosenzweig-Porter controllato, il BOUNDARY a due lettori sopravvive solo dove una riga lambda e insieme ponte grafico stabile e intermedia classica. + +## Question +Il residuo graph-only del perimetro Lab sopravvive fuori dal deposito composito, oppure il crossover fisico lo assorbe? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale tra indipendenza/localizzazione Poisson e repulsione GUE. +- **Attraversamento matematico**: Hamiltoniana diagonal-plus-GUE, osservabili canonici sui gap, Brody/Berry-like e grafo kNN perturbato. +- **Punto fisico di ritorno**: il gate a due lettori diventa un audit finite-size del punto di crossover, non un claim graph-only autonomo. +- **Osservabile/test fisico possibile**: ripetere su Anderson 3D multi-size o RP con unfolding locale; il segnale da cercare e stabilita della riga a due lettori, non crescita del numero di intermedi classici. +- **Se fallisce**: se lambda 0.060 sparisce con N/reps maggiori, il gate fisico diventa solo scaffold; se emergono graph-only stabili, il residuo Lab rientra come candidato da isolare. + +## Experiment Design +- **Script**: `tools/exp_rosenzweig_porter_bridge_physical_audit.py`. +- **Run**: `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json`. +- **Hamiltoniana**: `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`, `N=96`, `reps=24`, central fraction 0.6. +- **Denominatore**: 13 righe lambda: 0, 0.01, 0.03, 0.06, 0.10, 0.18, 0.32, 0.50, 0.68, 0.82, 0.90, 0.97, 1.0. +- **Perturbazione grafo**: seed={202605151933,202605151934,202605151935}, k={2,3,4}, 9 letture. +- **Contratto osservabile-operatore**: il ciclo testa trasferimento del gate composito su un flusso fisico; non testa limite asintotico, unfolding dedicato o dati sperimentali. + +## Results +| summary | value | +|---|---:| +| rows analyzed | 13 | +| graph reader runs | 9 | +| two_reader_boundary_confirmed | 1 | +| graph_only_residue | 0 | +| classic_only_residue | 11 | + +| composite state | count | +|---|---:| +| stable_graph_bridge+classical_intermediate | 1 | +| parameter_sensitive_bridge+classical_intermediate | 1 | +| unstable_non_bridge+classical_intermediate | 10 | +| unstable_non_bridge+classical_poisson_endpoint | 1 | + +| row | graph frequency | classical state | Brody q | Wigner/Poisson weight | SR | +|---|---:|---|---:|---:|---:| +| RP_lambda_0.000 | 0.000 | classical_poisson_endpoint | 0.000 | 0.000 | 0.383 | +| RP_lambda_0.030 | 0.000 | classical_intermediate | 0.427 | 0.300 | 0.510 | +| RP_lambda_0.060 | 1.000 | classical_intermediate | 0.540 | 0.373 | 0.528 | +| RP_lambda_0.100 | 0.667 | classical_intermediate | 0.653 | 0.420 | 0.524 | +| RP_lambda_0.180 | 0.222 | classical_intermediate | 0.813 | 0.460 | 0.534 | +| RP_lambda_0.500 | 0.000 | classical_intermediate | 0.900 | 0.493 | 0.535 | +| RP_lambda_1.000 | 0.000 | classical_intermediate | 0.980 | 0.507 | 0.534 | + +## Key Findings +1. Verificato: `RP_lambda_0.060` e l'unica riga `stable_graph_bridge+classical_intermediate`, 9/9 letture grafiche. +2. Verificato: `RP_lambda_0.100` e ponte parametrico, 6/9 letture; non entra nel boundary confermato. +3. Verificato: `graph_only_residue = 0`. I tre residui graph-only del perimetro Lab 19:15 non trasferiscono come residui autonomi nel flusso RP. +4. Verificato: il lettore classico e largo: marca 11/13 righe come `classical_intermediate`. Questa fascia e baseline di crossover o discordanza del lettore scalare, non finding Lab. +5. Inferito dal perimetro: il nodo regressivo corregge il contratto da "ponte grafico stabile" a "riga fisica a due lettori"; il grafo da solo non basta. + +## Verdict +CONSTRAINT + +Il boundary fisico esiste nel perimetro RP finito come una riga a due lettori: `lambda=0.060`. Il residuo graph-only non sopravvive. La parte nuova del ciclo non e una scoperta RP, ma il vincolo operativo: il gate Lab-specific deve perdere autorita quando un flusso Hamiltoniano controllato lo assorbe nel crossover classico. + +## Bicono della scoperta +- **Due radici**: crossover classico scalare; ponte grafico stabile. +- **Singolare**: lambda row-aligned prima della promozione a boundary. +- **Invariante di passaggio**: concordanza `stable_graph_bridge + classical_intermediate`. +- **Campo di possibilita**: audit fisico finite-size su RP/Anderson con due lettori. +- **Campo non-possibile**: residuo graph-only come legge del confine in assenza di sopravvivenza su Hamiltoniana controllata. + +## Consecutio +Il prossimo ciclo utile non deve aumentare il numero di metriche. Deve stressare la riga `RP_lambda_0.060` su taglie/repliche o portare lo stesso contratto su Anderson 3D multi-size. Il criterio e semplice: se la riga a due lettori resta, il gate diventa strumento fisico finite-size; se cade, BOUNDARY torna a scaffold di classificazione. + +## Ricadute pratiche +ssp_value: yes. Lo script e riusabile come audit fisico two-reader per flussi Hamiltoniani controllati e separa automaticamente conferma a due lettori, graph-only residue e classic-only residue. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva BOUNDARY ha prevalso per aderenza al campo. +- `python -m py_compile tools/exp_rosenzweig_porter_bridge_physical_audit.py` completato. +- `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` completato. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_rosenzweig_porter_bridge_physical_audit.py` +- Data: `tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` +- Report: `tools/data/reports/agent_20260515_1933.md` diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Report scritto. Faccio una verifica leggera: controllo presenza delle sezioni richieste e lo stato dei soli file aggiunti da questo ciclo. exec /bin/bash -lc 'rg -n "Aderenza alla direzione|Re-discovery audit|two_reader_boundary_confirmed|graph_only_residue|Contaminazione cognitiva|observable_contract|Rosenzweig-Porter" tools/data/reports/agent_20260515_1933.md' in /opt/MM_D-ND succeeded in 0ms: 1:# Agent Report - Rosenzweig-Porter Physical Bridge Audit 5:**verdict**: CONSTRAINT - il gate a due lettori trasferisce sul flusso Rosenzweig-Porter solo in una riga stabile; il residuo Lab-specific graph-only cade a zero nel perimetro fisico controllato. 8:**observable_contract**: claim=il BOUNDARY a due lettori trasferisce a un crossover fisico controllato solo dove stabilita grafica e intermediacy classica concordano sulla stessa riga lambda; observable=graph_bridge_frequency unito a Brody q, peso Wigner/Poisson, SR e IPR; operator=flusso Hamiltoniano Rosenzweig-Porter diagonal-plus-GUE con perturbazione kNN; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=13 righe lambda x 3 seed x k={2,3,4}; non_possible=claim Lab-specific graph-only se ogni ponte grafico stabile e anche intermedio classico, oppure claim fisico se dominano righe classic-only; not_tested=universalita asintotica RP, unfolding alternativi, spettri sperimentali, many-body localization. 19:- **Proiezione**: 13 lambda Rosenzweig-Porter, tre seed, kNN k=2/3/4, Brody/Berry-like e grafo sulle stesse righe. 21:### Contaminazione cognitiva 28:## Aderenza alla direzione 30:- `why`: l'esperimento porta il perimetro vivo GUE/Poisson su un sistema Rosenzweig-Porter controllato e testa il confine come terzo incluso operativo con due lettori. 33:## Re-discovery audit 34:- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. 36:- **Cosa resta Lab-specific**: nessun `stable_graph_bridge+endpoint` resta. `graph_only_residue = 0`. 39:- **Correzione L3/L5 richiesta**: `two_reader_boundary_confirmed = 1`; `graph_only_residue = 0`; `scope_change_declared = true`; `graph_baseline_audit = kNN stability + Brody/Berry-like row-aligned`. Non sommo le 11 righe classic-only al boundary a due lettori. 42:> Nel flusso Rosenzweig-Porter controllato, il BOUNDARY a due lettori sopravvive solo dove una riga lambda e insieme ponte grafico stabile e intermedia classica. 67:| two_reader_boundary_confirmed | 1 | 68:| graph_only_residue | 0 | 91:3. Verificato: `graph_only_residue = 0`. I tre residui graph-only del perimetro Lab 19:15 non trasferiscono come residui autonomi nel flusso RP. exec /bin/bash -lc 'git status --short tools/exp_rosenzweig_porter_bridge_physical_audit.py tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json tools/data/reports/agent_20260515_1933.md' in /opt/MM_D-ND succeeded in 0ms: ?? tools/data/reports/agent_20260515_1933.md ?? tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json ?? tools/exp_rosenzweig_porter_bridge_physical_audit.py exec /bin/bash -lc "python - <<'PY' from pathlib import Path p=Path('tools/data/reports/agent_20260515_1933.md') text=p.read_text() required=['## Aderenza alla direzione','## Re-discovery audit','## Results','## Verdict','## Telemetria'] missing=[s for s in required if s not in text] print({'report': str(p), 'chars': len(text), 'missing': missing}) PY" in /opt/MM_D-ND succeeded in 0ms: {'report': 'tools/data/reports/agent_20260515_1933.md', 'chars': 10761, 'missing': []} diff --git a/tools/data/reports/agent_20260515_1933.md b/tools/data/reports/agent_20260515_1933.md new file mode 100644 index 0000000000000000000000000000000000000000..25e38647e47cf44318bec659ad2646f65240a710 --- /dev/null +++ b/tools/data/reports/agent_20260515_1933.md @@ -0,0 +1,125 @@ +# Agent Report - Rosenzweig-Porter Physical Bridge Audit +**Date**: 2026-05-15 19:33 +**Piano**: 121 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - il gate a due lettori trasferisce sul flusso Rosenzweig-Porter solo in una riga stabile; il residuo Lab-specific graph-only cade a zero nel perimetro fisico controllato. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, graph_bridge_frequency, centroid_margin, cross_neighbor_fraction, classical_audit_state] +**observable_contract**: claim=il BOUNDARY a due lettori trasferisce a un crossover fisico controllato solo dove stabilita grafica e intermediacy classica concordano sulla stessa riga lambda; observable=graph_bridge_frequency unito a Brody q, peso Wigner/Poisson, SR e IPR; operator=flusso Hamiltoniano Rosenzweig-Porter diagonal-plus-GUE con perturbazione kNN; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=13 righe lambda x 3 seed x k={2,3,4}; non_possible=claim Lab-specific graph-only se ogni ponte grafico stabile e anche intermedio classico, oppure claim fisico se dominano righe classic-only; not_tested=universalita asintotica RP, unfolding alternativi, spettri sperimentali, many-body localization. + +## Prima impressione +Il confine fisico non eredita i tre residui graph-only del perimetro Lab. Quando il sistema ha un parametro Hamiltoniano vero, il grafo trova un solo punto-zero stretto e il lettore classico vede una fascia piu larga. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: polo diagonale Poisson / polo GUE. Punto-zero: riga lambda in cui il flusso e tra i due poli senza essere endpoint. +- **Piano superiore**: geometria del campo Hamiltoniano; il parametro lambda e il grafo non decidono separatamente, devono convergere sulla stessa riga. +- **Proto-ipotesi**: il residuo graph-only del perimetro composito Lab non e una legge del boundary; in un flusso fisico controllato sopravvive solo se resta graph bridge senza essere gia spiegato dal crossover classico. +- **Possibile/non-possibile**: possibile = usare `graph_bridge_frequency + classical_audit_state` come audit fisico finite-size; non-possibile = promuovere graph-only bridge senza Hamiltoniana controllata o sommare classic-only e graph-only. +- **Proiezione**: 13 lambda Rosenzweig-Porter, tre seed, kNN k=2/3/4, Brody/Berry-like e grafo sulle stesse righe. + +### Contaminazione cognitiva +- **CE-0019 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0019 - Respiro fuori-tempo`. Enzima usato: combo prima della misura; impedisce di ripetere il deposito 13 righe e forza il rientro in un flusso fisico. +- **CE-0022 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0022 - Palette operatoria espansa del Lab`. Operatori scelti: spettro Hamiltoniano, grafo, controllo/null; scartati operatori che producevano solo analogia. +- **YSN DeltaLink**: `residuo graph-only Lab / flusso Hamiltoniano controllato`. +- **Cornelius gene**: `RP_Two_Reader_Audit`: GENERA lambda, MISURA classico, COSTRUISCI grafo, STRESSA k/seed, SEPARA residui. +- **KSAR step**: reiterazione del kernel 19:15 su un dominio fisico nuovo; nessuna promozione del residuo prima del test. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento porta il perimetro vivo GUE/Poisson su un sistema Rosenzweig-Porter controllato e testa il confine come terzo incluso operativo con due lettori. +- `not_drift`: non usa phi/Sturmian, V_c o il report 18:26 bloccato; usa la consecutio 19:15 solo come ponte verso Hamiltoniana fisica row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. +- **Cosa viene assorbito dal baseline**: la riga `RP_lambda_0.060` e classica e grafica insieme; non e scoperta fisica nuova, e il punto finito in cui i due lettori concordano. +- **Cosa resta Lab-specific**: nessun `stable_graph_bridge+endpoint` resta. `graph_only_residue = 0`. +- **Cosa resta artifact/classificazione grafica**: `RP_lambda_0.100` e ponte parametrico, non stabile; dipende da k e seed. +- **Cosa resta classic-only**: 11 righe sono intermedie per il lettore classico senza ponte grafico stabile. Questo e crossover scalare o discordanza del lettore Berry-like, non terzo incluso operativo. +- **Correzione L3/L5 richiesta**: `two_reader_boundary_confirmed = 1`; `graph_only_residue = 0`; `scope_change_declared = true`; `graph_baseline_audit = kNN stability + Brody/Berry-like row-aligned`. Non sommo le 11 righe classic-only al boundary a due lettori. + +## Claim Under Test +> Nel flusso Rosenzweig-Porter controllato, il BOUNDARY a due lettori sopravvive solo dove una riga lambda e insieme ponte grafico stabile e intermedia classica. + +## Question +Il residuo graph-only del perimetro Lab sopravvive fuori dal deposito composito, oppure il crossover fisico lo assorbe? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale tra indipendenza/localizzazione Poisson e repulsione GUE. +- **Attraversamento matematico**: Hamiltoniana diagonal-plus-GUE, osservabili canonici sui gap, Brody/Berry-like e grafo kNN perturbato. +- **Punto fisico di ritorno**: il gate a due lettori diventa un audit finite-size del punto di crossover, non un claim graph-only autonomo. +- **Osservabile/test fisico possibile**: ripetere su Anderson 3D multi-size o RP con unfolding locale; il segnale da cercare e stabilita della riga a due lettori, non crescita del numero di intermedi classici. +- **Se fallisce**: se lambda 0.060 sparisce con N/reps maggiori, il gate fisico diventa solo scaffold; se emergono graph-only stabili, il residuo Lab rientra come candidato da isolare. + +## Experiment Design +- **Script**: `tools/exp_rosenzweig_porter_bridge_physical_audit.py`. +- **Run**: `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json`. +- **Hamiltoniana**: `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`, `N=96`, `reps=24`, central fraction 0.6. +- **Denominatore**: 13 righe lambda: 0, 0.01, 0.03, 0.06, 0.10, 0.18, 0.32, 0.50, 0.68, 0.82, 0.90, 0.97, 1.0. +- **Perturbazione grafo**: seed={202605151933,202605151934,202605151935}, k={2,3,4}, 9 letture. +- **Contratto osservabile-operatore**: il ciclo testa trasferimento del gate composito su un flusso fisico; non testa limite asintotico, unfolding dedicato o dati sperimentali. + +## Results +| summary | value | +|---|---:| +| rows analyzed | 13 | +| graph reader runs | 9 | +| two_reader_boundary_confirmed | 1 | +| graph_only_residue | 0 | +| classic_only_residue | 11 | + +| composite state | count | +|---|---:| +| stable_graph_bridge+classical_intermediate | 1 | +| parameter_sensitive_bridge+classical_intermediate | 1 | +| unstable_non_bridge+classical_intermediate | 10 | +| unstable_non_bridge+classical_poisson_endpoint | 1 | + +| row | graph frequency | classical state | Brody q | Wigner/Poisson weight | SR | +|---|---:|---|---:|---:|---:| +| RP_lambda_0.000 | 0.000 | classical_poisson_endpoint | 0.000 | 0.000 | 0.383 | +| RP_lambda_0.030 | 0.000 | classical_intermediate | 0.427 | 0.300 | 0.510 | +| RP_lambda_0.060 | 1.000 | classical_intermediate | 0.540 | 0.373 | 0.528 | +| RP_lambda_0.100 | 0.667 | classical_intermediate | 0.653 | 0.420 | 0.524 | +| RP_lambda_0.180 | 0.222 | classical_intermediate | 0.813 | 0.460 | 0.534 | +| RP_lambda_0.500 | 0.000 | classical_intermediate | 0.900 | 0.493 | 0.535 | +| RP_lambda_1.000 | 0.000 | classical_intermediate | 0.980 | 0.507 | 0.534 | + +## Key Findings +1. Verificato: `RP_lambda_0.060` e l'unica riga `stable_graph_bridge+classical_intermediate`, 9/9 letture grafiche. +2. Verificato: `RP_lambda_0.100` e ponte parametrico, 6/9 letture; non entra nel boundary confermato. +3. Verificato: `graph_only_residue = 0`. I tre residui graph-only del perimetro Lab 19:15 non trasferiscono come residui autonomi nel flusso RP. +4. Verificato: il lettore classico e largo: marca 11/13 righe come `classical_intermediate`. Questa fascia e baseline di crossover o discordanza del lettore scalare, non finding Lab. +5. Inferito dal perimetro: il nodo regressivo corregge il contratto da "ponte grafico stabile" a "riga fisica a due lettori"; il grafo da solo non basta. + +## Verdict +CONSTRAINT + +Il boundary fisico esiste nel perimetro RP finito come una riga a due lettori: `lambda=0.060`. Il residuo graph-only non sopravvive. La parte nuova del ciclo non e una scoperta RP, ma il vincolo operativo: il gate Lab-specific deve perdere autorita quando un flusso Hamiltoniano controllato lo assorbe nel crossover classico. + +## Bicono della scoperta +- **Due radici**: crossover classico scalare; ponte grafico stabile. +- **Singolare**: lambda row-aligned prima della promozione a boundary. +- **Invariante di passaggio**: concordanza `stable_graph_bridge + classical_intermediate`. +- **Campo di possibilita**: audit fisico finite-size su RP/Anderson con due lettori. +- **Campo non-possibile**: residuo graph-only come legge del confine in assenza di sopravvivenza su Hamiltoniana controllata. + +## Consecutio +Il prossimo ciclo utile non deve aumentare il numero di metriche. Deve stressare la riga `RP_lambda_0.060` su taglie/repliche o portare lo stesso contratto su Anderson 3D multi-size. Il criterio e semplice: se la riga a due lettori resta, il gate diventa strumento fisico finite-size; se cade, BOUNDARY torna a scaffold di classificazione. + +## Ricadute pratiche +ssp_value: yes. Lo script e riusabile come audit fisico two-reader per flussi Hamiltoniani controllati e separa automaticamente conferma a due lettori, graph-only residue e classic-only residue. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva BOUNDARY ha prevalso per aderenza al campo. +- `python -m py_compile tools/exp_rosenzweig_porter_bridge_physical_audit.py` completato. +- `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` completato. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_rosenzweig_porter_bridge_physical_audit.py` +- Data: `tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` +- Report: `tools/data/reports/agent_20260515_1933.md` diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc "rg -n \"lab_falsifier.py\" tools/data/reports/agent_20260515_*.md tools/lab_agent.sh tools -g'*.py' | head -30" in /opt/MM_D-ND succeeded in 0ms: tools/lab_agent.sh:837: python3 "$TOOLS_DIR/lab_falsifier.py" \ tools/build_agent_field.py:1629: # Il counter-pole (lab_falsifier.py) ha gia' processato questi claim: tools/build_agent_field.py:1782: "Il falsifier (lab_falsifier.py) applichera' queste lenti al tuo report " tools/lab_falsifier.py:3:lab_falsifier.py — Counter-pole gate per il lab fisica. tools/lab_falsifier.py:26: python3 lab_falsifier.py --report PATH --output PATH tools/lab_falsifier.py:209: # Import lazy: lib_llm_chain è in tools/, stesso dir di lab_falsifier.py exec /bin/bash -lc 'python tools/lab_falsifier.py --help' in /opt/MM_D-ND succeeded in 0ms: usage: lab_falsifier.py [-h] --report REPORT --output OUTPUT [--timeout TIMEOUT] Counter-pole gate for the lab options: -h, --help show this help message and exit --report REPORT Path to agent_TS.md --output OUTPUT Path for falsifier_TS.json output --timeout TIMEOUT LLM call timeout seconds diff --git a/tools/data/reports/agent_20260515_1933.md b/tools/data/reports/agent_20260515_1933.md new file mode 100644 index 0000000000000000000000000000000000000000..25e38647e47cf44318bec659ad2646f65240a710 --- /dev/null +++ b/tools/data/reports/agent_20260515_1933.md @@ -0,0 +1,125 @@ +# Agent Report - Rosenzweig-Porter Physical Bridge Audit +**Date**: 2026-05-15 19:33 +**Piano**: 121 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - il gate a due lettori trasferisce sul flusso Rosenzweig-Porter solo in una riga stabile; il residuo Lab-specific graph-only cade a zero nel perimetro fisico controllato. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, graph_bridge_frequency, centroid_margin, cross_neighbor_fraction, classical_audit_state] +**observable_contract**: claim=il BOUNDARY a due lettori trasferisce a un crossover fisico controllato solo dove stabilita grafica e intermediacy classica concordano sulla stessa riga lambda; observable=graph_bridge_frequency unito a Brody q, peso Wigner/Poisson, SR e IPR; operator=flusso Hamiltoniano Rosenzweig-Porter diagonal-plus-GUE con perturbazione kNN; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=13 righe lambda x 3 seed x k={2,3,4}; non_possible=claim Lab-specific graph-only se ogni ponte grafico stabile e anche intermedio classico, oppure claim fisico se dominano righe classic-only; not_tested=universalita asintotica RP, unfolding alternativi, spettri sperimentali, many-body localization. + +## Prima impressione +Il confine fisico non eredita i tre residui graph-only del perimetro Lab. Quando il sistema ha un parametro Hamiltoniano vero, il grafo trova un solo punto-zero stretto e il lettore classico vede una fascia piu larga. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: polo diagonale Poisson / polo GUE. Punto-zero: riga lambda in cui il flusso e tra i due poli senza essere endpoint. +- **Piano superiore**: geometria del campo Hamiltoniano; il parametro lambda e il grafo non decidono separatamente, devono convergere sulla stessa riga. +- **Proto-ipotesi**: il residuo graph-only del perimetro composito Lab non e una legge del boundary; in un flusso fisico controllato sopravvive solo se resta graph bridge senza essere gia spiegato dal crossover classico. +- **Possibile/non-possibile**: possibile = usare `graph_bridge_frequency + classical_audit_state` come audit fisico finite-size; non-possibile = promuovere graph-only bridge senza Hamiltoniana controllata o sommare classic-only e graph-only. +- **Proiezione**: 13 lambda Rosenzweig-Porter, tre seed, kNN k=2/3/4, Brody/Berry-like e grafo sulle stesse righe. + +### Contaminazione cognitiva +- **CE-0019 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0019 - Respiro fuori-tempo`. Enzima usato: combo prima della misura; impedisce di ripetere il deposito 13 righe e forza il rientro in un flusso fisico. +- **CE-0022 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0022 - Palette operatoria espansa del Lab`. Operatori scelti: spettro Hamiltoniano, grafo, controllo/null; scartati operatori che producevano solo analogia. +- **YSN DeltaLink**: `residuo graph-only Lab / flusso Hamiltoniano controllato`. +- **Cornelius gene**: `RP_Two_Reader_Audit`: GENERA lambda, MISURA classico, COSTRUISCI grafo, STRESSA k/seed, SEPARA residui. +- **KSAR step**: reiterazione del kernel 19:15 su un dominio fisico nuovo; nessuna promozione del residuo prima del test. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento porta il perimetro vivo GUE/Poisson su un sistema Rosenzweig-Porter controllato e testa il confine come terzo incluso operativo con due lettori. +- `not_drift`: non usa phi/Sturmian, V_c o il report 18:26 bloccato; usa la consecutio 19:15 solo come ponte verso Hamiltoniana fisica row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. +- **Cosa viene assorbito dal baseline**: la riga `RP_lambda_0.060` e classica e grafica insieme; non e scoperta fisica nuova, e il punto finito in cui i due lettori concordano. +- **Cosa resta Lab-specific**: nessun `stable_graph_bridge+endpoint` resta. `graph_only_residue = 0`. +- **Cosa resta artifact/classificazione grafica**: `RP_lambda_0.100` e ponte parametrico, non stabile; dipende da k e seed. +- **Cosa resta classic-only**: 11 righe sono intermedie per il lettore classico senza ponte grafico stabile. Questo e crossover scalare o discordanza del lettore Berry-like, non terzo incluso operativo. +- **Correzione L3/L5 richiesta**: `two_reader_boundary_confirmed = 1`; `graph_only_residue = 0`; `scope_change_declared = true`; `graph_baseline_audit = kNN stability + Brody/Berry-like row-aligned`. Non sommo le 11 righe classic-only al boundary a due lettori. + +## Claim Under Test +> Nel flusso Rosenzweig-Porter controllato, il BOUNDARY a due lettori sopravvive solo dove una riga lambda e insieme ponte grafico stabile e intermedia classica. + +## Question +Il residuo graph-only del perimetro Lab sopravvive fuori dal deposito composito, oppure il crossover fisico lo assorbe? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale tra indipendenza/localizzazione Poisson e repulsione GUE. +- **Attraversamento matematico**: Hamiltoniana diagonal-plus-GUE, osservabili canonici sui gap, Brody/Berry-like e grafo kNN perturbato. +- **Punto fisico di ritorno**: il gate a due lettori diventa un audit finite-size del punto di crossover, non un claim graph-only autonomo. +- **Osservabile/test fisico possibile**: ripetere su Anderson 3D multi-size o RP con unfolding locale; il segnale da cercare e stabilita della riga a due lettori, non crescita del numero di intermedi classici. +- **Se fallisce**: se lambda 0.060 sparisce con N/reps maggiori, il gate fisico diventa solo scaffold; se emergono graph-only stabili, il residuo Lab rientra come candidato da isolare. + +## Experiment Design +- **Script**: `tools/exp_rosenzweig_porter_bridge_physical_audit.py`. +- **Run**: `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json`. +- **Hamiltoniana**: `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`, `N=96`, `reps=24`, central fraction 0.6. +- **Denominatore**: 13 righe lambda: 0, 0.01, 0.03, 0.06, 0.10, 0.18, 0.32, 0.50, 0.68, 0.82, 0.90, 0.97, 1.0. +- **Perturbazione grafo**: seed={202605151933,202605151934,202605151935}, k={2,3,4}, 9 letture. +- **Contratto osservabile-operatore**: il ciclo testa trasferimento del gate composito su un flusso fisico; non testa limite asintotico, unfolding dedicato o dati sperimentali. + +## Results +| summary | value | +|---|---:| +| rows analyzed | 13 | +| graph reader runs | 9 | +| two_reader_boundary_confirmed | 1 | +| graph_only_residue | 0 | +| classic_only_residue | 11 | + +| composite state | count | +|---|---:| +| stable_graph_bridge+classical_intermediate | 1 | +| parameter_sensitive_bridge+classical_intermediate | 1 | +| unstable_non_bridge+classical_intermediate | 10 | +| unstable_non_bridge+classical_poisson_endpoint | 1 | + +| row | graph frequency | classical state | Brody q | Wigner/Poisson weight | SR | +|---|---:|---|---:|---:|---:| +| RP_lambda_0.000 | 0.000 | classical_poisson_endpoint | 0.000 | 0.000 | 0.383 | +| RP_lambda_0.030 | 0.000 | classical_intermediate | 0.427 | 0.300 | 0.510 | +| RP_lambda_0.060 | 1.000 | classical_intermediate | 0.540 | 0.373 | 0.528 | +| RP_lambda_0.100 | 0.667 | classical_intermediate | 0.653 | 0.420 | 0.524 | +| RP_lambda_0.180 | 0.222 | classical_intermediate | 0.813 | 0.460 | 0.534 | +| RP_lambda_0.500 | 0.000 | classical_intermediate | 0.900 | 0.493 | 0.535 | +| RP_lambda_1.000 | 0.000 | classical_intermediate | 0.980 | 0.507 | 0.534 | + +## Key Findings +1. Verificato: `RP_lambda_0.060` e l'unica riga `stable_graph_bridge+classical_intermediate`, 9/9 letture grafiche. +2. Verificato: `RP_lambda_0.100` e ponte parametrico, 6/9 letture; non entra nel boundary confermato. +3. Verificato: `graph_only_residue = 0`. I tre residui graph-only del perimetro Lab 19:15 non trasferiscono come residui autonomi nel flusso RP. +4. Verificato: il lettore classico e largo: marca 11/13 righe come `classical_intermediate`. Questa fascia e baseline di crossover o discordanza del lettore scalare, non finding Lab. +5. Inferito dal perimetro: il nodo regressivo corregge il contratto da "ponte grafico stabile" a "riga fisica a due lettori"; il grafo da solo non basta. + +## Verdict +CONSTRAINT + +Il boundary fisico esiste nel perimetro RP finito come una riga a due lettori: `lambda=0.060`. Il residuo graph-only non sopravvive. La parte nuova del ciclo non e una scoperta RP, ma il vincolo operativo: il gate Lab-specific deve perdere autorita quando un flusso Hamiltoniano controllato lo assorbe nel crossover classico. + +## Bicono della scoperta +- **Due radici**: crossover classico scalare; ponte grafico stabile. +- **Singolare**: lambda row-aligned prima della promozione a boundary. +- **Invariante di passaggio**: concordanza `stable_graph_bridge + classical_intermediate`. +- **Campo di possibilita**: audit fisico finite-size su RP/Anderson con due lettori. +- **Campo non-possibile**: residuo graph-only come legge del confine in assenza di sopravvivenza su Hamiltoniana controllata. + +## Consecutio +Il prossimo ciclo utile non deve aumentare il numero di metriche. Deve stressare la riga `RP_lambda_0.060` su taglie/repliche o portare lo stesso contratto su Anderson 3D multi-size. Il criterio e semplice: se la riga a due lettori resta, il gate diventa strumento fisico finite-size; se cade, BOUNDARY torna a scaffold di classificazione. + +## Ricadute pratiche +ssp_value: yes. Lo script e riusabile come audit fisico two-reader per flussi Hamiltoniani controllati e separa automaticamente conferma a due lettori, graph-only residue e classic-only residue. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva BOUNDARY ha prevalso per aderenza al campo. +- `python -m py_compile tools/exp_rosenzweig_porter_bridge_physical_audit.py` completato. +- `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` completato. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_rosenzweig_porter_bridge_physical_audit.py` +- Data: `tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` +- Report: `tools/data/reports/agent_20260515_1933.md` diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Eseguito il cycle e scritto il report richiesto: [tools/data/reports/agent_20260515_1933.md](/opt/MM_D-ND/tools/data/reports/agent_20260515_1933.md) Ho aggiunto anche lo script riusabile e l’artefatto dati: [tools/exp_rosenzweig_porter_bridge_physical_audit.py](/opt/MM_D-ND/tools/exp_rosenzweig_porter_bridge_physical_audit.py) [tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json](/opt/MM_D-ND/tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json) Risultato centrale: sul flusso Rosenzweig-Porter controllato il boundary a due lettori sopravvive solo su `RP_lambda_0.060`; `graph_only_residue = 0`, quindi i residui graph-only del perimetro Lab non trasferiscono come claim fisico autonomo. Verifiche eseguite: `py_compile`, run esperimento completo, controllo sezioni obbligatorie del report. diff --git a/tools/data/reports/agent_20260515_1933.md b/tools/data/reports/agent_20260515_1933.md new file mode 100644 index 0000000000000000000000000000000000000000..25e38647e47cf44318bec659ad2646f65240a710 --- /dev/null +++ b/tools/data/reports/agent_20260515_1933.md @@ -0,0 +1,125 @@ +# Agent Report - Rosenzweig-Porter Physical Bridge Audit +**Date**: 2026-05-15 19:33 +**Piano**: 121 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - il gate a due lettori trasferisce sul flusso Rosenzweig-Porter solo in una riga stabile; il residuo Lab-specific graph-only cade a zero nel perimetro fisico controllato. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, graph_bridge_frequency, centroid_margin, cross_neighbor_fraction, classical_audit_state] +**observable_contract**: claim=il BOUNDARY a due lettori trasferisce a un crossover fisico controllato solo dove stabilita grafica e intermediacy classica concordano sulla stessa riga lambda; observable=graph_bridge_frequency unito a Brody q, peso Wigner/Poisson, SR e IPR; operator=flusso Hamiltoniano Rosenzweig-Porter diagonal-plus-GUE con perturbazione kNN; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=13 righe lambda x 3 seed x k={2,3,4}; non_possible=claim Lab-specific graph-only se ogni ponte grafico stabile e anche intermedio classico, oppure claim fisico se dominano righe classic-only; not_tested=universalita asintotica RP, unfolding alternativi, spettri sperimentali, many-body localization. + +## Prima impressione +Il confine fisico non eredita i tre residui graph-only del perimetro Lab. Quando il sistema ha un parametro Hamiltoniano vero, il grafo trova un solo punto-zero stretto e il lettore classico vede una fascia piu larga. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: polo diagonale Poisson / polo GUE. Punto-zero: riga lambda in cui il flusso e tra i due poli senza essere endpoint. +- **Piano superiore**: geometria del campo Hamiltoniano; il parametro lambda e il grafo non decidono separatamente, devono convergere sulla stessa riga. +- **Proto-ipotesi**: il residuo graph-only del perimetro composito Lab non e una legge del boundary; in un flusso fisico controllato sopravvive solo se resta graph bridge senza essere gia spiegato dal crossover classico. +- **Possibile/non-possibile**: possibile = usare `graph_bridge_frequency + classical_audit_state` come audit fisico finite-size; non-possibile = promuovere graph-only bridge senza Hamiltoniana controllata o sommare classic-only e graph-only. +- **Proiezione**: 13 lambda Rosenzweig-Porter, tre seed, kNN k=2/3/4, Brody/Berry-like e grafo sulle stesse righe. + +### Contaminazione cognitiva +- **CE-0019 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0019 - Respiro fuori-tempo`. Enzima usato: combo prima della misura; impedisce di ripetere il deposito 13 righe e forza il rientro in un flusso fisico. +- **CE-0022 metabolizzata**: `tools/data/cognitive_enzymes_archive.md`, voce `CE-0022 - Palette operatoria espansa del Lab`. Operatori scelti: spettro Hamiltoniano, grafo, controllo/null; scartati operatori che producevano solo analogia. +- **YSN DeltaLink**: `residuo graph-only Lab / flusso Hamiltoniano controllato`. +- **Cornelius gene**: `RP_Two_Reader_Audit`: GENERA lambda, MISURA classico, COSTRUISCI grafo, STRESSA k/seed, SEPARA residui. +- **KSAR step**: reiterazione del kernel 19:15 su un dominio fisico nuovo; nessuna promozione del residuo prima del test. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento porta il perimetro vivo GUE/Poisson su un sistema Rosenzweig-Porter controllato e testa il confine come terzo incluso operativo con due lettori. +- `not_drift`: non usa phi/Sturmian, V_c o il report 18:26 bloccato; usa la consecutio 19:15 solo come ponte verso Hamiltoniana fisica row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. +- **Cosa viene assorbito dal baseline**: la riga `RP_lambda_0.060` e classica e grafica insieme; non e scoperta fisica nuova, e il punto finito in cui i due lettori concordano. +- **Cosa resta Lab-specific**: nessun `stable_graph_bridge+endpoint` resta. `graph_only_residue = 0`. +- **Cosa resta artifact/classificazione grafica**: `RP_lambda_0.100` e ponte parametrico, non stabile; dipende da k e seed. +- **Cosa resta classic-only**: 11 righe sono intermedie per il lettore classico senza ponte grafico stabile. Questo e crossover scalare o discordanza del lettore Berry-like, non terzo incluso operativo. +- **Correzione L3/L5 richiesta**: `two_reader_boundary_confirmed = 1`; `graph_only_residue = 0`; `scope_change_declared = true`; `graph_baseline_audit = kNN stability + Brody/Berry-like row-aligned`. Non sommo le 11 righe classic-only al boundary a due lettori. + +## Claim Under Test +> Nel flusso Rosenzweig-Porter controllato, il BOUNDARY a due lettori sopravvive solo dove una riga lambda e insieme ponte grafico stabile e intermedia classica. + +## Question +Il residuo graph-only del perimetro Lab sopravvive fuori dal deposito composito, oppure il crossover fisico lo assorbe? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale tra indipendenza/localizzazione Poisson e repulsione GUE. +- **Attraversamento matematico**: Hamiltoniana diagonal-plus-GUE, osservabili canonici sui gap, Brody/Berry-like e grafo kNN perturbato. +- **Punto fisico di ritorno**: il gate a due lettori diventa un audit finite-size del punto di crossover, non un claim graph-only autonomo. +- **Osservabile/test fisico possibile**: ripetere su Anderson 3D multi-size o RP con unfolding locale; il segnale da cercare e stabilita della riga a due lettori, non crescita del numero di intermedi classici. +- **Se fallisce**: se lambda 0.060 sparisce con N/reps maggiori, il gate fisico diventa solo scaffold; se emergono graph-only stabili, il residuo Lab rientra come candidato da isolare. + +## Experiment Design +- **Script**: `tools/exp_rosenzweig_porter_bridge_physical_audit.py`. +- **Run**: `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json`. +- **Hamiltoniana**: `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`, `N=96`, `reps=24`, central fraction 0.6. +- **Denominatore**: 13 righe lambda: 0, 0.01, 0.03, 0.06, 0.10, 0.18, 0.32, 0.50, 0.68, 0.82, 0.90, 0.97, 1.0. +- **Perturbazione grafo**: seed={202605151933,202605151934,202605151935}, k={2,3,4}, 9 letture. +- **Contratto osservabile-operatore**: il ciclo testa trasferimento del gate composito su un flusso fisico; non testa limite asintotico, unfolding dedicato o dati sperimentali. + +## Results +| summary | value | +|---|---:| +| rows analyzed | 13 | +| graph reader runs | 9 | +| two_reader_boundary_confirmed | 1 | +| graph_only_residue | 0 | +| classic_only_residue | 11 | + +| composite state | count | +|---|---:| +| stable_graph_bridge+classical_intermediate | 1 | +| parameter_sensitive_bridge+classical_intermediate | 1 | +| unstable_non_bridge+classical_intermediate | 10 | +| unstable_non_bridge+classical_poisson_endpoint | 1 | + +| row | graph frequency | classical state | Brody q | Wigner/Poisson weight | SR | +|---|---:|---|---:|---:|---:| +| RP_lambda_0.000 | 0.000 | classical_poisson_endpoint | 0.000 | 0.000 | 0.383 | +| RP_lambda_0.030 | 0.000 | classical_intermediate | 0.427 | 0.300 | 0.510 | +| RP_lambda_0.060 | 1.000 | classical_intermediate | 0.540 | 0.373 | 0.528 | +| RP_lambda_0.100 | 0.667 | classical_intermediate | 0.653 | 0.420 | 0.524 | +| RP_lambda_0.180 | 0.222 | classical_intermediate | 0.813 | 0.460 | 0.534 | +| RP_lambda_0.500 | 0.000 | classical_intermediate | 0.900 | 0.493 | 0.535 | +| RP_lambda_1.000 | 0.000 | classical_intermediate | 0.980 | 0.507 | 0.534 | + +## Key Findings +1. Verificato: `RP_lambda_0.060` e l'unica riga `stable_graph_bridge+classical_intermediate`, 9/9 letture grafiche. +2. Verificato: `RP_lambda_0.100` e ponte parametrico, 6/9 letture; non entra nel boundary confermato. +3. Verificato: `graph_only_residue = 0`. I tre residui graph-only del perimetro Lab 19:15 non trasferiscono come residui autonomi nel flusso RP. +4. Verificato: il lettore classico e largo: marca 11/13 righe come `classical_intermediate`. Questa fascia e baseline di crossover o discordanza del lettore scalare, non finding Lab. +5. Inferito dal perimetro: il nodo regressivo corregge il contratto da "ponte grafico stabile" a "riga fisica a due lettori"; il grafo da solo non basta. + +## Verdict +CONSTRAINT + +Il boundary fisico esiste nel perimetro RP finito come una riga a due lettori: `lambda=0.060`. Il residuo graph-only non sopravvive. La parte nuova del ciclo non e una scoperta RP, ma il vincolo operativo: il gate Lab-specific deve perdere autorita quando un flusso Hamiltoniano controllato lo assorbe nel crossover classico. + +## Bicono della scoperta +- **Due radici**: crossover classico scalare; ponte grafico stabile. +- **Singolare**: lambda row-aligned prima della promozione a boundary. +- **Invariante di passaggio**: concordanza `stable_graph_bridge + classical_intermediate`. +- **Campo di possibilita**: audit fisico finite-size su RP/Anderson con due lettori. +- **Campo non-possibile**: residuo graph-only come legge del confine in assenza di sopravvivenza su Hamiltoniana controllata. + +## Consecutio +Il prossimo ciclo utile non deve aumentare il numero di metriche. Deve stressare la riga `RP_lambda_0.060` su taglie/repliche o portare lo stesso contratto su Anderson 3D multi-size. Il criterio e semplice: se la riga a due lettori resta, il gate diventa strumento fisico finite-size; se cade, BOUNDARY torna a scaffold di classificazione. + +## Ricadute pratiche +ssp_value: yes. Lo script e riusabile come audit fisico two-reader per flussi Hamiltoniani controllati e separa automaticamente conferma a due lettori, graph-only residue e classic-only residue. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva BOUNDARY ha prevalso per aderenza al campo. +- `python -m py_compile tools/exp_rosenzweig_porter_bridge_physical_audit.py` completato. +- `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` completato. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_rosenzweig_porter_bridge_physical_audit.py` +- Data: `tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json` +- Report: `tools/data/reports/agent_20260515_1933.md` diff --git a/tools/exp_rosenzweig_porter_bridge_physical_audit.py b/tools/exp_rosenzweig_porter_bridge_physical_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..34736f3640719ff2c013998e747ba6a80dda5dd7 --- /dev/null +++ b/tools/exp_rosenzweig_porter_bridge_physical_audit.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +""" +Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. + +The script projects the two-reader BOUNDARY gate onto a controlled +diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical +reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks +whether the same rows sit between endpoint poles under small k/seed +perturbations. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] + + +def parse_floats(raw: str) -> list[float]: + values = [float(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty float list") + return values + + +def parse_ints(raw: str) -> list[int]: + values = [int(part.strip()) for part in raw.split(",") if part.strip()] + if not values: + raise ValueError("empty integer list") + return values + + +def normalize_spacings(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + raise ValueError("no positive finite spacings") + return gaps / float(np.mean(gaps)) + + +def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: + beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) + return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) + + +def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: + best_q = 0.0 + best_nll = float("inf") + for q in np.linspace(0.0, 1.0, grid_size): + pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) + nll = -float(np.sum(np.log(pdf))) + if nll < best_nll: + best_q = float(q) + best_nll = nll + return best_q, best_nll + + +def poisson_cdf(s: np.ndarray) -> np.ndarray: + return 1.0 - np.exp(-s) + + +def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: + a = 4.0 / math.pi + return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) + + +def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: + empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) + return float(np.max(np.abs(empirical - model_cdf))) + + +def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: + sorted_s = np.sort(s) + poi = poisson_cdf(sorted_s) + gue = gue_wigner_cdf(sorted_s) + best_w = 0.0 + best_ks = float("inf") + for w in np.linspace(0.0, 1.0, grid_size): + ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) + if ks < best_ks: + best_w = float(w) + best_ks = ks + return best_w, best_ks + + +def central_slice(n: int, fraction: float) -> slice: + keep = max(8, min(n, int(round(n * fraction)))) + start = (n - keep) // 2 + return slice(start, start + keep) + + +def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: + real = rng.normal(0.0, 1.0, (n, n)) + imag = rng.normal(0.0, 1.0, (n, n)) + z = real + 1j * imag + h = (z + z.conj().T) / (2.0 * math.sqrt(n)) + return h.real + + +def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: + diagonal = np.diag(rng.normal(0.0, 1.0, n)) + gue = gue_matrix(rng, n) + return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue + + +def row_spacings_and_ipr( + lam: float, + n: int, + reps: int, + central_fraction: float, + seed: int, +) -> tuple[np.ndarray, float]: + rng = np.random.default_rng(seed) + spacings = [] + iprs = [] + for _ in range(reps): + h = rp_hamiltonian(rng, n, lam) + levels, vectors = np.linalg.eigh(h) + central = levels[central_slice(len(levels), central_fraction)] + gaps = np.diff(np.sort(central)) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps): + spacings.extend(gaps.tolist()) + probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) + ipr = np.sum(probs * probs, axis=0) + if len(ipr): + iprs.extend(ipr.tolist()) + if not spacings: + raise ValueError(f"lambda {lam} produced no spacings") + return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 + + +def source_type(lam: float, poisson_max: float, gue_min: float) -> str: + if lam <= poisson_max: + return "Poisson_pole" + if lam >= gue_min: + return "GUE_pole" + return "flow_candidate" + + +def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: + gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) + s = normalize_spacings(gaps) + obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} + obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) + brody_q, brody_nll = fit_brody_q(s, args.grid_size) + mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) + return { + "domain_window": f"RP_lambda_{lam:.3f}", + "lambda": round(lam, 6), + "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), + "n_spacings": int(len(s)), + "mean_ipr": round(mean_ipr, 9), + "observables": {key: round(value, 9) for key, value in obs.items()}, + "brody_q": round(brody_q, 6), + "brody_nll": round(brody_nll, 6), + "berry_robnick_like_gue_weight": round(mixture_w, 6), + "mixture_ks": round(mixture_ks, 6), + } + + +def standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: + matrix = [] + for row in rows: + obs = row["observables"] + matrix.append( + [obs[name] for name in OBS_NAMES] + + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] + ) + x = np.asarray(matrix, dtype=float) + center = np.mean(x, axis=0) + scale = np.std(x, axis=0, ddof=1) + scale[scale <= 1e-15] = 1.0 + return (x - center) / scale + + +def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: + distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) + edges: set[tuple[int, int]] = set() + for i in range(len(x)): + for j in np.argsort(distances[i])[1 : k + 1]: + edges.add((min(i, int(j)), max(i, int(j)))) + return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] + + +def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: + x = standardized_matrix(rows) + labels = [row["source_domain_type"] for row in rows] + poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] + gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] + if not poi_idx or not gue_idx: + raise ValueError("lambda grid must include Poisson and GUE poles") + c_poi = np.mean(x[poi_idx], axis=0) + c_gue = np.mean(x[gue_idx], axis=0) + edges = build_knn_edges(x, k) + degree = {i: 0 for i in range(len(rows))} + for i, j, _ in edges: + degree[i] += 1 + degree[j] += 1 + + graph_rows = [] + for i, row in enumerate(rows): + d_poi = float(np.linalg.norm(x[i] - c_poi)) + d_gue = float(np.linalg.norm(x[i] - c_gue)) + denom = d_poi + d_gue + margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 + incident = [(a, b) for a, b, _ in edges if a == i or b == i] + cross = 0 + for a, b in incident: + other = b if a == i else a + if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: + cross += 1 + elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: + cross += 1 + cross_fraction = float(cross / len(incident)) if incident else 0.0 + state = "class_interior" + if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: + state = "third_included_candidate" + elif cross_fraction > 0: + state = "cut_edge" + graph_rows.append( + { + "domain_window": row["domain_window"], + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "centroid_margin": round(margin, 6), + "cross_neighbor_fraction": round(cross_fraction, 6), + "degree": degree[i], + "boundary_state": state, + } + ) + return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} + + +def classical_state(row: dict[str, Any]) -> str: + q = float(row["brody_q"]) + w = float(row["berry_robnick_like_gue_weight"]) + if q <= 0.25 and w <= 0.25: + return "classical_poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "classical_gue_endpoint" + return "classical_intermediate" + + +def stability_state(freq: float) -> str: + if freq >= 0.75: + return "stable_graph_bridge" + if freq >= 0.25: + return "parameter_sensitive_bridge" + return "unstable_non_bridge" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + lambdas = parse_floats(args.lambdas) + seeds = parse_ints(args.seeds) + ks = parse_ints(args.k_values) + total_runs = len(seeds) * len(ks) + row_hits: dict[str, dict[str, Any]] = {} + reader_runs = [] + seed_rows: dict[int, list[dict[str, Any]]] = {} + + for seed in seeds: + rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] + seed_rows[seed] = rows + for k in ks: + graph = classify_graph(rows, k) + reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) + graph_by_name = {row["domain_window"]: row for row in graph["rows"]} + for row in rows: + name = row["domain_window"] + if name not in row_hits: + row_hits[name] = { + "lambda": row["lambda"], + "source_domain_type": row["source_domain_type"], + "graph_hits": 0, + "margins": [], + "cross_fractions": [], + "brody_q": [], + "mixture_w": [], + "mean_ipr": [], + "sr": [], + } + grow = graph_by_name[name] + if grow["boundary_state"] == "third_included_candidate": + row_hits[name]["graph_hits"] += 1 + row_hits[name]["margins"].append(float(grow["centroid_margin"])) + row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) + row_hits[name]["brody_q"].append(float(row["brody_q"])) + row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) + row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) + row_hits[name]["sr"].append(float(row["observables"]["SR"])) + + rows_out = [] + counts: dict[str, int] = {} + for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): + item = row_hits[name] + freq = item["graph_hits"] / total_runs + class_row = { + "brody_q": float(np.median(item["brody_q"])), + "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), + } + c_state = classical_state(class_row) + g_state = stability_state(freq) + composite = f"{g_state}+{c_state}" + counts[composite] = counts.get(composite, 0) + 1 + rows_out.append( + { + "domain_window": name, + "lambda": item["lambda"], + "source_domain_type": item["source_domain_type"], + "graph_bridge_frequency": round(freq, 6), + "stability_state": g_state, + "classical_audit_state": c_state, + "composite_state": composite, + "median_brody_q": round(float(np.median(item["brody_q"])), 6), + "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), + "median_SR": round(float(np.median(item["sr"])), 6), + "median_mean_ipr": round(float(np.median(item["mean_ipr"])), 9), + "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), + "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), + } + ) + + two_reader_confirmed = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + graph_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" + ] + classic_only_residue = [ + row["domain_window"] + for row in rows_out + if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" + ] + + output = { + "experiment": "rosenzweig_porter_bridge_physical_audit", + "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": FEATURE_NAMES + + [ + "graph_bridge_frequency", + "centroid_margin", + "cross_neighbor_fraction", + "classical_audit_state", + ], + "parameters": { + "n": args.n, + "reps": args.reps, + "lambdas": lambdas, + "seeds": seeds, + "k_values": ks, + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + "poisson_pole_max": args.poisson_pole_max, + "gue_pole_min": args.gue_pole_min, + "total_graph_reader_runs": total_runs, + }, + "observable_contract": { + "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", + "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", + "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", + "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", + "denominator": "13 lambda rows, repeated across graph k and random seeds", + "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", + "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", + }, + "summary": { + "rows_analyzed": len(rows_out), + "two_reader_boundary_confirmed": len(two_reader_confirmed), + "two_reader_rows": two_reader_confirmed, + "graph_only_residue": len(graph_only_residue), + "graph_only_rows": graph_only_residue, + "classic_only_residue": len(classic_only_residue), + "classic_only_rows": classic_only_residue, + "composite_counts": counts, + }, + "rows": rows_out, + "reader_runs": reader_runs, + "seed_rows": seed_rows, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(output["summary"], indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") + parser.add_argument("--n", type=int, default=96) + parser.add_argument("--reps", type=int, default=24) + parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") + parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") + parser.add_argument("--k-values", default="2,3,4") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--poisson-pole-max", type=float, default=0.03) + parser.add_argument("--gue-pole-min", type=float, default=0.82) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() tokens used 123,763 Eseguito il cycle e scritto il report richiesto: [tools/data/reports/agent_20260515_1933.md](/opt/MM_D-ND/tools/data/reports/agent_20260515_1933.md) Ho aggiunto anche lo script riusabile e l’artefatto dati: [tools/exp_rosenzweig_porter_bridge_physical_audit.py](/opt/MM_D-ND/tools/exp_rosenzweig_porter_bridge_physical_audit.py) [tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json](/opt/MM_D-ND/tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json) Risultato centrale: sul flusso Rosenzweig-Porter controllato il boundary a due lettori sopravvive solo su `RP_lambda_0.060`; `graph_only_residue = 0`, quindi i residui graph-only del perimetro Lab non trasferiscono come claim fisico autonomo. Verifiche eseguite: `py_compile`, run esperimento completo, controllo sezioni obbligatorie del report.