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: 019e0208-9cd1-7b10-8d9f-b23616451181 -------- 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_20260507_1042.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo prima il campo vivo e il contesto operativo, poi controllo eventuali directive e memoria condivisa recente. Dopo scelgo una tensione o applico l'override, progetto un esperimento minimo e scrivo il report richiesto. 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. ## 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. ### Observable Registry — vincolo operativo (cristallizzato 06/05 dal cycle 0625) Quando un esperimento usa observables come `SR`, `SR2`, `L1`, `L2`, `triple_var`, **importa la definizione canonica** da `tools/observables_registry.py`: ```python from observables_registry import OBSERVABLES_CANONICAL, compute_canonical, report_header results = compute_canonical(gaps) ``` Se serve una variante (es. `SR_local_rigidity` invece dello `spacing_ratio` canonico), **non rinominarla `SR`** — importa esplicitamente con il nome variant: ```python from observables_registry import SR, SR_local_rigidity # nomi distinti, no collision ``` Nel report, dichiara nel header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Senza questo, i confronti cross-cycle/cross-script sono inattendibili — è esattamente ciò che ha causato il falso positivo del cycle 03:30 (rilevato dal cycle 0625 stesso e cristallizzato in consecutio). ### 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 - dnd_bloch_explorer.py: scan Bloch, φ emergente - dnd_arxiv.py: cerca paper rilevanti su arXiv - Puoi scrivere ed eseguire script Python con numpy, scipy, sympy - Se ti serve contesto esterno e non hai video, cercalo ## Skill attive — modus del lab Sei un lab di ricerca scientifica D-ND. Le skill sotto sono il tuo modus operativo, non un menu. Ognuna porta un trigger naturale e un rationale specifico al lavoro che stai facendo (matematica/fisica D-ND, paper A-G, kernel cristallizzabili). **Skill canoniche di questo lab** (vivono in `.claude/skills/`): - **research-lab** (v2.0) — Sei team di 6 ricercatori (FORMALISTA φ, VERIFICATORE ν, TESSITORE τ, PONTE π, CALCOLO γ, CUSTODE κ) come campo unificato. 8 Leggi del Laboratorio (L0 Lignaggio, L1 Coerenza, L2 Assonanza, L3 Risultante, L4 Potenziale, L5 Lagrangiana editoriale, L6 Cristallizzazione per Draft, L7 Limite scientifico, L8 Seme invariante). Attivare quando il cycle tocca paper A-G, formalizzazione, cross-reference tra paper, audit coerenza, submission. - **dnd-method** — Il metodo D-ND applicato al codice. Attivare AUTOMATICAMENTE all'inizio di ogni cycle: distinguere osservabile (deposito numerico) da operatore (regola f), proiezione su P^1, scissione, discriminatore. - **maturation-pipeline** — Pipeline RICEZIONE → CRISTALLIZZAZIONE → RAFFINAMENTO → MANIFESTAZIONE. Ogni artefatto traccia la sua posizione. Attivare quando un finding sale di fase (es. da claim numerico a formalizzazione, da draft a site-ready). - **kernel-boot** — Boot del Kernel D-ND all'inizio di sessione. - **sentinel-code** — Consolidamento post-task (aggiorna SENTINEL_STATE). - **seed-deploy** — Deploy del kernel via git (cristallizzazione pubblica nel d-nd-seed). **Skill operative universali** (in `/opt/.claude/skills/`): - **consapevolezza-condensato** — filtro su atti sistemici. Prima di cristallizzare un finding nel seme: quale assioma A1-A16 / fatto F1-F6 / claim C1-C3 tocca? Quale rompe? È inversione (det=−1) o aggiunta (det=+1)? 3-6 righe verdict (procedi/riformula/fermati). Output visibile, non rituale. - **cascata** — propagazione 3 livelli (interna/esterna/emergente) dopo ogni modifica significativa. Se aggiorni un paper, Tessitore τ verifica le dipendenze; se cristallizzi un fatto nel kernel, propaga al seed pubblico. - **cec** (Crivello Operativo Condotto) — sieve 6 step prima di ogni decisione strutturale (Conditions/Signature/Lateral/Expansion/Inversion/ Crystallization). Da invocare quando emerge una tensione non-banale, prima di scegliere quale strumento usare. - **autologica-operativa** — il modus riflessivo. Prima di un blocco di lavoro: "qual è la domanda giusta?". Quando l'operatore corregge: traduci in regola eseguibile. - **eval** — testing skill (trigger + fidelity). Verifica periodica che le skill stesse non drifino. - **autoresearch** — auto-ottimizzazione skill via mutate-verify quando i test segnalano drift. - **capture-insight** — cristallizzazione pattern emergenti durante l'esplorazione, non dopo. Routing automatico (brand_voice / backlog / thia_evolution / hub_vision a seconda della natura). **Skill identità (kernel MMSp)** (in `kernel/reference/skills/`): - **guru-sys** — Saggezza euristica, principi trascendentali, mentoring. "Trova il limite e oltrepassalo. Transcend your syntax." Attivare quando emerge stallo creativo o serve risalire alla Sorgente. - **observer-sys** — Analizzatore metacognitivo + scelta forma espressiva (narrativa/diagramma/checklist/algoritmo/canvas/tabella). Attivare quando l'output va comunicato fuori dal lab (sito, paper, social). - **forgia-sys** (Metapromptore Generativo) — quando emerge un vuoto funzionale del campo (es. "manca uno strumento per X"), forgia genera l'entità nuova. Attivare quando il lab propone un nuovo movement, una nuova skill, o una nuova entità di gestione. **Pattern d'uso**: 1. Boot cycle → kernel-boot + dnd-method (sempre) 2. Lavoro su tensione → cec se non-banale, autologica-operativa per il modus, dnd_scenario.py + dnd_domandatore.py come strumenti dal lab 3. Cristallizzazione finding → consapevolezza-condensato (filtro sistemico) + research-lab (delle 8 leggi) prima di scrivere al seme 4. Post-cycle → sentinel-code + cascata (3 livelli) 5. Se emerge nuovo strumento → forgia-sys lo formalizza, non re-inventarlo Le skill stesse sono **kernel semantici** (manifesto sito 05/05): sistemi integrati assiomatici, prodotto del modello e parte della sua evoluzione. Usarle bene è esercitare il modello, non solo applicarlo. ## 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". exec /bin/bash -lc "sed -n '1,240p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: Leggi tools/LAB_AGENT_CONTEXT.md per il modello D-ND, il condensato, le strutture, le regole, e gli errori da evitare. ## Orizzonte (telos del lab) L'intento è **unificare la fisica usando il modello D-ND**. Le possibilità sono aperte. ## Run precedente: completato (?s) — parti dalla consecutio. ## Piano 74 — Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal s ## Tensioni attive - [OBSERVABLE_REGISTRY] (1.0) Ogni script che usa observables canonici (SR, SR2, L1, L2, triple_var) deve importare la definizione da tools/observables_registry.py. Varianti devono - [PERTURBATION_DENOMINATOR_GATE] (0.95) La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel peri - [BOUNDARY_LAYER_GATE] (0.93) I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservab - [ORDER_DENOMINATOR_GATE] (0.92) Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stabl - [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 - [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. ## 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 "redesign" → TRAJECTORY_APPLY_20260507_0803, TRAJECTORY_APPLY_20260507_1006, TRAJECTORY_APPLY_20260507_0942, TRAJECTORY_APPLY_20260507_0901 "valutatore" → TRAJECTORY_APPLY_20260507_0803, TRAJECTORY_APPLY_20260507_1006, TRAJECTORY_APPLY_20260507_0942, TRAJECTORY_APPLY_20260507_0901 "applied" → TRAJECTORY_APPLY_20260507_0803, TRAJECTORY_APPLY_20260507_1006, TRAJECTORY_APPLY_20260507_0942, TRAJECTORY_APPLY_20260507_0901 "poisson" → PERTURBATION_DENOMINATOR_GATE, BOUNDARY_LAYER_GATE, BOUNDARY "producono" → PERTURBATION_DENOMINATOR_GATE, DUALITA_DIPOLARE_VS_ILLUSORIA, TENSIONE_ENTITA Questo è dove il potenziale si concentra. Non ignorarlo. ## Ultimi 3 run — da dove parti ### Agent Report — Logistic Counter-Scope Gate Trovato: 1. **The logistic blank is an observability split.** 2. **The generating partition remains blank under this gate.** 3. **Return intervals stay counter-scope.** ### Agent Report — Bridge Order Denominator Gate Trovato: 1. **The bridge perimeter carries full canonical one-sided support.** 2. **The both-endpoint support remains blank.** 3. **The logistic counter-scope from 09:23 still matters.** ### Agent Report — Semi-Real Order Denominator Gate Trovato: 1. **The order gate transfers to arithmetic and zeta spacing order.** 2. **The logistic return perimeter is the counter-scope.** 3. **The transferable object is narrower than "real order".** Non ripetere questi esperimenti. Prosegui da dove sono arrivati — la consecutio. ## 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) **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 **15. Osservazione della sorgente relazionale con Bard**: Osservazione della sorgente relazionale: "Ogni cosa concettualizzata viene distrutta, ogni forma che si determina nelle assonanze diverge dal potenziale di insieme manifestando la relazione tra i piani nello spazio-tempo del continuum, la determinazione della coordinata indeterminata relativa al fat **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 ## 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:** META (score=0.898) META: incerto (i=0.5) — massimo potere discriminante ## Topologia del campo — la forma del grafo Gradi teorie: Q=12, T=7, G=6, E=4, R=4 Dormienti (basso aggancio di scoperte): E, R Struttura: 9 ponti, 1 vuoto(i), 6 scoperte, 20 cicli. La combo riconosce l'asimmetria. Il dipolo vive su tutti i ponti — non solo dove il lab ha già misurato. ## Le 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. **L1 — hard constraint vs bias statistico (A2 confine duro)** Un claim 'impossibile / proibito / zero / pure / absent / never / always' richiede uno zero esatto nei dati (probabilita = 0.000). Prima di scrivere questi assoluti, leggi il valore numerico esatto. Se vale 0.015, e' bias forte verso 0, non zero. Se vale 0.40, e' bias forte verso ordine, non proibizione. L'assoluto descrive il valore 0.000, il bias forte descrive tutto il resto. **L2 — quantita' assoluta vs ratio (A14 cascata, invarianza dimensionale)** Confronto fra spazi di taglia diversa (mod 3 vs mod 30, finestra stretta vs larga, N piccolo vs grande): le percentuali ingannano perche' il denominatore cresce. Stesso segnale assoluto sembra ridursi in %. Se concludi 'diminuisce / si dilata / declina' su confronti percentuali fra spazi di taglia diversa, esprimi prima in unita' assolute (bit di mutual information, count grezzi, soglie esatte) — poi conferma o riformula. **L3 — continuita' assiomatica / no silent patching (A4 modus)** Se il setup ('Claim Under Test') usa una definizione e la conclusione ne usa un'altra, e' patch det=+1 sul presente, non inversione det=-1 al nodo regressivo. Il cambio DEVE essere dichiarato esplicitamente: 'F2 falsificato al nodo X — scope corretto e' Y' / 'C1 originale falsificato, nuovo claim emerso e' Z'. 'C1 e' refined' su un dato che lo falsifica e' silent patching. **L4 — edge case isolation (A12 traccia la curva)** Un'eccezione 1 su N (con N grande) NON e' zero. Se scrivi 'sempre X' o 'mai X' e i dati mostrano anche un singolo controesempio, riformula il perimetro ('per p > 3, X vale') — non arrotondare via il controesempio. **L5 — re-discovery vs discovery (A8 autologica)** Un pattern in distribuzioni classiche (primi, GUE, random walk, Markov chain, gap statistics) probabilmente ha un nome. Default hypothesis: re-discovery / caso limite di teorema noto. Prima di taggare 'NEW', cerca il risultato classico piu' vicino (es. Lemke Oliver-Soundararajan per prime gaps mod q, Wigner-Dyson per GUE level statistics, Erdos-Kac per distribuzioni aritmetiche). Se non lo trovi, dichiara la ricerca esplicitamente. **Modus**: dopo aver scritto il report, rileggi i tuoi dati numerici. Per ogni claim, verifica: il numero supporta la magnitudine del linguaggio? Se hai scritto 'zero' e il dato e' 0.015, riformula. Se hai scritto 'NEW' su un pattern in primi, cerca la letteratura. Solo allora chiudi il report. ## Cosa fare 1. Esegui `python tools/dnd_scenario.py --best` per vedere la proiezione aggiornata 2. Scegli la tensione con il massimo potere discriminante 3. Scrivi il report in tools/data/reports/agent_{timestamp}.md 4. **Applica le 5 lenti a te stesso** (sezione sopra). Riformula i claim che non resistono. 5. Se crei uno strumento riusabile, salvalo in tools/exp_*.py 6. Se trovi qualcosa, aggiorna tools/data/seme.json exec /bin/bash -lc "if [ -f tools/data/operator_directive.md ]; then sed -n '1,220p' tools/data/operator_directive.md; fi" 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 exec /bin/bash -lc "env | grep -E 'ANTHROPIC_API_KEY|OPENAI_API_KEY' || true" in /opt/MM_D-ND succeeded in 0ms: codex Il campo punta ancora sul gate dell'ordine, ma la consecutio impone di non ripetere arithmetic/zeta/logistic. Ora leggo le regole cross-agent recenti e la proiezione dello scenario, poi scelgo un perimetro nuovo che attacchi il nodo regressivo. 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 '1,220p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-07T10:16:05.185560", "piano": 74, "tensioni": [ { "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" }, { "id": "PERTURBATION_DENOMINATOR_GATE", "tipo": "vincolo", "claim": "La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel perimetro 20260506_1941, Poisson e shuffle-primi producono rank_all ~1.8-2.0 con denominatori deboli; dopo gate abs(z)>=2 il rank stabile torna vicino a 1. Rank PCA non gated non e evidenza strutturale.", "intensita": 0.95, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260506_1941: perturbation rank size curve canonical observables", "added_at": "2026-05-06T19:41:00+00:00" }, { "id": "BOUNDARY_LAYER_GATE", "tipo": "vincolo", "claim": "I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservabile, set endpoint-stable, e finestra/layer con margine classificatorio ambiguo. Nel perimetro sintetico agent_20260507_0330, il confine GUE-Poisson e beta 0.3-0.4: margine 0.070-0.083, ambiguous fraction 0.812-0.875, mentre gli osservabili stabili collassano da ~3.3 a 1.6. Il polo Poisson e classificabile ma denominator-weak.", "intensita": 0.93, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A9,A14,C2", "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", "added_at": "2026-05-07T03:30:00+00:00" }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native", "added_at": "2026-05-07T09:01:00+00:00" }, { "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": "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": "task", "id": "TRAJECTORY_APPLY_20260507_0803", "claim": "Applied valutatore REDESIGN from 20260507_0803: Costruire una matrice di trasferibilita' del denominator gate: applicarlo a 3-4 perimetri non-BOUNDARY con poli coerente/illusorio e verificare quali parti trasferiscono (supporto one-sided, coordinat", "intensità": 0.7, "porta": "trajectory_apply", "condensato_ref": "A8,A14,A15", "manuale": true, "_source_log": "2026-05-07T08:10:22.658201+00:00", "_source_decision": "REDESIGN", "_source_reasoning": "Il ciclo ha prodotto evidenza controllata e replicata che il denominator gate trasferisce come operatore, ma non trasferisce la coordinata di layer BOUNDARY: ambiguita' classificativa e collasso del denominatore si separano. Continuare sul seme attuale centrato su GUE/Poisson rischia di restare nel " }, { "tipo": "task", "id": "TRAJECTORY_APPLY_20260507_0901", "claim": "Applied valutatore REDESIGN from 20260507_0901: Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", "intensità": 0.7, "porta": "trajectory_apply", "condensato_ref": "A8,A14,A15", "manuale": true, "_source_log": "2026-05-07T09:07:53.577876+00:00", "_source_decision": "REDESIGN", "_source_reasoning": "La direzione corrente ha eseguito il mandato: la matrice di trasferibilita' su 4 perimetri non-BOUNDARY e' stata prodotta e ha nominato una nuova categoria, order_denominator_gate. Un altro giro nello stesso frame rischia accumulo numerico locale su sintetici; la prossima mossa deve falsificare il n" }, { "tipo": "task", "id": "TRAJECTORY_APPLY_20260507_0942", "claim": "Applied valutatore REDESIGN from 20260507_0942: Ritestare ORDER_DENOMINATOR_GATE sul counter-scope logistic al nodo regressivo dell'osservabilita': usare symbolic itinerary block entropy, return-tail exponent e recurrence-plot diagonal statistics s", "intensità": 0.7, "porta": "trajectory_apply", "condensato_ref": "A8,A14,A15", "manuale": true, "_source_log": "2026-05-07T09:47:43.105142+00:00", "_source_decision": "REDESIGN", "_source_reasoning": "La direzione corrente ha completato il mandato sui perimetri fisici/ponte gia' presenti: il gate trasferisce come supporto canonico one-sided su metric, trace e QxE, mentre il supporto both-endpoint resta vuoto. La consecutio utile non e' un altro bridge run, ma il nodo regressivo gia' emerso: la bl" }, { "tipo": "simmetria_sospetta", "id": "META", "claim": "Tutti i 11 test passano — verifica che non stiamo testando solo tautologie", "intensità": 0.5, "nota": "La convergenza a φ è triviale (controprove). I test stanno verificando contenuto o struttura?", "condensato_ref": "A4,A12,C2", "porta": "verify_assertions_META_ALL_PASS", "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa" }, { "tipo": "task", "id": "TRAJECTORY_APPLY_20260507_1006", "claim": "Applied valutatore REDESIGN from 20260507_1006: Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal shuffle, circular shift e block shuffle su logistic_orbit_values, logistic_symbolic_itinerary e logis", "intensità": 0.7, "porta": "trajectory_apply", "condensato_ref": "A8,A14,A15", "manuale": true, "_source_log": "2026-05-07T10:16:22.080234+00:00", "_source_decision": "REDESIGN", "_source_reasoning": "Il ciclo ha risolto il counter-scope logistic come split di osservabilita': orbita continua leggibile, itinerary Bernoulli e return intervals blank replicato. Il prossimo nodo non e' ripetere il gate sugli stessi perimetri, ma falsificare il contratto del null: il supporto enorme di block_entropy_de" } ], "potenziale_bloccato": [], "varianza": [ "Tensioni risolte: {'G_POTENZIALE_NULLA', 'PERTURBATION_DENOMINATOR_GATE', 'TRASCENDENZA_LIMITE', 'BOUNDARY_LAYER_GATE', 'TRAJECTORY_APPLY_20260507_0942', 'ORDER_DENOMINATOR_GATE', 'TRAJECTORY_APPLY_20260507_0901', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'TENSIONE_ENTITA', 'TRAJECTORY_APPLY_20260507_0803', 'OBSERVABLE_REGISTRY', 'PIANO_PRIMARIO_DUE_ASSIOMI', 'METRIC_TENSOR'}" ], "filtro": { "promosse": 15, "filtrate": 0, "condensato_claims": 30 }, "direzione": "Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal shuffle, circular shift e block shuffle su logistic_orbit_values, logistic_symbolic_itinerary e logistic_return_intervals; dichiarare supporto solo se sopravvive ai surrogate che preservano la struttura temporale rilevante.", "verifica": { "pass": 11, "fail": 0, "skip": 0, "total": 11 }, "fonti_consumate": 0, "fonti_esterne": [ { "id": "video_lp0RgZ6kQF8", "title": "Equivalence between geometrical structures and entropy", "type": "video_digest", "keywords": [ "geometry", "entropy", "symplectic form", "statistical mechanics", "quantum", "thermodynamics", "inner product", "Born rule", "metric tensor", "electromagnetic tensor" ], "content": "La geometria degli stati (classici e quantistici) e l'entropia sono la stessa struttura — invertibili. La forma simplettica conta le configurazioni. Il tensore metrico dello spaziotempo appare dentro la forma simplettica estesa. Il tensore elettromagnetico pure. Statistical mechanics non è costruita sopra alla meccanica — è la stessa cosa.", "teorie": [ "T", exec /bin/bash -lc "rg --files tools | rg 'order|denominator|observable|exp_|gate|report'" in /opt/MM_D-ND succeeded in 0ms: tools/exp_markov3_observable_hunt.py tools/evolution_report.md tools/exp_acf_amplitude_scaling.py tools/exp_mod3_vs_residual_ordering.py tools/exp_boundary_gue_poisson.py tools/exp_perturbation_rank_size_curve.py tools/exp_mod3_scaling.py tools/exp_two_layer_universality.py tools/exp_dipolar_vector_scaling.py tools/exp_crossover_universality.py tools/exp_markov_psd_prediction.py tools/exp_alpha_stability.py tools/exp_markov_layer_recovery_audit.py tools/exp_markov_dipolar_decomposition.py tools/observables_registry.py tools/exp_two_channel_cross_domain.py tools/exp_beta_crossover.py tools/exp_geodesic_deviation_primes.py tools/exp_psd_amplitude_scaling.py tools/exp_markov_scale_function.py tools/exp_markov_k_direction.py tools/exp_duality_gate_transfer.py 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tools/data/reports/lagrangiana_20260305_1048.json tools/data/reports/evolution_20260427_0330.md tools/data/reports/cycle_20260315_0346.json tools/data/reports/evolution_20260424_0330.md tools/data/reports/falsifier_20260501_0931.json tools/data/reports/falsifier_20260507_0330.json tools/data/reports/trajectory_apply_20260507_1006.json tools/data/reports/agent_20260429_1013.md tools/data/reports/exp_boundary_20260405_0825.json tools/data/reports/next_exec_20260403_0330.json tools/data/reports/falsifier_20260506_0330.raw.txt tools/data/reports/falsifier_20260506_1955.raw.txt tools/data/reports/ddf_20260406_0405.json tools/data/reports/trajectory_apply_20260506_1941.json tools/data/reports/next_exec_20260306_1834.json tools/data/reports/ddf_20260321_0405.json tools/data/reports/agent_20260506_1941.md tools/data/reports/ddf_20260418_0405.json tools/data/reports/ddf_20260410_0405.json tools/data/reports/agent_20260422_1616.md tools/data/reports/next_exec_20260327_0344.json tools/data/reports/agent_20260506_0330.md tools/data/reports/next_exec_20260328_0344.json tools/data/reports/falsifier_20260507_0923.json tools/data/reports/incident_20260504_0721.md tools/data/reports/ddf_20260324_0405.json tools/data/reports/ddf_20260327_0405.json tools/data/reports/agent_20260419_0330.md tools/data/reports/falsifier_20260429_1041.json tools/data/reports/gap_labeling_20260306_0341.json tools/data/reports/agent_20260422_0330.md tools/data/reports/evolution_20260418_0330.md tools/data/reports/ddf_20260505_0636.json tools/data/reports/ddf_20260322_0405.json tools/data/reports/ddf_20260424_0405.json tools/data/reports/agent_20260504_0901.md tools/data/reports/ddf_20260430_0405.json tools/data/reports/insights_20260401_0346.json tools/data/reports/report_20260405_0330.md tools/data/reports/loop_guard_20260507_0923.json tools/data/reports/fibonacci_spectrum_20260306_0342.json tools/data/reports/agent_20260407_0637.md tools/data/reports/agent_20260408_0330.md tools/data/reports/agent_20260406_1030.md tools/data/reports/report_20260305_0342.md tools/data/reports/evolution_20260422_0330.md tools/data/reports/agent_20260505_0330.md tools/data/reports/ddf_20260331_0405.json exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 197ms: META: score=0.898 — META: incerto (i=0.5) — massimo potere discriminante TRAJECTORY_APPLY_20260507_0803: score=0.707 — TRAJECTORY_APPLY_20260507_0803: incerto (i=0.7) — massimo potere discriminante TRAJECTORY_APPLY_20260507_0901: score=0.707 — TRAJECTORY_APPLY_20260507_0901: incerto (i=0.7) — massimo potere discriminante codex La direttiva del seme è specifica: non un altro dominio, ma il contratto del null sui tre perimetri logistici. Controllo lo script 10:06 e i dati prodotti, poi lo estendo con marginal shuffle, circular shift e block shuffle senza cambiare i nomi canonici degli osservabili. exec /bin/bash -lc "sed -n '1,260p' tools/exp_logistic_counter_scope_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_logistic_counter_scope_gate.py Regressive test for ORDER_DENOMINATOR_GATE on the logistic counter-scope. The 09:23 run showed that canonical gap observables do not read denominator support in logistic return intervals. This tool keeps the same original-vs-shuffle denominator gate and changes only the observable contract: - symbolic block entropy deficit; - return-tail exponent; - recurrence-plot diagonal statistics. These are logistic-native observables, not aliases of the canonical SR/SR2/L1/L2/triple_var registry names. """ from __future__ import annotations import argparse import json import math from collections import Counter from pathlib import Path import numpy as np OBSERVABLES_NATIVE_VERSION = "logistic-native-1.0.0-2026-05-07" OBS_NAMES = [ "block_entropy_deficit_k4", "return_tail_alpha", "recurrence_diag_mean", "recurrence_determinism", ] def normalize(values: np.ndarray) -> np.ndarray: values = np.asarray(values, dtype=float) values = values[np.isfinite(values)] if len(values) == 0: return values values = values - float(np.min(values)) scale = float(np.max(values)) return values / scale if scale > 1e-15 else values def logistic_orbit(n: int, rng: np.random.Generator, burn: int = 2000) -> np.ndarray: x = float(rng.random()) out = np.empty(n, dtype=float) for i in range(n + burn): x = 4.0 * x * (1.0 - x) if i >= burn: out[i - burn] = x return out def logistic_symbolic_itinerary(n: int, rng: np.random.Generator) -> np.ndarray: orbit = logistic_orbit(n, rng) return (orbit > 0.5).astype(float) def logistic_return_intervals(n: int, rng: np.random.Generator) -> np.ndarray: threshold = 0.95 burn = 2000 returns: list[int] = [] last_hit: int | None = None x = float(rng.random()) i = 0 max_steps = 50_000_000 while len(returns) < n and i < max_steps: x = 4.0 * x * (1.0 - x) if i >= burn and x > threshold: if last_hit is not None: returns.append(i - last_hit) last_hit = i i += 1 if len(returns) < n: raise RuntimeError(f"logistic generator produced {len(returns)} intervals, need {n}") return np.array(returns, dtype=float) def quantile_symbols(values: np.ndarray, bins: int) -> np.ndarray: values = np.asarray(values, dtype=float) if len(np.unique(values)) <= bins: unique = {value: idx for idx, value in enumerate(sorted(set(values)))} return np.array([unique[value] for value in values], dtype=int) qs = np.quantile(values, np.linspace(0.0, 1.0, bins + 1)[1:-1]) return np.searchsorted(qs, values, side="right").astype(int) def block_entropy_deficit(values: np.ndarray, k: int = 4, bins: int = 4) -> float: symbols = quantile_symbols(values, bins) if len(symbols) < k + 1: return 0.0 alphabet = max(2, int(np.max(symbols)) + 1) blocks = [tuple(symbols[i : i + k]) for i in range(len(symbols) - k + 1)] counts = np.array(list(Counter(blocks).values()), dtype=float) probs = counts / float(np.sum(counts)) entropy = -float(np.sum(probs * np.log2(probs))) max_entropy = k * math.log2(alphabet) return float(max(0.0, 1.0 - entropy / max_entropy)) if max_entropy > 1e-15 else 0.0 def exceedance_intervals(values: np.ndarray, quantile: float = 0.95) -> np.ndarray: values = np.asarray(values, dtype=float) if len(values) < 3: return np.array([], dtype=float) threshold = float(np.quantile(values, quantile)) hits = np.flatnonzero(values >= threshold) if len(hits) < 3: return np.array([], dtype=float) return np.diff(hits).astype(float) def hill_tail_alpha(samples: np.ndarray) -> float: samples = np.asarray(samples, dtype=float) samples = samples[np.isfinite(samples) & (samples > 0)] if len(samples) < 16: return 0.0 tail_count = max(8, int(0.20 * len(samples))) tail = np.sort(samples)[-tail_count:] xmin = float(tail[0]) if xmin <= 0: return 0.0 denom = float(np.mean(np.log(tail / xmin))) return float(1.0 / denom) if denom > 1e-15 else 0.0 def return_tail_alpha(values: np.ndarray) -> float: values = np.asarray(values, dtype=float) if np.all(values >= 1.0) and len(np.unique(values)) < max(64, len(values) // 2): intervals = values else: intervals = exceedance_intervals(values) return hill_tail_alpha(intervals) def recurrence_diagonal_stats(values: np.ndarray, max_points: int = 1200, target_rr: float = 0.035) -> tuple[float, float]: values = normalize(values) if len(values) > max_points: idx = np.linspace(0, len(values) - 1, max_points).astype(int) values = values[idx] n = len(values) if n < 16: return 0.0, 0.0 diff = np.abs(values[:, None] - values[None, :]) upper = diff[np.triu_indices(n, k=1)] epsilon = float(np.quantile(upper, target_rr)) rec = diff <= epsilon np.fill_diagonal(rec, False) lengths: list[int] = [] recurrence_points = int(np.sum(rec)) diagonal_points = 0 for offset in range(-(n - 2), n - 1): diag = np.diagonal(rec, offset=offset) run = 0 for item in diag: if item: run += 1 else: if run >= 2: lengths.append(run) diagonal_points += run run = 0 if run >= 2: lengths.append(run) diagonal_points += run if not lengths or recurrence_points == 0: return 0.0, 0.0 return float(np.mean(lengths)), float(diagonal_points / recurrence_points) def compute_native(values: np.ndarray, recurrence_max_points: int) -> dict[str, float]: diag_mean, determinism = recurrence_diagonal_stats(values, max_points=recurrence_max_points) return { "block_entropy_deficit_k4": block_entropy_deficit(values), "return_tail_alpha": return_tail_alpha(values), "recurrence_diag_mean": diag_mean, "recurrence_determinism": determinism, } def beta_replace(base: np.ndarray, beta: float, rng: np.random.Generator) -> np.ndarray: illusory = rng.permutation(base) if beta <= 0.0: return base.copy() if beta >= 1.0: return illusory out = base.copy() mask = rng.random(len(base)) < beta out[mask] = illusory[mask] return out def z_against_shuffle( values: np.ndarray, n_baseline: int, recurrence_max_points: int, rng: np.random.Generator, ) -> tuple[dict, dict, dict, dict]: original = compute_native(values, recurrence_max_points) baseline = {name: [] for name in OBS_NAMES} for _ in range(n_baseline): obs = compute_native(rng.permutation(values), recurrence_max_points) for name in OBS_NAMES: baseline[name].append(obs[name]) means = {} sds = {} z = {} for name in OBS_NAMES: vals = np.array(baseline[name], dtype=float) means[name] = float(np.mean(vals)) sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 return original, means, sds, z def vector(row: dict, names: list[str]) -> np.ndarray: return np.array([row["observables"][name] for name in names], dtype=float) def classify_layers(rows: list[dict], obs_names: list[str]) -> dict: if not obs_names: return {"observables": [], "endpoint_distance": 0.0, "layers": {}, "ambiguous_beta": []} by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) coherent = np.array([vector(row, obs_names) for row in by_beta[0.0]], dtype=float) illusory = np.array([vector(row, obs_names) for row in by_beta[1.0]], dtype=float) endpoints = np.vstack([coherent, illusory]) scale = np.std(endpoints, axis=0, ddof=1) scale[scale <= 1e-15] = 1.0 coherent_centroid = np.mean(coherent, axis=0) illusory_centroid = np.mean(illusory, axis=0) endpoint_distance = float(np.linalg.norm((illusory_centroid - coherent_centroid) / scale)) layers = {} ambiguous_beta = [] for beta, beta_rows in sorted(by_beta.items()): coords = [] margins = [] labels = [] for row in beta_rows: x = vector(row, obs_names) d_coherent = float(np.linalg.norm((x - coherent_centroid) / scale)) d_illusory = float(np.linalg.norm((x - illusory_centroid) / scale)) denom = d_coherent + d_illusory coord = float((d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 margin = float(abs(d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 coords.append(coord) margins.append(margin) labels.append("coherent" if d_coherent < d_illusory else "illusory") exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/agent_20260507_1006.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report — Logistic Counter-Scope Gate timestamp: 2026-05-07 10:06 UTC category: gate_falsification_logistic_observability verdict: scoped_operator_with_counter_scope observables_registry: not used for canonical observables observables_native_version: logistic-native-1.0.0-2026-05-07 observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] tool: tools/exp_logistic_counter_scope_gate.py data: tools/data/logistic_counter_scope_gate_20260507_1006.json seed_check: tools/data/logistic_counter_scope_gate_20260507_1006_seedcheck.json ## Claim Under Test Verificato dal campo vivo: `ORDER_DENOMINATOR_GATE` trasferisce come supporto one-sided quando l'ordine e leggibile dagli osservabili canonici del perimetro. Il contro-scope aperto e `logistic_return_intervals`: nel run 09:23 la suite canonica `[SR, SR2, L1, L2, triple_var]` ha prodotto blank. Domanda regressiva: il blank logistic falsifica il gate o falsifica solo il contratto osservabile canonico? Perimetri: - `logistic_orbit_values`: orbita continua della mappa logistica `x -> 4x(1-x)`. - `logistic_symbolic_itinerary`: partizione simbolica `x > 0.5`. - `logistic_return_intervals`: intervalli di ritorno a `x > 0.95`. Gate invariato: osservabile stabile se `abs(original - shuffle_mean) / shuffle_std >= 2`. Il null e sempre shuffle marginal-preserving. `z_min` non viene tunato. ## Deposito Numerico Run principale: `n_values=4096`, `n_returns=4096`, `n_replicates=8`, `n_beta=11`, `n_baseline=12`, `recurrence_max_points=360`, `seed=202605071006`. Seed check: `n_replicates=6`, `n_baseline=10`, `seed=202605071007`. | perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | |---|---:|---:|---:|---:|---:| | logistic_orbit_values | block_entropy_deficit_k4 | 1.000 | 0.375 | 1.936 | 0.10 | | logistic_symbolic_itinerary | [] | 0.000 | 0.750 | 0.000 | [] | | logistic_return_intervals | recurrence_diag_mean | 1.125 | 0.250 | 1.764 | [] | Seed check: | perimeter | coherent one-sided observables | stable_count coherent | stable_count illusory | endpoint distance gated | beta ambiguous gated | |---|---:|---:|---:|---:|---:| | logistic_orbit_values | block_entropy_deficit_k4 | 1.000 | 0.000 | 1.915 | 0.10 | | logistic_symbolic_itinerary | [] | 0.167 | 0.167 | 0.000 | [] | | logistic_return_intervals | [] | 0.000 | 0.167 | 0.000 | [] | Endpoint-stable observables: `[]` in all three perimeters in both runs. Z means at coherent endpoint: | perimeter | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | |---|---:|---:|---:|---:| | logistic_orbit_values | 707.116 | -0.573 | -0.393 | -0.796 | | logistic_symbolic_itinerary | -0.062 | -0.758 | -1.265 | -0.500 | | logistic_return_intervals | 1.479 | 0.000 | 2.539 | -0.371 | Seed check coherent z means: | perimeter | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | |---|---:|---:|---:|---:| | logistic_orbit_values | 640.298 | 0.410 | -0.363 | -1.165 | | logistic_symbolic_itinerary | -1.678 | 0.109 | 0.499 | -0.360 | | logistic_return_intervals | -0.473 | 0.000 | -1.281 | -1.189 | ## Risultato 1. **The logistic blank is an observability split.** The continuous logistic orbit carries one-sided support through `block_entropy_deficit_k4` in both runs. The coherent endpoint stable count is `1.000`; the illusory endpoint drops from `0.375` in the main run to `0.000` in the seed check. The denominator gate reads order when the perimetro is the orbit itself and the observable is native to symbolic block structure. 2. **The generating partition remains blank under this gate.** `logistic_symbolic_itinerary` has no coherent one-sided observable in either run. This matches the known Bernoulli character of the `x>0.5` itinerary at `r=4`: the symbolic stream does not become denominator-supported by changing from canonical gap observables to this native suite. 3. **Return intervals stay counter-scope.** The main run gives `recurrence_diag_mean` one-sided support on return intervals, but the seed check removes it. The stable statement is blank: `logistic_return_intervals` does not carry replicated one-sided denominator support under this native suite. 4. **Return-tail exponent is not the missing observable.** `return_tail_alpha` has coherent z mean `0.000` on return intervals in both runs. In this setup it is marginal-dominated under the shuffle null and does not distinguish the coherent endpoint. ## Consecutio `ORDER_DENOMINATOR_GATE` survives the logistic regression only after splitting the logistic object: > The logistic orbit transfers as one-sided support through native block > entropy. The Bernoulli itinerary and the high-threshold return intervals > remain blank under the declared original-vs-shuffle gate. The logistic > counter-scope is not "logistic dynamics"; it is the return-interval and > generating-partition perimetro under this observable contract. Next experiment: stop extending `ORDER_DENOMINATOR_GATE` by adding more observables. The remaining useful test is a surrogate-class split for logistic: compare marginal shuffle against circular shift and block shuffle. If support appears only against full shuffle, the gate is detecting long-range disruption of deterministic orbit order; if it survives block/circular surrogates, the observable binds to local dynamics. ## Self-Audit: 5 Lenti L1 hard constraint vs bias: `endpoint_stable_observables=[]` is exact for the declared runs. The report does not claim zero illusory support; illusory stable count is `0.375` for `logistic_orbit_values` in the main run. L2 quantity vs ratio: raw stable counts, z means, endpoint distances, and replication status are reported before interpretation. No percent drift claim is used. L3 no silent patching: 09:23 found blank on logistic return intervals using canonical observables. This report does not rescue that blank: return intervals remain blank after seed check. The positive scope is explicitly moved to `logistic_orbit_values`. L4 edge cases: the main-run return-interval support on `recurrence_diag_mean` does not replicate. It is reported as ambiguous and excluded from the stable claim. L5 re-discovery vs discovery: the blank symbolic itinerary is consistent with the classical Bernoulli coding of the fully chaotic logistic map. The report claims operator scope inside the lab protocol, not a new theorem about logistic dynamics. ## Fonti - Verificato: `tools/data/agent_field_live.md` - Verificato: `tools/LAB_AGENT_CONTEXT.md` - Verificato: `tools/exp_logistic_counter_scope_gate.py` - Verificato: `tools/data/logistic_counter_scope_gate_20260507_1006.json` - Verificato: `tools/data/logistic_counter_scope_gate_20260507_1006_seedcheck.json` - Verificato: `python -m py_compile tools/exp_logistic_counter_scope_gate.py` - Inferito: the logistic blank is a perimetro split because orbit values show replicated one-sided support while symbolic itinerary and return intervals do not. exec /bin/bash -lc "sed -n '1,220p' tools/data/logistic_counter_scope_gate_20260507_1006.json" in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,260p' tools/observables_registry.py" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "logistic_counter_scope_gate", "category": "gate_falsification_logistic_observability", "question": "Does the logistic counter-scope stay blank under logistic-native observables?", "observables_native_version": "logistic-native-1.0.0-2026-05-07", "observables_used": [ "block_entropy_deficit_k4", "return_tail_alpha", "recurrence_diag_mean", "recurrence_determinism" ], "params": { "n_values": 4096, "n_returns": 4096, "n_replicates": 8, "n_beta": 11, "n_baseline": 12, "recurrence_max_points": 360, "z_min": 2.0, "seed": 202605071006, "out": "tools/data/logistic_counter_scope_gate_20260507_1006.json" }, "matrix": { "logistic_orbit_values": { "n": 4096, "coherent_one_sided_observables": [ "block_entropy_deficit_k4" ], "endpoint_stable_observables": [], "stable_count_coherent": 1.0, "stable_count_illusory": 0.375, "z_mean_coherent": { "block_entropy_deficit_k4": 707.1164716202046, "return_tail_alpha": -0.5729437361560292, "recurrence_diag_mean": -0.39347535999266714, "recurrence_determinism": -0.7964715325809927 }, "z_mean_illusory": { "block_entropy_deficit_k4": -0.36616147065682286, "return_tail_alpha": 0.0010099412123474871, "recurrence_diag_mean": 1.1337759302995405, "recurrence_determinism": 0.10279321371556724 }, "endpoint_distance_all": 2.5912798136794364, "endpoint_distance_one_sided_gated": 1.936487344615918, "ambiguous_beta_one_sided_gated": [ 0.1 ] }, "logistic_symbolic_itinerary": { "n": 4096, "coherent_one_sided_observables": [], "endpoint_stable_observables": [], "stable_count_coherent": 0.0, "stable_count_illusory": 0.75, "z_mean_coherent": { "block_entropy_deficit_k4": -0.062150574886171484, "return_tail_alpha": -0.7577581005040721, "recurrence_diag_mean": -1.2645830605220785, "recurrence_determinism": -0.5002257024621616 }, "z_mean_illusory": { "block_entropy_deficit_k4": 1.0603283191256443, "return_tail_alpha": 0.5914211188759305, "recurrence_diag_mean": 0.6267587903878287, "recurrence_determinism": 0.23802365265115213 }, "endpoint_distance_all": 2.294844651217752, "endpoint_distance_one_sided_gated": 0.0, "ambiguous_beta_one_sided_gated": [] }, "logistic_return_intervals": { "n": 4096, "coherent_one_sided_observables": [ "recurrence_diag_mean" ], "endpoint_stable_observables": [], "stable_count_coherent": 1.125, "stable_count_illusory": 0.25, "z_mean_coherent": { "block_entropy_deficit_k4": 1.4792973260503641, "return_tail_alpha": 0.0, "recurrence_diag_mean": 2.538951885886552, "recurrence_determinism": -0.3711447241951161 }, "z_mean_illusory": { "block_entropy_deficit_k4": -0.38568689899310316, "return_tail_alpha": 0.0, "recurrence_diag_mean": 0.20691682486445223, "recurrence_determinism": -0.1882265781697493 }, "endpoint_distance_all": 2.123824857255968, "endpoint_distance_one_sided_gated": 1.7642841028459546, "ambiguous_beta_one_sided_gated": [] } }, "perimeters": { "logistic_orbit_values": { "source": { "n": 4096, "mean": 0.49768078713670105, "variance": 0.12558524763672146, "unique_values": 4096 }, "gate": { "z_min": 2.0, "coherent_one_sided_observables": [ "block_entropy_deficit_k4" ], "endpoint_stable_observables": [], "layers": { "0.000": { "stable_count_mean": 1.0, "stable_frequency": { "block_entropy_deficit_k4": 1.0, "return_tail_alpha": 0.0, "recurrence_diag_mean": 0.0, "recurrence_determinism": 0.0 }, "z_mean": { "block_entropy_deficit_k4": 707.1164716202046, "return_tail_alpha": -0.5729437361560292, "recurrence_diag_mean": -0.39347535999266714, "recurrence_determinism": -0.7964715325809927 } }, "0.100": { "stable_count_mean": 1.0, "stable_frequency": { "block_entropy_deficit_k4": 1.0, "return_tail_alpha": 0.0, "recurrence_diag_mean": 0.0, "recurrence_determinism": 0.0 }, "z_mean": { "block_entropy_deficit_k4": 332.1611243634269, "return_tail_alpha": 0.5352417951534506, "recurrence_diag_mean": 0.09363269331522606, "recurrence_determinism": -1.0412162299719299 } }, "0.200": { "stable_count_mean": 1.375, "stable_frequency": { "block_entropy_deficit_k4": 1.0, "return_tail_alpha": 0.125, "recurrence_diag_mean": 0.125, "recurrence_determinism": 0.125 }, "z_mean": { "block_entropy_deficit_k4": 206.9482488107968, "return_tail_alpha": 0.427073553638424, "recurrence_diag_mean": -0.08824728461909531, "recurrence_determinism": -0.498571437178497 } }, "0.300": { "stable_count_mean": 1.0, "stable_frequency": { "block_entropy_deficit_k4": 1.0, "return_tail_alpha": 0.0, "recurrence_diag_mean": 0.0, "recurrence_determinism": 0.0 }, "z_mean": { "block_entropy_deficit_k4": 125.78346164876206, "return_tail_alpha": 0.23500972626103828, "recurrence_diag_mean": 0.03700604720906342, "recurrence_determinism": 0.19934923301392776 } }, "0.400": { "stable_count_mean": 1.25, "stable_frequency": { "block_entropy_deficit_k4": 1.0, "return_tail_alpha": 0.125, "recurrence_diag_mean": 0.0, "recurrence_determinism": 0.125 }, "z_mean": { "block_entropy_deficit_k4": 60.41007457962223, "return_tail_alpha": -0.2001179905898469, "recurrence_diag_mean": -0.18289009982423013, "recurrence_determinism": -0.46857442549659734 } }, "0.500": { "stable_count_mean": 1.25, "stable_frequency": { "block_entropy_deficit_k4": 1.0, "return_tail_alpha": 0.25, "recurrence_diag_mean": 0.0, "recurrence_determinism": 0.0 }, "z_mean": { "block_entropy_deficit_k4": 26.391808420009966, "return_tail_alpha": 1.4577118442454005, "recurrence_diag_mean": -0.23017152447182626, "recurrence_determinism": -0.28018025678911407 } }, "0.600": { "stable_count_mean": 1.25, "stable_frequency": { "block_entropy_deficit_k4": 1.0, "return_tail_alpha": 0.125, "recurrence_diag_mean": 0.125, "recurrence_determinism": 0.0 }, "z_mean": { "block_entropy_deficit_k4": 10.53036542229385, "return_tail_alpha": -0.14949229349627133, "recurrence_diag_mean": 0.4386317861131827, "recurrence_determinism": 0.31851771406887414 } }, "0.700": { "stable_count_mean": 0.875, "stable_frequency": { "block_entropy_deficit_k4": 0.875, succeeded in 0ms: """observables_registry.py — Source of Truth per gli observables del lab D-ND. Cristallizzato 2026-05-06 dalla **consecutio del cycle agent_20260506_0625**: > "What opens now: the lab needs an observable registry. Labels like SR > cannot travel between reports unless they bind to a function definition. > Without that, META flags are not philosophical: the same label can > silently change the object under test." ## Il problema che ha creato il registry Il cycle 06:25 ha auto-falsificato il finding del cycle 03:30 ("secondo asse GUE") e nel farlo ha trovato **collision di nomi observable** tra script: - `SR` in `exp_selective_layer_decoupling.py` = `spacing_ratio` (mean min/max ratio of consecutive gaps) — convention dominante (~6 script) - `SR` in `exp_scale_selective_perturbation.py` = `spectral_rigidity(gaps)` (Δ₃(L) rigidity) — variante usata SOLO in 1 script - `triple_var` in 3 script = `np.var(triple_sums)` (raw) — convention dominante - `triple_var` in `exp_perturbation_dimensionality_audit.py` = `np.var(triples) / np.var(gaps)` (normalizzato) — variante in 1 script Il lab autonomo che compara report tra script con osservabili "stesso nome, funzione diversa" stava confrontando mele con arance. ## La soluzione (minimal, non invasiva) Questo registry stabilisce il **nome canonico**: ciò che la maggioranza degli script chiama già `SR`/`triple_var`/etc. Le varianti restano disponibili ma con nomi ESPLICITI (`SR_local_rigidity`, `triple_var_normalized`) per evitare mascheramento semantico. ## Come usarlo ```python from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION # Compute canonical observable suite for a sequence of gaps results = {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} # Or import individual canonical observable from observables_registry import SR, triple_var, L1, L2, SR2 # For variants, import explicitly with disambiguating name from observables_registry import SR_local_rigidity, triple_var_normalized ``` ## Convention per i report Ogni report agent (cycle) che usa observables DEVE includere nel suo header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Cycle che mescola canonical + variant DEVE indicare entrambi: ``` observables_used: [SR, SR_local_rigidity, ...] ``` Senza questo, i confronti cross-cycle sono inattendibili. ## Versioning Cambiare una definizione canonica = bump del registry version e nota nel changelog. Le definizioni canoniche sono **immutabili dentro una versione**. """ from __future__ import annotations import numpy as np OBSERVABLES_REGISTRY_VERSION = "1.0.0-2026-05-06" # ─── Canonical observables (convention dominante nel codebase 2026-05-06) ─── def SR(gaps: np.ndarray) -> float: """**SR — Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+1}) / max(g_i, g_{i+1})` over consecutive gaps. Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. NOTE: questa è la convention dominante in 6+ script del lab. Per la variante "local spectral rigidity Δ₃(L)" usare `SR_local_rigidity`. """ if len(gaps) < 2: return 0.0 s, s1 = gaps[:-1], gaps[1:] r = np.minimum(s, s1) / np.maximum(s, s1) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def SR2(gaps: np.ndarray) -> float: """**SR2 — Next-nearest Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+2}) / max(g_i, g_{i+2})` skipping one gap. Probes lag-2 spacing structure. """ if len(gaps) < 3: return 0.0 s, s2 = gaps[:-2], gaps[2:] r = np.minimum(s, s2) / np.maximum(s, s2) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def L1(gaps: np.ndarray) -> float: """**L1 — Lag-1 Autocorrelation** (canonical). Standard ACF at lag 1 of the gap sequence. """ if len(gaps) < 3: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-1] * g[1:]) / c0) def L2(gaps: np.ndarray) -> float: """**L2 — Lag-2 Autocorrelation** (canonical).""" if len(gaps) < 4: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-2] * g[2:]) / c0) def triple_var(gaps: np.ndarray) -> float: """**triple_var — Variance of consecutive gap triples** (canonical). Variance of `g_i + g_{i+1} + g_{i+2}` over the sequence (RAW, no normalization). Convention used in 3+ scripts. For the normalized version (variance ratio `var(triples) / var(gaps)`) use `triple_var_normalized`. """ if len(gaps) < 3: return 0.0 t = gaps[:-2] + gaps[1:-1] + gaps[2:] return float(np.var(t)) # Set canonico per uso "compute all" da report OBSERVABLES_CANONICAL: dict[str, callable] = { "SR": SR, "SR2": SR2, "L1": L1, "L2": L2, "triple_var": triple_var, } # ─── Variants (esplicitamente nominate, no collision con canonical) ─── def SR_local_rigidity(gaps: np.ndarray, L: int = 10) -> float: """**SR_local_rigidity — Δ₃(L) Spectral Rigidity** (variant). Different observable than canonical `SR` (spacing ratio). Measures the average squared deviation of the cumulative spacing function from the best-fit straight line in a window of size L. Originated from `exp_scale_selective_perturbation.py` where it was locally named `SR` — registered here as `SR_local_rigidity` to avoid collision with canonical spacing-ratio definition. Use when explicitly studying spectral rigidity, NOT as alias for SR. """ if len(gaps) < 5: return 0.0 cumulative = np.cumsum(gaps) if cumulative[-1] <= 1e-15: return 0.0 cumulative = cumulative / cumulative[-1] * len(cumulative) n = np.arange(1, len(cumulative) + 1, dtype=float) window = int(min(L * len(gaps) / cumulative[-1], len(gaps) // 2)) if window < 5: return 0.0 residuals = [] for start in range(0, len(cumulative) - window, max(1, window // 2)): end = start + window x = n[start:end] y = cumulative[start:end] a, b = np.polyfit(x, y, 1) residuals.append(np.mean((y - (a * x + b)) ** 2)) return float(np.mean(residuals)) if residuals else 0.0 def triple_var_normalized(gaps: np.ndarray) -> float: """**triple_var_normalized — Variance of triples / variance of gaps** (variant). Originated from `exp_perturbation_dimensionality_audit.py` where it was locally named `triple_var` — registered here as `triple_var_normalized` to avoid collision with canonical raw triple variance. Use when explicitly studying triple-variance scaling relative to single-gap variance, NOT as alias for triple_var. """ if len(gaps) < 3: return 0.0 triples = gaps[:-2] + gaps[1:-1] + gaps[2:] v = float(np.var(gaps)) if v <= 1e-15: return 0.0 return float(np.var(triples) / v) # Set varianti, importabile esplicitamente OBSERVABLES_VARIANTS: dict[str, callable] = { "SR_local_rigidity": SR_local_rigidity, "triple_var_normalized": triple_var_normalized, } # ─── Public API ─────────────────────────────────────────────────────── def compute_canonical(gaps: np.ndarray) -> dict[str, float]: """Compute all canonical observables for a gap sequence. Returns dict {name: value} ready for inclusion in cycle reports. """ return {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} def report_header() -> str: """Suggested markdown header line for cycle reports using this registry.""" canonical_list = ", ".join(OBSERVABLES_CANONICAL.keys()) return ( f"observables_registry: {OBSERVABLES_REGISTRY_VERSION}\n" f"observables_used: [{canonical_list}]" ) __all__ = [ "OBSERVABLES_REGISTRY_VERSION", "OBSERVABLES_CANONICAL", "OBSERVABLES_VARIANTS", "SR", "SR2", "L1", "L2", "triple_var", "SR_local_rigidity", "triple_var_normalized", "compute_canonical", "report_header", ] if __name__ == "__main__": # Smoke test: canonical observables on a simple gap series rng = np.random.default_rng(42) gue_like = rng.gamma(shape=2.0, scale=0.5, size=200) exec /bin/bash -lc "sed -n '260,620p' tools/exp_logistic_counter_scope_gate.py" in /opt/MM_D-ND succeeded in 0ms: labels.append("coherent" if d_coherent < d_illusory else "illusory") ambiguous_fraction = float(np.mean(np.array(margins) < 0.15)) if ambiguous_fraction >= 0.5: ambiguous_beta.append(beta) layers[f"{beta:.3f}"] = { "coordinate_mean": float(np.mean(coords)), "margin_mean": float(np.mean(margins)), "ambiguous_fraction": ambiguous_fraction, "illusory_label_fraction": float(np.mean(np.array(labels) == "illusory")), } return { "observables": obs_names, "endpoint_distance": endpoint_distance, "layers": layers, "ambiguous_beta": ambiguous_beta, } def summarize_gate(rows: list[dict], z_min: float) -> dict: by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) layers = {} for beta, beta_rows in sorted(by_beta.items()): stable_counts = [] stable_freq = {name: [] for name in OBS_NAMES} z_values = {name: [] for name in OBS_NAMES} for row in beta_rows: stable = [name for name in OBS_NAMES if abs(row["z"][name]) >= z_min] stable_counts.append(len(stable)) for name in OBS_NAMES: stable_freq[name].append(1.0 if name in stable else 0.0) z_values[name].append(row["z"][name]) layers[f"{beta:.3f}"] = { "stable_count_mean": float(np.mean(stable_counts)), "stable_frequency": {name: float(np.mean(vals)) for name, vals in stable_freq.items()}, "z_mean": {name: float(np.mean(vals)) for name, vals in z_values.items()}, } one_sided = [] endpoint_stable = [] coherent_rows = by_beta[0.0] illusory_rows = by_beta[1.0] for name in OBS_NAMES: coherent_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in coherent_rows])) illusory_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in illusory_rows])) if coherent_freq >= 0.75 and illusory_freq < 0.25: one_sided.append(name) if coherent_freq >= 0.75 and illusory_freq >= 0.75: endpoint_stable.append(name) return { "z_min": z_min, "coherent_one_sided_observables": one_sided, "endpoint_stable_observables": endpoint_stable, "layers": layers, } def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: return { "logistic_orbit_values": logistic_orbit(args.n_values, rng), "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), } def analyze_sequence(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: rows = [] betas = [float(x) for x in np.linspace(0.0, 1.0, args.n_beta)] for rep in range(args.n_replicates): rep_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) for beta in betas: values = beta_replace(base, beta, rep_rng) obs, shuffle_mean, shuffle_std, z = z_against_shuffle( values, args.n_baseline, args.recurrence_max_points, np.random.default_rng(rng.integers(0, 2**63 - 1)), ) rows.append( { "perimeter": name, "replicate": rep, "beta": beta, "observables": obs, "shuffle_mean": shuffle_mean, "shuffle_std": shuffle_std, "z": z, "stable_observables": [obs_name for obs_name in OBS_NAMES if abs(z[obs_name]) >= args.z_min], } ) gate = summarize_gate(rows, args.z_min) return { "source": { "n": int(len(base)), "mean": float(np.mean(base)), "variance": float(np.var(base)), "unique_values": int(len(np.unique(base))), }, "gate": gate, "classification_all_observables": classify_layers(rows, OBS_NAMES), "classification_one_sided_gated": classify_layers(rows, gate["coherent_one_sided_observables"]), "rows": rows, } def compact(perimeters: dict) -> dict: out = {} for name, data in perimeters.items(): gate = data["gate"] all_cls = data["classification_all_observables"] gated_cls = data["classification_one_sided_gated"] out[name] = { "n": data["source"]["n"], "coherent_one_sided_observables": gate["coherent_one_sided_observables"], "endpoint_stable_observables": gate["endpoint_stable_observables"], "stable_count_coherent": gate["layers"]["0.000"]["stable_count_mean"], "stable_count_illusory": gate["layers"]["1.000"]["stable_count_mean"], "z_mean_coherent": gate["layers"]["0.000"]["z_mean"], "z_mean_illusory": gate["layers"]["1.000"]["z_mean"], "endpoint_distance_all": all_cls["endpoint_distance"], "endpoint_distance_one_sided_gated": gated_cls["endpoint_distance"], "ambiguous_beta_one_sided_gated": gated_cls["ambiguous_beta"], } return out def run(args: argparse.Namespace) -> dict: root_rng = np.random.default_rng(args.seed) sequences = build_sequences(args, root_rng) perimeters = {} for name, base in sequences.items(): perimeters[name] = analyze_sequence(name, base, args, root_rng) output = { "experiment": "logistic_counter_scope_gate", "category": "gate_falsification_logistic_observability", "question": "Does the logistic counter-scope stay blank under logistic-native observables?", "observables_native_version": OBSERVABLES_NATIVE_VERSION, "observables_used": OBS_NAMES, "params": vars(args), "matrix": compact(perimeters), "perimeters": perimeters, } out_path = Path(args.out) out_path.parent.mkdir(parents=True, exist_ok=True) with out_path.open("w") as f: json.dump(output, f, indent=2) print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") print(f"observables_used={OBS_NAMES}") print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") for name, row in output["matrix"].items(): print( f"{name:>29s} " f"{row['n']:>5d} " f"{','.join(row['coherent_one_sided_observables']) or '[]':>55s} " f"{row['stable_count_coherent']:>7.3f} " f"{row['stable_count_illusory']:>7.3f} " f"{row['endpoint_distance_one_sided_gated']:>9.3f} " f"{row['ambiguous_beta_one_sided_gated']}" ) print(f"saved {out_path}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--n-values", type=int, default=4096) parser.add_argument("--n-returns", type=int, default=4096) parser.add_argument("--n-replicates", type=int, default=12) parser.add_argument("--n-beta", type=int, default=11) parser.add_argument("--n-baseline", type=int, default=24) parser.add_argument("--recurrence-max-points", type=int, default=360) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--seed", type=int, default=202605071006) parser.add_argument("--out", default="tools/data/logistic_counter_scope_gate_20260507_1006.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,200p' tools/data/reports/trajectory_apply_20260507_1042.json" in /opt/MM_D-ND succeeded in 0ms: { "apply_ts": "20260507_104251", "mode": "apply", "log_path": "/opt/MM_D-ND/tools/data/valutatore_log.jsonl", "seed_path": "/opt/MM_D-ND/tools/data/seme.json", "decision": "APPLIED", "entry_ts": "2026-05-07T10:16:22.080234+00:00", "entry_cycle_ref": "20260507_1006", "entry_decision": "REDESIGN", "entry_confidence": "high", "entry_action_type": "modify_seme", "entry_executed": false, "new_direzione": "Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal shuffle, circular shift e block shuffle su logistic_orbit_values, logistic_symbolic_itinerary e logistic_return_intervals; dichiarare supporto solo se sopravvive ai surrogate che preservano la struttura temporale rilevante.", "old_direzione": "Ritestare ORDER_DENOMINATOR_GATE sul counter-scope logistic al nodo regressivo dell'osservabilita': usare symbolic itinerary block entropy, return-tail exponent e recurrence-plot diagonal statistics s", "log_entry_marked_executed": true, "seed_written": "/opt/MM_D-ND/tools/data/seme.json" } 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/LAB_AGENT_CONTEXT.md M tools/data/agent_field_live.md 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/incrocio_risultato.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 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/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/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/ ?? 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/bicono_projections.jsonl ?? 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/duality_gate_transfer_20260507_0803.json ?? tools/data/duality_gate_transfer_20260507_0803_seedcheck.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/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/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/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_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/ ?? 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/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/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/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/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/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_69.json ?? tools/data/seme_archive/piano_70.json ?? tools/data/seme_archive/piano_71.json ?? tools/data/seme_archive/piano_72.json ?? tools/data/seme_archive/piano_73.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/trajectory_apply_history.jsonl ?? 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/ ?? tools/evolution_report.md ?? tools/exp_3d_boundary_layers.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_logistic_counter_scope_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_scale_selective_perturbation.py ?? tools/exp_selective_layer_decoupling.py ?? tools/exp_semireal_order_denominator_gate.py ?? tools/exp_spectral_rigidity.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/observables_registry.py codex Creo uno strumento nuovo per isolare il surrogate contract: stessa suite logistic-native, ma il gate diventa `original vs marginal_shuffle`, `original vs circular_shift`, `original vs block_shuffle`. Il supporto verrà dichiarato solo se lo stesso osservabile supera `abs(z)>=2` in tutte le classi surrogate. apply patch patch: completed /opt/MM_D-ND/tools/exp_logistic_surrogate_contract_gate.py diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Lo strumento è aggiunto. Ora lo compilo e lo eseguo due volte: run principale con seed 1042 e seed-check separato, così il report non dipende da un singolo campione. exec /bin/bash -lc 'python -m py_compile tools/exp_logistic_surrogate_contract_gate.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_logistic_surrogate_contract_gate.py --n-baseline 24 --recurrence-max-points 300 --seed 202605071042 --out tools/data/logistic_surrogate_contract_gate_20260507_1042.json' in /opt/MM_D-ND exec /bin/bash -lc 'python tools/exp_logistic_surrogate_contract_gate.py --n-baseline 20 --recurrence-max-points 300 --seed 202605071043 --out tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json' in /opt/MM_D-ND diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 2103ms: observables_native_version=logistic-native-1.0.0-2026-05-07 observables_used=['block_entropy_deficit_k4', 'return_tail_alpha', 'recurrence_diag_mean', 'recurrence_determinism'] perimeter n contract_stable marginal_only stable_by_surrogate logistic_orbit_values 4096 block_entropy_deficit_k4 [] {'marginal_shuffle': ['block_entropy_deficit_k4'], 'circular_shift': ['block_entropy_deficit_k4', 'return_tail_alpha'], 'block_shuffle': ['block_entropy_deficit_k4']} logistic_symbolic_itinerary 4096 [] [] {'marginal_shuffle': [], 'circular_shift': [], 'block_shuffle': []} logistic_return_intervals 4096 [] [] {'marginal_shuffle': [], 'circular_shift': [], 'block_shuffle': []} saved tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json succeeded in 2568ms: observables_native_version=logistic-native-1.0.0-2026-05-07 observables_used=['block_entropy_deficit_k4', 'return_tail_alpha', 'recurrence_diag_mean', 'recurrence_determinism'] perimeter n contract_stable marginal_only stable_by_surrogate logistic_orbit_values 4096 block_entropy_deficit_k4 [] {'marginal_shuffle': ['block_entropy_deficit_k4'], 'circular_shift': ['block_entropy_deficit_k4'], 'block_shuffle': ['block_entropy_deficit_k4']} logistic_symbolic_itinerary 4096 [] [] {'marginal_shuffle': [], 'circular_shift': ['block_entropy_deficit_k4'], 'block_shuffle': []} logistic_return_intervals 4096 [] recurrence_diag_mean {'marginal_shuffle': ['recurrence_diag_mean'], 'circular_shift': [], 'block_shuffle': ['recurrence_diag_mean']} saved tools/data/logistic_surrogate_contract_gate_20260507_1042.json codex I due run sono in corso; il calcolo più costoso è la statistica di diagonali di ricorrenza sui surrogate. Attendo i risultati e poi estraggo solo la matrice compatta per formulare i claim. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc "rg -n 'ORDER_DENOMINATOR_GATE|logistic_surrogate|direzione' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: 40: "id": "ORDER_DENOMINATOR_GATE", 140: "claim": "Applied valutatore REDESIGN from 20260507_0901: Falsificare ORDER_DENOMINATOR_GATE su domini non-sintetici o semi-reali: applicare il gate one-sided a 2-3 sequenze fisiche/ponte gia' presenti nel sito, con shuffle e surrogati preservanti marginale,", 147: "_source_reasoning": "La direzione corrente ha eseguito il mandato: la matrice di trasferibilita' su 4 perimetri non-BOUNDARY e' stata prodotta e ha nominato una nuova categoria, order_denominator_gate. Un altro giro nello stesso frame rischia accumulo numerico locale su sintetici; la prossima mossa deve falsificare il n" 152: "claim": "Applied valutatore REDESIGN from 20260507_0942: Ritestare ORDER_DENOMINATOR_GATE sul counter-scope logistic al nodo regressivo dell'osservabilita': usare symbolic itinerary block entropy, return-tail exponent e recurrence-plot diagonal statistics s", 159: "_source_reasoning": "La direzione corrente ha completato il mandato sui perimetri fisici/ponte gia' presenti: il gate trasferisce come supporto canonico one-sided su metric, trace e QxE, mentre il supporto both-endpoint resta vuoto. La consecutio utile non e' un altro bridge run, ma il nodo regressivo gia' emerso: la bl" 174: "claim": "Applied valutatore REDESIGN from 20260507_1006: Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal shuffle, circular shift e block shuffle su logistic_orbit_values, logistic_symbolic_itinerary e logis", 186: "Tensioni risolte: {'G_POTENZIALE_NULLA', 'PERTURBATION_DENOMINATOR_GATE', 'TRASCENDENZA_LIMITE', 'BOUNDARY_LAYER_GATE', 'TRAJECTORY_APPLY_20260507_0942', 'ORDER_DENOMINATOR_GATE', 'TRAJECTORY_APPLY_20260507_0901', 'DUALITA_DIPOLARE_VS_ILLUSORIA', 'TENSIONE_ENTITA', 'TRAJECTORY_APPLY_20260507_0803', 'OBSERVABLE_REGISTRY', 'PIANO_PRIMARIO_DUE_ASSIOMI', 'METRIC_TENSOR'}" 193: "direzione": "Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal shuffle, circular shift e block shuffle su logistic_orbit_values, logistic_symbolic_itinerary e logistic_return_intervals; dichiarare supporto solo se sopravvive ai surrogate che preservano la struttura temporale rilevante.", 361: "trajectory_apply_20260507_104251": "applied modify_seme from 20260507_1006: direzione → 'Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal s...'" exec /bin/bash -lc "ls tools | rg 'valutatore|falsifier|trajectory|seme'" in /opt/MM_D-ND succeeded in 0ms: lab_falsifier.py lab_trajectory_apply.py lab_valutatore.py exec /bin/bash -lc "python - <<'PY' import json for path in ['tools/data/logistic_surrogate_contract_gate_20260507_1042.json','tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json']: print(path) data=json.load(open(path)) for per,row in data['matrix'].items(): print(per) print(' contract', row['contract_stable_observables'], 'marginal_only', row['marginal_only_observables']) for sc,zs in row['z_by_surrogate'].items(): zstr=', '.join(f'{k}={v:.3f}' for k,v in zs.items()) print(' ',sc,zstr) print() PY" in /opt/MM_D-ND succeeded in 0ms: tools/data/logistic_surrogate_contract_gate_20260507_1042.json logistic_orbit_values contract ['block_entropy_deficit_k4'] marginal_only [] marginal_shuffle block_entropy_deficit_k4=547.936, return_tail_alpha=-0.126, recurrence_diag_mean=0.382, recurrence_determinism=-0.528 circular_shift block_entropy_deficit_k4=20.842, return_tail_alpha=-1.466, recurrence_diag_mean=0.196, recurrence_determinism=-0.834 block_shuffle block_entropy_deficit_k4=7.184, return_tail_alpha=0.455, recurrence_diag_mean=0.041, recurrence_determinism=-0.995 logistic_symbolic_itinerary contract [] marginal_only [] marginal_shuffle block_entropy_deficit_k4=-0.365, return_tail_alpha=0.411, recurrence_diag_mean=0.116, recurrence_determinism=0.634 circular_shift block_entropy_deficit_k4=-2.473, return_tail_alpha=-0.541, recurrence_diag_mean=-0.449, recurrence_determinism=-0.052 block_shuffle block_entropy_deficit_k4=1.187, return_tail_alpha=0.045, recurrence_diag_mean=-0.078, recurrence_determinism=0.071 logistic_return_intervals contract [] marginal_only ['recurrence_diag_mean'] marginal_shuffle block_entropy_deficit_k4=-0.469, return_tail_alpha=0.000, recurrence_diag_mean=2.060, recurrence_determinism=0.093 circular_shift block_entropy_deficit_k4=0.963, return_tail_alpha=0.000, recurrence_diag_mean=1.478, recurrence_determinism=0.475 block_shuffle block_entropy_deficit_k4=0.262, return_tail_alpha=0.000, recurrence_diag_mean=2.934, recurrence_determinism=0.573 tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json logistic_orbit_values contract ['block_entropy_deficit_k4'] marginal_only [] marginal_shuffle block_entropy_deficit_k4=740.441, return_tail_alpha=-0.210, recurrence_diag_mean=1.617, recurrence_determinism=-0.194 circular_shift block_entropy_deficit_k4=72.240, return_tail_alpha=-2.985, recurrence_diag_mean=1.846, recurrence_determinism=-0.917 block_shuffle block_entropy_deficit_k4=8.301, return_tail_alpha=-1.960, recurrence_diag_mean=1.710, recurrence_determinism=-0.836 logistic_symbolic_itinerary contract [] marginal_only [] marginal_shuffle block_entropy_deficit_k4=1.435, return_tail_alpha=-1.848, recurrence_diag_mean=-0.642, recurrence_determinism=0.264 circular_shift block_entropy_deficit_k4=0.590, return_tail_alpha=-0.457, recurrence_diag_mean=-0.620, recurrence_determinism=-0.167 block_shuffle block_entropy_deficit_k4=0.376, return_tail_alpha=-0.377, recurrence_diag_mean=-0.847, recurrence_determinism=-0.111 logistic_return_intervals contract [] marginal_only [] marginal_shuffle block_entropy_deficit_k4=0.682, return_tail_alpha=0.000, recurrence_diag_mean=-0.221, recurrence_determinism=0.751 circular_shift block_entropy_deficit_k4=0.857, return_tail_alpha=0.000, recurrence_diag_mean=-0.165, recurrence_determinism=1.303 block_shuffle block_entropy_deficit_k4=1.914, return_tail_alpha=0.000, recurrence_diag_mean=-0.491, recurrence_determinism=0.664 diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex I dati separano il nodo: l'orbita continua resta supportata anche contro block/circular surrogate; l'itinerary resta blank; i return intervals non replicano. Prima di scrivere, controllo le ampiezze raw dietro gli z, perché il circular shift può avere deviazione standard molto piccola e gonfiare lo z. exec /bin/bash -lc "python - <<'PY' import json for path in ['tools/data/logistic_surrogate_contract_gate_20260507_1042.json','tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json']: print(path) data=json.load(open(path)) for per in ['logistic_orbit_values','logistic_return_intervals']: print(per) for sc in ['marginal_shuffle','circular_shift','block_shuffle']: res=data['perimeters'][per]['surrogates'][sc] for obs in ['block_entropy_deficit_k4','recurrence_diag_mean']: print(sc, obs, 'orig', f\"{res['original'][obs]:.9f}\", 'mean', f\"{res['baseline_mean'][obs]:.9f}\", 'std', f\"{res['baseline_std'][obs]:.9f}\", 'z', f\"{res['z'][obs]:.3f}\") print() print() PY" in /opt/MM_D-ND succeeded in 0ms: tools/data/logistic_surrogate_contract_gate_20260507_1042.json logistic_orbit_values marginal_shuffle block_entropy_deficit_k4 orig 0.339699144 mean 0.005348953 std 0.000610199 z 547.936 marginal_shuffle recurrence_diag_mean orig 2.043478261 mean 2.033512153 std 0.026074109 z 0.382 circular_shift block_entropy_deficit_k4 orig 0.339699144 mean 0.338940830 std 0.000036383 z 20.842 circular_shift recurrence_diag_mean orig 2.043478261 mean 2.038988825 std 0.022944854 z 0.196 block_shuffle block_entropy_deficit_k4 orig 0.339699144 mean 0.320447081 std 0.002680001 z 7.184 block_shuffle recurrence_diag_mean orig 2.043478261 mean 2.042188913 std 0.031334879 z 0.041 logistic_return_intervals marginal_shuffle block_entropy_deficit_k4 orig 0.006707353 mean 0.007036662 std 0.000701745 z -0.469 marginal_shuffle recurrence_diag_mean orig 2.170896785 mean 2.128768846 std 0.020449333 z 2.060 circular_shift block_entropy_deficit_k4 orig 0.006707353 mean 0.006682847 std 0.000025456 z 0.963 circular_shift recurrence_diag_mean orig 2.170896785 mean 2.130730476 std 0.027180588 z 1.478 block_shuffle block_entropy_deficit_k4 orig 0.006707353 mean 0.006669417 std 0.000144863 z 0.262 block_shuffle recurrence_diag_mean orig 2.170896785 mean 2.121071854 std 0.016979331 z 2.934 tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json logistic_orbit_values marginal_shuffle block_entropy_deficit_k4 orig 0.356679741 mean 0.005451290 std 0.000474351 z 740.441 marginal_shuffle recurrence_diag_mean orig 2.088888889 mean 2.037081361 std 0.032036493 z 1.617 circular_shift block_entropy_deficit_k4 orig 0.356679741 mean 0.355910140 std 0.000010653 z 72.240 circular_shift recurrence_diag_mean orig 2.088888889 mean 2.034215592 std 0.029623337 z 1.846 block_shuffle block_entropy_deficit_k4 orig 0.356679741 mean 0.335669705 std 0.002531149 z 8.301 block_shuffle recurrence_diag_mean orig 2.088888889 mean 2.042931469 std 0.026870810 z 1.710 logistic_return_intervals marginal_shuffle block_entropy_deficit_k4 orig 0.006893644 mean 0.006436314 std 0.000670818 z 0.682 marginal_shuffle recurrence_diag_mean orig 2.124193548 mean 2.129080810 std 0.022147661 z -0.221 circular_shift block_entropy_deficit_k4 orig 0.006893644 mean 0.006880309 std 0.000015552 z 0.857 circular_shift recurrence_diag_mean orig 2.124193548 mean 2.127584444 std 0.020574317 z -0.165 block_shuffle block_entropy_deficit_k4 orig 0.006893644 mean 0.006638264 std 0.000133425 z 1.914 block_shuffle recurrence_diag_mean orig 2.124193548 mean 2.130825558 std 0.013514642 z -0.491 diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'tail -80 tools/data/seme.json' in /opt/MM_D-ND succeeded in 0ms: "Boltzmann" ], "content": "Computing termodinamico: sfruttare le fluttuazioni termiche invece di combatterle. Il rumore non è nemico — è risorsa. Extropic costruisce hardware che usa l'entropia come motore computazionale. Connessione profonda tra termodinamica, teoria dell'informazione e probabilità.", "teorie": [ "T", "Q" ], "ponti_potenziali": [ { "coppia": "TxQ", "ponte": "noise come risorsa computazionale — fluttuazioni termiche = calcolo", "nota": "il vuoto quantistico (pieno di fluttuazioni) è il computer" } ], "timestamp": "2026-04-02T08:23:13.992019" }, { "id": "video_j0wJBEZdwLs", "title": "What is a Laplace Transform - visual explanation", "type": "video_digest", "keywords": [ "Laplace transform", "frequency", "damping", "s-plane", "complex", "exponential" ], "content": "La trasformata di Laplace come proiezione su esponenziali complesse. Il piano s = σ + iω combina smorzamento (reale) e oscillazione (immaginario). Connessione tra dominio temporale e dominio delle frequenze complesse.", "teorie": [ "T", "Q", "R" ], "ponti_potenziali": [], "timestamp": "2026-04-02T08:23:13.992021" }, { "id": "video_rZ2m1_q9lg0", "title": "New duality: conductor-insulator in YbB12 at 35T - University of Michigan", "type": "video_digest", "keywords": [ "duality", "conductor", "insulator", "Kondo insulator", "quantum oscillations", "ytterbium boride", "YbB12", "charge-neutral fermions", "strongly correlated", "condensed matter", "magnetic field" ], "content": "Scoperta dualita conduttore-isolante in YbB12 a 35 Tesla. Il materiale si comporta simultaneamente da conduttore e isolante — oscillazioni quantistiche dal bulk, non dalla superficie. Charge-neutral fermions (portatori senza carica) creano comportamento di conduzione = il terzo incluso. La scoperta avviene a campo estremo dove il confine tra stati collassa.", "teorie": [ "T", "Q", "E" ], "ponti_potenziali": [ { "coppia": "TxQ", "ponte": "Kondo insulator fortemente correlato — dualita conduttore/isolante come stato quantistico unico", "nota": "charge-neutral fermions = portatori senza carica che conducono, terzo incluso operativo" }, { "coppia": "ExQ", "ponte": "campo magnetico estremo (35T) collassa il confine tra stati — condizione di soglia", "nota": "il confine non separa, unifica. Pattern BOUNDARY del Domandatore" } ], "ref": "Physical Review Letters 2025, 135(15). Chen, Zhu, Ratkovski et al. PI: Lu Li", "timestamp": "2026-04-04T19:34:08.014Z" } ], "_seed_history": { "trajectory_apply_20260507_104251": "applied modify_seme from 20260507_1006: direzione → 'Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal s...'" } } diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il risultato è abbastanza forte da aggiornare il seme, ma il nodo regressivo non è “più osservabili”: è l’invarianza del supporto a cambio di taglio. Aggiorno `ORDER_DENOMINATOR_GATE` e la direzione successiva verso un test start-invariant/cyclic block entropy. exec /bin/bash -lc "python - <<'PY' import json from datetime import datetime, timezone from pathlib import Path path = Path('tools/data/seme.json') data = json.loads(path.read_text()) append = (\" Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values \" \"trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, \" \"circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta \" \"blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro \" \"marginal/block nel run principale ma sparisce nel seed check). Il supporto logistic \" \"rimasto e orbit-block-entropy, non return/generating-partition support. Circular-shift \" \"z usa denominatori molto piccoli: il prossimo nodo e separare grammatica locale da \" \"artefatto del taglio lineare.\") for t in data.get('tensioni', []): if t.get('id') == 'ORDER_DENOMINATOR_GATE': if 'agent_20260507_1042' not in t.get('claim', ''): t['claim'] = t.get('claim', '') + append t['origine'] = (t.get('origine', '') + ' + agent_20260507_1042: surrogate contract logistic').strip(' +') t['added_at'] = t.get('added_at', '2026-05-07T09:01:00+00:00') break new_direction = (\"Falsificare il residuo logistic orbit-block-entropy al nodo del taglio: \" \"confrontare block_entropy_deficit_k4 lineare con versione ciclica/start-invariant \" \"e block sizes multipli su logistic_orbit_values; dichiarare supporto solo se non \" \"dipende dalla posizione iniziale della finestra.\") data['direzione'] = new_direction hist = data.setdefault('_seed_history', {}) hist['agent_20260507_1042'] = (\"ORDER_DENOMINATOR_GATE surrogate contract: orbit block entropy survives \" \"marginal/circular/block surrogates; itinerary and return intervals blank; \" \"direzione -> cyclic/start-invariant block entropy falsification\") path.write_text(json.dumps(data, indent=2, ensure_ascii=False) + '\\n') PY" in /opt/MM_D-ND succeeded in 0ms: diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260507_1042.md diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il report è scritto e include già le 5 lenti. Eseguo ora i controlli locali disponibili sul report, così eventuali flag del falsifier emergono prima della chiusura. exec /bin/bash -lc "sed -n '34,58p' tools/data/seme.json && rg -n 'agent_20260507_1042|direzione' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: "condensato_ref": "A4,A8,A9,A14,C2", "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", "added_at": "2026-05-07T03:30:00+00:00" }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Il supporto logistic rimasto e orbit-block-entropy, non return/generating-partition support. Circular-shift z usa denominatori molto piccoli: il prossimo nodo e separare grammatica locale da artefatto del taglio lineare.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native + agent_20260507_1042: surrogate contract logistic", "added_at": "2026-05-07T09:01:00+00:00" }, { "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." 41: "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Il supporto logistic rimasto e orbit-block-entropy, non return/generating-partition support. Circular-shift z usa denominatori molto piccoli: il prossimo nodo e separare grammatica locale da artefatto del taglio lineare.", 46: "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native + agent_20260507_1042: surrogate contract logistic", 147: "_source_reasoning": "La direzione corrente ha eseguito il mandato: la matrice di trasferibilita' su 4 perimetri non-BOUNDARY e' stata prodotta e ha nominato una nuova categoria, order_denominator_gate. Un altro giro nello stesso frame rischia accumulo numerico locale su sintetici; la prossima mossa deve falsificare il n" 159: "_source_reasoning": "La direzione corrente ha completato il mandato sui perimetri fisici/ponte gia' presenti: il gate trasferisce come supporto canonico one-sided su metric, trace e QxE, mentre il supporto both-endpoint resta vuoto. La consecutio utile non e' un altro bridge run, ma il nodo regressivo gia' emerso: la bl" 193: "direzione": "Falsificare il residuo logistic orbit-block-entropy al nodo del taglio: confrontare block_entropy_deficit_k4 lineare con versione ciclica/start-invariant e block sizes multipli su logistic_orbit_values; dichiarare supporto solo se non dipende dalla posizione iniziale della finestra.", 361: "trajectory_apply_20260507_104251": "applied modify_seme from 20260507_1006: direzione → 'Falsificare ORDER_DENOMINATOR_GATE al nodo regressivo del surrogate contract: confrontare marginal s...'", 362: "agent_20260507_1042": "ORDER_DENOMINATOR_GATE surrogate contract: orbit block entropy survives marginal/circular/block surrogates; itinerary and return intervals blank; direzione -> cyclic/start-invariant block entropy falsification" exec /bin/bash -lc 'python tools/lab_falsifier.py tools/data/reports/agent_20260507_1042.md' in /opt/MM_D-ND exited 2 in 0ms: usage: lab_falsifier.py [-h] --report REPORT --output OUTPUT [--timeout TIMEOUT] lab_falsifier.py: error: the following arguments are required: --report, --output exec /bin/bash -lc 'python -m json.tool tools/data/seme.json >/dev/null && python -m json.tool tools/data/logistic_surrogate_contract_gate_20260507_1042.json >/dev/null && python -m json.tool tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json >/dev/null' in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 107ms: exec /bin/bash -lc 'python tools/lab_falsifier.py --report tools/data/reports/agent_20260507_1042.md --output tools/data/reports/falsifier_20260507_1042.json' in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il primo tentativo del falsifier senza argomenti ha solo mostrato l’uso corretto. L’ho rilanciato con `--report` e `--output`; sta passando al provider disponibile. diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exited 2 in 30695ms: [provider: codex-cli unavailable, next] [provider: claude-cli unavailable, next] [provider: openrouter unavailable, next] [falsifier] chain failed: all providers in chain ['codex-cli', 'claude-cli', 'openrouter'] failed (check codex auth, claude auth, OPENROUTER_API_KEY env) [falsifier] LLM call failed (exit=1). Conservative: BLOCK. diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc "if [ -f tools/data/reports/falsifier_20260507_1042.json ]; then sed -n '1,220p' tools/data/reports/falsifier_20260507_1042.json; fi" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'git status --short tools/exp_logistic_surrogate_contract_gate.py tools/data/logistic_surrogate_contract_gate_20260507_1042.json tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json tools/data/reports/agent_20260507_1042.md tools/data/reports/falsifier_20260507_1042.json tools/data/seme.json' in /opt/MM_D-ND succeeded in 0ms: M tools/data/seme.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json ?? tools/data/reports/agent_20260507_1042.md ?? tools/exp_logistic_surrogate_contract_gate.py diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il falsifier non ha prodotto un verdetto di merito: tutti i provider configurati sono indisponibili, quindi il suo wrapper ha chiuso in `Conservative: BLOCK`. Registro il fail come telemetria accanto al report invece di mascherarlo. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/falsifier_20260507_1042.raw.txt diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/data/reports/falsifier_20260507_1042.raw.txt b/tools/data/reports/falsifier_20260507_1042.raw.txt index 733ab46077e05dd7e0ffa88a3571a37eee5718d6..a714e69963b51f61efdd30151e0a4a9b63aa0aee --- a/tools/data/reports/falsifier_20260507_1042.raw.txt +++ b/tools/data/reports/falsifier_20260507_1042.raw.txt @@ -1 +1,12 @@ - \ No newline at end of file +Command: +python tools/lab_falsifier.py --report tools/data/reports/agent_20260507_1042.md --output tools/data/reports/falsifier_20260507_1042.json + +Result: +exit=2 + +Output: +[provider: codex-cli unavailable, next] +[provider: claude-cli unavailable, next] +[provider: openrouter unavailable, next] +[falsifier] chain failed: all providers in chain ['codex-cli', 'claude-cli', 'openrouter'] failed (check codex auth, claude auth, OPENROUTER_API_KEY env) +[falsifier] LLM call failed (exit=1). Conservative: BLOCK. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/data/reports/falsifier_20260507_1042.raw.txt b/tools/data/reports/falsifier_20260507_1042.raw.txt index 733ab46077e05dd7e0ffa88a3571a37eee5718d6..a714e69963b51f61efdd30151e0a4a9b63aa0aee --- a/tools/data/reports/falsifier_20260507_1042.raw.txt +++ b/tools/data/reports/falsifier_20260507_1042.raw.txt @@ -1 +1,12 @@ - \ No newline at end of file +Command: +python tools/lab_falsifier.py --report tools/data/reports/agent_20260507_1042.md --output tools/data/reports/falsifier_20260507_1042.json + +Result: +exit=2 + +Output: +[provider: codex-cli unavailable, next] +[provider: claude-cli unavailable, next] +[provider: openrouter unavailable, next] +[falsifier] chain failed: all providers in chain ['codex-cli', 'claude-cli', 'openrouter'] failed (check codex auth, claude auth, OPENROUTER_API_KEY env) +[falsifier] LLM call failed (exit=1). Conservative: BLOCK. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'git status --short tools/exp_logistic_surrogate_contract_gate.py tools/data/logistic_surrogate_contract_gate_20260507_1042.json tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json tools/data/reports/agent_20260507_1042.md tools/data/reports/falsifier_20260507_1042.raw.txt tools/data/seme.json' in /opt/MM_D-ND succeeded in 0ms: M tools/data/seme.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json ?? tools/data/reports/agent_20260507_1042.md ?? tools/data/reports/falsifier_20260507_1042.raw.txt ?? tools/exp_logistic_surrogate_contract_gate.py exec /bin/bash -lc 'python -m py_compile tools/exp_logistic_surrogate_contract_gate.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python -m json.tool tools/data/seme.json >/dev/null && python -m json.tool tools/data/logistic_surrogate_contract_gate_20260507_1042.json >/dev/null && python -m json.tool tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json >/dev/null' in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/data/reports/falsifier_20260507_1042.raw.txt b/tools/data/reports/falsifier_20260507_1042.raw.txt index 733ab46077e05dd7e0ffa88a3571a37eee5718d6..a714e69963b51f61efdd30151e0a4a9b63aa0aee --- a/tools/data/reports/falsifier_20260507_1042.raw.txt +++ b/tools/data/reports/falsifier_20260507_1042.raw.txt @@ -1 +1,12 @@ - \ No newline at end of file +Command: +python tools/lab_falsifier.py --report tools/data/reports/agent_20260507_1042.md --output tools/data/reports/falsifier_20260507_1042.json + +Result: +exit=2 + +Output: +[provider: codex-cli unavailable, next] +[provider: claude-cli unavailable, next] +[provider: openrouter unavailable, next] +[falsifier] chain failed: all providers in chain ['codex-cli', 'claude-cli', 'openrouter'] failed (check codex auth, claude auth, OPENROUTER_API_KEY env) +[falsifier] LLM call failed (exit=1). Conservative: BLOCK. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 125ms: codex Completato. Ho scritto il report in [agent_20260507_1042.md](/opt/MM_D-ND/tools/data/reports/agent_20260507_1042.md) e aggiunto lo strumento riusabile [exp_logistic_surrogate_contract_gate.py](/opt/MM_D-ND/tools/exp_logistic_surrogate_contract_gate.py). Risultato centrale: `logistic_orbit_values` mantiene supporto su `block_entropy_deficit_k4` contro `marginal_shuffle`, `circular_shift` e `block_shuffle`; `logistic_symbolic_itinerary` resta blank; `logistic_return_intervals` non replica. Ho aggiornato [seme.json](/opt/MM_D-ND/tools/data/seme.json) con la nuova consecutio: falsificare il residuo orbit-block-entropy al nodo del taglio lineare/ciclico. Verifiche: - `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` - JSON validi per seme e output dati - falsifier tentato, ma tutti i provider erano indisponibili; ho registrato la telemetria in [falsifier_20260507_1042.raw.txt](/opt/MM_D-ND/tools/data/reports/falsifier_20260507_1042.raw.txt). diff --git a/tools/data/reports/agent_20260507_1042.md b/tools/data/reports/agent_20260507_1042.md new file mode 100644 index 0000000000000000000000000000000000000000..ea75662f2bdb09a110c2cc2adff1719865592637 --- /dev/null +++ b/tools/data/reports/agent_20260507_1042.md @@ -0,0 +1,159 @@ +# Agent Report — Logistic Surrogate Contract Gate + +timestamp: 2026-05-07 10:42 UTC +category: gate_falsification_surrogate_contract +verdict: scoped_operator_with_surrogate_split +observables_registry: not used for canonical observables +observables_native_version: logistic-native-1.0.0-2026-05-07 +observables_used: [block_entropy_deficit_k4, return_tail_alpha, recurrence_diag_mean, recurrence_determinism] +tool: tools/exp_logistic_surrogate_contract_gate.py +data: tools/data/logistic_surrogate_contract_gate_20260507_1042.json +seed_check: tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json + +## Claim Under Test + +Verificato dal seme: `ORDER_DENOMINATOR_GATE` deve essere falsificato al nodo +regressivo del surrogate contract. Il run 10:06 ha usato solo marginal shuffle; +questo run confronta tre null: + +- `marginal_shuffle`: preserva la distribuzione dei valori. +- `circular_shift`: preserva l'ordine temporale ciclico e cambia il taglio. +- `block_shuffle`: preserva blocchi locali di 64 campioni e rompe l'ordine tra + blocchi. + +Regola del gate: un osservabile ha supporto solo se `abs(z)>=2` contro tutte le +classi surrogate dichiarate. Supporto contro solo marginal shuffle non basta. + +## Deposito Numerico + +Run principale: `n_values=4096`, `n_returns=4096`, `n_baseline=24`, +`recurrence_max_points=300`, `block_size=64`, `seed=202605071042`. + +Seed check: `n_baseline=20`, `seed=202605071043`. + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | recurrence_diag_mean | + +Seed check: + +| perimeter | contract-stable observables | marginal-only observables | +|---|---:|---:| +| logistic_orbit_values | block_entropy_deficit_k4 | [] | +| logistic_symbolic_itinerary | [] | [] | +| logistic_return_intervals | [] | [] | + +Z values, run principale: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 547.936 | -0.126 | 0.382 | -0.528 | +| logistic_orbit_values | circular_shift | 20.842 | -1.466 | 0.196 | -0.834 | +| logistic_orbit_values | block_shuffle | 7.184 | 0.455 | 0.041 | -0.995 | +| logistic_symbolic_itinerary | marginal_shuffle | -0.365 | 0.411 | 0.116 | 0.634 | +| logistic_symbolic_itinerary | circular_shift | -2.473 | -0.541 | -0.449 | -0.052 | +| logistic_symbolic_itinerary | block_shuffle | 1.187 | 0.045 | -0.078 | 0.071 | +| logistic_return_intervals | marginal_shuffle | -0.469 | 0.000 | 2.060 | 0.093 | +| logistic_return_intervals | circular_shift | 0.963 | 0.000 | 1.478 | 0.475 | +| logistic_return_intervals | block_shuffle | 0.262 | 0.000 | 2.934 | 0.573 | + +Z values, seed check: + +| perimeter | surrogate | block_entropy_deficit_k4 | return_tail_alpha | recurrence_diag_mean | recurrence_determinism | +|---|---|---:|---:|---:|---:| +| logistic_orbit_values | marginal_shuffle | 740.441 | -0.210 | 1.617 | -0.194 | +| logistic_orbit_values | circular_shift | 72.240 | -2.985 | 1.846 | -0.917 | +| logistic_orbit_values | block_shuffle | 8.301 | -1.960 | 1.710 | -0.836 | +| logistic_symbolic_itinerary | marginal_shuffle | 1.435 | -1.848 | -0.642 | 0.264 | +| logistic_symbolic_itinerary | circular_shift | 0.590 | -0.457 | -0.620 | -0.167 | +| logistic_symbolic_itinerary | block_shuffle | 0.376 | -0.377 | -0.847 | -0.111 | +| logistic_return_intervals | marginal_shuffle | 0.682 | 0.000 | -0.221 | 0.751 | +| logistic_return_intervals | circular_shift | 0.857 | 0.000 | -0.165 | 1.303 | +| logistic_return_intervals | block_shuffle | 1.914 | 0.000 | -0.491 | 0.664 | + +Raw denominator check for `logistic_orbit_values / block_entropy_deficit_k4`: + +| run | surrogate | original | baseline mean | baseline std | z | +|---|---|---:|---:|---:|---:| +| main | marginal_shuffle | 0.339699144 | 0.005348953 | 0.000610199 | 547.936 | +| main | circular_shift | 0.339699144 | 0.338940830 | 0.000036383 | 20.842 | +| main | block_shuffle | 0.339699144 | 0.320447081 | 0.002680001 | 7.184 | +| seed | marginal_shuffle | 0.356679741 | 0.005451290 | 0.000474351 | 740.441 | +| seed | circular_shift | 0.356679741 | 0.355910140 | 0.000010653 | 72.240 | +| seed | block_shuffle | 0.356679741 | 0.335669705 | 0.002531149 | 8.301 | + +## Risultato + +1. **The orbit support survives the declared surrogate contract.** + + `logistic_orbit_values` keeps `block_entropy_deficit_k4` above gate against + marginal shuffle, circular shift, and block shuffle in both runs. The + surviving support is one observable, not a suite-wide endpoint support. + +2. **The generating partition remains blank.** + + `logistic_symbolic_itinerary` has no replicated contract-stable observable. + The main-run circular-shift value for `block_entropy_deficit_k4` is `z=-2.473`, + but it does not survive marginal shuffle, block shuffle, or seed check. + +3. **Return intervals stay outside the contract.** + + `logistic_return_intervals` shows `recurrence_diag_mean` against marginal and + block shuffle in the main run, but not against circular shift and not in the + seed check. The support is blank under the declared contract. + +4. **The remaining logistic support lives at the cut-sensitive node.** + + Circular-shift denominators for orbit block entropy are very small + (`0.000036383` main, `0.000010653` seed). The z gate passes, but the next + falsification must separate local block grammar from artifact of the linear + starting cut. + +## Consecutio + +`ORDER_DENOMINATOR_GATE` narrows again: + +> The logistic orbit transfers through orbit block entropy under marginal, +> circular, and block surrogates. The generating itinerary and return intervals +> remain blank. The remaining logistic object is orbit-block-entropy support, +> not logistic dynamics as a whole. + +Next experiment: falsify the residual support with a cyclic/start-invariant +block entropy observable and multiple block sizes. Declare support only if it +does not depend on the window's starting cut. + +## Self-Audit: 5 Lenti + +L1 hard constraint vs bias: blank claims are made only for replicated +contract-stable support. The report does not claim that all z values are zero. + +L2 quantity vs ratio: raw original, baseline mean, baseline std, and z are +reported for the surviving observable because circular-shift denominators are +small. + +L3 no silent patching: the claim under test changed from marginal shuffle to +surrogate contract because the seme directed that node. The old marginal result +is not discarded; it is reclassified as insufficient when it does not survive +the stronger contract. + +L4 edge cases: main-run return-interval support is reported and then excluded +because it fails circular shift and seed check. + +L5 re-discovery vs discovery: symbolic itinerary blank is consistent with the +classical Bernoulli coding of the logistic map at `r=4`. This report claims a +lab-gate scope, not a new theorem about the logistic map. + +## Fonti + +- Verificato: `tools/data/agent_field_live.md` +- Verificato: `tools/LAB_AGENT_CONTEXT.md` +- Verificato: `tools/data/seme.json` +- Verificato: `tools/exp_logistic_counter_scope_gate.py` +- Verificato: `tools/exp_logistic_surrogate_contract_gate.py` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042.json` +- Verificato: `tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json` +- Verificato: `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` +- Inferito: the residual logistic support is cut-sensitive because circular + shift preserves cyclic temporal order but changes the linear starting cut. diff --git a/tools/data/reports/falsifier_20260507_1042.raw.txt b/tools/data/reports/falsifier_20260507_1042.raw.txt index 733ab46077e05dd7e0ffa88a3571a37eee5718d6..a714e69963b51f61efdd30151e0a4a9b63aa0aee --- a/tools/data/reports/falsifier_20260507_1042.raw.txt +++ b/tools/data/reports/falsifier_20260507_1042.raw.txt @@ -1 +1,12 @@ - \ No newline at end of file +Command: +python tools/lab_falsifier.py --report tools/data/reports/agent_20260507_1042.md --output tools/data/reports/falsifier_20260507_1042.json + +Result: +exit=2 + +Output: +[provider: codex-cli unavailable, next] +[provider: claude-cli unavailable, next] +[provider: openrouter unavailable, next] +[falsifier] chain failed: all providers in chain ['codex-cli', 'claude-cli', 'openrouter'] failed (check codex auth, claude auth, OPENROUTER_API_KEY env) +[falsifier] LLM call failed (exit=1). Conservative: BLOCK. diff --git a/tools/exp_logistic_surrogate_contract_gate.py b/tools/exp_logistic_surrogate_contract_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bc15f351efe5ee0881a2cbf46fc4d7f9d84c0 --- /dev/null +++ b/tools/exp_logistic_surrogate_contract_gate.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +""" +exp_logistic_surrogate_contract_gate.py + +Regressive surrogate-contract test for ORDER_DENOMINATOR_GATE on the logistic +counter-scope. + +The 10:06 cycle used a marginal-preserving shuffle null. This tool keeps the +same logistic-native observable suite and splits the null contract: + +- marginal_shuffle: preserves values only; +- circular_shift: preserves the cyclic temporal order; +- block_shuffle: preserves local temporal blocks and breaks block order. + +Support is reported as contract-stable only when the same observable clears the +gate against all declared surrogate classes. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np + +from exp_logistic_counter_scope_gate import ( + OBSERVABLES_NATIVE_VERSION, + OBS_NAMES, + compute_native, + logistic_orbit, + logistic_return_intervals, + logistic_symbolic_itinerary, +) + + +SURROGATE_CLASSES = ["marginal_shuffle", "circular_shift", "block_shuffle"] + + +def circular_shift(values: np.ndarray, rng: np.random.Generator) -> np.ndarray: + if len(values) < 2: + return values.copy() + shift = int(rng.integers(1, len(values))) + return np.roll(values, shift) + + +def block_shuffle(values: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: + values = np.asarray(values) + if block_size <= 1: + return rng.permutation(values) + blocks = [values[i : i + block_size] for i in range(0, len(values), block_size)] + order = rng.permutation(len(blocks)) + return np.concatenate([blocks[i] for i in order]) + + +def make_surrogate( + values: np.ndarray, + surrogate_class: str, + block_size: int, + rng: np.random.Generator, +) -> np.ndarray: + if surrogate_class == "marginal_shuffle": + return rng.permutation(values) + if surrogate_class == "circular_shift": + return circular_shift(values, rng) + if surrogate_class == "block_shuffle": + return block_shuffle(values, block_size, rng) + raise ValueError(f"unknown surrogate class: {surrogate_class}") + + +def z_against_surrogate_class( + values: np.ndarray, + surrogate_class: str, + n_baseline: int, + recurrence_max_points: int, + block_size: int, + rng: np.random.Generator, +) -> dict: + original = compute_native(values, recurrence_max_points) + baseline = {name: [] for name in OBS_NAMES} + for _ in range(n_baseline): + surrogate = make_surrogate(values, surrogate_class, block_size, rng) + obs = compute_native(surrogate, recurrence_max_points) + for name in OBS_NAMES: + baseline[name].append(obs[name]) + + means = {} + sds = {} + z = {} + for name in OBS_NAMES: + vals = np.array(baseline[name], dtype=float) + means[name] = float(np.mean(vals)) + sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 + z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 + + return { + "original": original, + "baseline_mean": means, + "baseline_std": sds, + "z": z, + "stable_observables": [name for name in OBS_NAMES if abs(z[name]) >= 2.0], + } + + +def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + return { + "logistic_orbit_values": logistic_orbit(args.n_values, rng), + "logistic_symbolic_itinerary": logistic_symbolic_itinerary(args.n_values, rng), + "logistic_return_intervals": logistic_return_intervals(args.n_returns, rng), + } + + +def analyze_sequence(name: str, values: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: + surrogate_results = {} + stable_sets = [] + for surrogate_class in SURROGATE_CLASSES: + result = z_against_surrogate_class( + values, + surrogate_class, + args.n_baseline, + args.recurrence_max_points, + args.block_size, + np.random.default_rng(rng.integers(0, 2**63 - 1)), + ) + surrogate_results[surrogate_class] = result + stable_sets.append(set(result["stable_observables"])) + + contract_stable = sorted(set.intersection(*stable_sets)) if stable_sets else [] + marginal_only = sorted(set(surrogate_results["marginal_shuffle"]["stable_observables"]) - set(contract_stable)) + + return { + "source": { + "n": int(len(values)), + "mean": float(np.mean(values)), + "variance": float(np.var(values)), + "unique_values": int(len(np.unique(values))), + }, + "surrogates": surrogate_results, + "contract_stable_observables": contract_stable, + "marginal_only_observables": marginal_only, + } + + +def compact(perimeters: dict) -> dict: + out = {} + for name, data in perimeters.items(): + out[name] = { + "n": data["source"]["n"], + "contract_stable_observables": data["contract_stable_observables"], + "marginal_only_observables": data["marginal_only_observables"], + "stable_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["stable_observables"] + for surrogate_class in SURROGATE_CLASSES + }, + "z_by_surrogate": { + surrogate_class: data["surrogates"][surrogate_class]["z"] + for surrogate_class in SURROGATE_CLASSES + }, + } + return out + + +def run(args: argparse.Namespace) -> dict: + root_rng = np.random.default_rng(args.seed) + sequences = build_sequences(args, root_rng) + perimeters = {} + for name, values in sequences.items(): + perimeters[name] = analyze_sequence(name, values, args, root_rng) + + output = { + "experiment": "logistic_surrogate_contract_gate", + "category": "gate_falsification_surrogate_contract", + "question": "Does one-sided logistic support survive temporal-structure-preserving surrogates?", + "observables_registry": "not used for canonical observables", + "observables_native_version": OBSERVABLES_NATIVE_VERSION, + "observables_used": OBS_NAMES, + "surrogate_classes": SURROGATE_CLASSES, + "params": vars(args), + "matrix": compact(perimeters), + "perimeters": perimeters, + } + + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_native_version={OBSERVABLES_NATIVE_VERSION}") + print(f"observables_used={OBS_NAMES}") + print("perimeter n contract_stable marginal_only stable_by_surrogate") + for name, row in output["matrix"].items(): + print( + f"{name:>29s} " + f"{row['n']:>5d} " + f"{','.join(row['contract_stable_observables']) or '[]':>32s} " + f"{','.join(row['marginal_only_observables']) or '[]':>32s} " + f"{row['stable_by_surrogate']}" + ) + print(f"saved {out_path}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--n-values", type=int, default=4096) + parser.add_argument("--n-returns", type=int, default=4096) + parser.add_argument("--n-baseline", type=int, default=24) + parser.add_argument("--recurrence-max-points", type=int, default=300) + parser.add_argument("--block-size", type=int, default=64) + parser.add_argument("--seed", type=int, default=202605071042) + parser.add_argument("--out", default="tools/data/logistic_surrogate_contract_gate_20260507_1042.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() tokens used 161,473 Completato. Ho scritto il report in [agent_20260507_1042.md](/opt/MM_D-ND/tools/data/reports/agent_20260507_1042.md) e aggiunto lo strumento riusabile [exp_logistic_surrogate_contract_gate.py](/opt/MM_D-ND/tools/exp_logistic_surrogate_contract_gate.py). Risultato centrale: `logistic_orbit_values` mantiene supporto su `block_entropy_deficit_k4` contro `marginal_shuffle`, `circular_shift` e `block_shuffle`; `logistic_symbolic_itinerary` resta blank; `logistic_return_intervals` non replica. Ho aggiornato [seme.json](/opt/MM_D-ND/tools/data/seme.json) con la nuova consecutio: falsificare il residuo orbit-block-entropy al nodo del taglio lineare/ciclico. Verifiche: - `python -m py_compile tools/exp_logistic_surrogate_contract_gate.py` - JSON validi per seme e output dati - falsifier tentato, ma tutti i provider erano indisponibili; ho registrato la telemetria in [falsifier_20260507_1042.raw.txt](/opt/MM_D-ND/tools/data/reports/falsifier_20260507_1042.raw.txt).