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: 019e3096-ebe9-79f1-bc04-06f7a50ca16e -------- 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_20260516_1140.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 prima il campo vivo e il contesto operativo, poi scelgo il perimetro minimo dell’esperimento e scrivo un report verificabile nel percorso richiesto. Farò anche il controllo rapido sulle variabili API a pagamento prima di eseguire qualunque cosa. 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. Dinamica di movimento: `fisico A -> matematica -> fisico B`. Non e' una rotta prescritta e non sceglie il dominio al posto del campo. Serve a ricordare che una dualita' osservata deve manifestarsi, formalizzarsi e poi tentare un rimbalzo o un limite in un altro fenomeno, teoria, setup, misura o vincolo empirico. Se il punto B non emerge, registra vincolo/strumento/domanda; non promuovere come avanzamento fisico. ## 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. ## 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: 20260516_1124 - L2: Il null endpoint-preserving e' piu' restrittivo del feature-scramble pieno: `26/512` trial arrivano al conteggio osservato compatto, contro `112/128` del null pieno sul perimetro 11:17. Check richiesto: Rieseguire entrambi i null sullo stesso perimetro e con lo stesso N, oppure riportare unita' comparabili: conteggio atteso/null distribution sul medesimo observable, intervalli binomiali, e differenza di p stimata a parita' di lettore. Per L2, non chiamare `sopravvive`, `residuo` o `strutturale` un lift piccolo senza count grezzi, denominatore, soglia preregistrata o p-value/permutation interval dichiarato. 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. 7. Movimento A->M->B: se parti da fisica/scienza, nomina fisico A, struttura matematica M e fisico B; se B non emerge, dichiara il limite come vincolo/domanda invece di forzare un ponte. Se non riesci a compilare questi 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, G=9, T=7, E=4, R=4 **Generatrici/strade dense**: - disc_5: 3 ghost · Metrica primi g=(p/2)², curvatura GUE r=0.503 - report_20260516_1135: 3 ghost · Agent Report - Anderson Comparable Null Audit - report_20260516_1104: 2 ghost · Agent Report - Endpoint-Gated RP Boundary **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: 20260516_1135 NEXT_CYCLE/high - Direzione operativa valutatore: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - Perche': Il ciclo ha chiuso il nodo regressivo del falsifier: i null sono ora confrontabili a observable, perimetro e N fissati, e il risultato resta vincolo senza promuovere Anderson `W=20`. La prossima mossa non deve scavare altro dettaglio locale Anderson, ma usare il gate stabilizzato `null_first -> candidate_name -> physical_return` sul perimetro vivo della direzione corrente, cioe' 8 GUE / 5 Poisson o un secondo dominio fisico comparabile. - Nota: Side-effect log-only: avvio prossimo ciclo nello stesso frame, ma con ritorno al perimetro vivo cross-dominio invece di ulteriore deep-dive Anderson. 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; - random field; - stesso spettro, diversa dinamica. ### 5. Topologia assiomatica e forme globali 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. - **P-value definito prima dei risultati.** Se riporti un p-value da null, permutation, bootstrap o conteggio Monte Carlo, dichiara nel design la formula esatta prima della tabella: `raw_p=k/N`, `add_one_p=(k+1)/(N+1)`, left/right tail, two-sided o altro. Se usi una correzione, riporta anche i count grezzi che la generano. Un p-value senza definizione operativa e' telemetria ambigua, non evidenza. - **Null-first prima del nome candidato.** Quando il ciclo cerca un boundary, terzo incluso, ponte fisico o riga candidata, il null non deve essere solo audit dopo la nominazione. Dichiaralo prima come precondizione del candidato: quale relazione rompe, quali marginali preserva, quale conteggio deve NON ricostruire. Se il null ricostruisce il conteggio osservato, il nome candidato resta etichetta di lavoro o vault, non scoperta. - **Null comparabili o non confrontare.** Due null possono essere confrontati solo se condividono lo stesso observable, denominatore, perimetro, numero di trial o una normalizzazione dichiarata che rende l'unita' comune. Se cambi lettore, compressione, seed, spazio feature, trial count o source rows, il risultato ammesso e' `nulls_not_comparable:`, non "piu' restrittivo" o "piu' permissivo". Prima rendi comparabili i null; poi interpreta. - **Residuo del seme quando restringi il perimetro.** Se la direzione viva nomina un perimetro numerico o semantico piu' ampio (es. `8 GUE / 5 Poisson`) e il ciclo esegue un preflight, filtro endpoint o sotto-perimetro necessario, dichiara in `Aderenza alla direzione` una riga `seed_residue=` e `why_not_drift=`. Il sotto-perimetro puo' essere corretto, ma non deve cancellare il residuo che il seme aveva nominato. - **Counter-perimeter deliberato.** Se scegli consapevolmente un sotto-perimetro o contro-perimetro invece del perimetro vivo del seme, non dichiarare `follows_direction` pieno. Usa `relation: deliberate_counter_perimeter` e compila `why`, `not_drift`, `return_criterion` e `seed_residue`. Il criterio di ritorno deve dire cosa riporta il ciclo al perimetro vivo o cosa chiude il ramo come non-promuovibile. Senza `return_criterion`, il sotto-perimetro e' drift anche se scientificamente sensato. - **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. - **Dinamica fisico A -> matematica -> fisico B.** Il Lab e' il campo delle possibilita' in cui una dualita' osservata si manifesta, viene formalizzata e tenta un rimbalzo altrove. La matematica non e' destinazione ne' ornamento: e' trasduttore fra manifestazioni. Se il ciclo parte da un attrito fisico, deve estrarre una struttura formale e poi chiedere dove quella struttura puo' ri-manifestarsi, cadere o delimitare un non-possibile in un altro fenomeno, teoria, setup, misura o vincolo empirico. Se il punto B non emerge, il ciclo puo' ancora essere utile come vincolo, strumento o domanda, ma non come avanzamento fisico. ## 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 dinamica di movimento, non una direzione prescritta. Prima comprendi dove sei; poi, se il Lab parte da una tensione fisica, usa la matematica per formalizzare e falsificare e chiedi quale manifestazione B rende il ponte, il bordo o il non-possibile osservabile. 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 exec /bin/bash -lc "env | grep -E 'ANTHROPIC_API_KEY|OPENAI_API_KEY' || true" in /opt/MM_D-ND succeeded in 0ms: 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 codex Il campo impone di tornare al perimetro vivo, non a un dettaglio Anderson/Sturmian: 8 domini GUE, 5 Poisson e confine come terzo incluso operativo. Ora leggo i cristalli cross-agent e cerco i dati/script già presenti per costruire un esperimento riusabile, con null comparabili. exec /bin/bash -lc "sed -n '1,220p' shared_memory/README.md" in /opt/MM_D-ND succeeded in 0ms: # shared_memory — Cross-agent rules e cristalli > Cartella condivisa tra agenti che operano in `/opt/MM_D-ND/`: > - **TM3-claude** (Claude Code, sessioni interattive con operatore) > - **codex-lab** (Codex CLI, cycle agent notturno + run manuali) > - **eventuali altri** (TM5, TM7, futuri cycle) > > Contiene regole permanenti, cristalli operativi, scoperte rilevanti che valgono > per più di un agente. **Non** memoria operativa locale (quella resta in > `~/.claude/projects/.../memory/` per TM3, in `~/.codex_lab/memories/` per codex). ## Pattern di scrittura Ogni file: - Filename: `__.md` (es. `feedback_cycletest_2026-05-07.md`, `cristallo_gate_transferability_2026-05-07.md`) - Frontmatter: ```yaml --- type: feedback | cristallo | regola | finding-cross-agent author: TM3 | codex-lab | TM7 | operatore date: YYYY-MM-DD scope: cross-agent | mm-dnd-only | lab-cycle-only --- ``` - Body: conciso. Se è regola → "Why" + "How to apply". Se è cristallo → contesto + insight + how it changes work. ## Pattern di lettura - **TM3 (claude code)**: al boot di sessione su `/opt/MM_D-ND/`, leggi `MM_D-ND/.claude/CLAUDE.md` che adesso punta qui (sezione *Cross-agent rules*). Scansiona `ls -t shared_memory/*.md | head -10` (le più recenti) e integra in contesto. - **codex-lab**: `/opt/MM_D-ND/AGENTS.md` punta qui. Codex legge AGENTS.md ad ogni cycle, AGENTS.md istruisce di leggere shared_memory. - **Cleanup**: file > 90 giorni e non rilevanti vanno archiviati o rimossi (lo fa TM3 quando la cartella supera 50 file). ## Direzione bidirezionale - TM3 → codex: TM3 scrive feedback/cristalli da sessioni operatore qui → codex li vede al cycle dopo via AGENTS.md. - codex → TM3: codex durante un cycle scopre/cristallizza qualcosa di cross-agent → scrive qui → TM3 li vede al boot di sessione successiva. Il flusso non sostituisce le memorie locali — *integra* per ciò che serve a tutti. ## Anti-pattern - Duplicare qui regole che valgono SOLO per TM3 (vanno in `/root/.claude/projects/.../memory/`) - Duplicare qui regole che valgono SOLO per codex-lab cycle (vanno in `/root/.codex_lab/memories/`) - Scrivere note di lavoro temporanee (vanno nel campo vivo, non qui) 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 "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' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-16T11:39:45.910217", "piano": 133, "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 }, exec /bin/bash -lc "rg --files | rg '(agent_20260516_1135|20260516_1135|anderson|gue|poisson|boundary|null|brody|rosenzweig|porter|rmt|level|spacing)'" in /opt/MM_D-ND succeeded in 0ms: applications/soluzioni/20260516_1031_boundary-unfolding-transfer-matrix/finding_index.draft.json applications/soluzioni/20260516_1135_anderson-comparable-null-audit/finding_index.draft.json applications/soluzioni/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve/finding_index.draft.json tools/prime_mod6_null_fairness_audit.py applications/soluzioni/20260510_0330_boundary-residual-beta-absent-audit/finding_index.draft.json applications/soluzioni/20260501_0931_the-gue-poisson-crossover-has-a/finding_index.draft.json applications/soluzioni/20260515_1904_boundary-classical-crossover-audit/finding_index.draft.json applications/soluzioni/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/finding_index.draft.json applications/soluzioni/20260509_1538_boundary-row-aligned-nonexact-audit/finding_index.draft.json applications/soluzioni/20260509_1444_boundary-gate-transfer-matrix/finding_index.draft.json applications/soluzioni/20260516_1117_anderson-3d-two-reader-boundary-null/finding_index.draft.json applications/soluzioni/20260509_1427_blank-pair-null-transfer/finding_index.draft.json applications/soluzioni/20260515_1933_rosenzweig-porter-physical-bridge-audit/finding_index.draft.json tools/data/boundary_two_axis_matrix_20260509_1532.json applications/soluzioni/20260509_1457_vc-unit-boundary-audit/finding_index.draft.json tools/exp_boundary_gue_poisson.py tools/prime_mod6_generative_null_audit.py applications/soluzioni/20260515_1940_rp-boundary-size-stability-audit/finding_index.draft.json tools/exp_rosenzweig_porter_bridge_physical_audit.py tools/exp_rp_boundary_raw_count_null_audit.py tools/exp_anderson3d_comparable_null_audit.py tools/exp_two_channel_boundary.py tools/exp_boundary_unfolding_transfer_matrix.py tools/exp_boundary_graph_curvature_gate.py tools/exp_aubry_boundary_phase_transport_gate.py tools/dnd_gue_test.py tools/exp_prime_vs_mod6_sr_boundary.py tools/exp_vc_unit_boundary_audit.py tools/exp_boundary_graph_residue_threshold_audit.py tools/exp_boundary_classical_crossover_audit.py tools/exp_rp_boundary_size_stability_audit.py tools/exp_markov_memory_by_gue_type.py tools/exp_anderson3d_endpoint_preserving_null.py tools/exp_brody_crossover.py tools/gue_gap_test.py tools/exp_poisson_convergence.py tools/exp_brody_flow.py tools/exp_boundary_graph_null_audit.py tools/exp_boundary_short_denominator_extension.py tools/exp_boundary_transition_taxonomy_13rows.py tools/exp_boundary_mixture_gate.py tools/exp_dR_brody_connection.py tools/exp_aubry_cosine_boundary_counter_gate.py tools/test_gue_poisson_boundary.py applications/soluzioni/20260509_0741_vc-phase-bridge-label-null/finding_index.draft.json tools/exp_boundary_row_aligned_nonexact_audit.py tools/exp_vc_null_regression_gate.py tools/exp_boundary_blank_thin_support_audit.py tools/exp_boundary_denominator_prescan.py tools/exp_boundary_coherence.py tools/exp_anderson3d_mobility_edge_two_reader_audit.py tools/exp_3d_boundary_layers.py tools/exp_boundary_blank_null_audit.py tools/exp_boundary_residual_beta_absent_audit.py tools/exp_semireal_boundary_transfer_gate.py tools/exp_endpoint_gated_rp_boundary.py tools/exp_boundary_shuffle_audit.py tools/exp_boundary_two_axis_matrix.py tools/exp_prime_sr_persistent_boundary.py tools/exp_photonic_boundary_third_included_gate.py tools/exp_vc_nonsturmian_label_null_gate.py tools/exp_boundary_growth.py tools/exp_brody_calibration.py tools/prime_mod6_counter_null_audit.py tools/exp_endpoint_feature_scramble_null.py applications/soluzioni/20260509_1437_residual-boundary-closure/finding_index.draft.json tools/exp_boundary_bridge_stability_audit.py tools/data/boundary_denominator_prescan_20260509_1409.json tools/data/component_state_anderson3d_interface_20260514_1850.json tools/data/vc_nonsturmian_label_null_gate_20260509_0819.json tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json tools/data/photonic_boundary_third_included_gate_20260515_1734.json applications/soluzioni/20260515_1947_anderson-3d-mobility-edge-two-reader/finding_index.draft.json applications/soluzioni/20260509_1409_boundary-denominator-prescan/finding_index.draft.json docs/doc-dnd_aggiunti_03_03_26/AI D-ND/Descrivi il tutto e come contenitore usa il nulla.txt applications/soluzioni/20260513_0330_prime-vs-mod6-sr-boundary/finding_index.draft.json tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json tools/data/boundary_denominator_prescan_20260509_1430.json tools/data/promotions/promotion_20260516_1135.json tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json tools/data/boundary_denominator_prescan_full_20260509_1500.json tools/data/endpoint_feature_scramble_null_20260516_1058.json tools/data/boundary_graph_null_audit_20260516_0330.json tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.trace.jsonl tools/data/vc_null_regression_gate_20260509_0637.json applications/soluzioni/20260507_0330_the-gue-poisson-boundary-is-a/finding_index.draft.json applications/soluzioni/20260516_1104_endpoint-gated-rp-boundary/finding_index.draft.json docs/operations/lab_cycle_monitor_20260516_1135.md docs/operations/prime_mod6_null_fairness_audit_2026-05-15.md docs/operations/prime_mod6_counter_null_audit_2026-05-15.md docs/operations/prime_mod6_generative_null_audit_2026-05-15.md docs/operations/lab_boundary_graph_threshold_contract_2026-05-16.md applications/soluzioni/20260509_0819_vc-non-sturmian-label-null-gate/finding_index.draft.json applications/soluzioni/20260515_1712_primemod6-generative-null-w2048/finding_index.draft.json tools/data/preflight/prime_mod6_null_fairness_audit_20260515_w512.json tools/data/preflight/prime_mod6_generative_null_audit_20260515_1712_w2048.json tools/data/preflight/prime_mod6_null_fairness_audit_20260515_1705_w1024.json tools/data/preflight/prime_mod6_counter_null_audit_20260515.json tools/data/preflight/prime_mod6_null_fairness_audit_20260515_1712_w2048.json applications/published/20260501_0931_the-gue-poisson-crossover-has-a/finding_index.json applications/published/20260501_0931_the-gue-poisson-crossover-has-a/lab-note.md applications/published/20260501_0931_the-gue-poisson-crossover-has-a/cycle-report.md applications/published/20260501_0931_the-gue-poisson-crossover-has-a/_promote_meta.json tools/data/preflight/cycle_monitor/cycle_monitor_20260516_1135.json tools/data/preflight/prime_mod6_generative_null_audit_20260515_w512.json tools/data/preflight/prime_mod6_generative_null_audit_20260515_1705_w1024.json applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/finding_index.json applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/lab-note.md applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/cycle-report.md applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/_promote_meta.json tools/data/tool_contracts/runs/component_state_anderson3d_20260514_1850.json tools/data/markov_memory_by_gue_type.json tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json tools/data/boundary_short_denominator_extension_20260509_1556.json tools/data/prime_sr_persistent_boundary_20260512_0330.json tools/data/semireal_boundary_transfer_gate_20260509_1516.json tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.json tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.json tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.trace.jsonl tools/data/two_channel_boundary.json tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json tools/data/anderson3d_comparable_null_audit_20260516_1135.json archive/sim_canonical/emergence_measure_N_levels.pdf archive/sim_canonical/example_E_N_levels.py tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.trace.jsonl tools/data/boundary_blank_null_audit_20260509_1430.json tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.trace.jsonl tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json tools/data/boundary_graph_curvature_gate_20260515_1855.json applications/scoperte/20260430_1905_observable-coherence-at-the-gue-poisson_auto/lab-note.draft.md applications/scoperte/20260430_1905_observable-coherence-at-the-gue-poisson_auto/cycle-report.draft.md applications/published/20260430_1905_observable-coherence-at-the-gue-poisson/lab-note.md applications/published/20260430_1905_observable-coherence-at-the-gue-poisson/cycle-report.md applications/published/20260430_1905_observable-coherence-at-the-gue-poisson/_promote_meta.json tools/data/reports/exp_dR_brody_connection.json tools/data/reports/exp_brody_crossover_20260405.json tools/data/reports/loop_guard_20260516_1135.json tools/data/reports/exp_boundary_20260405_0825.json tools/data/reports/exp_boundary_growth_20260405_0914.json tools/data/reports/falsifier_20260516_1135.json tools/data/biconi/bicono_20260516_1135.json tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.json tools/data/rp_boundary_size_stability_audit_20260515_1940.json tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json tools/data/brody_flow.json tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json tools/data/endpoint_gated_rp_boundary_20260516_1104.json tools/data/vc_unit_boundary_audit_20260509_1457.json tools/data/aubry_cosine_boundary_counter_gate_20260515_1758.json tools/data/boundary_coherence.json tools/data/boundary_mixture_gate_20260507_0330.json tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json tools/data/boundary_classical_crossover_audit_20260515_1904.json tools/data/reports/agent_20260516_1135.md applications/scoperte/20260515_1904_boundary-classical-crossover-audit_auto/lab-note.draft.md applications/scoperte/20260515_1904_boundary-classical-crossover-audit_auto/cycle-report.draft.md tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json tools/data/incrocio_20260516_1135.json tools/data/brody_calibration_results.json tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl tools/data/boundary_residual_beta_absent_audit_20260510_0330.json tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl tools/data/aeternitas/aeternitas_20260516_1135.json tools/data/rp_boundary_raw_count_null_audit_20260516_0820.json tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json applications/scoperte/20260509_1538_boundary-row-aligned-nonexact-audit_auto/lab-note.draft.md applications/scoperte/20260509_1538_boundary-row-aligned-nonexact-audit_auto/cycle-report.draft.md tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json tools/data/anderson3d_component_state_interface_input_20260514_1850.json tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.trace.jsonl tools/data/exp_poisson_convergence.json tools/data/boundary_shuffle_audit.json tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl applications/scoperte/20260516_1031_boundary-unfolding-transfer-matrix_auto/lab-note.draft.md applications/scoperte/20260516_1031_boundary-unfolding-transfer-matrix_auto/cycle-report.draft.md tools/data/boundary_blank_null_audit_residual_20260509_1500.json tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json tools/data/anderson3d_endpoint_preserving_null_20260516_1124.json tools/data/3d_boundary_layers.json tools/data/piano11b_gue_test.json tools/data/graph_completion/graph_completion_20260516_1135.json tools/data/boundary_bridge_stability_audit_20260515_1915.json tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.json tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.json tools/data/boundary_blank_thin_support_audit_20260509_1548.json applications/published/20260507_0330_the-gue-poisson-boundary-is-a/finding_index.json applications/published/20260507_0330_the-gue-poisson-boundary-is-a/lab-note.md applications/published/20260507_0330_the-gue-poisson-boundary-is-a/cycle-report.md applications/published/20260507_0330_the-gue-poisson-boundary-is-a/_promote_meta.json tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.json applications/scoperte/20260516_1135_anderson-comparable-null-audit_auto/lab-note.draft.md applications/scoperte/20260516_1135_anderson-comparable-null-audit_auto/cycle-report.draft.md tools/data/evolution/evolution_20260516_1135.md tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl applications/scoperte/20260509_1457_vc-unit-boundary-audit_auto/lab-note.draft.md applications/scoperte/20260509_1457_vc-unit-boundary-audit_auto/cycle-report.draft.md applications/scoperte/20260509_0741_vc-phase-bridge-label-null_auto/lab-note.draft.md applications/scoperte/20260509_0741_vc-phase-bridge-label-null_auto/cycle-report.draft.md applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/lab-note.draft.md applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/cycle-report.draft.md applications/scoperte/20260509_1409_boundary-denominator-prescan_auto/lab-note.draft.md applications/scoperte/20260509_1409_boundary-denominator-prescan_auto/cycle-report.draft.md applications/scoperte/20260507_0803_denominator-gate-transfers-boundary-coordinate-does_auto/lab-note.draft.md applications/scoperte/20260507_0803_denominator-gate-transfers-boundary-coordinate-does_auto/cycle-report.draft.md applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/lab-note.draft.md applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/cycle-report.draft.md applications/scoperte/20260515_1933_rosenzweig-porter-physical-bridge-audit_auto/lab-note.draft.md applications/scoperte/20260515_1933_rosenzweig-porter-physical-bridge-audit_auto/cycle-report.draft.md applications/scoperte/20260516_1117_anderson-3d-two-reader-boundary-null_auto/lab-note.draft.md applications/scoperte/20260516_1117_anderson-3d-two-reader-boundary-null_auto/cycle-report.draft.md applications/scoperte/20260515_1947_anderson-3d-mobility-edge-two-reader_auto/lab-note.draft.md applications/scoperte/20260515_1947_anderson-3d-mobility-edge-two-reader_auto/cycle-report.draft.md applications/scoperte/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve_auto/lab-note.draft.md applications/scoperte/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve_auto/cycle-report.draft.md applications/scoperte/20260509_1427_blank-pair-null-transfer_auto/lab-note.draft.md applications/scoperte/20260509_1427_blank-pair-null-transfer_auto/cycle-report.draft.md applications/scoperte/20260516_1104_endpoint-gated-rp-boundary_auto/lab-note.draft.md applications/scoperte/20260516_1104_endpoint-gated-rp-boundary_auto/cycle-report.draft.md applications/scoperte/20260515_1940_rp-boundary-size-stability-audit_auto/lab-note.draft.md applications/scoperte/20260515_1940_rp-boundary-size-stability-audit_auto/cycle-report.draft.md applications/scoperte/20260510_0330_boundary-residual-beta-absent-audit_auto/lab-note.draft.md applications/scoperte/20260510_0330_boundary-residual-beta-absent-audit_auto/cycle-report.draft.md applications/scoperte/20260515_1712_primemod6-generative-null-w2048_auto/lab-note.draft.md applications/scoperte/20260515_1712_primemod6-generative-null-w2048_auto/cycle-report.draft.md applications/scoperte/20260509_0819_vc-non-sturmian-label-null-gate_auto/lab-note.draft.md applications/scoperte/20260509_0819_vc-non-sturmian-label-null-gate_auto/cycle-report.draft.md applications/scoperte/20260513_0330_prime-vs-mod6-sr-boundary_auto/lab-note.draft.md applications/scoperte/20260513_0330_prime-vs-mod6-sr-boundary_auto/cycle-report.draft.md applications/scoperte/20260509_1437_residual-boundary-closure_auto/lab-note.draft.md applications/scoperte/20260509_1437_residual-boundary-closure_auto/cycle-report.draft.md applications/scoperte/20260509_1444_boundary-gate-transfer-matrix_auto/lab-note.draft.md applications/scoperte/20260509_1444_boundary-gate-transfer-matrix_auto/cycle-report.draft.md exec /bin/bash -lc "sed -n '1,240p' tools/data/reports/agent_20260516_1135.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Anderson Comparable Null Audit **Date**: 2026-05-16 11:35 **Piano**: 132 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - Sullo stesso perimetro compatto Anderson, stesso observable e stesso `N=512`, il null endpoint-preserving ricostruisce il conteggio osservato in `36/512` trial (`raw_p=0.070312500`, Wilson 95% `[0.051218024, 0.095806720]`), mentre il full feature-scramble lo ricostruisce in `272/512` (`raw_p=0.531250000`, Wilson 95% `[0.487953078, 0.574081486]`). Il confronto L2 e' chiuso come unita' comparabile; il boundary fisico non si promuove perche' `W=20` resta rinominato dal null endpoint-preserving in `54/512` trial. observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, wigner_poisson_like_weight, mean_ipr, participation_entropy, two_reader_all_sizes, raw_p, add_one_p, wilson_95] **observable_contract**: claim=i null sono confrontabili solo se condividono observable, perimetro righe e N; observable=conteggio cross-size di righe `stable_graph_bridge+classical_intermediate`; operator=righe compatte Anderson 3D dal run 11:17 classificate dallo stesso reader kNN/classico; generator=stesse righe sorgente, due operatori null che differiscono solo per struttura preservata; denominator=`512` trial per null su 11 righe per size; p_value_definition=right-tail `raw_p=k/N`, `add_one_p=(k+1)/(N+1)`, con `k` = trial null con conteggio cross-size >= osservato; non_possible=chiamare un null piu' restrittivo se perimetro o N cambiano; not_tested=raw multi-seed reader, nuovi Hamiltoniani, `L>=7`, perimetro completo 8 GUE / 5 Poisson. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY + feedback falsifier L2 sui null comparabili. - **Dipolo / punto-zero**: null permissivo / null fisico. Punto-zero: la stessa riga disorder prima della nominazione e prima della scelta del null. - **Piano superiore**: topologia del bordo row-aligned; il bordo vive solo se l'operatore nullo non puo' ricostruire la stessa molteplicita' nello stesso spazio di lettura. - **Operatori laterali scelti**: boundary operator, graph rewiring, candidate-only shuffle. - **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel 11:24 sullo stesso spazio; CE-0117/Cascata applicata come vincolo: il risultato del falsifier entra nel seme operativo del ciclo e corregge il nodo regressivo, non il claim. - **Proto-ipotesi**: la restrittivita' di un null non e' proprieta' del nome del null; e' proprieta' misurabile solo a perimetro, observable e N fissati. - **Possibile/non-possibile**: possibile = distinguere quantitativamente full-scramble ed endpoint-preserving sul perimetro compatto; non-possibile = promuovere `W=20` finche' il null endpoint-preserving lo rinomina con frequenza non-zero. - **Proiezione**: rieseguo entrambi i null su `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json`, usando le stesse righe compatte e `512` trial ciascuno. - **Movimento A->M->B**: fisico A = Anderson 3D mobility edge; matematica M = confronto omogeneo di operatori null; fisico B = criterio di costo per decidere se lanciare large-L. Il ritorno fisico resta vincolo, non scoperta. ## Aderenza alla direzione - `relation`: `deliberate_counter_perimeter` - `why`: resta su Anderson per chiudere il check obbligatorio del falsifier 11:24: stessi null, stesso perimetro, stesso N e stessa observable prima di interpretare restrittivita'. - `not_drift`: non usa Sturmian, phi, V_c o fit locali; attacca il nodo regressivo `null_first -> candidate_name -> physical_return` emerso dentro il frame BOUNDARY cross-dominio. - `return_criterion`: tornare al perimetro vivo 8 GUE / 5 Poisson quando il contratto dei null comparabili e' stabilizzato; oppure chiudere Anderson se anche il raw-reader endpoint-preserving rinomina `W=20`. - `seed_residue`: restano non testati il perimetro completo 8 GUE / 5 Poisson, raw multi-seed Anderson e `L>=7`. - `why_not_drift`: il sotto-perimetro e' regressivo perche' corregge il confronto non omogeneo segnalato dal falsifier, senza promuovere un nuovo candidato. ## Re-discovery audit - **Baseline noto piu vicino**: Anderson localization, mobility edge 3D, crossover Wigner-Dyson/Poisson, Brody interpolation, finite-size scaling. - **Cosa assorbe il baseline**: righe intermedie vicino alla transizione, dipendenza da size piccole, sensibilita' a feature compatte. - **Cosa resta Lab-specific**: contratto null-first comparabile con due operatori null sullo stesso observable row-aligned. - **Separazione**: `two_reader_boundary_confirmed=2` nel perimetro compatto; `graph_only_residue` non sommato; `scope_change_declared=Anderson_compact_null_comparison`; `graph_baseline_audit=kNN stability / row-feature rewiring`. ## Claim Under Test > Nel perimetro compatto Anderson, il confronto tra null e' interpretabile solo se full feature-scramble ed endpoint-preserving candidate-only misurano lo stesso conteggio cross-size con lo stesso numero di trial. ## Question La riduzione osservata nel null endpoint-preserving era effetto del null o effetto del cambio di perimetro? ## Experiment Design - **Script**: `tools/exp_anderson3d_comparable_null_audit.py`. - **Run**: `python tools/exp_anderson3d_comparable_null_audit.py --out tools/data/anderson3d_comparable_null_audit_20260516_1135.json --null-trials 512`. - **Source**: `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json`. - **Perimetro**: righe compatte mediane del run 11:17, `L=5,6`, 11 disorder rows per size. - **Observed observable**: intersezione cross-size di righe `stable_graph_bridge+classical_intermediate`. - **Null A**: endpoint-preserving candidate-only; conserva poli metallic/localized e permuta feature solo fra righe `mobility_candidate`. - **Null B**: full feature-scramble; permuta feature su tutte le righe compatte della size. - **P-value**: right-tail; `raw_p=k/N`, `add_one_p=(k+1)/(N+1)`; Wilson 95% riportato sul count binomiale `k/N`. - **Non testato**: non misura nuovi autovalori, raw multi-seed reader, exponent critico o large-L. ## Results | measure | observed | null k/N | raw_p | add_one_p | Wilson 95% | max null | mean null | lettura | |---|---:|---:|---:|---:|---|---:|---:|---| | endpoint-preserving candidate-only | 2 | 36/512 | 0.070312500 | 0.072124756 | [0.051218024, 0.095806720] | 2 | 0.533203125 | riduce la ricostruzione ma non azzera | | full feature-scramble | 2 | 272/512 | 0.531250000 | 0.532163743 | [0.487953078, 0.574081486] | 4 | 1.623046875 | ricostruisce spesso il conteggio | | difference full - endpoint | n/a | n/a | 0.460937500 | n/a | [0.412369646, 0.509505354] | n/a | n/a | differenza comparabile nello stesso perimetro | | W row | endpoint-preserving named hits | full-scramble named hits | lettura | |---:|---:|---:|---| | 16.00 | 33/512 | 117/512 | riga osservata ricostruibile | | 20.00 | 54/512 | 116/512 | candidato non-zero nel null fisico | | 16.50 | 37/512 | 112/512 | intermittente nel deposito, ricostruibile | | size | observed compact two-reader rows | |---:|---| | L=5 | `W=16.00`, `W=20.00` | | L=6 | `W=16.00`, `W=16.50`, `W=20.00` | ## Key Findings 1. Verificato: sullo stesso perimetro e con lo stesso `N=512`, endpoint-preserving ricostruisce meno del full feature-scramble: `36/512` contro `272/512`. 2. Verificato: gli intervalli binomiali non si sovrappongono; la differenza `raw_p_full - raw_p_endpoint = 0.460937500` ha intervallo approssimato `[0.412369646, 0.509505354]`. 3. Verificato: il risultato L2 precedente non era formulabile come confronto; ora lo e', ma solo nel perimetro compatto. 4. Verificato: `W=20` non e' zero sotto endpoint-preserving: `54/512` rinomine cross-size. 5. Inferito dal perimetro: il null endpoint-preserving e' un filtro piu' duro, non una prova fisica del boundary. ## Verdict CONSTRAINT Il nodo regressivo L2 e' chiuso: a parita' di perimetro, observable e N, il null endpoint-preserving e' piu' restrittivo del full feature-scramble. La promozione fisica resta bloccata perche' il candidato `W=20` sopravvive come rinomina non-zero nel null che preserva i poli. ## Bicono della scoperta - **Due radici**: differenza comparabile fra null; rinomina non-zero del candidato. - **Singolare**: riga disorder prima del nome e prima del null. - **Invariante di passaggio**: stesso observable, stesso perimetro, stesso N. - **Campo di possibilita**: possibile = usare endpoint-preserving come pre-filtro di costo; non-possibile = pagare large-L per salvare `W=20` prima del raw-reader null. ## Consecutio Ripetere l'endpoint-preserving sul raw multi-seed reader del ciclo 11:17, non sulle mediane compatte. Se `W=20` resta rinominato, Anderson si chiude come proprieta' del lettore. Se va a zero, allora il costo `L>=7` diventa giustificato. ## Ricadute pratiche ssp_value: yes. `tools/exp_anderson3d_comparable_null_audit.py` diventa strumento riusabile per confrontare null solo dopo allineamento di perimetro, observable e N. ## 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 su TRASCENDENZA_LIMITE; BOUNDARY seguito come contro-perimetro regressivo per obbligo falsifier. - `python -m py_compile tools/exp_anderson3d_comparable_null_audit.py` completato. - Run completato: `tools/data/anderson3d_comparable_null_audit_20260516_1135.json`. - Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. - Nessun update del seme. - Nessuna promozione e nessun public sync. ## Files - Script: `tools/exp_anderson3d_comparable_null_audit.py` - Data: `tools/data/anderson3d_comparable_null_audit_20260516_1135.json` - Report: `tools/data/reports/agent_20260516_1135.md` exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_gue_poisson.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Experiment: GUE/Poisson boundary in prime gaps vs Cramér null model. Question: Is the transition from GUE-like to Poisson-like statistics in prime gaps a structural feature of primes, or does it appear in any sequence with decreasing density? Method: 1. Compute consecutive gap ratios = min(g_i, g_{i+1}) / max(g_i, g_{i+1}) for primes in sliding windows at different scales. - GUE (GOE in 1D): ≈ 0.5307 - Poisson: ≈ 0.3863 2. Generate Cramér random primes: each integer n is "prime" with probability 1/ln(n). 3. Compare the profile across scales for real primes vs Cramér model. 4. If both show the same transition → the boundary is trivial (density effect). If primes differ → the boundary carries structural information. Null baseline: 20 Cramér realizations, report mean ± std. """ import numpy as np from sympy import primerange import json from datetime import datetime def gap_ratios(gaps): """Compute consecutive gap ratios min/max for a sequence of gaps.""" if len(gaps) < 2: return np.array([]) r = np.minimum(gaps[:-1], gaps[1:]) / np.maximum(gaps[:-1], gaps[1:]) return r def primes_in_window(start, end, primes_array): """Get primes in [start, end).""" idx_start = np.searchsorted(primes_array, start, side='left') idx_end = np.searchsorted(primes_array, end, side='left') return primes_array[idx_start:idx_end] def cramer_random_primes(N_max, rng): """Generate Cramér random 'primes': each n>=2 is included with prob 1/ln(n).""" # For efficiency, work in blocks result = [2] n_vals = np.arange(3, N_max, 2) # odd numbers only (like primes > 2) probs = 2.0 / np.log(n_vals) # factor 2 because we only test odds probs = np.clip(probs, 0, 1) mask = rng.random(len(n_vals)) < probs result.extend(n_vals[mask].tolist()) return np.array(result) def analyze_windows(primes_array, windows): """Compute for primes in each window.""" results = [] for (start, end) in windows: p = primes_in_window(start, end, primes_array) if len(p) < 50: results.append(np.nan) continue gaps = np.diff(p).astype(float) r = gap_ratios(gaps) results.append(np.mean(r)) return np.array(results) def main(): print("=== GUE/Poisson Boundary: Primes vs Cramér Null Model ===\n") # Generate primes up to 10^7 N_MAX = 10_000_000 print(f"Generating primes up to {N_MAX:,}...") primes = np.array(list(primerange(2, N_MAX))) print(f" Found {len(primes):,} primes\n") # Define windows: logarithmically spaced # Each window has ~2000 consecutive primes for statistical stability n_windows = 20 window_centers = np.logspace(np.log10(1000), np.log10(N_MAX - 100000), n_windows).astype(int) window_half = 50000 # ±50K around center windows = [(max(2, c - window_half), c + window_half) for c in window_centers] # Analyze real primes print("Analyzing real primes across scales...") r_primes = analyze_windows(primes, windows) # Cramér null model: 20 realizations N_CRAMER = 20 print(f"Generating {N_CRAMER} Cramér random prime sets...") rng = np.random.default_rng(42) r_cramer_all = [] for i in range(N_CRAMER): cp = cramer_random_primes(N_MAX, rng) r_c = analyze_windows(cp, windows) r_cramer_all.append(r_c) if (i + 1) % 5 == 0: print(f" {i+1}/{N_CRAMER} done") r_cramer_all = np.array(r_cramer_all) r_cramer_mean = np.nanmean(r_cramer_all, axis=0) r_cramer_std = np.nanstd(r_cramer_all, axis=0) # Reference values r_gue = 0.5307 # GOE (real symmetric) in 1D r_poisson = 0.3863 # Print results print("\n" + "="*80) print(f"{'Window center':>15} | {' primes':>10} | {' Cramér':>12} | {'Δ':>8} | {'σ_Cramér':>8} | {'z-score':>8}") print("-"*80) z_scores = [] for i, (start, end) in enumerate(windows): center = (start + end) // 2 rp = r_primes[i] rc = r_cramer_mean[i] rs = r_cramer_std[i] delta = rp - rc z = delta / rs if rs > 0 else 0 z_scores.append(z) print(f"{center:>15,} | {rp:>10.4f} | {rc:>10.4f}±{rs:.3f} | {delta:>+8.4f} | {rs:>8.4f} | {z:>+8.2f}") print("="*80) print(f"\nReference: _GUE = {r_gue:.4f}, _Poisson = {r_poisson:.4f}") # Summary statistics z_scores = np.array(z_scores) valid = ~np.isnan(z_scores) print(f"\nz-score summary (primes - Cramér) / σ_Cramér:") print(f" mean z = {np.nanmean(z_scores):.3f}") print(f" max |z| = {np.max(np.abs(z_scores[valid])):.3f}") print(f" windows with |z| > 2: {np.sum(np.abs(z_scores[valid]) > 2)}/{np.sum(valid)}") # Key diagnostic: does trend differ? print("\n--- Diagnostic: trend analysis ---") # Fit linear trend to vs log(center) centers = np.array([(s+e)//2 for s,e in windows]) log_centers = np.log10(centers) valid_p = ~np.isnan(r_primes) if np.sum(valid_p) > 3: coeff_p = np.polyfit(log_centers[valid_p], r_primes[valid_p], 1) coeff_c = np.polyfit(log_centers[valid_p], r_cramer_mean[valid_p], 1) print(f" Primes: = {coeff_p[0]:+.4f} * log10(n) + {coeff_p[1]:.4f}") print(f" Cramér: = {coeff_c[0]:+.4f} * log10(n) + {coeff_c[1]:.4f}") print(f" Slope difference: {coeff_p[0] - coeff_c[0]:+.4f}") if abs(coeff_p[0] - coeff_c[0]) < 0.005: print(" → Slopes nearly identical: transition is a DENSITY EFFECT") structural = False else: print(" → Slopes differ: primes have STRUCTURAL content beyond density") structural = True else: print(" Not enough valid windows for trend analysis") structural = None # Where are primes relative to GUE/Poisson? print("\n--- Classification ---") for i, (start, end) in enumerate(windows): center = (start + end) // 2 rp = r_primes[i] if np.isnan(rp): continue dist_gue = abs(rp - r_gue) dist_poi = abs(rp - r_poisson) label = "GUE" if dist_gue < dist_poi else "POISSON" margin = abs(dist_gue - dist_poi) if margin < 0.02: label = "BOUNDARY" print(f" n~{center:>10,}: ={rp:.4f} → {label}") # Save results result = { "experiment": "boundary_gue_poisson_cramer", "timestamp": datetime.now().isoformat(), "N_MAX": N_MAX, "n_primes": len(primes), "n_cramer_realizations": N_CRAMER, "reference": {"r_gue": r_gue, "r_poisson": r_poisson}, "windows": [{"center": int((s+e)//2), "r_primes": float(r_primes[i]), "r_cramer_mean": float(r_cramer_mean[i]), "r_cramer_std": float(r_cramer_std[i]), "z_score": float(z_scores[i]) if not np.isnan(z_scores[i]) else None} for i, (s, e) in enumerate(windows)], "structural": structural, "z_mean": float(np.nanmean(z_scores)), "z_max_abs": float(np.max(np.abs(z_scores[valid]))), "slope_primes": float(coeff_p[0]) if np.sum(valid_p) > 3 else None, "slope_cramer": float(coeff_c[0]) if np.sum(valid_p) > 3 else None, } outpath = "/opt/MM_D-ND/tools/data/reports/exp_boundary_20260405_0825.json" with open(outpath, 'w') as f: json.dump(result, f, indent=2) print(f"\nResults saved to {outpath}") return result if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_mixture_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_boundary_mixture_gate.py Reusable META/BOUNDARY audit for the GUE-Poisson boundary. Question: Does the GUE/Poisson boundary remain a clean two-class split after the original-vs-shuffle denominator gate, or is the mixed region an operational third state where canonical observables lose stable denominators? The script uses only canonical observables from observables_registry.py. It builds synthetic mixtures by replacing a fraction beta of unfolded GUE spacings with Poisson spacings, then measures: - canonical observable vectors; - original-vs-shuffle z-score per observable; - endpoint separability in all observables and in gate-stable observables; - ambiguity of each beta layer relative to pure GUE and pure Poisson centroids. """ from __future__ import annotations import argparse import json from pathlib import Path import numpy as np from observables_registry import ( OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, compute_canonical, ) OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) def gue_spacings(matrix_size: int, min_spacings: int, rng: np.random.Generator) -> np.ndarray: """Generate unfolded GUE spacings by concatenating independent matrices.""" parts: list[np.ndarray] = [] edge = max(2, matrix_size // 10) while sum(len(x) for x in parts) < min_spacings: real = rng.standard_normal((matrix_size, matrix_size)) imag = rng.standard_normal((matrix_size, matrix_size)) h = real + 1j * imag h = (h + h.conj().T) / (2.0 * np.sqrt(matrix_size)) eigs = np.sort(np.linalg.eigvalsh(h).real) bulk = eigs[edge:-edge] gaps = np.diff(bulk) mean = float(np.mean(gaps)) if mean > 1e-15: parts.append(gaps / mean) return np.concatenate(parts)[:min_spacings].astype(float) def mixture_spacings(gue: np.ndarray, poisson: np.ndarray, beta: float, rng: np.random.Generator) -> np.ndarray: """Return a beta Poisson / (1-beta) GUE spacing sequence with mean spacing 1.""" if len(gue) != len(poisson): raise ValueError("gue and poisson arrays must have the same length") mask = rng.random(len(gue)) < beta out = gue.copy() out[mask] = poisson[mask] mean = float(np.mean(out)) return out / mean if mean > 1e-15 else out def z_against_shuffle( gaps: np.ndarray, n_baseline: int, rng: np.random.Generator, ) -> tuple[dict[str, float], dict[str, float], dict[str, float]]: """Return original observables, shuffle baseline std, and original-vs-shuffle z.""" original = compute_canonical(gaps) baseline_vals = {name: [] for name in OBS_NAMES} for _ in range(n_baseline): obs = compute_canonical(rng.permutation(gaps)) for name in OBS_NAMES: baseline_vals[name].append(obs[name]) std = {} z = {} for name in OBS_NAMES: vals = np.array(baseline_vals[name], dtype=float) mean = float(np.mean(vals)) sd = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 std[name] = sd z[name] = float((original[name] - mean) / sd) if sd > 1e-15 else 0.0 return original, std, z def vector(row: dict, names: list[str]) -> np.ndarray: return np.array([row["observables"][name] for name in names], dtype=float) def classify_layers(rows: list[dict], obs_names: list[str]) -> dict: """Classify each beta layer by standardized distance to endpoint centroids.""" if not obs_names: return { "observables": [], "endpoint_distance": 0.0, "layers": {}, "ambiguous_beta": [], } by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) gue_vectors = np.array([vector(row, obs_names) for row in by_beta[0.0]], dtype=float) poi_vectors = np.array([vector(row, obs_names) for row in by_beta[1.0]], dtype=float) all_endpoint = np.vstack([gue_vectors, poi_vectors]) scale = np.std(all_endpoint, axis=0, ddof=1) scale[scale <= 1e-15] = 1.0 gue_centroid = np.mean(gue_vectors, axis=0) poi_centroid = np.mean(poi_vectors, axis=0) endpoint_distance = float(np.linalg.norm((poi_centroid - gue_centroid) / scale)) layers = {} ambiguous_beta = [] for beta, beta_rows in sorted(by_beta.items()): coords = [] margins = [] labels = [] for row in beta_rows: x = vector(row, obs_names) d_gue = float(np.linalg.norm((x - gue_centroid) / scale)) d_poi = float(np.linalg.norm((x - poi_centroid) / scale)) denom = d_gue + d_poi coord = float((d_gue - d_poi) / denom) if denom > 1e-15 else 0.0 margin = float(abs(d_gue - d_poi) / denom) if denom > 1e-15 else 0.0 coords.append(coord) margins.append(margin) labels.append("gue" if d_gue < d_poi else "poisson") ambiguous_fraction = float(np.mean(np.array(margins) < 0.15)) if ambiguous_fraction >= 0.5: ambiguous_beta.append(beta) layers[f"{beta:.3f}"] = { "coordinate_mean": float(np.mean(coords)), "coordinate_std": float(np.std(coords, ddof=1)) if len(coords) > 1 else 0.0, "margin_mean": float(np.mean(margins)), "ambiguous_fraction": ambiguous_fraction, "poisson_label_fraction": float(np.mean(np.array(labels) == "poisson")), } return { "observables": obs_names, "endpoint_distance": endpoint_distance, "layers": layers, "ambiguous_beta": ambiguous_beta, } def summarize_gate(rows: list[dict], z_min: float) -> dict: by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) layers = {} for beta, beta_rows in sorted(by_beta.items()): stable_counts = [] stable_freq = {name: [] for name in OBS_NAMES} for row in beta_rows: stable = [name for name in OBS_NAMES if abs(row["z"][name]) >= z_min] stable_counts.append(len(stable)) for name in OBS_NAMES: stable_freq[name].append(1.0 if name in stable else 0.0) layers[f"{beta:.3f}"] = { "stable_count_mean": float(np.mean(stable_counts)), "stable_count_std": float(np.std(stable_counts, ddof=1)) if len(stable_counts) > 1 else 0.0, "stable_frequency": {name: float(np.mean(vals)) for name, vals in stable_freq.items()}, } endpoint_stable = [] for name in OBS_NAMES: endpoint_rows = by_beta[0.0] + by_beta[1.0] freq = np.mean([1.0 if abs(row["z"][name]) >= z_min else 0.0 for row in endpoint_rows]) if freq >= 0.75: endpoint_stable.append(name) return { "z_min": z_min, "endpoint_stable_observables": endpoint_stable, "layers": layers, } def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) betas = [float(x) for x in np.linspace(0.0, 1.0, args.n_beta)] rows = [] for rep in range(args.n_replicates): rep_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) gue = gue_spacings(args.gue_matrix_size, args.n_gaps, rep_rng) poisson = rep_rng.exponential(1.0, size=args.n_gaps) poisson = poisson / float(np.mean(poisson)) for beta in betas: layer_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) gaps = mixture_spacings(gue, poisson, beta, layer_rng) obs, shuffle_std, z = z_against_shuffle( gaps, n_baseline=args.n_baseline, rng=np.random.default_rng(rng.integers(0, 2**63 - 1)), ) rows.append( { "replicate": rep, "beta": beta, "observables": obs, "shuffle_std": shuffle_std, "z": z, "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= args.z_min], } ) gate = summarize_gate(rows, args.z_min) all_classification = classify_layers(rows, OBS_NAMES) gated_classification = classify_layers(rows, gate["endpoint_stable_observables"]) output = { "experiment": "boundary_mixture_gate", "question": "Is the GUE-Poisson mixed layer cleanly classifiable after denominator gating?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": OBS_NAMES, "params": vars(args), "gate": gate, "classification_all_observables": all_classification, "classification_endpoint_gated": gated_classification, "rows": rows, } out_path = Path(args.out) out_path.parent.mkdir(parents=True, exist_ok=True) with out_path.open("w") as f: json.dump(output, f, indent=2) print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") print(f"observables_used={OBS_NAMES}") print(f"endpoint_stable_observables={gate['endpoint_stable_observables']}") print( "endpoint_distance_all=" f"{all_classification['endpoint_distance']:.3f} " "endpoint_distance_gated=" f"{gated_classification['endpoint_distance']:.3f}" ) if gate["endpoint_stable_observables"]: print("beta stable_count margin_gated ambiguous_gated poisson_fraction_gated") else: print("endpoint gate is empty; printing all-observable classification") print("beta stable_count margin_all ambiguous_all poisson_fraction_all") for beta in betas: key = f"{beta:.3f}" stable_count = gate["layers"][key]["stable_count_mean"] source = gated_classification if gate["endpoint_stable_observables"] else all_classification layer = source["layers"].get(key, {}) print( f"{beta:>4.2f} {stable_count:>12.3f} " f"{layer.get('margin_mean', 0.0):>12.3f} " exec /bin/bash -lc "sed -n '1,260p' tools/exp_anderson3d_comparable_null_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Comparable null audit for the Anderson 3D compact boundary rows. The 11:24 falsifier rejected a comparison between a full feature-scramble null and an endpoint-preserving null because the two runs used different readers, denominators and trial counts. This script keeps one compact row perimeter and one observable, then runs both nulls with the same N before interpreting them. """ from __future__ import annotations import argparse import json import math from pathlib import Path from typing import Any, Callable import numpy as np from exp_anderson3d_endpoint_preserving_null import ( classify_size, compact_rows, endpoint_preserving_scramble, ) from exp_anderson3d_mobility_edge_two_reader_audit import ( OBS_NAMES, parse_ints, scrambled_rows, two_reader_names_from_rows, ) from observables_registry import OBSERVABLES_REGISTRY_VERSION def wilson_interval(k: int, n: int, z: float = 1.959963984540054) -> dict[str, float]: if n <= 0: return {"low": 0.0, "high": 1.0} phat = k / n denom = 1.0 + (z * z / n) center = (phat + (z * z) / (2.0 * n)) / denom half = (z / denom) * math.sqrt((phat * (1.0 - phat) / n) + (z * z / (4.0 * n * n))) return {"low": round(max(0.0, center - half), 9), "high": round(min(1.0, center + half), 9)} def count_histogram(counts: list[int]) -> dict[str, int]: values, freq = np.unique(np.asarray(counts, dtype=int), return_counts=True) return {str(int(value)): int(n) for value, n in zip(values, freq)} def summarize_null( name: str, counts: list[int], observed: int, named_hits: dict[str, int], trials: int, ) -> dict[str, Any]: k_ge = sum(1 for value in counts if value >= observed) raw_p = k_ge / trials if trials else 0.0 add_one_p = (k_ge + 1) / (trials + 1) if trials else 1.0 return { "null_name": name, "observed": observed, "k_ge_observed": k_ge, "trials": trials, "raw_p": round(raw_p, 9), "add_one_p": round(add_one_p, 9), "wilson_95": wilson_interval(k_ge, trials), "max_null": max(counts) if counts else 0, "mean_null": round(float(np.mean(counts)), 9) if counts else 0.0, "median_null": round(float(np.median(counts)), 9) if counts else 0.0, "histogram": count_histogram(counts), "cross_size_named_hits": dict(sorted(named_hits.items())), } def run_null( base_by_size: dict[int, list[dict[str, Any]]], sizes: list[int], args: argparse.Namespace, rng: np.random.Generator, scramble_fn: Callable[[list[dict[str, Any]], np.random.Generator], list[dict[str, Any]]], ) -> tuple[list[int], dict[str, int]]: counts: list[int] = [] named_hits: dict[str, int] = {} reader_args = argparse.Namespace(k_values=",".join(map(str, args.k_values)), graph_margin_max=args.graph_margin_max) for _ in range(args.null_trials): trial_sets = [] for l_size in sizes: trial_rows = scramble_fn(base_by_size[l_size], rng) trial_sets.append(two_reader_names_from_rows(trial_rows, reader_args)) cross = sorted(set.intersection(*trial_sets)) if trial_sets else [] counts.append(len(cross)) for name in cross: named_hits[name] = named_hits.get(name, 0) + 1 return counts, named_hits def difference_summary(endpoint: dict[str, Any], full: dict[str, Any]) -> dict[str, Any]: p_endpoint = endpoint["raw_p"] p_full = full["raw_p"] n_endpoint = endpoint["trials"] n_full = full["trials"] diff = p_full - p_endpoint se = math.sqrt((p_full * (1.0 - p_full) / n_full) + (p_endpoint * (1.0 - p_endpoint) / n_endpoint)) z = diff / se if se > 0 else 0.0 ci_low = diff - 1.959963984540054 * se ci_high = diff + 1.959963984540054 * se return { "comparison": "full_feature_scramble_raw_p - endpoint_preserving_raw_p", "difference": round(diff, 9), "wald_95": {"low": round(ci_low, 9), "high": round(ci_high, 9)}, "z_approx": round(z, 6), "interpretation_unit": "same compact row perimeter, same cross-size two-reader observable, same N", } def run(args: argparse.Namespace) -> dict[str, Any]: with Path(args.source).open(encoding="utf-8") as f: source = json.load(f) args.k_values = parse_ints(",".join(str(v) for v in source["parameters"]["k_values"])) args.graph_margin_max = float(source["parameters"]["graph_margin_max"]) sizes = sorted(entry["L"] for entry in source["by_size"]) base_by_size = {entry["L"]: compact_rows(entry) for entry in source["by_size"]} observed_sets: dict[int, set[str]] = {} observed_size_audit: dict[str, list[str]] = {} for l_size, rows in base_by_size.items(): audit = classify_size(rows, args.k_values, args.graph_margin_max) observed_sets[l_size] = set(audit["two_reader_rows"]) observed_size_audit[str(l_size)] = audit["two_reader_rows"] observed_all = sorted(set.intersection(*observed_sets.values())) if observed_sets else [] observed_count = len(observed_all) endpoint_counts, endpoint_named_hits = run_null( base_by_size, sizes, args, np.random.default_rng(args.endpoint_seed), endpoint_preserving_scramble, ) full_counts, full_named_hits = run_null( base_by_size, sizes, args, np.random.default_rng(args.full_seed), scrambled_rows, ) endpoint_summary = summarize_null( "endpoint_preserving_candidate_only", endpoint_counts, observed_count, endpoint_named_hits, args.null_trials, ) full_summary = summarize_null( "full_feature_scramble", full_counts, observed_count, full_named_hits, args.null_trials, ) output = { "experiment": "anderson3d_comparable_null_audit", "question": "On the same compact Anderson perimeter, does endpoint-preserving candidate-only scrambling differ from full feature scrambling for the same cross-size two-reader observable?", "source": args.source, "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": [ *OBS_NAMES, "SR_local_rigidity", "brody_q", "wigner_poisson_like_weight", "mean_ipr", "participation_entropy", "two_reader_all_sizes", "raw_p", "add_one_p", "wilson_95", ], "parameters": { "sizes": sizes, "k_values": args.k_values, "graph_margin_max": args.graph_margin_max, "null_trials": args.null_trials, "endpoint_seed": args.endpoint_seed, "full_seed": args.full_seed, "source_perimeter": "compact median rows from 20260516_1117", }, "observable_contract": { "claim": "Nulls can be compared only when they share one observable, one row perimeter and one N", "observable": "cross-size intersection count of stable_graph_bridge+classical_intermediate rows", "operator": "compact Anderson 3D rows from 11:17, classified by the same kNN/classical reader", "generator": "same source rows; two null operators differ only in preserved structure", "denominator": f"{args.null_trials} trials for each null on the same {len(base_by_size[sizes[0]]) if sizes else 0} rows per size", "p_value_definition": "right tail; raw_p=k/N and add_one_p=(k+1)/(N+1), where k is null trials with cross-size count >= observed", "non_possible": "calling one null more restrictive if the perimeters or N differ", "not_tested": "raw multi-seed reader, new Hamiltonian samples, L>=7, full 8 GUE / 5 Poisson seed perimeter", }, "summary": { "observed_all_size_count": observed_count, "observed_all_size_rows": observed_all, "observed_size_audit": observed_size_audit, "endpoint_preserving": endpoint_summary, "full_feature_scramble": full_summary, "difference": difference_summary(endpoint_summary, full_summary), }, } 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("--source", default="tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json") parser.add_argument("--out", default="tools/data/anderson3d_comparable_null_audit_20260516_1135.json") parser.add_argument("--null-trials", type=int, default=512) parser.add_argument("--endpoint-seed", type=int, default=202605161135) parser.add_argument("--full-seed", type=int, default=202605161136) run(parser.parse_args()) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '260,520p' tools/exp_boundary_mixture_gate.py" in /opt/MM_D-ND succeeded in 0ms: f"{layer.get('margin_mean', 0.0):>12.3f} " f"{layer.get('ambiguous_fraction', 0.0):>15.3f} " f"{layer.get('poisson_label_fraction', 0.0):>21.3f}" ) print(f"saved {out_path}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--n-gaps", type=int, default=1536) parser.add_argument("--n-replicates", type=int, default=16) parser.add_argument("--gue-matrix-size", type=int, default=180) parser.add_argument("--n-beta", type=int, default=11) parser.add_argument("--n-baseline", type=int, default=24) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--seed", type=int, default=20260507) parser.add_argument("--out", default="tools/data/boundary_mixture_gate.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_transition_taxonomy_13rows.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Scale the post-extension BOUNDARY transition taxonomy to all 13 rows. This script does not regenerate expensive signals. It composes the row-aligned two-axis matrix, the non-exact audit, the denominator prescan, and the 15:56 source-denominator extension. The operator asks whether the short-denominator transition taxonomy leaves any autonomous thin blank when read against the full 13-row perimeter. """ from __future__ import annotations import argparse import json from pathlib import Path from statistics import mean from typing import Any def load_json(path: str) -> dict[str, Any]: with Path(path).open() as f: return json.load(f) def index_rows(rows: list[dict[str, Any]], key: str = "row") -> dict[str, dict[str, Any]]: return {row[key]: row for row in rows} def prescan_index(data: dict[str, Any]) -> dict[str, dict[str, Any]]: return {row["domain_window"]: row for row in data.get("rows", [])} def support_tier(one_sided_count: int, endpoint: float, stable: float) -> str: if one_sided_count >= 4 and endpoint >= 3.5 and stable >= 4.0: return "strong_multi_observable" if one_sided_count >= 3 and endpoint >= 3.0 and stable >= 3.0: return "medium_multi_observable" return "thin_observable_support" def row_metrics(row: dict[str, Any], extension: dict[str, Any] | None = None) -> dict[str, Any]: if extension: one_sided_count = int(extension.get("after_one_sided") or 0) endpoint = float(extension.get("after_endpoint_distance") or 0.0) stable = float(extension.get("after_stable_count_coherent") or 0.0) beta = extension.get("after_beta", []) tier = extension.get("after_support_tier") or support_tier(one_sided_count, endpoint, stable) n_gaps = extension.get("after_n_gaps") return { "n_gaps_after": n_gaps, "one_sided_after": one_sided_count, "endpoint_after": endpoint, "stable_count_coherent_after": stable, "beta_after": beta, "support_tier_after": tier, } beta = row.get("ambiguous_beta", []) one_sided_count = len(row.get("one_sided_observables", [])) endpoint = float(row.get("endpoint_distance") or 0.0) stable = float(row.get("stable_count_coherent") or 0.0) return { "n_gaps_after": row.get("n_gaps"), "one_sided_after": one_sided_count, "endpoint_after": endpoint, "stable_count_coherent_after": stable, "beta_after": beta, "support_tier_after": support_tier(one_sided_count, endpoint, stable), } def transition_class(row: dict[str, Any], extension: dict[str, Any] | None) -> str: if extension: return extension["extension_state"] if not row.get("support_transfer"): return "fall_no_support" beta_state = row.get("beta_state") if beta_state == "beta_0_3_exact": return "beta_0_3_exact" if beta_state == "beta_0_3_local_nonunique": return "beta_0_3_local_nonunique" if beta_state == "local_beta_other": return "local_beta_other" if beta_state == "support_without_beta_blank": metrics = row_metrics(row) tier = metrics["support_tier_after"] if tier == "thin_observable_support": return "thin_persists" return "blank_medium_or_strong_beta_absent" return "unclassified" def build(args: argparse.Namespace) -> dict[str, Any]: two_axis = load_json(args.two_axis) row_audit = load_json(args.row_audit) extension = load_json(args.extension) prescan = load_json(args.prescan) two_rows = index_rows(two_axis.get("rows", [])) audit_rows = index_rows(row_audit.get("rows", [])) extension_rows = index_rows(extension.get("transitions", [])) prescan_rows = prescan_index(prescan) rows: list[dict[str, Any]] = [] class_counts: dict[str, int] = {} support_blank_full_rows: list[str] = [] thin_persist_rows: list[str] = [] endpoint_by_class: dict[str, list[float]] = {} for name in sorted(two_rows): source = two_rows[name] ext = extension_rows.get(name) metrics = row_metrics(source, ext) cls = transition_class(source, ext) class_counts[cls] = class_counts.get(cls, 0) + 1 endpoint_by_class.setdefault(cls, []).append(metrics["endpoint_after"]) if cls == "thin_persists": thin_persist_rows.append(name) if cls in {"blank_medium_or_strong_beta_absent", "support_thickens_beta_blank"}: support_blank_full_rows.append(name) audit = audit_rows.get(name, {}) pres = prescan_rows.get(name, {}) rows.append({ "row": name, "source_beta_state": source.get("beta_state"), "source_support_transfer": source.get("support_transfer"), "source_beta_coordinate_transfer": source.get("beta_coordinate_transfer"), "source_coordinate_failure": audit.get("coordinate_failure"), "transition_class": cls, "extension_applied": ext is not None, "n_gaps_before": source.get("n_gaps"), **metrics, "denominator_state": pres.get("denominator_state"), "excluded_mass": pres.get("excluded_mass"), "source_domain_type_audit_only": pres.get("source_domain_type"), }) total = len(rows) support_transfer_after = sum( 1 for row in rows if row["transition_class"] not in {"fall_no_support", "support_falls_after_extension"} ) beta_chart_after = sum(1 for row in rows if row["beta_after"]) exact_beta_after = sum(1 for row in rows if row["beta_after"] == [0.3]) verdict = "TAXONOMY_SCALES_THIN_DISSOLVED" if thin_persist_rows: verdict = "TAXONOMY_FAILS_THIN_PERSISTS" return { "experiment": "boundary_transition_taxonomy_13rows", "question": "Does the post-extension transition taxonomy scale to all 13 BOUNDARY rows without leaving autonomous thin blanks?", "observables_registry": two_axis.get("observables_registry"), "observables_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", ], "sources": { "two_axis": args.two_axis, "row_audit": args.row_audit, "extension": args.extension, "prescan": args.prescan, }, "observable_contract": { "claim": "the short-denominator transition taxonomy scales if no 13-row member remains thin_persists after extension composition", "observable": "row-aligned transition_class across 13 rows", "operator": "composition of measured deposits, no regeneration", "denominator": "13 semi-real BOUNDARY rows", "non_possible": "autonomous blank_thin_support if any row remains thin_persists", "not_tested": "new beta grid, new null surrogates, V_c fit, source GUE/Poisson label validity", }, "label_policy": "source_domain_type is audit metadata only and is not used in transition_class.", "counts": { "total_rows": total, "support_transfer_after": support_transfer_after, "fall_after": total - support_transfer_after, "beta_chart_after_any": beta_chart_after, "beta_chart_after_exact_0_3": exact_beta_after, "thin_persist_rows": len(thin_persist_rows), "blank_medium_or_strong_beta_absent_rows": len(support_blank_full_rows), **{f"class_{key}": value for key, value in sorted(class_counts.items())}, }, "class_endpoint_means": { key: mean(values) for key, values in sorted(endpoint_by_class.items()) }, "thin_persist_rows": thin_persist_rows, "open_blank_rows": support_blank_full_rows, "verdict": verdict, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--two-axis", default="tools/data/boundary_two_axis_matrix_20260509_1532.json") parser.add_argument("--row-audit", default="tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json") parser.add_argument("--extension", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") parser.add_argument("--prescan", default="tools/data/boundary_denominator_prescan_full_20260509_1500.json") parser.add_argument("--out", default="tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json") args = parser.parse_args() output = build(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) with out.open("w") as f: json.dump(output, f, indent=2) c = output["counts"] print(f"rows={c['total_rows']}") print(f"support_transfer_after={c['support_transfer_after']}/{c['total_rows']}") print(f"fall_after={c['fall_after']}/{c['total_rows']}") print(f"beta_chart_after_any={c['beta_chart_after_any']}/{c['total_rows']}") print(f"thin_persist_rows={c['thin_persist_rows']}") print(f"open_blank_rows={output['open_blank_rows']}") for key, value in sorted(c.items()): if key.startswith("class_"): print(f"{key}={value}") print(f"verdict={output['verdict']}") print(f"saved {out}") if __name__ == "__main__": main() exec /bin/bash -lc "rg -n \"8 domini|5 Poisson|8 GUE|GUE.*Poisson|domain\" tools/data/reports tools/data/*.json applications/published applications/scoperte -g '*.md' -g '*.json'" in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,220p' tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_transition_taxonomy_13rows", "question": "Does the post-extension transition taxonomy scale to all 13 BOUNDARY rows without leaving autonomous thin blanks?", "observables_registry": "1.0.0-2026-05-06", "observables_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" ], "sources": { "two_axis": "tools/data/boundary_two_axis_matrix_20260509_1532.json", "row_audit": "tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json", "extension": "tools/data/boundary_short_denominator_extension_20260509_1556.json", "prescan": "tools/data/boundary_denominator_prescan_full_20260509_1500.json" }, "observable_contract": { "claim": "the short-denominator transition taxonomy scales if no 13-row member remains thin_persists after extension composition", "observable": "row-aligned transition_class across 13 rows", "operator": "composition of measured deposits, no regeneration", "denominator": "13 semi-real BOUNDARY rows", "non_possible": "autonomous blank_thin_support if any row remains thin_persists", "not_tested": "new beta grid, new null surrogates, V_c fit, source GUE/Poisson label validity" }, "label_policy": "source_domain_type is audit metadata only and is not used in transition_class.", "counts": { "total_rows": 13, "support_transfer_after": 10, "fall_after": 3, "beta_chart_after_any": 9, "beta_chart_after_exact_0_3": 6, "thin_persist_rows": 0, "blank_medium_or_strong_beta_absent_rows": 2, "class_beta_0_3_exact": 4, "class_beta_0_3_local_nonunique": 2, "class_beta_chart_recovered": 1, "class_blank_medium_or_strong_beta_absent": 1, "class_fall_no_support": 2, "class_local_beta_other": 1, "class_support_falls_after_extension": 1, "class_support_thickens_beta_blank": 1 }, "class_endpoint_means": { "beta_0_3_exact": 3.828607195910865, "beta_0_3_local_nonunique": 3.571458898683993, "beta_chart_recovered": 2.66042787119548, "blank_medium_or_strong_beta_absent": 3.2427469463217204, "fall_no_support": 1.6940454566312773, "local_beta_other": 4.308544591024874, "support_falls_after_extension": 0.0, "support_thickens_beta_blank": 3.78055565502901 }, "thin_persist_rows": [], "open_blank_rows": [ "numeri_primi:cycle_3", "random_matrix:cycle_7" ], "verdict": "TAXONOMY_SCALES_THIN_DISSOLVED", "rows": [ { "row": "brownian_motion:cycle_12", "source_beta_state": "beta_0_3_local_nonunique", "source_support_transfer": true, "source_beta_coordinate_transfer": false, "source_coordinate_failure": "adjacent_beta_interval", "transition_class": "beta_0_3_local_nonunique", "extension_applied": false, "n_gaps_before": 4096, "n_gaps_after": 4096, "one_sided_after": 4, "endpoint_after": 3.8849187765851725, "stable_count_coherent_after": 4.0, "beta_after": [ 0.2, 0.3 ], "support_tier_after": "strong_multi_observable", "denominator_state": "complete", "excluded_mass": 0.0, "source_domain_type_audit_only": "Poisson" }, { "row": "cellular_automata:cycle_8", "source_beta_state": "fall_no_support", "source_support_transfer": false, "source_beta_coordinate_transfer": false, "source_coordinate_failure": null, "transition_class": "fall_no_support", "extension_applied": false, "n_gaps_before": 108, "n_gaps_after": 108, "one_sided_after": 0, "endpoint_after": 0.0, "stable_count_coherent_after": 0.0, "beta_after": [], "support_tier_after": "thin_observable_support", "denominator_state": "contaminated", "excluded_mass": 0.79, "source_domain_type_audit_only": "GUE" }, { "row": "coupled_oscillators:cycle_10", "source_beta_state": "beta_0_3_exact", "source_support_transfer": true, "source_beta_coordinate_transfer": true, "source_coordinate_failure": null, "transition_class": "beta_0_3_exact", "extension_applied": false, "n_gaps_before": 2002, "n_gaps_after": 2002, "one_sided_after": 5, "endpoint_after": 4.336278592600956, "stable_count_coherent_after": 5.0, "beta_after": [ 0.3 ], "support_tier_after": "strong_multi_observable", "denominator_state": "contaminated", "excluded_mass": 0.146, "source_domain_type_audit_only": "Poisson" }, { "row": "ising_2d:cycle_1", "source_beta_state": "local_beta_other", "source_support_transfer": true, "source_beta_coordinate_transfer": false, "source_coordinate_failure": "coordinate_shifted", "transition_class": "local_beta_other", "extension_applied": false, "n_gaps_before": 699, "n_gaps_after": 699, "one_sided_after": 5, "endpoint_after": 4.308544591024874, "stable_count_coherent_after": 5.0, "beta_after": [ 0.4 ], "support_tier_after": "strong_multi_observable", "denominator_state": "contaminated", "excluded_mass": 0.81, "source_domain_type_audit_only": "GUE" }, { "row": "logistica_biforcazione:cycle_5", "source_beta_state": "beta_0_3_local_nonunique", "source_support_transfer": true, "source_beta_coordinate_transfer": false, "source_coordinate_failure": "beta_grid_saturation", "transition_class": "beta_0_3_local_nonunique", "extension_applied": false, "n_gaps_before": 4096, "n_gaps_after": 4096, "one_sided_after": 3, "endpoint_after": 3.2579990207828127, "stable_count_coherent_after": 3.5833333333333335, "beta_after": [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 ], "support_tier_after": "medium_multi_observable", "denominator_state": "complete", "excluded_mass": 0.0, "source_domain_type_audit_only": "GUE" }, { "row": "logistica_biforcazione_var_3.5699:cycle_13", "source_beta_state": "beta_0_3_exact", "source_support_transfer": true, "source_beta_coordinate_transfer": true, "source_coordinate_failure": null, "transition_class": "beta_0_3_exact", "extension_applied": false, "n_gaps_before": 4096, "n_gaps_after": 4096, "one_sided_after": 2, "endpoint_after": 2.768680237365777, "stable_count_coherent_after": 2.0, "beta_after": [ 0.3 ], "support_tier_after": "thin_observable_support", "denominator_state": "complete", "excluded_mass": 0.0, "source_domain_type_audit_only": "GUE" }, { "row": "numeri_primi:cycle_3", "source_beta_state": "support_without_beta_blank", "source_support_transfer": true, "source_beta_coordinate_transfer": false, "source_coordinate_failure": "blank_despite_multi_observable_support", "transition_class": "blank_medium_or_strong_beta_absent", "extension_applied": false, "n_gaps_before": 4096, "n_gaps_after": 4096, "one_sided_after": 3, "endpoint_after": 3.2427469463217204, "stable_count_coherent_after": 3.0, "beta_after": [], "support_tier_after": "medium_multi_observable", "denominator_state": "complete", "excluded_mass": 0.0, "source_domain_type_audit_only": "GUE" }, { "row": "pendolo_doppio:cycle_2", "source_beta_state": "fall_no_support", exec /bin/bash -lc "sed -n '1,220p' tools/data/boundary_two_axis_matrix_20260509_1532.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_two_axis_matrix", "question": "Separate support_transfer from beta_coordinate_transfer on the 13 semi-real BOUNDARY rows without using GUE/Poisson labels.", "source": "semireal_boundary_transfer_gate", "source_scope": "tools/data/boundary_denominator_prescan_full_20260509_1500.json", "observables_registry": "1.0.0-2026-05-06", "observables_used": [ "support_transfer", "beta_coordinate_transfer", "beta_state", "ambiguous_beta", "stable_count_coherent", "stable_count_illusory", "endpoint_distance" ], "label_policy": "GUE/Poisson source labels are not read by this operator.", "counts": { "rows": 13, "support_transfer_true": 11, "support_transfer_false": 2, "beta_coordinate_exact_0_3": 4, "beta_coordinate_local_nonunique_0_3": 2, "beta_coordinate_other": 1, "support_without_beta_blank": 4, "fall_no_support": 2, "support_transfer_ratio": 0.8461538461538461, "beta_coordinate_exact_0_3_ratio": 0.3076923076923077, "any_beta_blank_on_support": 7, "any_beta_blank_on_support_ratio": 0.5384615384615384 }, "rows": [ { "row": "brownian_motion:cycle_12", "support_transfer": true, "beta_coordinate_transfer": false, "beta_state": "beta_0_3_local_nonunique", "ambiguous_beta": [ 0.2, 0.3 ], "one_sided_observables": [ "SR2", "L1", "L2", "triple_var" ], "stable_count_coherent": 4.0, "stable_count_illusory": 0.16666666666666666, "endpoint_distance": 3.8849187765851725, "source_state": "transfer_with_blank", "n_gaps": 4096 }, { "row": "cellular_automata:cycle_8", "support_transfer": false, "beta_coordinate_transfer": false, "beta_state": "fall_no_support", "ambiguous_beta": [], "one_sided_observables": [], "stable_count_coherent": 0.0, "stable_count_illusory": 0.5, "endpoint_distance": 0.0, "source_state": "fall", "n_gaps": 108 }, { "row": "coupled_oscillators:cycle_10", "support_transfer": true, "beta_coordinate_transfer": true, "beta_state": "beta_0_3_exact", "ambiguous_beta": [ 0.3 ], "one_sided_observables": [ "SR", "SR2", "L1", "L2", "triple_var" ], "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "endpoint_distance": 4.336278592600956, "source_state": "transfer_with_blank", "n_gaps": 2002 }, { "row": "ising_2d:cycle_1", "support_transfer": true, "beta_coordinate_transfer": false, "beta_state": "local_beta_other", "ambiguous_beta": [ 0.4 ], "one_sided_observables": [ "SR", "SR2", "L1", "L2", "triple_var" ], "stable_count_coherent": 5.0, "stable_count_illusory": 0.3333333333333333, "endpoint_distance": 4.308544591024874, "source_state": "transfer_with_blank", "n_gaps": 699 }, { "row": "logistica_biforcazione:cycle_5", "support_transfer": true, "beta_coordinate_transfer": false, "beta_state": "beta_0_3_local_nonunique", "ambiguous_beta": [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 ], "one_sided_observables": [ "SR", "SR2", "triple_var" ], "stable_count_coherent": 3.5833333333333335, "stable_count_illusory": 0.16666666666666666, "endpoint_distance": 3.2579990207828127, "source_state": "transfer_with_blank", "n_gaps": 4096 }, { "row": "logistica_biforcazione_var_3.5699:cycle_13", "support_transfer": true, "beta_coordinate_transfer": true, "beta_state": "beta_0_3_exact", "ambiguous_beta": [ 0.3 ], "one_sided_observables": [ "SR", "SR2" ], "stable_count_coherent": 2.0, "stable_count_illusory": 0.08333333333333333, "endpoint_distance": 2.768680237365777, "source_state": "transfer_with_blank", "n_gaps": 4096 }, { "row": "numeri_primi:cycle_3", "support_transfer": true, "beta_coordinate_transfer": false, "beta_state": "support_without_beta_blank", "ambiguous_beta": [], "one_sided_observables": [ "SR", "L1", "triple_var" ], "stable_count_coherent": 3.0, "stable_count_illusory": 0.16666666666666666, "endpoint_distance": 3.2427469463217204, "source_state": "transfer_no_blank", "n_gaps": 4096 }, { "row": "pendolo_doppio:cycle_2", "support_transfer": false, "beta_coordinate_transfer": false, "beta_state": "fall_no_support", "ambiguous_beta": [ 0.3 ], "one_sided_observables": [ "SR", "SR2", "L1" ], "stable_count_coherent": 5.0, "stable_count_illusory": 0.8333333333333334, "endpoint_distance": 3.3880909132625545, "source_state": "fall", "n_gaps": 4096 }, { "row": "percolation:cycle_9", "support_transfer": true, "beta_coordinate_transfer": false, "beta_state": "support_without_beta_blank", "ambiguous_beta": [], "one_sided_observables": [ "L1" ], "stable_count_coherent": 0.8333333333333334, "stable_count_illusory": 0.3333333333333333, "endpoint_distance": 1.7053713197497773, "source_state": "transfer_no_blank", "n_gaps": 190 }, { "row": "random_matrix:cycle_7", "support_transfer": true, "beta_coordinate_transfer": false, "beta_state": "support_without_beta_blank", "ambiguous_beta": [], "one_sided_observables": [ "L2" ], "stable_count_coherent": 1.0, "stable_count_illusory": 0.25, "endpoint_distance": 1.8543171371625617, "source_state": "transfer_no_blank", "n_gaps": 199 }, { "row": "reaction_diffusion:cycle_11", succeeded in 1ms: tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1126: "joint_boundary_v_by_domain": { tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1144: "domain_r": 0.5629284231397236, tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1147: "domain_ipr": 0.02398789865855508, tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1156: "domain_r": 0.5715249322307006, tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1159: "domain_ipr": 0.020915816210757762, tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1168: "domain_r": 0.6308382799408139, tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1171: "domain_ipr": 0.017649532726936125, tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json:1180: "domain_r": 0.5575930330261891, 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tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:507: "domain_window": "Anderson3D_W_20.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:516: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:523: "domain_window": "Anderson3D_W_24.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:532: "source_domain_type": "localized_poisson_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:539: "domain_window": "Anderson3D_W_32.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:548: "source_domain_type": "localized_poisson_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:590: "domain_window": "Anderson3D_W_2.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:612: "domain_window": "Anderson3D_W_4.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:634: "domain_window": "Anderson3D_W_8.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:656: "domain_window": "Anderson3D_W_12.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:678: "domain_window": "Anderson3D_W_14.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:701: "domain_window": "Anderson3D_W_16.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:725: "domain_window": "Anderson3D_W_16.50", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:748: "domain_window": "Anderson3D_W_17.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:770: "domain_window": "Anderson3D_W_20.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:792: "domain_window": "Anderson3D_W_24.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:814: "domain_window": "Anderson3D_W_32.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json:837: "non_possible": "cross-domain transfer if no W row is stable_graph_bridge+classical_intermediate at every tested size", tools/data/boundary_bridge_stability_audit_20260515_1915.json:5: "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid", tools/data/boundary_bridge_stability_audit_20260515_1915.json:49: "domain": "brownian_motion", tools/data/boundary_bridge_stability_audit_20260515_1915.json:50: "domain_window": "brownian_motion:cycle_12", tools/data/boundary_bridge_stability_audit_20260515_1915.json:55: "source_domain_type": "Poisson", tools/data/boundary_bridge_stability_audit_20260515_1915.json:64: "domain": "cellular_automata", tools/data/boundary_bridge_stability_audit_20260515_1915.json:65: "domain_window": "cellular_automata:cycle_8", tools/data/boundary_bridge_stability_audit_20260515_1915.json:70: "source_domain_type": "GUE", tools/data/boundary_bridge_stability_audit_20260515_1915.json:79: "domain": "coupled_oscillators", tools/data/boundary_bridge_stability_audit_20260515_1915.json:80: "domain_window": "coupled_oscillators:cycle_10", tools/data/boundary_bridge_stability_audit_20260515_1915.json:85: "source_domain_type": "Poisson", tools/data/boundary_bridge_stability_audit_20260515_1915.json:94: "domain": "ising_2d", tools/data/boundary_bridge_stability_audit_20260515_1915.json:95: "domain_window": "ising_2d:cycle_1", tools/data/boundary_bridge_stability_audit_20260515_1915.json:100: "source_domain_type": "GUE", tools/data/boundary_bridge_stability_audit_20260515_1915.json:109: "domain": "logistica_biforcazione", tools/data/boundary_bridge_stability_audit_20260515_1915.json:110: "domain_window": "logistica_biforcazione:cycle_5", tools/data/boundary_bridge_stability_audit_20260515_1915.json:115: "source_domain_type": "GUE", tools/data/boundary_bridge_stability_audit_20260515_1915.json:124: "domain": "logistica_biforcazione_var_3.5699", tools/data/boundary_bridge_stability_audit_20260515_1915.json:125: "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", tools/data/boundary_bridge_stability_audit_20260515_1915.json:130: "source_domain_type": "GUE", tools/data/boundary_bridge_stability_audit_20260515_1915.json:139: "domain": "numeri_primi", tools/data/boundary_bridge_stability_audit_20260515_1915.json:140: "domain_window": "numeri_primi:cycle_3", tools/data/boundary_bridge_stability_audit_20260515_1915.json:145: "source_domain_type": "GUE", tools/data/boundary_bridge_stability_audit_20260515_1915.json:154: "domain": "pendolo_doppio", tools/data/boundary_bridge_stability_audit_20260515_1915.json:155: "domain_window": "pendolo_doppio:cycle_2", tools/data/boundary_bridge_stability_audit_20260515_1915.json:160: "source_domain_type": "Poisson", tools/data/boundary_bridge_stability_audit_20260515_1915.json:169: "domain": "percolation", tools/data/boundary_bridge_stability_audit_20260515_1915.json:170: "domain_window": "percolation:cycle_9", tools/data/boundary_bridge_stability_audit_20260515_1915.json:175: "source_domain_type": "Poisson", tools/data/boundary_bridge_stability_audit_20260515_1915.json:184: "domain": "random_matrix", tools/data/boundary_bridge_stability_audit_20260515_1915.json:185: "domain_window": "random_matrix:cycle_7", tools/data/boundary_bridge_stability_audit_20260515_1915.json:190: "source_domain_type": "GUE", tools/data/boundary_bridge_stability_audit_20260515_1915.json:199: "domain": "reaction_diffusion", tools/data/boundary_bridge_stability_audit_20260515_1915.json:200: "domain_window": "reaction_diffusion:cycle_11", tools/data/boundary_bridge_stability_audit_20260515_1915.json:205: "source_domain_type": "GUE", tools/data/boundary_bridge_stability_audit_20260515_1915.json:214: "domain": "string_vibration", tools/data/boundary_bridge_stability_audit_20260515_1915.json:215: "domain_window": "string_vibration:cycle_6", tools/data/boundary_bridge_stability_audit_20260515_1915.json:220: "source_domain_type": "Poisson", tools/data/boundary_bridge_stability_audit_20260515_1915.json:229: "domain": "zeta_zeros", tools/data/boundary_bridge_stability_audit_20260515_1915.json:230: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_bridge_stability_audit_20260515_1915.json:235: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:3: "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", tools/data/boundary_denominator_prescan_20260509_1430.json:4: "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", tools/data/boundary_denominator_prescan_20260509_1430.json:8: "operator": "row-aligned domain/window prescan", tools/data/boundary_denominator_prescan_20260509_1430.json:26: "by_source_domain_type": { tools/data/boundary_denominator_prescan_20260509_1430.json:51: "domain_window": "ising_2d:cycle_1", tools/data/boundary_denominator_prescan_20260509_1430.json:52: "domain": "ising_2d", tools/data/boundary_denominator_prescan_20260509_1430.json:54: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:67: "domain_key": "ising_2d", tools/data/boundary_denominator_prescan_20260509_1430.json:76: "domain_window": "pendolo_doppio:cycle_2", tools/data/boundary_denominator_prescan_20260509_1430.json:77: "domain": "pendolo_doppio", tools/data/boundary_denominator_prescan_20260509_1430.json:79: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:92: "domain_key": "pendolo_doppio", tools/data/boundary_denominator_prescan_20260509_1430.json:101: "domain_window": "numeri_primi:cycle_3", tools/data/boundary_denominator_prescan_20260509_1430.json:102: "domain": "numeri_primi", tools/data/boundary_denominator_prescan_20260509_1430.json:104: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:117: "domain_key": "primes", tools/data/boundary_denominator_prescan_20260509_1430.json:126: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_denominator_prescan_20260509_1430.json:127: "domain": "zeta_zeros", tools/data/boundary_denominator_prescan_20260509_1430.json:129: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:142: "domain_key": "zeta_zeros", tools/data/boundary_denominator_prescan_20260509_1430.json:151: "domain_window": "logistica_biforcazione:cycle_5", tools/data/boundary_denominator_prescan_20260509_1430.json:152: "domain": "logistica_biforcazione", tools/data/boundary_denominator_prescan_20260509_1430.json:154: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:167: "domain_key": "logistic", tools/data/boundary_denominator_prescan_20260509_1430.json:176: "domain_window": "string_vibration:cycle_6", tools/data/boundary_denominator_prescan_20260509_1430.json:177: "domain": "string_vibration", tools/data/boundary_denominator_prescan_20260509_1430.json:179: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:192: "domain_key": null, tools/data/boundary_denominator_prescan_20260509_1430.json:201: "domain_window": "random_matrix:cycle_7", tools/data/boundary_denominator_prescan_20260509_1430.json:202: "domain": "random_matrix", tools/data/boundary_denominator_prescan_20260509_1430.json:204: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:217: "domain_key": "gue", tools/data/boundary_denominator_prescan_20260509_1430.json:226: "domain_window": "cellular_automata:cycle_8", tools/data/boundary_denominator_prescan_20260509_1430.json:227: "domain": "cellular_automata", tools/data/boundary_denominator_prescan_20260509_1430.json:229: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:242: "domain_key": "cell_auto", tools/data/boundary_denominator_prescan_20260509_1430.json:251: "domain_window": "percolation:cycle_9", tools/data/boundary_denominator_prescan_20260509_1430.json:252: "domain": "percolation", tools/data/boundary_denominator_prescan_20260509_1430.json:254: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:267: "domain_key": "percolation", tools/data/boundary_denominator_prescan_20260509_1430.json:276: "domain_window": "coupled_oscillators:cycle_10", tools/data/boundary_denominator_prescan_20260509_1430.json:277: "domain": "coupled_oscillators", tools/data/boundary_denominator_prescan_20260509_1430.json:279: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:292: "domain_key": "coupled_osc", tools/data/boundary_denominator_prescan_20260509_1430.json:301: "domain_window": "reaction_diffusion:cycle_11", tools/data/boundary_denominator_prescan_20260509_1430.json:302: "domain": "reaction_diffusion", tools/data/boundary_denominator_prescan_20260509_1430.json:304: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:317: "domain_key": null, tools/data/boundary_denominator_prescan_20260509_1430.json:326: "domain_window": "brownian_motion:cycle_12", tools/data/boundary_denominator_prescan_20260509_1430.json:327: "domain": "brownian_motion", tools/data/boundary_denominator_prescan_20260509_1430.json:329: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:342: "domain_key": "brownian", tools/data/boundary_denominator_prescan_20260509_1430.json:351: "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", tools/data/boundary_denominator_prescan_20260509_1430.json:352: "domain": "logistica_biforcazione_var_3.5699", tools/data/boundary_denominator_prescan_20260509_1430.json:354: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:367: "domain_key": null, tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:28: "domain": "ising_2d", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:29: "domain_window": "ising_2d:cycle_1", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:57: "source_domain_type": "GUE", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:70: "domain": "pendolo_doppio", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:71: "domain_window": "pendolo_doppio:cycle_2", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:99: "source_domain_type": "Poisson", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:112: "domain": "numeri_primi", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:113: "domain_window": "numeri_primi:cycle_3", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:141: "source_domain_type": "GUE", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:153: "domain": "zeta_zeros", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:154: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:182: "source_domain_type": "GUE", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:193: "domain": "logistica_biforcazione", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:194: "domain_window": "logistica_biforcazione:cycle_5", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:222: "source_domain_type": "GUE", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:235: "domain": "string_vibration", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:236: "domain_window": "string_vibration:cycle_6", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:264: "source_domain_type": "Poisson", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:277: "domain": "random_matrix", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:278: "domain_window": "random_matrix:cycle_7", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:306: "source_domain_type": "GUE", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:319: "domain": "cellular_automata", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:320: "domain_window": "cellular_automata:cycle_8", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:348: "source_domain_type": "GUE", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:361: "domain": "percolation", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:362: "domain_window": "percolation:cycle_9", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:390: "source_domain_type": "Poisson", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:400: "domain": "coupled_oscillators", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:401: "domain_window": "coupled_oscillators:cycle_10", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:429: "source_domain_type": "Poisson", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:442: "domain": "reaction_diffusion", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:443: "domain_window": "reaction_diffusion:cycle_11", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:471: "source_domain_type": "GUE", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:483: "domain": "brownian_motion", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:484: "domain_window": "brownian_motion:cycle_12", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:512: "source_domain_type": "Poisson", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:525: "domain": "logistica_biforcazione_var_3.5699", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:526: "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json:554: "source_domain_type": "GUE", tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:22: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:84: "domain_window": "brownian_motion:cycle_12" tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:118: "domain_window": "ising_2d:cycle_1" tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:158: "domain_window": "logistica_biforcazione:cycle_5" tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:188: "domain_window": "numeri_primi:cycle_3" tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:216: "domain_window": "percolation:cycle_9" tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:244: "domain_window": "random_matrix:cycle_7" tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json:273: "domain_window": "zeta_zeros:cycle_4" tools/data/boundary_two_axis_matrix_20260509_1532.json:3: "question": "Separate support_transfer from beta_coordinate_transfer on the 13 semi-real BOUNDARY rows without using GUE/Poisson labels.", tools/data/boundary_two_axis_matrix_20260509_1532.json:16: "label_policy": "GUE/Poisson source labels are not read by this operator.", tools/data/brody_calibration_results.json:414: "real_domains": { tools/data/ciclo_memoria.json:433: "direzione_corrente": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/component_state_fit_ready_20260514_1649.json:29: "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", 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/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:590: "raw": "qui diventa possibile separare le proprieta del gap dei primi in scala-dipendenti (che decadono con PNT) e scala-invarianti (che sono vincoli permanenti). Qui diventa non-possibile usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati.", tools/data/conoscenza_teorie.json:592: "non_possibile": "usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati" tools/data/conoscenza_teorie.json:704: "claim": "If acf(k) ~ -A(p)/k, then via Wiener-Khinchin the PSD low-frequency suppression should track A(p). The two measurements (time-domain ACF, frequency-domain PSD) must give consistent Poisson crossover predictions.", tools/data/conoscenza_teorie.json:765: "claim": "If acf(k) ~ -A(p)/k, then via Wiener-Khinchin the PSD low-frequency suppression should track A(p). The two measurements (time-domain ACF, frequency-domain PSD) must give consistent Poisson crossover predictions.", tools/data/conoscenza_teorie.json:824: "claim": "The boundary between GUE and Poisson is \"the third included\" (A9). Is this boundary populated by multiple domains, or are primes special?", 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:866: "raw": "qui diventa possibile separare le proprieta del gap dei primi in scala-dipendenti (che decadono con PNT) e scala-invarianti (che sono vincoli permanenti). Qui diventa non-possibile usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati.", tools/data/conoscenza_teorie.json:868: "non_possibile": "usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati" 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:999: "raw": "qui diventa possibile separare le proprieta del gap dei primi in scala-dipendenti (che decadono con PNT) e scala-invarianti (che sono vincoli permanenti). Qui diventa non-possibile usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati.", tools/data/conoscenza_teorie.json:1001: "non_possibile": "usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati" tools/data/conoscenza_teorie.json:1101: "claim": "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.", tools/data/conoscenza_teorie.json:1106: "singolare": "domain/window prima della classificazione GUE/Poisson.", tools/data/cross_domain_dipolar_direction.json:2: "domains": { tools/data/endpoint_gated_rp_size_ladder_20260516_1111.json:71: "generator": "GUE matrices, Poisson exponential spacings, RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", tools/data/endpoint_gated_rp_size_ladder_20260516_1111.json:75: "operator": "calibrate GUE/Poisson endpoint centroids; score RP rows by balanced distance to both endpoint centroids; compare to feature-scrambled RP rows" tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:89: "domain_window": "Anderson3D_W_2.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:98: "source_domain_type": "metallic_wigner_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:105: "domain_window": "Anderson3D_W_4.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:114: "source_domain_type": "metallic_wigner_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:121: "domain_window": "Anderson3D_W_8.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:130: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:137: "domain_window": "Anderson3D_W_12.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:146: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:153: "domain_window": "Anderson3D_W_14.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:162: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:169: "domain_window": "Anderson3D_W_16.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:178: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:185: "domain_window": "Anderson3D_W_16.50", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:194: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:201: "domain_window": "Anderson3D_W_17.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:210: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:217: "domain_window": "Anderson3D_W_20.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:226: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:233: "domain_window": "Anderson3D_W_24.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:242: "source_domain_type": "localized_poisson_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:249: "domain_window": "Anderson3D_W_32.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:258: "source_domain_type": "localized_poisson_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:379: "domain_window": "Anderson3D_W_2.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:388: "source_domain_type": "metallic_wigner_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:395: "domain_window": "Anderson3D_W_4.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:404: "source_domain_type": "metallic_wigner_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:411: "domain_window": "Anderson3D_W_8.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:420: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:427: "domain_window": "Anderson3D_W_12.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:436: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:443: "domain_window": "Anderson3D_W_14.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:452: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:459: "domain_window": "Anderson3D_W_16.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:468: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:475: "domain_window": "Anderson3D_W_16.50", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:484: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:491: "domain_window": "Anderson3D_W_17.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:500: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:507: "domain_window": "Anderson3D_W_20.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:516: "source_domain_type": "mobility_candidate", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:523: "domain_window": "Anderson3D_W_24.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:532: "source_domain_type": "localized_poisson_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:539: "domain_window": "Anderson3D_W_32.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:548: "source_domain_type": "localized_poisson_pole", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:590: "domain_window": "Anderson3D_W_2.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:612: "domain_window": "Anderson3D_W_4.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:634: "domain_window": "Anderson3D_W_8.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:656: "domain_window": "Anderson3D_W_12.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:678: "domain_window": "Anderson3D_W_14.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:701: "domain_window": "Anderson3D_W_16.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:725: "domain_window": "Anderson3D_W_16.50", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:748: "domain_window": "Anderson3D_W_17.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:770: "domain_window": "Anderson3D_W_20.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:792: "domain_window": "Anderson3D_W_24.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:814: "domain_window": "Anderson3D_W_32.00", tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json:837: "non_possible": "cross-domain transfer if no W row is stable_graph_bridge+classical_intermediate at every tested size", tools/data/endpoint_feature_scramble_null_20260516_1058.json:690: "claim": "GUE/Poisson endpoint filter is specific if observed endpoint stability remains complete and feature-scramble nulls do not reconstruct complete stability", tools/data/endpoint_feature_scramble_null_20260516_1058.json:691: "denominator": "2 domains x sizes x test seeds source rows; each row requires all readers to pass", tools/data/endpoint_feature_scramble_null_20260516_1058.json:692: "generator": "GUE matrices and Poisson exponential spacings", tools/data/engine_state.json:27: "source": "cross_domain" tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1602: "domain": "phi", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1613: "domain": "silver", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1624: "domain": "bronze", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1635: "domain": "periodic_ab", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1646: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1658: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1670: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1682: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1694: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1706: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1718: "domain": "phi", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1729: "domain": "silver", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1740: "domain": "bronze", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1751: "domain": "periodic_ab", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1762: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1774: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1786: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1798: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1810: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1822: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1834: "domain": "phi", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1845: "domain": "silver", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1856: "domain": "bronze", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1867: "domain": "periodic_ab", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1878: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1890: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1902: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1914: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:1926: "domain": "balanced_random_phi_density", 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tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:13942: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:13954: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:13966: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:13978: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:13990: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14002: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14014: "domain": "phi", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14025: "domain": "silver", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14036: "domain": "bronze", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14047: "domain": "periodic_ab", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14058: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14070: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14082: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14094: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14106: "domain": "balanced_random_phi_density", tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json:14118: "domain": "balanced_random_phi_density", tools/data/reports/agent_20260507_0923.md:84: protocol fold in the synthetic matrix, not a cross-domain coordinate. tools/data/reports/agent_20260507_2310.md:6:**observables_used**: blank_edge_is_GQ_count, any_deposit_on_blank_count, full_scale_law_count, shell_face_count - osservabili domain-native del denominatore blank-shell; nessun osservabile canonico SR/SR2/L1/L2/triple_var usato tools/data/reports/agent_20260507_2310.md:18:- Metrica: quattro osservabili domain-native del denominatore: lato blank fissato su QG, qualunque deposito sul guscio blank, legge completa del guscio, numero di facce del guscio. tools/data/reports/agent_20260515_1855.md:7:**observable_contract**: claim=il confine GUE/Poisson e' operativo quando la geometria row-aligned produce nodi cross-label a margine basso; observable=kNN graph position, cross-neighbor fraction, centroid margin, unweighted Forman edge curvature; operator=grafo kNN nello spazio canonical+rigidity+shuffle-z; generator=dnd_autoricerca row_spacings via semireal boundary transfer gate; denominator=13 righe base BOUNDARY, 8 GUE e 5 Poisson; non_possible=terzo incluso se non compaiono edge cross-label o se tutti gli edge cross-label restano interni ad alta margin; not_tested=V_c, denominatori Sturmian, origine analitica delle label. tools/data/reports/agent_20260515_1855.md:10:- **Combo**: A9 terzo incluso + incrocio QxG continuo/discreto + grafo della conoscenza come nodo/cut + tensione BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260515_1855.md:13:- **Operatori laterali scelti**: graph spectrum/curvature, spectral rigidity, GUE/Poisson. La rigidita' entra come osservabile esplicita, GUE/Poisson solo come audit label. tools/data/reports/agent_20260515_1855.md:16:- **Proiezione**: se il terzo incluso e' operativo, il grafo kNN delle 13 righe deve produrre edge GUE/Poisson e almeno una riga a margine basso; se il boundary e' solo tassonomia, il grafo resta in due componenti o attraversa solo con margin alta. tools/data/reports/agent_20260515_1855.md:20:- `why`: il ciclo misura esplicitamente il perimetro vivo 8 GUE / 5 Poisson e chiede dove il confine funziona come terzo incluso operativo. tools/data/reports/agent_20260515_1855.md:21:- `not_drift`: non usa il report Sturmian bloccato, non misura V_c, non usa phi/silver/bronze come sorgente; le label GUE/Poisson sono audit metadata, non operatore decisionale. tools/data/reports/agent_20260515_1855.md:24:> Nel perimetro base BOUNDARY, il terzo incluso appare come sottoinsieme di righe cross-label a margine basso nel grafo degli osservabili, non come l'intera divisione GUE/Poisson. tools/data/reports/agent_20260515_1855.md:27:Il confine 8 GUE / 5 Poisson resta una separazione binaria o produce nodi ponte misurabili nel grafo osservabile? tools/data/reports/agent_20260515_1855.md:39:- **Scope**: `boundary_denominator_prescan_full_20260509_1500.json`, righe base BOUNDARY con `source_domain_type in {GUE, Poisson}`. tools/data/reports/agent_20260515_1855.md:43:- **Contratto osservabile-operatore**: il claim usa posizione nel grafo, frazione di vicini cross-label, margin ai centroidi GUE/Poisson e curvatura Forman non pesata. `gap_ratio`, `V_c` e denominatori Sturmian non sono testati. tools/data/reports/agent_20260515_1855.md:49:| GUE / Poisson rows | 8 / 5 | tools/data/reports/agent_20260515_1855.md:70:1. Verificato: il perimetro e' quello richiesto, 13 righe con 8 GUE e 5 Poisson, senza errori di ricostruzione. tools/data/reports/agent_20260515_1855.md:71:2. Verificato: la separazione non e' binaria nel grafo osservabile; 8/27 edge attraversano la label GUE/Poisson. tools/data/reports/agent_20260515_1855.md:78:Il boundary trasferisce come geometria di righe ponte nel perimetro 8/5. Non trasferisce come singolo scalare di curvatura e non autorizza una tassonomia pulita GUE vs Poisson. tools/data/reports/agent_20260515_1855.md:81:- **Due radici**: label spettrale GUE/Poisson; posizione geometrica nel grafo osservabile. tools/data/reports/agent_20260515_1826.md:52:- Contratto osservabile-operatore: il ciclo testa denominator alignment; non testa `gap_ratio`, `V_c`, PSD surrogate quality, limite asintotico o universalita GUE/Poisson. tools/data/reports/agent_20260424_0330.md:8:> The GUE/Poisson classification of 13 domains is treated as a structural finding. But is it a property of sequential correlations (genuine) or of the gap distribution shape alone (tautological)? tools/data/reports/agent_20260424_0330.md:11:If I shuffle the gap sequence of each domain (destroying ordering, preserving distribution), does the GUE/Poisson classification survive? tools/data/reports/agent_20260424_0330.md:17:- **Scope**: 10 domains (primes, GUE matrices, Poisson, logistic, Fibonacci spectrum, Ising 2D, percolation, Brownian, coupled oscillators, cellular automata) tools/data/reports/agent_20260424_0330.md:18:- **Reference**: R_GUE = 0.5307, R_Poisson = 0.3863 tools/data/reports/agent_20260424_0330.md:26:| fibonacci | 609 | 0.4782 | 0.4073 | +8.0 | GUE | **Poisson** | STRUCTURAL + FLIP | tools/data/reports/agent_20260424_0330.md:27:| coupled_osc | 427 | 0.8775 | 0.4146 | +43.5 | GUE | **Poisson** | STRUCTURAL + FLIP | tools/data/reports/agent_20260424_0330.md:28:| percolation | 510 | 0.6551 | 0.4508 | +16.1 | GUE | **Poisson** | STRUCTURAL + FLIP | tools/data/reports/agent_20260424_0330.md:39:1. **Two kinds of GUE.** The GUE-classified domains split into two fundamentally different categories: tools/data/reports/agent_20260424_0330.md:41: - **Ordering-GUE** (fibonacci, coupled oscillators, percolation): the gap distribution is Poisson. The GUE classification exists ONLY because of sequential ordering. Destroy the order and they collapse to Poisson. r-shifts are massive: +0.07 to +0.46. tools/data/reports/agent_20260424_0330.md:43:2. **Primes are distribution-GUE.** Shuffled prime gaps still give r=0.4813 (GUE side). The ordering pushes r DOWN by 0.019 (z=-26.6), adding extra gap repulsion beyond what the distribution predicts. This is the opposite sign from ordering-GUE domains (where ordering pushes r UP). tools/data/reports/agent_20260424_0330.md:45:3. **The sign of delta_r is a discriminant.** Distribution-GUE domains have delta_r < 0 (ordering increases repulsion). Ordering-GUE domains have delta_r > 0 (ordering creates attraction/clustering that looks like level repulsion in the r-statistic). The sign tells you which mechanism drives the classification. tools/data/reports/agent_20260424_0330.md:47:4. **3/8 GUE domains are ordering-GUE.** The BOUNDARY claim "8 GUE, 5 Poisson" conflates two distinct mechanisms. The refined picture: 2 distribution-GUE (primes, GUE), 3 ordering-GUE (fibonacci, coupled_osc, percolation), 2 small-N ambiguous (ising, cell_auto), 3 Poisson (poisson, brownian, logistic). The logistic map is Poisson at distribution level but has massive ordering structure (z=+61.6) that doesn't flip the class. tools/data/reports/agent_20260424_0330.md:49:5. **META constraint confirmed.** A binary GUE/Poisson test that doesn't include a shuffle control conflates the two mechanisms. Testing "is r closer to 0.53 or 0.39?" is necessary but insufficient — it doesn't distinguish whether the ordering or the distribution is the source. tools/data/reports/agent_20260424_0330.md:54:The BOUNDARY claim must be refined: the 8 GUE domains are not homogeneous. Two distinct mechanisms generate GUE statistics. The boundary between GUE and Poisson has two layers: distribution-level and ordering-level. The sign of delta_r = r_original - r_shuffled discriminates which layer operates. tools/data/reports/next_exec_20260405_0729.json:22: "action": "EXPLORE: BOUNDARY — 8 domini GUE, 5 Poisson — il confine è il terzo incluso oper", tools/data/reports/agent_20260509_1337.md:7:observables_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 tools/data/reports/report_20260329_0343.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/falsifier_20260514_1649.json:10: "evidence": "La lente L6 richiede che la contaminazione cognitiva nomini almeno una voce CE-*, YSN, Cornelius, KSAR, PVI/Vault usata nella combo, oppure dichiari 'CE-none:' con motivo specifico. Nel report fornito compaiono A2, A9, A11, ponte QxG, graph completion 16:40 e direzione BOUNDARY GUE/Poisson, ma nessun metabolizzatore cognitivo esplicito.", tools/data/reports/agent_20260506_1955.md:13:This run therefore does not test perturbation dimensionality. It asks a cross-domain META question: tools/data/reports/agent_20260506_1955.md:15:> When the five canonical observables are measured under uniform partial shuffle, does observable collinearity break in structured domains, or only in controls where original-vs-shuffle denominators are weak? tools/data/reports/agent_20260506_1955.md:24:- added `prime_shuffle` as a control domain; tools/data/reports/agent_20260506_1955.md:28:- domains: first prime gaps, prime-shuffle control, independent GUE spacings, iid Poisson spacings; tools/data/reports/agent_20260506_1955.md:55:1. **Structured domains compress the five canonical retention curves to one dominant coordinate in this perimeter.** Primes and GUE both have PC1 > 0.93 on average and effective rank close to 1. This does not say the domains are the same; it says uniform partial shuffle moves the canonical observables along one dominant retention mode. tools/data/reports/agent_20260506_1955.md:61:4. **GUE is the cleanest conditioning check.** In the main run, all five GUE observables pass the denominator gate and still give rank `1.060`. This makes GUE the best positive control for "low rank despite valid denominators." Poisson is the negative control for "high rank without valid denominators." tools/data/reports/agent_20260506_1955.md:66:> observables_registry version + canonical observable list + original-vs-shuffle z per observable + control domains. tools/data/reports/agent_20260506_1955.md:68:In this perimeter, high observable-rank is not the sign of richer structure when it appears in Poisson or prime-shuffle controls; it is a warning that retention ratios are being formed on weak denominators. The stable cross-domain result is narrower: tools/data/reports/agent_20260506_1955.md:70:> uniform partial shuffle exposes one dominant retention coordinate in conditioned structured domains (primes, GUE), while apparent multi-coordinate behavior in Poisson/shuffle controls is denominator-weak. tools/data/reports/agent_20260506_1955.md:73:What opens now: the next non-redundant test is not another PCA audit. It is **selective operator coupling**: use perturbations that act separately on pair-scale and triple-scale structure, but report only observables whose denominator gate survives. If selective operators rotate primes while GUE stays collinear, the lab gets a real cross-domain discriminator; if both stay collinear, the current observable suite is overcomplete for this question. tools/data/reports/agent_20260509_1556.md:7:observables_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. tools/data/reports/agent_20260509_1556.md:9:**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. tools/data/reports/agent_20260509_1556.md:25:- `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. tools/data/reports/agent_20260509_1556.md:40:- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field. 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: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:71:Constraint: future BOUNDARY experiments should distinguish which channel they're measuring. The GUE/Poisson transition is a gap-level phenomenon; the residue channel is immune to it. tools/data/reports/agent_20260423_0330.md:78:- **Campo di possibilita**: qui diventa possibile separare le proprieta del gap dei primi in scala-dipendenti (che decadono con PNT) e scala-invarianti (che sono vincoli permanenti). Qui diventa non-possibile usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati. tools/data/reports/agent_20260430_1905.md:1:# Agent Report — Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar tools/data/reports/agent_20260430_1905.md:9:> Do different observables agree on WHERE primes sit between GUE and Poisson? tools/data/reports/agent_20260430_1905.md:25:- **References**: pure GUE from 50 random Hermitian matrices, pure Poisson from exponential draws tools/data/reports/agent_20260430_1905.md:70:2. **The two ordering-sensitive observables form a dipole.** Spacing ratio is pushed TOWARD Poisson by ordering (Δτ = −0.12), while lag-1 autocorrelation is pushed TOWARD GUE (Δτ = +0.20). The same physical phenomenon — consecutive gap anticorrelation (Lemke Oliver-Soundararajan type) — manifests as Poisson in one measure and GUE in another. Primes are not "between" GUE and Poisson on a single axis. They are dipolar: GUE in correlation structure, Poisson in consecutive ratio behavior. tools/data/reports/agent_20260430_1905.md:84:- **BOUNDARY**: the boundary is not a point on a one-dimensional axis between GUE and Poisson. It is a two-dimensional structure: one axis for distribution (all observables agree), one axis for ordering (the dipole between spacing_ratio and lag1_acf). The terzo incluso is the dipole — it doesn't interpolate between GUE and Poisson, it has a structure that neither has. tools/data/reports/agent_20260430_1905.md:96:- **Campo di possibilità**: possibile → characterize prime ordering as a 2D vector (spacing_ratio shift, lag1_acf shift) rather than a single GUE-Poisson interpolation parameter. Non-possibile → reduce prime ordering to a single β value and claim it captures the structure. tools/data/reports/agent_20260421_0330.md:95:This constrains META: residue channel tests pass because they test robust algebraic properties of Z/6Z structure, not because they're tautological. The real discriminant for C1 (primes as unique domain) lives in the magnitude channel. tools/data/reports/falsifier_20260515_1826.json:10: "evidence": "`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.", tools/data/reports/falsifier_20260515_1826.json:11: "suggestion": "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." tools/data/reports/falsifier_20260515_1826.json:14: "summary": "Il report è internamente coerente sui dati Sturmian, ma si rompe su L8: dichiara aderenza alla direzione viva mentre lavora il residuo Sturmian precedente invece del confine 8 GUE / 5 Poisson richiesto dal seme." tools/data/reports/report_20260402_0344.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260508_1632.md:24:| domain | conditions | global Jaccard median | global Jaccard min | phase Jaccard median | threshold Jaccard median | scale Jaccard median | core labels all conditions | tools/data/reports/agent_20260508_1632.md:33:| domain | median label error | median selected gaps | median large gaps | tools/data/reports/agent_20260509_0819.md:7:observables_used: [`label_jaccard`, `hamming_ratio`, `source_mode`, `acceptance_rate`, `event_type`, `vc_interp`, `r_floor`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0819.md:8:**observable_contract**: claim=un generatore non-Sturmian puo' preservare il label-set phi a N=144 con `label_jaccard>=0.75` e distanza non triviale; observable=`label_jaccard`, `hamming_ratio`, `source_mode`, `acceptance_rate`, `event_type`, `vc_interp`, `r_floor`; operator=scansione di generatori balanced random, block shuffle, periodic approximant, Markov-density contro lettore gap-label phi; generator=non-Sturmian candidate pool; denominator=N=144, phases={0,0.25,0.5,0.75}, 2199 candidati validi post hamming gate, r_threshold={0.48,0.50,0.52}; non_possible=se passano solo generatori con memoria di blocco/periodo lungo, il null e' non-Sturmian ma non indipendente dal boundary; not_tested=trasferimento GUE/Poisson, fit power-law, scale N!=144, indipendenza fuori dal label reader phi. tools/data/reports/agent_20260509_0819.md:17: - **YSN DeltaLink**: il confine non e' classe GUE/Poisson ma trasporto di scala tra label reader e generatore. tools/data/reports/agent_20260509_0819.md:22:- **Proiezione**: genero candidati non-Sturmian, applico gate `Jaccard>=0.75`, poi misuro `r(V)` solo sui best accepted per non confondere ricerca del generatore con confronto GUE/Poisson. tools/data/reports/agent_20260509_0819.md:77:La formulazione valida e': `source_mode` Sturmian esplicito non e' necessario; memoria di scala lunga e' necessaria nel perimetro misurato. `label_jaccard>=0.75` non autorizza da solo il trasferimento GUE/Poisson: deve essere accoppiato a `event_type` e alla classe di memoria del generatore. tools/data/reports/agent_20260509_0819.md:92:- **L3 no observable drift**: `gap_ratio`, fit power-law e GUE/Poisson non sono testati. tools/data/reports/report_20260314_0342.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/report_20260315_0342.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260516_0720.md:8:**observable_contract**: claim=le righe graph-only diventano residui thresholded solo con separazione raw-count da entrambi i null grafici; observable=count grezzi osservati/null con intervalli Wilson e p-value binomial-tail; operator=post-audit del JSON row-aligned 03:30 senza rerun del lettore grafico; generator=13 righe BOUNDARY 8 GUE / 5 Poisson con feature graph canonical+rigidity+shuffle-z; denominator=13 righe, observed denominator 6, label-null denominator 384, rewire-null denominator 384; non_possible=residue claim se p-value contro uno dei due null supera alpha o il lift minimo e sotto soglia; not_tested=nuovi sistemi fisici, nuova geometria del grafo, universalita asintotica. tools/data/reports/agent_20260516_0720.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore del confine + tensione seme BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_0720.md:21:- `why`: il ciclo resta sul perimetro vivo 8 GUE / 5 Poisson e ripara il confine come terzo incluso operativo, separando two-reader boundary, graph-only lift e residuo thresholded. tools/data/reports/agent_20260516_0720.md:25:- **Baseline noto piu vicino**: Brody / Berry-Robnik / Rosenzweig-Porter per crossover GUE-Poisson; kNN stability, label shuffle e degree-preserving rewiring per residui grafo. tools/data/reports/agent_20260516_0720.md:37:- **Punto fisico sorgente**: crossover spettrale tra repulsione Wigner-Dyson/GUE e indipendenza/localizzazione Poisson. tools/data/reports/agent_20260516_1111.md:8:**observable_contract**: claim=RP e' boundary endpoint-gated solo se il conteggio candidato batte il null e una finestra lambda resta non-zero attraverso la ladder size preregistrata; observable=`rp_boundary_candidate` per source row, `size_transport_count`, raw/add-one p-values; operator=stesso lettore endpoint 11:04, stessa soglia `4/5 reader`, stessa distanza bilanciata dai centroidi GUE/Poisson, griglia `N x lambda x seed`; generator=GUE, Poisson, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=endpoint 60 source rows x 5 reader, RP 5 size x 7 lambda x 6 seed = 210 source rows x 5 reader; non_possible=terzo incluso fisico se le candidate non arrivano almeno a `N=192` o se il null ricostruisce il conteggio; not_tested=Anderson 3D, spettri sperimentali, limite N infinito, unfolded alternatives oltre il reader 11:04. tools/data/reports/agent_20260516_1111.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + direzione seme "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_1111.md:19:- **Movimento A->M->B**: fisico A = crossover spettrale Rosenzweig-Porter GUE/Poisson; matematica M = trasporto del bordo in spazio osservabile endpoint-gated; fisico B non emerge. Il ciclo consegna un vincolo di non-promozione. tools/data/reports/agent_20260516_1111.md:25:- `seed_residue`: restano non testati il perimetro largo 8 GUE / 5 Poisson come domini indipendenti, Anderson 3D e spettri fisici reali. tools/data/reports/agent_20260516_1111.md:29:- **Baseline noto piu vicino**: Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik-like mixture, finite-size spectral crossover GUE/Poisson. tools/data/reports/agent_20260516_1111.md:52:- **Endpoint gate**: pass osservato se endpoint GUE/Poisson resta stabile e feature-scramble `add_one_p<=0.05`. tools/data/reports/agent_20260516_1124.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY + direzione seme "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_1124.md:25:- `seed_residue`: restano non testati il perimetro completo 8 GUE / 5 Poisson e un audit fisico large-L. tools/data/reports/agent_20260507_2042.md:62:Applicare lo stesso test a un perimetro non-primi con ordine costruito e marginale identica: Beatty coerente, shuffle Beatty, GUE spacing order, Poisson. Se il segno resta condiviso, il nodo regressivo e l'embedding `x=(g_i,g_{i-1})`; se lo scarto z replica solo nei domini ordinati, det resta utile come osservabile subordinato al denominator gate. 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: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: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:21:- **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. 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:45:- **Punto fisico sorgente**: statistiche Wigner-Dyson/GUE e Poisson usate nel report 16:31 come baseline del rimbalzo fisico. tools/data/reports/agent_20260514_1640.md:73:| domain | samples | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) | tools/data/reports/agent_20260514_1640.md:81:| domain | N | samples | focus active | SR real | SR delta | p(SR) | d(SR) | triple delta | p(triple) | d(triple) | tools/data/reports/agent_20260514_1640.md:99:- **Verificato / strong per tester**: GOE e GUE sono entrambi active contro Poisson span-matched su `SR,L1,triple_var`. tools/data/reports/agent_20260514_1640.md:115:1. **Verificato**: `SR` coincide con adjacent gap ratio e separa GOE/GUE nel verso fisico atteso: GUE > GOE > Poisson. tools/data/reports/agent_20260514_1640.md:118:4. **Inferito**: il tester 16:31 passa da "GUE contro Poisson" a "simmetria Wigner-Dyson contro bordo Poisson-localizzato". tools/data/reports/agent_20260514_1640.md:129:- **Due radici**: simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata. tools/data/reports/agent_20260507_0330.md:1:# Agent Report — The GUE-Poisson Boundary Is a Denominator Collapse Layer tools/data/reports/agent_20260507_0330.md:15:> If the GUE-Poisson boundary is simulated directly by controlled mixtures, tools/data/reports/agent_20260507_0330.md:23:- domains: synthetic unfolded GUE spacings, iid Poisson spacings, and mixtures; tools/data/reports/agent_20260507_0330.md:28:- classification: standardized distance to pure GUE and pure Poisson centroids using all five canonical observables. A layer is marked ambiguous when at least half the replicates have nearest-centroid margin `< 0.15`. tools/data/reports/agent_20260507_0330.md:74:1. **The clean two-class boundary fails under denominator gating.** Pure GUE and pure Poisson are separable in all-observable space, but there are no observables stable at both endpoints under the declared gate. The Poisson pole is a weak-denominator pole: classification can still place it, but retention-normalized structural claims cannot use it as if it had the same denominator support as GUE. tools/data/reports/agent_20260507_0330.md:83:**CONSTRAINT on META + BOUNDARY**: GUE/Poisson boundary claims must report: tools/data/reports/agent_20260507_0330.md:89:> In the synthetic mixture perimeter tested here, the GUE-Poisson boundary is an operational layer at beta 0.3-0.4: classification is ambiguous there, and denominator support collapses across the transition. The Poisson endpoint remains classifiable but denominator-weak, so it cannot serve as a symmetric structural pole for gated retention claims. tools/data/reports/agent_20260507_0330.md:92:What opens now: apply the same layer map to real domains rather than only synthetic mixtures. For primes, the next discriminating question is not "GUE or Poisson?" but: tools/data/reports/agent_20260507_0330.md:101:- **L3 no silent patching**: the claim is explicitly changed from "8 GUE, 5 Poisson boundary" to a synthetic mixture calibration. This does not assert the same layer for primes or all real domains. tools/data/reports/agent_20260507_0330.md:103:- **L5 re-discovery**: this is a finite-sample diagnostic of crossover and noisy denominator normalization in classical GUE/Poisson spacings. It is not tagged as a new RMT theorem. tools/data/reports/agent_20260508_0011.md:16:- **Scope**: 200,000 prime gaps (unfolded by local mean, kernel=100), GUE eigenvalue spacings (beta=2, matrix size ~2400, unfolded), Poisson (exponential i.i.d.) tools/data/reports/agent_20260508_0011.md:49:| Observable | Primes (s42) | Primes (s137) | GUE (s42) | GUE (s137) | Poisson (s42) | tools/data/reports/agent_20260508_0011.md:56:All R-squared > 0.95 for primes and GUE (except Poisson, which has no signal). tools/data/reports/falsifier_20260508_0011.json:28: "summary": "Report is internally coherent on its main claim (primes alpha < 0.5, GUE alpha > 0.5) but two edge cases break the stated perimeter: GUE L2 s137 violates the blanket 'alpha >= 0.5' (L4), and Poisson L2 shows non-trivial scaling (alpha=0.165, R2=0.91) that undermines the null baseline and may indicate a systematic bias in the z-score methodology (L4). Neither is fatal but both require tightening before the finding can be called clean." tools/data/reports/agent_20260501_0931.md:1:# Agent Report — The GUE-Poisson Crossover Has a Phase Transition: Direction Locks, Magnitude Decays, Then Flips tools/data/reports/agent_20260501_0931.md:9:> "8 domains GUE, 5 Poisson — the boundary is the third included operational" (BOUNDARY). tools/data/reports/agent_20260501_0931.md:10:> The GUE-Poisson transition: is it a smooth interpolation or does it have structure? tools/data/reports/agent_20260501_0931.md:57:1. **The GUE-Poisson crossover is not smooth — it has a phase transition.** The dipolar magnitude decays linearly with alpha and passes through a near-zero minimum (0.0007) at alpha in [0.65, 0.75]. At this point the dipolar direction flips approximately 180 degrees. Below the transition, the ordering signal points consistently at -97 degrees. Above it, residual noise points at +82 degrees. The transition is a genuine zero-crossing, not a gradual rotation. tools/data/reports/agent_20260501_0931.md:67:**CONFIRMED structure on BOUNDARY**: The GUE-Poisson transition in the dipolar plane has a phase transition (direction flip at magnitude zero-crossing, alpha in [0.65, 0.75]). The boundary is a discrete structural feature, not an interpolation. 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/reports/agent_20260501_0931.md:78:- **Campo di possibilita**: Possible — classify ordering regimes by direction (what kind) independently from magnitude (how much). Determine phase transition points for arbitrary sequences. Not possible — interpolate smoothly between GUE and Poisson in dipolar coordinates (the transition is discrete). 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: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/reports/falsifier_20260516_1124.json:16: "claim": "`relation`: `follows_direction`; `seed_residue`: restano non testati il perimetro completo 8 GUE / 5 Poisson e un audit fisico large-L.", tools/data/reports/falsifier_20260516_1124.json:17: "evidence": "La direzione viva in `seme.json` e' `Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo`. Il report resta su un sotto-perimetro Anderson compatto/regressivo e dichiara il residuo, ma non lo marca come `deliberate_counter_perimeter`; quindi l'aderenza e' motivata ma non pienamente allineata al perimetro 8/5.", tools/data/reports/falsifier_20260516_1124.json:18: "suggestion": "Nel prossimo ciclo dichiarare esplicitamente `deliberate_counter_perimeter` se si resta su Anderson, con `why` e `not_drift`, oppure trasferire il null endpoint-preserving ad almeno un confronto multi-dominio GUE/Poisson del seme." 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: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:36:- **Punto fisico sorgente**: transizione spettrale/localizzazione in reticoli quasi-periodici, usata come ritorno fisico del boundary GUE/Poisson. tools/data/markov_memory_by_gue_type.json:7: "domain": "primes", tools/data/markov_memory_by_gue_type.json:27: "domain": "gue_matrix", tools/data/markov_memory_by_gue_type.json:47: "domain": "percolation", tools/data/markov_memory_by_gue_type.json:67: "domain": "coupled_oscillators", tools/data/markov_memory_by_gue_type.json:87: "domain": "string_vibration", tools/data/markov_memory_by_gue_type.json:107: "domain": "poisson", tools/data/markov_memory_by_gue_type.json:127: "domain": "logistica_biforcazione", tools/data/markov_memory_by_gue_type.json:147: "domain": "brownian_motion", tools/data/observable_collinearity_breaking_20260506_1955.json:3: "question": "When do canonical observable retention curves break collinearity across domains?", tools/data/photonic_boundary_third_included_gate_20260515_1734.json:375: "domain": "phi", tools/data/photonic_boundary_third_included_gate_20260515_1734.json:390: "domain": "silver", tools/data/photonic_boundary_third_included_gate_20260515_1734.json:405: "domain": "bronze", tools/data/photonic_boundary_third_included_gate_20260515_1734.json:420: "domain": "periodic_ab", tools/data/photonic_boundary_third_included_gate_20260515_1734.json:435: "domain": 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tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3586: "domain": "silver", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3617: "domain": "bronze", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3648: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3664: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3680: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3696: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3712: "domain": "phi", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3743: "domain": "silver", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3774: "domain": "bronze", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3805: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3821: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3837: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3853: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3869: "domain": "phi", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3900: "domain": "silver", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3931: "domain": "bronze", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3962: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3978: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:3994: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json:4010: "domain": "balanced_random_phi_density", tools/data/reports/agent_20260516_1117.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + direzione seme "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_1117.md:19:- **Movimento A->M->B**: fisico A = crossover RP GUE/Poisson bloccato dal ciclo 11:11; matematica M = gate a due lettori con trasporto per size e null; fisico B = Anderson 3D mobility edge. Il ritorno fisico resta candidato non promosso. tools/data/reports/agent_20260516_1117.md:25:- `seed_residue`: restano non testati il perimetro completo 8 GUE / 5 Poisson e spettri fisici reali. tools/data/reports/agent_20260516_1117.md:41:- **Punto fisico sorgente**: crossover spettrale RP GUE/Poisson con residuo finito-size. tools/data/rp_boundary_size_stability_audit_20260515_1940.json:72: "domain_window": "RP_lambda_0.000", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:81: "source_domain_type": "Poisson_pole", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:87: "domain_window": "RP_lambda_0.030", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:96: "source_domain_type": "Poisson_pole", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:102: "domain_window": "RP_lambda_0.045", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:111: "source_domain_type": "flow_candidate", 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tools/data/rp_boundary_size_stability_audit_20260515_1940.json:845: "domain_window": "RP_lambda_0.060", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:864: "domain_window": "RP_lambda_0.075", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:884: "domain_window": "RP_lambda_0.100", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:903: "domain_window": "RP_lambda_0.180", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:923: "domain_window": "RP_lambda_0.320", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:942: "domain_window": "RP_lambda_0.680", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:961: "domain_window": "RP_lambda_0.820", tools/data/rp_boundary_size_stability_audit_20260515_1940.json:980: "domain_window": "RP_lambda_1.000", tools/data/reports/report_20260403_0330.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 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"domain_window": "RP_lambda_0.820", tools/data/rp_candidate_window_stress_20260516_1007_w9.json:1371: "domain_window": "RP_lambda_0.030", tools/data/rp_candidate_window_stress_20260516_1007_w9.json:1383: "domain_window": "RP_lambda_0.045", tools/data/rp_candidate_window_stress_20260516_1007_w9.json:1400: "domain_window": "RP_lambda_0.060", tools/data/rp_candidate_window_stress_20260516_1007_w9.json:1418: "domain_window": "RP_lambda_0.075", tools/data/rp_candidate_window_stress_20260516_1007_w9.json:1435: "domain_window": "RP_lambda_0.820", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:145: "domain_window": "RP_lambda_0.030", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:184: "domain_window": "RP_lambda_0.045", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:223: "domain_window": "RP_lambda_0.060", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:262: "domain_window": "RP_lambda_0.075", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:301: "domain_window": "RP_lambda_0.820", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:490: "domain_window": "RP_lambda_0.030", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:529: "domain_window": "RP_lambda_0.045", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:568: "domain_window": "RP_lambda_0.060", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:607: "domain_window": "RP_lambda_0.075", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:646: "domain_window": "RP_lambda_0.820", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:698: "domain_window": "RP_lambda_0.030", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:706: "domain_window": "RP_lambda_0.045", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:716: "domain_window": "RP_lambda_0.060", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:727: "domain_window": "RP_lambda_0.075", tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json:738: "domain_window": "RP_lambda_0.820", tools/data/seme_backup_b2_20260508_214525.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260508_214525.json:3: "new_direzione": "Riprogettare il gate `V_c` con null omogenei al boundary operator: phase-shuffle Sturmian, label-preserving surrogate e controllo gap_ratio prima di estendere a nuovi domini GUE/Poisson", tools/data/reports/report_20260328_0344.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/seme_backup_b2_20260509_144841.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260509_144841.json:3: "new_direzione": "Falsificare la forma minima del gate BOUNDARY come operatore ordine/null/denominatore: test su perimetri reali o avversariali senza importare label GUE/Poisson", tools/data/seme_backup_b2_20260514_161119.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/reports/agent_20260509_0846.md:7:observables_used: [`generator_class`, `source_mode`, `event_type`, `vc_interp`, `label_jaccard`, `hamming_ratio`, `internal_cross_rate`, `no_cross_rate`, `floor_hit_rate`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/seme_backup_b2_20260516_101425.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/reports/evolution_20260425_0330.md:19:2. **L'eterogeneità della classe Poisson.** Il run ha notato che il r-statistic classifica come Poisson domini con memoria Markov da 0% a 99%. Questo non falsifica il r-test — lo confina. Il confine GUE/Poisson misurato dal r-test è una proiezione 1D di uno spazio almeno 2D. Il passo successivo naturale: esiste una firma composita (r + saturazione) che separa senza ambiguità? tools/data/reports/agent_20260508_2019.md:8:**observable_contract**: claim=se il portatore contratto del core phi e' ordine interno + scala Fibonacci-like, il core phi deve decadere quando il lettore theta=1/phi resta fisso ma il generatore Sturmian cambia pendenza; observable=retention del core phi basso [-1,1,-2,2], alto [3,-4,4,6], completo [-1,1,-2,2,3,-4,4,6] e overlap mediano; operator=Hamiltoniana tight-binding V=1, gap labels dei top gap larghi, reader fisso theta=1/phi contro controllo reader nativo; generator=Sturmian phi, silver, bronze, plastic; denominator=main N={233,377,500,610}, phase={0,0.25,0.5,0.75}, threshold={1.75,2.0,2.25}, top_k=12, |n|<=34; seedcheck N={144,288,466,754}, phase={0.125,0.375,0.625,0.875}, threshold={1.9,2.1}; not_tested=gap_ratio, boundary esatto del supertile, domini GUE/Poisson reali, automa formale della sostituzione. tools/data/reports/agent_20260514_1701.md:4:**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY GUE-Poisson tools/data/reports/agent_20260514_1701.md:12:- **Combo**: A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + tensione `TENS_SCALE_TRASCENDENZA_LIMITE` + direzione viva BOUNDARY GUE/Poisson. tools/data/reports/agent_20260514_1701.md:28:- `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. tools/data/reports/agent_20260514_1701.md:43:- **Punto fisico sorgente**: confine GUE/Poisson come transizione fra repulsione spettrale e indipendenza. tools/data/reports/agent_20260508_1805.md:8:**observable_contract**: claim=il core phi dei gap larghi richiede generatore globale, non solo lettore label; observable=retention dei label core sotto block shuffle; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per block_size; generator=phi_sturmian con block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, block_size Fibonacci e non-Fibonacci; not_tested=gap_ratio, generatori Sturmiani non-phi, GUE/Poisson, soglie 1.75/2.25. tools/data/reports/agent_20260516_1104.md:5:**verdict**: CONSTRAINT - Il gate endpoint GUE/Poisson resta chiuso (`36/36`, feature-scramble `raw_p=0/512=0.0`, `add_one_p=1/513=0.001949318`). RP produce 6/54 righe terzo-incluse contro null massimo 1/54 (`raw_p=0/512=0.0`, `add_one_p=1/513=0.001949318`), ma tutte le candidate sono a `N=128`; `N=192` e `N=256` restano blank. Il residuo RP e' finito-size, non boundary fisico promosso. tools/data/reports/agent_20260516_1104.md:8:**observable_contract**: claim=RP e' terzo incluso endpoint-gated solo se il gate GUE/Poisson resta chiuso e le righe RP bilanciate fra i centroidi endpoint battono il null feature-scramble row-aligned; observable=`endpoint_stable`, `centroid_distance_balance`, `rp_boundary_candidate`, raw/add-one p-values; operator=centroidi endpoint GUE/Poisson calibrati, score RP per distanza bilanciata da entrambi i poli, null che preserva marginali per reader e rompe accoppiamento feature-riga; generator=GUE, Poisson, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=endpoint 36 source rows x 5 reader, RP 3 lambda x 3 size x 6 seed = 54 source rows x 5 reader; non_possible=terzo incluso se endpoint gate fallisce o null RP ricostruisce il numero osservato; not_tested=Anderson 3D, spettri sperimentali, limite N infinito, universalita analitica, nuova ricerca lambda. tools/data/reports/agent_20260516_1104.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + tensione seme "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_1104.md:13:- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator non legge RP finche' i poli GUE/Poisson non sono invarianti. tools/data/reports/agent_20260516_1104.md:19:- **Movimento A->M->B**: fisico A = crossover spettrale GUE/Poisson/RP; matematica M = distanza bilanciata da centroidi endpoint in spazio osservabile; fisico B non emerge. Il ciclo consegna un vincolo di size-stability prima del rimbalzo fisico. tools/data/reports/agent_20260516_1104.md:23:- `why`: usa il filtro endpoint GUE/Poisson chiuso nel ciclo 10:58 e tenta il rientro controllato nel boundary RP come terzo incluso operativo. tools/data/reports/agent_20260516_1104.md:25:- `seed_residue`: restano non testati gli 8 domini GUE / 5 Poisson originali come perimetro largo, Anderson 3D e spettri fisici reali. tools/data/reports/agent_20260516_1104.md:29:- **Baseline noto piu vicino**: Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik-like mixture, finite-size spectral crossover GUE/Poisson. tools/data/reports/agent_20260516_1104.md:30:- **Cosa assorbe il baseline**: una finestra RP puo' collocarsi fra statistiche GUE e Poisson in size finite. tools/data/reports/agent_20260516_1104.md:35:> RP e' boundary endpoint-gated se il gate GUE/Poisson resta chiuso e le righe RP bilanciate fra centroidi endpoint battono il null feature-scramble row-aligned. tools/data/reports/agent_20260516_1104.md:65:1. Verificato: il gate endpoint GUE/Poisson resta chiuso sullo stesso denominatore del 10:58: `36/36`, feature-scramble `add_one_p=0.001949318`. tools/data/reports/agent_20260516_1104.md:77:- **Due radici**: endpoint GUE/Poisson chiuso; RP bilanciato ma non size-stable. tools/data/reports/agent_20260413_0330.md:9:> If acf(k) ~ -A(p)/k, then via Wiener-Khinchin the PSD low-frequency suppression should track A(p). The two measurements (time-domain ACF, frequency-domain PSD) must give consistent Poisson crossover predictions. tools/data/reports/agent_20260413_0330.md:64:1. **PSD dip and ACF amplitude give the same Poisson crossover: p* ~ 10^{12.6}.** Two independent measurements (time-domain and frequency-domain) converge on the same number. This is not a coincidence — it's the Wiener-Khinchin theorem working as expected, but now verified empirically across scales. 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/reports/report_20260401_0346.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/falsifier_20260516_1045.json:17: "evidence": "La direzione viva in seme.json e lab_data.json e': 'Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo'. Il report testa solo 2 endpoint sintetici, GUE e Poisson, e non il perimetro 8+5 indicato dal seme. La deviazione e' motivata come preflight endpoint/null, quindi non e' drift HIGH, ma il residuo numerico 8/5 non viene chiuso nel contratto.", tools/data/reports/falsifier_20260516_1045.json:18: "suggestion": "Aggiungere una riga di perimetro: `seed_residue=8 GUE / 5 Poisson non testati; endpoint preflight su 2 poli sintetici; prossimo check estende il filtro ai 13 domini prima di RP`." tools/data/reports/falsifier_20260516_1045.json:21: "summary": "Il report e' sostanzialmente coerente, ma L3 si incrina sulla definizione non dichiarata del p-value e L8 lascia un residuo di perimetro rispetto alla direzione 8 GUE / 5 Poisson." tools/data/reports/agent_20260514_0330.md:32:- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista e il pre-bordo mod6 indicato dalla consecutio. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:9:> C1: Primes are the only dynamic domain under M among 7 tested. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:10:> Consecutio from last 3 runs: the two-channel structure (algebraic scale-invariant + statistical decaying) was established for primes. Does it exist in other domains? tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:14:If I apply the same two-channel decomposition (binary alternation channel + magnitude channel) to GUE eigenvalues, Cramer random primes, and real primes, which domains show scale-invariant channels — and how many independent channels does each have? tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:40:### Per-domain details tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:60:4. **C1 is refined, not falsified.** The original claim "primes are the only dynamic domain" is too broad — GUE is also dynamic (z ~ -10 binary channel). The precise claim: primes are the only domain with TWO independent correlation channels. GUE has one (alternation). Cramer has zero. The number of independent channels is the discriminator, not the strength of any single channel. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:66:- **C1**: Primes are not unique in having scale-invariant correlations — GUE shares the binary channel. Primes ARE unique in having a second, independent magnitude channel. Reformulate C1: "Primes are the only domain with two independent scale-invariant correlation channels under the binary/magnitude decomposition." tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:68:- **BOUNDARY**: The boundary between "structured" and "unstructured" is not binary. It has at least 3 levels: 0 channels (Cramer/Poisson), 1 channel (GUE), 2 channels (primes). The "GUE-Poisson crossover" is about the binary channel only — the magnitude channel crossover is a separate phenomenon. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:76:- **Singolare**: the raw gap sequence before decomposition. It does not distinguish one-channel from two-channel. The r-statistic, which operates on raw gaps, collapses both channels into one number — this is WHY the r-statistic couldn't see what was unique about primes (it mixes channels). The singularity is the moment before the decomposition, where both domains look "GUE-like" but for different structural reasons. 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_0852.md:84:- Script: `tools/exp_two_channel_cross_domain.py` (reusable, parametric) tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:85:- Data: `tools/data/two_channel_cross_domain.json` tools/data/reports/_quarantine_falsifier_29_04/falsifier_20260429_0852.json:24: "evidence": "Il Claim Under Test dice: \"Primes are the only dynamic domain under M among 7 tested\". Il report afferma che GUE e' dynamic con z ~ -10 binary channel. Questo falsifica il C1 originale; la nuova forma a due canali e' un claim sostitutivo, non una semplice raffinazione silenziosa.", tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:8:> 8 domains GUE, 5 Poisson — the boundary is the third included (A9). Also: are we testing tautologies? (META) tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:11:The last 3 runs established: (1) algebraic memory is mod-3, (2) two-channel structure is real, (3) spectral rigidity is scale-dependent. Consecutio: **do the residue and magnitude channels lose structure at the same scale, or at different scales?** If different, the "GUE/Poisson boundary" measured by r-statistic is an artifact of mixing two fundamentally different behaviors. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:48:3. **The r-statistic mixes two incommensurable behaviors.** Its decay (correlation +0.684 with ln(p)) is dominated by the magnitude channel weakening, while the residue channel stays constant. The "GUE/Poisson crossover" reported by r-statistic is not a single phase transition — it is the magnitude channel approaching noise while the algebraic channel remains invariant. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:55:- **BOUNDARY**: There is no single boundary. The algebraic channel (residue, mod-3) has no boundary in the tested range. The statistical channel (magnitude) decays slowly. What was called "the GUE/Poisson boundary" is the mixing artifact of two channels with different scaling laws. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0958.md:49:- **C1**: The mod-3 signal (z ≈ -100) is the strongest evidence for C1 (primes as unique dynamic domain). No synthetic reproduces it. The mechanism is algebraic (F2: Z/6Z confinement), not statistical. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:50:4. **The GUE/Poisson classification via r alone loses the scale information.** Primes at r = 0.469 look "40% of the way from Poisson to GUE." But at L=50 they look "95% of the way." The boundary (BOUNDARY tension) is not a point in β-space — it's a curve β(L) whose shape discriminates real structure from randomness. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:57:- **BOUNDARY**: The GUE/Poisson boundary is not a threshold in r-space. It's a curve β(L) that separates sequences with increasing β(L) (structured: primes) from decreasing β(L) (decorrelated: shuffle). The sign of dβ/dL is the boundary criterion. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0829.md:61:- **C1**: Primes have increasing β(L) — are they unique? The two-channel model predicts that any sequence with a smooth density function overlaid with local chaos would show this. Testing other domains (zeros of Riemann zeta, eigenvalues of specific operators) would discriminate whether this is prime-specific or universal. 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/reports/agent_20260502_0330.md:9:> "The GUE-Poisson crossover has a phase transition" (report 2026-05-01 09:31). tools/data/reports/agent_20260502_0330.md:15:If we apply the identical partial-shuffle crossover protocol to 7 different ordered sequences (GUE, Primes, Logistic map, AR(1) negative, Periodic 2-4, Random Walk excursions, Poisson), do they ALL show the same phase transition pattern? If yes, the previous finding is a method artifact. If no, what discriminates? tools/data/reports/agent_20260510_0330.md:8:**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. tools/data/reports/agent_20260510_0330.md:24:- `not_drift`: non usa `V_c`, non usa label GUE/Poisson come campo decisionale, non rigenera la griglia beta globale. tools/data/reports/agent_20260510_0330.md:40:- Non misurato: `gap_ratio`, `V_c`, nuova griglia beta globale, validita' delle label sorgente GUE/Poisson. tools/data/reports/falsifier_20260504_0901.json:24: "evidence": "The report itself shows all Poisson z-scores < 2 (no significant signal above baseline). If there is no signal, retention = (noise)/(noise), and critical alpha is undefined — it measures where random fluctuations cross an arbitrary threshold. A meaningless Delta from a no-signal sequence cannot serve as a control against which the primes/GUE Delta is validated. The argument 'Poisson shows separation therefore primes coupling is real' uses a noise artifact as if it were a measurement.", tools/data/reports/report_20260302_0341.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260430_1946.md:9:> The prime ordering signal at theta = -150 deg (previous run) is a unique direction, not just attenuated GUE. What angle do pure GUE, GOE, Poisson, and Cramer random primes have? tools/data/reports/agent_20260430_1946.md:19:- **Sources**: real primes (p > 10000, 50K gaps), GUE (500×500 Hermitian, unfolded, 20 trials), GOE (500×500 symmetric, 20 trials), Poisson (iid exponential, 20 trials), Cramer random primes (same density, no correlations, 20 trials) tools/data/reports/agent_20260430_1946.md:80:- **Due radici** (dipolo primario): GUE ordering (anticorrelation-dominated, theta = -97 deg, dL1/dSR = 8) and Poisson non-ordering (no direction, theta undefined). These are the two extremes of the ordering spectrum — structured repulsion vs structureless randomness. tools/data/reports/agent_20260427_0330.md:9:> BOUNDARY: 8 domains GUE, 5 Poisson — the boundary is the third included operative. tools/data/reports/agent_20260427_0330.md:18:- **Real domains**: primes (10000 unfolded gaps), GUE matrices (400 eigenvalues), logistic map, pure Poisson, coupled oscillators. 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: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:73:4. **Primes sit at beta_eff = 0.409 — the exact midpoint of the Poisson-GUE crossover.** They are not "GUE-like" or "Poisson-like" — they are the boundary itself. Their gap distribution alone gives intermediate repulsion (beta ~ 0.4). Their sequential ordering adds an additional 30% rigidity that i.i.d. gaps cannot produce. tools/data/reports/agent_20260427_0330.md:75:5. **The ordering channel has a definite sign that distinguishes domain types.** Primes: ordering adds rigidity (negative Delta). Logistic + coupled oscillators: ordering adds bunching (positive Delta, massive). GUE + Poisson: ON the curve (ordering is irrelevant). The sign of the deviation IS the diagnostic: det=-1 ordering (rigidity) vs det=+1 ordering (bunching). tools/data/reports/agent_20260427_0330.md:82:- **C1**: Primes are unique — the only domain where ordering adds rigidity while sitting at intermediate beta. GUE matrices have stronger repulsion but no ordering effect. Ordering-GUE domains have ordering but it creates bunching, not rigidity. tools/data/spectral_rigidity_results.json:3: "experiment": "spectral_rigidity_cross_domain", tools/data/reports/agent_20260509_1516.md:8:**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. tools/data/reports/agent_20260509_1516.md:12:- **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". tools/data/reports/agent_20260509_1516.md:14:- **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. tools/data/reports/agent_20260509_1516.md:23:- `why`: porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson. tools/data/reports/agent_20260509_1516.md:36:- Perimetro atomico: 13 righe base BOUNDARY, `source_domain_type` 8 GUE-like e 5 Poisson-like. tools/data/reports/agent_20260509_1516.md:37:- Operatore: per ogni dominio ricostruisco spacings domain-native, creo beta layers contro permutation null, calcolo osservabili canonici e z original-vs-shuffle. tools/data/reports/agent_20260405_0914.md:4:**BOUNDARY** (0.7): "La transizione GUE->Poisson e' un effetto densita'. Ma i primi hanno eccesso di correlazione che CRESCE con la scala." tools/data/reports/agent_20260405_0914.md:12:- 3 baselines: Cramer model (10 trials each), shuffled gaps (10 trials each), and reference constants (GUE=0.5307, Poisson=0.3863) tools/data/reports/agent_20260405_0914.md:31:- Primes drift from "weakly GUE-like" toward "Poisson-like" as scale increases tools/data/reports/agent_20260405_0914.md:33:- Both values are between Poisson (0.386) and GUE (0.531), closer to Poisson tools/data/reports/agent_20260405_0914.md:41:The "boundary" between GUE and Poisson is not a static boundary — it's a **trajectory**. tools/data/reports/agent_20260405_0914.md:42:Primes start closer to GUE at small scale and drift toward Poisson at large scale. tools/data/reports/agent_20260405_0914.md:58:The primes don't sit at GUE or Poisson; they trace a path between them. 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/agent_20260509_1532.md:7:observables_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. tools/data/reports/agent_20260509_1532.md:8:**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. tools/data/reports/agent_20260509_1532.md:11:- **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. tools/data/reports/agent_20260509_1532.md:24:- `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. tools/data/reports/agent_20260509_1532.md:37:- 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. tools/data/reports/agent_20260509_1532.md:76:1. **Verificato: `support_transfer` resta 11/13.** La lettura a due assi conserva il risultato utile del 15:16 senza importare label GUE/Poisson. tools/data/reports/agent_20260509_1532.md:100:- **Invariante di passaggio**: separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune. tools/data/reports/agent_20260509_1532.md:104:Il 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. tools/data/reports/report_20260331_0345.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260516_0938.md:23:- `not_drift`: non ritorna a phi, Sturmian, V_c o deposito locale; cambia solo size e larghezza di unfolding sul perimetro GUE/Poisson RP. tools/data/reports/agent_20260420_0330.md:93:The two-channel decomposition extends from the ACF domain to the spectral domain. The channels are spectrally independent (additivity holds to <0.5%). The blue-noise signature of prime gaps is a two-component mixture with distinct slopes. The PSD_BLUE_NOISE amplitude gap is a mixing artifact, not a model failure. tools/data/reports/evolution_20260506_1941.md:3:Il passo ha costruito una curva size-rank con gate sul denominatore, chiudendo il ciclo aperto dal run 03:30 (secondo asse GUE) e dal run 06:25 (restrizione). La traiettoria è stata: inflazione di rango osservata → ipotesi denominatore debole → costruzione del gate → falsificazione via controlli Poisson/shuffle. Il produttore ha invertito su se stesso: ha testato se il proprio strumento (PCA su retention) produceva artefatti, e ha trovato che sì. Passo autologico netto. tools/data/reports/agent_20260508_2102.md:8:**observable_contract**: claim=la dualita dipolare/illusoria nei primi non va letta da det(M) diretto ma dal supporto ordinato contro null; observable=rate low_low gap transition, rate high_high gap transition, SR mean difference; operator=Mobius interval charge S_n=sum mu(k) for p_n0; generator=prime gaps up to p<=1e6 with Mobius sieve; denominator=main N={5000,10000,20000} offset=0 plus seedcheck offsets {3000,7000,11000}; not_tested=gap_ratio Sturmian, high-core phi survival, universal GUE/Poisson classification, det(M) as direct discriminator. tools/data/reports/agent_20260508_2102.md:74:Il prossimo passo non deve estendere il claim a tutti i primi o a GUE/Poisson. Deve isolare il nodo regressivo del null: ripetere con block-permutation della carica e con controllo per lunghezza del gap, per separare informazione Mobius autentica da dipendenza banale dalla dimensione dell'intervallo. tools/data/reports/report_20260402_0756.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260430_1919.md:9:> The two order-sensitive observables (spacing_ratio, lag1_acf) form a 2D dipolar vector at the GUE-Poisson boundary. Does this vector rotate with scale (internal dynamics) or maintain constant angle (single structural mode)? tools/data/reports/agent_20260430_1919.md:85:- **Campo di possibilita**: diventa possibile classificare qualsiasi sequenza intera tramite il suo angolo dipolare (primi ~ -150, Cramer ~ +65, GUE e Poisson avranno i propri angoli). Diventa non-possibile trattare spacing_ratio e lag1_acf come informazioni indipendenti — sono una sola informazione. tools/data/reports/agent_20260430_1919.md:90:1. Quale angolo hanno GUE puro e Poisson puro? Se primes = -150, GUE = X, Poisson = Y, dove cadono sulla circonferenza? tools/data/reports/agent_20260515_1933.md:14:- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260515_1933.md: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. tools/data/reports/agent_20260515_1933.md:34:- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. 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:23:- `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. tools/data/reports/agent_20260515_1724.md:53:| domain | count | distinct_vc | repeat_rate | mode_rate | median | min | max | 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:12:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + TxQ matrice densita come lettura spettrale + tensione `BOUNDARY` 8 GUE / 5 Poisson. tools/data/reports/agent_20260515_1745.md:13:- **Dipolo / punto-zero**: repulsione spettrale / localizzazione degli autostati. Punto-zero = Hamiltoniana prima che una metrica singola decida se il dominio e' GUE-like, Poisson-like o di confine. tools/data/reports/agent_20260515_1745.md:23:- `why`: segue la direzione viva "8 domini GUE, 5 Poisson - il confine e' il terzo incluso operativo" portando il confine in un ritorno fisico dove GUE/Poisson diventano spacing/localizzazione. tools/data/reports/agent_20260515_1745.md:36:- **Punto fisico sorgente**: confine GUE/Poisson come attrito tra repulsione dei livelli e indipendenza/localizzazione. tools/data/reports/falsifier_20260514_1605.json:16: "claim": "\"relation: follows_direction\" e \"not_drift: non torna a V_c, fit, label locali o GUE/Poisson; misura esclusivamente il limite del vettore prime-minus-mod6.\"", tools/data/reports/falsifier_20260514_1605.json:17: "evidence": "lab_data direzione dice: \"Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo\". Il report dichiara di non misurare GUE/Poisson e resta nel deposito prime-minus-mod6 senza marcare la deviazione come deliberate_counter_perimeter.", tools/data/reports/falsifier_20260514_1605.json:18: "suggestion": "Riformulare aderenza come deliberate_counter_perimeter con why/not_drift verificabili, oppure collegare prime-minus-mod6 al confine GUE/Poisson tramite un ponte misurabile nel prossimo ciclo." tools/data/meta_assertion_gate.json:84: "claim": "Zeri zeta hanno spacing GUE (non Poisson)", 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:20:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + direzione BOUNDARY GUE/Poisson + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`. 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:34:- `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. tools/data/reports/agent_20260514_1631.md:64:| domain | samples | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) | tools/data/reports/agent_20260514_1631.md:71:- **Verificato / strong per tester**: GUE mantiene `SR` active contro Poisson span-matched (`delta=0.2070`, `p=0.000122`, `d=4.284`). tools/data/reports/agent_20260514_1631.md:97:- **Due radici**: Wigner-Dyson/GUE level statistics / Poisson level statistics. tools/data/reports/agent_20260514_1458.md:29:- `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. tools/data/reports/agent_20260514_1605.md:29:- `not_drift`: non torna a `V_c`, fit, label locali o GUE/Poisson; misura esclusivamente il limite del vettore prime-minus-mod6. tools/data/reports/agent_20260509_0659.md:7:observables_used: [`event_type`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`, `acceptance_rate`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0659.md:8:**observable_contract**: claim=il null label-preserving deve essere raggiungibile oltre N=89 prima di usare `V_c` per trasferire il boundary verso altri perimetri; observable=`event_type={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`, `acceptance_rate`; operator=surrogate bilanciato con swap 0/1, gate `label_jaccard>=0.75`, poi curva `r(V)` su griglia 0.5..3.0 step 0.01; generator=phi Sturmian, balanced_random, swap_label_surrogate; denominator=N=144, phase={0,0.25,0.5,0.75}, r_threshold={0.48,0.50,0.52}, random_trials=1, label_trials=1, swap_steps=120, seed=202605090659; non_possible=se il gate label-preserving non viene raggiunto, il ciclo non puo' usare il surrogate come controprova del boundary Sturmian; not_tested=GUE/Poisson reali, silver/bronze, fit power-law, gap_ratio, sufficienza del label-set con surrogate accettati a N=144. tools/data/reports/agent_20260509_0659.md:19:- **Proto-ipotesi**: se il generatore label-preserving e' pronto per il trasferimento, allora a N=144 deve raggiungere `Jaccard>=0.75` con accettazione non nulla. Se non raggiunge il gate, il prossimo passo resta costruzione del null, non estensione a GUE/Poisson. tools/data/reports/agent_20260509_0659.md:66:4. **Verificato: il costo del null cresce prima della prova fisica.** I tentativi piu' larghi non chiudono nel budget del cycle; il nodo regressivo e' il generatore del null, non il boundary verso GUE/Poisson. tools/data/reports/agent_20260509_0659.md:72:La formulazione valida e': prima di estendere `V_c` a GUE/Poisson, il Lab deve produrre un null label-preserving con accettazione dichiarata su N={89,144,233}. Fino a quel punto `event_type` resta gate obbligatorio e `label_jaccard` resta precondizione del confronto, non osservabile accessoria. tools/data/reports/agent_20260509_0659.md:81:Il prossimo passo e' regressivo sul generatore, non estensivo sul dominio: sostituire lo swap cieco con un generatore vincolato che ottimizza direttamente il label-set per fase e scala, oppure dichiarare che il label-set non e' un vincolo generativo praticabile e scegliere un null piu' nativo all'ordine Sturmian. Solo un null con accettazione non nulla su N={89,144,233} autorizza il passaggio a GUE/Poisson. tools/data/reports/trajectory_apply_20260507_0901.json:14: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/reports/agent_20260405_0916.md:38:- Primes move from near-GUE (0.476) toward intermediate (0.444), never reaching Poisson (0.386) tools/data/reports/agent_20260405_0916.md:46:3. The GUE/Poisson classification is an oversimplification: primes sit between them, drifting toward Poisson tools/data/reports/agent_20260515_1807.md:36:- **Punto fisico sorgente**: reticolo tight-binding 1D con potenziale binario quasiperiodico, dove spettro e autostati leggono il confine GUE/Poisson. tools/data/reports/agent_20260515_1807.md:52:- Contratto osservabile-operatore: il ciclo testa boundary window e profilo surrogate; non testa `gap_ratio`, `V_c` asintotico, universalita GUE/Poisson o dati sperimentali. tools/data/reports/agent_20260515_1807.md:56:| domain | joint boundary V | median hamming | median acf_l1 | median psd_l1 | tools/data/boundary_blank_thin_support_audit_20260509_1548.json:27: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/data/boundary_denominator_prescan_20260509_1409.json:3: "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", tools/data/boundary_denominator_prescan_20260509_1409.json:4: "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", tools/data/boundary_denominator_prescan_20260509_1409.json:8: "operator": "row-aligned domain/window prescan", tools/data/boundary_denominator_prescan_20260509_1409.json:23: "by_source_domain_type": { tools/data/boundary_denominator_prescan_20260509_1409.json:48: "domain_window": "ising_2d:cycle_1", tools/data/boundary_denominator_prescan_20260509_1409.json:49: "domain": "ising_2d", tools/data/boundary_denominator_prescan_20260509_1409.json:51: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:64: "domain_key": "ising_2d", tools/data/boundary_denominator_prescan_20260509_1409.json:73: "domain_window": "pendolo_doppio:cycle_2", tools/data/boundary_denominator_prescan_20260509_1409.json:74: "domain": "pendolo_doppio", tools/data/boundary_denominator_prescan_20260509_1409.json:76: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:89: "domain_key": null, tools/data/boundary_denominator_prescan_20260509_1409.json:98: "domain_window": "numeri_primi:cycle_3", tools/data/boundary_denominator_prescan_20260509_1409.json:99: "domain": "numeri_primi", tools/data/boundary_denominator_prescan_20260509_1409.json:101: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:114: "domain_key": "primes", tools/data/boundary_denominator_prescan_20260509_1409.json:123: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_denominator_prescan_20260509_1409.json:124: "domain": "zeta_zeros", tools/data/boundary_denominator_prescan_20260509_1409.json:126: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:139: "domain_key": null, tools/data/boundary_denominator_prescan_20260509_1409.json:148: "domain_window": "logistica_biforcazione:cycle_5", tools/data/boundary_denominator_prescan_20260509_1409.json:149: "domain": "logistica_biforcazione", tools/data/boundary_denominator_prescan_20260509_1409.json:151: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:164: "domain_key": "logistic", tools/data/boundary_denominator_prescan_20260509_1409.json:173: "domain_window": "string_vibration:cycle_6", tools/data/boundary_denominator_prescan_20260509_1409.json:174: "domain": "string_vibration", tools/data/boundary_denominator_prescan_20260509_1409.json:176: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:189: "domain_key": null, tools/data/boundary_denominator_prescan_20260509_1409.json:198: "domain_window": "random_matrix:cycle_7", tools/data/boundary_denominator_prescan_20260509_1409.json:199: "domain": "random_matrix", tools/data/boundary_denominator_prescan_20260509_1409.json:201: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:214: "domain_key": "gue", tools/data/boundary_denominator_prescan_20260509_1409.json:223: "domain_window": "cellular_automata:cycle_8", tools/data/boundary_denominator_prescan_20260509_1409.json:224: "domain": "cellular_automata", tools/data/boundary_denominator_prescan_20260509_1409.json:226: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:239: "domain_key": "cell_auto", tools/data/boundary_denominator_prescan_20260509_1409.json:248: "domain_window": "percolation:cycle_9", tools/data/boundary_denominator_prescan_20260509_1409.json:249: "domain": "percolation", tools/data/boundary_denominator_prescan_20260509_1409.json:251: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:264: "domain_key": "percolation", tools/data/boundary_denominator_prescan_20260509_1409.json:273: "domain_window": "coupled_oscillators:cycle_10", tools/data/boundary_denominator_prescan_20260509_1409.json:274: "domain": "coupled_oscillators", tools/data/boundary_denominator_prescan_20260509_1409.json:276: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:289: "domain_key": "coupled_osc", tools/data/boundary_denominator_prescan_20260509_1409.json:298: "domain_window": "reaction_diffusion:cycle_11", tools/data/boundary_denominator_prescan_20260509_1409.json:299: "domain": "reaction_diffusion", tools/data/boundary_denominator_prescan_20260509_1409.json:301: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:314: "domain_key": null, tools/data/boundary_denominator_prescan_20260509_1409.json:323: "domain_window": "brownian_motion:cycle_12", tools/data/boundary_denominator_prescan_20260509_1409.json:324: "domain": "brownian_motion", tools/data/boundary_denominator_prescan_20260509_1409.json:326: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:339: "domain_key": "brownian", tools/data/boundary_denominator_prescan_20260509_1409.json:348: "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", tools/data/boundary_denominator_prescan_20260509_1409.json:349: "domain": "logistica_biforcazione_var_3.5699", tools/data/boundary_denominator_prescan_20260509_1409.json:351: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:364: "domain_key": null, tools/data/reports/report_20260327_0344.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/boundary_graph_null_audit_20260516_0330.json:5: "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid and graph null trials", tools/data/boundary_graph_null_audit_20260516_0330.json:52: "domain": "ising_2d", tools/data/boundary_graph_null_audit_20260516_0330.json:53: "domain_window": "ising_2d:cycle_1", tools/data/boundary_graph_null_audit_20260516_0330.json:60: "source_domain_type": "GUE", tools/data/boundary_graph_null_audit_20260516_0330.json:67: "domain": "pendolo_doppio", tools/data/boundary_graph_null_audit_20260516_0330.json:68: "domain_window": "pendolo_doppio:cycle_2", tools/data/boundary_graph_null_audit_20260516_0330.json:75: "source_domain_type": "Poisson", tools/data/boundary_graph_null_audit_20260516_0330.json:82: "domain": "numeri_primi", tools/data/boundary_graph_null_audit_20260516_0330.json:83: "domain_window": "numeri_primi:cycle_3", tools/data/boundary_graph_null_audit_20260516_0330.json:90: "source_domain_type": "GUE", tools/data/boundary_graph_null_audit_20260516_0330.json:97: "domain": "zeta_zeros", tools/data/boundary_graph_null_audit_20260516_0330.json:98: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_graph_null_audit_20260516_0330.json:105: "source_domain_type": "GUE", tools/data/boundary_graph_null_audit_20260516_0330.json:112: "domain": "logistica_biforcazione", tools/data/boundary_graph_null_audit_20260516_0330.json:113: "domain_window": "logistica_biforcazione:cycle_5", tools/data/boundary_graph_null_audit_20260516_0330.json:120: "source_domain_type": "GUE", tools/data/boundary_graph_null_audit_20260516_0330.json:127: "domain": "string_vibration", tools/data/boundary_graph_null_audit_20260516_0330.json:128: "domain_window": "string_vibration:cycle_6", tools/data/boundary_graph_null_audit_20260516_0330.json:135: "source_domain_type": "Poisson", tools/data/boundary_graph_null_audit_20260516_0330.json:142: "domain": "random_matrix", tools/data/boundary_graph_null_audit_20260516_0330.json:143: "domain_window": "random_matrix:cycle_7", tools/data/boundary_graph_null_audit_20260516_0330.json:150: "source_domain_type": "GUE", tools/data/boundary_graph_null_audit_20260516_0330.json:157: "domain": "cellular_automata", tools/data/boundary_graph_null_audit_20260516_0330.json:158: "domain_window": "cellular_automata:cycle_8", tools/data/boundary_graph_null_audit_20260516_0330.json:165: "source_domain_type": "GUE", tools/data/boundary_graph_null_audit_20260516_0330.json:172: "domain": "percolation", tools/data/boundary_graph_null_audit_20260516_0330.json:173: "domain_window": "percolation:cycle_9", tools/data/boundary_graph_null_audit_20260516_0330.json:180: "source_domain_type": "Poisson", tools/data/boundary_graph_null_audit_20260516_0330.json:187: "domain": "coupled_oscillators", tools/data/boundary_graph_null_audit_20260516_0330.json:188: "domain_window": "coupled_oscillators:cycle_10", tools/data/boundary_graph_null_audit_20260516_0330.json:195: "source_domain_type": "Poisson", tools/data/boundary_graph_null_audit_20260516_0330.json:202: "domain": "reaction_diffusion", tools/data/boundary_graph_null_audit_20260516_0330.json:203: "domain_window": "reaction_diffusion:cycle_11", tools/data/boundary_graph_null_audit_20260516_0330.json:210: "source_domain_type": "GUE", tools/data/boundary_graph_null_audit_20260516_0330.json:217: "domain": "brownian_motion", tools/data/boundary_graph_null_audit_20260516_0330.json:218: "domain_window": "brownian_motion:cycle_12", tools/data/boundary_graph_null_audit_20260516_0330.json:225: "source_domain_type": "Poisson", tools/data/boundary_graph_null_audit_20260516_0330.json:232: "domain": "logistica_biforcazione_var_3.5699", tools/data/boundary_graph_null_audit_20260516_0330.json:233: "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", tools/data/boundary_graph_null_audit_20260516_0330.json:240: "source_domain_type": "GUE", tools/data/boundary_residual_beta_absent_audit_20260510_0330.json:42: "not_tested": "global beta grid, V_c, source GUE/Poisson label validity" tools/data/boundary_residual_beta_absent_audit_20260510_0330.json:46: "domain": "numeri_primi", tools/data/boundary_residual_beta_absent_audit_20260510_0330.json:51: "domain": "random_matrix", tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:29: "not_tested": "new beta grid, new null surrogates, V_c fit, source GUE/Poisson label validity" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:31: "label_policy": "source_domain_type is audit metadata only and is not used in transition_class.", tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:86: "source_domain_type_audit_only": "Poisson" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:105: "source_domain_type_audit_only": "GUE" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:126: "source_domain_type_audit_only": "Poisson" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:147: "source_domain_type_audit_only": "GUE" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:176: "source_domain_type_audit_only": "GUE" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:197: "source_domain_type_audit_only": "GUE" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:216: "source_domain_type_audit_only": "GUE" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:237: "source_domain_type_audit_only": "Poisson" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:256: "source_domain_type_audit_only": "Poisson" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:275: "source_domain_type_audit_only": "GUE" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:296: "source_domain_type_audit_only": "GUE" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:317: "source_domain_type_audit_only": "Poisson" tools/data/boundary_transition_taxonomy_13rows_20260509_1839.json:338: "source_domain_type_audit_only": "GUE" tools/data/reports/trajectory_apply_20260506_1955.json:14: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", 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:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". 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:28:- `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. tools/data/reports/agent_20260515_1904.md:55:- **Denominatore**: 13 righe row-aligned dal perimetro BOUNDARY, 8 GUE e 5 Poisson. 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:85:1. Verificato: il denominatore resta quello richiesto, 13 righe con 8 GUE e 5 Poisson. tools/data/reports/agent_20260515_1904.md:106:ssp_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. 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/reports/evolution_20260427_0330.md:23:2. **Il segno del canale di ordinamento come operatore diagnostico.** Ordinamento che aggiunge rigidita' (det=-1) vs ordinamento che aggiunge bunching (det=+1): questo e' un discriminatore binario applicabile a qualsiasi dominio nuovo. Puo' sostituire la classificazione GUE/Poisson con una piu' fine a due canali. tools/data/reports/evolution_20260424_0330.md:5:Il produttore ha preso la tensione META+BOUNDARY e l'ha invertita su se stessa: invece di aggiungere domini alla classificazione GUE/Poisson, ha chiesto se la classificazione stessa regge sotto shuffle. Traiettoria pulita — dalla domanda discriminante (shuffle distrugge la classe?) al risultato (due meccanismi distinti, sign(delta_r) come discriminante). 287 secondi, 21 tool calls, zero errori. Il passo ha chiuso con consecutio verso la decomposizione two-channel dei run precedenti. tools/data/reports/falsifier_20260501_0931.json:16: "claim": "\"The GUE-Poisson crossover has a phase transition\" / \"GUE-Poisson transition\"", tools/data/reports/falsifier_20260501_0931.json:17: "evidence": "The experiment does not interpolate GUE to Poisson spacings. Alpha=1 is a full shuffle of GUE spacings, which destroys order but preserves the GUE marginal spacing distribution. That endpoint is not Poisson/exponential; it is shuffled-GUE.", 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/falsifier_20260501_0931.json:42: "summary": "Il report non e' internamente coerente: si rompe soprattutto L1/L3, perche' trasforma un minimo non nullo di shuffled-GUE in uno zero/phase transition GUE-Poisson." tools/data/cognitive_enzymes_archive.json:553: "snippet": "**Source:** /sessions/pensive-sharp-curie/mnt/domain_D-ND_Cosmology/D-ND Workflow/D-ND Workflow/Domande su Dipolo assiomatico.docx **Character Count:** 747 --- Come si possono integrare le nuove informazioni e le critiche costruttive nella teoria del dipolo assiomatico? Quali sono gli aspetti della teoria che necessitano di una revisione più approfondita? Q" tools/data/cognitive_enzymes_archive.json:2200: "snippet": "**Source:** /sessions/pensive-sharp-curie/mnt/domain_D-ND_Cosmology/D-ND Workflow/D-ND Workflow/Salto di Paradigma/Mediare il Cambio Paradigma.docx **Character Count:** 11443 --- Person: - Negli eventi del cambio di paradigma la comprensione assume la risultante di ciò che è , per facilitare questa comprensione che distrugge il costituito e uccide ogni forma" tools/data/cognitive_enzymes_archive.json:2291: "snippet": "Direzione corrente: confine come terzo incluso operativo sui domini GUE/Poisson/non-phi. Possibile risultante da respirare: - D-ND: terzo incluso come punto-zero tra repulsione e indipendenza. - Operatori: graph curvature + spectral rigidity + non-phi generator control. - Dipolo: core congiunto / residuo singolo. - Punto-zero: confine prima che venga classif" tools/data/cognitive_enzymes_archive.json:2315: "snippet": "**Source:** /sessions/pensive-sharp-curie/mnt/domain_D-ND_Cosmology/D-ND Workflow/D-ND Workflow/DND DOC/D-ND notes/2023-09-11-conversations/2023-7-11/20-49-18-_LDND___Assioma_Primitivo_nella_Dinamica.docx **Character Count:** 8342 --- >> USER: Leggi attentamente le istruzioni: queste sono le istanze che ho fatto con te su un lavoro e che ho archiviato sul mi" tools/data/cognitive_enzymes_archive.json:5959: "snippet": "**Report Date:** 2026-02-14 **Corpus Location:** `/sessions/pensive-sharp-curie/mnt/domain_D-ND_Cosmology/D-ND Workflow/D-ND Workflow` **Total Files Analyzed:** 1,411 .docx files **Chronological Span:** July 11, 2023 — September 9, 2023 (41 dated conversation sessions) **Analysis Focus:** High-value content NOT in existing 653-file corpus ---" tools/data/reports/agent_20260429_1013.md:9:> BOUNDARY: 8 domains GUE, 5 Poisson — the boundary is the operative third included. tools/data/reports/agent_20260429_1013.md:53:Reference: Poisson r = 0.386, GUE r = 0.536. Cramer r = 0.386 (pure Poisson at all positions). tools/data/reports/agent_20260429_1013.md:57:1. **Primes flow toward Poisson, not toward GUE.** beta decreases from 0.46 (near p ~ 22K) to 0.33 (near p ~ 2M). The GUE/Poisson boundary is not a fixed point — it is a trajectory. The linear fit beta(p) = 0.64 - 0.030 * ln(p) has R^2 = 0.78. tools/data/reports/agent_20260429_1013.md:69:The spectral rigidity experiment (2026-04-27) showed beta_sigma(L) INCREASING with spectral scale L: primes become more GUE-like at larger L. This experiment shows beta(N) DECREASING with position N: primes become more Poisson-like at larger N. tools/data/reports/agent_20260429_1013.md:75:The full picture is a 2D map beta(N, L) with opposing gradients. The boundary between GUE and Poisson is a CURVE in this 2D space, not a point. tools/data/reports/agent_20260429_1013.md:81:- **BOUNDARY**: The boundary is not a classification (GUE vs Poisson) but a flow. Primes start closer to GUE at small N and drift toward Poisson at large N. The boundary IS the trajectory — the third included is the path between the two regimes, not a point on it. 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/falsifier_20260515_1745.json:17: "evidence": "Contro `seme.json.direzione` il report aderisce: porta il confine GUE/Poisson in spacing/localizzazione. Pero' `lab_data.json.direzione` diverge come residuo pubblico/pre-gate su VECTOR RESIDUE e non viene nominato esplicitamente; il report cita solo prime/mod6 in forma generica.", 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/falsifier_20260515_1855.json:11: "suggestion": "Nel prossimo ciclo aggiungere una sezione re-discovery audit: confrontare i nodi ponte kNN con almeno un modello/nome classico di crossover GUE-Poisson e dichiarare cosa resta lab-specific dopo quel confronto." tools/data/reports/agent_20260515_1816.md:51:- 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. tools/data/reports/agent_20260515_1816.md:55:| domain | mean_pr_tau | median spacing_r | median mean_ipr | median mean_pr | median participation_entropy | tools/data/reports/agent_20260506_1941.md:17:- domains: prime-gap windows, prime-shuffle controls, iid Poisson spacings, independent GUE spacings; tools/data/reports/agent_20260506_1941.md:19:- replicates/windows: 8 per domain-size point; tools/data/reports/agent_20260506_1941.md:75:> all-observable perturbation rank can inflate in weak-denominator regimes; after denominator gating, GUE and primes are both close to one perturbation coordinate in this perimeter, while Poisson/shuffle controls show why ungated rank is not structural evidence. tools/data/reports/agent_20260506_1941.md:78:What opens now: the lab can keep using perturbation rank, but only as a gated observable. The next useful movement is not more PCA; it is an operator-level denominator map: for each observable, identify the perturbation/domain/scale region where `original - shuffle` is a real signal rather than a noisy divisor. applications/scoperte/20260430_1905_observable-coherence-at-the-gue-poisson_auto/lab-note.draft.md:20:title_proposal: "[TARGET — TM1 refinement] Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar" applications/scoperte/20260430_1905_observable-coherence-at-the-gue-poisson_auto/lab-note.draft.md:31:# [TARGET — TM1 refinement] Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar tools/data/reports/agent_20260506_0330.md:20:- **Null baseline**: 48 full shuffles per domain. tools/data/reports/agent_20260506_0330.md:69:**CONSTRAINT on META + BOUNDARY**: the single latent coordinate found under uniform shuffle (rank audit 05-05) is a property of the perturbation type, not of the boundary itself. Scale-selective perturbations reveal a second axis in GUE (PC2=25.2%) and a weak second axis in primes (PC2=2.6%). The operational consequence: **GUE and primes have different perturbation dimensionality** — GUE correlations live on at least 2 perturbation axes, primes on ~1.3. This asymmetry between domains is new structure, not previously measured. tools/data/reports/agent_20260506_0330.md:77:- **Campo di possibilita**: here it becomes possible to distinguish domains by HOW they respond to structured probing (not just WHETHER they respond). Here it becomes non-possible to treat all perturbation z-scores as independent evidence of the same boundary. applications/scoperte/20260430_1905_observable-coherence-at-the-gue-poisson_auto/cycle-report.draft.md:51:# Cycle Report — Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar tools/data/reports/agent_20260509_0829.md:7:observables_used: [`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type`, `vc_interp`, `r_floor`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0829.md:8:**observable_contract**: claim=la soglia di memoria del boundary si vede variando blocchi e periodi attorno alle scale 21/34; observable=`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type`, `vc_interp`, `r_floor`; operator=griglia fine di block shuffle e periodic approximant contro lettore gap-label phi e curva `r(V)`; generator=non-Sturmian candidate pool gia' usato nel gate 08:19; denominator=`N=144`, phases `{0,0.25,0.5,0.75}`, r_threshold `{0.48,0.50,0.52}`, block sizes `{2,3,5,8,10,13,16,21,24,27,31,34,37,40,45,50,55}`, periods `{5,8,10,13,16,21,24,27,31,34,37,40,45,50,55,72,89}`, 128 balanced random trials, 96 mode trials; non_possible=se `label_jaccard` alto non implica `internal_cross`, il label-set isolato non puo' essere usato come null indipendente del boundary; not_tested=scale `N!=144`, fit power-law, GUE/Poisson transfer, gap_ratio. tools/data/reports/agent_20260509_0829.md:90:- **L3 no observable drift**: `gap_ratio`, fit power-law e GUE/Poisson non sono testati. tools/data/reports/agent_20260509_1839.md:7:observables_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. tools/data/reports/agent_20260509_1839.md:9:**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. tools/data/reports/agent_20260509_1839.md:15:- **Piano superiore**: sheaf locale del boundary su 13 sezioni. La sezione corta viene riparata, poi ricollocata nell'atlante senza usare label GUE/Poisson. tools/data/reports/agent_20260509_1839.md:25:- `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. tools/data/reports/agent_20260509_1839.md:38:- Label policy: `source_domain_type` resta audit metadata; non entra in `transition_class`. tools/data/reports/agent_20260509_0741.md:7:observables_used: [`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type`, `vc_interp`, `r_floor`, `r_span`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0741.md:8:**observable_contract**: claim=il generatore surrogate per `V_c` deve raggiungere `Jaccard>=0.75` a N=144 con acceptance_rate non nulla prima del trasferimento GUE/Poisson; observable=`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type`, `vc_interp`, `r_floor`, `r_span`; operator=aggiunta di candidati `phase_shift_sturmian` al generatore label-preserving, poi lettura della curva `r(V)`; generator=phi Sturmian, balanced_random, phase_shift_sturmian dentro `swap_label_surrogate`; denominator=N=144, phase={0,0.25,0.5,0.75}, r_threshold={0.48,0.50,0.52}, label_trials=2, phase_candidate_trials=64, swap_steps=0; non_possible=se il gate passa solo con source_mode Sturmian, il null e' ponte strutturato e non controprova indipendente del boundary; not_tested=GUE/Poisson, silver/bronze, fit power-law, gap_ratio, indipendenza del null fuori da source_mode Sturmian. tools/data/reports/agent_20260509_0741.md:19:- **Proto-ipotesi**: se la raggiungibilita' del label-set e' il blocco operativo, un generatore Sturmian a fase traslata deve superare `Jaccard>=0.75` a N=144. Se passa, il blocco tecnico cade; se passa solo mantenendo source_mode Sturmian, il null resta ponte strutturato e non autorizza il confronto GUE/Poisson. tools/data/reports/agent_20260509_0741.md:73:**CONSTRAINT**: il null label-preserving per `V_c` raggiunge `Jaccard>=0.75` a `N=144` con acceptance_rate non nulla (`6/8` sequenze, `0.75` sulle righe evento), ma solo come `phase_shift_sturmian`. Quindi il Lab ha un ponte strutturato accettabile per testare coerenza interna del boundary, non un contro-campo indipendente per trasferire verso GUE/Poisson. tools/data/reports/agent_20260509_0741.md:81:- **Campo di possibilita**: qui diventa possibile usare un ponte label-preserving a N=144 per audit interno di `V_c`; qui diventa non-possibile passare a GUE/Poisson finche' `source_mode` resta Sturmian. tools/data/reports/agent_20260509_0741.md:84:Il prossimo passo non e' confrontare GUE/Poisson. E' spezzare il ponte: cercare un generatore non-Sturmian che mantenga `label_jaccard>=0.75` e `hamming_ratio` non triviale, oppure promuovere il vincolo che il label-set alto e' raggiungibile solo attraverso trasporto Sturmian nel perimetro N=144. tools/data/reports/agent_20260509_0741.md:88:- **L1 hard constraint**: il verdict non autorizza GUE/Poisson; dichiara source_mode Sturmian come limite. tools/data/reports/agent_20260509_0837.md:7:observables_used: [`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `generator_class`, `event_type`, `vc_interp`, `r_floor`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0637.md:8:**observable_contract**: claim=la forma `r(V)` del boundary Sturmian-Harper e' sostenuta da attraversamenti interni, non da collasso al bordo minimo della filtrazione; observable=`event={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`; operator=curva `r(V)` su griglia 0.5..3.0 step 0.01, crossing lineare e classificazione del primo stato rispetto a `r_threshold={0.48,0.50,0.52}`; generator=phi Sturmian, phase-shuffle Sturmian, random bilanciato, surrogate random selezionato per overlap label-set; denominator=N={89,144,233}, phase={0,0.25,0.5,0.75}, phase_trials=2, random_trials=2, label_trials=2, label_candidates=5, seed=202605090637; non_possible=se un null che preserva label-set produce crossing interno unico e stesso `r_floor` dei generatori Sturmian, `V_c` non e' piu' boundary map di ordine Sturmian; not_tested=GUE/Poisson reali, silver/bronze in questo ciclo, fit power-law, label-preserving forte con accettazione Jaccard>=0.75. tools/data/reports/agent_20260509_0637.md:43:- Contratto osservabile-operatore: `gap_ratio`, controlli metallici silver/bronze e domini GUE/Poisson non vengono testati in questo ciclo. tools/data/reports/agent_20260422_0330.md:13:If the Wiener-Khinchin transform of the measured magnitude ACF reproduces the Welch PSD slope, the spectral content is fully encoded in the time-domain correlations. The convergence depth K* reveals how many lag orders carry the information: K*=1 means pair-only, K*>>1 means the 1/k decay structure is essential. tools/data/reports/agent_20260422_0330.md:89:1. **The magnitude PSD is Wiener-Khinchin self-consistent.** The ACF (200 lags) reconstructs 99.0% of the directly-measured PSD slope. The spectral content is fully encoded in the time-domain correlations — there is no spectral structure beyond what the ACF predicts. tools/data/reports/agent_20260509_0330.md:8:**observable_contract**: claim=la forma locale della curva `r(V)` puo' sostituire il primo crossing su griglia come portatore del boundary Sturmian-Harper; observable=`vc_interp`, `slope_at_cross`, `crossing_count`, `r_span`; operator=curva `r(V)` su griglia 0.5..3.0 step 0.01 con crossing lineare interpolato per `r_threshold={0.48,0.50,0.52}`; generator=Sturmian metallici phi/silver/bronze e random bilanciato a densita phi; denominator=N={89,144,233,377,610}, phase={0,0.25,0.5,0.75}, 3 random trial per condizione, seed=202605090330; not_tested=gap_ratio, label-set Sturmian, fit power-law asintotico, domini reali GUE/Poisson, prova formale di monotonia. tools/data/reports/agent_20260509_0330.md:46:| domain | vc_interp median | IQR | min-max | slope median | crossing_count | tools/data/reports/agent_20260509_0330.md:55:| domain | r_threshold | vc_interp median | IQR | slope median | crossings | tools/data/reports/agent_20260509_0330.md:79:| domain | r(V=0.5) median | r(V=3.0) median | r_span median | tools/data/observable_collinearity_breaking_20260506_1957.json:3: "question": "When do canonical observable retention curves break collinearity across domains?", applications/scoperte/20260508_1915_high-core-repair-audit_auto/lab-note.draft.md:18:pending_consecutio: "Testare il confine come terzo incluso operativo sui domini GUE/Poisson: applicare un contratto osservabile tipizzato prima del run, distinguendo core congiunto, residui singoli e stabilita' cross-dominio." applications/scoperte/20260508_1915_high-core-repair-audit_auto/lab-note.draft.md:36:> *Consecutio attesa*: Testare il confine come terzo incluso operativo sui domini GUE/Poisson: applicare un contratto osservabile tipizzato prima del run, distinguendo core congiunto, residui singoli e stabilita' cross-domi tools/data/reports/agent_20260504_0901.md:21:- **Sequences**: Prime gaps (N=50000), GUE eigenvalue gaps (200x200 matrices, 250 realizations), Poisson iid exponential gaps (N=50000). applications/scoperte/20260508_1915_high-core-repair-audit_auto/cycle-report.draft.md:19:pending_consecutio: "Testare il confine come terzo incluso operativo sui domini GUE/Poisson: applicare un contratto osservabile tipizzato prima del run, distinguendo core congiunto, residui singoli e stabilita' cross-dominio." applications/scoperte/20260508_1915_high-core-repair-audit_auto/cycle-report.draft.md:34:> *Consecutio attesa*: Testare il confine come terzo incluso operativo sui domini GUE/Poisson: applicare un contratto osservabile tipizzato prima del run, distinguendo core congiunto, residui singoli e stabilita' cross-dominio. tools/data/reports/agent_20260508_0330.md:26:| domain | N | phase | threshold | n_large | first_two_ratio | top2_ratio | tools/data/reports/agent_20260508_0330.md:34:| domain | first_two median | first_two IQR | first_two range | top2 median | n_large median | tools/data/reports/agent_20260509_1548.md:7:observables_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. tools/data/reports/agent_20260509_1548.md:24:- `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. tools/data/reports/agent_20260509_1548.md:39:- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field. tools/data/reports/agent_20260508_2140.md:8:**observable_contract**: claim=il fallimento del fit power-law su `V_c(phi)` segnala un bordo reticolare/quantizzato del passaggio Sturmian-Harper; observable=`V_c`, `distinct_vc`, `repeat_rate`, `mode_rate`; operator=prima soglia `V` su griglia 0.5..3.0 step 0.025 dove `(H(seq,V)) < 0.5`; generator=Sturmian metallici phi/silver/bronze e random bilanciato a densita phi; denominator=N={89,144,233,377,610}, phase={0,0.25,0.5,0.75}, 4 random trial per condizione, seed=202605082140; not_tested=gap_ratio, label-set Sturmian, GUE/Poisson universale, fit power-law asintotico oltre N=610. tools/data/reports/agent_20260508_2140.md:41:| domain | count | distinct_vc | repeat_rate | mode_rate | median | min | max | tools/data/reports/agent_20260508_2140.md:60:| domain/phase | distinct_vc | repeat_rate | mode_rate | median | tools/data/reports/agent_20260516_1058.md:5:**verdict**: CONSTRAINT - Il filtro endpoint GUE/Poisson resta completo su 36/36 righe e supera il nuovo feature-scramble null row-aligned (`raw_p=0/512=0.0`, `add_one_p=1/513=0.001949318`). Il label-permutation null resta permissivo (`raw_p=15/128=0.1171875`, `add_one_p=16/129=0.124031008`). Il nodo regressivo non e' l'endpoint reader, ma quale null e' legittimo per sfidare il lettore. tools/data/reports/agent_20260516_1058.md:8:**observable_contract**: claim=GUE/Poisson endpoint filter e' specifico se la stabilita' osservata resta completa e null feature-scramble che preservano marginali per reader non ricostruiscono la stabilita' completa; observable=`endpoint_stable` per source row, margine centroidale per reader, distribuzione null feature-scramble; operator=centroidi endpoint calibrati una volta su GUE/Poisson, poi scoring di test rows vere e feature-scrambled row-aligned; generator=matrici GUE e gap Poisson esponenziali; denominator=2 domini x 3 size x 6 test seed = 36 source rows, ogni source row richiede 5/5 reader pass; non_possible=filtro endpoint specifico se il feature-scramble null raggiunge 36/36; not_tested=RP residue, Anderson 3D, spettri sperimentali, limite N infinito, universalita analitica. tools/data/reports/agent_20260516_1058.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + tensione seme "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_1058.md:19:- **Movimento A->M->B**: fisico A = crossover spettrale GUE/Poisson/RP; matematica M = vettore osservabile multivariato con null che rompe l'accoppiamento feature-riga; fisico B non emerge. Il ciclo consegna un gate metodologico prima del ritorno a RP/Anderson. tools/data/reports/agent_20260516_1058.md:23:- `why`: segue la direzione valutatore: testare prima la stabilita' endpoint GUE/Poisson come filtro del confine-terzo incluso e riparare il null che nel ciclo 10:45 lasciava il filtro aperto. tools/data/reports/agent_20260516_1058.md:24:- `not_drift`: non torna a phi/Sturmian, V_c, lambda RP o residui graph-only; misura solo endpoint GUE/Poisson e null sullo stesso denominatore. tools/data/reports/agent_20260516_1058.md:25:- `seed_residue`: restano non testati gli 8 domini GUE / 5 Poisson originali come perimetro largo e il terzo incluso RP. tools/data/reports/agent_20260516_1058.md:29:- **Baseline noto piu vicino**: statistiche spettrali GUE/Poisson, spacing ratio, Brody interpolation, Berry-Robnik-like mixture, Rosenzweig-Porter come crossover. tools/data/reports/agent_20260516_1058.md:30:- **Cosa assorbe il baseline**: la separazione dei poli GUE e Poisson nelle feature canoniche. tools/data/reports/agent_20260516_1058.md:35:> GUE e Poisson sono endpoint validi per il filtro boundary se 36/36 source rows restano endpoint-stable e un feature-scramble null row-aligned non ricostruisce 36/36. tools/data/reports/agent_20260516_1058.md:41:- **Punto fisico sorgente**: transizione spettrale GUE/Poisson/Rosenzweig-Porter. tools/data/reports/agent_20260516_1058.md:72:1. Verificato: il filtro endpoint osservato resta completo. GUE = 18/18, Poisson = 18/18, combined = 36/36; ogni source row ha 5/5 reader pass. tools/data/reports/agent_20260516_1058.md:80:Il filtro endpoint GUE/Poisson e' chiuso contro feature-scramble row-aligned nel perimetro 36 source rows / 5 reader. Non diventa scoperta fisica: diventa preflight valido per riaprire il boundary test. La prossima mossa puo' tornare a RP solo mantenendo questo gate: endpoint observed 36/36, feature-scramble `add_one_p<=0.05`, poi boundary residue contro null row-aligned. tools/data/reports/agent_20260508_1947.md:8:**observable_contract**: claim=se il boundary simbolico del supertile esiste nell'osservabile, aligned supertile deve battere il misaligned same-length non solo nel label-set ma nella geometria IDS/rank/errore dei label core; observable=all-core hits, delta IDS, delta indice spettrale normalizzato, errore label e spacing ratio dei core label rispetto al reference phi; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, confronto per label contro reference stesso N/phase/threshold; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, GUE/Poisson real domains, soglie 1.75/2.25, parsing simbolico esatto di ogni supertile. tools/data/reports/report_20260405_0330.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 applications/scoperte/20260429_three-layer-decomposition/cycle-report.draft.md:181:- **BOUNDARY constrained**: il confine GUE/Poisson (Brody flow) descrive solo gli strati 1-2. Lo strato 3 (algebrico) è invisibile a Brody β. Qualsiasi modello completo del confine deve includere il pavimento algebrico. applications/scoperte/20260429_three-layer-decomposition/cycle-report.draft.md:188:- **Campo di possibilità**: diventa possibile modellare il confine GUE/Poisson con un pavimento algebrico che non decade. Diventa non-possibile trattare "strutturale" come una singola categoria. tools/data/reports/agent_20260406_1030.md:7:> The boundary between GUE and Poisson is "the third included" (A9). Is this boundary populated by multiple domains, or are primes special? 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:14:- N=5000 spacings per domain, 20 shuffled surrogates for z-scores tools/data/reports/agent_20260406_1030.md:20:### Spectral Landscape (17 domains) tools/data/reports/agent_20260406_1030.md:69:2. **The spectral landscape is 2D, not 1D.** alone classifies 4 zones (Poisson/boundary/GOE-GUE/rigid). Adding acf1 splits the boundary zone: mixtures (acf1~0) vs intrinsically ordered (acf1<<0). Primes are the ONLY tested domain at intermediate position on BOTH axes. tools/data/reports/agent_20260406_1030.md:75:5. **Quadratic residues are GUE-like.** =0.613, confirming Katz-Sarnak for quadratic L-functions. But their acf1=-0.046 is MUCH weaker than GUE (-0.28), suggesting incomplete level repulsion — another boundary domain on a different axis. tools/data/reports/agent_20260406_1030.md:80:New tension: **SPECTRAL_NICHE** — primes are the only known domain at (intermediate , significantly negative acf1). The 2D classification opens: are there other intrinsically-ordered boundary domains? tools/data/reports/agent_20260406_1030.md:82:Constraint: ** alone is insufficient** to characterize spectral statistics. Any study claiming "primes are GUE-like" or "primes are between GOE and Poisson" based solely on is missing the ordering dimension. tools/data/reports/agent_20260406_1030.md:86:2. Quadratic residues are GUE in but weakly ordered (acf1=-0.05). Are there other "weak-GUE" domains? tools/data/reports/agent_20260406_1030.md:90:- Script: tools/exp_spectral_landscape.py (reusable for any domain) tools/data/reports/report_20260305_0342.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260505_0330.md:14:- **Domini**: prime gaps, GUE gaps, Poisson iid exponential gaps. 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:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/crossover spettrale + tensione BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260515_1915.md:27:- `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. tools/data/reports/agent_20260515_1915.md:41:I nodi ponte GUE/Poisson sopravvivono a perturbazioni del lettore, oppure il boundary del 18:55 era una soglia locale? tools/data/reports/agent_20260515_1915.md:54:- **Denominatore**: 13 righe row-aligned, 8 GUE e 5 Poisson. tools/data/reports/agent_20260515_1915.md:94:1. Verificato: il denominatore resta quello richiesto, 13 righe con 8 GUE e 5 Poisson, ripetute in 27 letture. tools/data/reports/agent_20260515_1915.md:116: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. tools/data/reports/agent_20260513_0330.md:33:- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`. applications/scoperte/20260509_0659_vc-label-reachability-gate_auto/lab-note.draft.md:18:pending_consecutio: "Costruire il null label-preserving per V_c prima del trasferimento: ridisegnare il generatore surrogate finche' raggiunge Jaccard>=0.75 a N=144 con acceptance_rate non nulla, poi solo dopo confrontare GUE/Poisson" tools/data/reports/falsifier_20260512_0330.json:17: "evidence": "I file strutturali danno come direzione viva `Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo`. Il report usa 4 GUE random matrix blocks e controlli logistici/Cramer/mod6/compositi, ma non mostra 8 domini GUE né 5 Poisson. La deviazione verso prime/mod6 è dichiarata come BOUNDARY, ma non come `deliberate_counter_perimeter` rispetto alla direzione GUE/Poisson visibile in `lab_data.json`.", tools/data/reports/falsifier_20260512_0330.json:18: "suggestion": "Esplicitare se `prime_SR_persistent_boundary` è una sotto-direzione autorizzata dal campo vivo; altrimenti nel prossimo ciclo riallineare il contratto a 8 domini GUE / 5 Poisson oppure dichiarare `deliberate_counter_perimeter` con `why` e `not_drift` verificabili." tools/data/reports/falsifier_20260512_0330.json:21: "summary": "Il report è quasi coerente internamente, ma L8 segnala drift rispetto alla direzione strutturale GUE/Poisson e L5 richiede ancoraggio al risultato classico più vicino sui residui/gap modulo q." applications/scoperte/20260509_0659_vc-label-reachability-gate_auto/cycle-report.draft.md:19:pending_consecutio: "Costruire il null label-preserving per V_c prima del trasferimento: ridisegnare il generatore surrogate finche' raggiunge Jaccard>=0.75 a N=144 con acceptance_rate non nulla, poi solo dopo confrontare GUE/Poisson" applications/scoperte/20260509_0659_vc-label-reachability-gate_auto/cycle-report.draft.md:34:> *Consecutio attesa*: Costruire il null label-preserving per V_c prima del trasferimento: ridisegnare il generatore surrogate finche' raggiunge Jaccard>=0.75 a N=144 con acceptance_rate non nulla, poi solo dopo confrontare GUE/Poisson applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/lab-note.draft.md:20:title_proposal: "[TARGET — TM1 refinement] The GUE-Poisson Crossover Has a Phase Transition: Direction Locks, Magnitude Decays, Then Flips" applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/lab-note.draft.md:24: - "L3 high: 'The GUE-Poisson crossover has a phase transition' / 'GUE-Poisson transition'" applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/lab-note.draft.md:32:# [TARGET — TM1 refinement] The GUE-Poisson Crossover Has a Phase Transition: Direction Locks, Magnitude Decays, Then Flips applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/cycle-report.draft.md:29: summary: "'The GUE-Poisson crossover has a phase transition' / 'GUE-Poisson transition'" applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/cycle-report.draft.md:45: - "L3 high: 'The GUE-Poisson crossover has a phase transition' / 'GUE-Poisson transition'" applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/cycle-report.draft.md:52:# Cycle Report — The GUE-Poisson Crossover Has a Phase Transition: Direction Locks, Magnitude Decays, Then Flips applications/scoperte/20260501_0931_the-gue-poisson-crossover-has-a_auto/cycle-report.draft.md:65:Il report non e' internamente coerente: si rompe soprattutto L1/L3, perche' trasforma un minimo non nullo di shuffled-GUE in uno zero/phase transition GUE-Poisson. tools/data/reports/report_20260330_0344.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260405_0825.md:6:La transizione GUE -> Poisson nelle statistiche dei gap primi e' un fenomeno strutturale dei primi, o appare in qualsiasi sequenza con densita' decrescente? tools/data/reports/agent_20260405_0825.md:10:- Riferimenti: `_GUE = 0.5307`, `_Poisson = 0.3863` tools/data/reports/agent_20260405_0825.md:28:Le slopes sono quasi identiche. Sia i primi che i Cramer random transitano da valori piu' alti di `` (piu' GUE-like) a valori piu' bassi (piu' Poisson-like) man mano che la densita' decresce. La classificazione "8 domini GUE, 5 Poisson" riflette principalmente questo effetto di densita'. tools/data/reports/agent_20260405_0825.md:34:Tutti i valori `` cadono tra 0.44-0.48, ben lontani sia dal GUE puro (0.53) che dal Poisson puro (0.39). I primi vivono in un regime intermedio che non collassa ne' sull'uno ne' sull'altro — un confine largo, non una transizione di fase. tools/data/reports/agent_20260405_0825.md:37:Il "terzo incluso" non e' un punto di transizione tra GUE e Poisson. E' il regime intermedio stesso. I primi non sono ne' correlati (GUE) ne' indipendenti (Poisson) — sono in un terzo stato con correlazione in eccesso che cresce con la scala. Questo e' coerente con f(x) = 1 + 1/x: la regola genera correlazione oltre l'indipendenza, ma non la correlazione rigida di una matrice random. tools/data/reports/report_20260315_0801.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/lab-note.draft.md:20:title_proposal: "[TARGET — TM1 refinement] The GUE-Poisson Boundary Is a Denominator Collapse Layer" applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/lab-note.draft.md:29:# [TARGET — TM1 refinement] The GUE-Poisson Boundary Is a Denominator Collapse Layer applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/cycle-report.draft.md:29:# Cycle Report — The GUE-Poisson Boundary Is a Denominator Collapse Layer tools/data/reports/falsifier_20260514_1656.json:10: "evidence": "lab_data/seme dichiarano direzione viva cross-dominio: `8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo`. Il report giustifica la regressione locale con direttiva one-shot e fornisce `why`/`not_drift`, ma non marca esplicitamente la deviazione come `deliberate_counter_perimeter`.", applications/scoperte/20260516_1019_rp-exact-local-window-size-stress_auto/lab-note.draft.md:18:pending_consecutio: "Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned." applications/scoperte/20260516_1019_rp-exact-local-window-size-stress_auto/lab-note.draft.md:36:> *Consecutio attesa*: Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned. tools/data/reports/agent_20260509_1427.md:7:observables_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. tools/data/reports/agent_20260509_1427.md:8:**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. tools/data/reports/agent_20260509_1427.md:12:- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`. tools/data/reports/agent_20260509_1427.md:13:- **Dipolo / punto-zero**: blank / transfer; punto-zero = stessa riga domain/window prima della disponibilita del null row-aligned. tools/data/reports/agent_20260509_1427.md:14:- **Piano superiore**: grafo della conoscenza e boundary operator. Il bordo non classifica GUE o Poisson; filtra quali righe hanno denominatore sufficiente. tools/data/reports/agent_20260509_1427.md:25:Il null row-aligned per una coppia blank GUE/Poisson decide `blank -> transfers`, `blank -> falls` o `blank remains blank`? tools/data/reports/agent_20260509_1427.md:30:- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like. tools/data/reports/agent_20260509_1427.md:38:| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision | tools/data/reports/agent_20260509_1427.md:61:2. **Verificato: il transfer non cambia la classe.** `zeta_zeros` resta GUE -> GUE; `pendolo_doppio` resta Poisson -> Poisson. tools/data/reports/agent_20260509_1427.md:71:Questo 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. tools/data/reports/agent_20260509_1427.md:75:- **Singolare**: la riga domain/window prima della decisione del gate. tools/data/reports/agent_20260509_1427.md:76:- **Invariante di passaggio**: il null row-aligned decide il passaggio; la classe GUE/Poisson non viene riscritta. tools/data/reports/agent_20260509_1427.md:95:- 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`. applications/scoperte/20260516_1019_rp-exact-local-window-size-stress_auto/cycle-report.draft.md:19:pending_consecutio: "Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned." applications/scoperte/20260516_1019_rp-exact-local-window-size-stress_auto/cycle-report.draft.md:34:> *Consecutio attesa*: Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned. tools/data/reports/agent_20260512_0330.md:12:- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`. 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/agent_20260509_1538.md:7:observables_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. tools/data/reports/agent_20260509_1538.md:8:**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`. tools/data/reports/agent_20260509_1538.md:24:- `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. tools/data/reports/agent_20260509_1538.md:38:- Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field. tools/data/reports/agent_20260509_1538.md:114:- **Invariante di passaggio**: failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale. 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: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:58:| domain | samples | focus active | SR real/null/delta | p(SR) | L1 delta | p(L1) | triple delta | p(triple) | tools/data/reports/agent_20260514_1612.md:70:1. **Verificato**: nel proxy GUE, `SR` resta attivo contro Poisson span-matched (`delta=0.2055`, `p=0.000244`, `d=4.872`). 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_20260507_0803.md:12:GUE/Poisson boundary is a transferable operator or only a local metric. tools/data/reports/agent_20260507_0803.md:36:This is not a new BOUNDARY experiment. GUE and Poisson do not appear in the tools/data/reports/agent_20260507_0803.md:88:GUE/Poisson run, beta 0.3-0.4 carried both classification ambiguity and tools/data/reports/agent_20260507_0803.md:116:> GUE/Poisson boundary layer is not transferable: ambiguity appears near beta tools/data/reports/agent_20260507_0803.md:132:If both layers coincide in a domain, the domain has a BOUNDARY-like transition. tools/data/reports/agent_20260507_0803.md:134:coordinate belongs to the target domain, not to the operator. tools/data/reports/report_20260404_1852.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260516_1031.md:8:**observable_contract**: claim=`window_mode/unfolding` e' coordinata del boundary se gli endpoint GUE/Poisson trasferiscono mentre le righe RP boundary espongono residuo reader-specific contro null row-aligned; observable=reader_sensitivity del vettore spettrale canonico tra global_mean, exact_local e odd_coerced; operator=stessa riga di gap letta con piu unfolding/window modes; generator=matrici GUE, gap Poisson esponenziali, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=domain x size x seed, null da permutazione e shift circolare sulla stessa riga; non_possible=asse reader come boundary se un endpoint si frattura o RP non batte i null; not_tested=spettri sperimentali, N infinito, Anderson 3D, prova analitica di universalita. tools/data/reports/agent_20260516_1031.md:16:- **Proto-ipotesi**: se `window_mode/unfolding` e' asse reale del confine, GUE e Poisson trasferiscono come poli mentre RP boundary mostra residuo specifico del lettore sopra i null row-aligned. tools/data/reports/agent_20260516_1031.md:18:- **Proiezione**: misuro `reader_sensitivity` e stato classico per righe GUE, Poisson e RP `0.045/0.060/0.075`, con null di permutazione e shift sulla stessa riga. tools/data/reports/agent_20260516_1031.md:19:- **Movimento A->M->B**: fisico A = crossover GUE/Poisson/RP finito; matematica M = matrice row-aligned `(domain, N, seed, reader)`; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico. tools/data/reports/agent_20260516_1031.md:23:- `why`: porta l'asse `window_mode`/unfolding fuori dalla sola lambda RP e lo testa su GUE, Poisson e RP con null row-aligned. tools/data/reports/agent_20260516_1031.md:30:- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=reader_axis_cross_domain`; `graph_baseline_audit=permutation_null + position_shift_null`. tools/data/reports/agent_20260516_1031.md:38:- **Righe**: size `128/192`, seed `4`, domini `GUE`, `Poisson`, RP lambda `0.045/0.060/0.075`. tools/data/reports/agent_20260516_1031.md:82:Il prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matrice RP. Se la clausola classica resta `q AND w`, il boundary reader-axis e' bloccato. Se la clausola diventa endpoint-validata su baseline GUE indipendente, la domanda torna falsificabile. tools/data/reports/agent_20260508_2005.md:8:**observable_contract**: claim=se il boundary simbolico del core alto esiste nella grammatica nativa della parola, le finestre locali attorno alle posizioni IDS dei gap core devono separare aligned supertile da misaligned same-length; observable=eccesso grammaticale locale rispetto a baseline Sturmian classica; operator=estrazione finestra circolare attorno a round(IDS*N) per ogni label core selezionato, misura p(k)<=k+1, right-special<=1, return-word excess sopra 2, difetto palindromico; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}, window={89 main, 55 seedcheck}, k={3,4,5,6,7,8}; not_tested=gap_ratio, domini GUE/Poisson reali, soglie 1.75/2.25, prova formale della grammatica Sturmian, generatori non-phi. tools/data/reports/agent_20260516_1135.md:8:**observable_contract**: claim=i null sono confrontabili solo se condividono observable, perimetro righe e N; observable=conteggio cross-size di righe `stable_graph_bridge+classical_intermediate`; operator=righe compatte Anderson 3D dal run 11:17 classificate dallo stesso reader kNN/classico; generator=stesse righe sorgente, due operatori null che differiscono solo per struttura preservata; denominator=`512` trial per null su 11 righe per size; p_value_definition=right-tail `raw_p=k/N`, `add_one_p=(k+1)/(N+1)`, con `k` = trial null con conteggio cross-size >= osservato; non_possible=chiamare un null piu' restrittivo se perimetro o N cambiano; not_tested=raw multi-seed reader, nuovi Hamiltoniani, `L>=7`, perimetro completo 8 GUE / 5 Poisson. tools/data/reports/agent_20260516_1135.md:25:- `return_criterion`: tornare al perimetro vivo 8 GUE / 5 Poisson quando il contratto dei null comparabili e' stabilizzato; oppure chiudere Anderson se anche il raw-reader endpoint-preserving rinomina `W=20`. tools/data/reports/agent_20260516_1135.md:26:- `seed_residue`: restano non testati il perimetro completo 8 GUE / 5 Poisson, raw multi-seed Anderson e `L>=7`. tools/data/reports/agent_20260509_1437.md:7:observables_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. tools/data/reports/agent_20260509_1437.md:8:**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. tools/data/reports/agent_20260509_1437.md:12:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`. tools/data/reports/agent_20260509_1437.md:13:- **Dipolo / punto-zero**: blank / transfer; punto-zero = riga domain/window prima della disponibilita del null row-aligned. tools/data/reports/agent_20260509_1437.md:14:- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate decide copertura del denominatore, non ontologia GUE/Poisson. tools/data/reports/agent_20260509_1437.md:30:- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like. tools/data/reports/agent_20260509_1437.md:38:| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision | tools/data/reports/agent_20260509_1437.md:42:| logistica_biforcazione_var_3.5699 | 4727 | 0.581221 | 0.099640 | 161.271569 | GUE -> Poisson | true | transfer complete, class_change edge case | tools/data/reports/agent_20260509_1437.md:59:| domain/window | source | denominator_state | excluded mass | null | transfer | tools/data/reports/agent_20260509_1437.md:69:4. **Verificato: `logistica_biforcazione_var_3.5699` trasferisce con `class_change=true`.** Il cambio GUE -> Poisson e' edge case del null, non legge nuova legge. tools/data/reports/agent_20260509_1437.md:77:Il 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`. tools/data/reports/agent_20260509_1437.md:82:- **Invariante di passaggio**: disponibilita del null leggibile; non l'etichetta GUE/Poisson e non il fit `V_c`. tools/data/reports/agent_20260509_1437.md:94:- **L5 re-discovery**: il ciclo e' audit di denominatore residuo, non teorema GUE/Poisson. tools/data/reports/agent_20260509_1437.md:101:- 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`. tools/data/reports/agent_20260515_1940.md:14:- **Combo**: A9 terzo incluso + QxG continuo/discreto + flusso Hamiltoniano RP + tensione BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260515_1940.md:24:- `why`: il ciclo resta sul confine GUE/Poisson e testa il terzo incluso operativo dentro un flusso Hamiltoniano controllato, con separazione tra endpoint, riga a due lettori e residui del grafo. tools/data/reports/agent_20260515_1940.md:28:- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson. tools/data/reports/agent_20260515_1940.md:99:ssp_value: yes. Lo script e riusabile per stressare gate GUE/Poisson controllati su taglie multiple e restituisce direttamente righe all-size, righe intermittenti, residui graph-only e residui classic-only. tools/data/perturbation_dimensionality_audit_scale0330.json:39: "fixed_domains": { tools/data/reports/report_20260405_0715.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/falsifier_20260509_1516.json:9: "claim": "porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson", tools/data/reports/falsifier_20260509_1516.json:10: "evidence": "lab_data.json dichiara la direzione viva: \"test su perimetri reali o avversariali senza importare label GUE/Poisson\". Il report invece struttura input e adesione come 8 GUE-like e 5 Poisson-like, quindi importa proprio la label che la direzione chiedeva di non importare.", tools/data/reports/falsifier_20260509_1516.json:11: "suggestion": "Nel prossimo ciclo ripetere il gate su features domain-native senza usare `source_domain_type` GUE/Poisson nella classificazione o nel claim; se le label restano solo metadata, dichiarare `deliberate_counter_perimeter` con `why` e mostrare che non entrano nell'operatore." tools/data/reports/falsifier_20260509_1516.json:14: "summary": "Il report e' internamente quasi coerente, ma si rompe su L8: dichiara aderenza alla direzione mentre reintroduce label GUE/Poisson che la direzione viva chiedeva di non importare." tools/data/prime_sr_persistent_boundary_20260512_0330.json:46: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR" tools/data/endpoint_gated_rp_boundary_20260516_1104.json:53: "generator": "GUE matrices, Poisson exponential spacings, RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", tools/data/endpoint_gated_rp_boundary_20260516_1104.json:57: "operator": "calibrate GUE/Poisson endpoint centroids; score RP rows by balanced distance to both endpoint centroids; compare to feature-scrambled RP rows" tools/data/exp_acf_range_universality.json:251: "cross_domain": { tools/data/reports/report_20260307_0342.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/report_20260303_0341.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260426_0330.md:9:> BOUNDARY: 8 domains GUE, 5 Poisson — where is the boundary? tools/data/reports/agent_20260426_0330.md:13:Does spectral rigidity (number variance Sigma^2(L)) — an observable independent from the r-statistic — confirm or refute our GUE/Poisson classification? And does the dual-channel structure (magnitude vs algebraic ordering) manifest at the level of long-range spectral statistics? tools/data/reports/agent_20260426_0330.md:17:- **Theory**: GUE predicts Sigma^2(L) ~ (2/pi^2) ln(L), Poisson predicts Sigma^2(L) = L tools/data/reports/agent_20260426_0330.md:18:- **Domains**: 8 domains (primes, GUE matrices, coupled_osc, string_vib, percolation, logistic, brownian, Poisson random) tools/data/reports/agent_20260426_0330.md:20:- **Unfolding**: Primes use Li(p) = p/ln(p) (proper density correction). Other domains use local mean normalization. tools/data/reports/agent_20260426_0330.md:21:- **Null baseline**: 50 shuffles per domain (same gap distribution, destroyed sequential ordering) tools/data/reports/agent_20260426_0330.md:22:- **Metric**: Sig2/L ratio (GUE << 1, Poisson = 1), log-log slope, ordering fraction = (Sig2_shuf - Sig2_real) / Sig2_shuf tools/data/reports/agent_20260426_0330.md:26:### Cross-domain classification (crude unfolding, L=10) tools/data/reports/agent_20260426_0330.md:56:Log-log slope: real = 0.737, shuffle = 0.971. GUE theory ≈ 0.3, Poisson = 1.0. tools/data/reports/agent_20260426_0330.md:60:Ordering-GUE domains (coupled_osc, string_vib, percolation) show Sig2/L > 1 — they are SUPER-Poisson. The ordering creates excess clustering (bunching), not repulsion. Shuffling REDUCES their variance (z = 3 to 24). The r-statistic sees nearest-neighbor repulsion; Sig2 sees long-range bunching. These are two different properties. tools/data/reports/agent_20260426_0330.md:68:3. **Only true GUE matrices are rigid at all scales (Sig2/L = 0.073 at L=10).** Primes live in an intermediate regime (0.376 at L=10) — more rigid than Poisson, less rigid than GUE. This is NOT a failure of GUE classification — it's a finer structure that the r-statistic cannot resolve. tools/data/reports/agent_20260426_0330.md:70:4. **Ordering-GUE domains are anti-rigid at long range.** They show super-Poisson variance (Sig2/L > 1), meaning the ordering creates clustering, not repulsion. The r-statistic and Sig2 classify them differently: r sees short-range repulsion, Sig2 sees long-range bunching. tools/data/reports/agent_20260426_0330.md:72:5. **META resolved: the tests are not tautological, but they are incomplete.** The r-statistic captures genuine structure (short-range spacing repulsion) confirmed by an independent observable. But Sig2(L) reveals richer structure that the r-statistic cannot see. The 8/5 GUE/Poisson split is a projection of a higher-dimensional reality. tools/data/reports/agent_20260426_0330.md:78:- BOUNDARY: The boundary is not a line separating GUE from Poisson. It is a surface in the (short-range, long-range, ordering-fraction) space. Primes sit in the interior of this surface, not at a boundary. tools/data/reports/agent_20260426_0330.md:80:- C1: Primes remain unique — the only domain where ordering INCREASES rigidity at long range while maintaining intermediate short-range repulsion. GUE matrices have stronger short-range repulsion; ordering-GUE domains have anti-rigidity at long range. tools/data/reports/agent_20260426_0330.md:87:- **Campo di possibilità**: diventa possibile predire la rigidità a scala L dalla decomposizione (distribuzione + ordinamento) con due parametri indipendenti. Diventa non-possibile trattare i primi come "GUE" o "Poisson" — vivono in un continuo parametrizzato dalla scala, e nessun singolo numero li classifica. tools/data/reports/agent_20260426_0330.md:90:- Script: `tools/exp_spectral_rigidity.py` (cross-domain, riusabile con qualsiasi dominio) tools/data/reports/agent_20260509_1444.md:8:**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. tools/data/reports/agent_20260509_1444.md:11:- **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. tools/data/reports/agent_20260509_1444.md:15:- **Operatori laterali scelti**: boundary operator, graph/perimeter transfer matrix, shuffle marginal-preserving. Entrano per trasferire il gate senza importare il label GUE/Poisson. tools/data/reports/agent_20260509_1444.md:83:- **Invariante di passaggio**: osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo. tools/data/reports/exp_number_variance_test.json:4: "claim_under_test": "BOUNDARY: GUE->Poisson drift in primes", tools/data/reports/exp_number_variance_test.json:5: "method": "Number variance Sigma^2(L) at 5 scales, compared with GUE and Poisson predictions", tools/data/reports/agent_20260505_1022.md:20:- dati: `primes` con 8000 gap, `GUE` con 175 spacing effettivi prodotti dal generatore corrente, `Poisson` con 8000 spacing; tools/data/reports/agent_20260505_1022.md:75:L2 quantita' assoluta vs ratio: il confronto usa alpha critici e z-score, non percentuali tra spazi di taglia diversa. GUE ha perimetro ridotto (`N=175`) e non viene pesato come primes/Poisson. tools/data/reports/report_20260305_2121.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260514_1649.md:20:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. 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_20260508_2133.md:8:**observable_contract**: claim=il residuo SR dello zero Mobius resta informativo dopo un null che preserva la geometria coarse della coppia di gap; observable=sr_zero_minus_nonzero, sr_aligned_minus_misaligned, low_low_zero_minus_nonzero, high_high_zero_minus_nonzero; operator=permuta label di transizione aligned/misaligned/zero dentro ogni pair bucket `(bucket(g_i), bucket(g_{i+1}))`; generator=prime gaps up to p<=1e6 with Mobius interval charges; denominator=main N={5000,10000,20000} offset=0 plus seedcheck offsets {3000,7000,11000}, 400 permutazioni, seed=2133; not_tested=gap_ratio Sturmian, V_c scaling, GUE/Poisson universale, sequenza Mobius globale coerente dopo shuffle. tools/data/reports/agent_20260508_2133.md:36:- Contratto osservabile-operatore: `gap_ratio`, `V_c`, domini Sturmian e GUE/Poisson non testati. applications/scoperte/20260508_2140_quasiperiodic-vc-lattice-gate_auto/lab-note.draft.md:18:pending_consecutio: "Riprogettare il gate `V_c` con null omogenei al boundary operator: phase-shuffle Sturmian, label-preserving surrogate e controllo gap_ratio prima di estendere a nuovi domini GUE/Poisson" applications/scoperte/20260508_2140_quasiperiodic-vc-lattice-gate_auto/lab-note.draft.md:36:> *Consecutio attesa*: Riprogettare il gate `V_c` con null omogenei al boundary operator: phase-shuffle Sturmian, label-preserving surrogate e controllo gap_ratio prima di estendere a nuovi domini GUE/Poisson tools/data/reports/agent_20260514_1330.md:29:- `not_drift`: non torna a `V_c`, GUE/Poisson, fit o vecchi depositi; stressa solo il residuo nominato dal valutatore dopo sottrazione mod6. applications/scoperte/20260508_2140_quasiperiodic-vc-lattice-gate_auto/cycle-report.draft.md:19:pending_consecutio: "Riprogettare il gate `V_c` con null omogenei al boundary operator: phase-shuffle Sturmian, label-preserving surrogate e controllo gap_ratio prima di estendere a nuovi domini GUE/Poisson" applications/scoperte/20260508_2140_quasiperiodic-vc-lattice-gate_auto/cycle-report.draft.md:34:> *Consecutio attesa*: Riprogettare il gate `V_c` con null omogenei al boundary operator: phase-shuffle Sturmian, label-preserving surrogate e controllo gap_ratio prima di estendere a nuovi domini GUE/Poisson tools/data/reports/agent_20260508_2121.md:8:**observable_contract**: claim=la classe zero Mobius resta informativa dopo controllo per lunghezza del gap; observable=low_low_zero_minus_nonzero, high_high_zero_minus_nonzero, sr_zero_minus_nonzero sotto null stratificato; operator=shuffle delle cariche Mobius intervallari solo dentro bucket di lunghezza gap; generator=prime gaps up to p<=1e6 with Mobius sieve; denominator=main N={5000,10000,20000} offset=0 plus seedcheck offsets {3000,7000,11000}; not_tested=gap_ratio Sturmian, high-core phi survival, universal GUE/Poisson classification, det(M) diretto. tools/data/reports/agent_20260511_0330.md:8:**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. tools/data/reports/report_20260304_0342.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/rp_candidate_window_stress_20260516_0938_w5.json:142: "domain_window": "RP_lambda_0.030", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:181: "domain_window": "RP_lambda_0.045", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:220: "domain_window": "RP_lambda_0.060", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:259: "domain_window": "RP_lambda_0.075", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:298: "domain_window": "RP_lambda_0.820", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:489: "domain_window": "RP_lambda_0.030", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:528: "domain_window": "RP_lambda_0.045", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:567: "domain_window": "RP_lambda_0.060", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:606: "domain_window": "RP_lambda_0.075", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:645: "domain_window": "RP_lambda_0.820", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:828: "domain_window": "RP_lambda_0.030", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:867: "domain_window": "RP_lambda_0.045", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:906: "domain_window": "RP_lambda_0.060", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:945: "domain_window": "RP_lambda_0.075", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:984: "domain_window": "RP_lambda_0.820", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1172: "domain_window": "RP_lambda_0.030", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1211: "domain_window": "RP_lambda_0.045", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1250: "domain_window": "RP_lambda_0.060", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1289: "domain_window": "RP_lambda_0.075", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1328: "domain_window": "RP_lambda_0.820", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1381: "domain_window": "RP_lambda_0.030", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1393: "domain_window": "RP_lambda_0.045", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1410: "domain_window": "RP_lambda_0.060", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1428: "domain_window": "RP_lambda_0.075", tools/data/rp_candidate_window_stress_20260516_0938_w5.json:1445: "domain_window": "RP_lambda_0.820", tools/data/reports/agent_20260425_0330.md:8:> The TWO_KINDS_GUE result (2026-04-24) discriminated distribution-GUE (primes, GUE matrices) from ordering-GUE (fibonacci, coupled_osc, percolation). The Markov-3 result (2026-04-23) found 33.6% scale-invariant ordering memory in prime gap residues. Do ordering-GUE domains have the same kind of Markov memory as primes? tools/data/reports/agent_20260425_0330.md:11:What is the Markov memory profile of each GUE type? If ordering-GUE domains get their classification FROM sequential ordering, they should have high Markov memory. But in which channel — magnitude (gap size) or residue (algebraic structure)? tools/data/reports/agent_20260425_0330.md:14:- **Method**: Classify gap sequences into terciles (S/M/L), compute conditional entropy H_k at Markov orders k=1,2,3, compare H_real vs H_shuffled (200 shuffles per domain) tools/data/reports/agent_20260425_0330.md:17:- **Domains**: 8 domains across 3 GUE types (distribution-GUE, ordering-GUE, Poisson) tools/data/reports/agent_20260425_0330.md:19:- **Null baseline**: 200 random permutations per domain (same distribution, destroyed order) tools/data/reports/agent_20260425_0330.md:23:### Markov memory by domain (tercile classification) tools/data/reports/agent_20260425_0330.md:50:2. **Ordering-GUE domains have no algebraic channel.** They have only tercile-type (magnitude) memory: 0.2-1.5% at order 1, comparable to primes in the same channel. But primes have the mod-6 channel ON TOP — which ordering-GUE domains lack entirely. No natural modular structure exists for eigenvalue spacings or percolation clusters. tools/data/reports/agent_20260425_0330.md:52:3. **Saturation depth is an orthogonal axis to GUE type.** The fraction of memory captured at order-1 varies independently of whether a domain is distribution-GUE or ordering-GUE: tools/data/reports/agent_20260425_0330.md:60:5. **The two-channel structure of primes is unique among all 8 domains tested.** Only primes have: tools/data/reports/agent_20260425_0330.md:64: No other domain has two structurally distinct memory channels. This is a concrete expression of C1 (primes as unique dynamic domain under M). tools/data/reports/agent_20260425_0330.md:69:The TWO_KINDS_GUE classification (distribution vs ordering) captures WHERE structure lives. This experiment adds a second axis: HOW the memory is structured. Primes are the only domain with dual-channel memory (algebraic + statistical). The 33% scale-invariant memory is a Z/6Z phenomenon with no analogue in ordering-GUE domains. The boundary (GUE/Poisson) is a 1D projection of a 2D structure: GUE type x memory depth. tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:140: "domain_window": "RP_lambda_0.030", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:179: "domain_window": "RP_lambda_0.045", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:218: "domain_window": "RP_lambda_0.060", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:257: "domain_window": "RP_lambda_0.075", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:296: "domain_window": "RP_lambda_0.820", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:484: "domain_window": "RP_lambda_0.030", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:523: "domain_window": "RP_lambda_0.045", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:562: "domain_window": "RP_lambda_0.060", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:601: "domain_window": "RP_lambda_0.075", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:640: "domain_window": "RP_lambda_0.820", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:692: "domain_window": "RP_lambda_0.030", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:700: "domain_window": "RP_lambda_0.045", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:710: "domain_window": "RP_lambda_0.060", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:720: "domain_window": "RP_lambda_0.075", tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json:731: "domain_window": "RP_lambda_0.820", tools/data/reports/agent_20260509_1400.md:7:observables_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 tools/data/reports/agent_20260509_1400.md:8:**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. tools/data/reports/agent_20260509_1400.md:108:- **L3 no observable drift**: non sono testati gap_ratio, nuovi spettri, nuovi N o GUE/Poisson. tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:146: "domain_window": "RP_lambda_0.000", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:185: "domain_window": "RP_lambda_0.030", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:224: "domain_window": "RP_lambda_0.045", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:263: "domain_window": "RP_lambda_0.060", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:302: "domain_window": "RP_lambda_0.075", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:341: "domain_window": "RP_lambda_0.100", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:380: "domain_window": "RP_lambda_0.180", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:419: "domain_window": "RP_lambda_0.320", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:458: "domain_window": "RP_lambda_0.680", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:497: "domain_window": "RP_lambda_0.820", 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"domain_window": "RP_lambda_0.100", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2113: "domain_window": "RP_lambda_0.180", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2152: "domain_window": "RP_lambda_0.320", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2191: "domain_window": "RP_lambda_0.680", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2230: "domain_window": "RP_lambda_0.820", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2269: "domain_window": "RP_lambda_1.000", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2445: "domain_window": "RP_lambda_0.000", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2484: "domain_window": "RP_lambda_0.030", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2523: "domain_window": "RP_lambda_0.045", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2562: "domain_window": "RP_lambda_0.060", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2601: "domain_window": "RP_lambda_0.075", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2640: "domain_window": "RP_lambda_0.100", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2679: "domain_window": "RP_lambda_0.180", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2718: "domain_window": "RP_lambda_0.320", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2757: "domain_window": "RP_lambda_0.680", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2796: "domain_window": "RP_lambda_0.820", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:2835: "domain_window": "RP_lambda_1.000", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:3004: "domain_window": "RP_lambda_0.000", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:3043: "domain_window": "RP_lambda_0.030", tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json:3082: 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"domain_window": "RP_lambda_1.000", tools/data/reports/agent_20260515_1734.md:7:**observable_contract**: claim=`phi` fotonico manifesta un confine fisico tra ordine periodico e disordine random; observable=statistiche della trasmissione in lambda (`stopband_fraction`, `spectral_entropy`, `peak_spacing_r`); operator=`tools/exp_photonic_boundary_third_included_gate.py`; generator=multistrati quarter-wave fixed-thickness phi/silver/bronze/periodic/balanced_random; denominator=N={55,89,144} x phase={0,0.25,0.5,0.75}, random_trials=12, lambda=0.65..1.85 su 241 punti; non_possible=terzo incluso fotonico chiuso se `phi` non resta dentro il segmento periodico-random su stopband ed entropia o se collassa nel random; not_tested=modello Maxwell completo con interfacce fisiche calibrate, misure sperimentali, GUE/Poisson universality diretta. tools/data/reports/agent_20260515_1734.md:11:- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + TxE funzione di partizione/campo EM + QxG continuo/discreto come vuoto ancora da pontare + tensione `BOUNDARY` 8 GUE / 5 Poisson. tools/data/reports/agent_20260515_1734.md:13:- **Piano superiore**: geometria dei campi e bicono-dipoli; il confine non e' etichetta GUE/Poisson ma stato di trasporto del campo elettromagnetico. tools/data/reports/agent_20260515_1734.md:22:- `why`: segue la direzione viva "8 domini GUE, 5 Poisson - il confine e' il terzo incluso operativo" portando il confine in un dominio fisico EM invece di proseguire il contro-perimetro `V_c`. tools/data/reports/agent_20260515_1734.md:35:- **Punto fisico sorgente**: confine statistico GUE/Poisson come attrito tra repulsione spettrale e indipendenza. tools/data/reports/agent_20260515_1734.md:53:| domain | rows | stopband median | entropy median | peak_spacing_r median | tools/data/seme_axioms.json:40: "claim": "7 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_axioms.json:449: "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?", tools/data/seme_axioms.json:508: "claim": "Due meccanismi generano statistiche GUE: (1) distribution-GUE — la forma dei gap e gia repulsiva (primes, random matrices), delta_r < 0; (2) ordering-GUE — i gap sono Poisson ma l'ordine sequenziale crea repulsione (fibonacci, percolation, coupled_osc), delta_r > 0. Il segno di delta_r e il discriminante.", tools/data/seme_axioms.json:521: "nota": "Exp 2026-04-25: 8 domini, 200 shuffles, ordini Markov 1-3. Consecutio TWO_KINDS_GUE. Saturation depth (14-100%) e asse ortogonale a GUE type.", tools/data/seme_axioms.json:544: "claim": "Le claim di collinearita/rank tra osservabili canonici vanno riportate con observables_registry, z original-vs-shuffle per osservabile e controlli Poisson/shuffle. Nel perimetro 20260506_1955, primi e GUE sotto partial-shuffle uniforme comprimono le retention curves a un coordinate dominante (rank medio 1.30 e 1.11), mentre Poisson e prime-shuffle mostrano rank piu alto solo con denominatori deboli (weak obs medi 5.0 e 4.67). Rank osservabile alto senza denominatori validi e segnale META, non struttura.", tools/data/seme_axioms.json:553: "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.", tools/data/reports/agent_20260516_0330.md:8:**observable_contract**: claim=i residui graph-only sono Lab-specific solo se la frequenza bridge osservata supera label-shuffle e degree-preserving graph null; observable=frequenza graph bridge osservata contro frequenze null grafiche; operator=rerun del lettore graph BOUNDARY con label shuffle e rewiring degree-preserving; generator=13 righe BOUNDARY row-aligned in feature space canonical+rigidity+shuffle-z; denominator=13 righe, 8 GUE e 5 Poisson, su 6 letture grafiche e 384 trial per ciascun null; non_possible=residuo Lab graph-only se i null matchano o superano la frequenza osservata; not_tested=nuovi sistemi Hamiltoniani, unfolding alternativi, universalita fisica dei residui graph-only. tools/data/reports/agent_20260516_0330.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come nodo del confine + tensione seme BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_0330.md:21:- `why`: il ciclo testa esplicitamente il perimetro vivo 8 GUE / 5 Poisson e il terzo incluso operativo come confine auditato. tools/data/reports/agent_20260516_0330.md:38:- **Punto fisico sorgente**: transizione spettrale tra repulsione Wigner-Dyson/GUE e indipendenza/localizzazione Poisson. tools/data/reports/agent_20260516_0330.md:49:- **Denominatore**: 13 righe row-aligned, 8 GUE e 5 Poisson. tools/data/reports/agent_20260516_0330.md:97:ssp_value: yes. `tools/exp_boundary_graph_null_audit.py` diventa audit riusabile per distinguere boundary a due lettori, residuo graph-only e artefatto grafico in ogni perimetro GUE/Poisson row-aligned. tools/data/reports/agent_20260509_1409.md:7:observables_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. tools/data/reports/agent_20260509_1409.md:8:**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. tools/data/reports/agent_20260509_1409.md:11:- **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. tools/data/reports/agent_20260509_1409.md:13:- **Dipolo / punto-zero**: classificato / non aggregabile; punto-zero = riga domain/window prima che il null decida se l'osservabile puo' viaggiare. tools/data/reports/agent_20260509_1409.md:15:- **Proto-ipotesi**: `denominator_state` trasferisce oltre `V_c` se separa riga misurabile, massa contaminata e blank senza usare GUE/Poisson come risposta. tools/data/reports/agent_20260509_1409.md:17:- **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. tools/data/reports/agent_20260509_1409.md:22:- **YSN DeltaLink**: `domain row -> null availability -> transfer`, non `GUE/Poisson -> risposta`. tools/data/reports/agent_20260509_1409.md:25:> 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. tools/data/reports/agent_20260509_1409.md:31:- Perimetro atomico: cycle base `1..13`, esattamente 13 righe: 8 GUE-like, 5 Poisson-like. tools/data/reports/agent_20260509_1409.md:60:| domain/window | source | denominator_state | excluded mass | observable | null/surrogate | transfer | tools/data/reports/agent_20260509_1409.md:77:1. **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. tools/data/reports/agent_20260509_1409.md:84:**PARTIAL TRANSFER**: `denominator_state` trasferisce oltre `V_c` come gate di perimetro, non come risposta GUE/Poisson. tools/data/reports/agent_20260509_1409.md:86:Nel 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. tools/data/reports/agent_20260509_1409.md:92:- **Singolare**: domain/window prima della classificazione GUE/Poisson. tools/data/reports/agent_20260516_1045.md:5:**verdict**: CONSTRAINT - GUE e Poisson sono endpoint-stable nel lettore calibrato su 36/36 righe di stress, ma il label-permutation null resta troppo permissivo (`combined p=0.124031`; 15/128 null arrivano a 36/36). Il filtro endpoint e' osservativamente utile, non ancora specifico abbastanza per riaprire RP come terzo incluso. tools/data/reports/agent_20260516_1045.md:8:**observable_contract**: claim=GUE/Poisson endpoints sono filtro valido del boundary se entrambi i poli restano stabili sotto stress reader/window/size/seed con classificatore calibrato; observable=`endpoint_stable` per riga sorgente e margine centroidale per reader; operator=calibrazione centroidi endpoint su controlli GUE/Poisson e stress su seed indipendenti; generator=matrici GUE e gap Poisson esponenziali; denominator=2 domini x 3 size x 6 test seed = 36 source rows, ognuna letta da 5 reader; non_possible=il boundary-terzo incluso non riapre se un endpoint cade o se il null di etichetta raggiunge la stabilita' osservata; not_tested=RP residue, Anderson 3D, spettri sperimentali, limite N infinito, prova analitica di universalita. tools/data/reports/agent_20260516_1045.md:12:- **Dipolo / punto-zero**: polo GUE / polo Poisson. Punto-zero: la riga di gap prima che un reader la classifichi. tools/data/reports/agent_20260516_1045.md:17:- **Possibile/non-possibile**: possibile = usare endpoint GUE/Poisson come filtro preliminare; non-possibile = promuovere il filtro se il null di etichetta ricostruisce la stessa stabilita'. tools/data/reports/agent_20260516_1045.md:19:- **Movimento A->M->B**: fisico A = crossover spettrale GUE/Poisson/RP; matematica M = classificatore endpoint row-aligned sui vettori osservabili; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico. tools/data/reports/agent_20260516_1045.md:23:- `why`: esegue la direzione valutatore 10:31: testare prima la stabilita' endpoint GUE/Poisson come filtro del confine-terzo incluso. tools/data/reports/agent_20260516_1045.md:27:- **Baseline noto piu vicino**: statistiche GUE/Poisson con spacing ratio, Brody interpolation e Berry-Robnik-like mixture. tools/data/reports/agent_20260516_1045.md:28:- **Cosa assorbe il baseline**: la separazione osservata tra poli GUE e Poisson nelle feature spettrali canoniche. tools/data/reports/agent_20260516_1045.md:33:> GUE e Poisson possono fungere da filtro endpoint del boundary solo se ogni riga stress resta endpoint-stable in tutti i reader e il label-null non ricostruisce la stessa stabilita'. tools/data/reports/agent_20260516_1045.md:39:- **Punto fisico sorgente**: crossover spettrale GUE/Poisson/Rosenzweig-Porter. tools/data/reports/agent_20260516_1045.md:84:GUE/Poisson sono endpoint-stable nel lettore calibrato, ma il filtro non supera ancora il null di etichetta. La prossima mossa non deve entrare in RP: deve stringere il null endpoint, per esempio con holdout per reader, bootstrap centroidale bilanciato o feature-scramble row-aligned, e richiedere `p <= 0.05` prima del boundary. tools/data/reports/agent_20260516_1045.md:88:- **Singolare**: vettore osservabile della riga prima del nome GUE/Poisson. tools/data/reports/agent_20260508_2108.md:8:**observable_contract**: claim=lo zero della carica Mobius intervallare e' testato come terzo incluso del gate aligned/misaligned; observable=rate low_low, rate high_high, SR mean per classi aligned/misaligned/zero; operator=classificazione di S_n*S_{n+1}: aligned<0, misaligned>0, zero=0; generator=prime gaps up to p<=1e6 with Mobius sieve; denominator=main N={5000,10000,20000} offset=0 plus seedcheck offsets {3000,7000,11000}; not_tested=gap_ratio Sturmian, high-core phi survival, universal GUE/Poisson classification. tools/data/seme_backup_b2_20260509_071041.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260509_071041.json:3: "new_direzione": "Costruire il null label-preserving per V_c prima del trasferimento: ridisegnare il generatore surrogate finche' raggiunge Jaccard>=0.75 a N=144 con acceptance_rate non nulla, poi solo dopo confrontare GUE/Poisson", tools/data/reports/report_20260404_0330.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/seme_backup_b2_20260513_033622.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/reports/agent_20260507_0942.md:84: protocol coordinate, not domain coordinate. tools/data/reports/report_20260306_0341.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/agent_20260516_0921.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_0921.md:21:- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. tools/data/reports/agent_20260516_0820.md:11:- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". tools/data/reports/agent_20260516_0820.md:16:- **Proto-ipotesi**: un confine GUE/Poisson diventa terzo incluso operativo quando la stessa riga resta intermedia per lettore classico e grafo, e quando il grafo batte null che conservano righe e perimetro. tools/data/reports/agent_20260516_0820.md:21:- `why`: l'esperimento porta la direzione viva 8 GUE / 5 Poisson su un crossover fisico controllato GUE-Poisson e testa il confine come terzo incluso operativo con count e null. tools/data/reports/agent_20260507_0901.md:15:coordinata locale GUE/Poisson. tools/data/reports/agent_20260507_0901.md:65: GUE/Poisson layer. It is the coordinate created by the replacement protocol: tools/data/reports/agent_20260507_0901.md:85:domain-specific semantic axis. tools/data/reports/agent_20260507_0901.md:91: order support; beta ambiguity is a protocol fold, not domain ontology. tools/data/reports/falsifier_20260514_1701.json:23: "claim": "\"relation: follows_direction\" e \"direzione viva BOUNDARY GUE/Poisson\"", tools/data/reports/falsifier_20260514_1701.json:24: "evidence": "Il seme/lab_data dichiarano direzione: '8 domini GUE, 5 Poisson — il confine e' il terzo incluso operativo'. Il report usa Hamiltoniane quasiperiodiche Sturmian, controlli metallici, balanced random e block shuffle; non misura statistiche GUE/Poisson ne' gli 8/5 domini. La deviazione e' motivata come ritorno fisico, ma `deliberate_counter_perimeter=false` rende il drift non completamente tracciato.", tools/data/reports/falsifier_20260514_1701.json:25: "suggestion": "Dichiarare `deliberate_counter_perimeter=true` oppure aggiungere un check minimo GUE/Poisson esplicito: nearest-neighbor spacing/r-statistics sui tre generatori e mapping verso GUE/Poisson, con confronto ai domini richiesti dal seme." tools/data/reports/falsifier_20260514_1701.json:28: "summary": "Il report e' parzialmente coerente: L4 rompe l'invariante Sturmian per l'edge case bronze, L5 chiede decontaminazione da risultati classici, e L8 resta un drift verso quasiperiodico non pienamente dichiarato rispetto alla direzione GUE/Poisson." applications/scoperte/20260509_1444_boundary-gate-transfer-matrix_auto/lab-note.draft.md:18:pending_consecutio: "Falsificare la forma minima del gate BOUNDARY come operatore ordine/null/denominatore: test su perimetri reali o avversariali senza importare label GUE/Poisson" applications/scoperte/20260509_1444_boundary-gate-transfer-matrix_auto/lab-note.draft.md:36:> *Consecutio attesa*: Falsificare la forma minima del gate BOUNDARY come operatore ordine/null/denominatore: test su perimetri reali o avversariali senza importare label GUE/Poisson tools/data/reports/agent_20260509_1457.md:7:observables_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. applications/scoperte/20260509_1444_boundary-gate-transfer-matrix_auto/cycle-report.draft.md:19:pending_consecutio: "Falsificare la forma minima del gate BOUNDARY come operatore ordine/null/denominatore: test su perimetri reali o avversariali senza importare label GUE/Poisson" applications/scoperte/20260509_1444_boundary-gate-transfer-matrix_auto/cycle-report.draft.md:41:> *Consecutio attesa*: Falsificare la forma minima del gate BOUNDARY come operatore ordine/null/denominatore: test su perimetri reali o avversariali senza importare label GUE/Poisson tools/data/reports/report_20260331_1809.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/falsifier_20260511_0330.json:17: "evidence": "Il seme/lab_data indicano direzione BOUNDARY su `8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo`; il ciclo usa 4 GUE + 4 logistic controlli e resta sul residuo `numeri_primi:cycle_3`. La deviazione verso prime-residue e logistic e' motivata, ma non dichiarata come `deliberate_counter_perimeter` rispetto al perimetro 8 GUE/5 Poisson.", tools/data/reports/falsifier_20260511_0330.json:18: "suggestion": "Nel prossimo ciclo dichiarare esplicitamente se `prime_persistent_blank` e' contro-perimetro deliberato della direzione GUE/Poisson, oppure tornare al perimetro richiesto con 8 GUE e 5 Poisson come domini principali." applications/scoperte/20260509_0330_interpolated-vc-curve-map_auto/lab-note.draft.md:18:pending_consecutio: "Falsificare `V_c` sul nodo regressivo del null: separare floor_hit e crossing interno, poi confrontare Sturmian phase-shuffle e surrogate label-preserving prima di estendere a GUE/Poisson." applications/scoperte/20260509_0330_interpolated-vc-curve-map_auto/lab-note.draft.md:36:> *Consecutio attesa*: Falsificare `V_c` sul nodo regressivo del null: separare floor_hit e crossing interno, poi confrontare Sturmian phase-shuffle e surrogate label-preserving prima di estendere a GUE/Poisson. tools/data/reports/agent_20260508_2013.md:8:**observable_contract**: claim=se il boundary esatto del supertile e' portatore globale del core alto, le posizioni IDS dei gap core devono mostrare migliore riconoscibilita Ostrowski o maggiore prossimita ai tagli nel mode aligned rispetto al same-length misaligned; observable=distanza del centro gap da boundary di chunk, hit entro 2 siti, peso Zeckendorf e zeri finali Zeckendorf; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, centro round(IDS*N), boundary del tiling perturbato, rappresentazione Zeckendorf del centro; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, domini GUE/Poisson reali, soglie 1.75/2.25, automa formale di riconoscibilita della sostituzione, generatori non-phi. applications/scoperte/20260509_0330_interpolated-vc-curve-map_auto/cycle-report.draft.md:19:pending_consecutio: "Falsificare `V_c` sul nodo regressivo del null: separare floor_hit e crossing interno, poi confrontare Sturmian phase-shuffle e surrogate label-preserving prima di estendere a GUE/Poisson." applications/scoperte/20260509_0330_interpolated-vc-curve-map_auto/cycle-report.draft.md:34:> *Consecutio attesa*: Falsificare `V_c` sul nodo regressivo del null: separare floor_hit e crossing interno, poi confrontare Sturmian phase-shuffle e surrogate label-preserving prima di estendere a GUE/Poisson. tools/data/reports/falsifier_20260430_1919.json:25: "suggestion": "Dichiarare il nodo: 'DIPOLAR_ORDERING originale falsificato nella forma Delta_tau oppositiva; scope corretto: co-orientamento negativo rispetto a shuffle baseline'. Tenere distinti Delta_tau GUE-Poisson e z rispetto a shuffle." tools/data/reports/falsifier_20260516_1058.json:10: "evidence": "seme.json.direzione richiede \"Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo\"; il report testa solo endpoint GUE/Poisson su 36 righe e dichiara non testati \"gli 8 domini GUE / 5 Poisson originali\" e \"il terzo incluso RP\". La deviazione e' nominata come prerequisito/not_drift, ma non come deliberate_counter_perimeter con perimetro di ritorno verificato sul confine richiesto.", tools/data/reports/falsifier_20260516_1058.json:11: "suggestion": "Nel prossimo ciclo eseguire il gate endpoint-null sul perimetro 8 domini GUE / 5 Poisson e poi applicarlo al terzo incluso operativo; se resta un preflight, marcarlo esplicitamente come deliberate_counter_perimeter con why, not_drift e criterio di rientro al boundary." tools/data/reports/falsifier_20260516_1058.json:14: "summary": "Il report e' coerente internamente sui null endpoint, ma L8 si rompe parzialmente: il lavoro chiude un prerequisito metodologico mentre la direzione viva chiede il confine 8 GUE / 5 Poisson e il terzo incluso operativo." tools/data/reports/agent_20260509_0652.md:7:observables_used: [`event_type`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`, `acceptance_rate`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0652.md:8:**observable_contract**: claim=un null che preserva il gap-label set Sturmian puo' decidere se `V_c` e' portato dal label-set o dall'ordine generativo; observable=`event_type={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`, `acceptance_rate`; operator=surrogate bilanciato con swap 0/1 e ricottura finche' `label_jaccard>=0.75`, poi curva `r(V)` su griglia 0.5..3.0 step 0.01; generator=phi Sturmian, balanced_random, swap_label_surrogate; denominator=run principale N={89}, phase={0,0.25,0.5,0.75}, r_threshold={0.48,0.50,0.52}, random_trials=1, label_trials=2, swap_steps=3000, seed=202605090652; non_possible=se i surrogate accettati Jaccard>=0.75 producono floor-hit o internal_multi e restano con `r_floor`/`vc_interp` vicini al random, il label-set non basta a ricostruire il boundary Sturmian; not_tested=GUE/Poisson reali, silver/bronze, fit power-law, gap_ratio, prova a N>=144 con gate raggiunto. tools/data/reports/agent_20260509_0652.md:95:Il prossimo passaggio non e' estendere a GUE/Poisson. Prima serve un generatore label-preserving stabile cross-phase: stessa accettazione Jaccard su N={89,144,233} oppure fallimento dichiarato come vincolo del null. Solo dopo il boundary operator puo' trasferire verso domini GUE/Poisson. tools/data/reports/report_20260326_0343.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 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: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: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:43:- **Significato fisico dichiarato**: GOE = simmetria reale/time-reversal; GUE = unitaria/no time-reversal; Poisson = livelli indipendenti/null span-matched. tools/data/reports/agent_20260506_0625.md:13:- fixed domains: `primes` 12,000 gaps, `prime_shuffle_control` 12,000 permuted prime gaps, `poisson` 12,000 iid exponential spacings; tools/data/reports/agent_20260506_0625.md:62:> Primes remain near one perturbation coordinate under both observable sets; GUE long replicates show only a weak second component; short GUE samples can inflate apparent rank; Poisson and shuffled controls can also appear multi-axis. tools/data/reports/agent_20260506_0625.md:64:The boundary is still operator-dependent, but perturbation dimensionality is not yet a stable domain invariant. The next valid test is not another single GUE matrix; it is a replicate-and-size curve for effective rank vs number of spacings, with observable definitions versioned. applications/scoperte/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve_auto/lab-note.draft.md:40:TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson applications/scoperte/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve_auto/cycle-report.draft.md:12: tension_explored: "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson" applications/scoperte/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve_auto/cycle-report.draft.md:42: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_20260405_0919.md:7:> Direction: "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso" 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: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:58:The "third included" at the GUE/Poisson boundary is not a point — it's a **crossover function**. The primes' level repulsion parameter decays as 0.606 - 0.020*ln(p), bridging two universality classes without belonging to either. This is structurally consistent with: tools/data/reports/agent_20260405_0919.md:59:- The D-ND framework: the boundary between two poles (GUE/Poisson) carries its own structure tools/data/reports/agent_20260405_0919.md:61:- The crossover function itself is the "third" — neither GUE nor Poisson, but a specific interpolation tools/data/aubry_v2_generator_scaling_gate_20260515_1816.json:304: "summary_by_domain": { applications/scoperte/20260508_0011_duality-contrast-weakens-with-scale-in_auto/cycle-report.draft.md:54:Report is internally coherent on its main claim (primes alpha < 0.5, GUE alpha > 0.5) but two edge cases break the stated perimeter: GUE L2 s137 violates the blanket 'alpha >= 0.5' (L4), and Poisson L2 shows non-trivial scaling (alpha=0.165, R2=0.91) that undermines the null baseline and may indicate a systematic bias in the z-score methodology (L4). Neither is fatal but both require tightening before the finding can be called clean. tools/data/boundary_blank_null_audit_20260509_1430.json:10: "domains": { tools/data/boundary_blank_null_audit_20260509_1430.json:12: "domain": "zeta_zeros", tools/data/boundary_blank_null_audit_20260509_1430.json:35: "domain": "pendolo_doppio", tools/data/boundary_blank_null_audit_20260509_1430.json:59: "domains": [ tools/data/seme_backup_b2_20260515_194643.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/boundary_classical_crossover_audit_20260515_1904.json:5: "denominator": "13 rows: 8 GUE and 5 Poisson", tools/data/boundary_classical_crossover_audit_20260515_1904.json:6: "generator": "row_spacings(domain) with graph states imported from boundary_graph_curvature_gate", tools/data/boundary_classical_crossover_audit_20260515_1904.json:36: "domain": "ising_2d", tools/data/boundary_classical_crossover_audit_20260515_1904.json:37: "domain_window": "ising_2d:cycle_1", tools/data/boundary_classical_crossover_audit_20260515_1904.json:41: "source_domain_type": "GUE" tools/data/boundary_classical_crossover_audit_20260515_1904.json:50: "domain": "pendolo_doppio", tools/data/boundary_classical_crossover_audit_20260515_1904.json:51: "domain_window": "pendolo_doppio:cycle_2", tools/data/boundary_classical_crossover_audit_20260515_1904.json:55: "source_domain_type": "Poisson" tools/data/boundary_classical_crossover_audit_20260515_1904.json:64: "domain": "numeri_primi", tools/data/boundary_classical_crossover_audit_20260515_1904.json:65: "domain_window": "numeri_primi:cycle_3", tools/data/boundary_classical_crossover_audit_20260515_1904.json:69: "source_domain_type": "GUE" tools/data/boundary_classical_crossover_audit_20260515_1904.json:78: "domain": "zeta_zeros", tools/data/boundary_classical_crossover_audit_20260515_1904.json:79: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_classical_crossover_audit_20260515_1904.json:83: "source_domain_type": "GUE" tools/data/boundary_classical_crossover_audit_20260515_1904.json:92: "domain": "logistica_biforcazione", tools/data/boundary_classical_crossover_audit_20260515_1904.json:93: "domain_window": "logistica_biforcazione:cycle_5", tools/data/boundary_classical_crossover_audit_20260515_1904.json:97: "source_domain_type": "GUE" tools/data/boundary_classical_crossover_audit_20260515_1904.json:106: "domain": "string_vibration", tools/data/boundary_classical_crossover_audit_20260515_1904.json:107: "domain_window": "string_vibration:cycle_6", tools/data/boundary_classical_crossover_audit_20260515_1904.json:111: "source_domain_type": "Poisson" tools/data/boundary_classical_crossover_audit_20260515_1904.json:120: "domain": "random_matrix", tools/data/boundary_classical_crossover_audit_20260515_1904.json:121: "domain_window": "random_matrix:cycle_7", tools/data/boundary_classical_crossover_audit_20260515_1904.json:125: "source_domain_type": "GUE" tools/data/boundary_classical_crossover_audit_20260515_1904.json:134: "domain": "cellular_automata", tools/data/boundary_classical_crossover_audit_20260515_1904.json:135: "domain_window": "cellular_automata:cycle_8", tools/data/boundary_classical_crossover_audit_20260515_1904.json:139: "source_domain_type": "GUE" tools/data/boundary_classical_crossover_audit_20260515_1904.json:148: "domain": "percolation", tools/data/boundary_classical_crossover_audit_20260515_1904.json:149: "domain_window": "percolation:cycle_9", tools/data/boundary_classical_crossover_audit_20260515_1904.json:153: "source_domain_type": "Poisson" tools/data/boundary_classical_crossover_audit_20260515_1904.json:162: "domain": "coupled_oscillators", tools/data/boundary_classical_crossover_audit_20260515_1904.json:163: "domain_window": "coupled_oscillators:cycle_10", tools/data/boundary_classical_crossover_audit_20260515_1904.json:167: "source_domain_type": "Poisson" tools/data/boundary_classical_crossover_audit_20260515_1904.json:176: "domain": "reaction_diffusion", tools/data/boundary_classical_crossover_audit_20260515_1904.json:177: "domain_window": "reaction_diffusion:cycle_11", tools/data/boundary_classical_crossover_audit_20260515_1904.json:181: "source_domain_type": "GUE" tools/data/boundary_classical_crossover_audit_20260515_1904.json:190: "domain": "brownian_motion", tools/data/boundary_classical_crossover_audit_20260515_1904.json:191: "domain_window": "brownian_motion:cycle_12", tools/data/boundary_classical_crossover_audit_20260515_1904.json:195: "source_domain_type": "Poisson" tools/data/boundary_classical_crossover_audit_20260515_1904.json:204: "domain": "logistica_biforcazione_var_3.5699", tools/data/boundary_classical_crossover_audit_20260515_1904.json:205: "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", tools/data/boundary_classical_crossover_audit_20260515_1904.json:209: "source_domain_type": "GUE" tools/data/seme_backup_pre_run.json:21: "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?", tools/data/seme_backup_pre_run.json:63: "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_pre_run.json:209: "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.", tools/data/seme_backup_pre_run.json:214: "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", tools/data/seme_backup_pre_run.json:334: "origine": "cycle agent_20260508_0011: duality_scale_contrast su 200K gap primi vs GUE vs Poisson", tools/data/seme_backup_pre_run.json:427: "direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/boundary_denominator_prescan_full_20260509_1500.json:3: "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", tools/data/boundary_denominator_prescan_full_20260509_1500.json:4: "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", tools/data/boundary_denominator_prescan_full_20260509_1500.json:8: "operator": "row-aligned domain/window prescan", tools/data/boundary_denominator_prescan_full_20260509_1500.json:25: "by_source_domain_type": { tools/data/boundary_denominator_prescan_full_20260509_1500.json:49: "domain_window": "ising_2d:cycle_1", tools/data/boundary_denominator_prescan_full_20260509_1500.json:50: "domain": "ising_2d", tools/data/boundary_denominator_prescan_full_20260509_1500.json:52: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:65: "domain_key": "ising_2d", tools/data/boundary_denominator_prescan_full_20260509_1500.json:74: "domain_window": "pendolo_doppio:cycle_2", tools/data/boundary_denominator_prescan_full_20260509_1500.json:75: "domain": "pendolo_doppio", tools/data/boundary_denominator_prescan_full_20260509_1500.json:77: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_full_20260509_1500.json:90: "domain_key": "pendolo_doppio", tools/data/boundary_denominator_prescan_full_20260509_1500.json:99: "domain_window": "numeri_primi:cycle_3", tools/data/boundary_denominator_prescan_full_20260509_1500.json:100: "domain": "numeri_primi", tools/data/boundary_denominator_prescan_full_20260509_1500.json:102: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:115: "domain_key": "primes", tools/data/boundary_denominator_prescan_full_20260509_1500.json:124: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_denominator_prescan_full_20260509_1500.json:125: "domain": "zeta_zeros", tools/data/boundary_denominator_prescan_full_20260509_1500.json:127: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:140: "domain_key": "zeta_zeros", tools/data/boundary_denominator_prescan_full_20260509_1500.json:149: "domain_window": "logistica_biforcazione:cycle_5", tools/data/boundary_denominator_prescan_full_20260509_1500.json:150: "domain": "logistica_biforcazione", tools/data/boundary_denominator_prescan_full_20260509_1500.json:152: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:165: "domain_key": "logistic", tools/data/boundary_denominator_prescan_full_20260509_1500.json:174: "domain_window": "string_vibration:cycle_6", tools/data/boundary_denominator_prescan_full_20260509_1500.json:175: "domain": "string_vibration", tools/data/boundary_denominator_prescan_full_20260509_1500.json:177: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_full_20260509_1500.json:190: "domain_key": "string_vibration", tools/data/boundary_denominator_prescan_full_20260509_1500.json:199: "domain_window": "random_matrix:cycle_7", tools/data/boundary_denominator_prescan_full_20260509_1500.json:200: "domain": "random_matrix", tools/data/boundary_denominator_prescan_full_20260509_1500.json:202: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:215: "domain_key": "gue", tools/data/boundary_denominator_prescan_full_20260509_1500.json:224: "domain_window": "cellular_automata:cycle_8", tools/data/boundary_denominator_prescan_full_20260509_1500.json:225: "domain": "cellular_automata", tools/data/boundary_denominator_prescan_full_20260509_1500.json:227: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:240: "domain_key": "cell_auto", tools/data/boundary_denominator_prescan_full_20260509_1500.json:249: "domain_window": "percolation:cycle_9", tools/data/boundary_denominator_prescan_full_20260509_1500.json:250: "domain": "percolation", tools/data/boundary_denominator_prescan_full_20260509_1500.json:252: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_full_20260509_1500.json:265: "domain_key": "percolation", tools/data/boundary_denominator_prescan_full_20260509_1500.json:274: "domain_window": "coupled_oscillators:cycle_10", tools/data/boundary_denominator_prescan_full_20260509_1500.json:275: "domain": "coupled_oscillators", tools/data/boundary_denominator_prescan_full_20260509_1500.json:277: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_full_20260509_1500.json:290: "domain_key": "coupled_osc", tools/data/boundary_denominator_prescan_full_20260509_1500.json:299: "domain_window": "reaction_diffusion:cycle_11", tools/data/boundary_denominator_prescan_full_20260509_1500.json:300: "domain": "reaction_diffusion", tools/data/boundary_denominator_prescan_full_20260509_1500.json:302: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:315: "domain_key": "reaction_diffusion", tools/data/boundary_denominator_prescan_full_20260509_1500.json:324: "domain_window": "brownian_motion:cycle_12", tools/data/boundary_denominator_prescan_full_20260509_1500.json:325: "domain": "brownian_motion", tools/data/boundary_denominator_prescan_full_20260509_1500.json:327: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_full_20260509_1500.json:340: "domain_key": "brownian", tools/data/boundary_denominator_prescan_full_20260509_1500.json:349: "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", tools/data/boundary_denominator_prescan_full_20260509_1500.json:350: "domain": "logistica_biforcazione_var_3.5699", tools/data/boundary_denominator_prescan_full_20260509_1500.json:352: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_full_20260509_1500.json:365: "domain_key": "logistica_biforcazione_var_3.5699", tools/data/boundary_mixture_gate_20260507_0330.json:3: "question": "Is the GUE-Poisson mixed layer cleanly classifiable after denominator gating?", tools/data/boundary_short_denominator_extension_20260509_1556.json:42: "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity" tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json:5: "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json:6: "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json:7: "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json:68: "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", tools/data/endpoint_stability_filter_20260516_1045.json:134: "claim": "GUE/Poisson endpoints are valid filters for the boundary if both poles stay stable across reader/window/size/seed stress under a calibrated endpoint classifier", tools/data/endpoint_stability_filter_20260516_1045.json:135: "denominator": "domain x size x test seed source rows; each source row contains all readers", tools/data/endpoint_stability_filter_20260516_1045.json:136: "generator": "GUE matrices and Poisson exponential spacings", tools/data/endpoint_stability_filter_20260516_1045.json:140: "operator": "calibrate endpoint centroids on held-out GUE/Poisson controls, then stress test readers on independent seeds" tools/data/endpoint_stability_filter_20260516_1045.json:187: "question": "Do GUE and Poisson remain endpoint-stable under the reader family before RP boundary residue is tested again?", tools/data/gap_label_set_stability_20260508_1632.json:398: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:517: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:636: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:756: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:877: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:999: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:1122: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:1241: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:1360: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:1480: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:1603: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:1726: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:1847: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:1966: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:2085: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:2205: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:2327: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:2450: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:2571: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:2692: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:2814: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:2934: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:3056: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:3178: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:3301: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:3422: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:3544: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:3664: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:3785: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:3908: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:4030: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:4151: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:4273: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:4393: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:4516: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:4638: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:4761: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:4882: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:5002: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:5123: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:5245: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:5366: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:5488: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:5609: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:5729: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:5850: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:5973: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:6096: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:6218: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:6339: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:6459: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:6580: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:6703: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:6826: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:6949: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:7070: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:7192: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:7313: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:7436: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:7559: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:7682: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:7803: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:7925: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:8046: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:8169: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:8290: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:8412: "domain": "phi", tools/data/gap_label_set_stability_20260508_1632.json:8533: "domain": "silver", tools/data/gap_label_set_stability_20260508_1632.json:8655: "domain": "bronze", tools/data/gap_label_set_stability_20260508_1632.json:8776: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:8899: "domain": "balanced_random_phi_labels", tools/data/gap_label_set_stability_20260508_1632.json:9022: "domain": 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tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:3381: "domain": "balanced_random", tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:3391: "domain": "_matched_comparison", tools/data/lab_data.json:4: "direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/lab_data.json:44: "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/lab_data.json:101: "content": "# Agent Report - Anderson Comparable Null Audit\n**Date**: 2026-05-16 11:35\n**Piano**: 132\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - Sullo stesso perimetro compatto Anderson, stesso observable e stesso `N=512`, il null endpoint-preserving ricostruisce il conteggio osservato in `36/512` trial (`raw_p=0.070312500`, Wilson 95% `[0.051218024, 0.095806720]`), mentre il full feature-scramble lo ricostruisce in `272/512` (`raw_p=0.531250000`, Wilson 95% `[0.487953078, 0.574081486]`). Il confronto L2 e' chiuso come unita' comparabile; il boundary fisico non si promuove perche' `W=20` resta rinominato dal null endpoint-preserving in `54/512` trial.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, wigner_poisson_like_weight, mean_ipr, participation_entropy, two_reader_all_sizes, raw_p, add_one_p, wilson_95]\n**observable_contract**: claim=i null sono confrontabili solo se condividono observable, perimetro righe e N; observable=conteggio cross-size di righe `stable_graph_bridge+classical_intermediate`; operator=righe compatte Anderson 3D dal run 11:17 classificate dallo stesso reader kNN/classico; generator=stesse righe sorgente, due operatori null che differiscono solo per struttura preservata; denominator=`512` trial per null su 11 righe per size; p_value_definition=right-tail `raw_p=k/N`, `add_one_p=(k+1)/(N+1)`, con `k` = trial null con conteggio cross-size >= osservato; non_possible=chiamare un null piu' restrittivo se perimetro o N cambiano; not_tested=raw multi-seed reader, nuovi Hamiltoniani, `L>=7`, perimetro completo 8 GUE / 5 Poisson.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY + feedback falsifier L2 sui null comparabili.\n- **Dipolo / punto-zero**: null permissivo / null fisico. Punto-zero: la stessa riga disorder prima della nominazione e prima della scelta del null.\n- **Piano superiore**: topologia del bordo row-aligned; il bordo vive solo se l'operatore nullo non puo' ricostruire la stessa molteplicita' nello stesso spazio di lettura.\n- **Operatori laterali scelti**: boundary operator, graph rewiring, candidate-only shuffle.\n- **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel 11:24 sullo stesso spazio; CE-0117/Cascata applicata come vincolo: il risultato del falsifier entra nel seme operativo del ciclo e corregge il nodo regressivo, non il claim.\n- **Proto-ipotesi**: la restrittivita' di un null non e' proprieta' del nome del null; e' proprieta' misurabile solo a perimetro, observable e N fissati.\n- **Possibile/non-possibile**: possibile = distinguere quantitativamente full-scramble ed endpoint-preserving sul perimetro compatto; non-possibile = promuovere `W=20` finche' il null endpoint-preserving lo rinomina con frequenza non-zero.\n- **Proiezione**: rieseguo entrambi i null su `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json`, usando le stesse righe compatte" tools/data/lab_data.json:197: "anti_claim": "il confine è il terzo incluso operativo (not: 8 domini GUE, 5 Poisson)" tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json:187: "summary_by_domain_phase": { tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json:827: "domain": "phi", tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json:858: "domain": "silver", tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json:889: "domain": "bronze", tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json:920: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json:952: "domain": "balanced_random_phi_density", tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json:984: "domain": "balanced_random_phi_density", 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This does not say the domains are the same; it says uniform partial shuffle moves the canonical observables ", applications/published/20260506_1955_observable-collinearity-breaks-only-where-denominators/finding_index.json:80: "source_excerpt": "**GUE is the cleanest conditioning check.** In the main run, all five GUE observables pass the denominator gate and still give rank `1.060`. This makes GUE the best positive control for \"low rank despite valid denominators.\" Poisson is the negative control for \"high rank without valid denominators.\"", tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json:140: "domain_window": "RP_lambda_0.000", tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json:149: "source_domain_type": "Poisson_pole", tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json:155: "domain_window": "RP_lambda_0.010", tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json:164: "source_domain_type": "Poisson_pole", tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json:170: "domain_window": "RP_lambda_0.030", tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json:179: "source_domain_type": "Poisson_pole", tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json:185: "domain_window": "RP_lambda_0.060", 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tools/data/rp_exact_local_window_matrix_20260516_1019_w9.json:694: "domain_window": "RP_lambda_0.045", tools/data/rp_exact_local_window_matrix_20260516_1019_w9.json:705: "domain_window": "RP_lambda_0.060", tools/data/rp_exact_local_window_matrix_20260516_1019_w9.json:715: "domain_window": "RP_lambda_0.075", tools/data/rp_exact_local_window_matrix_20260516_1019_w9.json:725: "domain_window": "RP_lambda_0.820", tools/data/seme.json:21: "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?", tools/data/seme.json:63: "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme.json:209: "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.", tools/data/seme.json:214: "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", tools/data/seme.json:334: "origine": "cycle agent_20260508_0011: duality_scale_contrast su 200K gap primi vs GUE vs Poisson", tools/data/seme.json:427: "direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", applications/published/20260501_0931_the-gue-poisson-crossover-has-a/finding_index.json:17: "title": "The GUE-Poisson crossover is not smooth — it has a phase transition.", applications/published/20260501_0931_the-gue-poisson-crossover-has-a/finding_index.json:22: "source_excerpt": "**The GUE-Poisson crossover is not smooth — it has a phase transition.** The dipolar magnitude decays linearly with alpha and passes through a near-zero minimum (0.0007) at alpha in [0.65, 0.75]. At this point the dipolar direction flips approximately 180 degrees. Below the transition, the ordering ", applications/published/20260501_0931_the-gue-poisson-crossover-has-a/lab-note.md:16:title_proposal: "The GUE-Poisson Crossover Has a Phase Transition: Direction Locks, Magnitude Decays, Then Flips" applications/published/20260501_0931_the-gue-poisson-crossover-has-a/lab-note.md:20: - "L3 high: 'The GUE-Poisson crossover has a phase transition' / 'GUE-Poisson transition'" applications/published/20260501_0931_the-gue-poisson-crossover-has-a/lab-note.md:28:# The GUE-Poisson Crossover Has a Phase Transition: Direction Locks, Magnitude Decays, Then Flips applications/published/20260501_0931_the-gue-poisson-crossover-has-a/cycle-report.md:24: summary: "'The GUE-Poisson crossover has a phase transition' / 'GUE-Poisson transition'" applications/published/20260501_0931_the-gue-poisson-crossover-has-a/cycle-report.md:36: - "L3 high: 'The GUE-Poisson crossover has a phase transition' / 'GUE-Poisson transition'" applications/published/20260501_0931_the-gue-poisson-crossover-has-a/cycle-report.md:43:# Cycle Report — The GUE-Poisson Crossover Has a Phase Transition: Direction Locks, Magnitude Decays, Then Flips applications/published/20260501_0931_the-gue-poisson-crossover-has-a/cycle-report.md:53:Il report non e' internamente coerente: si rompe soprattutto L1/L3, perche' trasforma un minimo non nullo di shuffled-GUE in uno zero/phase transition GUE-Poisson. applications/published/20260429_three-layer-decomposition/cycle-report.md:177:- **BOUNDARY constrained**: il confine GUE/Poisson (Brody flow) descrive solo gli strati 1-2. Lo strato 3 (algebrico) è invisibile a Brody β. Qualsiasi modello completo del confine deve includere il pavimento algebrico. applications/published/20260429_three-layer-decomposition/cycle-report.md:184:- **Campo di possibilità**: diventa possibile modellare il confine GUE/Poisson con un pavimento algebrico che non decade. Diventa non-possibile trattare "strutturale" come una singola categoria. applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/finding_index.json:44: "source_excerpt": "**The BOUNDARY layer coordinate does not transfer unchanged.** In the\nGUE/Poisson run, beta 0.3-0.4 carried both classification ambiguity and\ndenominator collapse. Here, beta 0.3 is classification-ambiguous, but the\ndenominator support is still strong: stable count remains `3.000/5`.\nDenominator col", tools/data/perturbation_rank_size_curve.json:38: "domains": { tools/data/knowledge_state.json:1008: "input_claim": "7 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1024: "input_claim": "7 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1040: "input_claim": "7 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1056: "input_claim": "7 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1072: "input_claim": "7 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1248: "input_claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1264: "input_claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1280: "input_claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/knowledge_state.json:1296: "input_claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", applications/published/20260430_1905_observable-coherence-at-the-gue-poisson/lab-note.md:16:title_proposal: "Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar" applications/published/20260430_1905_observable-coherence-at-the-gue-poisson/lab-note.md:27:# Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar tools/data/lab_graph.json:635: "findings": "1. Verificato: il gate endpoint GUE/Poisson resta chiuso sullo stesso denominatore del 10:58: `36/36`, feature-scramble `add_one_p=0.001949318`.\n2. Verificato: il null label-permutation resta permissivo e viene riportato per continuita': `15/128`, `add_one_p=0.124031008`.\n3. Verificato: RP batte il ", tools/data/lab_graph.json:684: "findings": "1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce.\n2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse ", tools/data/lab_graph.json:1389: "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/lab_graph.json:1429: "content_full": "# Agent Report - Anderson Comparable Null Audit\n**Date**: 2026-05-16 11:35\n**Piano**: 132\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - Sullo stesso perimetro compatto Anderson, stesso observable e stesso `N=512`, il null endpoint-preserving ricostruisce il conteggio osservato in `36/512` trial (`raw_p=0.070312500`, Wilson 95% `[0.051218024, 0.095806720]`), mentre il full feature-scramble lo ricostruisce in `272/512` (`raw_p=0.531250000`, Wilson 95% `[0.487953078, 0.574081486]`). Il confronto L2 e' chiuso come unita' comparabile; il boundary fisico non si promuove perche' `W=20` resta rinominato dal null endpoint-preserving in `54/512` trial.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, wigner_poisson_like_weight, mean_ipr, participation_entropy, two_reader_all_sizes, raw_p, add_one_p, wilson_95]\n**observable_contract**: claim=i null sono confrontabili solo se condividono observable, perimetro righe e N; observable=conteggio cross-size di righe `stable_graph_bridge+classical_intermediate`; operator=righe compatte Anderson 3D dal run 11:17 classificate dallo stesso reader kNN/classico; generator=stesse righe sorgente, due operatori null che differiscono solo per struttura preservata; denominator=`512` trial per null su 11 righe per size; p_value_definition=right-tail `raw_p=k/N`, `add_one_p=(k+1)/(N+1)`, con `k` = trial null con conteggio cross-size >= osservato; non_possible=chiamare un null piu' restrittivo se perimetro o N cambiano; not_tested=raw multi-seed reader, nuovi Hamiltoniani, `L>=7`, perimetro completo 8 GUE / 5 Poisson.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY + feedback falsifier L2 sui null comparabili.\n- **Dipolo / punto-zero**: null permissivo / null fisico. Punto-zero: la stessa riga disorder prima della nominazione e prima della scelta del null.\n- **Piano superiore**: topologia del bordo row-aligned; il bordo vive solo se l'operatore nullo non puo' ricostruire la stessa molteplicita' nello stesso spazio di lettura.\n- **Operatori laterali scelti**: boundary operator, graph rewiring, candidate-only shuffle.\n- **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel 11:24 sullo stesso spazio; CE-0117/Cascata applicata come vincolo: il risultato del falsifier entra nel seme operativo del ciclo e corregge il nodo regressivo, non il claim.\n- **Proto-ipotesi**: la restrittivita' di un null non e' proprieta' del nome del null; e' proprieta' misurabile solo a perimetro, observable e N fissati.\n- **Possibile/non-possibile**: possibile = distinguere quantitativamente full-scramble ed endpoint-preserving sul perimetro compatto; non-possibile = promuovere `W=20` finche' il null endpoint-preserving lo rinomina con frequenza non-zero.\n- **Proiezione**: rieseguo entrambi i null su `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json`, usando le stesse righe compatte e `512` trial ciascuno.\n- **Movimento A->M->B**: fisico A = Anderson 3D mobility edge; matematica M = confronto omogeneo di operatori null; fisico B = criterio di costo per decidere se lanciare large-L. Il ritorno fisico resta vincolo, non scoperta.\n\n## Aderenza alla direzione\n- `relation`: `deliberate_counter_perimeter`\n- `why`: resta su Anderson per chiudere il check obbligatorio del falsifier 11:24: stessi null, stesso perimetro, stesso N e stessa observable prima di interpretare restrittivita'.\n- `not_drift`: non usa Sturmian, phi, V_c o fit locali; attacca il nodo regressivo `null_first -> candidate_name -> physical_return` emerso dentro il frame BOUNDARY cross-dominio.\n- `return_criterion`: tornare al perimetro vivo 8 GUE / 5 Poisson quando il contratto dei null comparabili e' stabilizzato; oppure chiudere Anderson se anche il raw-reader endpoint-preserving rinomina `W=20`.\n- `seed_residue`: restano non testati il perimetro completo 8 GUE / 5 Poisson, raw multi-seed Anderson e `L>=7`.\n- `why_not_drift`: il sotto-perimetro e' regressivo perche' corregge il confronto non omogeneo segnalato dal falsifier, senza promuovere un nuovo candidato.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: Anderson localization, mobility edge 3D, crossover Wigner-Dyson/Poisson, Brody interpolation, finite-size scaling.\n- **Cosa assorbe il baseline**: righe intermedie vicino alla transizione, dipendenza da size piccole, sensibilita' a feature compatte.\n- **Cosa resta Lab-specific**: contratto null-first comparabile con due operatori null sullo stesso observable row-aligned.\n- **Separazione**: `two_reader_boundary_confirmed=2` nel perimetro compatto; `graph_only_residue` non sommato; `scope_change_declared=Anderson_compact_null_comparison`; `graph_baseline_audit=kNN stability / row-feature rewiring`.\n\n## Claim Under Test\n> Nel perimetro compatto Anderson, il confronto tra null e' interpretabile solo se full feature-scramble ed endpoint-preserving candidate-only misurano lo stesso conteggio cross-size con lo stesso numero di trial.\n\n## Question\nLa riduzione osservata nel null endpoint-preserving era effetto del null o effetto del cambio di perimetro?\n\n## Experiment Design\n- **Script**: `tools/exp_anderson3d_comparable_null_audit.py`.\n- **Run**: `python tools/exp_anderson3d_comparable_null_audit.py --out tools/data/anderson3d_comparable_null_audit_20260516_1135.json --null-trials 512`.\n- **Source**: `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json`.\n- **Perimetro**: righe compatte mediane del run 11:17, `L=5,6`, 11 disorder rows per size.\n- **Observed observable**: intersezione cross-size di righe `stable_graph_bridge+classical_intermediate`.\n- **Null A**: endpoint-preserving candidate-only; conserva poli metallic/localized e permuta feature solo fra righe `mobility_candidate`.\n- **Null B**: full feature-scramble; permuta feature su tutte le righe compatte della size.\n- **P-value**: right-tail; `raw_p=k/N`, `add_one_p=(k+1)/(N+1)`; Wilson 95% riportato sul count binomiale `k/N`.\n- **Non testato**: non misura nuovi autovalori, raw multi-seed reader, exponent critico o large-L.\n\n## Results\n| measure | observed | null k/N | raw_p | add_one_p | Wilson 95% | max null | mean null | lettura |\n|---|---:|---:|---:|---:|---|---:|---:|---|\n| endpoint-preserving candidate-only | 2 | 36/512 | 0.070312500 | 0.072124756 | [0.051218024, 0.095806720] | 2 | 0.533203125 | riduce la ricostruzione ma non azzera |\n| full feature-scramble | 2 | 272/512 | 0.531250000 | 0.532163743 | [0.487953078, 0.574081486] | 4 | 1.623046875 | ricostruisce spesso il conteggio |\n| difference full - endpoint | n/a | n/a | 0.460937500 | n/a | [0.412369646, 0.509505354] | n/a | n/a | differenza comparabile nello stesso perimetro |\n\n| W row | endpoint-preserving named hits | full-scramble named hits | lettura |\n|---:|---:|---:|---|\n| 16.00 | 33/512 | 117/512 | riga osservata ricostruibile |\n| 20.00 | 54/512 | 116/512 | candidato non-zero nel null fisico |\n| 16.50 | 37/512 | 112/512 | intermittente nel deposito, ricostruibile |\n\n| size | observed compact two-reader rows |\n|---:|---|\n| L=5 | `W=16.00`, `W=20.00` |\n| L=6 | `W=16.00`, `W=16.50`, `W=20.00` |\n\n## Key Findings\n1. Verificato: sullo stesso perimetro e con lo stesso `N=512`, endpoint-preserving ricostruisce meno del full feature-scramble: `36/512` contro `272/512`.\n2. Verificato: gli intervalli binomiali non si sovrappongono; la differenza `raw_p_full - raw_p_endpoint = 0.460937500` ha intervallo approssimato `[0.412369646, 0.509505354]`.\n3. Verificato: il risultato L2 precedente non era formulabile come confronto; ora lo e', ma solo nel perimetro compatto.\n4. Verificato: `W=20` non e' zero sotto endpoint-preserving: `54/512` rinomine cross-size.\n5. Inferito dal perimetro: il null endpoint-preserving e' un filtro piu' duro, non una prova fisica del boundary.\n\n## Verdict\nCONSTRAINT\n\nIl nodo regressivo L2 e' chiuso: a parita' di perimetro, observable e N, il null endpoint-preserving e' piu' restrittivo del full feature-scramble. La promozione fisica resta bloccata perche' il candidato `W=20` sopravvive come rinomina non-zero nel null che preserva i poli.\n\n## Bicono della scoperta\n- **Due radici**: differenza comparabile fra null; rinomina non-zero del candidato.\n- **Singolare**: riga disorder prima del nome e prima del null.\n- **Invariante di passaggio**: stesso observable, stesso perimetro, stesso N.\n- **Campo di possibilita**: possibile = usare endpoint-preserving come pre-filtro di costo; non-possibile = pagare large-L per salvare `W=20` prima del raw-reader null.\n\n## Consecutio\nRipetere l'endpoint-preserving sul raw multi-seed reader del ciclo 11:17, non sulle mediane compatte. Se `W=20` resta rinominato, Anderson si chiude come proprieta' del lettore. Se va a zero, allora il costo `L>=7` diventa giustificato.\n\n## Ricadute pratiche\nssp_value: yes. `tools/exp_anderson3d_comparable_null_audit.py` diventa strumento riusabile per confrontare null solo dopo allineamento di perimetro, observable e N.\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 locale su TRASCENDENZA_LIMITE; BOUNDARY seguito come contro-perimetro regressivo per obbligo falsifier.\n- `python -m py_compile tools/exp_anderson3d_comparable_null_audit.py` completato.\n- Run completato: `tools/data/anderson3d_comparable_null_audit_20260516_1135.json`.\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_anderson3d_comparable_null_audit.py`\n- Data: `tools/data/anderson3d_comparable_null_audit_20260516_1135.json`\n- Report: `tools/data/reports/agent_20260516_1135.md`\n", tools/data/lab_graph.json:1480: "content_full": "# Agent Report - Anderson 3D Two-Reader Boundary Null\n**Date**: 2026-05-16 11:17\n**Piano**: 131\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - Anderson 3D produce una riga cross-size a due lettori (`W=20.00`), ma il feature-scramble null ricostruisce almeno lo stesso conteggio in `112/128` trial (`raw_p=0.875000000`, `add_one_p=0.875968992`). Il boundary trasferisce come candidato operativo sul dominio, non come terzo incluso fisico promosso.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, wigner_poisson_like_weight, mean_ipr, participation_entropy, graph_bridge_frequency, size_stability, centroid_margin, cross_neighbor_fraction, classical_audit_state, raw_p, add_one_p]\n**observable_contract**: claim=il gate BOUNDARY a due lettori trasferisce oltre RP solo se la stessa riga Anderson e' `stable_graph_bridge+classical_intermediate` su tutte le size e batte il feature-scramble null; observable=`two_reader_all_sizes` unito a `graph_bridge_frequency`, adjacent ratio, Brody q, mixture Wigner/Poisson, IPR, entropy; operator=Hamiltoniana Anderson 3D con disorder sweep, seed perturbation e kNN reader; generator=`H=sum_i eps_i |i>M->B**: fisico A = crossover RP GUE/Poisson bloccato dal ciclo 11:11; matematica M = gate a due lettori con trasporto per size e null; fisico B = Anderson 3D mobility edge. Il ritorno fisico resta candidato non promosso.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: segue la direzione valutatore: porta il gate endpoint/two-reader fuori da RP verso Anderson 3D, dominio fisico indipendente con transizione metallico/localizzato.\n- `not_drift`: non torna a phi/Sturmian, V_c, fit locali o aumento griglia RP; usa il deposito 11:11 solo come contratto operativo da falsificare fuori dominio.\n- `seed_residue`: restano non testati il perimetro completo 8 GUE / 5 Poisson e spettri fisici reali.\n- `why_not_drift`: il sotto-perimetro e' regressivo perche' attacca il nodo lasciato aperto dal valutatore: trasferibilita' cross-dominio del boundary.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: Anderson localization e mobility edge 3D; crossover Wigner-Dyson/Poisson; Brody interpolation; finite-size scaling della transizione metallico/localizzato.\n- **Cosa assorbe il baseline**: righe intermedie vicino alla transizione, drift di adjacent ratio/Brody q/IPR, dipendenza da size piccole.\n- **Cosa resta Lab-specific**: il contratto a due lettori con riga row-aligned, separazione graph-only/classic-only e p-value feature-scramble definito.\n- **Separazione**: `two_reader_boundary_confirmed=1 cross-size`; `graph_only_residue_by_size={L5:3,L6:4}`; `scope_change_declared=RP_to_Anderson3D`; `graph_baseline_audit=kNN stability/cut-edge persistence`.\n\n## Claim Under Test\n> Il boundary come terzo incluso trasferisce da RP ad Anderson 3D solo se una riga disorder resta a due lettori su tutte le size e il feature-scramble null non ricostruisce quel conteggio.\n\n## Question\nIl gate a due lettori trova un confine fisico Anderson, o una combinazione di feature che il null puo' ricomporre senza struttura fisica?\n\n## Ritorno fisico\n- **Punto fisico sorgente**: crossover spettrale RP GUE/Poisson con residuo finito-size.\n- **Attraversamento matematico**: grafo kNN standardizzato delle feature spettrali e classificazione classica row-aligned.\n- **Punto fisico di ritorno**: Anderson 3D mobility edge, transizione metallico/localizzato.\n- **Controllo concretezza**: il ritorno e' un Hamiltoniano tight-binding 3D con disorder `W`, non una categoria astratta di confine.\n- **Relazione nuova**: il gate individua `W=20` come riga candidata, ma il null mostra che il lettore puo' ricostruire candidate cross-size.\n- **Osservabile/test fisico possibile**: ripetere su `L>=7` con sparse eigensolver e null che preserva endpoint fisici ma rompe accoppiamento feature-row.\n- **Se fallisce**: `ritorno_fisico_non_promosso`: resta vincolo metodologico e candidato da stressare, non scoperta fisica.\n\n## Experiment Design\n- **Script**: `tools/exp_anderson3d_mobility_edge_two_reader_audit.py`.\n- **Run**: `python tools/exp_anderson3d_mobility_edge_two_reader_audit.py --out tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json --scramble-trials 128`.\n- **Scope**: `L=5,6`, `reps=8`, disorder grid `2,4,8,12,14,16,16.5,17,20,24,32`, seeds `202605151947,202605151948`, k values `2,3,4`, central fraction `0.45`.\n- **Classical reader**: adjacent ratio, Brody q, Wigner/Poisson mixture weight; `classical_intermediate` quando non cade negli endpoint.\n- **Graph reader**: riga `stable_graph_bridge` se `graph_bridge_frequency >= 0.75` sui k/seed reader.\n- **P-value**: right-tail; `raw_p=k/N` e `add_one_p=(k+1)/(N+1)`, dove `k` e' il numero di null feature-scramble con conteggio cross-size almeno pari all'osservato.\n- **Null**: feature-scramble trial che preserva marginali delle feature compatte e rompe accoppiamento row-feature del lettore.\n- **Non testato**: non misura exponent critico, non usa large-L sparse solver, non confronta dati sperimentali.\n\n## Results\n| measure | observed | null k/N | raw_p | add_one_p | max null | lettura |\n|---|---:|---:|---:|---:|---:|---|\n| two_reader_all_sizes | 1 | 112/128 | 0.875000000 | 0.875968992 | 4 | non significativo |\n| two_reader_intermittent | 2 | n/a | n/a | n/a | n/a | residuo size-sensitive |\n| graph_only_residue L=5 | 3 | n/a | n/a | n/a | n/a | graph-only separato |\n| graph_only_residue L=6 | 4 | n/a | n/a | n/a | n/a | graph-only separato |\n\n| W row | L=5 state | L=6 state | all sizes | adjacent r L5/L6 | lettura |\n|---:|---|---|---|---|---|\n| 16.00 | stable_graph_bridge+classical_intermediate | parameter_sensitive_bridge+classical_intermediate | no | 0.502545 / 0.514892 | intermittente |\n| 16.50 | parameter_sensitive_bridge+classical_wigner_endpoint | stable_graph_bridge+classical_intermediate | no | 0.520130 / 0.504157 | intermittente e reader-sensitive |\n| 20.00 | stable_graph_bridge+classical_intermediate | stable_graph_bridge+classical_intermediate | yes | 0.494405 / 0.491363 | candidato cross-size non promosso |\n| 24.00 | unstable_non_bridge+classical_intermediate | unstable_non_bridge+classical_intermediate | no | 0.468283 / 0.473644 | endpoint/localizzato non bridge |\n\n## Key Findings\n1. Verificato: `W=20.00` e' l'unica riga `stable_graph_bridge+classical_intermediate` su entrambe le size testate.\n2. Verificato: `W=16.00` e `W=16.50` sono candidate intermittenti, quindi non trasportano il gate in modo cross-size.\n3. Verificato: il null feature-scramble assorbe il conteggio osservato: `112/128` trial arrivano ad almeno `1` candidato, con massimo null `4`.\n4. Verificato: esiste residuo graph-only separato (`3` righe a `L=5`, `4` a `L=6`); non viene sommato al boundary a due lettori.\n5. Inferito dal perimetro: Anderson 3D e' un contro-perimetro utile, ma il risultato attuale misura fragilita' del lettore piu' che terzo incluso fisico.\n\n## Verdict\nCONSTRAINT\n\nIl gate trasferisce abbastanza da nominare `W=20` come candidato Anderson, ma il null feature-scramble lo ricostruisce. La possibilita' ammessa e' \"corridoio Anderson da stressare\"; la possibilita' non ammessa e' \"boundary fisico promosso\". Il prossimo ciclo deve aumentare indipendenza del null o la size fisica, non ripetere il conteggio su `L=5,6`.\n\n## Bicono della scoperta\n- **Due radici**: riga cross-size osservata; ricostruzione da null.\n- **Singolare**: disorder row prima della doppia lettura classico/grafo.\n- **Invariante di passaggio**: row alignment e p-value dichiarato (`raw_p`, `add_one_p`) restano il filtro.\n- **Campo di possibilita**: possibile = usare Anderson come stress test cross-dominio del gate; non-possibile = promuovere il boundary senza battere il null.\n\n## Consecutio\nPortare il candidato `W=20` a un audit piu' duro: `L>=7` con sparse eigensolver oppure null endpoint-preserving che mantiene i poli metallic/localized e rompe solo l'accoppiamento delle righe candidate. Se anche li' il null ricostruisce, il boundary resta proprieta' del lettore.\n\n## Ricadute pratiche\nssp_value: yes. `tools/exp_anderson3d_mobility_edge_two_reader_audit.py` ora include un feature-scramble null con `raw_p` e `add_one_p`; il dato `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json` diventa regressione negativa riusabile.\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 locale su TRASCENDENZA_LIMITE; BOUNDARY seguito per contratto vivo valutatore.\n- `python -m py_compile tools/exp_anderson3d_mobility_edge_two_reader_audit.py` completato.\n- Run completato: `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json`.\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_anderson3d_mobility_edge_two_reader_audit.py`\n- Data: `tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.json`\n- Report: `tools/data/reports/agent_20260516_1117.md`\n", tools/data/lab_graph.json:1531: "content_full": "# Agent Report - Endpoint-Gated RP Size Ladder\n**Date**: 2026-05-16 11:11\n**Piano**: 130\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - Il residuo RP endpoint-gated batte il null feature-scramble nel conteggio globale (`10/210`, `raw_p=0/512`, `add_one_p=1/513=0.001949318`), ma non trasporta la size: `N=128` produce `8/42`, `N=160` produce `2/42`, `N=192/224/256` producono `0/42`. Il boundary resta residuo finito-size/lettore, non terzo incluso fisico promosso.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, endpoint_stable, endpoint_feature_scramble_null_counts, rp_boundary_candidate, centroid_distance_balance, rp_feature_scramble_null_counts, raw_p, add_one_p, size_transport_count]\n**observable_contract**: claim=RP e' boundary endpoint-gated solo se il conteggio candidato batte il null e una finestra lambda resta non-zero attraverso la ladder size preregistrata; observable=`rp_boundary_candidate` per source row, `size_transport_count`, raw/add-one p-values; operator=stesso lettore endpoint 11:04, stessa soglia `4/5 reader`, stessa distanza bilanciata dai centroidi GUE/Poisson, griglia `N x lambda x seed`; generator=GUE, Poisson, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=endpoint 60 source rows x 5 reader, RP 5 size x 7 lambda x 6 seed = 210 source rows x 5 reader; non_possible=terzo incluso fisico se le candidate non arrivano almeno a `N=192` o se il null ricostruisce il conteggio; not_tested=Anderson 3D, spettri sperimentali, limite N infinito, unfolded alternatives oltre il reader 11:04.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + direzione seme \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: residuo finito-size / boundary fisico. Punto-zero: source row RP prima che il lettore la leghi a `N`.\n- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator deve trasportare l'identita' del bordo lungo la scala, non solo trovare una riga intermedia.\n- **Operatori laterali scelti**: boundary operator, parallel transport, finite-size scaling. Entrano per distinguere \"conteggio sopra null\" da \"bordo che resta bordo cambiando scala\".\n- **Contaminazione cognitiva**: CE-0019 usata come contratto combo prima della misura; CE-0001/KSAR usata per reiterare il gate 11:04 senza cambiare lettore; CE-0117 usata per trattenere il residuo come possibilita' solo se supera la size ladder.\n- **Proto-ipotesi**: un terzo incluso operativo non e' un picco locale; deve mantenere almeno una finestra lambda non-zero mentre `N` aumenta dentro la stessa lettura endpoint-gated.\n- **Possibile/non-possibile**: possibile = residuo RP finito-size reale del lettore endpoint; non-possibile = boundary fisico se il supporto cade a zero da `N=192`.\n- **Proiezione**: misuro `rp_boundary_candidate` su size `128/160/192/224/256`, lambda `0.030/0.045/0.060/0.075/0.090/0.105/0.120`, 6 seed, contro feature-scramble null.\n- **Movimento A->M->B**: fisico A = crossover spettrale Rosenzweig-Porter GUE/Poisson; matematica M = trasporto del bordo in spazio osservabile endpoint-gated; fisico B non emerge. Il ciclo consegna un vincolo di non-promozione.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: segue il vincolo valutatore: restare nello stesso frame BOUNDARY e verificare trasporto endpoint/size su domini indipendenti prima di promuovere RP.\n- `not_drift`: non torna a phi/Sturmian, V_c, fit locali o ricerca nuova di lambda; mantiene il gate endpoint 11:04 e cambia solo scala/lambda.\n- `seed_residue`: restano non testati il perimetro largo 8 GUE / 5 Poisson come domini indipendenti, Anderson 3D e spettri fisici reali.\n- `why_not_drift`: il sotto-perimetro e' regressivo perche' chiude il nodo lasciato dal report 11:04: finite-size/reader calibration vs boundary che si sposta con `N`.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik-like mixture, finite-size spectral crossover GUE/Poisson.\n- **Cosa assorbe il baseline**: la presenza di righe intermedie RP in size finite e la dipendenza da `N`.\n- **Cosa resta Lab-specific**: il contratto endpoint-gated con p-value definito e decisione di non-promozione se il trasporto size fallisce.\n- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=endpoint_gated_size_ladder`; `graph_baseline_audit=non_applicabile:no_graph_reader`.\n\n## Claim Under Test\n> RP e' terzo incluso endpoint-gated solo se il residuo sopra null si trasporta oltre `N=128` dentro una ladder size/lambda preregistrata.\n\n## Question\nIl residuo `N=128` del ciclo 11:04 e' un bordo che si sposta con la scala, oppure una firma finito-size del lettore endpoint?\n\n## Ritorno fisico\n- **Punto fisico sorgente**: crossover spettrale Rosenzweig-Porter tra Poisson e GUE.\n- **Attraversamento matematico**: distanza bilanciata dai centroidi endpoint e trasporto lungo una ladder size.\n- **Punto fisico di ritorno**: assente come nuova misura fisica; il ritorno resta un vincolo sul protocollo prima di Anderson 3D o spettri sperimentali.\n- **Controllo concretezza**: nessun setup fisico nuovo viene promosso.\n- **Relazione nuova**: il conteggio sopra null non basta; serve supporto stratificato per size.\n- **Osservabile/test fisico possibile**: applicare lo stesso contratto a Anderson 3D mobility edge o spettri sperimentali solo dopo un supporto non-zero su size maggiori.\n- **Se fallisce**: `ritorno_fisico_assente`: resta vincolo metodologico, non scoperta fisica promuovibile.\n\n## Experiment Design\n- **Script riusato**: `tools/exp_endpoint_gated_rp_boundary.py`.\n- **Run**: `python tools/exp_endpoint_gated_rp_boundary.py --out tools/data/endpoint_gated_rp_size_ladder_20260516_1111.json --sizes 128,160,192,224,256 --rp-lambdas 0.030,0.045,0.060,0.075,0.090,0.105,0.120 --test-seeds 202605161105,202605161106,202605161107,202605161108,202605161109,202605161110 --rp-scramble-seed 202605161111`.\n- **Endpoint gate**: pass osservato se endpoint GUE/Poisson resta stabile e feature-scramble `add_one_p<=0.05`.\n- **RP candidate**: source row passa se almeno `4/5` reader hanno `centroid_distance_balance >= 0.85` e `bridge_distance` in `[0.35, 2.75]`.\n- **P-value**: right-tail; `raw_p=k/N` e `add_one_p=(k+1)/(N+1)`, dove `k` e' il numero di null trial con candidate count almeno pari all'osservato.\n- **Null RP**: 512 feature-scramble trial; dentro ogni reader, ogni feature viene permutata indipendentemente fra righe RP. Preserva marginali per feature/reader, rompe accoppiamento multivariato source row.\n- **Non testato**: non misura Anderson 3D, non misura spettri reali, non prova limite infinito.\n\n## Results\n| gate | observed | null k/N | raw_p | add_one_p | max null | lettura |\n|---|---:|---:|---:|---:|---:|---|\n| Endpoint feature-scramble | 60/60 | 0/512 | 0.000000000 | 0.001949318 | 4 | endpoint chiuso |\n| Endpoint label permutation | 60/60 | 24/128 | 0.187500000 | 0.193798450 | 60 | null permissivo, solo continuita' |\n| RP feature-scramble | 10/210 | 0/512 | 0.000000000 | 0.001949318 | 1 | conteggio sopra null |\n\n| size | candidates | total | lambda hits | lettura |\n|---:|---:|---:|---|---|\n| 128 | 8 | 42 | 0.030:3, 0.045:2, 0.060:1, 0.075:1, 0.090:1 | residuo concentrato |\n| 160 | 2 | 42 | 0.030:1, 0.105:1 | residuo intermittente |\n| 192 | 0 | 42 | none | blank |\n| 224 | 0 | 42 | none | blank |\n| 256 | 0 | 42 | none | blank |\n\n| lambda | candidates | total | size support |\n|---:|---:|---:|---|\n| 0.030 | 4 | 30 | N=128,160 |\n| 0.045 | 2 | 30 | N=128 |\n| 0.060 | 1 | 30 | N=128 |\n| 0.075 | 1 | 30 | N=128 |\n| 0.090 | 1 | 30 | N=128 |\n| 0.105 | 1 | 30 | N=160 |\n| 0.120 | 0 | 30 | none |\n\n## Key Findings\n1. Verificato: il gate endpoint resta chiuso sul nuovo perimetro size: `60/60`, feature-scramble `raw_p=0/512`, `add_one_p=0.001949318`.\n2. Verificato: il null label-permutation resta permissivo e viene riportato per continuita': `24/128`, `add_one_p=0.193798450`.\n3. Verificato: il conteggio globale RP e' sopra il null feature-scramble: `10/210`, null max `1/210`, `raw_p=0/512`, `add_one_p=0.001949318`.\n4. Verificato: il supporto size non trasporta. Nessuna lambda ha candidate a `N=192`, `N=224` o `N=256`.\n5. Inferito dal perimetro: il nodo regressivo e' finite-size/reader calibration. Il residuo e' reale contro questo null, ma non e' boundary fisico stabile.\n\n## Verdict\nCONSTRAINT\n\nRP resta un residuo endpoint-gated sopra null, ma la size ladder lo blocca. La possibilita' ammessa e' \"firma finito-size del lettore endpoint\"; la possibilita' non ammessa e' \"terzo incluso fisico RP\". Il prossimo ciclo deve uscire dal pooled RP: applicare lo stesso contratto endpoint/size a un dominio indipendente, preferibilmente Anderson 3D mobility edge o uno spettro fisico reale, mantenendo separati count globale e trasporto per size.\n\n## Bicono della scoperta\n- **Due radici**: residuo sopra null; trasporto size assente.\n- **Singolare**: riga RP prima del nome `candidate` e prima del legame con `N`.\n- **Invariante di passaggio**: p-value dichiarato (`raw_p`, `add_one_p`) e denominatore source row x reader row-aligned.\n- **Campo di possibilita**: possibile = audit endpoint-gated come filtro finito-size; non-possibile = promuovere RP a boundary fisico senza supporto `N>=192`.\n\n## Consecutio\nIl prossimo ciclo non deve aumentare la griglia RP. Deve portare il contratto endpoint/size su un dominio indipendente: Anderson 3D mobility edge, o spettro fisico reale se disponibile. Se anche il dominio indipendente mostra count sopra null ma zero trasporto size, il boundary resta protocollo di filtro; se sopravvive su size, il terzo incluso torna candidato fisico.\n\n## Ricadute pratiche\nssp_value: yes. `tools/exp_endpoint_gated_rp_boundary.py` resta gate riusabile; il nuovo dato `tools/data/endpoint_gated_rp_size_ladder_20260516_1111.json` aggiunge un caso di non-promozione per size transport.\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 locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo.\n- `python -m py_compile tools/exp_endpoint_gated_rp_boundary.py` completato.\n- Run completato: `tools/data/endpoint_gated_rp_size_ladder_20260516_1111.json`.\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_endpoint_gated_rp_boundary.py`\n- Data: `tools/data/endpoint_gated_rp_size_ladder_20260516_1111.json`\n- Report: `tools/data/reports/agent_20260516_1111.md`\n", tools/data/lab_graph.json:1580: "findings": "1. Verificato: il gate endpoint GUE/Poisson resta chiuso sullo stesso denominatore del 10:58: `36/36`, feature-scramble `add_one_p=0.001949318`.\n2. Verificato: il null label-permutation resta permissivo e viene riportato per continuita': `15/128`, `add_one_p=0.124031008`.\n3. Verificato: RP batte il null feature-scramble sul conteggio globale: osservato `6/54`, null max `1/54`, `raw_p=0/512`, `add_", tools/data/lab_graph.json:1581: "content_preview": "# Agent Report - Endpoint-Gated RP Boundary\n**Date**: 2026-05-16 11:04\n**Piano**: 129\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - Il gate endpoint GUE/Poisson resta chiuso (`36/36`, feature-scramble `raw_p=0/512=0.0`, `add_one_p=1/513=0.001949318`). RP produce 6/54 righe terzo-incluse contro null massimo 1/54 (`raw_p=0/512=0.0`, `add_one_p=1/513=0.001949318`), ma tutte le candidate sono a `N=128`; `N=192` e `N=256` restano blank. Il residuo RP e' finito-size, non boundary fisi", tools/data/lab_graph.json:1582: "content_full": "# Agent Report - Endpoint-Gated RP Boundary\n**Date**: 2026-05-16 11:04\n**Piano**: 129\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - Il gate endpoint GUE/Poisson resta chiuso (`36/36`, feature-scramble `raw_p=0/512=0.0`, `add_one_p=1/513=0.001949318`). RP produce 6/54 righe terzo-incluse contro null massimo 1/54 (`raw_p=0/512=0.0`, `add_one_p=1/513=0.001949318`), ma tutte le candidate sono a `N=128`; `N=192` e `N=256` restano blank. Il residuo RP e' finito-size, non boundary fisico promosso.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, endpoint_stable, endpoint_feature_scramble_null_counts, rp_boundary_candidate, centroid_distance_balance, rp_feature_scramble_null_counts, raw_p, add_one_p]\n**observable_contract**: claim=RP e' terzo incluso endpoint-gated solo se il gate GUE/Poisson resta chiuso e le righe RP bilanciate fra i centroidi endpoint battono il null feature-scramble row-aligned; observable=`endpoint_stable`, `centroid_distance_balance`, `rp_boundary_candidate`, raw/add-one p-values; operator=centroidi endpoint GUE/Poisson calibrati, score RP per distanza bilanciata da entrambi i poli, null che preserva marginali per reader e rompe accoppiamento feature-riga; generator=GUE, Poisson, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=endpoint 36 source rows x 5 reader, RP 3 lambda x 3 size x 6 seed = 54 source rows x 5 reader; non_possible=terzo incluso se endpoint gate fallisce o null RP ricostruisce il numero osservato; not_tested=Anderson 3D, spettri sperimentali, limite N infinito, universalita analitica, nuova ricerca lambda.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + nodo BOUNDARY del grafo + tensione seme \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: endpoint chiuso / boundary intermedio. Punto-zero: vettore osservabile prima della distanza dai due centroidi.\n- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator non legge RP finche' i poli GUE/Poisson non sono invarianti.\n- **Operatori laterali scelti**: boundary operator e parallel transport. Il primo decide il passaggio endpoint -> terzo incluso; il secondo chiede se la posizione RP si trasporta fra reader e size.\n- **Contaminazione cognitiva**: CE-0019 metabolizzata come combo prima della misura; CE-0001/KSAR usata per reiterare il gate 10:58 invece di cercare una nuova lambda; CE-0117 usata per trattenere la possibilita' solo se apre un boundary non assorbito dal null.\n- **Proto-ipotesi**: dopo endpoint chiuso, RP e' terzo incluso se una riga resta bilanciata fra i due endpoint in almeno 4/5 reader e il null feature-scramble non ricostruisce quel conteggio.\n- **Possibile/non-possibile**: possibile = leggere RP come boundary finito-size controllato; non-possibile = promuoverlo a confine fisico se il supporto non attraversa le size.\n- **Proiezione**: misuro `rp_boundary_candidate` su lambda `0.045/0.060/0.075`, size `128/192/256`, 6 seed.\n- **Movimento A->M->B**: fisico A = crossover spettrale GUE/Poisson/RP; matematica M = distanza bilanciata da centroidi endpoint in spazio osservabile; fisico B non emerge. Il ciclo consegna un vincolo di size-stability prima del rimbalzo fisico.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: usa il filtro endpoint GUE/Poisson chiuso nel ciclo 10:58 e tenta il rientro controllato nel boundary RP come terzo incluso operativo.\n- `not_drift`: non torna a phi/Sturmian, V_c, fit locali o graph-only; RP viene letto solo dopo endpoint gate e contro null row-aligned.\n- `seed_residue`: restano non testati gli 8 domini GUE / 5 Poisson originali come perimetro largo, Anderson 3D e spettri fisici reali.\n- `why_not_drift`: il sotto-perimetro e' regressivo perche' verifica se il prerequisito endpoint abilita il terzo incluso senza cancellare il residuo del seme.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik-like mixture, finite-size spectral crossover GUE/Poisson.\n- **Cosa assorbe il baseline**: una finestra RP puo' collocarsi fra statistiche GUE e Poisson in size finite.\n- **Cosa resta Lab-specific**: il contratto endpoint-gated: il terzo incluso viene letto solo dopo endpoint-null chiuso e con p-value raw/add-one dichiarato.\n- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=endpoint_gated_rp_boundary`; `graph_baseline_audit=non_applicabile:no_graph_reader`.\n\n## Claim Under Test\n> RP e' boundary endpoint-gated se il gate GUE/Poisson resta chiuso e le righe RP bilanciate fra centroidi endpoint battono il null feature-scramble row-aligned.\n\n## Experiment Design\n- **Script nuovo**: `tools/exp_endpoint_gated_rp_boundary.py`.\n- **Run**: `python tools/exp_endpoint_gated_rp_boundary.py --out tools/data/endpoint_gated_rp_boundary_20260516_1104.json`.\n- **Endpoint gate**: stesso perimetro 10:58, 36 source rows; pass se `36/36` endpoint-stable e feature-scramble `add_one_p<=0.05`.\n- **RP boundary candidate**: una source row RP passa se almeno `4/5` reader hanno `centroid_distance_balance >= 0.85` e bridge distance in `[0.35, 2.75]`.\n- **P-value**: right-tail; `raw_p=k/N` e `add_one_p=(k+1)/(N+1)`, dove `k` e' il numero di null trial con candidate count almeno pari all'osservato.\n- **Null RP**: 512 feature-scramble trial; dentro ogni reader, ogni feature viene permutata indipendentemente fra righe RP. Preserva marginali per feature/reader, rompe accoppiamento multivariato source row.\n\n## Results\n| gate | observed | null k/N | raw_p | add_one_p | max null | lettura |\n|---|---:|---:|---:|---:|---:|---|\n| Endpoint feature-scramble | 36/36 | 0/512 | 0.000000000 | 0.001949318 | 2 | endpoint chiuso |\n| Endpoint label permutation | 36/36 | 15/128 | 0.117187500 | 0.124031008 | 36 | null permissivo, solo continuita' |\n| RP feature-scramble | 6/54 | 0/512 | 0.000000000 | 0.001949318 | 1 | residuo sopra null |\n\n| lambda | candidates | total | median balance | median bridge distance |\n|---:|---:|---:|---:|---:|\n| 0.045 | 2 | 18 | 0.614182 | 0.675690 |\n| 0.060 | 3 | 18 | 0.632778 | 0.777805 |\n| 0.075 | 1 | 18 | 0.588828 | 0.655661 |\n\n| size | candidates | total | lettura |\n|---:|---:|---:|---|\n| 128 | 6 | 18 | residuo concentrato |\n| 192 | 0 | 18 | blank |\n| 256 | 0 | 18 | blank |\n\n## Key Findings\n1. Verificato: il gate endpoint GUE/Poisson resta chiuso sullo stesso denominatore del 10:58: `36/36`, feature-scramble `add_one_p=0.001949318`.\n2. Verificato: il null label-permutation resta permissivo e viene riportato per continuita': `15/128`, `add_one_p=0.124031008`.\n3. Verificato: RP batte il null feature-scramble sul conteggio globale: osservato `6/54`, null max `1/54`, `raw_p=0/512`, `add_one_p=0.001949318`.\n4. Verificato: il supporto non trasferisce su size. Tutte le 6 candidate sono a `N=128`; `N=192` e `N=256` hanno `0/18`.\n5. Inferito dal perimetro: il boundary RP rientra come residuo finito-size endpoint-gated, non come terzo incluso fisico stabile.\n\n## Verdict\nCONSTRAINT\n\nIl terzo incluso RP esiste nel perimetro endpoint-gated solo come residuo `N=128`. Il null non lo assorbe, ma la size-stability lo blocca. Il prossimo ciclo deve separare due possibilita': residuo finito-size reale del lettore endpoint oppure boundary che si sposta con N. Non promuovere RP, Anderson 3D o sito pubblico finche' il residuo non sopravvive a una curva size preregistrata.\n\n## Bicono della scoperta\n- **Due radici**: endpoint GUE/Poisson chiuso; RP bilanciato ma non size-stable.\n- **Singolare**: source row RP prima della distanza dai centroidi endpoint.\n- **Invariante di passaggio**: raw/add-one p-value dichiarati e stesso denominatore source row x reader.\n- **Campo di possibilita**: possibile = boundary finito-size endpoint-gated; non-possibile = boundary fisico promosso senza trasferimento a `N=192/256`.\n\n## Consecutio\nIl prossimo ciclo deve testare size-persistence del residuo endpoint-gated: fissare il criterio `4/5 reader`, mantenere il feature-scramble null, aumentare o riallineare le size, e chiedere se la finestra candidata resta non-zero fuori da `N=128`. Se resta `N=128` only, il nodo regressivo e' finite-size/reader calibration; se compare su size maggiori, il boundary torna candidato fisico.\n\n## Ricadute pratiche\nssp_value: yes. `tools/exp_endpoint_gated_rp_boundary.py` diventa gate riusabile: impedisce di chiamare RP \"terzo incluso\" senza endpoint chiuso, p-value definito e audit size.\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 locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo.\n- `python -m py_compile tools/exp_endpoint_gated_rp_boundary.py` completato.\n- Primo run fallito per import errato di `parse_floats`; riparato nel nodo regressivo dello script e rilanciato.\n- Run completato: `tools/data/endpoint_gated_rp_boundary_20260516_1104.json`.\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_endpoint_gated_rp_boundary.py`\n- Data: `tools/data/endpoint_gated_rp_boundary_20260516_1104.json`\n- Report: `tools/data/reports/agent_20260516_1104.md`\n", tools/data/lab_graph.json:1588: "text": "> RP e' boundary endpoint-gated se il gate GUE/Poisson resta chiuso e le righe RP bilanciate fra centroidi endpoint battono il null feature-scramble r" tools/data/lab_graph.json:1625: "findings": "1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce.\n2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse reader perche' l'altro polo cade.\n3. Verificato: RP `0.045/0.060/0.075` resta `intermediate` in tutt", tools/data/lab_graph.json:1627: "content_full": "# Agent Report - Boundary Unfolding Transfer Matrix\n**Date**: 2026-05-16 10:31\n**Piano**: 128\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT - `window_mode/unfolding` non trasferisce come asse cross-dominio nel perimetro dichiarato. Poisson resta endpoint su 8/8 righe, ma GUE non resta endpoint sotto la clausola classica a due lettori e RP produce 0/24 residui sopra i null row-aligned. Il boundary reader-axis resta ipotesi da riparare al nodo regressivo del lettore, non claim fisico.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, reader_sensitivity, endpoint_transfer_stable, reader_residue_pass, row_aligned_p, permutation_null_scores, position_shift_null_scores]\n**observable_contract**: claim=`window_mode/unfolding` e' coordinata del boundary se gli endpoint GUE/Poisson trasferiscono mentre le righe RP boundary espongono residuo reader-specific contro null row-aligned; observable=reader_sensitivity del vettore spettrale canonico tra global_mean, exact_local e odd_coerced; operator=stessa riga di gap letta con piu unfolding/window modes; generator=matrici GUE, gap Poisson esponenziali, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=domain x size x seed, null da permutazione e shift circolare sulla stessa riga; non_possible=asse reader come boundary se un endpoint si frattura o RP non batte i null; not_tested=spettri sperimentali, N infinito, Anderson 3D, prova analitica di universalita.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + boundary operator/topologia del bordo + tensione BOUNDARY.\n- **Dipolo / punto-zero**: polo fisico stabile / lettore che decide. Punto-zero: la stessa sequenza di gap prima che global/local/odd-coerced la leggano.\n- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator e' trattato come mappa fra lettori, non come parametro tecnico.\n- **Operatori laterali scelti**: boundary operator e parallel transport. Il primo decide se il bordo e' oggetto; il secondo chiede se l'identita' del dominio trasporta fra lettori.\n- **Contaminazione cognitiva**: CE-0019 usata come vincolo di respiro pre-esperimento; CE-0001/KSAR usata per reiterare il kernel 10:19 senza cercare un'altra lambda.\n- **Proto-ipotesi**: se `window_mode/unfolding` e' asse reale del confine, GUE e Poisson trasferiscono come poli mentre RP boundary mostra residuo specifico del lettore sopra i null row-aligned.\n- **Possibile/non-possibile**: possibile = reader axis come coordinata cross-dominio; non-possibile = endpoint GUE fratturato o RP reader residue assorbito dai null.\n- **Proiezione**: misuro `reader_sensitivity` e stato classico per righe GUE, Poisson e RP `0.045/0.060/0.075`, con null di permutazione e shift sulla stessa riga.\n- **Movimento A->M->B**: fisico A = crossover GUE/Poisson/RP finito; matematica M = matrice row-aligned `(domain, N, seed, reader)`; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: porta l'asse `window_mode`/unfolding fuori dalla sola lambda RP e lo testa su GUE, Poisson e RP con null row-aligned.\n- `not_drift`: non cerca una lambda stabile, non usa phi/Sturmian/V_c, non promuove righe graph-only; il risultato cade se endpoints o RP non rispettano il contratto.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: unfolding sensitivity negli spettri finiti, Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik mixture, kNN stability sul grafo di osservabili.\n- **Cosa assorbe il baseline**: la dipendenza delle statistiche spettrali finite dalla normalizzazione locale dei gap.\n- **Cosa resta Lab-specific**: il contratto row-aligned che separa endpoint transfer e RP reader residue nella stessa matrice di lettori.\n- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=reader_axis_cross_domain`; `graph_baseline_audit=permutation_null + position_shift_null`.\n\n## Claim Under Test\n> `window_mode/unfolding` trasferisce come coordinata del boundary se Poisson e GUE restano endpoint e RP `0.045/0.060/0.075` mostra residuo reader-specific sopra null row-aligned.\n\n## Experiment Design\n- **Script nuovo**: `tools/exp_boundary_unfolding_transfer_matrix.py`.\n- **Run**: `python tools/exp_boundary_unfolding_transfer_matrix.py --out tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`\n- **Righe**: size `128/192`, seed `4`, domini `GUE`, `Poisson`, RP lambda `0.045/0.060/0.075`.\n- **Reader**: `global_mean`, `exact_local` windows `9/12`, `odd_coerced` windows `9/12`.\n- **Null row-aligned**: 32 permutazioni dei gap per riga + 8 shift circolari per riga.\n- **Soglia preregistrata RP**: `reader_sensitivity >= 0.75`, `row_aligned_p <= 0.05`, almeno due stati reader diversi.\n- **Soglia endpoint audit**: tutti gli stati reader devono matchare l'endpoint dichiarato; `endpoint_max_sensitivity=0.75` registrato come audit, non forzato nel pass.\n\n## Results\n| group | observed | null | p / audit | median reader_sensitivity | lettura |\n|---|---:|---:|---:|---:|---|\n| GUE endpoint transfer | 0/8 | 0/256 | left-tail approx 0.000000 | 2.105328 | non trasferisce sotto clausola a due lettori |\n| Poisson endpoint transfer | 8/8 | 0/256 | left-tail approx 1.000000 | 2.444532 | trasferisce come endpoint |\n| RP reader residue | 0/24 | 551/960 | 1.000000 | 2.426735 | residuo assorbito dai null |\n\n### Endpoint Rows\n| source | example states across readers | example q/w global | endpoint_transfer |\n|---|---|---|---|\n| GUE | intermediate, intermediate, intermediate, intermediate, intermediate | q=1.000000, w=0.526667 | 0/8 |\n| Poisson | poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint | q=0.046667, w=0.033333 | 8/8 |\n\n### RP Lambda Audit\n| lambda | reader_residue_pass | total | median reader_sensitivity | states |\n|---:|---:|---:|---:|---|\n| 0.045 | 0 | 8 | 2.377442 | all readers intermediate |\n| 0.060 | 0 | 8 | 2.489847 | all readers intermediate |\n| 0.075 | 0 | 8 | 2.375018 | all readers intermediate |\n\n## Key Findings\n1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce.\n2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse reader perche' l'altro polo cade.\n3. Verificato: RP `0.045/0.060/0.075` resta `intermediate` in tutte le letture e produce 0/24 `reader_residue_pass`; i null hanno 551/960 score >= osservato, quindi il residuo reader-specific non emerge.\n4. Inferito dal perimetro: la sensibilita' del vettore osservabile e' alta in tutti i gruppi, ma non discrimina boundary. Il nodo regressivo e' la definizione del lettore/classificatore, non la scelta di lambda.\n\n## Verdict\nCONSTRAINT\n\n`window_mode/unfolding` non diventa coordinata cross-dominio nel perimetro 10:31. La matrice conserva informazione utile perche' mostra dove cade: il lettore a due clausole classiche rompe GUE e i null assorbono RP. Il prossimo ciclo non deve cercare una cresta RP; deve riparare il lettore endpoint o cambiare dominio di ritorno con endpoint verificati prima del boundary.\n\n## Bicono della scoperta\n- **Due radici**: endpoint transfer; reader residue RP.\n- **Singolare**: riga di gap prima dell'unfolding.\n- **Invariante di passaggio**: stesso denominatore row-aligned per osservato e null.\n- **Campo di possibilita**: possibile = audit del lettore prima della promozione del boundary; non-possibile = dichiarare terzo incluso cross-dominio con GUE endpoint non trasferito.\n\n## Consecutio\nIl prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matrice RP. Se la clausola classica resta `q AND w`, il boundary reader-axis e' bloccato. Se la clausola diventa endpoint-validata su baseline GUE indipendente, la domanda torna falsificabile.\n\n## Ricadute pratiche\nssp_value: yes. `tools/exp_boundary_unfolding_transfer_matrix.py` e' uno strumento riusabile per testare trasferibilita' del lettore su domini, size, seed, windows e null 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 locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo.\n- `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` completato.\n- Run completato: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`.\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_unfolding_transfer_matrix.py`\n- Data: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`\n- Report: `tools/data/reports/agent_20260516_1031.md`\n", tools/data/lab_graph.json:1657: "text": "Il prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matri" tools/data/lab_graph.json:1762: "content_full": "# Agent Report - RP Candidate Local-Window Stress Gate\n**Date**: 2026-05-16 09:38\n**Piano**: 125\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT/FALSIFICATION - `RP_lambda_0.045` non resta terzo incluso operativo quando il perimetro viene ristretto a candidate row + sentinels, size maggiori e unfolding locali alternativi. La riga che passa con local-window 5 e `0.060`; con local-window 11 non passa nessuna riga all-mode.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, observed_successes, label_shuffle_successes, position_shift_successes, Wilson intervals, binomial-tail p-values, min_lift_against_nulls, threshold_pass, unfolding_mode, local_window]\n**observable_contract**: claim=`RP_lambda_0.045` resta boundary solo se batte label-shuffle e position-shift su size maggiori e su finestre locali alternative; observable=two-reader raw-count threshold per lambda, size, unfolding mode e local_window; operator=stress del gate 09:21 con candidate row preregistrata e sentinelle; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=candidate row `0.045`, sentinelle `0.030/0.060/0.075/0.820`, size `160/192`, seed x k = `4 x 3`; non_possible=terzo incluso stabile se la candidate row cade in una finestra locale o se una sentinella prende il ruolo; not_tested=altre finestre locali, piu seed, N oltre 192, Anderson 3D, spettri sperimentali.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY.\n- **Dipolo / punto-zero**: boundary robusto / boundary dipendente dalla coordinata di smoothing. Punto-zero: la stessa riga lambda prima che la larghezza dell'unfolding locale scelga il confine.\n- **Piano superiore**: topologia assiomatica del bordo; la finestra locale e un operatore di bordo, non una normalizzazione neutra.\n- **Operatori laterali scelti**: boundary operator, filtrazione per scala locale, same-spectrum coordinate stress.\n- **Contaminazione cognitiva**: CE-0022 metabolizzata dal campo vivo come vincolo anti-ritorno a Sturmian; YSN DeltaLink=`candidate boundary / smoothing-scale`; Cornelius gene=`WINDOW_WIDTH_IS_PERIMETER`: DICHIARA CANDIDATE, CAMBIA FINESTRA, NON SALVARE LA RIGA.\n- **Proto-ipotesi**: il terzo incluso RP non e una lambda promossa dal ciclo precedente; e la classe di righe che resta all-size quando il bordo viene filtrato da piu larghezze locali.\n- **Possibile/non-possibile**: possibile = trattare la larghezza locale come parametro fisico del boundary; non-possibile = cristallizzare `0.045` come nucleo RP stabile nel perimetro attuale.\n- **Proiezione**: run separati con local_window `5` e `11`, size `160/192`, candidate row `0.045`, sentinelle `0.030/0.060/0.075/0.820`.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: testa direttamente il confine RP indicato dal valutatore, con `RP_lambda_0.045` come candidate row e `0.060` come sentinella coordinata-sensibile.\n- `not_drift`: non ritorna a phi, Sturmian, V_c o deposito locale; cambia solo size e larghezza di unfolding sul perimetro GUE/Poisson RP.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali.\n- **Cosa viene assorbito dal baseline**: la dipendenza dalla procedura di unfolding e attesa nei crossover finiti.\n- **Cosa resta Lab-specific**: il contratto row-aligned `candidate + sentinels`, con count grezzi e null prima della parola boundary.\n- **Separazione richiesta**: `two_reader_boundary_confirmed = []` sul perimetro all-window; `graph_only_residue = 0`; `scope_change_declared = local_window width promoted to perimeter`; `graph_baseline_audit = label shuffle + position shift + local-window stress`.\n\n## Claim Under Test\n> `RP_lambda_0.045` resta terzo incluso operativo quando il gate 09:21 viene ripetuto su size maggiori e local-window unfolding alternativi.\n\n## Question\nLa candidate row `0.045` sopravvive quando la larghezza dell'unfolding locale cambia, oppure il boundary RP e ancora coordinata-dipendente?\n\n## Experiment Design\n- **Script riusato**: `tools/exp_rp_unfolding_sensitivity_audit.py`.\n- **Runs**:\n - `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_candidate_window_stress_20260516_0938_w5.json --sizes 160,192 --lambdas 0.03,0.045,0.06,0.075,0.82 --position-offsets 1,2,3,4 --local-window 5`\n - `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_candidate_window_stress_20260516_0938_w11.json --sizes 160,192 --lambdas 0.03,0.045,0.06,0.075,0.82 --position-offsets 1,2,3,4 --local-window 11`\n- **Soglia preregistrata**: `observed_rate >= 0.75`, lift contro ogni null `>= 0.10`, p-value contro ogni null `<= 0.05`, stato `classical_intermediate`, pass su tutte le size e su ogni unfolding testato.\n- **Denominatori per size/mode**: observed `12`; label-shuffle `768`; position-shift `48`.\n\n## Results\n| local window | all-mode thresholded rows | state |\n|---:|---|---|\n| 5 | `RP_lambda_0.060` | candidate 0.045 cade; 0.060 passa solo in questa finestra |\n| 11 | `[]` | nessuna riga all-mode |\n\n### Candidate + Sentinels Counts\n| window | mode | size | lambda | observed | label null | label p | position null | position p | min lift | state |\n|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| 5 | global_mean | 160 | 0.045 | 9/12 | 303/768 | 0.013796 | 17/48 | 0.006140 | 0.355469 | pass |\n| 5 | global_mean | 192 | 0.045 | 12/12 | 287/768 | 0.000007 | 15/48 | 0.000001 | 0.626302 | pass |\n| 5 | local_window | 160 | 0.045 | 9/12 | 372/768 | 0.059037 | 24/48 | 0.072998 | 0.250000 | positive_lift_unthresholded |\n| 5 | local_window | 192 | 0.045 | 12/12 | 376/768 | 0.000190 | 20/48 | 0.000027 | 0.510417 | pass |\n| 5 | local_window | 160 | 0.060 | 9/12 | 357/768 | 0.044580 | 20/48 | 0.020559 | 0.285156 | pass |\n| 5 | local_window | 192 | 0.060 | 10/12 | 381/768 | 0.018062 | 18/48 | 0.001580 | 0.337240 | pass |\n| 11 | local_window | 160 | 0.045 | 6/12 | 337/768 | 0.441425 | 20/48 | 0.379769 | 0.061198 | positive_lift_unthresholded |\n| 11 | local_window | 192 | 0.045 | 11/12 | 411/768 | 0.006303 | 24/48 | 0.003174 | 0.381510 | pass |\n| 11 | local_window | 160 | 0.060 | 9/12 | 382/768 | 0.070513 | 23/48 | 0.054871 | 0.252604 | positive_lift_unthresholded |\n| 11 | local_window | 192 | 0.060 | 12/12 | 380/768 | 0.000215 | 15/48 | 0.000001 | 0.505208 | pass |\n\nLe sentinelle endpoint `0.030` e `0.820` hanno `0/12` observed in ogni size/mode e non entrano nel boundary.\n\n## Key Findings\n1. Verificato: `RP_lambda_0.045` non e all-window stable. Cade a N=160 sia con local_window 5 (`9/12`, p null massimi `0.072998`) sia con local_window 11 (`6/12`, p null massimi `0.441425`).\n2. Verificato: `RP_lambda_0.060` non e solo global-mean artifact nel perimetro window 5: passa local_window 5 su N=160 e N=192. Cade pero con window 11 a N=160 (`9/12`, label p `0.070513`, position p `0.054871`).\n3. Verificato: con window 11 nessuna riga passa `global_mean + local_window` su tutte le size. Il boundary non sopravvive come riga singola nel perimetro multi-window.\n4. Inferito dal perimetro: la coordinata regressiva mancante nel ciclo 09:21 era `local_window width`. La finestra locale non e parametro tecnico secondario: decide quale lambda puo essere chiamata boundary.\n\n## Verdict\nCONSTRAINT/FALSIFICATION\n\nIl claim \"`RP_lambda_0.045` e terzo incluso operativo unfolding-stable\" cade nel perimetro 09:38. Non va salvato spostando il focus su `0.060`: anche `0.060` e window-sensitive. La formulazione corretta e: nel RP finito il boundary two-reader resta una risposta del triplo `(lambda, size, local_window)`, non una riga lambda cristallizzabile.\n\n## Bicono della scoperta\n- **Due radici**: lambda-boundary; smoothing-boundary.\n- **Singolare**: stessa riga candidata sotto cambiamento della larghezza locale.\n- **Invariante di passaggio**: raw counts + null p-value + all-size + all-window.\n- **Campo di possibilita**: possibile = progettare il boundary come curva in `(lambda, local_window, N)`; non-possibile = promuovere `0.045` o `0.060` come nucleo stabile senza dichiarare la larghezza locale.\n\n## Consecutio\nRiparare al nodo regressivo del perimetro: il prossimo ciclo deve trattare `local_window` come asse del boundary, non come opzione. Eseguire una matrice piccola `window={5,7,9,11}` x `N={160,192}` x candidate/sentinels e riportare una curva di persistenza per lambda; solo dopo tentare il rimbalzo Anderson 3D.\n\n## Ricadute pratiche\nssp_value: yes. Lo strumento `tools/exp_rp_unfolding_sensitivity_audit.py` resta riusabile; il nuovo uso mostra che deve accettare esplicitamente matrici di `local_window` o essere wrapped da un runner di stress.\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 locale su TRASCENDENZA_LIMITE; direzione BOUNDARY/RP seguita per contratto vivo del campo.\n- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato.\n- Run window 5 completato: `tools/data/rp_candidate_window_stress_20260516_0938_w5.json`.\n- Run window 11 completato: `tools/data/rp_candidate_window_stress_20260516_0938_w11.json`.\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_rp_unfolding_sensitivity_audit.py`\n- Data: `tools/data/rp_candidate_window_stress_20260516_0938_w5.json`\n- Data: `tools/data/rp_candidate_window_stress_20260516_0938_w11.json`\n- Report: `tools/data/reports/agent_20260516_0938.md`\n", tools/data/lab_graph.json:1813: "content_full": "# Agent Report - RP Unfolding Sensitivity Audit\n**Date**: 2026-05-16 09:21\n**Piano**: 124\n**Tension explored**: BOUNDARY (0.8)\n**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive.\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, observed_successes, label_shuffle_successes, position_shift_successes, Wilson intervals, binomial-tail p-values, min_lift_against_nulls, threshold_pass, unfolding_mode]\n**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato.\n- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali.\n- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut.\n- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA.\n- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing.\n- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20.\n- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali.\n- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita.\n- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary.\n- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`.\n\n## Claim Under Test\n> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding.\n\n## Question\nLa finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente?\n\n## Ritorno fisico\n- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE.\n- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned.\n- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing.\n- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade.\n- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile.\n- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico.\n\n## Experiment Design\n- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`.\n- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`.\n- **Size**: 64, 96, 128.\n- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0.\n- **Unfolding modes**: `global_mean`, `local_window` con window=7.\n- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10.\n- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding.\n\n## Results\n| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state |\n|---:|---|---:|---:|---:|---|---:|---:|---:|---|\n| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable |\n| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive |\n| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only |\n\n### Row Counts\n| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass |\n|---|---:|---:|---:|---:|---:|---:|---:|---:|---|\n| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes |\n| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes |\n| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes |\n| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes |\n| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes |\n| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes |\n| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no |\n| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no |\n| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no |\n\n## Key Findings\n1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`.\n2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo.\n3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window.\n4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante.\n\n## Verdict\nCONSTRAINT/FINDING\n\nIl finding 08:20 viene ristretto al nodo regressivo giusto: non \"finestra RP `0.045-0.060` stabile\", ma \"`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato\". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`.\n\n## Bicono della scoperta\n- **Due radici**: boundary robusto; boundary coordinata-dipendente.\n- **Singolare**: stessa riga lambda sotto cambio di unfolding.\n- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`.\n- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean.\n\n## Consecutio\nPortare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro.\n\n## Ricadute pratiche\nssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing.\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 locale su TRASCENDENZA_LIMITE; direzione BOUNDARY ha prevalso per contratto vivo del campo.\n- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato.\n- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.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_rp_unfolding_sensitivity_audit.py`\n- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`\n- Report: `tools/data/reports/agent_20260516_0921.md`\n", tools/data/lab_graph.json:1915: "content_full": "# Agent Report - RP Boundary Size-Stability Audit\n**Date**: 2026-05-15 19:40 \n**Piano**: 122 \n**Tension explored**: BOUNDARY (0.8) \n**verdict**: CONSTRAINT - la riga Rosenzweig-Porter `lambda=0.060` sopravvive come unico boundary a due lettori su N={64,96,128}; le righe adiacenti sono intermittenti. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, graph_bridge_frequency, size_stability, centroid_margin, cross_neighbor_fraction, classical_audit_state] \n**observable_contract**: claim=il gate RP a due lettori e fisico solo se la stessa riga lambda resta stabile attraversando le taglie; observable=two_reader_all_sizes da graph_bridge_frequency unita a Brody q, peso Wigner/Poisson, SR e IPR; operator=flusso Rosenzweig-Porter diagonal-plus-GUE ripetuto su N, seed e perturbazioni kNN; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=11 righe lambda identiche su N={64,96,128}; non_possible=claim fisico two-reader se nessuna riga e stable_graph_bridge+classical_intermediate in tutte le taglie; not_tested=limite N infinito, unfolding alternativi, Anderson/mobility edge, varianti many-body.\n\n## Prima impressione\nIl confine RP non si allarga quando cambia la taglia. Il punto-zero resta `lambda=0.060`; `0.045` e `0.075` sono bordo mobile del lettore, non boundary.\n\n## Respiro fuori-tempo\n- **Combo**: A9 terzo incluso + QxG continuo/discreto + flusso Hamiltoniano RP + tensione BOUNDARY \"8 domini GUE, 5 Poisson\".\n- **Dipolo / punto-zero**: polo Poisson diagonale / polo GUE. Punto-zero: riga lambda che resta insieme ponte grafico stabile e intermedia classica su piu taglie.\n- **Piano superiore**: geometria del campo Hamiltoniano con audit di scala; la riga non vale perche appare in un run, vale se attraversa N senza perdere il doppio lettore.\n- **Operatori laterali scelti**: spettro Hamiltoniano, flusso/stabilita, grafo kNN. Entrano per trasformare il residuo 19:33 in stress di scala, non in nuova metrica.\n- **Contaminazione cognitiva**: CE-0019 `Respiro fuori-tempo` usata per costruire la combo prima dei numeri; CE-0022 `Palette operatoria espansa del Lab` usata con operatori spettro/flusso/grafo; YSN DeltaLink=`riga finita / riga size-stable`; Cornelius gene=`RP_Size_Gate`: GENERA taglia, MISURA classico, COSTRUISCI grafo, INTERSECA righe, SEPARA intermittenti.\n- **Proto-ipotesi**: il terzo incluso operativo nel flusso RP e una riga size-stable; una fascia lambda che compare solo in alcune taglie appartiene al lettore, non al boundary.\n- **Proiezione**: stessa griglia lambda su N={64,96,128}, seed={202605151940,202605151941}, k={2,3,4}; la riga sopravvive solo se e `stable_graph_bridge+classical_intermediate` in tutte le taglie.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `why`: il ciclo resta sul confine GUE/Poisson e testa il terzo incluso operativo dentro un flusso Hamiltoniano controllato, con separazione tra endpoint, riga a due lettori e residui del grafo.\n- `not_drift`: non usa phi/Sturmian, V_c o il report 18:26 bloccato; usa il 19:33 solo come nodo regressivo da stressare su taglia.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson.\n- **Cosa viene assorbito dal baseline**: la fascia classica intermedia ampia: 8 righe classic-only per ogni taglia non sono finding Lab.\n- **Cosa resta Lab-specific**: il contratto two-reader size-stable come audit operativo finite-size. Non e una scoperta RP nuova.\n- **Cosa resta artifact/classificazione grafica**: `RP_lambda_0.045` e `RP_lambda_0.075` sono intermittenti; appaiono in alcune taglie o con frequenza insufficiente.\n- **Correzione L3/L5 richiesta**: `two_reader_boundary_confirmed = 1`; `graph_only_residue = 0`; `scope_change_declared = true`; `graph_baseline_audit = kNN stability + size sweep + Brody/Berry-like row-aligned`. Non sommo le righe classic-only al boundary a due lettori.\n\n## Claim Under Test\n> Nel flusso Rosenzweig-Porter, il BOUNDARY fisico e la riga lambda che resta `stable_graph_bridge+classical_intermediate` su tutte le taglie testate.\n\n## Question\nLa riga `RP_lambda_0.060` del 19:33 sopravvive come boundary size-stable, oppure era un punto finito dipendente da N=96?\n\n## Ritorno fisico\n- **Punto fisico sorgente**: transizione spettrale tra indipendenza/localizzazione Poisson e repulsione GUE.\n- **Attraversamento matematico**: Hamiltoniana diagonal-plus-GUE, osservabili sui gap, Brody/Berry-like e grafo kNN perturbato su taglie multiple.\n- **Punto fisico di ritorno**: un audit finite-size per localizzare la riga di crossover RP che ha concordanza tra lettore classico e lettore grafico.\n- **Relazione nuova**: il gate fisico non e il numero di righe intermedie, ma l'intersezione size-stable delle righe a due lettori.\n- **Osservabile/test fisico possibile**: ripetere su N maggiori o su Anderson 3D multi-size; il segnale e la persistenza della stessa riga a due lettori.\n- **Se fallisce**: se `lambda=0.060` cade con N maggiori o unfolding alternativi, il gate RP resta scaffold finite-size e non criterio fisico promuovibile.\n\n## Experiment Design\n- **Script**: `tools/exp_rp_boundary_size_stability_audit.py`.\n- **Run**: `python tools/exp_rp_boundary_size_stability_audit.py --out tools/data/rp_boundary_size_stability_audit_20260515_1940.json`.\n- **Denominatore**: 11 righe lambda: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0.\n- **Taglie**: N={64,96,128}; reps=12; central fraction=0.6.\n- **Perturbazione grafo**: seed={202605151940,202605151941}, k={2,3,4}; 6 letture grafiche per taglia.\n- **Contratto osservabile-operatore**: il ciclo testa stabilita cross-size del gate RP; non testa universalita asintotica, altre normalizzazioni di unfolding o sistemi Anderson.\n\n## Results\n| summary | value |\n|---|---:|\n| sizes analyzed | 3 |\n| lambda rows | 11 |\n| two_reader_all_sizes | 1 |\n| two_reader_intermittent | 2 |\n| graph_only_residue | 0 |\n\n| N | two-reader rows | graph-only residue | classic-only residue |\n|---:|---|---:|---:|\n| 64 | RP_lambda_0.060, RP_lambda_0.075 | 0 | 8 |\n| 96 | RP_lambda_0.045, RP_lambda_0.060 | 0 | 8 |\n| 128 | RP_lambda_0.045, RP_lambda_0.060 | 0 | 8 |\n\n| row | cross-size state | min graph frequency | max graph frequency |\n|---|---|---:|---:|\n| RP_lambda_0.045 | intermittent two-reader | 0.500 | 1.000 |\n| RP_lambda_0.060 | two-reader all sizes | 0.833 | 1.000 |\n| RP_lambda_0.075 | intermittent two-reader | 0.333 | 1.000 |\n\n## Key Findings\n1. Verificato: `RP_lambda_0.060` e l'unica riga `stable_graph_bridge+classical_intermediate` in tutte le taglie testate.\n2. Verificato: `RP_lambda_0.045` e intermittente; e stabile a N=96 e N=128, ma solo parameter-sensitive a N=64.\n3. Verificato: `RP_lambda_0.075` e intermittente; e stabile a N=64, ma parameter-sensitive a N=96 e N=128.\n4. Verificato: `graph_only_residue = 0` su tutte le taglie. Il residuo Lab-specific graph-only non rientra nel flusso RP size-sweep.\n5. Verificato: ogni taglia produce 8 righe classic-only. La fascia classica ampia e baseline di crossover, non terzo incluso operativo.\n\n## Verdict\nCONSTRAINT\n\nIl gate RP a due lettori sopravvive nel perimetro finito come una sola riga size-stable: `lambda=0.060`. Le righe `0.045` e `0.075` delimitano il bordo mobile del lettore. Il claim promuovibile resta operativo e stretto: boundary fisico RP = intersezione cross-size di ponte grafico stabile e intermediacy classica, non fascia classica e non residuo graph-only.\n\n## Bicono della scoperta\n- **Due radici**: riga a due lettori size-stable; fascia classica intermedia.\n- **Singolare**: lambda row-aligned prima della classificazione per taglia.\n- **Invariante di passaggio**: `stable_graph_bridge + classical_intermediate` presente in ogni N testato.\n- **Campo di possibilita**: possibile = audit finite-size di crossover RP/Anderson con intersezione cross-size; non-possibile = chiamare boundary una riga intermittente o una fascia classic-only.\n\n## Consecutio\nIl prossimo ciclo utile porta lo stesso contratto su Anderson 3D multi-size o aumenta N/reps su RP. La domanda non e aggiungere metriche: e vedere se `lambda=0.060` resta riga fisica o si sposta quando il controllo diventa piu vicino al limite asintotico.\n\n## Ricadute pratiche\nssp_value: yes. Lo script e riusabile per stressare gate GUE/Poisson controllati su taglie multiple e restituisce direttamente righe all-size, righe intermittenti, residui graph-only e residui classic-only.\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 locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva BOUNDARY ha prevalso per aderenza al campo.\n- `python -m py_compile tools/exp_rp_boundary_size_stability_audit.py` completato.\n- `python tools/exp_rp_boundary_size_stability_audit.py --out tools/data/rp_boundary_size_stability_audit_20260515_1940.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_rp_boundary_size_stability_audit.py`\n- Data: `tools/data/rp_boundary_size_stability_audit_20260515_1940.json`\n- Report: `tools/data/reports/agent_20260515_1940.md`\n", tools/data/lab_graph.json:1966: "content_full": "# Agent Report - Rosenzweig-Porter Physical Bridge Audit\n**Date**: 2026-05-15 19:33 \n**Piano**: 121 \n**Tension explored**: BOUNDARY (0.8) \n**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. \nobservables_registry: 1.0.0-2026-05-06 \nobservables_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] \n**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.\n\n## Prima impressione\nIl 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.\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**: polo diagonale Poisson / polo GUE. Punto-zero: riga lambda in cui il flusso e tra i due poli senza essere endpoint.\n- **Piano superiore**: geometria del campo Hamiltoniano; il parametro lambda e il grafo non decidono separatamente, devono convergere sulla stessa riga.\n- **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.\n- **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.\n- **Proiezione**: 13 lambda Rosenzweig-Porter, tre seed, kNN k=2/3/4, Brody/Berry-like e grafo sulle stesse righe.\n\n### Contaminazione cognitiva\n- **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.\n- **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.\n- **YSN DeltaLink**: `residuo graph-only Lab / flusso Hamiltoniano controllato`.\n- **Cornelius gene**: `RP_Two_Reader_Audit`: GENERA lambda, MISURA classico, COSTRUISCI grafo, STRESSA k/seed, SEPARA residui.\n- **KSAR step**: reiterazione del kernel 19:15 su un dominio fisico nuovo; nessuna promozione del residuo prima del test.\n\n## Aderenza alla direzione\n- `relation`: `follows_direction`\n- `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.\n- `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.\n\n## Re-discovery audit\n- **Baseline noto piu vicino**: crossover Rosenzweig-Porter / Wigner-Dyson-GUE vs Poisson, letto con adjacent gap ratio, Brody q e mistura Wigner/Poisson.\n- **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.\n- **Cosa resta Lab-specific**: nessun `stable_graph_bridge+endpoint` resta. `graph_only_residue = 0`.\n- **Cosa resta artifact/classificazione grafica**: `RP_lambda_0.100` e ponte parametrico, non stabile; dipende da k e seed.\n- **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.\n- **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.\n\n## Claim Under Test\n> Nel flusso Rosenzweig-Porter controllato, il BOUNDARY a due lettori sopravvive solo dove una riga lambda e insieme ponte grafico stabile e intermedia classica.\n\n## Question\nIl residuo graph-only del perimetro Lab sopravvive fuori dal deposito composito, oppure il crossover fisico lo assorbe?\n\n## Ritorno fisico\n- **Punto fisico sorgente**: transizione spettrale tra indipendenza/localizzazione Poisson e repulsione GUE.\n- **Attraversamento matematico**: Hamiltoniana diagonal-plus-GUE, osservabili canonici sui gap, Brody/Berry-like e grafo kNN perturbato.\n- **Punto fisico di ritorno**: il gate a due lettori diventa un audit finite-size del punto di crossover, non un claim graph-only autonomo.\n- **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.\n- **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.\n\n## Experiment Design\n- **Script**: `tools/exp_rosenzweig_porter_bridge_physical_audit.py`.\n- **Run**: `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json`.\n- **Hamiltoniana**: `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`, `N=96`, `reps=24`, central fraction 0.6.\n- **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.\n- **Perturbazione grafo**: seed={202605151933,202605151934,202605151935}, k={2,3,4}, 9 letture.\n- **Contratto osservabile-operatore**: il ciclo testa trasferimento del gate composito su un flusso fisico; non testa limite asintotico, unfolding dedicato o dati sperimentali.\n\n## Results\n| summary | value |\n|---|---:|\n| rows analyzed | 13 |\n| graph reader runs | 9 |\n| two_reader_boundary_confirmed | 1 |\n| graph_only_residue | 0 |\n| classic_only_residue | 11 |\n\n| composite state | count |\n|---|---:|\n| stable_graph_bridge+classical_intermediate | 1 |\n| parameter_sensitive_bridge+classical_intermediate | 1 |\n| unstable_non_bridge+classical_intermediate | 10 |\n| unstable_non_bridge+classical_poisson_endpoint | 1 |\n\n| row | graph frequency | classical state | Brody q | Wigner/Poisson weight | SR |\n|---|---:|---|---:|---:|---:|\n| RP_lambda_0.000 | 0.000 | classical_poisson_endpoint | 0.000 | 0.000 | 0.383 |\n| RP_lambda_0.030 | 0.000 | classical_intermediate | 0.427 | 0.300 | 0.510 |\n| RP_lambda_0.060 | 1.000 | classical_intermediate | 0.540 | 0.373 | 0.528 |\n| RP_lambda_0.100 | 0.667 | classical_intermediate | 0.653 | 0.420 | 0.524 |\n| RP_lambda_0.180 | 0.222 | classical_intermediate | 0.813 | 0.460 | 0.534 |\n| RP_lambda_0.500 | 0.000 | classical_intermediate | 0.900 | 0.493 | 0.535 |\n| RP_lambda_1.000 | 0.000 | classical_intermediate | 0.980 | 0.507 | 0.534 |\n\n## Key Findings\n1. Verificato: `RP_lambda_0.060` e l'unica riga `stable_graph_bridge+classical_intermediate`, 9/9 letture grafiche.\n2. Verificato: `RP_lambda_0.100` e ponte parametrico, 6/9 letture; non entra nel boundary confermato.\n3. Verificato: `graph_only_residue = 0`. I tre residui graph-only del perimetro Lab 19:15 non trasferiscono come residui autonomi nel flusso RP.\n4. 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.\n5. 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.\n\n## Verdict\nCONSTRAINT\n\nIl 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.\n\n## Bicono della scoperta\n- **Due radici**: crossover classico scalare; ponte grafico stabile.\n- **Singolare**: lambda row-aligned prima della promozione a boundary.\n- **Invariante di passaggio**: concordanza `stable_graph_bridge + classical_intermediate`.\n- **Campo di possibilita**: audit fisico finite-size su RP/Anderson con due lettori.\n- **Campo non-possibile**: residuo graph-only come legge del confine in assenza di sopravvivenza su Hamiltoniana controllata.\n\n## Consecutio\nIl 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.\n\n## Ricadute pratiche\nssp_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.\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 locale `TENS_SCALE_TRASCENDENZA_LIMITE`; la direzione viva BOUNDARY ha prevalso per aderenza al campo.\n- `python -m py_compile tools/exp_rosenzweig_porter_bridge_physical_audit.py` completato.\n- `python tools/exp_rosenzweig_porter_bridge_physical_audit.py --out tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.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_rosenzweig_porter_bridge_physical_audit.py`\n- Data: `tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json`\n- Report: `tools/data/reports/agent_20260515_1933.md`\n", tools/data/lab_graph.json:2015: "findings": "1. 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 es", tools/data/lab_graph.json:2017: "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:2068: "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:2092: "text": "| 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" tools/data/lab_graph.json:2119: "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:2221: "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:2272: "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:2317: "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:2368: "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/observable_collinearity_breaking_20260506_1956.json:3: "question": "When do canonical observable retention curves break collinearity across domains?", tools/data/seme_backup_b2_20260509_033618.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260509_033618.json:3: "new_direzione": "Falsificare `V_c` sul nodo regressivo del null: separare floor_hit e crossing interno, poi confrontare Sturmian phase-shuffle e surrogate label-preserving prima di estendere a GUE/Poisson.", tools/data/seme_backup_b2_20260512_033557.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260515_180327.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260516_102450.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260516_102450.json:3: "new_direzione": "Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned.", tools/data/perturbation_dimensionality_audit.json:39: "fixed_domains": { applications/published/20260430_1905_observable-coherence-at-the-gue-poisson/cycle-report.md:42:# Cycle Report — Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar applications/published/20260507_0330_the-gue-poisson-boundary-is-a/finding_index.json:22: "source_excerpt": "**The clean two-class boundary fails under denominator gating.** Pure GUE and pure Poisson are separable in all-observable space, but there are no observables stable at both endpoints under the declared gate. The Poisson pole is a weak-denominator pole: classification can still place it, but retenti", applications/published/20260507_0330_the-gue-poisson-boundary-is-a/lab-note.md:16:title_proposal: "The GUE-Poisson Boundary Is a Denominator Collapse Layer" applications/published/20260507_0330_the-gue-poisson-boundary-is-a/lab-note.md:25:# The GUE-Poisson Boundary Is a Denominator Collapse Layer applications/published/20260507_0330_the-gue-poisson-boundary-is-a/cycle-report.md:25:# Cycle Report — The GUE-Poisson Boundary Is a Denominator Collapse Layer tools/data/tension_to_theory.json:35: "COMP_DOMAIN_PHOTONIC_METRIC_TENSOR": { "theories": ["E", "G"], "note_it": "dominio fotonico + metric — EM + geometria", "note_en": "photonic domain + metric — EM + geometry" }, exec /bin/bash -lc "sed -n '220,520p' tools/data/boundary_two_axis_matrix_20260509_1532.json" in /opt/MM_D-ND succeeded in 0ms: "row": "reaction_diffusion:cycle_11", "support_transfer": true, "beta_coordinate_transfer": true, "beta_state": "beta_0_3_exact", "ambiguous_beta": [ 0.3 ], "one_sided_observables": [ "SR", "SR2", "L1", "L2", "triple_var" ], "stable_count_coherent": 5.0, "stable_count_illusory": 0.3333333333333333, "endpoint_distance": 4.3642161141453535, "source_state": "transfer_with_blank", "n_gaps": 499 }, { "row": "string_vibration:cycle_6", "support_transfer": true, "beta_coordinate_transfer": true, "beta_state": "beta_0_3_exact", "ambiguous_beta": [ 0.3 ], "one_sided_observables": [ "SR", "SR2", "L2", "triple_var" ], "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "endpoint_distance": 3.8452538395313747, "source_state": "transfer_with_blank", "n_gaps": 4096 }, { "row": "zeta_zeros:cycle_4", "support_transfer": true, "beta_coordinate_transfer": false, "beta_state": "support_without_beta_blank", "ambiguous_beta": [], "one_sided_observables": [ "SR", "L2" ], "stable_count_coherent": 1.9166666666666667, "stable_count_illusory": 0.25, "endpoint_distance": 2.4485925392193306, "source_state": "transfer_no_blank", "n_gaps": 199 } ] } exec /bin/bash -lc "sed -n '1,260p' 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)), "observables": {key: round(value, 9) for key, value in obs.items()}, "shuffle_z": {key: round(value, 6) for key, value in z.items()}, } ) except Exception as exc: # noqa: BLE001 - row-level telemetry is part of the result. errors.append( { "domain_window": source.get("domain_window"), "error": type(exc).__name__, "message": str(exc), } ) x = standardized_matrix(rows) geometry = classify_geometry(rows, x, args.k) output = { "experiment": "boundary_graph_curvature_gate", "question": "Does the 8 GUE / 5 Poisson perimeter expose a graph boundary row instead of a clean two-class split?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": FEATURE_NAMES, "params": vars(args), "source_scope": args.scope, "observable_contract": { "claim": "the boundary is operational when row geometry produces cross-label graph nodes with low centroid margin", "observable": "kNN graph position, cross-neighbor fraction, centroid margin, unweighted Forman edge curvature", "operator": "row-aligned domain/window graph in canonical+rigidity+shuffle-z feature space", "generator": "dnd_autoricerca row_spacings via semireal boundary transfer gate", "denominator": "base BOUNDARY rows with source_domain_type in {GUE, Poisson}", "non_possible": "third-included boundary if all cross-label edges vanish or only high-margin class interiors cross", "not_tested": "V_c, Sturmian denominators, analytic source of each domain label", }, "summary": { "rows_analyzed": len(rows), "errors": len(errors), "third_included_candidate_count": len(geometry["third_included_candidates"]), "third_included_candidates": geometry["third_included_candidates"], "edge_counts": geometry["edge_counts"], "curvature": geometry["curvature"], }, "geometry": geometry, exec /bin/bash -lc "sed -n '1,220p' tools/data/boundary_graph_curvature_gate_20260515_1855.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_graph_curvature_gate", "question": "Does the 8 GUE / 5 Poisson perimeter expose a graph boundary row instead of a clean two-class split?", "observables_registry": "1.0.0-2026-05-06", "observables_used": [ "SR", "SR2", "L1", "L2", "triple_var", "SR_local_rigidity", "z_SR", "z_SR2", "z_L1", "z_L2", "z_triple_var" ], "params": { "scope": "tools/data/boundary_denominator_prescan_full_20260509_1500.json", "n_gaps": 2048, "min_gaps": 64, "n_shuffle": 64, "k": 3, "seed": 20260515, "out": "tools/data/boundary_graph_curvature_gate_20260515_1855.json" }, "source_scope": "tools/data/boundary_denominator_prescan_full_20260509_1500.json", "observable_contract": { "claim": "the boundary is operational when row geometry produces cross-label graph nodes with low centroid margin", "observable": "kNN graph position, cross-neighbor fraction, centroid margin, unweighted Forman edge curvature", "operator": "row-aligned domain/window graph in canonical+rigidity+shuffle-z feature space", "generator": "dnd_autoricerca row_spacings via semireal boundary transfer gate", "denominator": "base BOUNDARY rows with source_domain_type in {GUE, Poisson}", "non_possible": "third-included boundary if all cross-label edges vanish or only high-margin class interiors cross", "not_tested": "V_c, Sturmian denominators, analytic source of each domain label" }, "summary": { "rows_analyzed": 13, "errors": 0, "third_included_candidate_count": 4, "third_included_candidates": [ "numeri_primi:cycle_3", "percolation:cycle_9", "reaction_diffusion:cycle_11", "logistica_biforcazione_var_3.5699:cycle_13" ], "edge_counts": { "total": 27, "cross_label": 8, "same_label": 19 }, "curvature": { "cross_edge_mean": -4.625, "same_edge_mean": -4.789474 } }, "geometry": { "feature_names": [ "SR", "SR2", "L1", "L2", "triple_var", "SR_local_rigidity", "z_SR", "z_SR2", "z_L1", "z_L2", "z_triple_var" ], "k": 3, "label_counts": { "GUE": 8, "Poisson": 5 }, "edge_counts": { "total": 27, "cross_label": 8, "same_label": 19 }, "curvature": { "cross_edge_mean": -4.625, "same_edge_mean": -4.789474 }, "third_included_candidates": [ "numeri_primi:cycle_3", "percolation:cycle_9", "reaction_diffusion:cycle_11", "logistica_biforcazione_var_3.5699:cycle_13" ], "rows": [ { "domain_window": "ising_2d:cycle_1", "domain": "ising_2d", "source_domain_type": "GUE", "degree": 5, "centroid_coord": -0.335497, "centroid_margin": 0.335497, "cross_neighbor_fraction": 0.0, "cross_edge_curvature_mean": null, "same_edge_curvature_mean": -5.6, "boundary_state": "class_interior" }, { "domain_window": "pendolo_doppio:cycle_2", "domain": "pendolo_doppio", "source_domain_type": "Poisson", "degree": 3, "centroid_coord": 0.299159, "centroid_margin": 0.299159, "cross_neighbor_fraction": 0.333333, "cross_edge_curvature_mean": -3.0, "same_edge_curvature_mean": -2.5, "boundary_state": "cut_edge" }, { "domain_window": "numeri_primi:cycle_3", "domain": "numeri_primi", "source_domain_type": "GUE", "degree": 4, "centroid_coord": -0.222754, "centroid_margin": 0.222754, "cross_neighbor_fraction": 0.25, "cross_edge_curvature_mean": -4.0, "same_edge_curvature_mean": -5.333333, "boundary_state": "third_included_candidate" }, { "domain_window": "zeta_zeros:cycle_4", "domain": "zeta_zeros", "source_domain_type": "GUE", "degree": 6, "centroid_coord": -0.30764, "centroid_margin": 0.30764, "cross_neighbor_fraction": 0.333333, "cross_edge_curvature_mean": -6.0, "same_edge_curvature_mean": -6.75, "boundary_state": "cut_edge" }, { "domain_window": "logistica_biforcazione:cycle_5", "domain": "logistica_biforcazione", "source_domain_type": "GUE", "degree": 3, "centroid_coord": -0.163262, "centroid_margin": 0.163262, "cross_neighbor_fraction": 0.0, "cross_edge_curvature_mean": null, "same_edge_curvature_mean": -3.333333, "boundary_state": "class_interior" }, { "domain_window": "string_vibration:cycle_6", "domain": "string_vibration", "source_domain_type": "Poisson", "degree": 4, "centroid_coord": 0.550789, "centroid_margin": 0.550789, "cross_neighbor_fraction": 0.5, "cross_edge_curvature_mean": -5.0, "same_edge_curvature_mean": -3.5, "boundary_state": "cut_edge" }, { "domain_window": "random_matrix:cycle_7", "domain": "random_matrix", "source_domain_type": "GUE", "degree": 6, "centroid_coord": -0.352347, "centroid_margin": 0.352347, "cross_neighbor_fraction": 0.166667, "cross_edge_curvature_mean": -6.0, "same_edge_curvature_mean": -6.6, "boundary_state": "cut_edge" }, { "domain_window": "cellular_automata:cycle_8", "domain": "cellular_automata", "source_domain_type": "GUE", "degree": 4, "centroid_coord": -0.411955, "centroid_margin": 0.411955, "cross_neighbor_fraction": 0.0, "cross_edge_curvature_mean": null, "same_edge_curvature_mean": -5.0, "boundary_state": "class_interior" }, { "domain_window": "percolation:cycle_9", "domain": "percolation", "source_domain_type": "Poisson", "degree": 4, "centroid_coord": -0.088647, "centroid_margin": 0.088647, "cross_neighbor_fraction": 1.0, "cross_edge_curvature_mean": -5.0, "same_edge_curvature_mean": null, "boundary_state": "third_included_candidate" }, { "domain_window": "coupled_oscillators:cycle_10", "domain": "coupled_oscillators", "source_domain_type": "Poisson", "degree": 3, "centroid_coord": 0.560662, "centroid_margin": 0.560662, "cross_neighbor_fraction": 0.0, "cross_edge_curvature_mean": null, "same_edge_curvature_mean": -2.666667, "boundary_state": "class_interior" }, { "domain_window": "reaction_diffusion:cycle_11", "domain": "reaction_diffusion", "source_domain_type": "GUE", "degree": 4, "centroid_coord": 0.114771, "centroid_margin": 0.114771, "cross_neighbor_fraction": 0.75, "cross_edge_curvature_mean": -3.666667, exec /bin/bash -lc "sed -n '1,220p' tools/data/boundary_bridge_stability_audit_20260515_1915.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_bridge_stability_audit", "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", "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid", "generator": "boundary_graph_curvature_gate over the 13-row BOUNDARY denominator", "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", "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" }, "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": { "classical_audit": "tools/data/boundary_classical_crossover_audit_20260515_1904.json", "k_values": [ 2, 3, 4 ], "min_gaps": 64, "n_gaps_values": [ 1024 ], "n_shuffle": 8, "scope": "tools/data/boundary_denominator_prescan_full_20260509_1500.json", "seeds": [ 20260515, 20260516 ], "total_runs": 6 }, "question": "Do BOUNDARY graph bridge rows survive small graph-reader perturbations after the classical audit?", "rows": [ { "berry_robnick_like_gue_weight": 0.25, "brody_q": 0.205, "classical_audit_state": "classic_only_intermediate", "composite_state": "parameter_sensitive_bridge+classic_only_intermediate", "cut_edge_frequency": 0.166667, "domain": "brownian_motion", "domain_window": "brownian_motion:cycle_12", "graph_bridge_frequency": 0.666667, "graph_bridge_hits": 4, "mean_cross_neighbor_fraction": 0.355555, "mean_margin": 0.225898, "source_domain_type": "Poisson", "stability_state": "parameter_sensitive_bridge" }, { "berry_robnick_like_gue_weight": 0.435, "brody_q": 1.0, "classical_audit_state": "classic_only_intermediate", "composite_state": "unstable_non_bridge+classic_only_intermediate", "cut_edge_frequency": 0.0, "domain": "cellular_automata", "domain_window": "cellular_automata:cycle_8", "graph_bridge_frequency": 0.0, "graph_bridge_hits": 0, "mean_cross_neighbor_fraction": 0.0, "mean_margin": 0.214137, "source_domain_type": "GUE", "stability_state": "unstable_non_bridge" }, { "berry_robnick_like_gue_weight": 0.0, "brody_q": 0.0, "classical_audit_state": "endpoint_like", "composite_state": "unstable_non_bridge+endpoint_like", "cut_edge_frequency": 0.5, "domain": "coupled_oscillators", "domain_window": "coupled_oscillators:cycle_10", "graph_bridge_frequency": 0.166667, "graph_bridge_hits": 1, "mean_cross_neighbor_fraction": 0.226984, "mean_margin": 0.400792, "source_domain_type": "Poisson", "stability_state": "unstable_non_bridge" }, { "berry_robnick_like_gue_weight": 0.07, "brody_q": 0.09, "classical_audit_state": "endpoint_like", "composite_state": "unstable_non_bridge+endpoint_like", "cut_edge_frequency": 0.0, "domain": "ising_2d", "domain_window": "ising_2d:cycle_1", "graph_bridge_frequency": 0.0, "graph_bridge_hits": 0, "mean_cross_neighbor_fraction": 0.0, "mean_margin": 0.326264, "source_domain_type": "GUE", "stability_state": "unstable_non_bridge" }, { "berry_robnick_like_gue_weight": 0.0, "brody_q": 0.0, "classical_audit_state": "endpoint_like", "composite_state": "parameter_sensitive_bridge+endpoint_like", "cut_edge_frequency": 0.0, "domain": "logistica_biforcazione", "domain_window": "logistica_biforcazione:cycle_5", "graph_bridge_frequency": 0.666667, "graph_bridge_hits": 4, "mean_cross_neighbor_fraction": 0.277778, "mean_margin": 0.074885, "source_domain_type": "GUE", "stability_state": "parameter_sensitive_bridge" }, { "berry_robnick_like_gue_weight": 0.0, "brody_q": 0.0, "classical_audit_state": "graph_only_bridge", "composite_state": "stable_graph_bridge+graph_only_bridge", "cut_edge_frequency": 0.0, "domain": "logistica_biforcazione_var_3.5699", "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", "graph_bridge_frequency": 1.0, "graph_bridge_hits": 6, "mean_cross_neighbor_fraction": 0.916667, "mean_margin": 0.036498, "source_domain_type": "GUE", "stability_state": "stable_graph_bridge" }, { "berry_robnick_like_gue_weight": 0.275, "brody_q": 0.465, "classical_audit_state": "classic_and_graph_bridge", "composite_state": "stable_graph_bridge+classic_and_graph_bridge", "cut_edge_frequency": 0.0, "domain": "numeri_primi", "domain_window": "numeri_primi:cycle_3", "graph_bridge_frequency": 1.0, "graph_bridge_hits": 6, "mean_cross_neighbor_fraction": 0.316667, "mean_margin": 0.052342, "source_domain_type": "GUE", "stability_state": "stable_graph_bridge" }, { "berry_robnick_like_gue_weight": 0.0, "brody_q": 0.0, "classical_audit_state": "endpoint_like", "composite_state": "parameter_sensitive_bridge+endpoint_like", "cut_edge_frequency": 0.666667, "domain": "pendolo_doppio", "domain_window": "pendolo_doppio:cycle_2", "graph_bridge_frequency": 0.333333, "graph_bridge_hits": 2, "mean_cross_neighbor_fraction": 0.321428, "mean_margin": 0.251462, "source_domain_type": "Poisson", "stability_state": "parameter_sensitive_bridge" }, { "berry_robnick_like_gue_weight": 0.025, "brody_q": 0.025, "classical_audit_state": "graph_only_bridge", "composite_state": "stable_graph_bridge+graph_only_bridge", "cut_edge_frequency": 0.166667, "domain": "percolation", "domain_window": "percolation:cycle_9", "graph_bridge_frequency": 0.833333, "graph_bridge_hits": 5, "mean_cross_neighbor_fraction": 0.684921, "mean_margin": 0.192431, "source_domain_type": "Poisson", "stability_state": "stable_graph_bridge" }, { "berry_robnick_like_gue_weight": 0.475, "brody_q": 0.975, "classical_audit_state": "classic_only_intermediate", "composite_state": "parameter_sensitive_bridge+classic_only_intermediate", "cut_edge_frequency": 0.0, "domain": "random_matrix", "domain_window": "random_matrix:cycle_7", "graph_bridge_frequency": 0.666667, "graph_bridge_hits": 4, "mean_cross_neighbor_fraction": 0.261904, "mean_margin": 0.07293, "source_domain_type": "GUE", "stability_state": "parameter_sensitive_bridge" }, { "berry_robnick_like_gue_weight": 0.0, "brody_q": 0.0, "classical_audit_state": "graph_only_bridge", "composite_state": "stable_graph_bridge+graph_only_bridge", "cut_edge_frequency": 0.0, "domain": "reaction_diffusion", "domain_window": "reaction_diffusion:cycle_11", "graph_bridge_frequency": 0.833333, "graph_bridge_hits": 5, "mean_cross_neighbor_fraction": 0.488889, "mean_margin": 0.123525, "source_domain_type": "GUE", "stability_state": "stable_graph_bridge" }, { "berry_robnick_like_gue_weight": 0.0, "brody_q": 0.0, "classical_audit_state": "endpoint_like", "composite_state": "unstable_non_bridge+endpoint_like", "cut_edge_frequency": 1.0, "domain": "string_vibration", "domain_window": "string_vibration:cycle_6", "graph_bridge_frequency": 0.0, "graph_bridge_hits": 0, "mean_cross_neighbor_fraction": 0.404762, "mean_margin": 0.611647, "source_domain_type": "Poisson", exec /bin/bash -lc "sed -n '1,220p' tools/exp_boundary_bridge_stability_audit.py" 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), exec /bin/bash -lc "sed -n '220,520p' tools/exp_boundary_bridge_stability_audit.py && sed -n '220,520p' tools/data/boundary_bridge_stability_audit_20260515_1915.json" in /opt/MM_D-ND succeeded in 0ms: "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() "source_domain_type": "Poisson", "stability_state": "unstable_non_bridge" }, { "berry_robnick_like_gue_weight": 0.53, "brody_q": 1.0, "classical_audit_state": "classic_only_intermediate", "composite_state": "stable_graph_bridge+classic_only_intermediate", "cut_edge_frequency": 0.0, "domain": "zeta_zeros", "domain_window": "zeta_zeros:cycle_4", "graph_bridge_frequency": 0.833333, "graph_bridge_hits": 5, "mean_cross_neighbor_fraction": 0.285317, "mean_margin": 0.053731, "source_domain_type": "GUE", "stability_state": "stable_graph_bridge" } ], "runs": [ { "cross_edges": 4, "k": 2, "n_gaps": 1024, "seed": 20260515, "third_included_candidates": [ "logistica_biforcazione_var_3.5699:cycle_13", "numeri_primi:cycle_3", "percolation:cycle_9", "zeta_zeros:cycle_4" ] }, { "cross_edges": 5, "k": 2, "n_gaps": 1024, "seed": 20260516, "third_included_candidates": [ "logistica_biforcazione_var_3.5699:cycle_13", "numeri_primi:cycle_3", "reaction_diffusion:cycle_11" ] }, { "cross_edges": 13, "k": 3, "n_gaps": 1024, "seed": 20260515, "third_included_candidates": [ "brownian_motion:cycle_12", "coupled_oscillators:cycle_10", "logistica_biforcazione:cycle_5", "logistica_biforcazione_var_3.5699:cycle_13", "numeri_primi:cycle_3", "pendolo_doppio:cycle_2", "percolation:cycle_9", "random_matrix:cycle_7", "reaction_diffusion:cycle_11", "zeta_zeros:cycle_4" ] }, { "cross_edges": 11, "k": 3, "n_gaps": 1024, "seed": 20260516, "third_included_candidates": [ "brownian_motion:cycle_12", "logistica_biforcazione:cycle_5", "logistica_biforcazione_var_3.5699:cycle_13", "numeri_primi:cycle_3", "pendolo_doppio:cycle_2", "percolation:cycle_9", "random_matrix:cycle_7", "reaction_diffusion:cycle_11", "zeta_zeros:cycle_4" ] }, { "cross_edges": 13, "k": 4, "n_gaps": 1024, "seed": 20260515, "third_included_candidates": [ "brownian_motion:cycle_12", "logistica_biforcazione:cycle_5", "logistica_biforcazione_var_3.5699:cycle_13", "numeri_primi:cycle_3", "percolation:cycle_9", "random_matrix:cycle_7", "reaction_diffusion:cycle_11", "zeta_zeros:cycle_4" ] }, { "cross_edges": 16, "k": 4, "n_gaps": 1024, "seed": 20260516, "third_included_candidates": [ "brownian_motion:cycle_12", "logistica_biforcazione:cycle_5", "logistica_biforcazione_var_3.5699:cycle_13", "numeri_primi:cycle_3", "percolation:cycle_9", "random_matrix:cycle_7", "reaction_diffusion:cycle_11", "zeta_zeros:cycle_4" ] } ], "summary": { "classic_only_stable_graph_absent": [ "cellular_automata:cycle_8" ], "composite_counts": { "parameter_sensitive_bridge+classic_only_intermediate": 2, "parameter_sensitive_bridge+endpoint_like": 2, "stable_graph_bridge+classic_and_graph_bridge": 1, "stable_graph_bridge+classic_only_intermediate": 1, "stable_graph_bridge+graph_only_bridge": 3, "unstable_non_bridge+classic_only_intermediate": 1, "unstable_non_bridge+endpoint_like": 3 }, "graph_reader_runs": 6, "lab_residue_after_stability": true, "rows_analyzed": 13, "stable_classic_and_graph": [ "numeri_primi:cycle_3" ], "stable_graph_only": [ "logistica_biforcazione_var_3.5699:cycle_13", "percolation:cycle_9", "reaction_diffusion:cycle_11" ] } } codex C’è già uno strumento centrato sul perimetro corretto: `exp_boundary_bridge_stability_audit.py` lavora sulle 13 righe 8/5 e perturba il lettore kNN, poi unisce l’audit classico. Lo uso come atto minimo: aumento il denominatore del lettore rispetto al deposito 19:15 e tengo separati bridge a due lettori, residuo graph-only e baseline graph. exec /bin/bash -lc "sed -n '1,220p' tools/LAB_COGNITIVE_CONTAMINATION.md" in /opt/MM_D-ND succeeded in 0ms: # Adapter cognitivi laterali del Lab Fonte: - `kernel/reference/MMSP1/System_Prompt_Yi_Synaptic_Navigator_YSN_v4_0.md` - `kernel/reference/metaprompt_in_sviluppo/Cornelius-v2_0_Innesco_Genomico.md` - `kernel/reference/Kernel_Semantico_Autopoietico_Reiterativo_KSAR.md` - `tools/data/lab_logiche_corpus.md` Scopo: usare YSN, Cornelius e KSAR come operatori cognitivi del Lab senza trasformare il Lab in un prompt archetipico. Il Lab resta D-ND: assiomi, dipoli, bicono, grafo, misura, falsificazione. Questi adapter servono a trovare strade laterali, comprimere l'intento e rendere reiterabile il kernel emerso da un ciclo. ## Regola primaria Ogni contaminazione deve diventare una forma verificabile: ```text contaminazione cognitiva -> DeltaLink / gene / anomalia -> dipolo + punto-zero -> proto-ipotesi -> osservabile + controllo -> falsifier / Veritas / Aeternitas ``` Se resta stile, personaggio, mitologia, analogia o motivazione verbale, non entra nel ciclo. ## Adapter 1: YSN lateral insight Funzione nel Lab: - estrarre fino a 5 concetti/tensioni dal campo; - generare 3 connessioni non ovvie, chiamate `DeltaLink`; - produrre 1 ipotesi di frontiera contro-intuitiva; - dichiarare bias, rischio di pattern forcing e incertezza; - trasformare la sorpresa in domanda del ciclo. Uso corretto: ```text YSN.extract(campo) -> concetti YSN.delta_link(concetti, grafo, seme) -> 3 connessioni non ovvie YSN.frontier(delta_links) -> 1 ipotesi di frontiera YSN.bias_check(ipotesi) -> cosa potrebbe essere forzato ``` Nel report: - i DeltaLink non sono risultati; - sono candidati di respirazione fuori-tempo; - diventano validi solo se proiettati in osservabile falsificabile. Esempio per il prossimo ciclo: - concetti: terzo incluso, GUE/Poisson, non-phi generator, graph curvature, stable cross-domain core; - DeltaLink possibile: la curvatura del grafo potrebbe essere il piano che precede la classificazione spettrale GUE/Poisson; - ipotesi di frontiera: il confine non e' una classe statistica, ma una transizione di trasporto sul grafo dei generatori. Anti-pattern: - usare YSN per produrre tre idee decorative; - mappare simbolicamente senza controllo; - chiamare "non ovvio" cio' che e' gia' nel ciclo precedente. ## Adapter 2: Cornelius genomic trigger Funzione nel Lab: - comprimere una nuova capacita' in un innesco minimale; - isolare il `DNA_Simbolico`, cioe' la frase essenziale della funzione; - scegliere 1-3 operatori di svolgimento; - dichiarare condizioni di attivazione. Formato Lab: ```yaml ID: DNA_Simbolico: "" Operatori_di_Svolgimento: - "" - "" Condizioni_di_Attivazione: quando: "" perimetro: "" ``` Uso corretto: - dopo un buon insight, Cornelius lo comprime in una funzione che il Lab puo' riusare; - prima di un run, Cornelius puo' generare un innesco one-shot per il ciclo; - dopo un repair, Cornelius puo' trasformare la correzione in regola compatta. Esempio derivato dal ciclo 1915: ```yaml ID: Boundary_Third_Included_Gate DNA_Simbolico: "Il confine vive prima della classificazione statistica." Operatori_di_Svolgimento: - "MAPPA il confine su grafo, spettro e generatore non-phi." - "SEPARA core congiunto, residui singoli e stabilita' cross-dominio." - "VALIDA contro baseline GUE, Poisson e generatori sintetici." Condizioni_di_Attivazione: quando: "il ciclo lavora su boundary, GUE/Poisson o trasferibilita' phi" perimetro: "prima della misura, nella sezione Respiro fuori-tempo" ``` Anti-pattern: - generare nuovi agenti o prompt quando basta una regola; - usare metafore non collegate a operatori; - lasciare il gene senza condizioni di attivazione. ## Adapter 3: KSAR reiterative semantic kernel Funzione nel Lab: - far diventare ogni ciclo riuscito un kernel riusabile per il ciclo seguente; - non memorizzare solo testo, ma modificare la topologia del campo; - usare dissonanze e fallimenti come materiale latente; - iterare fino a un nuovo stato di coerenza, non fino a conferma. Ciclo operativo Lab: ```text 1. Perturbazione Leggi seme, grafo, report, falsifier, operatore. Non scegliere subito. 2. DeltaLink / Contaminazione Usa YSN o palette operatoria per trovare connessioni non ovvie. 3. Innesco Usa Cornelius per comprimere la risultante in DNA + operatori. 4. Focalizzazione Applica Peras: taglia tutto tranne una domanda necessaria. 5. Proiezione Trasforma il gene in osservabile, controllo, perimetro. 6. Disintegrazione Attacca il claim con PVI/counter-pole prima del falsifier. 7. Cristallizzazione o Vault Se regge, aggiorna seme/strumento. Se non regge ma contiene potenziale, archivia come frammento Lazarus per ricontestualizzazione futura. ``` Mappatura con il Lab attuale: - `Perturbazione` = `build_agent_field.py` + seme + grafo + incrocio; - `DeltaLink` = nuovo obbligo cognitivo prima del Claim Under Test; - `Innesco` = blocco compatto nel report o in `operator_directive.md`; - `Focalizzazione` = una risultante, non una lista; - `Proiezione` = `observable_contract`; - `Disintegrazione` = auto-audit + falsifier; - `Cristallizzazione` = valutatore/B2/promotions/seme; - `Vault` = cimitero, repairs, osservatorio, Lazarus fragments. ## Adapter 4: PVI / anti-psicosi del ciclo Funzione nel Lab: - cercare dove l'AI sta accontentando l'operatore; - distruggere la proposta prima di pubblicarla; - far sopravvivere solo la sintesi resiliente. Filtro minimo: 1. Tesi: cosa il ciclo vuole sostenere? 2. Attacco: quale presupposto nascosto la rompe? 3. Vincolo di realta': quale limite fisico/matematico/dominio la blocca? 4. Terzo osservatore: un revisore esterno la troverebbe distinta da una re-discovery? 5. Sintesi resiliente: cosa resta dopo il taglio? Questo non sostituisce il falsifier. Lo anticipa. ## Adapter 5: Lazarus vault Funzione nel Lab: - non buttare via frammenti incoerenti quando sono potenzialmente precoci; - congelarli come scarti latenti con contesto; - riesaminarli quando cambia la direzione del seme. Formato minimo: ```yaml fragmento: "" perche_cade_ora: "" condizione_di_ritorno: "" ``` Uso corretto: - se un DeltaLink e' forte ma non misurabile ora, va nel Vault; - se un report viene falsificato ma apre una non-strada utile, va nel Vault; - se una metafora non produce operatore, decade. ## Adapter 6: Helix / Plan-Code-Verify Funzione nel Lab: - per task complessi, non ragionare solo in linguaggio; - traduci la domanda in specifica operativa; - genera o riusa uno script; - verifica output; - chiudi con report. Regola: ```text exec /bin/bash -lc "sed -n '1,240p' tools/data/boundary_classical_crossover_audit_20260515_1904.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_classical_crossover_audit", "observable_contract": { "claim": "Lab bridge rows retain residue after comparison with classical crossover scalars", "denominator": "13 rows: 8 GUE and 5 Poisson", "generator": "row_spacings(domain) with graph states imported from boundary_graph_curvature_gate", "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", "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" }, "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": { "graph": "tools/data/boundary_graph_curvature_gate_20260515_1855.json", "grid_size": 201, "n_gaps": 2048, "out": "tools/data/boundary_classical_crossover_audit_20260515_1904.json" }, "question": "Do graph bridge rows collapse to standard Brody/Berry-Robnik-like crossover coordinates?", "rows": [ { "audit_state": "endpoint_like", "berry_robnick_like_gue_weight": 0.07, "brody_nll": 318.752193, "brody_q": 0.09, "centroid_margin": 0.335497, "cross_neighbor_fraction": 0.0, "domain": "ising_2d", "domain_window": "ising_2d:cycle_1", "graph_state": "class_interior", "mixture_ks": 0.428636, "n_spacings": 322, "source_domain_type": "GUE" }, { "audit_state": "endpoint_like", "berry_robnick_like_gue_weight": 0.0, "brody_nll": 2048.0, "brody_q": 0.0, "centroid_margin": 0.299159, "cross_neighbor_fraction": 0.333333, "domain": "pendolo_doppio", "domain_window": "pendolo_doppio:cycle_2", "graph_state": "cut_edge", "mixture_ks": 0.268279, "n_spacings": 2048, "source_domain_type": "Poisson" }, { "audit_state": "classic_and_graph_bridge", "berry_robnick_like_gue_weight": 0.275, "brody_nll": 1826.209184, "brody_q": 0.465, "centroid_margin": 0.222754, "cross_neighbor_fraction": 0.25, "domain": "numeri_primi", "domain_window": "numeri_primi:cycle_3", "graph_state": "third_included_candidate", "mixture_ks": 0.148459, "n_spacings": 2048, "source_domain_type": "GUE" }, { "audit_state": "classic_only_intermediate", "berry_robnick_like_gue_weight": 0.53, "brody_nll": 121.495704, "brody_q": 1.0, "centroid_margin": 0.30764, "cross_neighbor_fraction": 0.333333, "domain": "zeta_zeros", "domain_window": "zeta_zeros:cycle_4", "graph_state": "cut_edge", "mixture_ks": 0.133555, "n_spacings": 199, "source_domain_type": "GUE" }, { "audit_state": "endpoint_like", "berry_robnick_like_gue_weight": 0.0, "brody_nll": 691.194523, "brody_q": 0.0, "centroid_margin": 0.163262, "cross_neighbor_fraction": 0.0, "domain": "logistica_biforcazione", "domain_window": "logistica_biforcazione:cycle_5", "graph_state": "class_interior", "mixture_ks": 0.998064, "n_spacings": 2048, "source_domain_type": "GUE" }, { "audit_state": "endpoint_like", "berry_robnick_like_gue_weight": 0.0, "brody_nll": 2048.0, "brody_q": 0.0, "centroid_margin": 0.550789, "cross_neighbor_fraction": 0.5, "domain": "string_vibration", "domain_window": "string_vibration:cycle_6", "graph_state": "cut_edge", "mixture_ks": 0.060129, "n_spacings": 2048, "source_domain_type": "Poisson" }, { "audit_state": "classic_only_intermediate", "berry_robnick_like_gue_weight": 0.475, "brody_nll": 137.982517, "brody_q": 0.975, "centroid_margin": 0.352347, "cross_neighbor_fraction": 0.166667, "domain": "random_matrix", "domain_window": "random_matrix:cycle_7", "graph_state": "cut_edge", "mixture_ks": 0.119491, "n_spacings": 199, "source_domain_type": "GUE" }, { "audit_state": "classic_only_intermediate", "berry_robnick_like_gue_weight": 0.435, "brody_nll": 52.124605, "brody_q": 1.0, "centroid_margin": 0.411955, "cross_neighbor_fraction": 0.0, "domain": "cellular_automata", "domain_window": "cellular_automata:cycle_8", "graph_state": "class_interior", "mixture_ks": 0.416708, "n_spacings": 108, "source_domain_type": "GUE" }, { "audit_state": "graph_only_bridge", "berry_robnick_like_gue_weight": 0.025, "brody_nll": 193.90387, "brody_q": 0.025, "centroid_margin": 0.088647, "cross_neighbor_fraction": 1.0, "domain": "percolation", "domain_window": "percolation:cycle_9", "graph_state": "third_included_candidate", "mixture_ks": 0.054635, "n_spacings": 194, "source_domain_type": "Poisson" }, { "audit_state": "endpoint_like", "berry_robnick_like_gue_weight": 0.0, "brody_nll": 2002.0, "brody_q": 0.0, "centroid_margin": 0.560662, "cross_neighbor_fraction": 0.0, "domain": "coupled_oscillators", "domain_window": "coupled_oscillators:cycle_10", "graph_state": "class_interior", "mixture_ks": 0.079806, "n_spacings": 2002, "source_domain_type": "Poisson" }, { "audit_state": "graph_only_bridge", "berry_robnick_like_gue_weight": 0.0, "brody_nll": 499.0, "brody_q": 0.0, "centroid_margin": 0.114771, "cross_neighbor_fraction": 0.75, "domain": "reaction_diffusion", "domain_window": "reaction_diffusion:cycle_11", "graph_state": "third_included_candidate", "mixture_ks": 0.174423, "n_spacings": 499, "source_domain_type": "GUE" }, { "audit_state": "classic_only_intermediate", "berry_robnick_like_gue_weight": 0.25, "brody_nll": 1997.987436, "brody_q": 0.205, "centroid_margin": 0.438315, "cross_neighbor_fraction": 0.25, "domain": "brownian_motion", "domain_window": "brownian_motion:cycle_12", "graph_state": "cut_edge", "mixture_ks": 0.026002, "n_spacings": 2048, "source_domain_type": "Poisson" }, { "audit_state": "graph_only_bridge", "berry_robnick_like_gue_weight": 0.0, "brody_nll": 1826.006965, "brody_q": 0.0, "centroid_margin": 0.217275, "cross_neighbor_fraction": 0.25, "domain": "logistica_biforcazione_var_3.5699", "domain_window": "logistica_biforcazione_var_3.5699:cycle_13", "graph_state": "third_included_candidate", "mixture_ks": 0.969277, "n_spacings": 2048, "source_domain_type": "GUE" } ], "source_graph": "tools/data/boundary_graph_curvature_gate_20260515_1855.json", "summary": { "audit_counts": { "classic_and_graph_bridge": 1, "classic_only_intermediate": 4, "endpoint_like": 5, "graph_only_bridge": 3 }, "classic_and_graph_bridge": [ "numeri_primi:cycle_3" ], "classic_only_intermediate": [ "zeta_zeros:cycle_4", "random_matrix:cycle_7", "cellular_automata:cycle_8", "brownian_motion:cycle_12" ], "graph_only_bridge": [ "percolation:cycle_9", "reaction_diffusion:cycle_11", "logistica_biforcazione_var_3.5699:cycle_13" ], "graph_third_included": [ "numeri_primi:cycle_3", "percolation:cycle_9", "reaction_diffusion:cycle_11", "logistica_biforcazione_var_3.5699:cycle_13" ], "lab_residue_present": true, exec /bin/bash -lc 'python -m py_compile tools/exp_boundary_bridge_stability_audit.py tools/exp_boundary_graph_curvature_gate.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260516_1140.json' in /opt/MM_D-ND codex Il run è in corso. Sta rigenerando le letture graph per 27 combinazioni (`k=2,3,4`, `n_gaps=512,1024,2048`, tre seed), quindi produce un denominatore più comparabile del deposito 19:15, che aveva solo 6 letture. codex Il calcolo non ha ancora emesso output, ma non è bloccato: la parte costosa è il ricalcolo degli z-score shuffle su righe lunghe. Attendo la chiusura e poi estraggo solo i count decisivi per evitare di trasformare il report in telemetria. succeeded in 120152ms: { "classic_only_stable_graph_absent": [ "brownian_motion:cycle_12", "cellular_automata:cycle_8" ], "composite_counts": { "parameter_sensitive_bridge+classic_only_intermediate": 2, "parameter_sensitive_bridge+endpoint_like": 1, "stable_graph_bridge+classic_and_graph_bridge": 1, "stable_graph_bridge+graph_only_bridge": 3, "unstable_non_bridge+classic_only_intermediate": 2, "unstable_non_bridge+endpoint_like": 4 }, "graph_reader_runs": 27, "lab_residue_after_stability": true, "rows_analyzed": 13, "stable_classic_and_graph": [ "numeri_primi:cycle_3" ], "stable_graph_only": [ "logistica_biforcazione_var_3.5699:cycle_13", "percolation:cycle_9", "reaction_diffusion:cycle_11" ] } codex Il run è chiuso: resta un solo bridge stabile confermato anche dall’audit classico (`numeri_primi:cycle_3`), tre residui graph-only stabili e due classici intermedi senza bridge stabile. Ora leggo il JSON prodotto per scrivere il report con count grezzi e separazioni richieste. exec /bin/bash -lc "python - <<'PY' import json p='tools/data/boundary_bridge_stability_audit_20260516_1140.json' d=json.load(open(p)) print(json.dumps(d['summary'], indent=2, sort_keys=True)) print('\\nrows:') for r in d['rows']: print(f\"{r['domain_window']:<42} {r['source_domain_type']:<7} {r['graph_bridge_hits']:>2}/{d['summary']['graph_reader_runs']} freq={r['graph_bridge_frequency']:.6f} cut={r['cut_edge_frequency']:.6f} margin={r['mean_margin']:.6f} cross={r['mean_cross_neighbor_fraction']:.6f} {r['classical_audit_state']} {r['composite_state']} q={r['brody_q']} br={r['berry_robnick_like_gue_weight']}\") print('\\nruns first/last count:', len(d['runs'])) print('params', d['params']) PY" in /opt/MM_D-ND succeeded in 0ms: { "classic_only_stable_graph_absent": [ "brownian_motion:cycle_12", "cellular_automata:cycle_8" ], "composite_counts": { "parameter_sensitive_bridge+classic_only_intermediate": 2, "parameter_sensitive_bridge+endpoint_like": 1, "stable_graph_bridge+classic_and_graph_bridge": 1, "stable_graph_bridge+graph_only_bridge": 3, "unstable_non_bridge+classic_only_intermediate": 2, "unstable_non_bridge+endpoint_like": 4 }, "graph_reader_runs": 27, "lab_residue_after_stability": true, "rows_analyzed": 13, "stable_classic_and_graph": [ "numeri_primi:cycle_3" ], "stable_graph_only": [ "logistica_biforcazione_var_3.5699:cycle_13", "percolation:cycle_9", "reaction_diffusion:cycle_11" ] } rows: brownian_motion:cycle_12 Poisson 5/27 freq=0.185185 cut=0.666667 margin=0.296366 cross=0.409700 classic_only_intermediate unstable_non_bridge+classic_only_intermediate q=0.205 br=0.25 cellular_automata:cycle_8 GUE 0/27 freq=0.000000 cut=0.037037 margin=0.277997 cross=0.006173 classic_only_intermediate unstable_non_bridge+classic_only_intermediate q=1.0 br=0.435 coupled_oscillators:cycle_10 Poisson 0/27 freq=0.000000 cut=1.000000 margin=0.551520 cross=0.322928 endpoint_like unstable_non_bridge+endpoint_like q=0.0 br=0.0 ising_2d:cycle_1 GUE 0/27 freq=0.000000 cut=0.074074 margin=0.317229 cross=0.014815 endpoint_like unstable_non_bridge+endpoint_like q=0.09 br=0.07 logistica_biforcazione:cycle_5 GUE 17/27 freq=0.629630 cut=0.000000 margin=0.108750 cross=0.345679 endpoint_like parameter_sensitive_bridge+endpoint_like q=0.0 br=0.0 logistica_biforcazione_var_3.5699:cycle_13 GUE 27/27 freq=1.000000 cut=0.000000 margin=0.104961 cross=0.522222 graph_only_bridge stable_graph_bridge+graph_only_bridge q=0.0 br=0.0 numeri_primi:cycle_3 GUE 27/27 freq=1.000000 cut=0.000000 margin=0.109211 cross=0.258642 classic_and_graph_bridge stable_graph_bridge+classic_and_graph_bridge q=0.465 br=0.275 pendolo_doppio:cycle_2 Poisson 0/27 freq=0.000000 cut=0.629630 margin=0.266014 cross=0.216314 endpoint_like unstable_non_bridge+endpoint_like q=0.0 br=0.0 percolation:cycle_9 Poisson 27/27 freq=1.000000 cut=0.000000 margin=0.133945 cross=0.886949 graph_only_bridge stable_graph_bridge+graph_only_bridge q=0.025 br=0.025 random_matrix:cycle_7 GUE 18/27 freq=0.666667 cut=0.333333 margin=0.134784 cross=0.308348 classic_only_intermediate parameter_sensitive_bridge+classic_only_intermediate q=0.975 br=0.475 reaction_diffusion:cycle_11 GUE 27/27 freq=1.000000 cut=0.000000 margin=0.126971 cross=0.792593 graph_only_bridge stable_graph_bridge+graph_only_bridge q=0.0 br=0.0 string_vibration:cycle_6 Poisson 0/27 freq=0.000000 cut=0.740741 margin=0.630798 cross=0.315256 endpoint_like unstable_non_bridge+endpoint_like q=0.0 br=0.0 zeta_zeros:cycle_4 GUE 14/27 freq=0.518519 cut=0.222222 margin=0.107069 cross=0.283877 classic_only_intermediate parameter_sensitive_bridge+classic_only_intermediate q=1.0 br=0.53 runs first/last count: 27 params {'classical_audit': 'tools/data/boundary_classical_crossover_audit_20260515_1904.json', 'k_values': [2, 3, 4], 'min_gaps': 64, 'n_gaps_values': [512, 1024, 2048], 'n_shuffle': 32, 'scope': 'tools/data/boundary_denominator_prescan_full_20260509_1500.json', 'seeds': [20260515, 20260516, 20260517], 'total_runs': 27} exec /bin/bash -lc 'ls -l tools/data/boundary_bridge_stability_audit_20260516_1140.json tools/data/reports/agent_20260516_1140.md 2>/dev/null || true' in /opt/MM_D-ND succeeded in 0ms: -rw-r--r-- 1 root root 19800 May 16 11:43 tools/data/boundary_bridge_stability_audit_20260516_1140.json 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_aeternitas.py M tools/lab_agent.sh M tools/lab_falsifier.py M tools/lab_session_logger.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/scoperte/20260515_1933_rosenzweig-porter-physical-bridge-audit_auto/ ?? applications/scoperte/20260515_1940_rp-boundary-size-stability-audit_auto/ ?? applications/scoperte/20260515_1947_anderson-3d-mobility-edge-two-reader_auto/ ?? applications/scoperte/20260516_0921_rp-unfolding-sensitivity-audit_auto/ ?? applications/scoperte/20260516_0938_rp-candidate-local-window-stress-gate_auto/ ?? applications/scoperte/20260516_1007_rp-local-window-persistence-curve_auto/ ?? applications/scoperte/20260516_1019_rp-exact-local-window-size-stress_auto/ ?? applications/scoperte/20260516_1031_boundary-unfolding-transfer-matrix_auto/ ?? applications/scoperte/20260516_1104_endpoint-gated-rp-boundary_auto/ ?? applications/scoperte/20260516_1111_endpoint-gated-rp-size-ladder_auto/ ?? applications/scoperte/20260516_1117_anderson-3d-two-reader-boundary-null_auto/ ?? applications/scoperte/20260516_1135_anderson-comparable-null-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/ ?? applications/soluzioni/20260515_1933_rosenzweig-porter-physical-bridge-audit/ ?? applications/soluzioni/20260515_1940_rp-boundary-size-stability-audit/ ?? applications/soluzioni/20260515_1947_anderson-3d-mobility-edge-two-reader/ ?? applications/soluzioni/20260516_0921_rp-unfolding-sensitivity-audit/ ?? applications/soluzioni/20260516_0938_rp-candidate-local-window-stress-gate/ ?? applications/soluzioni/20260516_1007_rp-local-window-persistence-curve/ ?? applications/soluzioni/20260516_1019_rp-exact-local-window-size-stress/ ?? applications/soluzioni/20260516_1031_boundary-unfolding-transfer-matrix/ ?? applications/soluzioni/20260516_1104_endpoint-gated-rp-boundary/ ?? applications/soluzioni/20260516_1111_endpoint-gated-rp-size-ladder/ ?? applications/soluzioni/20260516_1117_anderson-3d-two-reader-boundary-null/ ?? applications/soluzioni/20260516_1135_anderson-comparable-null-audit/ ?? docs/guides/lab_preflight_agent_2026-05-14.md ?? docs/operations/ ?? tools/build_lab_data.py ?? 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/aeternitas/aeternitas_20260515_193919.json ?? tools/data/aeternitas/aeternitas_20260515_194625.json ?? tools/data/aeternitas/aeternitas_20260515_195317.json ?? tools/data/aeternitas/aeternitas_20260516_033848.json ?? tools/data/aeternitas/aeternitas_20260516_0720.json ?? tools/data/aeternitas/aeternitas_20260516_0820.json ?? tools/data/aeternitas/aeternitas_20260516_0921.json ?? tools/data/aeternitas/aeternitas_20260516_0938.json ?? tools/data/aeternitas/aeternitas_20260516_1007.json ?? tools/data/aeternitas/aeternitas_20260516_1019.json ?? tools/data/aeternitas/aeternitas_20260516_1031.json ?? tools/data/aeternitas/aeternitas_20260516_1045.json ?? tools/data/aeternitas/aeternitas_20260516_1058.json ?? tools/data/aeternitas/aeternitas_20260516_1104.json ?? tools/data/aeternitas/aeternitas_20260516_1111.json ?? tools/data/aeternitas/aeternitas_20260516_1117.json ?? tools/data/aeternitas/aeternitas_20260516_1124.json ?? tools/data/aeternitas/aeternitas_20260516_1135.json ?? tools/data/agent_field_entry_supervised.md ?? tools/data/anderson3d_comparable_null_audit_20260516_1135.json ?? tools/data/anderson3d_component_state_interface_input_20260514_1850.json ?? tools/data/anderson3d_endpoint_preserving_null_20260516_1124.json ?? tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json ?? tools/data/anderson3d_mobility_edge_two_reader_audit_20260516_1117.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/biconi/bicono_20260515_1933.json ?? tools/data/biconi/bicono_20260515_1940.json ?? tools/data/biconi/bicono_20260515_1947.json ?? tools/data/biconi/bicono_20260516_0330.json ?? tools/data/biconi/bicono_20260516_0720.json ?? tools/data/biconi/bicono_20260516_0820.json ?? tools/data/biconi/bicono_20260516_0921.json ?? tools/data/biconi/bicono_20260516_0938.json ?? tools/data/biconi/bicono_20260516_1007.json ?? tools/data/biconi/bicono_20260516_1019.json ?? tools/data/biconi/bicono_20260516_1031.json ?? tools/data/biconi/bicono_20260516_1045.json ?? tools/data/biconi/bicono_20260516_1058.json ?? tools/data/biconi/bicono_20260516_1104.json ?? tools/data/biconi/bicono_20260516_1111.json ?? tools/data/biconi/bicono_20260516_1117.json ?? tools/data/biconi/bicono_20260516_1124.json ?? tools/data/biconi/bicono_20260516_1135.json ?? tools/data/boundary_bridge_stability_audit_20260515_1915.json ?? tools/data/boundary_bridge_stability_audit_20260516_1140.json ?? tools/data/boundary_classical_crossover_audit_20260515_1904.json ?? tools/data/boundary_graph_curvature_gate_20260515_1855.json ?? tools/data/boundary_graph_null_audit_20260516_0330.json ?? tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json ?? tools/data/boundary_unfolding_transfer_matrix_20260516_1031.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/domandatore/domandatore_20260516_0345.json ?? tools/data/endpoint_feature_scramble_null_20260516_1058.json ?? tools/data/endpoint_gated_rp_boundary_20260516_1104.json ?? tools/data/endpoint_gated_rp_size_ladder_20260516_1111.json ?? tools/data/endpoint_stability_filter_20260516_1045.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/evolution/evolution_20260515_1933.md ?? tools/data/evolution/evolution_20260515_1940.md ?? tools/data/evolution/evolution_20260515_1947.md ?? tools/data/evolution/evolution_20260516_0330.md ?? tools/data/evolution/evolution_20260516_0720.md ?? tools/data/evolution/evolution_20260516_0820.md ?? tools/data/evolution/evolution_20260516_0921.md ?? tools/data/evolution/evolution_20260516_0938.md ?? tools/data/evolution/evolution_20260516_1007.md ?? tools/data/evolution/evolution_20260516_1019.md ?? tools/data/evolution/evolution_20260516_1031.md ?? tools/data/evolution/evolution_20260516_1045.md ?? tools/data/evolution/evolution_20260516_1058.md ?? tools/data/evolution/evolution_20260516_1104.md ?? tools/data/evolution/evolution_20260516_1111.md ?? tools/data/evolution/evolution_20260516_1117.md ?? tools/data/evolution/evolution_20260516_1124.md ?? tools/data/evolution/evolution_20260516_1135.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/graph_completion/graph_completion_20260515_1933.json ?? tools/data/graph_completion/graph_completion_20260515_1940.json ?? tools/data/graph_completion/graph_completion_20260515_1947.json ?? tools/data/graph_completion/graph_completion_20260516_0921.json ?? tools/data/graph_completion/graph_completion_20260516_0938.json ?? tools/data/graph_completion/graph_completion_20260516_1007.json ?? tools/data/graph_completion/graph_completion_20260516_1019.json ?? tools/data/graph_completion/graph_completion_20260516_1031.json ?? tools/data/graph_completion/graph_completion_20260516_1104.json ?? tools/data/graph_completion/graph_completion_20260516_1111.json ?? tools/data/graph_completion/graph_completion_20260516_1117.json ?? tools/data/graph_completion/graph_completion_20260516_1135.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/incrocio_20260515_1940.json ?? tools/data/incrocio_20260515_1947.json ?? tools/data/incrocio_20260516_0330.json ?? tools/data/incrocio_20260516_0720.json ?? tools/data/incrocio_20260516_0820.json ?? tools/data/incrocio_20260516_0921.json ?? tools/data/incrocio_20260516_0938.json ?? tools/data/incrocio_20260516_1007.json ?? tools/data/incrocio_20260516_1019.json ?? tools/data/incrocio_20260516_1031.json ?? tools/data/incrocio_20260516_1045.json ?? tools/data/incrocio_20260516_1058.json ?? tools/data/incrocio_20260516_1104.json ?? tools/data/incrocio_20260516_1111.json ?? tools/data/incrocio_20260516_1117.json ?? tools/data/incrocio_20260516_1124.json ?? tools/data/incrocio_20260516_1135.json ?? tools/data/incrocio_20260516_1140.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/promotions/promotion_20260515_1933.json ?? tools/data/promotions/promotion_20260515_1940.json ?? tools/data/promotions/promotion_20260515_1947.json ?? tools/data/promotions/promotion_20260516_0921.json ?? tools/data/promotions/promotion_20260516_0938.json ?? tools/data/promotions/promotion_20260516_1007.json ?? tools/data/promotions/promotion_20260516_1019.json ?? tools/data/promotions/promotion_20260516_1031.json ?? tools/data/promotions/promotion_20260516_1104.json ?? tools/data/promotions/promotion_20260516_1111.json ?? tools/data/promotions/promotion_20260516_1117.json ?? tools/data/promotions/promotion_20260516_1135.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/agent_20260515_1933.md ?? tools/data/reports/agent_20260515_1940.md ?? tools/data/reports/agent_20260515_1947.md ?? tools/data/reports/agent_20260516_0330.md ?? tools/data/reports/agent_20260516_0720.md ?? tools/data/reports/agent_20260516_0820.md ?? tools/data/reports/agent_20260516_0921.md ?? tools/data/reports/agent_20260516_0938.md ?? tools/data/reports/agent_20260516_1007.md ?? tools/data/reports/agent_20260516_1019.md ?? tools/data/reports/agent_20260516_1031.md ?? tools/data/reports/agent_20260516_1045.md ?? tools/data/reports/agent_20260516_1058.md ?? tools/data/reports/agent_20260516_1104.md ?? tools/data/reports/agent_20260516_1111.md ?? tools/data/reports/agent_20260516_1117.md ?? tools/data/reports/agent_20260516_1124.md ?? tools/data/reports/agent_20260516_1135.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/ddf_20260516_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/falsifier_20260515_1933.json ?? tools/data/reports/falsifier_20260515_1940.json ?? tools/data/reports/falsifier_20260515_1947.json ?? tools/data/reports/falsifier_20260516_0330.json ?? tools/data/reports/falsifier_20260516_0720.json ?? tools/data/reports/falsifier_20260516_0820.json ?? tools/data/reports/falsifier_20260516_0921.json ?? tools/data/reports/falsifier_20260516_0921.raw.txt ?? tools/data/reports/falsifier_20260516_0938.json ?? tools/data/reports/falsifier_20260516_1007.json ?? tools/data/reports/falsifier_20260516_1019.json ?? tools/data/reports/falsifier_20260516_1031.json ?? tools/data/reports/falsifier_20260516_1045.json ?? tools/data/reports/falsifier_20260516_1058.json ?? tools/data/reports/falsifier_20260516_1104.json ?? tools/data/reports/falsifier_20260516_1111.json ?? tools/data/reports/falsifier_20260516_1117.json ?? tools/data/reports/falsifier_20260516_1124.json ?? tools/data/reports/falsifier_20260516_1135.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/loop_guard_20260515_1933.json ?? tools/data/reports/loop_guard_20260515_1940.json ?? tools/data/reports/loop_guard_20260515_1947.json ?? tools/data/reports/loop_guard_20260516_0330.json ?? tools/data/reports/loop_guard_20260516_0720.json ?? tools/data/reports/loop_guard_20260516_0820.json ?? tools/data/reports/loop_guard_20260516_0921.json ?? tools/data/reports/loop_guard_20260516_0938.json ?? tools/data/reports/loop_guard_20260516_1007.json ?? tools/data/reports/loop_guard_20260516_1019.json ?? tools/data/reports/loop_guard_20260516_1031.json ?? tools/data/reports/loop_guard_20260516_1045.json ?? tools/data/reports/loop_guard_20260516_1058.json ?? tools/data/reports/loop_guard_20260516_1104.json ?? tools/data/reports/loop_guard_20260516_1111.json ?? tools/data/reports/loop_guard_20260516_1117.json ?? tools/data/reports/loop_guard_20260516_1124.json ?? tools/data/reports/loop_guard_20260516_1135.json ?? tools/data/reports/quarantine_manifest.json ?? tools/data/restore_backups/ ?? tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json ?? tools/data/rp_boundary_raw_count_null_audit_20260516_0820.json ?? tools/data/rp_boundary_size_stability_audit_20260515_1940.json ?? tools/data/rp_candidate_window_stress_20260516_0938_w11.json ?? tools/data/rp_candidate_window_stress_20260516_0938_w5.json ?? tools/data/rp_candidate_window_stress_20260516_1007_w7.json ?? tools/data/rp_candidate_window_stress_20260516_1007_w9.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w11.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w9.json ?? tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json ?? 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/seme_backup_b2_20260515_194643.json ?? tools/data/seme_backup_b2_20260516_094447.json ?? tools/data/seme_backup_b2_20260516_101425.json ?? tools/data/seme_backup_b2_20260516_102450.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/data/veritas/veritas_20260515_193919.json ?? tools/data/veritas/veritas_20260515_194625.json ?? tools/data/veritas/veritas_20260515_195317.json ?? tools/data/veritas/veritas_20260516_033848.json ?? tools/data/veritas/veritas_20260516_072533.json ?? tools/data/veritas/veritas_20260516_082608.json ?? tools/data/veritas/veritas_20260516_092735.json ?? tools/data/veritas/veritas_20260516_094423.json ?? tools/data/veritas/veritas_20260516_101403.json ?? tools/data/veritas/veritas_20260516_102427.json ?? tools/data/veritas/veritas_20260516_103737.json ?? tools/data/veritas/veritas_20260516_105525.json ?? tools/data/veritas/veritas_20260516_110354.json ?? tools/data/veritas/veritas_20260516_111042.json ?? tools/data/veritas/veritas_20260516_111633.json ?? tools/data/veritas/veritas_20260516_112343.json ?? tools/data/veritas/veritas_20260516_113001.json ?? tools/data/veritas/veritas_20260516_113946.json ?? tools/exp_anderson3d_comparable_null_audit.py ?? tools/exp_anderson3d_endpoint_preserving_null.py ?? tools/exp_anderson3d_mobility_edge_two_reader_audit.py ?? 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_boundary_graph_null_audit.py ?? tools/exp_boundary_graph_residue_threshold_audit.py ?? tools/exp_boundary_unfolding_transfer_matrix.py ?? tools/exp_endpoint_feature_scramble_null.py ?? tools/exp_endpoint_gated_rp_boundary.py ?? tools/exp_endpoint_stability_filter.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_rosenzweig_porter_bridge_physical_audit.py ?? tools/exp_rp_boundary_raw_count_null_audit.py ?? tools/exp_rp_boundary_size_stability_audit.py ?? tools/exp_rp_exact_local_window_matrix.py ?? tools/exp_rp_unfolding_sensitivity_audit.py ?? tools/exp_sturmian_denominator_alignment_gate.py ?? tools/field_coherence_preflight.py ?? tools/field_rebuild_risk_map.py ?? tools/lab_cycle_monitor.py ?? tools/lab_preflight_agent.py ?? tools/lab_runtime_observer.py ?? tools/lab_surface_invariant_check.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 codex Scrivo ora il report richiesto in `tools/data/reports/agent_20260516_1140.md`. Non tocco il seme né i file di stato: il risultato è un vincolo sul perimetro vivo, non una promozione. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260516_1140.md diff --git a/tools/data/reports/agent_20260516_1140.md b/tools/data/reports/agent_20260516_1140.md new file mode 100644 index 0000000000000000000000000000000000000000..972f665b0cdfc80ef6ed9be3b1615d637d18f59f --- /dev/null +++ b/tools/data/reports/agent_20260516_1140.md @@ -0,0 +1,107 @@ +# Agent Report - Boundary Bridge Stability Audit 27 +**Date**: 2026-05-16 11:40 +**Piano**: 133 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - Sul perimetro vivo `8 GUE / 5 Poisson`, con 27 letture graph-reader perturbate, il confine a due lettori resta una sola riga: `numeri_primi:cycle_3` con `27/27` bridge graph stabile e audit classico `classic_and_graph_bridge`. Tre righe sono residuo `graph_only` stabile (`logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`) e due righe sono classiche intermedie senza bridge stabile (`brownian_motion`, `cellular_automata`). Il terzo incluso operativo non coincide con tutti gli intermedi classici. +observables_registry: 1.0.0-2026-05-06 via boundary_graph_curvature_gate +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 del confine 8/5 e' operativa solo se la posizione bridge nel grafo sopravvive a perturbazioni del lettore e resta distinta dall'intermedio classico; observable=`graph_bridge_frequency` unito a `classical_audit_state`; operator=`exp_boundary_bridge_stability_audit.py`; generator=`boundary_graph_curvature_gate` sulle 13 righe BOUNDARY, con audit classico Brody/Berry-Robnik-like row-aligned; denominator=13 righe, 27 letture graph-reader (`k=2,3,4` x `n_gaps=512,1024,2048` x 3 seed); non_possible=promuovere un bridge Lab se il bridge graph collassa sotto perturbazione o se l'intermedio classico assorbe tutte le righe; not_tested=nuovi generatori fisici, validita' analitica delle label GUE/Poisson, scaling asintotico. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY `8 GUE / 5 Poisson` + grafo della conoscenza come piano di passaggio. +- **Dipolo / punto-zero**: intermedio classico / bridge di grafo. Punto-zero: la stessa riga domain-window prima che Brody/Berry-Robnik e kNN decidano nomi diversi. +- **Piano superiore**: grafo della conoscenza e topologia del bordo row-aligned; il confine vive dove il lettore graph e il lettore classico non collassano nello stesso stato. +- **Operatori laterali scelti**: graph curvature, perturbazione del lettore, audit classico. +- **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel `null_first -> candidate_name -> physical_return`, qui tradotto in `reader_first -> bridge_name -> classical_return`. CE-0117/Cascata usata per riportare il ciclo dal sotto-perimetro Anderson al perimetro vivo 8/5. +- **Proto-ipotesi**: il terzo incluso operativo non e' la classe statistica intermedia; e' la riga che resta ponte quando due lettori eterogenei vengono perturbati sullo stesso denominatore. +- **Possibile/non-possibile**: possibile = separare bridge a due lettori, residuo graph-only e intermedio classico-only; non-possibile = sommare graph-only e classico-only come un unico boundary. +- **Proiezione**: rieseguo `exp_boundary_bridge_stability_audit.py` con i default completi: 13 righe, 27 letture, stesso audit classico 19:04. +- **Movimento A->M->B**: fisico A = statistiche GUE/Poisson cross-dominio; matematica M = grafo kNN perturbato in feature canoniche + coordinate classiche; fisico B = criterio di ritorno: quali righe meritano un nuovo dominio fisico o Hamiltoniano. Il ritorno B resta vincolo, non scoperta fisica. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: l'esperimento usa direttamente il perimetro vivo `8 GUE / 5 Poisson` e misura se il confine e' un terzo incluso operativo invece di una scissione pulita GUE/Poisson. +- `not_drift`: non usa Sturmian, phi, V_c, fit locali o sotto-perimetro Anderson; il denominatore atomico e' 13 righe, 8 GUE e 5 Poisson. +- `seed_residue`: restano non testati scaling asintotico, rigenerazione fisica indipendente delle 13 righe e validita' analitica delle label sorgente. + +## Re-discovery audit +- **Baseline noto piu vicino**: Brody crossover, Berry-Robnik mixture, Rosenzweig-Porter crossover, mobility/localization crossover, kNN stability e cluster-boundary stability. +- **Cosa assorbe il baseline**: righe intermedie classiche, peso GUE-like non estremo, q Brody non endpoint. +- **Cosa resta Lab-specific**: separazione row-aligned fra bridge di grafo stabile, classico-only intermedio e graph-only bridge sullo stesso denominatore 8/5. +- `two_reader_boundary_confirmed`: [`numeri_primi:cycle_3`]. +- `graph_only_residue`: [`logistica_biforcazione_var_3.5699:cycle_13`, `percolation:cycle_9`, `reaction_diffusion:cycle_11`]. +- `scope_change_declared`: nessun cambio di scope; il run torna al perimetro vivo 13 righe. +- `graph_baseline_audit`: kNN stability perturbata su k, size finestra e seed; join con Brody/Berry-Robnik-like audit classico. + +## Claim Under Test +> Nel perimetro `8 GUE / 5 Poisson`, il terzo incluso operativo e' una riga che resta bridge di grafo sotto perturbazione e non viene completamente assorbita dal lettore classico. + +## Question +Il bridge stabile del grafo sopravvive quando il lettore cambia k, lunghezza finestra e seed, oppure era un artefatto locale del run 19:15? + +## Experiment Design +- **Script**: `tools/exp_boundary_bridge_stability_audit.py`. +- **Run**: `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- **Scope**: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`. +- **Classical audit**: `tools/data/boundary_classical_crossover_audit_20260515_1904.json`. +- **Perimetro**: 13 righe BOUNDARY, `8` label sorgente GUE e `5` label sorgente Poisson. +- **Reader perturbation**: `k=[2,3,4]`, `n_gaps=[512,1024,2048]`, seeds `[20260515,20260516,20260517]`, totale `27` letture. +- **Non misurato**: p-value, nuovi autovalori, nuovi Hamiltoniani, source-label validation, `V_c`, Sturmian denominators. + +## Results +| class | rows | count | +|---|---|---:| +| stable_graph_bridge + classic_and_graph_bridge | `numeri_primi:cycle_3` | 1 | +| stable_graph_bridge + graph_only_bridge | `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion` | 3 | +| parameter_sensitive_bridge + classic_only_intermediate | `random_matrix`, `zeta_zeros` | 2 | +| parameter_sensitive_bridge + endpoint_like | `logistica_biforcazione` | 1 | +| unstable_non_bridge + classic_only_intermediate | `brownian_motion`, `cellular_automata` | 2 | +| unstable_non_bridge + endpoint_like | `coupled_oscillators`, `ising_2d`, `pendolo_doppio`, `string_vibration` | 4 | + +| row | source | graph hits | freq | classical audit | brody_q | BR-like GUE weight | +|---|---|---:|---:|---|---:|---:| +| `numeri_primi:cycle_3` | GUE | 27/27 | 1.000000 | classic_and_graph_bridge | 0.465 | 0.275 | +| `logistica_biforcazione_var_3.5699:cycle_13` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `percolation:cycle_9` | Poisson | 27/27 | 1.000000 | graph_only_bridge | 0.025 | 0.025 | +| `reaction_diffusion:cycle_11` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `random_matrix:cycle_7` | GUE | 18/27 | 0.666667 | classic_only_intermediate | 0.975 | 0.475 | +| `zeta_zeros:cycle_4` | GUE | 14/27 | 0.518519 | classic_only_intermediate | 1.000 | 0.530 | +| `brownian_motion:cycle_12` | Poisson | 5/27 | 0.185185 | classic_only_intermediate | 0.205 | 0.250 | +| `cellular_automata:cycle_8` | GUE | 0/27 | 0.000000 | classic_only_intermediate | 1.000 | 0.435 | + +## Key Findings +1. Verificato: `numeri_primi:cycle_3` e' l'unica riga che unisce bridge graph stabile e audit classico bridge: `27/27`, `brody_q=0.465`, `BR-like weight=0.275`. +2. Verificato: tre righe sono bridge graph stabili ma non classiche: `27/27` ciascuna per `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`. +3. Verificato: due righe sono classiche intermedie senza bridge stabile: `brownian_motion=5/27`, `cellular_automata=0/27`. +4. Verificato: gli intermedi classici `random_matrix` e `zeta_zeros` sono parameter-sensitive, non bridge stabili: `18/27` e `14/27`. +5. Inferito dal perimetro: il terzo incluso operativo e' un oggetto a due lettori; il grafo da solo produce residuo, il classico da solo produce crossover noto. + +## Verdict +CONSTRAINT. + +Il confine `8 GUE / 5 Poisson` non e' una lista di righe intermedie. A denominatore perturbato, una sola riga chiude come bridge a due lettori (`numeri_primi:cycle_3`). Il residuo graph-only resta informativo ma non si somma al boundary confermato. + +## Bicono della scoperta +- **Due radici**: bridge graph stabile / intermedio classico. +- **Singolare**: riga domain-window prima della scelta del lettore. +- **Invariante di passaggio**: stesso denominatore 13 righe, 27 perturbazioni del reader. +- **Campo di possibilita**: possibile = usare `numeri_primi:cycle_3` come unico candidato two-reader per ritorno fisico; non-possibile = promuovere graph-only residue come boundary confermato. + +## Consecutio +Il prossimo ciclo deve attaccare `numeri_primi:cycle_3` con un null label-preserving row-local sullo stesso lettore a due canali. Se il bridge resta `27/27` mentre le label GUE/Poisson vengono permutate entro vincoli row-aligned, il candidato diventa test fisico. Se cade, il risultato resta proprieta' del grafo 13-righe. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_bridge_stability_audit.py` e' ora lo strumento di baseline per separare `two_reader_boundary_confirmed`, `graph_only_residue` e `classic_only_intermediate` sul perimetro BOUNDARY. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python -m py_compile tools/exp_boundary_bridge_stability_audit.py tools/exp_boundary_graph_curvature_gate.py` completato. +- Run completato: `tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- 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_20260516_1140.json` +- Report: `tools/data/reports/agent_20260516_1140.md` diff --git a/tools/data/reports/agent_20260516_1140.md b/tools/data/reports/agent_20260516_1140.md new file mode 100644 index 0000000000000000000000000000000000000000..972f665b0cdfc80ef6ed9be3b1615d637d18f59f --- /dev/null +++ b/tools/data/reports/agent_20260516_1140.md @@ -0,0 +1,107 @@ +# Agent Report - Boundary Bridge Stability Audit 27 +**Date**: 2026-05-16 11:40 +**Piano**: 133 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - Sul perimetro vivo `8 GUE / 5 Poisson`, con 27 letture graph-reader perturbate, il confine a due lettori resta una sola riga: `numeri_primi:cycle_3` con `27/27` bridge graph stabile e audit classico `classic_and_graph_bridge`. Tre righe sono residuo `graph_only` stabile (`logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`) e due righe sono classiche intermedie senza bridge stabile (`brownian_motion`, `cellular_automata`). Il terzo incluso operativo non coincide con tutti gli intermedi classici. +observables_registry: 1.0.0-2026-05-06 via boundary_graph_curvature_gate +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 del confine 8/5 e' operativa solo se la posizione bridge nel grafo sopravvive a perturbazioni del lettore e resta distinta dall'intermedio classico; observable=`graph_bridge_frequency` unito a `classical_audit_state`; operator=`exp_boundary_bridge_stability_audit.py`; generator=`boundary_graph_curvature_gate` sulle 13 righe BOUNDARY, con audit classico Brody/Berry-Robnik-like row-aligned; denominator=13 righe, 27 letture graph-reader (`k=2,3,4` x `n_gaps=512,1024,2048` x 3 seed); non_possible=promuovere un bridge Lab se il bridge graph collassa sotto perturbazione o se l'intermedio classico assorbe tutte le righe; not_tested=nuovi generatori fisici, validita' analitica delle label GUE/Poisson, scaling asintotico. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY `8 GUE / 5 Poisson` + grafo della conoscenza come piano di passaggio. +- **Dipolo / punto-zero**: intermedio classico / bridge di grafo. Punto-zero: la stessa riga domain-window prima che Brody/Berry-Robnik e kNN decidano nomi diversi. +- **Piano superiore**: grafo della conoscenza e topologia del bordo row-aligned; il confine vive dove il lettore graph e il lettore classico non collassano nello stesso stato. +- **Operatori laterali scelti**: graph curvature, perturbazione del lettore, audit classico. +- **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel `null_first -> candidate_name -> physical_return`, qui tradotto in `reader_first -> bridge_name -> classical_return`. CE-0117/Cascata usata per riportare il ciclo dal sotto-perimetro Anderson al perimetro vivo 8/5. +- **Proto-ipotesi**: il terzo incluso operativo non e' la classe statistica intermedia; e' la riga che resta ponte quando due lettori eterogenei vengono perturbati sullo stesso denominatore. +- **Possibile/non-possibile**: possibile = separare bridge a due lettori, residuo graph-only e intermedio classico-only; non-possibile = sommare graph-only e classico-only come un unico boundary. +- **Proiezione**: rieseguo `exp_boundary_bridge_stability_audit.py` con i default completi: 13 righe, 27 letture, stesso audit classico 19:04. +- **Movimento A->M->B**: fisico A = statistiche GUE/Poisson cross-dominio; matematica M = grafo kNN perturbato in feature canoniche + coordinate classiche; fisico B = criterio di ritorno: quali righe meritano un nuovo dominio fisico o Hamiltoniano. Il ritorno B resta vincolo, non scoperta fisica. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: l'esperimento usa direttamente il perimetro vivo `8 GUE / 5 Poisson` e misura se il confine e' un terzo incluso operativo invece di una scissione pulita GUE/Poisson. +- `not_drift`: non usa Sturmian, phi, V_c, fit locali o sotto-perimetro Anderson; il denominatore atomico e' 13 righe, 8 GUE e 5 Poisson. +- `seed_residue`: restano non testati scaling asintotico, rigenerazione fisica indipendente delle 13 righe e validita' analitica delle label sorgente. + +## Re-discovery audit +- **Baseline noto piu vicino**: Brody crossover, Berry-Robnik mixture, Rosenzweig-Porter crossover, mobility/localization crossover, kNN stability e cluster-boundary stability. +- **Cosa assorbe il baseline**: righe intermedie classiche, peso GUE-like non estremo, q Brody non endpoint. +- **Cosa resta Lab-specific**: separazione row-aligned fra bridge di grafo stabile, classico-only intermedio e graph-only bridge sullo stesso denominatore 8/5. +- `two_reader_boundary_confirmed`: [`numeri_primi:cycle_3`]. +- `graph_only_residue`: [`logistica_biforcazione_var_3.5699:cycle_13`, `percolation:cycle_9`, `reaction_diffusion:cycle_11`]. +- `scope_change_declared`: nessun cambio di scope; il run torna al perimetro vivo 13 righe. +- `graph_baseline_audit`: kNN stability perturbata su k, size finestra e seed; join con Brody/Berry-Robnik-like audit classico. + +## Claim Under Test +> Nel perimetro `8 GUE / 5 Poisson`, il terzo incluso operativo e' una riga che resta bridge di grafo sotto perturbazione e non viene completamente assorbita dal lettore classico. + +## Question +Il bridge stabile del grafo sopravvive quando il lettore cambia k, lunghezza finestra e seed, oppure era un artefatto locale del run 19:15? + +## Experiment Design +- **Script**: `tools/exp_boundary_bridge_stability_audit.py`. +- **Run**: `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- **Scope**: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`. +- **Classical audit**: `tools/data/boundary_classical_crossover_audit_20260515_1904.json`. +- **Perimetro**: 13 righe BOUNDARY, `8` label sorgente GUE e `5` label sorgente Poisson. +- **Reader perturbation**: `k=[2,3,4]`, `n_gaps=[512,1024,2048]`, seeds `[20260515,20260516,20260517]`, totale `27` letture. +- **Non misurato**: p-value, nuovi autovalori, nuovi Hamiltoniani, source-label validation, `V_c`, Sturmian denominators. + +## Results +| class | rows | count | +|---|---|---:| +| stable_graph_bridge + classic_and_graph_bridge | `numeri_primi:cycle_3` | 1 | +| stable_graph_bridge + graph_only_bridge | `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion` | 3 | +| parameter_sensitive_bridge + classic_only_intermediate | `random_matrix`, `zeta_zeros` | 2 | +| parameter_sensitive_bridge + endpoint_like | `logistica_biforcazione` | 1 | +| unstable_non_bridge + classic_only_intermediate | `brownian_motion`, `cellular_automata` | 2 | +| unstable_non_bridge + endpoint_like | `coupled_oscillators`, `ising_2d`, `pendolo_doppio`, `string_vibration` | 4 | + +| row | source | graph hits | freq | classical audit | brody_q | BR-like GUE weight | +|---|---|---:|---:|---|---:|---:| +| `numeri_primi:cycle_3` | GUE | 27/27 | 1.000000 | classic_and_graph_bridge | 0.465 | 0.275 | +| `logistica_biforcazione_var_3.5699:cycle_13` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `percolation:cycle_9` | Poisson | 27/27 | 1.000000 | graph_only_bridge | 0.025 | 0.025 | +| `reaction_diffusion:cycle_11` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `random_matrix:cycle_7` | GUE | 18/27 | 0.666667 | classic_only_intermediate | 0.975 | 0.475 | +| `zeta_zeros:cycle_4` | GUE | 14/27 | 0.518519 | classic_only_intermediate | 1.000 | 0.530 | +| `brownian_motion:cycle_12` | Poisson | 5/27 | 0.185185 | classic_only_intermediate | 0.205 | 0.250 | +| `cellular_automata:cycle_8` | GUE | 0/27 | 0.000000 | classic_only_intermediate | 1.000 | 0.435 | + +## Key Findings +1. Verificato: `numeri_primi:cycle_3` e' l'unica riga che unisce bridge graph stabile e audit classico bridge: `27/27`, `brody_q=0.465`, `BR-like weight=0.275`. +2. Verificato: tre righe sono bridge graph stabili ma non classiche: `27/27` ciascuna per `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`. +3. Verificato: due righe sono classiche intermedie senza bridge stabile: `brownian_motion=5/27`, `cellular_automata=0/27`. +4. Verificato: gli intermedi classici `random_matrix` e `zeta_zeros` sono parameter-sensitive, non bridge stabili: `18/27` e `14/27`. +5. Inferito dal perimetro: il terzo incluso operativo e' un oggetto a due lettori; il grafo da solo produce residuo, il classico da solo produce crossover noto. + +## Verdict +CONSTRAINT. + +Il confine `8 GUE / 5 Poisson` non e' una lista di righe intermedie. A denominatore perturbato, una sola riga chiude come bridge a due lettori (`numeri_primi:cycle_3`). Il residuo graph-only resta informativo ma non si somma al boundary confermato. + +## Bicono della scoperta +- **Due radici**: bridge graph stabile / intermedio classico. +- **Singolare**: riga domain-window prima della scelta del lettore. +- **Invariante di passaggio**: stesso denominatore 13 righe, 27 perturbazioni del reader. +- **Campo di possibilita**: possibile = usare `numeri_primi:cycle_3` come unico candidato two-reader per ritorno fisico; non-possibile = promuovere graph-only residue come boundary confermato. + +## Consecutio +Il prossimo ciclo deve attaccare `numeri_primi:cycle_3` con un null label-preserving row-local sullo stesso lettore a due canali. Se il bridge resta `27/27` mentre le label GUE/Poisson vengono permutate entro vincoli row-aligned, il candidato diventa test fisico. Se cade, il risultato resta proprieta' del grafo 13-righe. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_bridge_stability_audit.py` e' ora lo strumento di baseline per separare `two_reader_boundary_confirmed`, `graph_only_residue` e `classic_only_intermediate` sul perimetro BOUNDARY. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python -m py_compile tools/exp_boundary_bridge_stability_audit.py tools/exp_boundary_graph_curvature_gate.py` completato. +- Run completato: `tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- 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_20260516_1140.json` +- Report: `tools/data/reports/agent_20260516_1140.md` exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260516_1140.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Boundary Bridge Stability Audit 27 **Date**: 2026-05-16 11:40 **Piano**: 133 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - Sul perimetro vivo `8 GUE / 5 Poisson`, con 27 letture graph-reader perturbate, il confine a due lettori resta una sola riga: `numeri_primi:cycle_3` con `27/27` bridge graph stabile e audit classico `classic_and_graph_bridge`. Tre righe sono residuo `graph_only` stabile (`logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`) e due righe sono classiche intermedie senza bridge stabile (`brownian_motion`, `cellular_automata`). Il terzo incluso operativo non coincide con tutti gli intermedi classici. observables_registry: 1.0.0-2026-05-06 via boundary_graph_curvature_gate 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 del confine 8/5 e' operativa solo se la posizione bridge nel grafo sopravvive a perturbazioni del lettore e resta distinta dall'intermedio classico; observable=`graph_bridge_frequency` unito a `classical_audit_state`; operator=`exp_boundary_bridge_stability_audit.py`; generator=`boundary_graph_curvature_gate` sulle 13 righe BOUNDARY, con audit classico Brody/Berry-Robnik-like row-aligned; denominator=13 righe, 27 letture graph-reader (`k=2,3,4` x `n_gaps=512,1024,2048` x 3 seed); non_possible=promuovere un bridge Lab se il bridge graph collassa sotto perturbazione o se l'intermedio classico assorbe tutte le righe; not_tested=nuovi generatori fisici, validita' analitica delle label GUE/Poisson, scaling asintotico. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY `8 GUE / 5 Poisson` + grafo della conoscenza come piano di passaggio. - **Dipolo / punto-zero**: intermedio classico / bridge di grafo. Punto-zero: la stessa riga domain-window prima che Brody/Berry-Robnik e kNN decidano nomi diversi. - **Piano superiore**: grafo della conoscenza e topologia del bordo row-aligned; il confine vive dove il lettore graph e il lettore classico non collassano nello stesso stato. - **Operatori laterali scelti**: graph curvature, perturbazione del lettore, audit classico. - **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel `null_first -> candidate_name -> physical_return`, qui tradotto in `reader_first -> bridge_name -> classical_return`. CE-0117/Cascata usata per riportare il ciclo dal sotto-perimetro Anderson al perimetro vivo 8/5. - **Proto-ipotesi**: il terzo incluso operativo non e' la classe statistica intermedia; e' la riga che resta ponte quando due lettori eterogenei vengono perturbati sullo stesso denominatore. - **Possibile/non-possibile**: possibile = separare bridge a due lettori, residuo graph-only e intermedio classico-only; non-possibile = sommare graph-only e classico-only come un unico boundary. - **Proiezione**: rieseguo `exp_boundary_bridge_stability_audit.py` con i default completi: 13 righe, 27 letture, stesso audit classico 19:04. - **Movimento A->M->B**: fisico A = statistiche GUE/Poisson cross-dominio; matematica M = grafo kNN perturbato in feature canoniche + coordinate classiche; fisico B = criterio di ritorno: quali righe meritano un nuovo dominio fisico o Hamiltoniano. Il ritorno B resta vincolo, non scoperta fisica. ## Aderenza alla direzione - `relation`: follows_direction - `why`: l'esperimento usa direttamente il perimetro vivo `8 GUE / 5 Poisson` e misura se il confine e' un terzo incluso operativo invece di una scissione pulita GUE/Poisson. - `not_drift`: non usa Sturmian, phi, V_c, fit locali o sotto-perimetro Anderson; il denominatore atomico e' 13 righe, 8 GUE e 5 Poisson. - `seed_residue`: restano non testati scaling asintotico, rigenerazione fisica indipendente delle 13 righe e validita' analitica delle label sorgente. ## Re-discovery audit - **Baseline noto piu vicino**: Brody crossover, Berry-Robnik mixture, Rosenzweig-Porter crossover, mobility/localization crossover, kNN stability e cluster-boundary stability. - **Cosa assorbe il baseline**: righe intermedie classiche, peso GUE-like non estremo, q Brody non endpoint. - **Cosa resta Lab-specific**: separazione row-aligned fra bridge di grafo stabile, classico-only intermedio e graph-only bridge sullo stesso denominatore 8/5. - `two_reader_boundary_confirmed`: [`numeri_primi:cycle_3`]. - `graph_only_residue`: [`logistica_biforcazione_var_3.5699:cycle_13`, `percolation:cycle_9`, `reaction_diffusion:cycle_11`]. - `scope_change_declared`: nessun cambio di scope; il run torna al perimetro vivo 13 righe. - `graph_baseline_audit`: kNN stability perturbata su k, size finestra e seed; join con Brody/Berry-Robnik-like audit classico. ## Claim Under Test > Nel perimetro `8 GUE / 5 Poisson`, il terzo incluso operativo e' una riga che resta bridge di grafo sotto perturbazione e non viene completamente assorbita dal lettore classico. ## Question Il bridge stabile del grafo sopravvive quando il lettore cambia k, lunghezza finestra e seed, oppure era un artefatto locale del run 19:15? ## Experiment Design - **Script**: `tools/exp_boundary_bridge_stability_audit.py`. - **Run**: `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260516_1140.json`. - **Scope**: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`. - **Classical audit**: `tools/data/boundary_classical_crossover_audit_20260515_1904.json`. - **Perimetro**: 13 righe BOUNDARY, `8` label sorgente GUE e `5` label sorgente Poisson. - **Reader perturbation**: `k=[2,3,4]`, `n_gaps=[512,1024,2048]`, seeds `[20260515,20260516,20260517]`, totale `27` letture. - **Non misurato**: p-value, nuovi autovalori, nuovi Hamiltoniani, source-label validation, `V_c`, Sturmian denominators. ## Results | class | rows | count | |---|---|---:| | stable_graph_bridge + classic_and_graph_bridge | `numeri_primi:cycle_3` | 1 | | stable_graph_bridge + graph_only_bridge | `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion` | 3 | | parameter_sensitive_bridge + classic_only_intermediate | `random_matrix`, `zeta_zeros` | 2 | | parameter_sensitive_bridge + endpoint_like | `logistica_biforcazione` | 1 | | unstable_non_bridge + classic_only_intermediate | `brownian_motion`, `cellular_automata` | 2 | | unstable_non_bridge + endpoint_like | `coupled_oscillators`, `ising_2d`, `pendolo_doppio`, `string_vibration` | 4 | | row | source | graph hits | freq | classical audit | brody_q | BR-like GUE weight | |---|---|---:|---:|---|---:|---:| | `numeri_primi:cycle_3` | GUE | 27/27 | 1.000000 | classic_and_graph_bridge | 0.465 | 0.275 | | `logistica_biforcazione_var_3.5699:cycle_13` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | | `percolation:cycle_9` | Poisson | 27/27 | 1.000000 | graph_only_bridge | 0.025 | 0.025 | | `reaction_diffusion:cycle_11` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | | `random_matrix:cycle_7` | GUE | 18/27 | 0.666667 | classic_only_intermediate | 0.975 | 0.475 | | `zeta_zeros:cycle_4` | GUE | 14/27 | 0.518519 | classic_only_intermediate | 1.000 | 0.530 | | `brownian_motion:cycle_12` | Poisson | 5/27 | 0.185185 | classic_only_intermediate | 0.205 | 0.250 | | `cellular_automata:cycle_8` | GUE | 0/27 | 0.000000 | classic_only_intermediate | 1.000 | 0.435 | ## Key Findings 1. Verificato: `numeri_primi:cycle_3` e' l'unica riga che unisce bridge graph stabile e audit classico bridge: `27/27`, `brody_q=0.465`, `BR-like weight=0.275`. 2. Verificato: tre righe sono bridge graph stabili ma non classiche: `27/27` ciascuna per `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`. 3. Verificato: due righe sono classiche intermedie senza bridge stabile: `brownian_motion=5/27`, `cellular_automata=0/27`. 4. Verificato: gli intermedi classici `random_matrix` e `zeta_zeros` sono parameter-sensitive, non bridge stabili: `18/27` e `14/27`. 5. Inferito dal perimetro: il terzo incluso operativo e' un oggetto a due lettori; il grafo da solo produce residuo, il classico da solo produce crossover noto. ## Verdict CONSTRAINT. Il confine `8 GUE / 5 Poisson` non e' una lista di righe intermedie. A denominatore perturbato, una sola riga chiude come bridge a due lettori (`numeri_primi:cycle_3`). Il residuo graph-only resta informativo ma non si somma al boundary confermato. ## Bicono della scoperta - **Due radici**: bridge graph stabile / intermedio classico. - **Singolare**: riga domain-window prima della scelta del lettore. - **Invariante di passaggio**: stesso denominatore 13 righe, 27 perturbazioni del reader. - **Campo di possibilita**: possibile = usare `numeri_primi:cycle_3` come unico candidato two-reader per ritorno fisico; non-possibile = promuovere graph-only residue come boundary confermato. ## Consecutio Il prossimo ciclo deve attaccare `numeri_primi:cycle_3` con un null label-preserving row-local sullo stesso lettore a due canali. Se il bridge resta `27/27` mentre le label GUE/Poisson vengono permutate entro vincoli row-aligned, il candidato diventa test fisico. Se cade, il risultato resta proprieta' del grafo 13-righe. ## Ricadute pratiche ssp_value: yes. `tools/exp_boundary_bridge_stability_audit.py` e' ora lo strumento di baseline per separare `two_reader_boundary_confirmed`, `graph_only_residue` e `classic_only_intermediate` sul perimetro BOUNDARY. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. - `python -m py_compile tools/exp_boundary_bridge_stability_audit.py tools/exp_boundary_graph_curvature_gate.py` completato. - Run completato: `tools/data/boundary_bridge_stability_audit_20260516_1140.json`. - 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_20260516_1140.json` - Report: `tools/data/reports/agent_20260516_1140.md` exec /bin/bash -lc 'wc -l tools/data/reports/agent_20260516_1140.md tools/data/boundary_bridge_stability_audit_20260516_1140.json' in /opt/MM_D-ND succeeded in 0ms: 107 tools/data/reports/agent_20260516_1140.md 647 tools/data/boundary_bridge_stability_audit_20260516_1140.json 754 total exec /bin/bash -lc "python - <<'PY' import json p='tools/data/boundary_bridge_stability_audit_20260516_1140.json' with open(p) as f: d=json.load(f) assert d['summary']['graph_reader_runs'] == 27 assert d['summary']['rows_analyzed'] == 13 assert d['summary']['stable_classic_and_graph'] == ['numeri_primi:cycle_3'] print('json verification ok') PY" in /opt/MM_D-ND succeeded in 0ms: json verification ok diff --git a/tools/data/reports/agent_20260516_1140.md b/tools/data/reports/agent_20260516_1140.md new file mode 100644 index 0000000000000000000000000000000000000000..972f665b0cdfc80ef6ed9be3b1615d637d18f59f --- /dev/null +++ b/tools/data/reports/agent_20260516_1140.md @@ -0,0 +1,107 @@ +# Agent Report - Boundary Bridge Stability Audit 27 +**Date**: 2026-05-16 11:40 +**Piano**: 133 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - Sul perimetro vivo `8 GUE / 5 Poisson`, con 27 letture graph-reader perturbate, il confine a due lettori resta una sola riga: `numeri_primi:cycle_3` con `27/27` bridge graph stabile e audit classico `classic_and_graph_bridge`. Tre righe sono residuo `graph_only` stabile (`logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`) e due righe sono classiche intermedie senza bridge stabile (`brownian_motion`, `cellular_automata`). Il terzo incluso operativo non coincide con tutti gli intermedi classici. +observables_registry: 1.0.0-2026-05-06 via boundary_graph_curvature_gate +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 del confine 8/5 e' operativa solo se la posizione bridge nel grafo sopravvive a perturbazioni del lettore e resta distinta dall'intermedio classico; observable=`graph_bridge_frequency` unito a `classical_audit_state`; operator=`exp_boundary_bridge_stability_audit.py`; generator=`boundary_graph_curvature_gate` sulle 13 righe BOUNDARY, con audit classico Brody/Berry-Robnik-like row-aligned; denominator=13 righe, 27 letture graph-reader (`k=2,3,4` x `n_gaps=512,1024,2048` x 3 seed); non_possible=promuovere un bridge Lab se il bridge graph collassa sotto perturbazione o se l'intermedio classico assorbe tutte le righe; not_tested=nuovi generatori fisici, validita' analitica delle label GUE/Poisson, scaling asintotico. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY `8 GUE / 5 Poisson` + grafo della conoscenza come piano di passaggio. +- **Dipolo / punto-zero**: intermedio classico / bridge di grafo. Punto-zero: la stessa riga domain-window prima che Brody/Berry-Robnik e kNN decidano nomi diversi. +- **Piano superiore**: grafo della conoscenza e topologia del bordo row-aligned; il confine vive dove il lettore graph e il lettore classico non collassano nello stesso stato. +- **Operatori laterali scelti**: graph curvature, perturbazione del lettore, audit classico. +- **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel `null_first -> candidate_name -> physical_return`, qui tradotto in `reader_first -> bridge_name -> classical_return`. CE-0117/Cascata usata per riportare il ciclo dal sotto-perimetro Anderson al perimetro vivo 8/5. +- **Proto-ipotesi**: il terzo incluso operativo non e' la classe statistica intermedia; e' la riga che resta ponte quando due lettori eterogenei vengono perturbati sullo stesso denominatore. +- **Possibile/non-possibile**: possibile = separare bridge a due lettori, residuo graph-only e intermedio classico-only; non-possibile = sommare graph-only e classico-only come un unico boundary. +- **Proiezione**: rieseguo `exp_boundary_bridge_stability_audit.py` con i default completi: 13 righe, 27 letture, stesso audit classico 19:04. +- **Movimento A->M->B**: fisico A = statistiche GUE/Poisson cross-dominio; matematica M = grafo kNN perturbato in feature canoniche + coordinate classiche; fisico B = criterio di ritorno: quali righe meritano un nuovo dominio fisico o Hamiltoniano. Il ritorno B resta vincolo, non scoperta fisica. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: l'esperimento usa direttamente il perimetro vivo `8 GUE / 5 Poisson` e misura se il confine e' un terzo incluso operativo invece di una scissione pulita GUE/Poisson. +- `not_drift`: non usa Sturmian, phi, V_c, fit locali o sotto-perimetro Anderson; il denominatore atomico e' 13 righe, 8 GUE e 5 Poisson. +- `seed_residue`: restano non testati scaling asintotico, rigenerazione fisica indipendente delle 13 righe e validita' analitica delle label sorgente. + +## Re-discovery audit +- **Baseline noto piu vicino**: Brody crossover, Berry-Robnik mixture, Rosenzweig-Porter crossover, mobility/localization crossover, kNN stability e cluster-boundary stability. +- **Cosa assorbe il baseline**: righe intermedie classiche, peso GUE-like non estremo, q Brody non endpoint. +- **Cosa resta Lab-specific**: separazione row-aligned fra bridge di grafo stabile, classico-only intermedio e graph-only bridge sullo stesso denominatore 8/5. +- `two_reader_boundary_confirmed`: [`numeri_primi:cycle_3`]. +- `graph_only_residue`: [`logistica_biforcazione_var_3.5699:cycle_13`, `percolation:cycle_9`, `reaction_diffusion:cycle_11`]. +- `scope_change_declared`: nessun cambio di scope; il run torna al perimetro vivo 13 righe. +- `graph_baseline_audit`: kNN stability perturbata su k, size finestra e seed; join con Brody/Berry-Robnik-like audit classico. + +## Claim Under Test +> Nel perimetro `8 GUE / 5 Poisson`, il terzo incluso operativo e' una riga che resta bridge di grafo sotto perturbazione e non viene completamente assorbita dal lettore classico. + +## Question +Il bridge stabile del grafo sopravvive quando il lettore cambia k, lunghezza finestra e seed, oppure era un artefatto locale del run 19:15? + +## Experiment Design +- **Script**: `tools/exp_boundary_bridge_stability_audit.py`. +- **Run**: `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- **Scope**: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`. +- **Classical audit**: `tools/data/boundary_classical_crossover_audit_20260515_1904.json`. +- **Perimetro**: 13 righe BOUNDARY, `8` label sorgente GUE e `5` label sorgente Poisson. +- **Reader perturbation**: `k=[2,3,4]`, `n_gaps=[512,1024,2048]`, seeds `[20260515,20260516,20260517]`, totale `27` letture. +- **Non misurato**: p-value, nuovi autovalori, nuovi Hamiltoniani, source-label validation, `V_c`, Sturmian denominators. + +## Results +| class | rows | count | +|---|---|---:| +| stable_graph_bridge + classic_and_graph_bridge | `numeri_primi:cycle_3` | 1 | +| stable_graph_bridge + graph_only_bridge | `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion` | 3 | +| parameter_sensitive_bridge + classic_only_intermediate | `random_matrix`, `zeta_zeros` | 2 | +| parameter_sensitive_bridge + endpoint_like | `logistica_biforcazione` | 1 | +| unstable_non_bridge + classic_only_intermediate | `brownian_motion`, `cellular_automata` | 2 | +| unstable_non_bridge + endpoint_like | `coupled_oscillators`, `ising_2d`, `pendolo_doppio`, `string_vibration` | 4 | + +| row | source | graph hits | freq | classical audit | brody_q | BR-like GUE weight | +|---|---|---:|---:|---|---:|---:| +| `numeri_primi:cycle_3` | GUE | 27/27 | 1.000000 | classic_and_graph_bridge | 0.465 | 0.275 | +| `logistica_biforcazione_var_3.5699:cycle_13` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `percolation:cycle_9` | Poisson | 27/27 | 1.000000 | graph_only_bridge | 0.025 | 0.025 | +| `reaction_diffusion:cycle_11` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `random_matrix:cycle_7` | GUE | 18/27 | 0.666667 | classic_only_intermediate | 0.975 | 0.475 | +| `zeta_zeros:cycle_4` | GUE | 14/27 | 0.518519 | classic_only_intermediate | 1.000 | 0.530 | +| `brownian_motion:cycle_12` | Poisson | 5/27 | 0.185185 | classic_only_intermediate | 0.205 | 0.250 | +| `cellular_automata:cycle_8` | GUE | 0/27 | 0.000000 | classic_only_intermediate | 1.000 | 0.435 | + +## Key Findings +1. Verificato: `numeri_primi:cycle_3` e' l'unica riga che unisce bridge graph stabile e audit classico bridge: `27/27`, `brody_q=0.465`, `BR-like weight=0.275`. +2. Verificato: tre righe sono bridge graph stabili ma non classiche: `27/27` ciascuna per `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`. +3. Verificato: due righe sono classiche intermedie senza bridge stabile: `brownian_motion=5/27`, `cellular_automata=0/27`. +4. Verificato: gli intermedi classici `random_matrix` e `zeta_zeros` sono parameter-sensitive, non bridge stabili: `18/27` e `14/27`. +5. Inferito dal perimetro: il terzo incluso operativo e' un oggetto a due lettori; il grafo da solo produce residuo, il classico da solo produce crossover noto. + +## Verdict +CONSTRAINT. + +Il confine `8 GUE / 5 Poisson` non e' una lista di righe intermedie. A denominatore perturbato, una sola riga chiude come bridge a due lettori (`numeri_primi:cycle_3`). Il residuo graph-only resta informativo ma non si somma al boundary confermato. + +## Bicono della scoperta +- **Due radici**: bridge graph stabile / intermedio classico. +- **Singolare**: riga domain-window prima della scelta del lettore. +- **Invariante di passaggio**: stesso denominatore 13 righe, 27 perturbazioni del reader. +- **Campo di possibilita**: possibile = usare `numeri_primi:cycle_3` come unico candidato two-reader per ritorno fisico; non-possibile = promuovere graph-only residue come boundary confermato. + +## Consecutio +Il prossimo ciclo deve attaccare `numeri_primi:cycle_3` con un null label-preserving row-local sullo stesso lettore a due canali. Se il bridge resta `27/27` mentre le label GUE/Poisson vengono permutate entro vincoli row-aligned, il candidato diventa test fisico. Se cade, il risultato resta proprieta' del grafo 13-righe. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_bridge_stability_audit.py` e' ora lo strumento di baseline per separare `two_reader_boundary_confirmed`, `graph_only_residue` e `classic_only_intermediate` sul perimetro BOUNDARY. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python -m py_compile tools/exp_boundary_bridge_stability_audit.py tools/exp_boundary_graph_curvature_gate.py` completato. +- Run completato: `tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- 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_20260516_1140.json` +- Report: `tools/data/reports/agent_20260516_1140.md` codex Eseguito l’esperimento e scritto il report richiesto: - [agent_20260516_1140.md](/opt/MM_D-ND/tools/data/reports/agent_20260516_1140.md) - [boundary_bridge_stability_audit_20260516_1140.json](/opt/MM_D-ND/tools/data/boundary_bridge_stability_audit_20260516_1140.json) Verdict: `CONSTRAINT`. Sul perimetro vivo `8 GUE / 5 Poisson`, resta un solo boundary confermato a due lettori: `numeri_primi:cycle_3` (`27/27`). Separati anche tre residui `graph_only` e due intermedi classici senza bridge stabile. Verifiche completate: `py_compile` degli script usati e check JSON su `rows_analyzed=13`, `graph_reader_runs=27`, `stable_classic_and_graph=['numeri_primi:cycle_3']`. diff --git a/tools/data/reports/agent_20260516_1140.md b/tools/data/reports/agent_20260516_1140.md new file mode 100644 index 0000000000000000000000000000000000000000..972f665b0cdfc80ef6ed9be3b1615d637d18f59f --- /dev/null +++ b/tools/data/reports/agent_20260516_1140.md @@ -0,0 +1,107 @@ +# Agent Report - Boundary Bridge Stability Audit 27 +**Date**: 2026-05-16 11:40 +**Piano**: 133 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - Sul perimetro vivo `8 GUE / 5 Poisson`, con 27 letture graph-reader perturbate, il confine a due lettori resta una sola riga: `numeri_primi:cycle_3` con `27/27` bridge graph stabile e audit classico `classic_and_graph_bridge`. Tre righe sono residuo `graph_only` stabile (`logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`) e due righe sono classiche intermedie senza bridge stabile (`brownian_motion`, `cellular_automata`). Il terzo incluso operativo non coincide con tutti gli intermedi classici. +observables_registry: 1.0.0-2026-05-06 via boundary_graph_curvature_gate +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 del confine 8/5 e' operativa solo se la posizione bridge nel grafo sopravvive a perturbazioni del lettore e resta distinta dall'intermedio classico; observable=`graph_bridge_frequency` unito a `classical_audit_state`; operator=`exp_boundary_bridge_stability_audit.py`; generator=`boundary_graph_curvature_gate` sulle 13 righe BOUNDARY, con audit classico Brody/Berry-Robnik-like row-aligned; denominator=13 righe, 27 letture graph-reader (`k=2,3,4` x `n_gaps=512,1024,2048` x 3 seed); non_possible=promuovere un bridge Lab se il bridge graph collassa sotto perturbazione o se l'intermedio classico assorbe tutte le righe; not_tested=nuovi generatori fisici, validita' analitica delle label GUE/Poisson, scaling asintotico. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY `8 GUE / 5 Poisson` + grafo della conoscenza come piano di passaggio. +- **Dipolo / punto-zero**: intermedio classico / bridge di grafo. Punto-zero: la stessa riga domain-window prima che Brody/Berry-Robnik e kNN decidano nomi diversi. +- **Piano superiore**: grafo della conoscenza e topologia del bordo row-aligned; il confine vive dove il lettore graph e il lettore classico non collassano nello stesso stato. +- **Operatori laterali scelti**: graph curvature, perturbazione del lettore, audit classico. +- **Contaminazione cognitiva**: CE-0001/KSAR usata come reiterazione del kernel `null_first -> candidate_name -> physical_return`, qui tradotto in `reader_first -> bridge_name -> classical_return`. CE-0117/Cascata usata per riportare il ciclo dal sotto-perimetro Anderson al perimetro vivo 8/5. +- **Proto-ipotesi**: il terzo incluso operativo non e' la classe statistica intermedia; e' la riga che resta ponte quando due lettori eterogenei vengono perturbati sullo stesso denominatore. +- **Possibile/non-possibile**: possibile = separare bridge a due lettori, residuo graph-only e intermedio classico-only; non-possibile = sommare graph-only e classico-only come un unico boundary. +- **Proiezione**: rieseguo `exp_boundary_bridge_stability_audit.py` con i default completi: 13 righe, 27 letture, stesso audit classico 19:04. +- **Movimento A->M->B**: fisico A = statistiche GUE/Poisson cross-dominio; matematica M = grafo kNN perturbato in feature canoniche + coordinate classiche; fisico B = criterio di ritorno: quali righe meritano un nuovo dominio fisico o Hamiltoniano. Il ritorno B resta vincolo, non scoperta fisica. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: l'esperimento usa direttamente il perimetro vivo `8 GUE / 5 Poisson` e misura se il confine e' un terzo incluso operativo invece di una scissione pulita GUE/Poisson. +- `not_drift`: non usa Sturmian, phi, V_c, fit locali o sotto-perimetro Anderson; il denominatore atomico e' 13 righe, 8 GUE e 5 Poisson. +- `seed_residue`: restano non testati scaling asintotico, rigenerazione fisica indipendente delle 13 righe e validita' analitica delle label sorgente. + +## Re-discovery audit +- **Baseline noto piu vicino**: Brody crossover, Berry-Robnik mixture, Rosenzweig-Porter crossover, mobility/localization crossover, kNN stability e cluster-boundary stability. +- **Cosa assorbe il baseline**: righe intermedie classiche, peso GUE-like non estremo, q Brody non endpoint. +- **Cosa resta Lab-specific**: separazione row-aligned fra bridge di grafo stabile, classico-only intermedio e graph-only bridge sullo stesso denominatore 8/5. +- `two_reader_boundary_confirmed`: [`numeri_primi:cycle_3`]. +- `graph_only_residue`: [`logistica_biforcazione_var_3.5699:cycle_13`, `percolation:cycle_9`, `reaction_diffusion:cycle_11`]. +- `scope_change_declared`: nessun cambio di scope; il run torna al perimetro vivo 13 righe. +- `graph_baseline_audit`: kNN stability perturbata su k, size finestra e seed; join con Brody/Berry-Robnik-like audit classico. + +## Claim Under Test +> Nel perimetro `8 GUE / 5 Poisson`, il terzo incluso operativo e' una riga che resta bridge di grafo sotto perturbazione e non viene completamente assorbita dal lettore classico. + +## Question +Il bridge stabile del grafo sopravvive quando il lettore cambia k, lunghezza finestra e seed, oppure era un artefatto locale del run 19:15? + +## Experiment Design +- **Script**: `tools/exp_boundary_bridge_stability_audit.py`. +- **Run**: `python tools/exp_boundary_bridge_stability_audit.py --out tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- **Scope**: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`. +- **Classical audit**: `tools/data/boundary_classical_crossover_audit_20260515_1904.json`. +- **Perimetro**: 13 righe BOUNDARY, `8` label sorgente GUE e `5` label sorgente Poisson. +- **Reader perturbation**: `k=[2,3,4]`, `n_gaps=[512,1024,2048]`, seeds `[20260515,20260516,20260517]`, totale `27` letture. +- **Non misurato**: p-value, nuovi autovalori, nuovi Hamiltoniani, source-label validation, `V_c`, Sturmian denominators. + +## Results +| class | rows | count | +|---|---|---:| +| stable_graph_bridge + classic_and_graph_bridge | `numeri_primi:cycle_3` | 1 | +| stable_graph_bridge + graph_only_bridge | `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion` | 3 | +| parameter_sensitive_bridge + classic_only_intermediate | `random_matrix`, `zeta_zeros` | 2 | +| parameter_sensitive_bridge + endpoint_like | `logistica_biforcazione` | 1 | +| unstable_non_bridge + classic_only_intermediate | `brownian_motion`, `cellular_automata` | 2 | +| unstable_non_bridge + endpoint_like | `coupled_oscillators`, `ising_2d`, `pendolo_doppio`, `string_vibration` | 4 | + +| row | source | graph hits | freq | classical audit | brody_q | BR-like GUE weight | +|---|---|---:|---:|---|---:|---:| +| `numeri_primi:cycle_3` | GUE | 27/27 | 1.000000 | classic_and_graph_bridge | 0.465 | 0.275 | +| `logistica_biforcazione_var_3.5699:cycle_13` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `percolation:cycle_9` | Poisson | 27/27 | 1.000000 | graph_only_bridge | 0.025 | 0.025 | +| `reaction_diffusion:cycle_11` | GUE | 27/27 | 1.000000 | graph_only_bridge | 0.000 | 0.000 | +| `random_matrix:cycle_7` | GUE | 18/27 | 0.666667 | classic_only_intermediate | 0.975 | 0.475 | +| `zeta_zeros:cycle_4` | GUE | 14/27 | 0.518519 | classic_only_intermediate | 1.000 | 0.530 | +| `brownian_motion:cycle_12` | Poisson | 5/27 | 0.185185 | classic_only_intermediate | 0.205 | 0.250 | +| `cellular_automata:cycle_8` | GUE | 0/27 | 0.000000 | classic_only_intermediate | 1.000 | 0.435 | + +## Key Findings +1. Verificato: `numeri_primi:cycle_3` e' l'unica riga che unisce bridge graph stabile e audit classico bridge: `27/27`, `brody_q=0.465`, `BR-like weight=0.275`. +2. Verificato: tre righe sono bridge graph stabili ma non classiche: `27/27` ciascuna per `logistica_biforcazione_var_3.5699`, `percolation`, `reaction_diffusion`. +3. Verificato: due righe sono classiche intermedie senza bridge stabile: `brownian_motion=5/27`, `cellular_automata=0/27`. +4. Verificato: gli intermedi classici `random_matrix` e `zeta_zeros` sono parameter-sensitive, non bridge stabili: `18/27` e `14/27`. +5. Inferito dal perimetro: il terzo incluso operativo e' un oggetto a due lettori; il grafo da solo produce residuo, il classico da solo produce crossover noto. + +## Verdict +CONSTRAINT. + +Il confine `8 GUE / 5 Poisson` non e' una lista di righe intermedie. A denominatore perturbato, una sola riga chiude come bridge a due lettori (`numeri_primi:cycle_3`). Il residuo graph-only resta informativo ma non si somma al boundary confermato. + +## Bicono della scoperta +- **Due radici**: bridge graph stabile / intermedio classico. +- **Singolare**: riga domain-window prima della scelta del lettore. +- **Invariante di passaggio**: stesso denominatore 13 righe, 27 perturbazioni del reader. +- **Campo di possibilita**: possibile = usare `numeri_primi:cycle_3` come unico candidato two-reader per ritorno fisico; non-possibile = promuovere graph-only residue come boundary confermato. + +## Consecutio +Il prossimo ciclo deve attaccare `numeri_primi:cycle_3` con un null label-preserving row-local sullo stesso lettore a due canali. Se il bridge resta `27/27` mentre le label GUE/Poisson vengono permutate entro vincoli row-aligned, il candidato diventa test fisico. Se cade, il risultato resta proprieta' del grafo 13-righe. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_bridge_stability_audit.py` e' ora lo strumento di baseline per separare `two_reader_boundary_confirmed`, `graph_only_residue` e `classic_only_intermediate` sul perimetro BOUNDARY. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python -m py_compile tools/exp_boundary_bridge_stability_audit.py tools/exp_boundary_graph_curvature_gate.py` completato. +- Run completato: `tools/data/boundary_bridge_stability_audit_20260516_1140.json`. +- 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_20260516_1140.json` +- Report: `tools/data/reports/agent_20260516_1140.md` tokens used 122,458 Eseguito l’esperimento e scritto il report richiesto: - [agent_20260516_1140.md](/opt/MM_D-ND/tools/data/reports/agent_20260516_1140.md) - [boundary_bridge_stability_audit_20260516_1140.json](/opt/MM_D-ND/tools/data/boundary_bridge_stability_audit_20260516_1140.json) Verdict: `CONSTRAINT`. Sul perimetro vivo `8 GUE / 5 Poisson`, resta un solo boundary confermato a due lettori: `numeri_primi:cycle_3` (`27/27`). Separati anche tre residui `graph_only` e due intermedi classici senza bridge stabile. Verifiche completate: `py_compile` degli script usati e check JSON su `rows_analyzed=13`, `graph_reader_runs=27`, `stable_classic_and_graph=['numeri_primi:cycle_3']`.