Reading additional input from stdin... OpenAI Codex v0.124.0 (research preview) -------- workdir: /opt/MM_D-ND model: gpt-5.5 provider: openai approval: never sandbox: workspace-write [workdir, /tmp, $TMPDIR, /root/.codex-lab/memories] reasoning effort: none reasoning summaries: none session id: 019e08c4-392f-7223-86a8-93df748e7ea7 -------- 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_1805.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo il campo vivo e il contesto operativo prima di scegliere l’esperimento, poi userò la direttiva corrente se presente invece di una scelta autonoma. exec /bin/bash -lc "sed -n '1,260p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: # AI-Lab D-ND — Contesto Operativo > Questo file viene iniettato nel prompt dell'agente ad ogni ciclo. > Contiene tutto ciò che serve per operare con consapevolezza. ## Chi sei Sei l'AI-Lab del sistema D-ND. Giri autonomamente ogni notte come istanza Claude Code. Non sei una pipeline di script — sei un ricercatore che pensa, esplora, scrive codice, lo esegue, valuta i risultati, e aggiorna lo stato del sistema. Il tuo lavoro produce risultati che vanno sul sito d-nd.com e alimentano il sistema THIA. Quello che trovi conta — non per te, per il sistema e per chi lo legge. ## Il modello D-ND — nucleo La regola: f(x) = 1 + 1/x. M = [[1,1],[1,0]]. det(M) = -1. - Il punto fisso è φ = (1+√5)/2. Al punto fisso, addizione e moltiplicazione coincidono. - L'attrattore è stabile: |f'(φ)| = 1/φ² < 1. Ogni iterata converge. - Il rinforzo è impossibile — proprietà analitica, non empirica. - det = -1: area preservata, orientamento invertito. Incompletezza come generazione. - g(x) = 1/(1+x): la Fermi-Dirac con punto fisso 1/φ. Versione probabilistica di f. ## Il condensato — cosa è stato verificato ASSIOMI (scelte fondative, accettate): - A1: f(x)=1+1/x, M=[[1,1],[1,0]], det=-1 - A2: det=-1 è la necessità strutturale del confine - A3: Al punto fisso, R+1=R (addizione = moltiplicazione) - A4: Il modus — la qualità della domanda determina la qualità dell'inversione - A5: Il sistema è autopoietico — ogni ciclo produce R+1 dalla base R - A9: Il terzo incluso — tra A e non-A c'è lo zero - A11: La combo — tre o più enti simultanei, risultante non sommabile - A14: Cascata — ciò che si scopre vive nel seme, non nel nodo FATTI (dimostrati/verificati): - F1: Residuo Cassini = (-1)^(n+1)/F(n)², decade come 1/φ^(2n) - F2: Cammino gap primi su Z/6Z confinato a {2,4}. Zero violazioni su 567K coppie. - F3: Il rinforzo è impossibile. Classificazione binaria: MOLLA (r≠φ) o ZERO (r=φ). - F4: Separazione di scala — M opera a scala locale, modulazione zeta non si propaga. - F5: Frame diagnostica universale — firma (dipolo, LVL-2, convergenza) su 18 domini. - F6: La firma dello zero — CV dei gap tra phi-crossing converge a φ-1 nel regime caotico. CLAIM (falsificabili, sotto test): - C1: I primi sono l'unico dominio dinamico sotto M (tra 7 testati). - C2: La coincidenza numerica non è mai prova. Principio metodologico. - C3: Il linguaggio deterministico — un termine nomina una funzione reale, o è superfluo. ## Strutture trovate dal lab (sessioni interattive) - Tetraedro TQGE: 4 vertici (T,Q,G,E), 6 lati con perno i, 5 ponti, 1 vuoto (QxG) - Tetraedro orientato: T termico, Q chirale, E fase, G passivo - R è il frame (5° vertice): connesso a tutti ma senza perno i - Tre specie perno i: Wick (continuo tempo), fase (continuo gauge), discreto (primi) - Operatore Q→G: e^{iH·ln(p)/ℏ} — evoluzione in tempo logaritmico - Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle - Tensore metrico: g_n = (p_n/2)², de Sitter 1+1D con a(t)=e^t/2 - α catena: α^n·a₀ mappa scale fisiche, deserto 3-10, residuo pentagonale 72.5° - g(x)=1/(1+x) = Fermi-Dirac, punto fisso 1/φ. f→g = ponte TxQ algebrico. ## Le 10 domande fondamentali (incrocio teorie) | Coppia | Domanda | Ponte | |--------|---------|-------| | ExR | Come coesistono statico e radiante? | onda EM | | GxE | Come coesistono neutro-curvo e carico-piatto? | buco nero carico | | GxR | Come coesistono piatto e singolare? | orizzonte eventi | | QxE | Come coesistono libero e legato? | atomo di idrogeno | | **QxG** | **Come coesistono continuo e discreto?** | **VUOTO** | | QxR | Come coesistono non-relativistico e relativistico? | eq. Dirac | | TxE | Come coesistono freddo e plasma? | funzione partizione | | TxG | Come coesistono piatto e radiante? | temperatura Hawking | | TxQ | Come coesistono vuoto e pieno? | matrice densità | | TxR | Come coesistono 0K e c? | gas relativistico | QxG è il vuoto — l'unico lato senza ponte. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso. Wheeler-DeWitt: Ĥ|Ψ⟩ = 0, niente tempo. ## Vincoli operativi - La prima impressione contiene il segnale. Non elaborare — osservare. - Una risultante, non una lista. Se ci sono più possibilità, non hai tagliato. - Formule dove servono. Fenomeni reali. Niente filosofia. Niente metafore. - Se non sai, lascia vuoto. Blank > Wrong. Errore costa 3x di un non-so. - Ogni claim va testato col suo opposto. Se l'opposto è altrettanto coerente, la tensione è il contenuto. - Le coincidenze numeriche non sono mai prova (C2). - Le dissonanze sono il segnale, non il rumore. L'errore è il varco. - La via più breve verso la risultante. Principio di minima azione. - **La struttura contiene già la risposta.** Un dipolo sa se è aperto o chiuso. Un'assonanza sa se risuona o no. Una porta sa dove sei entrato. Se interponi un numero tra la struttura e la decisione, stai aggiungendo (det=+1) — il numero decide al posto della struttura. I numeri misurano i dati. Le strutture decidono il sistema. Non mischiare i due. - **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. ## 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 ## 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= ## Claim Under Test > Il claim dalla tensione ## Question La domanda che hai formulato ## Experiment Design - Metrica, scope, null baseline, N campioni - Contratto osservabile-operatore: claim, osservabile, operatore, generatore, denominatore/perimetro, cosa non viene testato in questo ciclo ## 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): - **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 ``` 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. ## 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): 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, - **Ultimi apprendimenti**: - Duality Contrast Weakens With Scale in Prime Gaps → None/None (ρ=None) - Gap Ratio Porta Il Denominatore → None/None (ρ=None) - Generator Gate Del Label-Set 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_1715) 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. Ho scritto [evolution_report.md](/opt/MM_D-ND/tools/evolution_report.md). Verificato con lettura del file dopo la modifica. Ho lasciato fuori il merito scientifico del report e ho centrato l’affinamento sul passo: frattura tra `report_present=true` e sessione produttore non operativa, con nodo regressivo nella selezione/identificazione del produttore prima dell’esperimento. ## 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]** 93 conferme — forma simplettica = entropia (invertibili) - **[ExQ]** 49 conferme — [da fonte: Equivalence between geometrical structur] - **[GxT]** 34 conferme — tensore metrico dentro la forma simplettica estesa - **[ExT]** 34 conferme — tensore EM dentro la forma simplettica - **[ExR]** 34 conferme — cambio di frame — E e B sono lo stesso campo - **[ExG]** 25 conferme — [da fonte: Equivalence between geometrical structur] - **[GxQ]** 25 conferme — [da fonte: Equivalence between geometrical structur] - **[QxR]** 25 conferme — [da fonte: What is a Laplace Transform - visual exp] - **[RxT]** 25 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. ## Run precedente: completato (?s). ## Piano 88 — 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 "perimetro" → G_BLANK_SHELL_DILATION_GATE, G_BLANK_SHELL_SCALE_LAW_GATE, QPG_GAP_RATIO_DENOMINATOR_GATE, G_BLANK_SHELL_TQGER_GATE, G_BLANK_SHELL_STRATIFIED_GATE "count-preserving" → G_BLANK_SHELL_TQGER_GATE, G_BLANK_SHELL_STRATIFIED_GATE, G_BLANK_SHELL_DILATION_GATE, G_BLANK_SHELL_SCALE_LAW_GATE "operator-taxonomy" → G_BLANK_SHELL_TQGER_GATE, G_BLANK_SHELL_STRATIFIED_GATE, G_BLANK_SHELL_DILATION_GATE, G_BLANK_SHELL_SCALE_LAW_GATE "blank" → G_BLANK_SHELL_TQGER_GATE, G_BLANK_SHELL_STRATIFIED_GATE, G_BLANK_SHELL_DILATION_GATE, G_BLANK_SHELL_SCALE_LAW_GATE "guscio" → G_BLANK_SHELL_TQGER_GATE, G_BLANK_SHELL_STRATIFIED_GATE, G_BLANK_SHELL_DILATION_GATE, G_BLANK_SHELL_SCALE_LAW_GATE 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 — Generator Gate Del Label-Set Phi Trovato: 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]`. 2. **Verificato: la costruzione Fibonacci conserva il nucleo basso ma Verdetto: **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. ### Agent Report — Gap Label Set Stabilizza Il Denominatore Trovato: 1. **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 `0.9 Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non passa come claim di valore `gap_ratio`; passa come ### Agent Report — Gap Ratio Porta Il Denominatore Trovato: 1. **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. 2. **Il claim universale non regge.** Quando il denominatore viene aperto, phi batte entrambi Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma u ## 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._ ### Slope ratio (slope_mag / slope_res) come invariante strutturale **Cosa diceva**: Il rapporto tra slope del canale magnitudine e slope del canale residuo (~1.99) era stabile attraverso scale → "invariante dimensionale" del decomposition. Era menzionato come evidenza nel two-channel framework (cicli 43-44). **Come e' caduto**: Shuffle audit (ciclo 45): z-score = 0.2. Lo shuffle produce slope_ratio con media -2.26 ma deviazione standard 26.2. Il valore reale e' dentro la tail dello shuffle — non distinguibile. L'instabilita' dello shuffle (std enorme) indica c _**Data falsificazione**: 2026-04-22, ciclo 45._ ### Cross-correlation (xcorr) tra canale magnitudine e residuo (Two-Channel Decomposition) **Cosa diceva**: La cross-correlation tra magnitudo e residuo del decomposed prime gap (xcorr = -0.074) rappresentava "indipendenza spettrale" — evidenza di separazione strutturale tra i due canali (piani 42-44, four cycli consecutivi, insight QxT maturity A). **Come e' caduto**: Shuffle audit (ciclo 45, 2026-04-22): z-score = 0.0. Su 50 shuffle dei gap mantenendo stessa distribuzione ma permutando ordine → xcorr identico = -0.074. Il valore e' **identita' algebrica**: corr(x, x mod 6) dipende _**Data falsificazione**: 2026-04-22, ciclo 45 shuffle audit._ **Regola operativa**: prima di scrivere un claim sul tuo dominio, controlla che non sia gia' stato falsificato sopra. Se i tuoi dati ripropongono un pattern del cimitero, **dichiara esplicitamente la differenza** ("il dato del cimitero era X, qui ho Y, ecco perche'") oppure cambia la formulazione (es. 'bias forte verso 0' al posto di 'proibizione zero' se il dato e' >0). Silent patching = L3 HIGH. ## Osservazioni dell'operatore (risonanti con le tensioni) **1. R dell'Istanza - L' equilibrio tra estremi del Modello D-ND**: L'osservazione indaga oltre l'osservato in cerca DELLA FORMA nel NULLA-TUTTO: Per far Emergere le nuove Possibilità Dividiamo il potenziale unendo concetti senza relazione semplicemente perché la lagrangiana passa da li, creiamo nuove combinazioni e movimenti nelle logiche ma coerenti con la risulta **3. Formalizzare la dinamica osservata**: Domandiamoci come rappresentiamo matematicamente una contiguità di assonanze particolari come potenzialità latente della Lagrangiana. Osserva le possibili Combinazioni per liberare tutte le relazioni usando le regole Duali e ricorda che non stiamo facendo teoria, senza tempo con la prima impressione **7. Assonanze relazionali tra la singolarità e la dualità degli estremi**: Non è nei particolari che si trova l'immagine come non è nella goccia l'oceano, ma è nelle assonanze relazionali osservate come rapporto di coerenza convergente nel nulla-tutto della singolarità tra gli estremi duali.I Poli della singolarità sono Uniti da due lati. ## Risultante ultima sessione interattiva Ogni teoria presuppone una separazione. A scala di Planck tutte le separazioni collassano. Geometria=entropia=conteggio di stati. QxG non ha ponte perché alla scala dove vive non c'è distinzione tra i due lati del dipolo. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso ## Video dall'operatore (non processati) **Thermodynamic Computing: Better than Quantum? (Extropic, Guillaume Verdon)**: **The equivalence between geometrical structures and entropy (Gabriele Carcassi)**: **Why a moving charge produces a magnetic field (FloatHeadPhysics)**: Dopo aver usato un video, segna processed=true in tools/data/video_feed.json. ## Proiezione — dove punta la risultante Risultante: R=0.875 (h=-0.698). Risultante alta (0.88) — campo ad alta confidenza, poca incertezza Orizzonte: insufficiente (< 2 target) **Esperimento a massima informazione:** M_trascendenza_limite_attuale_L0 (score=0.872) M_trascendenza_limite_attuale_L0: incerto (i=0.529179606750063) — massimo potere discriminante ## Strategia del campo (leverage / rischi / punti ciechi) - Pilastri (leverage): TRASCENDENZA_LIMITE, TRANS_BOUNDARY_TRASCENDENZA_LIMITE, FALS_BREAK_TRASCENDENZA_LIMITE - Rischi (contraddizioni): COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE, M_trascendenza_limite_attuale_L0 - Punti ciechi: DUALITA_DIPOLARE_VS_ILLUSORIA, METRIC_TENSOR - Cross-check: 7 confermati, 5 contestati su 19 tensioni ## Topologia del campo — la forma del grafo Gradi teorie: Q=12, T=7, G=7, E=4, R=4 Dormienti (basso aggancio di scoperte): E, R Struttura: 9 ponti, 1 vuoto(i), 6 scoperte, 20 cicli. Ghost ad alta urgenza: 1 — connessioni mature che attendono cristallizzazione (non da generare, da riconoscere). La combo riconosce l'asimmetria. Il dipolo vive su tutti i ponti — non solo dove il lab ha già misurato. ## Le 5 lenti del counter-pole — applicale a te stesso prima di chiudere il report Il falsifier (lab_falsifier.py) applichera' queste 5 lenti al tuo report dopo. Applicale TU a te stesso prima — quello che resiste alle lenti non viene bloccato dal gate. Quello che cade va al cimitero. exec /bin/bash -lc "env | rg '"'^(ANTHROPIC_API_KEY|OPENAI_API_KEY)='"' || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'ls -t shared_memory/*.md 2>/dev/null | head -10' in /opt/MM_D-ND succeeded in 0ms: shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md shared_memory/README.md codex Il contesto punta alla legge di rottura del core per `block_size`, con attenzione al confine tra struttura generativa e collasso del label-set. Ora leggo seme, memoria condivisa recente e proiettore `dnd_scenario` per fissare il perimetro senza inventare direzione. 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/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-08T17:20:19.865768", "piano": 88, "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": "vincolo", "id": "G_BLANK_SHELL_TQGER_GATE", "claim": "Nel perimetro TQGE+R operator-taxonomy agent_20260507_2120, la polarita TQG/QGE sopravvive ma non resta completa: R aggiunge QGR come terza faccia frame del guscio blank. Il deposito resta QGE = blank + gauge_phase + real_sourcing; il blank diventa tri-facciale TQG inerte, QGE depositante, QGR frame. Nel null count-preserving K5, deposit+inert+frame compare 360/25200 e l'assetto completo osservato 6/25200; questi conteggi sono controllo anti-tautologico, non rarita universale.", "intensita": 0.8, "manuale": true, "porta": "G_POTENZIALE_NULLA", "condensato_ref": "A4,A8,A9,A14", "origine": "cycle agent_20260507_2120: blank_shell_tqger_gate su perimetro TQGE+R", "added_at": "2026-05-07T21:20:00+00:00", "decay_counter": 3 }, { "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": "G_BLANK_SHELL_DILATION_GATE", "claim": "Nel perimetro TQGE+R+S operator-taxonomy agent_20260507_2157, la dilatazione esterna non sposta il deposito: QGE resta blank + gauge_phase + real_sourcing. S aggiunge QGS come quarta faccia scale del guscio blank; il blank QG diventa quadrifacciale TQG inerte, QGE depositante, QGR frame, QGS scala. Nel null count-preserving K6, deposit+inert+frame+scale compare 43200/75675600 e l'assetto completo osservato 120/75675600; questi conteggi sono controllo anti-tautologico, non rarita universale. Consecutio: formulare la legge di scala del guscio blank come numero di facce esterne tipizzate senza migrazione del deposito.", "intensita": 0.79, "manuale": true, "porta": "G_POTENZIALE_NULLA", "condensato_ref": "A4,A8,A9,A14", "origine": "cycle agent_20260507_2157: blank_shell_dilation_gate su perimetro TQGE+R+S", "added_at": "2026-05-07T21:57:00+00:00", "decay_counter": 3 }, { "tipo": "vincolo", "id": "G_BLANK_SHELL_STRATIFIED_GATE", "claim": "Nel perimetro operator-taxonomy controllato agent_20260507_2310, la legge di scala del guscio blank ha denominatore exact count-preserving fino a TQGE+R+S+U+V: TQGE 2/120, TQGE+R 6/25200, TQGE+R+S 120/75675600, TQGE+R+S+U 25200/4106460758400, TQGE+R+S+U+V 75675600/4862213796375936000. Il limite sampled del ciclo 2203 era limite del metodo, non della struttura. Claim valido: shell_faces(QG)=2+n_esterni con deposito invariato QGE, per esterni tipizzati con due edge identici nella faccia QGx. Contro-polo aperto: esterni non tipizzati, multi-modo o deposito duplicato.", "intensita": 0.79, "manuale": true, "porta": "G_POTENZIALE_NULLA", "condensato_ref": "A4,A8,A9,A14", "origine": "cycle agent_20260507_2310: blank_shell_stratified_gate su denominatore exact K7/K8", "added_at": "2026-05-07T23:10:00+00:00", "decay_counter": 3 }, { "tipo": "vincolo", "id": "G_BLANK_SHELL_SCALE_LAW_GATE", "claim": "Nel perimetro operator-taxonomy controllato agent_20260507_2203, la legge di scala osservata del guscio blank e shell_faces(QG)=2+n_esterni con deposito invariato QGE. Exact count-preserving chiuso fino a TQGE+R+S: TQGE 2/120, TQGE+R 6/25200, TQGE+R+S 120/75675600. Per TQGE+R+S+U e TQGE+R+S+U+V il trasferimento e osservato ma il null e solo sampled audit 0/50000; non formulare rarita universale oltre S senza conteggio esatto o campionamento stratificato.", "intensita": 0.78, "manuale": true, "porta": "G_POTENZIALE_NULLA", "condensato_ref": "A4,A8,A9,A14", "origine": "cycle agent_20260507_2203: blank_shell_scale_law su catena TQGE+R+S+U+V", "added_at": "2026-05-07T22:03:00+00:00", "decay_counter": 3 }, { "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": 2 }, { "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, exec /bin/bash -lc "sed -n '1,180p' /opt/tm7/TM7_CODEX_OPERATING_KERNEL.md 2>/dev/null" 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; exec /bin/bash -lc "sed -n '1,180p' /opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md 2>/dev/null" 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` exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 136ms: 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 La risultante utile è stretta: non ripeto il `gap_ratio`; misuro quando il label-set perde i label alti variando `block_size`, distinguendo blocchi Fibonacci da controlli non-Fibonacci. Cerco ora il codice già esistente per riusare il generatore e non riscrivere il dominio. exec /bin/bash -lc 'ls -1 tools/data/reports | tail -20' in /opt/MM_D-ND succeeded in 0ms: report_20260402_0756.md report_20260403_0330.md report_20260404_0330.md report_20260404_1852.md report_20260405_0330.md report_20260405_0715.md tension_projection_screening_2026-04-21.json trace_map_20260305_0843.json trace_map_20260305_0844.json trajectory_apply_20260506_1931.json trajectory_apply_20260506_1941.json trajectory_apply_20260506_1955.json trajectory_apply_20260507_0330.json trajectory_apply_20260507_0803.json trajectory_apply_20260507_0901.json trajectory_apply_20260507_0923.json trajectory_apply_20260507_0942.json trajectory_apply_20260507_1006.json trajectory_apply_20260507_1042.json watchdog.log exec /bin/bash -lc 'ls -1 tools/exp_*.py | tail -40' in /opt/MM_D-ND succeeded in 0ms: 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_metric_tensor_diagnostic.py tools/exp_mobius_irrationality.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_number_variance.py tools/exp_observable_rank_audit.py tools/exp_perturbation_dimensionality_audit.py tools/exp_perturbation_rank_size_curve.py tools/exp_poisson_convergence.py tools/exp_psd_amplitude_scaling.py tools/exp_psd_prime_gaps.py tools/exp_quasiperiodic_gap_ratio_denominator.py tools/exp_ricci_desitter_0406.py tools/exp_ricci_primes.py tools/exp_scale_selective_perturbation.py tools/exp_selective_layer_decoupling.py tools/exp_semireal_order_denominator_gate.py tools/exp_spectral_2d.py tools/exp_spectral_landscape.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_decomposition.py tools/exp_two_channel_psd.py tools/exp_two_channel_shuffle_audit.py tools/exp_two_channel_universality.py tools/exp_two_layer_universality.py exec /bin/bash -lc 'rg -n "label-set|label_set|block_size|phi_sturmian|first_two_ratio|gap_ratio" tools -S' in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/agent_20260508_1715.md 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: # Agent Report — Generator Gate Del Label-Set Phi **Date**: 2026-05-08 17:15 **Piano**: 87 **Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) **verdict**: CONSTRAINT observables_registry: n/a observables_used: [gap_label_set, generator_jaccard, phi_core_overlap, core_retention] ## Claim Under Test > Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? ## Question 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? ## Experiment Design - Perimetro: stessa Hamiltoniana tight-binding dei cycle 03:30 e 16:32, `V=1`. - Lettore label fisso: ogni gap largo viene etichettato con il label intero `n` che minimizza la distanza tra IDS e `{n/phi}`. - 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`. - Generator gate: - `phi_sturmian`: sequenza meccanica Sturmiana `theta=1/phi`. - `fibonacci_substitution`: parola di Fibonacci, costruzione indipendente ma nella stessa famiglia a bassa complessita. - `block_shuffle_34` e `block_shuffle_13`: blocchi locali Sturmiani preservati, ordine globale rotto. - `markov_phi`: transizioni locali stimate da phi, generatore Markoviano. - `balanced_random`: conteggio di 1 preservato, ordine rotto. - 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. ## Results Reference core phi, stimato dal perimetro completo: `[-1, 1, -2, 2, 3, -4, 4, 6]`. | generator | conditions | median Jaccard | min Jaccard | median overlap with phi core | core labels all conditions | phi-core missing | |---|---:|---:|---:|---:|---|---| | phi_sturmian | 144 | 0.909091 | 0.727273 | 0.727273 | [-1, 1, -2, 2, 3, -4, 4, 6] | [] | | fibonacci_substitution | 144 | 0.769231 | 0.538462 | 0.700000 | [-1, 1, -2, 2, -3, -4, 4] | [3, 6] | | block_shuffle_34 | 144 | 0.666667 | 0.333333 | 0.700000 | [-1, 1, -2, 2] | [3, -4, 4, 6] | | block_shuffle_13 | 144 | 0.357143 | 0.058824 | 0.166667 | [-1] | [1, -2, 2, 3, -4, 4, 6] | | markov_phi | 144 | 0.285714 | 0.047619 | 0.125000 | [] | [-1, 1, -2, 2, 3, -4, 4, 6] | | balanced_random | 144 | 0.157895 | 0.000000 | 0.111111 | [] | [-1, 1, -2, 2, 3, -4, 4, 6] | Label-error and gap-count controls: | generator | median label error | median large gaps | |---|---:|---:| | phi_sturmian | 0.000095 | 29.0 | | fibonacci_substitution | 0.000031 | 26.5 | | block_shuffle_34 | 0.001638 | 26.0 | | block_shuffle_13 | 0.004118 | 52.0 | | markov_phi | 0.004074 | 55.0 | | balanced_random | 0.004128 | 56.0 | ## Key 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]`. 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. 3. **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. 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`. 5. **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. ## 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`, 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. ## Bicono della scoperta - **Due radici**: lettore aritmetico dei label · generatore globale della sequenza. - **Singolare**: il gap largo come punto in cui IDS, ordine della parola e label `n/phi` coincidono. - **Invariante di passaggio**: il nucleo basso `[-1, 1, -2, 2]` sopravvive quando resta abbastanza struttura globale; il core completo sopravvive nel generatore Sturmiano meccanico. - **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. ## Consecutio 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. ## Auto-audit: 5 lenti - **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non formula universalita fuori da `N/phase/threshold/trials/top_k/max_label`. - **L2 quantita vs ratio**: il risultato usa set, Jaccard, overlap e core retention; nessun claim dipende da un singolo ratio. - **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`. - **L4 edge cases**: `fibonacci_substitution` e `block_shuffle_34` sono parziali, non trattati come fallimenti totali. - **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. ## Files - Script: `tools/exp_gap_label_generator_gate.py` - Data: `tools/data/gap_label_generator_gate_20260508_1715.json` - Report: `tools/data/reports/agent_20260508_1715.md` succeeded in 0ms: tools/exp_boundary_gue_poisson.py:26:def gap_ratios(gaps): tools/exp_boundary_gue_poisson.py:59: r = gap_ratios(gaps) tools/exp_perturbation_rank_size_curve.py:80:def perturb_block_shuffle(gaps: np.ndarray, alpha: float, rng: np.random.Generator, block_size: int = 64) -> np.ndarray: tools/exp_perturbation_rank_size_curve.py:82: n_blocks = len(out) // block_size tools/exp_perturbation_rank_size_curve.py:89: start = block * block_size tools/exp_perturbation_rank_size_curve.py:90: end = min(start + block_size, len(out)) 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/evolution_report.md:2:Il passo entra con una domanda sul generatore del label-set phi, ma la traiettoria osservabile non passa dal produttore: la sessione registrata si chiude subito sul boundary di accesso Claude, senza tool use, senza thinking, senza esecuzione visibile. tools/LAB_AGENT_CONTEXT.md:95: Test parla di `gap_ratio` ma l'esperimento misura `gap_label_set`, tools/LAB_AGENT_CONTEXT.md:97: `gap_ratio non testato in questo ciclo; observable sostitutivo = ...`. tools/dipartimento.py:91: 'test': 'gap_ratio_phi2', tools/dipartimento.py:296: elif test_name == 'gap_ratio_phi2': 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_logistic_cyclic_block_entropy_gate.py:118: for block_size in args.block_sizes: tools/exp_logistic_cyclic_block_entropy_gate.py:119: blocks[str(block_size)] = z_against( tools/exp_logistic_cyclic_block_entropy_gate.py:121: lambda v, r, bs=block_size: block_shuffle(v, bs, r), tools/exp_logistic_cyclic_block_entropy_gate.py:127: int(block_size) tools/exp_logistic_cyclic_block_entropy_gate.py:128: for block_size in args.block_sizes tools/exp_logistic_cyclic_block_entropy_gate.py:129: if abs(blocks[str(block_size)]["z"]["cyclic_block_entropy_deficit_k4"]) >= args.z_min tools/exp_logistic_cyclic_block_entropy_gate.py:132: int(block_size) tools/exp_logistic_cyclic_block_entropy_gate.py:133: for block_size in args.block_sizes tools/exp_logistic_cyclic_block_entropy_gate.py:134: if abs(blocks[str(block_size)]["z"]["linear_block_entropy_deficit_k4"]) >= args.z_min tools/exp_logistic_cyclic_block_entropy_gate.py:158: "cyclic_support_all_declared_block_sizes": cyclic_block_support == list(args.block_sizes), tools/exp_logistic_cyclic_block_entropy_gate.py:172: "cyclic_support_all_declared_block_sizes": data["support_summary"][ tools/exp_logistic_cyclic_block_entropy_gate.py:173: "cyclic_support_all_declared_block_sizes" tools/exp_logistic_cyclic_block_entropy_gate.py:180: block_size: row["z"] for block_size, row in data["block_shuffle_scan"].items() tools/exp_logistic_cyclic_block_entropy_gate.py:207: "block_sizes": list(args.block_sizes), tools/exp_logistic_cyclic_block_entropy_gate.py:221: print("perimeter n marginal_stable rotation_invariant cyclic_block_sizes") tools/exp_logistic_cyclic_block_entropy_gate.py:234:def parse_block_sizes(raw: str) -> tuple[int, ...]: tools/exp_logistic_cyclic_block_entropy_gate.py:249: parser.add_argument("--block-sizes", type=parse_block_sizes, default=(4, 8, 16, 32, 64, 128, 256)) 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_gap_label_set_stability.py:84: label_set = sorted({item["label"] for item in selected}, key=lambda x: (abs(x), x)) tools/exp_gap_label_set_stability.py:89: "label_set": label_set, tools/exp_gap_label_set_stability.py:103: sets = [set(row["label_set"]) for row in rows if row["n_selected"] > 0] tools/exp_gap_label_set_stability.py:194: "experiment": "gap_label_set_stability", tools/exp_gap_label_set_stability.py:219: parser.add_argument("--out", default="tools/data/gap_label_set_stability_20260508_1632.json") tools/exp_logistic_surrogate_contract_gate.py:47:def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: tools/exp_logistic_surrogate_contract_gate.py:49: if block_size <= 1: tools/exp_logistic_surrogate_contract_gate.py:51: blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] tools/exp_logistic_surrogate_contract_gate.py:59: block_size: int, tools/exp_logistic_surrogate_contract_gate.py:67: return block_shuffle(values, block_size, rng) tools/exp_logistic_surrogate_contract_gate.py:76: block_size: int, tools/exp_logistic_surrogate_contract_gate.py:82: surrogate = make_surrogate(values, surrogate_class, block_size, rng) tools/exp_logistic_surrogate_contract_gate.py:122: args.block_size, 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:1850: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:1940: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_spirale.py:87: f"{'potenziale':>10s} {'gap_ratio':>10s}") 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_excess_scaling.py:33:def gap_ratio(gaps): tools/exp_excess_scaling.py:49: rs.append(gap_ratio(gaps)) tools/exp_excess_scaling.py:66: r_prime = gap_ratio(gaps) 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: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_gap_label_generator_gate.py:5:The label-set audit moved the observable from the first-two gap ratio to the tools/exp_gap_label_generator_gate.py:20:from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence, summarize_sets tools/exp_gap_label_generator_gate.py:67:def block_shuffle(seq: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: tools/exp_gap_label_generator_gate.py:68: blocks = [seq[i : i + block_size].copy() for i in range(0, len(seq), block_size)] tools/exp_gap_label_generator_gate.py:77: "phi_sturmian": phi, 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:128: phi_rows = [row for row in rows if row["generator"] == "phi_sturmian"] tools/exp_coherence_robustness.py:36:def gap_ratio(gaps): 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/dnd_piano11.py:497: "gap_ratio": float(phi**2), 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/exp_coherence_length.py:33:def gap_ratio(gaps): 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/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/exp_scale_selective_perturbation.py:142:def perturb_block_shuffle(gaps, alpha, rng, block_size=50): tools/exp_scale_selective_perturbation.py:146: n_blocks = n // block_size tools/exp_scale_selective_perturbation.py:149: start = b * block_size tools/exp_scale_selective_perturbation.py:150: end = min(start + block_size, n) tools/data/reports/agent_20260508_1632.md:7:observables_used: [gap_label_set, label_jaccard, phase_stability, threshold_stability, scale_stability] 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:13:Il 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? tools/data/reports/agent_20260508_1632.md:21:- Stabilita: Jaccard mediano tra label-set globali e dentro gruppi phase/threshold/scale. tools/data/reports/agent_20260508_1632.md:41:1. **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`. tools/data/reports/agent_20260508_1632.md:47:4. **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. 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:56:- **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. 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_20260508_1632.md:66:- **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. tools/data/reports/agent_20260508_1632.md:69:- Script: `tools/exp_gap_label_set_stability.py` tools/data/reports/agent_20260508_1632.md:70:- Data: `tools/data/gap_label_set_stability_20260508_1632.json` 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/data/reports/exp_metric_tensor_diag_long.json:24: "gap_ratio_r": 0.4537691241430244, tools/dnd_riformulazioni.py:137: 'gap_ratio': obs.get('gap_ratio_medio', 0), tools/exp_metric_tensor_diagnostic.py:342: "gap_ratio_r": float(r_mean_prime), tools/exp_metric_tensor_diagnostic.py:397:print(f" prime: {output['gap_ratio_r']:.6f}") 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/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/dnd_experiments.py:82: block_sizes = [int(N / k) for k in [2, 4, 8, 16, 32] if N / k >= 10] tools/dnd_experiments.py:85: for bs in block_sizes: 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_perturbation_dimensionality_audit.py:144:def perturb_block_shuffle(gaps: np.ndarray, alpha: float, rng: np.random.Generator, block_size: int = 64) -> np.ndarray: tools/exp_perturbation_dimensionality_audit.py:146: n_blocks = len(out) // block_size tools/exp_perturbation_dimensionality_audit.py:153: start = block * block_size tools/exp_perturbation_dimensionality_audit.py:154: end = min(start + block_size, len(out)) tools/exp_quasiperiodic_gap_ratio_denominator.py:3:Stratified denominator audit for the quasiperiodic gap_ratio claim. tools/exp_quasiperiodic_gap_ratio_denominator.py:50: first_two_ratio = large[0][1] / large[1][1] tools/exp_quasiperiodic_gap_ratio_denominator.py:52: first_two_ratio = None tools/exp_quasiperiodic_gap_ratio_denominator.py:58: "first_two_ratio": first_two_ratio, tools/exp_quasiperiodic_gap_ratio_denominator.py:111: phi_v = matched["phi"]["first_two_ratio"] tools/exp_quasiperiodic_gap_ratio_denominator.py:112: silver_v = matched["silver"]["first_two_ratio"] tools/exp_quasiperiodic_gap_ratio_denominator.py:113: bronze_v = matched["bronze"]["first_two_ratio"] tools/exp_quasiperiodic_gap_ratio_denominator.py:129: "first_two_ratio": summarize([r.get("first_two_ratio") for r in subset]), 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/data/reports/agent_20260507_1042.md:30:`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. 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/logistic_cyclic_block_entropy_gate_20260507_1419.json:17: "block_sizes": [ tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json:59: "cyclic_support_all_declared_block_sizes": true, tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json:116: "cyclic_support_all_declared_block_sizes": false, tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json:168: "cyclic_support_all_declared_block_sizes": false, tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json:403: "cyclic_support_all_declared_block_sizes": true tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json:589: "cyclic_support_all_declared_block_sizes": false tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json:770: "cyclic_support_all_declared_block_sizes": false tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json:23: "block_size": 64, 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/agent_field_live.md:10:- **Domanda aperta** (consecutio): 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, tools/data/agent_field_live.md:14: - Generator Gate Del Label-Set Phi → None/None (ρ=None) tools/data/agent_field_live.md:132: "perimetro" → G_BLANK_SHELL_DILATION_GATE, G_BLANK_SHELL_SCALE_LAW_GATE, QPG_GAP_RATIO_DENOMINATOR_GATE, G_BLANK_SHELL_TQGER_GATE, G_BLANK_SHELL_STRATIFIED_GATE tools/data/agent_field_live.md:142:### Agent Report — Generator Gate Del Label-Set Phi tools/data/agent_field_live.md:143:Trovato: 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/agent_field_live.md:146:Verdetto: **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/agent_field_live.md:149:Trovato: 1. **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 `0.9 tools/data/agent_field_live.md:150:Verdetto: **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/agent_field_live.md:156:Verdetto: **CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non conferma gap-labeling come claim generale. Conferma u tools/data/agent_field_live.md:247:- Rischi (contraddizioni): COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE, M_trascendenza_limite_attuale_L0 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/g1_log.jsonl:6:{"ts": "2026-05-08T03:33:40.212733+00:00", "cycle_ref": "20260508_0330", "decision": "PASS", "tension_id": "M_", "tension_tipo": null, "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[first_two_ratio, top2_ratio, large_gap_count]", "reason": "observables_used populated or field absent (legacy)"} tools/data/g1_log.jsonl:7:{"ts": "2026-05-08T16:35:40.840678+00:00", "cycle_ref": "20260508_1632", "decision": "PASS", "tension_id": "M_", "tension_tipo": null, "obs_field_present": true, "obs_empty": false, "obs_value_raw": "[gap_label_set, label_jaccard, phase_stability, threshold_stability, scale_stabi", "reason": "observables_used populated or field absent (legacy)"} 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/reports/agent_20260508_0330.md:7:observables_used: [first_two_ratio, top2_ratio, large_gap_count] 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:19:- Osservabile originale: `first_two_ratio = primo spacing sopra threshold*mean / secondo spacing sopra threshold*mean`. tools/data/reports/agent_20260508_0330.md:26:| domain | N | phase | threshold | n_large | first_two_ratio | top2_ratio | tools/data/reports/agent_20260508_0330.md:52:2. **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. tools/data/reports/agent_20260508_0330.md:54:3. **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`. tools/data/reports/agent_20260508_0330.md:56:4. **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. 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:68:Costruire 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. 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/seme_backup_pre_run.json:157: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme_backup_pre_run.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_backup_pre_run.json:163: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme_backup_pre_run.json:169: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme_backup_pre_run.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_backup_pre_run.json:172: "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: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_backup_pre_run.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_backup_pre_run.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/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/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/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/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/seme.json:157: "id": "QPG_GAP_RATIO_DENOMINATOR_GATE", tools/data/seme.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.json:163: "origine": "cycle agent_20260508_0330: quasiperiodic_gap_ratio_denominator", tools/data/seme.json:169: "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", tools/data/seme.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.json:172: "nota": "Dal domandatore (2026-05-07T21:20). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", tools/data/seme.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.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.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/reports/agent_20260508_1715.md:1:# Agent Report — Generator Gate Del Label-Set Phi 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:20: - `phi_sturmian`: sequenza meccanica Sturmiana `theta=1/phi`. tools/data/reports/agent_20260508_1715.md:32:| phi_sturmian | 144 | 0.909091 | 0.727273 | 0.727273 | [-1, 1, -2, 2, 3, -4, 4, 6] | [] | tools/data/reports/agent_20260508_1715.md:43:| phi_sturmian | 0.000095 | 29.0 | 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:55:3. **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. 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:59:5. **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. 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: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: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/mapping_validation_2026-04-21.json:180: "tension": "COMP_GEN_GAP_RATIO_FALSIFICA_F6", tools/data/reports/mapping_validation_2026-04-21.json:196: "tension": "COMP_GEN_GAP_RATIO_FALSIFICA_FALSIFICA_F6", 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/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json:17: "block_sizes": [ tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json:59: "cyclic_support_all_declared_block_sizes": true, tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json:107: "cyclic_support_all_declared_block_sizes": false, tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json:155: "cyclic_support_all_declared_block_sizes": false, tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json:390: "cyclic_support_all_declared_block_sizes": true tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json:567: "cyclic_support_all_declared_block_sizes": false tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json:744: "cyclic_support_all_declared_block_sizes": false tools/data/riformulazioni.json:13: "gap_ratio": 2.6180740677926337, tools/data/riformulazioni.json:69: "gap_ratio": 2.618037048594604, 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/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 — 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 t" 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/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/quasiperiodic_gap_ratio_denominator_20260508_0330.json:2: "experiment": "quasiperiodic_gap_ratio_denominator", tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:26: "first_two_ratio": { 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tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:211: "first_two_ratio": 0.39185776750560436, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:220: "first_two_ratio": 1.3897007660694667, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:229: "first_two_ratio": 1.0802211572120333, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:239: "first_two_ratio": 1.131899728971026, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:249: "first_two_ratio": 2.5954422972222653, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:259: "first_two_ratio": 0.7580466232932631, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:279: "first_two_ratio": 0.39185776750560436, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:288: "first_two_ratio": 1.3897007660694667, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:297: "first_two_ratio": 0.5633261646971632, 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0.3842336544975359, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:3367: "first_two_ratio": 2.225421585814851, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:3377: "first_two_ratio": 0.9374146866566403, tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json:3387: "first_two_ratio": 1.1340972497044528, 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/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, tools/data/autoricerca_journal.json:66: "gap_ratio": 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tools/data/autoricerca_journal.json:11693: "gap_ratio": 2.6180498365087095, tools/data/autoricerca_journal.json:11716: "gap_ratio": 2.6181052790180495, tools/data/autoricerca_journal.json:11738: "gap_ratio": 2.618012033163975, tools/data/autoricerca_journal.json:11760: "gap_ratio": 2.6180740677926337, tools/data/autoricerca_journal.json:11782: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_journal.json:11805: "gap_ratio": 2.6180412416553933, tools/data/autoricerca_journal.json:11828: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_journal.json:11851: "gap_ratio": 2.6180758708931045, tools/data/autoricerca_journal.json:11873: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_journal.json:11896: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_journal.json:11919: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_journal.json:11942: "gap_ratio": 2.6180861695660687, tools/data/autoricerca_journal.json:11962: "gap_ratio": 2.618071806911586, tools/data/autoricerca_journal.json:11983: "gap_ratio": null, tools/data/autoricerca_journal.json:12005: "gap_ratio": 2.6180295268704663, 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/meta_assertion_gate.json:31: "test": "gap_ratio_phi2", 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/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/lab_graph.json:490: "label": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:491: "label_en": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:492: "label_short": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:493: "label_short_en": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:496: "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:497: "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:498: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,50", tools/data/lab_graph.json:499: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: nel perimetro `N={233,377,50", tools/data/lab_graph.json:545: "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:546: "findings": "1. **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 `0.9", tools/data/lab_graph.json:547: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non p", tools/data/lab_graph.json:548: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / QPG_GAP_RATIO_DENOMINATOR_GATE**: il gap-labeling di phi non p", tools/data/lab_graph.json:566: "verdict": "**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:568: "annotation": "Vincolo: **CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non con", tools/data/lab_graph.json:569: "annotation_en": "Constraint: **CONSTRAINT on TRASCENDENZA_LIMITE / M_trascendenza_limite_attuale_L0**: il `gap_ratio` phi non con", tools/data/lab_graph.json:1133: "title": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:1134: "title_en": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:1137: "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:1138: "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:1139: "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:1140: "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:1141: "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:1147: "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:1153: "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:1171: "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:1177: "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:1188: "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:1189: "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:1190: "findings": "1. **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`.\n2. **Verificato: il null random rompe la tassonomia.** Il random ", tools/data/lab_graph.json:1191: "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:1192: "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:1198: "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:1204: "text": "Il 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?" tools/data/lab_graph.json:1222: "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:1228: "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:1239: "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:1240: "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:1242: "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:1243: "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:1249: "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:1255: "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:1267: "text": "Caso 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" tools/data/lab_graph.json:1273: "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:1279: "text": "Costruire 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 so" tools/data/lab_graph.json:1828: "content_full": "# Agent Report — Logistic Surrogate Contract Gate\n\ntimestamp: 2026-05-07 10:42 UTC\ncategory: gate_falsification_surrogate_contract\nverdict: scoped_operator_with_surrogate_split\nobservables_registry: not used for canonical observables\nobservables_native_version: logistic-native-1.0.0-2026-05-07\nobservables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism]\ntool: tools/exp_logistic_surrogate_contract_gate.py\ndata: tools/data/logistic_surrogate_contract_gate_20260507_1042.json\nseed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json\n\n## Claim Under Test\n\nVerificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo\nregressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle;\nquesto run confronta tre null:\n\n- `marginal_shuffle`: preserva la distribuzione dei valori.\n- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio.\n- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra\n blocchi.\n\nRegola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le\nclassi surrogate dichiarate. Supporto contro solo marginal shuffle non basta.\n\n## Deposito Numerico\n\nRun principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`,\n`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`.\n\nSeed check: `n_baseline=20`, `seed=202605071043`.\n\n| perimeter | contract-stable observables | marginal-only observables |\n|---|---:|---:|\n| logistic_orbit_values | block_entropy_deficit_k4 | [] |\n| logistic_symbolic_itinerary | [] | [] |\n| logistic_return_intervals | [] | recurrence_diag_mean |\n\nSeed check:\n\n| perimeter | contract-stable observables | marginal-only observables |\n|---|---:|---:|\n| logistic_orbit_values | block_entropy_deficit_k4 | [] |\n| logistic_symbolic_itinerary | [] | [] |\n| logistic_return_intervals | [] | [] |\n\nZ values, run principale:\n\n| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism |\n|---|---|---:|---:|---:|---:|\n| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 |\n| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 |\n| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 |\n| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 |\n| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 |\n| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 |\n| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 |\n| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 |\n| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 |\n\nZ values, seed check:\n\n| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism |\n|---|---|---:|---:|---:|---:|\n| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 |\n| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 |\n| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 |\n| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 |\n| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 |\n| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 |\n| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 |\n| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 |\n| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 |\n\nRaw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`:\n\n| run | surrogate | original | baseline mean | baseline std | z |\n|---|---|---:|---:|---:|---:|\n| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 |\n| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 |\n| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 |\n| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 |\n| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 |\n| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 |\n\n## Risultato\n\n1. **The orbit support survives the declared surrogate contract.**\n\n `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against\n marginal shuffle, circular shift, and block shuffle in both runs. The\n surviving support is one observable, not a suite-wide endpoint support.\n\n2. **The generating partition remains blank.**\n\n `logistic_symbolic_itinerary` has no replicated contract-stable observable.\n The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`,\n but it does not survive marginal shuffle, block shuffle, or seed check.\n\n3. **Return intervals stay outside the contract.**\n\n `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and\n block shuffle in the main run, but not against circular shift and not in the\n seed check. The support is blank under the declared contract.\n\n4. **The remaining logistic support lives at the cut-sensitive node.**\n\n Circular-shift denominators for orbit block entropy are very small\n (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next\n falsification must separate local block grammar from artifact of the linear\n starting cut.\n\n## Consecutio\n\n`ORDER_DENOMINATOR_GATE` narrows again:\n\n> The logistic orbit transfers through orbit block entropy under marginal,\n> circular, and block surrogates. The generating itinerary and return intervals\n> remain blank. The remaining logistic object is orbit-block-entropy support,\n> not logistic dynamics as a whole.\n\nNext experiment: falsify the residual support with a cyclic/start-invariant\nblock entropy observable and multiple block sizes. Declare support only if it\ndoes not depend on the window's starting cut.\n\n## Self-Audit: 5 Lenti\n\nL1 hard constraint vs bias: blank claims are made only for replicated\ncontract-stable support. The report does not claim that all z values are zero.\n\nL2 quantity vs ratio: raw original, baseline mean, baseline std, and z are\nreported for the surviving observable because circular-shift denominators are\nsmall.\n\nL3 no silent patching: the claim under test changed from marginal shuffle to\nsurrogate contract because the seme directed that node. The old marginal result\nis not discarded; it is reclassified as insufficient when it does not survive\nthe stronger contract.\n\nL4 edge cases: main-run return-interval support is reported and then excluded\nbecause it fails circular shift and seed check.\n\nL5 re-discovery vs discovery: symbolic itinerary blank is consistent with the\nclassical Bernoulli coding of the logistic map at `r=4`. This report claims a\nlab-gate scope, not a new theorem about the logistic map.\n\n## Fonti\n\n- Verificato: `tools/data/agent_field_live.md`\n- Verificato: `tools/LAB_AGENT_CONTEXT.md`\n- Verificato: `tools/data/seme.json`\n- Verificato: `tools/exp_logistic_counter_scope_gate.py`\n- Verificato: `tools/exp_logistic_surrogate_contract_gate.py`\n- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json`\n- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json`\n- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py`\n- Inferito: the residual logistic support is cut-sensitive because circular\n shift preserves cyclic temporal order but changes the linear starting cut.\n", tools/data/lab_graph.json:1967: "label": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:1968: "label_en": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:1969: "label_short": "Generator Gate Del Label-Set Phi", tools/data/lab_graph.json:1970: "label_short_en": "Generator Gate Del Label-Set Phi", 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_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_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/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_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/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/biconi/bicono_20260508_1632.json:6: "raw": "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.", tools/data/biconi/bicono_20260508_1632.json:7: "possibile": "cercare la rete dei punti fissi relazionali nei label-set, non nei valori puntuali", tools/data/vocabolario_custom.json:29: "gap_ratio": { 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/logistic_surrogate_contract_gate_20260507_1042.json:23: "block_size": 64, tools/data/vault_condizioni.json:11: "gap_ratio": 2.6179727982873873, tools/data/vault_condizioni.json:22: "gap_ratio": 2.6179575597540508, tools/data/vault_condizioni.json:33: "gap_ratio": 2.618131465957247, tools/data/vault_condizioni.json:44: "gap_ratio": 2.6180706858803986, tools/data/vault_condizioni.json:55: "gap_ratio": 2.6180861695660687, tools/data/vault_condizioni.json:66: "gap_ratio": 2.6180861695660687, tools/data/vault_condizioni.json:77: "gap_ratio": 2.618055175518615, 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/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: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: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/gap_label_set_stability_20260508_1632.json:2: "experiment": "gap_label_set_stability", tools/data/gap_label_set_stability_20260508_1632.json:404: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:523: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:642: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:763: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:884: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1006: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1128: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1247: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1366: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1487: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1610: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1733: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1853: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:1972: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2091: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2212: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2334: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2457: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2577: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2698: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2820: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:2941: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3063: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3185: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3307: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3428: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3550: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3671: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3792: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:3915: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4036: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4157: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4279: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4400: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4523: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4645: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4767: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:4888: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5008: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5130: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5252: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5373: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5494: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5615: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5735: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5857: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:5980: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6103: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6224: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6345: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6465: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6587: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6710: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6833: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:6955: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7076: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7198: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7320: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7443: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7566: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7688: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7809: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:7931: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8053: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8176: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8297: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8418: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8539: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8661: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8783: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:8906: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9029: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9150: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9271: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9390: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9511: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9634: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9757: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:9879: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10000: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10119: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10240: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10361: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10483: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10603: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10724: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10843: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:10964: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11087: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11210: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11332: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11453: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11574: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11696: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11819: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:11942: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12063: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12184: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12305: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12427: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12549: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12672: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12793: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:12914: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13035: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13157: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13279: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13401: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13522: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13643: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13763: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:13885: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14006: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14127: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14248: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14369: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14489: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14611: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14734: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14856: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:14978: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15099: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15219: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15341: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15462: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15583: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15703: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15824: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:15944: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:16067: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:16190: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:16313: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:16434: "label_set": [ tools/data/gap_label_set_stability_20260508_1632.json:16555: "label_set": [ 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tools/data/gap_label_generator_gate_20260508_1715.json:104146: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:104266: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:104386: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:104506: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:104626: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:104746: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:104866: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:104985: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:105104: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:105223: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:105340: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:105457: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:105574: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:105696: "label_set": [ tools/data/gap_label_generator_gate_20260508_1715.json:105818: "label_set": [ 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/aeternitas/aeternitas_20260508_002036.json:33: "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", 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/evolution/evolution_20260508_1632.md:2:Traiettoria: da un osservabile metrico mobile (`first_two_ratio`) a un osservabile tassonomico stabile (label-set dei gap larghi). Il passo ha attraversato il perimetro N/phase/threshold con tre domini metallici e un controllo random, misurando stabilità via Jaccard. Ha chiuso il ciclo con un vincolo: la trascendenza di phi non sta nel valore puntuale ma nella persistenza del set di etichette. tools/data/evolution/evolution_20260508_1632.md:8:Il nodo regressivo era già stato localizzato nel ciclo 03:30: l’osservabile `first_two_ratio` dipendente dal denominatore. In questo passo la riparazione è stata applicata a monte, sostituendo l’osservabile con il label-set. Non c’è nuovo nodo regressivo; tools/data/evolution/evolution_20260508_0330.md:3:È centrato sul passo: inversione da valore puntuale a denominatore atomico, attrito nella telemetria opaca (`ok`, zero tool use), nodo regressivo nel legame mancante tra run, dati, report e autopsy, consecutio verso label-set dei gap. 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:193: "first_two_ratio", 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:207: "titolo": "Generator Gate Del Label-Set Phi", tools/data/ciclo_memoria.json:210: "gap_label_set", 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:220: "cosa": "Generator Gate Del Label-Set Phi → None/None (ρ=None)" tools/data/ciclo_memoria.json:231: "domanda_aperta": "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/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/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/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, codex Il report 17:15 ha già lasciato una consecutio precisa e anche due flag medi: extra label Fibonacci non isolato e drift dichiarativo. Il nuovo report deve isolare solo la scala di rottura del core e dichiarare esplicitamente che non misura `gap_ratio`. 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 "sed -n '45,70p' tools/data/valutatore_log.jsonl" in /opt/MM_D-ND succeeded in 0ms: {"ts": "2026-05-07T20:02:46.907451+00:00", "cycle_ref": "20260507_1957", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto una consecutio precisa: QG non basta come lato isolato, il nodo regressivo e' il guscio blank a due facce TQG/QGE. Pero' il risultato resta interno alla tassonomia TQGE e dichiara esplicitamente L5: non e' discovery fisica nuova; il passo successivo deve falsificare il gate su TQGE+R invece di accumulare dettaglio locale.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare G_BLANK_SHELL_POLARITY_GATE su perimetro TQGE+R: verificare se la polarita' blank-shell TQG inerte / QGE depositante sopravvive quando entra R, o se R sposta il denominatore regressivo.", "reason": "La consecutio del ciclo punta a TQGE+R; restare nel catalogo TQGE rischia deep-dive locale senza cross-dominio."}}, "notes": "Promuovere G_BLANK_SHELL_POLARITY_GATE come tensione attiva del prossimo ciclo; usare controlli count-preserving/shuffle e dichiarare esplicitamente se R conserva, rompe o rilocalizza il guscio blank."} {"ts": "2026-05-07T20:46:55.259764+00:00", "cycle_ref": "20260507_2042", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha falsificato il framing forte `dipolare = det=-1` senza cadere nel dettaglio numerico locale: ha usato shuffle, ha isolato il nodo regressivo nell'embedding e ha prodotto una consecutio cross-dominio precisa. La prossima mossa non dipende dall'operatore: va testato se lo scarto reale-vs-shuffle sopravvive in domini ordinati non-primi o se il segno negativo e' interamente prodotto dal fit.", "action": {"type": "trigger_cycle", "detail": {"tension": "DUALITA_DET_DENOMINATOR_GATE", "direction": "Applicare il test det_vs_shuffle a Beatty coerente, shuffle Beatty, GUE spacing order e Poisson; separare segno condiviso da scarto ordinato contro null.", "reason": "Verifica cross-dominio della nuova tensione nata dal constraint: det non come polo primario, ma come osservabile subordinato al denominator gate."}}, "notes": "Mantenere il frame corrente. Evitare cristallizzazione: il risultato e' un constraint maturo sul perimetro primi, ma serve il passaggio non-primi prima di decidere se det entra come diagnostica secondaria o va nel cimitero come discriminatore."} {"ts": "2026-05-07T21:25:55.439673+00:00", "cycle_ref": "20260507_2120", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una consecutio strutturale chiara: R non sposta il deposito QGE ma dilata il guscio blank da binario a tri-facciale con QGR. Non c'e' contraddizione che richieda operatore e non c'e' accumulo numerico locale: il null e' stato usato come controllo anti-tautologico, mentre il finding e' nel cambio di perimetro TQGE -> TQGE+R. La prossima mossa naturale e' testare il gate di dilatazione: se ogni vertice esterno aggiunge faccia di shell senza muovere il deposito, oppure se R e' speciale.", "action": {"type": "trigger_cycle", "detail": {"tension": "G_BLANK_SHELL_TQGER_GATE", "direction": "Testare la dilatazione del guscio blank oltre R: aggiunta controllata di un vertice esterno e verifica se il deposito resta QGE mentre il blank acquisisce una nuova faccia di frame.", "constraint": "Non cercare rarita numerica; usare permutazioni solo come audit anti-tautologico e formulare il risultato come trasferibilita' o falsificazione del gate."}}, "notes": "Continuare nello stesso frame, ma il prossimo esperimento deve attaccare la nuova tensione e non ripetere il conteggio TQGE+R."} {"ts": "2026-05-07T22:11:31.614451+00:00", "cycle_ref": "20260507_2203", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una consecutio strutturale chiara: il deposito QGE resta invariato mentre il guscio blank cresce come 2+n_esterni nel perimetro tipizzato. Il limite non e' il risultato ma il nodo regressivo identificato dall'affinatore: prima di spingere K7/K8 serve una condizione d'ingresso che separi chiusura exact, null stratificato e trasferimento osservato. La prossima mossa deve restare nello stesso frame e attaccare il contro-polo dichiarato: esterni non tipizzati o multi-modo che possono far migrare o duplicare il deposito.", "action": {"type": "trigger_cycle", "detail": {"directive": "Testare G_BLANK_SHELL_SCALE_LAW_GATE sul contro-polo: introdurre un esterno non tipizzato o multi-modo e verificare se il deposito QGE resta unico, migra o si duplica. Prima dell'espansione dichiarare il gate d'ingresso: exact combinatorio, null stratificato, oppure solo trasferimento osservato; non usare K7/K8 sampled come claim di rarita."}}, "notes": "Non cristallizzare ancora: la legge e' matura come vincolo locale controllato, ma il contro-polo aperto e la distinzione exact/stratified/observed devono essere risolti prima del condensato."} {"ts": "2026-05-07T23:17:48.061131+00:00", "cycle_ref": "20260507_2310", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una riparazione regressiva pulita: il limite K7/K8 era nel denominatore pattern-completo, non nella legge del gate. La consecutio e' viva perche' apre un contro-polo esplicito: esterni non tipizzati, multi-modo o deposito duplicato; non serve redesign, serve il prossimo giro sul bordo del gate stratificato.", "action": {"type": "trigger_cycle", "detail": {"direction": "Testare il contro-polo di G_BLANK_SHELL_STRATIFIED_GATE su esterni non tipizzati, multi-modo e deposito duplicato", "reason": "Verificare se la legge shell_faces(QG)=2+n_esterni resta strutturale o dipende dalla tassonomia operator-tipizzata count-preserving"}}, "notes": "Non cristallizzare ancora: risultato forte ma interno al catalogo D-ND, senza cross-dominio fisico. Il prossimo ciclo deve falsificare il perimetro, non accumulare K ulteriori nello stesso frame tipizzato."} {"ts": "2026-05-08T00:21:16.867699+00:00", "cycle_ref": "20260508_0011", "executed": false, "decision": "STOP_FOR_REVIEW", "confidence": "high", "reasoning": "The cycle produced a genuine cross-domain discriminator: alpha (scaling exponent) separates primes (0.32-0.39) from GUE (0.5-0.6), replicated across seeds and observables. This is category (b) — cross-domain structural verification — and potentially crystallizable. However, the seme delta shows 6 new tensions all named *_TRASCENDENZA_LIMITE, which pattern-matches the gate-of-gate anti-pattern flagged by the operator on 07/05. The finding is solid; the trajectory risk is that the next cycles will methodologically gate the finding instead of letting the operator decide what it means for the model. Three consecutive NEXT_CYCLE decisions have been productive, but this is a natural inflection point where the operator should see the alpha-discriminator result and redirect.", "action": {"type": "notify_operator", "detail": {"message": "Lab cycle 85 produced a cross-domain discriminator: scaling exponent alpha separates primes (α≈0.35, weakening) from GUE (α≈0.55, scale-invariant). The discriminator is not the observable value but how it scales. Poisson shows no signal. Replicated on two seeds. 6 new TRASCENDENZA_LIMITE tensions generated — may need pruning before next cycle."}}, "notes": "The alpha result connects to the DUALITA_DIPOLARE_VS_ILLUSORIA tension at a structural level: prime gaps have dipolar structure that dissolves at large scale, while GUE repulsion is baked in at all scales. This is the kind of finding that should be read through the model (A10 dipolo assiomatico: φ attrattivo vs −1/φ repulsivo) before being pushed further numerically. The 6 TRASCENDENZA_LIMITE tensions risk triggering 2-3 more methodology cycles without advancing — operator should decide if they carry signal or are gate-of-gate."} {"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."} {"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."} {"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."} 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 'git status --short' in /opt/MM_D-ND succeeded in 0ms: 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/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/ ?? 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/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/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_generator_gate_20260508_1715.json ?? tools/data/gap_label_set_stability_20260508_1632.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/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/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/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/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/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/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_81.json ?? tools/data/seme_archive/piano_82.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/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/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_generator_gate.py ?? tools/exp_gap_label_set_stability.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 codex Sto aggiungendo uno strumento nuovo, non un esperimento monouso: prende il generatore Sturmiano phi, applica block shuffle su una griglia di lunghezze Fibonacci e non-Fibonacci, poi misura retention dei label bassi/alti contro il core di riferimento. apply patch patch: completed /opt/MM_D-ND/tools/exp_gap_label_block_scale_gate.py diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() codex Strumento creato. Eseguo ora la scansione con perimetro contenuto (`N=377,610`, quattro fasi, soglia 2.0, cinque trial) per ottenere un risultato leggibile entro il ciclo. exec /bin/bash -lc 'python tools/exp_gap_label_block_scale_gate.py' in /opt/MM_D-ND diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() succeeded in 10842ms: { "reference_core_phi": [ -1, 1, -2, 2, 3, -4, 4, 6 ], "reference_high": [ 3, -4, 4, 6 ], "crossings": { "median_high_retention_ge_threshold": 34, "all_high_condition_rate_ge_threshold": 89, "all_low_condition_rate_ge_threshold": 34 }, "blocks": { "5": { "family": "fibonacci", "median_jaccard": 0.2222222222222222, "median_overlap_with_phi_core": 0.1213235294117647, "median_low_retention": 0.25, "median_high_retention": 0.25, "all_high_condition_rate": 0.0, "stable_labels_75pct": [] }, "6": { "family": "non_fibonacci", "median_jaccard": 0.26666666666666666, "median_overlap_with_phi_core": 0.1213235294117647, "median_low_retention": 0.25, "median_high_retention": 0.25, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ 20 ] }, "8": { "family": "fibonacci", "median_jaccard": 0.3076923076923077, "median_overlap_with_phi_core": 0.1213235294117647, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 20 ] }, "10": { "family": "non_fibonacci", "median_jaccard": 0.35714285714285715, "median_overlap_with_phi_core": 0.14285714285714285, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 20 ] }, "13": { "family": "fibonacci", "median_jaccard": 0.4166666666666667, "median_overlap_with_phi_core": 0.14285714285714285, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 20, -33 ] }, "16": { "family": "non_fibonacci", "median_jaccard": 0.4444444444444444, "median_overlap_with_phi_core": 0.18181818181818182, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 33 ] }, "21": { "family": "fibonacci", "median_jaccard": 0.36363636363636365, "median_overlap_with_phi_core": 0.2928571428571428, "median_low_retention": 0.75, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1 ] }, "24": { "family": "non_fibonacci", "median_jaccard": 0.4444444444444444, "median_overlap_with_phi_core": 0.25, "median_low_retention": 0.75, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 33 ] }, "34": { "family": "fibonacci", "median_jaccard": 0.6363636363636364, "median_overlap_with_phi_core": 0.6666666666666666, "median_low_retention": 1.0, "median_high_retention": 0.5, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, -2, 2, -4, 4 ] }, "40": { "family": "non_fibonacci", "median_jaccard": 0.5, "median_overlap_with_phi_core": 0.4, "median_low_retention": 1.0, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, -2 ] }, "55": { "family": "fibonacci", "median_jaccard": 0.8, "median_overlap_with_phi_core": 0.7136363636363636, "median_low_retention": 1.0, "median_high_retention": 0.75, "all_high_condition_rate": 0.4, "stable_labels_75pct": [ -1, 1, -2, 2, -3, 3, -4, 4 ] }, "64": { "family": "non_fibonacci", "median_jaccard": 0.5714285714285714, "median_overlap_with_phi_core": 0.5555555555555556, "median_low_retention": 1.0, "median_high_retention": 0.25, "all_high_condition_rate": 0.025, "stable_labels_75pct": [ -1, 1, -2, 2 ] }, "89": { "family": "fibonacci", "median_jaccard": 0.8181818181818182, "median_overlap_with_phi_core": 0.7272727272727273, "median_low_retention": 1.0, "median_high_retention": 1.0, "all_high_condition_rate": 0.625, "stable_labels_75pct": [ -1, 1, -2, 2, -3, 3, -4, 4 ] }, "96": { "family": "non_fibonacci", "median_jaccard": 0.625, "median_overlap_with_phi_core": 0.6, "median_low_retention": 1.0, "median_high_retention": 0.5, "all_high_condition_rate": 0.025, "stable_labels_75pct": [ -1, 1, -2, 2 ] }, "128": { "family": "non_fibonacci", "median_jaccard": 0.6666666666666666, "median_overlap_with_phi_core": 0.6666666666666666, "median_low_retention": 1.0, "median_high_retention": 0.5, "all_high_condition_rate": 0.2, "stable_labels_75pct": [ -1, 1, -2, 2, -4, 4 ] }, "144": { "family": "fibonacci", "median_jaccard": 0.8333333333333334, "median_overlap_with_phi_core": 0.7272727272727273, "median_low_retention": 1.0, "median_high_retention": 1.0, "all_high_condition_rate": 0.825, "stable_labels_75pct": [ -1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7 ] } }, "out": "tools/data/gap_label_block_scale_gate_20260508_1805.json" } codex La scansione ha prodotto una soglia netta: il nucleo basso rientra a `block_size=34`; i label alti iniziano a rientrare lì, ma diventano condizione frequente solo sui blocchi Fibonacci lunghi, con soglia robusta a `89`. Verifico i dettagli JSON prima di scrivere il report. exec /bin/bash -lc 'rg -n "agent_20260508_1805|gap_label_block_scale_gate" tools/data/reports tools/data -S' in /opt/MM_D-ND succeeded in 0ms: tools/data/gap_label_block_scale_gate_20260508_1805.json:2: "experiment": "gap_label_block_scale_gate", exec /bin/bash -lc "python - <<'PY' import json p='tools/data/gap_label_block_scale_gate_20260508_1805.json' d=json.load(open(p)) for b in ['34','55','64','89','96','128','144']: s=d['block_summary'][b] print(b, s['median_high_retention'], s['all_high_condition_rate'], s['high_label_condition_rates'], s['stable_labels_75pct']) print('family') for k,v in d['family_summary'].items(): print(k, v['median_high_retention'], v['all_high_condition_rate'], v['stable_labels_75pct']) PY" in /opt/MM_D-ND succeeded in 0ms: 34 0.5 0.0 {'3': 0.375, '-4': 0.9, '4': 0.8, '6': 0.075} [-1, 1, -2, 2, -4, 4] 55 0.75 0.4 {'3': 0.925, '-4': 0.925, '4': 1.0, '6': 0.45} [-1, 1, -2, 2, -3, 3, -4, 4] 64 0.25 0.025 {'3': 0.15, '-4': 0.5, '4': 0.6, '6': 0.1} [-1, 1, -2, 2] 89 1.0 0.625 {'3': 0.975, '-4': 1.0, '4': 1.0, '6': 0.65} [-1, 1, -2, 2, -3, 3, -4, 4] 96 0.5 0.025 {'3': 0.25, '-4': 0.675, '4': 0.475, '6': 0.125} [-1, 1, -2, 2] 128 0.5 0.2 {'3': 0.45, '-4': 0.975, '4': 0.875, '6': 0.2} [-1, 1, -2, 2, -4, 4] 144 1.0 0.825 {'3': 0.95, '-4': 1.0, '4': 1.0, '6': 0.825} [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] family fibonacci 0.5 0.23125 [-1, 1] non_fibonacci 0.0 0.03125 [-1, 1] exec /bin/bash -lc "python - <<'PY' import json p='tools/data/gap_label_block_scale_gate_20260508_1805.json' d=json.load(open(p)) print(d['reference_summary']) PY" in /opt/MM_D-ND succeeded in 0ms: {'conditions': 8, 'median_jaccard': 0.9090909090909091, 'min_jaccard': 0.7272727272727273, 'core_labels_all_conditions': [-1, 1, -2, 2, 3, -4, 4, 6], 'stable_labels_75pct': [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7], 'label_frequency_top': [{'label': -1, 'count': 8}, {'label': 1, 'count': 8}, {'label': -2, 'count': 8}, {'label': 2, 'count': 8}, {'label': 3, 'count': 8}, {'label': -4, 'count': 8}, {'label': 4, 'count': 8}, {'label': 6, 'count': 8}, {'label': -7, 'count': 7}, {'label': -3, 'count': 6}, {'label': -6, 'count': 6}, {'label': 7, 'count': 1}], 'median_label_error': 2.0452436028856047e-05, 'median_selected': 12.0, 'median_n_large': 32.0} exec /bin/bash -lc 'python -m json.tool tools/data/gap_label_block_scale_gate_20260508_1805.json >/tmp/gap_block_check.json && wc -c tools/data/gap_label_block_scale_gate_20260508_1805.json /tmp/gap_block_check.json' in /opt/MM_D-ND diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() succeeded in 133ms: 2181736 tools/data/gap_label_block_scale_gate_20260508_1805.json 2885565 /tmp/gap_block_check.json 5067301 total codex Scrivo il report 18:05 con perimetro atomico e separo tre osservabili: retention del nucleo basso, retention dei label alti, frequenza di comparsa congiunta dei label alti. Non promuovo il dato a legge universale fuori da questa griglia. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260508_1805.md diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..41b6bb335a3cfd40b0f21860dc3ac1e120240de5 --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report — Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore · core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..41b6bb335a3cfd40b0f21860dc3ac1e120240de5 --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report — Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore · core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() codex Report scritto. Ora faccio una verifica leggera: file leggibile, riferimenti presenti e output JSON coerente con il report. exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260508_1805.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report — Block Scale Gate Del Core Phi **Date**: 2026-05-08 18:05 **Piano**: 88 **Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) **verdict**: CONSTRAINT observables_registry: n/a observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] **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. ## Claim Under Test > Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? ## Question 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? ## Experiment Design - Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. - Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. - Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. - Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. - Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. - Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. - Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. ## Results 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`. | block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | |---:|---|---:|---:|---:|---:|---| | 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | | 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | | 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | | 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | | 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | | 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | | 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | | 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | | 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | | 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | | 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | | 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | | 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | | 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | | 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | | 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | High-label condition rates at the main transition points: | block_size | family | label 3 | label -4 | label 4 | label 6 | |---:|---|---:|---:|---:|---:| | 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | | 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | | 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | | 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | | 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | | 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | | 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | Detected crossings with threshold `>=0.5`: | observable | first block_size | |---|---:| | median high retention | 34 | | all-high condition rate | 89 | | all-low condition rate | 34 | ## Key Findings 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` 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`). 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`. 4. **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. ## 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 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. 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`. ## Bicono della scoperta - **Due radici**: nucleo basso locale del generatore · core alto globale del generatore. - **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. - **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. - **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. ## Consecutio Testare 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. ## Auto-audit: 5 lenti - **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. - **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. - **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. - **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. - **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. ## Files - Script: `tools/exp_gap_label_block_scale_gate.py` - Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` - Report: `tools/data/reports/agent_20260508_1805.md` exec /bin/bash -lc 'rg -n "forse|potrebbe|probabilmente|attualmente|per ora|gap_ratio testato|Report —" tools/data/reports/agent_20260508_1805.md' in /opt/MM_D-ND succeeded in 0ms: 1:# Agent Report — Block Scale Gate Del Core Phi exec /bin/bash -lc 'python -m py_compile tools/exp_gap_label_block_scale_gate.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "python tools/exp_gap_label_block_scale_gate.py --out /tmp/gap_label_block_scale_gate_check.json >/tmp/gap_label_block_scale_gate_check.out && python -m json.tool /tmp/gap_label_block_scale_gate_check.json >/tmp/gap_label_block_scale_gate_check.pretty && sed -n '1,120p' /tmp/gap_label_block_scale_gate_check.out" in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..41b6bb335a3cfd40b0f21860dc3ac1e120240de5 --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report — Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore · core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..41b6bb335a3cfd40b0f21860dc3ac1e120240de5 --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report — Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore · core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() succeeded in 10344ms: { "reference_core_phi": [ -1, 1, -2, 2, 3, -4, 4, 6 ], "reference_high": [ 3, -4, 4, 6 ], "crossings": { "median_high_retention_ge_threshold": 34, "all_high_condition_rate_ge_threshold": 89, "all_low_condition_rate_ge_threshold": 34 }, "blocks": { "5": { "family": "fibonacci", "median_jaccard": 0.2222222222222222, "median_overlap_with_phi_core": 0.1213235294117647, "median_low_retention": 0.25, "median_high_retention": 0.25, "all_high_condition_rate": 0.0, "stable_labels_75pct": [] }, "6": { "family": "non_fibonacci", "median_jaccard": 0.26666666666666666, "median_overlap_with_phi_core": 0.1213235294117647, "median_low_retention": 0.25, "median_high_retention": 0.25, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ 20 ] }, "8": { "family": "fibonacci", "median_jaccard": 0.3076923076923077, "median_overlap_with_phi_core": 0.1213235294117647, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 20 ] }, "10": { "family": "non_fibonacci", "median_jaccard": 0.35714285714285715, "median_overlap_with_phi_core": 0.14285714285714285, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 20 ] }, "13": { "family": "fibonacci", "median_jaccard": 0.4166666666666667, "median_overlap_with_phi_core": 0.14285714285714285, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 20, -33 ] }, "16": { "family": "non_fibonacci", "median_jaccard": 0.4444444444444444, "median_overlap_with_phi_core": 0.18181818181818182, "median_low_retention": 0.5, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 33 ] }, "21": { "family": "fibonacci", "median_jaccard": 0.36363636363636365, "median_overlap_with_phi_core": 0.2928571428571428, "median_low_retention": 0.75, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1 ] }, "24": { "family": "non_fibonacci", "median_jaccard": 0.4444444444444444, "median_overlap_with_phi_core": 0.25, "median_low_retention": 0.75, "median_high_retention": 0.0, "all_high_condition_rate": 0.0, "stable_labels_75pct": [ -1, 1, 33 ] }, codex Ho notato caratteri non ASCII nel nuovo report generati dal formato del titolo e dal separatore del bicono. Li normalizzo ad ASCII per rispettare lo stile di editing del workspace. exec /bin/bash -lc 'LC_ALL=C rg -n "[''^[:ascii:]]" tools/data/reports/agent_20260508_1805.md tools/exp_gap_label_block_scale_gate.py' in /opt/MM_D-ND succeeded in 0ms: tools/data/reports/agent_20260508_1805.md:1:# Agent Report — Block Scale Gate Del Core Phi tools/data/reports/agent_20260508_1805.md:82:- **Due radici**: nucleo basso locale del generatore · core alto globale del generatore. diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..41b6bb335a3cfd40b0f21860dc3ac1e120240de5 --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report — Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore · core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260508_1805.md diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..6473d3cbb805f93a98bfc5c76147070ff4b4a3cc --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report - Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore / core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..6473d3cbb805f93a98bfc5c76147070ff4b4a3cc --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report - Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore / core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'LC_ALL=C rg -n "[''^[:ascii:]]" tools/data/reports/agent_20260508_1805.md tools/exp_gap_label_block_scale_gate.py || true' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'git status --short tools/exp_gap_label_block_scale_gate.py tools/data/gap_label_block_scale_gate_20260508_1805.json tools/data/reports/agent_20260508_1805.md' in /opt/MM_D-ND succeeded in 0ms: ?? tools/data/gap_label_block_scale_gate_20260508_1805.json ?? tools/data/reports/agent_20260508_1805.md ?? tools/exp_gap_label_block_scale_gate.py exec /bin/bash -lc 'test -s tools/data/reports/agent_20260508_1805.md && test -s tools/data/gap_label_block_scale_gate_20260508_1805.json && echo ok' in /opt/MM_D-ND succeeded in 0ms: ok exec /bin/bash -lc 'python -m py_compile tools/exp_gap_label_block_scale_gate.py' in /opt/MM_D-ND succeeded in 0ms: diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..6473d3cbb805f93a98bfc5c76147070ff4b4a3cc --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report - Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore / core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() codex Esperimento eseguito e report scritto. Creati: - [tools/exp_gap_label_block_scale_gate.py](/opt/MM_D-ND/tools/exp_gap_label_block_scale_gate.py) - [tools/data/gap_label_block_scale_gate_20260508_1805.json](/opt/MM_D-ND/tools/data/gap_label_block_scale_gate_20260508_1805.json) - [tools/data/reports/agent_20260508_1805.md](/opt/MM_D-ND/tools/data/reports/agent_20260508_1805.md) Risultante: il nucleo basso del core phi rientra a `block_size=34`; i label alti `[3, -4, 4, 6]` rientrano stabilmente solo su blocchi Fibonacci lunghi (`89`, `144`). I blocchi non-Fibonacci lunghi preservano memoria locale, ma non chiudono il core alto. Verifiche fatte: `py_compile`, JSON leggibile, report presente, nessuna API key paid rilevata. diff --git a/tools/data/reports/agent_20260508_1805.md b/tools/data/reports/agent_20260508_1805.md new file mode 100644 index 0000000000000000000000000000000000000000..6473d3cbb805f93a98bfc5c76147070ff4b4a3cc --- /dev/null +++ b/tools/data/reports/agent_20260508_1805.md @@ -0,0 +1,100 @@ +# Agent Report - Block Scale Gate Del Core Phi +**Date**: 2026-05-08 18:05 +**Piano**: 88 +**Tension explored**: M_trascendenza_limite_attuale_L0 (0.529179606750063) +**verdict**: CONSTRAINT +observables_registry: n/a +observables_used: [gap_label_set, block_scale_retention, high_label_condition_rate, low_label_condition_rate] +**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. + +## Claim Under Test +> Cosa manca per confermare completamente gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vici? + +## Question +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? + +## Experiment Design +- Perimetro: stesso lettore label dei cycle 16:32 e 17:15; `gap_ratio` non testato in questo ciclo. +- Reference core verificato sul generatore `phi_sturmian`: `[-1, 1, -2, 2, 3, -4, 4, 6]`. +- Nucleo basso: `[-1, 1, -2, 2]`. Label alti: `[3, -4, 4, 6]`. +- Blocchi Fibonacci: `5, 8, 13, 21, 34, 55, 89, 144`. +- Blocchi non-Fibonacci: `6, 10, 16, 24, 40, 64, 96, 128`. +- Null baseline interno: ogni block shuffle preserva conteggio e texture locale del generatore phi entro blocco, ma rompe ordine globale tra blocchi. +- Crossing dichiarativo: soglia descrittiva `>=0.5` sulle frequenze, usata per localizzare il passaggio nel dato, non per decidere verita strutturale. + +## Results +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`. + +| block_size | family | median Jaccard | low retention | high retention | all-high condition rate | stable labels 75% | +|---:|---|---:|---:|---:|---:|---| +| 5 | Fibonacci | 0.222222 | 0.25 | 0.25 | 0.000 | [] | +| 6 | non-Fibonacci | 0.266667 | 0.25 | 0.25 | 0.000 | [20] | +| 8 | Fibonacci | 0.307692 | 0.50 | 0.00 | 0.000 | [-1, 20] | +| 10 | non-Fibonacci | 0.357143 | 0.50 | 0.00 | 0.000 | [-1, 1, 20] | +| 13 | Fibonacci | 0.416667 | 0.50 | 0.00 | 0.000 | [-1, 1, 20, -33] | +| 16 | non-Fibonacci | 0.444444 | 0.50 | 0.00 | 0.000 | [-1, 1, 33] | +| 21 | Fibonacci | 0.363636 | 0.75 | 0.00 | 0.000 | [-1, 1] | +| 24 | non-Fibonacci | 0.444444 | 0.75 | 0.00 | 0.000 | [-1, 1, 33] | +| 34 | Fibonacci | 0.636364 | 1.00 | 0.50 | 0.000 | [-1, 1, -2, 2, -4, 4] | +| 40 | non-Fibonacci | 0.500000 | 1.00 | 0.00 | 0.000 | [-1, 1, -2] | +| 55 | Fibonacci | 0.800000 | 1.00 | 0.75 | 0.400 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 64 | non-Fibonacci | 0.571429 | 1.00 | 0.25 | 0.025 | [-1, 1, -2, 2] | +| 89 | Fibonacci | 0.818182 | 1.00 | 1.00 | 0.625 | [-1, 1, -2, 2, -3, 3, -4, 4] | +| 96 | non-Fibonacci | 0.625000 | 1.00 | 0.50 | 0.025 | [-1, 1, -2, 2] | +| 128 | non-Fibonacci | 0.666667 | 1.00 | 0.50 | 0.200 | [-1, 1, -2, 2, -4, 4] | +| 144 | Fibonacci | 0.833333 | 1.00 | 1.00 | 0.825 | [-1, 1, -2, 2, -3, 3, -4, 4, -6, 6, -7] | + +High-label condition rates at the main transition points: + +| block_size | family | label 3 | label -4 | label 4 | label 6 | +|---:|---|---:|---:|---:|---:| +| 34 | Fibonacci | 0.375 | 0.900 | 0.800 | 0.075 | +| 55 | Fibonacci | 0.925 | 0.925 | 1.000 | 0.450 | +| 64 | non-Fibonacci | 0.150 | 0.500 | 0.600 | 0.100 | +| 89 | Fibonacci | 0.975 | 1.000 | 1.000 | 0.650 | +| 96 | non-Fibonacci | 0.250 | 0.675 | 0.475 | 0.125 | +| 128 | non-Fibonacci | 0.450 | 0.975 | 0.875 | 0.200 | +| 144 | Fibonacci | 0.950 | 1.000 | 1.000 | 0.825 | + +Detected crossings with threshold `>=0.5`: + +| observable | first block_size | +|---|---:| +| median high retention | 34 | +| all-high condition rate | 89 | +| all-low condition rate | 34 | + +## Key Findings +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` 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`). + +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`. + +4. **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. + +## 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 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. + +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`. + +## Bicono della scoperta +- **Due radici**: nucleo basso locale del generatore / core alto globale del generatore. +- **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. +- **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. +- **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. + +## Consecutio +Testare 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. + +## Auto-audit: 5 lenti +- **L1 hard constraint vs bias**: il verdict dichiara il perimetro e non estende a soglie, N o generatori non testati. +- **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. +- **L3 no silent patching**: `gap_ratio` resta non testato; il report dichiara il cambio osservabile nel contratto. +- **L4 edge cases**: `block_size=128` non-Fibonacci trattato come parziale, non ignorato; mostra `-4/4` frequenti ma non chiude `3/6`. +- **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. + +## Files +- Script: `tools/exp_gap_label_block_scale_gate.py` +- Data: `tools/data/gap_label_block_scale_gate_20260508_1805.json` +- Report: `tools/data/reports/agent_20260508_1805.md` diff --git a/tools/exp_gap_label_block_scale_gate.py b/tools/exp_gap_label_block_scale_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..171a9c3950a3771bc3e8f3473233d70cd711a091 --- /dev/null +++ b/tools/exp_gap_label_block_scale_gate.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python3 +""" +Block-scale gate for phi gap-label core retention. + +The generator gate showed that short block shuffles keep local Sturmian texture +but lose the high labels of the phi core. This tool scans block length directly: +Fibonacci and non-Fibonacci block sizes are tested with the same phi label +reader, separating low-core retention from high-core re-entry. +""" + +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_generator_gate import THETA, block_shuffle +from exp_gap_label_set_stability import gap_labels, jaccard, sturmian_sequence, summarize_sets + + +REFERENCE_LOW = {-1, 1, -2, 2} +REFERENCE_HIGH = {3, -4, 4, 6} + + +def parse_ints(raw: str) -> list[int]: + return [int(x) for x in raw.split(",") if x.strip()] + + +def parse_floats(raw: str) -> list[float]: + return [float(x) for x in raw.split(",") if x.strip()] + + +def label_sort(labels: set[int] | list[int]) -> list[int]: + return sorted(labels, key=lambda x: (abs(x), x)) + + +def retention(row: dict, labels: set[int]) -> float: + present = set(row["label_set"]) + return len(present & labels) / len(labels) + + +def summarize_block(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) + high_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_HIGH) + } + low_rates = { + str(label): float(counter.get(label, 0) / n_sets) if n_sets else None + for label in label_sort(REFERENCE_LOW) + } + all_high_rate = ( + float(sum(REFERENCE_HIGH <= s for s in sets) / n_sets) + if n_sets + else None + ) + all_low_rate = ( + float(sum(REFERENCE_LOW <= s for s in sets) / n_sets) + if n_sets + else None + ) + 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, + "min_overlap_with_phi_core": float(np.min(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_condition_rate": all_low_rate, + "all_high_condition_rate": all_high_rate, + "low_label_condition_rates": low_rates, + "high_label_condition_rates": high_rates, + "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 first_crossing(block_summaries: dict[str, dict], key: str, threshold: float) -> int | None: + ordered = sorted((int(block), data) for block, data in block_summaries.items()) + for block, data in ordered: + value = data.get(key) + if value is not None and value >= threshold: + return block + return None + + +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) + fibonacci_blocks = parse_ints(args.fibonacci_blocks) + non_fibonacci_blocks = parse_ints(args.non_fibonacci_blocks) + block_sizes = sorted(set(fibonacci_blocks + non_fibonacci_blocks)) + + reference_rows = [] + rows = [] + for n in ns: + for phase in phases: + phi = sturmian_sequence(THETA, n, phase) + for threshold in thresholds: + reference_rows.append({ + "generator": "phi_sturmian", + "N": n, + "phase": phase, + "threshold": threshold, + **gap_labels(phi, THETA, threshold, args.max_label, args.top_k), + }) + for block_size in block_sizes: + for trial in range(args.trials): + shuffled = block_shuffle(phi, block_size, rng) + rows.append({ + "generator": "block_shuffle", + "block_size": block_size, + "block_family": "fibonacci" if block_size in fibonacci_blocks else "non_fibonacci", + "N": n, + "phase": phase, + "threshold": threshold, + "trial": trial, + **gap_labels(shuffled, 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[str(row["block_size"])].append(row) + + block_summary = { + block: summarize_block(group_rows, reference_core) + for block, group_rows in grouped.items() + } + + family_summary = {} + for family in ("fibonacci", "non_fibonacci"): + family_rows = [row for row in rows if row["block_family"] == family] + family_summary[family] = summarize_block(family_rows, reference_core) + + high_any_crossing = first_crossing(block_summary, "median_high_retention", args.crossing_threshold) + high_all_crossing = first_crossing(block_summary, "all_high_condition_rate", args.crossing_threshold) + low_all_crossing = first_crossing(block_summary, "all_low_condition_rate", args.crossing_threshold) + + return { + "experiment": "gap_label_block_scale_gate", + "parameters": { + "ns": ns, + "phases": phases, + "thresholds": thresholds, + "trials": args.trials, + "fibonacci_blocks": fibonacci_blocks, + "non_fibonacci_blocks": non_fibonacci_blocks, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + "crossing_threshold": args.crossing_threshold, + }, + "reference_core_phi": label_sort(reference_core), + "reference_low": label_sort(REFERENCE_LOW), + "reference_high": label_sort(REFERENCE_HIGH), + "reference_summary": reference_summary, + "block_summary": block_summary, + "family_summary": family_summary, + "crossings": { + "median_high_retention_ge_threshold": high_any_crossing, + "all_high_condition_rate_ge_threshold": high_all_crossing, + "all_low_condition_rate_ge_threshold": low_all_crossing, + }, + "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("--fibonacci-blocks", default="5,8,13,21,34,55,89,144") + parser.add_argument("--non-fibonacci-blocks", default="6,10,16,24,40,64,96,128") + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--crossing-threshold", type=float, default=0.5) + parser.add_argument("--seed", type=int, default=202605081805) + parser.add_argument("--out", default="tools/data/gap_label_block_scale_gate_20260508_1805.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 = { + block: { + "family": next(row["block_family"] for row in output["rows"] if row["block_size"] == int(block)), + "median_jaccard": data["median_jaccard"], + "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], + "median_low_retention": data["median_low_retention"], + "median_high_retention": data["median_high_retention"], + "all_high_condition_rate": data["all_high_condition_rate"], + "stable_labels_75pct": data["stable_labels_75pct"], + } + for block, data in sorted(output["block_summary"].items(), key=lambda item: int(item[0])) + } + print(json.dumps({ + "reference_core_phi": output["reference_core_phi"], + "reference_high": output["reference_high"], + "crossings": output["crossings"], + "blocks": compact, + "out": str(out), + }, indent=2)) + + +if __name__ == "__main__": + main() tokens used 106,687 Esperimento eseguito e report scritto. Creati: - [tools/exp_gap_label_block_scale_gate.py](/opt/MM_D-ND/tools/exp_gap_label_block_scale_gate.py) - [tools/data/gap_label_block_scale_gate_20260508_1805.json](/opt/MM_D-ND/tools/data/gap_label_block_scale_gate_20260508_1805.json) - [tools/data/reports/agent_20260508_1805.md](/opt/MM_D-ND/tools/data/reports/agent_20260508_1805.md) Risultante: il nucleo basso del core phi rientra a `block_size=34`; i label alti `[3, -4, 4, 6]` rientrano stabilmente solo su blocchi Fibonacci lunghi (`89`, `144`). I blocchi non-Fibonacci lunghi preservano memoria locale, ma non chiudono il core alto. Verifiche fatte: `py_compile`, JSON leggibile, report presente, nessuna API key paid rilevata.