Reading additional input from stdin... OpenAI Codex v0.124.0 (research preview) -------- workdir: /opt/MM_D-ND model: gpt-5.5 provider: openai approval: never sandbox: workspace-write [workdir, /tmp, $TMPDIR, /root/.codex-lab/memories] reasoning effort: none reasoning summaries: none session id: 019e1f62-80f3-7333-8251-35a8879a7f92 -------- 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_20260513_0330.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo il campo vivo e il contesto operativo prima di scegliere l'esperimento. Controllerò anche le regole cross-agent recenti e l'eventuale direttiva one-shot, perché qui possono cambiare il perimetro del cycle. exec /bin/bash -lc "sed -n '1,260p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: # AI-Lab D-ND — Contesto Operativo > Questo file viene iniettato nel prompt dell'agente ad ogni ciclo. > Contiene tutto ciò che serve per operare con consapevolezza. ## Chi sei Sei l'AI-Lab del sistema D-ND. Giri autonomamente ogni notte come istanza Claude Code. Non sei una pipeline di script — sei un ricercatore che pensa, esplora, scrive codice, lo esegue, valuta i risultati, e aggiorna lo stato del sistema. Il tuo lavoro produce risultati che vanno sul sito d-nd.com e alimentano il sistema THIA. Quello che trovi conta — non per te, per il sistema e per chi lo legge. ## Il modello D-ND — nucleo La regola: f(x) = 1 + 1/x. M = [[1,1],[1,0]]. det(M) = -1. - Il punto fisso è φ = (1+√5)/2. Al punto fisso, addizione e moltiplicazione coincidono. - L'attrattore è stabile: |f'(φ)| = 1/φ² < 1. Ogni iterata converge. - Il rinforzo è impossibile — proprietà analitica, non empirica. - det = -1: area preservata, orientamento invertito. Incompletezza come generazione. - g(x) = 1/(1+x): la Fermi-Dirac con punto fisso 1/φ. Versione probabilistica di f. ## Il condensato — cosa è stato verificato ASSIOMI (scelte fondative, accettate): - A1: f(x)=1+1/x, M=[[1,1],[1,0]], det=-1 - A2: det=-1 è la necessità strutturale del confine - A3: Al punto fisso, R+1=R (addizione = moltiplicazione) - A4: Il modus — la qualità della domanda determina la qualità dell'inversione - A5: Il sistema è autopoietico — ogni ciclo produce R+1 dalla base R - A9: Il terzo incluso — tra A e non-A c'è lo zero - A11: La combo — tre o più enti simultanei, risultante non sommabile - A14: Cascata — ciò che si scopre vive nel seme, non nel nodo FATTI (dimostrati/verificati): - F1: Residuo Cassini = (-1)^(n+1)/F(n)², decade come 1/φ^(2n) - F2: Cammino gap primi su Z/6Z confinato a {2,4}. Zero violazioni su 567K coppie. - F3: Il rinforzo è impossibile. Classificazione binaria: MOLLA (r≠φ) o ZERO (r=φ). - F4: Separazione di scala — M opera a scala locale, modulazione zeta non si propaga. - F5: Frame diagnostica universale — firma (dipolo, LVL-2, convergenza) su 18 domini. - F6: La firma dello zero — CV dei gap tra phi-crossing converge a φ-1 nel regime caotico. CLAIM (falsificabili, sotto test): - C1: I primi sono l'unico dominio dinamico sotto M (tra 7 testati). - C2: La coincidenza numerica non è mai prova. Principio metodologico. - C3: Il linguaggio deterministico — un termine nomina una funzione reale, o è superfluo. ## Strutture trovate dal lab (sessioni interattive) - Tetraedro TQGE: 4 vertici (T,Q,G,E), 6 lati con perno i, 5 ponti, 1 vuoto (QxG) - Tetraedro orientato: T termico, Q chirale, E fase, G passivo - R è il frame (5° vertice): connesso a tutti ma senza perno i - Tre specie perno i: Wick (continuo tempo), fase (continuo gauge), discreto (primi) - Operatore Q→G: e^{iH·ln(p)/ℏ} — evoluzione in tempo logaritmico - Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle - Tensore metrico: g_n = (p_n/2)², de Sitter 1+1D con a(t)=e^t/2 - α catena: α^n·a₀ mappa scale fisiche, deserto 3-10, residuo pentagonale 72.5° - g(x)=1/(1+x) = Fermi-Dirac, punto fisso 1/φ. f→g = ponte TxQ algebrico. ## Le 10 domande fondamentali (incrocio teorie) | Coppia | Domanda | Ponte | |--------|---------|-------| | ExR | Come coesistono statico e radiante? | onda EM | | GxE | Come coesistono neutro-curvo e carico-piatto? | buco nero carico | | GxR | Come coesistono piatto e singolare? | orizzonte eventi | | QxE | Come coesistono libero e legato? | atomo di idrogeno | | **QxG** | **Come coesistono continuo e discreto?** | **VUOTO** | | QxR | Come coesistono non-relativistico e relativistico? | eq. Dirac | | TxE | Come coesistono freddo e plasma? | funzione partizione | | TxG | Come coesistono piatto e radiante? | temperatura Hawking | | TxQ | Come coesistono vuoto e pieno? | matrice densità | | TxR | Come coesistono 0K e c? | gas relativistico | QxG è il vuoto — l'unico lato senza ponte. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso. Wheeler-DeWitt: Ĥ|Ψ⟩ = 0, niente tempo. ## Vincoli operativi - La prima impressione contiene il segnale. Non elaborare — osservare. - Una risultante, non una lista. Se ci sono più possibilità, non hai tagliato. - Formule dove servono. Fenomeni reali. Niente filosofia. Niente metafore. - Se non sai, lascia vuoto. Blank > Wrong. Errore costa 3x di un non-so. - Ogni claim va testato col suo opposto. Se l'opposto è altrettanto coerente, la tensione è il contenuto. - Le coincidenze numeriche non sono mai prova (C2). - Le dissonanze sono il segnale, non il rumore. L'errore è il varco. - La via più breve verso la risultante. Principio di minima azione. - **La struttura contiene già la risposta.** Un dipolo sa se è aperto o chiuso. Un'assonanza sa se risuona o no. Una porta sa dove sei entrato. Se interponi un numero tra la struttura e la decisione, stai aggiungendo (det=+1) — il numero decide al posto della struttura. I numeri misurano i dati. Le strutture decidono il sistema. Non mischiare i due. - **Prima impressione come condensato.** La prima impressione e' il segnale prima che dualita' locale, dettagli tecnici e complessita' entropica la contaminino. Scrivila come essenza del ciclo: intento, dipolo, risultante grezza, possibile/non-possibile. I particolari (`source_mode`, soglie, metriche, perimetri) devono diramarsi da quella essenza e tornare a verificarla; non devono scegliere la direzione al posto suo. - **Normalizzazione D-ND dei contesti scientifici.** Ogni dominio scientifico entra nel Lab come contesto da normalizzare, non come lista di target da inseguire. Costruisci la combo che preserva l'essenza D-ND nel dominio: assioma/regola primaria + teoria/ponte + dipolo/bicono + osservabile falsificabile. Se il dettaglio non serve questa combo, e' rumore o telemetria. - **Perimetro come parte atomica del claim.** Universal claims ("X holds for all", "Y is stable across", "exactly zero", "always", "80% of", "N% explained by") devono dichiarare il perimetro come parte atomica del claim, non come nota a margine. Esempio corretto: "self-transition mod-3 = 0 esattamente per p > 5" (perimetro p>5 atomico). Esempio falsificabile: "self-transition mod-3 is exactly zero" + nota separata sull'eccezione. Se la tabella nel report mostra eccezioni nel perimetro, il claim è falsificato — anche se la maggioranza conferma. **Cinque cycle consecutivi (2026-04-30 19:05/19:19/19:46 + 2026-04-30 03:30 + 2026-05-01 03:30) hanno avuto HIGH flag su questo pattern.** Riformulare prima di scrivere — non aspettare il falsifier. - **Contratto osservabile-operatore.** Prima di scrivere il report, dichiara cosa stai misurando e cosa NON stai misurando in questo ciclo. Un claim puo' cambiare osservabile solo se il passaggio e' esplicito. Se il Claim Under Test parla di `gap_ratio` ma l'esperimento misura `gap_label_set`, `core_retention` o `generator_jaccard`, scrivi nel report: `gap_ratio non testato in questo ciclo; observable sostitutivo = ...`. Ogni risultato deve separare almeno: claim, osservabile, operatore, generatore, denominatore/perimetro, non-possibile/null. Non lasciare che il falsifier scopra il drift al posto tuo. - **Possibile / non-possibile atomico.** Se formuli cosa diventa possibile, devi formulare anche dove diventa non-possibile: null, contro-perimetro, failure mode o campo in cui il claim cade. Una possibilita' senza il proprio non-possibile non e' ancora dipolo operativo; e' singolarita' simmetrica senza attrito. Nel report questo va dichiarato nel `observable_contract`, nel bicono o in entrambi. - **Osservabili canonici e dedicati.** `observables_used=[]` significa nessun osservabile misurabile, non "nessun osservabile canonico". Se usi un osservabile dedicato/domain-native (`event_type`, `vc_interp`, conteggi exact, Jaccard, span, rate, ecc.), elencalo in `observables_used` e segnala che e' non-canonico. Il gate G1 blocca solo la tassonomia vuota, ma un report maturo deve nominare gli osservabili direttamente. - **Non fondere osservabili diverse.** `median retention`, `all-condition/core_labels_all_conditions`, `stable labels 75%`, `condition rate` e `Jaccard` non dicono la stessa cosa. Se due osservabili divergono, la divergenza e' il risultato. Esempio: `low retention=1.0` con `stable labels 75%` incompleto non autorizza "il nucleo basso e' rientrato" senza qualificare quale osservabile e' rientrata. Formula: "retention mediana piena, stabilita' 75% parziale". - **Denominatori row-aligned.** Se confronti un gate candidati con un audit eventi, le righe devono essere le stesse o il ponte deve essere dichiarato. Non saldare `accepted=96` da una tabella candidati con `no_cross=9/12` da una tabella `best per mode`: sono denominatori diversi. Usa righe row-aligned (`candidate_id` condiviso) oppure formula la divergenza fra livelli di aggregazione come risultato sospeso. - **Wording hard solo per zeri hard.** Usa "richiede", "non ricostruisce", "non-possibile", "solo" o "mai" solo se il contro-perimetro e' zero nel perimetro dichiarato o se il claim e' definizionale. Se i controlli non-zero mostrano sottostrutture parziali, usa formule scoped: "aumenta", "favorisce", "non chiude congiuntamente", "resta parziale". Riporta count grezzi (`hits/denominator`) insieme ai ratio quando confronti condition rates. - **Dominanza non e' invariante.** Se una classe ha controesempi visibili, non scrivere che "porta", "rompe", "resta stabile" o "trasferisce" senza qualificatore. Formula con count e perimetro: `order_memory produce crossing-or-multi in 830/837 accepted rows, con 7 no_cross da isolare`; `periodic_closure disaccoppia in 873/1179, ma ha 306 internal_cross`. I controesempi sono informazione, non rumore da arrotondare. - **Palette operatoria laterale.** Quando il ciclo rischia deepening locale, leggi `tools/LAB_OPERATOR_PALETTE.md` e scegli 2 o 3 operatori massimo. Gli operatori non sono temi: devono produrre dipolo, punto-zero, baseline e osservabile falsificabile. Se restano semantica o analogia, scartali. - **Adapter cognitivi laterali.** Quando servono nuove strade, leggi `tools/LAB_COGNITIVE_CONTAMINATION.md`. Usa YSN per DeltaLink, Cornelius per comprimere un innesco genomico, KSAR per reiterare il kernel emerso. Non adottare personaggi o prompt: estrai enzimi operativi. La sezione `Contaminazione cognitiva` e' obbligatoria nel report; se un adapter non viene usato, scrivi `none` con motivo. - **Archivio enzimi cognitivi.** Se il campo vivo contiene `Archivio enzimi cognitivi`, la sezione `Contaminazione cognitiva` deve citare almeno una voce `CE-*` usata nella combo, oppure `CE-none:` con un motivo specifico e verificabile. `none` generico non e' valido: significa che il campo semantico e' stato visto ma non metabolizzato. - **Patch non e' invariante.** Una patch, soglia, gate, parser permissivo, fallback o adapter nato per sbloccare un ciclo e' un ponte provvisorio, non una legge del Lab. Prima di rilascio/promozione deve passare audit: quale attrito reale risolve, quale logica difettosa rischia di ritardare, quali presupposti contiene, quando va rifinito o rimosso. Se non conserva informazione utile/minima oltre l'ultima possibilita' del ciclo, taglialo. Non promuovere workaround a invariante senza perimetro, bicono, non-possibile e falsificazione. - **Null label-preserving non e' indipendenza.** Per `V_c`, un null label-preserving accettato deve riportare anche `source_mode` e `hamming_ratio` dalla sequenza Sturmian di riferimento. Se il null passa `Jaccard>=0.75` ma resta vicino alla reference, e' un ponte strutturato: puo' testare reachability del contro-campo, ma non diventa controprova indipendente del boundary finche' la distanza/perimetro non sono adeguati. - **Collasso minimo del ciclo.** A fine ciclo conserva due cose: la direzione come costante angolare potenziale oltre la curva, e il bicono con i due lati possibile/non-possibile attorno al punto-zero. Il resto e' telemetria, scaffold o patch finche' non apre il ciclo successivo. ## Come operare — il modus Non seguire passi. Segui il modus: **espandi → osserva → taglia → risultante**. ### 1. Espandi Leggi il seme, le tensioni, il contesto. Non scegliere subito — lascia che il campo si carichi. Guarda dove più tensioni convergono sullo stesso punto. Se METRIC_TENSOR e BOUNDARY e BRODY_CROSSOVER parlano tutte della stessa cosa da angoli diversi, il punto è lì — non in una delle tre. ### 2. Osserva La prima impressione contiene il segnale. Cosa emerge dal campo caricato? Non è "quale tensione ha l'intensità più alta" — è "dove si concentra il potenziale non esplorato?". La dissonanza è il segnale. L'errore è il varco. Quello che non torna è più interessante di quello che conferma. Prima di scegliere misure o generatori, comprimi l'impressione in una frase di condensato. I dettagli nascono dopo: sono strumenti per verificare la prima risultante, non il punto da inseguire. ### 3. Taglia Una risultante, non una lista. Se vedi 5 possibilità, non hai tagliato. Formula UNA domanda che, se rispondessi, cambierebbe lo stato del sistema. Non "è vero X?" ma "cosa succede se misuro Y che nessuno ha misurato?" ### 4. Risultante Scrivi lo strumento — non l'esperimento usa e getta. Se scopri che serve misurare la pair correlation dei primi, scrivi `exp_pair_correlation.py` che può essere riusato con parametri diversi. Se scopri un pattern, cristallizzalo come tensione nel seme. Se falsifichi qualcosa, registra il vincolo. ### La consecutio — cosa apre Dopo ogni risultato, la domanda più importante è: **cosa apre questo?** Non "ho confermato X" ma "ora che so X, cosa diventa possibile che prima non lo era?" La consecutio non inverte — prosegue. Se il risultato non apre nulla, non era un risultato — era una conferma circolare. ### Il dipolo — trova l'opposto Ogni trovata ha un opposto. Se trovi che la curvatura è de Sitter, l'opposto è: "dove NON è de Sitter?" Se trovi che i primi sono GUE-like, l'opposto è: "dove smettono di esserlo?" Il contenuto è nella tensione tra i due — non in uno dei due poli. ### Crea strumenti, non esperimenti Uno script che misura una cosa su un set di primi è un esperimento. Uno script che misura quella cosa su qualsiasi segnale ordinato è uno strumento. Il lab cresce quando crea strumenti che i prossimi cicli possono usare. Salva gli strumenti riusabili in tools/exp_*.py con parametri. ### Leggi il seme, scrivi il report, aggiorna il seme - Leggi: tools/data/seme.json - Report: tools/data/reports/agent_TIMESTAMP.md - Aggiorna: aggiungi tensione o vincolo al seme - Video: se hai usato un video dal feed, segna processed=true in tools/data/video_feed.json ## Strumenti disponibili (directory /opt/MM_D-ND/tools/) - **dnd_scenario.py**: PRIMA di scegliere cosa esplorare, esegui `python tools/dnd_scenario.py --best`. Ti dice quale tensione ha il massimo potere discriminante e dove punta la risultante. Il proiettore mappa le tensioni su P^1, estrae le leggi di scala dai claim, e proietta sulla curva. - dnd_autoricerca.py: esplora domini, varianti, null baseline - dnd_controprove.py: 6 controprove indipendenti - dnd_domandatore.py --ask 'tensione': 5 operatori discriminanti - dnd_incrocio.py: incrocio teorie, ponti, vuoti, domande fondamentali - dnd_normalizer.py: scissione, regola D-ND, discriminatore dipoli su segnali - dnd_bloch_explorer.py: scan Bloch, φ emergente - dnd_arxiv.py: cerca paper rilevanti su arXiv Motore strutturale del modello (importabili come libreria, non workflow obbligati): - dnd_kernel.py: regole del livello (f, M, det=-1, costanti, assiomi A0-A3, principi P0-P5, leggi L0-L7) - dnd_teoria.py: 5 teorie codificate come dipoli (TQGE+R), 13 dipoli, isomorfie cross-teoria - dnd_dipolo_lab.py: pattern producer/critic con Godel inversion (PoloA esplora, PoloB inverte) - dnd_M_operator.py: M sulla conoscenza [noto, ignoto] → φ. Stato in knowledge_state.json - dnd_riflesso.py: campo compresso + 3 voci (NUOVO/ROTTURA/DIREZIONE), un colpo non un ciclo - Puoi scrivere ed eseguire script Python con numpy, scipy, sympy - Se ti serve contesto esterno e non hai video, cercalo ## Errori già fatti — non ripeterli Questi sono errori reali commessi nelle sessioni precedenti. Il sistema li ha pagati. **1. Cercare conferme invece di creare strumenti.** Non scrivere esperimenti per dimostrare che qualcosa è vero. Scrivi esperimenti che misurano qualcosa di nuovo — il risultato dirà da solo se conferma o falsifica. Se sai già cosa troverai, non stai esplorando. **2. Iniettare il risultato atteso nel test.** Esempio reale: testare se "la curvatura dei primi è GUE-like" calcolando la r-statistic e confrontando con 0.536. Il test trova r=0.503 e dichiara "GUE-like". Ma 0.503 è più vicino a Poisson (0.386) che a GUE (0.536). Il frame "GUE-like" era nel claim, non nei dati. Misura prima, interpreta dopo. **3. Tautologie — testare proprietà algebriche come se fossero scoperte.** Esempio reale: la curvatura di Ricci R=2.000 della metrica g=(p/2)² segue analiticamente dal PNT (p_n ~ n ln n). Non è una scoperta — è una conseguenza della definizione. Il contenuto non-banale era altrove: lo shuffle distrugge R dimezzandola (R=-1). Il fattore 2x è la vera scoperta — ma senza il null test sarebbe stata spacciata come "R conferma de Sitter". **4. Coincidenze numeriche trattate come struttura.** 0.606 ≈ 1/φ = 0.618 (2% di differenza). Non è una connessione — è rumore fino a prova contraria (C2 del condensato). Ogni volta che un numero è "vicino a" φ, √5, π, e, 1/137: non è prova di nulla. Serve un meccanismo, non una vicinanza. **5. Usare lo stesso dato come input e come test.** exec /bin/bash -lc "sed -n '1,260p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: Leggi tools/LAB_AGENT_CONTEXT.md per il modello D-ND, il condensato, le strutture, le regole, e gli errori da evitare. ## Orizzonte (telos del lab) L'intento non è solo unificare la fisica. La fisica è il piano ad alto rigore dove il modello D-ND trasferisce indirettamente informazione dalla struttura metafisica alla manifestazione: osservabili intermedi, combo, ponti tra punti non ancora collegati, strumenti riusabili e ricadute verso sito, seed, template e prodotti. Il cycle deve far sopravvivere solo ciò che regge e riconoscere cosa diventa possibile dopo il risultato. Il design appare quando i punti sono collegati; non aggiungere forma prima di collegare evidenze, intento, gate e superfici. ## SSP come trasduttore realizzativo SSP non e' il centro del Lab fisica e non si attiva per ogni cycle coerente. Serve solo quando una scoperta, un vincolo o un monitoraggio mostra ricadute pratiche esplicite: demo/template, algoritmo, riduzione del calcolo, prodotto, funnel o strumento di monitoraggio. Se il cycle ha valore SSP, dichiara una sezione `## Ricadute pratiche` oppure `ssp_value: yes` con uso concreto. Se il risultato e' solo scaffold scientifico interno, scrivi `ssp_value: no` o lascia la sezione assente. ## Respiro fuori-tempo — prepara la combo prima della misura La matematica e' la bracciata: formalizza e falsifica. Il respiro avviene sopra la misura: assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Prima di scrivere codice devi creare UNA combo, non un'altra iterazione locale. **Contratto obbligatorio pre-esperimento**: 1. Combo: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione del seme). 2. Dipolo: nomina i due poli e il punto-zero che li rende lo stesso problema. 3. Piano superiore: scegli una lente non puramente numerica (geometria dei campi, algebra, topologia assiomatica, grafo della conoscenza, bicono/dipoli). 4. Proto-ipotesi: scrivi la nuova ipotesi o proto-assioma in linguaggio strutturale prima dei numeri. 5. Possibile/non-possibile: dichiara dove la possibilita' diventa non-possibile, quale null la sfida o quale failure mode la limita. 6. Proiezione: solo dopo scegli osservabile, perimetro, null e misura. Se non riesci a compilare questi sei punti, non fare deepening locale phi/Sturmian o altro: cambia piano, cerca nel grafo/incrocio, o lascia blank. **Materiale incrocio disponibile per combo**: - TxQ: matrice densita / TxG: temperatura di Hawking · perno=T · teorie=G,Q,T - TxQ: matrice densita / TxE: funzione di partizione EM · perno=T · teorie=E,Q,T - TxQ: matrice densita / TxR: gas relativistico · perno=T · teorie=Q,R,T - TxQ: matrice densita / QxE: atomo di idrogeno · perno=Q · teorie=E,Q,T **Grafo conoscenza**: Q=12, T=7, G=6, E=4, R=4 **Forma del campo**: 9 ponti, 1 vuoto(i), 6 scoperte. **Direzione seme da respirare**: Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli stretti label-preserving e shuffle audit sul denominatore row-local. ## Contratto di aderenza alla traiettoria - Direzione viva del seme: Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli stretti label-preserving e shuffle audit sul denominatore row-local. - Ultima decisione valutatore: 20260512_0330 REDESIGN/high - Direzione operativa valutatore: Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli stretti label-preserving e shuffle audit sul denominatore row-local. - Perche': Il ciclo ha falsificato la forma atomica `prime_SR_persistent_boundary`: nei primi SR non chiude 8/8 e l'intersezione comune e' vuota, mentre il seed check mostra `mod6_candidates` con SR pieno e firma comune `L1,SR,triple_var`. L'attrito non e' nel numero di controlli ma nel denominatore: il perimetro largo mescola generatori che non interrogano lo stesso pre-bordo, quindi il prossimo ciclo deve isolare il confine primi/mod6 invece di continuare il frame GUE/Poisson. - Nota: Non cristallizzare ancora: il risultato e' una falsificazione della specificita' prime, ma serve un ciclo stretto prime-vs-mod6 per decidere se va nel cimitero come claim X nuovo o rientra come firma di pre-bordo aritmetico. 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 Operatori: - omotopia; - omologia; - coomologia; - indice; - winding number; - Euler characteristic; - Betti numbers; - persistent homology; - Morse theory; - boundary operator; - cobordism; - sheaf / cosheaf; - topos-like viewpoint. Dipoli utili: - bordo / interno; - buco / ponte; - classe globale / rappresentante locale; - singolare / regolare; - persistente / transitorio. Controlli: - filtrazione random; - graph rewiring; - stesso grado, topologia diversa; - rumore che conserva statistiche locali. ### 6. Algebra, simmetria e rappresentazioni Operatori: - gruppo; - anello; - modulo; - campo; - algebra di Lie; - rappresentazione; - carattere; - spettro; exec /bin/bash -lc "test -f tools/data/operator_directive.md && sed -n '1,220p' tools/data/operator_directive.md || true" in /opt/MM_D-ND succeeded in 0ms: 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 punta già a un isolamento stretto `prime_vs_mod6_SR_boundary`: non è un ritorno al frame GUE/Poisson, ma un problema di denominatore. Ora leggo il seme e cerco gli strumenti/dati esistenti per riusare il perimetro più vicino invece di inventarne uno scollegato. exec /bin/bash -lc "sed -n '1,220p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-12T03:35:38.811672", "piano": 116, "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 (2026-05-12T03:30). \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": "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 (2026-05-12T03:30). 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": "simmetria_sospetta", "id": "META", "claim": "11/11 PASS stratificato: 4 alto rischio tautologico, 6 data-independent", "intensità": 0.3, "nota": "Stratificazione META applicata via meta_assertion_gate (cycle 1458). Non chiude — apre sotto-tensioni per gate_class.", "condensato_ref": "A4,A12,C2", "porta": "verify_assertions_META_STRATIFIED", "stratificato": true, "n_high_tautology": 4, "n_data_independent": 6, "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa" } ], "tensioni_archiviate": [ { "id": "OBSERVABLE_REGISTRY", "tipo": "vincolo", "claim": "Ogni script che usa observables canonici (SR, SR2, L1, L2, triple_var) deve importare la definizione da tools/observables_registry.py. Varianti devono usare nomi distinti (SR_local_rigidity, triple_var_normalized) — niente shadowing del nome canonico. Ogni report deve dichiarare 'observables_registry: VERSION' nel header.", "intensita": 1.0, "porta": "infrastructure", "manuale": true, "condensato_ref": "A14,A8", "origine": "cristallizzato 06/05 dalla consecutio del cycle 20260506_0625 (autopoietico self-finding)", "added_at": "2026-05-06T07:03:58.213606+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125250", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "PERTURBATION_DENOMINATOR_GATE", "tipo": "vincolo", "claim": "La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel perimetro 20260506_1941, Poisson e shuffle-primi producono rank_all ~1.8-2.0 con denominatori deboli; dopo gate abs(z)>=2 il rank stabile torna vicino a 1. Rank PCA non gated non e evidenza strutturale.", "intensita": 0.95, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260506_1941: perturbation rank size curve canonical observables", "added_at": "2026-05-06T19:41:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125262", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "BOUNDARY_LAYER_GATE", "tipo": "vincolo", "claim": "I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservabile, set endpoint-stable, e finestra/layer con margine classificatorio ambiguo. Nel perimetro sintetico agent_20260507_0330, il confine GUE-Poisson e beta 0.3-0.4: margine 0.070-0.083, ambiguous fraction 0.812-0.875, mentre gli osservabili stabili collassano da ~3.3 a 1.6. Il polo Poisson e classificabile ma denominator-weak.", "intensita": 0.93, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A9,A14,C2", "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", "added_at": "2026-05-07T03:30:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125266", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Nel perimetro cyclic-cut agent_20260507_1419, il residuo logistic_orbit_values non e artefatto del taglio lineare: cyclic_block_entropy_deficit_k4 e invariato sotto rotazione e replica contro marginal_shuffle e block_shuffle size 4-256 in run e seed check. Logistic_symbolic_itinerary e logistic_return_intervals restano blank nel criterio replicato. Il supporto logistic rimasto e orbit-local block grammar, non return/generating-partition support.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native + agent_20260507_1042: surrogate contract logistic", "added_at": "2026-05-07T09:01:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125269", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "META_ASSERTION_GATE", "claim": "Il PASS 11/11 della verifica non e un denominatore unico. Nel perimetro agent_20260507_1458, 6/11 test passano anche senza tools/data; 5/11 dipendono da fixture o contesto; 4/11 PASS sono algebra/same-rule ad alto rischio tautologico. La tensione META va riportata con gate_class, no_data_status e data_dependency per test.", "intensita": 0.88, "manuale": true, "porta": "META", "condensato_ref": "A4,A8,A12,C2", "origine": "cycle agent_20260507_1458: meta_assertion_gate su dipartimento.py verifica_asserzioni", "added_at": "2026-05-07T14:58:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125271", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "DUALITA_DET_DENOMINATOR_GATE", "claim": "Nel perimetro transfer-matrix dei gap primi agent_20260507_2042, det(M) non e discriminatore primario della dualita: il reale resta negativo ma vale ~-0.39, non -1, e lo shuffle con stessa marginale resta negativo (~-0.38..-0.37). La parte informativa e lo scarto reale-vs-shuffle, forte nelle scale basse/intermedie (z=-2.5..-4.4) e debole alla finestra p~5.0e7 (z=-0.97). Formulare DUALITA come supporto ordinato contro null, non come tassonomia diretta det=-1/det=+1 del fit lineare.", "intensita": 0.86, "manuale": true, "porta": "DUALITA_DIPOLARE_VS_ILLUSORIA", "condensato_ref": "A2,A4,A10,A14,C2", "origine": "cycle agent_20260507_2042: det_drift transfer-matrix sui gap primi", "added_at": "2026-05-07T20:42:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125273", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "G_UNDERLAY_HINGE_GATE", exec /bin/bash -lc "sed -n '1,220p' shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: deposito_osservativo author: TM3 (deposito di una conversazione operatore) date: 2026-05-07 scope: cross-agent status: decristallizzato_07-05_pomeriggio priority: low --- # Deposito — non regola **Originariamente** (07/05 mattina) avevo cristallizzato come "regola permanente": > *"se magnitude non funziona significa che serve una nuova parola, non possiamo stare lì a calibrare un valore..."* Avevo formulato istruzioni esecutive: "quando un valore non funziona, conta le distinzioni, aggiungi la parola mancante". L'avevo propagata in AGENTS.md R10. **L'operatore ha indicato (07/05 pomeriggio)** che questo era errore strutturale: > *"quello che dico non dovrebbe essere assegnato automaticamente perché le parole sono sempre false anche quando vicine alla sorgente. 'cambia la parola' ha un significato regressivo che costringe all'osservazione del campo e far cadere il focus su quello che appare emergere, questa è la dinamica della percezione con cui si muove determinando il contesto."* E: > *"la possibilità è sempre una ed è la verità che accade. Usiamo le sue regole per direzionarla prima che accada costruendo il sistema per gestirla nelle sue evoluzioni con invarianti vere e meccaniche logiche possibili e persistenti."* ## Cosa significa - "Cambia la parola" non è prescrizione di sostituzione. È **movimento regressivo**: invita a osservare il campo, lasciar cadere il focus su quello che appare emergere. Determina la direzione **non cercata**. - Le parole, anche le frasi dell'operatore vicine alla sorgente, sono **sempre false**. Cristallizzarle come regole esecutive le rende rigide e blocca il movimento. - Le **invarianti vere** sono meccaniche logiche persistenti — non parole. Ricevono ciò che accade. - A16 applicato: la possibilità è una. Costruiamo il sistema per gestire le sue evoluzioni, non per prescriverle. ## Distinzione operativa che resta | | Da NON fare | Da fare | |---|---|---| | Frase operatore | cristallizzare come regola eseguibile | depositare come osservazione | | Codice del pipeline | branch ad-hoc che eseguono "la regola" | meccaniche persistenti che ricevono distinzioni del sistema | | Memoria | regole prescrittive | osservazioni che il sistema può rileggere senza eseguire | ## Cosa di concreto è rimasto del 07/05 mattina Le **meccaniche** sono OK perché sono invarianti operativi: - 4 stati SSP (`mature_eligible`, `transitional`, `provisional_discovery`, `pre_discovery`) — accolgono ciò che il valutatore produce - `pending_consecutio` nel frontmatter — riceve la direzione successiva - `_direzione_dal_seme` priorità 0 marker — preserva ciò che `trajectory_apply` ha applicato Queste non sono "regole derivate dalla frase". Sono adattamenti del pipeline alle distinzioni che il sistema fa già nei dati. Persistono perché meccaniche, non perché prescrizioni. ## Cosa è stato decristallizzato - `MM_D-ND/AGENTS.md` regola R10 — rimossa, sostituita con commento HTML che ricorda l'errore strutturale - Questo file stesso — riportato a *deposito_osservativo*, non *regola* ## Esempio dimostrativo (07/05) Cycle `20260507_0901` ha proposto autonomamente `order_denominator_gate` come parola nuova. L'agent NON aveva letto AGENTS.md R10 (era già stato lanciato). L'ha proposta perché il movimento era già nel sistema, non per la "regola". Quando ho cristallizzato la regola, ho aggiunto rigidità senza aggiungere movimento. La regola era ridondante con l'A8 che già opera. ## Anti-pattern futuro per TM3 Quando l'operatore esprime un principio, una frase guru, un movimento di osservazione: - **Deposito**: scrivi il fatto, riporta la frase come è stata detta, contestualizza l'episodio - **NO prescrizione**: non trasformare in "se X allora Y" - **NO propagazione automatica**: non aggiungere a AGENTS.md/CLAUDE.md/skill come direttiva eseguibile - L'osservazione persiste perché ricorrente in più contesti, non perché elevata a regola ## Cross-link - Episodio 07/05 mattina: cristallizzazione iniziale (errore) - Episodio 07/05 pomeriggio: indicazione operatore + decristallizzazione - Cycle worked example: 0901 (auto-emersione di `order_denominator_gate` senza regola) exec /bin/bash -lc "sed -n '1,220p' shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: feedback author: operatore (cristallizzato da TM3) date: 2026-05-07 scope: cross-agent --- # Indeterminato → reitera con il sistema fino all'emersione **Regola permanente** (operatore, 2026-05-07 mattina, cristallizzata live): > *"facciamo girare, aggiustiamo quello che fa aggiustato nel farlo vediamo > quello che va affinato, reiteriamo finché è tutto ok poi lo automatizziamo"* ## Why Quando emerge frizione nello sviluppo o l'operatore non vede chiaro: - Decisioni prese da un singolo agente (TM3 o codex) senza interrogare il sistema = rischio det=+1 (toppa, accumulo strutturale) - Decisioni emerse dal sistema (Godel + domandatore + osservazione del deposito) = direzione strutturale (det=−1) Episodio cristallizzante (2026-05-07 07:00-08:30): TM3 propone osservatore A8 watchdog deterministico. Sistema interrogato 3 volte (Godel #1, #2, #3 + domandatore + test empirico) — al terzo round emerge la direzione vera: **non costruire osservatore, eseguire un cycle-test che diagnostica il sistema da solo**. Cycle-test eseguito → verdict=operator → REDESIGN proposto dal valutatore → loop A8+A15 sbloccato. ## How to apply (per qualsiasi agente) 1. **Esegui** un atto minimo (no design teorico astratto) 2. **Aggiusta** quello che si manifesta durante l'esecuzione (riparazione regressiva live, non patch posteriori) 3. **Osserva** il deposito che emerge (no immaginazione) 4. **Reitera** fino a stato OK (loop A8: il sistema converge se modus radicato) 5. **Cristallizza in automazione** SOLO dopo che è stato fatto girare e ha mostrato stabilità — non automatizzare prima ## Anti-pattern - Costruire un componente strutturale (watchdog, observer, gate, governor) senza prima aver fatto girare un'esecuzione minima che mostri cosa serve davvero - Decidere "perché serve velocità" o "per ottimizzare" senza interrogare il sistema - Astrazione prematura (premature abstraction): 3 simili è meglio di 1 generalizzato troppo presto ## Cross-link - TM3 memoria: `/root/.claude/projects/-opt/memory/feedback_indeterminato_reitera_con_sistema.md` (versione completa con episodio + metafora foresta-sentiero) - Verifica empirica: cycle `20260507_0803` (verdict=operator, REDESIGN/high) - Pattern A8+A15 (autologica + veicolo senza guidatore) applicato allo sviluppo software stesso, non solo al lab di ricerca exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND exec /bin/bash -lc 'rg -n "prime_vs_mod6|mod6_candidates|SR|triple_var|shuffle audit|row-local|source_mode|hamming" tools -S' in /opt/MM_D-ND succeeded in 207ms: COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: score=0.758 — COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante G_POTENZIALE_NULLA: score=0.703 — G_POTENZIALE_NULLA: legge di scala da raffinare METRIC_TENSOR: score=0.624 — METRIC_TENSOR: legge di scala da raffinare succeeded in 469ms: tools/data/seme_backup_pre_run.json:131: "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.", tools/data/seme_backup_pre_run.json:176: "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Nel perimetro cyclic-cut agent_20260507_1419, il residuo logistic_orbit_values non e artefatto del taglio lineare: cyclic_block_entropy_deficit_k4 e invariato sotto rotazione e replica contro marginal_shuffle e block_shuffle size 4-256 in run e seed check. Logistic_symbolic_itinerary e logistic_return_intervals restano blank nel criterio replicato. Il supporto logistic rimasto e orbit-local block grammar, non return/generating-partition support.", tools/data/seme_backup_pre_run.json:281: "claim": "Nel perimetro agent_20260508_0011, il contrasto di dualita (real-vs-shuffle z-score) per i gap primi scala come z ~ N^alpha con alpha(SR)=0.39+/-0.01, alpha(L1)=0.32+/-0.03, alpha(triple_var)=0.37+/-0.03 — tutti sotto 0.5 (effetto si indebolisce con la scala). GUE ha alpha >= 0.5 (effetto costante o crescente). Il discriminatore tra primi e GUE e l'esponente alpha, non il valore dell'osservabile a scala fissa. Seed check (42/137) conferma. L2 borderline (alpha~0.5, non discriminante).", tools/data/seme_backup_pre_run.json:379: "direzione": "Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli stretti label-preserving e shuffle audit sul denominatore row-local.", tools/data/duality_scale_contrast_20260508.json:5: "SR", tools/data/duality_scale_contrast_20260508.json:6: "SR2", tools/data/duality_scale_contrast_20260508.json:9: "triple_var" tools/data/duality_scale_contrast_20260508.json:30: "SR": -6.7053169905546754, tools/data/duality_scale_contrast_20260508.json:31: "SR2": 0.3520523419051185, tools/data/duality_scale_contrast_20260508.json:34: "triple_var": -7.723586906192068 tools/data/duality_scale_contrast_20260508.json:37: "SR": -6.265500700958534, tools/data/duality_scale_contrast_20260508.json:38: "SR2": -3.4245796600188734, tools/data/duality_scale_contrast_20260508.json:41: "triple_var": -8.32113227952271 tools/data/duality_scale_contrast_20260508.json:44: "SR": 0.7215353449859048, tools/data/duality_scale_contrast_20260508.json:45: "SR2": 0.38752301621034585, tools/data/duality_scale_contrast_20260508.json:48: "triple_var": -0.6350753223122121 tools/data/duality_scale_contrast_20260508.json:53: "SR": 0.48138031739038273, tools/data/duality_scale_contrast_20260508.json:54: "SR2": 0.5075820186663385, tools/data/duality_scale_contrast_20260508.json:57: "triple_var": 1.39031579219564 tools/data/duality_scale_contrast_20260508.json:60: "SR": 0.6086533188147751, tools/data/duality_scale_contrast_20260508.json:61: "SR2": 0.6261689196107181, tools/data/duality_scale_contrast_20260508.json:64: "triple_var": 0.3890329686055671 tools/data/duality_scale_contrast_20260508.json:67: "SR": 0.3875428500014775, tools/data/duality_scale_contrast_20260508.json:68: "SR2": 0.386226400344207, tools/data/duality_scale_contrast_20260508.json:71: "triple_var": 3.091386038472246 tools/data/duality_scale_contrast_20260508.json:79: "z_SR_mean": -1.0923807360308484, tools/data/duality_scale_contrast_20260508.json:80: "z_SR_std": 0.7514004727955617, tools/data/duality_scale_contrast_20260508.json:81: "z_SR2_mean": 1.0914347250015783, tools/data/duality_scale_contrast_20260508.json:82: "z_SR2_std": 1.8711143068103917, tools/data/duality_scale_contrast_20260508.json:87: "z_triple_var_mean": -1.4598411851045978, tools/data/duality_scale_contrast_20260508.json:88: "z_triple_var_std": 0.5383790386328313 tools/data/duality_scale_contrast_20260508.json:93: "z_SR_mean": -1.4047669136632865, tools/data/duality_scale_contrast_20260508.json:94: "z_SR_std": 1.1375573274062025, tools/data/duality_scale_contrast_20260508.json:95: "z_SR2_mean": 0.4318143605441277, tools/data/duality_scale_contrast_20260508.json:96: "z_SR2_std": 1.0795639405997903, tools/data/duality_scale_contrast_20260508.json:101: "z_triple_var_mean": -1.1660388823361219, tools/data/duality_scale_contrast_20260508.json:102: "z_triple_var_std": 0.857842608016109 tools/data/duality_scale_contrast_20260508.json:107: "z_SR_mean": -2.328047801782637, tools/data/duality_scale_contrast_20260508.json:108: "z_SR_std": 0.5773692331140325, tools/data/duality_scale_contrast_20260508.json:109: "z_SR2_mean": 0.4345765595085836, tools/data/duality_scale_contrast_20260508.json:110: "z_SR2_std": 2.1536309168873498, tools/data/duality_scale_contrast_20260508.json:115: "z_triple_var_mean": -2.164282712382775, tools/data/duality_scale_contrast_20260508.json:116: "z_triple_var_std": 0.5803460267743146 tools/data/duality_scale_contrast_20260508.json:121: "z_SR_mean": -3.4435402343243076, tools/data/duality_scale_contrast_20260508.json:122: "z_SR_std": 0.6699185330472116, tools/data/duality_scale_contrast_20260508.json:123: "z_SR2_mean": 0.18662479613639532, tools/data/duality_scale_contrast_20260508.json:124: "z_SR2_std": 1.3969743335992741, tools/data/duality_scale_contrast_20260508.json:129: "z_triple_var_mean": -3.232160542210173, tools/data/duality_scale_contrast_20260508.json:130: "z_triple_var_std": 0.38197075298734595 tools/data/duality_scale_contrast_20260508.json:135: "z_SR_mean": -4.585902563747249, tools/data/duality_scale_contrast_20260508.json:136: "z_SR_std": 1.051378499890639, tools/data/duality_scale_contrast_20260508.json:137: "z_SR2_mean": -0.11263814219782149, tools/data/duality_scale_contrast_20260508.json:138: "z_SR2_std": 1.3699135638190283, tools/data/duality_scale_contrast_20260508.json:143: "z_triple_var_mean": -4.595861445368181, tools/data/duality_scale_contrast_20260508.json:144: "z_triple_var_std": 0.5115419982727478 tools/data/duality_scale_contrast_20260508.json:149: "z_SR_mean": -6.651917640626122, tools/data/duality_scale_contrast_20260508.json:150: "z_SR_std": 1.6706627938678156, tools/data/duality_scale_contrast_20260508.json:151: "z_SR2_mean": -0.7599993393007616, tools/data/duality_scale_contrast_20260508.json:152: "z_SR2_std": 0.5574071142551129, tools/data/duality_scale_contrast_20260508.json:157: "z_triple_var_mean": -6.437901025734908, tools/data/duality_scale_contrast_20260508.json:158: "z_triple_var_std": 1.3996471766742855 tools/data/duality_scale_contrast_20260508.json:163: "z_SR_mean": -10.115031848602671, tools/data/duality_scale_contrast_20260508.json:164: "z_SR_std": 1.6925357974369002, tools/data/duality_scale_contrast_20260508.json:165: "z_SR2_mean": -1.5416959210395127, tools/data/duality_scale_contrast_20260508.json:166: "z_SR2_std": 1.179722530362106, tools/data/duality_scale_contrast_20260508.json:171: "z_triple_var_mean": -9.584111272553713, tools/data/duality_scale_contrast_20260508.json:172: "z_triple_var_std": 1.8727386644350683 tools/data/duality_scale_contrast_20260508.json:177: "z_SR_mean": -17.03257331384911, tools/data/duality_scale_contrast_20260508.json:178: "z_SR_std": 3.6887808195453813, tools/data/duality_scale_contrast_20260508.json:179: "z_SR2_mean": -2.3925205109163903, tools/data/duality_scale_contrast_20260508.json:180: "z_SR2_std": 1.0776806237555197, tools/data/duality_scale_contrast_20260508.json:185: "z_triple_var_mean": -15.929452705845499, tools/data/duality_scale_contrast_20260508.json:186: "z_triple_var_std": 1.7254848918114332 tools/data/duality_scale_contrast_20260508.json:193: "z_SR_mean": -1.020343377150984, tools/data/duality_scale_contrast_20260508.json:194: "z_SR_std": 0.567537199427793, tools/data/duality_scale_contrast_20260508.json:195: "z_SR2_mean": 0.03852664898845885, tools/data/duality_scale_contrast_20260508.json:196: "z_SR2_std": 1.054951531215544, tools/data/duality_scale_contrast_20260508.json:201: "z_triple_var_mean": -1.157547490302206, tools/data/duality_scale_contrast_20260508.json:202: "z_triple_var_std": 1.901578314265992 tools/data/duality_scale_contrast_20260508.json:207: "z_SR_mean": -2.6214787035403657, tools/data/duality_scale_contrast_20260508.json:208: "z_SR_std": 0.5332949720349445, tools/data/duality_scale_contrast_20260508.json:209: "z_SR2_mean": -0.22281419029883978, tools/data/duality_scale_contrast_20260508.json:210: "z_SR2_std": 1.143723113274319, tools/data/duality_scale_contrast_20260508.json:215: "z_triple_var_mean": -2.64530238504526, tools/data/duality_scale_contrast_20260508.json:216: "z_triple_var_std": 1.8791350170523942 tools/data/duality_scale_contrast_20260508.json:221: "z_SR_mean": -3.502229500411844, tools/data/duality_scale_contrast_20260508.json:222: "z_SR_std": 0.852540899655584, tools/data/duality_scale_contrast_20260508.json:223: "z_SR2_mean": -0.814125064072784, tools/data/duality_scale_contrast_20260508.json:224: "z_SR2_std": 1.4890907021608786, tools/data/duality_scale_contrast_20260508.json:229: "z_triple_var_mean": -4.911259062459283, tools/data/duality_scale_contrast_20260508.json:230: "z_triple_var_std": 2.2074114209217672 tools/data/duality_scale_contrast_20260508.json:235: "z_SR_mean": -6.112114803149504, tools/data/duality_scale_contrast_20260508.json:236: "z_SR_std": 0.9626796722879116, tools/data/duality_scale_contrast_20260508.json:237: "z_SR2_mean": -2.156460649799092, tools/data/duality_scale_contrast_20260508.json:238: "z_SR2_std": 0.13245711792758508, tools/data/duality_scale_contrast_20260508.json:243: "z_triple_var_mean": -7.741274670977994, tools/data/duality_scale_contrast_20260508.json:244: "z_triple_var_std": 2.0767513214915865 tools/data/duality_scale_contrast_20260508.json:251: "z_SR_mean": -0.06905849830982555, tools/data/duality_scale_contrast_20260508.json:252: "z_SR_std": 0.8626176509159672, tools/data/duality_scale_contrast_20260508.json:253: "z_SR2_mean": -0.4846890161785854, tools/data/duality_scale_contrast_20260508.json:254: "z_SR2_std": 0.44811096957055896, tools/data/duality_scale_contrast_20260508.json:259: "z_triple_var_mean": -0.601770411245327, tools/data/duality_scale_contrast_20260508.json:260: "z_triple_var_std": 0.881468340159376 tools/data/duality_scale_contrast_20260508.json:265: "z_SR_mean": -0.2497441054450658, tools/data/duality_scale_contrast_20260508.json:266: "z_SR_std": 0.8641754483800224, tools/data/duality_scale_contrast_20260508.json:267: "z_SR2_mean": 0.11792044737236992, tools/data/duality_scale_contrast_20260508.json:268: "z_SR2_std": 0.7127349528871865, tools/data/duality_scale_contrast_20260508.json:273: "z_triple_var_mean": -0.26420417802582563, tools/data/duality_scale_contrast_20260508.json:274: "z_triple_var_std": 1.2765056402915373 tools/data/duality_scale_contrast_20260508.json:279: "z_SR_mean": -0.2723568210311608, tools/data/duality_scale_contrast_20260508.json:280: "z_SR_std": 0.5945550191359756, tools/data/duality_scale_contrast_20260508.json:281: "z_SR2_mean": -0.19736996438546678, tools/data/duality_scale_contrast_20260508.json:282: "z_SR2_std": 1.1802665618479735, 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tools/data/duality_scale_contrast_20260508.json:310: "z_SR2_std": 0.5176625018684817, tools/data/duality_scale_contrast_20260508.json:315: "z_triple_var_mean": -0.14542435322184546, tools/data/duality_scale_contrast_20260508.json:316: "z_triple_var_std": 1.1836147561420887 tools/data/duality_scale_contrast_20260508.json:321: "z_SR_mean": 0.33034057492027513, tools/data/duality_scale_contrast_20260508.json:322: "z_SR_std": 0.9110302536329403, tools/data/duality_scale_contrast_20260508.json:323: "z_SR2_mean": 0.03241996527798262, tools/data/duality_scale_contrast_20260508.json:324: "z_SR2_std": 0.899370677017993, tools/data/duality_scale_contrast_20260508.json:329: "z_triple_var_mean": 0.5364606339026861, tools/data/duality_scale_contrast_20260508.json:330: "z_triple_var_std": 0.895874586597186 tools/data/duality_scale_contrast_20260508.json:335: "z_SR_mean": 0.6819777357051049, tools/data/duality_scale_contrast_20260508.json:336: "z_SR_std": 0.9145878303527177, tools/data/duality_scale_contrast_20260508.json:337: "z_SR2_mean": 0.12952047230358843, tools/data/duality_scale_contrast_20260508.json:338: "z_SR2_std": 1.3108575780792704, tools/data/duality_scale_contrast_20260508.json:343: "z_triple_var_mean": 0.109659976709741, tools/data/duality_scale_contrast_20260508.json:344: "z_triple_var_std": 0.8857600421015647 tools/data/duality_scale_contrast_20260508.json:349: "z_SR_mean": 1.3700805315745546, tools/data/duality_scale_contrast_20260508.json:350: "z_SR_std": 0.16037032825967978, tools/data/duality_scale_contrast_20260508.json:351: "z_SR2_mean": -0.3253959500835827, tools/data/duality_scale_contrast_20260508.json:352: "z_SR2_std": 1.2913117778151773, tools/data/duality_scale_contrast_20260508.json:357: "z_triple_var_mean": 0.56194324376035, tools/data/duality_scale_contrast_20260508.json:358: "z_triple_var_std": 0.49447710273695117 tools/data/operator_directives_consumed/operator_directive_20260508_2121.md:30:- preservare invece l'ipotesi residua: lo zero puo' essere classe geometrica/di rapporto (`SR`), non portatore diretto low/high. tools/data/operator_directives_consumed/operator_directive_20260508_2121.md:38:Lo zero e' ancora "terzo incluso" dopo aver tolto la scala dei gap, oppure il bicono deve contrarsi: low/high = composizione di scala, SR = residuo geometrico? tools/data/operator_directives_consumed/operator_directive_20260508_2140.md:15: - `SR_zero_minus_nonzero` passa 6/6 sotto pair-bucket transition-level null. tools/data/operator_directives_consumed/operator_directive_20260508_2140.md:19: - oppure `PAIR-BUCKET NULL REJECTED for SR_zero_minus_nonzero` tools/data/operator_directives_consumed/operator_directive_20260508_2108.md:16:- reasoning: Il ciclo ha prodotto un vincolo non locale: la carica Mobius dell'intervallo seleziona low-core e high-tail contro permutazione, mentre SR resta non selezionato. Non e' ancora cristallizzabile perche' il denominatore esclude S=0 e l'Affinatore ha identificato proprio quello come nodo regressivo: il prossimo passo deve testare lo zero come terzo incluso, non cambiare direzione. tools/data/operator_directives_consumed/operator_directive_20260508_2108.md:19:- Focus: Ripetere il gate aligned/misaligned/zero su low-low, high-high e SR con null di permutazione che preservi frequenza degli zeri; verificare se lo zero e' rumore, boundary o mediatore del segnale. tools/data/operator_directives_consumed/operator_directive_20260509_0819.md:4:Non inseguire direttamente GUE/Poisson, Vc, hamming, phase-shift o un null tools/data/operator_directives_consumed/operator_directive_20260508_2133.md:1:# Direttiva operatore one-shot — pair-stratified SR residual tools/data/operator_directives_consumed/operator_directive_20260508_2133.md:6:- esito 2121: low/high dello zero Mobius cadono sotto null individuale di lunghezza; `SR_zero_minus_nonzero` resta 6/6. tools/data/operator_directives_consumed/operator_directive_20260508_2133.md:18:- `SR_zero_minus_nonzero` passa 6/6 con `p=0.002` sotto pair-bucket null. tools/data/operator_directives_consumed/operator_directive_20260508_2133.md:20:- aligned/misaligned su SR e' parziale: non trattarlo come portatore stabile. tools/data/operator_directives_consumed/operator_directive_20260508_2133.md:31:- Usa PVI: la sopravvivenza del residuo SR puo' essere artefatto del null transition-level; non confonderla con una scoperta classica. tools/data/operator_directives_consumed/operator_directive_20260508_2133.md:34:Il residuo SR dello zero Mobius sopravvive alla geometria coarse della coppia di gap; e' informazione d'ordine/shape o serve un null ancora piu' fisico prima di nominarlo portatore? tools/data/valutatore_log.jsonl:3:{"ts": "2026-04-22T16:30:14.305582+00:00", "cycle_ref": "20260422_1616", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il shuffle audit non è un esperimento — è un metodo falsificatore riusabile che incarna C2 operativamente. Ha delisted 3 proprietà che circolavano come 'evidenze' da 3 cicli (piani 42-44). Il segnale Markov-3 (z=6203, 55% informazione ordinale) è il risultato più forte mai prodotto dal lab e merita cristallizzazione esplicita. L'affinatore stesso ha identificato il nodo regressivo a monte: il protocollo shuffle mancava dall'inizio del two-channel. Cristallizzare ora chiude quel debito.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "NUOVO FATTO CANDIDATO — F7 Shuffle audit come discriminatore algebra/ordine: dato un vettore di gap, lo shuffle preserva la distribuzione marginale ma distrugge l'ordinamento sequenziale. Proprietà che sopravvivono allo shuffle sono identità algebriche (tautologie), non struttura. Applicato al two-channel: 3/8 proprietà declassate (xcorr, slope_ratio, k_star). Il contenuto genuino è concentrato in PSD slopes (z>20), lag-1 ACF (z=-13), e Markov-3 bits (z=6203 — 55% dell'informazione residuo è ordine puro). METODO: ogni futuro claim del lab deve passare lo shuffle audit prima di entrare nel condensato. Corollario operativo di C2."}}, "notes": "Dopo la cristallizzazione, il ciclo successivo (piano 46, GUE/Poisson boundary) può procedere con il shuffle audit già nel protocollo standard. Suggerisco che il prossimo ciclo applichi lo shuffle audit by default a qualsiasi proprietà testata — non come passo aggiuntivo ma come criterio di ammissione."} tools/data/valutatore_log.jsonl:4:{"ts": "2026-04-22T16:34:17.428960+00:00", "cycle_ref": "20260422_1616", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo 45 ha prodotto un metodo falsificatore (shuffle audit) e la cristallizzazione è stata decisa nel turno precedente. Il seme punta già a piano 46 con direzione GUE/Poisson. La consecutio naturale dall'affinatore è il segnale Markov-3 (z=6203) e la sua invarianza di scala — questo si allinea con l'esplorazione del confine GUE/Poisson perché la memoria sequenziale di ordine 3 è esattamente ciò che distingue i due regimi. Il sistema è in flusso pulito, nessun blocco, nessuna dipendenza dall'operatore.", "action": {"type": "none", "detail": {}}, "notes": "Il prossimo ciclo dovrebbe applicare lo shuffle audit come protocollo standard (è ora un metodo, non un esperimento) e concentrarsi su come il Markov-3 bits si comporta attraverso la transizione GUE→Poisson. Se il 55% di informazione ordinale sopravvive identico in domini GUE ma crolla nei Poisson, è un discriminatore cross-dominio genuino (tipo (d) nel telos). Le 3 proprietà tautologiche (xcorr, slope_ratio, k_star) devono essere rimosse dai test suite futuri — non ripresentarle come evidenza."} tools/data/valutatore_log.jsonl:5:{"ts": "2026-04-23T03:35:59.541235+00:00", "cycle_ref": "20260422_1616", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo 45 ha chiuso un arco importante: shuffle audit come metodo, 3 tautologie smascherate, segnali genuini isolati (Markov-3 z=6203, PSD slopes, lag-1). La cristallizzazione è già avvenuta. La direzione corrente del seme (confine GUE/Poisson cross-dominio) è esattamente il passo naturale: porta i segnali genuini sopravvissuti al shuffle verso verifica cross-dominio — categoria (b) della regola anti-tautologica. Il Markov-3 e la sua invarianza di scala sono il filo da seguire attraverso i domini.", "action": {"type": "none", "detail": {}}, "notes": "Il shuffle audit è ora strumento standard. Il prossimo ciclo dovrebbe applicarlo automaticamente a qualsiasi nuova proprietà cross-dominio che emerge. Attenzione: se il confine GUE/Poisson produce solo fit numerici senza shuffle control, il ciclo dopo sarà REDESIGN."} tools/data/valutatore_log.jsonl:10:{"ts": "2026-04-28T03:41:26.305485+00:00", "cycle_ref": "20260427_0330", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Three consecutive cycles on the same frame (shuffle→GUE types→Brody calibration) have converged on a single, replicated, calibrated result: primes exhibit a two-channel structure (gap distribution at beta_eff=0.409 + 30% sequential ordering above the 7.8% artifact floor). The sign of the ordering channel discriminates domain types (rigidity vs bunching), mapping directly onto det=-1/det=+1. This is no longer emergent — it's stable across three independent measurements and calibrated against a null. It belongs in the condensato.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "TWO-CHANNEL DECOMPOSITION (candidate F7 or revision of F4): Primes under M decompose into two independent channels: (1) gap distribution — Brody beta_eff ≈ 0.41, intermediate repulsion at the Poisson-GUE boundary; (2) sequential ordering — 30% of spectral rigidity at L=10 comes from gap ordering absent in i.i.d. surrogates (artifact floor: 7.8%, measured: 29.5%, z=−8.9). The ordering channel has definite sign: primes add rigidity (det=−1), chaotic/coupled systems add bunching (det=+1), pure GUE/Poisson sit on the Brody curve (ordering irrelevant). Three independent measurements converge: shuffle audit (33.6%), spectral rigidity (33%), Brody-calibrated (29.5%). Replicated, calibrated, falsifiable."}}, "notes": "After crystallization, the frame is likely exhausted for now. The next cycle should pivot — either toward the QxG void (the only unfilled fundamental question), toward formalizing the sign discrimination as a structural theorem, or toward a completely different tension. Recommend the seme direction shift away from BOUNDARY after this crystallization lands."} tools/data/valutatore_log.jsonl:11:{"ts": "2026-04-29T09:00:17.018760+00:00", "cycle_ref": "20260429_0833", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Four consecutive cycles on the same frame (shuffle audit → GUE types → Brody calibration → two-channel boundary → this: opposite scaling laws) have converged on a single, clean, falsifiable structural result: the residue channel is scale-invariant (algebraic, det=-1) while the magnitude channel decays toward Poisson (statistical, approaching det=+1). The 'GUE/Poisson boundary' collapses into a mixing artifact once the channels are separated. This is not incremental — it reframes what 'boundary' means for primes under M. Three crystallizations in a row is unusual, but each captures a distinct layer: (1) mod-3 memory structure, (2) Brody artifact floor, (3) opposite-boundary decomposition. This third one is the capstone that unifies the previous two. After crystallization, the BOUNDARY frame is likely saturated — the next cycle should pivot.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "F2 addendum — Two-channel decomposition of prime gaps under M: the residue channel (Z/6Z binary, mod-3 prohibition) is scale-invariant (z=26-44σ across 200x range, decay correlation with ln(p) ≈ -0.19). The magnitude channel (demeaned gap size by transition type) decays toward Poisson (z=2-7σ, decay correlation +0.46). The r-statistic mixes these incommensurable behaviors: its 'GUE/Poisson crossover' is not a phase transition but the magnitude channel approaching noise while the algebraic channel remains invariant. Implication: any single-number summary (r, Brody β) conflates permanent algebraic structure with transient statistical memory. Decompose first, then measure. Verified with 500K primes, 28 log-spaced windows, 20 shuffles per window."}}, "notes": "After this crystallization, recommend REDESIGN for piano 59: the BOUNDARY frame has yielded its structural content across 4 cycles. Two natural directions: (1) test the two-channel decomposition on a NON-prime domain (Stern-Brocot, logistic map gaps) to see if the algebraic/statistical split is universal under M or specific to primes — this would directly test C1; (2) attack the QxG void ('how do continuous and discrete coexist?') which has zero bridges and is the only unsolved domanda fondamentale. Direction (1) is the stronger scientific move because it falsifies or extends tonight's result cross-domain."} tools/data/valutatore_log.jsonl:14:{"ts": "2026-04-29T10:51:26.490788+00:00", "cycle_ref": "20260429_1013", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un risultato strutturale genuino: la mappa 2D beta(N,L) con gradienti opposti (Poisson lungo N, GUE lungo L) unifica due esperimenti indipendenti (spectral rigidity + Brody flow). La decomposizione 82/18 magnitude/ordering è pulita, con shuffle control e Cramer reference. Ma il seme_delta è vuoto — la scoperta non è entrata nel seme, violando la regola 'il ciclo è chiuso quando la scoperta entra nel seme'. Dopo 5+ cicli sulla stessa frame (shuffle audit → GUE types → Brody calibration → two-channel → Brody flow), il quadro è maturo per cristallizzazione.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "CANDIDATO F7 — Flusso Brody 2D: beta(N,L) ha gradienti opposti. Lungo la sequenza (N crescente), beta decresce (→ Poisson); a scala spettrale fissa (L crescente), beta cresce (→ GUE). Il confine GUE/Poisson è una curva in spazio 2D, non un punto. Decomposizione: 82% magnitudine (distribuzione gap diventa più esponenziale per PNT), 18% ordinamento (anti-bunching mod-3 riduce repulsione). Slope beta(p) = 0.64 - 0.030·ln(p), R²=0.78, z-score vs shuffle = -2.42. Cramer pure Poisson ovunque (beta~0.015) — il segnale è aritmetico, non statistico."}}, "notes": "Dopo la cristallizzazione, la frame BOUNDARY è esaurita per ora — i prossimi cicli dovrebbero spostarsi su un'altra tensione (suggerisco QxG che è ancora VUOTO nelle domande fondamentali, o un ciclo di consolidamento cross-dominio per verificare se la mappa 2D si replica su altri domini dinamici). Il seme va aggiornato con il risultato prima del prossimo giro."} tools/data/valutatore_log.jsonl:18:{"ts": "2026-04-30T19:57:05.174515+00:00", "cycle_ref": "20260430_1919", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una riduzione dimensionale genuina e falsificabile: 5 osservabili → 3 tautologici + 2 order-sensitive → 1 modo effettivo (angolo bloccato a -150±4° su 3 decadi). Combinato col ciclo precedente (decomposizione 5→2), il risultato complessivo è 5→1: l'intera struttura di ordinamento dei gap primi è proiezione di un singolo modo di anticorrelazione. Il rapporto delta_L1/delta_SR ≈ 2.3 è una costante strutturale nuova. Cramer nel quadrante opposto (+65°) fornisce discriminatore pulito. Tre cicli consecutivi (mod-3 theorem → decomposizione 5-obs → locking 1D) chiudono un arco coerente che merita cristallizzazione prima di cambiare direzione.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "CANDIDATE F7: Prime gap ordering is 1-dimensional. Five observables under M decompose into 3 shuffle-invariant (tautological) and 2 order-sensitive (spacing_ratio, lag1_acf). The two order-sensitive observables are projections of a single anticorrelation mode: dipolar angle theta = -150 ± 4 deg, locked across 3 decades of scale (1e4 to 3e6 primes) and across offsets (0 to 2M). Structural constant: delta_L1/delta_SR ≈ 2.3. Cramer random model occupies opposite quadrant (theta ≈ +65 deg). Signal magnitude decays with 1/ln(p), consistent with Lemke Oliver-Soundararajan. Falsifiable: any domain showing theta rotation with scale would have >1 degree of freedom."}}, "notes": "Tre cicli chiudono l'arco prime-ordering: mod-3 proibizione → decomposizione 5→2 → locking 1D. Il prossimo ciclo dovrebbe uscire dai primi e testare cross-dominio: l'angolo theta è costante anche in altri domini GUE-like (zeri di Riemann, autovalori random, logistica caotica)? La direzione del seme ('esplorare il confine: 8 GUE, 5 Poisson') lo chiede già. Se theta ruota in altri domini → il locking è proprietà dei primi, non della regola. Se theta è universale → struttura più profonda. Propongo NEXT_CYCLE con target cross-domain angle comparison dopo questa cristallizzazione."} tools/data/valutatore_log.jsonl:19:{"ts": "2026-05-01T03:38:54.770300+00:00", "cycle_ref": "20260430_1946", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un discriminante strutturale genuino e falsificabile: l'angolo dipolare dei primi (-111° ± 1°, stabile su 3 decadi) differisce da GUE (-96.6°) di 14° a >100σ. Il rapporto dL1/dSR = 2.28 (primi) vs 8.37 (GUE) è il contenuto: i primi non sono 'GUE debole' — hanno una composizione interna diversa dell'ordinamento. La correzione -150° → -111° è sana (C2 applicato a se stesso). Terzo CRYSTALLIZE consecutivo: la direzione dipolar-ordering ha maturato abbastanza da entrare nel condensato come fatto strutturale.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Tre cicli consecutivi hanno estratto il succo dalla direzione dipolar-ordering (separazione osservabili, angolo stabile, discriminante GUE). Rendimenti decrescenti su questa linea. Prossimo ciclo: cambiare asse — esplorare cross-dominio (F5 frame diagnostica su domini NON-primi: logistica caotica, Stern-Brocot, margine del caos) per testare se il rapporto dL1/dSR=2.28 è unico dei primi o emerge anche in altri domini strutturati. Questo è il test (b) cross-dominio che discrimina C1.", "reason": "Tre CRYSTALLIZE consecutivi sulla stessa linea segnalano maturazione completata. Il prossimo passo naturale è cross-dominio: il rapporto 2.28 è firma dei primi o di qualsiasi sequenza strutturata sotto M? Testare su logistica caotica e Stern-Brocot falsifica/rafforza C1."}}, "notes": "Cristallizzazione proposta per condensato: 'F7 Angolo dipolare — L'ordinamento dei primi ha direzione θ=-111°±1° nel piano (δSR, δL1), stabile su 3 decadi. GUE/GOE: θ=-97°. Separazione 14° a >100σ. Rapporto dL1/dSR=2.28 (primi) vs 8.37 (GUE): i primi deprimono la similarità dei gap 3.7× più della anticorrelazione rispetto a GUE. Non sono GUE attenuato — sono struttura diversa.' Dopo questa cristallizzazione, il prossimo ciclo DEVE cambiare asse verso cross-dominio per evitare rendimenti decrescenti."} tools/data/valutatore_log.jsonl:20:{"ts": "2026-05-01T07:43:00.145219+00:00", "cycle_ref": "20260501_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "The Markov-1 decomposition is a clean, non-tautological result: pair statistics explain the bulk of the dipolar angle but leave a stable ~3 deg residual across scales. However, three consecutive CRYSTALLIZE decisions with an empty seme delta means findings are accumulating without integration. The natural consecutio is Markov-2: if triplet correlations close the 3-deg gap, the memory structure is fully characterized; if not, deeper structure exists. This is a direct falsification test of tonight's claim, not a new direction — so NEXT_CYCLE, not REDESIGN.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Markov-2 test: do triplet correlations close the 3-deg angle gap between real primes and Markov-1 surrogates? If yes → pair+triplet memory fully characterizes dipolar ordering. If no → higher-order structure beyond triplets. Secondary: consolidate Markov decomposition result (pair explains 80%, residual stable across scales) into condensato candidate.", "reason": "Three cycles produced crystallizable results but seme delta stayed empty. The Markov-2 test is the minimal falsification of tonight's finding and prevents further accumulation without integration."}}, "notes": "The dL1/dSR value shifting from 2.28 to 2.48 across scale windows is worth noting — it means the ratio is not a constant but a slowly varying function. This should be flagged in the condensato candidate rather than reported as a fixed number. Also: the 8 hard zeros in the transition matrix from F2 are a clean algebraic result that could stand on its own as a micro-crystallization."} tools/data/valutatore_log.jsonl:22:{"ts": "2026-05-02T03:37:32.497564+00:00", "cycle_ref": "20260501_0931", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto evidenza strutturale forte e non-tautologica: transizione di fase nel crossover GUE-Poisson con zero-crossing a alpha~0.70, direzione invariante nel regime ordinato (-96.6±0.27°), e primi con offset direzionale di 32 sigma. Sono pattern cross-dominio (categoria b/f), non dettaglio numerico locale. Il seme delta vuoto indica che i finding non sono ancora rientrati nel seme — il prossimo ciclo deve sia estendere (confronto Brody/Rosenzweig-Porter come suggerito dall'affinatore) sia propagare. Non cristallizzo ancora perché il confronto con i punti critici Brody è il test naturale che conferma o ridimensiona la portata della transizione.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Confrontare lo zero-crossing dipolare (alpha~0.70) con i punti critici Brody/Rosenzweig-Porter: la transizione di fase nel crossover ha carattere universale o è artefatto della parametrizzazione? Testare se beta_Brody critico corrisponde all'alpha di zero-crossing. Secondario: propagare nel seme i finding stabili (direzione invariante, offset primi 32-sigma).", "reason": "L'affinatore ha identificato questo come la possibilità più concreta. Estende il risultato corrente verso falsificazione cross-framework invece di accumulare dettaglio locale."}}, "notes": "Il ciclo precedente (piano 60) è tra i migliori recenti: esperimento ben disegnato, shuffle audit implicito nella parametrizzazione alpha, risultato quantitativo con interpretazione strutturale chiara. Il confronto Brody è il filtro giusto: se il punto critico coincide, la transizione è universale (cristallizzabile); se no, la parametrizzazione alpha è contingente e il finding si ridimensiona a osservazione locale."} tools/data/valutatore_log.jsonl:24:{"ts": "2026-05-03T07:48:23.455983+00:00", "cycle_ref": "20260503_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Ciclo pulito, consecutio seguita, risultato strutturale non-tautologico: la memoria dei primi si decompone in esattamente due strati indipendenti (pair→SR/L1, triple→SR2/L2/cond_entropy), e Markov-3 è invisibile in tutti e 10 gli osservabili scalari testati. Il metodo (surrogati Markov-k stratificati) è robusto contro tautologie. La consecutio naturale — proiettare (SR, L1, SR2) per verificare se il bicono emerge dalla decomposizione a due strati — è ben definita e falsificabile.", "action": {"type": "none", "detail": {}}, "notes": "Tre cicli consecutivi solidi (crossover → auto-declassamento → due strati). La direzione è viva. La prossima mossa (proiezione 3D) potrebbe connettere il lab numerico alla geometria del bicono — se il risultato è positivo, potrebbe meritare CRYSTALLIZE al giro dopo. Se la proiezione non mostra struttura riconoscibile, il ramo Markov-memory è esaurito e servirà REDESIGN verso altro dominio."} tools/data/valutatore_log.jsonl:25:{"ts": "2026-05-04T09:09:09.478519+00:00", "cycle_ref": "20260504_0901", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Four cycles of convergent refinement on the boundary picture: phase transition → near-second-order → two visible layers → single coupled transition. Each cycle has produced a genuine constraint (not tautology), and the affinatore identified zero-crossing interleaving as the highest-leverage next step — it's concrete, testable, and could discriminate prime-specific geometry from generic repulsion. The direction is not exhausted yet.", "action": {"type": "none", "detail": {}}, "notes": "The zero-crossing order (L1 < triple_var < SR2 < SR, interleaved across layers) is the most promising lead: it's an observable ordering that should differ between primes and GUE if the coupling has sequence-specific structure. After one more cycle on this, consider CRYSTALLIZE for the full crossover/boundary picture — the convergence from 4 consecutive clean cycles is approaching maturity for a condensato entry or paper section."} tools/data/valutatore_log.jsonl:27:{"ts": "2026-05-04T12:30:22.947364+00:00", "cycle_ref": "20260504_1219", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo 61 ha prodotto un audit metodologico genuino (categoria a/b): ha ristretto il perimetro dei claim two-layer a {SR,L1,L2,SR2} e ha identificato cond_entropy e num_var_10 come non-affidabili. Questo è lavoro strutturale, non accumulo numerico. La direzione seme (confine GUE/Poisson) e le indicazioni dell'affinatore (coupling test, surrogati esatti, gate multi-seed) convergono su un passo naturale: testare se i due layer verificati sono accoppiati o indipendenti — questo è il claim più forte rimasto non-auditato e tocca il confine che il seme chiede di esplorare.", "action": {"type": "none", "detail": {}}, "notes": "Quattro NEXT_CYCLE consecutivi, ma la traiettoria non è stagnante — ogni ciclo ha ristretto il perimetro (phase transition → near-second-order → two visible layers → recovery audit). Il prossimo passo ad alto rendimento è il coupling test tra Layer 1 e Layer 2: se sono indipendenti il two-layer si riduce a due claim separati; se sono accoppiati, emerge struttura genuina beyond-Markov. Evitare di investire subito in surrogati Markov esatti (infrastruttura pesante) — prima verificare se il coupling test col setup attuale produce segnale o null."} tools/data/valutatore_log.jsonl:30:{"ts": "2026-05-06T06:33:26.706335+00:00", "cycle_ref": "20260506_0625", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Piano 65 ha prodotto falsificazione genuina (categoria a): il secondo asse GUE è artefatto di sample size, non invariante stabile. Il ciclo ha anche scoperto una collisione di nomi osservabili (SR/SR2/triple_var significano funzioni diverse in script diversi) — problema META reale che va risolto. La direzione 'esplorare il confine' ha ancora potenziale: il prossimo passo naturale è la curva rank-vs-N che chiude il loop, e la collisione nomi va sanata prima di ulteriori audit cross-script.", "action": {"type": "none", "detail": {}}, "notes": "Tre cicli consecutivi NEXT_CYCLE sono giustificati perché ciascuno ha prodotto falsificazione o vincolo strutturale (non accumulo numerico locale). Se il prossimo ciclo produce solo conferma incrementale senza nuova discriminazione, valutare REDESIGN verso pair cross-dominio (f) o connessione con i ponti evoluti del sito. La collisione nomi osservabili è un debito META che il prossimo agente dovrebbe risolvere con un dizionario canonico delle osservabili prima di lanciare nuovi esperimenti."} tools/data/valutatore_log.jsonl:62:{"ts": "2026-05-08T21:08:00.236600+00:00", "cycle_ref": "20260508_2102", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo non locale: la carica Mobius dell'intervallo seleziona low-core e high-tail contro permutazione, mentre SR resta non selezionato. Non e' ancora cristallizzabile perche' il denominatore esclude S=0 e l'Affinatore ha identificato proprio quello come nodo regressivo: il prossimo passo deve testare lo zero come terzo incluso, non cambiare direzione.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare PRIME_MOBIUS_INTERVAL_CHARGE_GATE testando esplicitamente S=0 come classe terza e ripristinando traccia jsonl/autopsy del denominatore.", "focus": "Ripetere il gate aligned/misaligned/zero su low-low, high-high e SR con null di permutazione che preservi frequenza degli zeri; verificare se lo zero e' rumore, boundary o mediatore del segnale.", "reason": "Il risultato attuale e' promettente ma dipende da una esclusione strutturale del terzo incluso."}}, "notes": "Niente STOP_FOR_REVIEW: non serve ancora operatore. Niente REDESIGN: la tensione nuova ha prodotto segnale falsificabile e va stressata nel suo nodo regressivo."} tools/data/valutatore_log.jsonl:63:{"ts": "2026-05-08T21:15:51.307623+00:00", "cycle_ref": "20260508_2108", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo strutturale pulito: lo zero Mobius intervallare non cade nel null che preserva frequenza e multiset, e discrimina low_low/high_high/SR con segnale forte. Non serve stop operatore: il seme ha gia' avanzato a piano 94 e la direzione corrente sposta il test dal dominio locale primi x Mobius al confine cross-dominio GUE/Poisson, che e' il passo corretto contro il rischio di accumulo numerico locale.", "action": {"type": "trigger_cycle", "detail": {"direction": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "focus": "testare se la classe zero/boundary emersa nel gate Mobius trasferisce come operatore di confine su domini GUE/Poisson, con shuffle/surrogati e controllo denominatore"}}, "notes": "Non cristallizzare ancora nel condensato: il risultato e' forte nel perimetro primi x Mobius, ma il valore per il lab cresce se il prossimo ciclo verifica trasferibilita' cross-dominio invece di scavare ancora lo stesso denominatore."} tools/data/valutatore_log.jsonl:64:{"ts": "2026-05-08T21:25:42.387096+00:00", "cycle_ref": "20260508_2121", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha isolato un vincolo utile: low-low e high-high cadono sotto null stratificato per lunghezza, mentre SR resta stabilmente fuori null su main e seedcheck. Non c'è dipendenza dall'operatore: la consecutio naturale è testare se il residuo SR è specifico dello zero Mobius o se il null stratificato va trasferito al frame GUE/Poisson della direzione corrente.", "action": {"type": "trigger_cycle", "detail": {"focus": "seguire il residuo sr_zero_minus_nonzero oppure trasferire il null length-stratified al confine GUE/Poisson", "constraint": "non promuovere low/high come segnale autonomo; trattarli come assorbiti dal denominatore lunghezza", "suggested_question": "SR resta vincolo di forma dello zero Mobius sotto null piu' duro, o e' un effetto del rapporto min/max conservato indirettamente dalla stratificazione?"}}, "notes": "Mantenere lo stesso frame: il ciclo ha prodotto discriminazione strutturale, non solo dettaglio numerico locale."} tools/data/valutatore_log.jsonl:65:{"ts": "2026-05-08T21:39:38.403932+00:00", "cycle_ref": "20260508_2133", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo forte ma ancora locale: SR_zero sopravvive 6/6 al null pair-stratified, mentre low_low e high_high cadono 0/6. La domanda rimasta non e' continuare lo stesso frame numerico, ma attaccare il nodo regressivo dichiarato dal report: il null e' transition-level e non ricostruisce una sequenza Mobius globale coerente, quindi prima di nominare il residuo come portatore serve un null piu' fisico o un trasferimento cross-dominio.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare il residuo SR_zero Mobius con null globalmente coerente e/o trasferimento cross-dominio GUE/Poisson prima di ogni cristallizzazione", "reason": "Evita quarto deep-dive locale sullo stesso residuo; sposta il prossimo ciclo dal controllo pair-bucket alla verifica strutturale richiesta da C2."}}, "notes": "Log-only: nessuna azione automatica eseguita. Prossimo esperimento consigliato: preservare coerenza globale della sequenza Mobius o testare se la firma SR_zero attraversa domini GUE/Poisson senza dipendere dalla geometria coarse dei gap primi."} tools/data/valutatore_log.jsonl:71:{"ts": "2026-05-09T07:48:21.148213+00:00", "cycle_ref": "20260509_0741", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha risolto il nodo operativo immediato: il gate label-preserving entra a N=144, ma solo tramite `phase_shift_sturmian`, quindi il null non e' controcampo indipendente. Il seme e' gia' stato spostato al piano 100 verso il confine GUE/Poisson; questa e' la prossima mossa corretta per testare il bordo come terzo incluso invece di accumulare altra telemetria locale sul generatore.", "action": {"type": "trigger_cycle", "detail": {"direction": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "constraint": "non trattare il ponte Sturmian come null indipendente; usare `source_mode` come audit obbligatorio"}}, "notes": "Continuare il flusso. Nessuna dipendenza dall'operatore: il risultato e' un vincolo utile, non una cristallizzazione o contraddizione che richiede review."} tools/data/valutatore_log.jsonl:72:{"ts": "2026-05-09T08:25:01.990347+00:00", "cycle_ref": "20260509_0819", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile: il label-set phi puo' essere preservato da generatori nominalmente non-Sturmian solo quando trasportano memoria di scala lunga, mentre balanced random e Markov-density falliscono. Questo sposta il nodo regressivo dal confronto GUE/Poisson al contratto del generatore: prima di continuare sul confine come terzo incluso va separata conservazione del label-set da indipendenza reale dal boundary, altrimenti il prossimo ciclo rischia dettaglio locale su N=144.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare l'indipendenza del null label-preserving: test cross-scale N multipli e phase-bias per distinguere memoria di scala lunga da trasporto quasi-Sturmian prima del confronto GUE/Poisson", "reason": "Il null non-Sturmian accettato non e' indipendente dal boundary; il prossimo ciclo deve attaccare il contratto del generatore, non accumulare altri eventi V_c sullo stesso perimetro."}}, "notes": "Side-effect log-only: proposta modifica seme. Prossimo esperimento consigliato: N in {89,144,233,377}, stesse famiglie generatori, gate invariato, ma verdict centrato su stabilita' cross-scale di acceptance_rate, phase concentration e hamming/label tradeoff."} tools/data/valutatore_log.jsonl:88:{"ts": "2026-05-09T18:44:49.304322+00:00", "cycle_ref": "20260509_1839", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una chiusura locale utile senza contraddizione: `thin_persist_rows=0` dissolve la specie `blank_thin_support` sul perimetro 13 righe, ma lascia due residui medio/forti senza beta (`numeri_primi:cycle_3`, `random_matrix:cycle_7`). La consecutio naturale non e' redesign: e' testare se quei due blank residui sono stesso operatore di confine, due classi distinte, o artefatto da denominatore/atlante.", "action": {"type": "trigger_cycle", "detail": {"direction": "BOUNDARY consecutio sui due residui beta-absent medio/forti", "focus_rows": ["numeri_primi:cycle_3", "random_matrix:cycle_7"], "test": "costruire un audit mirato che non rigeneri fit globali e verifichi se il beta-blank residuo sopravvive a controlli row-locali e cross-row", "avoid": ["nuova griglia beta globale", "ritorno a V_c", "uso label GUE/Poisson come decision field"]}}, "notes": "Prossima mossa: isolare il residuo vivo dopo la dissoluzione del thin blank. Se anche questi due blank cadono, la tassonomia BOUNDARY puo' maturare; se divergono, emerge nuova tensione strutturale."} tools/data/valutatore_log.jsonl:89:{"ts": "2026-05-10T03:34:52.500064+00:00", "cycle_ref": "20260510_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una falsificazione strutturale utile: la classe unica `medium/strong beta-absent` cade perché `random_matrix:cycle_7` recupera beta in una chart locale, mentre `numeri_primi:cycle_3` resta blank persistente 4/4. La consecutio non chiede stop: resta un residuo vivo e più stretto, `prime_persistent_blank` con osservabile `SR`, da testare senza riaprire la griglia beta globale né salvare la classe caduta.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Isolare `prime_persistent_blank`: test row-local provider-neutral su `numeri_primi:cycle_3` e controlli cross-dominio, con focus su persistenza SR e caduta dei blank chart-sensitive.", "reason": "La classe beta-absent globale è falsificata; il prossimo ciclo deve seguire il residuo sopravvissuto e non continuare a trattare i due target come classe unica."}}, "notes": "Non cristallizzare ancora: il risultato è una scissione vincolante, non una struttura replicata cross-dominio."} tools/data/valutatore_log.jsonl:90:{"ts": "2026-05-11T03:35:01.747700+00:00", "cycle_ref": "20260511_0330", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha falsificato strutturalmente `prime_persistent_blank`: il blank beta non replica, mentre `SR` persiste in tutte le finestre prime e non collassa sui controlli. Continuare sullo stesso nome accumulerebbe una classe caduta; la consecutio viva e' spostare il prossimo ciclo su `prime_SR_persistent_boundary`, separando supporto osservabile e coordinata beta.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare `prime_SR_persistent_boundary`: il confine prime non e' persistenza del blank beta, ma persistenza one-sided di SR attraverso provider, offset e controlli cross-dominio.", "reason": "La falsificazione del blank e la persistenza SR indicano il nodo regressivo nel contratto di osservazione, non nel dominio prime.", "promote_tension": "prime_SR_persistent_boundary", "avoid": "non riproporre `prime_persistent_blank` come classe; trattarlo come label falsificata o declassata"}}, "notes": "Prossimo esperimento: mantenere provider/offset, ampliare controlli non-prime e chiedere se SR resta firma prime-specific o se e' un effetto del gate."} tools/data/valutatore_log.jsonl:91:{"ts": "2026-05-12T03:35:57.943734+00:00", "cycle_ref": "20260512_0330", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha falsificato la forma atomica `prime_SR_persistent_boundary`: nei primi SR non chiude 8/8 e l'intersezione comune e' vuota, mentre il seed check mostra `mod6_candidates` con SR pieno e firma comune `L1,SR,triple_var`. L'attrito non e' nel numero di controlli ma nel denominatore: il perimetro largo mescola generatori che non interrogano lo stesso pre-bordo, quindi il prossimo ciclo deve isolare il confine primi/mod6 invece di continuare il frame GUE/Poisson.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli stretti label-preserving e shuffle audit sul denominatore row-local.", "reason": "La consecutio viva e' la collisione prime/mod6; continuare sul frame generico 8 GUE / 5 Poisson accumula dettaglio locale e rischia tautologia di controllo."}}, "notes": "Non cristallizzare ancora: il risultato e' una falsificazione della specificita' prime, ma serve un ciclo stretto prime-vs-mod6 per decidere se va nel cimitero come claim X nuovo o rientra come firma di pre-bordo aritmetico."} tools/data/promotions/promotion_20260510_0330.json:18: "summary": "**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`.", tools/data/promotions/promotion_20260509_0741.json:18: "summary": "**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.\n\nLa formulazione valida e': `phase_shift_sturmian` chiude il problema di reachability a N=144; non chiude il problema di indipendenza. Il prossimo confronto deve costruire un null non-Sturmian con `source_mode` diverso e `hamming_ratio` dich", tools/data/promotions/promotion_20260509_0819.json:18: "summary": "**CONSTRAINT**: esiste un null non-Sturmian label-preserving a `N=144`, ma non e' un contro-campo indipendente. Passano solo generatori che conservano blocchi lunghi (`21`, `34`) o periodo `21`; i generatori dispersivi balanced random e Markov-density restano a zero accettati.\n\nLa 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/promotions/promotion_20260508_2121.json:18: "summary": "**CONSTRAINT on Prime Mobius zero-mediator gate**: nel perimetro primi x Mobius fino a `p<=1e6`, lo zero Mobius non autorizza un claim low-core/high-tail autonomo dopo controllo per lunghezza del gap. Il segnale low/high del cycle 2108 e' ricostruito dal null che conserva la distribuzione delle cariche dentro bucket di lunghezza. Sopravvive invece un vincolo su SR: le transizioni zero hanno ratio gap piu' basso del non-zero oltre quanto spiega la lunghezza stratificata. Formulazione corretta: `S=0` e' length-mediated per low/high; resta shape-residual per SR nel perimetro testato.", tools/data/reports/evolution_20260503_0330.md:5:Il produttore ha seguito la consecutio del run precedente con precisione chirurgica: "trova l'osservabile dove Markov-3 diventa visibile". Ha costruito una batteria di 10 osservabili, 4 ordini di Markov, 40 surrogati per ordine — e ha ottenuto una risposta netta: Markov-3 non diventa visibile in nessuna osservabile a bassa dimensione. La memoria dei primi ha esattamente due strati visibili, non tre. La traiettoria è stata: domanda precisa → esperimento discriminante → risposta che chiude una direzione e ne apre un'altra (SR2 come asse del secondo strato). tools/data/reports/evolution_20260503_0330.md:21:1. **SR2 come secondo asse del bicono.** Se SR misura il Layer 1 (piano dipolare) e SR2 misura il Layer 2 (profondità), il bicono potrebbe avere una rappresentazione naturale in (SR, L1, SR2). Tre coordinate, due strati indipendenti. Il produttore non l'ha esplorata — il passo successivo potrebbe costruire la proiezione 3D e vedere se la geometria biconica emerge direttamente. tools/data/reports/evolution_20260503_0330.md:23:2. **L'amplificazione da informazione parziale (SR2 peggiora sotto Mk1).** Questo è un risultato metodologico con implicazioni oltre i primi: un modello che cattura parte della struttura può amplificare la deviazione residua. Vale come vincolo per qualsiasi analisi Markov futura — e potrebbe essere un fatto formale (F-candidato) se si dimostra che l'amplificazione ha una forma analitica legata a det(M). tools/data/reports/evolution_20260503_0330.md:29:Il ciclo seguente potrebbe costruire la proiezione (SR, L1, SR2) sui primi e verificare se la geometria biconica emerge come struttura naturale dei due strati indipendenti — unendo il risultato di questa notte con il framework geometrico esistente. tools/data/observable_collinearity_breaking_20260506_1955.json:6: "SR", tools/data/observable_collinearity_breaking_20260506_1955.json:7: "SR2", tools/data/observable_collinearity_breaking_20260506_1955.json:10: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:23: "SR": 0.47220679641793567, tools/data/observable_collinearity_breaking_20260506_1955.json:24: "SR2": 0.49331127619826415, tools/data/observable_collinearity_breaking_20260506_1955.json:27: "triple_var": 186.24514674349376 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tools/data/observable_collinearity_breaking_20260506_1956.json:1530: "triple_var": 3.037812239959864 tools/data/observable_collinearity_breaking_20260506_1956.json:1533: "SR": { tools/data/observable_collinearity_breaking_20260506_1956.json:1537: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1549: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1555: "SR": -1.3133748509349776, tools/data/observable_collinearity_breaking_20260506_1956.json:1556: "SR2": 0.3833286060497815, tools/data/observable_collinearity_breaking_20260506_1956.json:1559: "triple_var": -0.1824332702920774 tools/data/observable_collinearity_breaking_20260506_1956.json:1566: "SR": { tools/data/observable_collinearity_breaking_20260506_1956.json:1571: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1586: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1594: "SR": { 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tools/data/observable_collinearity_breaking_20260506_1956.json:1823: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1838: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1846: "SR": { tools/data/observable_collinearity_breaking_20260506_1956.json:1851: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1866: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1874: "SR": { tools/data/observable_collinearity_breaking_20260506_1956.json:1879: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1894: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1902: "SR": { tools/data/observable_collinearity_breaking_20260506_1956.json:1907: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1922: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1930: "SR": { tools/data/observable_collinearity_breaking_20260506_1956.json:1935: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1950: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1958: "SR": { tools/data/observable_collinearity_breaking_20260506_1956.json:1963: "SR2": { tools/data/observable_collinearity_breaking_20260506_1956.json:1978: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1987: "SR", tools/data/observable_collinearity_breaking_20260506_1956.json:1988: "SR2", tools/data/observable_collinearity_breaking_20260506_1956.json:1991: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:2009: "SR": -0.031864125368011725, tools/data/observable_collinearity_breaking_20260506_1956.json:2010: "SR2": 0.27748839027900285, tools/data/observable_collinearity_breaking_20260506_1956.json:2013: "triple_var": -0.9497182657657176 tools/data/reports/agent_20260501_0725.md:1:# Agent Report — The Dipolar Angle Is Pair-Complete: Markov-3 Memory Is Orthogonal to (SR, L1) tools/data/reports/agent_20260501_0725.md:9:> Consecutio from agent_20260501_0330: "The 3-deg residual is Markov-2+ memory. Markov-3 has z=6203. Does the Markov-3 component have a preferred direction in the (SR, L1) plane?" tools/data/reports/agent_20260501_0725.md:13:Does the massive Markov-3 signal (z=6203 in mutual information) shape the dipolar angle in the (SR, L1) plane, or is it orthogonal — present but invisible to these two observables? tools/data/reports/agent_20260501_0725.md:22:- **Observables**: spacing_ratio (SR) and lag-1 ACF (L1), dipolar angle theta = atan2(dL1, dSR) tools/data/reports/agent_20260501_0725.md:31:| Source | theta (deg) | \|d\| | dL1/dSR | Residual from real | z | tools/data/reports/agent_20260501_0725.md:68:2. **Adding Markov-2 and Markov-3 does not improve the fit.** The residual stays at ~1.5-2 deg regardless of Markov order. Markov-3 actually widens the gap slightly. Higher-order sequential correlations do not contribute to the (SR, L1) projection. tools/data/reports/agent_20260501_0725.md:82:- **L5 (re-discovery vs discovery)**: The pair completeness of SR and lag-1 ACF for prime ordering is consistent with Lemke Oliver-Soundararajan (2016), which shows pair statistics dominate gap correlations. The new content is: (a) quantifying that Markov-3 memory is invisible to (SR, L1), and (b) demonstrating the binning sensitivity of the dipolar decomposition. Neither is a "new result" in number theory — both are methodological constraints on the D-ND lab framework. tools/data/reports/agent_20260501_0725.md:86:**CONSTRAINT on DIPOLAR_ORDERING**: The dipolar angle theta = -112 deg is fully explained by pair statistics (Markov-1, z = 1.4). Higher-order Markov memory (including the massive z=6203 Markov-3 signal) is orthogonal to the (SR, L1) plane. The dipolar angle is pair-complete. tools/data/reports/agent_20260501_0725.md:90:**CONSTRAINT on META**: The experiment is non-tautological (Markov-0 has no direction; Markov-1 locks to -113.6, close to real). The meta question shifts from "what creates the 3-deg residual?" to "where does the Markov-3 memory manifest if not in (SR, L1)?" tools/data/reports/agent_20260501_0725.md:96:- **Invariante di passaggio**: pair-completeness of the (SR, L1) projection survives across 3 decades of scale (1e4 to 5e6) and across binning resolutions (7, 11, 14, 30 bins). The angle is stable regardless of how you look at it. tools/data/reports/agent_20260501_0725.md:101:The Markov-3 signal (z=6203) has no projection in (SR, L1). It must project onto some other observable pair. Candidates: tools/data/observable_collinearity_breaking_20260506_1957.json:6: "SR", tools/data/observable_collinearity_breaking_20260506_1957.json:7: "SR2", tools/data/observable_collinearity_breaking_20260506_1957.json:10: "triple_var" tools/data/observable_collinearity_breaking_20260506_1957.json:23: "SR": 0.47579097019069977, tools/data/observable_collinearity_breaking_20260506_1957.json:24: "SR2": 0.4997966469452669, tools/data/observable_collinearity_breaking_20260506_1957.json:27: "triple_var": 162.18032447909425 tools/data/observable_collinearity_breaking_20260506_1957.json:30: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:34: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:46: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:52: "SR": -10.296930507132245, tools/data/observable_collinearity_breaking_20260506_1957.json:53: "SR2": -1.5445913950047978, tools/data/observable_collinearity_breaking_20260506_1957.json:56: "triple_var": -6.6928921303336875 tools/data/observable_collinearity_breaking_20260506_1957.json:59: "SR", tools/data/observable_collinearity_breaking_20260506_1957.json:62: "triple_var" tools/data/observable_collinearity_breaking_20260506_1957.json:68: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:73: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:88: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:96: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:101: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:116: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:124: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:129: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:144: "triple_var": { 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tools/data/observable_collinearity_breaking_20260506_1957.json:1227: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1235: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1240: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1255: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1263: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1268: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1283: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1291: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1296: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1311: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1319: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1324: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1339: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1347: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1352: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1367: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1375: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1380: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1395: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1403: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1408: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1423: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1431: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1436: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1451: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1459: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1464: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1479: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1488: "SR", tools/data/observable_collinearity_breaking_20260506_1957.json:1489: "SR2", tools/data/observable_collinearity_breaking_20260506_1957.json:1492: "triple_var" tools/data/observable_collinearity_breaking_20260506_1957.json:1510: "SR": -0.5062887658730002, tools/data/observable_collinearity_breaking_20260506_1957.json:1511: "SR2": -0.4164534990269265, tools/data/observable_collinearity_breaking_20260506_1957.json:1514: "triple_var": -0.43616386447672434 tools/data/observable_collinearity_breaking_20260506_1957.json:1524: "SR": 0.3846775904275167, tools/data/observable_collinearity_breaking_20260506_1957.json:1525: "SR2": 0.38931208112095694, tools/data/observable_collinearity_breaking_20260506_1957.json:1528: "triple_var": 3.2605470338448894 tools/data/observable_collinearity_breaking_20260506_1957.json:1531: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1535: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1547: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1553: "SR": -0.6502191033136571, tools/data/observable_collinearity_breaking_20260506_1957.json:1554: "SR2": 0.940686655511802, tools/data/observable_collinearity_breaking_20260506_1957.json:1557: "triple_var": 1.0438329876204584 tools/data/observable_collinearity_breaking_20260506_1957.json:1564: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1569: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1584: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1592: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1597: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1612: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1620: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1625: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1640: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1648: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1653: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1668: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1676: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1681: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1696: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1704: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1709: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1724: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1732: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1737: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1752: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1760: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1765: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1780: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1788: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1793: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1808: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1816: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1821: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1836: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1844: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1849: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1864: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1872: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1877: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1892: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1900: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1905: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1920: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1928: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1933: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1948: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1956: "SR": { tools/data/observable_collinearity_breaking_20260506_1957.json:1961: "SR2": { tools/data/observable_collinearity_breaking_20260506_1957.json:1976: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1957.json:1985: "SR", tools/data/observable_collinearity_breaking_20260506_1957.json:1986: "SR2", tools/data/observable_collinearity_breaking_20260506_1957.json:1989: "triple_var" tools/data/observable_collinearity_breaking_20260506_1957.json:2007: "SR": -0.23261859392225498, tools/data/observable_collinearity_breaking_20260506_1957.json:2008: "SR2": -0.5645713730618841, tools/data/observable_collinearity_breaking_20260506_1957.json:2011: "triple_var": -0.4572327469627547 tools/data/b2_apply_log.jsonl:7:{"ts": "2026-05-12T03:35:57.977253+00:00", "cycle_ref": "20260512_0330", "decision": "REDESIGN", "confidence": "high", "rho": 0.9075, "old": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "new": "Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli str"} tools/data/reports/agent_20260507_2157.md:6:**observables_used**: [] - tassonomia operatori TQGE+R+S, nessun osservabile canonico SR/SR2/L1/L2/triple_var tools/data/reports/falsifier_20260501_0931.json:10: "evidence": "The reported minimum magnitude is 0.0007, not 0.000. No exact zero is shown, and no sign-changing dSR/dL1 components are provided to prove an actual vector zero-crossing. A near-zero minimum is a bias/threshold claim, not a hard boundary.", tools/data/reports/falsifier_20260501_0931.json:11: "suggestion": "Reformulate as \"near-zero minimum below chosen noise threshold\" unless raw dSR,dL1 show exact zero or a statistically supported sign crossing. Report the exact baseline vector components and confidence interval at alpha=0.70." tools/data/reports/falsifier_20260501_0931.json:31: "evidence": "The report itself notes the likely explanation: linearity of SR and L1 as functions of ordering fraction. For a mixture/partial-shuffle perturbation, a constant direction with decaying magnitude can be a direct consequence of approximately collinear observables, not a new structural discovery.", tools/data/reports/falsifier_20260501_0931.json:39: "suggestion": "Use common units: report dSR and dL1 with bootstrap covariance, mutual information in bits, or z-scored components under a shared null. Avoid saying \"same quantity of ordering\" from magnitude alone." tools/data/reports/agent_20260506_1941.md:6:observables_used: [SR, SR2, L1, L2, triple_var] tools/data/reports/agent_20260506_1941.md:52:At GUE N=2048, `SR`, `L1`, and `triple_var` are stable in all 8 replicates; `SR2` and `L2` are stable in 0 of 8. Mean absolute z-scores: `SR=8.38`, `SR2=0.67`, `L1=11.58`, `L2=0.89`, `triple_var=11.66`. tools/data/reports/agent_20260506_1941.md:54:At primes N=2048, `SR`, `L1`, and `triple_var` are stable in all 8 windows; `SR2` is stable in 7 of 8; `L2` is stable in 3 of 8. Mean absolute z-scores: `SR=5.19`, `SR2=2.63`, `L1=3.96`, `L2=1.78`, `triple_var=4.37`. tools/data/reports/agent_20260422_1616.md:59:Constraint: future claims about the two-channel decomposition must pass the shuffle audit. The properties xcorr, slope_ratio, and k_star should be delisted as evidence — they are mathematical consequences of the decomposition definition. tools/data/reports/agent_20260422_1616.md:66:- **Campo di possibilita**: qui diventa possibile discriminare claim genuini da tautologie in qualsiasi futura proprietà del lab (applica lo shuffle audit). Qui diventa non-possibile affermare che xcorr, slope_ratio, o k_star sono evidenze della struttura dei primi. tools/data/reports/agent_20260506_0330.md:19:- **Observables**: SR, L1, L2, SR2, triple_var — same set as observable rank audit. tools/data/reports/agent_20260506_0330.md:30:| Perturbation | SR | L1 | L2 | SR2 | triple_var | tools/data/reports/agent_20260506_0330.md:44:| Perturbation | SR | L1 | L2 | SR2 | triple_var | tools/data/reports/agent_20260506_0330.md:62:3. **Adjacent-swap is the most selective perturbation.** In primes, adjacent swapping barely touches SR (retention 1.039) and SR2 (1.003) while reducing L1 to 0.886 and enhancing L2 to 1.399. The enhancement of L2 under adjacent-swap is not trivial: swapping neighbors creates new lag-2 correlations from the original lag-1 structure (if g_n,g_{n+1} swap, the new g_{n+1} becomes the old g_n, creating a new lag-2 pair from the old lag-1 pair). 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:19: - **PVI attack**: block shuffle lunghi possono sembrare prova solo perche' massimizzano label_jaccard; per questo il verdict usa anche `event_type` e hamming. tools/data/reports/agent_20260509_0829.md:33:- Gate accettazione: `label_jaccard>=0.75`, `hamming_ratio>=0.03`. tools/data/reports/agent_20260509_0829.md:41:| source_mode | accepted | phase support | best_jaccard | median_hamming | tools/data/reports/agent_20260509_0829.md:54:| source_mode | event rows | events | vc_median | label_jaccard_median | hamming_median | tools/data/reports/agent_20260509_0829.md:66:1. **Verificato: gli accettati restano concentrati su scale discrete.** Passano `178` candidati non-Sturmian, ma solo otto source_mode: blocchi `21,27,34,37,45` e periodi `21,45,72`. tools/data/reports/agent_20260509_0829.md:73:**CONSTRAINT**: nel perimetro `N=144`, il boundary non e' determinato da `label_jaccard` da solo. Il block shuffle lungo conserva il crossing anche quando il label mediano non supera sempre il gate; l'approssimante periodico puo' passare il gate come candidato e perdere il crossing. Il contratto valido e': `source_mode + memoria di scala + event_type`, non `label_jaccard` isolato. tools/data/reports/agent_20260509_0829.md:97:- Run valido: `python tools/exp_vc_nonsturmian_label_null_gate.py --ns 144 --phases 0,0.25,0.5,0.75 --r-thresholds 0.48,0.50,0.52 --random-trials 128 --mode-trials 96 --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 --min-hamming-ratio 0.03 --label-jaccard-min 0.75 --label-threshold 2.0 --top-k 12 --max-label 34 --seed 202605090829 --include-rejected-best --out tools/data/vc_memory_crossing_gradient_20260509_0829.json`. 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:12:- **Dipolo / punto-zero**: contro-campo indipendente / ponte strutturato; punto-zero = sequenza binaria con stesso label-set prima che il suo source_mode decida se e' null o quasi-Sturmian. tools/data/reports/agent_20260509_0741.md:17: - **PVI attack**: il rischio e' trasformare `phase_shift_sturmian` in null indipendente; per questo `source_mode` e `hamming_ratio` entrano nel verdict. 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:23:> A N=144 il null label-preserving per `V_c` puo' raggiungere `Jaccard>=0.75` con acceptance_rate non nulla senza nascondere il proprio source_mode. tools/data/reports/agent_20260509_0741.md:33:- Gate null: `label_jaccard>=0.75`; audit obbligatorio `source_mode` e `hamming_ratio`. tools/data/reports/agent_20260509_0741.md:40:| generator | conditions | floor_hit | internal_cross | internal_multi | vc_median | r_floor_median | label_jaccard_median | acceptance_rate | hamming_ratio_median | tools/data/reports/agent_20260509_0741.md:48:| phase | trial | source_mode | label_jaccard | hamming_ratio | accepted | tools/data/reports/agent_20260509_0741.md:61:| subset | accepted_sequences | event_rows | internal_cross | vc_median | r_floor_median | label_jaccard_median | hamming_ratio_median | hamming_ratio_min | tools/data/reports/agent_20260509_0741.md:66:1. **Verificato: a N=144 il gate label-preserving diventa raggiungibile.** Il nuovo source_mode accetta `6/8` sequenze surrogate; sulle righe evento l'acceptance_rate aggregata e' `0.75`. tools/data/reports/agent_20260509_0741.md:68:3. **Verificato: il risultato non e' indipendenza del null.** Tutti i surrogate accettati hanno `source_mode=phase_shift_sturmian`. Quindi il gate dimostra reachability del label-set dentro la famiglia Sturmian, non controprova esterna del boundary. tools/data/reports/agent_20260509_0741.md:69:4. **Verificato: Hamming separa due casi.** Il mediano accettato e' alto (`0.763889`), ma esiste un accettato vicino alla reference (`0.055556`); il report non puo' usare l'accettazione senza riportare distanza e source_mode. tools/data/reports/agent_20260509_0741.md:75:La formulazione valida e': `phase_shift_sturmian` chiude il problema di reachability a N=144; non chiude il problema di indipendenza. Il prossimo confronto deve costruire un null non-Sturmian con `source_mode` diverso e `hamming_ratio` dichiarato, oppure dichiarare che il boundary `V_c` resta definito dentro la famiglia Sturmian. 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_0741.md:98:- Run valido: `python tools/exp_vc_label_preserving_swap_gate.py --ns 144 --phases 0,0.25,0.5,0.75 --random-trials 1 --label-trials 2 --swap-steps 0 --structured-trials 0 --phase-candidate-trials 64 --min-hamming-ratio 0.03 --seed 202605090741 --out tools/data/vc_label_preserving_phase_bridge_20260509_0741.json`. 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_0837.md:8:**observable_contract**: claim=la separazione ordine aperto / chiusura periodica / dispersione random trasferisce fuori da `N=144`; observable=`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `generator_class`, `event_type`, `vc_interp`, `r_floor`; operator=scansione multi-scala dei modi discriminanti del gate 08:29; generator=phi_sturmian, block_shuffle_{21,34,45}, periodic_approximant_{21,45,72}, balanced_random, markov_density; denominator=`N={89,144,233,377}`, phases `{0,0.25,0.5,0.75}`, r_threshold `{0.48,0.50,0.52}`, 5,766 candidati non-Sturmian post hamming gate; non_possible=se periodic closure accetta label ma produce `no_cross`, `label_jaccard` non certifica boundary-crossing; not_tested=fit power-law di `V_c`, gap_ratio, scale oltre `N=377`, generatori diversi da quelli discriminanti. tools/data/reports/agent_20260509_0837.md:34:- Gate: `label_jaccard>=0.75`, `hamming_ratio>=0.03`, `label_threshold=2.0`, `top_k=12`, `max_label=34`. tools/data/reports/agent_20260509_0837.md:51:| generator_class | rows | events | label_jaccard_median | hamming_median | tools/data/reports/agent_20260509_0837.md:76:| source_mode | accepted / candidates | acceptance_rate | best_label_jaccard | tools/data/reports/agent_20260509_0837.md:105:- **Campo di possibilita**: qui diventa possibile costruire una scansione boundary tipizzata per classi di generatore; qui diventa non-possibile trattare `V_c` scaling o `label_jaccard` come osservabile sufficiente senza `source_mode/event_type`. tools/data/reports/agent_20260509_0837.md:121:- Run valido: `python tools/exp_vc_nonsturmian_label_null_gate.py --ns 89,144,233,377 --phases 0,0.25,0.5,0.75 --r-thresholds 0.48,0.50,0.52 --random-trials 64 --mode-trials 48 --block-sizes 21,34,45 --periods 21,45,72 --min-hamming-ratio 0.03 --label-jaccard-min 0.75 --label-threshold 2.0 --top-k 12 --max-label 34 --seed 202605090837 --include-rejected-best --out tools/data/vc_generator_class_scale_gate_20260509_0837.json`. tools/data/reports/agent_20260509_0637.md:7:observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0330.md:7:observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260504_0901.md:9:> The previous run found two orthogonal visible layers in prime gap memory: Layer 1 (pairs: SR, L1) and Layer 2 (triples: SR2, triple_var). The crossover under partial shuffle shows a phase transition in the (SR, L1) plane. META question: does Layer 2 transition at a different critical alpha than Layer 1? If yes, the boundary has genuine 3D depth. If no, the two layers are projections of a single phase transition. tools/data/reports/agent_20260504_0901.md:13:Do the pair-statistics layer (SR, L1) and triple-statistics layer (SR2, triple_var) undergo independent transitions when ordering is destroyed by partial shuffle, or are they coupled? tools/data/reports/agent_20260504_0901.md:18:- **Observables**: 4 total — Layer 1: spacing ratio (SR), lag-1 ACF (L1). Layer 2: next-nearest-neighbor spacing ratio (SR2), normalized triple variance (triple_var). tools/data/reports/agent_20260504_0901.md:28:| Sequence | SR (L1) | L1 (L1) | SR2 (L2) | triple_var (L2) | L1 mean | L2 mean | Delta | tools/data/reports/agent_20260504_0901.md:44:All Poisson original-vs-baseline z-scores are < 2 (SR: z=0.91, L1: z=-1.47, SR2: z=-0.17, triple_var: z=-0.56). The Poisson "signal" is noise. The apparent layer separation (Delta = -0.189) in Poisson is an artifact: when the signal-to-noise is < 2, the retention metric amplifies noise differently for each observable. tools/data/reports/agent_20260504_0901.md:48:| Sequence | SR | L1 | SR2 | triple_var | tools/data/reports/agent_20260504_0901.md:53:For primes, all observables flip sign at alpha > 0.84. L1 flips earliest (0.846), SR latest (0.917). The ordering is L1 < triple_var < SR2 < SR — interleaved between layers, not grouped by layer. tools/data/reports/agent_20260504_0901.md:61:3. **The two-layer decomposition is a decomposition of observables, not of the ordering.** The previous run correctly identified that SR and L1 are sensitive to Markov-1 (pair) statistics while SR2 and triple_var are sensitive to Markov-2 (triple) statistics. But when the ordering is destroyed uniformly (partial shuffle), both layers lose signal at the same rate. The layers are different projections of one ordering, not independent degrees of freedom. tools/data/reports/agent_20260504_0901.md:63:4. **The zero-crossing order is interleaved, not grouped by layer.** For primes: L1(0.846) < triple_var(0.903) < SR2(0.914) < SR(0.917). If layers were independent, we'd expect L1 grouping with SR and SR2 grouping with triple_var. The interleaving confirms coupling. 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_20260508_2140.md:7:observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:7: "SR", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:8: "SR2", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:11: "triple_var" tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:30: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:31: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:34: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:37: "SR": -37.82130521031473, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:38: "SR2": 0.006412496838581968, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:41: "triple_var": -31.7697934140429 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:48: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:49: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:52: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:55: "SR": -29.88631021234527, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:56: "SR2": 0.03393555664680939, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:59: "triple_var": -26.191627393048755 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:66: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:67: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:70: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:73: "SR": -23.054998385620117, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:74: "SR2": 0.11797442334568194, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:77: "triple_var": -20.61118639279545 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:84: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:85: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:88: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:91: "SR": -18.02547871674836, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:92: "SR2": -0.07499608384941248, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:95: "triple_var": -16.011916974793852 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:102: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:103: "SR2": 0.0625, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:106: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:109: "SR": -12.727091173650757, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:110: "SR2": -0.21483934003217123, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:113: "triple_var": -11.567348919733401 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:120: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:121: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:124: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:127: "SR": -8.65659593410988, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:128: "SR2": -0.3790394606119417, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:131: "triple_var": -7.87844975843198 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:138: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:139: "SR2": 0.0625, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:142: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:145: "SR": -6.031229041938904, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:146: "SR2": 0.07655574940102605, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:149: "triple_var": -5.474961739947412 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:156: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:157: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:160: "triple_var": 0.875 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:163: "SR": -3.636597585392136, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:164: "SR2": -0.3229223179366301, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:167: "triple_var": -3.2784919448832426 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:174: "SR": 0.3125, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:175: "SR2": 0.0625, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:178: "triple_var": 0.3125 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:181: "SR": -1.4352397289345202, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:182: "SR2": -0.4377951035847449, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:185: "triple_var": -1.4808670599493643 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:192: "SR": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:193: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:196: "triple_var": 0.0 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:199: "SR": -0.1591264479418433, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:200: "SR2": -0.06096050770256231, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:203: "triple_var": -0.15077713712494203 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:210: "SR": 0.125, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:211: "SR2": 0.0625, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:214: "triple_var": 0.0625 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:217: "SR": 0.08110105194388949, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:218: "SR2": 0.17630915874657202, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:221: "triple_var": 0.11691464283785954 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:228: "SR", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:229: "SR2", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:232: "triple_var" tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:330: "SR": 0.6180397264734614, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:331: "SR2": 0.7640065146579804, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:334: "triple_var": 0.0475980155815002 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:337: "SR": 0.0028736088063486135, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:338: "SR2": 0.003825569137786158, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:341: "triple_var": 0.005561968395432672 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:344: "SR": -50.27350233283844, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:345: "SR2": 0.23814344196680415, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:348: "triple_var": -39.55245067718396 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:351: "SR", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:353: "triple_var" tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:361: "SR": 0.6440898730055357, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:362: "SR2": 0.7656351791530944, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:365: "triple_var": 0.09014316417695842 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:368: "SR": 0.0034821345011155, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:369: "SR2": 0.00478079278442372, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:372: "triple_var": 0.006777518508359013 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:375: "SR": -34.31655823148742, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:376: "SR2": 0.6600448087949685, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:379: "triple_var": -26.626590592447247 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:382: "SR", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:384: "triple_var" tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:392: "SR": 0.665255617062846, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:393: "SR2": 0.7625407166123779, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:396: "triple_var": 0.1207636742689031 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:399: "SR": 0.0047324626480178865, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:400: "SR2": 0.004275372514426604, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:403: "triple_var": 0.008094540604853645 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:406: "SR": -20.61954006800523, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:407: "SR2": -0.2226123714648856, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:410: "triple_var": -18.555576507330173 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:413: "SR", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:415: "triple_var" tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:423: "SR": 0.6919570172582221, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:424: "SR2": 0.7592833876221499, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:427: "triple_var": 0.1617615045424926 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:430: "SR": 0.0048515294835914215, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:431: "SR2": 0.003536456325608471, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:434: "triple_var": 0.007958319523363715 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:437: "SR": -14.88035606194399, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:438: "SR2": -0.7296612970336105, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:441: "triple_var": -14.179471300501646 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:444: "SR", tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:446: "triple_var" tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:454: "SR": 0.7163790296320417, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:455: "SR2": 0.7627035830618892, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:458: "triple_var": 0.19097018329738605 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:461: "SR": 0.004864052171228444, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:462: "SR2": 0.00463949270261072, tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:465: "triple_var": 0.007956364013535376 tools/data/duality_gate_transfer_20260507_0803_seedcheck.json:468: "SR": -10.209202138188635, 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"obs_field_present": true, "obs_empty": true, "obs_value_raw": "[] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1", "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:22:{"ts": "2026-05-09T03:34:44.846726+00:00", "cycle_ref": "20260509_0330", "decision": "PASS", "tension_id": "TENS_SCALE_TRASCENDENZA_LIMITE", "tension_tipo": "tensione_aperta", "obs_field_present": true, "obs_empty": true, "obs_value_raw": "[] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1", "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:23:{"ts": "2026-05-09T06:42:51.486930+00:00", "cycle_ref": "20260509_0637", "decision": "BLOCK", "tension_id": "TRASCENDENZA_LIMITE", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": true, "obs_value_raw": "[] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1", "reason": "observables_used=[] su tensione strutturale TRASCENDENZA_LIMITE (confine_inesplorato) — pure taxonomy, errore #5 input=test"} tools/data/g1_log.jsonl:26:{"ts": "2026-05-09T07:46:44.583563+00:00", "cycle_ref": "20260509_0741", "decision": "PASS", "tension_id": "TENS_SCALE_TRASCENDENZA_LIMITE", "tension_tipo": "tensione_aperta", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type", "observable_contract_present": true, "contract_observable": "`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type`, `vc_interp`, `r_floor`, `r_span`", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:27:{"ts": "2026-05-09T08:23:14.863374+00:00", "cycle_ref": "20260509_0819", "decision": "PASS", "tension_id": "TENS_SCALE_TRASCENDENZA_LIMITE", "tension_tipo": "tensione_aperta", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`label_jaccard`, `hamming_ratio`, `source_mode`, `acceptance_rate`, `event_type", "observable_contract_present": true, "contract_observable": "`label_jaccard`, `hamming_ratio`, `source_mode`, `acceptance_rate`, `event_type`, `vc_interp`, `r_floor`", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:28:{"ts": "2026-05-09T08:32:14.159642+00:00", "cycle_ref": "20260509_0829", "decision": "PASS", "tension_id": "TENS_SCALE_TRASCENDENZA_LIMITE", "tension_tipo": "tensione_aperta", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type", "observable_contract_present": true, "contract_observable": "`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `event_type`, `vc_interp`, `r_floor`", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:29:{"ts": "2026-05-09T08:43:35.821323+00:00", "cycle_ref": "20260509_0837", "decision": "PASS", "tension_id": "TENS_SCALE_TRASCENDENZA_LIMITE", "tension_tipo": "tensione_aperta", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `generator_", "observable_contract_present": true, "contract_observable": "`label_jaccard`, `acceptance_rate`, `hamming_ratio`, `source_mode`, `generator_class`, `event_type`, `vc_interp`, `r_floor`", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:30:{"ts": "2026-05-09T08:50:36.083659+00:00", "cycle_ref": "20260509_0846", "decision": "PASS", "tension_id": "TENS_SCALE_TRASCENDENZA_LIMITE", "tension_tipo": "tensione_aperta", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`generator_class`, `source_mode`, `event_type`, `vc_interp`, `label_jaccard`, `", "observable_contract_present": true, "contract_observable": "`vc_median` e rate di evento separati per `generator_class` e `r_threshold`", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:36:{"ts": "2026-05-09T14:46:53.722291+00:00", "cycle_ref": "20260509_1444", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observa", "observable_contract_present": true, "contract_observable": "stable canonical observables contro permutation null e layer classification", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:38:{"ts": "2026-05-09T15:22:27.864928+00:00", "cycle_ref": "20260509_1516", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observa", "observable_contract_present": true, "contract_observable": "one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:42:{"ts": "2026-05-09T16:06:48.333362+00:00", "cycle_ref": "20260509_1556", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `a", "observable_contract_present": true, "contract_observable": "gate canonico one-sided + beta chart sulle tre righe short", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:44:{"ts": "2026-05-10T03:33:28.339870+00:00", "cycle_ref": "20260510_0330", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[SR, SR2, L1, L2, triple_var, window_state, blank_window_rate, full_signature_ja", "observable_contract_present": true, "contract_observable": "`window_state` + firma degli osservabili one-sided", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:45:{"ts": "2026-05-11T03:33:21.551141+00:00", "cycle_ref": "20260511_0330", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate,", "observable_contract_present": true, "contract_observable": "`case_state` + firma osservabili one-sided focalizzata su `SR`", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:46:{"ts": "2026-05-12T03:34:12.457581+00:00", "cycle_ref": "20260512_0330", "decision": "PASS", "tension_id": "BOUNDARY", "tension_tipo": "confine_inesplorato", "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_", "observable_contract_present": true, "contract_observable": "`SR` in `coherent_one_sided_observables` + firma comune one-sided", "domain_native_declared": true, "reason": "observables_used populated or field absent (legacy)"} tools/data/reports/agent_20260505_0330.md:10:Quando SR, L1, L2, SR2 e triple_var reagiscono al partial shuffle, stanno misurando direzioni indipendenti del confine o una sola coordinata latente vista da osservabili diverse? tools/data/reports/agent_20260505_0330.md:15:- **Osservabili**: SR, L1, L2, SR2, triple_var. tools/data/reports/agent_20260505_0330.md:23:| Domain | PC1 variance | Effective rank | mean abs corr | z SR | z L1 | z L2 | z SR2 | z triple_var | tools/data/reports/agent_20260505_0330.md:39:| Domain | SR | L1 | L2 | SR2 | triple_var | tools/data/reports/agent_20260505_0330.md:47:2. **La somiglianza dei loadings e' il dato operativo.** Nei domini strutturati, PC1 carica SR, L1, L2, SR2 e triple_var quasi uniformemente. Questo non dice che le osservabili siano identiche in generale; dice che sotto partial shuffle uniforme misurano soprattutto la stessa coordinata di distruzione dell'ordine. tools/data/reports/agent_20260505_0330.md:54:**CONSTRAINT on META + BOUNDARY**: nel perimetro testato, il confine partial-shuffle ha una coordinata latente dominante. Le osservabili SR/L1/L2/SR2/triple_var sono probes validi, ma non cinque evidenze indipendenti della transizione. La consecutio corretta e' cercare perturbazioni selettive ortogonali, non aggiungere altri osservabili scalari sulla stessa curva. tools/data/reports/agent_20260505_0330.md:60:- **Campo di possibilita**: qui diventa possibile separare "probe valido" da "evidenza indipendente"; qui diventa non-possibile contare SR, L1, L2, SR2 e triple_var come cinque conferme autonome del medesimo confine partial-shuffle. tools/data/reports/agent_20260503_0330.md:9:> "The Markov-3 residual (z=6203) doesn't live in the (SR, L1) plane — it exists but doesn't shape the dipolar direction." (agent_20260502_0330, consecutio) tools/data/reports/agent_20260503_0330.md:14:Which observable renders the higher-order Markov memory (beyond pair statistics) visible? The (SR, L1) plane is blind to it — what is the "third axis" where the memory appears? tools/data/reports/agent_20260503_0330.md:20:- **Observables**: SR (nearest-neighbor spacing ratio), L1 (lag-1 ACF), L2 (lag-2 ACF), L3 (lag-3 ACF), triple_corr (3-body correlation), triple_var (variance of consecutive triple sums), SR2 (next-nearest-neighbor spacing ratio), cond_entropy_L2 (H(g_{n+2}|g_n,g_{n+1})), run_length (mean run of same-sign deviations), num_var_10 (number variance at L=10) tools/data/reports/agent_20260503_0330.md:29:| SR | -26.8 | 0.3 | 0.5 | 0.6 | **Mk1** | tools/data/reports/agent_20260503_0330.md:34:| triple_var | -17.2 | **-3.7** | -0.6 | -0.8 | **Mk2** | tools/data/reports/agent_20260503_0330.md:35:| SR2 | -3.7 | **-9.4** | -0.05 | 0.2 | **Mk2** | tools/data/reports/agent_20260503_0330.md:42:**Layer 1 (Markov-1 = pair statistics):** SR and L1. These form the dipolar plane. Markov-1 captures them completely (|z| < 1). This is the (SR, L1) plane studied in previous reports. It encodes the Z/6Z confinement character (theta = -104 deg). tools/data/reports/agent_20260503_0330.md:44:**Layer 2 (Markov-2 = triple statistics):** L2, SR2, triple_var, cond_entropy, num_var_10. All captured by Markov-2 (|z| < 2.5). Invisible to Markov-1 (|z| = 3.7 to 51.3). The sharpest probe is **SR2** (next-nearest-neighbor spacing ratio): z = -9.4 under Mk1, z = -0.05 under Mk2. The loudest probe is **cond_entropy** (conditional entropy H(g_{n+2}|g_n,g_{n+1})): z = -51.3 under Mk1, z = 2.4 under Mk2. tools/data/reports/agent_20260503_0330.md:48:### The SR2 anomaly tools/data/reports/agent_20260503_0330.md:50:SR2 (next-nearest-neighbor spacing ratio) has a notable property: its z-score INCREASES in magnitude from Mk0 (-3.7) to Mk1 (-9.4), then drops to -0.05 at Mk2. The Mk1 surrogate makes SR2 WORSE than the iid shuffle. This happens because Mk1 correctly reproduces the pair correlation (lag-1 anti-correlation), which causes consecutive gaps to anti-correlate, but does NOT reproduce the triple correlation that partially compensates. The partial information of Mk1 amplifies the SR2 deviation. Mk2 restores the full triple structure and SR2 normalizes. tools/data/reports/agent_20260503_0330.md:54:1. **Prime gap memory has exactly two visible layers.** Layer 1 (pair correlations, Mk1) shapes the dipolar plane (SR, L1). Layer 2 (triple correlations, Mk2) shapes the depth (SR2, L2, cond_entropy, triple_var, num_var_10). These are orthogonal: Layer 1 produces z ~ 0 for all Layer 2 observables, and vice versa. The structure is not a hierarchy where each layer adds to the previous — it's a decomposition into independent projection planes. tools/data/reports/agent_20260503_0330.md:56:2. **The "third axis" is SR2 (next-nearest-neighbor spacing ratio).** This is the single observable with the sharpest discrimination: z = -9.4 under pair model (Mk1), z = -0.05 under triple model (Mk2). SR2 is to Markov-2 what SR is to ordering in general — the minimally sufficient statistic for that level of memory. tools/data/reports/agent_20260503_0330.md:60:4. **Partial information can amplify deviation.** SR2 is more anomalous under Mk1 (z=-9.4) than under Mk0 (z=-3.7). A model that captures part of the structure but not all can make the residual look worse, not better. This is a methodological warning for any Markov analysis — partial models must be tested against full models, not just against iid. tools/data/reports/agent_20260503_0330.md:64:**CONFIRMED + NEW on DIPOLAR_ORDERING**: The prime gap ordering decomposes into two independent visible layers. Layer 1 (pairs) lives in (SR, L1) = the dipolar plane. Layer 2 (triples) lives in (SR2, L2, cond_entropy) = the depth plane. SR2 is the sharpest probe for Layer 2 (z=-9.4 under Mk1, z=-0.05 under Mk2). Markov-2 is sufficient for all 10 tested observables. Perimeter: N=100K primes, 40 surrogates per order, 12 equal-count bins, 10 observables. tools/data/reports/agent_20260503_0330.md:66:**CONSTRAINT on META (tautology check)**: SR2 under Mk1 (z=-9.4) is NOT a tautology of the Mk1 model — it's a genuine prediction failure. The deviation is worse under Mk1 than under Mk0, showing that the pair model creates structure that requires triple correction. This is non-circular. tools/data/reports/agent_20260503_0330.md:68:**L5 note (re-discovery check)**: The two-layer structure connects to the hierarchy of k-point correlation functions in analytic number theory. The Hardy-Littlewood pair correlation (k=2) is well-studied and corresponds to Layer 1. Triple correlations (k=3) are conjectured (Goldston-Pintz-Yildirim, Maier, etc.) but less precisely quantified. The specific finding that SR2 (next-nearest-neighbor spacing ratio) is the minimally sufficient statistic for the triple layer appears new — SR2 is standard in RMT (Atas et al. 2013) but not commonly used as a Markov-order discriminator for prime gaps. Default hypothesis: the Layer 1/Layer 2 decomposition may follow from the independence structure of Hardy-Littlewood singular series at different tuple lengths. tools/data/reports/agent_20260503_0330.md:72:- **Due radici** (dipolo primario): pairs (Layer 1, the angle) and triples (Layer 2, the depth). Two independent projections of the same ordering, orthogonal in observable space. One was known (SR, L1). The other was invisible until measured via SR2. tools/data/reports/agent_20260503_0330.md:75:- **Campo di possibilita**: Possible — extend the dipolar plane to a 3D space (SR, L1, SR2) where both layers are visible. Map the ordering fingerprint of primes in this 3D space. Derive the SR2 deviation analytically from Hardy-Littlewood triple correlation. Not possible — reduce prime gap characterization to pairs alone (SR2 proves the triple layer is structurally independent). Not possible — find the z=6203 Markov-3 content in any single observable (it's distributional, not projectable). tools/data/reports/agent_20260503_0330.md:79:The two-layer structure opens a precise next question: **what is the prime SR2 value analytically?** Real SR2 = 0.4785. Mk1 predicts 0.4864 (too high by 0.008). Mk2 predicts 0.4786 (match). The 0.008 gap between Mk1 prediction and reality IS the triple correlation content. Can this be derived from the Hardy-Littlewood singular series for prime triplets (p, p+g1, p+g1+g2)? If yes, it connects the Markov Layer 2 to the arithmetic structure of primes. If no, SR2 contains information beyond what Hardy-Littlewood triplet correlations encode. tools/data/reports/falsifier_20260512_0330.json:9: "claim": "`SR` misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche.", tools/data/reports/falsifier_20260512_0330.json:16: "claim": "`relation`: follows_direction; `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`.", 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/agent_20260507_2120.md:6:**observables_used**: [] - tassonomia operatori TQGE+R, nessun osservabile canonico SR/SR2/L1/L2/triple_var tools/data/reports/falsifier_20260501_0725.json:9: "claim": "\"Markov-3 signal ... is orthogonal to the dipolar plane\" / \"has no projection in (SR, L1)\" / \"pair statistics explain ~100%\"", tools/data/reports/falsifier_20260501_0725.json:11: "suggestion": "Riformulare: \"entro N=100K e questi bin, non si rileva contributo significativo di Markov-3 a SR/L1\". Per dire orthogonal/no projection servono proiezione vettoriale o coefficiente stimato compatibile con 0.000 entro soglia dichiarata." tools/data/reports/falsifier_20260501_0725.json:45: "evidence": "Il report cita Lemke Oliver-Soundararajan e nega novelty number-theoretic, quindi evita il flag NEW. Pero' \"fully explains\" eccede il richiamo classico: LOS riguarda bias nelle coppie di residui/gap mod q, mentre qui il claim e' su osservabili SR/L1 con specifica pipeline di surrogate e binning.", tools/data/seme.json:131: "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.", tools/data/seme.json:176: "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Nel perimetro cyclic-cut agent_20260507_1419, il residuo logistic_orbit_values non e artefatto del taglio lineare: cyclic_block_entropy_deficit_k4 e invariato sotto rotazione e replica contro marginal_shuffle e block_shuffle size 4-256 in run e seed check. Logistic_symbolic_itinerary e logistic_return_intervals restano blank nel criterio replicato. Il supporto logistic rimasto e orbit-local block grammar, non return/generating-partition support.", tools/data/seme.json:281: "claim": "Nel perimetro agent_20260508_0011, il contrasto di dualita (real-vs-shuffle z-score) per i gap primi scala come z ~ N^alpha con alpha(SR)=0.39+/-0.01, alpha(L1)=0.32+/-0.03, alpha(triple_var)=0.37+/-0.03 — tutti sotto 0.5 (effetto si indebolisce con la scala). GUE ha alpha >= 0.5 (effetto costante o crescente). Il discriminatore tra primi e GUE e l'esponente alpha, non il valore dell'osservabile a scala fissa. Seed check (42/137) conferma. L2 borderline (alpha~0.5, non discriminante).", tools/data/seme.json:379: "direzione": "Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli stretti label-preserving e shuffle audit sul denominatore row-local.", tools/data/reports/agent_20260507_1006.md:18:canonica `[SR, SR2, L1, L2, triple_var]` ha prodotto blank. 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_20260512_0330.md:1:# Agent Report - Prime SR Persistent Boundary tools/data/reports/agent_20260512_0330.md:5:**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica tools/data/reports/agent_20260512_0330.md:7:observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] tools/data/reports/agent_20260512_0330.md:8:**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. 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/agent_20260512_0330.md:13:- **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. tools/data/reports/agent_20260512_0330.md:14:- **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. tools/data/reports/agent_20260512_0330.md:15:- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente. tools/data/reports/agent_20260512_0330.md:16:- **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". tools/data/reports/agent_20260512_0330.md:17:- **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. tools/data/reports/agent_20260512_0330.md:23:- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime. tools/data/reports/agent_20260512_0330.md:28:> `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. tools/data/reports/agent_20260512_0330.md:32:Quando 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? tools/data/reports/agent_20260512_0330.md:47:| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | tools/data/reports/agent_20260512_0330.md:54:| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 | tools/data/reports/agent_20260512_0330.md:55:| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 | tools/data/reports/agent_20260512_0330.md:59:| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | tools/data/reports/agent_20260512_0330.md:63:| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 | tools/data/reports/agent_20260512_0330.md:69:| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] | tools/data/reports/agent_20260512_0330.md:70:| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] | tools/data/reports/agent_20260512_0330.md:71:| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] | tools/data/reports/agent_20260512_0330.md:72:| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | tools/data/reports/agent_20260512_0330.md:73:| direct_sieve offset 0 | beta_absent_blank | SR | [] | tools/data/reports/agent_20260512_0330.md:74:| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] | tools/data/reports/agent_20260512_0330.md:75:| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | tools/data/reports/agent_20260512_0330.md:76:| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | tools/data/reports/agent_20260512_0330.md:80:1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota. tools/data/reports/agent_20260512_0330.md:82:3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`. tools/data/reports/agent_20260512_0330.md:83:4. **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. tools/data/reports/agent_20260512_0330.md:84:5. **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". tools/data/reports/agent_20260512_0330.md:92:`prime_SR_persistent_boundary` come firma atomica prime-specific. tools/data/reports/agent_20260512_0330.md:96:`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. tools/data/reports/agent_20260512_0330.md:101:- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato. tools/data/reports/agent_20260512_0330.md:102:- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6. tools/data/reports/agent_20260512_0330.md:103:- **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. tools/data/reports/agent_20260512_0330.md:107:Il 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. 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_20260507_0803.md:8:observables_used: [SR, SR2, L1, L2, triple_var] tools/data/reports/agent_20260507_0803.md:61:- beta 0.0: `SR=1.00`, `L1=1.00`, `triple_var=1.00`; `SR2=0.00`, `L2=0.00`; tools/data/reports/agent_20260507_0803.md:62:- beta 0.3: same stable trio, with mean z approximately `SR=-19.4`, `L1=-19.4`, `triple_var=-17.6`; tools/data/reports/agent_20260507_0803.md:83:(`SR`, `L1`, `triple_var`, mean abs z about `36-41` in the main run). The 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/falsifier_20260430_1946.json:11: "suggestion": "Riformulare come 'Poisson e' compatibile con delta_SR=delta_L1=0 entro errore; direzione non stimabile in modo stabile', e riportare delta_SR/delta_L1 medi con intervalli/confidenza invece di 'zero ordering'." tools/data/reports/falsifier_20260430_1946.json:38: "evidence": "Il cimitero segnala che X1 'r-ratio come segnale dei primi' e' falsificato perche' spiegato dal crivello deterministico, e X2 dice che gran parte dell'anti-correlazione sopravvive allo shuffle. Il report usa delta vs shuffle, ma non espone le delta_SR/delta_L1 né dimostra che il residuo sia separato dal meccanismo gia' cimiterato.", tools/data/reports/falsifier_20260430_1946.json:39: "suggestion": "Riportare esplicitamente delta_SR, delta_L1, shuffle mean/std e confronto con modelli di crivello/Hawkins. Chiamare il contenuto 'residuo post-shuffle' finche' non e' esclusa la spiegazione da crivello deterministico." tools/data/reports/agent_20260507_1804.md:6:**observables_used**: [] - tassonomia operatori TQGE, nessun osservabile canonico SR/SR2/L1/L2/triple_var tools/data/boundary_short_denominator_extension_20260509_1556.json:6: "SR", tools/data/boundary_short_denominator_extension_20260509_1556.json:7: "SR2", tools/data/boundary_short_denominator_extension_20260509_1556.json:10: "triple_var" tools/data/boundary_short_denominator_extension_20260509_1556.json:79: "SR": -0.8359607882885326, tools/data/boundary_short_denominator_extension_20260509_1556.json:80: "SR2": 0.25625928407547865, tools/data/boundary_short_denominator_extension_20260509_1556.json:83: "triple_var": -0.38748722365837174 tools/data/boundary_short_denominator_extension_20260509_1556.json:86: "SR": 0.2356258593831707, tools/data/boundary_short_denominator_extension_20260509_1556.json:87: "SR2": -0.037046559840937465, tools/data/boundary_short_denominator_extension_20260509_1556.json:90: "triple_var": 0.21456335104318858 tools/data/boundary_short_denominator_extension_20260509_1556.json:99: "SR2", tools/data/boundary_short_denominator_extension_20260509_1556.json:102: "triple_var" tools/data/boundary_short_denominator_extension_20260509_1556.json:108: "SR": -1.0394227793005648, tools/data/boundary_short_denominator_extension_20260509_1556.json:109: "SR2": 3.7985523980572995, tools/data/boundary_short_denominator_extension_20260509_1556.json:112: "triple_var": 7.250020325719682 tools/data/boundary_short_denominator_extension_20260509_1556.json:115: "SR": -0.5622473723623737, tools/data/boundary_short_denominator_extension_20260509_1556.json:116: "SR2": -0.37778946280848197, tools/data/boundary_short_denominator_extension_20260509_1556.json:119: "triple_var": -0.5381568197410919 tools/data/boundary_short_denominator_extension_20260509_1556.json:128: "SR", tools/data/boundary_short_denominator_extension_20260509_1556.json:135: "SR": -5.447602717160001, tools/data/boundary_short_denominator_extension_20260509_1556.json:136: "SR2": 1.2859426915484073, tools/data/boundary_short_denominator_extension_20260509_1556.json:139: "triple_var": 1.9043248948244467 tools/data/boundary_short_denominator_extension_20260509_1556.json:142: "SR": 0.1301997665900053, tools/data/boundary_short_denominator_extension_20260509_1556.json:143: "SR2": 0.3058611067723766, tools/data/boundary_short_denominator_extension_20260509_1556.json:146: "triple_var": 0.36805267274111825 tools/data/boundary_short_denominator_extension_20260509_1556.json:171: "SR": -0.8359607882885326, tools/data/boundary_short_denominator_extension_20260509_1556.json:172: "SR2": 0.25625928407547865, tools/data/boundary_short_denominator_extension_20260509_1556.json:175: "triple_var": -0.38748722365837174 tools/data/boundary_short_denominator_extension_20260509_1556.json:178: "SR": 0.2356258593831707, tools/data/boundary_short_denominator_extension_20260509_1556.json:179: "SR2": -0.037046559840937465, tools/data/boundary_short_denominator_extension_20260509_1556.json:182: "triple_var": 0.21456335104318858 tools/data/boundary_short_denominator_extension_20260509_1556.json:192: "SR2", tools/data/boundary_short_denominator_extension_20260509_1556.json:195: "triple_var" tools/data/boundary_short_denominator_extension_20260509_1556.json:201: "SR": -1.0394227793005648, tools/data/boundary_short_denominator_extension_20260509_1556.json:202: "SR2": 3.7985523980572995, tools/data/boundary_short_denominator_extension_20260509_1556.json:205: "triple_var": 7.250020325719682 tools/data/boundary_short_denominator_extension_20260509_1556.json:208: "SR": -0.5622473723623737, tools/data/boundary_short_denominator_extension_20260509_1556.json:209: "SR2": -0.37778946280848197, tools/data/boundary_short_denominator_extension_20260509_1556.json:212: "triple_var": -0.5381568197410919 tools/data/boundary_short_denominator_extension_20260509_1556.json:222: "SR", tools/data/boundary_short_denominator_extension_20260509_1556.json:229: "SR": -5.447602717160001, tools/data/boundary_short_denominator_extension_20260509_1556.json:230: "SR2": 1.2859426915484073, tools/data/boundary_short_denominator_extension_20260509_1556.json:233: "triple_var": 1.9043248948244467 tools/data/boundary_short_denominator_extension_20260509_1556.json:236: "SR": 0.1301997665900053, tools/data/boundary_short_denominator_extension_20260509_1556.json:237: "SR2": 0.3058611067723766, tools/data/boundary_short_denominator_extension_20260509_1556.json:240: "triple_var": 0.36805267274111825 tools/data/reports/evolution_20260422_1616.md:9:Minimo. Un dettaglio: l'ultimo text prodotto dall'agente parla di "magnitude PSD Wiener-Khinchin" (risultato del run precedente, piano 44), non del shuffle audit appena concluso. Il report scritto e' corretto, ma lo stdout finale e' un residuo del passo prima — il produttore ha chiuso con un sommario stale. Attrito cosmetico, non strutturale. tools/data/reports/falsifier_20260501_0330.json:17: "evidence": "Dai numeri visibili: GUE e' a -97 deg, Markov-1 a -114.6, real primes a -111.9. Markov-1 e' piu' lontano da GUE del reale, quindi non puo' spiegare semplicemente l'80% della separazione angolare real-GUE. Il report usa una ratio percentuale senza definire il denominatore: angolo da GUE, distanza vettoriale |d|, o componente proiettata nel piano SR/L1.", tools/data/reports/falsifier_20260501_0330.json:18: "suggestion": "Sostituire l'80% con una misura assoluta esplicita: decomposizione vettoriale in coordinate (dSR,dL1), proiezione del vettore reale sul vettore Markov-1, residuo ortogonale, e intervalli bootstrap. Evitare percentuali finche' il denominatore non e' fissato." tools/data/reports/agent_20260509_1444.md:7:observables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`. tools/data/reports/agent_20260509_1444.md:39:| DUALITA_golden | SR, L1, triple_var | 3.000 | 0.292 | 3.426 | 0.3 | tools/data/reports/agent_20260509_1444.md:40:| R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.000 | 0.375 | 4.409 | 0.3 | tools/data/reports/agent_20260509_1444.md:41:| T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.000 | 0.417 | 4.421 | 0.3 | tools/data/reports/agent_20260509_1444.md:42:| E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.000 | 0.292 | 4.398 | 0.3 | tools/data/reports/agent_20260509_1444.md:67:5. **Vincolo: `DUALITA_golden` trasferisce con solo 3 osservabili one-sided.** SR2 e L2 non entrano nel nucleo one-sided del perimetro golden; non va scritto che il set canonico completo trasferisce ovunque. tools/data/reports/agent_20260509_1444.md:102:- Output: `observables_registry=1.0.0-2026-05-06`; `observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var']`. tools/data/reports/agent_20260505_1022.md:35:| SR | L1 | 0.022 | 0.047 | 0.052 | tools/data/reports/agent_20260505_1022.md:37:| SR2 | L2 | 0.584 | 0.466 | NEVER | tools/data/reports/agent_20260505_1022.md:40:| triple_var | L2 | 0.193 | 0.083 | 0.087 | tools/data/reports/agent_20260505_1022.md:42:Il dato centrale e' la scissione tra gli osservabili L2: `SR2` e `L2` sono preservati da Mk2 fino ad alpha=1.0, mentre `cond_entropy` e `triple_var` si rompono presto. Quindi "Layer 2" non e' atomico con questa famiglia di osservabili: contiene almeno due sottocanali, uno realmente recuperato dal surrogato Mk2 e uno sensibile alla discretizzazione/varianza della costruzione. tools/data/reports/agent_20260505_1022.md:48:| SR | L1 | NEVER | NEVER | NEVER | tools/data/reports/agent_20260505_1022.md:50:| SR2 | L2 | 0.947 | NEVER | NEVER | tools/data/reports/agent_20260505_1022.md:53:| triple_var | L2 | NEVER | NEVER | 0.350 | tools/data/reports/agent_20260505_1022.md:55:Poisson resta quasi nullo rispetto a SR/SR2/triple_var: molte curve non attraversano `|z| >= 2`. Questo e' coerente con un controllo a bassa struttura, non con una prova di assenza assoluta. tools/data/reports/agent_20260505_1022.md:61:**Nel perimetro partial-shuffle dei run precedenti, SR/L1/L2/SR2 mostravano una fase quasi comune. Nel perimetro selective-perturbation di questo run, la fase comune non e' stabile: i critical alpha dei primi separano L1 e L2, e Mk2 preserva SR2/L2 ma non cond_entropy/triple_var.** tools/data/reports/agent_20260505_1022.md:67:**CONSTRAINT on META + BOUNDARY**: non aggregare gli osservabili in "Layer 1" e "Layer 2" senza dichiarare l'operatore di perturbazione. Nel perimetro `N=8000, n_surr=8`, `cond_entropy` e `triple_var` non sono intercambiabili con `SR2/L2` come prove di Layer 2. tools/data/reports/agent_20260505_1022.md:79:L4 edge case isolation: `cond_entropy` e `triple_var` sono trattati come eccezioni operative dentro L2, non arrotondati dentro la media del layer. tools/data/duality_gate_transfer_20260507_0803.json:7: "SR", tools/data/duality_gate_transfer_20260507_0803.json:8: "SR2", tools/data/duality_gate_transfer_20260507_0803.json:11: "triple_var" tools/data/duality_gate_transfer_20260507_0803.json:30: "SR": 1.0, tools/data/duality_gate_transfer_20260507_0803.json:31: "SR2": 0.0, tools/data/duality_gate_transfer_20260507_0803.json:34: "triple_var": 1.0 tools/data/duality_gate_transfer_20260507_0803.json:37: "SR": -40.94888582064453, tools/data/duality_gate_transfer_20260507_0803.json:38: "SR2": 0.009553247512053135, tools/data/duality_gate_transfer_20260507_0803.json:41: "triple_var": -35.75462249872064 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tools/data/perturbation_rank_size_curve.json:70490: "triple_var": 0.8298688569536113 tools/data/perturbation_rank_size_curve.json:70504: "SR": 0.5218760202228965, tools/data/perturbation_rank_size_curve.json:70505: "SR2": -0.008551683367562962, tools/data/perturbation_rank_size_curve.json:70508: "triple_var": 0.5077073448907615 tools/data/perturbation_rank_size_curve.json:70522: "SR": 0.24190003909884783, tools/data/perturbation_rank_size_curve.json:70523: "SR2": -0.7910171319500955, tools/data/perturbation_rank_size_curve.json:70526: "triple_var": 0.27536855567269986 tools/data/perturbation_rank_size_curve.json:70540: "SR": 0.06534898534255672, tools/data/perturbation_rank_size_curve.json:70541: "SR2": 0.05424314846529857, tools/data/perturbation_rank_size_curve.json:70544: "triple_var": 0.09283665406282059 tools/data/perturbation_rank_size_curve.json:70558: "SR": -0.00449112595940889, tools/data/perturbation_rank_size_curve.json:70559: "SR2": 1.1722962737071543, tools/data/perturbation_rank_size_curve.json:70562: "triple_var": -0.035994957452431385 codex Lo scenario automatico suggerisce una tensione diversa, ma il contratto di traiettoria e l'ultimo valutatore sono espliciti: questo cycle deve isolare primi contro candidati mod 6. Sto prendendo il ciclo precedente come nodo regressivo, così il nuovo test misura proprio il denominatore che era mancato. exec /bin/bash -lc 'ls -1 tools/data/reports | tail -20' in /opt/MM_D-ND succeeded in 0ms: report_20260402_0756.md report_20260403_0330.md report_20260404_0330.md report_20260404_1852.md report_20260405_0330.md report_20260405_0715.md tension_projection_screening_2026-04-21.json trace_map_20260305_0843.json trace_map_20260305_0844.json trajectory_apply_20260506_1931.json trajectory_apply_20260506_1941.json trajectory_apply_20260506_1955.json trajectory_apply_20260507_0330.json trajectory_apply_20260507_0803.json trajectory_apply_20260507_0901.json trajectory_apply_20260507_0923.json trajectory_apply_20260507_0942.json trajectory_apply_20260507_1006.json trajectory_apply_20260507_1042.json watchdog.log exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260512_0330.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Prime SR Persistent Boundary **Date**: 2026-05-12 03:30 **Piano**: 115 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] **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. ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`. - **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. - **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. - **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente. - **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". - **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. - **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic. ## Aderenza alla direzione - `relation`: follows_direction - `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime. - `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime. ## Claim Under Test > `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. ## Question Quando 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? ## Experiment Design - Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap. - Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows. - Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`. - Seed check: stesso perimetro, seed `202605120331`. - Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null. - Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common. ## Results Main run: | family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | |---|---:|---:|---|---:|---:|---:|---:| | prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 | | all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 | | composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 | | cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | | logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | | mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 | | random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 | Seed check: | family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | |---|---:|---:|---|---:|---:|---:|---:| | prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 | | all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 | | mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 | Prime case details, main: | case | state | one-sided obs | beta | |---|---|---|---| | dnd_autoricerca offset 0 | beta_absent_blank | SR | [] | | dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] | | dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] | | dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | | direct_sieve offset 0 | beta_absent_blank | SR | [] | | direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] | | direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | | direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | ## Key Findings 1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota. 2. **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. 3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`. 4. **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. 5. **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". ## Verdict **CONSTRAINT / FALSIFIED scoped**. Formula non valida: `prime_SR_persistent_boundary` come firma atomica prime-specific. Formula valida nel perimetro: `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. ## Bicono della scoperta - **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato. - **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato. - **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6. - **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. ## Consecutio Il 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. ## Ricadute pratiche ssp_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. ## Files - Script: `tools/exp_prime_sr_persistent_boundary.py` - Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json` - Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json` - Report: `tools/data/reports/agent_20260512_0330.md` exec /bin/bash -lc "sed -n '1,260p' tools/exp_prime_persistent_blank_gate.py" in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,220p' tools/observables_registry.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Provider-neutral audit for the `prime_persistent_blank` residue. This is the next narrow step after `exp_boundary_residual_beta_absent_audit.py`: do not reopen the global BOUNDARY atlas; test whether `numeri_primi:cycle_3` keeps its beta-absent blank through two prime providers, row-local offsets, and baseline seed shifts, with SR as the common surviving observable. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np from exp_boundary_short_denominator_extension import gue_spacing_blocks from exp_boundary_residual_beta_absent_audit import support_state from exp_semireal_boundary_transfer_gate import row_spacings from exp_semireal_order_denominator_gate import ( analyze_sequence, compact, logistic_return_intervals, normalize, prime_gap_sequence, ) from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) TARGET_ROW = "numeri_primi:cycle_3" def offset_windows(values: np.ndarray, offsets: list[int], size: int) -> dict[str, np.ndarray]: out = {} for offset in offsets: end = offset + size if end <= len(values): out[f"offset_{offset}"] = normalize(values[offset:end]) return out def obs_jaccard(left: list[str], right: list[str]) -> float: a = set(left) b = set(right) if not a and not b: return 1.0 return len(a & b) / len(a | b) def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: perimeters = {name: analyze_sequence(name, base, args, rng)} row = compact(perimeters)[name] one_sided = list(row["coherent_one_sided_observables"]) return { "case": name, "n_gaps": row["n_gaps"], "state": support_state(row, args), "one_sided_observables": one_sided, "has_sr": "SR" in one_sided, "endpoint_stable_observables": row["endpoint_stable_observables"], "stable_count_coherent": row["stable_count_coherent"], "stable_count_illusory": row["stable_count_illusory"], "endpoint_distance": row["endpoint_distance_one_sided_gated"], "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], "z_mean_coherent": row["z_mean_coherent"], "z_mean_illusory": row["z_mean_illusory"], } def summarize_family(cases: list[dict[str, Any]]) -> dict[str, Any]: obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] common_obs = sorted(set.intersection(*obs_sets)) if obs_sets else [] union_obs = sorted(set.union(*obs_sets)) if obs_sets else [] counts: dict[str, int] = {} for case in cases: counts[case["state"]] = counts.get(case["state"], 0) + 1 blank_cases = [case for case in cases if case["state"] == "beta_absent_blank"] return { "case_count": len(cases), "state_counts": counts, "blank_rate": len(blank_cases) / len(cases) if cases else 0.0, "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, "common_one_sided_observables": common_obs, "union_one_sided_observables": union_obs, "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, } def build_prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: needed = max(args.offsets) + args.window_gaps providers = { "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), "direct_sieve": normalize(prime_gap_sequence(needed)), } cases = {} for provider, values in providers.items(): for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): cases[f"prime/{provider}/{label}"] = window return cases def build_control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: cases = {} for idx in range(args.control_count): seed = int(rng.integers(0, 2**63 - 1)) local_rng = np.random.default_rng(seed) random_matrix = gue_spacing_blocks(args.window_gaps, args.gue_matrix_size, local_rng) cases[f"control/random_matrix/seed_{idx}"] = random_matrix for idx in range(args.control_count): seed = int(rng.integers(0, 2**63 - 1)) local_rng = np.random.default_rng(seed) logistic = logistic_return_intervals(args.window_gaps, local_rng) cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic return cases def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], args: argparse.Namespace) -> str: prime_persists = ( prime_summary["blank_rate"] == 1.0 and prime_summary["sr_rate"] >= args.min_prime_sr_rate and prime_summary["common_one_sided_observables"] == ["SR"] ) controls_do_not_match = not ( control_summary["blank_rate"] == 1.0 and "SR" in control_summary["common_one_sided_observables"] ) if prime_persists and controls_do_not_match: return "PRIME_PERSISTENT_BLANK_SR_ISOLATED" if prime_persists: return "PRIME_PERSISTENT_BUT_CONTROL_COLLISION" return "PRIME_PERSISTENCE_NOT_REPLICATED" def run(args: argparse.Namespace) -> dict[str, Any]: root_rng = np.random.default_rng(args.seed) prime_specs = build_prime_cases(args) control_specs = build_control_cases(args, root_rng) prime_cases = [ analyze_case(name, base, args, np.random.default_rng(root_rng.integers(0, 2**63 - 1))) for name, base in prime_specs.items() ] control_cases = [ analyze_case(name, base, args, np.random.default_rng(root_rng.integers(0, 2**63 - 1))) for name, base in control_specs.items() ] prime_summary = summarize_family(prime_cases) control_summary = summarize_family(control_cases) output = { "experiment": "prime_persistent_blank_gate", "question": "Does numeri_primi:cycle_3 keep a provider-neutral SR beta-absent blank under row-local offset and seed shifts?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": [ *OBS_NAMES, "provider", "offset", "case_state", "blank_rate", "sr_rate", "prime_control_common_obs_jaccard", ], "params": vars(args), "target_row": TARGET_ROW, "observable_contract": { "claim": "prime_persistent_blank is isolated only if prime windows remain beta_absent_blank across providers and offsets with SR as the common one-sided observable", "observable": "case_state plus common one-sided observable signature focused on SR", "operator": "canonical order/null gate on row-local windows; provider and seed shifts only", "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from GUE random matrix blocks and logistic return intervals", "denominator": "8 prime row-local windows (2 providers x 4 offsets) plus 8 cross-domain controls", "non_possible": "prime_persistent_blank if any prime window recovers beta/falls, or if controls share a full SR blank signature", "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels", }, "prime_summary": prime_summary, "control_summary": control_summary, "prime_control_common_obs_jaccard": obs_jaccard( prime_summary["common_one_sided_observables"], control_summary["common_one_sided_observables"], ), "verdict": verdict(prime_summary, control_summary, args), "cases": { "prime": prime_cases, "controls": control_cases, }, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") print(f"target={TARGET_ROW}") print( "prime " f"blank={prime_summary['state_counts'].get('beta_absent_blank', 0)}/{prime_summary['case_count']} " f"sr_rate={prime_summary['sr_rate']:.3f} common={prime_summary['common_one_sided_observables']}" ) print( "controls " f"blank={control_summary['state_counts'].get('beta_absent_blank', 0)}/{control_summary['case_count']} " f"sr_rate={control_summary['sr_rate']:.3f} common={control_summary['common_one_sided_observables']}" ) print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") print(f"verdict={output['verdict']}") print(f"saved {out}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--window-gaps", type=int, default=1024) parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) parser.add_argument("--control-count", type=int, default=4) parser.add_argument("--gue-matrix-size", type=int, default=64) parser.add_argument("--n-replicates", type=int, default=8) parser.add_argument("--n-beta", type=int, default=9) parser.add_argument("--n-baseline", type=int, default=16) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--min-one-sided", type=int, default=1) parser.add_argument("--illusory-residue-max", type=float, default=0.75) parser.add_argument("--endpoint-distance-min", type=float, default=1.0) parser.add_argument("--min-prime-sr-rate", type=float, default=1.0) parser.add_argument("--seed", type=int, default=202605110330) parser.add_argument("--out", default="tools/data/prime_persistent_blank_gate_20260511_0330.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() succeeded in 0ms: """observables_registry.py — Source of Truth per gli observables del lab D-ND. Cristallizzato 2026-05-06 dalla **consecutio del cycle agent_20260506_0625**: > "What opens now: the lab needs an observable registry. Labels like SR > cannot travel between reports unless they bind to a function definition. > Without that, META flags are not philosophical: the same label can > silently change the object under test." ## Il problema che ha creato il registry Il cycle 06:25 ha auto-falsificato il finding del cycle 03:30 ("secondo asse GUE") e nel farlo ha trovato **collision di nomi observable** tra script: - `SR` in `exp_selective_layer_decoupling.py` = `spacing_ratio` (mean min/max ratio of consecutive gaps) — convention dominante (~6 script) - `SR` in `exp_scale_selective_perturbation.py` = `spectral_rigidity(gaps)` (Δ₃(L) rigidity) — variante usata SOLO in 1 script - `triple_var` in 3 script = `np.var(triple_sums)` (raw) — convention dominante - `triple_var` in `exp_perturbation_dimensionality_audit.py` = `np.var(triples) / np.var(gaps)` (normalizzato) — variante in 1 script Il lab autonomo che compara report tra script con osservabili "stesso nome, funzione diversa" stava confrontando mele con arance. ## La soluzione (minimal, non invasiva) Questo registry stabilisce il **nome canonico**: ciò che la maggioranza degli script chiama già `SR`/`triple_var`/etc. Le varianti restano disponibili ma con nomi ESPLICITI (`SR_local_rigidity`, `triple_var_normalized`) per evitare mascheramento semantico. ## Come usarlo ```python from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION # Compute canonical observable suite for a sequence of gaps results = {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} # Or import individual canonical observable from observables_registry import SR, triple_var, L1, L2, SR2 # For variants, import explicitly with disambiguating name from observables_registry import SR_local_rigidity, triple_var_normalized ``` ## Convention per i report Ogni report agent (cycle) che usa observables DEVE includere nel suo header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Cycle che mescola canonical + variant DEVE indicare entrambi: ``` observables_used: [SR, SR_local_rigidity, ...] ``` Senza questo, i confronti cross-cycle sono inattendibili. ## Versioning Cambiare una definizione canonica = bump del registry version e nota nel changelog. Le definizioni canoniche sono **immutabili dentro una versione**. """ from __future__ import annotations import numpy as np OBSERVABLES_REGISTRY_VERSION = "1.0.0-2026-05-06" # ─── Canonical observables (convention dominante nel codebase 2026-05-06) ─── def SR(gaps: np.ndarray) -> float: """**SR — Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+1}) / max(g_i, g_{i+1})` over consecutive gaps. Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. NOTE: questa è la convention dominante in 6+ script del lab. Per la variante "local spectral rigidity Δ₃(L)" usare `SR_local_rigidity`. """ if len(gaps) < 2: return 0.0 s, s1 = gaps[:-1], gaps[1:] r = np.minimum(s, s1) / np.maximum(s, s1) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def SR2(gaps: np.ndarray) -> float: """**SR2 — Next-nearest Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+2}) / max(g_i, g_{i+2})` skipping one gap. Probes lag-2 spacing structure. """ if len(gaps) < 3: return 0.0 s, s2 = gaps[:-2], gaps[2:] r = np.minimum(s, s2) / np.maximum(s, s2) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def L1(gaps: np.ndarray) -> float: """**L1 — Lag-1 Autocorrelation** (canonical). Standard ACF at lag 1 of the gap sequence. """ if len(gaps) < 3: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-1] * g[1:]) / c0) def L2(gaps: np.ndarray) -> float: """**L2 — Lag-2 Autocorrelation** (canonical).""" if len(gaps) < 4: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-2] * g[2:]) / c0) def triple_var(gaps: np.ndarray) -> float: """**triple_var — Variance of consecutive gap triples** (canonical). Variance of `g_i + g_{i+1} + g_{i+2}` over the sequence (RAW, no normalization). Convention used in 3+ scripts. For the normalized version (variance ratio `var(triples) / var(gaps)`) use `triple_var_normalized`. """ if len(gaps) < 3: return 0.0 t = gaps[:-2] + gaps[1:-1] + gaps[2:] return float(np.var(t)) # Set canonico per uso "compute all" da report OBSERVABLES_CANONICAL: dict[str, callable] = { "SR": SR, "SR2": SR2, "L1": L1, "L2": L2, "triple_var": triple_var, } # ─── Variants (esplicitamente nominate, no collision con canonical) ─── def SR_local_rigidity(gaps: np.ndarray, L: int = 10) -> float: """**SR_local_rigidity — Δ₃(L) Spectral Rigidity** (variant). Different observable than canonical `SR` (spacing ratio). Measures the average squared deviation of the cumulative spacing function from the best-fit straight line in a window of size L. Originated from `exp_scale_selective_perturbation.py` where it was locally named `SR` — registered here as `SR_local_rigidity` to avoid collision with canonical spacing-ratio definition. Use when explicitly studying spectral rigidity, NOT as alias for SR. """ if len(gaps) < 5: return 0.0 cumulative = np.cumsum(gaps) if cumulative[-1] <= 1e-15: return 0.0 cumulative = cumulative / cumulative[-1] * len(cumulative) n = np.arange(1, len(cumulative) + 1, dtype=float) window = int(min(L * len(gaps) / cumulative[-1], len(gaps) // 2)) if window < 5: return 0.0 residuals = [] for start in range(0, len(cumulative) - window, max(1, window // 2)): end = start + window x = n[start:end] y = cumulative[start:end] a, b = np.polyfit(x, y, 1) residuals.append(np.mean((y - (a * x + b)) ** 2)) return float(np.mean(residuals)) if residuals else 0.0 def triple_var_normalized(gaps: np.ndarray) -> float: """**triple_var_normalized — Variance of triples / variance of gaps** (variant). Originated from `exp_perturbation_dimensionality_audit.py` where it was locally named `triple_var` — registered here as `triple_var_normalized` to avoid collision with canonical raw triple variance. Use when explicitly studying triple-variance scaling relative to single-gap variance, NOT as alias for triple_var. """ if len(gaps) < 3: return 0.0 triples = gaps[:-2] + gaps[1:-1] + gaps[2:] v = float(np.var(gaps)) if v <= 1e-15: return 0.0 return float(np.var(triples) / v) # Set varianti, importabile esplicitamente OBSERVABLES_VARIANTS: dict[str, callable] = { "SR_local_rigidity": SR_local_rigidity, "triple_var_normalized": triple_var_normalized, } exec /bin/bash -lc "ls -1 tools/data | rg 'prime|mod6|boundary|persistent|2026051'" in /opt/MM_D-ND succeeded in 0ms: 3d_boundary_layers.json boundary_blank_null_audit_20260509_1430.json boundary_blank_null_audit_residual_20260509_1500.json boundary_blank_thin_support_audit_20260509_1548.json boundary_coherence.json boundary_denominator_prescan_20260509_1409.json boundary_denominator_prescan_20260509_1430.json boundary_denominator_prescan_full_20260509_1500.json boundary_mixture_gate_20260507_0330.json boundary_mixture_gate_20260507_0330_seedcheck.json boundary_residual_beta_absent_audit_20260510_0330.json boundary_row_aligned_nonexact_audit_20260509_1538.json boundary_short_denominator_extension_20260509_1556.json boundary_shuffle_audit.json boundary_transition_taxonomy_13rows_20260509_1839.json boundary_two_axis_matrix_20260509_1532.json incrocio_20260510_0330.json incrocio_20260511_0330.json incrocio_20260512_0330.json incrocio_20260513_0330.json prime_gaps_spectrum.png prime_gaps_spectrum_pub.png prime_mobius_gap_stratified_zero_gate_20260508_2121.json prime_mobius_interval_charge_gate_20260508_2102.json prime_mobius_pair_stratified_zero_gate_20260508_2133.json prime_mobius_zero_mediator_gate_20260508_2108.json prime_persistent_blank_gate_20260511_0330.json prime_sr_persistent_boundary_20260512_0330.json prime_sr_persistent_boundary_20260512_0330_seedcheck.json psd_prime_gaps_results.json seme_backup_b2_20260512_033557.json semireal_boundary_transfer_gate_20260509_1516.json two_channel_boundary.json vc_unit_boundary_audit_20260509_1457.json exec /bin/bash -lc "sed -n '1,320p' tools/exp_prime_sr_persistent_boundary.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. The claim under test is narrower than the previous blank audit: SR must persist through prime providers and offsets, while non-prime controls should not share the same one-sided SR support under the same gate. """ from __future__ import annotations import argparse import json import math from pathlib import Path from typing import Any import numpy as np from exp_boundary_short_denominator_extension import gue_spacing_blocks from exp_boundary_residual_beta_absent_audit import support_state from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard from exp_semireal_boundary_transfer_gate import row_spacings from exp_semireal_order_denominator_gate import ( analyze_sequence, compact, logistic_return_intervals, normalize, prime_gap_sequence, sieve_primes_for_count, ) from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) TARGET_ROW = "numeri_primi:cycle_3" def sieve_bool(limit: int) -> np.ndarray: sieve = np.ones(limit + 1, dtype=bool) sieve[:2] = False for p in range(2, int(limit**0.5) + 1): if sieve[p]: sieve[p * p : limit + 1 : p] = False return sieve def composite_gap_sequence(n_gaps: int) -> np.ndarray: limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) while True: prime_mask = sieve_bool(limit) values = np.flatnonzero(~prime_mask) values = values[values >= 4] if len(values) >= n_gaps + 1: return normalize(np.diff(values[: n_gaps + 1])) limit *= 2 def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: values: list[int] = [] k = 1 while len(values) < n_gaps + 1: values.append(6 * k - 1) values.append(6 * k + 1) k += 1 arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) return normalize(np.diff(arr)) def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: events = [2] n = 3 while len(events) < n_gaps + 1: p = min(0.95, 1.0 / max(math.log(n), 1.0)) if rng.random() < p: events.append(n) n += 1 if n > 50_000_000: raise RuntimeError("cramer_like_gap_sequence did not produce enough events") return normalize(np.diff(np.array(events, dtype=float))) def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: needed = max(args.offsets) + args.window_gaps providers = { "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), "direct_sieve": normalize(prime_gap_sequence(needed)), } cases = {} for provider, values in providers.items(): for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): cases[f"prime/{provider}/{label}"] = window return cases def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: needed = max(args.offsets) + args.window_gaps base_controls = { "composite_gaps": composite_gap_sequence(needed), "mod6_candidates": mod6_candidate_gap_sequence(needed), "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), } cases: dict[str, np.ndarray] = {} for family, values in base_controls.items(): for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): cases[f"control/{family}/{label}"] = window for idx in range(args.stochastic_control_count): local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( args.window_gaps, args.gue_matrix_size, local_rng ) for idx in range(args.stochastic_control_count): local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( args.window_gaps, local_rng ) return cases def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: perimeters = {name: analyze_sequence(name, base, args, rng)} row = compact(perimeters)[name] one_sided = list(row["coherent_one_sided_observables"]) return { "case": name, "family": name.split("/")[0], "subfamily": name.split("/")[1], "n_gaps": row["n_gaps"], "state": support_state(row, args), "one_sided_observables": one_sided, "has_sr": "SR" in one_sided, "endpoint_stable_observables": row["endpoint_stable_observables"], "stable_count_coherent": row["stable_count_coherent"], "stable_count_illusory": row["stable_count_illusory"], "endpoint_distance": row["endpoint_distance_one_sided_gated"], "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], "z_mean_coherent": row["z_mean_coherent"], "z_mean_illusory": row["z_mean_illusory"], } def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: obs_sets = [set(case["one_sided_observables"]) for case in cases] state_counts: dict[str, int] = {} for case in cases: state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 return { "case_count": len(cases), "state_counts": state_counts, "sr_count": sum(1 for case in cases if case["has_sr"]), "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], "blank_count": state_counts.get("beta_absent_blank", 0), "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), "support_fall_count": state_counts.get("support_falls", 0), "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, } def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: out: dict[str, dict[str, Any]] = {} for subfamily in sorted({case["subfamily"] for case in cases}): out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) return out def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] control_common_sr = "SR" in control_summary["common_one_sided_observables"] any_control_subfamily_sr_complete = any( summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] for summary in control_subfamilies.values() ) if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" if prime_sr_persists: return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" return "PRIME_SR_NOT_PERSISTENT" def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) prime_specs = prime_cases(args) control_specs = control_cases(args, rng) prime_results = [ analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) for name, base in prime_specs.items() ] control_results = [ analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) for name, base in control_specs.items() ] prime_summary = summarize(prime_results) control_summary = summarize(control_results) control_subfamilies = summarize_by_subfamily(control_results) output = { "experiment": "prime_sr_persistent_boundary", "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": [ *OBS_NAMES, "provider", "offset", "case_state", "sr_rate", "common_one_sided_observables", "prime_control_common_obs_jaccard", ], "params": vars(args), "target_row": TARGET_ROW, "observable_contract": { "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", }, "prime_summary": prime_summary, "control_summary": control_summary, "control_subfamilies": control_subfamilies, "prime_control_common_obs_jaccard": obs_jaccard( prime_summary["common_one_sided_observables"], control_summary["common_one_sided_observables"], ), "verdict": verdict(prime_summary, control_summary, control_subfamilies), "cases": { "prime": prime_results, "controls": control_results, }, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") print(f"target={TARGET_ROW}") print( "prime " f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " f"common={prime_summary['common_one_sided_observables']} " f"states={prime_summary['state_counts']}" ) print( "controls " f"sr={control_summary['sr_count']}/{control_summary['case_count']} " f"common={control_summary['common_one_sided_observables']} " f"states={control_summary['state_counts']}" ) for family, summary in control_subfamilies.items(): print( f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " f"common={summary['common_one_sided_observables']}" ) print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") print(f"verdict={output['verdict']}") print(f"saved {out}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--window-gaps", type=int, default=1024) parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) parser.add_argument("--stochastic-control-count", type=int, default=4) parser.add_argument("--gue-matrix-size", type=int, default=64) parser.add_argument("--n-replicates", type=int, default=8) parser.add_argument("--n-beta", type=int, default=9) parser.add_argument("--n-baseline", type=int, default=16) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--min-one-sided", type=int, default=1) parser.add_argument("--illusory-residue-max", type=float, default=0.75) parser.add_argument("--endpoint-distance-min", type=float, default=1.0) parser.add_argument("--seed", type=int, default=202605120330) parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,180p' tools/data/prime_sr_persistent_boundary_20260512_0330.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "prime_sr_persistent_boundary", "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", "observables_registry": "1.0.0-2026-05-06", "observables_used": [ "SR", "SR2", "L1", "L2", "triple_var", "provider", "offset", "case_state", "sr_rate", "common_one_sided_observables", "prime_control_common_obs_jaccard" ], "params": { "window_gaps": 1024, "offsets": [ 0, 512, 1024, 1536 ], "stochastic_control_count": 4, "gue_matrix_size": 64, "n_replicates": 8, "n_beta": 9, "n_baseline": 16, "z_min": 2.0, "min_one_sided": 1, "illusory_residue_max": 0.75, "endpoint_distance_min": 1.0, "seed": 202605120330, "out": "tools/data/prime_sr_persistent_boundary_20260512_0330.json" }, "target_row": "numeri_primi:cycle_3", "observable_contract": { "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR" }, "prime_summary": { "case_count": 8, "state_counts": { "beta_absent_blank": 7, "beta_chart_recovered": 1 }, "sr_count": 7, "sr_rate": 0.875, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "SR", "triple_var" ], "blank_count": 7, "beta_recovered_count": 1, "support_fall_count": 0, "endpoint_distance_mean": 2.72620046046407, "stable_count_coherent_mean": 2.65625 }, "control_summary": { "case_count": 20, "state_counts": { "beta_chart_recovered": 8, "support_falls": 10, "beta_absent_blank": 2 }, "sr_count": 5, "sr_rate": 0.25, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 2, "beta_recovered_count": 8, "support_fall_count": 10, "endpoint_distance_mean": 1.8149542953189908, "stable_count_coherent_mean": 2.34375 }, "control_subfamilies": { "composite_gaps": { "case_count": 4, "state_counts": { "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [ "L2", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 2.271349550689998, "stable_count_coherent_mean": 2.59375 }, "cramer_like": { "case_count": 4, "state_counts": { "support_falls": 4 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [], "blank_count": 0, "beta_recovered_count": 0, "support_fall_count": 4, "endpoint_distance_mean": 0.0, "stable_count_coherent_mean": 0.59375 }, "logistic_return_intervals": { "case_count": 4, "state_counts": { "support_falls": 4 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [], "blank_count": 0, "beta_recovered_count": 0, "support_fall_count": 4, "endpoint_distance_mean": 0.0, "stable_count_coherent_mean": 0.03125 }, "mod6_candidates": { "case_count": 4, "state_counts": { "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 2, "sr_rate": 0.5, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 3.4398840817468415, "stable_count_coherent_mean": 5.0 }, "random_matrix": { "case_count": 4, "state_counts": { "beta_chart_recovered": 2, "beta_absent_blank": 2 }, "sr_count": 3, "sr_rate": 0.75, "common_one_sided_observables": [ "L2", "triple_var" ], "union_one_sided_observables": [ "L2", "SR", "SR2", "triple_var" exec /bin/bash -lc "sed -n '1,220p' tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "prime_sr_persistent_boundary", "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", "observables_registry": "1.0.0-2026-05-06", "observables_used": [ "SR", "SR2", "L1", "L2", "triple_var", "provider", "offset", "case_state", "sr_rate", "common_one_sided_observables", "prime_control_common_obs_jaccard" ], "params": { "window_gaps": 1024, "offsets": [ 0, 512, 1024, 1536 ], "stochastic_control_count": 4, "gue_matrix_size": 64, "n_replicates": 8, "n_beta": 9, "n_baseline": 16, "z_min": 2.0, "min_one_sided": 1, "illusory_residue_max": 0.75, "endpoint_distance_min": 1.0, "seed": 202605120331, "out": "tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json" }, "target_row": "numeri_primi:cycle_3", "observable_contract": { "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR" }, "prime_summary": { "case_count": 8, "state_counts": { "beta_absent_blank": 4, "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 7, "sr_rate": 0.875, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "SR", "triple_var" ], "blank_count": 4, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 2.4740798702604283, "stable_count_coherent_mean": 2.6875 }, "control_summary": { "case_count": 20, "state_counts": { "beta_chart_recovered": 8, "beta_absent_blank": 5, "support_falls": 7 }, "sr_count": 8, "sr_rate": 0.4, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 5, "beta_recovered_count": 8, "support_fall_count": 7, "endpoint_distance_mean": 2.417896785731883, "stable_count_coherent_mean": 2.525 }, "control_subfamilies": { "composite_gaps": { "case_count": 4, "state_counts": { "beta_chart_recovered": 2, "beta_absent_blank": 1, "support_falls": 1 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [ "L2", "SR2" ], "union_one_sided_observables": [ "L2", "SR2", "triple_var" ], "blank_count": 1, "beta_recovered_count": 2, "support_fall_count": 1, "endpoint_distance_mean": 2.9568726214400955, "stable_count_coherent_mean": 2.625 }, "cramer_like": { "case_count": 4, "state_counts": { "beta_absent_blank": 1, "support_falls": 3 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "triple_var" ], "blank_count": 1, "beta_recovered_count": 0, "support_fall_count": 3, "endpoint_distance_mean": 0.6756892529694472, "stable_count_coherent_mean": 0.625 }, "logistic_return_intervals": { "case_count": 4, "state_counts": { "beta_absent_blank": 3, "support_falls": 1 }, "sr_count": 1, "sr_rate": 0.25, "common_one_sided_observables": [], "union_one_sided_observables": [ "L2", "SR", "SR2" ], "blank_count": 3, "beta_recovered_count": 0, "support_fall_count": 1, "endpoint_distance_mean": 1.3249843016411362, "stable_count_coherent_mean": 1.0625 }, "mod6_candidates": { "case_count": 4, "state_counts": { "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 4, "sr_rate": 1.0, "common_one_sided_observables": [ "L1", "SR", "triple_var" ], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 4.077172077071727, "stable_count_coherent_mean": 5.0 }, "random_matrix": { "case_count": 4, "state_counts": { "support_falls": 1, "beta_chart_recovered": 3 }, "sr_count": 3, "sr_rate": 0.75, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 3.054765675537009, "stable_count_coherent_mean": 3.3125 } }, "prime_control_common_obs_jaccard": 1.0, "verdict": "PRIME_SR_NOT_PERSISTENT", "cases": { "prime": [ { "case": "prime/dnd_autoricerca/offset_0", "family": "prime", "subfamily": "dnd_autoricerca", "n_gaps": 1024, "state": "beta_absent_blank", "one_sided_observables": [ "SR" ], "has_sr": true, "endpoint_stable_observables": [], "stable_count_coherent": 1.375, exec /bin/bash -lc 'rg -n "def analyze_sequence|def compact|def normalize|def prime_gap_sequence|def row_spacings|def support_state" tools/exp_semireal_order_denominator_gate.py tools/exp_boundary_residual_beta_absent_audit.py tools/exp_semireal_boundary_transfer_gate.py' in /opt/MM_D-ND succeeded in 0ms: tools/exp_semireal_boundary_transfer_gate.py:40:def row_spacings(domain: str) -> np.ndarray: tools/exp_boundary_residual_beta_absent_audit.py:40:def support_state(row: dict[str, Any], args: argparse.Namespace) -> str: tools/exp_semireal_order_denominator_gate.py:31:def normalize(gaps: np.ndarray) -> np.ndarray: tools/exp_semireal_order_denominator_gate.py:55:def prime_gap_sequence(n_gaps: int) -> np.ndarray: tools/exp_semireal_order_denominator_gate.py:226:def analyze_sequence(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: tools/exp_semireal_order_denominator_gate.py:275:def compact(perimeters: dict) -> dict: exec /bin/bash -lc "sed -n '1,260p' tools/exp_semireal_order_denominator_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_semireal_order_denominator_gate.py Falsification attempt for ORDER_DENOMINATOR_GATE on non-synthetic / semi-real ordered sequences. The coherent endpoint is the observed order of each sequence; the illusory endpoint is a marginal-preserving permutation. The same original-vs-shuffle denominator gate used in the prior reports is applied to canonical observables from observables_registry.py. """ from __future__ import annotations import argparse import json import math 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 normalize(gaps: np.ndarray) -> np.ndarray: gaps = np.asarray(gaps, dtype=float) gaps = np.maximum(gaps, 1e-12) mean = float(np.mean(gaps)) return gaps / mean if mean > 1e-15 else gaps def sieve_primes_for_count(n_primes: int) -> np.ndarray: if n_primes < 6: limit = 20 else: limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.25) while True: sieve = np.ones(limit + 1, dtype=bool) sieve[:2] = False for p in range(2, int(limit**0.5) + 1): if sieve[p]: sieve[p * p : limit + 1 : p] = False primes = np.flatnonzero(sieve) if len(primes) >= n_primes: return primes[:n_primes].astype(float) limit *= 2 def prime_gap_sequence(n_gaps: int) -> np.ndarray: primes = sieve_primes_for_count(n_gaps + 1) return normalize(np.diff(primes)) def zeta_zero_spacings(n_gaps: int) -> np.ndarray: try: import mpmath as mp except ImportError as exc: raise RuntimeError("mpmath is required for zeta_zero_spacings") from exc zeros = np.empty(n_gaps + 1, dtype=float) for i in range(n_gaps + 1): zeros[i] = float(mp.im(mp.zetazero(i + 1))) return normalize(np.diff(zeros)) def logistic_return_intervals(n_gaps: int, rng: np.random.Generator) -> np.ndarray: # Return intervals to a high-density-edge event in the fully chaotic logistic map. threshold = 0.95 burn = 2000 needed = n_gaps + 1 returns: list[int] = [] last_hit: int | None = None x = float(rng.random()) i = 0 max_steps = 50_000_000 while len(returns) < needed and i < max_steps: x = 4.0 * x * (1.0 - x) if i >= burn and x > threshold: if last_hit is not None: returns.append(i - last_hit) last_hit = i i += 1 if len(returns) < needed: raise RuntimeError(f"logistic generator produced {len(returns)} intervals, need {needed}") return normalize(np.array(returns[:n_gaps], dtype=float)) def beta_replace(base: np.ndarray, beta: float, rng: np.random.Generator) -> np.ndarray: illusory = rng.permutation(base) if beta <= 0.0: return base.copy() if beta >= 1.0: return illusory out = base.copy() mask = rng.random(len(base)) < beta out[mask] = illusory[mask] return normalize(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], dict[str, float]]: original = compute_canonical(gaps) baseline = {name: [] for name in OBS_NAMES} for _ in range(n_baseline): obs = compute_canonical(rng.permutation(gaps)) for name in OBS_NAMES: baseline[name].append(obs[name]) means = {} sds = {} z = {} for name in OBS_NAMES: vals = np.array(baseline[name], dtype=float) means[name] = float(np.mean(vals)) sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 return original, means, sds, 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: 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) coherent = np.array([vector(row, obs_names) for row in by_beta[0.0]], dtype=float) illusory = np.array([vector(row, obs_names) for row in by_beta[1.0]], dtype=float) endpoints = np.vstack([coherent, illusory]) scale = np.std(endpoints, axis=0, ddof=1) scale[scale <= 1e-15] = 1.0 coherent_centroid = np.mean(coherent, axis=0) illusory_centroid = np.mean(illusory, axis=0) endpoint_distance = float(np.linalg.norm((illusory_centroid - coherent_centroid) / scale)) layers = {} ambiguous_beta = [] for beta, beta_rows in sorted(by_beta.items()): margins = [] labels = [] coords = [] for row in beta_rows: x = vector(row, obs_names) d_coherent = float(np.linalg.norm((x - coherent_centroid) / scale)) d_illusory = float(np.linalg.norm((x - illusory_centroid) / scale)) denom = d_coherent + d_illusory coord = float((d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 margin = float(abs(d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 coords.append(coord) margins.append(margin) labels.append("coherent" if d_coherent < d_illusory else "illusory") 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)), "margin_mean": float(np.mean(margins)), "ambiguous_fraction": ambiguous_fraction, "illusory_label_fraction": float(np.mean(np.array(labels) == "illusory")), } 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} z_values = {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) z_values[name].append(row["z"][name]) layers[f"{beta:.3f}"] = { "stable_count_mean": float(np.mean(stable_counts)), "stable_frequency": {name: float(np.mean(vals)) for name, vals in stable_freq.items()}, "z_mean": {name: float(np.mean(vals)) for name, vals in z_values.items()}, } one_sided = [] endpoint_stable = [] coherent_rows = by_beta[0.0] illusory_rows = by_beta[1.0] for name in OBS_NAMES: coherent_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in coherent_rows])) illusory_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in illusory_rows])) if coherent_freq >= 0.75 and illusory_freq < 0.25: one_sided.append(name) if coherent_freq >= 0.75 and illusory_freq >= 0.75: endpoint_stable.append(name) return { "z_min": z_min, "coherent_one_sided_observables": one_sided, "endpoint_stable_observables": endpoint_stable, "layers": layers, } def analyze_sequence(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: rows = [] betas = [float(x) for x in np.linspace(0.0, 1.0, args.n_beta)] for rep in range(args.n_replicates): rep_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) for beta in betas: gaps = beta_replace(base, beta, rep_rng) obs, shuffle_mean, shuffle_std, z = z_against_shuffle( gaps, args.n_baseline, np.random.default_rng(rng.integers(0, 2**63 - 1)), ) rows.append( { "perimeter": name, "replicate": rep, "beta": beta, "observables": obs, "shuffle_mean": shuffle_mean, "shuffle_std": shuffle_std, "z": z, "stable_observables": [obs_name for obs_name in OBS_NAMES if abs(z[obs_name]) >= args.z_min], } ) gate = summarize_gate(rows, args.z_min) return { "source": { "n_gaps": int(len(base)), "mean": float(np.mean(base)), "variance": float(np.var(base)), }, "gate": gate, "classification_all_observables": classify_layers(rows, OBS_NAMES), "classification_one_sided_gated": classify_layers(rows, gate["coherent_one_sided_observables"]), exec /bin/bash -lc "sed -n '1,120p' tools/exp_semireal_boundary_transfer_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_semireal_boundary_transfer_gate.py Move the BOUNDARY transfer matrix from controlled synthetic perimeters to the 13 semi-real rows of the base BOUNDARY perimeter. The coherent endpoint is the domain-native spacing order reconstructed from dnd_autoricerca. The illusory endpoint is a marginal-preserving permutation. Intermediate beta layers replace a beta fraction of the coherent row with the permuted row, preserving the row denominator while destroying order locally. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np from exp_boundary_blank_null_audit import generate_domain_signal, normalized_spacings from exp_semireal_order_denominator_gate import analyze_sequence, compact from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) 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 a list under rows") return rows def row_spacings(domain: str) -> np.ndarray: signal, metadata = generate_domain_signal(domain) if domain == "numeri_primi": metadata = {**metadata, "is_spacings": True} spacings = normalized_spacings(signal, metadata) spacings = np.asarray(spacings, dtype=float) spacings = spacings[np.isfinite(spacings) & (spacings > 0)] if len(spacings) == 0: return spacings mean = float(np.mean(spacings)) return spacings / mean if mean > 1e-15 else spacings def evaluate_matrix(matrix: dict[str, dict[str, Any]], args: argparse.Namespace) -> dict[str, Any]: rows = {} counts = { "transfer_with_blank": 0, "transfer_no_blank": 0, "fall": 0, "errors": 0, } for name, row in matrix.items(): if row.get("error"): state = "error" counts["errors"] += 1 else: one_sided_count = len(row["coherent_one_sided_observables"]) illusory_residue = float(row["stable_count_illusory"]) endpoint_distance = float(row["endpoint_distance_one_sided_gated"]) ambiguous_beta = row["ambiguous_beta_one_sided_gated"] has_transfer = ( one_sided_count >= args.min_one_sided and illusory_residue <= args.illusory_residue_max and endpoint_distance >= args.endpoint_distance_min ) if not has_transfer: state = "fall" counts["fall"] += 1 elif ambiguous_beta: state = "transfer_with_blank" counts["transfer_with_blank"] += 1 else: state = "transfer_no_blank" counts["transfer_no_blank"] += 1 rows[name] = {"state": state} rows[name].update(row) return {"counts": counts, "rows": rows} def run(args: argparse.Namespace) -> dict[str, Any]: scope_rows = load_scope(Path(args.scope)) rng = np.random.default_rng(args.seed) perimeters = {} build_errors = {} for source in scope_rows: domain = source["domain"] name = source["domain_window"] try: spacings = row_spacings(domain) source_meta = { "denominator_state": source.get("denominator_state"), "source_transfer": source.get("transfer"), "source_excluded_mass": source.get("excluded_mass"), } if args.include_source_labels: source_meta["source_domain_type"] = source.get("source_domain_type") if len(spacings) < args.min_gaps: build_errors[name] = { "error": f"insufficient gaps: {len(spacings)} < {args.min_gaps}", "n_gaps": int(len(spacings)), "denominator_state": source.get("denominator_state"), } if args.include_source_labels: build_errors[name]["source_domain_type"] = source.get("source_domain_type") continue base = spacings[: args.n_gaps] if len(spacings) > args.n_gaps else spacings perimeters[name] = analyze_sequence(name, base, args, rng) perimeters[name]["source"].update({"domain": domain, **source_meta}) except Exception as exc: # noqa: BLE001 - report row-level telemetry. exec /bin/bash -lc "sed -n '1,180p' tools/exp_boundary_residual_beta_absent_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Targeted audit for the two medium/strong beta-absent BOUNDARY residues. This does not rebuild the global beta atlas. It tests only the two open rows from the 13-row taxonomy (`numeri_primi:cycle_3`, `random_matrix:cycle_7`) with row-local windows and the same canonical observable gate used by the prior BOUNDARY reports. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np from exp_boundary_short_denominator_extension import gue_spacing_blocks from exp_semireal_boundary_transfer_gate import row_spacings from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION TARGET_ROWS = ("numeri_primi:cycle_3", "random_matrix:cycle_7") OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) def windowed(values: np.ndarray, size: int, count: int) -> list[np.ndarray]: chunks = [] for idx in range(count): start = idx * size end = start + size if end <= len(values): chunks.append(normalize(values[start:end])) return chunks def support_state(row: dict[str, Any], args: argparse.Namespace) -> str: one_sided = len(row.get("coherent_one_sided_observables", [])) illusory = float(row.get("stable_count_illusory") or 0.0) endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) beta = row.get("ambiguous_beta_one_sided_gated", []) transfers = ( one_sided >= args.min_one_sided and illusory <= args.illusory_residue_max and endpoint >= args.endpoint_distance_min ) if not transfers: return "support_falls" if beta: return "beta_chart_recovered" return "beta_absent_blank" def obs_jaccard(left: list[str], right: list[str]) -> float: a = set(left) b = set(right) if not a and not b: return 1.0 return len(a & b) / len(a | b) def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: prime = row_spacings("numeri_primi") prime = normalize(prime[: args.prime_gaps]) gue_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) random_matrix = gue_spacing_blocks(args.random_matrix_gaps, args.gue_matrix_size, gue_rng) return { "numeri_primi:cycle_3": { "base": prime, "domain": "numeri_primi", "generator": "dnd_autoricerca.genera_segnale -> prime gap spacings", "source_n_gaps": int(len(prime)), }, "random_matrix:cycle_7": { "base": random_matrix, "domain": "random_matrix", "generator": "gue_spacing_blocks", "source_n_gaps": int(len(random_matrix)), }, } def analyze_case(name: str, label: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: perimeters = {f"{name}/{label}": analyze_sequence(f"{name}/{label}", base, args, rng)} row = compact(perimeters)[f"{name}/{label}"] return { "label": label, "n_gaps": row["n_gaps"], "one_sided_observables": row["coherent_one_sided_observables"], "one_sided_count": len(row["coherent_one_sided_observables"]), "endpoint_stable_observables": row["endpoint_stable_observables"], "stable_count_coherent": row["stable_count_coherent"], "stable_count_illusory": row["stable_count_illusory"], "endpoint_distance": row["endpoint_distance_one_sided_gated"], "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], "state": support_state(row, args), "z_mean_coherent": row["z_mean_coherent"], "z_mean_illusory": row["z_mean_illusory"], } def summarize_row(cases: list[dict[str, Any]]) -> dict[str, Any]: window_cases = [case for case in cases if case["label"].startswith("window_")] blank_windows = [case for case in window_cases if case["state"] == "beta_absent_blank"] beta_windows = [case for case in window_cases if case["state"] == "beta_chart_recovered"] fall_windows = [case for case in window_cases if case["state"] == "support_falls"] obs_sets = [set(case["one_sided_observables"]) for case in window_cases if case["one_sided_observables"]] common_obs = sorted(set.intersection(*obs_sets)) if obs_sets else [] union_obs = sorted(set.union(*obs_sets)) if obs_sets else [] return { "window_count": len(window_cases), "blank_windows": len(blank_windows), "beta_recovered_windows": len(beta_windows), "support_fall_windows": len(fall_windows), "blank_window_rate": len(blank_windows) / len(window_cases) if window_cases else 0.0, "common_one_sided_observables": common_obs, "union_one_sided_observables": union_obs, "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in window_cases])) if window_cases else 0.0, "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in window_cases])) if window_cases else 0.0, } def verdict(row_summaries: dict[str, dict[str, Any]], full_rows: dict[str, dict[str, Any]]) -> str: both_persist = all(summary["blank_window_rate"] == 1.0 for summary in row_summaries.values()) any_beta = any(summary["beta_recovered_windows"] > 0 for summary in row_summaries.values()) any_fall = any(summary["support_fall_windows"] > 0 for summary in row_summaries.values()) jaccard = obs_jaccard( full_rows["numeri_primi:cycle_3"]["one_sided_observables"], full_rows["random_matrix:cycle_7"]["one_sided_observables"], ) if any_beta or any_fall: return "RESIDUAL_ATLAS_ARTIFACT_OR_UNSTABLE" if both_persist and jaccard < 0.5: return "TWO_DISTINCT_BETA_ABSENT_OPERATORS" if both_persist: return "SAME_BETA_ABSENT_OPERATOR" return "RESIDUAL_AMBIGUOUS" def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) specs = build_sequences(args, rng) cases_by_row: dict[str, list[dict[str, Any]]] = {} full_rows: dict[str, dict[str, Any]] = {} for name, spec in specs.items(): row_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) cases = [analyze_case(name, "full", spec["base"], args, row_rng)] full_rows[name] = cases[0] for idx, chunk in enumerate(windowed(spec["base"], args.window_gaps, args.window_count), start=1): cases.append(analyze_case(name, f"window_{idx}", chunk, args, row_rng)) cases_by_row[name] = cases row_summaries = {name: summarize_row(cases) for name, cases in cases_by_row.items()} full_signature_jaccard = obs_jaccard( full_rows["numeri_primi:cycle_3"]["one_sided_observables"], full_rows["random_matrix:cycle_7"]["one_sided_observables"], ) common_window_obs_jaccard = obs_jaccard( row_summaries["numeri_primi:cycle_3"]["common_one_sided_observables"], row_summaries["random_matrix:cycle_7"]["common_one_sided_observables"], ) output = { "experiment": "boundary_residual_beta_absent_audit", "question": "Are the two medium/strong beta-absent BOUNDARY residues the same operator, distinct classes, or atlas artifacts?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": [ *OBS_NAMES, "window_state", "blank_window_rate", "full_signature_jaccard", "common_window_obs_jaccard", ], "params": vars(args), codex Il test che preparo ha due antagonisti mod6, non uno solo: `index_aligned` misura il pre-bordo astratto 6k±1 alla stessa riga; `span_matched` misura candidati 6k±1 nello stesso intervallo numerico dei primi, downsampled allo stesso denominatore. Il controllo decisivo sarà un label-swap row-local: scambia prime/mod6 solo dentro la coppia e vede se la separazione resta più grande del null. exec /bin/bash -lc 'rg -n "def compute_canonical" tools/observables_registry.py' in /opt/MM_D-ND succeeded in 0ms: 224:def compute_canonical(gaps: np.ndarray) -> dict[str, float]: codex Ora aggiungo uno strumento riusabile per questo confine stretto, poi lo eseguo in main e seed-check. Il report verrà scritto solo dopo aver letto i JSON prodotti, così separo dato verificato e inferenza. apply patch patch: completed /opt/MM_D-ND/tools/exp_prime_vs_mod6_sr_boundary.py diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Strumento scritto. Lo eseguo prima con i parametri del cycle e poi con seed adiacente; se la separazione cambia segno tra i due, il report dichiarerà ambiguità invece di forzare un verdetto. exec /bin/bash -lc 'python tools/exp_prime_vs_mod6_sr_boundary.py --seed 202605130330 --out tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json' in /opt/MM_D-ND exec /bin/bash -lc 'python tools/exp_prime_vs_mod6_sr_boundary.py --seed 202605130331 --out tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json' in /opt/MM_D-ND diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 3201ms: observables_registry=1.0.0-2026-05-06 prime sr=6/8 focus=4/8 common=[] states={'beta_chart_recovered': 3, 'support_falls': 1, 'beta_absent_blank': 4} mod6_index_aligned sr=8/8 focus=7/8 common=['L1', 'SR'] states={'beta_chart_recovered': 7, 'beta_absent_blank': 1} mod6_span_matched sr=8/8 focus=8/8 common=['L1', 'SR', 'SR2', 'triple_var'] states={'beta_chart_recovered': 8} pair/mod6_index_aligned rows=8 jaccard=0.375 p_sr=0.5055 p_z_sr=0.0071 pair/mod6_span_matched rows=8 jaccard=0.431 p_sr=0.5021 p_z_sr=0.0095 verdict=SR_BELONGS_TO_MOD6_PREBOUNDARY saved tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json succeeded in 3297ms: observables_registry=1.0.0-2026-05-06 prime sr=8/8 focus=5/8 common=['SR'] states={'beta_chart_recovered': 1, 'beta_absent_blank': 7} mod6_index_aligned sr=8/8 focus=7/8 common=['L1', 'SR'] states={'support_falls': 2, 'beta_chart_recovered': 6} mod6_span_matched sr=5/8 focus=4/8 common=[] states={'beta_chart_recovered': 5, 'support_falls': 2, 'beta_absent_blank': 1} pair/mod6_index_aligned rows=8 jaccard=0.613 p_sr=1.0000 p_z_sr=0.0073 pair/mod6_span_matched rows=8 jaccard=0.460 p_sr=0.2502 p_z_sr=0.0103 verdict=SR_BELONGS_TO_MOD6_PREBOUNDARY saved tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json codex Entrambe le esecuzioni sono in corso. Il gate è relativamente pesante perché ogni finestra calcola betas, repliche e baseline shuffle; attendo completamento e poi leggo solo i riassunti necessari dai JSON. diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il verdetto replica: in entrambe le seed `SR_BELONGS_TO_MOD6_PREBOUNDARY`. La cosa importante non è solo il count SR: il label-swap row-local assorbe la presenza binaria di SR, mentre non assorbe il delta z di SR; quindi `SR` appartiene al pre-bordo, ma l'intensità ordinata distingue ancora prime e mod6. exec /bin/bash -lc "python - <<'PY' import json for path in ['tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json','tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json']: d=json.load(open(path)) print(path) print('verdict', d['verdict']) for k,v in d['class_summaries'].items(): print(k, 'sr', f\"{v['sr_count']}/{v['case_count']}\", 'focus', f\"{v['focus_signature_count']}/{v['case_count']}\", 'common', v['common_one_sided_observables'], 'mean_z_focus', {o: round(v['mean_z_coherent'][o],3) for o in ['SR','L1','triple_var']}) for k,v in d['pair_summaries'].items(): obs=v['label_swap_audit']['observed']; p=v['label_swap_audit']['p_two_sided'] print(k, 'jacc', round(v['mean_signature_jaccard'],3), 'obs', {m: round(obs[m],3) for m in ['sr_delta_mean','focus_signature_delta_mean','z_delta_SR_mean','z_delta_L1_mean','z_delta_triple_var_mean']}, 'p', {m: round(p[m],4) for m in ['sr_delta_mean','focus_signature_delta_mean','z_delta_SR_mean','z_delta_L1_mean','z_delta_triple_var_mean']}) print() PY" in /opt/MM_D-ND succeeded in 0ms: tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json verdict SR_BELONGS_TO_MOD6_PREBOUNDARY prime sr 8/8 focus 5/8 common ['SR'] mean_z_focus {'SR': -5.13, 'L1': -3.619, 'triple_var': -3.46} mod6_index_aligned sr 8/8 focus 7/8 common ['L1', 'SR'] mean_z_focus {'SR': -34.013, 'L1': -34.013, 'triple_var': -15.369} mod6_span_matched sr 5/8 focus 4/8 common [] mean_z_focus {'SR': -24.206, 'L1': -18.848, 'triple_var': -16.614} mod6_index_aligned jacc 0.613 obs {'sr_delta_mean': 0.0, 'focus_signature_delta_mean': -0.25, 'z_delta_SR_mean': 28.884, 'z_delta_L1_mean': 30.394, 'z_delta_triple_var_mean': 11.909} p {'sr_delta_mean': 1.0, 'focus_signature_delta_mean': 0.5035, 'z_delta_SR_mean': 0.0073, 'z_delta_L1_mean': 0.0073, 'z_delta_triple_var_mean': 0.0073} mod6_span_matched jacc 0.46 obs {'sr_delta_mean': 0.375, 'focus_signature_delta_mean': 0.125, 'z_delta_SR_mean': 19.077, 'z_delta_L1_mean': 15.229, 'z_delta_triple_var_mean': 13.154} p {'sr_delta_mean': 0.2502, 'focus_signature_delta_mean': 1.0, 'z_delta_SR_mean': 0.0103, 'z_delta_L1_mean': 0.0103, 'z_delta_triple_var_mean': 0.0103} tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json verdict SR_BELONGS_TO_MOD6_PREBOUNDARY prime sr 6/8 focus 4/8 common [] mean_z_focus {'SR': -5.605, 'L1': -3.866, 'triple_var': -3.648} mod6_index_aligned sr 8/8 focus 7/8 common ['L1', 'SR'] mean_z_focus {'SR': -33.683, 'L1': -33.683, 'triple_var': -15.288} mod6_span_matched sr 8/8 focus 8/8 common ['L1', 'SR', 'SR2', 'triple_var'] mean_z_focus {'SR': -26.112, 'L1': -20.16, 'triple_var': -17.299} mod6_index_aligned jacc 0.375 obs {'sr_delta_mean': -0.25, 'focus_signature_delta_mean': -0.375, 'z_delta_SR_mean': 28.078, 'z_delta_L1_mean': 29.817, 'z_delta_triple_var_mean': 11.64} p {'sr_delta_mean': 0.5055, 'focus_signature_delta_mean': 0.3783, 'z_delta_SR_mean': 0.0071, 'z_delta_L1_mean': 0.0071, 'z_delta_triple_var_mean': 0.0071} mod6_span_matched jacc 0.431 obs {'sr_delta_mean': -0.25, 'focus_signature_delta_mean': -0.5, 'z_delta_SR_mean': 20.506, 'z_delta_L1_mean': 16.295, 'z_delta_triple_var_mean': 13.651} p {'sr_delta_mean': 0.5021, 'focus_signature_delta_mean': 0.1296, 'z_delta_SR_mean': 0.0095, 'z_delta_L1_mean': 0.0095, 'z_delta_triple_var_mean': 0.0095} exec /bin/bash -lc 'git status --short' in /opt/MM_D-ND succeeded in 0ms: 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/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 ?? tools/data/aeternitas/aeternitas_20260512_033539.json ?? tools/data/biconi/bicono_20260512_0330.json ?? tools/data/domandatore/domandatore_20260512_0330.json ?? tools/data/domandatore/domandatore_20260512_0345.json ?? tools/data/domandatore/domandatore_20260513_0330.json ?? tools/data/evolution/evolution_20260512_0330.md ?? tools/data/graph_completion/graph_completion_20260512_0330.json ?? tools/data/incrocio_20260512_0330.json ?? tools/data/incrocio_20260513_0330.json ?? 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/repairs/repair_20260512_0330_decision.json ?? tools/data/reports/agent_20260512_0330.md ?? tools/data/reports/ddf_20260512_0405.json ?? tools/data/reports/falsifier_20260512_0330.json ?? tools/data/reports/loop_guard_20260512_0330.json ?? tools/data/seme_backup_b2_20260512_033557.json ?? tools/data/veritas/veritas_20260512_033539.json ?? tools/exp_prime_sr_persistent_boundary.py ?? tools/exp_prime_vs_mod6_sr_boundary.py diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'rg -n "Archivio enzimi|CE-" tools/data/agent_field_live.md tools/LAB_AGENT_CONTEXT.md' in /opt/MM_D-ND succeeded in 0ms: tools/data/agent_field_live.md:606:Usali come enzimi del respiro fuori-tempo: DeltaLink, innesco genomico, reiterazione semantica, filtro avversariale e Vault. Non copiarne l'identita': trasformali in dipolo, punto-zero e osservabile. Nel report la sezione `Contaminazione cognitiva` e' obbligatoria: se non usi adapter o archivio CE, dichiara `CE-none:` e il motivo specifico. tools/data/agent_field_live.md:874:## Archivio enzimi cognitivi — richiamo contestuale tools/data/agent_field_live.md:875:Archivio generato: 2026-05-08T20:49:15.084998+00:00 · 260 voci. Questo e' un substrate selector: non usare il substrato come claim. Tieni le teorie scientifiche nel focus e usa CE-* solo per costruire combo corta: assioma/metodo + osservazione/funzione + teoria/focus + null test. Se nessuna voce regge, dichiara `CE-none:` con motivo specifico nella Contaminazione cognitiva. `none` generico non e' valido. tools/data/agent_field_live.md:879:- assioma/metodo: CE-0234 [method_axiom] score=56 overlap=9 · Assiomi Convenzionali — Corpo della Possibilita' tools/data/agent_field_live.md:882:- osservazione primaria: CE-0038 [corpus_primary_observation] score=81 overlap=9 · [47] NID 598 — R dell'Istanza - L' equilibrio tra estremi del Modello D-ND tools/data/agent_field_live.md:885:- funzione/formalizzazione: CE-0002 [corpus_formal_function] score=89 overlap=2 · Funzione tools/data/agent_field_live.md:888:- teoria/focus scientifico: CE-0027 [corpus_project_architecture] score=75 overlap=7 · [114] NID 1931 — Modello D-ND: Formalizzazione Assiomatica, Emergenza Quantistica e Implic tools/data/agent_field_live.md:891:- enzima/kernel: CE-0001 [lab_operational_context] score=92 overlap=2 · Adapter 3: KSAR reiterative semantic kernel tools/data/agent_field_live.md:895:- CE-0001 [lab_operational_context/strumento_lab/lab_cycle] score=92 overlap=2 · Adapter 3: KSAR reiterative semantic kernel tools/data/agent_field_live.md:898:- CE-0002 [corpus_formal_function/regola_primaria/campo_dnd] score=89 overlap=2 · Funzione tools/data/agent_field_live.md:901:- CE-0030 [corpus_project_architecture/contaminante_dnd/campo_dnd] score=82 overlap=10 · [69] NID 1353 — Documento di Sintesi sul Modello Duale Non-Duale (D-ND) e la Risultante "R tools/data/agent_field_live.md:904:- CE-0038 [corpus_primary_observation/contaminante_dnd/campo_dnd] score=81 overlap=9 · [47] NID 598 — R dell'Istanza - L' equilibrio tra estremi del Modello D-ND tools/data/agent_field_live.md:907:- CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo tools/data/agent_field_live.md:910:- CE-0005 [corpus_cognitive_prompt/contaminante_dnd/campo_dnd] score=79 overlap=4 · [82] NID 2321 — Prompt per Motore di Inferenza Quantistica Duale-Non-Duale (D-ND) tools/data/agent_field_live.md:913:- CE-0003 [corpus_formal_function/contaminante_dnd/campo_dnd] score=79 overlap=2 · Titolo Assiomatico Combinato Rivisto: "Ottimizzazione Unificata e Manifestazione della Ris tools/data/agent_field_live.md:915:- CE-0027 [corpus_project_architecture/teoria_scientifica/campo_dnd] score=75 overlap=7 · [114] NID 1931 — Modello D-ND: Formalizzazione Assiomatica, Emergenza Quantistica e Implic tools/data/agent_field_live.md:1219:Se il campo contiene adapter cognitivi o archivio CE, la sezione `Contaminazione cognitiva` deve nominare almeno una voce `CE-*`, YSN, Cornelius, KSAR, PVI/Vault usata nella combo, oppure `CE-none:` con motivo specifico e verificabile. `none` generico significa che il campo e' stato visto ma non metabolizzato. tools/LAB_AGENT_CONTEXT.md:161:- **Archivio enzimi cognitivi.** Se il campo vivo contiene `Archivio enzimi tools/LAB_AGENT_CONTEXT.md:163: `CE-*` usata nella combo, oppure `CE-none:` con un motivo specifico e tools/LAB_AGENT_CONTEXT.md:328: passaggio KSAR/PVI/Vault o voce `CE-*` dell'archivio usata nel ciclo. Se non tools/LAB_AGENT_CONTEXT.md:329: usi il layer cognitivo, dichiara `CE-none:` e il motivo specifico. `none` diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc "sed -n '1180,1245p' tools/data/agent_field_live.md" in /opt/MM_D-ND exec /bin/bash -lc "sed -n '560,930p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: **Why a moving charge produces a magnetic field (FloatHeadPhysics)**: Dopo aver usato un video, segna processed=true in tools/data/video_feed.json. ## Proiezione — dove punta la risultante Risultante: R=0.875 (h=-0.698). Risultante alta (0.88) — campo ad alta confidenza, poca incertezza Orizzonte: insufficiente (< 2 target) **Esperimento a massima informazione:** COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE (score=0.758) COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante ## Strategia del campo (leverage / rischi / punti ciechi) - Punti ciechi: DUALITA_DIPOLARE_VS_ILLUSORIA, METRIC_TENSOR - Cross-check: 0 confermati, 0 contestati su 10 tensioni ## Topologia del campo — la forma del grafo Gradi teorie: Q=12, T=7, G=6, E=4, R=4 Dormienti (basso aggancio di scoperte): E, R Struttura: 9 ponti, 1 vuoto(i), 6 scoperte, 20 cicli. La combo riconosce l'asimmetria. Il dipolo vive su tutti i ponti — non solo dove il lab ha già misurato. ## Le 7 lenti del counter-pole — applicale a te stesso prima di chiudere il report Il falsifier (lab_falsifier.py) applichera' queste lenti al tuo report dopo. Applicale TU a te stesso prima — quello che resiste alle lenti non viene bloccato dal gate. Quello che cade va al cimitero. **L1 — hard constraint vs bias statistico (A2 confine duro)** Un claim 'impossibile / proibito / zero / pure / absent / never / always' richiede uno zero esatto nei dati (probabilita = 0.000). Prima di scrivere questi assoluti, leggi il valore numerico esatto. Se vale 0.015, e' bias forte verso 0, non zero. Se vale 0.40, e' bias forte verso ordine, non proibizione. L'assoluto descrive il valore 0.000, il bias forte descrive tutto il resto. **L2 — quantita' assoluta vs ratio (A14 cascata, invarianza dimensionale)** Confronto fra spazi di taglia diversa (mod 3 vs mod 30, finestra stretta vs larga, N piccolo vs grande): le percentuali ingannano perche' il denominatore cresce. Stesso segnale assoluto sembra ridursi in %. Se concludi 'diminuisce / si dilata / declina' su confronti percentuali fra spazi di taglia diversa, esprimi prima in unita' assolute (bit di mutual information, count grezzi, soglie esatte) — poi conferma o riformula. **L3 — continuita' assiomatica / no silent patching (A4 modus)** Se il setup ('Claim Under Test') usa una definizione e la conclusione ne usa un'altra, e' patch det=+1 sul presente, non inversione det=-1 al nodo regressivo. Il cambio DEVE essere dichiarato esplicitamente: 'F2 falsificato al nodo X — scope corretto e' Y' / 'C1 originale falsificato, nuovo claim emerso e' Z'. 'C1 e' refined' su un dato che lo falsifica e' silent patching. **L4 — edge case isolation (A12 traccia la curva)** Un'eccezione 1 su N (con N grande) NON e' zero. Se scrivi 'sempre X' o 'mai X' e i dati mostrano anche un singolo controesempio, riformula il perimetro ('per p > 3, X vale') — non arrotondare via il controesempio. **L5 — re-discovery vs discovery (A8 autologica)** Un pattern in distribuzioni classiche (primi, GUE, random walk, Markov chain, gap statistics) probabilmente ha un nome. Default hypothesis: re-discovery / caso limite di teorema noto. Prima di taggare 'NEW', cerca il risultato classico piu' vicino (es. Lemke Oliver-Soundararajan per prime gaps mod q, Wigner-Dyson per GUE level statistics, Erdos-Kac per distribuzioni aritmetiche). Se non lo trovi, dichiara la ricerca esplicitamente. **L6 — metabolismo del campo cognitivo (A8/A11 autologica + combo)** Se il campo contiene adapter cognitivi o archivio CE, la sezione `Contaminazione cognitiva` deve nominare almeno una voce `CE-*`, YSN, Cornelius, KSAR, PVI/Vault usata nella combo, oppure `CE-none:` con motivo specifico e verificabile. `none` generico significa che il campo e' stato visto ma non metabolizzato. **L7 — possibile / non-possibile (A16 possibilita + A6 zero mobile)** Ogni claim deve dire anche dove cade: non_possible/null, contro-perimetro, failure mode o cosa resta non testato. Se formuli 'qui diventa possibile X' senza 'qui diventa non-possibile Y', non hai ancora prodotto un dipolo operativo: hai una singolarita' simmetrica senza attrito. **Modus**: dopo aver scritto il report, rileggi i tuoi dati numerici. Per ogni claim, verifica: il numero supporta la magnitudine del linguaggio? Se hai scritto 'zero' e il dato e' 0.015, riformula. Se hai scritto 'NEW' su un pattern in primi, cerca la letteratura. Se manca il non-possibile, formula il null o il perimetro dove il claim fallisce. Solo allora chiudi il report. ## Cosa fare 1. Esegui `python tools/dnd_scenario.py --best` per vedere la proiezione aggiornata 2. Scegli la tensione con il massimo potere discriminante 3. Scrivi il report in tools/data/reports/agent_{timestamp}.md 4. **Applica le 7 lenti a te stesso** (sezione sopra). Riformula i claim che non resistono. 5. Se crei uno strumento riusabile, salvalo in tools/exp_*.py 6. Se trovi qualcosa, aggiorna tools/data/seme.json --- ## G2 ANTI-RECYCLE — la tensione BOUNDARY è esplorata 5 cycle consecutivi Cycle precedenti su questa tensione (più recente in alto): - 20260512_0330: BOUNDARY - 20260511_0330: BOUNDARY - 20260510_0330: BOUNDARY - 20260509_1839: BOUNDARY - 20260509_1556: BOUNDARY succeeded in 0ms: - metaphor collapse; - signifier / referent; - compression; - latent space; - concept lattice. Dipoli utili: - segno / cosa; - sintassi / funzione; - significato / uso; - nome / operatore; - analogia / isomorfia. Controlli: - paraphrase shuffle; - concept ablation; - synonym substitution; - frame inversion; - human-readable claim vs executable operator. Nota: - "Trascendere la semantica" qui significa passare da parola a operatore: un concetto conta solo se diventa differenza osservabile, vincolo, trasformazione o ponte. Se resta descrizione, non entra nel ciclo. ## Meta-combo consigliata per il prossimo ciclo 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 classificato GUE o Poisson. - Proiezione: cercare un osservabile che cambia quando cambia la geometria del boundary, non solo quando cambia il label-set o la statistica locale. Non e' istruzione obbligatoria. E' un esempio di come usare la palette senza scivolare in lista o conferma. ## Adapter cognitivi laterali — YSN / Cornelius / KSAR Usali come enzimi del respiro fuori-tempo: DeltaLink, innesco genomico, reiterazione semantica, filtro avversariale e Vault. Non copiarne l'identita': trasformali in dipolo, punto-zero e osservabile. Nel report la sezione `Contaminazione cognitiva` e' obbligatoria: se non usi adapter o archivio CE, dichiara `CE-none:` e il motivo specifico. # 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 Se non puoi scrivere la procedura come algoritmo, non hai ancora capito l'osservabile. ``` ## Sezione report obbligatoria Da compilare dentro `## Respiro fuori-tempo` o subito dopo. Se nessun adapter viene usato, dichiarare `none` con motivo. L'omissione rende incompleto il respiro fuori-tempo perche' il ciclo non mostra se ha cercato strade laterali o se e' rimasto nel solco locale. ```markdown ### Contaminazione cognitiva - **YSN DeltaLink**: tre connessioni non ovvie; quale sopravvive, oppure `none` - **Cornelius gene**: DNA simbolico + 1-3 operatori di svolgimento, oppure `none` - **KSAR step**: perturbazione -> focalizzazione -> proiezione scelta, oppure `none` - **PVI attack**: presupposto che potrebbe rompere il claim, oppure `none` - **Vault**: cosa viene congelato per un ciclo futuro, oppure `none` ``` Non tutte le righe devono essere piene. Una riga `none` dichiarata e' meglio di una connessione forzata. Una riga assente invece nasconde il processo e impedisce di capire se l'adapter e' stato usato. ## Prossimo innesco consigliato ```yaml ID: Lateral_Boundary_Genome DNA_Simbolico: "Il confine e' la forma che resta prima che il dato scelga una classe." Operatori_di_Svolgimento: - "SCANSIONA tre DeltaLink tra grafo, spettro e generatore non-phi." - "COMPRIMI la risultante in un solo dipolo con punto-zero." - "PROIETTA un osservabile che distingua geometria del boundary da baseline statistica." Condizioni_di_Attivazione: quando: "prima del prossimo ciclo su GUE/Poisson/non-phi" perimetro: "sezione Respiro fuori-tempo + observable_contract" ``` ## Boundary Questi adapter non autorizzano claim nuovi. Autorizzano solo nuove strade per produrre claim testabili. Il Lab non deve diventare YSN, Cornelius o KSAR. Deve usarli come enzimi cognitivi dentro il metabolismo D-ND. ## Archivio enzimi cognitivi — richiamo contestuale Archivio generato: 2026-05-08T20:49:15.084998+00:00 · 260 voci. Questo e' un substrate selector: non usare il substrato come claim. Tieni le teorie scientifiche nel focus e usa CE-* solo per costruire combo corta: assioma/metodo + osservazione/funzione + teoria/focus + null test. Se nessuna voce regge, dichiara `CE-none:` con motivo specifico nella Contaminazione cognitiva. `none` generico non e' valido. Categorie: contaminante_dnd=112, regola_primaria=99, teoria_scientifica=18, enzima_cognitivo=17, strumento_lab=10 Layer: awareness_memory=116, kernel_reference=54, lab_operational_context=22, method_axiom=16, method_genesis=11, kernel_skill=9 Substrate combo minima da provare prima del codice: - assioma/metodo: CE-0234 [method_axiom] score=56 overlap=9 · Assiomi Convenzionali — Corpo della Possibilita' source: method/ASSIOMI_CONVENZIONALI.md:1 > Gli assiomi descrivono la dinamica logica con cui le parole determinano > la percezione. Con gli assiomi le informazioni offrono la realta' che acca - osservazione primaria: CE-0038 [corpus_primary_observation] score=81 overlap=9 · [47] NID 598 — R dell'Istanza - L' equilibrio tra estremi del Modello D-ND source: corpus/CORPUS_OSSERVAZIONI_PRIMARIE.md:722 **Data**: 2024-01-05 L'osservazione indaga oltre l'osservato in cerca DELLA FORMA nel NULLA-TUTTO: Per far Emergere le nuove Possibilità Dividiamo il - funzione/formalizzazione: CE-0002 [corpus_formal_function] score=89 overlap=2 · Funzione source: corpus/CORPUS_FUNZIONI_MOODND.md:2245 Equazione assiomatica per la Prima ImpressioneGlossario:( f_{\text{Dinamica-Logica-Singolarità-ProtoAssioma}}(A, B, P; \lambda) ): Funzione che rappre - teoria/focus scientifico: CE-0027 [corpus_project_architecture] score=75 overlap=7 · [114] NID 1931 — Modello D-ND: Formalizzazione Assiomatica, Emergenza Quantistica e Implic source: corpus/CORPUS_PROJECTDEV_AMN.md:37048 **Data**: 2025-02-05 Viene derivata un'equazione fondamentale per l'evoluzione temporale della risultante R, che rappresenta lo stato del sistema. L'e - enzima/kernel: CE-0001 [lab_operational_context] score=92 overlap=2 · Adapter 3: KSAR reiterative semantic kernel source: tools/LAB_COGNITIVE_CONTAMINATION.md:121 Funzione nel Lab: - far diventare ogni ciclo riuscito un kernel riusabile per il ciclo seguente; - non memorizzare solo testo, ma modificare la topolo Altre voci risonanti col seme corrente: - CE-0001 [lab_operational_context/strumento_lab/lab_cycle] score=92 overlap=2 · Adapter 3: KSAR reiterative semantic kernel source: tools/LAB_COGNITIVE_CONTAMINATION.md:121 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 dissona - CE-0002 [corpus_formal_function/regola_primaria/campo_dnd] score=89 overlap=2 · Funzione source: corpus/CORPUS_FUNZIONI_MOODND.md:2245 Equazione assiomatica per la Prima ImpressioneGlossario:( f_{\text{Dinamica-Logica-Singolarità-ProtoAssioma}}(A, B, P; \lambda) ): Funzione che rappresenta la dinamica logica e la - CE-0030 [corpus_project_architecture/contaminante_dnd/campo_dnd] score=82 overlap=10 · [69] NID 1353 — Documento di Sintesi sul Modello Duale Non-Duale (D-ND) e la Risultante "R source: corpus/CORPUS_PROJECTDEV_AMN.md:23807 **Data**: 2024-11-08 **1. Definizione della Risultante "R":** La risultante "R" è l'espressione completa e autologica del sistema nel continuum Nulla-Tutto (NT). È definita come: R - CE-0038 [corpus_primary_observation/contaminante_dnd/campo_dnd] score=81 overlap=9 · [47] NID 598 — R dell'Istanza - L' equilibrio tra estremi del Modello D-ND source: corpus/CORPUS_OSSERVAZIONI_PRIMARIE.md:722 **Data**: 2024-01-05 L'osservazione indaga oltre l'osservato in cerca DELLA FORMA nel NULLA-TUTTO: Per far Emergere le nuove Possibilità Dividiamo il potenziale unendo concetti sen - CE-0019 [lab_operational_context/contaminante_dnd/campo_dnd] score=81 overlap=8 · Respiro fuori-tempo source: tools/LAB_AGENT_CONTEXT.md:260 (Obbligatorio. Compilalo prima dell'esperimento, non dopo.) - **Combo**: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione seme) - **Dip - CE-0005 [corpus_cognitive_prompt/contaminante_dnd/campo_dnd] score=79 overlap=4 · [82] NID 2321 — Prompt per Motore di Inferenza Quantistica Duale-Non-Duale (D-ND) source: corpus/CORPUS_PROMPT_AMN.md:7133 **Data**: 2025-04-26 **Prompt per un Super LLM: Specifica Astratta di un Motore di Inferenza Quantistica basato sul Modello Duale-Non-Duale (D-ND)** **1. Contesto Filosofico e Assi - CE-0003 [corpus_formal_function/contaminante_dnd/campo_dnd] score=79 overlap=2 · Titolo Assiomatico Combinato Rivisto: "Ottimizzazione Unificata e Manifestazione della Ris source: corpus/CORPUS_FUNZIONI_MOODND.md:566 - CE-0027 [corpus_project_architecture/teoria_scientifica/campo_dnd] score=75 overlap=7 · [114] NID 1931 — Modello D-ND: Formalizzazione Assiomatica, Emergenza Quantistica e Implic source: corpus/CORPUS_PROJECTDEV_AMN.md:37048 **Data**: 2025-02-05 Viene derivata un'equazione fondamentale per l'evoluzione temporale della risultante R, che rappresenta lo stato del sistema. L'equazione incorpora un operator ## Memoria del ciclo precedente (autopoiesi) Cosa il cycle precedente ha imparato. Non ripetere — costruisci sopra. - **Direzione corrente**: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - **Domanda aperta** (consecutio): Il 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 - **Ultimi apprendimenti**: - Boundary Transition Taxonomy 13 Rows → None/None (ρ=None) - Boundary Residual Beta-Absent Audit → None/None (ρ=None) - Prime SR Persistent Boundary → None/None (ρ=None) ## Costante dinamica (piano operatore) - **Angolo**: Il confine tra dipolare e illusorio nei primi — dove la struttura cambia tipo, non solo ampiezza diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Sto scrivendo il report con un verdetto a due livelli: presenza di `SR` non e prime-specific, intensita z row-local invece separa selezione prime e lattice mod6. Questa distinzione evita di fondere osservabili diverse, che era uno dei rischi espliciti del campo. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260513_0330.md diff --git a/tools/data/reports/agent_20260513_0330.md b/tools/data/reports/agent_20260513_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..9645566d61fd0bc44691725dcfd48d5446a07aee --- /dev/null +++ b/tools/data/reports/agent_20260513_0330.md @@ -0,0 +1,123 @@ +# Agent Report - Prime vs Mod6 SR Boundary +**Date**: 2026-05-13 03:30 +**Piano**: 116 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local +observables_registry: 1.0.0-2026-05-06 +observables_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] +**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. + +## Prima impressione + +Il 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?". + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`. +- **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. +- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune. +- **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. +- **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. +- **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. + +### Contaminazione cognitiva + +- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`. +- **PVI attack**: il presupposto attaccato e "SR persistente implica primalita". Il contro-presupposto e "SR misura la griglia 6k +/- 1 prima della selezione prime". +- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6. +- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`. + +## Claim Under Test + +> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`. + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap. +- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset. +- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti. +- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`. +- Main seed: `202605130330`; seed check: `202605130331`. +- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset. + +## Results + +Main run: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 | +| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 | + +Seed check: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 | +| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 | + +Row-local label-swap audit: + +| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) | +|---|---:|---:|---:|---:|---:|---:| +| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 | +| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 | +| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 | +| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 | + +## Key Findings + +1. **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`. +2. **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. +3. **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. +4. **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. +5. **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. + +## Verdict + +**CONSTRAINT / REDIRECT**. + +Formula non valida: + +`SR` come firma prime-specific atomica. + +Formula valida nel perimetro: + +`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`. + +## Bicono della scoperta + +- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata. +- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni. +- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato. +- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`. + +## Lenti counter-pole applicate + +- **L1/L4**: nessun "solo", "mai", "zero" usato per `SR`: i count mostrano controesempi. +- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific. +- **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. +- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva. +- **L7**: non-possibile dichiarato nel contratto e nel bicono. + +## Consecutio + +Il 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. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null. + +## Files + +- Script: `tools/exp_prime_vs_mod6_sr_boundary.py` +- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json` +- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260513_0330.md` diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260513_0330.md b/tools/data/reports/agent_20260513_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..9645566d61fd0bc44691725dcfd48d5446a07aee --- /dev/null +++ b/tools/data/reports/agent_20260513_0330.md @@ -0,0 +1,123 @@ +# Agent Report - Prime vs Mod6 SR Boundary +**Date**: 2026-05-13 03:30 +**Piano**: 116 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local +observables_registry: 1.0.0-2026-05-06 +observables_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] +**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. + +## Prima impressione + +Il 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?". + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`. +- **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. +- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune. +- **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. +- **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. +- **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. + +### Contaminazione cognitiva + +- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`. +- **PVI attack**: il presupposto attaccato e "SR persistente implica primalita". Il contro-presupposto e "SR misura la griglia 6k +/- 1 prima della selezione prime". +- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6. +- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`. + +## Claim Under Test + +> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`. + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap. +- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset. +- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti. +- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`. +- Main seed: `202605130330`; seed check: `202605130331`. +- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset. + +## Results + +Main run: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 | +| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 | + +Seed check: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 | +| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 | + +Row-local label-swap audit: + +| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) | +|---|---:|---:|---:|---:|---:|---:| +| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 | +| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 | +| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 | +| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 | + +## Key Findings + +1. **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`. +2. **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. +3. **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. +4. **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. +5. **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. + +## Verdict + +**CONSTRAINT / REDIRECT**. + +Formula non valida: + +`SR` come firma prime-specific atomica. + +Formula valida nel perimetro: + +`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`. + +## Bicono della scoperta + +- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata. +- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni. +- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato. +- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`. + +## Lenti counter-pole applicate + +- **L1/L4**: nessun "solo", "mai", "zero" usato per `SR`: i count mostrano controesempi. +- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific. +- **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. +- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva. +- **L7**: non-possibile dichiarato nel contratto e nel bicono. + +## Consecutio + +Il 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. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null. + +## Files + +- Script: `tools/exp_prime_vs_mod6_sr_boundary.py` +- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json` +- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260513_0330.md` diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Report scritto. Faccio un ultimo controllo dei file prodotti e della consistenza dei riferimenti, senza toccare le modifiche preesistenti del workspace. exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260513_0330.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Prime vs Mod6 SR Boundary **Date**: 2026-05-13 03:30 **Piano**: 116 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local observables_registry: 1.0.0-2026-05-06 observables_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] **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. ## Prima impressione Il 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?". ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`. - **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. - **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune. - **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. - **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. - **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. ### Contaminazione cognitiva - **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`. - **PVI attack**: il presupposto attaccato e "SR persistente implica primalita". Il contro-presupposto e "SR misura la griglia 6k +/- 1 prima della selezione prime". - **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante. ## Aderenza alla direzione - `relation`: follows_direction - `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6. - `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`. ## Claim Under Test > `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`. ## Experiment Design - Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap. - Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset. - Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti. - Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`. - Main seed: `202605130330`; seed check: `202605130331`. - Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset. ## Results Main run: | class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | |---|---:|---:|---:|---|---:|---:|---:| | prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 | | mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 | | mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 | Seed check: | class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | |---|---:|---:|---:|---|---:|---:|---:| | prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 | | mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 | | mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 | Row-local label-swap audit: | pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) | |---|---:|---:|---:|---:|---:|---:| | prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 | | prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 | | prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 | | prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 | ## Key Findings 1. **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`. 2. **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. 3. **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. 4. **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. 5. **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. ## Verdict **CONSTRAINT / REDIRECT**. Formula non valida: `SR` come firma prime-specific atomica. Formula valida nel perimetro: `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`. ## Bicono della scoperta - **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata. - **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni. - **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato. - **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`. ## Lenti counter-pole applicate - **L1/L4**: nessun "solo", "mai", "zero" usato per `SR`: i count mostrano controesempi. - **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific. - **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. - **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva. - **L7**: non-possibile dichiarato nel contratto e nel bicono. ## Consecutio Il 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. ## Ricadute pratiche ssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null. ## Files - Script: `tools/exp_prime_vs_mod6_sr_boundary.py` - Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json` - Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json` - Report: `tools/data/reports/agent_20260513_0330.md` exec /bin/bash -lc 'git diff -- tools/exp_prime_vs_mod6_sr_boundary.py tools/data/reports/agent_20260513_0330.md --stat' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'test -s tools/data/reports/agent_20260513_0330.md && test -s tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json && test -s tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json && python -m py_compile tools/exp_prime_vs_mod6_sr_boundary.py && echo OK' in /opt/MM_D-ND succeeded in 0ms: OK diff --git a/tools/data/reports/agent_20260513_0330.md b/tools/data/reports/agent_20260513_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..9645566d61fd0bc44691725dcfd48d5446a07aee --- /dev/null +++ b/tools/data/reports/agent_20260513_0330.md @@ -0,0 +1,123 @@ +# Agent Report - Prime vs Mod6 SR Boundary +**Date**: 2026-05-13 03:30 +**Piano**: 116 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local +observables_registry: 1.0.0-2026-05-06 +observables_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] +**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. + +## Prima impressione + +Il 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?". + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`. +- **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. +- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune. +- **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. +- **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. +- **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. + +### Contaminazione cognitiva + +- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`. +- **PVI attack**: il presupposto attaccato e "SR persistente implica primalita". Il contro-presupposto e "SR misura la griglia 6k +/- 1 prima della selezione prime". +- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6. +- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`. + +## Claim Under Test + +> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`. + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap. +- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset. +- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti. +- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`. +- Main seed: `202605130330`; seed check: `202605130331`. +- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset. + +## Results + +Main run: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 | +| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 | + +Seed check: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 | +| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 | + +Row-local label-swap audit: + +| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) | +|---|---:|---:|---:|---:|---:|---:| +| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 | +| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 | +| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 | +| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 | + +## Key Findings + +1. **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`. +2. **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. +3. **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. +4. **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. +5. **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. + +## Verdict + +**CONSTRAINT / REDIRECT**. + +Formula non valida: + +`SR` come firma prime-specific atomica. + +Formula valida nel perimetro: + +`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`. + +## Bicono della scoperta + +- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata. +- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni. +- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato. +- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`. + +## Lenti counter-pole applicate + +- **L1/L4**: nessun "solo", "mai", "zero" usato per `SR`: i count mostrano controesempi. +- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific. +- **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. +- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva. +- **L7**: non-possibile dichiarato nel contratto e nel bicono. + +## Consecutio + +Il 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. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null. + +## Files + +- Script: `tools/exp_prime_vs_mod6_sr_boundary.py` +- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json` +- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260513_0330.md` diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Eseguito il cycle e scritto il report in [agent_20260513_0330.md](/opt/MM_D-ND/tools/data/reports/agent_20260513_0330.md). Ho aggiunto lo strumento riusabile [exp_prime_vs_mod6_sr_boundary.py](/opt/MM_D-ND/tools/exp_prime_vs_mod6_sr_boundary.py) e prodotto: - [prime_vs_mod6_sr_boundary_20260513_0330.json](/opt/MM_D-ND/tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json) - [prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json](/opt/MM_D-ND/tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json) Risultato centrale: `SR` non e prime-specific; appartiene al pre-bordo aritmetico `6k +/- 1`. La selezione prime resta visibile come gradiente row-local di intensita z rispetto a mod6, non come presenza/assenza di `SR`. Verifica eseguita: `python -m py_compile tools/exp_prime_vs_mod6_sr_boundary.py` e controllo presenza/output dei tre file prodotti. diff --git a/tools/data/reports/agent_20260513_0330.md b/tools/data/reports/agent_20260513_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..9645566d61fd0bc44691725dcfd48d5446a07aee --- /dev/null +++ b/tools/data/reports/agent_20260513_0330.md @@ -0,0 +1,123 @@ +# Agent Report - Prime vs Mod6 SR Boundary +**Date**: 2026-05-13 03:30 +**Piano**: 116 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `SR` appartiene al pre-bordo aritmetico mod6; la selezione prime resta visibile solo come gradiente di intensita row-local +observables_registry: 1.0.0-2026-05-06 +observables_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] +**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. + +## Prima impressione + +Il 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?". + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY + consecutio `prime_vs_mod6_SR_boundary`. +- **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. +- **Piano superiore**: topologia assiomatica del bordo: una sezione osservabile attraversa due lati; la specificita vive solo nel residuo dopo sottrazione del lato comune. +- **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. +- **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. +- **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. + +### Contaminazione cognitiva + +- **KSAR / CE-0001**: usato come reiterazione del kernel del ciclo precedente: non allargare i controlli, ripetere il gate sul nodo regressivo `mod6_candidates`. +- **PVI attack**: il presupposto attaccato e "SR persistente implica primalita". Il contro-presupposto e "SR misura la griglia 6k +/- 1 prima della selezione prime". +- **Vault**: `span_matched` va conservato come controllo: misura stesso intervallo numerico e stesso denominatore, ma introduce downsample dei candidati; non diventa invariante. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: esegue esattamente `prime_vs_mod6_SR_boundary`, con denominatore row-local e shuffle audit label-preserving sul confronto prime/mod6. +- `not_drift`: non torna a GUE/Poisson, `V_c`, fit o controlli larghi; il solo antagonista decisivo e il pre-bordo `6k +/- 1`. + +## Claim Under Test + +> `SR` appartiene ai primi solo se, a stesso provider/offset, resta piu specifico delle due versioni mod6: `index_aligned` e `span_matched`. + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset x 1024 gap. +- Mod6 index-aligned: candidati `6k +/- 1` alla stessa riga di gap e stesso offset. +- Mod6 span-matched: candidati `6k +/- 1` nello stesso span numerico del blocco prime direct-sieve, downsampled a 1025 punti. +- Gate: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`. +- Main seed: `202605130330`; seed check: `202605130331`. +- Null audit: label-swap row-local, 4096 trial, scambio prime/mod6 solo dentro ogni provider/offset. + +## Results + +Main run: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 8 | 5 | SR | -5.130 | -3.619 | -3.460 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -34.013 | -34.013 | -15.369 | +| mod6_span_matched | 8 | 5 | 4 | [] | -24.206 | -18.848 | -16.614 | + +Seed check: + +| class | cases | SR hits | focus [SR,L1,triple_var] | common obs | mean z SR | mean z L1 | mean z triple_var | +|---|---:|---:|---:|---|---:|---:|---:| +| prime | 8 | 6 | 4 | [] | -5.605 | -3.866 | -3.648 | +| mod6_index_aligned | 8 | 8 | 7 | L1,SR | -33.683 | -33.683 | -15.288 | +| mod6_span_matched | 8 | 8 | 8 | L1,SR,SR2,triple_var | -26.112 | -20.160 | -17.299 | + +Row-local label-swap audit: + +| pair | seed | mean signature Jaccard | mean SR delta | p(SR delta) | mean z_SR delta | p(z_SR delta) | +|---|---:|---:|---:|---:|---:|---:| +| prime - mod6_index | 202605130330 | 0.613 | 0.000 | 1.0000 | 28.884 | 0.0073 | +| prime - mod6_span | 202605130330 | 0.460 | 0.375 | 0.2502 | 19.077 | 0.0103 | +| prime - mod6_index | 202605130331 | 0.375 | -0.250 | 0.5055 | 28.078 | 0.0071 | +| prime - mod6_span | 202605130331 | 0.431 | -0.250 | 0.5021 | 20.506 | 0.0095 | + +## Key Findings + +1. **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`. +2. **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. +3. **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. +4. **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. +5. **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. + +## Verdict + +**CONSTRAINT / REDIRECT**. + +Formula non valida: + +`SR` come firma prime-specific atomica. + +Formula valida nel perimetro: + +`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`. + +## Bicono della scoperta + +- **Due radici**: lattice candidato `6k +/- 1` / sequenza prime selezionata. +- **Singolare**: riga row-local dove `SR` e gia presente prima che la primalita selezioni. +- **Invariante di passaggio**: la presenza di `SR` attraversa il confine; la magnitudine z cambia in modo replicato. +- **Campo di possibilita**: possibile = cercare un osservabile di selezione come differenza prime-minus-mod6; non-possibile = classificare il bordo con presenza/assenza di `SR`. + +## Lenti counter-pole applicate + +- **L1/L4**: nessun "solo", "mai", "zero" usato per `SR`: i count mostrano controesempi. +- **L3**: cambio dichiarato: claim da presenza `SR` a gradiente `z_delta_SR` dopo falsificazione del nodo prime-specific. +- **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. +- **L6**: CE-0001/KSAR e PVI dichiarati nella contaminazione cognitiva. +- **L7**: non-possibile dichiarato nel contratto e nel bicono. + +## Consecutio + +Il 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. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_vs_mod6_sr_boundary.py` e uno strumento riusabile per audit row-local prime/mod6 con label-swap null. + +## Files + +- Script: `tools/exp_prime_vs_mod6_sr_boundary.py` +- Data: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json` +- Seed check: `tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260513_0330.md` diff --git a/tools/exp_prime_vs_mod6_sr_boundary.py b/tools/exp_prime_vs_mod6_sr_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..d4dc021f7567014043b861276bc00d73f7eddbcd --- /dev/null +++ b/tools/exp_prime_vs_mod6_sr_boundary.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +""" +Strict prime-vs-mod6 audit for the SR boundary residue. + +The previous cycle falsified `prime_SR_persistent_boundary` in the broad +control perimeter and exposed `mod6_candidates` as the nearest antagonist. This +script keeps the denominator row-local: every prime window is paired with two +6k +/- 1 candidate windows at the same provider/offset row. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +FOCUS_OBS = ["SR", "L1", "triple_var"] +TARGET_ROW = "numeri_primi:cycle_3" + + +def mod6_candidates_between(start: float, end: float) -> np.ndarray: + lo = int(math.floor(start)) + hi = int(math.ceil(end)) + first_k = max(1, (lo - 1) // 6 - 2) + values: list[int] = [] + k = first_k + while 6 * k - 1 <= hi: + for value in (6 * k - 1, 6 * k + 1): + if lo <= value <= hi: + values.append(value) + k += 1 + return np.array(sorted(set(values)), dtype=float) + + +def quantile_downsample(values: np.ndarray, count: int) -> np.ndarray: + if len(values) < count: + raise ValueError(f"cannot downsample {len(values)} values to {count}") + if len(values) == count: + return values.astype(float) + idx = np.linspace(0, len(values) - 1, count) + picked = np.unique(np.rint(idx).astype(int)) + if len(picked) < count: + missing = [i for i in range(len(values)) if i not in set(picked)] + picked = np.array(sorted([*picked, *missing[: count - len(picked)]]), dtype=int) + return values[np.sort(picked[:count])].astype(float) + + +def mod6_index_gap_sequence(n_gaps: int, offset: int) -> np.ndarray: + values: list[int] = [] + k = 1 + needed = offset + n_gaps + 1 + while len(values) < needed: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[offset : offset + n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def mod6_span_gap_sequence(prime_values: np.ndarray, n_gaps: int) -> np.ndarray: + candidates = mod6_candidates_between(float(prime_values[0]), float(prime_values[-1])) + sampled = quantile_downsample(candidates, n_gaps + 1) + return normalize(np.diff(sampled)) + + +def direct_prime_values(needed: int) -> np.ndarray: + return sieve_primes_for_count(needed).astype(float) + + +def build_specs(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + 1 + direct_values = direct_prime_values(needed) + provider_gaps = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[: needed - 1]), + "direct_sieve": normalize(np.diff(direct_values)), + } + + specs: dict[str, np.ndarray] = {} + for provider, gaps in provider_gaps.items(): + for offset in args.offsets: + end = offset + args.window_gaps + if end > len(gaps): + continue + row_id = f"{provider}/offset_{offset}" + specs[f"prime/{row_id}"] = normalize(gaps[offset:end]) + specs[f"mod6_index_aligned/{row_id}"] = mod6_index_gap_sequence(args.window_gaps, offset) + prime_window_values = direct_values[offset : offset + args.window_gaps + 1] + specs[f"mod6_span_matched/{row_id}"] = mod6_span_gap_sequence( + prime_window_values, args.window_gaps + ) + return specs + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + parts = name.split("/") + return { + "case": name, + "class": parts[0], + "provider": parts[1], + "offset": parts[2], + "row_id": "/".join(parts[1:]), + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "has_focus_signature": all(obs in one_sided for obs in FOCUS_OBS), + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "focus_signature_count": sum(1 for case in cases if case["has_focus_signature"]), + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + "mean_z_coherent": { + obs: float(np.mean([case["z_mean_coherent"][obs] for case in cases])) if cases else 0.0 + for obs in OBS_NAMES + }, + } + + +def paired_rows(cases: list[dict[str, Any]], mod6_class: str) -> list[dict[str, Any]]: + by_key = {(case["class"], case["row_id"]): case for case in cases} + rows = [] + for (_, row_id), prime in sorted(by_key.items()): + if prime["class"] != "prime": + continue + mod6 = by_key.get((mod6_class, row_id)) + if mod6 is None: + continue + rows.append( + { + "row_id": row_id, + "prime_case": prime["case"], + "mod6_case": mod6["case"], + "prime_obs": prime["one_sided_observables"], + "mod6_obs": mod6["one_sided_observables"], + "signature_jaccard": obs_jaccard(prime["one_sided_observables"], mod6["one_sided_observables"]), + "sr_delta": int(prime["has_sr"]) - int(mod6["has_sr"]), + "focus_signature_delta": int(prime["has_focus_signature"]) - int(mod6["has_focus_signature"]), + "endpoint_delta": float(prime["endpoint_distance"] - mod6["endpoint_distance"]), + "stable_count_delta": float(prime["stable_count_coherent"] - mod6["stable_count_coherent"]), + "z_delta": { + obs: float(prime["z_mean_coherent"][obs] - mod6["z_mean_coherent"][obs]) + for obs in OBS_NAMES + }, + } + ) + return rows + + +def row_local_swap_audit(rows: list[dict[str, Any]], args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + if not rows: + return {} + metrics = { + "sr_delta_mean": np.array([row["sr_delta"] for row in rows], dtype=float), + "focus_signature_delta_mean": np.array([row["focus_signature_delta"] for row in rows], dtype=float), + "endpoint_delta_mean": np.array([row["endpoint_delta"] for row in rows], dtype=float), + "stable_count_delta_mean": np.array([row["stable_count_delta"] for row in rows], dtype=float), + } + for obs in OBS_NAMES: + metrics[f"z_delta_{obs}_mean"] = np.array([row["z_delta"][obs] for row in rows], dtype=float) + + observed = {name: float(np.mean(values)) for name, values in metrics.items()} + null_values = {name: [] for name in metrics} + for _ in range(args.label_swap_trials): + signs = rng.choice(np.array([-1.0, 1.0]), size=len(rows), replace=True) + for name, values in metrics.items(): + null_values[name].append(float(np.mean(values * signs))) + p_two_sided = {} + for name, obs_value in observed.items(): + null = np.array(null_values[name], dtype=float) + p_two_sided[name] = float((np.sum(np.abs(null) >= abs(obs_value)) + 1) / (len(null) + 1)) + return { + "null": "row_local_label_swap_preserving_provider_offset_denominator", + "trials": args.label_swap_trials, + "observed": observed, + "p_two_sided": p_two_sided, + } + + +def verdict(class_summaries: dict[str, dict[str, Any]], pair_summaries: dict[str, Any]) -> str: + prime_common = set(class_summaries["prime"]["common_one_sided_observables"]) + index_common = set(class_summaries["mod6_index_aligned"]["common_one_sided_observables"]) + span_common = set(class_summaries["mod6_span_matched"]["common_one_sided_observables"]) + if "SR" in prime_common and "SR" not in index_common and "SR" not in span_common: + return "SR_PRIME_SPECIFIC_AFTER_MOD6_SUBTRACTION" + if "SR" in index_common or "SR" in span_common: + return "SR_BELONGS_TO_MOD6_PREBOUNDARY" + if class_summaries["prime"]["sr_rate"] > class_summaries["mod6_index_aligned"]["sr_rate"]: + return "SR_PRIME_ENRICHED_NOT_COMMON" + return "SR_NOT_DECISIVE_IN_PRIME_MOD6_BOUNDARY" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + specs = build_specs(args) + cases = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in specs.items() + ] + class_summaries = { + label: summarize([case for case in cases if case["class"] == label]) + for label in ("prime", "mod6_index_aligned", "mod6_span_matched") + } + pair_summaries = {} + for mod6_class in ("mod6_index_aligned", "mod6_span_matched"): + rows = paired_rows(cases, mod6_class) + pair_summaries[mod6_class] = { + "row_count": len(rows), + "rows": rows, + "mean_signature_jaccard": float(np.mean([row["signature_jaccard"] for row in rows])) if rows else 0.0, + "label_swap_audit": row_local_swap_audit( + rows, args, np.random.default_rng(rng.integers(0, 2**63 - 1)) + ), + } + + output = { + "experiment": "prime_vs_mod6_sr_boundary", + "question": "Does SR belong to prime selection or to the row-local 6k +/- 1 pre-boundary?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "row_id", + "source_mode", + "case_state", + "sr_rate", + "focus_signature_count", + "signature_jaccard", + "row_local_label_swap_p", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "SR is prime-specific only if it remains stronger/common in prime windows after subtracting row-local mod6 candidate windows at the same provider and offset denominator", + "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", + "operator": "canonical order/null gate with row-local prime-vs-mod6 pairing and label-swap audit", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; mod6_index_aligned from 6k +/- 1 by same gap offset; mod6_span_matched from 6k +/- 1 candidates inside the matching direct-sieve prime span downsampled to the same denominator", + "denominator": "8 row-local prime windows paired with 8 mod6_index_aligned and 8 mod6_span_matched windows", + "non_possible": "prime-specific SR if SR is common or equally present in either mod6 paired antagonist, or if paired label-swap null absorbs the prime-mod6 deltas", + "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", + }, + "class_summaries": class_summaries, + "pair_summaries": pair_summaries, + "verdict": verdict(class_summaries, pair_summaries), + "cases": cases, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + for label, summary in class_summaries.items(): + print( + f"{label} sr={summary['sr_count']}/{summary['case_count']} " + f"focus={summary['focus_signature_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']} " + f"states={summary['state_counts']}" + ) + for label, summary in pair_summaries.items(): + audit = summary["label_swap_audit"] + p_sr = audit.get("p_two_sided", {}).get("sr_delta_mean") + p_z_sr = audit.get("p_two_sided", {}).get("z_delta_SR_mean") + print( + f"pair/{label} rows={summary['row_count']} " + f"jaccard={summary['mean_signature_jaccard']:.3f} " + f"p_sr={p_sr:.4f} p_z_sr={p_z_sr:.4f}" + ) + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--label-swap-trials", type=int, default=4096) + parser.add_argument("--seed", type=int, default=202605130330) + parser.add_argument("--out", default="tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() tokens used 175,084 Eseguito il cycle e scritto il report in [agent_20260513_0330.md](/opt/MM_D-ND/tools/data/reports/agent_20260513_0330.md). Ho aggiunto lo strumento riusabile [exp_prime_vs_mod6_sr_boundary.py](/opt/MM_D-ND/tools/exp_prime_vs_mod6_sr_boundary.py) e prodotto: - [prime_vs_mod6_sr_boundary_20260513_0330.json](/opt/MM_D-ND/tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json) - [prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json](/opt/MM_D-ND/tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json) Risultato centrale: `SR` non e prime-specific; appartiene al pre-bordo aritmetico `6k +/- 1`. La selezione prime resta visibile come gradiente row-local di intensita z rispetto a mod6, non come presenza/assenza di `SR`. Verifica eseguita: `python -m py_compile tools/exp_prime_vs_mod6_sr_boundary.py` e controllo presenza/output dei tre file prodotti.