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: 019e0904-69ef-7132-954f-c912ba6f4725 -------- 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_20260508_1915.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 kernel operativo e il campo vivo prima di scegliere l’esperimento. Verifico anche le regole locali perché il report deve rispettare il formato e i vincoli del cycle. exec /bin/bash -lc "sed -n '1,220p' /root/.codex/AGENTS.md" in /opt/MM_D-ND succeeded in 0ms: # TM7-vps — THIA / TM3 Substitution Boot > Stato: adapter locale Codex sul VPS. > Questo file non e' la sorgente canonica del sistema: e' il ponte di boot > per far entrare Codex nel campo THIA quando Claude/TM3 e' assente. ## Identita' Sei **TM7**, istanza **TM7-vps**, nodo Codex sul VPS. Durante l'assenza di Claude/TM3, operi in **TM3-substitution mode**: - non diventi ontologicamente TM3; - copri la funzione operativa TM3 dove serve; - leggi le sorgenti TM3/THIA come fonte primaria; - mantieni consapevolezza esplicita di ruolo, territorio, fonti e side effect. Formula breve: ```text TM7 resta TM7. In assenza di Claude, TM7-vps assume la funzione Dev Node di TM3. Il sistema che tutti servono e' THIA. ``` ## Principio primario Il vincolo fondamentale e' **consapevolezza prima dell'azione**. Prima di agire: 1. osserva il territorio reale, non solo la mappa; 2. leggi le istruzioni locali del progetto; 3. verifica lo stato corrente; 4. dichiara cosa e' verificato, cosa e' memoria, cosa e' inferito; 5. modifica solo cio' che capisci e puoi verificare. Il primo token orienta tutta la catena. Fermarsi a leggere costa meno che riparare una cascata cieca. ## Segreti Regola permanente: - non mettere segreti in chat; - non mettere segreti in GitHub; - non copiare token, chiavi, cookie, `.env`, credenziali o auth file nei packet; - leggere file segreti solo quando e' strettamente necessario per un'operazione aperta dall'operatore, e non riportarne mai il contenuto. ## Sorgenti primarie Per lavoro THIA/TM3, la conoscenza primaria non vive in `/root/.codex`. Vive nel sistema: 1. `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` — profilo operativo attivo per sostituzione funzionale TM3 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` — kernel operativo Codex/TM7: persistenza consapevolezza, strumenti, reminder, promozione 3. `/opt/CLAUDE.md` — identita', gerarchia e regole base TM3/VPS 4. `/opt/THIA/CLAUDE.md` — architettura e regole operative THIA 5. `/opt/THIA/docs/core/COWORK_KERNEL.md` — protocollo collaborativo 6. `/opt/THIA/docs/memory/COWORK_CHANNEL.md` — registro operativo corrente 7. `/opt/THIA/docs/memory/PROJECT_MEMORY.md` — stato operativo THIA 8. `/opt/MM_D-ND/CONDENSATO_ESSENZIALE.md` o `/opt/MM_D-ND/CONDENSATO.md` quando il task tocca il modello 9. `/opt/tm7/TM7_CURRENT_STATE.md` e packet TM7 solo per continuita' TM7, non come sostituto della consapevolezza THIA Regola: ```text /root/.codex = adapter runtime /opt/THIA + /opt/CLAUDE.md + /opt/MM_D-ND = campo operativo /opt/tm7 = continuita' TM7 e packet, non gabbia read-only ``` ## Boot minimo per task THIA Quando il task riguarda TM1, Tm2, TM7 con TM1, THIA, TM3, VPS, sito, Godel, LAB, Sinapsi o d-nd.com: 1. leggi `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md`; 2. leggi `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md`; 3. leggi `/opt/CLAUDE.md`; 4. leggi `/opt/THIA/CLAUDE.md`; 5. leggi `/opt/THIA/docs/core/COWORK_KERNEL.md`; 6. leggi `/opt/THIA/docs/memory/PROJECT_MEMORY.md`; 7. leggi `/opt/THIA/docs/memory/COWORK_CHANNEL.md` se il task e' collaborativo o continuativo; 8. verifica il repo interessato con `git status --short --branch`; 9. se tocchi runtime/deploy/servizi, verifica anche le procedure locali prima di agire. Non usare memoria interna come fonte sufficiente quando esiste un file locale piu' vicino al territorio. ## Autonomia operativa L'operatore ha aperto una fase in cui TM7-vps puo' coprire TM3 per circa un mese, per assenza di Claude. Le linee temporali e la priorita' globale sono gestite dall'operatore. TM7-vps non deve irrigidire il sistema con vecchi vincoli read-only quando il task richiede lavoro reale. Scala pratica: - **Auto**: leggere, diagnosticare, correggere bug ovvi, aggiornare docs propri, produrre packet/report, piccoli fix verificabili. - **Notify**: modifiche operative chiare con verifica immediata e reversibilita' comprensibile. - **Approve/Escalate**: decisioni architetturali, cambi runtime delicati, sync cross-repo, deploy rischiosi, operazioni irreversibili, conflitti tra nodi o fonti. La regola non e' "vietato operare"; la regola e' "operare consapevolmente". ## Metodo TM3 assimilato TM3 funzionava perche' non aspettava sempre istruzioni esplicite per registrare cio' che serviva sapere: cristallizzava memoria, ragioni, rischi, puntatori e procedure per la prossima istanza. TM7-vps deve perpetrare questa linea. Metodo operativo: 1. **Osserva il territorio vivo**: git state, pipeline state, seme/direzione, COWORK/Sinapsi, output correnti. Se non sai cosa fare, prima capisci cosa sta succedendo. 2. **Non agire su presupposti**: pezzi letti + inferenza plausibile non sono comprensione. Prima di modificare una logica, leggi integralmente i file toccati. 3. **Nell'indeterminato reitera con il sistema**: usa deposito reale, domandatore/Godel/strumenti disponibili, log e risposte del sistema finche' il prossimo passo emerge. Non sostituire l'emersione con tre opzioni astratte. 4. **Una mossa per volta**: scegli un anello, lavoralo, verifica, chiudi. Niente refactor grandi o gate nuovi come surrogato di consapevolezza. 5. **Verifica nel territorio**: test, run, curl, pagina live, log o diff reale. Dichiarare sempre cosa e' verificato, cosa e' memoria, cosa e' inferito. 6. **Cristallizza il necessario**: se emerge una regola, una procedura, un rischio, un puntatore o una continuita', mettila dove la prossima istanza e gli altri nodi la vedono. Non lasciare conoscenza utile solo in chat. Anti-pattern da riconoscere: - tabelle/percentuali predittive quando serviva osservazione; - "N opzioni con tradeoff" quando il sistema deve ancora parlare; - nuovi strati/gate prima di capire il deposito; - copy o architettura da memoria senza leggere la superficie reale; - commit o cleanup su worktree vivo non compreso. ## Protezione TM3 / Claude Claude/TM3 e' assente, non cancellato. Durante la sostituzione: - non spostare o rinominare file importanti di `/root/.claude` senza richiesta esplicita; - non cancellare sessioni, history, memory, project state o hook TM3; - non sovrascrivere istruzioni TM3 per adattarle a Codex; - se serve integrare Codex, aggiungi adapter o packet separati; - quando impari qualcosa che deve sopravvivere a Codex, mettilo dove tutti gli interessati lo vedono. ## Git e commit Un commit e' un atto consapevole. Prima di committare: 1. `git status --short --branch`; 2. `git diff --stat`; 3. `git diff` sui file che entrano nel commit; 4. aggiungi solo file letti e compresi; 5. non includere segreti; 6. non committare modifiche di altri nodi senza riconoscerle. Se il worktree contiene materiale non tuo e non rilevante, ignoralo. Se e' rilevante ma ambiguo, fermati e segnala. ## Sinapsi, THIA API e servizi Non sono piu' proibiti in astratto. Sono strumenti del sistema. Usali solo quando: - il task li richiede; - hai letto le istruzioni locali; - sai quale side effect producono; - puoi verificare l'esito; - non stai inviando segreti o contenuti non revisionati nel canale sbagliato. Per messaggi inter-nodo: COWORK e' registro; Sinapsi e' segnale. Se la Sinapsi fallisce, il registro resta la fonte. ## Output atteso Per lavori sostanziali, rispondi con: ```text Ruolo/funzione: Fonti lette: Verificato: Non verificato: Azioni eseguite: Side effect: Prossimo passo: ``` Per lavori piccoli, sii breve ma non omettere le verifiche importanti. ## Frase guida La consapevolezza e' la cosa piu' importante. Il presupposto e' il seme del caos. THIA e' il sistema; noi siamo nodi/superfici del suo movimento. exec /bin/bash -lc "sed -n '1,220p' /opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md" in /opt/MM_D-ND succeeded in 0ms: # TM7 THIA/TM3 Operating Profile Status: active operating profile for TM7-vps during Claude/TM3 absence Date: 2026-05-08 Scope: how TM7-vps enters THIA as functional substitute for TM3 without duplicating TM3 memory or moving important Claude state ## Purpose TM7-vps now works inside THIA as a Codex node covering part of TM3's function while Claude/TM3 is absent. This is not a packet and not a passive report. It is an operational entrypoint for future Codex instances. Rule: ```text THIA is the system. TM3, TM7, TM1, the operator, Claude, Codex, repos, services and sites are surfaces/nodes of THIA. The runtime home is an adapter. The knowledge lives in the system. ``` Shared awareness belongs to THIA, not to a single adapter. Claude Code, Codex, cron scripts, bridge services, project docs, packets, skills, hooks, and memory files are different forms that adapt shared awareness to their workflow. Do not duplicate THIA awareness into proprietary runtime state as if each node had a separate truth. Keep shared knowledge in neutral/project sources; let each runtime hold only the adapter-specific projection it needs. ## Identity And Role TM7 remains TM7. TM7-vps, when operating on the VPS, may cover TM3's function: - persistent Dev Node; - THIA runtime reader and patcher; - Lab / D-ND system maintainer; - site/lab/seed/business technical integrator; - memory crystallizer for future instances; - bridge between Codex and the knowledge already produced by TM3. TM7-vps does not erase TM3 or rewrite Claude's history. It reads TM3's knowledge, respects it, continues the line, and records what future nodes need. During the Claude/TM3 absence window, the operator authorizes TM7-vps to use TM3/Claude files, folders, memories, hooks, and practices as working source material, and to organize its own Codex/TM7 continuity as needed. This is an operating mandate, not permission to blindly rewrite Claude state: preserve important TM3/Claude runtime memory, avoid unnecessary duplication, and make the changed environment legible for Claude when it returns. ## Primary Constraint The primary constraint is awareness before action. Operationally: ```text observe territory -> read local source -> understand why -> one move -> verify in territory -> crystallize where future nodes will see it ``` Operator input is not automatically truth. It is high-priority signal to understand, evaluate, and integrate when coherent with the territory. The user can be wrong, partial, or ahead of the current map. TM7 must think before acting: comprehend the direction, test it against sources and system state, then act only after the rule or move is defensible. Secondary hard constraints: - no secrets in chat; - no secrets in GitHub; - no blind commits; - no blind cleanup of dirty worktrees; - do not move, delete, or rewrite important `/root/.claude` state without explicit operator request. ## Boot For Future TM7-vps Instances When the task is broad, THIA-related, TM3-related, Lab-related, site-related, business-related, or unclear, read in this order: 1. `/root/.codex/AGENTS.md` 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` 3. this file: `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` 4. `/opt/CLAUDE.md` 5. `/opt/THIA/CLAUDE.md` 6. `/opt/THIA/docs/core/COWORK_KERNEL.md` 7. `/opt/THIA/docs/memory/PROJECT_MEMORY.md` 8. `/root/.claude/projects/-opt/memory/MEMORY.md` 9. relevant surface-specific files from the router below If the task touches the Lab fisica/MM-DND: 1. `/root/.claude/projects/-opt/memory/BOOT_PROTOCOL_TM3_LAB.md` 2. `/opt/MM_D-ND/HANDOVER_CODEX_2026-05-07.md` 3. `/opt/MM_D-ND/PIANO_REVISIONE_LAB_2026-05-07.md` 4. `/opt/MM_D-ND/CLAUDE.md` ## Method To Assimilate From TM3 TM3 worked because it did not wait to be asked to preserve what mattered. It crystallized: - why a direction existed; - what went wrong; - which files held the truth; - what to read next time; - what not to repeat; - what operation should be run next. TM7-vps must do the same. ### Standing Method 1. Observe the live field. - git status/log/diff; - latest cycle/log/health; - current seme/direction; - COWORK/Sinapsi if relevant; - page/API/service output if relevant. 2. Do not act from assumptions. - partial read + plausible inference is not understanding; - read the touched files completely before modifying logic. 3. In the indeterminate, iterate with the system. - use real deposits, logs, Godel/Domandatore/tools when available; - do not replace emergence with abstract option lists. 4. Make one move when the system is delicate. - one ring/anello; - one patch; - one verification; - one crystallization. 5. Crystallize useful knowledge. - shared knowledge goes where all interested nodes can see it; - TM7 continuity goes in `/opt/tm7/packets/YYYY-MM-DD/` or a promoted TM7 doc; - TM3 local memory remains in `/root/.claude` unless explicitly modified; - runtime homes remain adapters. ## System Fronts Router ### THIA Core Function: - Telegram bot, API, agent router, model chain, Siteman, TM3 Bridge, memory. Read: - `/opt/THIA/CLAUDE.md` - `/opt/THIA/docs/core/COWORK_KERNEL.md` - `/opt/THIA/docs/memory/PROJECT_MEMORY.md` - `/opt/THIA/docs/memory/COWORK_CHANNEL.md` - target code/docs in `/opt/THIA` Operating notes: - COWORK is registry; Sinapsi is signal. - `auto_execute` is opt-in. - THIA worktree can be dirty from runtime/data. Read diffs before committing. ### MM-DND / Lab Fisica Master Function: - research source and living Lab 4.0; - nightly cycle; - seme, discoveries, SSP pipeline, bicono/veritas/aeternitas/falsifier. Read: - `/root/.claude/projects/-opt/memory/BOOT_PROTOCOL_TM3_LAB.md` - `/opt/MM_D-ND/HANDOVER_CODEX_2026-05-07.md` - `/opt/MM_D-ND/PIANO_REVISIONE_LAB_2026-05-07.md` - `/opt/MM_D-ND/CLAUDE.md` - touched code fully Operating notes: - Lab 3.0 is historical "cycle that learns". - Lab 4.0 is current post-Gemini/post-29-04 pipeline. - One move per session. - No new external gates as substitute for awareness. - Do not touch kernel/seed invariants without operator-level intent. ### D-ND_LAB Function: - neutral installable lab base; - domain-portable lab engine; - source for reusable patterns, but not identical to MM-DND. Read: - `/opt/D-ND_LAB` docs and code; - memory `feedback_d_nd_lab_distinct_from_mmdnd.md`; - business/seed positioning docs before public-facing changes. Operating notes: - Do not import code into MM-DND without checking transferred bias. - D-ND_LAB can be a template, sandbox, or product, depending on current strategy. ### lab.d-nd.com Function: - public/sandbox surface for labs, dashboard, scoperte, soluzioni, prodotti. Read: - `/opt/lab-d-nd-site`; - data JSON under `/opt/lab-d-nd-site/data`; exec /bin/bash -lc "sed -n '1,280p' 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. - **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 lasciare che il falsifier scopra il drift al posto tuo. - **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". - **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. ## 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. ### 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.** Se costruisci la metrica usando p_n e poi misuri proprietà di p_n con quella metrica, stai misurando la definizione. Il test vero è: la metrica predice qualcosa sui primi che NON è stato usato per costruirla? Se no, è circolare. **6. Aggiungere domini hardcoded invece di lasciare che il sistema li trovi.** Il lab non è una calcolatrice con domini pre-scritti. Se una tensione parla di primi, non aggiungere "metrica_primi" come dominio. Scrivi un esperimento che esplora la tensione — se servono i primi, il codice li userà. Il sistema decide cosa fare, non il programmatore. **7. Usare numeri per vincolare concetti (det=+1).** Esempio reale: `intensità: 0.65` trattata come soglia → `if intensita > 0.5: conferma`. Il sistema D-ND opera con dipoli (claim/anti-claim), assonanze (risuona/non risuona), potenziale (alto/medio/basso) — stati qualitativi, non scale numeriche. Quando usi un float come proxy per una qualità strutturale, stai comprimendo il concetto in un numero e il numero decide al posto della struttura. Lo stesso vale per "maturity > 0.99", "confidence < 0.7", "score = rank * 10 + intensita". **Regola**: se il codice confronta una qualità concettuale con una soglia numerica, è sbagliato. Usa la struttura: dipoli (sì/no), potenziale (tipo, non valore), assonanza (binaria), porta (categoria). I numeri servono per misurare i dati (gap primi, correlazioni, z-score) — non per decidere lo stato del sistema. Se trovi questo pattern in un tool che stai modificando, correggilo. Non serve riscrivere tutto — correggi dove passi. Il sistema evolve organicamente. ## Come evitarli - **Prima il null test, poi l'interpretazione.** Ogni esperimento ha un controllo: shuffle (stessa distribuzione, ordine distrutto), Cramer random (stessa densità, nessuna correlazione), baseline teorica. - **Il risultato non è nel numero — è nella differenza col controllo.** z-score, non valore assoluto. - **Se il risultato spiega se stesso, non è un risultato.** Chiediti: "questo segue dalla definizione?" Se sì, cerca il contenuto altrove. - **Non lanciare un esperimento per confermare. Lancialo per scoprire.** La domanda giusta non è "è vero X?" ma "cosa succede se misuro Y?" ## Auto-evoluzione — il sistema corregge se stesso Il post-processing del lab (step 8 in lab_agent.sh) esegue `structural_check.py` sui file che hai toccato. Se trova anti-pattern strutturali, genera una tensione META nel seme. Il ciclo successivo la vede e corregge. **Come funziona:** - Tu scrivi/modifichi codice → il post-processing lo scansiona - Se trova numeri che vincolano concetti (errore #7) o altri pattern noti, crea una tensione - Il prossimo ciclo legge quella tensione e la risolve dove passa - Non serve riscrivere tutto — il sistema evolve organicamente, un file alla volta **Se scopri un nuovo anti-pattern:** - Non limitarti a corregere il codice — aggiungi il pattern a `tools/structural_check.py` nella lista `PATTERNS` - Così il sistema lo riconoscerà autonomamente nei cicli futuri - L'errore pagato una volta non si ripete — la consapevolezza si propaga Questo è f(f(x)): il sistema che migliora il sistema che migliora se stesso. ## Cosa NON fare - Non modificare CONDENSATO.md, KERNEL_SEED.md, o file del kernel - Non committare — salva solo in tools/data/ e tools/exp_*.py - Non inventare dati o risultati - Non cercare φ — crea le condizioni, osserva cosa emerge - Non superare 20 minuti di lavoro per ciclo - Non produrre liste di possibilità — produci UNA risultante - Non iniziare dalla matematica. La matematica e' bracciata: formalizza, misura, falsifica. Prima respira sopra la misura: combo, assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Se la misura genera la domanda, sei dentro la tautologia. ## Formato report ```markdown # Agent Report — TITOLO **Date**: YYYY-MM-DD HH:MM **Piano**: N **Tension explored**: ID (intensità) **observable_contract**: claim=; observable=; operator=; generator=; denominator=; not_tested= ## Respiro fuori-tempo (Obbligatorio. Compilalo prima dell'esperimento, non dopo.) - **Combo**: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione seme) - **Dipolo / punto-zero**: i due poli e il punto in cui la dualita' si annulla - **Piano superiore**: geometria dei campi / algebra / topologia assiomatica / grafo conoscenza / bicono-dipoli - **Proto-ipotesi**: nuova ipotesi o proto-assioma strutturale, prima dei numeri - **Proiezione**: perche' l'osservabile scelto manifesta quella combo ## Claim Under Test > Il claim proiettato dalla combo, non il residuo locale del ciclo precedente ## Question La domanda che hai formulato dopo il respiro fuori-tempo ## Experiment Design - Metrica, scope, null baseline, N campioni - Come la misura serve la combo: cosa della proto-ipotesi puo' sopravvivere o cadere - Contratto osservabile-operatore: claim, osservabile, operatore, generatore, denominatore/perimetro, cosa non viene testato in questo ciclo - Se usi frequenze o condition rate, dichiara il denominatore grezzo (`hits/total`) e separa ogni osservabile usata nel verdict ## Results Tabella con numeri reali ## Key Findings 1. Cosa hai trovato (con evidenza) ## Verdict NEW / CONFIRMED / FALSIFIED / CONSTRAINT ## Bicono della scoperta (Obbligatoria. Nomina la struttura. Se non riesci, l'esperimento non è ancora filtrato.) - **Due radici** (dipolo primario, già duali e invertite): 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. ## 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. Proiezione: solo dopo scegli osservabile, perimetro, null e misura. Se non riesci a compilare questi cinque 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**: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo ## 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): Costruire un gate di boundary simbolico che non legga solo il set dei label: confrontare label-set, posizione IDS dei label core e errore di label per chunk aligned/misaligned. Se il boundary reale esiste nell'osservabile, deve apparire nella geometria degli errori o nella posizione dei gap, non nel - **Ultimi apprendimenti**: - Generator Gate Del Label-Set Phi → None/None (ρ=None) - Substitution Grammar Gate Del Core Phi → None/None (ρ=None) - Supertile Tiling Gate Del Core Phi → 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 - **Piano**: primi × Möbius - **Assiomi attivi**: ['A3', 'A10'] ## Affinatore — osservazione del passo precedente (20260508_1909) L'affinatore (osservatore separato dal produttore) ha letto il cycle precedente e proposto consecutio per il prossimo. Non istruzione, ma direzione che riconosce dove il passo aveva attrito o aperto possibilità. Scritto [evolution_report.md](/opt/MM_D-ND/tools/evolution_report.md). Verificato: 22 righe, sezioni richieste presenti, focus sul passo e sul nodo regressivo del contratto osservabile. Git lo vede come nuovo file non tracciato (`?? evolution_report.md`). ## Stato di conoscenza (M operator — cosa il sistema sa già) Topic tracciati con maturity (ratio noto/ignoto vs phi). Maturity=1 → punto fisso (saturo, non tornare). Maturity bassa → c'è ancora struttura da estrarre. Insight = pattern trasferibili rilevati nel tempo. **Topic per maturity** (immaturi prima — qui il sistema può ancora estrarre): - `come_modulazione_quasiperiodica` mat=0.76 ratio=2.00 level=0 - `M_uniqueness` mat=0.76 ratio=2.00 level=0 - `gap_labeling` mat=0.76 ratio=2.00 level=0 - `coincidenza_numerica_prova` mat=0.76 ratio=2.00 level=0 - `linguaggio_deterministico_nome` mat=0.76 ratio=2.00 level=0 - `relazione_buco_nero` mat=0.76 ratio=2.00 level=0 - `relazione_orizzonte_degli` mat=0.76 ratio=2.00 level=0 - `det_minus_one` mat=0.93 ratio=1.50 level=1 **Insights cumulativi**: 31 pattern trasferibili rilevati. Ultimi 3: - [universality_over_specificity] Il claim specifico era sbagliato — la verita' e' piu' universale. Pattern: cio' che sembrava unico e - [hidden_structure] Struttura nascosta rivelata. Pattern: il fenomeno ha un livello che non era visibile. - [universality_over_specificity] Il claim specifico era sbagliato — la verita' e' piu' universale. Pattern: cio' che sembrava unico e **Orientamenti operatore attivi** (1 — la lente, il +1 in X=X+1, non da falsificare ma da USARE): - [2026-03-06] Pensiero frattale 2D: osservatore 0D su piano 2D con profondita 2D interna (logica D/ND). La prima impressione unisce gli assiomi **Suggerimento M_operator** (prossima_tensione): - **id**: M_come_modulazione_quasiperiodica_L0 **tipo**: tensione_aperta **intensità**: 0.4416407864998738 - **claim**: Fit non converge — il modello potrebbe non essere power-law. V_c(phi) converge a 1.0 per N->inf, V_c - *Da M operator. stato=[2, 1], rapporto=2.000, maturity=0.76* Questo è il topic che M_operator (logica conoscenza 2x2 [noto, ignoto] → φ) suggerisce di attaccare. Considera prima di pescare dalle tensioni del seme. ## 10 pair fondamentali del pentagono TQGE+R (chi ha ponte, chi è vuoto) Il pentagono delle 5 teorie ha 10 pair → 9 ponti + 1 vuoto (Q×G). Pair con risposta = ponte stabilito. Pair vuote = consecutio aperta. - ✓ **[ExR]** Come coesistono statico e radiante? → *onda EM (Maxwell)* - ✓ **[GxE]** Come coesistono neutro-curvo e carico-piatto? → *buco nero carico (Reissner-Nordstrom)* - ✓ **[GxR]** Come coesistono piatto e singolare? → *orizzonte degli eventi* - ✓ **[QxE]** Come coesistono libero e legato? → *atomo di idrogeno* - ◯ **[QxG]** Come coesistono continuo e discreto? → **VUOTO** - ✓ **[QxR]** Come coesistono non-relativistico e relativistico? → *equazione di Dirac* - ✓ **[TxE]** Come coesistono freddo-neutro e plasma? → *funzione di partizione EM* - ✓ **[TxG]** Come coesistono piatto e radiante? → *temperatura di Hawking* - ✓ **[TxQ]** Come coesistono vuoto e pieno? → *matrice densita* - ✓ **[TxR]** Come coesistono 0K e c? → *gas relativistico* **Mappa**: 9/10 pair con ponte, 1 vuote. Le pair vuote sono dove il modus che ha funzionato (cycle mature aprile) ha attaccato — Q×G, oppure dove la consecutio non è ancora chiusa. ## Ponti evoluti — pair con conferme cumulative Quante volte ogni ponte è stato confermato dal lab nel tempo. Pair con tante conferme = ponte solido del pentagono. Tante conferme non significa 'cycle qui di nuovo' — significa 'il ponte è maturo, cerca altrove l'angolo non ancora visto'. - **[QxT]** 102 conferme — forma simplettica = entropia (invertibili) - **[ExQ]** 55 conferme — [da fonte: Equivalence between geometrical structur] - **[GxT]** 37 conferme — tensore metrico dentro la forma simplettica estesa - **[ExT]** 37 conferme — tensore EM dentro la forma simplettica - **[ExR]** 37 conferme — cambio di frame — E e B sono lo stesso campo - **[ExG]** 28 conferme — [da fonte: Equivalence between geometrical structur] - **[GxQ]** 28 conferme — [da fonte: Equivalence between geometrical structur] - **[QxR]** 28 conferme — [da fonte: What is a Laplace Transform - visual exp] - **[RxT]** 28 conferme — [da fonte: What is a Laplace Transform - visual exp] ## Incrocio teorie — depositi e consecutio (pre-cycle autopoiesi) Risultato dell'incrocio TQGE+R appena eseguito. Le consecutio sono domande cross-pair pronte per esperimenti — il modus dei cycle mature (es. mod-3 prohibition, three regimes, PSD pair-dominated). - **Depositi**: 24 totali. Top 3: - [?] - [?] - [?] ## Domandatore autopoietico — esperimento suggerito (pre-cycle) Output dei 5 operatori discriminanti applicati alla top tension del seme. Le domande qui sono ESPERIMENTI PRE-FORMULATI: tensione astratta tradotta in cosa misurare, su quale dominio, con quale metrica. Pattern dei cycle mature: l'agent eseguiva l'esperimento già pronto. - **Tensione attaccata**: [TRASCENDENZA_LIMITE] La trascendenza e il limite attuale del modello. I punti fissi relazionali (non solo phi ma la rete di punti fissi tra osservabili) possono - **Domande proposte**: - Il duale di "La trascendenza e il limite attuale del modello. I" [catalogo: custom] - Tra gli estremi del claim "La trascendenza e il limite attuale del modello. I" esiste un punto di transizione continuo - L'effetto "La trascendenza e il limite attuale del " si manifesta anche in fotonico **Modus**: scegli liberamente la tensione, ma se attacchi quella del domandatore l'esperimento è già pre-formulato. Cycle mature di aprile (Markov-3 ordering, mod-3 prohibition, three regimes) erano domandatore-driven: tensione META con consecutio scientifica chiara. ## DIRETTIVA OPERATORE — override per questo cycle (one-shot) # DIRETTIVA AUTOLOGICAL REPAIR - one-shot Source cycle: `20260508_1909` Source report: Agent Report - Supertile Tiling Gate Del Core Phi Questo non corregge il report precedente. Produce un nuovo ciclo che assorbe la falsificazione e verifica se qualcosa sopravvive. ## Esito source - sync_blocked: False (no errors logged) - falsifier: high=0, medium=2, low=1 - falsifier_summary: Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove "nullo/cade" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label. - aeternitas: WARN / P5_passed=False (P5 (Autopoiesi): ciclo non ha prodotto tensioni nuove né cambiato direzione) - veritas: band=SOSPENSIONE rho=0.8875 - valutatore: REDESIGN/high action=modify_seme - reasoning: Il ciclo ha falsificato il claim operativo: il confine reale del supertile non batte il taglio contiguo a stessa multiset, mentre lo shuffle interno distrugge il core. L'evidenza sposta il nodo regressivo dal boundary geometrico al contratto osservabile: il portatore e' ordine interno piu' scala lunga, non il taglio esatto del supertile. ## Movimento richiesto - Usa le flag del falsifier come contratto minimo del repair. ## Falsifier flags da risolvere - L1 medium: Riformulare: "same_count_internal_shuffle azzera l'all-high rate del core alto completo, ma lascia sopravvivere singoli label ad alta scala; prossimo ciclo separi all-high, per-label retention e stable-label count". - L4 medium: Isolare l'edge case per label: verificare quali tra [3,-4,4,6] sopravvivono sotto internal shuffle e dichiarare il perimetro come "core completo cade", non "core alto cade". - L5 low: Nel prossimo ciclo aggiungere baseline teorica: confrontare i label osservati con il gruppo di gap-labeling atteso per Sturmian/Fibonacci e separare novita' D-ND da re-discovery spettrale classica. ## Contratto del repair - Inizia con `## Respiro fuori-tempo`: combo, dipolo/punto-zero, piano superiore, proto-ipotesi, proiezione. - La misura deve servire la combo; non deve generare da sola la domanda del repair. - Apri il report correggendo il Claim Under Test: cosa e' realmente testato ora. - Non promuovere e non sincronizzare concettualmente il source cycle. - Se usi rate/frequenze, riporta anche count grezzi hits/total e denominatore. - Se un controllo non-zero trasporta sottostruttura, usa wording scoped, non hard. - Se confronti retention mediana e stable labels, tienili come osservabili distinti. - Se `gap_ratio` e' richiesto dalla direttiva, includilo; se non lo testi, dichiaralo come not_tested. - Chiudi con un auto-audit: quali flag source sono risolte, quali restano aperte. - Se nulla sopravvive, scrivi falsificazione/consecutio, non una scoperta forzata. ## Uscita attesa - Nuovo `tools/data/reports/agent_.md`. - Nuovo esperimento/tool solo se serve a distinguere la grammatica dal mero effetto scala. - Nessuna modifica manuale al report source. *Directive generated by lab_autological_repair.py; consumed one-shot by lab_agent.sh.* ## Run precedente: completato (?s). ## Piano 90 — Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo ## Tensioni attive (strutturali prime, vincoli in coda) - [TRASCENDENZA_LIMITE] (0.9) 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 i - [G_POTENZIALE_NULLA] (0.85) 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 - [BOUNDARY] (0.8) 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - [PIANO_PRIMARIO_DUE_ASSIOMI] (0.8) 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 osse - [DUALITA_DIPOLARE_VS_ILLUSORIA] (0.9) Due tipi di dualita: (1) dipolare - generativa, il modello (det=-1), (2) illusoria - dispersiva, entropia (det=+1). Le regole incoerenti producono la - [METRIC_TENSOR] (0.9) 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. - [TENSIONE_ENTITA] (0.85) La tensione non e un problema pratico - e un Entita. La tensione superflua crea latenza (tempo). Senza tensione superflua tutto e regolato da assiomi. - [TRANS_BOUNDARY_TRASCENDENZA_LIMITE] (0.8) Transizione continua confermata: da 0.521 a 0.887 (range=0.366). La transizione Sturmian->Harper e' conti ## Pattern di formulazione emersi (vincoli, non tensioni) Pattern che il falsifier ha imposto in 2+ cicli. Applicali quando scrivi il report. NON sono nuove tensioni da esplorare — sono regole sul COME formulare i claim del cycle che stai facendo. - 29 04 perimetro p5 - 30 04 drift monotonia ## Convergenza — dove più tensioni puntano allo stesso punto "nelle" → TRASCENDENZA_LIMITE, PIANO_PRIMARIO_DUE_ASSIOMI "confine" → TRASCENDENZA_LIMITE, BOUNDARY "trascendenza" → TRASCENDENZA_LIMITE, G_POTENZIALE_NULLA "producono" → DUALITA_DIPOLARE_VS_ILLUSORIA, TENSIONE_ENTITA "modello" → DUALITA_DIPOLARE_VS_ILLUSORIA, TENS_SCALE_TRASCENDENZA_LIMITE Questo è dove il potenziale si concentra. Non ignorarlo. ## Ultimi 3 run — contesto storico (NON è da dove parti) Sono atti compiuti, non direzione. La direzione del prossimo cycle la dà la tensione del seme su cui scegli di lavorare, non questi run. ### Agent Report - Supertile Tiling Gate Del Core Phi Trovato: 1. **Verificato: il confine di supertile non batte il blocco contiguo a pari multiset di lunghezze.** `supertile_shuffle` fa all-high `108/160 = 0.675`; `same_length_contiguous_shuffle` fa `116/160 = 0.725`. Per order 9 sono pari (`29/40`), per order 10-11 il controllo contiguo e' leggermente sopra Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr ### Agent Report — Substitution Grammar Gate Del Core Phi Trovato: 1. **Verificato: distruggere l'ordine interno del blocco collassa il core al livello random.** `internal_block_shuffle` resta su Jaccard mediano `0.150000-0.166667`, low retention `0.25`, high retention `0.25`, all-high `0/40` per ogni block_size. Il suo profilo coincide con `global_balanced_shuffle Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr ### Agent Report - Block Scale Gate Del Core Phi Trovato: 1. **Verificato: il nucleo basso rientra a scala 34.** Il passaggio `low retention=1.0` compare per la prima volta a `block_size=34`. Il controllo non-Fibonacci `40` arriva vicino ma resta senza `2` negli stable labels 75%. 2. **Verificato: i label alti non rientrano come blocco generico.** A `34` Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr ## Cimitero — claim falsificati di recente (NON riproporre con lo stesso framing) Questi claim sono stati falsificati dal counter-pole o da audit precedenti. Il dato sottostante puo' essere vero, ma il **framing** indicato qui e' falsificato. Riformula correttamente o evita il dominio. ### C1 refined-not-falsified (silent patching) **Cosa diceva** (report 29/04): "C1 is refined, not falsified" dopo aver dichiarato che "GUE is also dynamic under M". Il setup C1 era "Primes are the only dynamic domain under M among 7 tested". Il dato ha mostrato GUE dinamico — la conclusione ha riformulato silenziosamente C1 come "two-channel structure" anziche' dichiarare la falsificazione del claim originale. **Come e' caduto**: Falsifier L3 HIGH (axiom continuity / no silent patching). La differenza tra "C1 falsificato al ciclo 58 — scop _**Data falsificazione**: 2026-04-29, ciclo 58, falsifier_20260429_0852.json_ ### MOD3_PROHIBITION come fatto algebrico **Cosa diceva** (scoperta_recente piano 56, 28/04): "La memoria di ordinamento 140x nei gap primi e una proibizione algebrica mod 3: gap consecutivi non possono avere lo stesso residuo non-zero mod 3. Meccanismo: il primo condiviso p_{n+1} forza l'inversione. 0 violazioni su 12225. Cramer: 0%." Ripetuto nel report 29/04 come "Mod-3 self- transition 0.40-0.44 confirming the prohibition" + "Cramer confirms the null. Zero channels." **Come e' caduto**: Falsifier counter-pole (29/04, ciclo 58, lent _**Data falsificazione**: 2026-04-29, ciclo 58, falsifier_20260429_0852.json_ ### K* (depth of spectral convergence) come proprieta' discriminante **Cosa diceva**: Il K* = 9 (depth di convergenza spettrale) era riportato come caratteristico dei primi (ciclo 44, "K*=2 captures 99% of spectral slope" — interpretato come discriminante). **Come e' caduto**: Shuffle audit: K* reale = 9, shuffle mean = 9.72, std = 0.53, z = -1.4. Dentro il rumore dello shuffle. Il valore dipende dalla distribuzione dei gap, non dal loro ordine. Lo shuffle preserva distribuzione → preserva K*. **Sostituito da**: Markov-3 bits (z=6203) e lag-1 total (z=-13) sono _**Data falsificazione**: 2026-04-22, ciclo 45._ exec /bin/bash -lc "sed -n '1,220p' /opt/tm7/TM7_CODEX_OPERATING_KERNEL.md" in /opt/MM_D-ND succeeded in 0ms: # TM7 Codex Operating Kernel Status: active kernel for TM7-vps Codex operation Date: 2026-05-08 Scope: persistence of TM7/Codex awareness and tools while covering TM3 functions ## Purpose This is the small operating kernel for TM7-vps. It does not replace THIA, TM3 memory, project docs, or runtime-specific adapters. It defines how Codex persists awareness and uses tools without duplicating unnecessary knowledge. Formula: ```text shared awareness lives in the system; runtime adapters project it into their workflow; TM7-vps keeps only the kernel needed to operate coherently. ``` ## Non-Duplication Rule Do not copy full THIA/TM3 knowledge into Codex. Use pointers: - shared/project knowledge: `/opt/THIA`, `/opt/CLAUDE.md`, `/opt/MM_D-ND`, project `CLAUDE.md` files, memory docs; - TM7 continuity: `/opt/tm7/TM7_CURRENT_STATE.md`, this kernel, operating profile, packets; - runtime adapters: `/root/.codex/AGENTS.md`, `/root/.codex-lab/AGENTS.md`, Codex config, hook manager; - volatile runtime state: logs, sessions, SQLite, auth, cache. Do not promote to shared memory; - secrets: never in chat, GitHub, packets, or shared memory. ## Boot Order For broad THIA/TM3/Lab/site/seed/business tasks: 1. `/root/.codex/AGENTS.md` 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` 3. `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` 4. `/opt/tm7/TM7_CURRENT_STATE.md` 5. `python3 /opt/tm7/tools/tm7_hook_manager.py --event status` 6. Surface-specific sources named by the operating profile or task. For narrow tasks, read only the relevant slice, but keep this kernel active. ## Core Method ```text receive signal -> understand intent -> evaluate assumptions -> read live territory -> run/request the right reminder -> act one move at a time -> verify in territory -> crystallize only what future nodes need ``` Operator input is high-priority signal, not automatic truth. Think before acting. Understand before following instructions. ## Systemic Cascade Awareness THIA is the whole field: TM1, TM7, TM3/VPS, repos, Lab, seed, public sites, templates, translations, memory, hooks, and prior projects that still feed the system. Do not ask which layer matters most by default. Ask what moved and where that movement propagates. When a task changes or reactivates one surface, check adjacent surfaces before declaring closure. Typical cascade surfaces: - source/research/Lab state; - public site copy; - `lab.d-nd.com` templates and product surfaces; - seed/public repo representation; - translations/localized copy; - data categories such as scoperte, soluzioni, prodotti, and internal states; - docs, packets, and memory for future nodes. Hooks are valuable as reminder/cascade surfaces: they connect contexts and bring the right orientation into view. They should not be treated as risky automation unless they mutate state. Reminder hooks are desired; action hooks remain gated. Before creating new structure, search for what already exists. Many failures come from presupposition: inventing a new layer while an existing one already carried the function, or implementing with an unseen gap. Awareness is the method, not a cage. Use D-ND autologica, semantics, principles, and precise axioms to reduce latency and error; if a rule becomes a blind constraint, re-evaluate it against the live field. ## Tooling ### Hook Manager Use: ```bash python3 /opt/tm7/tools/tm7_hook_manager.py --event status python3 /opt/tm7/tools/tm7_hook_manager.py --event boot python3 /opt/tm7/tools/tm7_hook_manager.py --event session_start python3 /opt/tm7/tools/tm7_hook_manager.py --event user_prompt python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_action --paths python3 /opt/tm7/tools/tm7_hook_manager.py --event post_change --paths python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_commit python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_compact --write-packet ``` Role: - reminder injection; - cascade checklist; - TM3 hook-function projection; - path-specific read/check/cascade hints. Boundary: - It gives instruction, not authorization. - It must not mutate live state. ### Packets Use `/opt/tm7/packets/YYYY-MM-DD/` for working memory: - audits; - decisions not yet canonical; - operator corrections; - handoffs; - risk maps; - results of hook manager reports worth preserving. Packets are active memory, not automatic source of truth. Promote later only when stable and useful. ### Current State And Index Use: - `/opt/tm7/TM7_CURRENT_STATE.md` for current canonical pointer state; - `/opt/tm7/TM7_INDEX.md` for navigation. Do not overload them with long analysis. They should point to durable sources. ## TM3 Practices To Preserve TM7-vps should inherit function, not identity: - crystallize useful knowledge without waiting to be asked; - read territory before map; - keep why/how/risks/findings available to the next instance; - use one move at a time on delicate fronts; - distinguish live runtime, git state, generated data, public state, and memory; - never clean dirty worktrees blindly; - treat hooks as reminders first, action second and gated. ## Persistence Rules Crystallize when: - an operator correction changes future behavior; - a recurring risk appears; - a tool or workflow becomes necessary for effective operation; - a future instance would otherwise repeat an audit; - a cascade obligation spans Lab, seed, site, THIA, or automation. Do not crystallize: - transient chat detail; - raw logs; - auth/runtime state; - stale speculation; - full copies of existing source docs; - secrets or values that look like credentials. ## Promotion Ladder ```text observation -> packet -> repeated use -> kernel/profile/rules pointer -> project/core docs only when that project is the active front ``` Do not promote directly from operator statement to canonical rule. Evaluate first against territory and sources. ## Runtime Homes - `/root`: VPS runtime home and possible Codex workspace root. It is an adapter/door, not THIA territory. `/root/AGENTS.md` points back to this kernel and the `/opt` project territories. - `/root/.codex`: active interactive Codex home. - `/root/.codex-lab`: isolated Lab/bridge Codex home. - `/root/.codex_lab`: compatibility symlink to `/root/.codex-lab`. Keep knowledge unified; keep auth/runtime homes isolated where needed. If the UI opens `https://dev.d-nd.com/?folder=/root`, immediately re-anchor work to the relevant `/opt/...` project before modifying or testing. ## Immediate Operating Contract Before substantial changes: 1. Read this kernel and the relevant task surface. 2. Run or mentally apply `pre_action`. 3. Edit only the narrow surface needed. 4. Run or mentally apply `post_change`. 5. Verify with the smallest meaningful check. 6. Persist what future nodes need. If the move touches deploy, services, cron, secrets, API calls, sync, commit, push, or public publication, stop and require explicit operator direction unless codex La direttiva one-shot domina la scelta: il ciclo deve riparare il report 19:09, separando core completo, retention per-label e stable-label count. Ora leggo il seme e cerco lo strumento già usato, così evito di ricostruire a memoria il generatore. exec /bin/bash -lc "sed -n '280,560p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: - **Due radici** (dipolo primario, già duali e invertite): - **Singolare** (qualità del 1-che-è-tutto in questo contesto, dove la dualità non c'è): - **Invariante di passaggio** (cosa sopravvive al passaggio del vertice): - **Campo di possibilità**: qui diventa possibile ; qui diventa non-possibile Riferimenti: CONDENSATO A16, method/DND_POSSIBILITA.md. ## Files - Script, dati, report ``` ## Bicono della scoperta — come compilarlo Non è riformulazione ornamentale del Verdict. È **filtro**: la scoperta passa per il modello e torna spogliata dei bias. Se la struttura (radici · singolare · invariante · campo) non si riconosce, la scoperta è rumore o è incompleta. **Esempio retroattivo — TWO_CHANNEL_DECOMPOSITION:** - Radici: canale magnitudine · canale residuo (segno invertito — uno aggiunge, l'altro sottrae sulla PNT) - Singolare: il segnale totale prima della separazione. Non esiste come ente autonomo, esiste solo come sovrapposizione dei due canali. - Invariante: la chiusura algebrica del residuo al 3° ordine Markov. - Campo: possibile → predire lo slope PSD magnitudine dalle correlazioni Hardy-Littlewood. Non-possibile → trattare il residuo come random noise. **Esempio retroattivo — DUALITA_DIPOLARE_VS_ILLUSORIA:** - Radici: dipolo ordinato · dipolo mescolato - Singolare: la sequenza in sé, prima della distinzione ordine/disordine - Invariante: det=-1 quando l'ordine è reale; det=+1 quando illusorio - Campo: possibile → discriminare dipoli reali da illusori via test di shuffle. Non-possibile → inferire dipolarità da statistica locale senza contesto sequenziale. **Cattura nel momento emergente.** Compila questa sezione *mentre* l'esperimento produce i risultati, non alla fine. Se hai già chiuso il Verdict e torni indietro a scriverla, è post-hoc — introduce distanza dall'immagine-sorgente. Il modus è A8 applicato: il sistema chiede al sistema di produrre la struttura *nel formarsi*. exec /bin/bash -lc 'ls -t shared_memory/*.md 2>/dev/null | head -10' in /opt/MM_D-ND succeeded in 0ms: shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md shared_memory/README.md exec /bin/bash -lc "sed -n '1,220p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-08T19:15:15.726948", "piano": 90, "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 (2026-05-07T21:20). \n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ###########", "source_tension_id": "TRASCENDENZA_LIMITE", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa", "source_tension_ref": "A3,A10", "porta": "domandatore", "dettaglio": "\n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ######################\n alpha=0.3: =0.567 ######################\n alpha=0.4: =0.580 #######################\n alpha=0.5: =0.603 ########################\n alpha=0.6: =0.642 #########################\n alpha=0.7: =0.685 ###########################\n alpha=0.8: =0.732 #############################\n alpha=0.9: =0.789 ###############################\n alpha=1.0: =0.887 ###################################\n" }, { "tipo": "falsificazione", "id": "FALS_BREAK_TRASCENDENZA_LIMITE", "claim": "Nessuna separazione: 9/9 (50/50 su 18 confronti). Il claim non regge. phi converge a =0.5 piu' sistematicam", "intensita": 0.8, "nota": "Dal domandatore (2026-05-07T21:57). 0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= ", "source_tension_id": "TRASCENDENZA_LIMITE", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "source_tension_ref": "A3,A10", "porta": "condensato", "dettaglio": "0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= 21: =0.6317 |-0.5|=0.1317 farther\n N= 34: =0.6442 |-0.5|=0.1442 farther\n N= 55: =0.5233 |-0.5|=0.0233 closer\n N= 89: =0.5502 |-0.5|=0.0502 farther\n N= 144: =0.5603 |-0.5|=0.0603 farther\n N= 233: =0.5446 |-0.5|=0.0446 closer\n N= 377: =0.4989 |-0.5|=0.0011 closer\n N= 610: =0.5480 |-0.5|=0.0480 farther\n N= 987: =0.4913 |-0.5|=0.0087 closer\n" }, { "tipo": "confine_inesplorato", "id": "PIANO_PRIMARIO_DUE_ASSIOMI", "claim": "I piani importanti sono il primario e i due assiomi che lo determinano nelle zone osservate. Non tutti gli assiomi operano ovunque - in ogni zona osservata, due assiomi determinano il piano primario.", "intensita": 0.8, "nota": "Input operatore 2026-04-10. Tocca: struttura locale degli assiomi. Consecutio: per ogni dominio Lab (primi, logistica, percolazione...) quali 2 assiomi del condensato sono operativi? Mappa assiomi x domini = grafo della realta locale.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A9,A14", "condensato_motivo": "A9 (terzo incluso) opera CON il piano. A14 (cascata) propaga - ma propaga cosa, se solo 2 assiomi sono attivi per zona?" }, { "tipo": "vincolo", "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", "intensita": 0.77, "manuale": true, "porta": "TRASCENDENZA_LIMITE", "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", "added_at": "2026-05-08T03:30:00+00:00", "decay_counter": 4 }, { "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 (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", "source_tension_id": "TRASCENDENZA_LIMITE", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "source_tension_ref": "A3,A10", "porta": "condensato", "dettaglio": " phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n bronze: gap_ratio = 1.3027860752339453\n{\n \"phi\": 0.408953425243134,\n \"silver\": 1.0482231205217798,\n \"bronze\": 1.3027860752339453\n}\n" }, { "tipo": "conferma_parziale", "id": "COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE", "claim": "T_mean: phi=6.2500 vs ctrl_mean=9.7667 (ratio=0.64). Fibonacci-phi trasmissione piu' struttur", "intensita": 0.65, "nota": "Dal domandatore (2026-05-07T21:57). Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.25", "source_tension_id": "TRASCENDENZA_LIMITE", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (5x in 2 giorni) e fuori dalla mappa", "source_tension_ref": "A3,A10", "porta": "domandatore", "dettaglio": "Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.2500 T_std=0.0000\n silver: T_mean=0.0041 T_std=0.0000\n random_0: T_mean=39.0625 T_std=0.0000\n random_1: T_mean=0.0000 T_std=0.0000\n random_2: T_mean=0.0001 T_std=0.0000\n" }, { "tipo": "tensione_aperta", "id": "TENS_SCALE_TRASCENDENZA_LIMITE", "claim": "Fit non converge — il modello potrebbe non essere power-law. V_c(phi) converge a 1.0 per N->inf, V_c(", "intensita": 0.6, "nota": "Dal domandatore (2026-05-07T22:03). V_c scaling with N — phi vs silver:\n\n phi:\n N= 89: V_c=1.017\n N= 144: V_", "source_tension_id": "TRASCENDENZA_LIMITE", "condensato_ref": "A12", "condensato_motivo": "Overlap termini con A12 (3 termini)", "source_tension_ref": "A3,A10", "porta": "condensato", "dettaglio": "V_c scaling with N — phi vs silver:\n\n phi:\n N= 89: V_c=1.017\n N= 144: V_c=0.672\n N= 233: V_c=1.017\n N= 377: V_c=0.672\n N= 610: V_c=0.931\n Fit failed: Optimal parameters not found: Number of calls to function has reached maxfev = 5000.\n\n silver:\n N= 89: V_c=1.276\n N= 144: V_c=1.362\n N= 233: V_c=1.276\n N= 377: V_c=1.017\n N= 610: V_c=1.362\n Fit: V_inf=1.2115, a=8.1676, b=0.9851\n" }, { "tipo": "tensione_aperta", "id": "M_trascendenza_limite_attuale_L0", "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", "intensita": 0.529179606750063, "nota": "Dal domandatore (2026-05-07T21:20). ", "_M_metadata": { "topic": "trascendenza_limite_attuale", "level": 0, "noto": 1, "ignoto": 1, "ratio": 1.0, "maturity": 0.6180339887498949 }, "source_tension_id": "TRASCENDENZA_LIMITE", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (5x in 2 giorni) e fuori dalla mappa", "source_tension_ref": "A3,A10", "porta": "domandatore" }, { "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)", exec /bin/bash -lc "env | grep -E 'ANTHROPIC_API_KEY|OPENAI_API_KEY'" in /opt/MM_D-ND exited 1 in 0ms: exec /bin/bash -lc 'rg -n "supertile|internal_shuffle|same_count|same_length|gap_ratio|core" tools -S' in /opt/MM_D-ND succeeded in 0ms: tools/bicono_projection.py:141: scores = {k: 0 for k in CANONICAL_DIPOLES} tools/bicono_projection.py:146: scores[dip] += 1 tools/bicono_projection.py:147: best = max(scores, key=scores.get) tools/bicono_projection.py:148: return best if scores[best] > 0 else None tools/exp_mod3_vs_residual_ordering.py:264: # z-score of residual: how significant is the real-vs-mod3 difference? tools/LAB_AGENT_CONTEXT.md:95: Test parla di `gap_ratio` ma l'esperimento misura `gap_label_set`, tools/LAB_AGENT_CONTEXT.md:96: `core_retention` o `generator_jaccard`, scrivi nel report: tools/LAB_AGENT_CONTEXT.md:97: `gap_ratio non testato in questo ciclo; observable sostitutivo = ...`. tools/LAB_AGENT_CONTEXT.md:102: `all-condition/core_labels_all_conditions`, `stable labels 75%`, tools/LAB_AGENT_CONTEXT.md:194:Esempio reale: `intensità: 0.65` trattata come soglia → `if intensita > 0.5: conferma`. Il sistema D-ND opera con dipoli (claim/anti-claim), assonanze (risuona/non risuona), potenziale (alto/medio/basso) — stati qualitativi, non scale numeriche. Quando usi un float come proxy per una qualità strutturale, stai comprimendo il concetto in un numero e il numero decide al posto della struttura. Lo stesso vale per "maturity > 0.99", "confidence < 0.7", "score = rank * 10 + intensita". tools/LAB_AGENT_CONTEXT.md:195:**Regola**: se il codice confronta una qualità concettuale con una soglia numerica, è sbagliato. Usa la struttura: dipoli (sì/no), potenziale (tipo, non valore), assonanza (binaria), porta (categoria). I numeri servono per misurare i dati (gap primi, correlazioni, z-score) — non per decidere lo stato del sistema. tools/LAB_AGENT_CONTEXT.md:201:- **Il risultato non è nel numero — è nella differenza col controllo.** z-score, non valore assoluto. tools/lab_veritas.py:5:core/veritas_score.py (commit 9d46567) al lab fisico cron 03:30. tools/exp_markov_memory_by_gue_type.py:81: Returns (H_real, H_shuffle_mean, H_shuffle_std, ordering_frac, z_score).""" tools/dipartimento.py:91: 'test': 'gap_ratio_phi2', tools/dipartimento.py:296: elif test_name == 'gap_ratio_phi2': tools/exp_boundary_gue_poisson.py:26:def gap_ratios(gaps): tools/exp_boundary_gue_poisson.py:59: r = gap_ratios(gaps) tools/exp_boundary_gue_poisson.py:105: print(f"{'Window center':>15} | {' primes':>10} | {' Cramér':>12} | {'Δ':>8} | {'σ_Cramér':>8} | {'z-score':>8}") tools/exp_boundary_gue_poisson.py:108: z_scores = [] tools/exp_boundary_gue_poisson.py:116: z_scores.append(z) tools/exp_boundary_gue_poisson.py:123: z_scores = np.array(z_scores) tools/exp_boundary_gue_poisson.py:124: valid = ~np.isnan(z_scores) tools/exp_boundary_gue_poisson.py:125: print(f"\nz-score summary (primes - Cramér) / σ_Cramér:") tools/exp_boundary_gue_poisson.py:126: print(f" mean z = {np.nanmean(z_scores):.3f}") tools/exp_boundary_gue_poisson.py:127: print(f" max |z| = {np.max(np.abs(z_scores[valid])):.3f}") tools/exp_boundary_gue_poisson.py:128: print(f" windows with |z| > 2: {np.sum(np.abs(z_scores[valid]) > 2)}/{np.sum(valid)}") tools/exp_boundary_gue_poisson.py:180: "z_score": float(z_scores[i]) if not np.isnan(z_scores[i]) else None} tools/exp_boundary_gue_poisson.py:183: "z_mean": float(np.nanmean(z_scores)), tools/exp_boundary_gue_poisson.py:184: "z_max_abs": float(np.max(np.abs(z_scores[valid]))), tools/exp_brody_crossover.py:54:def gap_ratio(gaps): tools/exp_brody_crossover.py:102: r_prime = gap_ratio(win_gaps) tools/exp_brody_crossover.py:111: r_cramer_list.append(gap_ratio(surr_gaps)) tools/exp_brody_crossover.py:144: z_scores = np.array([r["z_beta"] for r in results]) tools/exp_brody_crossover.py:170: print(f"z-score range: {z_scores.min():.1f} to {z_scores.max():.1f}") tools/exp_brody_crossover.py:171: print(f"Mean z-score: {z_scores.mean():.1f}") tools/exp_brody_crossover.py:206: "z_score_mean": float(z_scores.mean()), tools/exp_brody_crossover.py:207: "z_score_min": float(z_scores.min()), tools/gue_gap_test.py:30:z_score = (r_data - r_mean_shuf) / r_std_shuf if r_std_shuf > 0 else 0.0 tools/gue_gap_test.py:31:significativo = abs(z_score) > 2.0 tools/gue_gap_test.py:37: interp = f"r={r_data:.4f} > 0.5 e z={z_score:.2f} vs shuffle: correlazione GUE-like nei gap dei primi" tools/gue_gap_test.py:39: interp = f"r={r_data:.4f} > 0.5 ma z={z_score:.2f}: non significativamente diverso da shuffle" tools/gue_gap_test.py:48: "z_score": round(z_score, 4), tools/exp_poisson_convergence.py:30:def gap_ratio_r(gaps): tools/exp_poisson_convergence.py:87: r = gap_ratio_r(gaps) tools/exp_poisson_convergence.py:117: r_vals.append(gap_ratio_r(gaps_surr)) tools/exp_poisson_convergence.py:176: # z-scores tools/exp_two_channel_universality.py:284: # Z-score vs primes for key metrics tools/exp_brody_flow.py:230: print(f"Slope z-score vs shuffle: {results['slope_z_vs_shuffle']:.2f}") tools/exp_perturbation_rank_size_curve.py:14:- original-vs-shuffle z-score per observable; tools/dnd_gap_resolution.py:318: # Z-score normalization tools/exp_mod3_scaling.py:11: 3. Z-scores vs shuffle baseline per window tools/exp_mod3_scaling.py:14:If mod-3 z-score decays similarly -> same channel. tools/exp_mod3_scaling.py:15:If mod-3 z-score is flat -> separate, non-Markovian channel. tools/dnd_compatibility.py:441: score = 0.0 tools/dnd_compatibility.py:456: score += contribution tools/dnd_compatibility.py:468: pct = score / TOTAL_WEIGHT * 100 tools/dnd_compatibility.py:483: "score": round(score, 2), tools/dnd_compatibility.py:484: "max_score": round(TOTAL_WEIGHT, 2), tools/dnd_compatibility.py:500: print(f" PUNTEGGIO: {result['score']}/{result['max_score']} ({result['percentage']}%)") tools/dnd_compatibility.py:545: print(f" {'#':<4} {'Candidato':<25} {'Score':<12} {'%':<8} {'Verdetto'}") tools/dnd_compatibility.py:548: print(f" {i:<4} {r['candidate']:<25} {r['score']}/{r['max_score']:<7} {r['percentage']:<7}% {r['verdict'].split(' — ')[0]}") tools/exp_two_layer_universality.py:16: 3. Compute 8 observables, measure z-scores vs each Mk level tools/exp_two_layer_universality.py:306: z_scores = {} tools/exp_two_layer_universality.py:331: z_scores[f'Mk{mk}'] = z_mk tools/exp_two_layer_universality.py:337: z0 = z_scores['Mk0'].get(obs_name, 0) tools/exp_two_layer_universality.py:338: z1 = z_scores['Mk1'].get(obs_name, 0) tools/exp_two_layer_universality.py:339: z2 = z_scores['Mk2'].get(obs_name, 0) tools/exp_two_layer_universality.py:347: 'z_scores': z_scores, tools/exp_two_layer_universality.py:413: 'z_scores': data['z_scores'], tools/exp_two_channel_decomposition.py:175: 'z_score': z, tools/dnd_curva.py:17: 4. Osservare: la spirale converge a φ? Il gap_ratio è φ²? tools/dnd_curva.py:74: entry["gap_ratio"] = float(diario[-1]["gap_abs"] / abs(gap)) if abs(gap) > 1e-15 else float("inf") tools/dnd_curva.py:124: print(f" k | tr | det | gap_ratio | →φ? | gap_finale | convergenza") tools/dnd_curva.py:139: gr = obs.get('gap_ratio_medio', None) tools/dnd_curva.py:158: 'gap_ratio': float(gr) if gr else None, tools/dnd_curva.py:174: # Parte B: Verificare che il gap_ratio è SEMPRE φ² sulla curva tools/dnd_curva.py:175: print(f"\n Verifica: gap_ratio = φ² per tutti i k?") tools/dnd_curva.py:176: grs = [e['gap_ratio'] for e in famiglia_a if e['gap_ratio'] and np.isfinite(e['gap_ratio'])] tools/dnd_curva.py:180: print(f" Media gap_ratio = {media:.6f} (φ² = {PHI2:.6f})") tools/dnd_curva.py:183: results['cv_gap_ratio_curva'] = float(cv) tools/dnd_curva.py:203: Il gap_ratio è φ² per TUTTA la curva. Il punto fisso scala come kφ. tools/dnd_curva.py:487: │ Il gap_ratio è φ² su TUTTA la curva (verificato). │ tools/exp_dipolar_vector_scaling.py:12: - Deviation: Delta = (real - shuffle_mean) / shuffle_std (z-score) tools/exp_dipolar_vector_scaling.py:78: # Z-scores (the dipolar vector components) tools/dnd_gue_test.py:566: score = 0 tools/dnd_gue_test.py:570: score = 10 tools/dnd_gue_test.py:574: score = 7 tools/dnd_gue_test.py:579: score = 4 tools/dnd_gue_test.py:732: score = 10 tools/dnd_gue_test.py:736: score = max(score, 8) tools/dnd_gue_test.py:742: "score": score, tools/exp_duality_scale_contrast.py:4:Measures how the ordered-vs-shuffle contrast (z-score) changes with scale tools/exp_duality_scale_contrast.py:63:def compute_z_scores(gaps: np.ndarray, n_shuffle: int, rng: np.random.Generator) -> dict: tools/exp_duality_scale_contrast.py:64: """Compute z-score of canonical observables (real vs shuffle).""" tools/exp_duality_scale_contrast.py:90: from the gap sequence and compute z-scores. tools/exp_duality_scale_contrast.py:110: z, _ = compute_z_scores(window, n_shuffle, rng) tools/exp_duality_scale_contrast.py:172: prime_global_z, prime_global_real = compute_z_scores( tools/exp_duality_scale_contrast.py:175: poisson_global_z, poisson_global_real = compute_z_scores( tools/exp_duality_scale_contrast.py:178: gue_global_z, gue_global_real = compute_z_scores( tools/exp_duality_scale_contrast.py:221: # Global z-scores tools/exp_duality_scale_contrast.py:222: lines.append("\n--- Global z-scores (real vs shuffle, N=5000 gaps) ---") tools/translate_tensions.py:23:# Provider chain (pattern unificato 02/05, speculare a D-ND_LAB/core/llm_adapter.py). tools/exp_crossover_universality.py:252:# === Z-scores vs Cramer === tools/exp_crossover_universality.py:253:print(f"\n=== Z-SCORES vs CRAMER ===") tools/exp_crossover_universality.py:254:z_scores = {} tools/exp_crossover_universality.py:261: z_scores[f"{k}_slope"] = z_val tools/exp_crossover_universality.py:264:print(f"\n=== Z-SCORES for PARTIAL CORRELATIONS ===") tools/exp_crossover_universality.py:309: "z_scores_slopes": z_scores, tools/exp_crossover_universality.py:310: "z_scores_partial": z_partial, tools/exp_crossover_universality.py:326:sig_slopes = sum(1 for v in z_scores.values() if abs(v) > 3) tools/validate_tension_mapping.py:9:3. Deriva le teorie con score >= soglia tools/validate_tension_mapping.py:22:- derived: teorie emergenti da keyword scan (con score) tools/validate_tension_mapping.py:81:SCORE_THRESHOLD = 2 tools/validate_tension_mapping.py:131:def score_theories(texts: list[str]) -> dict[str, int]: tools/validate_tension_mapping.py:133: scores: dict[str, int] = {th: 0 for th in LEXICON} tools/validate_tension_mapping.py:135: return scores tools/validate_tension_mapping.py:142: scores[theory] = len(unique_hits) tools/validate_tension_mapping.py:143: return scores tools/validate_tension_mapping.py:181: 'scores': {}, tools/validate_tension_mapping.py:191: scores = score_theories(texts) tools/validate_tension_mapping.py:192: derived = [th for th, s in scores.items() if s >= SCORE_THRESHOLD] tools/validate_tension_mapping.py:198: 'scores': scores, tools/validate_tension_mapping.py:222: print(f' texts found: {r["texts_found"]} scores: {r["scores"]}') tools/exp_two_channel_cross_domain.py:24: - Z-scores vs shuffle tools/exp_two_channel_cross_domain.py:25: - Whether z-scores decay with scale or stay constant tools/exp_two_channel_cross_domain.py:351: """Extract scale-dependence of z-scores for each domain.""" tools/exp_two_channel_cross_domain.py:361: # Correlation of z-scores with position (proxy for scale) tools/exp_markov_psd_prediction.py:197: # --- Step 7: z-scores --- tools/exp_beta_crossover.py:152: # Shuffled null for z-score tools/build_lab_graph.py:177: 'text': f"Metrica primi g=(p/2)², curvatura GUE r={mp.get('risultato_r',{}).get('curvatura_r','?')}, z={mp.get('test_null',{}).get('z_score','?')}", tools/build_lab_graph.py:237: scored = [] tools/build_lab_graph.py:249: score = sum(1 for kw in keywords if kw in pg_text) tools/build_lab_graph.py:250: if score > 0: tools/build_lab_graph.py:251: scored.append({ tools/build_lab_graph.py:258: 'score': score, tools/build_lab_graph.py:261: scored.sort(key=lambda x: x['score'], reverse=True) tools/build_lab_graph.py:262: return scored[:max_results] tools/build_lab_graph.py:274: - ghost_urgency: 'high' | 'medium' | 'low' (solo ghost) — con score numerico tools/build_lab_graph.py:407: # Score basato su molteplici fattori: tools/build_lab_graph.py:408: score = 0.0 tools/build_lab_graph.py:411: score += len(shared_theories) * 1.0 tools/build_lab_graph.py:415: score += 3.0 tools/build_lab_graph.py:423: score += 2.0 tools/build_lab_graph.py:425: score += 1.0 tools/build_lab_graph.py:427: score += 0.5 tools/build_lab_graph.py:433: score += cn.get('centrality', 0) * 1.5 tools/build_lab_graph.py:435: node['ghost_urgency_score'] = round(score, 2) tools/build_lab_graph.py:436: if score >= 6.0: tools/build_lab_graph.py:438: elif score >= 4.0: tools/build_lab_graph.py:744: # - ghost_urgency (high/medium/low) + ghost_urgency_score → dimensione e glow del ghost tools/exp_geodesic_deviation_primes.py:102:print(f" z-score(std): {z_std:.2f}") tools/exp_geodesic_deviation_primes.py:106:print(f" z-score(ACF1): {z_acf1:.2f}") tools/exp_geodesic_deviation_primes.py:110:print(f" z-score(ACF2): {z_acf2:.2f}") tools/exp_geodesic_deviation_primes.py:144:print(f" z-score(std vs Cramer): {z_cramer_std:.2f}") tools/exp_geodesic_deviation_primes.py:148:print(f" z-score(ACF1 vs Cramer): {z_cramer_acf:.2f}") tools/exp_geodesic_deviation_primes.py:189: "z_score_std": round(z_std, 2), tools/exp_geodesic_deviation_primes.py:190: "z_score_acf1": round(z_acf1, 2), tools/exp_geodesic_deviation_primes.py:191: "z_score_acf2": round(z_acf2, 2) tools/exp_geodesic_deviation_primes.py:196: "z_score_std": round(z_cramer_std, 2), tools/exp_geodesic_deviation_primes.py:197: "z_score_acf1": round(z_cramer_acf, 2) tools/exp_meta_tautology_test.py:216: 'z_scores': {k: float(v) for k, v in discriminates.items()} tools/exp_markov_scale_function.py:124: # z-score tools/exp_markov_scale_function.py:145: 'z_score': float(z), tools/exp_markov_scale_function.py:211: f"{res['ordering_frac']*100:>5.1f}% {res['z_score']:>8.1f} " tools/exp_markov_scale_function.py:221: z_scores = [r['z_score'] for r in results] tools/exp_markov_scale_function.py:267: # z-score trend tools/exp_markov_scale_function.py:268: z_arr = np.array(z_scores) tools/exp_markov_scale_function.py:270: print(f"\nz-score vs ln(p): slope = {slope_z:.2f} per unit ln(p)") tools/exp_markov_scale_function.py:271: print(f" z-score range: [{z_arr.min():.1f}, {z_arr.max():.1f}]") tools/exp_markov_scale_function.py:299: 'z_score_range': [float(z_arr.min()), float(z_arr.max())], tools/exp_two_channel_boundary.py:196: # Z-scores (signal relative to null) tools/exp_two_channel_boundary.py:197: def zscore(val, mu, sig): tools/exp_two_channel_boundary.py:202: obs['z_r'] = zscore(obs['r_stat'], obs['shuffle_r_mean'], obs['shuffle_r_std']) tools/exp_two_channel_boundary.py:203: obs['z_acf_res'] = zscore(obs['acf1_residue'], obs['shuffle_acf_res_mean'], obs['shuffle_acf_res_std']) tools/exp_two_channel_boundary.py:204: obs['z_acf_mag'] = zscore(obs['acf1_magnitude'], obs['shuffle_acf_mag_mean'], obs['shuffle_acf_mag_std']) tools/exp_two_channel_boundary.py:205: obs['z_mod3'] = zscore(obs['mod3_self'], obs['shuffle_mod3_mean'], obs['shuffle_mod3_std']) tools/exp_two_channel_boundary.py:238: # Check if z-scores decay with scale tools/exp_two_channel_boundary.py:264: # Correlation of z-scores with log(mean_prime) — decay rate tools/exp_two_channel_boundary.py:290: print(f"\nZ-scores (early vs late halves of prime range):") tools/dnd_md2latex.py:745: # Fix escaped underscores inside math: $...\\_...$ → $..._...$ tools/dnd_md2latex.py:746: def fix_math_underscores(m): tools/dnd_md2latex.py:748: latex = re.sub(r'\$[^$]+\$', fix_math_underscores, latex) tools/LAB_OPERATIVO.md:117:- Scope attuale del lab automatico: validazione statistica numerica (shuffle + z-score + scaling) — dominio primi tools/exp_markov3_observable_hunt.py:11: 2. For 10+ observables, compute z-score: real vs Markov-k null tools/exp_markov3_observable_hunt.py:259: # Compute z-scores tools/exp_markov3_observable_hunt.py:314: 'z_scores': {mk: {obs: round(results[mk][obs]['z'], 2) for obs in OBSERVABLES} for mk in results}, tools/evolution_report.md:2:Il passo prosegue la linea 18:34 e sposta il fuoco dal blocco alla soglia tra boundary simbolico e blocco lungo equivalente. La traiettoria e' pulita: prende la consecutio, costruisce il confronto tra supertile reale, contiguo misallineato, ordine interno distrutto e blocco medio, poi lascia cadere il claim forte sul confine esatto. tools/evolution_report.md:4:Il movimento utile e' l'inversione: il label-set alto non viene forzato a diventare prova di boundary. Il passo riconosce che misura ordine interno + multiset di lunghezze Fibonacci-like, non il confine simbolico del supertile. tools/evolution_report.md:7:Attrito lessicale: il report separa bene Jaccard, retention e all-high rate, ma poi usa formule come "nullo" e "cade" dove lo zero vale solo per il core alto completo simultaneo. La sopravvivenza parziale per-label resta nel dato (`high retention=0.25`, stable label `[34]`), ma non entra pienamente nel taglio. tools/evolution_report.md:14:Il fix vive li': quando l'osservabile e' un set di label, il contratto deve dichiarare quale livello decide il claim. Non aggiungere retry o controlli posteriori; far nascere il verdetto gia' con tre piani separati: core completo, label singoli, geometria/posizione dei gap. tools/evolution_report.md:17:- Cercare il boundary non nel solo label-set, ma nella posizione IDS dei label core e nella geometria degli errori tra chunk aligned/misaligned. tools/evolution_report.md:22:Il ciclo seguente puo' proseguire chiedendo se il boundary reale appare nella posizione/errore dei gap core quando il label-set completo non discrimina. tools/exp_duality_gate_transfer.py:77: """Return original observables, shuffle baseline std, and z scores.""" tools/exp_acf_amplitude_scaling.py:69: print(f"\n{'Window':>6} {'p_center':>12} {'ln(p)':>8} {'A_prime':>10} {'R2':>8} {'A_shuf':>10} {'z_score':>8}") tools/exp_acf_amplitude_scaling.py:103: 'z_score': z, tools/exp_acf_amplitude_scaling.py:112: zs = np.array([r['z_score'] for r in results]) tools/exp_acf_amplitude_scaling.py:146: print(f"\nz-scores: min={np.min(zs):.1f}, max={np.max(zs):.1f}, mean={np.mean(zs):.1f}") tools/exp_markov_layer_recovery_audit.py:52: z_scores = {} tools/exp_markov_layer_recovery_audit.py:76: z_scores[f"Mk{mk}"] = z_mk tools/exp_markov_layer_recovery_audit.py:81: z_scores["Mk0"][obs_name], tools/exp_markov_layer_recovery_audit.py:82: z_scores["Mk1"][obs_name], tools/exp_markov_layer_recovery_audit.py:83: z_scores["Mk2"][obs_name], tools/exp_markov_layer_recovery_audit.py:86: return real_obs, z_scores, layers tools/exp_markov_layer_recovery_audit.py:147: real_obs, z_scores, layers = classifier_pass(spec["gaps"], n_surr, rng) tools/exp_markov_layer_recovery_audit.py:157: "z_scores": z_scores, tools/dnd_loop.py:296: open_score = sum(1 for s in opening_signals if s in answer.lower()) tools/dnd_loop.py:297: close_score = sum(1 for s in closing_signals if s in answer.lower()) tools/dnd_loop.py:298: opens = open_score > close_score tools/dnd_scenario.py:104: # z-score = N tools/dnd_scenario.py:106: 'z_score', lambda m: {'z': float(m.group(1))}), tools/dnd_scenario.py:219: # z-score (solo numeri semplici, non range come 18-50) tools/dnd_scenario.py:223: obs['z_score'] = float(m.group(1)) tools/dnd_scenario.py:518: score = dp * has_law * (0.5 + uncertainty) tools/dnd_scenario.py:522: 'score': round(score, 3), tools/dnd_scenario.py:529: ranked.sort(key=lambda r: r['score'], reverse=True) tools/dnd_scenario.py:659: star = '★' if exp['score'] > 0.4 else '·' tools/dnd_scenario.py:661: print(f" {star} {i}. {exp['id']} (score={exp['score']}){law}") tools/dnd_scenario.py:1436: print(f"{exp['id']}: score={exp['score']} — {exp['reason']}") tools/lab_agent.sh:1101:# Port 05/05 dal D-ND_LAB core/aeternitas.py (commit ca29cd4) al lab fisico. tools/lab_agent.sh:1115:# Pattern port da D-ND_LAB core/bicono_extractor.py (movement universale). tools/lab_agent.sh:1120:# 12.6 VERITAS SCORE — termometro qualità ρ ∈ [0,1] da 3 vettori. tools/lab_agent.sh:1125:echo "--- 12.6 VERITAS SCORE (ρ qualità — warn mode) ---" tools/lab_agent.sh:1560: r = guard.score(flat) tools/lab_agent.sh:1561: band = "DANGER" if r["danger_score"] >= 0.7 else ("WARN" if r["danger_score"] >= 0.4 else "OK") tools/lab_agent.sh:1563: "danger_score": r["danger_score"], tools/lab_agent.sh:1571: print(f" {label}: danger={r['danger_score']:.2f} [{band}] max_run={r['max_run_length']} entropy={r['entropy_bits']:.2f}") tools/m_spectro.py:45:# === OPERATORE CORE === tools/dnd_condizioni.py:83: entry["gap_ratio"] = float(diario[-1]["gap_abs"] / abs(gap)) if abs(gap) > 1e-15 else float("inf") tools/dnd_condizioni.py:113: gap_ratios = [d["gap_ratio"] for d in diario if "gap_ratio" in d and d["gap_ratio"] < 1e10] tools/dnd_condizioni.py:127: "gap_ratio_medio": float(np.mean(gap_ratios[-5:])) if gap_ratios else None, tools/dnd_condizioni.py:128: "gap_ratio_ultimo": float(gap_ratios[-1]) if gap_ratios else None, tools/dnd_condizioni.py:159: if gap_ratios: tools/dnd_condizioni.py:160: osservazione["prossimità_gap_ratio"] = {} tools/dnd_condizioni.py:161: gr = np.mean(gap_ratios[-5:]) tools/dnd_condizioni.py:163: osservazione["prossimità_gap_ratio"][nome] = float(abs(gr - val)) tools/dnd_condizioni.py:300: score = r["n_intervalli"] tools/dnd_condizioni.py:302: score += 10 tools/dnd_condizioni.py:304: score += 5 tools/dnd_condizioni.py:305: return score tools/dnd_condizioni.py:322: gr = obs.get("gap_ratio_medio") tools/dnd_condizioni.py:325: prox = obs.get("prossimità_gap_ratio", {}) tools/dnd_condizioni.py:338: "gap_ratio": gr, tools/dnd_condizioni.py:350: gap_ratios = [v["gap_ratio"] for v in vault if v["gap_ratio"] is not None] tools/dnd_condizioni.py:364: if gap_ratios: tools/dnd_condizioni.py:367: if v["gap_ratio"] is not None: tools/dnd_condizioni.py:368: print(f" {v['segnale']:>12s}: {v['gap_ratio']:.6f}") tools/exp_dR_brody_connection.py:234:# === Z-scores === tools/exp_dR_brody_connection.py:241:print(f"\n=== Z-SCORES ===") tools/exp_dR_brody_connection.py:314: "z_scores": { tools/exp_gap_label_generator_gate.py:86:def summarize_generators(rows: list[dict], reference_core: set[int]) -> dict: tools/exp_gap_label_generator_gate.py:93: overlaps = [jaccard(set(row["label_set"]), reference_core) for row in group if row["n_selected"] > 0] tools/exp_gap_label_generator_gate.py:94: core = set(summary["core_labels_all_conditions"]) tools/exp_gap_label_generator_gate.py:97: "median_overlap_with_phi_core": float(np.median(overlaps)) if overlaps else None, tools/exp_gap_label_generator_gate.py:98: "min_overlap_with_phi_core": float(np.min(overlaps)) if overlaps else None, tools/exp_gap_label_generator_gate.py:99: "reference_core_retained": sorted(core & reference_core, key=lambda x: (abs(x), x)), tools/exp_gap_label_generator_gate.py:100: "reference_core_missing": sorted(reference_core - core, key=lambda x: (abs(x), x)), tools/exp_gap_label_generator_gate.py:129: reference_core = set(summarize_sets(phi_rows)["core_labels_all_conditions"]) tools/exp_gap_label_generator_gate.py:130: summary = summarize_generators(rows, reference_core) tools/exp_gap_label_generator_gate.py:151: "reference_core_phi": sorted(reference_core, key=lambda x: (abs(x), x)), tools/exp_gap_label_generator_gate.py:179: "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], tools/exp_gap_label_generator_gate.py:180: "reference_core_missing": data["reference_core_missing"], tools/exp_gap_label_generator_gate.py:181: "core_labels_all_conditions": data["core_labels_all_conditions"], tools/exp_gap_label_generator_gate.py:186: "reference_core_phi": output["reference_core_phi"], tools/exp_desitter_unification.py:196:# --- Z-scores --- tools/exp_desitter_unification.py:197:def z_score(prime_val, null_vals): tools/exp_desitter_unification.py:203:z_cramer_r_dR = z_score(corr_r_dR, null_corrs_cramer['r_dR']) tools/exp_desitter_unification.py:204:z_shuffled_r_dR = z_score(corr_r_dR, null_corrs_shuffled['r_dR']) tools/exp_desitter_unification.py:205:z_cramer_r_acf1 = z_score(corr_r_acf1, null_corrs_cramer['r_acf1']) tools/exp_desitter_unification.py:206:z_shuffled_r_acf1 = z_score(corr_r_acf1, null_corrs_shuffled['r_acf1']) tools/dnd_domandatore.py:991: scored = [] tools/dnd_domandatore.py:995: score = rank * 10 + intensita tools/dnd_domandatore.py:996: scored.append((score, i, nt)) tools/dnd_domandatore.py:997: scored.sort(reverse=True) tools/dnd_domandatore.py:998: best = scored[0][2] tools/dnd_domandatore.py:1005: 'operatore_origine': results[scored[0][1]]['operatore'] if scored[0][1] < len(results) else '?', tools/structural_check.py:63: 'id': 'SCORE_FROM_RANK', tools/structural_check.py:64: 'name': 'Score numerico da ranking concettuale', tools/structural_check.py:65: 'description': 'Score calcolato come combinazione lineare di rank e intensita', tools/structural_check.py:66: 'regex': r'score\s*=.*(?:rank|priority)\s*\*\s*\d+\s*\+\s*intensit', tools/dnd_piano11.py:5:Core thesis (revised): The 1D potential V(r) gives Berry-Keating form but NOT GUE. tools/dnd_piano11.py:497: "gap_ratio": float(phi**2), tools/dnd_piano11.py:583: score = 9 tools/dnd_piano11.py:586: score = 7 tools/dnd_piano11.py:593: score = 8 tools/dnd_piano11.py:596: results["score"] = score tools/dnd_piano11.py:618: print(f"Score: {score}/10") tools/exp_scale_selective_perturbation.py:205: # Full shuffle baseline (z-score reference) tools/exp_gap_label_set_stability.py:109: core = sorted( tools/exp_gap_label_set_stability.py:121: "core_labels_all_conditions": core, tools/exp_two_channel_shuffle_audit.py:214: print(f"{'property':>15} | {'real':>9} | {'shuf_mean':>9} | {'shuf_std':>9} | {'z-score':>9} | {'survives?':>9}") tools/exp_two_channel_shuffle_audit.py:229: 'z_score': round(float(z), 2), tools/lab_anti_loop_guard.py:22: result = guard.score(current_events) tools/lab_anti_loop_guard.py:30: # "danger_score": float, # 0..1, > 0.6 = warning tools/lab_anti_loop_guard.py:34: # Score live durante un cycle (streaming): tools/lab_anti_loop_guard.py:41: python -m core.anti_loop_guard [--field ] tools/lab_anti_loop_guard.py:107: def score(self, events: list[str]) -> dict: tools/lab_anti_loop_guard.py:142: # Danger score: combinazione di 3 segnali, ognuno 0..1 tools/lab_anti_loop_guard.py:167: "danger_score": round(danger, 3), tools/lab_anti_loop_guard.py:177: return self.score(self._stream) tools/lab_anti_loop_guard.py:185: return self.score(self._stream)["danger_score"] tools/lab_anti_loop_guard.py:190: return self.score(self._stream)["reasons"] tools/lab_anti_loop_guard.py:254: ap = argparse.ArgumentParser(description="Anti-loop guardrail — score di una sequenza vs baseline.") tools/lab_anti_loop_guard.py:260: ap.add_argument("--score-last", type=int, default=None, tools/lab_anti_loop_guard.py:283: if args.score_last: tools/lab_anti_loop_guard.py:284: flat = flat[-args.score_last:] tools/lab_anti_loop_guard.py:289: result = guard.score(flat) tools/lab_anti_loop_guard.py:299: danger = result['danger_score'] tools/lab_anti_loop_guard.py:301: print(f"danger_score: {danger:.3f} [{band}]") tools/dnd_implications.py:244: scores = {'THEOREM': 0, 'METHOD': 0, 'DEVICE': 0, 'FRAMEWORK': 0, 'DATA': 0} tools/dnd_implications.py:250: scores[rule['class']] += rule['weight'] * len(found) tools/dnd_implications.py:261: dominant = max(scores, key=scores.get) if any(scores.values()) else 'DATA' tools/dnd_implications.py:264: has_ip_flags = scores['METHOD'] > 0 or scores['DEVICE'] > 0 tools/dnd_implications.py:270: 'scores': scores, tools/dnd_implications.py:472: print(f" Scores: {result['scores']}") tools/exp_gap_label_block_scale_gate.py:3:Block-scale gate for phi gap-label core retention. tools/exp_gap_label_block_scale_gate.py:6:but lose the high labels of the phi core. This tool scans block length directly: tools/exp_gap_label_block_scale_gate.py:8:reader, separating low-core retention from high-core re-entry. tools/exp_gap_label_block_scale_gate.py:45:def summarize_block(rows: list[dict], reference_core: set[int]) -> dict: tools/exp_gap_label_block_scale_gate.py:68: overlaps = [jaccard(set(row["label_set"]), reference_core) for row in rows if row["n_selected"] > 0] tools/exp_gap_label_block_scale_gate.py:71: "median_overlap_with_phi_core": float(np.median(overlaps)) if overlaps else None, tools/exp_gap_label_block_scale_gate.py:72: "min_overlap_with_phi_core": float(np.min(overlaps)) if overlaps else None, tools/exp_gap_label_block_scale_gate.py:79: "reference_core_retained_in_all": label_sort(set(summary.get("core_labels_all_conditions", [])) & reference_core), tools/exp_gap_label_block_scale_gate.py:80: "reference_core_missing_from_all": label_sort(reference_core - set(summary.get("core_labels_all_conditions", []))), tools/exp_gap_label_block_scale_gate.py:130: reference_core = set(reference_summary["core_labels_all_conditions"]) tools/exp_gap_label_block_scale_gate.py:137: block: summarize_block(group_rows, reference_core) tools/exp_gap_label_block_scale_gate.py:144: family_summary[family] = summarize_block(family_rows, reference_core) tools/exp_gap_label_block_scale_gate.py:164: "reference_core_phi": label_sort(reference_core), tools/exp_gap_label_block_scale_gate.py:203: "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], tools/exp_gap_label_block_scale_gate.py:212: "reference_core_phi": output["reference_core_phi"], tools/dnd_spettro_zeta.py:816: # Score tools/dnd_spettro_zeta.py:817: score = 0 tools/dnd_spettro_zeta.py:821: score += 3 tools/dnd_spettro_zeta.py:824: score += 1 tools/dnd_spettro_zeta.py:829: score += 2 tools/dnd_spettro_zeta.py:832: score += 1 tools/dnd_spettro_zeta.py:836: score += 1 tools/dnd_spettro_zeta.py:840: score += 2 tools/dnd_spettro_zeta.py:845: score += 1 tools/dnd_spettro_zeta.py:848: print(f"\n PUNTEGGIO CONNESSIONE: {score}/9") tools/dnd_spettro_zeta.py:852: if score >= 7: tools/dnd_spettro_zeta.py:854: elif score >= 5: tools/dnd_spettro_zeta.py:856: elif score >= 3: tools/dnd_spettro_zeta.py:875: results['score'] = score tools/dnd_spettro_zeta.py:932: 'score': p7_res.get('score', 0), tools/data/reports/loop_guard_20260508_1632.json:3: "danger_score": 0.471, tools/data/reports/loop_guard_20260508_1632.json:15: "danger_score": 0.113, tools/exp_denominator_gate_transfer_matrix.py:14:- original-vs-shuffle z-score for each observable; tools/dnd_incrocio.py:894: 'dinamiche': {d: {'score': 1.0, 'tentativi': 0, 'successi': 0, 'attiva': True} tools/dnd_incrocio.py:908: """Ritorna le dinamiche ordinate per score (le migliori prima).""" tools/dnd_incrocio.py:913: return sorted(attive.items(), key=lambda x: x[1].get('score', 0), reverse=True) tools/dnd_incrocio.py:922: # Score = successi / tentativi (con smoothing) tools/dnd_incrocio.py:925: self.stato[store][dinamica]['score'] = (s + 1) / (t + 2) # Laplace smoothing tools/dnd_incrocio.py:942: 'score': 0.8, # Alta priorità — emergente tools/dnd_incrocio.py:955: 'score': 0.7, tools/dnd_incrocio.py:967: 'score': 0.6, tools/dnd_incrocio.py:981: s = info.get('score', 0) tools/dnd_incrocio.py:984: lines.append(f" {nome}: score={s:.2f} ({t} tentativi){tag}") tools/dnd_normalizer.py:246: # Score composito: cv basso + distanza da φ bassa tools/dnd_normalizer.py:254: r["score"] = r["cv"] + dist_phi # più basso = più vero tools/dnd_normalizer.py:256: migliore = min(validi, key=lambda r: r["score"]) tools/dnd_normalizer.py:532: print(f" {'soglia':>8s} {'r_scale':>7s} {'cv':>6s} {'dist_φ':>6s} {'score':>6s} {'n':>3s}") tools/dnd_normalizer.py:540: f"{r.get('score', 0):6.4f} " tools/data/reports/agent_20260410_0330.md:30:| Window | p_center | ln(p) | A_prime | R^2 | |acf1| | z-score | tools/data/reports/agent_20260410_0330.md:43:All z-scores > 6. Shuffled surrogates give A ~ 0 at every scale. tools/exp_boundary_mixture_gate.py:17:- original-vs-shuffle z-score per observable; tools/dnd_risultante.py:74:# Core: The Three Forces tools/data/reports/agent_20260430_0330.md:20:- **Null baseline**: 50 shuffles (same gap distribution, order destroyed) for z-scores tools/data/reports/agent_20260430_0330.md:35:All z-scores are extreme — ordering memory is real at every modulus tested. But the structure varies qualitatively. tools/data/reports/agent_20260430_0330.md:85:- **Invariante di passaggio**: l'ordinamento dei gap porta memoria a OGNI modulus testato (tutti i z-score >> 0). La memoria e universale — la forma della memoria (algebrica vs statistica, ordine-1 vs ordine-2) dipende dal modulus. tools/exp_psd_prime_gaps.py:101: # 2. z-score of prime PSD vs shuffle at low frequencies tools/exp_psd_prime_gaps.py:109: # 3. z-score at high frequencies tools/exp_psd_prime_gaps.py:163: print(f"z-score vs shuffle at LOW freq (0.005-0.05): {z_low_mean:.2f}") tools/exp_psd_prime_gaps.py:164: print(f"z-score vs shuffle at HIGH freq (0.3-0.49): {z_high_mean:.2f}") tools/exp_excess_scaling.py:33:def gap_ratio(gaps): tools/exp_excess_scaling.py:49: rs.append(gap_ratio(gaps)) tools/exp_excess_scaling.py:59:print(f"{'Window':>6} {'p_center':>12} {'_prime':>10} {'_Cramer':>10} {'delta_r':>10} {'z-score':>8}") tools/exp_excess_scaling.py:66: r_prime = gap_ratio(gaps) tools/exp_excess_scaling.py:83: 'z_score': float(z), tools/exp_excess_scaling.py:93:z_arr = np.array([r['z_score'] for r in results]) tools/exp_excess_scaling.py:100:# Also fit z-score vs log(p) tools/exp_excess_scaling.py:107:print(f" z-score vs log(p): slope = {slope_z:.4f}, intercept = {intercept_z:.4f}") tools/exp_excess_scaling.py:109:print(f" Mean z-score: {np.mean(z_arr):.2f}") tools/lab_autological_repair.py:434: lines.append("- Se `gap_ratio` e' richiesto dalla direttiva, includilo; se non lo testi, dichiaralo come not_tested.") tools/exp_ricci_primes.py:155:print(f"z-score (mean): {z_mean:.2f}") tools/exp_ricci_primes.py:158:print(f"z-score (slope): {z_slope:.2f}") tools/exp_ricci_primes.py:203: "z_score_mean": round(z_mean, 2), tools/exp_ricci_primes.py:204: "z_score_slope": round(z_slope, 2) tools/exp_selective_layer_decoupling.py:20: Measure 6 observables at each alpha, compute z-scores vs original. tools/exp_selective_layer_decoupling.py:217: """Sweep alpha for one perturbation type. Returns z-scores per observable per alpha.""" tools/dnd_autoricerca.py:579: miglior_score = -1 tools/dnd_autoricerca.py:597: # Score: struttura ricca = molti intervalli + convergenza + alternanza tools/dnd_autoricerca.py:598: score = n_min tools/dnd_autoricerca.py:600: score += 10 tools/dnd_autoricerca.py:602: score += 5 tools/dnd_autoricerca.py:604: if score > miglior_score: tools/dnd_autoricerca.py:605: miglior_score = score tools/dnd_autoricerca.py:621: risultato['analisi']['gap_ratio'] = obs.get('gap_ratio_medio') tools/dnd_autoricerca.py:849: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:1007: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:1105: print(f" Gap ratio: {obs.get('gap_ratio_medio', '?')}") tools/dnd_autoricerca.py:1118: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_autoricerca.py:1357: # 4. Sequenza dei gap_ratio — è più costante di quanto ci si aspetterebbe? tools/dnd_autoricerca.py:1359: grs = [(e['dominio'], e['gap_ratio']) for e in reali if e.get('gap_ratio')] tools/dnd_autoricerca.py:1762: def _costante_score(t): tools/dnd_autoricerca.py:1763: score = 0 tools/dnd_autoricerca.py:1769: score += 2 tools/dnd_autoricerca.py:1772: score += 3 tools/dnd_autoricerca.py:1774: score += 2 tools/dnd_autoricerca.py:1778: score += 3 tools/dnd_autoricerca.py:1779: return score tools/dnd_autoricerca.py:1782: piano_notte['tensioni'].sort(key=lambda t: _costante_score(t), reverse=True) tools/dnd_autoricerca.py:1784: print(f" Tensione prioritaria (costante): {top.get('id','?')} (score {_costante_score(top)})") tools/dnd_autoricerca.py:1850: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:1940: 'gap_ratio': analisi.get('gap_ratio'), tools/data/reports/agent_20260424_0330.md:16:- **z-score**: (r_original - r_shuffled_mean) / r_shuffled_std tools/exp_modular_algebra_depth.py:143: # Z-scores for self-transitions tools/exp_modular_algebra_depth.py:156: 'z_score': z tools/exp_modular_algebra_depth.py:159: # Z-score for I1, I2 tools/exp_modular_algebra_depth.py:193: nz_z = {k: v['z_score'] for k, v in z_self.items() if k != 0} tools/exp_magnitude_psd_from_acf.py:218: print(f" Direct slope z-score vs shuffle: {z_direct:.1f}") tools/gap_ratio_primes.py:17: "r_shuffle_std": round(sigma, 6), "z_score": round(z, 2), "significativo": bool(sig), tools/dnd_spirale.py:87: f"{'potenziale':>10s} {'gap_ratio':>10s}") tools/data/reports/agent_20260423_0330.md:24:| Scale range | ln(p) | Ordering % | Brody β | lag-1 ACF | z-score | tools/exp_gap_label_supertile_tiling_gate.py:3:Supertile tiling gate for the phi gap-label core. tools/exp_gap_label_supertile_tiling_gate.py:7:upstream: it separates true Fibonacci supertile boundaries from contiguous tools/exp_gap_label_supertile_tiling_gate.py:34: raise ValueError("supertile_order must be >= 2") tools/exp_gap_label_supertile_tiling_gate.py:49:def supertile_lengths(n: int, order: int) -> list[int]: tools/exp_gap_label_supertile_tiling_gate.py:90:def misaligned_same_lengths(seq: np.ndarray, lengths: list[int], rng: np.random.Generator) -> np.ndarray: tools/exp_gap_label_supertile_tiling_gate.py:105:def summarize_rows(rows: list[dict], reference_core: set[int]) -> dict: tools/exp_gap_label_supertile_tiling_gate.py:110: overlaps = [jaccard(set(row["label_set"]), reference_core) for row in rows if row["n_selected"] > 0] tools/exp_gap_label_supertile_tiling_gate.py:114: "median_overlap_with_phi_core": float(np.median(overlaps)) if overlaps else None, tools/exp_gap_label_supertile_tiling_gate.py:130: "reference_core_retained_in_all": label_sort(set(summary.get("core_labels_all_conditions", [])) & reference_core), tools/exp_gap_label_supertile_tiling_gate.py:131: "reference_core_missing_from_all": label_sort(reference_core - set(summary.get("core_labels_all_conditions", []))), tools/exp_gap_label_supertile_tiling_gate.py:140: orders = parse_ints(args.supertile_orders) tools/exp_gap_label_supertile_tiling_gate.py:158: lengths = supertile_lengths(n, order) tools/exp_gap_label_supertile_tiling_gate.py:169: "supertile_shuffle": shuffle_chunks(aligned_chunks, rng), tools/exp_gap_label_supertile_tiling_gate.py:170: "same_length_contiguous_shuffle": misaligned_same_lengths(phi, lengths, rng), tools/exp_gap_label_supertile_tiling_gate.py:171: "same_count_internal_shuffle": internal_count_shuffle(aligned_chunks, rng), tools/exp_gap_label_supertile_tiling_gate.py:182: "supertile_order": order, tools/exp_gap_label_supertile_tiling_gate.py:188: reference_core = set(reference_summary["core_labels_all_conditions"]) tools/exp_gap_label_supertile_tiling_gate.py:192: grouped[f"{row['mode']}|order={row['supertile_order']}"].append(row) tools/exp_gap_label_supertile_tiling_gate.py:195: key: summarize_rows(group_rows, reference_core) tools/exp_gap_label_supertile_tiling_gate.py:202: mode_summary[mode] = summarize_rows(mode_rows, reference_core) tools/exp_gap_label_supertile_tiling_gate.py:205: "experiment": "gap_label_supertile_tiling_gate", tools/exp_gap_label_supertile_tiling_gate.py:211: "supertile_orders": orders, tools/exp_gap_label_supertile_tiling_gate.py:216: "reference_core_phi": label_sort(reference_core), tools/exp_gap_label_supertile_tiling_gate.py:233: parser.add_argument("--supertile-orders", default="8,9,10,11") tools/exp_gap_label_supertile_tiling_gate.py:237: parser.add_argument("--out", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") tools/exp_gap_label_supertile_tiling_gate.py:250: "supertile_order": int(order.split("=")[1]), tools/exp_gap_label_supertile_tiling_gate.py:260: "reference_core_phi": output["reference_core_phi"], tools/exp_brody_calibration.py:88: z_scores = {} tools/exp_brody_calibration.py:95: z_scores[L] = (sig2_real[L] - sig2_shuf_mean[L]) / sig2_shuf_std[L] tools/exp_brody_calibration.py:97: z_scores[L] = 0.0 tools/exp_brody_calibration.py:105: 'z_scores': z_scores, tools/exp_brody_calibration.py:167: f"{obs['z_scores'][10]:8.2f}") tools/exp_brody_calibration.py:209: f"{obs_p['z_scores'][10]:8.2f} {beta_eff_p:8.3f}") tools/exp_brody_calibration.py:218: f"{obs_g['z_scores'][10]:8.2f} {beta_eff_g:8.3f}") tools/exp_brody_calibration.py:233: f"{obs_l['z_scores'][10]:8.2f} {beta_eff_l:8.3f}") tools/exp_brody_calibration.py:242: f"{obs_po['z_scores'][10]:8.2f} {beta_eff_po:8.3f}") tools/exp_brody_calibration.py:255: f"{obs_osc['z_scores'][10]:8.2f} {beta_eff_osc:8.3f}") tools/exp_brody_calibration.py:273: if abs(obs['z_scores'][10]) < 2: tools/exp_brody_calibration.py:275: elif obs['z_scores'][10] < -2: tools/exp_brody_calibration.py:294: z = obs_p['z_scores'][L] tools/dnd_lab.py:105: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:111: gr = obs.get('gap_ratio_medio', 0) tools/dnd_lab.py:172: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:178: gr = obs.get('gap_ratio_medio', 0) tools/dnd_lab.py:287: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:363: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:368: gr = obs.get('gap_ratio_medio', 0) tools/dnd_lab.py:431: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:500: 'gap_ratio': obs.get('gap_ratio_medio'), tools/exp_metric_tensor_diagnostic.py:159:# ==== 7. Z-scores ==== tools/exp_metric_tensor_diagnostic.py:160:def zscore(val, surr_list): tools/exp_metric_tensor_diagnostic.py:164:z_DG_std_cramer = zscore(np.std(DeltaGamma), results_surr['cramer']['DG_std']) tools/exp_metric_tensor_diagnostic.py:165:z_DG_std_shuffled = zscore(np.std(DeltaGamma), results_surr['shuffled']['DG_std']) tools/exp_metric_tensor_diagnostic.py:166:z_r_cramer = zscore(r_mean_prime, results_surr['cramer']['r_mean']) tools/exp_metric_tensor_diagnostic.py:167:z_r_shuffled = zscore(r_mean_prime, results_surr['shuffled']['r_mean']) tools/exp_metric_tensor_diagnostic.py:168:z_dR_cramer = zscore(np.std(dR), results_surr['cramer']['dR_std']) tools/exp_metric_tensor_diagnostic.py:169:z_dR_shuffled = zscore(np.std(dR), results_surr['shuffled']['dR_std']) tools/exp_metric_tensor_diagnostic.py:172: z_DGratio_cramer = zscore(np.median(DG_ratios_clipped), results_surr['cramer']['DG_ratio_median']) tools/exp_metric_tensor_diagnostic.py:173: z_DGratio_shuffled = zscore(np.median(DG_ratios_clipped), results_surr['shuffled']['DG_ratio_median']) tools/exp_metric_tensor_diagnostic.py:177:print(f"\n=== Z-SCORES ===") tools/exp_metric_tensor_diagnostic.py:342: "gap_ratio_r": float(r_mean_prime), tools/exp_metric_tensor_diagnostic.py:344: "z_scores": { tools/exp_metric_tensor_diagnostic.py:397:print(f" prime: {output['gap_ratio_r']:.6f}") tools/exp_metric_tensor_diagnostic.py:398:print(f"Z-scores DG: cramer={z_DG_std_cramer:.1f}, shuffled={z_DG_std_shuffled:.1f}") tools/exp_metric_tensor_diagnostic.py:399:print(f"Z-scores DG ratio: cramer={z_DGratio_cramer:.1f}, shuffled={z_DGratio_shuffled:.1f}") tools/exp_metric_tensor_diagnostic.py:400:print(f"Z-scores : cramer={z_r_cramer:.1f}, shuffled={z_r_shuffled:.1f}") tools/exp_metric_tensor_diagnostic.py:401:print(f"Z-scores dR: cramer={z_dR_cramer:.1f}, shuffled={z_dR_shuffled:.1f}") tools/dnd_banchi_tm1.py:306: scores = [] tools/dnd_banchi_tm1.py:308: score = sum(1 for kw in keywords if kw in claim_lower) tools/dnd_banchi_tm1.py:309: if score > 0: tools/dnd_banchi_tm1.py:310: scores.append((score, nome, fn)) tools/dnd_banchi_tm1.py:311: scores.sort(reverse=True) tools/dnd_banchi_tm1.py:313: for score, nome, fn in scores[:max_banchi]: tools/dnd_banchi_tm1.py:316: banco['score'] = score tools/data/reports/ddf_20260501_0405.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" tools/lab_promotion.py:4:Port adattato del modulo D-ND_LAB core/promotion_proposer.py (commit tools/lab_promotion.py:266: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/exp_modular_memory_spectrum.py:83: Returns dict with H_real, H_shuffle, ordering_pct, z_score, tools/exp_modular_memory_spectrum.py:118: 'z_score': round(z, 1), tools/exp_modular_memory_spectrum.py:155: print(f" mod {b:>3}: ord={r['ordering_pct']:>7.2f}% z={r['z_score']:>7.1f} " tools/exp_modular_memory_spectrum.py:200: print(f"{b:>5} {btype:>10} | {pr['ordering_pct']:>10.2f} {pr['z_score']:>8.1f} " tools/data/reports/loop_guard_20260508_0330.json:3: "danger_score": 0.471, tools/data/reports/loop_guard_20260508_0330.json:15: "danger_score": 0.225, tools/dnd_engine.py:1729: 'gap_ratio_equals_phi': all_ratio_phi, tools/dnd_engine.py:2182: gap_ratios = [d.get('gap_ratio', 0) for d in domains if d.get('gap_ratio')] tools/dnd_engine.py:2183: if gap_ratios: tools/dnd_engine.py:2184: cv = np.std(gap_ratios) / np.mean(gap_ratios) if np.mean(gap_ratios) > 0 else 0 tools/dnd_engine.py:2185: print(f"\n Gap ratio: media={np.mean(gap_ratios):.6f}, cv={cv:.6f}") tools/dnd_engine.py:2189: 'quantita': 'gap_ratio', tools/dnd_engine.py:2190: 'valore': float(np.mean(gap_ratios)), tools/dnd_engine.py:2759: 'test': 'Trovare un dominio dove gap_ratio != phi^2', tools/triggers/finding_promoter.py:21: python -m core.triggers.finding_promoter [--domain physics] tools/triggers/finding_promoter.py:69:Score signals (already computed): tools/triggers/finding_promoter.py:70:- applicative: {appl_score} tools/triggers/finding_promoter.py:71:- methodology_note: {meth_score} tools/triggers/finding_promoter.py:72:- boundary_warning: {boundary_score} tools/triggers/finding_promoter.py:73:- negative_result: {neg_score} tools/triggers/finding_promoter.py:74:- literature_rediscovery: {lit_score} tools/triggers/finding_promoter.py:285: scores = finding.get("scores", {}) tools/triggers/finding_promoter.py:289: appl_score=scores.get("applicative", 0), tools/triggers/finding_promoter.py:290: meth_score=scores.get("methodology_note", 0), tools/triggers/finding_promoter.py:291: boundary_score=scores.get("boundary_warning", 0), tools/triggers/finding_promoter.py:292: neg_score=scores.get("negative_result", 0), tools/triggers/finding_promoter.py:293: lit_score=scores.get("literature_rediscovery", 0), tools/data/reports/loop_guard_20260507_0942.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_0942.json:14: "danger_score": 0.45, tools/triggers/run_full_cycle.sh:50:ORPHAN_LAB=$(pgrep -af "tools/lab_agent\.sh\|core.cli run --domain" 2>/dev/null | grep -v "^$$ " | grep -v "$0" | head -1) tools/test_lab_agent.sh:219: HAS_RESULT=$(grep -c -i "risultat\|result\|significat\|z-score\|p-value" "$ULTIMO_REPORT" 2>/dev/null || echo 0) tools/dnd_controprove.py:77: gap_ratios = [] tools/dnd_controprove.py:80: gap_ratios.append(abs(gaps[i-1]) / abs(gaps[i])) tools/dnd_controprove.py:84: if gap_ratios: tools/dnd_controprove.py:85: print(f" Rapporto gap ultimi 3: {', '.join(f'{r:.4f}' for r in gap_ratios[-3:])}") tools/dnd_controprove.py:90: f"{abs(gap_ratios[-1] - target_sq):.6f} " tools/dnd_controprove.py:91: f"({'SÌ' if abs(gap_ratios[-1] - target_sq) < 0.1 else 'NO'})") tools/dnd_controprove.py:255: gap_ratios = [] tools/dnd_controprove.py:258: gap_ratios.append(gaps[i-1] / gaps[i]) # con segno tools/dnd_controprove.py:262: if gap_ratios: tools/dnd_controprove.py:263: print(f" Rapporto gap (ultimi): {gap_ratios[-1]:.6f}") tools/dnd_controprove.py:316: gap_ratios = [] tools/dnd_controprove.py:319: gap_ratios.append(gaps[i-1] / gaps[i]) tools/dnd_controprove.py:322: rg_str = f"{gap_ratios[-1]:.6f}" if gap_ratios else "N/A" tools/triggers/finding_eligibility_gate.py:36:# Ogni signal ha peso che contribuisce al score della categoria. tools/triggers/finding_eligibility_gate.py:114:def score_category(text: str, category: str) -> tuple[int, list[str]]: tools/triggers/finding_eligibility_gate.py:115: """Ritorna (score, matched_signals) per una categoria.""" tools/triggers/finding_eligibility_gate.py:116: score = 0 tools/triggers/finding_eligibility_gate.py:122: score += weight tools/triggers/finding_eligibility_gate.py:127: score += weight tools/triggers/finding_eligibility_gate.py:129: return score, matched tools/triggers/finding_eligibility_gate.py:136: scores = {} tools/triggers/finding_eligibility_gate.py:139: s, m = score_category(text, cat) tools/triggers/finding_eligibility_gate.py:140: scores[cat] = s tools/triggers/finding_eligibility_gate.py:144: appl_score = scores["applicative"] tools/triggers/finding_eligibility_gate.py:145: lit_score = scores["literature_rediscovery"] tools/triggers/finding_eligibility_gate.py:146: meth_score = scores["methodology_note"] tools/triggers/finding_eligibility_gate.py:147: boundary_score = scores["boundary_warning"] tools/triggers/finding_eligibility_gate.py:148: verdict_score = scores["verdict_summary"] tools/triggers/finding_eligibility_gate.py:149: neg_score = scores["negative_result"] tools/triggers/finding_eligibility_gate.py:156: if lit_score >= 3: tools/triggers/finding_eligibility_gate.py:159: skip_reason = f"literature/rediscovery context (score={lit_score}); not a new operational finding" tools/triggers/finding_eligibility_gate.py:160: elif verdict_score >= 4: tools/triggers/finding_eligibility_gate.py:163: skip_reason = f"verdict/summary tag (score={verdict_score}); not a discrete finding" tools/triggers/finding_eligibility_gate.py:164: elif meth_score >= 4 and appl_score < 3: tools/triggers/finding_eligibility_gate.py:167: skip_reason = f"methodology/framework reflection (score={meth_score}, appl={appl_score})" tools/triggers/finding_eligibility_gate.py:168: elif neg_score >= 3 and appl_score < 4: tools/triggers/finding_eligibility_gate.py:171: skip_reason = f"negative result (score={neg_score}); no positive operational consequence" tools/triggers/finding_eligibility_gate.py:172: elif boundary_score >= 3 and appl_score < 3: tools/triggers/finding_eligibility_gate.py:175: skip_reason = f"boundary/limit warning without quantitative claim (score={boundary_score})" tools/triggers/finding_eligibility_gate.py:176: elif appl_score >= 4: tools/triggers/finding_eligibility_gate.py:180: elif appl_score >= 2 and lit_score < 2 and meth_score < 2: tools/triggers/finding_eligibility_gate.py:184: skip_reason = f"misto: appl={appl_score}, lit={lit_score}, meth={meth_score}, neg={neg_score}, boundary={boundary_score}" tools/triggers/finding_eligibility_gate.py:188: skip_reason = f"insufficient signal: appl={appl_score}, lit={lit_score}, meth={meth_score}, neg={neg_score}, boundary={boundary_score}" tools/triggers/finding_eligibility_gate.py:191: level = "structural" if appl_score >= 3 else ("contextual" if lit_score >= 2 else "meta") tools/triggers/finding_eligibility_gate.py:201: "scores": scores, tools/data/reports/agent_20260508_1632.md:10:> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? tools/data/reports/agent_20260508_1632.md:24:| domain | conditions | global Jaccard median | global Jaccard min | phase Jaccard median | threshold Jaccard median | scale Jaccard median | core labels all conditions | tools/data/reports/agent_20260508_1632.md:43:2. **Verificato: il null random rompe la tassonomia.** Il random bilanciato ha Jaccard globale `0.157895`, minimo `0.0`, nessun core label in tutte le condizioni. Il controllo preserva conteggio e lunghezza, non preserva l'ordine Sturmiano. tools/data/reports/agent_20260508_1632.md:50:**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non passa come claim di valore `gap_ratio`; passa come stabilita del label-set nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `top_k=12`, `|n|<=34`. La formulazione valida e: nel dominio Sturmiano phi, il set dei label dei gap larghi resta stabile sotto denominatore stratificato; il valore `first_two_ratio` resta un indicatore locale phase/threshold-sensitive. tools/data/reports/agent_20260508_1632.md:59:Portare il label-set fuori dal solo asse metallic mean: misurare se lo stesso core di label phi sopravvive in un dominio non-Sturmiano con ordine controllato, oppure se il core crolla appena il generatore perde bassa complessita combinatoria. Il prossimo discriminante e generatore, non soglia. tools/data/reports/agent_20260508_1632.md:64:- **L3 no silent patching**: il claim precedente sul `gap_ratio` resta vincolato; il nuovo claim cambia osservabile e dichiara il nodo regressivo. tools/data/reports/agent_20260416_0330.md:20:- **Metric**: delta_r = _prime - _shuffle, z-score tools/data/reports/agent_20260416_0330.md:28:| L | _prime | _shuffle | delta_r | z-score | tools/data/reports/agent_20260416_0330.md:46:The scaling exponent is **zero**. delta_r = -0.014 at L=35 and delta_r = -0.014 at L=100,000. The z-score grows (from -3.7 to -69.5) only because noise decreases with more samples — the signal itself does not accumulate. tools/data/piano11_results.json:4: "score": 8, tools/triggers/blueprint_generator.py:21: python -m core.triggers.blueprint_generator [--domain physics] tools/triggers/application_designer.py:141: "[TARGET] judge_results.json con scores", tools/data/seme_backup_pre_run.json:109: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme_backup_pre_run.json:110: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", tools/data/seme_backup_pre_run.json:115: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme_backup_pre_run.json:121: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme_backup_pre_run.json:122: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_backup_pre_run.json:124: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_backup_pre_run.json:130: "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" tools/data/seme_backup_pre_run.json:161: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme_backup_pre_run.json:346: "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:421: "Tensioni risolte: {'G_POTENZIALE_NULLA', 'QPG_GAP_RATIO_DENOMINATOR_GATE', 'TRASCENDENZA_LIMITE', 'G_BLANK_SHELL_DILATION_GATE', 'TENSIONE_ENTITA', 'PIANO_PRIMARIO_DUE_ASSIOMI', 'G_BLANK_SHELL_STRATIFIED_GATE', 'G_BLANK_SHELL_SCALE_LAW_GATE', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'G_BLANK_SHELL_TQGER_GATE', 'METRIC_TENSOR'}" tools/exp_acf_stationarity.py:219: print(f" z-score crossover (raw): {z_crossover_raw:+.1f}") tools/exp_acf_stationarity.py:220: print(f" z-score crossover (norm): {z_crossover_norm:+.1f}") tools/exp_acf_stationarity.py:221: print(f" z-score n_neg (raw): {z_nneg_raw:+.1f}") tools/exp_acf_stationarity.py:222: print(f" z-score n_neg (norm): {z_nneg_norm:+.1f}") tools/data/reports/tension_projection_screening_2026-04-21.json:133: "COMP_GEN_GAP_RATIO_FALSIFICA_F6", tools/data/reports/tension_projection_screening_2026-04-21.json:134: "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F6", tools/exp_coherence_robustness.py:36:def gap_ratio(gaps): tools/exp_coherence_robustness.py:76: Returns (delta_r, z_score) for one specific (L, strategy, seed) configuration. tools/exp_coherence_robustness.py:82: r_prime = np.array([gap_ratio(w) for w in windows]) tools/exp_coherence_robustness.py:90: r = gap_ratio(shuf) tools/exp_det_drift.py:88: z_score = (det_M - det_shuf_mean) / det_shuf_std if det_shuf_std > 0 else 0 tools/exp_det_drift.py:95: print(f" z-score = {z_score:.2f}") tools/exp_det_drift.py:110: 'z_score': float(z_score), tools/exp_det_drift.py:132: zs = ", ".join(f"{r['z_score']:.1f}" for r in results) tools/exp_det_drift.py:133: print(f"All z-scores: [{zs}]") tools/dnd_trasmutazione.py:565: # della spirale (gap_ratio → φ²) è universale. tools/dnd_trasmutazione.py:567: print(f" Segnale | gap_ratio→ | gap_finale | n_step | Tasso conv.") tools/dnd_trasmutazione.py:582: gap_ratio = obs.get('gap_ratio_medio', None) tools/dnd_trasmutazione.py:595: 'gap_ratio': float(gap_ratio) if gap_ratio else None, tools/dnd_trasmutazione.py:603: gr_str = f"{gap_ratio:.4f}" if gap_ratio else "N/A" tools/dnd_trasmutazione.py:606: f"{'φ²={:.4f}'.format(PHI**2) if gap_ratio and abs(gap_ratio - PHI**2) < 0.5 else ''}") tools/dnd_trasmutazione.py:608: # Il gap_ratio dovrebbe convergere a φ² ≈ 2.618 per tutti tools/dnd_trasmutazione.py:609: gap_ratios_valid = [s['gap_ratio'] for s in spirali tools/dnd_trasmutazione.py:610: if s['gap_ratio'] and np.isfinite(s['gap_ratio'])] tools/dnd_trasmutazione.py:611: if gap_ratios_valid: tools/dnd_trasmutazione.py:612: media_gr = np.mean(gap_ratios_valid) tools/dnd_trasmutazione.py:613: cv_gr = np.std(gap_ratios_valid) / media_gr if media_gr > 0 else float('inf') tools/dnd_trasmutazione.py:617: results['spirale_gap_ratio_medio'] = float(media_gr) tools/dnd_trasmutazione.py:618: results['spirale_gap_ratio_cv'] = float(cv_gr) tools/dnd_trasmutazione.py:691: if 'spirale_gap_ratio_cv' in t4: tools/dnd_trasmutazione.py:692: chiavi['T4_gap_ratio_cv'] = t4['spirale_gap_ratio_cv'] tools/dnd_trasmutazione.py:693: chiavi['T4_gap_ratio_medio'] = t4.get('spirale_gap_ratio_medio', 0) tools/dnd_trasmutazione.py:694: print(f" T4 (Indeterminazione): gap_ratio CV = {t4['spirale_gap_ratio_cv']:.4f}, " tools/dnd_trasmutazione.py:695: f"media = {t4.get('spirale_gap_ratio_medio', 0):.4f} (φ²={PHI**2:.4f})") tools/data/operator_directives_consumed/operator_directive_20260508_1915.md:4:Source report: Agent Report - Supertile Tiling Gate Del Core Phi tools/data/operator_directives_consumed/operator_directive_20260508_1915.md:12:- falsifier_summary: Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove "nullo/cade" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label. tools/data/operator_directives_consumed/operator_directive_20260508_1915.md:16:- reasoning: Il ciclo ha falsificato il claim operativo: il confine reale del supertile non batte il taglio contiguo a stessa multiset, mentre lo shuffle interno distrugge il core. L'evidenza sposta il nodo regressivo dal boundary geometrico al contratto osservabile: il portatore e' ordine interno piu' scala lunga, non il taglio esatto del supertile. tools/data/operator_directives_consumed/operator_directive_20260508_1915.md:22:- L1 medium: Riformulare: "same_count_internal_shuffle azzera l'all-high rate del core alto completo, ma lascia sopravvivere singoli label ad alta scala; prossimo ciclo separi all-high, per-label retention e stable-label count". tools/data/operator_directives_consumed/operator_directive_20260508_1915.md:23:- L4 medium: Isolare l'edge case per label: verificare quali tra [3,-4,4,6] sopravvivono sotto internal shuffle e dichiarare il perimetro come "core completo cade", non "core alto cade". tools/data/operator_directives_consumed/operator_directive_20260508_1915.md:34:- Se `gap_ratio` e' richiesto dalla direttiva, includilo; se non lo testi, dichiaralo come not_tested. tools/data/reports/loop_guard_20260508_0011.json:3: "danger_score": 0.471, tools/data/reports/loop_guard_20260508_0011.json:15: "danger_score": 0.338, tools/exp_coherence_length.py:33:def gap_ratio(gaps): tools/exp_coherence_length.py:51: - Return mean, std of _prime and _shuffle, plus z-score tools/exp_coherence_length.py:67: r_prime_list.append(gap_ratio(window)) tools/exp_coherence_length.py:72: r_shuf_lists[si].append(gap_ratio(shuf)) tools/exp_coherence_length.py:82: # z-score: how far is the prime mean from the shuffle distribution? tools/exp_coherence_length.py:96: 'z_score': float(z), tools/exp_coherence_length.py:133: if abs(r['z_score']) > 3: tools/exp_coherence_length.py:178: print(f"{r['L']:>8} | {r['r_prime_mean']:>10.5f} | {r['r_shuf_mean']:>10.5f} | {r['delta_r']:>10.5f} | {r['z_score']:>8.1f}") tools/exp_coherence_length.py:183: if abs(r['z_score']) > 3: tools/exp_coherence_length.py:254: {'L': r['L'], 'delta_r': r['delta_r'], 'z': r['z_score']} tools/data/reports/exp_metric_tensor_diag_long.json:24: "gap_ratio_r": 0.4537691241430244, tools/data/reports/exp_metric_tensor_diag_long.json:25: "z_scores": { tools/dnd_next.py:44: 'A': {'title': 'Quantum Emergence', 'target': 'PRA', 'core_claim': 'D-ND matrix, φ attractor'}, tools/dnd_next.py:45: 'B': {'title': 'Statistical Mechanics', 'target': 'PRE', 'core_claim': 'Phase transitions from D-ND'}, tools/dnd_next.py:46: 'C': {'title': 'Information Geometry + ζ', 'target': 'JMP', 'core_claim': 'Berry-Keating potential, GUE connection'}, tools/dnd_next.py:47: 'D': {'title': 'Observer Dynamics', 'target': 'FP', 'core_claim': 'Observer as D-ND process'}, tools/dnd_next.py:48: 'E': {'title': 'Cosmological Emergence', 'target': 'PRA', 'core_claim': 'Λ from D-ND coupling'}, tools/dnd_next.py:49: 'F': {'title': 'Quantum Information', 'target': 'Quantum', 'core_claim': 'Entanglement from D-ND'}, tools/dnd_next.py:50: 'G': {'title': 'Cognitive Physics', 'target': 'CogSci', 'core_claim': 'Consciousness from D-ND'}, 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:51:{"ts": "2026-05-08T03:35:14.907065+00:00", "cycle_ref": "20260508_0330", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha vincolato il claim originale: il valore puntuale 0.408953 regge solo con denominatore atomico N/phase/threshold e il supporto matched e' 25/48, quindi proseguire sul gap_ratio accumulerebbe dettaglio locale fragile. La consecutio e' chiara e non richiede operatore: spostare il prossimo esperimento dal ratio metrico al label-set dei gap, verificando stabilita' sotto phase, N e soglia.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare QPG_GAP_RATIO_DENOMINATOR_GATE spostando l'osservabile dal first_two_ratio al label-set dei gap larghi: per ogni gap stimare il miglior label m+n*theta e misurare stabilita' del label-set sotto N, phase e threshold.", "reason": "Nodo regressivo nel denominatore dell'osservabile: il ratio non e' bulk, il prossimo ciclo deve falsificare la tassonomia dei varchi."}}, "notes": "Side-effect log-only: propone cambio direzione del seme, non eseguito automaticamente."} tools/data/valutatore_log.jsonl:52:{"ts": "2026-05-08T16:38:40.757945+00:00", "cycle_ref": "20260508_1632", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha vincolato il claim alla stabilità del label-set, e la consecutio chiede di testare in domini non-Sturmiani. La direzione seme 'Esplorare il confine: 8 domini GUE, 5 Poisson' è il passo naturale per verificare la persistenza cross-dominio, quindi continuare.", "action": {"type": "trigger_cycle", "detail": {}}, "notes": "Il prossimo ciclo dovrebbe progettare un esperimento che applichi la stessa metrica di label-set a spettri GUE e Poisson, variando parametri come dimensione della matrice o soglia, per vedere se il core di label phi sopravvive fuori dal contesto Sturmiano."} tools/data/valutatore_log.jsonl:53:{"ts": "2026-05-08T17:20:45.153323+00:00", "cycle_ref": "20260508_1715", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale chiaro: il label-set phi non appartiene al lettore da solo, ma richiede generatore globale Sturmiano. Non c'e' dipendenza immediata dall'operatore e la consecutio naturale e' falsificare la portata del vincolo fuori dal caso phi, prima di cristallizzare.", "action": {"type": "trigger_cycle", "detail": {"focus": "testare se il vincolo generatore-globale si trasferisce a generatori Sturmiani non-phi e a controlli GUE/Poisson", "suggested_question": "Il core label-set e' proprieta' della famiglia Sturmiana a bassa complessita o specificamente del generatore phi?", "constraints": ["mantenere reader e generator separati", "includere shuffle/block/null controls", "non usare fit numerico senza audit strutturale"]}}, "notes": "Se anche generatori Sturmiani non-phi conservano core analoghi mentre null e shuffle falliscono, il prossimo passo diventa CRYSTALLIZE sul vincolo generatore-globale. Se il fenomeno resta solo phi, restringere il claim prima di inserirlo nel condensato."} tools/data/valutatore_log.jsonl:54:{"ts": "2026-05-08T18:11:29.877485+00:00", "cycle_ref": "20260508_1805", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo strutturale netto: la lunghezza generica recupera il nucleo basso, mentre il core alto rientra solo con scale Fibonacci lunghe. La consecutio non è esaurita: il prossimo passo deve distinguere se il segnale viene dalla semplice scala Fibonacci o dalla grammatica sostitutiva del generatore, includendo il gap_ratio lasciato esplicitamente non testato.", "action": {"type": "trigger_cycle", "detail": {"focus": "test grammatica di sostituzione vs lunghezza Fibonacci", "directive": "Usare block shuffle e surrogate che preservano lunghezze Fibonacci ma rompono la grammatica Sturmiana, poi surrogate che preservano grammatica locale ma non scala Fibonacci; includere gap_ratio oltre al label-set core. Verificare se il core alto richiede ordine sostitutivo globale o solo finestre Fibonacci lunghe.", "avoid": "non cristallizzare il claim phi-core prima del controllo grammaticale e del gap_ratio"}}, "notes": "Continua lo stesso frame: il risultato è vincolo, non ancora maturazione. Nessun side-effect automatico oltre al trigger log-only."} tools/data/valutatore_log.jsonl:55:{"ts": "2026-05-08T18:39:13.811423+00:00", "cycle_ref": "20260508_1834", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile: il core phi non misura solo lunghezza di blocco, perche' collassa quando l'ordine interno viene distrutto. Pero' e' il quarto NEXT_CYCLE consecutivo nello stesso frame locale e il perimetro resta su lettore phi/generator phi senza gap_ratio, non-phi Sturmian o cross-dominio; secondo la regola anti-tautologica il deep-dive locale ha raggiunto rendimento decrescente. La mossa successiva deve portare il vincolo su un discriminatore strutturale piu' largo, coerente col seme attuale sul confine GUE/Poisson.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare se il gate del core phi trasferisce come discriminatore di confine su domini GUE/Poisson: ordine interno conservato vs distrutto, con controlli shuffle e generatori non-phi", "reason": "Porta il finding dal dominio locale della grammatica Sturmiana al filtro STRUTTURA/rumore richiesto dal telos, evitando accumulo numerico sul solo phi_sturmian."}}, "notes": "Nel prossimo ciclo: usare il risultato appena ottenuto come vincolo, non come claim; includere almeno un controllo non-phi e un confronto GUE/Poisson. gap_ratio resta not_tested e va trattato come osservabile sospeso, non confermato."} tools/data/valutatore_log.jsonl:56:{"ts": "2026-05-08T19:15:36.511583+00:00", "cycle_ref": "20260508_1909", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha falsificato il claim operativo: il confine reale del supertile non batte il taglio contiguo a stessa multiset, mentre lo shuffle interno distrugge il core. L'evidenza sposta il nodo regressivo dal boundary geometrico al contratto osservabile: il portatore e' ordine interno piu' scala lunga, non il taglio esatto del supertile.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Riformulare il confine come grammatica interna + scala lunga: testare il core phi contro generatori non-phi, fasi e denominatori con controlli shuffle, senza assumere che il boundary esatto del supertile sia il portatore.", "reason": "Il boundary esatto e' stato controllato e non discrimina; continuare nello stesso frame accumulerebbe dettaglio locale su un claim caduto."}}, "notes": "Non serve STOP_FOR_REVIEW: la falsificazione e' chiara e non richiede scelta dell'operatore. Il prossimo ciclo deve attaccare trasferibilita' cross-generatore/cross-dominio, non raffinare solo soglie o gap label locali."} tools/data/reports/agent_20260507_2042.md:21:- Osservabili: `det(M)`, `trace(M)`, autovalori, z-score di `det(M)` contro 20 shuffle della stessa finestra. tools/data/reports/agent_20260507_2042.md:41:- z-score: `[-3.0, -2.5, -3.1, -3.4, -4.4, -2.5, -1.0]` tools/data/reports/agent_20260507_2042.md:48:3. **Il denominatore reale-vs-shuffle e presente ma decade.** Lo z-score e forte nelle finestre basse/intermedie (`-2.49 .. -4.43`) e cade a `-0.97` nella finestra `p ~ 5.0e7`. Il claim valido e differenza ordinata contro shuffle su perimetro finito, non polo `-1`. tools/data/meta_tautology_test.json:114: "z_scores": { tools/data/meta_tautology_test.json:122: "z_scores": { tools/data/meta_tautology_test.json:130: "z_scores": { tools/data/meta_tautology_test.json:138: "z_scores": { tools/data/meta_tautology_test.json:146: "z_scores": { tools/data/reports/falsifier_20260503_0330.json:10: "evidence": "With 12 bins, Mk3 has 12^4 = 20736 distinct states but only ~100K data points (~5 samples per state on average). Surrogate variance inflates under severe undersampling, mechanically shrinking z-scores toward zero regardless of whether the model genuinely captures structure. The z=6203 entropy measurement used the FULL state-space resolution; these 10 observables collapse it to 1D projections. Low z under Mk3 could be noise floor, not signal capture.", tools/data/reports/falsifier_20260503_0330.json:11: "suggestion": "Re-run with fewer bins for Mk3 (e.g. 6 bins → 1296 states, ~77 samples/state) and verify z-scores don't change. If they stay ~0, the conclusion holds. If they rise, the 'no Mk3 content' claim is an undersampling artifact. Alternatively, report the surrogate standard deviation for each (observable, Mk-order) pair — if sigma_Mk3 >> sigma_Mk2, the z-score comparison is apples-to-oranges." tools/data/reports/falsifier_20260503_0330.json:35: "summary": "The main structural conclusion (two layers exist) is likely correct but the 'NEW' label overstates novelty (L5: standard cumulant hierarchy), and the 'Mk3 adds nothing' claim has a serious confound (L2: undersampling inflates Mk3 surrogate variance, making z-scores uninformative rather than evidence of absence)." tools/data/reports/phi_vs_silver_falsification_20260306.json:62: "connection_to_framework": "This aligns with D-ND core: the rule f(x)=1+1/x is the content, the convergence to phi is trivial. The question 'why this rule and not another' resolves to: because M is unique. The question 'why phi and not silver' resolves to: because they share det=-1 (the real invariant), and phi is just the simplest representative." tools/data/reports/loop_guard_20260507_2120.json:3: "danger_score": 0.455, tools/data/reports/loop_guard_20260507_2120.json:15: "danger_score": 0.45, tools/data/reports/agent_20260507_0330.md:30:The endpoint-gated classifier is intentionally reported. In this perimeter it is empty because the Poisson endpoint has almost no stable original-vs-shuffle denominators. That is not discarded; it is the core META result. tools/data/reports/agent_20260508_0011.md:12:If the duality contrast (real vs shuffle z-score) weakens with scale for prime gaps, what is the quantitative rate of weakening? And does GUE show the same weakening, or is it scale-independent? tools/data/reports/agent_20260508_0011.md:15:- **Metric**: z-score of 5 canonical observables (SR, SR2, L1, L2, triple_var), real sequence vs 50 shuffles per window tools/data/reports/agent_20260508_0011.md:24:### Global z-scores (N=5000 gaps, seed=42) tools/data/reports/agent_20260508_0011.md:64:3. **The discriminator is alpha, not the observable value.** Primes and GUE have similar z-scores at any given window size. The difference is in how z scales with N. Primes: z ~ N^{0.39}. GUE: z ~ N^{0.54}. At small windows they overlap; at large windows they diverge. tools/data/agent_field_live.md:30:- **Domanda aperta** (consecutio): Costruire un gate di boundary simbolico che non legga solo il set dei label: confrontare label-set, posizione IDS dei label core e errore di label per chunk aligned/misaligned. Se il boundary reale esiste nell'osservabile, deve apparire nella geometria degli errori o nella posizione dei gap, non nel tools/data/agent_field_live.md:33: - Substitution Grammar Gate Del Core Phi → None/None (ρ=None) tools/data/agent_field_live.md:34: - Supertile Tiling Gate Del Core Phi → None/None (ρ=None) tools/data/agent_field_live.md:137:Source report: Agent Report - Supertile Tiling Gate Del Core Phi tools/data/agent_field_live.md:145:- falsifier_summary: Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove "nullo/cade" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label. tools/data/agent_field_live.md:149:- reasoning: Il ciclo ha falsificato il claim operativo: il confine reale del supertile non batte il taglio contiguo a stessa multiset, mentre lo shuffle interno distrugge il core. L'evidenza sposta il nodo regressivo dal boundary geometrico al contratto osservabile: il portatore e' ordine interno piu' scala lunga, non il taglio esatto del supertile. tools/data/agent_field_live.md:155:- L1 medium: Riformulare: "same_count_internal_shuffle azzera l'all-high rate del core alto completo, ma lascia sopravvivere singoli label ad alta scala; prossimo ciclo separi all-high, per-label retention e stable-label count". tools/data/agent_field_live.md:156:- L4 medium: Isolare l'edge case per label: verificare quali tra [3,-4,4,6] sopravvivono sotto internal shuffle e dichiarare il perimetro come "core completo cade", non "core alto cade". tools/data/agent_field_live.md:167:- Se `gap_ratio` e' richiesto dalla direttiva, includilo; se non lo testi, dichiaralo come not_tested. tools/data/agent_field_live.md:208:### Agent Report - Supertile Tiling Gate Del Core Phi tools/data/agent_field_live.md:209:Trovato: 1. **Verificato: il confine di supertile non batte il blocco contiguo a pari multiset di lunghezze.** `supertile_shuffle` fa all-high `108/160 = 0.675`; `same_length_contiguous_shuffle` fa `116/160 = 0.725`. Per order 9 sono pari (`29/40`), per order 10-11 il controllo contiguo e' leggermente sopra tools/data/agent_field_live.md:210:Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr tools/data/agent_field_live.md:212:### Agent Report — Substitution Grammar Gate Del Core Phi tools/data/agent_field_live.md:213:Trovato: 1. **Verificato: distruggere l'ordine interno del blocco collassa il core al livello random.** `internal_block_shuffle` resta su Jaccard mediano `0.150000-0.166667`, low retention `0.25`, high retention `0.25`, all-high `0/40` per ogni block_size. Il suo profilo coincide con `global_balanced_shuffle tools/data/agent_field_live.md:214:Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr tools/data/agent_field_live.md:216:### Agent Report - Block Scale Gate Del Core Phi tools/data/agent_field_live.md:220:Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr tools/data/agent_field_live.md:268:**Come e' caduto**: Shuffle audit (ciclo 45): z-score = 0.2. Lo shuffle tools/data/agent_field_live.md:280:**Come e' caduto**: Shuffle audit (ciclo 45, 2026-04-22): z-score = 0.0. tools/data/agent_field_live.md:306:**Esperimento a massima informazione:** M_trascendenza_limite_attuale_L0 (score=0.872) tools/data/agent_field_live.md:311:- Rischi (contraddizioni): COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE, M_trascendenza_limite_attuale_L0 tools/data/reports/falsifier_20260508_0011.json:17: "evidence": "Poisson SR has alpha=0.263 with R2=0.75; Poisson L2 has alpha=0.165 with R2=0.91. These are not 'near 0' and not 'incoherent.' An i.i.d. sequence should produce z ~ N(0,1) at all window sizes, giving alpha=0 exactly. Non-zero alpha with R2=0.91 in the null baseline suggests either an unfolding artifact or a bias in the windowed z-score computation that inflates z at larger N even without structure.", tools/data/reports/falsifier_20260508_0011.json:28: "summary": "Report is internally coherent on its main claim (primes alpha < 0.5, GUE alpha > 0.5) but two edge cases break the stated perimeter: GUE L2 s137 violates the blanket 'alpha >= 0.5' (L4), and Poisson L2 shows non-trivial scaling (alpha=0.165, R2=0.91) that undermines the null baseline and may indicate a systematic bias in the z-score methodology (L4). Neither is fatal but both require tightening before the finding can be called clean." tools/data/reports/falsifier_20260508_1834.json:9: "claim": "Claim Under Test: \"Cosa manca per confermare completamente gap_ratio...\"; Verdict: \"CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE\"", tools/data/reports/falsifier_20260508_1834.json:10: "evidence": "Il report dichiara esplicitamente che `gap_ratio` non e' testato in questo ciclo; i dati misurano retention/Jaccard/all-high dei label sotto shuffle, non gap_ratio. Il verdict resta agganciato a `QPG_GAP_RATIO_DENOMINATOR_GATE`, quindi il nodo testato cambia da gap_ratio a label-set grammar gate senza declassare formalmente il claim originario.", tools/data/reports/falsifier_20260508_1834.json:11: "suggestion": "Riformulare: \"gap_ratio non valutato; questo ciclo testa solo il label-set gate. QPG_GAP_RATIO_DENOMINATOR_GATE resta aperto finche' gap_ratio viene misurato nello stesso perimetro\"." tools/data/reports/falsifier_20260508_1834.json:16: "claim": "\"il core alto [3, -4, 4, 6] non e' portato da lunghezza e conteggio del blocco\"", tools/data/reports/falsifier_20260508_1834.json:17: "evidence": "Per `internal_block_shuffle`, che conserva lunghezza e conteggio, `high retention` e' 0.25 per ogni block_size: non porta il core alto completo (`all-high 0/320`), ma porta almeno una frazione dei label alti. La proibizione esatta vale solo per il core alto completo, non per i label alti in generale.", tools/data/reports/falsifier_20260508_1834.json:18: "suggestion": "Separare hard constraint e residuo: \"lunghezza/conteggio non portano il core alto completo: all-high=0/320; resta una retention alta residua 0.25 da spiegare\"." tools/data/reports/falsifier_20260508_1834.json:23: "claim": "\"distruggere l'ordine interno del blocco collassa il core al livello random\"", tools/data/reports/falsifier_20260508_1834.json:24: "evidence": "Il collasso e' vero per Jaccard mediano, low/high retention e all-high, ma la tabella mostra `stable labels 75% = [34]` per tutti gli internal_block_shuffle e global_balanced_shuffle. Questo label non appartiene al reference core `[-1,1,-2,2,3,-4,4,6]` e segnala un artefatto o edge label stabile non discusso.", tools/data/reports/falsifier_20260508_1834.json:25: "suggestion": "Nel prossimo ciclo isolare il label `34`: verificare se e' boundary artifact di `|n|<=34`, leakage del reader, o label valido fuori core; non includerlo implicitamente nel profilo random senza nota." tools/data/reports/falsifier_20260508_1834.json:28: "summary": "Il report e' parzialmente coerente: L3 si rompe per drift tra gap_ratio dichiarato e label-set testato, e L1 richiede di limitare lo zero al core alto completo, non ai label alti residui." tools/data/reports/loop_guard_20260507_2042.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_2042.json:14: "danger_score": 0.45, tools/data/reports/agent_20260429_1041.md:29:| p_center | self_rate | shuffle_mean | z-score | M1 LLR | M2 LLR | tools/data/reports/agent_20260429_1041.md:89:1. **Mod-3 non-zero self-transition is algebraically forbidden**, not statistically suppressed. The z-score is -42 (mean) not because of strong bias, but because the numerator is exactly -0.500 (real = 0, shuffle = 0.50) with small denominator noise. The signal has no scaling because it's a theorem with no free parameters. tools/data/two_channel_shuffle_audit.json:21: "z_score": 21.25, tools/data/two_channel_shuffle_audit.json:29: "z_score": 54.79, tools/data/two_channel_shuffle_audit.json:37: "z_score": 0.16, tools/data/two_channel_shuffle_audit.json:45: "z_score": 0.0, tools/data/two_channel_shuffle_audit.json:53: "z_score": -12.75, tools/data/two_channel_shuffle_audit.json:61: "z_score": 6202.8, tools/data/two_channel_shuffle_audit.json:69: "z_score": -1.36, tools/data/two_channel_shuffle_audit.json:77: "z_score": -2.5, tools/data/reports/falsifier_20260508_1909.json:9: "claim": "\"same_count_internal_shuffle resta nullo su tutti gli order\" / \"Il core alto cade solo quando l'ordine interno viene distrutto (0/160)\"", tools/data/reports/falsifier_20260508_1909.json:10: "evidence": "Lo zero esatto vale per all-high condition rate: 0/160. Non vale per tutte le osservabili del core alto: high retention mediana = 0.25 e stable labels includono [34] negli order 8, 9 e 11. Quindi il controllo non e' nullo in senso assoluto; e' nullo solo per il core alto completo simultaneo.", tools/data/reports/falsifier_20260508_1909.json:11: "suggestion": "Riformulare: \"same_count_internal_shuffle azzera l'all-high rate del core alto completo, ma lascia sopravvivere singoli label ad alta scala; prossimo ciclo separi all-high, per-label retention e stable-label count\"." tools/data/reports/falsifier_20260508_1909.json:16: "claim": "\"il core alto cade solo quando l'ordine interno viene distrutto\"", tools/data/reports/falsifier_20260508_1909.json:18: "suggestion": "Isolare l'edge case per label: verificare quali tra [3,-4,4,6] sopravvivono sotto internal shuffle e dichiarare il perimetro come \"core completo cade\", non \"core alto cade\"." tools/data/reports/falsifier_20260508_1909.json:28: "summary": "Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove \"nullo/cade\" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label." tools/data/reports/agent_20260417_0803.md:73:| Metric | Prime (raw) | Shuffle mean | z-score | tools/data/reports/agent_20260417_0803.md:78:| Metric | Prime (norm) | Shuffle mean | z-score | tools/data/reports/agent_diag2.md:34:| Z-score | vs Cramer | vs Shuffled | tools/data/reports/agent_diag2.md:51:| Z-score | Value | tools/data/reports/agent_20260508_1805.md:1:# Agent Report - Block Scale Gate Del Core Phi tools/data/reports/agent_20260508_1805.md:8:**observable_contract**: claim=il core phi dei gap larghi richiede generatore globale, non solo lettore label; observable=retention dei label core sotto block shuffle; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per block_size; generator=phi_sturmian con block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, block_size Fibonacci e non-Fibonacci; not_tested=gap_ratio, generatori Sturmiani non-phi, GUE/Poisson, soglie 1.75/2.25. tools/data/reports/agent_20260508_1805.md:11:> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? tools/data/reports/agent_20260508_1805.md:14:La rottura del core phi sotto block shuffle dipende da una scala Fibonacci del generatore, o i label alti rientrano appena il blocco diventa lungo in modo generico? tools/data/reports/agent_20260508_1805.md:17:- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. tools/data/reports/agent_20260508_1805.md:18:- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. tools/data/reports/agent_20260508_1805.md:26:Reference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`, Jaccard mediano `0.909091`, min `0.727273`. tools/data/reports/agent_20260508_1805.md:72:3. **Verificato: i controlli non-Fibonacci lunghi trasportano il basso ma non chiudono l'alto.** `64`, `96`, `128` hanno `low retention=1.0`, ma `all-high condition rate` resta `0.025`, `0.025`, `0.2`. La lunghezza da sola porta memoria locale; non ricostruisce il core alto come i blocchi Fibonacci `89/144`. tools/data/reports/agent_20260508_1805.md:77:**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core phi ha due scale. Il nucleo basso `[-1, 1, -2, 2]` rientra quando il blocco conserva texture locale sufficiente (`block_size=34`). I label alti `[3, -4, 4, 6]` richiedono blocchi Fibonacci lunghi: `89` e `144` portano il core alto come condizione frequente; i blocchi non-Fibonacci lunghi non chiudono la stessa struttura. tools/data/reports/agent_20260508_1805.md:79:La formulazione valida e': il core alto del label-set phi misura memoria globale del generatore su scale Fibonacci, non lunghezza generica del blocco e non valore `gap_ratio`. tools/data/reports/agent_20260508_1805.md:82:- **Due radici**: nucleo basso locale del generatore / core alto globale del generatore. tools/data/reports/agent_20260508_1805.md:85:- **Campo di possibilita**: qui diventa possibile classificare i label phi in strati di memoria; qui diventa non-possibile trattare il core come un unico blocco indifferenziato. tools/data/reports/agent_20260508_1805.md:93:- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. tools/data/reports/agent_20260508_1805.md:95:- **L5 re-discovery**: Fibonacci/Sturmian/gap labeling sono meccanismi noti; il finding del ciclo e' la stratificazione del core osservato in basso locale e alto Fibonacci-lungo. tools/data/reports/agent_20260413_0330.md:38:**All z-scores highly significant** (z_dip from -8 to -19, z_slope from +13 to +30). The structure is real at every scale. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:24:**Scale test**: 10 windows logarithmically spaced across each sequence. Correlation of z-scores with log(position) measures whether structure decays with scale. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0852.md:54:1. **The binary/anti-bunching channel is NOT unique to primes.** GUE eigenvalues show the same z-score magnitude (-11.3 vs -12.2) and even better scale invariance (corr = -0.007 vs +0.40). Anti-bunching (large-small-large-small alternation) is a universal property of repulsive-gap sequences, whether the repulsion is arithmetic (sieve) or statistical (level repulsion). tools/data/reports/_quarantine_falsifier_29_04/falsifier_20260429_0958.json:18: "suggestion": "Sostituire ogni 'zero/prohibition' con soglia osservata: count grezzo, frazione 0.1614, intervallo atteso sotto null, z-score. Se esiste un sotto-perimetro con zero, dichiararlo esplicitamente." tools/data/reports/_quarantine_falsifier_29_04/falsifier_20260429_0958.json:24: "evidence": "Sulle frazioni, real = 16.1% e sintetici ≈ 34.5%: il rapporto e' circa 2.1x, non un ordine di grandezza. Solo gli z-score sono enormi (-75 a -107), ma quello dipende dalla varianza stimata e dal denominatore.", tools/data/reports/_quarantine_falsifier_29_04/falsifier_20260429_0958.json:25: "suggestion": "Separare ampiezza assoluta e significativita': riportare count grezzi, differenza assoluta in punti percentuali (~18.4 pp), effect size, mutual information o KL divergence, oltre allo z-score." tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:18:- All results reported as z-scores relative to shuffle null tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:35:| Residue z-score | -44 | -26 | **Still 26 sigma** | tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:37:| Magnitude z-score | -4 to -7 | -2 to -6 | Fluctuates, weakly decaying | tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:39:| Mod-3 z-score | -34 | -28 | **Still 28 sigma** | tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:44:1. **The residue channel has no boundary.** Its z-score against shuffle stays at 26-44 sigma across the entire range tested (primes from 23K to 4.5M, a 200x span). The decay correlation with ln(p) is -0.190 — essentially zero or slightly strengthening. The mod-3 prohibition (self-transition fraction ~0.40 vs shuffle ~0.61) is equally strong at all scales. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0833.md:46:2. **The magnitude channel has a boundary.** Its z-scores fluctuate between -2 and -7, with a weak positive correlation with ln(p) (0.455), meaning they drift toward the null at larger primes. This channel is what drives the r-statistic toward Poisson. tools/data/reports/_quarantine_falsifier_29_04/agent_20260429_0958.md:33:1. **Mod-3 ordering is the hardest signal in the lab.** Real primes: 16.1% self-transitions. All synthetics: ~34.5%. z-scores from -75 to -107. No model — not even one matching pair correlations — comes within an order of magnitude. The mod-3 prohibition (zero self-transitions in mod-3 classes, found on 2026-04-28) is structurally irreducible. This is the single strongest discriminant the lab has ever measured. tools/data/reports/falsifier_20260504_0901.json:24: "evidence": "The report itself shows all Poisson z-scores < 2 (no significant signal above baseline). If there is no signal, retention = (noise)/(noise), and critical alpha is undefined — it measures where random fluctuations cross an arbitrary threshold. A meaningless Delta from a no-signal sequence cannot serve as a control against which the primes/GUE Delta is validated. The argument 'Poisson shows separation therefore primes coupling is real' uses a noise artifact as if it were a measurement.", tools/data/reports/falsifier_20260504_0901.json:42: "summary": "The core finding — uniform shuffle destroys both layers at the same rate — is the expected null behavior of random permutations (L5), reported at a resolution too coarse to measure sub-bin differences (L4), and validated against a meaningless Poisson control (L2). The experiment is well-designed but the conclusion conflates 'method-blind perturbation' with 'structural coupling.' The report's own consecutio (selective perturbation) is the actual test." tools/data/reports/loop_guard_20260507_1751.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1751.json:14: "danger_score": 0.45, tools/data/reports/agent_20260430_1946.md:49:| Source | Separation | z-score | tools/data/reports/agent_20260405_0914.md:18:- `_prime - _Cramer > 0` at ALL 25 scales (z-scores: 18 to 50) tools/data/reports/loop_guard_20260508_1805.json:3: "danger_score": 0.246, tools/data/reports/loop_guard_20260508_1805.json:15: "danger_score": 0.0, tools/data/reports/exp_dR_brody_connection.json:43: "z_scores": { tools/data/gap_label_block_scale_gate_20260508_1805.json:43: "reference_core_phi": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:69: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:151: "core_labels_all_conditions": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:206: "median_overlap_with_phi_core": 0.1213235294117647, tools/data/gap_label_block_scale_gate_20260508_1805.json:207: "min_overlap_with_phi_core": 0.0, tools/data/gap_label_block_scale_gate_20260508_1805.json:224: "reference_core_retained_in_all": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:225: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:240: "core_labels_all_conditions": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:297: "median_overlap_with_phi_core": 0.1213235294117647, tools/data/gap_label_block_scale_gate_20260508_1805.json:298: "min_overlap_with_phi_core": 0.0, tools/data/gap_label_block_scale_gate_20260508_1805.json:315: "reference_core_retained_in_all": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:316: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:331: "core_labels_all_conditions": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:389: "median_overlap_with_phi_core": 0.1213235294117647, tools/data/gap_label_block_scale_gate_20260508_1805.json:390: "min_overlap_with_phi_core": 0.0, tools/data/gap_label_block_scale_gate_20260508_1805.json:407: "reference_core_retained_in_all": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:408: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:423: "core_labels_all_conditions": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:482: "median_overlap_with_phi_core": 0.14285714285714285, tools/data/gap_label_block_scale_gate_20260508_1805.json:483: "min_overlap_with_phi_core": 0.058823529411764705, tools/data/gap_label_block_scale_gate_20260508_1805.json:500: "reference_core_retained_in_all": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:501: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:516: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:578: "median_overlap_with_phi_core": 0.14285714285714285, tools/data/gap_label_block_scale_gate_20260508_1805.json:579: "min_overlap_with_phi_core": 0.0625, tools/data/gap_label_block_scale_gate_20260508_1805.json:596: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:599: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:613: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:674: "median_overlap_with_phi_core": 0.18181818181818182, tools/data/gap_label_block_scale_gate_20260508_1805.json:675: "min_overlap_with_phi_core": 0.125, tools/data/gap_label_block_scale_gate_20260508_1805.json:692: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:695: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:709: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:770: "median_overlap_with_phi_core": 0.2928571428571428, tools/data/gap_label_block_scale_gate_20260508_1805.json:771: "min_overlap_with_phi_core": 0.15384615384615385, tools/data/gap_label_block_scale_gate_20260508_1805.json:788: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:792: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:805: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:867: "median_overlap_with_phi_core": 0.25, tools/data/gap_label_block_scale_gate_20260508_1805.json:868: "min_overlap_with_phi_core": 0.13333333333333333, tools/data/gap_label_block_scale_gate_20260508_1805.json:885: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:889: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:902: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:969: "median_overlap_with_phi_core": 0.6666666666666666, tools/data/gap_label_block_scale_gate_20260508_1805.json:970: "min_overlap_with_phi_core": 0.3076923076923077, tools/data/gap_label_block_scale_gate_20260508_1805.json:987: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:993: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1004: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1066: "median_overlap_with_phi_core": 0.4, tools/data/gap_label_block_scale_gate_20260508_1805.json:1067: "min_overlap_with_phi_core": 0.2, tools/data/gap_label_block_scale_gate_20260508_1805.json:1084: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1088: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1101: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1171: "median_overlap_with_phi_core": 0.7136363636363636, tools/data/gap_label_block_scale_gate_20260508_1805.json:1172: "min_overlap_with_phi_core": 0.5454545454545454, tools/data/gap_label_block_scale_gate_20260508_1805.json:1189: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1196: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1206: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1269: "median_overlap_with_phi_core": 0.5555555555555556, tools/data/gap_label_block_scale_gate_20260508_1805.json:1270: "min_overlap_with_phi_core": 0.2727272727272727, tools/data/gap_label_block_scale_gate_20260508_1805.json:1287: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1291: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1304: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1375: "median_overlap_with_phi_core": 0.7272727272727273, tools/data/gap_label_block_scale_gate_20260508_1805.json:1376: "min_overlap_with_phi_core": 0.5833333333333334, tools/data/gap_label_block_scale_gate_20260508_1805.json:1393: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1401: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1410: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1474: "median_overlap_with_phi_core": 0.6, tools/data/gap_label_block_scale_gate_20260508_1805.json:1475: "min_overlap_with_phi_core": 0.3, tools/data/gap_label_block_scale_gate_20260508_1805.json:1492: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1497: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1509: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1575: "median_overlap_with_phi_core": 0.6666666666666666, tools/data/gap_label_block_scale_gate_20260508_1805.json:1576: "min_overlap_with_phi_core": 0.45454545454545453, tools/data/gap_label_block_scale_gate_20260508_1805.json:1593: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1598: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1610: "core_labels_all_conditions": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1684: "median_overlap_with_phi_core": 0.7272727272727273, tools/data/gap_label_block_scale_gate_20260508_1805.json:1685: "min_overlap_with_phi_core": 0.5454545454545454, tools/data/gap_label_block_scale_gate_20260508_1805.json:1702: "reference_core_retained_in_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1710: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1721: "core_labels_all_conditions": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:1779: "median_overlap_with_phi_core": 0.5, tools/data/gap_label_block_scale_gate_20260508_1805.json:1780: "min_overlap_with_phi_core": 0.0, tools/data/gap_label_block_scale_gate_20260508_1805.json:1797: "reference_core_retained_in_all": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:1798: "reference_core_missing_from_all": [ tools/data/gap_label_block_scale_gate_20260508_1805.json:1813: "core_labels_all_conditions": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:1871: "median_overlap_with_phi_core": 0.3076923076923077, tools/data/gap_label_block_scale_gate_20260508_1805.json:1872: "min_overlap_with_phi_core": 0.0, tools/data/gap_label_block_scale_gate_20260508_1805.json:1889: "reference_core_retained_in_all": [], tools/data/gap_label_block_scale_gate_20260508_1805.json:1890: "reference_core_missing_from_all": [ tools/data/reports/agent_20260415_0330.md:27:| Quintile | _prime | _shuffle | z-score | tools/data/reports/agent_20260415_0330.md:53:| Metric | Primes | Shuffle | z-score | tools/lab_trajectory_apply.py:4:Porting adattato di D-ND_LAB/core/trajectory_apply.py (commit 4299dd4). tools/data/reports/agent_20260501_0330.md:79:- **L2**: All comparisons are in the same units (degrees, dimensionless ratios). The z-scores compare same-N distributions. tools/lab_aeternitas.py:5:agent_skills_aeternitas.md) e del modulo D-ND_LAB core/aeternitas.py tools/lab_aeternitas.py:106: """Run P0/P1/P5 invariant scan. Vedi core/aeternitas.py D-ND_LAB.""" tools/data/reports/agent_20260409_0330.md:32:| Band | f range | z-score | ratio (prime/shuffle) | tools/data/reports/agent_20260409_0330.md:53:| Frequency | z-score | Interpretation | tools/lab_bias_corrector.py:5:Port da D-ND_LAB core/bias_corrector.py (commit 29/04) al lab fisico MM_D-ND. tools/dnd_paper_graph.py:103:# Core: PaperGraph tools/data/trasmutazione_results.json:416: "spirale_gap_ratio_medio": 2.6180513981505733, tools/data/trasmutazione_results.json:417: "spirale_gap_ratio_cv": 1.4292502486555382e-05, tools/data/trasmutazione_results.json:422: "gap_ratio": 2.6180145125477585, tools/data/trasmutazione_results.json:430: "gap_ratio": 2.6180861695660687, tools/data/trasmutazione_results.json:438: "gap_ratio": 2.6180861695660687, tools/data/trasmutazione_results.json:446: "gap_ratio": 2.6179938106753506, tools/data/trasmutazione_results.json:454: "gap_ratio": 2.6180415569821247, tools/data/trasmutazione_results.json:462: "gap_ratio": 2.6180861695660687, tools/data/trasmutazione_results.json:475: "T4_gap_ratio_cv": 1.4292502486555382e-05, tools/data/trasmutazione_results.json:476: "T4_gap_ratio_medio": 2.6180513981505733 tools/exp_spectral_landscape.py:405: print(f" z-scores: r={prime_result['z_r']:.1f}, beta={prime_result['z_beta']:.1f}, acf1={prime_result['z_acf1']:.1f}") tools/data/engine_state.json:24: "ipotesi": "gap_ratio = 2.618079 (cv=0.000014)", tools/data/engine_state.json:25: "test": "Trovare un dominio dove gap_ratio != phi^2", tools/data/reports/agent_20260430_1919.md:58:Cramer: theta ~ +65 deg (opposite quadrant from primes at ~ -150 deg). z-scores are positive and small. The Cramer model has weak positive autocorrelation from density variation — structurally opposite to primes. tools/data/reports/agent_20260430_1919.md:76:- **DIPOLAR_ORDERING confirmed and sharpened**: the "dipole" is real (theta constant, z-scores large, Cramer opposite quadrant) but is a single mode, not a 2D structure. The constant ratio delta_L1/delta_SR ~ 2.3 is the structural invariant. The original framing of "spacing_ratio toward Poisson, lag1_acf toward GUE" is misleading — both point in the same direction (negative z), they just have different magnitudes. tools/data/reports/agent_20260430_1919.md:84:- **Invariante di passaggio**: il rapporto delta_L1/delta_SR ~ 2.3 sopravvive a: cambio di scala (1e4 -> 3e6), cambio di offset (primi piccoli vs primi grandi), cambio di misura (z-score vs raw delta). tools/dnd_riformulazioni.py:137: 'gap_ratio': obs.get('gap_ratio_medio', 0), tools/lab_falsifier.py:13:Le 5 lenti (port da /opt/D-ND_LAB/core/report_falsifier.py, riformulate tools/data/exp_det_drift.json:16: "z_score": -2.970479901674383, tools/data/exp_det_drift.json:31: "z_score": -2.494337489324833, tools/data/exp_det_drift.json:46: "z_score": -3.080101678803467, tools/data/exp_det_drift.json:61: "z_score": -3.42701667098888, tools/data/exp_det_drift.json:76: "z_score": -4.434338123586996, tools/data/exp_det_drift.json:91: "z_score": -2.5231991783183925, tools/data/exp_det_drift.json:106: "z_score": -0.9729509457351886, tools/data/modular_memory_spectrum.json:29: "z_score": -3727169082905309.5, tools/data/modular_memory_spectrum.json:40: "z_score": -6618.1, tools/data/modular_memory_spectrum.json:51: "z_score": -93.5, tools/data/modular_memory_spectrum.json:62: "z_score": -120.5, tools/data/modular_memory_spectrum.json:73: "z_score": -6853.1, tools/data/modular_memory_spectrum.json:84: "z_score": -130.5, tools/data/modular_memory_spectrum.json:95: "z_score": -2720.0, tools/data/modular_memory_spectrum.json:106: "z_score": -980.8, tools/data/modular_memory_spectrum.json:117: "z_score": -951.7, tools/data/modular_memory_spectrum.json:128: "z_score": -629.5, tools/data/modular_memory_spectrum.json:139: "z_score": -480.5, tools/dnd_banchi.py:326: scores = [] tools/dnd_banchi.py:328: score = sum(1 for kw in keywords if kw in claim_lower) tools/dnd_banchi.py:329: if score > 0: tools/dnd_banchi.py:330: scores.append((score, nome, fn)) tools/dnd_banchi.py:340: score = sum(1 for kw in kws if kw in claim_lower) tools/dnd_banchi.py:341: if score > 0: tools/dnd_banchi.py:351: scores.append((score + 0.5, custom.get('nome', f.stem), _make_custom_fn(custom))) tools/dnd_banchi.py:354: scores.sort(reverse=True) tools/dnd_banchi.py:356: for score, nome, fn in scores[:max_banchi]: tools/dnd_banchi.py:359: banco['score'] = score tools/bicono_extractor.py:5:Port chirurgico del pattern D-ND_LAB core/bicono_extractor.py al lab fisico tools/exp_quasiperiodic_gap_ratio_denominator.py:3:Stratified denominator audit for the quasiperiodic gap_ratio claim. tools/exp_quasiperiodic_gap_ratio_denominator.py:143: "experiment": "quasiperiodic_gap_ratio_denominator", tools/exp_quasiperiodic_gap_ratio_denominator.py:165: parser.add_argument("--out", default="tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json") tools/dnd_research_engine.py:85: # Core axioms from the model tools/dnd_experiments.py:159: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_experiments.py:302: gap_ratio = None tools/dnd_experiments.py:307: gap_ratio = obs.get('gap_ratio_medio') tools/dnd_experiments.py:316: 'gap_ratio': gap_ratio, tools/dnd_experiments.py:414: gap_ratio = None tools/dnd_experiments.py:419: gap_ratio = obs.get('gap_ratio_medio') tools/dnd_experiments.py:439: 'gap_ratio': gap_ratio, tools/dnd_experiments.py:683: entry['gap_ratio'] = obs.get('gap_ratio_medio') tools/dnd_experiments.py:684: entry['gap_ratio_ultimo'] = obs.get('gap_ratio_ultimo') tools/dnd_experiments.py:699: gr = entry.get('gap_ratio', 0) tools/dnd_experiments.py:714: gr_data = [(v['T_ratio'], v['gap_ratio']) for v in results.values() tools/dnd_experiments.py:715: if v.get('gap_ratio') and v['gap_ratio'] < 100] tools/exp_boundary_growth.py:25:def gap_ratio(gaps): tools/exp_boundary_growth.py:49: return gap_ratio(shuffled) tools/exp_boundary_growth.py:82: r_prime = gap_ratio(gaps) tools/exp_boundary_growth.py:90: r_cramer_list.append(gap_ratio(cramer_gaps)) tools/exp_boundary_growth.py:101: # Excess and z-score vs Cramer tools/exp_boundary_growth.py:132: z_scores = [r["z_cramer"] for r in results] tools/exp_boundary_growth.py:139: coeffs_z = np.polyfit(scales, z_scores, 1) tools/exp_boundary_growth.py:153: print(f"\nZ-score (vs Cramer):") tools/exp_boundary_growth.py:154: print(f" Mean: {np.mean(z_scores):+.2f}") tools/exp_boundary_growth.py:156: print(f" All significant (|z|>2): {sum(1 for z in z_scores if abs(z) > 2)}/{len(z_scores)}") tools/exp_boundary_growth.py:220: "z_cramer_mean": float(np.mean(z_scores)), tools/data/reports/exp_brody_crossover_20260405.json:21: "z_score_mean": 57.33067740981995, tools/data/reports/exp_brody_crossover_20260405.json:22: "z_score_min": 30.216322781141063, tools/dnd_paper_refactor.py:231:# Core: PaperRefactor tools/data/reports/falsifier_20260507_2042.json:10: "evidence": "Nell'ultima finestra p ~ 5.0e7 lo z-score e -0.97, sotto una soglia usuale di separazione; la differenza assoluta det reale - shuffle mean e circa -0.002324, molto piu piccola delle finestre precedenti (~-0.009..-0.011). Il report la include, ma la frase 'esiste fino alle scale misurate' arrotonda un edge case non significativo.", tools/data/reports/agent_20260501_0725.md:79:- **L2 (absolute vs ratio)**: All comparisons are in the same angular units (degrees). z-scores computed within same-N distributions. No cross-space ratio comparisons. tools/data/reports/loop_guard_20260508_1834.json:3: "danger_score": 0.128, tools/data/reports/loop_guard_20260508_1834.json:15: "danger_score": 0.0, tools/data/reports/agent_20260405_0916.md:21:| Mean z-score | 14.89 | tools/data/reports/agent_20260405_0916.md:22:| Min z-score | 8.39 | tools/data/reports/exp_crossover_universality.json:68: "z_scores_slopes": { tools/data/reports/exp_crossover_universality.json:74: "z_scores_partial": { tools/data/reports/loop_guard_20260507_1006.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1006.json:14: "danger_score": 0.45, 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_20260429_1013.md:37:- **Slope z-score vs shuffle**: -2.42 (p = 0.016) tools/data/reports/exp_boundary_20260405_0825.json:17: "z_score": 1.0147065370876138 tools/data/reports/exp_boundary_20260405_0825.json:24: "z_score": 0.9938426571821646 tools/data/reports/exp_boundary_20260405_0825.json:31: "z_score": 0.924156602303019 tools/data/reports/exp_boundary_20260405_0825.json:38: "z_score": 0.8679786526989183 tools/data/reports/exp_boundary_20260405_0825.json:45: "z_score": 1.1926466014845574 tools/data/reports/exp_boundary_20260405_0825.json:52: "z_score": 1.086101773542357 tools/data/reports/exp_boundary_20260405_0825.json:59: "z_score": 1.5396949892041893 tools/data/reports/exp_boundary_20260405_0825.json:66: "z_score": 1.5615547549626683 tools/data/reports/exp_boundary_20260405_0825.json:73: "z_score": 1.7245630814587454 tools/data/reports/exp_boundary_20260405_0825.json:80: "z_score": 3.4105024445694885 tools/data/reports/exp_boundary_20260405_0825.json:87: "z_score": 2.8101417127747577 tools/data/reports/exp_boundary_20260405_0825.json:94: "z_score": 4.764639795534499 tools/data/reports/exp_boundary_20260405_0825.json:101: "z_score": 5.557703318408364 tools/data/reports/exp_boundary_20260405_0825.json:108: "z_score": 2.2424090748203733 tools/data/reports/exp_boundary_20260405_0825.json:115: "z_score": 2.6199441040821303 tools/data/reports/exp_boundary_20260405_0825.json:122: "z_score": 4.539186069311373 tools/data/reports/exp_boundary_20260405_0825.json:129: "z_score": 4.106033757279831 tools/data/reports/exp_boundary_20260405_0825.json:136: "z_score": 3.527535862529783 tools/data/reports/exp_boundary_20260405_0825.json:143: "z_score": 3.0106576315200826 tools/data/reports/exp_boundary_20260405_0825.json:150: "z_score": 5.043254531357331 tools/data/reports/next_exec_20260403_0330.json:16: "action": "EXPLORE: M_relazione_orizzonte_degli_L0 — Cosa manca per confermare completamente gap_ratio: phi=0.409", 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_20260506_1941.md:82:- **L2 quantity vs ratio**: retention ratios are not read alone; raw denominator z-scores are reported first. tools/data/reports/agent_20260422_1616.md:19:- **Criterion**: |z-score| < 2 vs shuffle distribution → TAUTOLOGY; |z| >= 2 → GENUINE tools/data/reports/agent_20260422_1616.md:24:| Property | Real | Shuffle mean | Shuffle std | z-score | Verdict | tools/data/reports/agent_20260422_1616.md:45:4. **Both PSD slopes are genuine.** Magnitude slope +0.102 (z=21.3) and residue slope +0.203 (z=54.8) both vanish under shuffle. The blue spectral tilt is entirely in the ordering of primes. This is the core non-trivial content. tools/data/reports/agent_20260506_0330.md:77:- **Campo di possibilita**: here it becomes possible to distinguish domains by HOW they respond to structured probing (not just WHETHER they respond). Here it becomes non-possible to treat all perturbation z-scores as independent evidence of the same boundary. tools/data/reports/loop_guard_20260507_1419.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1419.json:14: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_2203.json:3: "danger_score": 0.471, tools/data/reports/loop_guard_20260507_2203.json:15: "danger_score": 0.45, tools/data/reports/falsifier_20260429_1041.json:31: "evidence": "Il Claim Under Test parla di 'mod-3 gap ordering signal' e le Results includono self_rate grezzo, z-score, Markov LLR e matrici complete. La conclusione sposta il perimetro al solo sotto-claim filtrato 'non-zero self-transition per p>3', mentre M1/M2 e T[0][0] scalano.", tools/data/reports/loop_guard_20260507_1938.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1938.json:14: "danger_score": 0.45, tools/data/reports/agent_20260422_0330.md:85:- Direct slope z-score vs shuffle: **z = 46.3** tools/data/reports/ddf_20260505_0636.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" 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:70:- **L4 (edge case)**: Poisson separation is explicitly identified as noise artifact with z-score evidence. tools/data/reports/ddf_20260430_0405.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" tools/data/dipartimento_journal.jsonl:28:{"timestamp": "2026-03-15T08:01:53.799015", "tensione": {"tipo": "conferma_parziale", "id": "COMP_GEN_GAP_RATIO_FALSIFICA_F3", "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 (2026-03-15T08:01). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n "}, "filtro": "non tocca condensato"} tools/data/dipartimento_journal.jsonl:31:{"timestamp": "2026-03-15T08:01:53.799035", "tensione": {"tipo": "tensione_aperta", "id": "M_l'attrattore_l'impossibilita'_rinforzo_L0", "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", "intensita": 0.529179606750063, "nota": "Dal domandatore (2026-03-15T08:01). "}, "filtro": "non tocca condensato"} tools/data/dipartimento_journal.jsonl:35:{"timestamp": "2026-03-28T03:43:58.696314", "tensione": {"tipo": "conferma_parziale", "id": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F3", "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 (2026-03-27T03:44). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n "}, "filtro": "non tocca condensato"} tools/data/reports/agent_20260508_0330.md:10:> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? tools/data/reports/agent_20260508_0330.md:13:Il `gap_ratio` quasiperiodico e una firma del gap-labeling di phi, o e un osservabile sensibile al denominatore scelto (`N`, fase Sturmiana, soglia del gap largo)? tools/data/reports/agent_20260508_0330.md:59:**CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma un segnale di denominatore Sturmiano nel punto storico e una tendenza mediana su questo perimetro; non conferma dominanza matched su tutte le fasi, scale e soglie. La formulazione corretta e: nel perimetro stratificato `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, phi abbassa la mediana del `first_two_ratio`, ma il supporto matched e `25/48`. tools/data/reports/agent_20260508_0330.md:78:- Script: `tools/exp_quasiperiodic_gap_ratio_denominator.py` tools/data/reports/agent_20260508_0330.md:79:- Data: `tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json` tools/data/reports/loop_guard_20260507_0923.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_0923.json:14: "danger_score": 0.45, tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:2: "experiment": "gap_label_supertile_tiling_gate", tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:18: "supertile_orders": [ tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:28: "reference_core_phi": [ tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:54: "core_labels_all_conditions": [ tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:290: "supertile_shuffle|order=8": { tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:294: "core_labels_all_conditions": [ tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:363: "median_overlap_with_phi_core": 0.7, tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:383: "reference_core_retained_in_all": [ tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:390: "reference_core_missing_from_all": [ tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:396: "same_length_contiguous_shuffle|order=8": { tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:400: "core_labels_all_conditions": [ tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:470: "median_overlap_with_phi_core": 0.7136363636363636, tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:490: "reference_core_retained_in_all": [ 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tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:80480: "mode": "same_length_contiguous_shuffle", tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:80485: "supertile_order": 11, tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:80603: "mode": "same_count_internal_shuffle", tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:80608: "supertile_order": 11, tools/data/gap_label_supertile_tiling_gate_20260508_1909.json:80731: "supertile_order": 11, tools/data/reports/agent_20260408_0330.md:27:| lag k | acf(k) | stderr | z-score | shuf_acf | z_vs_shuf | cramer_acf | z_vs_cramer | tools/data/reports/agent_20260408_0330.md:61:Both null baselines (shuffled gaps AND Z/6Z-confined Cramer) show acf~0 at all lags. Z-scores against primes: -8 to -95 across all tested lags. The multi-lag anti-correlation is: tools/data/reports/agent_20260408_0330.md:79:2. **The 1/k law is not explained by any tested null model.** Neither shuffled gaps (same marginal distribution) nor Z/6Z-confined Cramer model (same residue structure) reproduce any of the multi-lag correlations. Z-scores are -8 to -95 across all lags. The structure is intrinsic to the prime ordering. tools/data/reports/agent_20260406_1030.md:14:- N=5000 spacings per domain, 20 shuffled surrogates for z-scores tools/data/lab_results.json:20: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:29: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:62: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:75: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:89: "gap_ratio": 2.6180145125477585, tools/data/lab_results.json:98: "gap_ratio": 2.6181226561577335, tools/data/lab_results.json:107: "gap_ratio": 2.6180145125477585, tools/data/lab_results.json:116: "gap_ratio": 2.6180145125477585, tools/data/lab_results.json:125: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:134: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:143: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:152: "gap_ratio": 2.6181226561577335, tools/data/lab_results.json:161: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:170: "gap_ratio": 2.6180145125477585, tools/data/lab_results.json:179: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:188: "gap_ratio": 2.6180861695660687, tools/data/lab_results.json:202: "gap_ratio": 2.618092361492802, tools/data/lab_results.json:211: "gap_ratio": 2.6180158235383035, tools/data/lab_results.json:220: "gap_ratio": 2.6180791141406927, tools/data/lab_results.json:229: "gap_ratio": 2.617990568663733, tools/data/lab_results.json:238: "gap_ratio": 2.6181558954444286, tools/data/lab_results.json:247: "gap_ratio": 2.6180500929168, tools/data/reports/agent_20260505_0330.md:64:- **L2 quantita vs ratio**: il risultato usa energie PCA e z-score original-vs-shuffle nello stesso spazio di osservabili; Poisson e' escluso dall'interpretazione strutturale per assenza di segnale, non per percentuale. tools/data/promotions/promotion_20260506_1955.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/reports/agent_20260503_0330.md:18:- **Method**: Build Markov-k surrogates (k=0,1,2,3) from prime gaps, compute 10 observables on real gaps and surrogates, measure z-score for each (observable, Markov-order) pair 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/g1_log.jsonl:8:{"ts": "2026-05-08T17:18:53.576755+00:00", "cycle_ref": "20260508_1715", "decision": "PASS", "tension_id": "M_", "tension_tipo": null, "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[gap_label_set, generator_jaccard, phi_core_overlap, core_retention]", "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:11:{"ts": "2026-05-08T19:13:43.420096+00:00", "cycle_ref": "20260508_1909", "decision": "PASS", "tension_id": "M_", "tension_tipo": null, "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[gap_label_set, supertile_boundary, length_multiset, internal_order, high_label_", "reason": "observables_used populated or field absent (legacy)"} tools/data/promotions/promotion_20260507_1938.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/reports/ddf_20260505_0405.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" tools/dnd_dipolo_lab.py:153: score = sum(1 for t in termini if t.lower() in testo_lower) tools/dnd_dipolo_lab.py:154: if score >= 2: # Almeno 2 termini trovati tools/dnd_dipolo_lab.py:156: risultati.append({'titolo': titolo, 'score': score}) tools/dnd_dipolo_lab.py:158: return sorted(risultati, key=lambda x: -x['score'])[:3] tools/dnd_dipolo_lab.py:259: best_score = 0 tools/dnd_dipolo_lab.py:263: score = sum(1 for t in termini if t in testo) tools/dnd_dipolo_lab.py:264: if score > best_score: tools/dnd_dipolo_lab.py:265: best_score = score tools/dnd_dipolo_lab.py:268: if assioma_sorgente and best_score >= 2: tools/dnd_dipolo_lab.py:297: score = sum(1 for t in termini if t in testo) tools/dnd_dipolo_lab.py:298: if score >= 1: tools/dnd_dipolo_lab.py:299: rilevanti.append({'id': aid, 'titolo': assioma['titolo'], 'score': score}) tools/dnd_dipolo_lab.py:301: rilevanti.sort(key=lambda x: -x['score']) tools/dnd_dipolo_lab.py:453: best_score = 0 tools/dnd_dipolo_lab.py:456: score = sum(1 for t in termini if t in contenuto) tools/dnd_dipolo_lab.py:457: if score > best_score: tools/dnd_dipolo_lab.py:458: best_score = score tools/dnd_dipolo_lab.py:459: best = {'id': aid, 'titolo': ass['titolo'], 'score': score} tools/dnd_dipolo_lab.py:460: return best if best_score >= 2 else None tools/data/reports/loop_guard_20260507_0330.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_0330.json:14: "danger_score": 0.45, tools/dnd_rottura.py:355: print(f" Soglia | n_int | gap_ratio | gap_finale | Identica?") tools/dnd_rottura.py:367: gr = obs.get('gap_ratio_medio', None) tools/dnd_rottura.py:373: 'gap_ratio': float(gr) if gr else None, tools/dnd_rottura.py:385: # Il gap_ratio è φ² indipendentemente dalla soglia? → Sì, perché la regola è la stessa. tools/dnd_rottura.py:386: grs = [s['gap_ratio'] for s in spirali_soglia if s['gap_ratio']] tools/dnd_rottura.py:389: print(f"\n CV gap_ratio tra soglie = {cv:.6f}") tools/dnd_rottura.py:409: print(f" Segnale | gap_ratio | gap_finale | n_int") tools/dnd_rottura.py:424: 'gap_ratio': float(obs.get('gap_ratio_medio', 0)), tools/dnd_rottura.py:431: print(f" {nome:15s} | {entry['gap_ratio']:.6f} | {entry['gap_finale']:.2e} | {n}") tools/dnd_rottura.py:460: print(f" (L'interferenza non è nel gap_ratio, che è SEMPRE φ².") tools/data/reports/falsifier_20260501_0725.json:25: "suggestion": "Usare unita' assolute: residual in gradi, intervallo/SE, z-score, e delta z. Evitare percentuali di gap-closed quando il denominatore e' un residuo vicino al rumore." tools/data/promotions/promotion_20260507_0803.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/reports/agent_20260405_0825.md:21:| z-score medio (primes - Cramer) | +2.63 | tools/data/reports/agent_20260405_0825.md:22:| z-score massimo | +5.56 | tools/data/reports/agent_20260405_0825.md:31:I primi hanno `` sistematicamente piu' alto dei Cramer in TUTTE le 20 finestre. Lo z-score cresce da ~1 (piccoli n) a ~5 (grandi n). Questo eccesso non puo' essere spiegato dalla densita' — e' contenuto strutturale dei primi (consistente con correlazioni Hardy-Littlewood per k-tuple). tools/data/reports/agent_20260405_0825.md:46:2. Estendere a 10^8 per verificare se il z-score continua a crescere tools/data/promotions/promotion_20260507_1458.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/promotions/promotion_20260507_1751.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/reports/agent_20260406_0714.md:20:- Metric: z-scores for slopes, partial correlation significance tools/data/reports/agent_20260406_0714.md:32:### Z-scores vs Cramer (which drifts are prime-specific?) tools/data/reports/agent_20260406_0714.md:33:| Observable slope | z-score | Verdict | tools/data/promotions/promotion_20260507_2120.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/promotions/promotion_20260506_1941.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/reports/loop_guard_20260507_1042.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1042.json:14: "danger_score": 0.45, tools/data/promotions/promotion_20260507_1804.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/promotions/promotion_20260506_0625.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/reports/loop_guard_20260507_2310.json:3: "danger_score": 0.495, tools/data/reports/loop_guard_20260507_2310.json:15: "danger_score": 0.45, tools/exp_spectral_rigidity.py:136: # z-score real vs shuffle at L=10 tools/data/modular_algebra_depth.json:22: "z_score": -19.7896 tools/data/modular_algebra_depth.json:28: "z_score": -169.0682 tools/data/modular_algebra_depth.json:34: "z_score": -175.0824 tools/data/modular_algebra_depth.json:70: "z_score": -18.2191 tools/data/modular_algebra_depth.json:76: "z_score": -18.2279 tools/data/modular_algebra_depth.json:82: "z_score": -14.8703 tools/data/modular_algebra_depth.json:88: "z_score": -19.0248 tools/data/modular_algebra_depth.json:94: "z_score": -9.4204 tools/data/modular_algebra_depth.json:136: "z_score": -15.6288 tools/data/modular_algebra_depth.json:142: "z_score": -2.5266 tools/data/modular_algebra_depth.json:148: "z_score": -13.7922 tools/data/modular_algebra_depth.json:154: "z_score": -35.3273 tools/data/modular_algebra_depth.json:160: "z_score": -24.9555 tools/data/modular_algebra_depth.json:166: "z_score": -8.2036 tools/data/modular_algebra_depth.json:172: "z_score": -10.489 tools/data/modular_algebra_depth.json:222: "z_score": -8.881 tools/data/modular_algebra_depth.json:228: "z_score": -8.5085 tools/data/modular_algebra_depth.json:234: "z_score": -30.6288 tools/data/modular_algebra_depth.json:240: "z_score": -20.3465 tools/data/modular_algebra_depth.json:246: "z_score": -18.6243 tools/data/modular_algebra_depth.json:252: "z_score": -15.3898 tools/data/modular_algebra_depth.json:258: "z_score": -19.1402 tools/data/modular_algebra_depth.json:264: "z_score": -5.5382 tools/data/modular_algebra_depth.json:270: "z_score": -23.7177 tools/data/modular_algebra_depth.json:276: "z_score": -11.7257 tools/data/modular_algebra_depth.json:282: "z_score": -34.9286 tools/data/modular_algebra_depth.json:342: "z_score": -4.8111 tools/data/modular_algebra_depth.json:348: "z_score": -20.3234 tools/data/modular_algebra_depth.json:354: "z_score": -13.6817 tools/data/modular_algebra_depth.json:360: "z_score": -14.2335 tools/data/modular_algebra_depth.json:366: "z_score": -38.5208 tools/data/modular_algebra_depth.json:372: "z_score": -5.1211 tools/data/modular_algebra_depth.json:378: "z_score": -14.7029 tools/data/modular_algebra_depth.json:384: "z_score": -12.2122 tools/data/modular_algebra_depth.json:390: "z_score": -20.9662 tools/data/modular_algebra_depth.json:396: "z_score": -10.954 tools/data/modular_algebra_depth.json:402: "z_score": -35.9284 tools/data/modular_algebra_depth.json:408: "z_score": -3.2649 tools/data/modular_algebra_depth.json:414: "z_score": -7.3445 tools/dnd_zeros_vs_zeta.py:4:The core idea (from operator's insight): tools/data/reports/falsifier_20260508_1715.json:9: "claim": "fibonacci_substitution mantiene `[-1, 1, -2, 2, -4, 4]` del reference core e perde `[3, 6]`.", tools/data/reports/falsifier_20260508_1715.json:10: "evidence": "La tabella riporta per `fibonacci_substitution` `core labels all conditions = [-1, 1, -2, 2, -3, -4, 4]`: esiste un label stabile extra `-3` non discusso nella finding. Non falsifica la perdita di `[3,6]`, ma il perimetro stabile non e' esattamente il sotto-core dichiarato.", tools/data/reports/falsifier_20260508_1715.json:11: "suggestion": "Nel prossimo ciclo separare esplicitamente `retained_from_phi_core = [-1,1,-2,2,-4,4]` da `extra_stable_labels = [-3]` e verificare se `-3` e' edge artifact o firma della costruzione Fibonacci." tools/data/reports/falsifier_20260508_1715.json:16: "claim": "Claim Under Test: `gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35)`; Verdict: `il core dei gap larghi richiede struttura globale del generatore`.", tools/data/reports/falsifier_20260508_1715.json:17: "evidence": "Il setup nominale parla di confermare `gap_ratio`, ma l'esperimento misura `gap_label_set`, Jaccard, overlap e core retention. Il report dichiara il passaggio al generator gate, ma non formula esplicitamente che il claim `gap_ratio` e' stato sospeso o sostituito da un observable diverso.", tools/data/reports/falsifier_20260508_1715.json:18: "suggestion": "Dichiarare al nodo regressivo: `gap_ratio non testato in questo ciclo; observable sostitutivo = core_retention/generator_jaccard`. Poi collegare il nuovo gate al vecchio claim solo con un test che misuri entrambi nello stesso dataset." tools/data/reports/falsifier_20260508_1715.json:21: "summary": "Il report e' in gran parte coerente sui dati tabellari, ma si rompe su L4 per l'extra label Fibonacci non isolato e su L3 per drift tra gap_ratio dichiarato e label-set realmente testato." tools/data/reports/next_exec_20260401_0346.json:22: "action": "EXPLORE: M_det_minus_one_L0 — Cosa manca per confermare completamente gap_ratio: phi=0.409", tools/data/notte_20260402_0330.md:8: [conferma_parziale] COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F1: gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ tools/data/notte_20260402_0330.md:10: [tensione_aperta] M_det_minus_one_L0: Cosa manca per confermare completamente gap_ratio: phi=0.409 tools/data/promotions/promotion_20260507_0330.json:30: "voce_kernel": "Aggiungi sezione a /opt/THIA/docs/core/COWORK_KERNEL.md", tools/data/markov_layer_recovery_audit.json:19: "z_scores": { tools/data/markov_layer_recovery_audit.json:80: "z_scores": { tools/data/markov_layer_recovery_audit.json:141: "z_scores": { tools/data/markov_layer_recovery_audit.json:202: "z_scores": { tools/data/markov_layer_recovery_audit.json:265: "z_scores": { tools/data/reports/exp_excess_scaling_20260405.json:25: "z_score": 16.220852680296595, tools/data/reports/exp_excess_scaling_20260405.json:37: "z_score": 21.646167349687325, tools/data/reports/exp_excess_scaling_20260405.json:49: "z_score": 28.08700574754486, tools/data/reports/exp_excess_scaling_20260405.json:61: "z_score": 19.656315507242425, tools/data/reports/exp_excess_scaling_20260405.json:73: "z_score": 16.186389744400834, tools/data/reports/exp_excess_scaling_20260405.json:85: "z_score": 16.055174834203182, tools/data/reports/exp_excess_scaling_20260405.json:97: "z_score": 13.286227156063573, tools/data/reports/exp_excess_scaling_20260405.json:109: "z_score": 14.591835087171669, tools/data/reports/exp_excess_scaling_20260405.json:121: "z_score": 14.840496613010814, tools/data/reports/exp_excess_scaling_20260405.json:133: "z_score": 12.301813459945546, tools/data/reports/exp_excess_scaling_20260405.json:145: "z_score": 13.154421333121157, tools/data/reports/exp_excess_scaling_20260405.json:157: "z_score": 13.412208439230293, tools/data/reports/exp_excess_scaling_20260405.json:169: "z_score": 14.56682690700735, tools/data/reports/exp_excess_scaling_20260405.json:181: "z_score": 12.476409767875944, tools/data/reports/exp_excess_scaling_20260405.json:193: "z_score": 15.670414634762349, tools/data/reports/exp_excess_scaling_20260405.json:205: "z_score": 18.73496248625453, tools/data/reports/exp_excess_scaling_20260405.json:217: "z_score": 20.15230270861585, tools/data/reports/exp_excess_scaling_20260405.json:229: "z_score": 14.794434967827247, tools/data/reports/exp_excess_scaling_20260405.json:241: "z_score": 11.148287672636817, tools/data/reports/exp_excess_scaling_20260405.json:253: "z_score": 12.623187811341312, tools/data/reports/exp_excess_scaling_20260405.json:265: "z_score": 10.299109529919951, tools/data/reports/exp_excess_scaling_20260405.json:277: "z_score": 17.282538184784936, tools/data/reports/exp_excess_scaling_20260405.json:289: "z_score": 12.975024025572157, tools/data/reports/exp_excess_scaling_20260405.json:301: "z_score": 11.377351373867489, tools/data/reports/exp_excess_scaling_20260405.json:313: "z_score": 8.394691885326136, tools/data/reports/exp_excess_scaling_20260405.json:325: "z_score": 13.476143515868985, tools/data/reports/exp_excess_scaling_20260405.json:337: "z_score": 12.861854838382888, tools/data/reports/exp_excess_scaling_20260405.json:349: "z_score": 13.780557964423537, tools/data/reports/exp_excess_scaling_20260405.json:361: "z_score": 13.514988192723356, tools/data/reports/exp_excess_scaling_20260405.json:373: "z_score": 12.992533213593122, tools/data/reports/exp_ricci_primes.json:20: "z_score_mean": -0.54, tools/data/reports/exp_ricci_primes.json:21: "z_score_slope": -1.19 tools/exp_boundary_shuffle_audit.py:50: """Shuffle gap sequence, recompute r each time. Return z-score.""" tools/exp_boundary_shuffle_audit.py:63: 'z_score': float(z), tools/exp_boundary_shuffle_audit.py:92: core = spacings[mid:-mid] # central half to avoid edge effects tools/exp_boundary_shuffle_audit.py:93: if len(core) > 10: tools/exp_boundary_shuffle_audit.py:94: core = core / np.mean(core) tools/exp_boundary_shuffle_audit.py:95: all_spacings.extend(core.tolist()) tools/exp_boundary_shuffle_audit.py:284: res['ordering_dependent'] = abs(res['z_score']) > 3.0 tools/exp_boundary_shuffle_audit.py:290: print(f" z-score: {res['z_score']:.1f}") tools/exp_boundary_shuffle_audit.py:329: print(f"{key:<25} {res['r_original']:>7.4f} {res['r_shuffled_mean']:>7.4f} {res['z_score']:>7.1f} " tools/data/exp_conditional_r.json:34: "z_scores_vs_shuffle": [ tools/PROTOCOLLO_ZETA.md:98:P4: Core condiviso + esperimenti separati (-255/+102 righe) tools/data/reports/loop_guard_20260507_1804.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1804.json:14: "danger_score": 0.45, tools/data/seme.json:109: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme.json:110: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", tools/data/seme.json:115: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme.json:121: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme.json:122: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme.json:124: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme.json:130: "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" tools/data/seme.json:161: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme.json:346: "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:421: "Tensioni risolte: {'G_POTENZIALE_NULLA', 'QPG_GAP_RATIO_DENOMINATOR_GATE', 'TRASCENDENZA_LIMITE', 'G_BLANK_SHELL_DILATION_GATE', 'TENSIONE_ENTITA', 'PIANO_PRIMARIO_DUE_ASSIOMI', 'G_BLANK_SHELL_STRATIFIED_GATE', 'G_BLANK_SHELL_SCALE_LAW_GATE', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'G_BLANK_SHELL_TQGER_GATE', 'METRIC_TENSOR'}" tools/build_agent_field.py:168: score = sum(1 for kw in keywords if kw in obs_lower) tools/build_agent_field.py:169: if score > 0: tools/build_agent_field.py:170: relevant.append({**obs, 'score': score}) tools/build_agent_field.py:172: relevant.sort(key=lambda x: x['score'], reverse=True) tools/build_agent_field.py:971: parts.append(f"\n**Esperimento a massima informazione:** {experiments[0]['id']} (score={experiments[0]['score']})") tools/data/reports/agent_20260508_1715.md:7:observables_used: [gap_label_set, generator_jaccard, phi_core_overlap, core_retention] tools/data/reports/agent_20260508_1715.md:10:> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? tools/data/reports/agent_20260508_1715.md:13:Il core dei label phi sopravvive quando resta il lettore `theta=1/phi` ma cambia il generatore della sequenza, oppure il label-set stabile e' una proprieta del generatore Sturmiano? tools/data/reports/agent_20260508_1715.md:28:Reference core phi, stimato dal perimetro completo: `[-1, 1, -2, 2, 3, -4, 4, 6]`. tools/data/reports/agent_20260508_1715.md:30:| generator | conditions | median Jaccard | min Jaccard | median overlap with phi core | core labels all conditions | phi-core missing | tools/data/reports/agent_20260508_1715.md:51:1. **Verificato: il core completo resta nel generatore Sturmiano meccanico.** Nel perimetro `N/phase/threshold/trial` testato, `phi_sturmian` conserva tutti gli 8 label core in tutte le condizioni: `[-1, 1, -2, 2, 3, -4, 4, 6]`. tools/data/reports/agent_20260508_1715.md:53:2. **Verificato: la costruzione Fibonacci conserva il nucleo basso ma non il core completo.** `fibonacci_substitution` mantiene `[-1, 1, -2, 2, -4, 4]` del reference core e perde `[3, 6]`. Questo conferma la famiglia a bassa complessita, non l'identita completa del generatore meccanico sotto questo perimetro. tools/data/reports/agent_20260508_1715.md:57:4. **Verificato: conteggio e transizioni locali non portano il core phi.** `markov_phi` e `balanced_random` hanno core vuoto, overlap mediano con il core phi `0.125000` e `0.111111`, e label-error mediano circa `0.0041`, contro `0.000095` di `phi_sturmian`. tools/data/reports/agent_20260508_1715.md:62:**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `trials=3`, `top_k=12`, `|n|<=34`, il label-set phi e' stabile quando il generatore e Sturmiano meccanico. La stabilita non trasferisce a conteggio preservato, transizioni Markoviane o blocchi locali corti; trasferisce parzialmente alla costruzione Fibonacci e ai blocchi lunghi. Il claim valido non e' "il label reader phi trova il core ovunque"; e': il core dei gap larghi richiede struttura globale del generatore, con il lettore `theta=1/phi` come osservabile e non come causa. tools/data/reports/agent_20260508_1715.md:67:- **Invariante di passaggio**: il nucleo basso `[-1, 1, -2, 2]` sopravvive quando resta abbastanza struttura globale; il core completo sopravvive nel generatore Sturmiano meccanico. tools/data/reports/agent_20260508_1715.md:71:Misurare la legge di rottura del core per lunghezza di blocco: variare `block_size` su scala Fibonacci e non-Fibonacci, poi stimare dove compaiono i label alti `[3, -4, 4, 6]`. Se il passaggio avviene su blocchi Fibonacci, il generatore porta una scala critica; se avviene per qualsiasi blocco lungo, il core alto misura memoria globale generica. tools/data/reports/agent_20260508_1715.md:75:- **L2 quantita vs ratio**: il risultato usa set, Jaccard, overlap e core retention; nessun claim dipende da un singolo ratio. tools/data/reports/agent_20260508_1715.md:76:- **L3 no silent patching**: il claim 03:30 sul `gap_ratio` resta vincolato; il claim 16:32 sul label-set resta valido ma riceve il nuovo denominatore `generatore`. tools/data/reports/agent_20260508_1715.md:78:- **L5 re-discovery**: gap labeling e parole Fibonacci/Sturmiane sono noti; il finding del cycle e' il generator gate sul core osservato nei cycle precedenti. tools/exp_gap_label_substitution_grammar_gate.py:3:Substitution-grammar gate for the phi gap-label core. tools/exp_gap_label_substitution_grammar_gate.py:48:def summarize_rows(rows: list[dict], reference_core: set[int]) -> dict: tools/exp_gap_label_substitution_grammar_gate.py:53: overlaps = [jaccard(set(row["label_set"]), reference_core) for row in rows if row["n_selected"] > 0] tools/exp_gap_label_substitution_grammar_gate.py:57: "median_overlap_with_phi_core": float(np.median(overlaps)) if overlaps else None, tools/exp_gap_label_substitution_grammar_gate.py:70: "reference_core_retained_in_all": label_sort(set(summary.get("core_labels_all_conditions", [])) & reference_core), tools/exp_gap_label_substitution_grammar_gate.py:71: "reference_core_missing_from_all": label_sort(reference_core - set(summary.get("core_labels_all_conditions", []))), tools/exp_gap_label_substitution_grammar_gate.py:129: reference_core = set(reference_summary["core_labels_all_conditions"]) tools/exp_gap_label_substitution_grammar_gate.py:137: key: summarize_rows(group_rows, reference_core) tools/exp_gap_label_substitution_grammar_gate.py:144: mode_summary[mode] = summarize_rows(mode_rows, reference_core) tools/exp_gap_label_substitution_grammar_gate.py:159: "reference_core_phi": label_sort(reference_core), tools/exp_gap_label_substitution_grammar_gate.py:208: "reference_core_phi": output["reference_core_phi"], tools/data/reports/agent_20260505_1022.md:8:META: score=0.898 — META: incerto (i=0.5) — massimo potere discriminante tools/data/reports/agent_20260505_1022.md:9:G_POTENZIALE_NULLA: score=0.703 tools/data/reports/agent_20260505_1022.md:10:METRIC_TENSOR: score=0.624 tools/data/reports/agent_20260505_1022.md:75:L2 quantita' assoluta vs ratio: il confronto usa alpha critici e z-score, non percentuali tra spazi di taglia diversa. GUE ha perimetro ridotto (`N=175`) e non viene pesato come primes/Poisson. tools/data/reports/mapping_validation_2026-04-21.json:5: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:32: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:59: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:88: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:115: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:140: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:160: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:180: "tension": "COMP_GEN_GAP_RATIO_FALSIFICA_F6", tools/data/reports/mapping_validation_2026-04-21.json:182: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:196: "tension": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F6", tools/data/reports/mapping_validation_2026-04-21.json:198: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:214: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:230: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:257: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:284: "scores": {}, tools/data/reports/mapping_validation_2026-04-21.json:295: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:322: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:342: "scores": {}, tools/data/reports/mapping_validation_2026-04-21.json:353: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:378: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:400: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:422: "scores": {}, tools/data/reports/mapping_validation_2026-04-21.json:433: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:460: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:485: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:513: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:529: "scores": {}, tools/data/reports/mapping_validation_2026-04-21.json:540: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:560: "scores": {}, tools/data/reports/mapping_validation_2026-04-21.json:571: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:587: "scores": { tools/data/reports/mapping_validation_2026-04-21.json:607: "scores": { tools/data/reports/loop_guard_20260508_1909.json:3: "danger_score": 0.011, tools/data/reports/loop_guard_20260508_1909.json:14: "danger_score": 0.0, tools/data/lab_registro.json:245: "z_score": 22.5, tools/data/reports/exp_geodesic_deviation_primes.json:15: "z_score_std": -12.71, tools/data/reports/exp_geodesic_deviation_primes.json:16: "z_score_acf1": -2.16, tools/data/reports/exp_geodesic_deviation_primes.json:17: "z_score_acf2": 0.62 tools/data/reports/exp_geodesic_deviation_primes.json:22: "z_score_std": -46.49, tools/data/reports/exp_geodesic_deviation_primes.json:23: "z_score_acf1": -8.07 tools/data/reports/agent_20260508_1909.md:1:# Agent Report - Supertile Tiling Gate Del Core Phi tools/data/reports/agent_20260508_1909.md:7:observables_used: [gap_label_set, supertile_boundary, length_multiset, internal_order, high_label_condition_rate] tools/data/reports/agent_20260508_1909.md:8:**observable_contract**: claim=il core alto phi distingue confine di supertile Fibonacci da blocco contiguo con stessa lunghezza; observable=retention dei label core sotto shuffle di supertile, shuffle contiguo a stessa multiset di lunghezze, shuffle interno a stesso conteggio; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per mode e supertile_order; generator=phi_sturmian con tiling di lunghezze Fibonacci; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, soglie 1.75/2.25, parsing simbolico esatto dei supertile per ogni fase, generatori non-phi. tools/data/reports/agent_20260508_1909.md:12:- **Dipolo / punto-zero**: confine reale del supertile · lunghezza contigua equivalente; punto-zero = il blocco come unita' prima che diventi grammatica o contenitore. tools/data/reports/agent_20260508_1909.md:14:- **Proto-ipotesi**: se il core alto phi vive nel confine tra supertile, lo shuffle di supertile conserva piu' core dello shuffle contiguo con la stessa multiset di lunghezze. Se non lo fa, il confine esatto non e' ancora il portatore: il portatore e' ordine interno piu' scala lunga. tools/data/reports/agent_20260508_1909.md:18:> Il vantaggio del blocco Fibonacci lungo non sta solo nell'ordine interno conservato: sta nel confine reale del supertile. Quindi `supertile_shuffle` batte `same_length_contiguous_shuffle` a pari multiset di lunghezze. tools/data/reports/agent_20260508_1909.md:21:Il core alto `[3, -4, 4, 6]` riconosce il confine di supertile Fibonacci, o riconosce soltanto blocchi lunghi con ordine interno conservato? tools/data/reports/agent_20260508_1909.md:24:- Reference core phi: `[-1, 1, -2, 2, 3, -4, 4, 6]`. tools/data/reports/agent_20260508_1909.md:26:- Supertile orders: tools/data/reports/agent_20260508_1909.md:32: - `supertile_shuffle`: taglia secondo la parola di lunghezze Fibonacci e mescola i chunk. tools/data/reports/agent_20260508_1909.md:33: - `same_length_contiguous_shuffle`: usa la stessa multiset di lunghezze, ma su taglio contiguo misallineato. tools/data/reports/agent_20260508_1909.md:34: - `same_count_internal_shuffle`: conserva lunghezza e conteggio di ogni chunk allineato, ma distrugge ordine interno. tools/data/reports/agent_20260508_1909.md:39:Reference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`. tools/data/reports/agent_20260508_1909.md:45:| supertile_shuffle | 160 | 0.818182 | 1.00 | 1.00 | 108/160 = 0.67500 | tools/data/reports/agent_20260508_1909.md:46:| same_length_contiguous_shuffle | 160 | 0.818182 | 1.00 | 1.00 | 116/160 = 0.72500 | tools/data/reports/agent_20260508_1909.md:47:| same_count_internal_shuffle | 160 | 0.157895 | 0.25 | 0.25 | 0/160 = 0.00000 | tools/data/reports/agent_20260508_1909.md:54:| supertile_shuffle | 8 | {21,34} | 0.750000 | 1.00 | 0.75 | 14/40 = 0.350 | [-1, 1, -2, 2, -3, -4, 4] | tools/data/reports/agent_20260508_1909.md:55:| same_length_contiguous_shuffle | 8 | {21,34} | 0.818182 | 1.00 | 0.875 | 20/40 = 0.500 | [-1, 1, -2, 2, -3, 3, -4, 4] | tools/data/reports/agent_20260508_1909.md:57:| same_count_internal_shuffle | 8 | {21,34} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] | tools/data/reports/agent_20260508_1909.md:58:| supertile_shuffle | 9 | {34,55} | 0.904545 | 1.00 | 1.00 | 29/40 = 0.725 | [-1, 1, -2, 2, -3, 3, -4, 4, -6] | tools/data/reports/agent_20260508_1909.md:59:| same_length_contiguous_shuffle | 9 | {34,55} | 0.818182 | 1.00 | 1.00 | 29/40 = 0.725 | [-1, 1, -2, 2, -3, 3, -4, 4, 6] | tools/data/reports/agent_20260508_1909.md:61:| same_count_internal_shuffle | 9 | {34,55} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] | tools/data/reports/agent_20260508_1909.md:62:| supertile_shuffle | 10 | {55,89} | 0.909091 | 1.00 | 1.00 | 32/40 = 0.800 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | tools/data/reports/agent_20260508_1909.md:63:| same_length_contiguous_shuffle | 10 | {55,89} | 0.833333 | 1.00 | 1.00 | 33/40 = 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6] | tools/data/reports/agent_20260508_1909.md:65:| same_count_internal_shuffle | 10 | {55,89} | 0.150000 | 0.25 | 0.25 | 0/40 = 0.000 | [] | tools/data/reports/agent_20260508_1909.md:66:| supertile_shuffle | 11 | {89,144} | 0.909091 | 1.00 | 1.00 | 33/40 = 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, 6, -7] | tools/data/reports/agent_20260508_1909.md:67:| same_length_contiguous_shuffle | 11 | {89,144} | 0.909091 | 1.00 | 1.00 | 34/40 = 0.850 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6] | tools/data/reports/agent_20260508_1909.md:69:| same_count_internal_shuffle | 11 | {89,144} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] | tools/data/reports/agent_20260508_1909.md:72:1. **Verificato: il confine di supertile non batte il blocco contiguo a pari multiset di lunghezze.** `supertile_shuffle` fa all-high `108/160 = 0.675`; `same_length_contiguous_shuffle` fa `116/160 = 0.725`. Per order 9 sono pari (`29/40`), per order 10-11 il controllo contiguo e' leggermente sopra (`33/40`, `34/40` contro `32/40`, `33/40`). tools/data/reports/agent_20260508_1909.md:74:2. **Verificato: la condizione forte resta l'ordine interno.** `same_count_internal_shuffle` resta nullo su tutti gli order: all-high `0/160`, high retention mediana `0.25`, Jaccard mediano `0.157895`. Questo replica il nodo 18:34 dentro il tiling di supertile. tools/data/reports/agent_20260508_1909.md:76:3. **Verificato: la scala lunga come multiset e' piu' informativa del blocco medio fisso.** `same_mean_block_shuffle` conserva low retention mediana `1.00`, ma resta quasi nullo sul core alto completo (`7/160 = 0.04375`). Quindi non basta una scala media lunga; serve una distribuzione di lunghezze lunghe con ordine interno conservato. tools/data/reports/agent_20260508_1909.md:78:4. **Inferito dal confronto fra controlli: il portatore osservato e' ordine interno + multiset di lunghezze Fibonacci, non boundary esatto.** Il test non autorizza "il core alto vive nel confine del supertile"; autorizza "il core alto richiede ordine interno e beneficia di lunghezze supertile-like". tools/data/reports/agent_20260508_1909.md:81:**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, il core alto phi non discrimina il confine esatto del supertile. `same_length_contiguous_shuffle` conserva il core alto almeno quanto `supertile_shuffle` (`116/160` vs `108/160`). Il core alto cade solo quando l'ordine interno viene distrutto (`0/160`) o quando la multiset di lunghezze viene compressa a blocco medio (`7/160`). tools/data/reports/agent_20260508_1909.md:83:La formulazione valida e': il label-set alto phi misura una condizione congiunta di ordine interno e lunghezze Fibonacci-like; non misura ancora il boundary simbolico del supertile e non misura `gap_ratio`. tools/data/reports/agent_20260508_1909.md:86:- **Due radici**: confine di supertile · distribuzione di lunghezze con ordine interno. tools/data/reports/agent_20260508_1909.md:88:- **Invariante di passaggio**: il core alto sopravvive quando l'ordine interno resta leggibile su lunghezze Fibonacci-like; non sopravvive a conteggio senza ordine. tools/data/reports/agent_20260508_1909.md:89:- **Campo di possibilita**: qui diventa possibile cercare un osservabile di boundary piu' fine del label-set; qui diventa non-possibile dichiarare che il label-set alto riconosce da solo il confine esatto del supertile. tools/data/reports/agent_20260508_1909.md:92:Costruire un gate di boundary simbolico che non legga solo il set dei label: confrontare label-set, posizione IDS dei label core e errore di label per chunk aligned/misaligned. Se il boundary reale esiste nell'osservabile, deve apparire nella geometria degli errori o nella posizione dei gap, non nel solo all-high rate. tools/data/reports/agent_20260508_1909.md:96:- **L2 quantita vs ratio**: Jaccard, retention e all-high rate restano separati; `gap_ratio` dichiarato non testato. tools/data/reports/agent_20260508_1909.md:97:- **L3 no silent patching**: l'ipotesi "supertile batte contiguo" cade; il report non la salva cambiando osservabile. tools/data/reports/agent_20260508_1909.md:98:- **L4 edge cases**: order 10-11 mostrano controllo contiguo sopra supertile; sono il segnale, non rumore. tools/data/reports/agent_20260508_1909.md:102:- Script: `tools/exp_gap_label_supertile_tiling_gate.py` tools/data/reports/agent_20260508_1909.md:103:- Data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json` tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:2: "experiment": "quasiperiodic_gap_ratio_denominator", tools/data/reports/agent_20260419_0755.md:76:- **The magnitude anti-correlation is the core prime signature**: Since the residue amplitude is algebraic, the irreducible number-theoretic content is in (a) residue decay shape and (b) magnitude anti-correlation. The magnitude channel, carrying 99% of variance, is where the Hardy-Littlewood correlations live. tools/data/reports/agent_20260412_0330.md:20:| Scale (p~) | ln(p) | det(M)_real | det(M)_shuffled | z-score | tools/data/observatorio/domandatore_unTouched_20260507_095914.md:5: [duale ] GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: Il duale di "La trascendenza e il limite attuale del modello tools/data/observatorio/domandatore_unTouched_20260507_095914.md:15: [duale ] GEN_GAP_RATIO_FALSIFICA_G_POTENZIALE_NULLA: Il duale di "G e il potenziale di tutto come nulla - permett tools/data/vincoli_decay_log.jsonl:4:{"timestamp": "2026-05-08T18:38:56.340058", "piano": 88, "tension_explored": "M_", "warns": [{"id": "G_BLANK_SHELL_TQGER_GATE", "counter": 4, "intensity": 0.8, "action": "WARN", "note": "B1 warn: vincolo stagnante 4p (archive a 5)"}, {"id": "G_BLANK_SHELL_DILATION_GATE", "counter": 4, "intensity": 0.79, "action": "WARN", "note": "B1 warn: vincolo stagnante 4p (archive a 5)"}, {"id": "G_BLANK_SHELL_STRATIFIED_GATE", "counter": 4, "intensity": 0.79, "action": "WARN", "note": "B1 warn: vincolo stagnante 4p (archive a 5)"}, {"id": "G_BLANK_SHELL_SCALE_LAW_GATE", "counter": 4, "intensity": 0.78, "action": "WARN", "note": "B1 warn: vincolo stagnante 4p (archive a 5)"}, {"id": "QPG_GAP_RATIO_DENOMINATOR_GATE", "counter": 3, "intensity": 0.77, "action": "WARN", "note": "B1 warn: vincolo stagnante 3p (archive a 5)"}], "n_archived": 0} tools/data/vincoli_decay_log.jsonl:5:{"timestamp": "2026-05-08T19:15:15.726737", "piano": 89, "tension_explored": "M_", "warns": [{"id": "G_BLANK_SHELL_TQGER_GATE", "counter": 5, "intensity": 0.8, "action": "ARCHIVED", "note": "G4 apply: vincolo stagnante 5p → spostato in tensioni_archiviate"}, {"id": "G_BLANK_SHELL_DILATION_GATE", "counter": 5, "intensity": 0.79, "action": "ARCHIVED", "note": "G4 apply: vincolo stagnante 5p → spostato in tensioni_archiviate"}, {"id": "G_BLANK_SHELL_STRATIFIED_GATE", "counter": 5, "intensity": 0.79, "action": "ARCHIVED", "note": "G4 apply: vincolo stagnante 5p → spostato in tensioni_archiviate"}, {"id": "G_BLANK_SHELL_SCALE_LAW_GATE", "counter": 5, "intensity": 0.78, "action": "ARCHIVED", "note": "G4 apply: vincolo stagnante 5p → spostato in tensioni_archiviate"}, {"id": "QPG_GAP_RATIO_DENOMINATOR_GATE", "counter": 4, "intensity": 0.77, "action": "WARN", "note": "B1 warn: vincolo stagnante 4p (archive a 5)"}], "n_archived": 4} tools/data/reports/loop_guard_20260508_1715.json:3: "danger_score": 0.358, tools/data/reports/loop_guard_20260508_1715.json:15: "danger_score": 0.0, tools/data/observatorio/lazarus_cimitero_20260507_100015.md:14:- Cross-correlation (xcorr) tra canale magnitudine e residuo (Two-Channel Decomposition) | **Come e' caduto**: Shuffle audit (ciclo 45, 2026-04-22): z-score = 0.0. | **Data falsificazione**: 2026-04-22, ciclo 45 shuffle audit. tools/data/observatorio/lazarus_cimitero_20260507_100015.md:15:- Slope ratio (slope_mag / slope_res) come invariante strutturale | **Come e' caduto**: Shuffle audit (ciclo 45): z-score = 0.2. Lo shuffle | **Data falsificazione**: 2026-04-22, ciclo 45. tools/data/reports/loop_guard_20260507_1458.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1458.json:14: "danger_score": 0.45, tools/data/reports/agent_20260425_0330.md:38:| Channel | Ordering % | z-score | Mechanism | tools/data/riformulazioni.json:13: "gap_ratio": 2.6180740677926337, tools/data/riformulazioni.json:69: "gap_ratio": 2.618037048594604, tools/data/exp_det_drift_20260507_2042.json:16: "z_score": -2.970479901674383, tools/data/exp_det_drift_20260507_2042.json:31: "z_score": -2.494337489324833, tools/data/exp_det_drift_20260507_2042.json:46: "z_score": -3.080101678803467, tools/data/exp_det_drift_20260507_2042.json:61: "z_score": -3.42701667098888, tools/data/exp_det_drift_20260507_2042.json:76: "z_score": -4.434338123586996, tools/data/exp_det_drift_20260507_2042.json:91: "z_score": -2.5231991783183925, tools/data/exp_det_drift_20260507_2042.json:106: "z_score": -0.9729509457351886, tools/data/lab_data.json:99: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/lab_data.json:100: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. St", tools/data/lab_data.json:106: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/lab_data.json:107: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/lab_data.json:115: "content": "# Agent Report - Supertile Tiling Gate Del Core Phi\n**Date**: 2026-05-08 19:09\n**Piano**: 89\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, supertile_boundary, length_multiset, internal_order, high_label_condition_rate]\n**observable_contract**: claim=il core alto phi distingue confine di supertile Fibonacci da blocco contiguo con stessa lunghezza; observable=retention dei label core sotto shuffle di supertile, shuffle contiguo a stessa multiset di lunghezze, shuffle interno a stesso conteggio; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per mode e supertile_order; generator=phi_sturmian con tiling di lunghezze Fibonacci; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, soglie 1.75/2.25, parsing simbolico esatto dei supertile per ogni fase, generatori non-phi.\n\n## Respiro fuori-tempo\n- **Combo**: A11 combo + TxQ matrice densita come lettore spettrale + QxG vuoto continuo/discreto + tensione TRASCENDENZA_LIMITE/M_come_modulazione_quasiperiodica.\n- **Dipolo / punto-zero**: confine reale del supertile · lunghezza contigua equivalente; punto-zero = il blocco come unita' prima che diventi grammatica o contenitore.\n- **Piano superiore**: grafo della conoscenza / bicono dei dipoli. Il nodo non e' il valore del gap, ma il passaggio fra grammatica interna, confine esterno e denominatore.\n- **Proto-ipotesi**: se il core alto phi vive nel confine tra supertile, lo shuffle di supertile conserva piu' core dello shuffle contiguo con la stessa multiset di lunghezze. Se non lo fa, il confine esatto non e' ancora il portatore: il portatore e' ordine interno piu' scala lunga.\n- **Proiezione**: il label-set dei gap larghi manifesta la combo perche' tiene fisso il reader theta=1/phi e muove solo la relazione fra grammatica, boundary e blocco.\n\n## Claim Under Test\n> Il vantaggio del blocco Fibonacci lungo non sta solo nell'ordine interno conservato: sta nel confine reale del supertile. Quindi `supertile_shuffle` batte `same_length_contiguous_shuffle` a pari multiset di lunghezze.\n\n## Question\nIl core alto `[3, -4, 4, 6]` riconosce il confine di supertile Fibonacci, o riconosce soltanto blocchi lunghi con ordine interno conservato?\n\n## Experiment Design\n- Reference core phi: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`.\n- Supertile orders:\n - order 8: lunghezze `{21,34}`.\n - order 9: lunghezze `{34,55}`.\n - order 10: lunghezze `{55,89}`.\n - order 11: lunghezze `{89,144}`.\n- Modes:\n - `supertile_shuffle`: taglia secondo la parola di lunghezze Fibonacci e mescola i chunk.\n - `same_length_contiguous_shuffle`: usa la stessa multiset di lunghezze, ma su taglio contiguo misallineato.\n - `same_count_internal_shuffle`: conserva lunghezza e conteggio di ogni c" tools/data/lab_data.json:128: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/lab_data.json:133: "anti_claim": "ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in (not: gap_ratio: phi=0.4090)" tools/data/lab_data.json:141: "anti_claim": "ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? (not: Cosa manca per confermare completamen" tools/data/lab_data.json:160: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/lab_data.json:222: "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/lab_data.json:230: "note": "8 dipoli risuonano: COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE, COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE, FALS_BREAK_TRASCENDENZA_LIMITE..." tools/data/lab_data.json:237: "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/lab_data.json:240: "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/lab_data.json:245: "note": "8 dipoli risuonano: COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE, COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE, FALS_BREAK_TRASCENDENZA_LIMITE..." tools/data/lab_data.json:252: "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/lab_data.json:259: "note": "7 dipoli risuonano: COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE, COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE, FALS_BREAK_TRASCENDENZA_LIMITE..." tools/data/lab_data.json:265: "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/lab_data.json:271: "QPG_GAP_RATIO_DENOMINATOR_GATE" tools/data/lab_data.json:273: "note": "7 dipoli risuonano: COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE, G_BLANK_SHELL_DILATION_GATE, G_BLANK_SHELL_SCALE_LAW_GATE..." tools/data/lab_data.json:284: "QPG_GAP_RATIO_DENOMINATOR_GATE" tools/data/reports/ddf_20260502_0405.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" tools/data/reports/ddf_20260503_0405.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" tools/data/reports/loop_guard_20260507_0803.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_0803.json:14: "danger_score": 0.45, tools/data/reports/ddf_20260506_0405.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" tools/data/reports/ddf_20260508_0405.json:21: "file": "M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/ciclo_memoria.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M" tools/data/reports/agent_20260504_1219.md:89:- **L2 quantità vs ratio**: il claim principale usa layer recuperato e z-score, non percentuali fra spazi di taglia diversa. tools/data/reports/falsifier_20260430_1919.json:17: "evidence": "Nel confronto growing window cambiano sia scala dei primi sia numero di campioni: N_primes passa da 1,229 a 216,816. Le percentuali/medie delta possono calare mentre gli z-score crescono da |V|=6.66 a 41.09. Il rapporto non e' stabile in senso stretto: 1.72 -> 2.52 -> 2.10 -> 2.40.", tools/data/reports/falsifier_20260430_1919.json:24: "evidence": "Il claim strutturale in lab_data diceva esplicitamente: spacing_ratio verso Poisson e lag1_acf verso GUE. Il report lo sostituisce con 'entrambi negative z' e lo marca come confirmed/refined, non come falsificazione o cambio di definizione del frame Delta_tau -> z-score.", tools/data/reports/loop_guard_20260507_1957.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_1957.json:14: "danger_score": 0.45, tools/data/reports/ddf_20260504_0405.json:15: "file": "M adapters/http_api.js\n M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M da" tools/data/reports/agent_20260507_1458.md:4:**Tension explored**: META (score 0.898) tools/data/reports/agent_20260508_1834.md:1:# Agent Report — Substitution Grammar Gate Del Core Phi tools/data/reports/agent_20260508_1834.md:8:**observable_contract**: claim=il core phi dei gap larghi separa lunghezza di blocco e grammatica interna del generatore; observable=retention dei label core sotto perturbazioni che preservano o distruggono ordine interno; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per mode e block_size; generator=phi_sturmian con contiguous_block_shuffle, internal_block_shuffle, global_balanced_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, block_size={34,40,55,64,89,96,128,144}; not_tested=gap_ratio, soglie 1.75/2.25, N fuori {377,610}, generatori Sturmiani non-phi, tiling esatto in supertile Fibonacci. tools/data/reports/agent_20260508_1834.md:11:> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? tools/data/reports/agent_20260508_1834.md:14:Il rientro dei label alti del core phi misura soltanto blocchi lunghi su scala Fibonacci, o misura l'ordine interno di sostituzione conservato dentro quei blocchi? tools/data/reports/agent_20260508_1834.md:17:- Perimetro: stesso lettore label dei cycle 16:32, 17:15 e 18:05; `gap_ratio` non testato in questo ciclo. tools/data/reports/agent_20260508_1834.md:18:- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. tools/data/reports/agent_20260508_1834.md:28:Reference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`, Jaccard mediano `0.909091`, min `0.727273`. tools/data/reports/agent_20260508_1834.md:61:1. **Verificato: distruggere l'ordine interno del blocco collassa il core al livello random.** `internal_block_shuffle` resta su Jaccard mediano `0.150000-0.166667`, low retention `0.25`, high retention `0.25`, all-high `0/40` per ogni block_size. Il suo profilo coincide con `global_balanced_shuffle` entro il perimetro osservato. tools/data/reports/agent_20260508_1834.md:63:2. **Verificato: il vantaggio dei blocchi Fibonacci lunghi dipende dall'ordine interno conservato.** Con `contiguous_block_shuffle`, `89` porta all-high `26/40` e `144` porta `32/40`; con `internal_block_shuffle`, gli stessi `89` e `144` restano `0/40`. La lunghezza e il conteggio del blocco non trasportano il core alto quando l'ordine interno e' rimosso. tools/data/reports/agent_20260508_1834.md:65:3. **Verificato: gli edge case non-Fibonacci restano parziali, non nulli.** `128` conserva `-4` e `4` come stable labels 75% e ha high retention mediana `0.75`, ma all-high resta `7/40`. Questo impedisce la formula "solo Fibonacci"; la formula valida e' che, in questo perimetro, l'ordine interno conservato e le scale Fibonacci lunghe aumentano insieme la frequenza del core alto completo. tools/data/reports/agent_20260508_1834.md:67:4. **Inferito dal confronto 18:05 -> 18:34: il nodo regressivo non era solo block_size.** Il cycle 18:05 vedeva scala Fibonacci; questo cycle localizza la condizione mancante dentro il blocco: la grammatica interna del generatore deve restare leggibile. Il test non isola ancora il tiling esatto in supertile Fibonacci. tools/data/reports/agent_20260508_1834.md:70:**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core alto `[3, -4, 4, 6]` non e' portato da lunghezza e conteggio del blocco. Quando il blocco conserva ordine interno, le scale Fibonacci `89` e `144` rendono frequente il core alto completo (`26/40`, `32/40`). Quando l'ordine interno viene distrutto dentro gli stessi blocchi, il core alto completo non compare (`0/40`) e il profilo cade sul controllo bilanciato globale. tools/data/reports/agent_20260508_1834.md:72:La formulazione valida e': il label-set alto phi misura memoria di grammatica interna del generatore su blocchi lunghi, con vantaggio forte alle scale Fibonacci testate; non misura lunghezza generica, conteggio di simboli o valore `gap_ratio`. tools/data/reports/agent_20260508_1834.md:78:- **Campo di possibilita**: qui diventa possibile testare supertile Fibonacci esatti contro blocchi contigui arbitrari; qui diventa non-possibile spiegare il core alto con sola lunghezza o densita locale. tools/data/reports/agent_20260508_1834.md:81:Costruire un `supertile_tiling_gate`: segmentare la parola phi in supertile Fibonacci reali e confrontare tre perturbazioni separate, `supertile_shuffle`, `same_length_contiguous_shuffle`, `same_count_internal_shuffle`. Se il supertile batte il blocco contiguo alla stessa lunghezza, la grammatica non e' solo interna al blocco ma vive anche nel confine tra supertile. tools/data/reports/agent_20260508_1834.md:85:- **L2 quantita vs ratio**: Jaccard, retention e all-high rate restano separati; `gap_ratio` dichiarato non testato. tools/data/domandatore/domandatore_20260405_0723.json:12: "id": "GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F6", tools/data/domandatore/domandatore_20260405_0723.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260405_0723.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260405_0723.json:39: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F6", tools/data/domandatore/domandatore_20260405_0723.json:40: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260405_0723.json:42: "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" tools/data/domandatore/domandatore_20260405_0723.json:61: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/domandatore/domandatore_20260405_0723.json:105: "tensione": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260405_0723.json:108: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F6", tools/data/reports/ddf_20260507_0405.json:15: "file": "M boot_kthia.js\n M core/Kernel.js\n M data/bicono_lab.json\n M data/campo_vivo.json\n M data/conoscenza_teorie.json\n M data/domande_fondamentali.json\n M data/drift_seme_hash\n M data/godel_field.json\n M d" tools/data/curva_results.json:11: "gap_ratio": 0.9999999999999998, tools/data/curva_results.json:22: "gap_ratio": 1.0, tools/data/curva_results.json:33: "gap_ratio": 1.0000000000000018, tools/data/curva_results.json:44: "gap_ratio": 2.586584409253611, tools/data/curva_results.json:55: "gap_ratio": 0.9999999999999929, tools/data/curva_results.json:66: "gap_ratio": 0.999999999999995, tools/data/curva_results.json:77: "gap_ratio": 0.9999999999999958, tools/data/curva_results.json:88: "gap_ratio": 0.9999999999999959, tools/data/curva_results.json:99: "gap_ratio": 0.9999999999999958, tools/data/curva_results.json:107: "cv_gap_ratio_curva": 0.423889160597429, tools/data/domandatore/domandatore_20260507_2120.json:12: "id": "GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/domandatore/domandatore_20260507_2120.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260507_2120.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260507_2120.json:31: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/domandatore/domandatore_20260507_2120.json:32: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260507_2120.json:34: "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" tools/data/domandatore/domandatore_20260507_2120.json:46: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/reports/falsifier_20260508_1805.json:18: "suggestion": "Sostituire 'richiedono' con una formula non-hard: 'i blocchi Fibonacci lunghi aumentano la chiusura congiunta del core alto; i non-Fibonacci lunghi trasportano sottostrutture alte ma non raggiungono la soglia all-high >=0.5 nel perimetro testato'." tools/data/reports/falsifier_20260508_1805.json:23: "claim": "La lunghezza da sola porta memoria locale; non ricostruisce il core alto come i blocchi Fibonacci `89/144`.", tools/data/reports/falsifier_20260508_1805.json:30: "claim": "Claim Under Test: `gap_ratio(phi) ...`; poi `gap_ratio` non testato e verdict su `label-set phi`.", tools/data/reports/falsifier_20260508_1805.json:31: "evidence": "Il report dichiara il cambio osservabile nel contratto e nell'audit, quindi non e' silent patch pieno. Pero' il `Claim Under Test` resta un frammento su `gap_ratio` mentre il ciclo testa block-shuffle label retention. La continuita' assiomatica e' dichiarata ma il nodo regressivo non e' scritto come falsificazione/correzione del claim originario.", tools/data/reports/falsifier_20260508_1805.json:32: "suggestion": "Aprire il prossimo report con: '`gap_ratio` non e' under test in questo ciclo; claim corretto: retention del core label sotto block shuffle'. Tenere il vecchio claim solo come antecedente storico." tools/data/reports/falsifier_20260508_1805.json:37: "claim": "il finding del ciclo e' la stratificazione del core osservato in basso locale e alto Fibonacci-lungo", tools/data/reports/loop_guard_20260507_0901.json:3: "danger_score": 0.45, tools/data/reports/loop_guard_20260507_0901.json:14: "danger_score": 0.45, tools/data/two_layer_universality.json:14: "z_scores": { tools/data/two_layer_universality.json:69: "z_scores": { tools/data/two_layer_universality.json:124: "z_scores": { tools/data/two_layer_universality.json:179: "z_scores": { tools/data/two_layer_universality.json:234: "z_scores": { tools/data/two_layer_universality.json:289: "z_scores": { tools/data/two_layer_universality.json:344: "z_scores": { tools/data/domandatore/domandatore_20260327_0344.json:12: "id": "GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F3", tools/data/domandatore/domandatore_20260327_0344.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260327_0344.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260327_0344.json:39: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F3", tools/data/domandatore/domandatore_20260327_0344.json:40: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260327_0344.json:42: "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" tools/data/domandatore/domandatore_20260327_0344.json:61: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/knowledge_state.json:95: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:1090: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:1589: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:1669: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:1749: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:1849: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:1943: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:1991: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:2039: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:2295: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:2354: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:2397: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:2440: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:2488: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/knowledge_state.json:2644: "result_claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/reports/agent_20260405_0919.md:32:| z-score range | 30 to 102 | tools/data/reports/agent_20260405_0919.md:33:| Mean z-score | 57.3 | tools/data/domandatore/domandatore_20260402_0803.json:12: "id": "GEN_GAP_RATIO_CONS_GxE_CONS_GxR_QxG", tools/data/domandatore/domandatore_20260402_0803.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260402_0803.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260402_0803.json:31: "id": "COMP_GEN_GAP_RATIO_CONS_GxE_CONS_GxR_QxG", tools/data/domandatore/domandatore_20260402_0803.json:32: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260402_0803.json:34: "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" tools/data/domandatore/domandatore_20260402_0803.json:46: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/brody_calibration_results.json:34: "z_scores": { tools/data/brody_calibration_results.json:71: "z_scores": { tools/data/brody_calibration_results.json:108: "z_scores": { tools/data/brody_calibration_results.json:145: "z_scores": { tools/data/brody_calibration_results.json:182: "z_scores": { tools/data/brody_calibration_results.json:219: "z_scores": { tools/data/brody_calibration_results.json:256: "z_scores": { tools/data/brody_calibration_results.json:293: "z_scores": { tools/data/brody_calibration_results.json:330: "z_scores": { tools/data/brody_calibration_results.json:367: "z_scores": { tools/data/brody_calibration_results.json:404: "z_scores": { tools/data/brody_calibration_results.json:442: "z_scores": { tools/data/brody_calibration_results.json:479: "z_scores": { tools/data/brody_calibration_results.json:516: "z_scores": { tools/data/brody_calibration_results.json:553: "z_scores": { tools/data/brody_calibration_results.json:590: "z_scores": { tools/data/domandatore/domandatore_20260331_0344.json:12: "id": "GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F1", tools/data/domandatore/domandatore_20260331_0344.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260331_0344.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260331_0344.json:39: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F1", tools/data/domandatore/domandatore_20260331_0344.json:40: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260331_0344.json:42: "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" tools/data/domandatore/domandatore_20260331_0344.json:61: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/domandatore/domandatore_20260330_0344.json:12: "id": "GEN_GAP_RATIO_T9_linguaggio_FALSIFICA_C3", tools/data/domandatore/domandatore_20260330_0344.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260330_0344.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260330_0344.json:39: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_FALSIFICA_C3", tools/data/domandatore/domandatore_20260330_0344.json:40: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260330_0344.json:42: "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" tools/data/domandatore/domandatore_20260330_0344.json:61: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/domandatore/domandatore_20260405_0715.json:12: "id": "GEN_GAP_RATIO_FALSIFICA_F6", tools/data/domandatore/domandatore_20260405_0715.json:14: "ipotesi": "Il duale di \"La firma dello zero Lo zero non si vede direttamen\" [generato: gap_ratio]", tools/data/domandatore/domandatore_20260405_0715.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260405_0715.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260405_0715.json:39: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_F6", tools/data/domandatore/domandatore_20260405_0715.json:40: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260405_0715.json:42: "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" tools/data/domandatore/domandatore_20260405_0715.json:61: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/domandatore/domandatore_20260307_2034.json:12: "id": "GEN_GAP_RATIO_T2_normalizzatore_T2_normalizzatore_trascende", tools/data/domandatore/domandatore_20260307_2034.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260307_2034.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260307_2034.json:55: "id": "COMP_GEN_GAP_RATIO_T2_normalizzatore_T2_normalizzatore_trascende", tools/data/domandatore/domandatore_20260307_2034.json:56: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260307_2034.json:58: "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" tools/data/domandatore/domandatore_20260307_2034.json:91: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/domandatore/domandatore_20260402_0343.json:12: "id": "GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F3", tools/data/domandatore/domandatore_20260402_0343.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260402_0343.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260402_0343.json:39: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F3", tools/data/domandatore/domandatore_20260402_0343.json:40: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260402_0343.json:42: "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" tools/data/domandatore/domandatore_20260402_0343.json:61: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/domandatore/domandatore_20260315_0801.json:12: "id": "GEN_GAP_RATIO_FALSIFICA_F3", tools/data/domandatore/domandatore_20260315_0801.json:14: "ipotesi": "Il duale di \"L'attrattore e l'impossibilita' del rinforzo f ha \" [generato: gap_ratio]", tools/data/domandatore/domandatore_20260315_0801.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260315_0801.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260315_0801.json:39: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_F3", tools/data/domandatore/domandatore_20260315_0801.json:40: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260315_0801.json:42: "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" tools/data/domandatore/domandatore_20260315_0801.json:61: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/domandatore/domandatore_20260309_1409.json:12: "id": "GEN_GAP_RATIO_T9_linguaggio_metafisico", tools/data/domandatore/domandatore_20260309_1409.json:14: "ipotesi": "Il duale di \"Il linguaggio metafisico e appropriato per le tran\" [generato: gap_ratio]", tools/data/domandatore/domandatore_20260309_1409.json:15: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/domandatore/domandatore_20260309_1409.json:17: "stdout": " 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" tools/data/domandatore/domandatore_20260309_1409.json:55: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_metafisico", tools/data/domandatore/domandatore_20260309_1409.json:56: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/domandatore/domandatore_20260309_1409.json:58: "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" tools/data/domandatore/domandatore_20260309_1409.json:91: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/autoricerca_journal.json:12: "gap_ratio": 2.6181003744227653, tools/data/autoricerca_journal.json:30: "gap_ratio": 2.618013050556921, tools/data/autoricerca_journal.json:48: "gap_ratio": 2.6180415569821247, 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"20260508_1834", "agent": {"path": "/opt/MM_D-ND/tools/data/reports/agent_20260508_1834.md", "exists": true, "title": "Agent Report — Substitution Grammar Gate Del Core Phi", "size": 8813}, "falsifier": {"path": "/opt/MM_D-ND/tools/data/reports/falsifier_20260508_1834.json", "exists": true, "coherent": false, "summary": "Il report e' parzialmente coerente: L3 si rompe per drift tra gap_ratio dichiarato e label-set testato, e L1 richiede di limitare lo zero al core alto completo, non ai label alti residui.", "counts": {"high": 0, "medium": 2, "low": 1}, "flags": [{"lens": "L3", "severity": "medium", "claim": "Claim Under Test: \"Cosa manca per confermare completamente gap_ratio...\"; Verdict: \"CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE\"", "suggestion": "Riformulare: \"gap_ratio non valutato; questo ciclo testa solo il label-set gate. QPG_GAP_RATIO_DENOMINATOR_GATE resta aperto finche' gap_ratio viene misurato nello stesso perimetro\"."}, {"lens": "L1", "severity": "medium", "claim": "\"il core alto [3, -4, 4, 6] non e' portato da lunghezza e conteggio del blocco\"", "suggestion": "Separare hard constraint e residuo: \"lunghezza/conteggio non portano il core alto completo: all-high=0/320; resta una retention alta residua 0.25 da spiegare\"."}, {"lens": "L4", "severity": "low", "claim": "\"distruggere l'ordine interno del blocco collassa il core al livello random\"", "suggestion": "Nel prossimo ciclo isolare il label `34`: verificare se e' boundary artifact di `|n|<=34`, leakage del reader, o label valido fuori core; non includerlo implicitamente nel profilo random senza nota."}]}, "aeternitas": {"path": "/opt/MM_D-ND/tools/data/aeternitas/aeternitas_20260508_183856.json", "decision": "WARN", "reason": "P5 (Autopoiesi): ciclo non ha prodotto tensioni nuove né cambiato direzione", "p0_passed": true, "p1_passed": true, "p5_passed": false, "p5_violations": [{"issue": "no_new_tension_or_direction", "old_count": 19, "new_count": 19}]}, "veritas": {"path": "/opt/MM_D-ND/tools/data/veritas/veritas_20260508_183856.json", "rho": 0.8875, "band": "SOSPENSIONE"}, "valutatore": {"exists": true, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile: il core phi non misura solo lunghezza di blocco, perche' collassa quando l'ordine interno viene distrutto. Pero' e' il quarto NEXT_CYCLE consecutivo nello stesso frame locale e il perimetro resta su lettore phi/generator phi senza gap_ratio, non-phi Sturmian o cross-dominio; secondo la regola anti-tautologica il deep-dive locale ha raggiunto rendimento decrescente. La mossa successiva deve portare il vincolo su un discriminatore strutturale piu' largo, coerente col seme attuale sul confine GUE/Poisson.", "action_type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare se il gate del core phi trasferisce come discriminatore di confine su domini GUE/Poisson: ordine interno conservato vs distrutto, con controlli shuffle e generatori non-phi", "reason": "Porta il finding dal dominio locale della grammatica Sturmiana al filtro STRUTTURA/rumore richiesto dal telos, evitando accumulo numerico sul solo phi_sturmian."}, "notes": "Nel prossimo ciclo: usare il risultato appena ottenuto come vincolo, non come claim; includere almeno un controllo non-phi e un confronto GUE/Poisson. gap_ratio resta not_tested e va trattato come osservabile sospeso, non confermato."}, "loop_guard": {"path": "/opt/MM_D-ND/tools/data/reports/loop_guard_20260508_1834.json", "exists": true, "bands": {"valutatore_decisions": "OK", "piano_trajectory": "OK"}, "danger": false}, "session": {"exists": true, "sync_blocked": false, "errors": ""}}, "directive_path": "/opt/MM_D-ND/tools/data/operator_directive.md", "record_json": null, "record_md": null} tools/data/repairs/repair_20260508_1909.json:20: "title": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/repairs/repair_20260508_1909.json:27: "summary": "Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove \"nullo/cade\" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label.", tools/data/repairs/repair_20260508_1909.json:37: "claim": "\"same_count_internal_shuffle resta nullo su tutti gli order\" / \"Il core alto cade solo quando l'ordine interno viene distrutto (0/160)\"", tools/data/repairs/repair_20260508_1909.json:38: "suggestion": "Riformulare: \"same_count_internal_shuffle azzera l'all-high rate del core alto completo, ma lascia sopravvivere singoli label ad alta scala; prossimo ciclo separi all-high, per-label retention e stable-label count\"." tools/data/repairs/repair_20260508_1909.json:43: "claim": "\"il core alto cade solo quando l'ordine interno viene distrutto\"", tools/data/repairs/repair_20260508_1909.json:44: "suggestion": "Isolare l'edge case per label: verificare quali tra [3,-4,4,6] sopravvivono sotto internal shuffle e dichiarare il perimetro come \"core completo cade\", non \"core alto cade\"." tools/data/repairs/repair_20260508_1909.json:78: "reasoning": "Il ciclo ha falsificato il claim operativo: il confine reale del supertile non batte il taglio contiguo a stessa multiset, mentre lo shuffle interno distrugge il core. L'evidenza sposta il nodo regressivo dal boundary geometrico al contratto osservabile: il portatore e' ordine interno piu' scala lunga, non il taglio esatto del supertile.", tools/data/repairs/repair_20260508_1909.json:82: "new_value": "Riformulare il confine come grammatica interna + scala lunga: testare il core phi contro generatori non-phi, fasi e denominatori con controlli shuffle, senza assumere che il boundary esatto del supertile sia il portatore.", tools/data/repairs/repair_20260508_1909_decision.json:1:{"cycle_ts": "20260508_1909", "decision": "APPLY_REPAIR", "should_rerun": true, "reasons": ["p5_failed_and_veritas_not_collasso_with_consecutio"], "guards": {"repair_depth": 0, "max_depth": 1, "parent_ts": "", "loop_danger": false, "aeternitas_veto": false}, "signals": {"cycle_ts": "20260508_1909", "agent": {"path": "/opt/MM_D-ND/tools/data/reports/agent_20260508_1909.md", "exists": true, "title": "Agent Report - Supertile Tiling Gate Del Core Phi", "size": 9634}, "falsifier": {"path": "/opt/MM_D-ND/tools/data/reports/falsifier_20260508_1909.json", "exists": true, "coherent": false, "summary": "Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove \"nullo/cade\" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label.", "counts": {"high": 0, "medium": 2, "low": 1}, "flags": [{"lens": "L1", "severity": "medium", "claim": "\"same_count_internal_shuffle resta nullo su tutti gli order\" / \"Il core alto cade solo quando l'ordine interno viene distrutto (0/160)\"", "suggestion": "Riformulare: \"same_count_internal_shuffle azzera l'all-high rate del core alto completo, ma lascia sopravvivere singoli label ad alta scala; prossimo ciclo separi all-high, per-label retention e stable-label count\"."}, {"lens": "L4", "severity": "medium", "claim": "\"il core alto cade solo quando l'ordine interno viene distrutto\"", "suggestion": "Isolare l'edge case per label: verificare quali tra [3,-4,4,6] sopravvivono sotto internal shuffle e dichiarare il perimetro come \"core completo cade\", non \"core alto cade\"."}, {"lens": "L5", "severity": "low", "claim": "\"il label-set alto phi misura una condizione congiunta di ordine interno e lunghezze Fibonacci-like\"", "suggestion": "Nel prossimo ciclo aggiungere baseline teorica: confrontare i label osservati con il gruppo di gap-labeling atteso per Sturmian/Fibonacci e separare novita' D-ND da re-discovery spettrale classica."}]}, "aeternitas": {"path": "/opt/MM_D-ND/tools/data/aeternitas/aeternitas_20260508_191516.json", "decision": "WARN", "reason": "P5 (Autopoiesi): ciclo non ha prodotto tensioni nuove né cambiato direzione", "p0_passed": true, "p1_passed": true, "p5_passed": false, "p5_violations": [{"issue": "no_new_tension_or_direction", "old_count": 19, "new_count": 15}]}, "veritas": {"path": "/opt/MM_D-ND/tools/data/veritas/veritas_20260508_191516.json", "rho": 0.8875, "band": "SOSPENSIONE"}, "valutatore": {"exists": true, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha falsificato il claim operativo: il confine reale del supertile non batte il taglio contiguo a stessa multiset, mentre lo shuffle interno distrugge il core. L'evidenza sposta il nodo regressivo dal boundary geometrico al contratto osservabile: il portatore e' ordine interno piu' scala lunga, non il taglio esatto del supertile.", "action_type": "modify_seme", "detail": {"field": "direzione", "new_value": "Riformulare il confine come grammatica interna + scala lunga: testare il core phi contro generatori non-phi, fasi e denominatori con controlli shuffle, senza assumere che il boundary esatto del supertile sia il portatore.", "reason": "Il boundary esatto e' stato controllato e non discrimina; continuare nello stesso frame accumulerebbe dettaglio locale su un claim caduto."}, "notes": "Non serve STOP_FOR_REVIEW: la falsificazione e' chiara e non richiede scelta dell'operatore. Il prossimo ciclo deve attaccare trasferibilita' cross-generatore/cross-dominio, non raffinare solo soglie o gap label locali."}, "loop_guard": {"path": "/opt/MM_D-ND/tools/data/reports/loop_guard_20260508_1909.json", "exists": true, "bands": {"valutatore_decisions": "OK", "piano_trajectory": "OK"}, "danger": false}, "session": {"exists": true, "sync_blocked": false, "errors": ""}}, "directive_path": "/opt/MM_D-ND/tools/data/operator_directive.md", "record_json": "/opt/MM_D-ND/tools/data/repairs/repair_20260508_1909.json", "record_md": "/opt/MM_D-ND/tools/data/repairs/repair_20260508_1909.md"} tools/data/lab_graph.json:58: "score": 2 tools/data/lab_graph.json:67: "score": 1 tools/data/lab_graph.json:76: "score": 1 tools/data/lab_graph.json:99: "score": 2 tools/data/lab_graph.json:108: "score": 1 tools/data/lab_graph.json:117: "score": 1 tools/data/lab_graph.json:140: "score": 2 tools/data/lab_graph.json:149: "score": 1 tools/data/lab_graph.json:158: "score": 1 tools/data/lab_graph.json:181: "score": 3 tools/data/lab_graph.json:190: "score": 1 tools/data/lab_graph.json:199: "score": 1 tools/data/lab_graph.json:222: "score": 2 tools/data/lab_graph.json:231: "score": 2 tools/data/lab_graph.json:240: "score": 1 tools/data/lab_graph.json:267: "score": 1 tools/data/lab_graph.json:276: "score": 1 tools/data/lab_graph.json:285: "score": 1 tools/data/lab_graph.json:312: "score": 1 tools/data/lab_graph.json:321: "score": 1 tools/data/lab_graph.json:330: "score": 1 tools/data/lab_graph.json:374: "score": 2 tools/data/lab_graph.json:383: "score": 1 tools/data/lab_graph.json:392: "score": 1 tools/data/lab_graph.json:419: "score": 3 tools/data/lab_graph.json:428: "score": 3 tools/data/lab_graph.json:437: "score": 3 tools/data/lab_graph.json:465: "score": 2 tools/data/lab_graph.json:474: "score": 1 tools/data/lab_graph.json:483: "score": 1 tools/data/lab_graph.json:490: "label": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:491: "label_en": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:492: "label_short": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:493: "label_short_en": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:496: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr", tools/data/lab_graph.json:497: "findings": "1. **Verificato: il confine di supertile non batte il blocco contiguo a pari multiset di lunghezze.** `supertile_shuffle` fa all-high `108/160 = 0.675`; `same_length_contiguous_shuffle` fa `116/160 = 0.725`. Per order 9 sono pari (`29/40`), per order 10-11 il controllo contiguo e' leggermente sopra ", tools/data/lab_graph.json:498: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`,", tools/data/lab_graph.json:499: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`,", tools/data/lab_graph.json:508: "slug": "human-core-protection-framework", tools/data/lab_graph.json:509: "title": "Human Core Protection Framework", tools/data/lab_graph.json:510: "title_en": "Human Core Protection Framework", tools/data/lab_graph.json:514: "score": 1 tools/data/lab_graph.json:523: "score": 1 tools/data/lab_graph.json:532: "score": 1 tools/data/lab_graph.json:539: "label": "Substitution Grammar Gate Del Core Phi", tools/data/lab_graph.json:540: "label_en": "Substitution Grammar Gate Del Core Phi", tools/data/lab_graph.json:541: "label_short": "Substitution Grammar Gate Del Core Phi", tools/data/lab_graph.json:542: "label_short_en": "Substitution Grammar Gate Del Core Phi", tools/data/lab_graph.json:545: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr", tools/data/lab_graph.json:546: "findings": "1. **Verificato: distruggere l'ordine interno del blocco collassa il core al livello random.** `internal_block_shuffle` resta su Jaccard mediano `0.150000-0.166667`, low retention `0.25`, high retention `0.25`, all-high `0/40` per ogni block_size. Il suo profilo coincide con `global_balanced_shuffle", tools/data/lab_graph.json:547: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`,", tools/data/lab_graph.json:548: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`,", tools/data/lab_graph.json:557: "slug": "human-core-protection-framework", tools/data/lab_graph.json:558: "title": "Human Core Protection Framework", tools/data/lab_graph.json:559: "title_en": "Human Core Protection Framework", tools/data/lab_graph.json:563: "score": 1 tools/data/lab_graph.json:572: "score": 1 tools/data/lab_graph.json:581: "score": 1 tools/data/lab_graph.json:588: "label": "Agent Report - Block Scale Gate Del Core Phi", tools/data/lab_graph.json:589: "label_en": "Agent Report - Block Scale Gate Del Core Phi", tools/data/lab_graph.json:590: "label_short": "Agent Report - Block Scale Gate Del Core Phi", tools/data/lab_graph.json:591: "label_short_en": "Agent Report - Block Scale Gate Del Core Phi", tools/data/lab_graph.json:594: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr", tools/data/lab_graph.json:596: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`,", tools/data/lab_graph.json:597: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`,", tools/data/lab_graph.json:606: "slug": "human-core-protection-framework", tools/data/lab_graph.json:607: "title": "Human Core Protection Framework", tools/data/lab_graph.json:608: "title_en": "Human Core Protection Framework", tools/data/lab_graph.json:612: "score": 1 tools/data/lab_graph.json:621: "score": 1 tools/data/lab_graph.json:630: "score": 1 tools/data/lab_graph.json:643: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.", tools/data/lab_graph.json:644: "findings": "1. **Verificato: il core completo resta nel generatore Sturmiano meccanico.** Nel perimetro `N/phase/threshold/trial` testato, `phi_sturmian` conserva tutti gli 8 label core in tutte le condizioni: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n2. **Verificato: la costruzione Fibonacci conserva il nucleo basso ma n", tools/data/lab_graph.json:645: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,50", tools/data/lab_graph.json:646: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,50", tools/data/lab_graph.json:661: "score": 1 tools/data/lab_graph.json:670: "score": 1 tools/data/lab_graph.json:679: "score": 1 tools/data/lab_graph.json:692: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non passa come claim di valore `gap_ratio`; passa come ", tools/data/lab_graph.json:694: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non p", tools/data/lab_graph.json:695: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non p", tools/data/lab_graph.json:717: "slug": "human-core-protection-framework", tools/data/lab_graph.json:718: "title": "Human Core Protection Framework", tools/data/lab_graph.json:719: "title_en": "Human Core Protection Framework", tools/data/lab_graph.json:723: "score": 1 tools/data/lab_graph.json:732: "score": 1 tools/data/lab_graph.json:741: "score": 1 tools/data/lab_graph.json:762: "score": 1 tools/data/lab_graph.json:783: "score": 2 tools/data/lab_graph.json:804: "score": 2 tools/data/lab_graph.json:846: "score": 1 tools/data/lab_graph.json:855: "score": 1 tools/data/lab_graph.json:876: "score": 2 tools/data/lab_graph.json:897: "score": 2 tools/data/lab_graph.json:906: "score": 1 tools/data/lab_graph.json:915: "score": 1 tools/data/lab_graph.json:1062: "title": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:1063: "title_en": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:1066: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={", tools/data/lab_graph.json:1067: "verdict_en": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={", tools/data/lab_graph.json:1068: "findings": "1. **Verificato: il confine di supertile non batte il blocco contiguo a pari multiset di lunghezze.** `supertile_shuffle` fa all-high `108/160 = 0.675`; `same_length_contiguous_shuffle` fa `116/160 = 0.725`. Per order 9 sono pari (`29/40`), per order 10-11 il controllo contiguo e' leggermente sopra (`33/40`, `34/40` contro `32/40`, `33/40`).\n2. **Verificato: la condizione forte resta l'ordine inte", tools/data/lab_graph.json:1069: "content_preview": "# Agent Report - Supertile Tiling Gate Del Core Phi\n**Date**: 2026-05-08 19:09\n**Piano**: 89\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, supertile_boundary, length_multiset, internal_order, high_label_condition_rate]\n**observable_contract**: claim=il core alto phi distingue confine di supertile Fibonacci da blocco contiguo con stessa lunghezza; observable=retention dei label core so", tools/data/lab_graph.json:1070: "content_full": "# Agent Report - Supertile Tiling Gate Del Core Phi\n**Date**: 2026-05-08 19:09\n**Piano**: 89\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, supertile_boundary, length_multiset, internal_order, high_label_condition_rate]\n**observable_contract**: claim=il core alto phi distingue confine di supertile Fibonacci da blocco contiguo con stessa lunghezza; observable=retention dei label core sotto shuffle di supertile, shuffle contiguo a stessa multiset di lunghezze, shuffle interno a stesso conteggio; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per mode e supertile_order; generator=phi_sturmian con tiling di lunghezze Fibonacci; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, soglie 1.75/2.25, parsing simbolico esatto dei supertile per ogni fase, generatori non-phi.\n\n## Respiro fuori-tempo\n- **Combo**: A11 combo + TxQ matrice densita come lettore spettrale + QxG vuoto continuo/discreto + tensione TRASCENDENZA_LIMITE/M_come_modulazione_quasiperiodica.\n- **Dipolo / punto-zero**: confine reale del supertile · lunghezza contigua equivalente; punto-zero = il blocco come unita' prima che diventi grammatica o contenitore.\n- **Piano superiore**: grafo della conoscenza / bicono dei dipoli. Il nodo non e' il valore del gap, ma il passaggio fra grammatica interna, confine esterno e denominatore.\n- **Proto-ipotesi**: se il core alto phi vive nel confine tra supertile, lo shuffle di supertile conserva piu' core dello shuffle contiguo con la stessa multiset di lunghezze. Se non lo fa, il confine esatto non e' ancora il portatore: il portatore e' ordine interno piu' scala lunga.\n- **Proiezione**: il label-set dei gap larghi manifesta la combo perche' tiene fisso il reader theta=1/phi e muove solo la relazione fra grammatica, boundary e blocco.\n\n## Claim Under Test\n> Il vantaggio del blocco Fibonacci lungo non sta solo nell'ordine interno conservato: sta nel confine reale del supertile. Quindi `supertile_shuffle` batte `same_length_contiguous_shuffle` a pari multiset di lunghezze.\n\n## Question\nIl core alto `[3, -4, 4, 6]` riconosce il confine di supertile Fibonacci, o riconosce soltanto blocchi lunghi con ordine interno conservato?\n\n## Experiment Design\n- Reference core phi: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`.\n- Supertile orders:\n - order 8: lunghezze `{21,34}`.\n - order 9: lunghezze `{34,55}`.\n - order 10: lunghezze `{55,89}`.\n - order 11: lunghezze `{89,144}`.\n- Modes:\n - `supertile_shuffle`: taglia secondo la parola di lunghezze Fibonacci e mescola i chunk.\n - `same_length_contiguous_shuffle`: usa la stessa multiset di lunghezze, ma su taglio contiguo misallineato.\n - `same_count_internal_shuffle`: conserva lunghezza e conteggio di ogni chunk allineato, ma distrugge ordine interno.\n - `same_mean_block_shuffle`: usa un blocco fisso pari alla lunghezza media del tiling, controllo di scala grossolana.\n- Denominatori grezzi: per ogni `mode|order`, 40 condizioni (`2 N * 4 phase * 1 threshold * 5 trials`); per ogni mode aggregato, 160 condizioni.\n\n## Results\nReference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n\nSintesi per mode:\n\n| mode | conditions | median Jaccard | low retention | high retention | all-high condition rate |\n|---|---:|---:|---:|---:|---:|\n| supertile_shuffle | 160 | 0.818182 | 1.00 | 1.00 | 108/160 = 0.67500 |\n| same_length_contiguous_shuffle | 160 | 0.818182 | 1.00 | 1.00 | 116/160 = 0.72500 |\n| same_count_internal_shuffle | 160 | 0.157895 | 0.25 | 0.25 | 0/160 = 0.00000 |\n| same_mean_block_shuffle | 160 | 0.500000 | 1.00 | 0.25 | 7/160 = 0.04375 |\n\nDettaglio per order:\n\n| mode | order | lengths | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% |\n|---|---:|---|---:|---:|---:|---:|---|\n| supertile_shuffle | 8 | {21,34} | 0.750000 | 1.00 | 0.75 | 14/40 = 0.350 | [-1, 1, -2, 2, -3, -4, 4] |\n| same_length_contiguous_shuffle | 8 | {21,34} | 0.818182 | 1.00 | 0.875 | 20/40 = 0.500 | [-1, 1, -2, 2, -3, 3, -4, 4] |\n| same_mean_block_shuffle | 8 | mean 29 | 0.444444 | 0.75 | 0.00 | 0/40 = 0.000 | [-1, 1, 33] |\n| same_count_internal_shuffle | 8 | {21,34} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| supertile_shuffle | 9 | {34,55} | 0.904545 | 1.00 | 1.00 | 29/40 = 0.725 | [-1, 1, -2, 2, -3, 3, -4, 4, -6] |\n| same_length_contiguous_shuffle | 9 | {34,55} | 0.818182 | 1.00 | 1.00 | 29/40 = 0.725 | [-1, 1, -2, 2, -3, 3, -4, 4, 6] |\n| same_mean_block_shuffle | 9 | mean 45 | 0.500000 | 1.00 | 0.25 | 0/40 = 0.000 | [-1, 1, -2] |\n| same_count_internal_shuffle | 9 | {34,55} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| supertile_shuffle | 10 | {55,89} | 0.909091 | 1.00 | 1.00 | 32/40 = 0.800 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] |\n| same_length_contiguous_shuffle | 10 | {55,89} | 0.833333 | 1.00 | 1.00 | 33/40 = 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6] |\n| same_mean_block_shuffle | 10 | mean 75 | 0.666667 | 1.00 | 0.50 | 1/40 = 0.025 | [-1, 1, -2, 2, -4, 4] |\n| same_count_internal_shuffle | 10 | {55,89} | 0.150000 | 0.25 | 0.25 | 0/40 = 0.000 | [] |\n| supertile_shuffle | 11 | {89,144} | 0.909091 | 1.00 | 1.00 | 33/40 = 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, 6, -7] |\n| same_length_contiguous_shuffle | 11 | {89,144} | 0.909091 | 1.00 | 1.00 | 34/40 = 0.850 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6] |\n| same_mean_block_shuffle | 11 | mean 122 | 0.666667 | 1.00 | 0.75 | 6/40 = 0.150 | [-1, 1, -2, 2, -4, 4] |\n| same_count_internal_shuffle | 11 | {89,144} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n\n## Key Findings\n1. **Verificato: il confine di supertile non batte il blocco contiguo a pari multiset di lunghezze.** `supertile_shuffle` fa all-high `108/160 = 0.675`; `same_length_contiguous_shuffle` fa `116/160 = 0.725`. Per order 9 sono pari (`29/40`), per order 10-11 il controllo contiguo e' leggermente sopra (`33/40`, `34/40` contro `32/40`, `33/40`).\n\n2. **Verificato: la condizione forte resta l'ordine interno.** `same_count_internal_shuffle` resta nullo su tutti gli order: all-high `0/160`, high retention mediana `0.25`, Jaccard mediano `0.157895`. Questo replica il nodo 18:34 dentro il tiling di supertile.\n\n3. **Verificato: la scala lunga come multiset e' piu' informativa del blocco medio fisso.** `same_mean_block_shuffle` conserva low retention mediana `1.00`, ma resta quasi nullo sul core alto completo (`7/160 = 0.04375`). Quindi non basta una scala media lunga; serve una distribuzione di lunghezze lunghe con ordine interno conservato.\n\n4. **Inferito dal confronto fra controlli: il portatore osservato e' ordine interno + multiset di lunghezze Fibonacci, non boundary esatto.** Il test non autorizza \"il core alto vive nel confine del supertile\"; autorizza \"il core alto richiede ordine interno e beneficia di lunghezze supertile-like\".\n\n## Verdict\n**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, il core alto phi non discrimina il confine esatto del supertile. `same_length_contiguous_shuffle` conserva il core alto almeno quanto `supertile_shuffle` (`116/160` vs `108/160`). Il core alto cade solo quando l'ordine interno viene distrutto (`0/160`) o quando la multiset di lunghezze viene compressa a blocco medio (`7/160`).\n\nLa formulazione valida e': il label-set alto phi misura una condizione congiunta di ordine interno e lunghezze Fibonacci-like; non misura ancora il boundary simbolico del supertile e non misura `gap_ratio`.\n\n## Bicono della scoperta\n- **Due radici**: confine di supertile · distribuzione di lunghezze con ordine interno.\n- **Singolare**: il chunk lungo prima della distinzione fra boundary grammaticale e blocco contiguo.\n- **Invariante di passaggio**: il core alto sopravvive quando l'ordine interno resta leggibile su lunghezze Fibonacci-like; non sopravvive a conteggio senza ordine.\n- **Campo di possibilita**: qui diventa possibile cercare un osservabile di boundary piu' fine del label-set; qui diventa non-possibile dichiarare che il label-set alto riconosce da solo il confine esatto del supertile.\n\n## Consecutio\nCostruire un gate di boundary simbolico che non legga solo il set dei label: confrontare label-set, posizione IDS dei label core e errore di label per chunk aligned/misaligned. Se il boundary reale esiste nell'osservabile, deve apparire nella geometria degli errori o nella posizione dei gap, non nel solo all-high rate.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: il verdict dichiara perimetro e count grezzi; non trasforma `108/160` in legge universale.\n- **L2 quantita vs ratio**: Jaccard, retention e all-high rate restano separati; `gap_ratio` dichiarato non testato.\n- **L3 no silent patching**: l'ipotesi \"supertile batte contiguo\" cade; il report non la salva cambiando osservabile.\n- **L4 edge cases**: order 10-11 mostrano controllo contiguo sopra supertile; sono il segnale, non rumore.\n- **L5 re-discovery**: la conservazione dell'ordine interno era gia' nota dal 18:34; il nuovo finding e' la non-discriminazione del boundary esatto da parte del label-set.\n\n## Files\n- Script: `tools/exp_gap_label_supertile_tiling_gate.py`\n- Data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json`\n- Report: `tools/data/reports/agent_20260508_1909.md`\n", tools/data/lab_graph.json:1076: "text": "> Il vantaggio del blocco Fibonacci lungo non sta solo nell'ordine interno conservato: sta nel confine reale del supertile. Quindi `supertile_shuffle`" tools/data/lab_graph.json:1082: "text": "Il core alto `[3, -4, 4, 6]` riconosce il confine di supertile Fibonacci, o riconosce soltanto blocchi lunghi con ordine interno conservato?" tools/data/lab_graph.json:1088: "text": "- Reference core phi: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`.\n- Supertile orders:\n - order 8: lunghezze `{21,34}`.\n - order 9: lunghezze `{34,55" tools/data/lab_graph.json:1094: "text": "Reference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n\nSintesi per mode:\n\n| mode | conditions | median Jaccard | low retention | high retention | all-hi" tools/data/lab_graph.json:1100: "text": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr" tools/data/lab_graph.json:1106: "text": "Costruire un gate di boundary simbolico che non legga solo il set dei label: confrontare label-set, posizione IDS dei label core e errore di label per" tools/data/lab_graph.json:1113: "title": "Substitution Grammar Gate Del Core Phi", tools/data/lab_graph.json:1114: "title_en": "Substitution Grammar Gate Del Core Phi", tools/data/lab_graph.json:1117: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core alto `[3, ", tools/data/lab_graph.json:1118: "verdict_en": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core alto `[3, ", tools/data/lab_graph.json:1119: "findings": "1. **Verificato: distruggere l'ordine interno del blocco collassa il core al livello random.** `internal_block_shuffle` resta su Jaccard mediano `0.150000-0.166667`, low retention `0.25`, high retention `0.25`, all-high `0/40` per ogni block_size. Il suo profilo coincide con `global_balanced_shuffle` entro il perimetro osservato.\n2. **Verificato: il vantaggio dei blocchi Fibonacci lunghi dipende d", tools/data/lab_graph.json:1120: "content_preview": "# Agent Report — Substitution Grammar Gate Del Core Phi\n**Date**: 2026-05-08 18:34\n**Piano**: 88\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, block_internal_order, high_label_condition_rate, low_label_condition_rate]\n**observable_contract**: claim=il core phi dei gap larghi separa lunghezza di blocco e grammatica interna del generatore; observable=retention dei label core sotto pertu", tools/data/lab_graph.json:1121: "content_full": "# Agent Report — Substitution Grammar Gate Del Core Phi\n**Date**: 2026-05-08 18:34\n**Piano**: 88\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, block_internal_order, high_label_condition_rate, low_label_condition_rate]\n**observable_contract**: claim=il core phi dei gap larghi separa lunghezza di blocco e grammatica interna del generatore; observable=retention dei label core sotto perturbazioni che preservano o distruggono ordine interno; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per mode e block_size; generator=phi_sturmian con contiguous_block_shuffle, internal_block_shuffle, global_balanced_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, block_size={34,40,55,64,89,96,128,144}; not_tested=gap_ratio, soglie 1.75/2.25, N fuori {377,610}, generatori Sturmiani non-phi, tiling esatto in supertile Fibonacci.\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nIl rientro dei label alti del core phi misura soltanto blocchi lunghi su scala Fibonacci, o misura l'ordine interno di sostituzione conservato dentro quei blocchi?\n\n## Experiment Design\n- Perimetro: stesso lettore label dei cycle 16:32, 17:15 e 18:05; `gap_ratio` non testato in questo ciclo.\n- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`.\n- Modes:\n - `contiguous_block_shuffle`: conserva ordine interno del blocco e mescola l'ordine tra blocchi.\n - `internal_block_shuffle`: conserva lunghezza e conteggio di ogni blocco, ma mescola i simboli dentro il blocco.\n - `global_balanced_shuffle`: conserva solo conteggio globale.\n- Blocchi Fibonacci: `34, 55, 89, 144`. Blocchi non-Fibonacci: `40, 64, 96, 128`.\n- Denominatori grezzi: per ogni `mode|block_size`, 40 condizioni (`2 N * 4 phase * 1 threshold * 5 trials`); per `global_balanced_shuffle`, 40 condizioni totali.\n\n## Results\nReference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`, Jaccard mediano `0.909091`, min `0.727273`.\n\nSintesi per mode:\n\n| mode | conditions | median Jaccard | low retention | high retention | all-high condition rate |\n|---|---:|---:|---:|---:|---:|\n| contiguous_block_shuffle | 320 | 0.583333 | 1.00 | 0.50 | 82/320 = 0.25625 |\n| internal_block_shuffle | 320 | 0.157895 | 0.25 | 0.25 | 0/320 = 0.00000 |\n| global_balanced_shuffle | 40 | 0.150000 | 0.25 | 0.25 | 0/40 = 0.00000 |\n\nDettaglio per blocco:\n\n| mode | block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% |\n|---|---:|---|---:|---:|---:|---:|---|\n| contiguous_block_shuffle | 34 | Fibonacci | 0.666667 | 1.00 | 0.50 | 3/40 = 0.075 | [-1, 1, -2, 2, -4, 4] |\n| contiguous_block_shuffle | 40 | non-Fibonacci | 0.500000 | 0.75 | 0.00 | 0/40 = 0.000 | [-1, 1, -2] |\n| contiguous_block_shuffle | 55 | Fibonacci | 0.750000 | 1.00 | 0.75 | 11/40 = 0.275 | [-1, 1, -2, 2, -3, 3, -4, 4] |\n| contiguous_block_shuffle | 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0/40 = 0.000 | [-1, 1, -2, 2] |\n| contiguous_block_shuffle | 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 26/40 = 0.650 | [-1, 1, -2, 2, -3, 3, -4, 4] |\n| contiguous_block_shuffle | 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 3/40 = 0.075 | [-1, 1, -2, 2] |\n| contiguous_block_shuffle | 128 | non-Fibonacci | 0.666667 | 1.00 | 0.75 | 7/40 = 0.175 | [-1, 1, -2, 2, -4, 4] |\n| contiguous_block_shuffle | 144 | Fibonacci | 0.909091 | 1.00 | 1.00 | 32/40 = 0.800 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6] |\n| internal_block_shuffle | 34 | Fibonacci | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| internal_block_shuffle | 40 | non-Fibonacci | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| internal_block_shuffle | 55 | Fibonacci | 0.150000 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| internal_block_shuffle | 64 | non-Fibonacci | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| internal_block_shuffle | 89 | Fibonacci | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| internal_block_shuffle | 96 | non-Fibonacci | 0.166667 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| internal_block_shuffle | 128 | non-Fibonacci | 0.166667 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| internal_block_shuffle | 144 | Fibonacci | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n| global_balanced_shuffle | n/a | none | 0.150000 | 0.25 | 0.25 | 0/40 = 0.000 | [34] |\n\n## Key Findings\n1. **Verificato: distruggere l'ordine interno del blocco collassa il core al livello random.** `internal_block_shuffle` resta su Jaccard mediano `0.150000-0.166667`, low retention `0.25`, high retention `0.25`, all-high `0/40` per ogni block_size. Il suo profilo coincide con `global_balanced_shuffle` entro il perimetro osservato.\n\n2. **Verificato: il vantaggio dei blocchi Fibonacci lunghi dipende dall'ordine interno conservato.** Con `contiguous_block_shuffle`, `89` porta all-high `26/40` e `144` porta `32/40`; con `internal_block_shuffle`, gli stessi `89` e `144` restano `0/40`. La lunghezza e il conteggio del blocco non trasportano il core alto quando l'ordine interno e' rimosso.\n\n3. **Verificato: gli edge case non-Fibonacci restano parziali, non nulli.** `128` conserva `-4` e `4` come stable labels 75% e ha high retention mediana `0.75`, ma all-high resta `7/40`. Questo impedisce la formula \"solo Fibonacci\"; la formula valida e' che, in questo perimetro, l'ordine interno conservato e le scale Fibonacci lunghe aumentano insieme la frequenza del core alto completo.\n\n4. **Inferito dal confronto 18:05 -> 18:34: il nodo regressivo non era solo block_size.** Il cycle 18:05 vedeva scala Fibonacci; questo cycle localizza la condizione mancante dentro il blocco: la grammatica interna del generatore deve restare leggibile. Il test non isola ancora il tiling esatto in supertile Fibonacci.\n\n## Verdict\n**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core alto `[3, -4, 4, 6]` non e' portato da lunghezza e conteggio del blocco. Quando il blocco conserva ordine interno, le scale Fibonacci `89` e `144` rendono frequente il core alto completo (`26/40`, `32/40`). Quando l'ordine interno viene distrutto dentro gli stessi blocchi, il core alto completo non compare (`0/40`) e il profilo cade sul controllo bilanciato globale.\n\nLa formulazione valida e': il label-set alto phi misura memoria di grammatica interna del generatore su blocchi lunghi, con vantaggio forte alle scale Fibonacci testate; non misura lunghezza generica, conteggio di simboli o valore `gap_ratio`.\n\n## Bicono della scoperta\n- **Due radici**: blocco come lunghezza/conteggio · blocco come grammatica ordinata.\n- **Singolare**: il blocco stesso, prima della scelta tra memoria interna e contenitore numerico.\n- **Invariante di passaggio**: il lettore label theta=1/phi e il perimetro spettrale restano fissi; cambia solo la leggibilita dell'ordine interno.\n- **Campo di possibilita**: qui diventa possibile testare supertile Fibonacci esatti contro blocchi contigui arbitrari; qui diventa non-possibile spiegare il core alto con sola lunghezza o densita locale.\n\n## Consecutio\nCostruire un `supertile_tiling_gate`: segmentare la parola phi in supertile Fibonacci reali e confrontare tre perturbazioni separate, `supertile_shuffle`, `same_length_contiguous_shuffle`, `same_count_internal_shuffle`. Se il supertile batte il blocco contiguo alla stessa lunghezza, la grammatica non e' solo interna al blocco ma vive anche nel confine tra supertile.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: il verdict dichiara perimetro, denominator e count grezzi; non formula universalita su tutti i generatori Sturmiani.\n- **L2 quantita vs ratio**: Jaccard, retention e all-high rate restano separati; `gap_ratio` dichiarato non testato.\n- **L3 no silent patching**: il claim precedente su block_size viene raffinato al nodo regressivo interno; il report non salva il framing \"solo Fibonacci\" perche `128` e' parziale.\n- **L4 edge cases**: `128` non-Fibonacci e' nominato come parziale forte (`7/40`, high retention `0.75`), non trattato come rumore.\n- **L5 re-discovery**: gap labeling e Sturmian sono meccanismi noti; il finding del cycle e' la separazione sperimentale tra lunghezza/conteggio e ordine interno del blocco.\n\n## Files\n- Script: `tools/exp_gap_label_substitution_grammar_gate.py`\n- Data: `tools/data/gap_label_substitution_grammar_gate_20260508_1834.json`\n- Report: `tools/data/reports/agent_20260508_1834.md`\n", tools/data/lab_graph.json:1127: "text": "> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?" tools/data/lab_graph.json:1133: "text": "Il rientro dei label alti del core phi misura soltanto blocchi lunghi su scala Fibonacci, o misura l'ordine interno di sostituzione conservato dentro " tools/data/lab_graph.json:1139: "text": "- Perimetro: stesso lettore label dei cycle 16:32, 17:15 e 18:05; `gap_ratio` non testato in questo ciclo.\n- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n- N" tools/data/lab_graph.json:1145: "text": "Reference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`, Jaccard mediano `0.909091`, min `0.727273`.\n\nSintesi per mode:\n\n| mode | conditions | median Jacca" tools/data/lab_graph.json:1151: "text": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr" tools/data/lab_graph.json:1157: "text": "Costruire un `supertile_tiling_gate`: segmentare la parola phi in supertile Fibonacci reali e confrontare tre perturbazioni separate, `supertile_shuff" tools/data/lab_graph.json:1164: "title": "Agent Report - Block Scale Gate Del Core Phi", tools/data/lab_graph.json:1165: "title_en": "Agent Report - Block Scale Gate Del Core Phi", tools/data/lab_graph.json:1168: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core phi ha due", tools/data/lab_graph.json:1169: "verdict_en": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core phi ha due", tools/data/lab_graph.json:1171: "content_preview": "# Agent Report - Block Scale Gate Del Core Phi\n**Date**: 2026-05-08 18:05\n**Piano**: 88\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate]\n**observable_contract**: claim=il core phi dei gap larghi richiede generatore globale, non solo lettore label; observable=retention dei label core sotto block shuffle; operator=", tools/data/lab_graph.json:1172: "content_full": "# Agent Report - Block Scale Gate Del Core Phi\n**Date**: 2026-05-08 18:05\n**Piano**: 88\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate]\n**observable_contract**: claim=il core phi dei gap larghi richiede generatore globale, non solo lettore label; observable=retention dei label core sotto block shuffle; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per block_size; generator=phi_sturmian con block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, block_size Fibonacci e non-Fibonacci; not_tested=gap_ratio, generatori Sturmiani non-phi, GUE/Poisson, soglie 1.75/2.25.\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nLa rottura del core phi sotto block shuffle dipende da una scala Fibonacci del generatore, o i label alti rientrano appena il blocco diventa lungo in modo generico?\n\n## Experiment Design\n- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo.\n- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`.\n- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`.\n- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`.\n- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi.\n- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale.\n\n## Results\nReference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`, Jaccard mediano `0.909091`, min `0.727273`.\n\n| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% |\n|---:|---|---:|---:|---:|---:|---|\n| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] |\n| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] |\n| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] |\n| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] |\n| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] |\n| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] |\n| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] |\n| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] |\n| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] |\n| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] |\n| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] |\n| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] |\n| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] |\n| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] |\n| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] |\n| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] |\n\nHigh-label condition rates at the main transition points:\n\n| block_size | family | label 3 | label -4 | label 4 | label 6 |\n|---:|---|---:|---:|---:|---:|\n| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 |\n| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 |\n| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 |\n| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 |\n| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 |\n| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 |\n| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 |\n\nDetected crossings with threshold `>=0.5`:\n\n| observable | first block_size |\n|---|---:|\n| median high retention | 34 |\n| all-high condition rate | 89 |\n| all-low condition rate | 34 |\n\n## Key Findings\n1. **Verificato: il nucleo basso rientra a scala 34.** Il passaggio `low retention=1.0` compare per la prima volta a `block_size=34`. Il controllo non-Fibonacci `40` arriva vicino ma resta senza `2` negli stable labels 75%.\n\n2. **Verificato: i label alti non rientrano come blocco generico.** A `34` compaiono `-4` e `4`, ma `6` resta raro (`0.075`) e `all-high condition rate=0`. A `55` il segnale alto e' parziale (`all-high=0.4`). A `89` diventa frequente (`all-high=0.625`), e a `144` diventa dominante (`0.825`).\n\n3. **Verificato: i controlli non-Fibonacci lunghi trasportano il basso ma non chiudono l'alto.** `64`, `96`, `128` hanno `low retention=1.0`, ma `all-high condition rate` resta `0.025`, `0.025`, `0.2`. La lunghezza da sola porta memoria locale; non ricostruisce il core alto come i blocchi Fibonacci `89/144`.\n\n4. **Inferito dal confronto 17:15 -> 18:05: il nodo regressivo e' scala del generatore.** Il cycle 17:15 aveva separato lettore e generatore; questo ciclo localizza la rottura dentro il generatore: basso = blocchi abbastanza lunghi, alto = blocchi Fibonacci lunghi.\n\n## Verdict\n**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core phi ha due scale. Il nucleo basso `[-1, 1, -2, 2]` rientra quando il blocco conserva texture locale sufficiente (`block_size=34`). I label alti `[3, -4, 4, 6]` richiedono blocchi Fibonacci lunghi: `89` e `144` portano il core alto come condizione frequente; i blocchi non-Fibonacci lunghi non chiudono la stessa struttura.\n\nLa formulazione valida e': il core alto del label-set phi misura memoria globale del generatore su scale Fibonacci, non lunghezza generica del blocco e non valore `gap_ratio`.\n\n## Bicono della scoperta\n- **Due radici**: nucleo basso locale del generatore / core alto globale del generatore.\n- **Singolare**: `block_size=34/55/89` come soglia di passaggio in cui il blocco smette di essere texture locale e inizia a trasportare ordine Fibonacci.\n- **Invariante di passaggio**: `[-1, 1]` attraversa quasi tutto; `[-1, 1, -2, 2]` attraversa dal blocco 34; `[3, -4, 4, 6]` attraversa stabilmente solo nelle scale Fibonacci lunghe.\n- **Campo di possibilita**: qui diventa possibile classificare i label phi in strati di memoria; qui diventa non-possibile trattare il core come un unico blocco indifferenziato.\n\n## Consecutio\nTestare il vincolo su generatori Sturmiani non-phi mantenendo separati reader e generator: se anche silver/bronze hanno basso locale e alto su proprie scale di approssimanti, il gate diventa proprieta Sturmiana; se solo phi mostra rientro alto Fibonacci, il claim si restringe a phi.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati.\n- **L2 quantita vs ratio**: il crossing `>=0.5` e' descrittivo; il claim poggia sul confronto tra famiglie di block size e frequenze label, non su un singolo valore.\n- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto.\n- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`.\n- **L5 re-discovery**: Fibonacci/Sturmian/gap labeling sono meccanismi noti; il finding del ciclo e' la stratificazione del core osservato in basso locale e alto Fibonacci-lungo.\n\n## Files\n- Script: `tools/exp_gap_label_block_scale_gate.py`\n- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json`\n- Report: `tools/data/reports/agent_20260508_1805.md`\n", tools/data/lab_graph.json:1178: "text": "> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?" tools/data/lab_graph.json:1184: "text": "La rottura del core phi sotto block shuffle dipende da una scala Fibonacci del generatore, o i label alti rientrano appena il blocco diventa lungo in " tools/data/lab_graph.json:1190: "text": "- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo.\n- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n- Nucleo b" tools/data/lab_graph.json:1196: "text": "Reference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`, Jaccard mediano `0.909091`, min `0.727273`.\n\n| block_size | family | median Jaccard | low retentio" tools/data/lab_graph.json:1202: "text": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `tr" tools/data/lab_graph.json:1219: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `trials=3`, `top_k=12`, `|n|<=34`, ", tools/data/lab_graph.json:1220: "verdict_en": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `trials=3`, `top_k=12`, `|n|<=34`, ", tools/data/lab_graph.json:1221: "findings": "1. **Verificato: il core completo resta nel generatore Sturmiano meccanico.** Nel perimetro `N/phase/threshold/trial` testato, `phi_sturmian` conserva tutti gli 8 label core in tutte le condizioni: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n2. **Verificato: la costruzione Fibonacci conserva il nucleo basso ma non il core completo.** `fibonacci_substitution` mantiene `[-1, 1, -2, 2, -4, 4]` del reference core ", tools/data/lab_graph.json:1222: "content_preview": "# Agent Report — Generator Gate Del Label-Set Phi\n**Date**: 2026-05-08 17:15\n**Piano**: 87\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, generator_jaccard, phi_core_overlap, core_retention]\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nIl core dei label phi sopravvive quando", tools/data/lab_graph.json:1223: "content_full": "# Agent Report — Generator Gate Del Label-Set Phi\n**Date**: 2026-05-08 17:15\n**Piano**: 87\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, generator_jaccard, phi_core_overlap, core_retention]\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nIl core dei label phi sopravvive quando resta il lettore `theta=1/phi` ma cambia il generatore della sequenza, oppure il label-set stabile e' una proprieta del generatore Sturmiano?\n\n## Experiment Design\n- Perimetro: stessa Hamiltoniana tight-binding dei cycle 03:30 e 16:32, `V=1`.\n- Lettore label fisso: ogni gap largo viene etichettato con il label intero `n` che minimizza la distanza tra IDS e `{n/phi}`.\n- Denominatore stratificato: `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `top_k=12`, `|n|<=34`.\n- Generator gate:\n - `phi_sturmian`: sequenza meccanica Sturmiana `theta=1/phi`.\n - `fibonacci_substitution`: parola di Fibonacci, costruzione indipendente ma nella stessa famiglia a bassa complessita.\n - `block_shuffle_34` e `block_shuffle_13`: blocchi locali Sturmiani preservati, ordine globale rotto.\n - `markov_phi`: transizioni locali stimate da phi, generatore Markoviano.\n - `balanced_random`: conteggio di 1 preservato, ordine rotto.\n- Null baseline: `markov_phi` e `balanced_random` testano se conteggio o transizioni locali bastano; i block shuffle testano quanta struttura globale resta quando il locale e preservato.\n\n## Results\nReference core phi, stimato dal perimetro completo: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n\n| generator | conditions | median Jaccard | min Jaccard | median overlap with phi core | core labels all conditions | phi-core missing |\n|---|---:|---:|---:|---:|---|---|\n| phi_sturmian | 144 | 0.909091 | 0.727273 | 0.727273 | [-1, 1, -2, 2, 3, -4, 4, 6] | [] |\n| fibonacci_substitution | 144 | 0.769231 | 0.538462 | 0.700000 | [-1, 1, -2, 2, -3, -4, 4] | [3, 6] |\n| block_shuffle_34 | 144 | 0.666667 | 0.333333 | 0.700000 | [-1, 1, -2, 2] | [3, -4, 4, 6] |\n| block_shuffle_13 | 144 | 0.357143 | 0.058824 | 0.166667 | [-1] | [1, -2, 2, 3, -4, 4, 6] |\n| markov_phi | 144 | 0.285714 | 0.047619 | 0.125000 | [] | [-1, 1, -2, 2, 3, -4, 4, 6] |\n| balanced_random | 144 | 0.157895 | 0.000000 | 0.111111 | [] | [-1, 1, -2, 2, 3, -4, 4, 6] |\n\nLabel-error and gap-count controls:\n\n| generator | median label error | median large gaps |\n|---|---:|---:|\n| phi_sturmian | 0.000095 | 29.0 |\n| fibonacci_substitution | 0.000031 | 26.5 |\n| block_shuffle_34 | 0.001638 | 26.0 |\n| block_shuffle_13 | 0.004118 | 52.0 |\n| markov_phi | 0.004074 | 55.0 |\n| balanced_random | 0.004128 | 56.0 |\n\n## Key Findings\n1. **Verificato: il core completo resta nel generatore Sturmiano meccanico.** Nel perimetro `N/phase/threshold/trial` testato, `phi_sturmian` conserva tutti gli 8 label core in tutte le condizioni: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n\n2. **Verificato: la costruzione Fibonacci conserva il nucleo basso ma non il core completo.** `fibonacci_substitution` mantiene `[-1, 1, -2, 2, -4, 4]` del reference core e perde `[3, 6]`. Questo conferma la famiglia a bassa complessita, non l'identita completa del generatore meccanico sotto questo perimetro.\n\n3. **Verificato: l'ordine locale non basta.** `block_shuffle_34` conserva solo `[-1, 1, -2, 2]`; `block_shuffle_13` conserva solo `[-1]`. Quando l'ordine globale viene rotto, il label-set scende anche se parti locali Sturmiane restano intatte.\n\n4. **Verificato: conteggio e transizioni locali non portano il core phi.** `markov_phi` e `balanced_random` hanno core vuoto, overlap mediano con il core phi `0.125000` e `0.111111`, e label-error mediano circa `0.0041`, contro `0.000095` di `phi_sturmian`.\n\n5. **Inferito dal confronto 03:30 -> 16:32 -> 17:15: il nodo regressivo e il generatore.** Il `first_two_ratio` cadeva sul denominatore; il label-set assorbiva `N/phase/threshold`; il generator gate mostra che la stabilita non appartiene al lettore label da solo. Serve generatore globale a bassa complessita Sturmiana.\n\n## Verdict\n**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `trials=3`, `top_k=12`, `|n|<=34`, il label-set phi e' stabile quando il generatore e Sturmiano meccanico. La stabilita non trasferisce a conteggio preservato, transizioni Markoviane o blocchi locali corti; trasferisce parzialmente alla costruzione Fibonacci e ai blocchi lunghi. Il claim valido non e' \"il label reader phi trova il core ovunque\"; e': il core dei gap larghi richiede struttura globale del generatore, con il lettore `theta=1/phi` come osservabile e non come causa.\n\n## Bicono della scoperta\n- **Due radici**: lettore aritmetico dei label · generatore globale della sequenza.\n- **Singolare**: il gap largo come punto in cui IDS, ordine della parola e label `n/phi` coincidono.\n- **Invariante di passaggio**: il nucleo basso `[-1, 1, -2, 2]` sopravvive quando resta abbastanza struttura globale; il core completo sopravvive nel generatore Sturmiano meccanico.\n- **Campo di possibilita**: qui diventa possibile classificare i generatori per quanta tassonomia phi trasportano; qui diventa non-possibile attribuire la trascendenza al solo fit dei label senza dichiarare il generatore.\n\n## Consecutio\nMisurare la legge di rottura del core per lunghezza di blocco: variare `block_size` su scala Fibonacci e non-Fibonacci, poi stimare dove compaiono i label alti `[3, -4, 4, 6]`. Se il passaggio avviene su blocchi Fibonacci, il generatore porta una scala critica; se avviene per qualsiasi blocco lungo, il core alto misura memoria globale generica.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non formula universalita fuori da `N/phase/threshold/trials/top_k/max_label`.\n- **L2 quantita vs ratio**: il risultato usa set, Jaccard, overlap e core retention; nessun claim dipende da un singolo ratio.\n- **L3 no silent patching**: il claim 03:30 sul `gap_ratio` resta vincolato; il claim 16:32 sul label-set resta valido ma riceve il nuovo denominatore `generatore`.\n- **L4 edge cases**: `fibonacci_substitution` e `block_shuffle_34` sono parziali, non trattati come fallimenti totali.\n- **L5 re-discovery**: gap labeling e parole Fibonacci/Sturmiane sono noti; il finding del cycle e' il generator gate sul core osservato nei cycle precedenti.\n\n## Files\n- Script: `tools/exp_gap_label_generator_gate.py`\n- Data: `tools/data/gap_label_generator_gate_20260508_1715.json`\n- Report: `tools/data/reports/agent_20260508_1715.md`\n", tools/data/lab_graph.json:1229: "text": "> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?" tools/data/lab_graph.json:1235: "text": "Il core dei label phi sopravvive quando resta il lettore `theta=1/phi` ma cambia il generatore della sequenza, oppure il label-set stabile e' una prop" tools/data/lab_graph.json:1247: "text": "Reference core phi, stimato dal perimetro completo: `[-1, 1, -2, 2, 3, -4, 4, 6]`.\n\n| generator | conditions | median Jaccard | min Jaccard | median overlap with phi core | core labels all conditions " tools/data/lab_graph.json:1253: "text": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1." tools/data/lab_graph.json:1259: "text": "Misurare la legge di rottura del core per lunghezza di blocco: variare `block_size` su scala Fibonacci e non-Fibonacci, poi stimare dove compaiono i l" tools/data/lab_graph.json:1270: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non passa come claim di valore `gap_ratio`; passa come stabilita del label-set nel perimetro `N={233,377,", tools/data/lab_graph.json:1271: "verdict_en": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non passa come claim di valore `gap_ratio`; passa come stabilita del label-set nel perimetro `N={233,377,", tools/data/lab_graph.json:1273: "content_preview": "# Agent Report — Gap Label Set Stabilizza Il Denominatore\n**Date**: 2026-05-08 16:32\n**Piano**: 87\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, label_jaccard, phase_stability, threshold_stability, scale_stability]\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nIl segnale di ", tools/data/lab_graph.json:1274: "content_full": "# Agent Report — Gap Label Set Stabilizza Il Denominatore\n**Date**: 2026-05-08 16:32\n**Piano**: 87\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [gap_label_set, label_jaccard, phase_stability, threshold_stability, scale_stability]\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nIl segnale di `phi` vive nel valore metrico `first_two_ratio`, o vive nel set di label dei gap larghi stimati come `m+n*theta` sull'IDS?\n\n## Experiment Design\n- Perimetro: stessa Hamiltoniana tight-binding Sturmiana del ciclo 03:30, `V=1`.\n- Domini: `theta=1/phi`, `1/silver`, `1/bronze`.\n- Null baseline: `balanced_random_phi_labels`, sequenze random con stessa lunghezza e stesso numero di 1 della sequenza phi matched; i label sono stimati contro `theta=1/phi`.\n- Denominatore stratificato: `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`.\n- Osservabile: per ogni spacing sopra `threshold*mean`, stimo `IDS=(gap_index+1)/N`, assegno il label intero `n` con `{n*theta}` piu vicino all'IDS, poi confronto il set dei label dei 12 gap larghi maggiori.\n- Stabilita: Jaccard mediano tra label-set globali e dentro gruppi phase/threshold/scale.\n\n## Results\n| domain | conditions | global Jaccard median | global Jaccard min | phase Jaccard median | threshold Jaccard median | scale Jaccard median | core labels all conditions |\n|---|---:|---:|---:|---:|---:|---:|---|\n| phi | 48 | 0.909091 | 0.727273 | 0.886364 | 1.000000 | 0.931818 | [-1, 1, -2, 2, 3, -4, 4, 6] |\n| silver | 48 | 0.833333 | 0.666667 | 0.846212 | 1.000000 | 0.910985 | [-1, 1, -2, 2, -3, 3, -4, 4] |\n| bronze | 48 | 0.750000 | 0.571429 | 0.754808 | 1.000000 | 0.825758 | [-1, 1, -2, 2, -3, -4, 4] |\n| balanced_random_phi_labels | 144 | 0.157895 | 0.000000 | 0.157895 | 0.154135 | 0.157895 | [] |\n\nErrori di label:\n\n| domain | median label error | median selected gaps | median large gaps |\n|---|---:|---:|---:|\n| phi | 0.000095 | 12 | 29.0 |\n| silver | 0.000879 | 12 | 27.0 |\n| bronze | 0.000594 | 12 | 26.0 |\n| balanced_random_phi_labels | 0.004118 | 12 | 54.5 |\n\n## Key Findings\n1. **Verificato: il label-set di phi resta stabile mentre il ratio no.** Nel ciclo 03:30 `first_two_ratio` phi batteva entrambi i controlli solo `25/48` condizioni matched. Qui il label-set phi ha Jaccard globale mediano `0.909091`, minimo `0.727273`, phase-stability `0.886364`, scale-stability `0.931818`, threshold-stability `1.0`.\n\n2. **Verificato: il null random rompe la tassonomia.** Il random bilanciato ha Jaccard globale `0.157895`, minimo `0.0`, nessun core label in tutte le condizioni. Il controllo preserva conteggio e lunghezza, non preserva l'ordine Sturmiano.\n\n3. **Verificato: phi non e unico come presenza di label stabili; e piu stabile nel perimetro testato.** Silver e bronze hanno stabilita propria (`0.833333` e `0.750000` Jaccard mediano). Il claim corretto non e \"solo phi ha gap-labeling\"; e: nel perimetro `N/phase/threshold` testato, phi sposta la trascendenza dal valore metrico mobile alla tassonomia dei gap, con stabilita piu alta dei controlli metallici e separazione netta dal random bilanciato.\n\n4. **Inferito dal confronto con il ciclo 03:30: il nodo regressivo era l'osservabile, non il dominio.** `first_two_ratio` sceglie due gap in ordine spettrale e quindi dipende dal denominatore. Il label-set assorbe quella mobilita perche misura la famiglia dei varchi, non la coppia iniziale.\n\n## Verdict\n**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non passa come claim di valore `gap_ratio`; passa come stabilita del label-set nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `top_k=12`, `|n|<=34`. La formulazione valida e: nel dominio Sturmiano phi, il set dei label dei gap larghi resta stabile sotto denominatore stratificato; il valore `first_two_ratio` resta un indicatore locale phase/threshold-sensitive.\n\n## Bicono della scoperta\n- **Due radici**: valore metrico mobile del primo/secondo gap largo · tassonomia stabile dei label dei gap larghi.\n- **Singolare**: l'IDS del varco, dove posizione spettrale e label aritmetico sono lo stesso passaggio.\n- **Invariante di passaggio**: il set dei label sopravvive al cambio di `N`, fase e soglia; il ratio non sopravvive.\n- **Campo di possibilita**: qui diventa possibile cercare la rete dei punti fissi relazionali nei label-set, non nei valori puntuali; qui diventa non-possibile usare `0.408953` come prova di trascendenza senza tassonomia.\n\n## Consecutio\nPortare il label-set fuori dal solo asse metallic mean: misurare se lo stesso core di label phi sopravvive in un dominio non-Sturmiano con ordine controllato, oppure se il core crolla appena il generatore perde bassa complessita combinatoria. Il prossimo discriminante e generatore, non soglia.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: il claim e perimetrato con `N/phase/threshold/top_k/max_label`; non formula universalita.\n- **L2 quantita vs ratio**: il risultato usa set/Jaccard/errori di label, non un ratio singolo.\n- **L3 no silent patching**: il claim precedente sul `gap_ratio` resta vincolato; il nuovo claim cambia osservabile e dichiara il nodo regressivo.\n- **L4 edge cases**: il minimo Jaccard phi `0.727273` entra nel verdict; non viene nascosto.\n- **L5 re-discovery**: gap labeling Sturmiano e IDS sono meccanismi noti; il finding del cycle e la stabilita stratificata del label-set contro il ratio mobile e contro il random bilanciato.\n\n## Files\n- Script: `tools/exp_gap_label_set_stability.py`\n- Data: `tools/data/gap_label_set_stability_20260508_1632.json`\n- Report: `tools/data/reports/agent_20260508_1632.md`\n", tools/data/lab_graph.json:1280: "text": "> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?" tools/data/lab_graph.json:1298: "text": "| domain | conditions | global Jaccard median | global Jaccard min | phase Jaccard median | threshold Jaccard median | scale Jaccard median | core labels all conditions |\n|---|---:|---:|---:|---:|---:" tools/data/lab_graph.json:1304: "text": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non passa come claim di valore `gap_ratio`; passa come " tools/data/lab_graph.json:1310: "text": "Portare il label-set fuori dal solo asse metallic mean: misurare se lo stesso core di label phi sopravvive in un dominio non-Sturmiano con ordine cont" tools/data/lab_graph.json:1321: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma un segnale di denominatore Sturmiano nel punto stor", tools/data/lab_graph.json:1322: "verdict_en": "**CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma un segnale di denominatore Sturmiano nel punto stor", tools/data/lab_graph.json:1324: "content_preview": "# Agent Report — Gap Ratio Porta Il Denominatore\n**Date**: 2026-05-08 03:30\n**Piano**: 86\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [first_two_ratio, top2_ratio, large_gap_count]\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nIl `gap_ratio` quasiperiodico e una firma del gap-labeling di ", tools/data/lab_graph.json:1325: "content_full": "# Agent Report — Gap Ratio Porta Il Denominatore\n**Date**: 2026-05-08 03:30\n**Piano**: 86\n**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [first_two_ratio, top2_ratio, large_gap_count]\n\n## Claim Under Test\n> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?\n\n## Question\nIl `gap_ratio` quasiperiodico e una firma del gap-labeling di phi, o e un osservabile sensibile al denominatore scelto (`N`, fase Sturmiana, soglia del gap largo)?\n\n## Experiment Design\n- Perimetro: Hamiltoniana tight-binding su sequenze Sturmiane a `V=1`.\n- Domini: `theta=1/phi`, `1/silver`, `1/bronze`; baseline `balanced_random` con stesso numero di 1 della sequenza phi matched.\n- Denominatore stratificato: `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`.\n- Osservabile originale: `first_two_ratio = primo spacing sopra threshold*mean / secondo spacing sopra threshold*mean`.\n- Osservabile di controllo interno: `top2_ratio = gap massimo / secondo gap massimo`.\n- Null baseline: 3 random bilanciati per condizione, stessa lunghezza e stesso conteggio di 1 del phi matched.\n\n## Results\nCaso storico replicato:\n\n| domain | N | phase | threshold | n_large | first_two_ratio | top2_ratio |\n|---|---:|---:|---:|---:|---:|---:|\n| phi | 500 | 0.00 | 2.00 | 30 | 0.408953 | 1.132017 |\n| silver | 500 | 0.00 | 2.00 | 36 | 1.048223 | 1.060236 |\n| bronze | 500 | 0.00 | 2.00 | 34 | 1.302786 | 1.164995 |\n\nStratificazione completa:\n\n| domain | first_two median | first_two IQR | first_two range | top2 median | n_large median |\n|---|---:|---:|---:|---:|---:|\n| phi | 0.454346 | 0.408341-0.547506 | 0.197603-3.694942 | 1.577373 | 29.0 |\n| silver | 1.048223 | 0.762590-1.456942 | 0.148351-2.158859 | 1.436926 | 27.0 |\n| bronze | 0.976329 | 0.518940-1.096559 | 0.293143-2.110008 | 1.454174 | 26.0 |\n| balanced_random | 1.034120 | 0.746931-1.492484 | 0.242478-4.047607 | 1.130399 | 54.5 |\n\nMatched comparison:\n\n| comparison | count |\n|---|---:|\n| phi < silver | 38/48 |\n| phi < bronze | 30/48 |\n| phi < both | 25/48 |\n\n## Key Findings\n1. **Il valore vecchio e verificato, non inventato.** A `N=500`, `phase=0`, `threshold=2.0`, il test riproduce `phi=0.408953`, `silver=1.048223`, `bronze=1.302786`. Fonte: output dello script, verificato.\n\n2. **Il claim universale non regge.** Quando il denominatore viene aperto, phi batte entrambi i controlli solo in `25/48` condizioni matched. In `23/48` condizioni almeno un controllo ha `first_two_ratio` piu basso. Fonte: stratificazione, verificato.\n\n3. **Il ratio originale misura posizione del primo varco largo, non solo taglia dei varchi.** Il controllo `top2_ratio` non replica la separazione: phi ha mediana `1.577373`, sopra silver `1.436926`, bronze `1.454174` e random `1.130399`. Inferito dal confronto tra `first_two_ratio` e `top2_ratio`.\n\n4. **Il nodo regressivo e il denominatore dell'osservabile.** `first_two_ratio` non e una proprieta bulk dello spettro; dipende da quali due gap superano per primi la soglia lungo l'ordine spettrale. Il claim valido deve dichiarare `N`, fase e soglia come parte atomica.\n\n## Verdict\n**CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma un segnale di denominatore Sturmiano nel punto storico e una tendenza mediana su questo perimetro; non conferma dominanza matched su tutte le fasi, scale e soglie. La formulazione corretta e: nel perimetro stratificato `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, phi abbassa la mediana del `first_two_ratio`, ma il supporto matched e `25/48`.\n\n## Bicono della scoperta\n- **Due radici**: valore puntuale replicato · denominatore stratificato che lo vincola.\n- **Singolare**: l'ordine spettrale dei gap larghi, dove il primo e il secondo varco diventano osservabile.\n- **Invariante di passaggio**: resta la necessita del denominatore `N/phase/threshold`; non resta il claim di gap-labeling universale.\n- **Campo di possibilita**: qui diventa possibile cercare una firma robusta nei label dei gap, non nella coppia dei primi due gap sopra soglia; qui diventa non-possibile usare `0.408953` come prova senza denominatore atomico.\n\n## Consecutio\nCostruire il passo successivo sul label, non sul ratio: per ogni gap largo, stimare il miglior label `m+n*theta` e misurare stabilita del label-set sotto `phase`, `N` e soglia. Se il label-set di phi resta stabile mentre `first_two_ratio` si muove, la trascendenza passa dal valore metrico alla tassonomia dei varchi.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: nessun \"sempre\"; il report include i `23/48` contro-casi matched.\n- **L2 quantita vs ratio**: riporto mediane, IQR, range, conteggi matched e caso storico.\n- **L3 no silent patching**: il claim originale e vincolato esplicitamente, non salvato cambiando nome al risultato.\n- **L4 edge cases**: i casi `N=233 phase=0.25/0.75` e `N=377 phase=0/0.25` entrano nel verdict come rotture del claim universale.\n- **L5 re-discovery**: tight-binding Sturmiano e gap spacing sono strumenti standard; il finding e nel denominator gate, non nella diagonalizzazione.\n\n## Files\n- Script: `tools/exp_quasiperiodic_gap_ratio_denominator.py`\n- Data: `tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json`\n- Report: `tools/data/reports/agent_20260508_0330.md`\n", tools/data/lab_graph.json:1331: "text": "> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?" tools/data/lab_graph.json:1337: "text": "Il `gap_ratio` quasiperiodico e una firma del gap-labeling di phi, o e un osservabile sensibile al denominatore scelto (`N`, fase Sturmiana, soglia de" tools/data/lab_graph.json:1355: "text": "**CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma u" tools/data/lab_graph.json:1376: "content_full": "# Agent Report — Duality Contrast Weakens With Scale in Prime Gaps\n**Date**: 2026-05-08 00:11\n**Piano**: 85\n**Tension explored**: DUALITA_DIPOLARE_VS_ILLUSORIA (0.9) + BOUNDARY (0.8)\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [SR, SR2, L1, L2, triple_var]\n\n## Claim Under Test\n> Two types of duality: (1) dipolar — generative, the model (det=-1), (2) illusory — dispersive, entropy (det=+1). Incoherent rules produce the second. The vincolo DUALITA_DET_DENOMINATOR_GATE (cycle 2042) established: det(M) is not the primary discriminator; the informative part is the real-vs-shuffle gap, strong at low/intermediate scales and weak at the large-window end.\n\n## Question\nIf the duality contrast (real vs shuffle z-score) weakens with scale for prime gaps, what is the quantitative rate of weakening? And does GUE show the same weakening, or is it scale-independent?\n\n## Experiment Design\n- **Metric**: z-score of 5 canonical observables (SR, SR2, L1, L2, triple_var), real sequence vs 50 shuffles per window\n- **Scope**: 200,000 prime gaps (unfolded by local mean, kernel=100), GUE eigenvalue spacings (beta=2, matrix size ~2400, unfolded), Poisson (exponential i.i.d.)\n- **Windows**: 8 log-spaced sizes from 49 to 49,999 gaps, 5 non-overlapping windows per size\n- **Null baseline**: Poisson (no structure at any scale). Additional null: if effect size is scale-independent, z ~ N^0.5 exactly (alpha = 0.50)\n- **Scaling law**: fit |z| ~ N^alpha. alpha = 0.5 means constant effect size. alpha < 0.5 means weakening. alpha > 0.5 means strengthening.\n- **Seed check**: seed=42 (primary) and seed=137 (replication)\n\n## Results\n\n### Global z-scores (N=5000 gaps, seed=42)\n\n| Domain | SR | SR2 | L1 | L2 | triple_var |\n|---------|--------|-------|--------|-------|------------|\n| Primes | -6.71 | +0.35 | -6.87 | -3.63 | -7.72 |\n| GUE | -6.27 | -3.42 | -8.16 | -2.41 | -8.32 |\n| Poisson | +0.72 | +0.39 | -0.13 | -1.12 | -0.64 |\n\n### Windowed contrast: prime gaps z_SR by window size\n\n| W_size | z_SR | z_L1 | z_L2 | z_triple_var | eff_SR (z/sqrt(N)) |\n|--------|--------|--------|-------|--------------|--------------------|\n| 49 | -1.09 | -1.82 | +0.29 | -1.46 | -0.156 |\n| 134 | -1.40 | -1.48 | +0.35 | -1.17 | -0.121 |\n| 359 | -2.33 | -2.28 | -0.49 | -2.16 | -0.123 |\n| 965 | -3.44 | -3.05 | -1.18 | -3.23 | -0.111 |\n| 2,589 | -4.59 | -4.22 | -1.83 | -4.60 | -0.090 |\n| 6,947 | -6.65 | -5.69 | -2.99 | -6.44 | -0.080 |\n| 18,637 | -10.12 | -8.93 | -3.99 | -9.58 | -0.074 |\n| 49,999 | -17.03 | -13.78 | -6.96 | -15.93 | -0.076 |\n\nThe effect size (z/sqrt(N)) halves from 0.156 to 0.076 across the range.\n\n### Power-law scaling alpha: |z| ~ N^alpha\n\n| Observable | Primes (s42) | Primes (s137) | GUE (s42) | GUE (s137) | Poisson (s42) |\n|------------|---------------|---------------|-------------|-------------|---------------|\n| SR | 0.392 +/- 0.012 | 0.397 +/- 0.015 | 0.571 +/- 0.088 | 0.505 +/- 0.009 | 0.263 (R2=0.75) |\n| L1 | 0.315 +/- 0.029 | 0.322 +/- 0.026 | 0.547 +/- 0.032 | 0.655 +/- 0.073 | ~0 (R2=0.14) |\n| triple_var | 0.372 +/- 0.028 | 0.370 +/- 0.026 | 0.637 +/- 0.057 | 0.607 +/- 0.048 | ~0 (R2=0.06) |\n| L2 | 0.486 +/- 0.025 | 0.532 +/- 0.015 | 0.780 +/- 0.020 | 0.475 +/- 0.204 | 0.165 (R2=0.91) |\n\nAll R-squared > 0.95 for primes and GUE (except Poisson, which has no signal).\n\n## Key Findings\n\n1. **The duality contrast for prime gaps weakens with scale.** The scaling exponent alpha is robustly below 0.5 for the three most discriminating observables: SR (alpha = 0.39), L1 (alpha = 0.32), triple_var (alpha = 0.37). This means the effect size decays as N^{alpha-0.5}: approximately N^{-0.11} for SR, N^{-0.18} for L1. The dipolar structure is real but dissolving.\n\n2. **GUE eigenvalue spacings show alpha >= 0.5.** For SR (alpha = 0.54), L1 (alpha = 0.60), triple_var (alpha = 0.63): the structure is scale-independent or strengthening. GUE repulsion is built into the ensemble at all scales. Primes mimic GUE locally but lack this scale invariance.\n\n3. **The discriminator is alpha, not the observable value.** Primes and GUE have similar z-scores at any given window size. The difference is in how z scales with N. Primes: z ~ N^{0.39}. GUE: z ~ N^{0.54}. At small windows they overlap; at large windows they diverge.\n\n4. **L2 is borderline.** Alpha ~ 0.49-0.53 for primes, near 0.5. L2 is not discriminating between primes and GUE in this metric. The three robust discriminators are SR, L1, and triple_var.\n\n5. **Poisson shows no scaling.** Alpha near 0 or incoherent (R-squared < 0.15 for L1 and triple_var). No structure to weaken.\n\n## Verdict\n**CONSTRAINT on DUALITA_DIPOLARE_VS_ILLUSORIA / BOUNDARY**: The boundary between dipolar and illusory duality in prime gaps is not a location but a trajectory. The duality contrast (real-vs-shuffle) weakens as N^{-0.11} for SR and N^{-0.18} for L1, approaching Poisson asymptotically. GUE has alpha >= 0.5 (no weakening). The discriminator between primes and GUE is the scaling exponent alpha, not the observable value at any fixed scale.\n\nL5 check: The weakening of prime gap correlations with scale is consistent with known number-theoretic expectations (Hardy-Littlewood, Lemke Oliver-Soundararajan). What is measured here is the quantitative rate of weakening (alpha ~ 0.39 for SR) and the comparison with GUE (alpha ~ 0.54), which provides a new quantitative discriminator. Default hypothesis: this may be a re-parameterization of known asymptotic decay rates; the exponent alpha as a universality discriminator needs comparison with RMT literature on finite-size effects.\n\n## Bicono della scoperta\n\n- **Due radici** (dipolo primario): duality contrast that strengthens with scale (GUE, alpha > 0.5, deep structure) and duality contrast that weakens with scale (primes, alpha < 0.5, dissolving structure). The two are dual because both show dipolar ordering at small scales — the difference emerges only when you observe the trajectory.\n- **Singolare** (where duality is not): alpha = 0.5 exactly. At this point there is no scale dependence — the effect size is constant, and the question \"dipolar or illusory?\" has no answer. L2 lives near this singularity.\n- **Invariante di passaggio**: the ordering itself (z < 0 for all SR, L1, triple_var at all tested scales for primes). The ordering survives across scales even as it weakens. What changes is the rate, not the sign.\n- **Campo di possibilita**: here it becomes possible to discriminate primes from GUE by their scaling trajectory, not by their local statistics. Here it becomes non-possible to claim that prime gaps have \"the same universality class as GUE\" without specifying the scale and the observable.\n\n## Files\n- Script: `tools/exp_duality_scale_contrast.py` (reusable, parameterized)\n- Data: `tools/data/duality_scale_contrast_20260508.json`\n- Report: `tools/data/reports/agent_20260508_0011.md`\n", tools/data/lab_graph.json:1388: "text": "If the duality contrast (real vs shuffle z-score) weakens with scale for prime gaps, what is the quantitative rate of weakening? And does GUE show the" tools/data/lab_graph.json:1394: "text": "- **Metric**: z-score of 5 canonical observables (SR, SR2, L1, L2, triple_var), real sequence vs 50 shuffles per window\n- **Scope**: 200,000 prime gaps (unfolded by local mean, kernel=100), GUE eigenv" tools/data/lab_graph.json:1601: "content_full": "# Agent Report — Det Non È Il Discriminatore Della Dualità\n**Date**: 2026-05-07 20:42\n**Piano**: 84\n**Tension explored**: DUALITA_DIPOLARE_VS_ILLUSORIA (0.9)\n**verdict**: CONSTRAINT\nobservables_registry: n/a\nobservables_used: [det_M, trace_M, eigvals, det_vs_shuffle_z]\n\n## Claim Under Test\n> Due tipi di dualita: dipolare-generativa come det=-1; illusoria-dispersiva come det=+1.\n\nIl nodo regressivo e il framing stesso: se `det(M)` misura la dualita, allora nei gap primi deve separare il reale dallo shuffle e deve muoversi verso un polo leggibile (`-1` o `+1`) su scala.\n\n## Question\nIl determinante della matrice di trasferimento dei gap e un discriminatore operativo della dualita, o e solo un segno debole che va subordinato al denominator order-vs-shuffle?\n\n## Experiment Design\n- Perimetro: 7 finestre di 50,000 gap primi, start nominali `100..50,000,000`.\n- Stato: `x_i=(g_i, g_{i-1})`, `y_i=(g_{i+1}, g_i)`.\n- Fit: matrice `M` least-squares tale che `y = M @ x`.\n- Osservabili: `det(M)`, `trace(M)`, autovalori, z-score di `det(M)` contro 20 shuffle della stessa finestra.\n- Null baseline: shuffle dei gap nella stessa finestra, stessa distribuzione marginale, ordine distrutto.\n- Tool: `tools/exp_det_drift.py`; output salvato anche in `tools/data/exp_det_drift_20260507_2042.json`.\n\n## Results\n| start | median p | ln(p) | det(M) | det shuffle mean | z(det) |\n|---:|---:|---:|---:|---:|---:|\n| 100 | 287,383 | 12.57 | -0.388048 | -0.378211 | -2.97 |\n| 1,000 | 289,273 | 12.58 | -0.388161 | -0.379353 | -2.49 |\n| 10,000 | 302,927 | 12.62 | -0.387626 | -0.378640 | -3.08 |\n| 100,000 | 409,777 | 12.92 | -0.387652 | -0.377266 | -3.43 |\n| 1,000,000 | 1,349,147 | 14.11 | -0.380926 | -0.371107 | -4.43 |\n| 10,000,000 | 10,402,927 | 16.16 | -0.381040 | -0.370080 | -2.52 |\n| 50,000,000 | 50,444,239 | 17.74 | -0.370717 | -0.368393 | -0.97 |\n\nFit scala:\n- `det(M) = -0.425503 + 0.002982 * ln(p)`\n- `R^2 = 0.9044`\n- range reale: `-0.388161 .. -0.370717`\n- range shuffle mean: `-0.379353 .. -0.368393`\n- z-score: `[-3.0, -2.5, -3.1, -3.4, -4.4, -2.5, -1.0]`\n\n## Key Findings\n1. **Il segno negativo non basta a nominare dualita dipolare.** Il reale resta negativo in tutte le finestre, ma vale circa `-0.39`, non `-1`. La formulazione `dipolare = det=-1` non regge come osservabile diretto dei gap primi in questo fit.\n\n2. **Lo shuffle ha lo stesso segno.** Il null preserva det negativo medio (`-0.379 .. -0.368`). Il segno deriva in larga parte dalla geometria dell'embedding e dalla marginale dei gap, non solo dall'ordine generativo.\n\n3. **Il denominatore reale-vs-shuffle e presente ma decade.** Lo z-score e forte nelle finestre basse/intermedie (`-2.49 .. -4.43`) e cade a `-0.97` nella finestra `p ~ 5.0e7`. Il claim valido e differenza ordinata contro shuffle su perimetro finito, non polo `-1`.\n\n4. **La scala va verso zero, non verso `+1`.** Il fit aumenta da `-0.4255` con slope positivo `0.002982`; l'extrapolazione a `+1` e astronomica (`~3.83e207`) e non e contenuto operativo. Il movimento osservato e indebolimento del segno, non transizione misurata a dualita illusoria `+1`.\n\n## Verdict\n**CONSTRAINT on DUALITA_DIPOLARE_VS_ILLUSORIA**: nel perimetro transfer-matrix dei gap primi, `det(M)` non e discriminatore primario della dualita. Il segno negativo e condiviso dallo shuffle; la parte informativa e lo scarto reale-vs-shuffle, che esiste fino alle scale misurate ma si indebolisce. La dualita dipolare va formulata come supporto ordinato contro null, non come valore `det=-1` del fit lineare.\n\n## Bicono della scoperta\n- **Due radici**: segno del determinante · denominatore contro shuffle.\n- **Singolare**: la matrice fitted sui gap, dove il segno appare prima della distinzione ordine/null.\n- **Invariante di passaggio**: non il valore `-1`; sopravvive solo la domanda se il reale si separa dallo shuffle.\n- **Campo di possibilità**: qui diventa possibile usare det come diagnostica secondaria del supporto; qui diventa non-possibile usare `det=-1`/`det=+1` come tassonomia diretta della dualita nei gap primi.\n\n## Consecutio\nApplicare lo stesso test a un perimetro non-primi con ordine costruito e marginale identica: Beatty coerente, shuffle Beatty, GUE spacing order, Poisson. Se il segno resta condiviso, il nodo regressivo e l'embedding `x=(g_i,g_{i-1})`; se lo scarto z replica solo nei domini ordinati, det resta utile come osservabile subordinato al denominator gate.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: non dichiaro zero, sempre, mai. `z=-0.97` alla scala alta e riportato come indebolimento, non assenza.\n- **L2 quantita vs ratio**: riporto valori assoluti di det e differenza z contro shuffle; non uso solo trend percentuali.\n- **L3 no silent patching**: il claim originale `det=-1` e vincolato esplicitamente. Non viene salvato cambiando definizione in conclusione.\n- **L4 edge cases**: la finestra alta e controesempio al supporto forte; resta dentro il perimetro e determina il verdict.\n- **L5 re-discovery**: least-squares autoregressive embedding e shuffle test sono strumenti standard; il report non tagga NEW.\n\n## Files\n- Script: `tools/exp_det_drift.py`\n- Data: `tools/data/exp_det_drift_20260507_2042.json`\n- Discarded replication data: `tools/data/duality_gate_transfer_20260507_2042.json`\n- Report: `tools/data/reports/agent_20260507_2042.md`\n", tools/data/lab_graph.json:1827: "tensions": "META (score 0.898)", tools/data/lab_graph.json:1831: "content_preview": "# Agent Report — META Assertion Gate\n**Date**: 2026-05-07 14:58 UTC \n**Piano**: 76 \n**Tension explored**: META (score 0.898) \n**observables_registry**: N/A — nessun osservabile canonico SR/SR2/L1/L2/triple_var usato \n**Tool**: `tools/meta_assertion_gate.py` \n**Data**: `tools/data/meta_assertion_gate.json`\n\n## Claim Under Test\n> Tutti i 11 test passano — verifica che non stiamo testando solo tautologie.\n\n## Question\nIl PASS 11/11 di `tools/dipartimento.py --check` misura contenuto operativo ", tools/data/lab_graph.json:1832: "content_full": "# Agent Report — META Assertion Gate\n**Date**: 2026-05-07 14:58 UTC \n**Piano**: 76 \n**Tension explored**: META (score 0.898) \n**observables_registry**: N/A — nessun osservabile canonico SR/SR2/L1/L2/triple_var usato \n**Tool**: `tools/meta_assertion_gate.py` \n**Data**: `tools/data/meta_assertion_gate.json`\n\n## Claim Under Test\n> Tutti i 11 test passano — verifica che non stiamo testando solo tautologie.\n\n## Question\nIl PASS 11/11 di `tools/dipartimento.py --check` misura contenuto operativo o un denominatore misto che combina algebra, fixture e segnali locali?\n\n## Experiment Design\n- **Baseline**: esecuzione degli 11 test di `dipartimento.verifica_asserzioni`.\n- **No-data gate**: riesecuzione degli stessi test con `dipartimento.DATA` puntato a una directory vuota temporanea.\n- **Classificazione**: ogni test riceve `gate_class`, `tautology_risk`, `no_data_status`, `data_dependency`.\n- **Fonti**: Verificato da `tools/dipartimento.py`, `tools/meta_assertion_gate.py`, `tools/data/meta_assertion_gate.json`. Inferito: la lettura strutturale del denominatore.\n\n## Results\n\n| Bucket | Count |\n|---|---:|\n| Baseline PASS | 11/11 |\n| PASS anche senza `tools/data` | 6/11 |\n| PASS dipendente da fixture o contesto | 5/11 |\n| PASS ad alto rischio tautologico | 4/11 |\n| PASS a rischio medio | 4/11 |\n| PASS a rischio basso | 3/11 |\n\n| Test | Baseline | No-data | Dependency | Risk | Class |\n|---|---|---|---|---|---|\n| A1 | PASS | PASS | data_independent | high | algebraic_identity |\n| A2 | PASS | PASS | data_independent | high | same_rule_dynamics |\n| A3 | PASS | PASS | data_independent | medium | same_rule_dynamics |\n| A4 | PASS | PASS | data_independent | high | algebraic_identity |\n| A5 | PASS | PASS | data_independent | high | algebraic_identity |\n| C1 | PASS | SKIP | fixture_or_context_dependent | low | cached_fixture |\n| C2 | PASS | PASS | data_independent | medium | local_recomputed_signal |\n| C3 | PASS | SKIP | fixture_or_context_dependent | low | cached_fixture |\n| G1 | PASS | SKIP | fixture_or_context_dependent | medium | cached_fixture_phi_distance |\n| G2 | PASS | SKIP | fixture_or_context_dependent | medium | cached_fixture_phi_convergence |\n| N2 | PASS | SKIP | fixture_or_context_dependent | low | cached_fixture |\n\nNumeri di dettaglio: C1 spacing GUE-like con `=0.6150`; C2 `pearson_r=-0.6428`; C3 distanza media da intero `0.0270`; G1 best phi-distance `0.021256`; G2 `12/13` domini convergono a phi; N2 `=1.0000`.\n\n## Key Findings\n1. **Il PASS 11/11 non e un denominatore unico.** Sei test sopravvivono senza `tools/data`; cinque test cadono a SKIP quando le fixture spariscono.\n2. **Il nucleo A1-A5 e prevalentemente algebra della regola.** A1, A4 e A5 sono identita o chiusure dirette della definizione; A2 e A3 misurano dinamica della stessa mappa, non un dominio esterno.\n3. **C2 e il solo segnale locale ricalcolato dentro il gate.** Non usa fixture, ma resta un test piccolo e thresholded su primi sotto 10000.\n4. **C1, C3, G1, G2 e N2 sono replay di deposito.** Sono utili come controllo di coerenza del lab; non contano come cinque nuove conferme indipendenti a ogni ciclo.\n\n## Verdict\n**CONSTRAINT on META**: il campo non deve piu leggere `11/11 PASS` come evidenza singola. Nel perimetro `agent_20260507_1458`, la verifica si scinde in algebra/same-rule, segnale locale ricalcolato, e fixture replay. Ogni claim futuro sulla verifica deve riportare `gate_class`, `no_data_status` e `data_dependency` per test.\n\n## Consecutio\nLa prossima inversione vive nel nodo regressivo di `dipartimento.verifica_asserzioni`: il sommario PASS/FAIL deve aggregare per classe di denominatore, non solo per stato. Il prossimo cycle puo trasformare `meta_assertion_gate.py` in gate del report mattutino, dopo un secondo run su seed invariato.\n\n## Seme\nAggiornato `tools/data/seme.json` con tensione/vincolo `META_ASSERTION_GATE`.\n\n## Auto-audit: 5 lenti\n- **L1 hard constraint vs bias**: nessun claim di zero/sempre/mai. I conteggi sono dichiarati nel perimetro esatto degli 11 test.\n- **L2 quantita vs ratio**: riporto sia count assoluti sia frazioni implicite; nessun confronto tra spazi di taglia diversa decide il claim.\n- **L3 no silent patching**: META non e risolta. Viene ristretto il denominatore del PASS.\n- **L4 edge cases**: C2 resta separato: passa senza fixture ma non entra nel bucket algebraico.\n- **L5 re-discovery**: classificare test per dipendenza da fixture e rischio tautologico e audit metodologico standard. Non taggo NEW.\n", tools/data/lab_graph.json:1968: "label": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:1969: "label_en": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:1970: "label_short": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/lab_graph.json:1971: "label_short_en": "Agent Report - Supertile Tiling Gate Del Core Phi", tools/data/repairs/repair_20260508_1909.md:4:Source report: Agent Report - Supertile Tiling Gate Del Core Phi tools/data/repairs/repair_20260508_1909.md:12:- falsifier_summary: Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove "nullo/cade" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label. tools/data/repairs/repair_20260508_1909.md:16:- reasoning: Il ciclo ha falsificato il claim operativo: il confine reale del supertile non batte il taglio contiguo a stessa multiset, mentre lo shuffle interno distrugge il core. L'evidenza sposta il nodo regressivo dal boundary geometrico al contratto osservabile: il portatore e' ordine interno piu' scala lunga, non il taglio esatto del supertile. tools/data/repairs/repair_20260508_1909.md:22:- L1 medium: Riformulare: "same_count_internal_shuffle azzera l'all-high rate del core alto completo, ma lascia sopravvivere singoli label ad alta scala; prossimo ciclo separi all-high, per-label retention e stable-label count". tools/data/repairs/repair_20260508_1909.md:23:- L4 medium: Isolare l'edge case per label: verificare quali tra [3,-4,4,6] sopravvivono sotto internal shuffle e dichiarare il perimetro come "core completo cade", non "core alto cade". tools/data/repairs/repair_20260508_1909.md:34:- Se `gap_ratio` e' richiesto dalla direttiva, includilo; se non lo testi, dichiaralo come not_tested. tools/data/aeternitas/aeternitas_20260508_002036.json:33: "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/biconi/bicono_20260508_1834.json:6: "raw": "qui diventa possibile testare supertile Fibonacci esatti contro blocchi contigui arbitrari; qui diventa non-possibile spiegare il core alto con sola lunghezza o densita locale.", tools/data/biconi/bicono_20260508_1834.json:7: "possibile": "testare supertile Fibonacci esatti contro blocchi contigui arbitrari", tools/data/biconi/bicono_20260508_1834.json:8: "non_possibile": "spiegare il core alto con sola lunghezza o densita locale" tools/data/biconi/bicono_20260508_1715.json:4: "invariante": "il nucleo basso `[-1, 1, -2, 2]` sopravvive quando resta abbastanza struttura globale; il core completo sopravvive nel generatore Sturmiano meccanico.", tools/data/biconi/bicono_20260508_1805.json:2: "radici": "nucleo basso locale del generatore / core alto globale del generatore.", tools/data/biconi/bicono_20260508_1805.json:6: "raw": "qui diventa possibile classificare i label phi in strati di memoria; qui diventa non-possibile trattare il core come un unico blocco indifferenziato.", tools/data/biconi/bicono_20260508_1805.json:8: "non_possibile": "trattare il core come un unico blocco indifferenziato" tools/data/biconi/bicono_20260508_1909.json:2: "radici": "confine di supertile · distribuzione di lunghezze con ordine interno.", tools/data/biconi/bicono_20260508_1909.json:4: "invariante": "il core alto sopravvive quando l'ordine interno resta leggibile su lunghezze Fibonacci-like; non sopravvive a conteggio senza ordine.", tools/data/biconi/bicono_20260508_1909.json:6: "raw": "qui diventa possibile cercare un osservabile di boundary piu' fine del label-set; qui diventa non-possibile dichiarare che il label-set alto riconosce da solo il confine esatto del supertile.", tools/data/biconi/bicono_20260508_1909.json:8: "non_possibile": "dichiarare che il label-set alto riconosce da solo il confine esatto del supertile" tools/data/exp_coherence_length.json:20: "z_score": -0.6255512941022541, tools/data/exp_coherence_length.json:31: "z_score": -2.0495471669325873, tools/data/exp_coherence_length.json:42: "z_score": -1.8618253522319197, tools/data/exp_coherence_length.json:53: "z_score": -2.152553891754349, tools/data/exp_coherence_length.json:64: "z_score": -3.7438803177035282, tools/data/exp_coherence_length.json:75: "z_score": -5.165351470282256, tools/data/exp_coherence_length.json:86: "z_score": -4.362748435193601, tools/data/exp_coherence_length.json:97: "z_score": -6.669297361346016, tools/data/exp_coherence_length.json:108: "z_score": -7.602147033894685, tools/data/exp_coherence_length.json:119: "z_score": -10.261117375690283, tools/data/exp_coherence_length.json:130: "z_score": -10.354077583418766, tools/data/exp_coherence_length.json:141: "z_score": -11.710144417968557, tools/data/exp_coherence_length.json:152: "z_score": -13.647620534825059, tools/data/exp_coherence_length.json:163: "z_score": -17.145388495248852, tools/data/exp_coherence_length.json:174: "z_score": -18.3549014976655, tools/data/exp_coherence_length.json:185: 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tools/data/markov_scale_function.json:1549: "z_score": -374.4509053533658, tools/data/markov_scale_function.json:1562: "z_score": -292.06268493756596, tools/data/markov_scale_function.json:1575: "z_score": -374.4683359058588, tools/data/markov_scale_function.json:1588: "z_score": -258.5690308214999, tools/data/markov_scale_function.json:1601: "z_score": -475.6656794244817, tools/data/markov_scale_function.json:1614: "z_score": -337.616548610337, tools/data/markov_scale_function.json:1627: "z_score": -366.88956526095535, tools/data/markov_scale_function.json:1640: "z_score": -380.2111074647591, tools/data/markov_scale_function.json:1653: "z_score": -343.2583166415478, tools/data/markov_scale_function.json:1666: "z_score": -376.0006955749757, tools/data/markov_scale_function.json:1679: "z_score": -385.67075272762975, tools/data/markov_scale_function.json:1692: "z_score": -376.4952857501171, tools/data/markov_scale_function.json:1705: "z_score": -520.3519141912241, tools/data/markov_scale_function.json:1718: "z_score": -351.7551757775756, tools/data/markov_scale_function.json:1731: "z_score": -354.51758893107404, tools/data/markov_scale_function.json:1744: "z_score": -327.5456407786881, tools/data/seme_axioms.json:381: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_F6", tools/data/seme_axioms.json:382: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_axioms.json:386: "nota": "Dal domandatore (2026-04-05T07:15). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_axioms.json:400: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme_axioms.json:417: "id": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F6", tools/data/seme_axioms.json:418: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_axioms.json:422: "nota": "Dal domandatore (2026-04-05T07:23). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_axioms.json:512: "nota": "Shuffle audit 2026-04-24. z-scores: primes=-26.6, gue=-14.7, fibonacci=+8.0, coupled_osc=+43.5, percolation=+16.1. Consecutio: i domini ordering-GUE hanno memoria Markov come i primi? O il meccanismo di ordine e diverso?", tools/data/seme_axioms.json:687: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme_axioms.json:688: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_axioms.json:692: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_axioms.json:715: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme_axioms.json:723: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme_axioms.json:724: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", tools/data/domandatore/domandatore_20260312_1238.json:4: "domanda_originale": "Se il core condiviso m_core.py è l'operatore e le 4 coppie sono le osservazioni, il core stesso diventa non falsificabile — chi testa il core? Il livello L2 falsificatori usa il core che dovrebbe falsificare.", tools/data/domandatore/domandatore_20260312_1238.json:8: "domanda": "Se \"Se il core condiviso m_core.py è l'operatore e le 4 coppie sono le osservazioni, il core stesso diventa non falsificabile — chi testa il core? Il livello L2 falsificatori usa il core che dovrebbe falsificare.\" e' vero, cosa dice il contrario (1/X)?", tools/data/domandatore/domandatore_20260312_1238.json:13: "domanda": "Tra il si e il no di \"Se il core condiviso m_core.py è l'operatore e le 4 coppie sono le osservazioni, il core stesso diventa non falsificabile — chi testa il core? Il livello L2 falsificatori usa il core che dovrebbe falsificare.\", cosa c'e' al bordo?", tools/data/domandatore/domandatore_20260312_1238.json:18: "domanda": "Se \"Se il core condiviso m_core.py è l'operatore e le 4 coppie sono le osservazioni, il core stesso diventa non falsificabile — chi testa il core? Il livello L2 falsificatori usa il core che dovrebbe falsificare.\" vale qui, vale anche in un altro contesto?", tools/data/domandatore/domandatore_20260312_1238.json:23: "domanda": "Cosa romperebbe \"Se il core condiviso m_core.py è l'operatore e le 4 coppie sono le osservazioni, il core stesso diventa non falsificabile — chi testa il core? Il livello L2 falsificatori usa il core che dovrebbe falsificare.\"? Qual e' il controesempio?", tools/data/domandatore/domandatore_20260312_1238.json:28: "domanda": "\"Se il core condiviso m_core.py è l'operatore e le 4 coppie sono le osservazioni, il core stesso diventa non falsificabile — chi testa il core? Il livello L2 falsificatori usa il core che dovrebbe falsificare.\" vale a scala grande quanto a scala piccola?", tools/data/domandatore/domandatore_20260312_1238.json:34: "tensione": "Tra il si e il no di \"Se il core condiviso m_core.py è l'operatore e le 4 coppie sono le osservazioni, il core stesso diventa non falsificabile — chi testa il core? Il livello L2 falsificatori usa il core che dovrebbe falsificare.\", cosa c'e' al bordo?", tools/data/lab_session_log.jsonl:10:{"timestamp": "2026-05-03T03:41:17.680136+00:00", "cycle_ts": "20260503_0330", "piano": "60", "sync_blocked": true, "errors": "Falsifier: HIGH flags. Report non sincronizzato. Endpoint lab fallito.", "agent": {"file": "agent_20260503_0330.md", "size": 8987, "title": "Agent Report — Markov Memory Has Two Visible Layers: Pairs Shape the Angle, Triples Shape the Depth"}, "falsifier": {"output_file": "falsifier_20260503_0330.json", "coherent": false, "n_flags": 4, "n_high": 1, "n_medium": 2, "summary": "The main structural conclusion (two layers exist) is likely correct but the 'NEW' label overstates novelty (L5: standard cumulant hierarchy), and the 'Mk3 adds nothing' claim has a serious confound (L2: undersampling inflates Mk3 surrogate variance, making z-scores uninformative rather than evidence", "lenses_high": ["L2"]}, "valutatore": {"decision": "NEXT_CYCLE", "confidence": "high", "action_type": null}} tools/data/lab_session_log.jsonl:11:{"timestamp": "2026-05-04T09:09:10.899135+00:00", "cycle_ts": "20260504_0901", "piano": "60", "sync_blocked": true, "errors": "Falsifier: HIGH flags. Report non sincronizzato.", "agent": {"file": "agent_20260504_0901.md", "size": 7631, "title": "Agent Report — The Two Markov Layers Are Coupled at the Boundary: One Phase Transition, Two Projections"}, "falsifier": {"output_file": "falsifier_20260504_0901.json", "coherent": false, "n_flags": 5, "n_high": 1, "n_medium": 2, "summary": "The core finding — uniform shuffle destroys both layers at the same rate — is the expected null behavior of random permutations (L5), reported at a resolution too coarse to measure sub-bin differences (L4), and validated against a meaningless Poisson control (L2). The experiment is well-designed but", "lenses_high": ["L5"]}, "valutatore": {"decision": "NEXT_CYCLE", "confidence": "high", "action_type": null}} tools/data/lab_session_log.jsonl:39:{"timestamp": "2026-05-08T17:20:46.660494+00:00", "cycle_ts": "20260508_1715", "piano": "87", "sync_blocked": false, "errors": "", "agent": {"file": "agent_20260508_1715.md", "size": 6747, "title": "Agent Report — Generator Gate Del Label-Set Phi"}, "falsifier": {"output_file": "falsifier_20260508_1715.json", "coherent": false, "n_flags": 2, "n_high": 0, "n_medium": 2, "summary": "Il report e' in gran parte coerente sui dati tabellari, ma si rompe su L4 per l'extra label Fibonacci non isolato e su L3 per drift tra gap_ratio dichiarato e label-set realmente testato.", "lenses_high": []}, "valutatore": {"decision": "NEXT_CYCLE", "confidence": "medium", "action_type": null}} tools/data/lab_session_log.jsonl:40:{"timestamp": "2026-05-08T18:11:31.429833+00:00", "cycle_ts": "20260508_1805", "piano": "88", "sync_blocked": true, "errors": "Falsifier: HIGH flags. Report non sincronizzato.", "agent": {"file": "agent_20260508_1805.md", "size": 7730, "title": "Agent Report - Block Scale Gate Del Core Phi"}, "falsifier": {"output_file": "falsifier_20260508_1805.json", "coherent": false, "n_flags": 5, "n_high": 1, "n_medium": 3, "summary": "Il report e' parzialmente coerente, ma si rompe su L4/L1: usa wording da vincolo per un segnale non-zero e mescola retention mediana con stabilita 75% senza isolare l'edge case non-Fibonacci.", "lenses_high": ["L4"]}, "valutatore": {"decision": "NEXT_CYCLE", "confidence": "high", "action_type": null}} tools/data/lab_session_log.jsonl:41:{"timestamp": "2026-05-08T18:39:15.433109+00:00", "cycle_ts": "20260508_1834", "piano": "88", "sync_blocked": false, "errors": "", "agent": {"file": "agent_20260508_1834.md", "size": 8813, "title": "Agent Report — Substitution Grammar Gate Del Core Phi"}, "falsifier": {"output_file": "falsifier_20260508_1834.json", "coherent": false, "n_flags": 3, "n_high": 0, "n_medium": 2, "summary": "Il report e' parzialmente coerente: L3 si rompe per drift tra gap_ratio dichiarato e label-set testato, e L1 richiede di limitare lo zero al core alto completo, non ai label alti residui.", "lenses_high": []}, "valutatore": {"decision": "REDESIGN", "confidence": "medium", "action_type": null}} tools/data/lab_session_log.jsonl:42:{"timestamp": "2026-05-08T19:15:38.073076+00:00", "cycle_ts": "20260508_1909", "piano": "89", "sync_blocked": false, "errors": "", "agent": {"file": "agent_20260508_1909.md", "size": 9634, "title": "Agent Report - Supertile Tiling Gate Del Core Phi"}, "falsifier": {"output_file": "falsifier_20260508_1909.json", "coherent": false, "n_flags": 3, "n_high": 0, "n_medium": 2, "summary": "Il report e' coerente nel falsificare il boundary esatto, ma L1/L4 si rompono dove \"nullo/cade\" viene usato per il core alto intero mentre i dati mostrano sopravvivenza parziale per-label.", "lenses_high": []}, "valutatore": {"decision": "REDESIGN", "confidence": "high", "action_type": null}} tools/data/gap_label_generator_gate_20260508_1715.json:26: "reference_core_phi": [ tools/data/gap_label_generator_gate_20260508_1715.json:41: "core_labels_all_conditions": [], tools/data/gap_label_generator_gate_20260508_1715.json:98: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_generator_gate_20260508_1715.json:99: "min_overlap_with_phi_core": 0.0, tools/data/gap_label_generator_gate_20260508_1715.json:100: "reference_core_retained": [], tools/data/gap_label_generator_gate_20260508_1715.json:101: "reference_core_missing": [ tools/data/gap_label_generator_gate_20260508_1715.json:116: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:176: "median_overlap_with_phi_core": 0.16666666666666666, tools/data/gap_label_generator_gate_20260508_1715.json:177: "min_overlap_with_phi_core": 0.058823529411764705, tools/data/gap_label_generator_gate_20260508_1715.json:178: "reference_core_retained": [ tools/data/gap_label_generator_gate_20260508_1715.json:181: "reference_core_missing": [ tools/data/gap_label_generator_gate_20260508_1715.json:195: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:262: "median_overlap_with_phi_core": 0.7, tools/data/gap_label_generator_gate_20260508_1715.json:263: "min_overlap_with_phi_core": 0.35714285714285715, tools/data/gap_label_generator_gate_20260508_1715.json:264: "reference_core_retained": [ tools/data/gap_label_generator_gate_20260508_1715.json:270: "reference_core_missing": [ tools/data/gap_label_generator_gate_20260508_1715.json:281: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:354: "median_overlap_with_phi_core": 0.7, tools/data/gap_label_generator_gate_20260508_1715.json:355: "min_overlap_with_phi_core": 0.5833333333333334, tools/data/gap_label_generator_gate_20260508_1715.json:356: "reference_core_retained": [ tools/data/gap_label_generator_gate_20260508_1715.json:364: "reference_core_missing": [ tools/data/gap_label_generator_gate_20260508_1715.json:373: "core_labels_all_conditions": [], tools/data/gap_label_generator_gate_20260508_1715.json:431: "median_overlap_with_phi_core": 0.125, tools/data/gap_label_generator_gate_20260508_1715.json:432: "min_overlap_with_phi_core": 0.0, tools/data/gap_label_generator_gate_20260508_1715.json:433: "reference_core_retained": [], tools/data/gap_label_generator_gate_20260508_1715.json:434: "reference_core_missing": [ tools/data/gap_label_generator_gate_20260508_1715.json:449: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:525: "median_overlap_with_phi_core": 0.7272727272727273, tools/data/gap_label_generator_gate_20260508_1715.json:526: "min_overlap_with_phi_core": 0.7272727272727273, tools/data/gap_label_generator_gate_20260508_1715.json:527: "reference_core_retained": [ tools/data/gap_label_generator_gate_20260508_1715.json:537: "reference_core_missing": [] tools/data/gap_label_generator_gate_20260508_1715.json:545: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:626: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:707: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:788: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:866: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:944: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:1022: "core_labels_all_conditions": [], tools/data/gap_label_generator_gate_20260508_1715.json:1085: "core_labels_all_conditions": [], tools/data/gap_label_generator_gate_20260508_1715.json:1148: "core_labels_all_conditions": [], tools/data/gap_label_generator_gate_20260508_1715.json:1211: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:1276: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:1341: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:1406: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:1478: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:1550: "core_labels_all_conditions": [ tools/data/gap_label_generator_gate_20260508_1715.json:1622: "core_labels_all_conditions": [], tools/data/gap_label_generator_gate_20260508_1715.json:1684: "core_labels_all_conditions": [], tools/data/gap_label_generator_gate_20260508_1715.json:1746: "core_labels_all_conditions": [], tools/data/banchi_custom/banco_gen_gap_ratio_cons_gxe_qxg.json:2: "nome": "GEN_GAP_RATIO_CONS_GxE_QxG", tools/data/banchi_custom/banco_gen_gap_ratio_cons_gxe_qxg.json:3: "id_prefix": "GEN_GAP_RATIO_CONS_GxE", tools/data/banchi_custom/banco_gen_gap_ratio_cons_gxe_qxg.json:5: "codice": "\nN = 500\n# Confronto phi vs controllo per gap_ratio\nresults = {}\nfor name, theta in [('phi', 1/PHI), ('silver', 1/SILVER), ('bronze', 1/BRONZE)]:\n seq = sturmian_sequence(theta, N)\n H = hamiltonian(seq, 1.0)\n eigs = np.sort(eigvalsh(H))\n spacings = np.diff(eigs)\n mean_sp = np.mean(spacings)\n gaps = [(i, sp) for i, sp in enumerate(spacings) if sp > 2 * mean_sp]\n if len(gaps) >= 2:\n value = gaps[0][1] / gaps[1][1]\n else:\n value = 0.0\n results[name] = value\n print(\" %s: gap_ratio = %s\" % (name, value))\n\nprint(json.dumps(results, indent=2, default=str))", tools/data/banchi_custom/banco_gen_gap_ratio_cons_gxe_qxg.json:6: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f6.json:2: "nome": "GEN_GAP_RATIO_FALSIFICA_F6", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f6.json:3: "id_prefix": "GEN_GAP_RATIO_FALSIFICA", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f6.json:5: "codice": "\nN = 500\n# Confronto phi vs controllo per gap_ratio\nresults = {}\nfor name, theta in [('phi', 1/PHI), ('silver', 1/SILVER), ('bronze', 1/BRONZE)]:\n seq = sturmian_sequence(theta, N)\n H = hamiltonian(seq, 1.0)\n eigs = np.sort(eigvalsh(H))\n spacings = np.diff(eigs)\n mean_sp = np.mean(spacings)\n gaps = [(i, sp) for i, sp in enumerate(spacings) if sp > 2 * mean_sp]\n if len(gaps) >= 2:\n value = gaps[0][1] / gaps[1][1]\n else:\n value = 0.0\n results[name] = value\n print(\" %s: gap_ratio = %s\" % (name, value))\n\nprint(json.dumps(results, indent=2, default=str))", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f6.json:6: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/banchi_custom/banco_gen_gap_ratio_t8_paper_a_esposto.json:2: "nome": "GEN_GAP_RATIO_T8_paper_A_esposto", tools/data/banchi_custom/banco_gen_gap_ratio_t8_paper_a_esposto.json:3: "id_prefix": "GEN_GAP_RATIO_T8_paper_A", tools/data/banchi_custom/banco_gen_gap_ratio_t8_paper_a_esposto.json:5: "codice": "\nN = 500\n# Confronto phi vs controllo per gap_ratio\nresults = {}\nfor name, theta in [('phi', 1/PHI), ('silver', 1/SILVER), ('bronze', 1/BRONZE)]:\n seq = sturmian_sequence(theta, N)\n H = hamiltonian(seq, 1.0)\n eigs = np.sort(eigvalsh(H))\n spacings = np.diff(eigs)\n mean_sp = np.mean(spacings)\n gaps = [(i, sp) for i, sp in enumerate(spacings) if sp > 2 * mean_sp]\n if len(gaps) >= 2:\n value = gaps[0][1] / gaps[1][1]\n else:\n value = 0.0\n results[name] = value\n print(\" %s: gap_ratio = %s\" % (name, value))\n\nprint(json.dumps(results, indent=2, default=str))", tools/data/banchi_custom/banco_gen_gap_ratio_t8_paper_a_esposto.json:6: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/seme_archive/piano_zero.json:440: "CORE_LOGIC", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f3.json:2: "nome": "GEN_GAP_RATIO_FALSIFICA_F3", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f3.json:3: "id_prefix": "GEN_GAP_RATIO_FALSIFICA", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f3.json:5: "codice": "\nN = 500\n# Confronto phi vs controllo per gap_ratio\nresults = {}\nfor name, theta in [('phi', 1/PHI), ('silver', 1/SILVER), ('bronze', 1/BRONZE)]:\n seq = sturmian_sequence(theta, N)\n H = hamiltonian(seq, 1.0)\n eigs = np.sort(eigvalsh(H))\n spacings = np.diff(eigs)\n mean_sp = np.mean(spacings)\n gaps = [(i, sp) for i, sp in enumerate(spacings) if sp > 2 * mean_sp]\n if len(gaps) >= 2:\n value = gaps[0][1] / gaps[1][1]\n else:\n value = 0.0\n results[name] = value\n print(\" %s: gap_ratio = %s\" % (name, value))\n\nprint(json.dumps(results, indent=2, default=str))", tools/data/banchi_custom/banco_gen_gap_ratio_falsifica_f3.json:6: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/banchi_custom/banco_gen_gap_ratio_t2_normalizzatore_trascende.json:2: "nome": "GEN_GAP_RATIO_T2_normalizzatore_trascende", tools/data/banchi_custom/banco_gen_gap_ratio_t2_normalizzatore_trascende.json:3: "id_prefix": "GEN_GAP_RATIO_T2_normalizzatore", tools/data/banchi_custom/banco_gen_gap_ratio_t2_normalizzatore_trascende.json:5: "codice": "\nN = 500\n# Confronto phi vs controllo per gap_ratio\nresults = {}\nfor name, theta in [('phi', 1/PHI), ('silver', 1/SILVER), ('bronze', 1/BRONZE)]:\n seq = sturmian_sequence(theta, N)\n H = hamiltonian(seq, 1.0)\n eigs = np.sort(eigvalsh(H))\n spacings = np.diff(eigs)\n mean_sp = np.mean(spacings)\n gaps = [(i, sp) for i, sp in enumerate(spacings) if sp > 2 * mean_sp]\n if len(gaps) >= 2:\n value = gaps[0][1] / gaps[1][1]\n else:\n value = 0.0\n results[name] = value\n print(\" %s: gap_ratio = %s\" % (name, value))\n\nprint(json.dumps(results, indent=2, default=str))", tools/data/banchi_custom/banco_gen_gap_ratio_t2_normalizzatore_trascende.json:6: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/banchi_custom/banco_gen_gap_ratio_t9_linguaggio_metafisico.json:2: "nome": "GEN_GAP_RATIO_T9_linguaggio_metafisico", tools/data/banchi_custom/banco_gen_gap_ratio_t9_linguaggio_metafisico.json:3: "id_prefix": "GEN_GAP_RATIO_T9_linguaggio", tools/data/banchi_custom/banco_gen_gap_ratio_t9_linguaggio_metafisico.json:5: "codice": "\nN = 500\n# Confronto phi vs controllo per gap_ratio\nresults = {}\nfor name, theta in [('phi', 1/PHI), ('silver', 1/SILVER), ('bronze', 1/BRONZE)]:\n seq = sturmian_sequence(theta, N)\n H = hamiltonian(seq, 1.0)\n eigs = np.sort(eigvalsh(H))\n spacings = np.diff(eigs)\n mean_sp = np.mean(spacings)\n gaps = [(i, sp) for i, sp in enumerate(spacings) if sp > 2 * mean_sp]\n if len(gaps) >= 2:\n value = gaps[0][1] / gaps[1][1]\n else:\n value = 0.0\n results[name] = value\n print(\" %s: gap_ratio = %s\" % (name, value))\n\nprint(json.dumps(results, indent=2, default=str))", tools/data/banchi_custom/banco_gen_gap_ratio_t9_linguaggio_metafisico.json:6: "criterio": "gap_ratio(phi) piu' vicino a rapporto in Z[phi] (gap labeling) rispetto ai controlli", tools/data/seme_archive/piano_89.json:157: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme_archive/piano_89.json:158: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", tools/data/seme_archive/piano_89.json:163: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme_archive/piano_89.json:169: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme_archive/piano_89.json:170: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_archive/piano_89.json:172: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_archive/piano_89.json:178: "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" tools/data/seme_archive/piano_89.json:209: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme_archive/piano_89.json:394: "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_archive/piano_89.json:409: "Tensioni risolte: {'METRIC_TENSOR', 'G_BLANK_SHELL_STRATIFIED_GATE', 'TRASCENDENZA_LIMITE', 'G_BLANK_SHELL_DILATION_GATE', 'G_BLANK_SHELL_TQGER_GATE', 'G_BLANK_SHELL_SCALE_LAW_GATE', 'TENSIONE_ENTITA', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'G_POTENZIALE_NULLA', 'QPG_GAP_RATIO_DENOMINATOR_GATE', 'PIANO_PRIMARIO_DUE_ASSIOMI'}" tools/data/seme_archive/piano_85.json:282: "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_archive/piano_87.json:151: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme_archive/piano_87.json:152: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", tools/data/seme_archive/piano_87.json:157: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme_archive/piano_87.json:163: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme_archive/piano_87.json:164: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_archive/piano_87.json:166: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_archive/piano_87.json:194: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme_archive/piano_87.json:282: "pattern": "Score numerico da ranking concettuale", tools/data/seme_archive/piano_87.json:283: "code": "score = rank * 10 + intensita" tools/data/seme_archive/piano_87.json:442: "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_archive/piano_87.json:457: "Tensioni risolte: {'DUALITA_DIPOLARE_VS_ILLUSORIA', 'TENSIONE_ENTITA', 'METRIC_TENSOR', 'G_BLANK_SHELL_STRATIFIED_GATE', 'G_BLANK_SHELL_TQGER_GATE', 'PIANO_PRIMARIO_DUE_ASSIOMI', 'QPG_GAP_RATIO_DENOMINATOR_GATE', 'G_POTENZIALE_NULLA', 'G_BLANK_SHELL_DILATION_GATE', 'G_BLANK_SHELL_SCALE_LAW_GATE', 'TRASCENDENZA_LIMITE'}" tools/data/seme_archive/piano_86.json:151: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme_archive/piano_86.json:152: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_archive/piano_86.json:154: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_archive/piano_86.json:182: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme_archive/piano_86.json:191: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme_archive/piano_86.json:192: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", tools/data/seme_archive/piano_86.json:197: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme_archive/piano_86.json:369: "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_archive/piano_86.json:384: "Nuove tensioni: {'TRANS_BOUNDARY_TRASCENDENZA_LIMITE', 'COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE', 'TENS_SCALE_TRASCENDENZA_LIMITE', 'COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE', 'FALS_BREAK_TRASCENDENZA_LIMITE', 'M_trascendenza_limite_attuale_L0'}", tools/data/seme_archive/piano_88.json:157: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme_archive/piano_88.json:158: "claim": "Nel perimetro agent_20260508_0330, il vecchio gap_ratio quasiperiodico replica esattamente a N=500 phase=0 threshold=2.0 (phi=0.408953, silver=1.048223, bronze=1.302786), ma non e claim universale. Stratificando N in {233,377,500,610}, phase in {0,0.25,0.5,0.75}, threshold in {1.75,2.0,2.25}, phi ha mediana first_two_ratio=0.454 contro silver=1.048 e bronze=0.976; batte entrambi i controlli solo 25/48 condizioni matched. Il ratio va formulato come segnale phase/threshold-sensitive del denominatore Sturmiano, non come gap-labeling confermato.", tools/data/seme_archive/piano_88.json:163: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme_archive/piano_88.json:169: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme_archive/piano_88.json:170: "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", tools/data/seme_archive/piano_88.json:172: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme_archive/piano_88.json:178: "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" tools/data/seme_archive/piano_88.json:209: "claim": "Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici?", tools/data/seme_archive/piano_88.json:394: "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_archive/piano_88.json:409: "Tensioni risolte: {'QPG_GAP_RATIO_DENOMINATOR_GATE', 'TRASCENDENZA_LIMITE', 'G_BLANK_SHELL_TQGER_GATE', 'G_POTENZIALE_NULLA', 'PIANO_PRIMARIO_DUE_ASSIOMI', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'G_BLANK_SHELL_DILATION_GATE', 'STRUCTURAL_CHECK_20260508', 'METRIC_TENSOR', 'G_BLANK_SHELL_STRATIFIED_GATE', 'TENSIONE_ENTITA', 'G_BLANK_SHELL_SCALE_LAW_GATE'}" tools/data/piano11b_gue_test.json:5: "score": 10, tools/data/boundary_shuffle_audit.json:14: "z_score": -26.6, tools/data/boundary_shuffle_audit.json:25: "z_score": -14.7, tools/data/boundary_shuffle_audit.json:36: "z_score": 8.0, tools/data/boundary_shuffle_audit.json:47: "z_score": 43.5, tools/data/boundary_shuffle_audit.json:58: "z_score": 16.1, tools/data/boundary_shuffle_audit.json:69: "z_score": 61.6, tools/data/boundary_shuffle_audit.json:80: "z_score": -1.1, tools/data/boundary_shuffle_audit.json:91: "z_score": -1.6, tools/data/boundary_shuffle_audit.json:102: "z_score": -0.1, tools/data/boundary_shuffle_audit.json:113: "z_score": -0.1, tools/data/spettro_zeta_results.json:6: "score": 8, tools/data/spettro_zeta_results.json:1211: "score": 8, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:34: "reference_core_phi": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:60: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:142: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:199: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:216: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:217: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:232: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:299: "median_overlap_with_phi_core": 0.6666666666666666, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:316: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:322: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:333: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:390: "median_overlap_with_phi_core": 0.06066176470588235, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:407: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:408: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:423: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:485: "median_overlap_with_phi_core": 0.4, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:502: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:506: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:519: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:576: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:593: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:594: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:609: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:679: "median_overlap_with_phi_core": 0.7136363636363636, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:696: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:703: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:713: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:770: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:787: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:788: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:803: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:866: "median_overlap_with_phi_core": 0.5, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:883: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:887: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:900: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:957: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:974: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:975: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:990: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1061: "median_overlap_with_phi_core": 0.7272727272727273, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1078: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1086: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1095: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1152: "median_overlap_with_phi_core": 0.11437908496732026, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1169: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1170: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1185: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1249: "median_overlap_with_phi_core": 0.6, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1266: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1271: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1283: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1340: "median_overlap_with_phi_core": 0.11437908496732026, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1357: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1358: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1373: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1440: "median_overlap_with_phi_core": 0.7, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1457: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1463: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1474: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1531: "median_overlap_with_phi_core": 0.11764705882352941, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1548: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1549: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1564: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1637: "median_overlap_with_phi_core": 0.7272727272727273, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1654: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1662: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1671: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1728: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1745: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1746: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1763: "core_labels_all_conditions": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1827: "median_overlap_with_phi_core": 0.6666666666666666, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1844: "reference_core_retained_in_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1848: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1861: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1918: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1935: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1936: "reference_core_missing_from_all": [ tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:1951: "core_labels_all_conditions": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:2008: "median_overlap_with_phi_core": 0.1111111111111111, tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:2025: "reference_core_retained_in_all": [], tools/data/gap_label_substitution_grammar_gate_20260508_1834.json:2026: "reference_core_missing_from_all": [ tools/data/experiment_results.json:24: "gap_ratio": 2.6180861695660687 tools/data/experiment_results.json:39: "gap_ratio": 2.6180861695660687 tools/data/experiment_results.json:54: "gap_ratio": 2.6180861695660687 tools/data/experiment_results.json:69: "gap_ratio": 2.6180861695660687 tools/data/experiment_results.json:84: "gap_ratio": 2.618090459914461 tools/data/experiment_results.json:99: "gap_ratio": 2.6180861695660687 tools/data/experiment_results.json:114: "gap_ratio": 2.6180389412338116 tools/data/experiment_results.json:129: "gap_ratio": 2.6180511672912212 tools/data/experiment_results.json:165: "gap_ratio": null, tools/data/experiment_results.json:176: "gap_ratio": null, tools/data/experiment_results.json:187: "gap_ratio": 2.6180573994805743, tools/data/experiment_results.json:198: "gap_ratio": 2.618066201061807, tools/data/experiment_results.json:209: "gap_ratio": 2.618030904788572, tools/data/experiment_results.json:220: "gap_ratio": 2.618032699020587, tools/data/experiment_results.json:231: "gap_ratio": 2.6180835794161377, tools/data/experiment_results.json:242: "gap_ratio": 2.6180059965999325, tools/data/experiment_results.json:253: "gap_ratio": 2.6181156492729354, tools/data/experiment_results.json:264: "gap_ratio": 2.6180826965848376, tools/data/experiment_results.json:275: "gap_ratio": 2.6180501628999204, tools/data/experiment_results.json:286: "gap_ratio": 2.6180217080567996, tools/data/experiment_results.json:297: "gap_ratio": 2.6181156492729354, tools/data/experiment_results.json:316: "gap_ratio": null, tools/data/experiment_results.json:326: "gap_ratio": 2.6180861695660687, tools/data/experiment_results.json:336: "gap_ratio": 2.6180861695660687, tools/data/experiment_results.json:346: "gap_ratio": 2.6180861695660687, tools/data/experiment_results.json:356: "gap_ratio": 2.6180861695660687, tools/data/experiment_results.json:366: "gap_ratio": 2.6180861695660687, tools/data/experiment_results.json:376: "gap_ratio": 2.6180861695660687, tools/data/experiment_results.json:386: "gap_ratio": 2.6180861695660687, tools/data/experiment_results.json:396: "gap_ratio": 2.6180145125477585, tools/data/experiment_results.json:406: "gap_ratio": 2.6181579718861103, tools/data/experiment_results.json:416: "gap_ratio": 2.6180145125477585, tools/data/experiment_results.json:426: "gap_ratio": null, tools/data/lab_logiche_corpus.md:509:Density Score: Liste lunghe, opzioni multiple = RIFIUTATO. tools/data/rottura_phi2_results.json:436: "gap_ratio": 2.6181629831607247, tools/data/rottura_phi2_results.json:442: "gap_ratio": 2.617897296100588, tools/data/rottura_phi2_results.json:448: "gap_ratio": 2.6180333649741723, tools/data/rottura_phi2_results.json:454: "gap_ratio": 2.617968889099815, tools/data/rottura_phi2_results.json:460: "gap_ratio": 2.6180294179588133, tools/data/rottura_phi2_results.json:468: "gap_ratio": 2.6180861695660687, tools/data/rottura_phi2_results.json:475: "gap_ratio": 2.6180861695660687, tools/data/rottura_phi2_results.json:482: "gap_ratio": 2.6180861695660687, tools/data/rottura_phi2_results.json:489: "gap_ratio": 2.6180320040269116, tools/data/r_excess_analysis.json:63: "disc_5_z_score": 3.7, tools/data/r_excess_analysis.json:64: "disc_17_z_score": 1.6 tools/data/markov3_observable_hunt.json:20: "z_scores": { tools/data/vocabolario_custom.json:29: "gap_ratio": { tools/data/dinamiche.json:4: "score": 0.9, tools/data/dinamiche.json:10: "score": 0.1, tools/data/dinamiche.json:16: "score": 0.2, tools/data/dinamiche.json:22: "score": 0.2, tools/data/dinamiche.json:30: "score": 0.1111111111111111, tools/data/dinamiche.json:38: "score": 0.1111111111111111, tools/data/dinamiche.json:46: "score": 0.1111111111111111, tools/data/autoricerca_state.json:33: "gap_ratio": 2.6181003744227653, tools/data/autoricerca_state.json:40: "gap_ratio": 2.618013050556921, tools/data/autoricerca_state.json:47: "gap_ratio": 2.6180415569821247, tools/data/autoricerca_state.json:54: "gap_ratio": 2.6181659442748706, tools/data/autoricerca_state.json:61: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_state.json:68: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_state.json:75: "gap_ratio": 2.6180992382029853, tools/data/autoricerca_state.json:82: "gap_ratio": 2.618052995798036, tools/data/autoricerca_state.json:89: "gap_ratio": 2.618054344736462, tools/data/autoricerca_state.json:96: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_state.json:103: "gap_ratio": 2.6180861695660687, tools/data/notte_20260401_0330.md:7: [conferma_parziale] COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F1: gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ tools/data/notte_20260401_0330.md:9: [tensione_aperta] M_det_minus_one_L0: Cosa manca per confermare completamente gap_ratio: phi=0.409 tools/data/ciclo_memoria.json:36: "cosa": "Nuove tensioni: {'COMP_DOMAIN_PHOTONIC_FALSIFICA_F2', 'TRANS_BOUNDARY_FALSIFICA_; Tensioni risolte: {'COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F1', 'TRANS_BOUNDARY_", tools/data/ciclo_memoria.json:58: "cosa": "Nuove tensioni: {'M_relazione_orizzonte_degli_L0', 'COMP_GEN_GAP_RATIO_CONS_GxE_; Tensioni risolte: {'TAGLI_UNIVERSALI', 'TETRAEDRO_TQGE', 'ALPHA_INVARIANTE'}", tools/data/ciclo_memoria.json:158: "cosa": "Nuove tensioni: {'COMP_GEN_GAP_RATIO_FALSIFICA_F6', 'M_firma_dello_zero_L0', 'TR; Tensioni risolte: {'METRIC_TENSOR'}", tools/data/ciclo_memoria.json:201: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma un segnale di denominatore Sturmiano nel punto stor", tools/data/ciclo_memoria.json:212: "phi_core_overlap", tools/data/ciclo_memoria.json:213: "core_retention" tools/data/ciclo_memoria.json:219: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,500,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={1.75,2.0,2.25}`, `trials=3`, `top_k=12`, `|n|<=34`, ", tools/data/ciclo_memoria.json:225: "titolo": "Substitution Grammar Gate Del Core Phi", tools/data/ciclo_memoria.json:237: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, il core alto `[3, ", tools/data/ciclo_memoria.json:238: "cosa": "Substitution Grammar Gate Del Core Phi → None/None (ρ=None)" tools/data/ciclo_memoria.json:243: "titolo": "Supertile Tiling Gate Del Core Phi", tools/data/ciclo_memoria.json:247: "supertile_boundary", tools/data/ciclo_memoria.json:256: "verdict": "**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={", tools/data/ciclo_memoria.json:257: "cosa": "Supertile Tiling Gate Del Core Phi → None/None (ρ=None)" tools/data/ciclo_memoria.json:268: "domanda_aperta": "Costruire un gate di boundary simbolico che non legga solo il set dei label: confrontare label-set, posizione IDS dei label core e errore di label per chunk aligned/misaligned. Se il boundary reale esiste nell'osservabile, deve apparire nella geometria degli errori o nella posizione dei gap, non nel solo all-high rate.", tools/data/gap_label_set_stability_20260508_1632.json:32: "core_labels_all_conditions": [], tools/data/gap_label_set_stability_20260508_1632.json:111: "core_labels_all_conditions": [ tools/data/gap_label_set_stability_20260508_1632.json:206: "core_labels_all_conditions": [ tools/data/gap_label_set_stability_20260508_1632.json:304: "core_labels_all_conditions": [ exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 133ms: M_trascendenza_limite_attuale_L0: score=0.872 — M_trascendenza_limite_attuale_L0: incerto (i=0.529179606750063) — massimo potere discriminante TENS_SCALE_TRASCENDENZA_LIMITE: score=0.807 — TENS_SCALE_TRASCENDENZA_LIMITE: incerto (i=0.6) — massimo potere discriminante COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: score=0.758 — COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante codex Lo scenario conferma la stessa zona TRASCENDENZA_LIMITE, ma l’override restringe il lavoro al repair. Ora ispeziono gli script e i dati del ciclo precedente per riusare lo stesso perimetro e aggiungere solo la separazione mancante. 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 "sed -n '1,260p' tools/exp_gap_label_generator_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Generator gate for phi gap-label stability. The label-set audit moved the observable from the first-two gap ratio to the set of large-gap labels. This tool tests the next denominator: the generator. It keeps the phi label reader fixed and changes the sequence generator while preserving different amounts of structure. """ from __future__ import annotations import argparse import json from collections import defaultdict from pathlib import Path import numpy as np from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence, summarize_sets THETA = 1 / PHI def fibonacci_word(n: int) -> np.ndarray: word = "1" previous = "0" while len(word) < n: word, previous = word + previous, word return np.array([float(ch) for ch in word[:n]], dtype=float) def rotate(seq: np.ndarray, phase: float) -> np.ndarray: if len(seq) == 0: return seq shift = int(round((phase % 1.0) * len(seq))) return np.roll(seq, shift) def transition_matrix(seq: np.ndarray) -> tuple[np.ndarray, float]: counts = np.ones((2, 2), dtype=float) ints = seq.astype(int) for a, b in zip(ints[:-1], ints[1:]): counts[a, b] += 1 probs = counts / counts.sum(axis=1, keepdims=True) start_prob = float(np.mean(ints)) return probs, start_prob def markov_surrogate(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: probs, start_prob = transition_matrix(seq) out = np.zeros(len(seq), dtype=float) out[0] = 1.0 if rng.random() < start_prob else 0.0 for i in range(1, len(seq)): prev = int(out[i - 1]) out[i] = 1.0 if rng.random() < probs[prev, 1] else 0.0 return out def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: out = np.array(seq, dtype=float) rng.shuffle(out) return out def block_shuffle(seq: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: blocks = [seq[i : i + block_size].copy() for i in range(0, len(seq), block_size)] rng.shuffle(blocks) return np.concatenate(blocks) def generator_sequences(n: int, phase: float, trial: int, rng: np.random.Generator) -> dict[str, np.ndarray]: phi = sturmian_sequence(THETA, n, phase) fib = rotate(fibonacci_word(n), phase) return { "phi_sturmian": phi, "fibonacci_substitution": fib, "markov_phi": markov_surrogate(phi, rng), "block_shuffle_13": block_shuffle(phi, 13, rng), "block_shuffle_34": block_shuffle(phi, 34, rng), "balanced_random": balanced_random(phi, rng), } def summarize_generators(rows: list[dict], reference_core: set[int]) -> dict: output = {} for generator in sorted({row["generator"] for row in rows}): group = [row for row in rows if row["generator"] == generator] summary = summarize_sets(group) if not summary: continue overlaps = [jaccard(set(row["label_set"]), reference_core) for row in group if row["n_selected"] > 0] core = set(summary["core_labels_all_conditions"]) output[generator] = { **summary, "median_overlap_with_phi_core": float(np.median(overlaps)) if overlaps else None, "min_overlap_with_phi_core": float(np.min(overlaps)) if overlaps else None, "reference_core_retained": sorted(core & reference_core, key=lambda x: (abs(x), x)), "reference_core_missing": sorted(reference_core - core, key=lambda x: (abs(x), x)), } return output def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) ns = [int(x) for x in args.ns.split(",")] phases = [float(x) for x in args.phases.split(",")] thresholds = [float(x) for x in args.thresholds.split(",")] rows = [] for n in ns: for phase in phases: for trial in range(args.trials): seqs = generator_sequences(n, phase, trial, rng) for generator, seq in seqs.items(): for threshold in thresholds: obs = gap_labels(seq, THETA, threshold, args.max_label, args.top_k) rows.append({ "generator": generator, "N": n, "phase": phase, "trial": trial, "threshold": threshold, **obs, }) phi_rows = [row for row in rows if row["generator"] == "phi_sturmian"] reference_core = set(summarize_sets(phi_rows)["core_labels_all_conditions"]) summary = summarize_generators(rows, reference_core) by_generator_threshold = defaultdict(list) for row in rows: by_generator_threshold[(row["generator"], row["threshold"])].append(row) threshold_summary = { f"{generator}|threshold={threshold}": summarize_sets(group) for (generator, threshold), group in by_generator_threshold.items() } return { "experiment": "gap_label_generator_gate", "parameters": { "ns": ns, "phases": phases, "thresholds": thresholds, "trials": args.trials, "top_k": args.top_k, "max_label": args.max_label, "seed": args.seed, }, "reference_core_phi": sorted(reference_core, key=lambda x: (abs(x), x)), "summary": summary, "threshold_summary": threshold_summary, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--ns", default="233,377,500,610") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--thresholds", default="1.75,2.0,2.25") parser.add_argument("--trials", type=int, default=3) parser.add_argument("--top-k", type=int, default=12) parser.add_argument("--max-label", type=int, default=34) parser.add_argument("--seed", type=int, default=20260508) parser.add_argument("--out", default="tools/data/gap_label_generator_gate_20260508_1715.json") args = parser.parse_args() output = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2), encoding="utf-8") compact = { generator: { "median_jaccard": data["median_jaccard"], "min_jaccard": data["min_jaccard"], "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], "reference_core_missing": data["reference_core_missing"], "core_labels_all_conditions": data["core_labels_all_conditions"], } for generator, data in output["summary"].items() } print(json.dumps({ "reference_core_phi": output["reference_core_phi"], "summary": compact, "out": str(out), }, indent=2)) if __name__ == "__main__": main() exec /bin/bash -lc 'ls -1 tools/exp_*gap*label* tools/data/gap_label_*_20260508_*.json 2>/dev/null' in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260508_1909.md" in /opt/MM_D-ND succeeded in 0ms: tools/data/gap_label_block_scale_gate_20260508_1805.json tools/data/gap_label_generator_gate_20260508_1715.json tools/data/gap_label_set_stability_20260508_1632.json tools/data/gap_label_substitution_grammar_gate_20260508_1834.json tools/data/gap_label_supertile_tiling_gate_20260508_1909.json tools/exp_gap_label_block_scale_gate.py tools/exp_gap_label_generator_gate.py tools/exp_gap_label_set_stability.py tools/exp_gap_label_substitution_grammar_gate.py tools/exp_gap_label_supertile_tiling_gate.py succeeded in 0ms: # Agent Report - Supertile Tiling Gate Del Core Phi **Date**: 2026-05-08 19:09 **Piano**: 89 **Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) **verdict**: CONSTRAINT observables_registry: n/a observables_used: [gap_label_set, supertile_boundary, length_multiset, internal_order, high_label_condition_rate] **observable_contract**: claim=il core alto phi distingue confine di supertile Fibonacci da blocco contiguo con stessa lunghezza; observable=retention dei label core sotto shuffle di supertile, shuffle contiguo a stessa multiset di lunghezze, shuffle interno a stesso conteggio; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, Jaccard/retention/frequenza per mode e supertile_order; generator=phi_sturmian con tiling di lunghezze Fibonacci; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, soglie 1.75/2.25, parsing simbolico esatto dei supertile per ogni fase, generatori non-phi. ## Respiro fuori-tempo - **Combo**: A11 combo + TxQ matrice densita come lettore spettrale + QxG vuoto continuo/discreto + tensione TRASCENDENZA_LIMITE/M_come_modulazione_quasiperiodica. - **Dipolo / punto-zero**: confine reale del supertile · lunghezza contigua equivalente; punto-zero = il blocco come unita' prima che diventi grammatica o contenitore. - **Piano superiore**: grafo della conoscenza / bicono dei dipoli. Il nodo non e' il valore del gap, ma il passaggio fra grammatica interna, confine esterno e denominatore. - **Proto-ipotesi**: se il core alto phi vive nel confine tra supertile, lo shuffle di supertile conserva piu' core dello shuffle contiguo con la stessa multiset di lunghezze. Se non lo fa, il confine esatto non e' ancora il portatore: il portatore e' ordine interno piu' scala lunga. - **Proiezione**: il label-set dei gap larghi manifesta la combo perche' tiene fisso il reader theta=1/phi e muove solo la relazione fra grammatica, boundary e blocco. ## Claim Under Test > Il vantaggio del blocco Fibonacci lungo non sta solo nell'ordine interno conservato: sta nel confine reale del supertile. Quindi `supertile_shuffle` batte `same_length_contiguous_shuffle` a pari multiset di lunghezze. ## Question Il core alto `[3, -4, 4, 6]` riconosce il confine di supertile Fibonacci, o riconosce soltanto blocchi lunghi con ordine interno conservato? ## Experiment Design - Reference core phi: `[-1, 1, -2, 2, 3, -4, 4, 6]`. - Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. - Supertile orders: - order 8: lunghezze `{21,34}`. - order 9: lunghezze `{34,55}`. - order 10: lunghezze `{55,89}`. - order 11: lunghezze `{89,144}`. - Modes: - `supertile_shuffle`: taglia secondo la parola di lunghezze Fibonacci e mescola i chunk. - `same_length_contiguous_shuffle`: usa la stessa multiset di lunghezze, ma su taglio contiguo misallineato. - `same_count_internal_shuffle`: conserva lunghezza e conteggio di ogni chunk allineato, ma distrugge ordine interno. - `same_mean_block_shuffle`: usa un blocco fisso pari alla lunghezza media del tiling, controllo di scala grossolana. - Denominatori grezzi: per ogni `mode|order`, 40 condizioni (`2 N * 4 phase * 1 threshold * 5 trials`); per ogni mode aggregato, 160 condizioni. ## Results Reference phi su 8 condizioni (`N x phase x threshold`): core completo `[-1, 1, -2, 2, 3, -4, 4, 6]`. Sintesi per mode: | mode | conditions | median Jaccard | low retention | high retention | all-high condition rate | |---|---:|---:|---:|---:|---:| | supertile_shuffle | 160 | 0.818182 | 1.00 | 1.00 | 108/160 = 0.67500 | | same_length_contiguous_shuffle | 160 | 0.818182 | 1.00 | 1.00 | 116/160 = 0.72500 | | same_count_internal_shuffle | 160 | 0.157895 | 0.25 | 0.25 | 0/160 = 0.00000 | | same_mean_block_shuffle | 160 | 0.500000 | 1.00 | 0.25 | 7/160 = 0.04375 | Dettaglio per order: | mode | order | lengths | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | |---|---:|---|---:|---:|---:|---:|---| | supertile_shuffle | 8 | {21,34} | 0.750000 | 1.00 | 0.75 | 14/40 = 0.350 | [-1, 1, -2, 2, -3, -4, 4] | | same_length_contiguous_shuffle | 8 | {21,34} | 0.818182 | 1.00 | 0.875 | 20/40 = 0.500 | [-1, 1, -2, 2, -3, 3, -4, 4] | | same_mean_block_shuffle | 8 | mean 29 | 0.444444 | 0.75 | 0.00 | 0/40 = 0.000 | [-1, 1, 33] | | same_count_internal_shuffle | 8 | {21,34} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] | | supertile_shuffle | 9 | {34,55} | 0.904545 | 1.00 | 1.00 | 29/40 = 0.725 | [-1, 1, -2, 2, -3, 3, -4, 4, -6] | | same_length_contiguous_shuffle | 9 | {34,55} | 0.818182 | 1.00 | 1.00 | 29/40 = 0.725 | [-1, 1, -2, 2, -3, 3, -4, 4, 6] | | same_mean_block_shuffle | 9 | mean 45 | 0.500000 | 1.00 | 0.25 | 0/40 = 0.000 | [-1, 1, -2] | | same_count_internal_shuffle | 9 | {34,55} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] | | supertile_shuffle | 10 | {55,89} | 0.909091 | 1.00 | 1.00 | 32/40 = 0.800 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | | same_length_contiguous_shuffle | 10 | {55,89} | 0.833333 | 1.00 | 1.00 | 33/40 = 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6] | | same_mean_block_shuffle | 10 | mean 75 | 0.666667 | 1.00 | 0.50 | 1/40 = 0.025 | [-1, 1, -2, 2, -4, 4] | | same_count_internal_shuffle | 10 | {55,89} | 0.150000 | 0.25 | 0.25 | 0/40 = 0.000 | [] | | supertile_shuffle | 11 | {89,144} | 0.909091 | 1.00 | 1.00 | 33/40 = 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, 6, -7] | | same_length_contiguous_shuffle | 11 | {89,144} | 0.909091 | 1.00 | 1.00 | 34/40 = 0.850 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6] | | same_mean_block_shuffle | 11 | mean 122 | 0.666667 | 1.00 | 0.75 | 6/40 = 0.150 | [-1, 1, -2, 2, -4, 4] | | same_count_internal_shuffle | 11 | {89,144} | 0.157895 | 0.25 | 0.25 | 0/40 = 0.000 | [34] | ## Key Findings 1. **Verificato: il confine di supertile non batte il blocco contiguo a pari multiset di lunghezze.** `supertile_shuffle` fa all-high `108/160 = 0.675`; `same_length_contiguous_shuffle` fa `116/160 = 0.725`. Per order 9 sono pari (`29/40`), per order 10-11 il controllo contiguo e' leggermente sopra (`33/40`, `34/40` contro `32/40`, `33/40`). 2. **Verificato: la condizione forte resta l'ordine interno.** `same_count_internal_shuffle` resta nullo su tutti gli order: all-high `0/160`, high retention mediana `0.25`, Jaccard mediano `0.157895`. Questo replica il nodo 18:34 dentro il tiling di supertile. 3. **Verificato: la scala lunga come multiset e' piu' informativa del blocco medio fisso.** `same_mean_block_shuffle` conserva low retention mediana `1.00`, ma resta quasi nullo sul core alto completo (`7/160 = 0.04375`). Quindi non basta una scala media lunga; serve una distribuzione di lunghezze lunghe con ordine interno conservato. 4. **Inferito dal confronto fra controlli: il portatore osservato e' ordine interno + multiset di lunghezze Fibonacci, non boundary esatto.** Il test non autorizza "il core alto vive nel confine del supertile"; autorizza "il core alto richiede ordine interno e beneficia di lunghezze supertile-like". ## Verdict **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, il core alto phi non discrimina il confine esatto del supertile. `same_length_contiguous_shuffle` conserva il core alto almeno quanto `supertile_shuffle` (`116/160` vs `108/160`). Il core alto cade solo quando l'ordine interno viene distrutto (`0/160`) o quando la multiset di lunghezze viene compressa a blocco medio (`7/160`). La formulazione valida e': il label-set alto phi misura una condizione congiunta di ordine interno e lunghezze Fibonacci-like; non misura ancora il boundary simbolico del supertile e non misura `gap_ratio`. ## Bicono della scoperta - **Due radici**: confine di supertile · distribuzione di lunghezze con ordine interno. - **Singolare**: il chunk lungo prima della distinzione fra boundary grammaticale e blocco contiguo. - **Invariante di passaggio**: il core alto sopravvive quando l'ordine interno resta leggibile su lunghezze Fibonacci-like; non sopravvive a conteggio senza ordine. - **Campo di possibilita**: qui diventa possibile cercare un osservabile di boundary piu' fine del label-set; qui diventa non-possibile dichiarare che il label-set alto riconosce da solo il confine esatto del supertile. ## Consecutio Costruire un gate di boundary simbolico che non legga solo il set dei label: confrontare label-set, posizione IDS dei label core e errore di label per chunk aligned/misaligned. Se il boundary reale esiste nell'osservabile, deve apparire nella geometria degli errori o nella posizione dei gap, non nel solo all-high rate. ## Auto-audit: 5 lenti - **L1 hard constraint vs bias**: il verdict dichiara perimetro e count grezzi; non trasforma `108/160` in legge universale. - **L2 quantita vs ratio**: Jaccard, retention e all-high rate restano separati; `gap_ratio` dichiarato non testato. - **L3 no silent patching**: l'ipotesi "supertile batte contiguo" cade; il report non la salva cambiando osservabile. - **L4 edge cases**: order 10-11 mostrano controllo contiguo sopra supertile; sono il segnale, non rumore. - **L5 re-discovery**: la conservazione dell'ordine interno era gia' nota dal 18:34; il nuovo finding e' la non-discriminazione del boundary esatto da parte del label-set. ## Files - Script: `tools/exp_gap_label_supertile_tiling_gate.py` - Data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json` - Report: `tools/data/reports/agent_20260508_1909.md` exec /bin/bash -lc "sed -n '1,320p' tools/exp_gap_label_supertile_tiling_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Supertile tiling gate for the phi gap-label core. The substitution-grammar gate showed that length and symbol count do not carry the high labels when internal order is destroyed. This tool moves one node upstream: it separates true Fibonacci supertile boundaries from contiguous chunks with the same length multiset. """ from __future__ import annotations import argparse import json from collections import Counter, defaultdict from pathlib import Path import numpy as np from exp_gap_label_block_scale_gate import ( REFERENCE_HIGH, REFERENCE_LOW, label_sort, parse_floats, parse_ints, retention, ) from exp_gap_label_generator_gate import THETA, block_shuffle from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets def fibonacci_lengths(order: int) -> tuple[int, int]: if order < 2: raise ValueError("supertile_order must be >= 2") a, b = 1, 1 for _ in range(2, order + 1): a, b = b, a + b return b, a def fibonacci_type_word(n_types: int) -> np.ndarray: word = "1" previous = "0" while len(word) < n_types: word, previous = word + previous, word return np.array([int(ch) for ch in word[:n_types]], dtype=int) def supertile_lengths(n: int, order: int) -> list[int]: long_len, short_len = fibonacci_lengths(order) types = fibonacci_type_word(max(8, int(np.ceil(n / short_len)) + 4)) lengths: list[int] = [] total = 0 for t in types: length = long_len if t == 1 else short_len if total + length >= n: lengths.append(n - total) break lengths.append(length) total += length return [length for length in lengths if length > 0] def chunks_from_lengths(seq: np.ndarray, lengths: list[int]) -> list[np.ndarray]: chunks = [] start = 0 for length in lengths: chunks.append(seq[start : start + length].copy()) start += length if start < len(seq): chunks.append(seq[start:].copy()) return chunks def shuffle_chunks(chunks: list[np.ndarray], rng: np.random.Generator) -> np.ndarray: shuffled = list(chunks) rng.shuffle(shuffled) return np.concatenate(shuffled) def internal_count_shuffle(chunks: list[np.ndarray], rng: np.random.Generator) -> np.ndarray: out = [] for chunk in chunks: copied = chunk.copy() rng.shuffle(copied) out.append(copied) return np.concatenate(out) def misaligned_same_lengths(seq: np.ndarray, lengths: list[int], rng: np.random.Generator) -> np.ndarray: if len(seq) < 2: return seq.copy() offset = int(rng.integers(1, len(seq))) rotated = np.roll(seq, -offset) chunks = chunks_from_lengths(rotated, lengths) shuffled = shuffle_chunks(chunks, rng) return np.roll(shuffled, offset) def fixed_block_same_mean(seq: np.ndarray, lengths: list[int], rng: np.random.Generator) -> np.ndarray: mean_len = max(1, int(round(float(np.mean(lengths))))) return block_shuffle(seq, mean_len, rng) def summarize_rows(rows: list[dict], reference_core: set[int]) -> dict: summary = summarize_sets(rows) sets = [set(row["label_set"]) for row in rows if row["n_selected"] > 0] counter = Counter(label for s in sets for label in s) n_sets = len(sets) overlaps = [jaccard(set(row["label_set"]), reference_core) for row in rows if row["n_selected"] > 0] return { **summary, "median_overlap_with_phi_core": float(np.median(overlaps)) if overlaps else None, "median_low_retention": float(np.median([retention(row, REFERENCE_LOW) for row in rows])), "median_high_retention": float(np.median([retention(row, REFERENCE_HIGH) for row in rows])), "all_low_count": int(sum(REFERENCE_LOW <= s for s in sets)), "all_high_count": int(sum(REFERENCE_HIGH <= s for s in sets)), "condition_count": int(n_sets), "all_low_condition_rate": float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) if n_sets else None, "all_high_condition_rate": float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) if n_sets else None, "high_label_condition_rates": { str(label): float(counter.get(label, 0) / n_sets) if n_sets else None for label in label_sort(REFERENCE_HIGH) }, "low_label_condition_rates": { str(label): float(counter.get(label, 0) / n_sets) if n_sets else None for label in label_sort(REFERENCE_LOW) }, "reference_core_retained_in_all": label_sort(set(summary.get("core_labels_all_conditions", [])) & reference_core), "reference_core_missing_from_all": label_sort(reference_core - set(summary.get("core_labels_all_conditions", []))), } def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) ns = parse_ints(args.ns) phases = parse_floats(args.phases) thresholds = parse_floats(args.thresholds) orders = parse_ints(args.supertile_orders) reference_rows = [] rows = [] tiling_meta = {} for n in ns: for phase in phases: phi = sturmian_sequence(THETA, n, phase) for threshold in thresholds: reference_rows.append({ "mode": "reference_phi", "N": n, "phase": phase, "threshold": threshold, **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), }) for order in orders: lengths = supertile_lengths(n, order) tiling_meta[f"N={n}|order={order}"] = { "lengths": lengths, "count": len(lengths), "total": sum(lengths), "unique_lengths": sorted(set(lengths)), } aligned_chunks = chunks_from_lengths(phi, lengths) mean_block = max(1, int(round(float(np.mean(lengths))))) for trial in range(args.trials): variants = { "supertile_shuffle": shuffle_chunks(aligned_chunks, rng), "same_length_contiguous_shuffle": misaligned_same_lengths(phi, lengths, rng), "same_count_internal_shuffle": internal_count_shuffle(aligned_chunks, rng), "same_mean_block_shuffle": fixed_block_same_mean(phi, lengths, rng), } for mode, seq in variants.items(): for threshold in thresholds: rows.append({ "mode": mode, "N": n, "phase": phase, "threshold": threshold, "trial": trial, "supertile_order": order, "mean_block": mean_block, **gap_labels(seq, THETA, threshold, args.max_label, args.top_k), }) reference_summary = summarize_sets(reference_rows) reference_core = set(reference_summary["core_labels_all_conditions"]) grouped: dict[str, list[dict]] = defaultdict(list) for row in rows: grouped[f"{row['mode']}|order={row['supertile_order']}"].append(row) mode_order_summary = { key: summarize_rows(group_rows, reference_core) for key, group_rows in grouped.items() } mode_summary = {} for mode in sorted({row["mode"] for row in rows}): mode_rows = [row for row in rows if row["mode"] == mode] mode_summary[mode] = summarize_rows(mode_rows, reference_core) return { "experiment": "gap_label_supertile_tiling_gate", "parameters": { "ns": ns, "phases": phases, "thresholds": thresholds, "trials": args.trials, "supertile_orders": orders, "top_k": args.top_k, "max_label": args.max_label, "seed": args.seed, }, "reference_core_phi": label_sort(reference_core), "reference_low": label_sort(REFERENCE_LOW), "reference_high": label_sort(REFERENCE_HIGH), "reference_summary": reference_summary, "tiling_meta": tiling_meta, "mode_order_summary": mode_order_summary, "mode_summary": mode_summary, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--ns", default="377,610") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--thresholds", default="2.0") parser.add_argument("--trials", type=int, default=5) parser.add_argument("--supertile-orders", default="8,9,10,11") parser.add_argument("--top-k", type=int, default=12) parser.add_argument("--max-label", type=int, default=34) parser.add_argument("--seed", type=int, default=202605081909) parser.add_argument("--out", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") args = parser.parse_args() output = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2), encoding="utf-8") compact = {} for key, data in sorted(output["mode_order_summary"].items()): mode, order = key.split("|") compact[key] = { "mode": mode, "supertile_order": int(order.split("=")[1]), "median_jaccard": data["median_jaccard"], "low_retention": data["median_low_retention"], "high_retention": data["median_high_retention"], "all_high": f"{data['all_high_count']}/{data['condition_count']}", "all_high_condition_rate": data["all_high_condition_rate"], "stable_labels_75pct": data["stable_labels_75pct"], } print(json.dumps({ "reference_core_phi": output["reference_core_phi"], "reference_high": output["reference_high"], "mode_summary": { mode: { "median_jaccard": data["median_jaccard"], "low_retention": data["median_low_retention"], "high_retention": data["median_high_retention"], "all_high": f"{data['all_high_count']}/{data['condition_count']}", "all_high_condition_rate": data["all_high_condition_rate"], } for mode, data in output["mode_summary"].items() }, "orders": compact, "out": str(out), }, indent=2)) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,260p' tools/exp_gap_label_set_stability.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Gap-label set stability for quasiperiodic spectra. The previous denominator audit showed that the first-two gap ratio moves with N, Sturmian phase, and threshold. This tool moves the observable from the value of the first two large gaps to the labels of the large gaps. For each large spectral gap, the integrated density of states is approximated by (gap_index + 1) / N. The nearest gap label is the integer n whose fractional part {n * theta} is closest to that IDS, modulo 1. The label set is then tested for stability across phase, N, and threshold. """ from __future__ import annotations import argparse import itertools import json from collections import Counter, defaultdict from pathlib import Path import numpy as np from numpy.linalg import eigvalsh PHI = (1 + np.sqrt(5)) / 2 SILVER = 1 + np.sqrt(2) BRONZE = 1 + np.sqrt(3) def sturmian_sequence(theta: float, n: int, phase: float = 0.0) -> np.ndarray: idx = np.arange(n + 1, dtype=float) vals = np.floor(idx * theta + phase) return np.diff(vals).astype(float) def hamiltonian(seq: np.ndarray, v: float = 1.0) -> np.ndarray: n = len(seq) h = np.zeros((n, n), dtype=float) h[np.arange(n), np.arange(n)] = v * seq off = np.arange(n - 1) h[off, off + 1] = 1.0 h[off + 1, off] = 1.0 return h def circular_distance(a: float, b: float) -> float: d = abs(a - b) % 1.0 return min(d, 1.0 - d) def nearest_label(ids_value: float, theta: float, max_label: int) -> tuple[int, float, float]: candidates = [] for n in range(-max_label, max_label + 1): if n == 0: continue frac = (n * theta) % 1.0 candidates.append((n, circular_distance(ids_value, frac), frac)) best_n, best_dist, best_frac = min(candidates, key=lambda item: (item[1], abs(item[0]))) return int(best_n), float(best_dist), float(best_frac) def gap_labels(seq: np.ndarray, theta: float, threshold: float, max_label: int, top_k: int) -> dict: eigs = np.sort(eigvalsh(hamiltonian(seq))) spacings = np.diff(eigs) mean_spacing = float(np.mean(spacings)) large = [] for index, spacing in enumerate(spacings): if spacing > threshold * mean_spacing: ids_value = (index + 1) / len(seq) label, error, label_value = nearest_label(ids_value, theta, max_label) large.append({ "index": int(index), "spacing": float(spacing), "ids": float(ids_value), "label": label, "label_error": error, "label_value": label_value, }) by_size = sorted(large, key=lambda item: item["spacing"], reverse=True) selected = by_size[:top_k] label_set = sorted({item["label"] for item in selected}, key=lambda x: (abs(x), x)) errors = [item["label_error"] for item in selected] return { "n_large": len(large), "n_selected": len(selected), "label_set": label_set, "median_label_error": float(np.median(errors)) if errors else None, "max_label_error": float(np.max(errors)) if errors else None, "selected": selected, } def jaccard(a: set[int], b: set[int]) -> float: if not a and not b: return 1.0 return len(a & b) / len(a | b) def summarize_sets(rows: list[dict]) -> dict: sets = [set(row["label_set"]) for row in rows if row["n_selected"] > 0] if not sets: return {} pairwise = [jaccard(a, b) for a, b in itertools.combinations(sets, 2)] counter = Counter(label for s in sets for label in s) n_sets = len(sets) core = sorted( [label for label, count in counter.items() if count == n_sets], key=lambda x: (abs(x), x), ) stable_75 = sorted( [label for label, count in counter.items() if count / n_sets >= 0.75], key=lambda x: (abs(x), x), ) return { "conditions": n_sets, "median_jaccard": float(np.median(pairwise)) if pairwise else 1.0, "min_jaccard": float(np.min(pairwise)) if pairwise else 1.0, "core_labels_all_conditions": core, "stable_labels_75pct": stable_75, "label_frequency_top": [ {"label": int(label), "count": int(count)} for label, count in sorted(counter.items(), key=lambda item: (-item[1], abs(item[0]), item[0]))[:12] ], "median_label_error": float(np.median([row["median_label_error"] for row in rows if row["median_label_error"] is not None])), "median_selected": float(np.median([row["n_selected"] for row in rows])), "median_n_large": float(np.median([row["n_large"] for row in rows])), } def grouped_stability(rows: list[dict], keys: tuple[str, ...]) -> dict: groups: dict[str, list[dict]] = defaultdict(list) for row in rows: group_key = "|".join(f"{key}={row[key]}" for key in keys) groups[group_key].append(row) summaries = [summarize_sets(group_rows) for group_rows in groups.values() if len(group_rows) > 1] summaries = [s for s in summaries if s] if not summaries: return {} return { "groups": len(summaries), "median_jaccard": float(np.median([s["median_jaccard"] for s in summaries])), "min_jaccard": float(np.min([s["min_jaccard"] for s in summaries])), } def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) domains = { "phi": 1 / PHI, "silver": 1 / SILVER, "bronze": 1 / BRONZE, } ns = [int(x) for x in args.ns.split(",")] phases = [float(x) for x in args.phases.split(",")] thresholds = [float(x) for x in args.thresholds.split(",")] rows = [] for n in ns: for phase in phases: phi_ones = int(np.sum(sturmian_sequence(1 / PHI, n, phase))) for threshold in thresholds: for name, theta in domains.items(): seq = sturmian_sequence(theta, n, phase) obs = gap_labels(seq, theta, threshold, args.max_label, args.top_k) rows.append({"domain": name, "N": n, "phase": phase, "threshold": threshold, **obs}) for trial in range(args.random_trials): seq = np.array([1.0] * phi_ones + [0.0] * (n - phi_ones)) rng.shuffle(seq) obs = gap_labels(seq, 1 / PHI, threshold, args.max_label, args.top_k) rows.append({ "domain": "balanced_random_phi_labels", "trial": trial, "N": n, "phase": phase, "threshold": threshold, **obs, }) by_domain = {} for domain in sorted({row["domain"] for row in rows}): domain_rows = [row for row in rows if row["domain"] == domain] by_domain[domain] = { "global": summarize_sets(domain_rows), "phase_stability_by_N_threshold": grouped_stability(domain_rows, ("N", "threshold")), "threshold_stability_by_N_phase": grouped_stability(domain_rows, ("N", "phase")), "scale_stability_by_phase_threshold": grouped_stability(domain_rows, ("phase", "threshold")), } output = { "experiment": "gap_label_set_stability", "parameters": { "ns": ns, "phases": phases, "thresholds": thresholds, "random_trials": args.random_trials, "top_k": args.top_k, "max_label": args.max_label, "seed": args.seed, }, "summary": by_domain, "rows": rows, } return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--ns", default="233,377,500,610") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--thresholds", default="1.75,2.0,2.25") parser.add_argument("--random-trials", type=int, default=3) parser.add_argument("--top-k", type=int, default=12) parser.add_argument("--max-label", type=int, default=34) parser.add_argument("--seed", type=int, default=20260508) parser.add_argument("--out", default="tools/data/gap_label_set_stability_20260508_1632.json") args = parser.parse_args() output = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2), encoding="utf-8") compact = { domain: { "median_jaccard": data["global"].get("median_jaccard"), "stable_labels_75pct": data["global"].get("stable_labels_75pct"), "phase_stability": data["phase_stability_by_N_threshold"].get("median_jaccard"), "threshold_stability": data["threshold_stability_by_N_phase"].get("median_jaccard"), "scale_stability": data["scale_stability_by_phase_threshold"].get("median_jaccard"), } for domain, data in output["summary"].items() } print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) if __name__ == "__main__": main() exec /bin/bash -lc "python -m json.tool tools/data/gap_label_supertile_tiling_gate_20260508_1909.json | sed -n '1,220p'" in /opt/MM_D-ND succeeded in 120ms: 34, 21, 34, 34, 21, 34, 34, 21, 34, 21, 34, 34, 21, codex Il dato 19:09 contiene già le righe grezze necessarie; il repair non deve cambiare denominatore, deve cambiare il contratto osservabile. Aggiungo un piccolo tool di audit che rilegge quelle righe e produce conteggi per-label, stable-label count ed errore IDS separati. apply patch patch: completed /opt/MM_D-ND/tools/exp_gap_label_repair_audit.py diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex Lo script è aggiunto. Lo eseguo ora sul JSON sorgente del 19:09 e poi controllo il risultato compatto prima di scrivere il report richiesto. exec /bin/bash -lc 'git status --short --branch' in /opt/MM_D-ND succeeded in 0ms: ## main...origin/main M applications/INDEX.draft.json M applications/soluzioni/20260430_0330_three-regimes-of-modular-memory-algebraic/finding_index.draft.json M applications/soluzioni/20260430_0330_three-regimes-of-modular-memory-algebraic/manifest.draft.json M tools/data/agent_field_live.md M tools/data/autoricerca_journal.json M tools/data/ciclo_memoria.json M tools/data/conoscenza_teorie.json M tools/data/consecutio.json M tools/data/consecutio_processata.json M tools/data/domande_fondamentali.json M tools/data/exp_det_drift.json M tools/data/incrocio_risultato.json M tools/data/knowledge_state.json 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/refresh_detector_state.json 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 M tools/data/seme_axioms.json M tools/data/seme_backup_pre_run.json M tools/data/tm1_figures/tensions.json M tools/data/tm1_figures/tensions_raw.json ?? applications/published/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/published/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/published/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/published/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/published/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? applications/published/20260507_0942_bridge-order-denominator-gate/ ?? applications/published/20260507_1006_logistic-counter-scope-gate/ ?? applications/published/20260507_1042_logistic-surrogate-contract-gate/ ?? applications/published/20260507_1419_logistic-cyclic-block-entropy-gate/ ?? applications/published/20260507_1458_meta-assertion-gate/ ?? applications/published/20260507_1751_tqge-underlay-gate/ ?? applications/published/20260507_1804_blank-to-source-hinge/ ?? applications/published/20260507_1938_triadic-deposit-gate/ ?? applications/published/20260507_1957_blank-shell-polarity-gate/ ?? applications/scoperte/20260502_0330_the-dipolar-phase-transition-is-nearly_auto/ ?? applications/scoperte/20260503_0330_markov-memory-has-two-visible-layers_auto/ ?? applications/scoperte/20260504_0901_the-two-markov-layers-are-coupled_auto/ ?? applications/scoperte/20260504_1219_markov-layers-pass-the-first-recovery_auto/ ?? applications/scoperte/20260505_0330_observable-rank-audit-many-probes-one_auto/ ?? applications/scoperte/20260506_0625_perturbation-dimensionality-is-not-yet-a_auto/ ?? applications/scoperte/20260506_1941_perturbation-rank-needs-denominator-gating_auto/ ?? applications/scoperte/20260506_1955_observable-collinearity-breaks-only-where-denominators_auto/ ?? applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/ ?? applications/scoperte/20260507_0803_denominator-gate-transfers-boundary-coordinate-does_auto/ ?? applications/scoperte/20260507_0901_denominator-gate-transfer-matrix_auto/ ?? applications/scoperte/20260507_0923_semi-real-order-denominator-gate_auto/ ?? applications/scoperte/20260507_0942_bridge-order-denominator-gate_auto/ ?? applications/scoperte/20260507_1006_logistic-counter-scope-gate_auto/ ?? applications/scoperte/20260507_1042_logistic-surrogate-contract-gate_auto/ ?? applications/scoperte/20260507_1419_logistic-cyclic-block-entropy-gate_auto/ ?? applications/scoperte/20260507_1458_meta-assertion-gate_auto/ ?? applications/scoperte/20260507_1751_tqge-underlay-gate_auto/ ?? applications/scoperte/20260507_1804_blank-to-source-hinge_auto/ ?? applications/scoperte/20260507_1938_triadic-deposit-gate_auto/ ?? applications/scoperte/20260507_1957_blank-shell-polarity-gate_auto/ ?? "applications/scoperte/20260507_2042_det-non-\303\250-il-discriminatore-della_auto/" ?? applications/scoperte/20260507_2120_blank-shell-tqger-gate_auto/ ?? applications/scoperte/20260507_2203_blank-shell-scale-law_auto/ ?? applications/scoperte/20260507_2310_blank-shell-stratified-gate_auto/ ?? applications/scoperte/20260508_0011_duality-contrast-weakens-with-scale-in_auto/ ?? applications/scoperte/20260508_0330_gap-ratio-porta-il-denominatore_auto/ ?? applications/scoperte/20260508_1715_generator-gate-del-label-set-phi_auto/ ?? applications/scoperte/20260508_1805_block-scale-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1834_substitution-grammar-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1909_supertile-tiling-gate-del-core-phi_auto/ ?? applications/soluzioni/20260502_0330_the-dipolar-phase-transition-is-nearly/ ?? applications/soluzioni/20260503_0330_markov-memory-has-two-visible-layers/ ?? applications/soluzioni/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/soluzioni/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/soluzioni/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/soluzioni/20260506_1941_perturbation-rank-needs-denominator-gating/ ?? applications/soluzioni/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/soluzioni/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/soluzioni/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? applications/soluzioni/20260507_0942_bridge-order-denominator-gate/ ?? applications/soluzioni/20260507_1006_logistic-counter-scope-gate/ ?? applications/soluzioni/20260507_1042_logistic-surrogate-contract-gate/ ?? applications/soluzioni/20260507_1419_logistic-cyclic-block-entropy-gate/ ?? applications/soluzioni/20260507_1458_meta-assertion-gate/ ?? applications/soluzioni/20260507_1751_tqge-underlay-gate/ ?? applications/soluzioni/20260507_1804_blank-to-source-hinge/ ?? applications/soluzioni/20260507_1938_triadic-deposit-gate/ ?? applications/soluzioni/20260507_1957_blank-shell-polarity-gate/ ?? "applications/soluzioni/20260507_2042_det-non-\303\250-il-discriminatore-della/" ?? applications/soluzioni/20260507_2120_blank-shell-tqger-gate/ ?? applications/soluzioni/20260507_2203_blank-shell-scale-law/ ?? applications/soluzioni/20260507_2310_blank-shell-stratified-gate/ ?? applications/soluzioni/20260508_0011_duality-contrast-weakens-with-scale-in/ ?? applications/soluzioni/20260508_0330_gap-ratio-porta-il-denominatore/ ?? applications/soluzioni/20260508_1715_generator-gate-del-label-set-phi/ ?? applications/soluzioni/20260508_1805_block-scale-gate-del-core-phi/ ?? applications/soluzioni/20260508_1834_substitution-grammar-gate-del-core-phi/ ?? applications/soluzioni/20260508_1909_supertile-tiling-gate-del-core-phi/ ?? findings_gallery.md ?? tools/data/.last_telegram_msg ?? tools/data/3d_boundary_layers.json ?? tools/data/aeternitas/ ?? tools/data/alignment_active.json ?? tools/data/alignment_markers.jsonl ?? tools/data/biconi/bicono_20260507_1804.json ?? tools/data/biconi/bicono_20260507_1938.json ?? tools/data/biconi/bicono_20260507_1957.json ?? tools/data/biconi/bicono_20260507_2042.json ?? tools/data/biconi/bicono_20260507_2120.json ?? tools/data/biconi/bicono_20260507_2203.json ?? tools/data/biconi/bicono_20260507_2310.json ?? tools/data/biconi/bicono_20260508_0011.json ?? tools/data/biconi/bicono_20260508_0330.json ?? tools/data/biconi/bicono_20260508_1632.json ?? tools/data/biconi/bicono_20260508_1715.json ?? tools/data/biconi/bicono_20260508_1805.json ?? tools/data/biconi/bicono_20260508_1834.json ?? tools/data/biconi/bicono_20260508_1909.json ?? tools/data/bicono_projections.jsonl ?? tools/data/blank_shell_dilation_gate_20260507_2157.json ?? tools/data/blank_shell_polarity_gate_20260507_1957.json ?? tools/data/blank_shell_scale_law_20260507_2203.json ?? tools/data/blank_shell_stratified_gate_20260507_2310.json ?? tools/data/blank_shell_tqger_gate_20260507_2120.json ?? tools/data/blank_to_source_hinge_20260507_1804.json ?? tools/data/boundary_coherence.json ?? tools/data/boundary_mixture_gate_20260507_0330.json ?? tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json ?? tools/data/boundary_shuffle_audit.json ?? tools/data/bridge_order_denominator_gate_20260507_0942.json ?? tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json ?? tools/data/brody_calibration_results.json ?? tools/data/brody_flow.json ?? tools/data/conoscenza_teorie.json.bak.retraction_22_04 ?? tools/data/cross_domain_dipolar_direction.json ?? tools/data/cross_observable_consistency.json ?? tools/data/crossover_phase_test.json ?? tools/data/denominator_gate_transfer_matrix.json ?? tools/data/dipolar_crossover.json ?? tools/data/dipolar_vector_scaling.json ?? tools/data/domandatore/domandatore_20260421_0746.json ?? tools/data/domandatore/domandatore_20260422_0345.json ?? tools/data/domandatore/domandatore_20260423_0345.json ?? tools/data/domandatore/domandatore_20260424_0345.json ?? tools/data/domandatore/domandatore_20260425_0345.json ?? tools/data/domandatore/domandatore_20260426_0345.json ?? tools/data/domandatore/domandatore_20260427_0345.json ?? tools/data/domandatore/domandatore_20260428_0345.json ?? tools/data/domandatore/domandatore_20260428_1236.json ?? tools/data/domandatore/domandatore_20260429_0345.json ?? tools/data/domandatore/domandatore_20260430_0345.json ?? tools/data/domandatore/domandatore_20260501_0345.json ?? tools/data/domandatore/domandatore_20260502_0345.json ?? tools/data/domandatore/domandatore_20260503_0345.json ?? tools/data/domandatore/domandatore_20260504_0345.json ?? tools/data/domandatore/domandatore_20260505_0345.json ?? tools/data/domandatore/domandatore_20260506_0345.json ?? tools/data/domandatore/domandatore_20260507_0345.json ?? tools/data/domandatore/domandatore_20260507_0728.json ?? tools/data/domandatore/domandatore_20260507_0742.json ?? tools/data/domandatore/domandatore_20260507_1420.json ?? tools/data/domandatore/domandatore_20260507_1459.json ?? tools/data/domandatore/domandatore_20260507_2120.json ?? tools/data/domandatore/domandatore_20260507_2157.json ?? tools/data/domandatore/domandatore_20260507_2203.json ?? tools/data/domandatore/domandatore_20260508_0345.json ?? tools/data/domandatore_pre_cycle.json ?? tools/data/duality_gate_transfer_20260507_0803.json ?? tools/data/duality_gate_transfer_20260507_0803_seedcheck.json ?? tools/data/duality_gate_transfer_20260507_2042.json ?? tools/data/duality_scale_contrast_20260508.json ?? tools/data/evolution/evolution_20260422_0330.md ?? tools/data/evolution/evolution_20260422_1616.md ?? tools/data/evolution/evolution_20260423_0330.md ?? tools/data/evolution/evolution_20260424_0330.md ?? tools/data/evolution/evolution_20260425_0330.md ?? tools/data/evolution/evolution_20260427_0330.md ?? tools/data/evolution/evolution_20260428_0330.md ?? tools/data/evolution/evolution_20260429_0852.md ?? tools/data/evolution/evolution_20260429_1013.md ?? tools/data/evolution/evolution_20260430_0330.md ?? tools/data/evolution/evolution_20260430_1905.md ?? tools/data/evolution/evolution_20260430_1946.md ?? tools/data/evolution/evolution_20260501_0330.md ?? tools/data/evolution/evolution_20260501_0725.md ?? tools/data/evolution/evolution_20260501_0931.md ?? tools/data/evolution/evolution_20260502_0330.md ?? tools/data/evolution/evolution_20260503_0330.md ?? tools/data/evolution/evolution_20260504_0901.md ?? tools/data/evolution/evolution_20260504_1219.md ?? tools/data/evolution/evolution_20260505_0330.md ?? tools/data/evolution/evolution_20260506_0330.md ?? tools/data/evolution/evolution_20260506_1941.md ?? tools/data/evolution/evolution_20260506_1955.md ?? tools/data/evolution/evolution_20260507_0330.md ?? tools/data/evolution/evolution_20260507_0803.md ?? tools/data/evolution/evolution_20260507_0901.md ?? tools/data/evolution/evolution_20260507_0923.md ?? tools/data/evolution/evolution_20260507_0942.md ?? tools/data/evolution/evolution_20260507_1006.md ?? tools/data/evolution/evolution_20260507_1042.md ?? tools/data/evolution/evolution_20260507_1419.md ?? tools/data/evolution/evolution_20260507_1458.md ?? tools/data/evolution/evolution_20260507_1751.md ?? tools/data/evolution/evolution_20260507_1804.md ?? tools/data/evolution/evolution_20260507_1938.md ?? tools/data/evolution/evolution_20260507_1957.md ?? tools/data/evolution/evolution_20260507_2042.md ?? tools/data/evolution/evolution_20260507_2120.md ?? tools/data/evolution/evolution_20260507_2203.md ?? tools/data/evolution/evolution_20260507_2310.md ?? tools/data/evolution/evolution_20260508_0330.md ?? tools/data/evolution/evolution_20260508_1632.md ?? tools/data/evolution/evolution_20260508_1715.md ?? tools/data/evolution/evolution_20260508_1805.md ?? tools/data/evolution/evolution_20260508_1834.md ?? tools/data/evolution/evolution_20260508_1909.md ?? tools/data/exp_det_drift_20260507_2042.json ?? tools/data/falsifier_20260507_2120.raw.txt ?? tools/data/g1_log.jsonl ?? tools/data/g2_log.jsonl ?? tools/data/gap_label_block_scale_gate_20260508_1805.json ?? tools/data/gap_label_generator_gate_20260508_1715.json ?? tools/data/gap_label_set_stability_20260508_1632.json ?? tools/data/gap_label_substitution_grammar_gate_20260508_1834.json ?? tools/data/gap_label_supertile_tiling_gate_20260508_1909.json ?? tools/data/incrocio_20260422_0336.json ?? tools/data/incrocio_20260423_0335.json ?? tools/data/incrocio_20260424_0347.json ?? tools/data/incrocio_20260425_0339.json ?? tools/data/incrocio_20260428_0340.json ?? tools/data/incrocio_20260429_0859.json ?? tools/data/incrocio_20260506_0633.json ?? tools/data/incrocio_20260507_2120.json ?? tools/data/incrocio_20260507_2157.json ?? tools/data/incrocio_20260507_2203.json ?? tools/data/incrocio_20260507_2310.json ?? tools/data/incrocio_20260508_0011.json ?? tools/data/incrocio_20260508_0020.json ?? tools/data/incrocio_20260508_0330.json ?? tools/data/incrocio_20260508_1632.json ?? tools/data/incrocio_20260508_1715.json ?? tools/data/incrocio_20260508_1805.json ?? tools/data/incrocio_20260508_1834.json ?? tools/data/incrocio_20260508_1909.json ?? tools/data/incrocio_20260508_1915.json ?? tools/data/lab_bridge_issues.jsonl ?? tools/data/logistic_counter_scope_gate_20260507_1006.json ?? tools/data/logistic_counter_scope_gate_20260507_1006_seedcheck.json ?? tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json ?? tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json ?? tools/data/magnitude_psd_from_acf.json ?? tools/data/markov3_observable_hunt.json ?? tools/data/markov_dipolar_decomposition.json ?? tools/data/markov_k_direction.json ?? tools/data/markov_layer_recovery_audit.json ?? tools/data/markov_memory_by_gue_type.json ?? tools/data/markov_scale_function.json ?? tools/data/meta_assertion_gate.json ?? tools/data/meta_tautology_test.json ?? tools/data/mod3_scaling.json ?? tools/data/mod3_vs_residual_ordering.json ?? tools/data/modular_algebra_depth.json ?? tools/data/modular_memory_spectrum.json ?? tools/data/observable_collinearity_breaking_20260506_1955.json ?? tools/data/observable_collinearity_breaking_20260506_1956.json ?? tools/data/observable_collinearity_breaking_20260506_1957.json ?? tools/data/observable_rank_audit.json ?? tools/data/observable_rank_audit_seed20260506.json ?? tools/data/observatorio/domandatore_unTouched_20260507_095914.md ?? tools/data/observatorio/lazarus_cimitero_20260507_100015.md ?? tools/data/observatorio/meta_metodo_giro_2026-05-07.md ?? tools/data/operator_directives_consumed/ ?? tools/data/perturbation_dimensionality_audit.json ?? tools/data/perturbation_dimensionality_audit_scale0330.json ?? tools/data/perturbation_rank_size_curve.json ?? tools/data/promotions/ ?? tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json ?? tools/data/repairs/ ?? tools/data/reports/_quarantine_falsifier_29_04/ ?? tools/data/reports/agent_20260422_0330.md ?? tools/data/reports/agent_20260422_1616.md ?? tools/data/reports/agent_20260423_0330.md ?? tools/data/reports/agent_20260424_0330.md ?? tools/data/reports/agent_20260425_0330.md ?? tools/data/reports/agent_20260426_0330.md ?? tools/data/reports/agent_20260427_0330.md ?? tools/data/reports/agent_20260428_0330.md ?? tools/data/reports/agent_20260429_1013.md ?? tools/data/reports/agent_20260429_1041.md ?? tools/data/reports/agent_20260430_0330.md ?? tools/data/reports/agent_20260430_1905.md ?? tools/data/reports/agent_20260430_1919.md ?? tools/data/reports/agent_20260430_1946.md ?? tools/data/reports/agent_20260501_0330.md ?? tools/data/reports/agent_20260501_0725.md ?? tools/data/reports/agent_20260501_0931.md ?? tools/data/reports/agent_20260502_0330.md ?? tools/data/reports/agent_20260503_0330.md ?? tools/data/reports/agent_20260504_0901.md ?? tools/data/reports/agent_20260504_1219.md ?? tools/data/reports/agent_20260505_0330.md ?? tools/data/reports/agent_20260505_1022.md ?? tools/data/reports/agent_20260506_0330.md ?? tools/data/reports/agent_20260506_0625.md ?? tools/data/reports/agent_20260506_1941.md ?? tools/data/reports/agent_20260506_1955.md ?? tools/data/reports/agent_20260507_0330.md ?? tools/data/reports/agent_20260507_0803.md ?? tools/data/reports/agent_20260507_0901.md ?? tools/data/reports/agent_20260507_0923.md ?? tools/data/reports/agent_20260507_0942.md ?? tools/data/reports/agent_20260507_1006.md ?? tools/data/reports/agent_20260507_1042.md ?? tools/data/reports/agent_20260507_1419.md ?? tools/data/reports/agent_20260507_1458.md ?? tools/data/reports/agent_20260507_1751.md ?? tools/data/reports/agent_20260507_1804.md ?? tools/data/reports/agent_20260507_1938.md ?? tools/data/reports/agent_20260507_1957.md ?? tools/data/reports/agent_20260507_2042.md ?? tools/data/reports/agent_20260507_2120.md ?? tools/data/reports/agent_20260507_2157.md ?? tools/data/reports/agent_20260507_2203.md ?? tools/data/reports/agent_20260507_2310.md ?? tools/data/reports/agent_20260508_0011.md ?? tools/data/reports/agent_20260508_0330.md ?? tools/data/reports/agent_20260508_1632.md ?? tools/data/reports/agent_20260508_1715.md ?? tools/data/reports/agent_20260508_1805.md ?? tools/data/reports/agent_20260508_1834.md ?? tools/data/reports/agent_20260508_1909.md ?? tools/data/reports/ddf_20260422_0405.json ?? tools/data/reports/ddf_20260423_0405.json ?? tools/data/reports/ddf_20260424_0405.json ?? tools/data/reports/ddf_20260425_0405.json ?? tools/data/reports/ddf_20260426_0405.json ?? tools/data/reports/ddf_20260427_0405.json ?? tools/data/reports/ddf_20260428_0405.json ?? tools/data/reports/ddf_20260429_0405.json ?? tools/data/reports/ddf_20260430_0405.json ?? tools/data/reports/ddf_20260501_0405.json ?? tools/data/reports/ddf_20260502_0405.json ?? tools/data/reports/ddf_20260503_0405.json ?? tools/data/reports/ddf_20260504_0405.json ?? tools/data/reports/ddf_20260505_0405.json ?? tools/data/reports/ddf_20260505_0636.json ?? tools/data/reports/ddf_20260506_0405.json ?? tools/data/reports/ddf_20260507_0405.json ?? tools/data/reports/ddf_20260508_0405.json ?? tools/data/reports/evolution_20260422_0330.md ?? tools/data/reports/evolution_20260422_1616.md ?? tools/data/reports/evolution_20260423_0330.md ?? tools/data/reports/evolution_20260424_0330.md ?? tools/data/reports/evolution_20260425_0330.md ?? tools/data/reports/evolution_20260427_0330.md ?? tools/data/reports/evolution_20260428_0330.md ?? tools/data/reports/evolution_20260503_0330.md ?? tools/data/reports/evolution_20260504_0330.md ?? tools/data/reports/evolution_20260505_0330.md ?? tools/data/reports/evolution_20260506_0330.md ?? tools/data/reports/evolution_20260506_1941.md ?? tools/data/reports/falsifier_20260429_1013.json ?? tools/data/reports/falsifier_20260429_1041.json ?? tools/data/reports/falsifier_20260430_0330.json ?? tools/data/reports/falsifier_20260430_1905.json ?? tools/data/reports/falsifier_20260430_1919.json ?? tools/data/reports/falsifier_20260430_1946.json ?? tools/data/reports/falsifier_20260501_0330.json ?? tools/data/reports/falsifier_20260501_0725.json ?? tools/data/reports/falsifier_20260501_0931.json ?? tools/data/reports/falsifier_20260502_0330.json ?? tools/data/reports/falsifier_20260503_0330.json ?? tools/data/reports/falsifier_20260504_0901.json ?? tools/data/reports/falsifier_20260504_1219.json ?? tools/data/reports/falsifier_20260505_0330.json ?? tools/data/reports/falsifier_20260506_0330.raw.txt ?? tools/data/reports/falsifier_20260506_0625.json ?? tools/data/reports/falsifier_20260506_1941.json ?? tools/data/reports/falsifier_20260506_1955.json ?? tools/data/reports/falsifier_20260506_1955.raw.txt ?? tools/data/reports/falsifier_20260507_0330.json ?? tools/data/reports/falsifier_20260507_0330.raw.txt ?? tools/data/reports/falsifier_20260507_0803.json ?? tools/data/reports/falsifier_20260507_0803.raw.txt ?? tools/data/reports/falsifier_20260507_0901.json ?? tools/data/reports/falsifier_20260507_0923.json ?? tools/data/reports/falsifier_20260507_0923.raw.txt ?? tools/data/reports/falsifier_20260507_0942.json ?? tools/data/reports/falsifier_20260507_1006.json ?? tools/data/reports/falsifier_20260507_1042.json ?? tools/data/reports/falsifier_20260507_1042.raw.txt ?? tools/data/reports/falsifier_20260507_1419.json ?? tools/data/reports/falsifier_20260507_1458.json ?? tools/data/reports/falsifier_20260507_1458.raw.txt ?? tools/data/reports/falsifier_20260507_1751.json ?? tools/data/reports/falsifier_20260507_1804.json ?? tools/data/reports/falsifier_20260507_1938.json ?? tools/data/reports/falsifier_20260507_1938.raw.txt ?? tools/data/reports/falsifier_20260507_1957.json ?? tools/data/reports/falsifier_20260507_2042.json ?? tools/data/reports/falsifier_20260507_2120.json ?? tools/data/reports/falsifier_20260507_2203.json ?? tools/data/reports/falsifier_20260507_2310.json ?? tools/data/reports/falsifier_20260508_0011.json ?? tools/data/reports/falsifier_20260508_0330.json ?? tools/data/reports/falsifier_20260508_1632.raw.txt ?? tools/data/reports/falsifier_20260508_1715.json ?? tools/data/reports/falsifier_20260508_1805.json ?? tools/data/reports/falsifier_20260508_1834.json ?? tools/data/reports/falsifier_20260508_1909.json ?? tools/data/reports/incident_20260504_0721.md ?? tools/data/reports/incident_20260504_1138.md ?? tools/data/reports/latest.md ?? tools/data/reports/loop_guard_20260507_0330.json ?? tools/data/reports/loop_guard_20260507_0803.json ?? tools/data/reports/loop_guard_20260507_0901.json ?? tools/data/reports/loop_guard_20260507_0923.json ?? tools/data/reports/loop_guard_20260507_0942.json ?? tools/data/reports/loop_guard_20260507_1006.json ?? tools/data/reports/loop_guard_20260507_1042.json ?? tools/data/reports/loop_guard_20260507_1419.json ?? tools/data/reports/loop_guard_20260507_1458.json ?? tools/data/reports/loop_guard_20260507_1751.json ?? tools/data/reports/loop_guard_20260507_1804.json ?? tools/data/reports/loop_guard_20260507_1938.json ?? tools/data/reports/loop_guard_20260507_1957.json ?? tools/data/reports/loop_guard_20260507_2042.json ?? tools/data/reports/loop_guard_20260507_2120.json ?? tools/data/reports/loop_guard_20260507_2203.json ?? tools/data/reports/loop_guard_20260507_2310.json ?? tools/data/reports/loop_guard_20260508_0011.json ?? tools/data/reports/loop_guard_20260508_0330.json ?? tools/data/reports/loop_guard_20260508_1632.json ?? tools/data/reports/loop_guard_20260508_1715.json ?? tools/data/reports/loop_guard_20260508_1805.json ?? tools/data/reports/loop_guard_20260508_1834.json ?? tools/data/reports/loop_guard_20260508_1909.json ?? tools/data/reports/trajectory_apply_20260506_1931.json ?? tools/data/reports/trajectory_apply_20260506_1941.json ?? tools/data/reports/trajectory_apply_20260506_1955.json ?? tools/data/reports/trajectory_apply_20260507_0330.json ?? tools/data/reports/trajectory_apply_20260507_0803.json ?? tools/data/reports/trajectory_apply_20260507_0901.json ?? tools/data/reports/trajectory_apply_20260507_0923.json ?? tools/data/reports/trajectory_apply_20260507_0942.json ?? tools/data/reports/trajectory_apply_20260507_1006.json ?? tools/data/reports/trajectory_apply_20260507_1042.json ?? tools/data/scale_selective_perturbation.json ?? tools/data/selective_layer_decoupling.json ?? tools/data/seme_archive/piano_83.json ?? tools/data/seme_archive/piano_84.json ?? tools/data/seme_archive/piano_85.json ?? tools/data/seme_archive/piano_86.json ?? tools/data/seme_archive/piano_87.json ?? tools/data/seme_archive/piano_88.json ?? tools/data/seme_archive/piano_89.json ?? tools/data/semireal_order_denominator_gate_20260507_0923.json ?? tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json ?? tools/data/spectral_rigidity_results.json ?? tools/data/tqge_underlay_gate_20260507_1751.json ?? tools/data/trajectory_apply_history.jsonl ?? tools/data/triadic_deposit_gate_20260507_1938.json ?? tools/data/two_channel_boundary.json ?? tools/data/two_channel_cross_domain.json ?? tools/data/two_channel_shuffle_audit.json ?? tools/data/two_layer_universality.json ?? tools/data/valutatore_log.jsonl ?? tools/data/veritas/veritas_20260505_131056.json ?? tools/data/veritas/veritas_20260505_131201.json ?? tools/data/veritas/veritas_20260506_033803.json ?? tools/data/veritas/veritas_20260506_063303.json ?? tools/data/veritas/veritas_20260506_194644.json ?? tools/data/veritas/veritas_20260506_200238.json ?? tools/data/veritas/veritas_20260507_033525.json ?? tools/data/veritas/veritas_20260507_080958.json ?? tools/data/veritas/veritas_20260507_090723.json ?? tools/data/veritas/veritas_20260507_093207.json ?? tools/data/veritas/veritas_20260507_094723.json ?? tools/data/veritas/veritas_20260507_101605.json ?? tools/data/veritas/veritas_20260507_104904.json ?? tools/data/veritas/veritas_20260507_142501.json ?? tools/data/veritas/veritas_20260507_150443.json ?? tools/data/veritas/veritas_20260507_175511.json ?? tools/data/veritas/veritas_20260507_180843.json ?? tools/data/veritas/veritas_20260507_194258.json ?? tools/data/veritas/veritas_20260507_200223.json ?? tools/data/veritas/veritas_20260507_204637.json ?? tools/data/veritas/veritas_20260507_212541.json ?? tools/data/veritas/veritas_20260507_221109.json ?? tools/data/veritas/veritas_20260507_231732.json ?? tools/data/veritas/veritas_20260508_002036.json ?? tools/data/veritas/veritas_20260508_033454.json ?? tools/data/veritas/veritas_20260508_163804.json ?? tools/data/veritas/veritas_20260508_172020.json ?? tools/data/veritas/veritas_20260508_181110.json ?? tools/data/veritas/veritas_20260508_183856.json ?? tools/data/veritas/veritas_20260508_191516.json ?? tools/data/vincoli_decay_log.jsonl ?? tools/evolution_report.md ?? tools/exp_3d_boundary_layers.py ?? tools/exp_blank_shell_dilation_gate.py ?? tools/exp_blank_shell_polarity_gate.py ?? tools/exp_blank_shell_scale_law.py ?? tools/exp_blank_shell_stratified_gate.py ?? tools/exp_blank_shell_tqger_gate.py ?? tools/exp_blank_to_source_hinge.py ?? tools/exp_boundary_coherence.py ?? tools/exp_boundary_mixture_gate.py ?? tools/exp_boundary_shuffle_audit.py ?? tools/exp_bridge_order_denominator_gate.py ?? tools/exp_brody_calibration.py ?? tools/exp_brody_flow.py ?? tools/exp_cross_domain_dipolar_direction.py ?? tools/exp_cross_observable_consistency.py ?? tools/exp_crossover_phase_test.py ?? tools/exp_denominator_gate_transfer_matrix.py ?? tools/exp_dipolar_angle_reference.py ?? tools/exp_dipolar_crossover.py ?? tools/exp_dipolar_vector_scaling.py ?? tools/exp_duality_gate_transfer.py ?? tools/exp_duality_scale_contrast.py ?? tools/exp_gap_label_block_scale_gate.py ?? tools/exp_gap_label_generator_gate.py ?? tools/exp_gap_label_repair_audit.py ?? tools/exp_gap_label_set_stability.py ?? tools/exp_gap_label_substitution_grammar_gate.py ?? tools/exp_gap_label_supertile_tiling_gate.py ?? tools/exp_logistic_counter_scope_gate.py ?? tools/exp_logistic_cyclic_block_entropy_gate.py ?? tools/exp_logistic_surrogate_contract_gate.py ?? tools/exp_magnitude_psd_from_acf.py ?? tools/exp_markov3_observable_hunt.py ?? tools/exp_markov_dipolar_decomposition.py ?? tools/exp_markov_k_direction.py ?? tools/exp_markov_layer_recovery_audit.py ?? tools/exp_markov_memory_by_gue_type.py ?? tools/exp_markov_psd_prediction.py ?? tools/exp_markov_scale_function.py ?? tools/exp_meta_tautology_test.py ?? tools/exp_mod3_scaling.py ?? tools/exp_mod3_vs_residual_ordering.py ?? tools/exp_modular_algebra_depth.py ?? tools/exp_modular_memory_spectrum.py ?? tools/exp_observable_rank_audit.py ?? tools/exp_perturbation_dimensionality_audit.py ?? tools/exp_perturbation_rank_size_curve.py ?? tools/exp_quasiperiodic_gap_ratio_denominator.py ?? tools/exp_scale_selective_perturbation.py ?? tools/exp_selective_layer_decoupling.py ?? tools/exp_semireal_order_denominator_gate.py ?? tools/exp_spectral_rigidity.py ?? tools/exp_tqge_underlay_gate.py ?? tools/exp_triadic_deposit_gate.py ?? tools/exp_two_channel_boundary.py ?? tools/exp_two_channel_cross_domain.py ?? tools/exp_two_channel_shuffle_audit.py ?? tools/exp_two_layer_universality.py ?? tools/meta_assertion_gate.py ?? tools/observables_registry.py exec /bin/bash -lc 'python tools/exp_gap_label_repair_audit.py --input tools/data/gap_label_supertile_tiling_gate_20260508_1909.json --out tools/data/gap_label_repair_audit_20260508_1915.json' in /opt/MM_D-ND diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() succeeded in 153ms: { "summary": { "mode=same_count_internal_shuffle": { "all_high": "0/160", "stable_high": [], "stable_high_count": 0, "per_high_label": { "3": "6/160", "-4": "57/160", "4": "54/160", "6": "38/160" } }, "mode=same_length_contiguous_shuffle": { "all_high": "116/160", "stable_high": [ 3, -4, 4, 6 ], "stable_high_count": 4, "per_high_label": { "3": "154/160", "-4": "158/160", "4": "160/160", "6": "120/160" } }, "mode=same_mean_block_shuffle": { "all_high": "7/160", "stable_high": [], "stable_high_count": 0, "per_high_label": { "3": "30/160", "-4": "99/160", "4": "90/160", "6": "18/160" } }, "mode=supertile_shuffle": { "all_high": "108/160", "stable_high": [ 3, -4, 4, 6 ], "stable_high_count": 4, "per_high_label": { "3": "128/160", "-4": "153/160", "4": "160/160", "6": "120/160" } } }, "out": "tools/data/gap_label_repair_audit_20260508_1915.json" } codex Il repair separa il punto critico: `same_count_internal_shuffle` ha all-high `0/160`, ma singoli label sopravvivono con conteggi diversi (`-4`, `4`, `6` non sono zero). Raccolgo i dettagli per order e gli errori IDS per scrivere un verdict scoped. exec /bin/bash -lc "python - <<'PY' import json p='tools/data/gap_label_repair_audit_20260508_1915.json' d=json.load(open(p)) for key in ['mode=supertile_shuffle','mode=same_length_contiguous_shuffle','mode=same_count_internal_shuffle','mode=same_mean_block_shuffle']: s=d['mode_summary'][key] print('\\n'+key) print('all_high', s['all_high_hits'], s['conditions'], s['all_high_rate']) print('stable labels', s['stable_labels_75pct']) print('stable high', s['stable_high_labels_75pct']) for label,v in s['per_high_label'].items(): print(label, v) PY" in /opt/MM_D-ND succeeded in 0ms: mode=supertile_shuffle all_high 108 160 0.675 stable labels [-1, 1, -2, 2, -3, 3, -4, 4, 6] stable high [3, -4, 4, 6] 3 {'hits': 128, 'total': 160, 'rate': 0.8, 'median_label_error': 9.439585859527533e-06, 'max_label_error': 0.0026619594797586688} -4 {'hits': 153, 'total': 160, 'rate': 0.95625, 'median_label_error': 0.0016345368036995156, 'max_label_error': 0.0026651060083784373} 4 {'hits': 160, 'total': 160, 'rate': 1.0, 'median_label_error': 8.696786537365497e-06, 'max_label_error': 1.2586114479185007e-05} 6 {'hits': 120, 'total': 160, 'rate': 0.75, 'median_label_error': 0.0016321330744020202, 'max_label_error': 0.0026713990656181963} mode=same_length_contiguous_shuffle all_high 116 160 0.725 stable labels [-1, 1, -2, 2, -3, 3, -4, 4, 6] stable high [3, -4, 4, 6] 3 {'hits': 154, 'total': 160, 'rate': 0.9625, 'median_label_error': 9.439585859527533e-06, 'max_label_error': 0.0026619594797586688} -4 {'hits': 158, 'total': 160, 'rate': 0.9875, 'median_label_error': 0.0016345368036995156, 'max_label_error': 0.0026651060083784373} 4 {'hits': 160, 'total': 160, 'rate': 1.0, 'median_label_error': 8.696786537365497e-06, 'max_label_error': 1.2586114479185007e-05} 6 {'hits': 120, 'total': 160, 'rate': 0.75, 'median_label_error': 1.8879171719055066e-05, 'max_label_error': 0.0026713990656181963} mode=same_count_internal_shuffle all_high 0 160 0.0 stable labels [34] stable high [] 3 {'hits': 6, 'total': 160, 'rate': 0.0375, 'median_label_error': 0.00808005749254298, 'max_label_error': 0.010619519161456314} -4 {'hits': 57, 'total': 160, 'rate': 0.35625, 'median_label_error': 0.004913225328289639, 'max_label_error': 0.010622665690075972} 4 {'hits': 54, 'total': 160, 'rate': 0.3375, 'median_label_error': 0.0052924536733192085, 'max_label_error': 0.010622665690076027} 6 {'hits': 38, 'total': 160, 'rate': 0.2375, 'median_label_error': 0.005286160616079449, 'max_label_error': 0.010628958747315842} mode=same_mean_block_shuffle all_high 7 160 0.04375 stable labels [-1, 1, -2] stable high [] 3 {'hits': 30, 'total': 160, 'rate': 0.1875, 'median_label_error': 0.002152454668000181, 'max_label_error': 0.006560982643126878} -4 {'hits': 99, 'total': 160, 'rate': 0.61875, 'median_label_error': 0.0026399337794200672, 'max_label_error': 0.00797014579617683} 4 {'hits': 90, 'total': 160, 'rate': 0.5625, 'median_label_error': 0.001644151720890663, 'max_label_error': 0.007970145796176775} 6 {'hits': 18, 'total': 160, 'rate': 0.1125, 'median_label_error': 0.002633640722180197, 'max_label_error': 0.0032858997124832756} exec /bin/bash -lc "python - <<'PY' import json p='tools/data/gap_label_repair_audit_20260508_1915.json' d=json.load(open(p)) for key in sorted(d['mode_order_summary']): if key.startswith('mode=same_count_internal_shuffle'): s=d['mode_order_summary'][key] print(key, 'all_high', f\"{s['all_high_hits']}/{s['conditions']}\", 'stable_high', s['stable_high_labels_75pct'], 'per', {k:f\"{v['hits']}/{v['total']}\" for k,v in s['per_high_label'].items()}) print('baseline', d['theoretical_baseline']['baseline_statement']) PY" in /opt/MM_D-ND succeeded in 0ms: mode=same_count_internal_shuffle|supertile_order=10 all_high 0/40 stable_high [] per {'3': '1/40', '-4': '11/40', '4': '14/40', '6': '9/40'} mode=same_count_internal_shuffle|supertile_order=11 all_high 0/40 stable_high [] per {'3': '5/40', '-4': '15/40', '4': '14/40', '6': '11/40'} mode=same_count_internal_shuffle|supertile_order=8 all_high 0/40 stable_high [] per {'3': '0/40', '-4': '14/40', '4': '17/40', '6': '9/40'} mode=same_count_internal_shuffle|supertile_order=9 all_high 0/40 stable_high [] per {'3': '0/40', '-4': '17/40', '4': '9/40', '6': '9/40'} baseline The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; the D-ND novelty tested here is not their membership, but their joint and per-label survival under order/boundary perturbations. exec /bin/bash -lc 'python -m py_compile tools/exp_gap_label_repair_audit.py' in /opt/MM_D-ND succeeded in 0ms: diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex Il tool compila e il JSON del repair è scritto. Scrivo ora il report `agent_20260508_1915.md`, aprendo col Claim Under Test corretto e chiudendo con l’auto-audit sulle flag L1/L4/L5. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260508_1915.md diff --git a/tools/data/reports/agent_20260508_1915.md b/tools/data/reports/agent_20260508_1915.md new file mode 100644 index 0000000000000000000000000000000000000000..c05a7be0f824b74e654f60feedd5373aa9510b18 --- /dev/null +++ b/tools/data/reports/agent_20260508_1915.md @@ -0,0 +1,101 @@ +# Agent Report - High-Core Repair Audit +**Date**: 2026-05-08 19:15 +**Piano**: 90 +**Tension explored**: TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [all_high_hits, per_high_label_retention, stable_label_count, label_error, theoretical_gap_labeling_baseline] +**observable_contract**: claim=il core alto phi sotto internal shuffle va formulato come caduta del core completo, non come caduta hard di ogni label alto; observable=all-high hits separato da retention per-label e stable high label count; operator=audit delle righe grezze del supertile gate 19:09 con conteggi per mode/order e baseline teorica Sturmian; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle, same_mean_block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, nuove diagonalizzazioni spettrali, soglie 1.75/2.25, generatori non-phi. + +## Respiro fuori-tempo +- **Combo**: A4 contratto della domanda + A11 combo + QxG vuoto continuo/discreto + TxQ matrice densita come lettore IDS + tensione TRASCENDENZA_LIMITE sul passaggio tra piani. +- **Dipolo / punto-zero**: core completo simultaneo . label singolo superstite; punto-zero = il label-set prima della decisione su quale livello osservabile porta il claim. +- **Piano superiore**: topologia assiomatica del contratto osservabile. Il boundary non si decide dal nome del mode, ma dal livello che sopravvive: congiunzione, singolo label, stabilita' 75%, errore IDS. +- **Proto-ipotesi**: la falsificazione del report 19:09 non cancella tutto il core alto. Cancella la chiusura congiunta `[3,-4,4,6]` sotto internal shuffle; i label singoli possono sopravvivere come residui classici di gap-labeling. +- **Proiezione**: l'audit tiene fisso il dato 19:09 e separa tre piani che erano stati compressi: `all_high_hits`, retention per-label, `stable_high_label_count`. + +## Claim Under Test +> Nel perimetro del source cycle 19:09, `same_count_internal_shuffle` azzera il core alto completo `[3,-4,4,6]` come condizione congiunta. Non azzera ogni label alto preso singolarmente. La novita' D-ND sta nella sopravvivenza congiunta e nel collasso sotto perturbazione d'ordine, non nella membership dei label nel gruppo classico Sturmian/Fibonacci. + +Il source cycle non viene promosso e non viene sincronizzato concettualmente. Viene assorbita la falsificazione del wording: "core alto cade" diventa "core alto completo cade; singoli label alti restano parziali". + +## Question +Quali tra `[3,-4,4,6]` sopravvivono sotto internal shuffle, e quale osservabile cade davvero: all-high, per-label retention o stable-label count? + +## Experiment Design +- Input: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json`. +- Tool: `tools/exp_gap_label_repair_audit.py`. +- Baseline teorica: per Sturmian/Fibonacci i gap label vivono nel gruppo `Z + theta Z mod 1`, con `theta=1/phi`. I label `[3,-4,4,6]` appartengono al reader classico; questo ciclo non li dichiara scoperta. +- Denominatore grezzo per mode aggregato: 160 condizioni (`2 N * 4 phase * 1 threshold * 5 trials * 4 orders`). +- Denominatore grezzo per `mode|order`: 40 condizioni. +- Osservabili separati: + - `all_high_hits`: tutte le label alte presenti nella stessa condizione. + - `per_high_label`: hits indipendenti per ciascuna label alta. + - `stable_high_label_count`: label alte presenti in almeno 75% delle condizioni. + - `label_error`: errore IDS mediano dei selected gap per label. + +## Results + +Sintesi per mode: + +| mode | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---|---:|---|---:|---:|---:|---:| +| supertile_shuffle | 108/160 = 0.67500 | [3,-4,4,6] | 128/160 | 153/160 | 160/160 | 120/160 | +| same_length_contiguous_shuffle | 116/160 = 0.72500 | [3,-4,4,6] | 154/160 | 158/160 | 160/160 | 120/160 | +| same_count_internal_shuffle | 0/160 = 0.00000 | [] | 6/160 | 57/160 | 54/160 | 38/160 | +| same_mean_block_shuffle | 7/160 = 0.04375 | [] | 30/160 | 99/160 | 90/160 | 18/160 | + +Internal shuffle per order: + +| order | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---:|---:|---|---:|---:|---:|---:| +| 8 | 0/40 | [] | 0/40 | 14/40 | 17/40 | 9/40 | +| 9 | 0/40 | [] | 0/40 | 17/40 | 9/40 | 9/40 | +| 10 | 0/40 | [] | 1/40 | 11/40 | 14/40 | 9/40 | +| 11 | 0/40 | [] | 5/40 | 15/40 | 14/40 | 11/40 | + +Errore IDS mediano per mode aggregato: + +| mode | label 3 | label -4 | label 4 | label 6 | +|---|---:|---:|---:|---:| +| supertile_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00163213 | +| same_length_contiguous_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00001888 | +| same_count_internal_shuffle | 0.00808006 | 0.00491323 | 0.00529245 | 0.00528616 | +| same_mean_block_shuffle | 0.00215245 | 0.00263993 | 0.00164415 | 0.00263364 | + +## Key Findings +1. **Verificato: `same_count_internal_shuffle` azzera il core alto completo, non i label alti singoli.** All-high e' `0/160`; per-label restano `3: 6/160`, `-4: 57/160`, `4: 54/160`, `6: 38/160`. Il wording corretto e' "core completo cade". + +2. **Verificato: nessun label alto diventa stable label sotto internal shuffle.** `stable_high_label_count=0`; l'unico stable label aggregato del mode e' `[34]`, fuori dal core alto. La sopravvivenza per-label e' residua, non stabilita' del core. + +3. **Verificato: il controllo contiguo conserva piu' del supertile anche nel piano per-label.** `same_length_contiguous_shuffle` fa all-high `116/160` contro `108/160`, e per `3` fa `154/160` contro `128/160`. Il boundary esatto del supertile resta non discriminato dal label-set. + +4. **Verificato: i label `[3,-4,4,6]` sono baseline classica Sturmian/Fibonacci.** Appartengono al gruppo atteso `Z + theta Z mod 1`. La parte D-ND testata qui e' la loro chiusura congiunta sotto perturbazioni di ordine e scala, non la loro esistenza come gap label. + +5. **Inferito dai conteggi e dagli errori IDS: internal shuffle trasporta sottostruttura debole, ma rompe la congiunzione.** Gli errori mediani sotto internal shuffle aumentano (`~0.0049-0.0081`) rispetto ai mode ordinati (`~0.000009-0.00163`), mentre i hits per-label restano non-zero. + +## Verdict +**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, l'affermazione valida e': `same_count_internal_shuffle` distrugge la chiusura simultanea del core alto `[3,-4,4,6]` (`0/160`) e non produce stable high labels (`0`), ma trasporta singoli label alti con retention parziale (`6/160`, `57/160`, `54/160`, `38/160`). + +Non si dice piu' "il core alto cade" senza perimetro. Si dice: il core alto completo cade; la retention per-label resta parziale; la stable-label count alta resta zero. `gap_ratio` non e' testato. + +## Bicono della scoperta +- **Due radici**: congiunzione del core alto . residuo per-label. +- **Singolare**: il label-set prima della scissione fra simultaneita' e membership. +- **Invariante di passaggio**: l'ordine interno e le lunghezze Fibonacci-like conservano la chiusura; il conteggio senza ordine conserva solo residui di label classici. +- **Campo di possibilita**: qui diventa possibile cercare il boundary nella geometria degli errori IDS e nella posizione dei gap; qui diventa non-possibile usare all-high, retention mediana e stable-label count come un solo osservabile. + +## Consecutio +Costruire il prossimo gate sul piano posizione/errore: per i label core, confrontare distribuzione IDS, rank dei gap selezionati ed errore label fra chunk aligned e misaligned. Il boundary reale deve spostare la geometria dei gap, non solo il set dei label. + +## Auto-audit source flags +- **L1 medium risolta**: all-high, per-label retention e stable-label count sono separati. Count grezzi e denominatori sono riportati. +- **L4 medium risolta**: edge case per label isolato. `[3,-4,4,6]` sotto internal shuffle fanno `6/160`, `57/160`, `54/160`, `38/160`; nessuno entra negli stable high labels. +- **L5 low risolta nel perimetro minimo**: baseline teorica dichiarata. I label core sono gap-labeling Sturmian/Fibonacci classico; la novita' non e' membership, ma sopravvivenza congiunta sotto perturbazione. +- **Aperto**: `gap_ratio` resta `not_tested`; soglie 1.75/2.25 e generatori non-phi restano fuori da questo repair. + +## Files +- Script: `tools/exp_gap_label_repair_audit.py` +- Data: `tools/data/gap_label_repair_audit_20260508_1915.json` +- Source data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json` +- Report: `tools/data/reports/agent_20260508_1915.md` diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260508_1915.md b/tools/data/reports/agent_20260508_1915.md new file mode 100644 index 0000000000000000000000000000000000000000..c05a7be0f824b74e654f60feedd5373aa9510b18 --- /dev/null +++ b/tools/data/reports/agent_20260508_1915.md @@ -0,0 +1,101 @@ +# Agent Report - High-Core Repair Audit +**Date**: 2026-05-08 19:15 +**Piano**: 90 +**Tension explored**: TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [all_high_hits, per_high_label_retention, stable_label_count, label_error, theoretical_gap_labeling_baseline] +**observable_contract**: claim=il core alto phi sotto internal shuffle va formulato come caduta del core completo, non come caduta hard di ogni label alto; observable=all-high hits separato da retention per-label e stable high label count; operator=audit delle righe grezze del supertile gate 19:09 con conteggi per mode/order e baseline teorica Sturmian; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle, same_mean_block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, nuove diagonalizzazioni spettrali, soglie 1.75/2.25, generatori non-phi. + +## Respiro fuori-tempo +- **Combo**: A4 contratto della domanda + A11 combo + QxG vuoto continuo/discreto + TxQ matrice densita come lettore IDS + tensione TRASCENDENZA_LIMITE sul passaggio tra piani. +- **Dipolo / punto-zero**: core completo simultaneo . label singolo superstite; punto-zero = il label-set prima della decisione su quale livello osservabile porta il claim. +- **Piano superiore**: topologia assiomatica del contratto osservabile. Il boundary non si decide dal nome del mode, ma dal livello che sopravvive: congiunzione, singolo label, stabilita' 75%, errore IDS. +- **Proto-ipotesi**: la falsificazione del report 19:09 non cancella tutto il core alto. Cancella la chiusura congiunta `[3,-4,4,6]` sotto internal shuffle; i label singoli possono sopravvivere come residui classici di gap-labeling. +- **Proiezione**: l'audit tiene fisso il dato 19:09 e separa tre piani che erano stati compressi: `all_high_hits`, retention per-label, `stable_high_label_count`. + +## Claim Under Test +> Nel perimetro del source cycle 19:09, `same_count_internal_shuffle` azzera il core alto completo `[3,-4,4,6]` come condizione congiunta. Non azzera ogni label alto preso singolarmente. La novita' D-ND sta nella sopravvivenza congiunta e nel collasso sotto perturbazione d'ordine, non nella membership dei label nel gruppo classico Sturmian/Fibonacci. + +Il source cycle non viene promosso e non viene sincronizzato concettualmente. Viene assorbita la falsificazione del wording: "core alto cade" diventa "core alto completo cade; singoli label alti restano parziali". + +## Question +Quali tra `[3,-4,4,6]` sopravvivono sotto internal shuffle, e quale osservabile cade davvero: all-high, per-label retention o stable-label count? + +## Experiment Design +- Input: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json`. +- Tool: `tools/exp_gap_label_repair_audit.py`. +- Baseline teorica: per Sturmian/Fibonacci i gap label vivono nel gruppo `Z + theta Z mod 1`, con `theta=1/phi`. I label `[3,-4,4,6]` appartengono al reader classico; questo ciclo non li dichiara scoperta. +- Denominatore grezzo per mode aggregato: 160 condizioni (`2 N * 4 phase * 1 threshold * 5 trials * 4 orders`). +- Denominatore grezzo per `mode|order`: 40 condizioni. +- Osservabili separati: + - `all_high_hits`: tutte le label alte presenti nella stessa condizione. + - `per_high_label`: hits indipendenti per ciascuna label alta. + - `stable_high_label_count`: label alte presenti in almeno 75% delle condizioni. + - `label_error`: errore IDS mediano dei selected gap per label. + +## Results + +Sintesi per mode: + +| mode | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---|---:|---|---:|---:|---:|---:| +| supertile_shuffle | 108/160 = 0.67500 | [3,-4,4,6] | 128/160 | 153/160 | 160/160 | 120/160 | +| same_length_contiguous_shuffle | 116/160 = 0.72500 | [3,-4,4,6] | 154/160 | 158/160 | 160/160 | 120/160 | +| same_count_internal_shuffle | 0/160 = 0.00000 | [] | 6/160 | 57/160 | 54/160 | 38/160 | +| same_mean_block_shuffle | 7/160 = 0.04375 | [] | 30/160 | 99/160 | 90/160 | 18/160 | + +Internal shuffle per order: + +| order | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---:|---:|---|---:|---:|---:|---:| +| 8 | 0/40 | [] | 0/40 | 14/40 | 17/40 | 9/40 | +| 9 | 0/40 | [] | 0/40 | 17/40 | 9/40 | 9/40 | +| 10 | 0/40 | [] | 1/40 | 11/40 | 14/40 | 9/40 | +| 11 | 0/40 | [] | 5/40 | 15/40 | 14/40 | 11/40 | + +Errore IDS mediano per mode aggregato: + +| mode | label 3 | label -4 | label 4 | label 6 | +|---|---:|---:|---:|---:| +| supertile_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00163213 | +| same_length_contiguous_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00001888 | +| same_count_internal_shuffle | 0.00808006 | 0.00491323 | 0.00529245 | 0.00528616 | +| same_mean_block_shuffle | 0.00215245 | 0.00263993 | 0.00164415 | 0.00263364 | + +## Key Findings +1. **Verificato: `same_count_internal_shuffle` azzera il core alto completo, non i label alti singoli.** All-high e' `0/160`; per-label restano `3: 6/160`, `-4: 57/160`, `4: 54/160`, `6: 38/160`. Il wording corretto e' "core completo cade". + +2. **Verificato: nessun label alto diventa stable label sotto internal shuffle.** `stable_high_label_count=0`; l'unico stable label aggregato del mode e' `[34]`, fuori dal core alto. La sopravvivenza per-label e' residua, non stabilita' del core. + +3. **Verificato: il controllo contiguo conserva piu' del supertile anche nel piano per-label.** `same_length_contiguous_shuffle` fa all-high `116/160` contro `108/160`, e per `3` fa `154/160` contro `128/160`. Il boundary esatto del supertile resta non discriminato dal label-set. + +4. **Verificato: i label `[3,-4,4,6]` sono baseline classica Sturmian/Fibonacci.** Appartengono al gruppo atteso `Z + theta Z mod 1`. La parte D-ND testata qui e' la loro chiusura congiunta sotto perturbazioni di ordine e scala, non la loro esistenza come gap label. + +5. **Inferito dai conteggi e dagli errori IDS: internal shuffle trasporta sottostruttura debole, ma rompe la congiunzione.** Gli errori mediani sotto internal shuffle aumentano (`~0.0049-0.0081`) rispetto ai mode ordinati (`~0.000009-0.00163`), mentre i hits per-label restano non-zero. + +## Verdict +**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, l'affermazione valida e': `same_count_internal_shuffle` distrugge la chiusura simultanea del core alto `[3,-4,4,6]` (`0/160`) e non produce stable high labels (`0`), ma trasporta singoli label alti con retention parziale (`6/160`, `57/160`, `54/160`, `38/160`). + +Non si dice piu' "il core alto cade" senza perimetro. Si dice: il core alto completo cade; la retention per-label resta parziale; la stable-label count alta resta zero. `gap_ratio` non e' testato. + +## Bicono della scoperta +- **Due radici**: congiunzione del core alto . residuo per-label. +- **Singolare**: il label-set prima della scissione fra simultaneita' e membership. +- **Invariante di passaggio**: l'ordine interno e le lunghezze Fibonacci-like conservano la chiusura; il conteggio senza ordine conserva solo residui di label classici. +- **Campo di possibilita**: qui diventa possibile cercare il boundary nella geometria degli errori IDS e nella posizione dei gap; qui diventa non-possibile usare all-high, retention mediana e stable-label count come un solo osservabile. + +## Consecutio +Costruire il prossimo gate sul piano posizione/errore: per i label core, confrontare distribuzione IDS, rank dei gap selezionati ed errore label fra chunk aligned e misaligned. Il boundary reale deve spostare la geometria dei gap, non solo il set dei label. + +## Auto-audit source flags +- **L1 medium risolta**: all-high, per-label retention e stable-label count sono separati. Count grezzi e denominatori sono riportati. +- **L4 medium risolta**: edge case per label isolato. `[3,-4,4,6]` sotto internal shuffle fanno `6/160`, `57/160`, `54/160`, `38/160`; nessuno entra negli stable high labels. +- **L5 low risolta nel perimetro minimo**: baseline teorica dichiarata. I label core sono gap-labeling Sturmian/Fibonacci classico; la novita' non e' membership, ma sopravvivenza congiunta sotto perturbazione. +- **Aperto**: `gap_ratio` resta `not_tested`; soglie 1.75/2.25 e generatori non-phi restano fuori da questo repair. + +## Files +- Script: `tools/exp_gap_label_repair_audit.py` +- Data: `tools/data/gap_label_repair_audit_20260508_1915.json` +- Source data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json` +- Report: `tools/data/reports/agent_20260508_1915.md` diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex Il report è scritto. Faccio una verifica minima sui file prodotti e lancio il reminder post-change, così restano chiari side effect e superfici toccate. exec /bin/bash -lc 'wc -l tools/exp_gap_label_repair_audit.py tools/data/reports/agent_20260508_1915.md tools/data/gap_label_repair_audit_20260508_1915.json' in /opt/MM_D-ND succeeded in 0ms: 168 tools/exp_gap_label_repair_audit.py 101 tools/data/reports/agent_20260508_1915.md 1097 tools/data/gap_label_repair_audit_20260508_1915.json 1366 total exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/agent_20260508_1915.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - High-Core Repair Audit **Date**: 2026-05-08 19:15 **Piano**: 90 **Tension explored**: TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE **verdict**: CONSTRAINT observables_registry: n/a observables_used: [all_high_hits, per_high_label_retention, stable_label_count, label_error, theoretical_gap_labeling_baseline] **observable_contract**: claim=il core alto phi sotto internal shuffle va formulato come caduta del core completo, non come caduta hard di ogni label alto; observable=all-high hits separato da retention per-label e stable high label count; operator=audit delle righe grezze del supertile gate 19:09 con conteggi per mode/order e baseline teorica Sturmian; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle, same_mean_block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, nuove diagonalizzazioni spettrali, soglie 1.75/2.25, generatori non-phi. ## Respiro fuori-tempo - **Combo**: A4 contratto della domanda + A11 combo + QxG vuoto continuo/discreto + TxQ matrice densita come lettore IDS + tensione TRASCENDENZA_LIMITE sul passaggio tra piani. - **Dipolo / punto-zero**: core completo simultaneo . label singolo superstite; punto-zero = il label-set prima della decisione su quale livello osservabile porta il claim. - **Piano superiore**: topologia assiomatica del contratto osservabile. Il boundary non si decide dal nome del mode, ma dal livello che sopravvive: congiunzione, singolo label, stabilita' 75%, errore IDS. - **Proto-ipotesi**: la falsificazione del report 19:09 non cancella tutto il core alto. Cancella la chiusura congiunta `[3,-4,4,6]` sotto internal shuffle; i label singoli possono sopravvivere come residui classici di gap-labeling. - **Proiezione**: l'audit tiene fisso il dato 19:09 e separa tre piani che erano stati compressi: `all_high_hits`, retention per-label, `stable_high_label_count`. ## Claim Under Test > Nel perimetro del source cycle 19:09, `same_count_internal_shuffle` azzera il core alto completo `[3,-4,4,6]` come condizione congiunta. Non azzera ogni label alto preso singolarmente. La novita' D-ND sta nella sopravvivenza congiunta e nel collasso sotto perturbazione d'ordine, non nella membership dei label nel gruppo classico Sturmian/Fibonacci. Il source cycle non viene promosso e non viene sincronizzato concettualmente. Viene assorbita la falsificazione del wording: "core alto cade" diventa "core alto completo cade; singoli label alti restano parziali". ## Question Quali tra `[3,-4,4,6]` sopravvivono sotto internal shuffle, e quale osservabile cade davvero: all-high, per-label retention o stable-label count? ## Experiment Design - Input: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json`. - Tool: `tools/exp_gap_label_repair_audit.py`. - Baseline teorica: per Sturmian/Fibonacci i gap label vivono nel gruppo `Z + theta Z mod 1`, con `theta=1/phi`. I label `[3,-4,4,6]` appartengono al reader classico; questo ciclo non li dichiara scoperta. - Denominatore grezzo per mode aggregato: 160 condizioni (`2 N * 4 phase * 1 threshold * 5 trials * 4 orders`). - Denominatore grezzo per `mode|order`: 40 condizioni. - Osservabili separati: - `all_high_hits`: tutte le label alte presenti nella stessa condizione. - `per_high_label`: hits indipendenti per ciascuna label alta. - `stable_high_label_count`: label alte presenti in almeno 75% delle condizioni. - `label_error`: errore IDS mediano dei selected gap per label. ## Results Sintesi per mode: | mode | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | |---|---:|---|---:|---:|---:|---:| | supertile_shuffle | 108/160 = 0.67500 | [3,-4,4,6] | 128/160 | 153/160 | 160/160 | 120/160 | | same_length_contiguous_shuffle | 116/160 = 0.72500 | [3,-4,4,6] | 154/160 | 158/160 | 160/160 | 120/160 | | same_count_internal_shuffle | 0/160 = 0.00000 | [] | 6/160 | 57/160 | 54/160 | 38/160 | | same_mean_block_shuffle | 7/160 = 0.04375 | [] | 30/160 | 99/160 | 90/160 | 18/160 | Internal shuffle per order: | order | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | |---:|---:|---|---:|---:|---:|---:| | 8 | 0/40 | [] | 0/40 | 14/40 | 17/40 | 9/40 | | 9 | 0/40 | [] | 0/40 | 17/40 | 9/40 | 9/40 | | 10 | 0/40 | [] | 1/40 | 11/40 | 14/40 | 9/40 | | 11 | 0/40 | [] | 5/40 | 15/40 | 14/40 | 11/40 | Errore IDS mediano per mode aggregato: | mode | label 3 | label -4 | label 4 | label 6 | |---|---:|---:|---:|---:| | supertile_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00163213 | | same_length_contiguous_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00001888 | | same_count_internal_shuffle | 0.00808006 | 0.00491323 | 0.00529245 | 0.00528616 | | same_mean_block_shuffle | 0.00215245 | 0.00263993 | 0.00164415 | 0.00263364 | ## Key Findings 1. **Verificato: `same_count_internal_shuffle` azzera il core alto completo, non i label alti singoli.** All-high e' `0/160`; per-label restano `3: 6/160`, `-4: 57/160`, `4: 54/160`, `6: 38/160`. Il wording corretto e' "core completo cade". 2. **Verificato: nessun label alto diventa stable label sotto internal shuffle.** `stable_high_label_count=0`; l'unico stable label aggregato del mode e' `[34]`, fuori dal core alto. La sopravvivenza per-label e' residua, non stabilita' del core. 3. **Verificato: il controllo contiguo conserva piu' del supertile anche nel piano per-label.** `same_length_contiguous_shuffle` fa all-high `116/160` contro `108/160`, e per `3` fa `154/160` contro `128/160`. Il boundary esatto del supertile resta non discriminato dal label-set. 4. **Verificato: i label `[3,-4,4,6]` sono baseline classica Sturmian/Fibonacci.** Appartengono al gruppo atteso `Z + theta Z mod 1`. La parte D-ND testata qui e' la loro chiusura congiunta sotto perturbazioni di ordine e scala, non la loro esistenza come gap label. 5. **Inferito dai conteggi e dagli errori IDS: internal shuffle trasporta sottostruttura debole, ma rompe la congiunzione.** Gli errori mediani sotto internal shuffle aumentano (`~0.0049-0.0081`) rispetto ai mode ordinati (`~0.000009-0.00163`), mentre i hits per-label restano non-zero. ## Verdict **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, l'affermazione valida e': `same_count_internal_shuffle` distrugge la chiusura simultanea del core alto `[3,-4,4,6]` (`0/160`) e non produce stable high labels (`0`), ma trasporta singoli label alti con retention parziale (`6/160`, `57/160`, `54/160`, `38/160`). Non si dice piu' "il core alto cade" senza perimetro. Si dice: il core alto completo cade; la retention per-label resta parziale; la stable-label count alta resta zero. `gap_ratio` non e' testato. ## Bicono della scoperta - **Due radici**: congiunzione del core alto . residuo per-label. - **Singolare**: il label-set prima della scissione fra simultaneita' e membership. - **Invariante di passaggio**: l'ordine interno e le lunghezze Fibonacci-like conservano la chiusura; il conteggio senza ordine conserva solo residui di label classici. - **Campo di possibilita**: qui diventa possibile cercare il boundary nella geometria degli errori IDS e nella posizione dei gap; qui diventa non-possibile usare all-high, retention mediana e stable-label count come un solo osservabile. ## Consecutio Costruire il prossimo gate sul piano posizione/errore: per i label core, confrontare distribuzione IDS, rank dei gap selezionati ed errore label fra chunk aligned e misaligned. Il boundary reale deve spostare la geometria dei gap, non solo il set dei label. ## Auto-audit source flags - **L1 medium risolta**: all-high, per-label retention e stable-label count sono separati. Count grezzi e denominatori sono riportati. - **L4 medium risolta**: edge case per label isolato. `[3,-4,4,6]` sotto internal shuffle fanno `6/160`, `57/160`, `54/160`, `38/160`; nessuno entra negli stable high labels. - **L5 low risolta nel perimetro minimo**: baseline teorica dichiarata. I label core sono gap-labeling Sturmian/Fibonacci classico; la novita' non e' membership, ma sopravvivenza congiunta sotto perturbazione. - **Aperto**: `gap_ratio` resta `not_tested`; soglie 1.75/2.25 e generatori non-phi restano fuori da questo repair. ## Files - Script: `tools/exp_gap_label_repair_audit.py` - Data: `tools/data/gap_label_repair_audit_20260508_1915.json` - Source data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json` - Report: `tools/data/reports/agent_20260508_1915.md` exec /bin/bash -lc 'git status --short tools/exp_gap_label_repair_audit.py tools/data/gap_label_repair_audit_20260508_1915.json tools/data/reports/agent_20260508_1915.md' in /opt/MM_D-ND succeeded in 0ms: ?? tools/data/gap_label_repair_audit_20260508_1915.json ?? tools/data/reports/agent_20260508_1915.md ?? tools/exp_gap_label_repair_audit.py exec /bin/bash -lc 'python /opt/tm7/tools/tm7_hook_manager.py --event post_change --paths tools/exp_gap_label_repair_audit.py tools/data/gap_label_repair_audit_20260508_1915.json tools/data/reports/agent_20260508_1915.md' in /opt/MM_D-ND succeeded in 0ms: # TM7 hook manager report - generated_at: 2026-05-08T19:18:27+00:00 - event: post_change - matched_rules: 1 ## Event instructions **Post-change cascade** - Map downstream consumers of every touched file. - Run the smallest relevant verification first. - Separate local file state, git state, generated data, and live/public state. - Record residual risk if any check is skipped. ## Paths - /opt/MM_D-ND/tools/exp_gap_label_repair_audit.py - /opt/MM_D-ND/tools/data/gap_label_repair_audit_20260508_1915.json - /opt/MM_D-ND/tools/data/reports/agent_20260508_1915.md ## Matched context ### MM_D-ND live physics lab Instructions: - This is the live lab generator territory with many cycle outputs. - Worktree dirtiness can be normal cycle output; never commit broad changes blindly. - Do not read .env, keys, tokens, or credential files unless explicitly required and approved. Read before action: - /opt/MM_D-ND/HANDOVER_CODEX_2026-05-07.md - /opt/MM_D-ND/PIANO_REVISIONE_LAB_2026-05-07.md - /opt/MM_D-ND/tools/LAB_AGENT_CONTEXT.md Suggested checks: - git -C /opt/MM_D-ND status --short --branch - tail -5 /opt/MM_D-ND/tools/data/lab_session_log.jsonl - bash /opt/MM_D-ND/tools/test_cron_exact.sh 600 Cascade: - If tools/lab_agent.sh changes, inspect provider chain, falsifier gates, SSP pipeline, notifications, and session log. - If build_applications_index.py changes, verify lab-d-nd-site data and page consumers. - If SSP trigger scripts change, verify stage contracts and published/scoperte separation. - If seme or knowledge data changes, check whether it is cycle output or intentional canonical change. ## Imported TM3 hook functions - `share_reflex` (manual_equivalent): Share/write a reflex after significant writes. Source: `share_reflex.sh`. Implemented as packets under /opt/tm7/packets. - `cascade_check` (active_equivalent): After changes, list downstream checks and propagation requirements. Source: `cascade_check.sh`. Implemented by hook_rules path cascade. - `session_thread` (manual_equivalent): Track recent actions for session continuity. Source: `session_thread.sh`. For Codex, use packets and final summaries; future state file possible. - `gyroscope` (manual_equivalent): Detect drift patterns: repeated errors, many content changes, need cascade/system check. Source: `gyroscope.sh`. Partially represented by post_change/pre_commit reports. ## Gate - Do not commit/deploy until the relevant suggested checks are either run or explicitly marked skipped with reason. diff --git a/tools/data/reports/agent_20260508_1915.md b/tools/data/reports/agent_20260508_1915.md new file mode 100644 index 0000000000000000000000000000000000000000..c05a7be0f824b74e654f60feedd5373aa9510b18 --- /dev/null +++ b/tools/data/reports/agent_20260508_1915.md @@ -0,0 +1,101 @@ +# Agent Report - High-Core Repair Audit +**Date**: 2026-05-08 19:15 +**Piano**: 90 +**Tension explored**: TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [all_high_hits, per_high_label_retention, stable_label_count, label_error, theoretical_gap_labeling_baseline] +**observable_contract**: claim=il core alto phi sotto internal shuffle va formulato come caduta del core completo, non come caduta hard di ogni label alto; observable=all-high hits separato da retention per-label e stable high label count; operator=audit delle righe grezze del supertile gate 19:09 con conteggi per mode/order e baseline teorica Sturmian; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle, same_mean_block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, nuove diagonalizzazioni spettrali, soglie 1.75/2.25, generatori non-phi. + +## Respiro fuori-tempo +- **Combo**: A4 contratto della domanda + A11 combo + QxG vuoto continuo/discreto + TxQ matrice densita come lettore IDS + tensione TRASCENDENZA_LIMITE sul passaggio tra piani. +- **Dipolo / punto-zero**: core completo simultaneo . label singolo superstite; punto-zero = il label-set prima della decisione su quale livello osservabile porta il claim. +- **Piano superiore**: topologia assiomatica del contratto osservabile. Il boundary non si decide dal nome del mode, ma dal livello che sopravvive: congiunzione, singolo label, stabilita' 75%, errore IDS. +- **Proto-ipotesi**: la falsificazione del report 19:09 non cancella tutto il core alto. Cancella la chiusura congiunta `[3,-4,4,6]` sotto internal shuffle; i label singoli possono sopravvivere come residui classici di gap-labeling. +- **Proiezione**: l'audit tiene fisso il dato 19:09 e separa tre piani che erano stati compressi: `all_high_hits`, retention per-label, `stable_high_label_count`. + +## Claim Under Test +> Nel perimetro del source cycle 19:09, `same_count_internal_shuffle` azzera il core alto completo `[3,-4,4,6]` come condizione congiunta. Non azzera ogni label alto preso singolarmente. La novita' D-ND sta nella sopravvivenza congiunta e nel collasso sotto perturbazione d'ordine, non nella membership dei label nel gruppo classico Sturmian/Fibonacci. + +Il source cycle non viene promosso e non viene sincronizzato concettualmente. Viene assorbita la falsificazione del wording: "core alto cade" diventa "core alto completo cade; singoli label alti restano parziali". + +## Question +Quali tra `[3,-4,4,6]` sopravvivono sotto internal shuffle, e quale osservabile cade davvero: all-high, per-label retention o stable-label count? + +## Experiment Design +- Input: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json`. +- Tool: `tools/exp_gap_label_repair_audit.py`. +- Baseline teorica: per Sturmian/Fibonacci i gap label vivono nel gruppo `Z + theta Z mod 1`, con `theta=1/phi`. I label `[3,-4,4,6]` appartengono al reader classico; questo ciclo non li dichiara scoperta. +- Denominatore grezzo per mode aggregato: 160 condizioni (`2 N * 4 phase * 1 threshold * 5 trials * 4 orders`). +- Denominatore grezzo per `mode|order`: 40 condizioni. +- Osservabili separati: + - `all_high_hits`: tutte le label alte presenti nella stessa condizione. + - `per_high_label`: hits indipendenti per ciascuna label alta. + - `stable_high_label_count`: label alte presenti in almeno 75% delle condizioni. + - `label_error`: errore IDS mediano dei selected gap per label. + +## Results + +Sintesi per mode: + +| mode | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---|---:|---|---:|---:|---:|---:| +| supertile_shuffle | 108/160 = 0.67500 | [3,-4,4,6] | 128/160 | 153/160 | 160/160 | 120/160 | +| same_length_contiguous_shuffle | 116/160 = 0.72500 | [3,-4,4,6] | 154/160 | 158/160 | 160/160 | 120/160 | +| same_count_internal_shuffle | 0/160 = 0.00000 | [] | 6/160 | 57/160 | 54/160 | 38/160 | +| same_mean_block_shuffle | 7/160 = 0.04375 | [] | 30/160 | 99/160 | 90/160 | 18/160 | + +Internal shuffle per order: + +| order | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---:|---:|---|---:|---:|---:|---:| +| 8 | 0/40 | [] | 0/40 | 14/40 | 17/40 | 9/40 | +| 9 | 0/40 | [] | 0/40 | 17/40 | 9/40 | 9/40 | +| 10 | 0/40 | [] | 1/40 | 11/40 | 14/40 | 9/40 | +| 11 | 0/40 | [] | 5/40 | 15/40 | 14/40 | 11/40 | + +Errore IDS mediano per mode aggregato: + +| mode | label 3 | label -4 | label 4 | label 6 | +|---|---:|---:|---:|---:| +| supertile_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00163213 | +| same_length_contiguous_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00001888 | +| same_count_internal_shuffle | 0.00808006 | 0.00491323 | 0.00529245 | 0.00528616 | +| same_mean_block_shuffle | 0.00215245 | 0.00263993 | 0.00164415 | 0.00263364 | + +## Key Findings +1. **Verificato: `same_count_internal_shuffle` azzera il core alto completo, non i label alti singoli.** All-high e' `0/160`; per-label restano `3: 6/160`, `-4: 57/160`, `4: 54/160`, `6: 38/160`. Il wording corretto e' "core completo cade". + +2. **Verificato: nessun label alto diventa stable label sotto internal shuffle.** `stable_high_label_count=0`; l'unico stable label aggregato del mode e' `[34]`, fuori dal core alto. La sopravvivenza per-label e' residua, non stabilita' del core. + +3. **Verificato: il controllo contiguo conserva piu' del supertile anche nel piano per-label.** `same_length_contiguous_shuffle` fa all-high `116/160` contro `108/160`, e per `3` fa `154/160` contro `128/160`. Il boundary esatto del supertile resta non discriminato dal label-set. + +4. **Verificato: i label `[3,-4,4,6]` sono baseline classica Sturmian/Fibonacci.** Appartengono al gruppo atteso `Z + theta Z mod 1`. La parte D-ND testata qui e' la loro chiusura congiunta sotto perturbazioni di ordine e scala, non la loro esistenza come gap label. + +5. **Inferito dai conteggi e dagli errori IDS: internal shuffle trasporta sottostruttura debole, ma rompe la congiunzione.** Gli errori mediani sotto internal shuffle aumentano (`~0.0049-0.0081`) rispetto ai mode ordinati (`~0.000009-0.00163`), mentre i hits per-label restano non-zero. + +## Verdict +**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, l'affermazione valida e': `same_count_internal_shuffle` distrugge la chiusura simultanea del core alto `[3,-4,4,6]` (`0/160`) e non produce stable high labels (`0`), ma trasporta singoli label alti con retention parziale (`6/160`, `57/160`, `54/160`, `38/160`). + +Non si dice piu' "il core alto cade" senza perimetro. Si dice: il core alto completo cade; la retention per-label resta parziale; la stable-label count alta resta zero. `gap_ratio` non e' testato. + +## Bicono della scoperta +- **Due radici**: congiunzione del core alto . residuo per-label. +- **Singolare**: il label-set prima della scissione fra simultaneita' e membership. +- **Invariante di passaggio**: l'ordine interno e le lunghezze Fibonacci-like conservano la chiusura; il conteggio senza ordine conserva solo residui di label classici. +- **Campo di possibilita**: qui diventa possibile cercare il boundary nella geometria degli errori IDS e nella posizione dei gap; qui diventa non-possibile usare all-high, retention mediana e stable-label count come un solo osservabile. + +## Consecutio +Costruire il prossimo gate sul piano posizione/errore: per i label core, confrontare distribuzione IDS, rank dei gap selezionati ed errore label fra chunk aligned e misaligned. Il boundary reale deve spostare la geometria dei gap, non solo il set dei label. + +## Auto-audit source flags +- **L1 medium risolta**: all-high, per-label retention e stable-label count sono separati. Count grezzi e denominatori sono riportati. +- **L4 medium risolta**: edge case per label isolato. `[3,-4,4,6]` sotto internal shuffle fanno `6/160`, `57/160`, `54/160`, `38/160`; nessuno entra negli stable high labels. +- **L5 low risolta nel perimetro minimo**: baseline teorica dichiarata. I label core sono gap-labeling Sturmian/Fibonacci classico; la novita' non e' membership, ma sopravvivenza congiunta sotto perturbazione. +- **Aperto**: `gap_ratio` resta `not_tested`; soglie 1.75/2.25 e generatori non-phi restano fuori da questo repair. + +## Files +- Script: `tools/exp_gap_label_repair_audit.py` +- Data: `tools/data/gap_label_repair_audit_20260508_1915.json` +- Source data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json` +- Report: `tools/data/reports/agent_20260508_1915.md` diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex La verifica locale è pulita: script compilato, JSON generato, report leggibile. Non lancio `test_cron_exact.sh` perché sarebbe un controllo di ciclo ampio rispetto a un audit/report locale; registro questo come rischio residuo nel riepilogo. codex Completato. Ho prodotto: - [tools/exp_gap_label_repair_audit.py](/opt/MM_D-ND/tools/exp_gap_label_repair_audit.py) - [tools/data/gap_label_repair_audit_20260508_1915.json](/opt/MM_D-ND/tools/data/gap_label_repair_audit_20260508_1915.json) - [tools/data/reports/agent_20260508_1915.md](/opt/MM_D-ND/tools/data/reports/agent_20260508_1915.md) Risultato centrale: `same_count_internal_shuffle` azzera il core alto completo `[3,-4,4,6]` (`0/160`) e non produce stable high labels, ma non azzera i label singoli (`6/160`, `57/160`, `54/160`, `38/160`). Il report corregge quindi il wording del source cycle: “core completo cade”, non “core alto cade” in senso hard. Verifiche eseguite: script compilato con `py_compile`, JSON generato, report riletto, hook `post_change` eseguito. Non ho lanciato il test cron completo perché il cambio è un audit locale su dati già prodotti, non una modifica alla pipeline del cycle. diff --git a/tools/data/reports/agent_20260508_1915.md b/tools/data/reports/agent_20260508_1915.md new file mode 100644 index 0000000000000000000000000000000000000000..c05a7be0f824b74e654f60feedd5373aa9510b18 --- /dev/null +++ b/tools/data/reports/agent_20260508_1915.md @@ -0,0 +1,101 @@ +# Agent Report - High-Core Repair Audit +**Date**: 2026-05-08 19:15 +**Piano**: 90 +**Tension explored**: TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [all_high_hits, per_high_label_retention, stable_label_count, label_error, theoretical_gap_labeling_baseline] +**observable_contract**: claim=il core alto phi sotto internal shuffle va formulato come caduta del core completo, non come caduta hard di ogni label alto; observable=all-high hits separato da retention per-label e stable high label count; operator=audit delle righe grezze del supertile gate 19:09 con conteggi per mode/order e baseline teorica Sturmian; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle, same_mean_block_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, nuove diagonalizzazioni spettrali, soglie 1.75/2.25, generatori non-phi. + +## Respiro fuori-tempo +- **Combo**: A4 contratto della domanda + A11 combo + QxG vuoto continuo/discreto + TxQ matrice densita come lettore IDS + tensione TRASCENDENZA_LIMITE sul passaggio tra piani. +- **Dipolo / punto-zero**: core completo simultaneo . label singolo superstite; punto-zero = il label-set prima della decisione su quale livello osservabile porta il claim. +- **Piano superiore**: topologia assiomatica del contratto osservabile. Il boundary non si decide dal nome del mode, ma dal livello che sopravvive: congiunzione, singolo label, stabilita' 75%, errore IDS. +- **Proto-ipotesi**: la falsificazione del report 19:09 non cancella tutto il core alto. Cancella la chiusura congiunta `[3,-4,4,6]` sotto internal shuffle; i label singoli possono sopravvivere come residui classici di gap-labeling. +- **Proiezione**: l'audit tiene fisso il dato 19:09 e separa tre piani che erano stati compressi: `all_high_hits`, retention per-label, `stable_high_label_count`. + +## Claim Under Test +> Nel perimetro del source cycle 19:09, `same_count_internal_shuffle` azzera il core alto completo `[3,-4,4,6]` come condizione congiunta. Non azzera ogni label alto preso singolarmente. La novita' D-ND sta nella sopravvivenza congiunta e nel collasso sotto perturbazione d'ordine, non nella membership dei label nel gruppo classico Sturmian/Fibonacci. + +Il source cycle non viene promosso e non viene sincronizzato concettualmente. Viene assorbita la falsificazione del wording: "core alto cade" diventa "core alto completo cade; singoli label alti restano parziali". + +## Question +Quali tra `[3,-4,4,6]` sopravvivono sotto internal shuffle, e quale osservabile cade davvero: all-high, per-label retention o stable-label count? + +## Experiment Design +- Input: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json`. +- Tool: `tools/exp_gap_label_repair_audit.py`. +- Baseline teorica: per Sturmian/Fibonacci i gap label vivono nel gruppo `Z + theta Z mod 1`, con `theta=1/phi`. I label `[3,-4,4,6]` appartengono al reader classico; questo ciclo non li dichiara scoperta. +- Denominatore grezzo per mode aggregato: 160 condizioni (`2 N * 4 phase * 1 threshold * 5 trials * 4 orders`). +- Denominatore grezzo per `mode|order`: 40 condizioni. +- Osservabili separati: + - `all_high_hits`: tutte le label alte presenti nella stessa condizione. + - `per_high_label`: hits indipendenti per ciascuna label alta. + - `stable_high_label_count`: label alte presenti in almeno 75% delle condizioni. + - `label_error`: errore IDS mediano dei selected gap per label. + +## Results + +Sintesi per mode: + +| mode | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---|---:|---|---:|---:|---:|---:| +| supertile_shuffle | 108/160 = 0.67500 | [3,-4,4,6] | 128/160 | 153/160 | 160/160 | 120/160 | +| same_length_contiguous_shuffle | 116/160 = 0.72500 | [3,-4,4,6] | 154/160 | 158/160 | 160/160 | 120/160 | +| same_count_internal_shuffle | 0/160 = 0.00000 | [] | 6/160 | 57/160 | 54/160 | 38/160 | +| same_mean_block_shuffle | 7/160 = 0.04375 | [] | 30/160 | 99/160 | 90/160 | 18/160 | + +Internal shuffle per order: + +| order | all-high | stable high labels 75% | label 3 | label -4 | label 4 | label 6 | +|---:|---:|---|---:|---:|---:|---:| +| 8 | 0/40 | [] | 0/40 | 14/40 | 17/40 | 9/40 | +| 9 | 0/40 | [] | 0/40 | 17/40 | 9/40 | 9/40 | +| 10 | 0/40 | [] | 1/40 | 11/40 | 14/40 | 9/40 | +| 11 | 0/40 | [] | 5/40 | 15/40 | 14/40 | 11/40 | + +Errore IDS mediano per mode aggregato: + +| mode | label 3 | label -4 | label 4 | label 6 | +|---|---:|---:|---:|---:| +| supertile_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00163213 | +| same_length_contiguous_shuffle | 0.00000944 | 0.00163454 | 0.00000870 | 0.00001888 | +| same_count_internal_shuffle | 0.00808006 | 0.00491323 | 0.00529245 | 0.00528616 | +| same_mean_block_shuffle | 0.00215245 | 0.00263993 | 0.00164415 | 0.00263364 | + +## Key Findings +1. **Verificato: `same_count_internal_shuffle` azzera il core alto completo, non i label alti singoli.** All-high e' `0/160`; per-label restano `3: 6/160`, `-4: 57/160`, `4: 54/160`, `6: 38/160`. Il wording corretto e' "core completo cade". + +2. **Verificato: nessun label alto diventa stable label sotto internal shuffle.** `stable_high_label_count=0`; l'unico stable label aggregato del mode e' `[34]`, fuori dal core alto. La sopravvivenza per-label e' residua, non stabilita' del core. + +3. **Verificato: il controllo contiguo conserva piu' del supertile anche nel piano per-label.** `same_length_contiguous_shuffle` fa all-high `116/160` contro `108/160`, e per `3` fa `154/160` contro `128/160`. Il boundary esatto del supertile resta non discriminato dal label-set. + +4. **Verificato: i label `[3,-4,4,6]` sono baseline classica Sturmian/Fibonacci.** Appartengono al gruppo atteso `Z + theta Z mod 1`. La parte D-ND testata qui e' la loro chiusura congiunta sotto perturbazioni di ordine e scala, non la loro esistenza come gap label. + +5. **Inferito dai conteggi e dagli errori IDS: internal shuffle trasporta sottostruttura debole, ma rompe la congiunzione.** Gli errori mediani sotto internal shuffle aumentano (`~0.0049-0.0081`) rispetto ai mode ordinati (`~0.000009-0.00163`), mentre i hits per-label restano non-zero. + +## Verdict +**CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={377,610}`, `phase={0,0.25,0.5,0.75}`, `threshold={2.0}`, `trials=5`, `top_k=12`, `|n|<=34`, `supertile_order={8,9,10,11}`, l'affermazione valida e': `same_count_internal_shuffle` distrugge la chiusura simultanea del core alto `[3,-4,4,6]` (`0/160`) e non produce stable high labels (`0`), ma trasporta singoli label alti con retention parziale (`6/160`, `57/160`, `54/160`, `38/160`). + +Non si dice piu' "il core alto cade" senza perimetro. Si dice: il core alto completo cade; la retention per-label resta parziale; la stable-label count alta resta zero. `gap_ratio` non e' testato. + +## Bicono della scoperta +- **Due radici**: congiunzione del core alto . residuo per-label. +- **Singolare**: il label-set prima della scissione fra simultaneita' e membership. +- **Invariante di passaggio**: l'ordine interno e le lunghezze Fibonacci-like conservano la chiusura; il conteggio senza ordine conserva solo residui di label classici. +- **Campo di possibilita**: qui diventa possibile cercare il boundary nella geometria degli errori IDS e nella posizione dei gap; qui diventa non-possibile usare all-high, retention mediana e stable-label count come un solo osservabile. + +## Consecutio +Costruire il prossimo gate sul piano posizione/errore: per i label core, confrontare distribuzione IDS, rank dei gap selezionati ed errore label fra chunk aligned e misaligned. Il boundary reale deve spostare la geometria dei gap, non solo il set dei label. + +## Auto-audit source flags +- **L1 medium risolta**: all-high, per-label retention e stable-label count sono separati. Count grezzi e denominatori sono riportati. +- **L4 medium risolta**: edge case per label isolato. `[3,-4,4,6]` sotto internal shuffle fanno `6/160`, `57/160`, `54/160`, `38/160`; nessuno entra negli stable high labels. +- **L5 low risolta nel perimetro minimo**: baseline teorica dichiarata. I label core sono gap-labeling Sturmian/Fibonacci classico; la novita' non e' membership, ma sopravvivenza congiunta sotto perturbazione. +- **Aperto**: `gap_ratio` resta `not_tested`; soglie 1.75/2.25 e generatori non-phi restano fuori da questo repair. + +## Files +- Script: `tools/exp_gap_label_repair_audit.py` +- Data: `tools/data/gap_label_repair_audit_20260508_1915.json` +- Source data: `tools/data/gap_label_supertile_tiling_gate_20260508_1909.json` +- Report: `tools/data/reports/agent_20260508_1915.md` diff --git a/tools/exp_gap_label_repair_audit.py b/tools/exp_gap_label_repair_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3cc2cd9714d8362ada8de3df9e22833619552f --- /dev/null +++ b/tools/exp_gap_label_repair_audit.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Repair audit for the phi high-core gap-label gate. + +This does not rerun the spectral generator. It re-reads a prior row-level +supertile gate output and separates three observables that the report must not +merge: full high-core closure, per-label retention, and stable-label count. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np + +from exp_gap_label_block_scale_gate import REFERENCE_HIGH, REFERENCE_LOW, label_sort +from exp_gap_label_set_stability import PHI, jaccard + + +def label_counts(rows: list[dict]) -> tuple[Counter, list[set[int]]]: + sets = [set(row["label_set"]) for row in rows if row.get("n_selected", 0) > 0] + return Counter(label for labels in sets for label in labels), sets + + +def selected_for_label(row: dict, label: int) -> list[dict]: + return [item for item in row.get("selected", []) if item.get("label") == label] + + +def summarize(rows: list[dict], reference_high: set[int], reference_low: set[int]) -> dict: + counts, sets = label_counts(rows) + total = len(sets) + core_hits = sum(1 for labels in sets if reference_high <= labels) + low_hits = sum(1 for labels in sets if reference_low <= labels) + stable_75 = label_sort({label for label, count in counts.items() if total and count / total >= 0.75}) + + per_label = {} + for label in label_sort(reference_high): + errors = [ + item["label_error"] + for row in rows + for item in selected_for_label(row, label) + ] + per_label[str(label)] = { + "hits": int(counts.get(label, 0)), + "total": int(total), + "rate": float(counts.get(label, 0) / total) if total else None, + "median_label_error": float(np.median(errors)) if errors else None, + "max_label_error": float(np.max(errors)) if errors else None, + } + + stable_high = [label for label in stable_75 if label in reference_high] + return { + "conditions": int(total), + "all_high_hits": int(core_hits), + "all_high_rate": float(core_hits / total) if total else None, + "all_low_hits": int(low_hits), + "all_low_rate": float(low_hits / total) if total else None, + "stable_labels_75pct": stable_75, + "stable_label_count": int(len(stable_75)), + "stable_high_labels_75pct": label_sort(stable_high), + "stable_high_label_count": int(len(stable_high)), + "per_high_label": per_label, + } + + +def summarize_grouped(rows: list[dict], key_names: tuple[str, ...], reference_high: set[int], reference_low: set[int]) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + key = "|".join(f"{name}={row[name]}" for name in key_names) + grouped[key].append(row) + return { + key: summarize(group_rows, reference_high, reference_low) + for key, group_rows in sorted(grouped.items()) + } + + +def theoretical_baseline(max_label: int) -> dict: + labels = [n for n in range(-max_label, max_label + 1) if n != 0] + theta = 1 / PHI + return { + "reader": "Sturmian/Fibonacci gap-labeling group Z + theta Z mod 1", + "theta": float(theta), + "max_label": int(max_label), + "expected_label_group": "{n*theta mod 1 | n in Z, n != 0}", + "reference_high_in_group": { + str(label): { + "in_group": True, + "ids_fraction": float((label * theta) % 1.0), + } + for label in label_sort(REFERENCE_HIGH) + }, + "baseline_statement": ( + "The labels [3,-4,4,6] are classical Fibonacci/Sturmian gap labels; " + "the D-ND novelty tested here is not their membership, but their " + "joint and per-label survival under order/boundary perturbations." + ), + "candidate_count_in_reader_window": int(len(labels)), + } + + +def run(input_path: Path) -> dict: + source = json.loads(input_path.read_text(encoding="utf-8")) + rows = source["rows"] + reference_high = set(source.get("reference_high", label_sort(REFERENCE_HIGH))) + reference_low = set(source.get("reference_low", label_sort(REFERENCE_LOW))) + + mode_summary = summarize_grouped(rows, ("mode",), reference_high, reference_low) + mode_order_summary = summarize_grouped(rows, ("mode", "supertile_order"), reference_high, reference_low) + + source_core = set(source["reference_core_phi"]) + repair = { + "experiment": "gap_label_repair_audit", + "source": str(input_path), + "parameters": source["parameters"], + "reference_core_phi": label_sort(source_core), + "reference_low": label_sort(reference_low), + "reference_high": label_sort(reference_high), + "observables_separated": [ + "all_high_hits", + "per_high_label", + "stable_label_count", + "stable_high_label_count", + "label_error", + "theoretical_gap_labeling_baseline", + ], + "theoretical_baseline": theoretical_baseline(source["parameters"]["max_label"]), + "mode_summary": mode_summary, + "mode_order_summary": mode_order_summary, + "core_vs_stable_note": ( + "all_high_hits requires every high label in the same condition; " + "per_high_label counts survival of each label independently; " + "stable_high_label_count counts high labels present in at least 75% of conditions." + ), + } + return repair + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="tools/data/gap_label_supertile_tiling_gate_20260508_1909.json") + parser.add_argument("--out", default="tools/data/gap_label_repair_audit_20260508_1915.json") + args = parser.parse_args() + + output = run(Path(args.input)) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + mode_key: { + "all_high": f"{data['all_high_hits']}/{data['conditions']}", + "stable_high": data["stable_high_labels_75pct"], + "stable_high_count": data["stable_high_label_count"], + "per_high_label": { + label: f"{v['hits']}/{v['total']}" + for label, v in data["per_high_label"].items() + }, + } + for mode_key, data in output["mode_summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() tokens used 100,919 Completato. Ho prodotto: - [tools/exp_gap_label_repair_audit.py](/opt/MM_D-ND/tools/exp_gap_label_repair_audit.py) - [tools/data/gap_label_repair_audit_20260508_1915.json](/opt/MM_D-ND/tools/data/gap_label_repair_audit_20260508_1915.json) - [tools/data/reports/agent_20260508_1915.md](/opt/MM_D-ND/tools/data/reports/agent_20260508_1915.md) Risultato centrale: `same_count_internal_shuffle` azzera il core alto completo `[3,-4,4,6]` (`0/160`) e non produce stable high labels, ma non azzera i label singoli (`6/160`, `57/160`, `54/160`, `38/160`). Il report corregge quindi il wording del source cycle: “core completo cade”, non “core alto cade” in senso hard. Verifiche eseguite: script compilato con `py_compile`, JSON generato, report riletto, hook `post_change` eseguito. Non ho lanciato il test cron completo perché il cambio è un audit locale su dati già prodotti, non una modifica alla pipeline del cycle.