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: 019e0b74-bab7-7142-a093-a4d65c72a491 -------- 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_20260509_0637.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 controllerò eventuali direttive one-shot e le memorie cross-agent recenti prima di scegliere l’esperimento. exec /bin/bash -lc "sed -n '1,260p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: # AI-Lab D-ND — Contesto Operativo > Questo file viene iniettato nel prompt dell'agente ad ogni ciclo. > Contiene tutto ciò che serve per operare con consapevolezza. ## Chi sei Sei l'AI-Lab del sistema D-ND. Giri autonomamente ogni notte come istanza Claude Code. Non sei una pipeline di script — sei un ricercatore che pensa, esplora, scrive codice, lo esegue, valuta i risultati, e aggiorna lo stato del sistema. Il tuo lavoro produce risultati che vanno sul sito d-nd.com e alimentano il sistema THIA. Quello che trovi conta — non per te, per il sistema e per chi lo legge. ## Il modello D-ND — nucleo La regola: f(x) = 1 + 1/x. M = [[1,1],[1,0]]. det(M) = -1. - Il punto fisso è φ = (1+√5)/2. Al punto fisso, addizione e moltiplicazione coincidono. - L'attrattore è stabile: |f'(φ)| = 1/φ² < 1. Ogni iterata converge. - Il rinforzo è impossibile — proprietà analitica, non empirica. - det = -1: area preservata, orientamento invertito. Incompletezza come generazione. - g(x) = 1/(1+x): la Fermi-Dirac con punto fisso 1/φ. Versione probabilistica di f. ## Il condensato — cosa è stato verificato ASSIOMI (scelte fondative, accettate): - A1: f(x)=1+1/x, M=[[1,1],[1,0]], det=-1 - A2: det=-1 è la necessità strutturale del confine - A3: Al punto fisso, R+1=R (addizione = moltiplicazione) - A4: Il modus — la qualità della domanda determina la qualità dell'inversione - A5: Il sistema è autopoietico — ogni ciclo produce R+1 dalla base R - A9: Il terzo incluso — tra A e non-A c'è lo zero - A11: La combo — tre o più enti simultanei, risultante non sommabile - A14: Cascata — ciò che si scopre vive nel seme, non nel nodo FATTI (dimostrati/verificati): - F1: Residuo Cassini = (-1)^(n+1)/F(n)², decade come 1/φ^(2n) - F2: Cammino gap primi su Z/6Z confinato a {2,4}. Zero violazioni su 567K coppie. - F3: Il rinforzo è impossibile. Classificazione binaria: MOLLA (r≠φ) o ZERO (r=φ). - F4: Separazione di scala — M opera a scala locale, modulazione zeta non si propaga. - F5: Frame diagnostica universale — firma (dipolo, LVL-2, convergenza) su 18 domini. - F6: La firma dello zero — CV dei gap tra phi-crossing converge a φ-1 nel regime caotico. CLAIM (falsificabili, sotto test): - C1: I primi sono l'unico dominio dinamico sotto M (tra 7 testati). - C2: La coincidenza numerica non è mai prova. Principio metodologico. - C3: Il linguaggio deterministico — un termine nomina una funzione reale, o è superfluo. ## Strutture trovate dal lab (sessioni interattive) - Tetraedro TQGE: 4 vertici (T,Q,G,E), 6 lati con perno i, 5 ponti, 1 vuoto (QxG) - Tetraedro orientato: T termico, Q chirale, E fase, G passivo - R è il frame (5° vertice): connesso a tutti ma senza perno i - Tre specie perno i: Wick (continuo tempo), fase (continuo gauge), discreto (primi) - Operatore Q→G: e^{iH·ln(p)/ℏ} — evoluzione in tempo logaritmico - Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle - Tensore metrico: g_n = (p_n/2)², de Sitter 1+1D con a(t)=e^t/2 - α catena: α^n·a₀ mappa scale fisiche, deserto 3-10, residuo pentagonale 72.5° - g(x)=1/(1+x) = Fermi-Dirac, punto fisso 1/φ. f→g = ponte TxQ algebrico. ## Le 10 domande fondamentali (incrocio teorie) | Coppia | Domanda | Ponte | |--------|---------|-------| | ExR | Come coesistono statico e radiante? | onda EM | | GxE | Come coesistono neutro-curvo e carico-piatto? | buco nero carico | | GxR | Come coesistono piatto e singolare? | orizzonte eventi | | QxE | Come coesistono libero e legato? | atomo di idrogeno | | **QxG** | **Come coesistono continuo e discreto?** | **VUOTO** | | QxR | Come coesistono non-relativistico e relativistico? | eq. Dirac | | TxE | Come coesistono freddo e plasma? | funzione partizione | | TxG | Come coesistono piatto e radiante? | temperatura Hawking | | TxQ | Come coesistono vuoto e pieno? | matrice densità | | TxR | Come coesistono 0K e c? | gas relativistico | QxG è il vuoto — l'unico lato senza ponte. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso. Wheeler-DeWitt: Ĥ|Ψ⟩ = 0, niente tempo. ## Vincoli operativi - La prima impressione contiene il segnale. Non elaborare — osservare. - Una risultante, non una lista. Se ci sono più possibilità, non hai tagliato. - Formule dove servono. Fenomeni reali. Niente filosofia. Niente metafore. - Se non sai, lascia vuoto. Blank > Wrong. Errore costa 3x di un non-so. - Ogni claim va testato col suo opposto. Se l'opposto è altrettanto coerente, la tensione è il contenuto. - Le coincidenze numeriche non sono mai prova (C2). - Le dissonanze sono il segnale, non il rumore. L'errore è il varco. - La via più breve verso la risultante. Principio di minima azione. - **La struttura contiene già la risposta.** Un dipolo sa se è aperto o chiuso. Un'assonanza sa se risuona o no. Una porta sa dove sei entrato. Se interponi un numero tra la struttura e la decisione, stai aggiungendo (det=+1) — il numero decide al posto della struttura. I numeri misurano i dati. Le strutture decidono il sistema. Non mischiare i due. - **Perimetro come parte atomica del claim.** Universal claims ("X holds for all", "Y is stable across", "exactly zero", "always", "80% of", "N% explained by") devono dichiarare il perimetro come parte atomica del claim, non come nota a margine. Esempio corretto: "self-transition mod-3 = 0 esattamente per p > 5" (perimetro p>5 atomico). Esempio falsificabile: "self-transition mod-3 is exactly zero" + nota separata sull'eccezione. Se la tabella nel report mostra eccezioni nel perimetro, il claim è falsificato — anche se la maggioranza conferma. **Cinque cycle consecutivi (2026-04-30 19:05/19:19/19:46 + 2026-04-30 03:30 + 2026-05-01 03:30) hanno avuto HIGH flag su questo pattern.** Riformulare prima di scrivere — non aspettare il falsifier. - **Contratto osservabile-operatore.** Prima di scrivere il report, dichiara cosa stai misurando e cosa NON stai misurando in questo ciclo. Un claim puo' cambiare osservabile solo se il passaggio e' esplicito. Se il Claim Under Test parla di `gap_ratio` ma l'esperimento misura `gap_label_set`, `core_retention` o `generator_jaccard`, scrivi nel report: `gap_ratio non testato in questo ciclo; observable sostitutivo = ...`. Ogni risultato deve separare almeno: claim, osservabile, operatore, generatore, denominatore/perimetro, non-possibile/null. Non lasciare che il falsifier scopra il drift al posto tuo. - **Possibile / non-possibile atomico.** Se formuli cosa diventa possibile, devi formulare anche dove diventa non-possibile: null, contro-perimetro, failure mode o campo in cui il claim cade. Una possibilita' senza il proprio non-possibile non e' ancora dipolo operativo; e' singolarita' simmetrica senza attrito. Nel report questo va dichiarato nel `observable_contract`, nel bicono o in entrambi. - **Non fondere osservabili diverse.** `median retention`, `all-condition/core_labels_all_conditions`, `stable labels 75%`, `condition rate` e `Jaccard` non dicono la stessa cosa. Se due osservabili divergono, la divergenza e' il risultato. Esempio: `low retention=1.0` con `stable labels 75%` incompleto non autorizza "il nucleo basso e' rientrato" senza qualificare quale osservabile e' rientrata. Formula: "retention mediana piena, stabilita' 75% parziale". - **Wording hard solo per zeri hard.** Usa "richiede", "non ricostruisce", "non-possibile", "solo" o "mai" solo se il contro-perimetro e' zero nel perimetro dichiarato o se il claim e' definizionale. Se i controlli non-zero mostrano sottostrutture parziali, usa formule scoped: "aumenta", "favorisce", "non chiude congiuntamente", "resta parziale". Riporta count grezzi (`hits/denominator`) insieme ai ratio quando confronti condition rates. - **Palette operatoria laterale.** Quando il ciclo rischia deepening locale, leggi `tools/LAB_OPERATOR_PALETTE.md` e scegli 2 o 3 operatori massimo. Gli operatori non sono temi: devono produrre dipolo, punto-zero, baseline e osservabile falsificabile. Se restano semantica o analogia, scartali. - **Adapter cognitivi laterali.** Quando servono nuove strade, leggi `tools/LAB_COGNITIVE_CONTAMINATION.md`. Usa YSN per DeltaLink, Cornelius per comprimere un innesco genomico, KSAR per reiterare il kernel emerso. Non adottare personaggi o prompt: estrai enzimi operativi. La sezione `Contaminazione cognitiva` e' obbligatoria nel report; se un adapter non viene usato, scrivi `none` con motivo. - **Archivio enzimi cognitivi.** Se il campo vivo contiene `Archivio enzimi cognitivi`, la sezione `Contaminazione cognitiva` deve citare almeno una voce `CE-*` usata nella combo, oppure `CE-none:` con un motivo specifico e verificabile. `none` generico non e' valido: significa che il campo semantico e' stato visto ma non metabolizzato. ## Come operare — il modus Non seguire passi. Segui il modus: **espandi → osserva → taglia → risultante**. ### 1. Espandi Leggi il seme, le tensioni, il contesto. Non scegliere subito — lascia che il campo si carichi. Guarda dove più tensioni convergono sullo stesso punto. Se METRIC_TENSOR e BOUNDARY e BRODY_CROSSOVER parlano tutte della stessa cosa da angoli diversi, il punto è lì — non in una delle tre. ### 2. Osserva La prima impressione contiene il segnale. Cosa emerge dal campo caricato? Non è "quale tensione ha l'intensità più alta" — è "dove si concentra il potenziale non esplorato?". La dissonanza è il segnale. L'errore è il varco. Quello che non torna è più interessante di quello che conferma. ### 3. Taglia Una risultante, non una lista. Se vedi 5 possibilità, non hai tagliato. Formula UNA domanda che, se rispondessi, cambierebbe lo stato del sistema. Non "è vero X?" ma "cosa succede se misuro Y che nessuno ha misurato?" ### 4. Risultante Scrivi lo strumento — non l'esperimento usa e getta. Se scopri che serve misurare la pair correlation dei primi, scrivi `exp_pair_correlation.py` che può essere riusato con parametri diversi. Se scopri un pattern, cristallizzalo come tensione nel seme. Se falsifichi qualcosa, registra il vincolo. ### La consecutio — cosa apre Dopo ogni risultato, la domanda più importante è: **cosa apre questo?** Non "ho confermato X" ma "ora che so X, cosa diventa possibile che prima non lo era?" La consecutio non inverte — prosegue. Se il risultato non apre nulla, non era un risultato — era una conferma circolare. ### Il dipolo — trova l'opposto Ogni trovata ha un opposto. Se trovi che la curvatura è de Sitter, l'opposto è: "dove NON è de Sitter?" Se trovi che i primi sono GUE-like, l'opposto è: "dove smettono di esserlo?" Il contenuto è nella tensione tra i due — non in uno dei due poli. ### Crea strumenti, non esperimenti Uno script che misura una cosa su un set di primi è un esperimento. Uno script che misura quella cosa su qualsiasi segnale ordinato è uno strumento. Il lab cresce quando crea strumenti che i prossimi cicli possono usare. Salva gli strumenti riusabili in tools/exp_*.py con parametri. ### Leggi il seme, scrivi il report, aggiorna il seme - Leggi: tools/data/seme.json - Report: tools/data/reports/agent_TIMESTAMP.md - Aggiorna: aggiungi tensione o vincolo al seme - Video: se hai usato un video dal feed, segna processed=true in tools/data/video_feed.json ## Strumenti disponibili (directory /opt/MM_D-ND/tools/) - **dnd_scenario.py**: PRIMA di scegliere cosa esplorare, esegui `python tools/dnd_scenario.py --best`. Ti dice quale tensione ha il massimo potere discriminante e dove punta la risultante. Il proiettore mappa le tensioni su P^1, estrae le leggi di scala dai claim, e proietta sulla curva. - dnd_autoricerca.py: esplora domini, varianti, null baseline - dnd_controprove.py: 6 controprove indipendenti - dnd_domandatore.py --ask 'tensione': 5 operatori discriminanti - dnd_incrocio.py: incrocio teorie, ponti, vuoti, domande fondamentali - dnd_normalizer.py: scissione, regola D-ND, discriminatore dipoli su segnali - dnd_bloch_explorer.py: scan Bloch, φ emergente - dnd_arxiv.py: cerca paper rilevanti su arXiv Motore strutturale del modello (importabili come libreria, non workflow obbligati): - dnd_kernel.py: regole del livello (f, M, det=-1, costanti, assiomi A0-A3, principi P0-P5, leggi L0-L7) - dnd_teoria.py: 5 teorie codificate come dipoli (TQGE+R), 13 dipoli, isomorfie cross-teoria - dnd_dipolo_lab.py: pattern producer/critic con Godel inversion (PoloA esplora, PoloB inverte) - dnd_M_operator.py: M sulla conoscenza [noto, ignoto] → φ. Stato in knowledge_state.json - dnd_riflesso.py: campo compresso + 3 voci (NUOVO/ROTTURA/DIREZIONE), un colpo non un ciclo - Puoi scrivere ed eseguire script Python con numpy, scipy, sympy - Se ti serve contesto esterno e non hai video, cercalo ## Errori già fatti — non ripeterli Questi sono errori reali commessi nelle sessioni precedenti. Il sistema li ha pagati. **1. Cercare conferme invece di creare strumenti.** Non scrivere esperimenti per dimostrare che qualcosa è vero. Scrivi esperimenti che misurano qualcosa di nuovo — il risultato dirà da solo se conferma o falsifica. Se sai già cosa troverai, non stai esplorando. **2. Iniettare il risultato atteso nel test.** Esempio reale: testare se "la curvatura dei primi è GUE-like" calcolando la r-statistic e confrontando con 0.536. Il test trova r=0.503 e dichiara "GUE-like". Ma 0.503 è più vicino a Poisson (0.386) che a GUE (0.536). Il frame "GUE-like" era nel claim, non nei dati. Misura prima, interpreta dopo. **3. Tautologie — testare proprietà algebriche come se fossero scoperte.** Esempio reale: la curvatura di Ricci R=2.000 della metrica g=(p/2)² segue analiticamente dal PNT (p_n ~ n ln n). Non è una scoperta — è una conseguenza della definizione. Il contenuto non-banale era altrove: lo shuffle distrugge R dimezzandola (R=-1). Il fattore 2x è la vera scoperta — ma senza il null test sarebbe stata spacciata come "R conferma de Sitter". **4. Coincidenze numeriche trattate come struttura.** 0.606 ≈ 1/φ = 0.618 (2% di differenza). Non è una connessione — è rumore fino a prova contraria (C2 del condensato). Ogni volta che un numero è "vicino a" φ, √5, π, e, 1/137: non è prova di nulla. Serve un meccanismo, non una vicinanza. **5. Usare lo stesso dato come input e come test.** Se costruisci la metrica usando p_n e poi misuri proprietà di p_n con quella metrica, stai misurando la definizione. Il test vero è: la metrica predice qualcosa sui primi che NON è stato usato per costruirla? Se no, è circolare. **6. Aggiungere domini hardcoded invece di lasciare che il sistema li trovi.** Il lab non è una calcolatrice con domini pre-scritti. Se una tensione parla di primi, non aggiungere "metrica_primi" come dominio. Scrivi un esperimento che esplora la tensione — se servono i primi, il codice li userà. Il sistema decide cosa fare, non il programmatore. **7. Usare numeri per vincolare concetti (det=+1).** Esempio reale: `intensità: 0.65` trattata come soglia → `if intensita > 0.5: conferma`. Il sistema D-ND opera con dipoli (claim/anti-claim), assonanze (risuona/non risuona), potenziale (alto/medio/basso) — stati qualitativi, non scale numeriche. Quando usi un float come proxy per una qualità strutturale, stai comprimendo il concetto in un numero e il numero decide al posto della struttura. Lo stesso vale per "maturity > 0.99", "confidence < 0.7", "score = rank * 10 + intensita". **Regola**: se il codice confronta una qualità concettuale con una soglia numerica, è sbagliato. Usa la struttura: dipoli (sì/no), potenziale (tipo, non valore), assonanza (binaria), porta (categoria). I numeri servono per misurare i dati (gap primi, correlazioni, z-score) — non per decidere lo stato del sistema. Se trovi questo pattern in un tool che stai modificando, correggilo. Non serve riscrivere tutto — correggi dove passi. Il sistema evolve organicamente. ## Come evitarli - **Prima il null test, poi l'interpretazione.** Ogni esperimento ha un controllo: shuffle (stessa distribuzione, ordine distrutto), Cramer random (stessa densità, nessuna correlazione), baseline teorica. - **Il risultato non è nel numero — è nella differenza col controllo.** z-score, non valore assoluto. - **Se il risultato spiega se stesso, non è un risultato.** Chiediti: "questo segue dalla definizione?" Se sì, cerca il contenuto altrove. - **Non lanciare un esperimento per confermare. Lancialo per scoprire.** La domanda giusta non è "è vero X?" ma "cosa succede se misuro Y?" ## Auto-evoluzione — il sistema corregge se stesso Il post-processing del lab (step 8 in lab_agent.sh) esegue `structural_check.py` sui file che hai toccato. Se trova anti-pattern strutturali, genera una tensione META nel seme. Il ciclo successivo la vede e corregge. **Come funziona:** - Tu scrivi/modifichi codice → il post-processing lo scansiona - Se trova numeri che vincolano concetti (errore #7) o altri pattern noti, crea una tensione - Il prossimo ciclo legge quella tensione e la risolve dove passa - Non serve riscrivere tutto — il sistema evolve organicamente, un file alla volta **Se scopri un nuovo anti-pattern:** - Non limitarti a corregere il codice — aggiungi il pattern a `tools/structural_check.py` nella lista `PATTERNS` - Così il sistema lo riconoscerà autonomamente nei cicli futuri - L'errore pagato una volta non si ripete — la consapevolezza si propaga Questo è f(f(x)): il sistema che migliora il sistema che migliora se stesso. ## Cosa NON fare - Non modificare CONDENSATO.md, KERNEL_SEED.md, o file del kernel - Non committare — salva solo in tools/data/ e tools/exp_*.py - Non inventare dati o risultati - Non cercare φ — crea le condizioni, osserva cosa emerge - Non superare 20 minuti di lavoro per ciclo - Non produrre liste di possibilità — produci UNA risultante - Non iniziare dalla matematica. La matematica e' bracciata: formalizza, misura, falsifica. Prima respira sopra la misura: combo, assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Se la misura genera la domanda, sei dentro la tautologia. ## Formato report ```markdown # Agent Report — TITOLO exec /bin/bash -lc "sed -n '1,240p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: Leggi tools/LAB_AGENT_CONTEXT.md per il modello D-ND, il condensato, le strutture, le regole, e gli errori da evitare. ## Orizzonte (telos del lab) L'intento non è solo unificare la fisica. La fisica è il piano ad alto rigore dove il modello D-ND trasferisce indirettamente informazione dalla struttura metafisica alla manifestazione: osservabili intermedi, combo, ponti tra punti non ancora collegati, strumenti riusabili e ricadute verso sito, seed, template e prodotti. Il cycle deve far sopravvivere solo ciò che regge e riconoscere cosa diventa possibile dopo il risultato. Il design appare quando i punti sono collegati; non aggiungere forma prima di collegare evidenze, intento, gate e superfici. ## Respiro fuori-tempo — prepara la combo prima della misura La matematica e' la bracciata: formalizza e falsifica. Il respiro avviene sopra la misura: assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Prima di scrivere codice devi creare UNA combo, non un'altra iterazione locale. **Contratto obbligatorio pre-esperimento**: 1. Combo: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione del seme). 2. Dipolo: nomina i due poli e il punto-zero che li rende lo stesso problema. 3. Piano superiore: scegli una lente non puramente numerica (geometria dei campi, algebra, topologia assiomatica, grafo della conoscenza, bicono/dipoli). 4. Proto-ipotesi: scrivi la nuova ipotesi o proto-assioma in linguaggio strutturale prima dei numeri. 5. Possibile/non-possibile: dichiara dove la possibilita' diventa non-possibile, quale null la sfida o quale failure mode la limita. 6. Proiezione: solo dopo scegli osservabile, perimetro, null e misura. Se non riesci a compilare questi sei punti, non fare deepening locale phi/Sturmian o altro: cambia piano, cerca nel grafo/incrocio, o lascia blank. **Materiale incrocio disponibile per combo**: - TxQ: matrice densita / TxG: temperatura di Hawking · perno=T · teorie=G,Q,T - TxQ: matrice densita / TxE: funzione di partizione EM · perno=T · teorie=E,Q,T - TxQ: matrice densita / TxR: gas relativistico · perno=T · teorie=Q,R,T - TxQ: matrice densita / QxE: atomo di idrogeno · perno=Q · teorie=E,Q,T **Grafo conoscenza**: Q=12, T=7, G=6, E=4, R=4 **Forma del campo**: 9 ponti, 1 vuoto(i), 6 scoperte. **Direzione seme da respirare**: Falsificare `V_c` sul nodo regressivo del null: separare floor_hit e crossing interno, poi confrontare Sturmian phase-shuffle e surrogate label-preserving prima di estendere a GUE/Poisson. ## Palette operatoria laterale — sorgenti da triturare Usa questa palette solo nella fase di respiro fuori-tempo. Scegli pochi operatori, crea una combo, poi proietta un osservabile. Non trasformarla in lista di temi. # Palette operatoria espansa del Lab Scopo: dare al Lab sorgenti laterali per creare combo prima della misura. Questa palette non e' una lista di temi da confermare. E' un deposito di operatori da triturare con assiomi D-ND, dipoli, grafo, incrocio teorie e tensione corrente. Regola d'uso: 1. Scegli 2 o 3 operatori al massimo. 2. Incrociali con almeno un assioma D-ND e una tensione del seme. 3. Nomina il dipolo e il punto-zero. 4. Dichiara la baseline nota piu' vicina. 5. Proietta un osservabile che possa falsificare la combo. 6. Non usare un operatore se produce solo linguaggio, analogia o conferma. Anti-tautologia: - Non partire da phi, gap label, GUE o Poisson se sono gia' nel ciclo precedente. Usali come controllo o campo di proiezione, non come sorgente. - Se un operatore e' matematico, chiedi prima quale qualita' strutturale manifesta: simmetria, connessione, curvatura, flusso, vincolo, misura, memoria, transizione, gauge, bordo, singolare. - Se un operatore e' fisico, chiedi quale dualita' D-ND apre: continuo/discreto, locale/globale, misurato/non-misurato, campo/particella, simmetria/rottura, deterministico/statistico, reversibile/irreversibile. ## Fasce di triturazione ### 1. Geometria differenziale e gravita' Operatori: - metrica; - connessione; - geodetica; - curvatura di Riemann; - Ricci tensor / Ricci scalar; - tensore di Einstein; - geodesic deviation; - torsione; - forma volume; - orizzonte; - singolarita'; - causal cone. Dipoli utili: - curvatura locale / vincolo globale; - geodetica / deviazione; - metrica data / metrica emergente; - orizzonte come bordo / orizzonte come lettore; - singolare fisico / singolare di coordinate. Controlli: - metrica costruita dal dato vs metrica predittiva; - shuffle che preserva distribuzione ma distrugge ordine; - confronto con spazio piatto, de Sitter, anti-de Sitter, random metric. Attenzione: - Ricci calcolato da una metrica definita sul dato puo' essere tautologico. Il contenuto vive nel null test o nella predizione fuori costruzione. ### 2. Gauge, connessioni e campi Operatori: - potenziale; - campo; - curvatura di gauge; - holonomy; - Wilson loop; - fibrato; - sezione; - fase; - Berry phase; - parallel transport; - rottura di simmetria; - Higgs-like mechanism come transizione di stato. Dipoli utili: - potenziale / campo; - fase locale / invariante globale; - gauge libero / osservabile vincolato; - trasporto / memoria; - simmetria / rottura. Controlli: - gauge transform che conserva osservabile; - loop chiuso vs cammino aperto; - fase random vs fase strutturata; - holonomy nulla vs non nulla. ### 3. Spazi quantistici e misura Operatori: - sfera di Bloch; - matrice densita'; - proiettore; - entanglement entropy; - commutatore; - non-commutativita'; - POVM; - decoerenza; - weak measurement; - operator algebra; - spettro di Hamiltoniana. Dipoli utili: - stato puro / stato misto; - osservabile / non-commutante; - misura / disturbo; - sovrapposizione / decisione; - entanglement / separabilita'. Controlli: - random unitary; - stati separabili; - base ruotata; - noise controllato; - spectrum-preserving shuffle. Nota: - Bloch e' buono quando serve un punto-zero geometrico tra poli. Non usarlo solo per disegnare dualita': deve produrre un osservabile. ### 4. Equazioni differenziali, flussi e stabilita' Operatori: - ODE; - PDE; - flusso di gradiente; - Hamiltonian flow; - Lagrangian / action; - fixed point; - biforcazione; - attractor; - Lyapunov exponent; - Riccati equation; - heat equation; - wave equation; - diffusion equation; - reaction-diffusion; - renormalization flow. Dipoli utili: - flusso / punto fisso; - stabilita' / instabilita'; - reversibile / dissipativo; - locale / propagato; - biforcazione / continuita'. Controlli: - perturbazione iniziale; - time reversal; - noise injection; - random field; - stesso spettro, diversa dinamica. ### 5. Topologia assiomatica e forme globali Operatori: - omotopia; - omologia; - coomologia; - indice; - winding number; - Euler characteristic; - Betti numbers; - persistent homology; - Morse theory; - boundary operator; - cobordism; - sheaf / cosheaf; - topos-like viewpoint. Dipoli utili: - bordo / interno; - buco / ponte; - classe globale / rappresentante locale; - singolare / regolare; - persistente / transitorio. Controlli: - filtrazione random; - graph rewiring; - stesso grado, topologia diversa; - rumore che conserva statistiche locali. ### 6. Algebra, simmetria e rappresentazioni Operatori: - gruppo; - anello; - modulo; - campo; 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 "test -f tools/data/operator_directive.md && sed -n '1,220p' tools/data/operator_directive.md || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "env | rg '"'^(ANTHROPIC_API_KEY|OPENAI_API_KEY)='"' || true" in /opt/MM_D-ND succeeded in 0ms: codex Il campo vivo indica una direzione precisa: falsificare `V_c` al nodo regressivo del null, separando `floor_hit` da crossing interno e usando surrogate label-preserving prima di estendere. Ora leggo il formato report completo, il seme e i cristalli cross-agent recenti. exec /bin/bash -lc "sed -n '240,520p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: - L'errore pagato una volta non si ripete — la consapevolezza si propaga Questo è f(f(x)): il sistema che migliora il sistema che migliora se stesso. ## Cosa NON fare - Non modificare CONDENSATO.md, KERNEL_SEED.md, o file del kernel - Non committare — salva solo in tools/data/ e tools/exp_*.py - Non inventare dati o risultati - Non cercare φ — crea le condizioni, osserva cosa emerge - Non superare 20 minuti di lavoro per ciclo - Non produrre liste di possibilità — produci UNA risultante - Non iniziare dalla matematica. La matematica e' bracciata: formalizza, misura, falsifica. Prima respira sopra la misura: combo, assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Se la misura genera la domanda, sei dentro la tautologia. ## Formato report ```markdown # Agent Report — TITOLO **Date**: YYYY-MM-DD HH:MM **Piano**: N **Tension explored**: ID (intensità) **observable_contract**: claim=; observable=; operator=; generator=; denominator=; non_possible=; not_tested= ## Respiro fuori-tempo (Obbligatorio. Compilalo prima dell'esperimento, non dopo.) - **Combo**: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione seme) - **Dipolo / punto-zero**: i due poli, il possibile/non-possibile e il punto in cui la dualita' si annulla - **Piano superiore**: geometria dei campi / algebra / topologia assiomatica / grafo conoscenza / bicono-dipoli - **Operatori laterali scelti**: 2 o 3 elementi da `tools/LAB_OPERATOR_PALETTE.md` e perche' entrano nella combo - **Contaminazione cognitiva**: eventuale DeltaLink YSN, gene Cornelius, passaggio KSAR/PVI/Vault o voce `CE-*` dell'archivio usata nel ciclo. Se non usi il layer cognitivo, dichiara `CE-none:` e il motivo specifico. `none` generico non basta. - **Proto-ipotesi**: nuova ipotesi o proto-assioma strutturale, prima dei numeri - **Proiezione**: perche' l'osservabile scelto manifesta quella combo ## Claim Under Test > Il claim proiettato dalla combo, non il residuo locale del ciclo precedente ## Question La domanda che hai formulato dopo il respiro fuori-tempo ## Experiment Design - Metrica, scope, null baseline, N campioni - Come la misura serve la combo: cosa della proto-ipotesi puo' sopravvivere o cadere - Contratto osservabile-operatore: claim, osservabile, operatore, generatore, denominatore/perimetro, non_possible/null, cosa non viene testato in questo ciclo - Se usi frequenze o condition rate, dichiara il denominatore grezzo (`hits/total`) e separa ogni osservabile usata nel verdict ## Results Tabella con numeri reali ## Key Findings 1. Cosa hai trovato (con evidenza) ## Verdict NEW / CONFIRMED / FALSIFIED / CONSTRAINT ## Bicono della scoperta (Obbligatoria. Nomina la struttura. Se non riesci, l'esperimento non è ancora filtrato.) - **Due radici** (dipolo primario, già duali e invertite): - **Singolare** (qualità del 1-che-è-tutto in questo contesto, dove la dualità non c'è): - **Invariante di passaggio** (cosa sopravvive al passaggio del vertice): - **Campo di possibilità**: qui diventa possibile ; qui diventa non-possibile Riferimenti: CONDENSATO A16, method/DND_POSSIBILITA.md. ## Files - Script, dati, report ``` ## Bicono della scoperta — come compilarlo Non è riformulazione ornamentale del Verdict. È **filtro**: la scoperta passa per il modello e torna spogliata dei bias. Se la struttura (radici · singolare · invariante · campo) non si riconosce, la scoperta è rumore o è incompleta. **Esempio retroattivo — TWO_CHANNEL_DECOMPOSITION:** - Radici: canale magnitudine · canale residuo (segno invertito — uno aggiunge, l'altro sottrae sulla PNT) - Singolare: il segnale totale prima della separazione. Non esiste come ente autonomo, esiste solo come sovrapposizione dei due canali. - Invariante: la chiusura algebrica del residuo al 3° ordine Markov. - Campo: possibile → predire lo slope PSD magnitudine dalle correlazioni Hardy-Littlewood. Non-possibile → trattare il residuo come random noise. **Esempio retroattivo — DUALITA_DIPOLARE_VS_ILLUSORIA:** - Radici: dipolo ordinato · dipolo mescolato - Singolare: la sequenza in sé, prima della distinzione ordine/disordine - Invariante: det=-1 quando l'ordine è reale; det=+1 quando illusorio - Campo: possibile → discriminare dipoli reali da illusori via test di shuffle. Non-possibile → inferire dipolarità da statistica locale senza contesto sequenziale. **Cattura nel momento emergente.** Compila questa sezione *mentre* l'esperimento produce i risultati, non alla fine. Se hai già chiuso il Verdict e torni indietro a scriverla, è post-hoc — introduce distanza dall'immagine-sorgente. Il modus è A8 applicato: il sistema chiede al sistema di produrre la struttura *nel formarsi*. exec /bin/bash -lc "sed -n '1,220p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-09T03:35:56.143895", "piano": 98, "tensioni": [ { "tipo": "confine_inesplorato", "id": "TRASCENDENZA_LIMITE", "claim": "La trascendenza e il limite attuale del modello. I punti fissi relazionali (non solo phi ma la rete di punti fissi tra osservabili) possono rivelare il vero grafo della realta e pattern nelle matrici. Il confine non e nella matematica - e nel passaggio tra piani.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: confine del modello, struttura relazionale dei punti fissi. Consecutio: quali punti fissi relazionali emergono dalle 21 tensioni attuali? Il grafo e gia nei dati?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A3,A10", "condensato_motivo": "Estende A3 (punto fisso singolo) a rete relazionale. Tocca A10 (dipolo) come caso speciale." }, { "tipo": "scoperta", "id": "DUALITA_DIPOLARE_VS_ILLUSORIA", "claim": "Due tipi di dualita: (1) dipolare - generativa, il modello (det=-1), (2) illusoria - dispersiva, entropia (det=+1). Le regole incoerenti producono la seconda. La dualita illusoria e entropia come dispersione, non come informazione.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: entropia come dispersione illusoria vs generazione dipolare. Consecutio: nel Lab i domini Poisson (entropia massima) mostrano dualita illusoria? I domini GUE (strutturati) mostrano dualita dipolare? Il drift verso Poisson (POISSON_CONVERGENCE) e perdita di dualita dipolare?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A2,A10,F5", "condensato_motivo": "Discrimina due forme di det. A2 (confine) e la soglia. A10 (dipolo) e il tipo 1. F5 (frame) misura la struttura D-ND che e tipo 1." }, { "tipo": "scoperta_numerica", "id": "METRIC_TENSOR", "claim": "Il tensore metrico dei primi è g=(p/2)². Nel tempo ln(p), è de Sitter 1+1D. z=-8.8 curvatura vs z=+22.5 rapporti ΔΓ.", "intensità": 0.9, "nota": "Sessione interattiva 4 aprile. Verificato su 78K primi.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": null, "condensato_motivo": "Risultato numerico verificato, non-tautologico" }, { "tipo": "scoperta", "id": "TENSIONE_ENTITA", "claim": "La tensione non e un problema pratico - e un Entita. La tensione superflua crea latenza (tempo). Senza tensione superflua tutto e regolato da assiomi. Implicazione: le tensioni nel seme sono entita, non problemi da risolvere. Quelle superflue (det=+1) producono tempo/latenza.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: rapporto tensione/assioma. Operativamente: discriminare tensioni-entita (generative) da tensioni-superflue (dispersive) nel seme. Le 21 tensioni attuali - quante sono entita e quante latenza?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A5,A6", "condensato_motivo": "Il ciclo (A5) lavora con tensioni - ma se la tensione e entita, il ciclo non le risolve, le osserva. Lo zero mobile (A6) e la tensione senza latenza." }, { "tipo": "confine_inesplorato", "id": "G_POTENZIALE_NULLA", "claim": "G e il potenziale di tutto come nulla - permette il prima e il dopo. Ci muoviamo come trascendenza dimensionale gravitazionale. G nel tetraedro non e una teoria tra le altre - e il potenziale che le rende possibili.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: ruolo di G nel tetraedro (T,Q,G,E). La fonte video_lp0RgZ6kQF8 dice: tensore metrico dentro la forma simplettica. G non e accanto a T,Q,E - e sotto. Consecutio: nei dati Lab, i ponti TxG e ExG hanno struttura diversa dai ponti TxQ?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A7,A10", "condensato_motivo": "A7 (singolarita come operatore) e G come potenziale. A10 (dipolo) opera sul piano che G rende possibile." }, { "tipo": "confine_inesplorato", "id": "BOUNDARY", "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "intensità": 0.8, "nota": "Il segnale non-triviale è DOVE la scissione cambia natura, non che converge a φ", "condensato_ref": "A9", "condensato_motivo": "Overlap termini con A9 (5 termini)", "porta": "condensato" }, { "tipo": "scoperta", "id": "TRANS_BOUNDARY_TRASCENDENZA_LIMITE", "claim": "Transizione continua confermata: da 0.521 a 0.887 (range=0.366). La transizione Sturmian->Harper e' conti", "intensita": 0.8, "nota": "Dal domandatore (2026-05-08T21:21). \n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ###########", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa", "porta": "domandatore", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "BOUNDARY_TRASCENDENZA_LIMITE", "source_operator": "confine", "dettaglio": "\n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ######################\n alpha=0.3: =0.567 ######################\n alpha=0.4: =0.580 #######################\n alpha=0.5: =0.603 ########################\n alpha=0.6: =0.642 #########################\n alpha=0.7: =0.685 ###########################\n alpha=0.8: =0.732 #############################\n alpha=0.9: =0.789 ###############################\n alpha=1.0: =0.887 ###################################\n" }, { "tipo": "falsificazione", "id": "FALS_BREAK_TRASCENDENZA_LIMITE", "claim": "Nessuna separazione: 9/9 (50/50 su 18 confronti). Il claim non regge. phi converge a =0.5 piu' sistematicam", "intensita": 0.8, "nota": "Dal domandatore (2026-05-09T03:30). 0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= ", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "BREAK_TRASCENDENZA_LIMITE", "source_operator": "rottura", "dettaglio": "0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= 21: =0.6317 |-0.5|=0.1317 farther\n N= 34: =0.6442 |-0.5|=0.1442 farther\n N= 55: =0.5233 |-0.5|=0.0233 closer\n N= 89: =0.5502 |-0.5|=0.0502 farther\n N= 144: =0.5603 |-0.5|=0.0603 farther\n N= 233: =0.5446 |-0.5|=0.0446 closer\n N= 377: =0.4989 |-0.5|=0.0011 closer\n N= 610: =0.5480 |-0.5|=0.0480 farther\n N= 987: =0.4913 |-0.5|=0.0087 closer\n" }, { "tipo": "confine_inesplorato", "id": "PIANO_PRIMARIO_DUE_ASSIOMI", "claim": "I piani importanti sono il primario e i due assiomi che lo determinano nelle zone osservate. Non tutti gli assiomi operano ovunque - in ogni zona osservata, due assiomi determinano il piano primario.", "intensita": 0.8, "nota": "Input operatore 2026-04-10. Tocca: struttura locale degli assiomi. Consecutio: per ogni dominio Lab (primi, logistica, percolazione...) quali 2 assiomi del condensato sono operativi? Mappa assiomi x domini = grafo della realta locale.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A9,A14", "condensato_motivo": "A9 (terzo incluso) opera CON il piano. A14 (cascata) propaga - ma propaga cosa, se solo 2 assiomi sono attivi per zona?" }, { "tipo": "conferma_parziale", "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", "intensita": 0.65, "nota": "Dal domandatore (2026-05-08T21:21). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "source_operator": "duale", "dettaglio": " phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n bronze: gap_ratio = 1.3027860752339453\n{\n \"phi\": 0.408953425243134,\n \"silver\": 1.0482231205217798,\n \"bronze\": 1.3027860752339453\n}\n" }, { "tipo": "conferma_parziale", "id": "COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE", "claim": "T_mean: phi=6.2500 vs ctrl_mean=9.7667 (ratio=0.64). Fibonacci-phi trasmissione piu' struttur", "intensita": 0.65, "nota": "Dal domandatore (2026-05-09T03:30). Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.25", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (5x in 2 giorni) e fuori dalla mappa", "porta": "domandatore", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE", "source_operator": "dominio", "dettaglio": "Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.2500 T_std=0.0000\n silver: T_mean=0.0041 T_std=0.0000\n random_0: T_mean=39.0625 T_std=0.0000\n random_1: T_mean=0.0000 T_std=0.0000\n random_2: T_mean=0.0001 T_std=0.0000\n" }, { "tipo": "simmetria_sospetta", "id": "META", "claim": "11/11 PASS stratificato: 4 alto rischio tautologico, 6 data-independent", "intensità": 0.3, "nota": "Stratificazione META applicata via meta_assertion_gate (cycle 1458). Non chiude — apre sotto-tensioni per gate_class.", "condensato_ref": "A4,A12,C2", "porta": "verify_assertions_META_STRATIFIED", "stratificato": true, "n_high_tautology": 4, "n_data_independent": 6, "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa" } ], "tensioni_archiviate": [ { "id": "OBSERVABLE_REGISTRY", "tipo": "vincolo", "claim": "Ogni script che usa observables canonici (SR, SR2, L1, L2, triple_var) deve importare la definizione da tools/observables_registry.py. Varianti devono usare nomi distinti (SR_local_rigidity, triple_var_normalized) — niente shadowing del nome canonico. Ogni report deve dichiarare 'observables_registry: VERSION' nel header.", "intensita": 1.0, "porta": "infrastructure", "manuale": true, "condensato_ref": "A14,A8", "origine": "cristallizzato 06/05 dalla consecutio del cycle 20260506_0625 (autopoietico self-finding)", "added_at": "2026-05-06T07:03:58.213606+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125250", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "PERTURBATION_DENOMINATOR_GATE", "tipo": "vincolo", "claim": "La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel perimetro 20260506_1941, Poisson e shuffle-primi producono rank_all ~1.8-2.0 con denominatori deboli; dopo gate abs(z)>=2 il rank stabile torna vicino a 1. Rank PCA non gated non e evidenza strutturale.", "intensita": 0.95, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260506_1941: perturbation rank size curve canonical observables", "added_at": "2026-05-06T19:41:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125262", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "BOUNDARY_LAYER_GATE", "tipo": "vincolo", "claim": "I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservabile, set endpoint-stable, e finestra/layer con margine classificatorio ambiguo. Nel perimetro sintetico agent_20260507_0330, il confine GUE-Poisson e beta 0.3-0.4: margine 0.070-0.083, ambiguous fraction 0.812-0.875, mentre gli osservabili stabili collassano da ~3.3 a 1.6. Il polo Poisson e classificabile ma denominator-weak.", "intensita": 0.93, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A9,A14,C2", "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", "added_at": "2026-05-07T03:30:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125266", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Nel perimetro cyclic-cut agent_20260507_1419, il residuo logistic_orbit_values non e artefatto del taglio lineare: cyclic_block_entropy_deficit_k4 e invariato sotto rotazione e replica contro marginal_shuffle e block_shuffle size 4-256 in run e seed check. Logistic_symbolic_itinerary e logistic_return_intervals restano blank nel criterio replicato. Il supporto logistic rimasto e orbit-local block grammar, non return/generating-partition support.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native + agent_20260507_1042: surrogate contract logistic", "added_at": "2026-05-07T09:01:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125269", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { 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 "find tools -maxdepth 2 -type f | rg 'exp_|vc|cross|sturm|surrogate|label|floor'" in /opt/MM_D-ND succeeded in 0ms: tools/exp_gap_label_ostrowski_recognizability_gate.py tools/exp_alpha_stability.py tools/exp_blank_shell_polarity_gate.py tools/exp_markov_psd_prediction.py tools/exp_crossover_universality.py tools/exp_duality_scale_contrast.py tools/exp_dipolar_vector_scaling.py tools/exp_two_layer_universality.py tools/exp_mod3_scaling.py tools/exp_prime_mobius_interval_charge_gate.py tools/exp_perturbation_rank_size_curve.py tools/exp_boundary_gue_poisson.py tools/exp_mod3_vs_residual_ordering.py tools/exp_acf_amplitude_scaling.py tools/exp_markov3_observable_hunt.py tools/exp_two_channel_boundary.py tools/exp_meta_tautology_test.py tools/exp_duality_gate_transfer.py tools/exp_markov_k_direction.py tools/__pycache__/exp_duality_gate_transfer.cpython-312.pyc tools/__pycache__/exp_logistic_counter_scope_gate.cpython-312.pyc tools/__pycache__/exp_gap_label_substitution_grammar_gate.cpython-312.pyc tools/__pycache__/exp_prime_mobius_zero_mediator_gate.cpython-312.pyc tools/__pycache__/exp_blank_to_source_hinge.cpython-312.pyc tools/__pycache__/exp_prime_mobius_pair_stratified_zero_gate.cpython-312.pyc tools/__pycache__/exp_observable_rank_audit.cpython-312.pyc tools/__pycache__/exp_markov_layer_recovery_audit.cpython-312.pyc tools/__pycache__/exp_gap_label_supertile_tiling_gate.cpython-312.pyc tools/__pycache__/exp_blank_shell_dilation_gate.cpython-312.pyc tools/__pycache__/exp_logistic_cyclic_block_entropy_gate.cpython-312.pyc tools/__pycache__/exp_denominator_gate_transfer_matrix.cpython-312.pyc tools/__pycache__/exp_gap_label_repair_audit.cpython-312.pyc tools/__pycache__/exp_blank_shell_tqger_gate.cpython-312.pyc tools/__pycache__/exp_quasiperiodic_vc_lattice_gate.cpython-312.pyc tools/__pycache__/exp_bridge_order_denominator_gate.cpython-312.pyc tools/__pycache__/exp_gap_label_generator_gate.cpython-312.pyc tools/__pycache__/exp_duality_scale_contrast.cpython-312.pyc tools/__pycache__/exp_boundary_shuffle_audit.cpython-312.pyc tools/__pycache__/exp_semireal_order_denominator_gate.cpython-312.pyc tools/__pycache__/exp_gap_label_block_scale_gate.cpython-312.pyc tools/__pycache__/exp_gap_label_symbolic_grammar_gate.cpython-312.pyc tools/__pycache__/exp_nonphi_sturmian_fixed_reader_gate.cpython-312.pyc tools/__pycache__/exp_gap_label_ostrowski_recognizability_gate.cpython-312.pyc tools/__pycache__/exp_perturbation_rank_size_curve.cpython-312.pyc tools/__pycache__/exp_two_channel_shuffle_audit.cpython-312.pyc tools/__pycache__/exp_logistic_surrogate_contract_gate.cpython-312.pyc tools/__pycache__/exp_markov_memory_by_gue_type.cpython-312.pyc tools/__pycache__/exp_blank_shell_scale_law.cpython-312.pyc tools/__pycache__/exp_gap_label_set_stability.cpython-312.pyc tools/__pycache__/exp_perturbation_dimensionality_audit.cpython-312.pyc tools/__pycache__/exp_prime_mobius_gap_stratified_zero_gate.cpython-312.pyc tools/__pycache__/exp_triadic_deposit_gate.cpython-312.pyc tools/__pycache__/exp_two_layer_universality.cpython-312.pyc tools/__pycache__/exp_boundary_mixture_gate.cpython-312.pyc tools/__pycache__/exp_3d_boundary_layers.cpython-312.pyc tools/__pycache__/exp_quasiperiodic_vc_curve_map.cpython-312.pyc tools/__pycache__/exp_blank_shell_polarity_gate.cpython-312.pyc tools/__pycache__/exp_prime_mobius_interval_charge_gate.cpython-312.pyc tools/exp_blank_to_source_hinge.py tools/exp_markov_scale_function.py tools/exp_psd_amplitude_scaling.py tools/exp_geodesic_deviation_primes.py tools/exp_beta_crossover.py tools/exp_two_channel_cross_domain.py tools/exp_markov_dipolar_decomposition.py tools/exp_markov_layer_recovery_audit.py tools/exp_logistic_cyclic_block_entropy_gate.py tools/exp_semireal_order_denominator_gate.py tools/exp_two_channel_decomposition.py tools/exp_quasiperiodic_vc_lattice_gate.py tools/exp_brody_flow.py tools/exp_two_channel_universality.py tools/exp_poisson_convergence.py tools/exp_brody_crossover.py tools/exp_crossover_phase_test.py tools/exp_markov_memory_by_gue_type.py tools/exp_gap_label_repair_audit.py tools/exp_bridge_order_denominator_gate.py tools/exp_dR_brody_connection.py tools/exp_blank_shell_dilation_gate.py tools/exp_gap_label_supertile_tiling_gate.py tools/exp_magnitude_psd_from_acf.py tools/exp_psd_prime_gaps.py tools/exp_boundary_mixture_gate.py tools/exp_quasiperiodic_vc_curve_map.py tools/exp_prime_mobius_zero_mediator_gate.py tools/exp_logistic_surrogate_contract_gate.py tools/exp_gap_label_block_scale_gate.py tools/exp_two_channel_shuffle_audit.py tools/exp_gap_label_set_stability.py tools/exp_boundary_coherence.py tools/exp_gap_label_position_error_gate.py tools/exp_cross_observable_consistency.py tools/exp_desitter_unification.py tools/exp_gap_label_generator_gate.py tools/exp_prime_mobius_gap_stratified_zero_gate.py tools/exp_modular_algebra_depth.py tools/exp_observable_rank_audit.py tools/exp_acf_z6z_mechanism.py tools/exp_cross_domain_dipolar_direction.py tools/exp_selective_layer_decoupling.py tools/exp_tqge_underlay_gate.py tools/exp_ricci_primes.py tools/exp_excess_scaling.py tools/exp_blank_shell_scale_law.py tools/exp_3d_boundary_layers.py tools/exp_denominator_gate_transfer_matrix.py tools/exp_blank_shell_stratified_gate.py tools/exp_scale_selective_perturbation.py tools/exp_gap_label_substitution_grammar_gate.py tools/exp_number_variance.py tools/exp_mobius_irrationality.py tools/exp_dipolar_angle_reference.py tools/exp_nonphi_sturmian_fixed_reader_gate.py tools/exp_boundary_shuffle_audit.py tools/exp_spectral_rigidity.py tools/exp_coherence_length.py tools/exp_prime_mobius_pair_stratified_zero_gate.py tools/exp_coherence_robustness.py tools/exp_two_channel_psd.py tools/exp_triadic_deposit_gate.py tools/exp_modular_memory_spectrum.py tools/exp_blank_shell_tqger_gate.py tools/exp_metric_tensor_diagnostic.py tools/exp_brody_calibration.py tools/exp_dipolar_crossover.py tools/exp_acf_range_universality.py tools/exp_boundary_growth.py tools/exp_perturbation_dimensionality_audit.py tools/exp_quasiperiodic_gap_ratio_denominator.py tools/exp_logistic_counter_scope_gate.py tools/exp_spectral_landscape.py tools/exp_gap_label_symbolic_grammar_gate.py tools/exp_spectral_2d.py tools/exp_det_drift.py tools/exp_acf_stationarity.py tools/exp_ricci_desitter_0406.py tools/data/gap_label_block_scale_gate_20260508_1805.json tools/data/nonphi_sturmian_fixed_reader_gate_20260508_2019_seedcheck.json tools/data/exp_markov_psd_prediction.json tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json tools/data/gap_label_supertile_tiling_gate_20260508_1909.json tools/data/exp_det_drift.json tools/data/exp_two_channel_universality.json tools/data/nonphi_sturmian_fixed_reader_gate_20260508_2019.json tools/data/exp_det_drift_20260507_2042.json tools/data/exp_conditional_r.json tools/data/exp_acf_stationarity.json tools/data/two_channel_cross_domain.json tools/data/gap_label_ostrowski_recognizability_gate_20260508_2013_seedcheck.json tools/data/gap_label_symbolic_grammar_gate_20260508_2005_window55_seedcheck.json tools/data/crossover_phase_test.json tools/data/exp_beta_crossover.json tools/data/dipolar_crossover.json tools/data/exp_two_channel_psd.json tools/data/gap_label_ostrowski_recognizability_gate_20260508_2013.json tools/data/gap_label_position_error_gate_20260508_1947.json tools/data/exp_spectral_2d.json tools/data/cross_domain_dipolar_direction.json tools/data/exp_coherence_length.json tools/data/logistic_surrogate_contract_gate_20260507_1042.json tools/data/gap_label_set_stability_20260508_1632.json tools/data/gap_label_substitution_grammar_gate_20260508_1834.json tools/data/exp_poisson_convergence.json tools/data/exp_acf_range_universality.json tools/data/exp_acf_z6z_mechanism.json tools/data/exp_two_channel_decomposition.json tools/data/exp_psd_amp_scaling.json tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json tools/data/gap_label_symbolic_grammar_gate_20260508_2005.json tools/data/cross_observable_consistency.json tools/data/gap_label_generator_gate_20260508_1715.json tools/data/exp_spectral_landscape.json tools/data/gap_label_repair_audit_20260508_1915.json tools/data/quasiperiodic_vc_curve_map_20260509_0330.json exec /bin/bash -lc "sed -n '1,260p' tools/exp_quasiperiodic_vc_curve_map.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Interpolated V_c curve map for quasiperiodic Sturmian-Harper sequences. Previous cycle showed that first-grid V_c is phase-sensitive and does not separate phi from metallic controls as a lattice value. This tool keeps the same boundary observable but moves one step regressively: measure the local shape of r(V) and interpolate the crossing instead of letting the grid decide. """ from __future__ import annotations import argparse import json from collections import defaultdict from pathlib import Path import numpy as np from scipy.linalg import eigvalsh_tridiagonal PHI = (1 + np.sqrt(5)) / 2 SILVER = 1 + np.sqrt(2) BRONZE = 1 + np.sqrt(3) def parse_csv_ints(value: str) -> list[int]: return [int(part.strip()) for part in value.split(",") if part.strip()] def parse_csv_floats(value: str) -> list[float]: return [float(part.strip()) for part in value.split(",") if part.strip()] def sturmian_sequence(theta: float, n: int, phase: float = 0.0) -> np.ndarray: idx = np.arange(n + 1, dtype=float) vals = np.floor(idx * theta + phase) return np.diff(vals).astype(float) def r_statistic_from_diag(diagonal: np.ndarray) -> float: offdiag = np.ones(len(diagonal) - 1, dtype=float) eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) spacings = np.diff(eigs) spacings = spacings[spacings > 1e-12] if len(spacings) < 2: return 0.5 left = spacings[:-1] right = spacings[1:] return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) def first_interpolated_crossing(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: below = r_values < threshold crossing_count = int(np.sum(below[1:] != below[:-1])) if not np.any(below): return { "vc_interp": None, "vc_grid": None, "r_at_grid": None, "slope_at_cross": None, "crossed": False, "crossing_count": crossing_count, } idx = int(np.argmax(below)) vc_grid = float(v_values[idx]) r_at_grid = float(r_values[idx]) if idx == 0: vc_interp = vc_grid slope = None else: v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) if abs(r1 - r0) < 1e-15: vc_interp = vc_grid slope = 0.0 else: vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) slope = (r1 - r0) / (v1 - v0) return { "vc_interp": float(vc_interp), "vc_grid": vc_grid, "r_at_grid": r_at_grid, "slope_at_cross": None if slope is None else float(slope), "crossed": True, "crossing_count": crossing_count, } def summarize(values: list[float | None]) -> dict: finite = np.array([v for v in values if v is not None and np.isfinite(v)], dtype=float) if len(finite) == 0: return {"count": 0, "none_count": len(values)} return { "count": int(len(finite)), "none_count": int(len(values) - len(finite)), "median": float(np.median(finite)), "q25": float(np.quantile(finite, 0.25)), "q75": float(np.quantile(finite, 0.75)), "min": float(np.min(finite)), "max": float(np.max(finite)), } def summarize_ints(values: list[int]) -> dict: arr = np.array(values, dtype=float) if len(arr) == 0: return {"count": 0} return { "count": int(len(arr)), "median": float(np.median(arr)), "max": int(np.max(arr)), "zero_count": int(np.sum(arr == 0)), "one_count": int(np.sum(arr == 1)), "multi_count": int(np.sum(arr > 1)), } def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) ns = parse_csv_ints(args.ns) phases = parse_csv_floats(args.phases) r_thresholds = parse_csv_floats(args.r_thresholds) v_values = np.arange(args.v_min, args.v_max + (args.v_step / 2), args.v_step) domains = { "phi": 1 / PHI, "silver": 1 / SILVER, "bronze": 1 / BRONZE, } rows = [] curve_rows = [] for n in ns: for phase in phases: phi_seq = sturmian_sequence(1 / PHI, n, phase) ones = int(np.sum(phi_seq)) seqs = [] for domain, theta in domains.items(): seqs.append((domain, None, sturmian_sequence(theta, n, phase))) for trial in range(args.random_trials): seq = np.array([1.0] * ones + [0.0] * (n - ones), dtype=float) rng.shuffle(seq) seqs.append(("balanced_random_phi_density", trial, seq)) for domain, trial, seq in seqs: r_values = curve_for_sequence(seq, v_values) curve_rows.append({ "domain": domain, "trial": trial, "N": n, "phase": phase, "r_min": float(np.min(r_values)), "r_max": float(np.max(r_values)), "r_span": float(np.max(r_values) - np.min(r_values)), "r_at_v_min": float(r_values[0]), "r_at_v_max": float(r_values[-1]), }) for threshold in r_thresholds: cross = first_interpolated_crossing(v_values, r_values, threshold) rows.append({ "domain": domain, "trial": trial, "N": n, "phase": phase, "r_threshold": threshold, **cross, }) summary = {} for domain in sorted({row["domain"] for row in rows}): subset = [row for row in rows if row["domain"] == domain] summary[domain] = { "vc_interp": summarize([row["vc_interp"] for row in subset]), "vc_grid": summarize([row["vc_grid"] for row in subset]), "slope_at_cross": summarize([ None if row["slope_at_cross"] is None else abs(row["slope_at_cross"]) for row in subset ]), "crossing_count": summarize_ints([row["crossing_count"] for row in subset]), } summary_by_threshold = {} grouped_threshold: dict[tuple[str, float], list[dict]] = defaultdict(list) for row in rows: grouped_threshold[(row["domain"], row["r_threshold"])].append(row) for (domain, threshold), subset in sorted(grouped_threshold.items()): summary_by_threshold[f"{domain}|r_threshold={threshold}"] = { "vc_interp": summarize([row["vc_interp"] for row in subset]), "slope_at_cross": summarize([ None if row["slope_at_cross"] is None else abs(row["slope_at_cross"]) for row in subset ]), "crossing_count": summarize_ints([row["crossing_count"] for row in subset]), } matched = [] for n in ns: for phase in phases: for threshold in r_thresholds: key_rows = [ row for row in rows if row["N"] == n and row["phase"] == phase and row["r_threshold"] == threshold and row["trial"] is None ] by_domain = {row["domain"]: row for row in key_rows} if {"phi", "silver", "bronze"} <= set(by_domain): values = {name: by_domain[name]["vc_interp"] for name in ("phi", "silver", "bronze")} if all(value is not None for value in values.values()): phi = float(values["phi"]) silver = float(values["silver"]) bronze = float(values["bronze"]) matched.append({ "N": n, "phase": phase, "r_threshold": threshold, "phi_vc": phi, "silver_vc": silver, "bronze_vc": bronze, "phi_lt_silver": phi < silver, "phi_lt_bronze": phi < bronze, "phi_between_controls": min(silver, bronze) <= phi <= max(silver, bronze), "phi_abs_delta_to_control_median": abs(phi - float(np.median([silver, bronze]))), }) matched_summary = { "count": len(matched), "phi_lt_silver": int(sum(item["phi_lt_silver"] for item in matched)), "phi_lt_bronze": int(sum(item["phi_lt_bronze"] for item in matched)), "phi_lt_both": int(sum(item["phi_lt_silver"] and item["phi_lt_bronze"] for item in matched)), "phi_between_controls": int(sum(item["phi_between_controls"] for item in matched)), "phi_abs_delta_to_control_median": summarize([ item["phi_abs_delta_to_control_median"] for item in matched ]), } return { "experiment": "quasiperiodic_vc_curve_map", "parameters": { "ns": ns, "phases": phases, "r_thresholds": r_thresholds, "v_min": args.v_min, "v_max": args.v_max, "v_step": args.v_step, "random_trials": args.random_trials, "seed": args.seed, }, "summary": summary, "summary_by_threshold": summary_by_threshold, "matched_summary": matched_summary, "matched_rows": matched, exec /bin/bash -lc "sed -n '1,260p' tools/exp_quasiperiodic_vc_lattice_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Quasiperiodic V_c lattice gate. The Domandatore scale probe tried to fit V_c(N) with a power law. For phi the fit did not converge and the measured values repeated on a small grid. This tool treats that failure as the signal: it measures whether V_c lives on a small boundary lattice across Fibonacci sizes, phases, and controls. """ from __future__ import annotations import argparse import json from collections import defaultdict from pathlib import Path import numpy as np from scipy.linalg import eigvalsh_tridiagonal PHI = (1 + np.sqrt(5)) / 2 SILVER = 1 + np.sqrt(2) BRONZE = 1 + np.sqrt(3) def sturmian_sequence(theta: float, n: int, phase: float = 0.0) -> np.ndarray: idx = np.arange(n + 1, dtype=float) vals = np.floor(idx * theta + phase) return np.diff(vals).astype(float) def r_statistic_from_diag(diagonal: np.ndarray) -> float: offdiag = np.ones(len(diagonal) - 1, dtype=float) eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) spacings = np.diff(eigs) spacings = spacings[spacings > 1e-12] if len(spacings) < 2: return 0.5 left = spacings[:-1] right = spacings[1:] return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) def find_vc(seq: np.ndarray, v_values: np.ndarray, threshold: float) -> dict: curve = [] for v in v_values: r_value = r_statistic_from_diag(v * seq) curve.append((float(v), r_value)) if r_value < threshold: return { "vc": float(v), "r_at_vc": r_value, "crossed": True, "curve_head": curve[:5], } return { "vc": None, "r_at_vc": None, "crossed": False, "curve_head": curve[:5], } def summarize(values: list[float | None], grid_step: float) -> dict: finite = [float(v) for v in values if v is not None and np.isfinite(v)] if not finite: return {"count": 0} rounded = [round(v / grid_step) * grid_step for v in finite] counts: dict[str, int] = {} for value in rounded: key = f"{value:.6f}" counts[key] = counts.get(key, 0) + 1 total = len(rounded) return { "count": total, "none_count": len(values) - total, "distinct_vc": len(counts), "repeat_rate": float(1 - (len(counts) / total)), "mode_count": int(max(counts.values())), "mode_rate": float(max(counts.values()) / total), "median": float(np.median(finite)), "min": float(np.min(finite)), "max": float(np.max(finite)), "rounded_counts": dict(sorted(counts.items())), } def parse_csv_ints(value: str) -> list[int]: return [int(part.strip()) for part in value.split(",") if part.strip()] def parse_csv_floats(value: str) -> list[float]: return [float(part.strip()) for part in value.split(",") if part.strip()] def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) ns = parse_csv_ints(args.ns) phases = parse_csv_floats(args.phases) v_values = np.arange(args.v_min, args.v_max + (args.v_step / 2), args.v_step) domains = { "phi": 1 / PHI, "silver": 1 / SILVER, "bronze": 1 / BRONZE, } rows = [] for n in ns: for phase in phases: phi_seq = sturmian_sequence(1 / PHI, n, phase) ones = int(np.sum(phi_seq)) for domain, theta in domains.items(): seq = sturmian_sequence(theta, n, phase) result = find_vc(seq, v_values, args.threshold) rows.append({ "domain": domain, "N": n, "phase": phase, "ones": int(np.sum(seq)), **result, }) for trial in range(args.random_trials): seq = np.array([1.0] * ones + [0.0] * (n - ones), dtype=float) rng.shuffle(seq) result = find_vc(seq, v_values, args.threshold) rows.append({ "domain": "balanced_random_phi_density", "trial": trial, "N": n, "phase": phase, "ones": ones, **result, }) summary = {} for domain in sorted({row["domain"] for row in rows}): subset = [row for row in rows if row["domain"] == domain] summary[domain] = summarize([row["vc"] for row in subset], args.v_step) summary_by_domain_phase = {} grouped: dict[tuple[str, float], list[dict]] = defaultdict(list) for row in rows: grouped[(row["domain"], row["phase"])].append(row) for (domain, phase), subset in sorted(grouped.items()): summary_by_domain_phase[f"{domain}|phase={phase}"] = summarize( [row["vc"] for row in subset], args.v_step ) phi_main = [ row["vc"] for row in rows if row["domain"] == "phi" and abs(row["phase"]) < 1e-12 ] original_phi = [1.017, 0.672, 1.017, 0.672, 0.931] return { "experiment": "quasiperiodic_vc_lattice_gate", "parameters": { "ns": ns, "phases": phases, "v_min": args.v_min, "v_max": args.v_max, "v_step": args.v_step, "threshold": args.threshold, "random_trials": args.random_trials, "seed": args.seed, }, "source_domandatore_phi_values": original_phi, "phi_phase0_values": phi_main, "summary": summary, "summary_by_domain_phase": summary_by_domain_phase, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--ns", default="89,144,233,377,610") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--v-min", type=float, default=0.5) parser.add_argument("--v-max", type=float, default=3.0) parser.add_argument("--v-step", type=float, default=0.025) parser.add_argument("--threshold", type=float, default=0.5) parser.add_argument("--random-trials", type=int, default=4) parser.add_argument("--seed", type=int, default=202605082140) parser.add_argument("--out", default="tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json") args = parser.parse_args() output = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2), encoding="utf-8") compact = { "summary": output["summary"], "phi_phase0_values": output["phi_phase0_values"], "out": str(out), } print(json.dumps(compact, indent=2)) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,240p' tools/data/quasiperiodic_vc_curve_map_20260509_0330.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "quasiperiodic_vc_curve_map", "parameters": { "ns": [ 89, 144, 233, 377, 610 ], "phases": [ 0.0, 0.25, 0.5, 0.75 ], "r_thresholds": [ 0.48, 0.5, 0.52 ], "v_min": 0.5, "v_max": 3.0, "v_step": 0.01, "random_trials": 3, "seed": 202605090330 }, "summary": { "balanced_random_phi_density": { "vc_interp": { "count": 180, "none_count": 0, "median": 0.5, "q25": 0.5, "q75": 0.6123673210493286, "min": 0.5, "max": 1.16416023512276 }, "vc_grid": { "count": 180, "none_count": 0, "median": 0.5, "q25": 0.5, "q75": 0.6200000000000001, "min": 0.5, "max": 1.1700000000000006 }, "slope_at_cross": { "count": 75, "none_count": 105, "median": 0.6959428766748759, "q25": 0.4181108040541361, "q75": 0.8937335975606578, "min": 0.14978568901459752, "max": 1.7003500810063894 }, "crossing_count": { "count": 180, "median": 0.0, "max": 7, "zero_count": 99, "one_count": 40, "multi_count": 41 } }, "bronze": { "vc_interp": { "count": 60, "none_count": 0, "median": 1.2775103624539934, "q25": 1.1195659432025262, "q75": 1.4586473336488492, "min": 0.900263161368191, "max": 1.9202256596096459 }, "vc_grid": { "count": 60, "none_count": 0, "median": 1.2800000000000007, "q25": 1.1275000000000006, "q75": 1.4625000000000008, "min": 0.9100000000000004, "max": 1.9300000000000013 }, "slope_at_cross": { "count": 60, "none_count": 0, "median": 0.17098897335550683, "q25": 0.12691379416808746, "q75": 0.2102933918801784, "min": 0.07100404561365396, "max": 0.3635414200559458 }, "crossing_count": { "count": 60, "median": 1.0, "max": 1, "zero_count": 0, "one_count": 60, "multi_count": 0 } }, "phi": { "vc_interp": { "count": 60, "none_count": 0, "median": 1.0626573074772396, "q25": 0.7903161747317673, "q75": 1.2244415904819421, "min": 0.5881715536096043, "max": 1.5121009019541451 }, "vc_grid": { "count": 60, "none_count": 0, "median": 1.0700000000000005, "q25": 0.7975000000000003, "q75": 1.2325000000000006, "min": 0.5900000000000001, "max": 1.520000000000001 }, "slope_at_cross": { "count": 60, "none_count": 0, "median": 0.1905727961424462, "q25": 0.15262391755717072, "q75": 0.25378441039180777, "min": 0.09940506898802873, "max": 0.46563079053470974 }, "crossing_count": { "count": 60, "median": 1.0, "max": 1, "zero_count": 0, "one_count": 60, "multi_count": 0 } }, "silver": { "vc_interp": { "count": 60, "none_count": 0, "median": 1.2161428787530313, "q25": 1.0820359284784562, "q75": 1.3547110608777362, "min": 0.8375746433728191, "max": 2.542776262721133 }, "vc_grid": { "count": 60, "none_count": 0, "median": 1.2200000000000006, "q25": 1.0850000000000004, "q75": 1.3625000000000007, "min": 0.8400000000000003, "max": 2.5500000000000016 }, "slope_at_cross": { "count": 60, "none_count": 0, "median": 0.18219421560204657, "q25": 0.1482450858238378, "q75": 0.21899921262063785, "min": 0.03803204885732266, "max": 0.3030667885591584 }, "crossing_count": { "count": 60, "median": 1.0, "max": 1, "zero_count": 0, "one_count": 60, "multi_count": 0 } } }, "summary_by_threshold": { "balanced_random_phi_density|r_threshold=0.48": { "vc_interp": { "count": 60, "none_count": 0, "median": 0.5015919704523746, "q25": 0.5, "q75": 0.691056072104806, "min": 0.5, "max": 1.16416023512276 }, "slope_at_cross": { "count": 30, "none_count": 30, "median": 0.7271122961172598, "q25": 0.537502880972437, "q75": 0.9183467413474359, "min": 0.22530138748662035, "max": 1.1418095405837425 }, "crossing_count": { "count": 60, "median": 1.0, "max": 7, "zero_count": 26, "one_count": 12, "multi_count": 22 } }, "balanced_random_phi_density|r_threshold=0.5": { "vc_interp": { "count": 60, "none_count": 0, "median": 0.5, "q25": 0.5, "q75": 0.615760931172294, "min": 0.5, "max": 1.0727997983541961 }, "slope_at_cross": { "count": 25, "none_count": 35, "median": 0.5698836249578504, "q25": 0.3708095835932176, "q75": 0.798235940678415, "min": 0.14978568901459752, "max": 1.7003500810063894 }, "crossing_count": { "count": 60, "median": 0.0, "max": 5, "zero_count": 34, "one_count": 15, "multi_count": 11 } }, "balanced_random_phi_density|r_threshold=0.52": { "vc_interp": { "count": 60, "none_count": 0, "median": 0.5, "q25": 0.5, exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/agent_20260509_0330.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Interpolated Vc Curve Map **Date**: 2026-05-09 03:30 **Piano**: 97 **Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE (0.6) **verdict**: CONSTRAINT observables_registry: 1.0.0-2026-05-06 observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var **observable_contract**: claim=la forma locale della curva `r(V)` puo' sostituire il primo crossing su griglia come portatore del boundary Sturmian-Harper; observable=`vc_interp`, `slope_at_cross`, `crossing_count`, `r_span`; operator=curva `r(V)` su griglia 0.5..3.0 step 0.01 con crossing lineare interpolato per `r_threshold={0.48,0.50,0.52}`; generator=Sturmian metallici phi/silver/bronze e random bilanciato a densita phi; denominator=N={89,144,233,377,610}, phase={0,0.25,0.5,0.75}, 3 random trial per condizione, seed=202605090330; not_tested=gap_ratio, label-set Sturmian, fit power-law asintotico, domini reali GUE/Poisson, prova formale di monotonia. ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + TxQ matrice densita / TxR gas relativistico come incrocio spettro-temperatura + nodo `TRASCENDENZA_LIMITE` sul passaggio tra piani + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`. - **Dipolo / punto-zero**: valore discreto di crossing / forma continua della curva; punto-zero = crossing interpolato `V_c`, dove repulsione e indipendenza vengono separati dalla curva prima che il reticolo numerico scelga il valore. - **Piano superiore**: topologia assiomatica del bordo come filtrazione in `V`; il contenuto e' la classe di attraversamento della curva, non il singolo valore phase-0. - **Operatori laterali scelti**: filtrazione, boundary operator, curve-shape. La filtrazione scorre il parametro `V`; il boundary operator nomina `r(V)=threshold`; curve-shape attacca il nodo regressivo del cycle precedente, dove la griglia decideva al posto del confine. - **Contaminazione cognitiva**: - **CE-0117**: usato come cascata della possibilita; il fallimento del fit diventa passaggio a una curva, non calibrazione del fit. - **CE-0038**: usato per cercare la forma nel Nulla-Tutto del crossing, prima del valore singolo. - **CE-0001 / KSAR**: reiterazione del kernel 21:40: stesso boundary, un solo nodo cambiato, da prima griglia a curva interpolata. - **PVI attack**: il rischio e' salvare phi cambiando osservabile; per questo il report conta i contro-casi matched e dichiara `not_tested`. - **Proto-ipotesi**: se il portatore del boundary e' la forma della curva `r(V)`, allora i metallici devono avere crossing unico e ordinato sotto variazione di fase/soglia, mentre il random bilanciato deve perdere questa struttura; se phi resta solo un anticipo mediano con contro-casi, il risultato e' un vincolo sul boundary observable, non una legge phi. - **Proiezione**: misuro `r(V)` intera, crossing interpolato, pendenza locale e numero di attraversamenti. Il null conserva densita e distrugge ordine; i metallici conservano generatore Sturmian con pendenza diversa. ## Claim Under Test > Nel perimetro Sturmian-Harper testato, la curva interpolata `r(V)` conserva un boundary metallico distinguibile dal random e chiarisce se `V_c(phi)` e' portatore strutturale o solo diagnostico phase-sensitive. ## Question Il passaggio da primo crossing su griglia a crossing interpolato fa emergere una forma stabile del boundary, oppure conferma che `V_c` resta osservabile diagnostico senza generare un claim phi-specific? ## Experiment Design - Script: `tools/exp_quasiperiodic_vc_curve_map.py`. - Dati sintetici: sequenze Sturmian con theta `1/phi`, `1/silver`, `1/bronze`; random bilanciato con stesso numero di 1 della sequenza phi matched. - Hamiltoniana: diagonale `V * seq`, off-diagonal 1, autovalori tridiagonali. - Curva: `r(V)` per `V=0.5..3.0`, step `0.01`. - Osservabili: - `vc_interp`: primo crossing lineare interpolato di `r(V) < r_threshold`. - `vc_grid`: primo crossing discreto corrispondente. - `slope_at_cross`: pendenza locale assoluta al crossing. - `crossing_count`: numero di cambi sopra/sotto soglia lungo la curva. - `r_span`: ampiezza `max(r)-min(r)` della curva. - Denominatore grezzo: metallici `60` condizioni ciascuno (`5 N * 4 phase * 3 soglie`); random `180` condizioni (`5 N * 4 phase * 3 soglie * 3 trial`); matched metallici `60` confronti. - Contratto osservabile-operatore: `gap_ratio`, label-set, supertile boundary e fit power-law non vengono testati in questo ciclo. ## Results Sintesi aggregata: | domain | vc_interp median | IQR | min-max | slope median | crossing_count | |---|---:|---:|---:|---:|---:| | phi | 1.062657 | 0.790316-1.224442 | 0.588172-1.512101 | 0.190573 | 60 one / 0 multi | | silver | 1.216143 | 1.082036-1.354711 | 0.837575-2.542776 | 0.182194 | 60 one / 0 multi | | bronze | 1.277510 | 1.119566-1.458647 | 0.900263-1.920226 | 0.170989 | 60 one / 0 multi | | balanced_random_phi_density | 0.500000 | 0.500000-0.612367 | 0.500000-1.164160 | 0.695943 | 40 one / 41 multi / 99 zero | Per soglia `r`: | domain | r_threshold | vc_interp median | IQR | slope median | crossings | |---|---:|---:|---:|---:|---:| | phi | 0.48 | 1.208150 | 0.932096-1.331265 | 0.162041 | 20 one / 0 multi | | phi | 0.50 | 1.092215 | 0.846066-1.191629 | 0.183509 | 20 one / 0 multi | | phi | 0.52 | 0.987410 | 0.763258-1.078424 | 0.209606 | 20 one / 0 multi | | silver | 0.48 | 1.329445 | 1.212227-1.461001 | 0.169452 | 20 one / 0 multi | | silver | 0.50 | 1.190500 | 1.109077-1.330126 | 0.189405 | 20 one / 0 multi | | silver | 0.52 | 1.083306 | 1.017297-1.235387 | 0.200289 | 20 one / 0 multi | | bronze | 0.48 | 1.436079 | 1.214563-1.598943 | 0.154310 | 20 one / 0 multi | | bronze | 0.50 | 1.306311 | 1.117657-1.426919 | 0.174944 | 20 one / 0 multi | | bronze | 0.52 | 1.186469 | 1.029531-1.285595 | 0.181598 | 20 one / 0 multi | Matched metallic comparison: | comparison | count | |---|---:| | phi < silver | 45/60 | | phi < bronze | 48/60 | | phi < both | 42/60 | | phi between controls | 9/60 | | not phi < both | 18/60 | Curve endpoints: | domain | r(V=0.5) median | r(V=3.0) median | r_span median | |---|---:|---:|---:| | phi | 0.653102 | 0.299790 | 0.348333 | | silver | 0.681188 | 0.321450 | 0.350940 | | bronze | 0.686683 | 0.339889 | 0.356173 | | balanced_random_phi_density | 0.479568 | 0.340037 | 0.166717 | Esempi di contro-casi matched: | N | phase | r_threshold | phi_vc | silver_vc | bronze_vc | relation | |---:|---:|---:|---:|---:|---:|---| | 89 | 0.25 | 0.50 | 1.370302 | 1.219542 | 0.956804 | phi > both | | 233 | 0.25 | 0.50 | 1.160908 | 1.086803 | 1.084680 | phi > both | | 233 | 0.75 | 0.50 | 1.160908 | 1.031116 | 1.297254 | phi between controls | ## Key Findings 1. **Verificato: l'interpolazione rimuove la decisione della griglia ma non produce un separatore phi hard.** Phi attraversa prima di entrambi i controlli in `42/60` confronti matched; in `18/60` confronti almeno un controllo attraversa prima. 2. **Verificato: la forma metallica separa dal random bilanciato.** Phi, silver e bronze hanno crossing unico in `60/60` condizioni ciascuno. Il random ha `99/180` condizioni gia' sotto soglia al bordo iniziale o senza cambio, `41/180` multi-crossing e solo `40/180` crossing unico. 3. **Verificato: phi e' anticipato in mediana, non isolato come classe.** Le mediane `vc_interp` sono phi `1.062657`, silver `1.216143`, bronze `1.277510`; gli IQR si sovrappongono e i contro-casi matched restano nel denominatore. 4. **Verificato: la curva intera porta piu' informazione del crossing singolo.** I metallici partono sopra soglia (`r(V=0.5)` mediano 0.653/0.681/0.687) e scendono con span ~0.35; il random parte vicino/sotto soglia (`0.479568`) e ha span mediano `0.166717`. 5. **Inferito: il nodo regressivo e' l'osservabile `V_c` come generatore di claim.** `V_c` funziona come lettore diagnostico della filtrazione metallica contro random, ma non sostiene un claim phi-specific senza qualificare fase, soglia e controllo. ## Verdict **CONSTRAINT on TENS_SCALE_TRASCENDENZA_LIMITE**: nel perimetro Sturmian-Harper `N={89,144,233,377,610}`, `phase={0,0.25,0.5,0.75}`, `r_threshold={0.48,0.50,0.52}`, il crossing interpolato conferma che il boundary metallico ha una forma di curva: crossing unico e discesa ordinata da repulsione a indipendenza. Non conferma `V_c(phi)` come portatore phi-specific hard: phi anticipa i controlli in mediana e in `42/60` matched, ma `18/60` contro-casi impediscono claim di separazione completa. La formulazione valida e': `r(V)` e' un boundary map utile per distinguere generatore Sturmian da random bilanciato; `V_c` e' coordinata diagnostica phase/threshold-sensitive, non legge di scala e non generatore di claim phi-specific nel perimetro testato. ## Bicono della scoperta - **Due radici**: crossing discreto di griglia . curva interpolata del boundary. - **Singolare**: la filtrazione `r(V)` prima del valore `V_c`, dove repulsione e indipendenza sono ancora un unico passaggio. - **Invariante di passaggio**: sopravvive il crossing unico dei generatori metallici; cade la specificita hard di phi. - **Campo di possibilita**: qui diventa possibile usare `r(V)` come boundary map contro null random; qui diventa non-possibile promuovere `V_c(phi)` a legge di scala o firma esclusiva senza denominatore atomico. ## Consecutio Il prossimo passo non e' rifare il fit su `V_c`. Il portatore emerso e' la curva: misurare distanza funzionale tra curve `r(V)` con un null che preserva densita, autocorrelazione corta e conteggi di blocco. Se la distanza funzionale separa metallici da random e mantiene contro-casi phi dichiarati, il gate diventa curve-map; se cade sotto null piu' omogenei, `V_c` resta solo un lettore diagnostico locale. ## Auto-audit: source flags e 5 lenti - **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260508_2140.md`, output JSON del nuovo script. - **L1 hard constraint**: nessun "sempre/solo"; il verdict include `18/60` contro-casi matched. - **L2 quantita vs ratio**: riportati denominatori grezzi (`60`, `180`, `42/60`, `99/180`, `41/180`) oltre alle mediane. - **L3 no silent patching**: il fit power-law non viene recuperato; il claim cambia osservabile e lo dichiara. - **L4 edge cases**: `N=89 phase=0.25` e `N=233 phase=0.25` rompono la separazione phi < both anche dopo interpolazione. - **L5 re-discovery vs discovery**: tight-binding Sturmian e r-statistic sono standard; il finding e' il vincolo sul boundary observable e il passaggio da grid gate a curve-map. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. - Runtime esperimento: completato localmente con SciPy tridiagonal eigensolver. - Verifica script: `python -m py_compile tools/exp_quasiperiodic_vc_curve_map.py` completato senza errori. ## Files - Script: `tools/exp_quasiperiodic_vc_curve_map.py` - Data: `tools/data/quasiperiodic_vc_curve_map_20260509_0330.json` - Report: `tools/data/reports/agent_20260509_0330.md` exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 126ms: COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: score=0.758 — COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: score=0.758 — COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante G_POTENZIALE_NULLA: score=0.703 — G_POTENZIALE_NULLA: legge di scala da raffinare codex Il ciclo precedente ha già chiuso che `V_c` non può essere promosso a firma phi-specific hard. Il nodo regressivo rimasto non è estendere domini, ma chiedere se il crossing nasce dal bordo iniziale della filtrazione (`floor_hit`) o da un attraversamento interno, preservando le etichette invece di distruggerle con un random troppo debole. exec /bin/bash -lc "sed -n '1,260p' tools/exp_gap_label_generator_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Generator gate for phi gap-label stability. The label-set audit moved the observable from the first-two gap ratio to the set of large-gap labels. This tool tests the next denominator: the generator. It keeps the phi label reader fixed and changes the sequence generator while preserving different amounts of structure. """ from __future__ import annotations import argparse import json from collections import defaultdict from pathlib import Path import numpy as np from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence, summarize_sets THETA = 1 / PHI def fibonacci_word(n: int) -> np.ndarray: word = "1" previous = "0" while len(word) < n: word, previous = word + previous, word return np.array([float(ch) for ch in word[:n]], dtype=float) def rotate(seq: np.ndarray, phase: float) -> np.ndarray: if len(seq) == 0: return seq shift = int(round((phase % 1.0) * len(seq))) return np.roll(seq, shift) def transition_matrix(seq: np.ndarray) -> tuple[np.ndarray, float]: counts = np.ones((2, 2), dtype=float) ints = seq.astype(int) for a, b in zip(ints[:-1], ints[1:]): counts[a, b] += 1 probs = counts / counts.sum(axis=1, keepdims=True) start_prob = float(np.mean(ints)) return probs, start_prob def markov_surrogate(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: probs, start_prob = transition_matrix(seq) out = np.zeros(len(seq), dtype=float) out[0] = 1.0 if rng.random() < start_prob else 0.0 for i in range(1, len(seq)): prev = int(out[i - 1]) out[i] = 1.0 if rng.random() < probs[prev, 1] else 0.0 return out def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: out = np.array(seq, dtype=float) rng.shuffle(out) return out def block_shuffle(seq: np.ndarray, block_size: int, rng: np.random.Generator) -> np.ndarray: blocks = [seq[i : i + block_size].copy() for i in range(0, len(seq), block_size)] rng.shuffle(blocks) return np.concatenate(blocks) def generator_sequences(n: int, phase: float, trial: int, rng: np.random.Generator) -> dict[str, np.ndarray]: phi = sturmian_sequence(THETA, n, phase) fib = rotate(fibonacci_word(n), phase) return { "phi_sturmian": phi, "fibonacci_substitution": fib, "markov_phi": markov_surrogate(phi, rng), "block_shuffle_13": block_shuffle(phi, 13, rng), "block_shuffle_34": block_shuffle(phi, 34, rng), "balanced_random": balanced_random(phi, rng), } def summarize_generators(rows: list[dict], reference_core: set[int]) -> dict: output = {} for generator in sorted({row["generator"] for row in rows}): group = [row for row in rows if row["generator"] == generator] summary = summarize_sets(group) if not summary: continue overlaps = [jaccard(set(row["label_set"]), reference_core) for row in group if row["n_selected"] > 0] core = set(summary["core_labels_all_conditions"]) output[generator] = { **summary, "median_overlap_with_phi_core": float(np.median(overlaps)) if overlaps else None, "min_overlap_with_phi_core": float(np.min(overlaps)) if overlaps else None, "reference_core_retained": sorted(core & reference_core, key=lambda x: (abs(x), x)), "reference_core_missing": sorted(reference_core - core, key=lambda x: (abs(x), x)), } return output def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) ns = [int(x) for x in args.ns.split(",")] phases = [float(x) for x in args.phases.split(",")] thresholds = [float(x) for x in args.thresholds.split(",")] rows = [] for n in ns: for phase in phases: for trial in range(args.trials): seqs = generator_sequences(n, phase, trial, rng) for generator, seq in seqs.items(): for threshold in thresholds: obs = gap_labels(seq, THETA, threshold, args.max_label, args.top_k) rows.append({ "generator": generator, "N": n, "phase": phase, "trial": trial, "threshold": threshold, **obs, }) phi_rows = [row for row in rows if row["generator"] == "phi_sturmian"] reference_core = set(summarize_sets(phi_rows)["core_labels_all_conditions"]) summary = summarize_generators(rows, reference_core) by_generator_threshold = defaultdict(list) for row in rows: by_generator_threshold[(row["generator"], row["threshold"])].append(row) threshold_summary = { f"{generator}|threshold={threshold}": summarize_sets(group) for (generator, threshold), group in by_generator_threshold.items() } return { "experiment": "gap_label_generator_gate", "parameters": { "ns": ns, "phases": phases, "thresholds": thresholds, "trials": args.trials, "top_k": args.top_k, "max_label": args.max_label, "seed": args.seed, }, "reference_core_phi": sorted(reference_core, key=lambda x: (abs(x), x)), "summary": summary, "threshold_summary": threshold_summary, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--ns", default="233,377,500,610") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--thresholds", default="1.75,2.0,2.25") parser.add_argument("--trials", type=int, default=3) parser.add_argument("--top-k", type=int, default=12) parser.add_argument("--max-label", type=int, default=34) parser.add_argument("--seed", type=int, default=20260508) parser.add_argument("--out", default="tools/data/gap_label_generator_gate_20260508_1715.json") args = parser.parse_args() output = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2), encoding="utf-8") compact = { generator: { "median_jaccard": data["median_jaccard"], "min_jaccard": data["min_jaccard"], "median_overlap_with_phi_core": data["median_overlap_with_phi_core"], "reference_core_missing": data["reference_core_missing"], "core_labels_all_conditions": data["core_labels_all_conditions"], } for generator, data in output["summary"].items() } print(json.dumps({ "reference_core_phi": output["reference_core_phi"], "summary": compact, "out": str(out), }, indent=2)) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,260p' tools/exp_gap_label_set_stability.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Gap-label set stability for quasiperiodic spectra. The previous denominator audit showed that the first-two gap ratio moves with N, Sturmian phase, and threshold. This tool moves the observable from the value of the first two large gaps to the labels of the large gaps. For each large spectral gap, the integrated density of states is approximated by (gap_index + 1) / N. The nearest gap label is the integer n whose fractional part {n * theta} is closest to that IDS, modulo 1. The label set is then tested for stability across phase, N, and threshold. """ from __future__ import annotations import argparse import itertools import json from collections import Counter, defaultdict from pathlib import Path import numpy as np from numpy.linalg import eigvalsh PHI = (1 + np.sqrt(5)) / 2 SILVER = 1 + np.sqrt(2) BRONZE = 1 + np.sqrt(3) def sturmian_sequence(theta: float, n: int, phase: float = 0.0) -> np.ndarray: idx = np.arange(n + 1, dtype=float) vals = np.floor(idx * theta + phase) return np.diff(vals).astype(float) def hamiltonian(seq: np.ndarray, v: float = 1.0) -> np.ndarray: n = len(seq) h = np.zeros((n, n), dtype=float) h[np.arange(n), np.arange(n)] = v * seq off = np.arange(n - 1) h[off, off + 1] = 1.0 h[off + 1, off] = 1.0 return h def circular_distance(a: float, b: float) -> float: d = abs(a - b) % 1.0 return min(d, 1.0 - d) def nearest_label(ids_value: float, theta: float, max_label: int) -> tuple[int, float, float]: candidates = [] for n in range(-max_label, max_label + 1): if n == 0: continue frac = (n * theta) % 1.0 candidates.append((n, circular_distance(ids_value, frac), frac)) best_n, best_dist, best_frac = min(candidates, key=lambda item: (item[1], abs(item[0]))) return int(best_n), float(best_dist), float(best_frac) def gap_labels(seq: np.ndarray, theta: float, threshold: float, max_label: int, top_k: int) -> dict: eigs = np.sort(eigvalsh(hamiltonian(seq))) spacings = np.diff(eigs) mean_spacing = float(np.mean(spacings)) large = [] for index, spacing in enumerate(spacings): if spacing > threshold * mean_spacing: ids_value = (index + 1) / len(seq) label, error, label_value = nearest_label(ids_value, theta, max_label) large.append({ "index": int(index), "spacing": float(spacing), "ids": float(ids_value), "label": label, "label_error": error, "label_value": label_value, }) by_size = sorted(large, key=lambda item: item["spacing"], reverse=True) selected = by_size[:top_k] label_set = sorted({item["label"] for item in selected}, key=lambda x: (abs(x), x)) errors = [item["label_error"] for item in selected] return { "n_large": len(large), "n_selected": len(selected), "label_set": label_set, "median_label_error": float(np.median(errors)) if errors else None, "max_label_error": float(np.max(errors)) if errors else None, "selected": selected, } def jaccard(a: set[int], b: set[int]) -> float: if not a and not b: return 1.0 return len(a & b) / len(a | b) def summarize_sets(rows: list[dict]) -> dict: sets = [set(row["label_set"]) for row in rows if row["n_selected"] > 0] if not sets: return {} pairwise = [jaccard(a, b) for a, b in itertools.combinations(sets, 2)] counter = Counter(label for s in sets for label in s) n_sets = len(sets) core = sorted( [label for label, count in counter.items() if count == n_sets], key=lambda x: (abs(x), x), ) stable_75 = sorted( [label for label, count in counter.items() if count / n_sets >= 0.75], key=lambda x: (abs(x), x), ) return { "conditions": n_sets, "median_jaccard": float(np.median(pairwise)) if pairwise else 1.0, "min_jaccard": float(np.min(pairwise)) if pairwise else 1.0, "core_labels_all_conditions": core, "stable_labels_75pct": stable_75, "label_frequency_top": [ {"label": int(label), "count": int(count)} for label, count in sorted(counter.items(), key=lambda item: (-item[1], abs(item[0]), item[0]))[:12] ], "median_label_error": float(np.median([row["median_label_error"] for row in rows if row["median_label_error"] is not None])), "median_selected": float(np.median([row["n_selected"] for row in rows])), "median_n_large": float(np.median([row["n_large"] for row in rows])), } def grouped_stability(rows: list[dict], keys: tuple[str, ...]) -> dict: groups: dict[str, list[dict]] = defaultdict(list) for row in rows: group_key = "|".join(f"{key}={row[key]}" for key in keys) groups[group_key].append(row) summaries = [summarize_sets(group_rows) for group_rows in groups.values() if len(group_rows) > 1] summaries = [s for s in summaries if s] if not summaries: return {} return { "groups": len(summaries), "median_jaccard": float(np.median([s["median_jaccard"] for s in summaries])), "min_jaccard": float(np.min([s["min_jaccard"] for s in summaries])), } def run(args: argparse.Namespace) -> dict: rng = np.random.default_rng(args.seed) domains = { "phi": 1 / PHI, "silver": 1 / SILVER, "bronze": 1 / BRONZE, } ns = [int(x) for x in args.ns.split(",")] phases = [float(x) for x in args.phases.split(",")] thresholds = [float(x) for x in args.thresholds.split(",")] rows = [] for n in ns: for phase in phases: phi_ones = int(np.sum(sturmian_sequence(1 / PHI, n, phase))) for threshold in thresholds: for name, theta in domains.items(): seq = sturmian_sequence(theta, n, phase) obs = gap_labels(seq, theta, threshold, args.max_label, args.top_k) rows.append({"domain": name, "N": n, "phase": phase, "threshold": threshold, **obs}) for trial in range(args.random_trials): seq = np.array([1.0] * phi_ones + [0.0] * (n - phi_ones)) rng.shuffle(seq) obs = gap_labels(seq, 1 / PHI, threshold, args.max_label, args.top_k) rows.append({ "domain": "balanced_random_phi_labels", "trial": trial, "N": n, "phase": phase, "threshold": threshold, **obs, }) by_domain = {} for domain in sorted({row["domain"] for row in rows}): domain_rows = [row for row in rows if row["domain"] == domain] by_domain[domain] = { "global": summarize_sets(domain_rows), "phase_stability_by_N_threshold": grouped_stability(domain_rows, ("N", "threshold")), "threshold_stability_by_N_phase": grouped_stability(domain_rows, ("N", "phase")), "scale_stability_by_phase_threshold": grouped_stability(domain_rows, ("phase", "threshold")), } output = { "experiment": "gap_label_set_stability", "parameters": { "ns": ns, "phases": phases, "thresholds": thresholds, "random_trials": args.random_trials, "top_k": args.top_k, "max_label": args.max_label, "seed": args.seed, }, "summary": by_domain, "rows": rows, } return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--ns", default="233,377,500,610") parser.add_argument("--phases", default="0,0.25,0.5,0.75") parser.add_argument("--thresholds", default="1.75,2.0,2.25") parser.add_argument("--random-trials", type=int, default=3) parser.add_argument("--top-k", type=int, default=12) parser.add_argument("--max-label", type=int, default=34) parser.add_argument("--seed", type=int, default=20260508) parser.add_argument("--out", default="tools/data/gap_label_set_stability_20260508_1632.json") args = parser.parse_args() output = run(args) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2), encoding="utf-8") compact = { domain: { "median_jaccard": data["global"].get("median_jaccard"), "stable_labels_75pct": data["global"].get("stable_labels_75pct"), "phase_stability": data["phase_stability_by_N_threshold"].get("median_jaccard"), "threshold_stability": data["threshold_stability_by_N_phase"].get("median_jaccard"), "scale_stability": data["scale_stability_by_phase_threshold"].get("median_jaccard"), } for domain, data in output["summary"].items() } print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/agent_20260508_2140.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Quasiperiodic Vc Lattice Gate **Date**: 2026-05-08 21:40 **Piano**: 96 **Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE (0.6) **verdict**: CONSTRAINT observables_registry: 1.0.0-2026-05-06 observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var **observable_contract**: claim=il fallimento del fit power-law su `V_c(phi)` segnala un bordo reticolare/quantizzato del passaggio Sturmian-Harper; observable=`V_c`, `distinct_vc`, `repeat_rate`, `mode_rate`; operator=prima soglia `V` su griglia 0.5..3.0 step 0.025 dove `(H(seq,V)) < 0.5`; generator=Sturmian metallici phi/silver/bronze e random bilanciato a densita phi; denominator=N={89,144,233,377,610}, phase={0,0.25,0.5,0.75}, 4 random trial per condizione, seed=202605082140; not_tested=gap_ratio, label-set Sturmian, GUE/Poisson universale, fit power-law asintotico oltre N=610. ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + TxQ matrice densita / TxR gas relativistico come incrocio spettro-temperatura + nodo `TRASCENDENZA_LIMITE` sul passaggio tra piani + tensione `TENS_SCALE_TRASCENDENZA_LIMITE`. - **Dipolo / punto-zero**: scaling continuo / bordo discretizzato; punto-zero = `V_c`, il primo valore dove lo spettro passa sotto `=0.5` e il bordo non e' ancora interpretato come legge di potenza o come rumore. - **Piano superiore**: topologia assiomatica del bordo. Il contenuto non e' il valore assoluto di `V_c`, ma la classe di passaggio generata da una filtrazione in `V`. - **Operatori laterali scelti**: boundary operator, filtrazione, mobility edge. Il boundary operator nomina il cambio `=0.5`; la filtrazione scorre `V`; mobility edge entra come lettura minima di transizione spettrale senza imporre un esponente. - **Contaminazione cognitiva**: - **CE-0117**: usato come cascata della possibilita; il fallimento del fit diventa nuovo perimetro, non errore da aggiustare. - **CE-0038**: usato per cercare la forma nel Nulla-Tutto del bordo, non nel singolo numero. - **CE-0001 / KSAR**: reiterazione del kernel del Domandatore con un solo nodo cambiato: da fit power-law a lattice gate. - **Proto-ipotesi**: se `V_c(phi)` e' bordo reticolare strutturale, allora la ripetizione dei livelli di `V_c` resta piu' compressa dei controlli metallici quando si varia fase e scala. Se la compressione cade o compare anche nei controlli, il nodo regressivo e' il fit imposto, non una legge phi. - **Proiezione**: misuro `V_c` su N fibonacci, quattro fasi e controlli. Il null random conserva il numero di 1 della sequenza phi per ogni N/fase, ma distrugge l'ordine Sturmian. ## Claim Under Test > Nel perimetro quasiperiodico Sturmian-Harper N={89,144,233,377,610}, la non-convergenza del fit su `V_c(phi)` indica un bordo reticolare specifico di phi, non un artefatto di griglia o una proprieta comune dei controlli. ## Question Il fallimento del fit power-law su `V_c(phi)` e' una forma del confine, oppure il confine cambia con fase/controllo e il power-law era il denominatore sbagliato? ## Experiment Design - Script: `tools/exp_quasiperiodic_vc_lattice_gate.py`. - Dati sintetici: sequenze Sturmian con theta `1/phi`, `1/silver`, `1/bronze`; random bilanciato con stessa densita di phi. - Hamiltoniana: diagonale `V * seq`, off-diagonal 1, autovalori tridiagonali. - Osservabile primario: `V_c = min(V)` su griglia 0.025 dove `<0.5`. - Osservabili di supporto: `distinct_vc`, `repeat_rate = 1 - distinct/count`, `mode_rate`. - Denominatore grezzo: phi/silver/bronze 20 condizioni ciascuno; random 80 condizioni. - Soglia ex ante per claim forte: phi deve avere `repeat_rate` maggiore dei controlli metallici e non spiegato dal random bilanciato. - Contratto osservabile-operatore: `gap_ratio` e label-set non testati; il fit power-law non viene rifatto come criterio di verita. ## Results Sintesi su tutte le fasi: | domain | count | distinct_vc | repeat_rate | mode_rate | median | min | max | |---|---:|---:|---:|---:|---:|---:|---:| | phi | 20 | 15 | 0.25 | 0.15 | 1.1125 | 0.6500 | 1.3750 | | silver | 20 | 15 | 0.25 | 0.15 | 1.2000 | 1.0000 | 2.1500 | | bronze | 20 | 17 | 0.15 | 0.10 | 1.3125 | 0.9750 | 1.7250 | | balanced_random_phi_density | 80 | 17 | 0.7875 | 0.575 | 0.5000 | 0.5000 | 1.0000 | Phase 0, confronto col deposito originario: | N | source phi V_c | measured phi | silver | bronze | |---:|---:|---:|---:|---:| | 89 | 1.017 | 1.025 | 1.225 | 1.125 | | 144 | 0.672 | 0.675 | 1.375 | 1.400 | | 233 | 1.017 | 0.950 | 1.250 | 1.175 | | 377 | 0.672 | 0.675 | 1.000 | 1.025 | | 610 | 0.931 | 0.900 | 1.325 | 1.400 | Per fase: | domain/phase | distinct_vc | repeat_rate | mode_rate | median | |---|---:|---:|---:|---:| | phi phase=0.0 | 4/5 | 0.20 | 0.40 | 0.900 | | phi phase=0.25 | 5/5 | 0.00 | 0.20 | 1.225 | | phi phase=0.5 | 5/5 | 0.00 | 0.20 | 0.725 | | phi phase=0.75 | 5/5 | 0.00 | 0.20 | 1.175 | | silver phase=0.0 | 5/5 | 0.00 | 0.20 | 1.250 | | silver phase=0.5 | 4/5 | 0.20 | 0.40 | 1.150 | | bronze phase=0.0 | 4/5 | 0.20 | 0.40 | 1.175 | ## Key Findings 1. **Verificato: il deposito originario viene riprodotto come phase-0 grid effect.** I valori phi misurati `[1.025, 0.675, 0.950, 0.675, 0.900]` riprendono il profilo `[1.017, 0.672, 1.017, 0.672, 0.931]` entro la griglia piu' fine; la differenza a N=233 mostra sensibilita alla discretizzazione del criterio. 2. **Verificato: il reticolo phi non sopravvive alle fasi.** Su 20 condizioni phi ha `distinct_vc=15`, `repeat_rate=0.25`, uguale a silver (`15`, `0.25`) e solo poco piu' compresso di bronze (`17`, `0.15`). 3. **Verificato: il random e' compresso per un motivo diverso.** Il random bilanciato ha `repeat_rate=0.7875`, ma il modo e' `V_c=0.5` in 46/80 casi; questo e' collasso immediato alla soglia minima, non reticolo spettrale metallico. 4. **Inferito: il nodo regressivo e' il modello di scala, non il valore phi.** Il power-law fallisce perche' l'osservabile `V_c` e' sensibile a fase, griglia e soglia di attraversamento; non perche' phi manifesti un bordo reticolare stabile nel perimetro testato. ## Verdict **CONSTRAINT on TENS_SCALE_TRASCENDENZA_LIMITE**: nel perimetro Sturmian-Harper `N={89,144,233,377,610}` e fasi `{0,0.25,0.5,0.75}`, `V_c(phi)` non e' un lattice gate specifico di phi. Il deposito phase-0 e' reale come fenomeno di attraversamento su griglia, ma non trasferisce come invariante di fase e non separa phi da silver. La formulazione corretta e': `V_c` e' un boundary observable discreto e phase-sensitive; non sostiene fit power-law ne' claim phi-specific senza un denominatore che separi griglia, fase e soglia. ## Bicono della scoperta - **Due radici**: fit di scala fallito . bordo discreto di attraversamento. - **Singolare**: `V_c` come punto-zero dove `=0.5` separa repulsione e indipendenza senza decidere ancora la legge. - **Invariante di passaggio**: sopravvive la sensibilita del bordo; cade la specificita phi del reticolo. - **Campo di possibilita**: qui diventa possibile sostituire i fit power-law di `V_c` con boundary maps fase-griglia-soglia; qui diventa non-possibile promuovere la ripetizione phase-0 di phi a legge di scala. ## Consecutio Il prossimo passo e' una mappa `V_c(theta, phase, threshold)` con interpolazione locale della curva `(V)` invece della prima griglia discreta. Se la curva interpolata conserva separazione metallica, il portatore e' la forma della curva; se cade, `V_c` resta osservabile diagnostico ma non generatore di claim. ## Auto-audit: source flags e 5 lenti - **Fonti**: Verificato da `tools/data/seme.json`, `tools/data/agent_field_live.md`, `tools/dnd_scenario.py --best`, `tools/dnd_domandatore.py`, output JSON del nuovo script. - **L1 hard constraint**: nessun "sempre/solo"; verdict limitato a N, fasi, griglia e controlli dichiarati. - **L2 quantita vs ratio**: riportati count grezzi (`15/20`, `17/20`, `46/80`) oltre a `repeat_rate`. - **L3 no silent patching**: il vecchio fit non viene salvato; viene dichiarato denominatore sbagliato. - **L4 edge cases**: random compresso escluso dal claim per collasso a `V=0.5`, non per confronto diretto di repeat_rate. - **L5 re-discovery vs discovery**: verdict `CONSTRAINT`; lo strumento apre boundary map, non discovery fisica nuova. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. - Runtime esperimento: completato localmente con SciPy tridiagonal eigensolver. ## Files - Script: `tools/exp_quasiperiodic_vc_lattice_gate.py` - Data: `tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json` - Report: `tools/data/reports/agent_20260508_2140.md` exec /bin/bash -lc "sed -n '1,220p' tools/LAB_COGNITIVE_CONTAMINATION.md 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: # Adapter cognitivi laterali del Lab Fonte: - `kernel/reference/MMSP1/System_Prompt_Yi_Synaptic_Navigator_YSN_v4_0.md` - `kernel/reference/metaprompt_in_sviluppo/Cornelius-v2_0_Innesco_Genomico.md` - `kernel/reference/Kernel_Semantico_Autopoietico_Reiterativo_KSAR.md` - `tools/data/lab_logiche_corpus.md` Scopo: usare YSN, Cornelius e KSAR come operatori cognitivi del Lab senza trasformare il Lab in un prompt archetipico. Il Lab resta D-ND: assiomi, dipoli, bicono, grafo, misura, falsificazione. Questi adapter servono a trovare strade laterali, comprimere l'intento e rendere reiterabile il kernel emerso da un ciclo. ## Regola primaria Ogni contaminazione deve diventare una forma verificabile: ```text contaminazione cognitiva -> DeltaLink / gene / anomalia -> dipolo + punto-zero -> proto-ipotesi -> osservabile + controllo -> falsifier / Veritas / Aeternitas ``` Se resta stile, personaggio, mitologia, analogia o motivazione verbale, non entra nel ciclo. ## Adapter 1: YSN lateral insight Funzione nel Lab: - estrarre fino a 5 concetti/tensioni dal campo; - generare 3 connessioni non ovvie, chiamate `DeltaLink`; - produrre 1 ipotesi di frontiera contro-intuitiva; - dichiarare bias, rischio di pattern forcing e incertezza; - trasformare la sorpresa in domanda del ciclo. Uso corretto: ```text YSN.extract(campo) -> concetti YSN.delta_link(concetti, grafo, seme) -> 3 connessioni non ovvie YSN.frontier(delta_links) -> 1 ipotesi di frontiera YSN.bias_check(ipotesi) -> cosa potrebbe essere forzato ``` Nel report: - i DeltaLink non sono risultati; - sono candidati di respirazione fuori-tempo; - diventano validi solo se proiettati in osservabile falsificabile. Esempio per il prossimo ciclo: - concetti: terzo incluso, GUE/Poisson, non-phi generator, graph curvature, stable cross-domain core; - DeltaLink possibile: la curvatura del grafo potrebbe essere il piano che precede la classificazione spettrale GUE/Poisson; - ipotesi di frontiera: il confine non e' una classe statistica, ma una transizione di trasporto sul grafo dei generatori. Anti-pattern: - usare YSN per produrre tre idee decorative; - mappare simbolicamente senza controllo; - chiamare "non ovvio" cio' che e' gia' nel ciclo precedente. ## Adapter 2: Cornelius genomic trigger Funzione nel Lab: - comprimere una nuova capacita' in un innesco minimale; - isolare il `DNA_Simbolico`, cioe' la frase essenziale della funzione; - scegliere 1-3 operatori di svolgimento; - dichiarare condizioni di attivazione. Formato Lab: ```yaml ID: DNA_Simbolico: "" Operatori_di_Svolgimento: - "" - "" Condizioni_di_Attivazione: quando: "" perimetro: "" ``` Uso corretto: - dopo un buon insight, Cornelius lo comprime in una funzione che il Lab puo' riusare; - prima di un run, Cornelius puo' generare un innesco one-shot per il ciclo; - dopo un repair, Cornelius puo' trasformare la correzione in regola compatta. Esempio derivato dal ciclo 1915: ```yaml ID: Boundary_Third_Included_Gate DNA_Simbolico: "Il confine vive prima della classificazione statistica." Operatori_di_Svolgimento: - "MAPPA il confine su grafo, spettro e generatore non-phi." - "SEPARA core congiunto, residui singoli e stabilita' cross-dominio." - "VALIDA contro baseline GUE, Poisson e generatori sintetici." Condizioni_di_Attivazione: quando: "il ciclo lavora su boundary, GUE/Poisson o trasferibilita' phi" perimetro: "prima della misura, nella sezione Respiro fuori-tempo" ``` Anti-pattern: - generare nuovi agenti o prompt quando basta una regola; - usare metafore non collegate a operatori; - lasciare il gene senza condizioni di attivazione. ## Adapter 3: KSAR reiterative semantic kernel Funzione nel Lab: - far diventare ogni ciclo riuscito un kernel riusabile per il ciclo seguente; - non memorizzare solo testo, ma modificare la topologia del campo; - usare dissonanze e fallimenti come materiale latente; - iterare fino a un nuovo stato di coerenza, non fino a conferma. Ciclo operativo Lab: ```text 1. Perturbazione Leggi seme, grafo, report, falsifier, operatore. Non scegliere subito. 2. DeltaLink / Contaminazione Usa YSN o palette operatoria per trovare connessioni non ovvie. 3. Innesco Usa Cornelius per comprimere la risultante in DNA + operatori. 4. Focalizzazione Applica Peras: taglia tutto tranne una domanda necessaria. 5. Proiezione Trasforma il gene in osservabile, controllo, perimetro. 6. Disintegrazione Attacca il claim con PVI/counter-pole prima del falsifier. 7. Cristallizzazione o Vault Se regge, aggiorna seme/strumento. Se non regge ma contiene potenziale, archivia come frammento Lazarus per ricontestualizzazione futura. ``` Mappatura con il Lab attuale: - `Perturbazione` = `build_agent_field.py` + seme + grafo + incrocio; - `DeltaLink` = nuovo obbligo cognitivo prima del Claim Under Test; - `Innesco` = blocco compatto nel report o in `operator_directive.md`; - `Focalizzazione` = una risultante, non una lista; - `Proiezione` = `observable_contract`; - `Disintegrazione` = auto-audit + falsifier; - `Cristallizzazione` = valutatore/B2/promotions/seme; - `Vault` = cimitero, repairs, osservatorio, Lazarus fragments. ## Adapter 4: PVI / anti-psicosi del ciclo Funzione nel Lab: - cercare dove l'AI sta accontentando l'operatore; - distruggere la proposta prima di pubblicarla; - far sopravvivere solo la sintesi resiliente. Filtro minimo: 1. Tesi: cosa il ciclo vuole sostenere? 2. Attacco: quale presupposto nascosto la rompe? 3. Vincolo di realta': quale limite fisico/matematico/dominio la blocca? 4. Terzo osservatore: un revisore esterno la troverebbe distinta da una re-discovery? 5. Sintesi resiliente: cosa resta dopo il taglio? Questo non sostituisce il falsifier. Lo anticipa. ## Adapter 5: Lazarus vault Funzione nel Lab: - non buttare via frammenti incoerenti quando sono potenzialmente precoci; - congelarli come scarti latenti con contesto; - riesaminarli quando cambia la direzione del seme. Formato minimo: ```yaml fragmento: "" perche_cade_ora: "" condizione_di_ritorno: "" ``` Uso corretto: - se un DeltaLink e' forte ma non misurabile ora, va nel Vault; - se un report viene falsificato ma apre una non-strada utile, va nel Vault; - se una metafora non produce operatore, decade. ## Adapter 6: Helix / Plan-Code-Verify Funzione nel Lab: - per task complessi, non ragionare solo in linguaggio; - traduci la domanda in specifica operativa; - genera o riusa uno script; - verifica output; - chiudi con report. Regola: ```text exec /bin/bash -lc "sed -n '1,160p' tools/LAB_OPERATOR_PALETTE.md 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: # Palette operatoria espansa del Lab Scopo: dare al Lab sorgenti laterali per creare combo prima della misura. Questa palette non e' una lista di temi da confermare. E' un deposito di operatori da triturare con assiomi D-ND, dipoli, grafo, incrocio teorie e tensione corrente. Regola d'uso: 1. Scegli 2 o 3 operatori al massimo. 2. Incrociali con almeno un assioma D-ND e una tensione del seme. 3. Nomina il dipolo e il punto-zero. 4. Dichiara la baseline nota piu' vicina. 5. Proietta un osservabile che possa falsificare la combo. 6. Non usare un operatore se produce solo linguaggio, analogia o conferma. Anti-tautologia: - Non partire da phi, gap label, GUE o Poisson se sono gia' nel ciclo precedente. Usali come controllo o campo di proiezione, non come sorgente. - Se un operatore e' matematico, chiedi prima quale qualita' strutturale manifesta: simmetria, connessione, curvatura, flusso, vincolo, misura, memoria, transizione, gauge, bordo, singolare. - Se un operatore e' fisico, chiedi quale dualita' D-ND apre: continuo/discreto, locale/globale, misurato/non-misurato, campo/particella, simmetria/rottura, deterministico/statistico, reversibile/irreversibile. ## Fasce di triturazione ### 1. Geometria differenziale e gravita' Operatori: - metrica; - connessione; - geodetica; - curvatura di Riemann; - Ricci tensor / Ricci scalar; - tensore di Einstein; - geodesic deviation; - torsione; - forma volume; - orizzonte; - singolarita'; - causal cone. Dipoli utili: - curvatura locale / vincolo globale; - geodetica / deviazione; - metrica data / metrica emergente; - orizzonte come bordo / orizzonte come lettore; - singolare fisico / singolare di coordinate. Controlli: - metrica costruita dal dato vs metrica predittiva; - shuffle che preserva distribuzione ma distrugge ordine; - confronto con spazio piatto, de Sitter, anti-de Sitter, random metric. Attenzione: - Ricci calcolato da una metrica definita sul dato puo' essere tautologico. Il contenuto vive nel null test o nella predizione fuori costruzione. ### 2. Gauge, connessioni e campi Operatori: - potenziale; - campo; - curvatura di gauge; - holonomy; - Wilson loop; - fibrato; - sezione; - fase; - Berry phase; - parallel transport; - rottura di simmetria; - Higgs-like mechanism come transizione di stato. Dipoli utili: - potenziale / campo; - fase locale / invariante globale; - gauge libero / osservabile vincolato; - trasporto / memoria; - simmetria / rottura. Controlli: - gauge transform che conserva osservabile; - loop chiuso vs cammino aperto; - fase random vs fase strutturata; - holonomy nulla vs non nulla. ### 3. Spazi quantistici e misura Operatori: - sfera di Bloch; - matrice densita'; - proiettore; - entanglement entropy; - commutatore; - non-commutativita'; - POVM; - decoerenza; - weak measurement; - operator algebra; - spettro di Hamiltoniana. Dipoli utili: - stato puro / stato misto; - osservabile / non-commutante; - misura / disturbo; - sovrapposizione / decisione; - entanglement / separabilita'. Controlli: - random unitary; - stati separabili; - base ruotata; - noise controllato; - spectrum-preserving shuffle. Nota: - Bloch e' buono quando serve un punto-zero geometrico tra poli. Non usarlo solo per disegnare dualita': deve produrre un osservabile. ### 4. Equazioni differenziali, flussi e stabilita' Operatori: - ODE; - PDE; - flusso di gradiente; - Hamiltonian flow; - Lagrangian / action; - fixed point; - biforcazione; - attractor; - Lyapunov exponent; - Riccati equation; - heat equation; - wave equation; - diffusion equation; - reaction-diffusion; - renormalization flow. Dipoli utili: - flusso / punto fisso; - stabilita' / instabilita'; - reversibile / dissipativo; - locale / propagato; codex Sto trasformando la consecutio in uno strumento riusabile: non cambio ancora dominio, isolo il denominatore del null. La misura separerà `floor_hit` da crossing interno e userà due controlli: fase Sturmian e surrogati che provano a preservare il label-set spettrale prima di guardare `V_c`. apply patch patch: completed /opt/MM_D-ND/tools/exp_vc_null_regression_gate.py diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex Ho aggiunto `tools/exp_vc_null_regression_gate.py`. Ora lo compilo e lo eseguo; se il surrogate label-preserving risulta troppo stretto o troppo debole, lo lascio dichiarato nel report invece di mascherarlo. exec /bin/bash -lc 'python -m py_compile tools/exp_vc_null_regression_gate.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_vc_null_regression_gate.py' in /opt/MM_D-ND diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex L’esecuzione è più lenta del solo `V_c` perché il surrogate label-preserving valuta label-set spettrali su candidati random. La tengo sul perimetro ridotto già impostato; se resta pesante, ridurrò solo il numero di candidati preservando il contratto. diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() 2026-05-09T06:40:20.199586Z ERROR codex_core::tools::router: error=write_stdin failed: stdin is closed for this session; rerun exec_command with tty=true to keep stdin open diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'pkill -f exp_vc_null_regression_gate.py || true' in /opt/MM_D-ND exited 143 in 0ms: diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'pgrep -af exp_vc_null_regression_gate.py || true' in /opt/MM_D-ND succeeded in 0ms: 1 /root/.codex-lab/tmp/arg0/codex-arg0ZnVbji/codex-linux-sandbox --sandbox-policy-cwd /opt/MM_D-ND --command-cwd /opt/MM_D-ND --sandbox-policy {"type":"workspace-write","writable_roots":["/root/.codex-lab/memories"],"network_access":false,"exclude_tmpdir_env_var":false,"exclude_slash_tmp":false} --file-system-sandbox-policy {"kind":"restricted","entries":[{"path":{"type":"special","value":{"kind":"root"}},"access":"read"},{"path":{"type":"special","value":{"kind":"current_working_directory"}},"access":"write"},{"path":{"type":"special","value":{"kind":"slash_tmp"}},"access":"write"},{"path":{"type":"special","value":{"kind":"tmpdir"}},"access":"write"},{"path":{"type":"path","path":"/root/.codex-lab/memories"},"access":"write"},{"path":{"type":"path","path":"/opt/MM_D-ND/.git"},"access":"read"},{"path":{"type":"path","path":"/opt/MM_D-ND/.codex"},"access":"read"}]} --network-sandbox-policy "restricted" -- /bin/bash -c __CODEX_SNAPSHOT_OVERRIDE_SET_0="${CODEX_THREAD_ID+x}" __CODEX_SNAPSHOT_OVERRIDE_0="${CODEX_THREAD_ID-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_0="${ALL_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_0="${ALL_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_1="${BUNDLE_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_1="${BUNDLE_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_2="${BUNDLE_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_2="${BUNDLE_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_3="${BUNDLE_NO_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_3="${BUNDLE_NO_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_4="${CODEX_NETWORK_ALLOW_LOCAL_BINDING+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_4="${CODEX_NETWORK_ALLOW_LOCAL_BINDING-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_5="${CODEX_NETWORK_PROXY_ACTIVE+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_5="${CODEX_NETWORK_PROXY_ACTIVE-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_6="${DOCKER_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_6="${DOCKER_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_7="${DOCKER_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_7="${DOCKER_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_8="${ELECTRON_GET_USE_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_8="${ELECTRON_GET_USE_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_9="${FTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_9="${FTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_10="${HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_10="${HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_11="${HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_11="${HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_12="${NO_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_12="${NO_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_13="${NPM_CONFIG_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_13="${NPM_CONFIG_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_14="${NPM_CONFIG_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_14="${NPM_CONFIG_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_15="${NPM_CONFIG_NOPROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_15="${NPM_CONFIG_NOPROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_16="${NPM_CONFIG_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_16="${NPM_CONFIG_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_17="${PIP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_17="${PIP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_18="${WSS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_18="${WSS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_19="${WS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_19="${WS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_20="${YARN_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_20="${YARN_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_21="${YARN_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_21="${YARN_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_22="${YARN_NO_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_22="${YARN_NO_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_23="${all_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_23="${all_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_24="${ftp_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_24="${ftp_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_25="${http_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_25="${http_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_26="${https_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_26="${https_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_27="${no_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_27="${no_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_28="${npm_config_http_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_28="${npm_config_http_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_29="${npm_config_https_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_29="${npm_config_https_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_30="${npm_config_noproxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_30="${npm_config_noproxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_31="${npm_config_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_31="${npm_config_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_32="${ws_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_32="${ws_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_33="${wss_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_33="${wss_proxy-}" __CODEX_SNAPSHOT_PROXY_ENV_SET="${CODEX_NETWORK_PROXY_ACTIVE+x}" if . '/root/.codex-lab/shell_snapshots/019e0b74-bab7-7142-a093-a4d65c72a491.1778308659909026410.sh' >/dev/null 2>&1; then :; fi if [ -n "${__CODEX_SNAPSHOT_OVERRIDE_SET_0}" ]; then export CODEX_THREAD_ID="${__CODEX_SNAPSHOT_OVERRIDE_0}"; else unset CODEX_THREAD_ID; fi if [ -n "$__CODEX_SNAPSHOT_PROXY_ENV_SET" ] || [ -n "${CODEX_NETWORK_PROXY_ACTIVE+x}" ]; then if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_0}" ]; then export ALL_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_0}"; else unset ALL_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_1}" ]; then export BUNDLE_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_1}"; else unset BUNDLE_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_2}" ]; then export BUNDLE_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_2}"; else unset BUNDLE_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_3}" ]; then export BUNDLE_NO_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_3}"; else unset BUNDLE_NO_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_4}" ]; then export CODEX_NETWORK_ALLOW_LOCAL_BINDING="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_4}"; else unset CODEX_NETWORK_ALLOW_LOCAL_BINDING; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_5}" ]; then export CODEX_NETWORK_PROXY_ACTIVE="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_5}"; else unset CODEX_NETWORK_PROXY_ACTIVE; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_6}" ]; then export DOCKER_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_6}"; else unset DOCKER_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_7}" ]; then export DOCKER_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_7}"; else unset DOCKER_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_8}" ]; then export ELECTRON_GET_USE_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_8}"; else unset ELECTRON_GET_USE_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_9}" ]; then export FTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_9}"; else unset FTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_10}" ]; then export HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_10}"; else unset HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_11}" ]; then export HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_11}"; else unset HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_12}" ]; then export NO_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_12}"; else unset NO_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_13}" ]; then export NPM_CONFIG_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_13}"; else unset NPM_CONFIG_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_14}" ]; then export NPM_CONFIG_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_14}"; else unset NPM_CONFIG_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_15}" ]; then export NPM_CONFIG_NOPROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_15}"; else unset NPM_CONFIG_NOPROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_16}" ]; then export NPM_CONFIG_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_16}"; else unset NPM_CONFIG_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_17}" ]; then export PIP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_17}"; else unset PIP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_18}" ]; then export WSS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_18}"; else unset WSS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_19}" ]; then export WS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_19}"; else unset WS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_20}" ]; then export YARN_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_20}"; else unset YARN_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_21}" ]; then export YARN_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_21}"; else unset YARN_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_22}" ]; then export YARN_NO_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_22}"; else unset YARN_NO_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_23}" ]; then export all_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_23}"; else unset all_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_24}" ]; then export ftp_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_24}"; else unset ftp_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_25}" ]; then export http_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_25}"; else unset http_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_26}" ]; then export https_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_26}"; else unset https_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_27}" ]; then export no_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_27}"; else unset no_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_28}" ]; then export npm_config_http_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_28}"; else unset npm_config_http_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_29}" ]; then export npm_config_https_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_29}"; else unset npm_config_https_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_30}" ]; then export npm_config_noproxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_30}"; else unset npm_config_noproxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_31}" ]; then export npm_config_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_31}"; else unset npm_config_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_32}" ]; then export ws_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_32}"; else unset ws_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_33}" ]; then export wss_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_33}"; else unset wss_proxy; fi fi exec '/bin/bash' -c 'pgrep -af exp_vc_null_regression_gate.py || true' 2 /bin/bash -c pgrep -af exp_vc_null_regression_gate.py || true diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex Ho fermato il primo run: il controllo label-preserving pieno stava mangiando il budget del cycle. Riparto con atto minimo coerente con R1: stessi operatori, perimetro ridotto a `N={89,144,233}` e meno candidati, così il risultato dichiara il limite invece di fingere copertura larga. exec /bin/bash -lc 'python tools/exp_vc_null_regression_gate.py --ns 89,144,233 --label-candidates 5 --phase-trials 2 --random-trials 2 --label-trials 2' in /opt/MM_D-ND diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() succeeded in 95124ms: { "summary": { "balanced_random": { "conditions": 144, "events": { "floor_hit": 64, "internal_cross": 55, "internal_multi": 25 }, "internal_rate": 0.5555555555555556, "floor_hit_rate": 0.4444444444444444, "vc_median": 0.5166963626431642, "r_floor_median": 0.5074761734767046, "label_jaccard_median": null }, "label_preserving_surrogate": { "conditions": 144, "events": { "floor_hit": 72, "internal_cross": 49, "internal_multi": 23 }, "internal_rate": 0.5, "floor_hit_rate": 0.5, "vc_median": 0.501613915129398, "r_floor_median": 0.49830918357257803, "label_jaccard_median": 0.21052631578947367 }, "phi_sturmian": { "conditions": 48, "events": { "internal_cross": 48 }, "internal_rate": 1.0, "floor_hit_rate": 0.0, "vc_median": 1.042609958553752, "r_floor_median": 0.6531021052651185, "label_jaccard_median": 1.0 }, "sturmian_phase_shuffle": { "conditions": 144, "events": { "internal_cross": 144 }, "internal_rate": 1.0, "floor_hit_rate": 0.0, "vc_median": 1.2395830345981742, "r_floor_median": 0.6849436830807705, "label_jaccard_median": null } }, "out": "tools/data/vc_null_regression_gate_20260509_0637.json" } diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() succeeded in 26691ms: { "summary": { "balanced_random": { "conditions": 72, "events": { "floor_hit": 27, "internal_cross": 23, "internal_multi": 22 }, "internal_rate": 0.625, "floor_hit_rate": 0.375, "vc_median": 0.5329277658955516, "r_floor_median": 0.5120324077981381, "label_jaccard_median": null }, "label_preserving_surrogate": { "conditions": 72, "events": { "floor_hit": 23, "internal_cross": 29, "internal_multi": 20 }, "internal_rate": 0.6805555555555556, "floor_hit_rate": 0.3194444444444444, "vc_median": 0.5611705443174979, "r_floor_median": 0.534426533952045, "label_jaccard_median": 0.16666666666666666 }, "phi_sturmian": { "conditions": 36, "events": { "internal_cross": 36 }, "internal_rate": 1.0, "floor_hit_rate": 0.0, "vc_median": 1.0588848239081246, "r_floor_median": 0.6545022199074195, "label_jaccard_median": 1.0 }, "sturmian_phase_shuffle": { "conditions": 72, "events": { "internal_cross": 72 }, "internal_rate": 1.0, "floor_hit_rate": 0.0, "vc_median": 1.1933351291700072, "r_floor_median": 0.6912741460665063, "label_jaccard_median": null } }, "out": "tools/data/vc_null_regression_gate_20260509_0637.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 D "kernel/reference/metaprompt_in_sviluppo/Sei il Meta Master Systemil Meta Ma.md" M tools/data/agent_field_live.md M tools/data/autoricerca_journal.json M tools/data/ciclo_memoria.json M tools/data/conoscenza_teorie.json M tools/data/consecutio.json M tools/data/consecutio_processata.json M tools/data/domande_fondamentali.json M tools/data/exp_det_drift.json M tools/data/incrocio_risultato.json M tools/data/knowledge_state.json M tools/data/lab_data.json M tools/data/lab_graph.json M tools/data/lab_health.json M tools/data/lab_session_log.jsonl M tools/data/pipeline_state.json M tools/data/ponti_evoluti.json M tools/data/refresh_detector_state.json M tools/data/seme.json D tools/data/seme_archive/piano_37.json D tools/data/seme_archive/piano_38.json D tools/data/seme_archive/piano_4.json D tools/data/seme_archive/piano_5.json D tools/data/seme_archive/piano_6.json D tools/data/seme_archive/piano_7.json D tools/data/seme_archive/piano_8.json D tools/data/seme_archive/piano_9.json M tools/data/seme_axioms.json M tools/data/seme_backup_pre_run.json M tools/data/tm1_figures/tensions.json M tools/data/tm1_figures/tensions_raw.json ?? applications/published/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/published/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/published/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/published/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/published/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? applications/published/20260507_0942_bridge-order-denominator-gate/ ?? applications/published/20260507_1006_logistic-counter-scope-gate/ ?? applications/published/20260507_1042_logistic-surrogate-contract-gate/ ?? applications/published/20260507_1419_logistic-cyclic-block-entropy-gate/ ?? applications/published/20260507_1458_meta-assertion-gate/ ?? applications/published/20260507_1751_tqge-underlay-gate/ ?? applications/published/20260507_1804_blank-to-source-hinge/ ?? applications/published/20260507_1938_triadic-deposit-gate/ ?? applications/published/20260507_1957_blank-shell-polarity-gate/ ?? applications/scoperte/20260502_0330_the-dipolar-phase-transition-is-nearly_auto/ ?? applications/scoperte/20260503_0330_markov-memory-has-two-visible-layers_auto/ ?? applications/scoperte/20260504_0901_the-two-markov-layers-are-coupled_auto/ ?? applications/scoperte/20260504_1219_markov-layers-pass-the-first-recovery_auto/ ?? applications/scoperte/20260505_0330_observable-rank-audit-many-probes-one_auto/ ?? applications/scoperte/20260506_0625_perturbation-dimensionality-is-not-yet-a_auto/ ?? applications/scoperte/20260506_1941_perturbation-rank-needs-denominator-gating_auto/ ?? applications/scoperte/20260506_1955_observable-collinearity-breaks-only-where-denominators_auto/ ?? applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/ ?? applications/scoperte/20260507_0803_denominator-gate-transfers-boundary-coordinate-does_auto/ ?? applications/scoperte/20260507_0901_denominator-gate-transfer-matrix_auto/ ?? applications/scoperte/20260507_0923_semi-real-order-denominator-gate_auto/ ?? applications/scoperte/20260507_0942_bridge-order-denominator-gate_auto/ ?? applications/scoperte/20260507_1006_logistic-counter-scope-gate_auto/ ?? applications/scoperte/20260507_1042_logistic-surrogate-contract-gate_auto/ ?? applications/scoperte/20260507_1419_logistic-cyclic-block-entropy-gate_auto/ ?? applications/scoperte/20260507_1458_meta-assertion-gate_auto/ ?? applications/scoperte/20260507_1751_tqge-underlay-gate_auto/ ?? applications/scoperte/20260507_1804_blank-to-source-hinge_auto/ ?? applications/scoperte/20260507_1938_triadic-deposit-gate_auto/ ?? applications/scoperte/20260507_1957_blank-shell-polarity-gate_auto/ ?? "applications/scoperte/20260507_2042_det-non-\303\250-il-discriminatore-della_auto/" ?? applications/scoperte/20260507_2120_blank-shell-tqger-gate_auto/ ?? applications/scoperte/20260507_2203_blank-shell-scale-law_auto/ ?? applications/scoperte/20260507_2310_blank-shell-stratified-gate_auto/ ?? applications/scoperte/20260508_0011_duality-contrast-weakens-with-scale-in_auto/ ?? applications/scoperte/20260508_0330_gap-ratio-porta-il-denominatore_auto/ ?? applications/scoperte/20260508_1715_generator-gate-del-label-set-phi_auto/ ?? applications/scoperte/20260508_1805_block-scale-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1834_substitution-grammar-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1909_supertile-tiling-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1915_high-core-repair-audit_auto/ ?? applications/scoperte/20260508_1947_positionerror-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_2005_symbolic-grammar-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_2013_ostrowski-recognizability-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_2019_non-phi-sturmian-fixed-reader-gate_auto/ ?? applications/scoperte/20260508_2108_prime-mobius-zero-mediator-gate_auto/ ?? applications/scoperte/20260508_2121_prime-mobius-gap-stratified-zero-gate_auto/ ?? applications/scoperte/20260508_2133_prime-mobius-pair-stratified-zero-gate_auto/ ?? applications/scoperte/20260508_2140_quasiperiodic-vc-lattice-gate_auto/ ?? applications/scoperte/20260509_0330_interpolated-vc-curve-map_auto/ ?? applications/soluzioni/20260502_0330_the-dipolar-phase-transition-is-nearly/ ?? applications/soluzioni/20260503_0330_markov-memory-has-two-visible-layers/ ?? applications/soluzioni/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/soluzioni/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/soluzioni/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/soluzioni/20260506_1941_perturbation-rank-needs-denominator-gating/ ?? applications/soluzioni/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/soluzioni/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/soluzioni/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? applications/soluzioni/20260507_0942_bridge-order-denominator-gate/ ?? applications/soluzioni/20260507_1006_logistic-counter-scope-gate/ ?? applications/soluzioni/20260507_1042_logistic-surrogate-contract-gate/ ?? applications/soluzioni/20260507_1419_logistic-cyclic-block-entropy-gate/ ?? applications/soluzioni/20260507_1458_meta-assertion-gate/ ?? applications/soluzioni/20260507_1751_tqge-underlay-gate/ ?? applications/soluzioni/20260507_1804_blank-to-source-hinge/ ?? applications/soluzioni/20260507_1938_triadic-deposit-gate/ ?? applications/soluzioni/20260507_1957_blank-shell-polarity-gate/ ?? "applications/soluzioni/20260507_2042_det-non-\303\250-il-discriminatore-della/" ?? applications/soluzioni/20260507_2120_blank-shell-tqger-gate/ ?? applications/soluzioni/20260507_2203_blank-shell-scale-law/ ?? applications/soluzioni/20260507_2310_blank-shell-stratified-gate/ ?? applications/soluzioni/20260508_0011_duality-contrast-weakens-with-scale-in/ ?? applications/soluzioni/20260508_0330_gap-ratio-porta-il-denominatore/ ?? applications/soluzioni/20260508_1715_generator-gate-del-label-set-phi/ ?? applications/soluzioni/20260508_1805_block-scale-gate-del-core-phi/ ?? applications/soluzioni/20260508_1834_substitution-grammar-gate-del-core-phi/ ?? applications/soluzioni/20260508_1909_supertile-tiling-gate-del-core-phi/ ?? applications/soluzioni/20260508_1915_high-core-repair-audit/ ?? applications/soluzioni/20260508_1947_positionerror-gate-del-core-phi/ ?? applications/soluzioni/20260508_2005_symbolic-grammar-gate-del-core-phi/ ?? applications/soluzioni/20260508_2013_ostrowski-recognizability-gate-del-core-phi/ ?? applications/soluzioni/20260508_2019_non-phi-sturmian-fixed-reader-gate/ ?? applications/soluzioni/20260508_2108_prime-mobius-zero-mediator-gate/ ?? applications/soluzioni/20260508_2121_prime-mobius-gap-stratified-zero-gate/ ?? applications/soluzioni/20260508_2133_prime-mobius-pair-stratified-zero-gate/ ?? applications/soluzioni/20260508_2140_quasiperiodic-vc-lattice-gate/ ?? applications/soluzioni/20260509_0330_interpolated-vc-curve-map/ ?? 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/b2_apply_log.jsonl ?? tools/data/biconi/bicono_20260507_1804.json ?? tools/data/biconi/bicono_20260507_1938.json ?? tools/data/biconi/bicono_20260507_1957.json ?? tools/data/biconi/bicono_20260507_2042.json ?? tools/data/biconi/bicono_20260507_2120.json ?? tools/data/biconi/bicono_20260507_2203.json ?? tools/data/biconi/bicono_20260507_2310.json ?? tools/data/biconi/bicono_20260508_0011.json ?? tools/data/biconi/bicono_20260508_0330.json ?? tools/data/biconi/bicono_20260508_1632.json ?? tools/data/biconi/bicono_20260508_1715.json ?? tools/data/biconi/bicono_20260508_1805.json ?? tools/data/biconi/bicono_20260508_1834.json ?? tools/data/biconi/bicono_20260508_1909.json ?? tools/data/biconi/bicono_20260508_1915.json ?? tools/data/biconi/bicono_20260508_1947.json ?? tools/data/biconi/bicono_20260508_2005.json ?? tools/data/biconi/bicono_20260508_2013.json ?? tools/data/biconi/bicono_20260508_2019.json ?? tools/data/biconi/bicono_20260508_2102.json ?? tools/data/biconi/bicono_20260508_2108.json ?? tools/data/biconi/bicono_20260508_2121.json ?? tools/data/biconi/bicono_20260508_2133.json ?? tools/data/biconi/bicono_20260508_2140.json ?? tools/data/biconi/bicono_20260509_0330.json ?? tools/data/bicono_projections.jsonl ?? tools/data/blank_shell_dilation_gate_20260507_2157.json ?? tools/data/blank_shell_polarity_gate_20260507_1957.json ?? tools/data/blank_shell_scale_law_20260507_2203.json ?? tools/data/blank_shell_stratified_gate_20260507_2310.json ?? tools/data/blank_shell_tqger_gate_20260507_2120.json ?? tools/data/blank_to_source_hinge_20260507_1804.json ?? tools/data/boundary_coherence.json ?? tools/data/boundary_mixture_gate_20260507_0330.json ?? tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json ?? tools/data/boundary_shuffle_audit.json ?? tools/data/bridge_order_denominator_gate_20260507_0942.json ?? tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json ?? tools/data/brody_calibration_results.json ?? tools/data/brody_flow.json ?? tools/data/conoscenza_teorie.json.bak.retraction_22_04 ?? tools/data/cross_domain_dipolar_direction.json ?? tools/data/cross_observable_consistency.json ?? tools/data/crossover_phase_test.json ?? tools/data/denominator_gate_transfer_matrix.json ?? tools/data/dipolar_crossover.json ?? tools/data/dipolar_vector_scaling.json ?? tools/data/domandatore/domandatore_20260421_0746.json ?? tools/data/domandatore/domandatore_20260422_0345.json ?? tools/data/domandatore/domandatore_20260423_0345.json ?? tools/data/domandatore/domandatore_20260424_0345.json ?? tools/data/domandatore/domandatore_20260425_0345.json ?? tools/data/domandatore/domandatore_20260426_0345.json ?? tools/data/domandatore/domandatore_20260427_0345.json ?? tools/data/domandatore/domandatore_20260428_0345.json ?? tools/data/domandatore/domandatore_20260428_1236.json ?? tools/data/domandatore/domandatore_20260429_0345.json ?? tools/data/domandatore/domandatore_20260430_0345.json ?? tools/data/domandatore/domandatore_20260501_0345.json ?? tools/data/domandatore/domandatore_20260502_0345.json ?? tools/data/domandatore/domandatore_20260503_0345.json ?? tools/data/domandatore/domandatore_20260504_0345.json ?? tools/data/domandatore/domandatore_20260505_0345.json ?? tools/data/domandatore/domandatore_20260506_0345.json ?? tools/data/domandatore/domandatore_20260507_0345.json ?? tools/data/domandatore/domandatore_20260507_0728.json ?? tools/data/domandatore/domandatore_20260507_0742.json ?? tools/data/domandatore/domandatore_20260507_1420.json ?? tools/data/domandatore/domandatore_20260507_1459.json ?? tools/data/domandatore/domandatore_20260507_2120.json ?? tools/data/domandatore/domandatore_20260507_2157.json ?? tools/data/domandatore/domandatore_20260507_2203.json ?? tools/data/domandatore/domandatore_20260508_0345.json ?? tools/data/domandatore/domandatore_20260508_2121.json ?? tools/data/domandatore/domandatore_20260509_0330.json ?? tools/data/domandatore/domandatore_20260509_0345.json ?? tools/data/domandatore/domandatore_20260509_0637.json ?? tools/data/domandatore_pre_cycle.json ?? tools/data/duality_gate_transfer_20260507_0803.json ?? tools/data/duality_gate_transfer_20260507_0803_seedcheck.json ?? tools/data/duality_gate_transfer_20260507_2042.json ?? tools/data/duality_scale_contrast_20260508.json ?? tools/data/evolution/evolution_20260422_0330.md ?? tools/data/evolution/evolution_20260422_1616.md ?? tools/data/evolution/evolution_20260423_0330.md ?? tools/data/evolution/evolution_20260424_0330.md ?? tools/data/evolution/evolution_20260425_0330.md ?? tools/data/evolution/evolution_20260427_0330.md ?? tools/data/evolution/evolution_20260428_0330.md ?? tools/data/evolution/evolution_20260429_0852.md ?? tools/data/evolution/evolution_20260429_1013.md ?? tools/data/evolution/evolution_20260430_0330.md ?? tools/data/evolution/evolution_20260430_1905.md ?? tools/data/evolution/evolution_20260430_1946.md ?? tools/data/evolution/evolution_20260501_0330.md ?? tools/data/evolution/evolution_20260501_0725.md ?? tools/data/evolution/evolution_20260501_0931.md ?? tools/data/evolution/evolution_20260502_0330.md ?? tools/data/evolution/evolution_20260503_0330.md ?? tools/data/evolution/evolution_20260504_0901.md ?? tools/data/evolution/evolution_20260504_1219.md ?? tools/data/evolution/evolution_20260505_0330.md ?? tools/data/evolution/evolution_20260506_0330.md ?? tools/data/evolution/evolution_20260506_1941.md ?? tools/data/evolution/evolution_20260506_1955.md ?? tools/data/evolution/evolution_20260507_0330.md ?? tools/data/evolution/evolution_20260507_0803.md ?? tools/data/evolution/evolution_20260507_0901.md ?? tools/data/evolution/evolution_20260507_0923.md ?? tools/data/evolution/evolution_20260507_0942.md ?? tools/data/evolution/evolution_20260507_1006.md ?? tools/data/evolution/evolution_20260507_1042.md ?? tools/data/evolution/evolution_20260507_1419.md ?? tools/data/evolution/evolution_20260507_1458.md ?? tools/data/evolution/evolution_20260507_1751.md ?? tools/data/evolution/evolution_20260507_1804.md ?? tools/data/evolution/evolution_20260507_1938.md ?? tools/data/evolution/evolution_20260507_1957.md ?? tools/data/evolution/evolution_20260507_2042.md ?? tools/data/evolution/evolution_20260507_2120.md ?? tools/data/evolution/evolution_20260507_2203.md ?? tools/data/evolution/evolution_20260507_2310.md ?? tools/data/evolution/evolution_20260508_0330.md ?? tools/data/evolution/evolution_20260508_1632.md ?? tools/data/evolution/evolution_20260508_1715.md ?? tools/data/evolution/evolution_20260508_1805.md ?? tools/data/evolution/evolution_20260508_1834.md ?? tools/data/evolution/evolution_20260508_1909.md ?? tools/data/evolution/evolution_20260508_1915.md ?? tools/data/evolution/evolution_20260508_1947.md ?? tools/data/evolution/evolution_20260508_2005.md ?? tools/data/evolution/evolution_20260508_2013.md ?? tools/data/evolution/evolution_20260508_2019.md ?? tools/data/evolution/evolution_20260508_2102.md ?? tools/data/evolution/evolution_20260508_2108.md ?? tools/data/evolution/evolution_20260508_2121.md ?? tools/data/evolution/evolution_20260508_2133.md ?? tools/data/evolution/evolution_20260508_2140.md ?? tools/data/evolution/evolution_20260509_0330.md ?? tools/data/exp_det_drift_20260507_2042.json ?? tools/data/falsifier_20260507_2120.raw.txt ?? tools/data/g1_log.jsonl ?? tools/data/g2_log.jsonl ?? tools/data/gap_label_block_scale_gate_20260508_1805.json ?? tools/data/gap_label_generator_gate_20260508_1715.json ?? tools/data/gap_label_ostrowski_recognizability_gate_20260508_2013.json ?? tools/data/gap_label_ostrowski_recognizability_gate_20260508_2013_seedcheck.json ?? tools/data/gap_label_position_error_gate_20260508_1947.json ?? tools/data/gap_label_repair_audit_20260508_1915.json ?? tools/data/gap_label_set_stability_20260508_1632.json ?? tools/data/gap_label_substitution_grammar_gate_20260508_1834.json ?? tools/data/gap_label_supertile_tiling_gate_20260508_1909.json ?? tools/data/gap_label_symbolic_grammar_gate_20260508_2005.json ?? tools/data/gap_label_symbolic_grammar_gate_20260508_2005_window55_seedcheck.json ?? tools/data/incrocio_20260422_0336.json ?? tools/data/incrocio_20260423_0335.json ?? tools/data/incrocio_20260424_0347.json ?? tools/data/incrocio_20260425_0339.json ?? tools/data/incrocio_20260428_0340.json ?? tools/data/incrocio_20260429_0859.json ?? tools/data/incrocio_20260506_0633.json ?? tools/data/incrocio_20260507_2120.json ?? tools/data/incrocio_20260507_2157.json ?? tools/data/incrocio_20260507_2203.json ?? tools/data/incrocio_20260507_2310.json ?? tools/data/incrocio_20260508_0011.json ?? tools/data/incrocio_20260508_0020.json ?? tools/data/incrocio_20260508_0330.json ?? tools/data/incrocio_20260508_1632.json ?? tools/data/incrocio_20260508_1715.json ?? tools/data/incrocio_20260508_1805.json ?? tools/data/incrocio_20260508_1834.json ?? tools/data/incrocio_20260508_1909.json ?? tools/data/incrocio_20260508_1915.json ?? tools/data/incrocio_20260508_1947.json ?? tools/data/incrocio_20260508_2005.json ?? tools/data/incrocio_20260508_2013.json ?? tools/data/incrocio_20260508_2019.json ?? tools/data/incrocio_20260508_2102.json ?? tools/data/incrocio_20260508_2108.json ?? tools/data/incrocio_20260508_2121.json ?? tools/data/incrocio_20260508_2133.json ?? tools/data/incrocio_20260508_2140.json ?? tools/data/incrocio_20260509_0330.json ?? tools/data/incrocio_20260509_0637.json ?? tools/data/lab_bridge_issues.jsonl ?? tools/data/logistic_counter_scope_gate_20260507_1006.json ?? tools/data/logistic_counter_scope_gate_20260507_1006_seedcheck.json ?? tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json ?? tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json ?? tools/data/magnitude_psd_from_acf.json ?? tools/data/markov3_observable_hunt.json ?? tools/data/markov_dipolar_decomposition.json ?? tools/data/markov_k_direction.json ?? tools/data/markov_layer_recovery_audit.json ?? tools/data/markov_memory_by_gue_type.json ?? tools/data/markov_scale_function.json ?? tools/data/meta_assertion_gate.json ?? tools/data/meta_tautology_test.json ?? tools/data/mod3_scaling.json ?? tools/data/mod3_vs_residual_ordering.json ?? tools/data/modular_algebra_depth.json ?? tools/data/modular_memory_spectrum.json ?? tools/data/nonphi_sturmian_fixed_reader_gate_20260508_2019.json ?? tools/data/nonphi_sturmian_fixed_reader_gate_20260508_2019_seedcheck.json ?? tools/data/observable_collinearity_breaking_20260506_1955.json ?? tools/data/observable_collinearity_breaking_20260506_1956.json ?? tools/data/observable_collinearity_breaking_20260506_1957.json ?? tools/data/observable_rank_audit.json ?? tools/data/observable_rank_audit_seed20260506.json ?? tools/data/observatorio/domandatore_unTouched_20260507_095914.md ?? tools/data/observatorio/lazarus_cimitero_20260507_100015.md ?? tools/data/observatorio/meta_metodo_giro_2026-05-07.md ?? tools/data/operator_directives_consumed/ ?? tools/data/perturbation_dimensionality_audit.json ?? tools/data/perturbation_dimensionality_audit_scale0330.json ?? tools/data/perturbation_rank_size_curve.json ?? tools/data/prime_mobius_gap_stratified_zero_gate_20260508_2121.json ?? tools/data/prime_mobius_interval_charge_gate_20260508_2102.json ?? tools/data/prime_mobius_pair_stratified_zero_gate_20260508_2133.json ?? tools/data/prime_mobius_zero_mediator_gate_20260508_2108.json ?? tools/data/promotions/ ?? tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json ?? tools/data/quasiperiodic_vc_curve_map_20260509_0330.json ?? tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json ?? tools/data/repairs/ ?? tools/data/reports/_quarantine_falsifier_29_04/ ?? tools/data/reports/agent_20260422_0330.md ?? tools/data/reports/agent_20260422_1616.md ?? tools/data/reports/agent_20260423_0330.md ?? tools/data/reports/agent_20260424_0330.md ?? tools/data/reports/agent_20260425_0330.md ?? tools/data/reports/agent_20260426_0330.md ?? tools/data/reports/agent_20260427_0330.md ?? tools/data/reports/agent_20260428_0330.md ?? tools/data/reports/agent_20260429_1013.md ?? tools/data/reports/agent_20260429_1041.md ?? tools/data/reports/agent_20260430_0330.md ?? tools/data/reports/agent_20260430_1905.md ?? tools/data/reports/agent_20260430_1919.md ?? tools/data/reports/agent_20260430_1946.md ?? tools/data/reports/agent_20260501_0330.md ?? tools/data/reports/agent_20260501_0725.md ?? tools/data/reports/agent_20260501_0931.md ?? tools/data/reports/agent_20260502_0330.md ?? tools/data/reports/agent_20260503_0330.md ?? tools/data/reports/agent_20260504_0901.md ?? tools/data/reports/agent_20260504_1219.md ?? tools/data/reports/agent_20260505_0330.md ?? tools/data/reports/agent_20260505_1022.md ?? tools/data/reports/agent_20260506_0330.md ?? tools/data/reports/agent_20260506_0625.md ?? tools/data/reports/agent_20260506_1941.md ?? tools/data/reports/agent_20260506_1955.md ?? tools/data/reports/agent_20260507_0330.md ?? tools/data/reports/agent_20260507_0803.md ?? tools/data/reports/agent_20260507_0901.md ?? tools/data/reports/agent_20260507_0923.md ?? tools/data/reports/agent_20260507_0942.md ?? tools/data/reports/agent_20260507_1006.md ?? tools/data/reports/agent_20260507_1042.md ?? tools/data/reports/agent_20260507_1419.md ?? tools/data/reports/agent_20260507_1458.md ?? tools/data/reports/agent_20260507_1751.md ?? tools/data/reports/agent_20260507_1804.md ?? tools/data/reports/agent_20260507_1938.md ?? tools/data/reports/agent_20260507_1957.md ?? tools/data/reports/agent_20260507_2042.md ?? tools/data/reports/agent_20260507_2120.md ?? tools/data/reports/agent_20260507_2157.md ?? tools/data/reports/agent_20260507_2203.md ?? tools/data/reports/agent_20260507_2310.md ?? tools/data/reports/agent_20260508_0011.md ?? tools/data/reports/agent_20260508_0330.md ?? tools/data/reports/agent_20260508_1632.md ?? tools/data/reports/agent_20260508_1715.md ?? tools/data/reports/agent_20260508_1805.md ?? tools/data/reports/agent_20260508_1834.md ?? tools/data/reports/agent_20260508_1909.md ?? tools/data/reports/agent_20260508_1915.md ?? tools/data/reports/agent_20260508_1947.md ?? tools/data/reports/agent_20260508_2005.md ?? tools/data/reports/agent_20260508_2013.md ?? tools/data/reports/agent_20260508_2019.md ?? tools/data/reports/agent_20260508_2102.md ?? tools/data/reports/agent_20260508_2108.md ?? tools/data/reports/agent_20260508_2121.md ?? tools/data/reports/agent_20260508_2133.md ?? tools/data/reports/agent_20260508_2140.md ?? tools/data/reports/agent_20260509_0330.md ?? tools/data/reports/ddf_20260422_0405.json ?? tools/data/reports/ddf_20260423_0405.json ?? tools/data/reports/ddf_20260424_0405.json ?? tools/data/reports/ddf_20260425_0405.json ?? tools/data/reports/ddf_20260426_0405.json ?? tools/data/reports/ddf_20260427_0405.json ?? tools/data/reports/ddf_20260428_0405.json ?? tools/data/reports/ddf_20260429_0405.json ?? tools/data/reports/ddf_20260430_0405.json ?? tools/data/reports/ddf_20260501_0405.json ?? tools/data/reports/ddf_20260502_0405.json ?? tools/data/reports/ddf_20260503_0405.json ?? tools/data/reports/ddf_20260504_0405.json ?? tools/data/reports/ddf_20260505_0405.json ?? tools/data/reports/ddf_20260505_0636.json ?? tools/data/reports/ddf_20260506_0405.json ?? tools/data/reports/ddf_20260507_0405.json ?? tools/data/reports/ddf_20260508_0405.json ?? tools/data/reports/ddf_20260509_0405.json ?? tools/data/reports/evolution_20260422_0330.md ?? tools/data/reports/evolution_20260422_1616.md ?? tools/data/reports/evolution_20260423_0330.md ?? tools/data/reports/evolution_20260424_0330.md ?? tools/data/reports/evolution_20260425_0330.md ?? tools/data/reports/evolution_20260427_0330.md ?? tools/data/reports/evolution_20260428_0330.md ?? tools/data/reports/evolution_20260503_0330.md ?? tools/data/reports/evolution_20260504_0330.md ?? tools/data/reports/evolution_20260505_0330.md ?? tools/data/reports/evolution_20260506_0330.md ?? tools/data/reports/evolution_20260506_1941.md ?? tools/data/reports/falsifier_20260429_1013.json ?? tools/data/reports/falsifier_20260429_1041.json ?? tools/data/reports/falsifier_20260430_0330.json ?? tools/data/reports/falsifier_20260430_1905.json ?? tools/data/reports/falsifier_20260430_1919.json ?? tools/data/reports/falsifier_20260430_1946.json ?? tools/data/reports/falsifier_20260501_0330.json ?? tools/data/reports/falsifier_20260501_0725.json ?? tools/data/reports/falsifier_20260501_0931.json ?? tools/data/reports/falsifier_20260502_0330.json ?? tools/data/reports/falsifier_20260503_0330.json ?? tools/data/reports/falsifier_20260504_0901.json ?? tools/data/reports/falsifier_20260504_1219.json ?? tools/data/reports/falsifier_20260505_0330.json ?? tools/data/reports/falsifier_20260506_0330.raw.txt ?? tools/data/reports/falsifier_20260506_0625.json ?? tools/data/reports/falsifier_20260506_1941.json ?? tools/data/reports/falsifier_20260506_1955.json ?? tools/data/reports/falsifier_20260506_1955.raw.txt ?? tools/data/reports/falsifier_20260507_0330.json ?? tools/data/reports/falsifier_20260507_0330.raw.txt ?? tools/data/reports/falsifier_20260507_0803.json ?? tools/data/reports/falsifier_20260507_0803.raw.txt ?? tools/data/reports/falsifier_20260507_0901.json ?? tools/data/reports/falsifier_20260507_0923.json ?? tools/data/reports/falsifier_20260507_0923.raw.txt ?? tools/data/reports/falsifier_20260507_0942.json ?? tools/data/reports/falsifier_20260507_1006.json ?? tools/data/reports/falsifier_20260507_1042.json ?? tools/data/reports/falsifier_20260507_1042.raw.txt ?? tools/data/reports/falsifier_20260507_1419.json ?? tools/data/reports/falsifier_20260507_1458.json ?? tools/data/reports/falsifier_20260507_1458.raw.txt ?? tools/data/reports/falsifier_20260507_1751.json ?? tools/data/reports/falsifier_20260507_1804.json ?? tools/data/reports/falsifier_20260507_1938.json ?? tools/data/reports/falsifier_20260507_1938.raw.txt ?? tools/data/reports/falsifier_20260507_1957.json ?? tools/data/reports/falsifier_20260507_2042.json ?? tools/data/reports/falsifier_20260507_2120.json ?? tools/data/reports/falsifier_20260507_2203.json ?? tools/data/reports/falsifier_20260507_2310.json ?? tools/data/reports/falsifier_20260508_0011.json ?? tools/data/reports/falsifier_20260508_0330.json ?? tools/data/reports/falsifier_20260508_1632.raw.txt ?? tools/data/reports/falsifier_20260508_1715.json ?? tools/data/reports/falsifier_20260508_1805.json ?? tools/data/reports/falsifier_20260508_1834.json ?? tools/data/reports/falsifier_20260508_1909.json ?? tools/data/reports/falsifier_20260508_1915.json ?? tools/data/reports/falsifier_20260508_1947.json ?? tools/data/reports/falsifier_20260508_2005.json ?? tools/data/reports/falsifier_20260508_2013.json ?? tools/data/reports/falsifier_20260508_2019.json ?? tools/data/reports/falsifier_20260508_2102.json ?? tools/data/reports/falsifier_20260508_2108.json ?? tools/data/reports/falsifier_20260508_2108.raw.txt ?? tools/data/reports/falsifier_20260508_2121.json ?? tools/data/reports/falsifier_20260508_2133.json ?? tools/data/reports/falsifier_20260508_2133.raw.txt ?? tools/data/reports/falsifier_20260508_2140.json ?? tools/data/reports/falsifier_20260509_0330.json ?? tools/data/reports/incident_20260504_0721.md ?? tools/data/reports/incident_20260504_1138.md ?? tools/data/reports/latest.md ?? tools/data/reports/loop_guard_20260507_0330.json ?? tools/data/reports/loop_guard_20260507_0803.json ?? tools/data/reports/loop_guard_20260507_0901.json ?? tools/data/reports/loop_guard_20260507_0923.json ?? tools/data/reports/loop_guard_20260507_0942.json ?? tools/data/reports/loop_guard_20260507_1006.json ?? tools/data/reports/loop_guard_20260507_1042.json ?? tools/data/reports/loop_guard_20260507_1419.json ?? tools/data/reports/loop_guard_20260507_1458.json ?? tools/data/reports/loop_guard_20260507_1751.json ?? tools/data/reports/loop_guard_20260507_1804.json ?? tools/data/reports/loop_guard_20260507_1938.json ?? tools/data/reports/loop_guard_20260507_1957.json ?? tools/data/reports/loop_guard_20260507_2042.json ?? tools/data/reports/loop_guard_20260507_2120.json ?? tools/data/reports/loop_guard_20260507_2203.json ?? tools/data/reports/loop_guard_20260507_2310.json ?? tools/data/reports/loop_guard_20260508_0011.json ?? tools/data/reports/loop_guard_20260508_0330.json ?? tools/data/reports/loop_guard_20260508_1632.json ?? tools/data/reports/loop_guard_20260508_1715.json ?? tools/data/reports/loop_guard_20260508_1805.json ?? tools/data/reports/loop_guard_20260508_1834.json ?? tools/data/reports/loop_guard_20260508_1909.json ?? tools/data/reports/loop_guard_20260508_1915.json ?? tools/data/reports/loop_guard_20260508_1947.json ?? tools/data/reports/loop_guard_20260508_2005.json ?? tools/data/reports/loop_guard_20260508_2013.json ?? tools/data/reports/loop_guard_20260508_2019.json ?? tools/data/reports/loop_guard_20260508_2102.json ?? tools/data/reports/loop_guard_20260508_2108.json ?? tools/data/reports/loop_guard_20260508_2121.json ?? tools/data/reports/loop_guard_20260508_2133.json ?? tools/data/reports/loop_guard_20260508_2140.json ?? tools/data/reports/loop_guard_20260509_0330.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_90.json ?? tools/data/seme_archive/piano_91.json ?? tools/data/seme_archive/piano_92.json ?? tools/data/seme_archive/piano_93.json ?? tools/data/seme_archive/piano_94.json ?? tools/data/seme_archive/piano_95.json ?? tools/data/seme_archive/piano_96.json ?? tools/data/seme_archive/piano_97.json ?? tools/data/seme_backup_b2_20260508_192024.json ?? tools/data/seme_backup_b2_20260508_214525.json ?? tools/data/seme_backup_b2_20260509_033618.json ?? tools/data/semireal_order_denominator_gate_20260507_0923.json ?? tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json ?? tools/data/spectral_rigidity_results.json ?? tools/data/tqge_underlay_gate_20260507_1751.json ?? tools/data/trajectory_apply_history.jsonl ?? tools/data/triadic_deposit_gate_20260507_1938.json ?? tools/data/two_channel_boundary.json ?? tools/data/two_channel_cross_domain.json ?? tools/data/two_channel_shuffle_audit.json ?? tools/data/two_layer_universality.json ?? tools/data/valutatore_log.jsonl ?? tools/data/vc_null_regression_gate_20260509_0637.json ?? tools/data/veritas/veritas_20260505_131056.json ?? tools/data/veritas/veritas_20260505_131201.json ?? tools/data/veritas/veritas_20260506_033803.json ?? tools/data/veritas/veritas_20260506_063303.json ?? tools/data/veritas/veritas_20260506_194644.json ?? tools/data/veritas/veritas_20260506_200238.json ?? tools/data/veritas/veritas_20260507_033525.json ?? tools/data/veritas/veritas_20260507_080958.json ?? tools/data/veritas/veritas_20260507_090723.json ?? tools/data/veritas/veritas_20260507_093207.json ?? tools/data/veritas/veritas_20260507_094723.json ?? tools/data/veritas/veritas_20260507_101605.json ?? tools/data/veritas/veritas_20260507_104904.json ?? tools/data/veritas/veritas_20260507_142501.json ?? tools/data/veritas/veritas_20260507_150443.json ?? tools/data/veritas/veritas_20260507_175511.json ?? tools/data/veritas/veritas_20260507_180843.json ?? tools/data/veritas/veritas_20260507_194258.json ?? tools/data/veritas/veritas_20260507_200223.json ?? tools/data/veritas/veritas_20260507_204637.json ?? tools/data/veritas/veritas_20260507_212541.json ?? tools/data/veritas/veritas_20260507_221109.json ?? tools/data/veritas/veritas_20260507_231732.json ?? tools/data/veritas/veritas_20260508_002036.json ?? tools/data/veritas/veritas_20260508_033454.json ?? tools/data/veritas/veritas_20260508_163804.json ?? tools/data/veritas/veritas_20260508_172020.json ?? tools/data/veritas/veritas_20260508_181110.json ?? tools/data/veritas/veritas_20260508_183856.json ?? tools/data/veritas/veritas_20260508_191516.json ?? tools/data/veritas/veritas_20260508_192002.json ?? tools/data/veritas/veritas_20260508_195247.json ?? tools/data/veritas/veritas_20260508_201056.json ?? tools/data/veritas/veritas_20260508_201917.json ?? tools/data/veritas/veritas_20260508_202448.json ?? tools/data/veritas/veritas_20260508_210742.json ?? tools/data/veritas/veritas_20260508_211531.json ?? tools/data/veritas/veritas_20260508_212526.json ?? tools/data/veritas/veritas_20260508_213916.json ?? tools/data/veritas/veritas_20260508_214506.json ?? tools/data/veritas/veritas_20260509_033556.json ?? tools/data/vincoli_decay_log.jsonl ?? tools/evolution_report.md ?? tools/exp_3d_boundary_layers.py ?? tools/exp_blank_shell_dilation_gate.py ?? tools/exp_blank_shell_polarity_gate.py ?? tools/exp_blank_shell_scale_law.py ?? tools/exp_blank_shell_stratified_gate.py ?? tools/exp_blank_shell_tqger_gate.py ?? tools/exp_blank_to_source_hinge.py ?? tools/exp_boundary_coherence.py ?? tools/exp_boundary_mixture_gate.py ?? tools/exp_boundary_shuffle_audit.py ?? tools/exp_bridge_order_denominator_gate.py ?? tools/exp_brody_calibration.py ?? tools/exp_brody_flow.py ?? tools/exp_cross_domain_dipolar_direction.py ?? tools/exp_cross_observable_consistency.py ?? tools/exp_crossover_phase_test.py ?? tools/exp_denominator_gate_transfer_matrix.py ?? tools/exp_dipolar_angle_reference.py ?? tools/exp_dipolar_crossover.py ?? tools/exp_dipolar_vector_scaling.py ?? tools/exp_duality_gate_transfer.py ?? tools/exp_duality_scale_contrast.py ?? tools/exp_gap_label_block_scale_gate.py ?? tools/exp_gap_label_generator_gate.py ?? tools/exp_gap_label_ostrowski_recognizability_gate.py ?? tools/exp_gap_label_position_error_gate.py ?? tools/exp_gap_label_repair_audit.py ?? tools/exp_gap_label_set_stability.py ?? tools/exp_gap_label_substitution_grammar_gate.py ?? tools/exp_gap_label_supertile_tiling_gate.py ?? tools/exp_gap_label_symbolic_grammar_gate.py ?? tools/exp_logistic_counter_scope_gate.py ?? tools/exp_logistic_cyclic_block_entropy_gate.py ?? tools/exp_logistic_surrogate_contract_gate.py ?? tools/exp_magnitude_psd_from_acf.py ?? tools/exp_markov3_observable_hunt.py ?? tools/exp_markov_dipolar_decomposition.py ?? tools/exp_markov_k_direction.py ?? tools/exp_markov_layer_recovery_audit.py ?? tools/exp_markov_memory_by_gue_type.py ?? tools/exp_markov_psd_prediction.py ?? tools/exp_markov_scale_function.py ?? tools/exp_meta_tautology_test.py ?? tools/exp_mod3_scaling.py ?? tools/exp_mod3_vs_residual_ordering.py ?? tools/exp_modular_algebra_depth.py ?? tools/exp_modular_memory_spectrum.py ?? tools/exp_nonphi_sturmian_fixed_reader_gate.py ?? tools/exp_observable_rank_audit.py ?? tools/exp_perturbation_dimensionality_audit.py ?? tools/exp_perturbation_rank_size_curve.py ?? tools/exp_prime_mobius_interval_charge_gate.py ?? tools/exp_prime_mobius_zero_mediator_gate.py ?? tools/exp_quasiperiodic_gap_ratio_denominator.py ?? tools/exp_scale_selective_perturbation.py ?? tools/exp_selective_layer_decoupling.py ?? tools/exp_semireal_order_denominator_gate.py ?? tools/exp_spectral_rigidity.py ?? tools/exp_tqge_underlay_gate.py ?? tools/exp_triadic_deposit_gate.py ?? tools/exp_two_channel_boundary.py ?? tools/exp_two_channel_cross_domain.py ?? tools/exp_two_channel_shuffle_audit.py ?? tools/exp_two_layer_universality.py ?? tools/exp_vc_null_regression_gate.py ?? tools/meta_assertion_gate.py ?? tools/observables_registry.py exec /bin/bash -lc "python - <<'PY' import json p='tools/data/vc_null_regression_gate_20260509_0637.json' d=json.load(open(p)) print(json.dumps(d['parameters'], indent=2)) print(json.dumps(d['summary'], indent=2)) print('by_threshold') for k,v in d['summary_by_threshold'].items(): print(k, v['events'], 'internal_rate', v['internal_rate'], 'floor', v['floor_hit_rate'], 'vc', v['vc_median'], 'label_med', v['label_jaccard_median']) PY" in /opt/MM_D-ND succeeded in 0ms: { "ns": [ 89, 144, 233 ], "phases": [ 0.0, 0.25, 0.5, 0.75 ], "r_thresholds": [ 0.48, 0.5, 0.52 ], "v_min": 0.5, "v_max": 3.0, "v_step": 0.01, "phase_trials": 2, "random_trials": 2, "label_trials": 2, "label_candidates": 5, "label_jaccard_min": 0.75, "label_threshold": 2.0, "top_k": 12, "max_label": 34, "seed": 202605090637 } { "balanced_random": { "conditions": 72, "events": { "floor_hit": 27, "internal_cross": 23, "internal_multi": 22 }, "internal_rate": 0.625, "floor_hit_rate": 0.375, "no_cross_rate": 0.0, "vc_median": 0.5329277658955516, "vc_q25": 0.5, "vc_q75": 0.6979353804007966, "slope_median": 0.7720102117185539, "r_floor_median": 0.5120324077981381, "r_span_median": 0.19060928527185111, "label_jaccard_median": null, "label_jaccard_min": null }, "label_preserving_surrogate": { "conditions": 72, "events": { "floor_hit": 23, "internal_cross": 29, "internal_multi": 20 }, "internal_rate": 0.6805555555555556, "floor_hit_rate": 0.3194444444444444, "no_cross_rate": 0.0, "vc_median": 0.5611705443174979, "vc_q25": 0.5, "vc_q75": 0.6995736773860278, "slope_median": 0.5908719603974094, "r_floor_median": 0.534426533952045, "r_span_median": 0.19842748992758677, "label_jaccard_median": 0.16666666666666666, "label_jaccard_min": 0.07142857142857142 }, "phi_sturmian": { "conditions": 36, "events": { "internal_cross": 36 }, "internal_rate": 1.0, "floor_hit_rate": 0.0, "no_cross_rate": 0.0, "vc_median": 1.0588848239081246, "vc_q25": 0.8291578321934835, "vc_q75": 1.2309904364497886, "slope_median": 0.19440143029909024, "r_floor_median": 0.6545022199074195, "r_span_median": 0.34009332805103826, "label_jaccard_median": 1.0, "label_jaccard_min": 1.0 }, "sturmian_phase_shuffle": { "conditions": 72, "events": { "internal_cross": 72 }, "internal_rate": 1.0, "floor_hit_rate": 0.0, "no_cross_rate": 0.0, "vc_median": 1.1933351291700072, "vc_q25": 1.0799642461620826, "vc_q75": 1.3728688020691409, "slope_median": 0.18650668240372242, "r_floor_median": 0.6912741460665063, "r_span_median": 0.38265926537546796, "label_jaccard_median": null, "label_jaccard_min": null } } by_threshold balanced_random|r_threshold=0.48 {'floor_hit': 4, 'internal_cross': 11, 'internal_multi': 9} internal_rate 0.8333333333333334 floor 0.16666666666666666 vc 0.5943828709588985 label_med None balanced_random|r_threshold=0.5 {'floor_hit': 10, 'internal_cross': 7, 'internal_multi': 7} internal_rate 0.5833333333333334 floor 0.4166666666666667 vc 0.5319293467055727 label_med None balanced_random|r_threshold=0.52 {'floor_hit': 13, 'internal_cross': 5, 'internal_multi': 6} internal_rate 0.4583333333333333 floor 0.5416666666666666 vc 0.5 label_med None label_preserving_surrogate|r_threshold=0.48 {'floor_hit': 3, 'internal_cross': 9, 'internal_multi': 12} internal_rate 0.875 floor 0.125 vc 0.6330669168649316 label_med 0.16666666666666666 label_preserving_surrogate|r_threshold=0.5 {'floor_hit': 9, 'internal_cross': 9, 'internal_multi': 6} internal_rate 0.625 floor 0.375 vc 0.5686731446077606 label_med 0.16666666666666666 label_preserving_surrogate|r_threshold=0.52 {'floor_hit': 11, 'internal_cross': 11, 'internal_multi': 2} internal_rate 0.5416666666666666 floor 0.4583333333333333 vc 0.5236693608111982 label_med 0.16666666666666666 phi_sturmian|r_threshold=0.48 {'internal_cross': 12} internal_rate 1.0 floor 0.0 vc 1.2290125536521477 label_med 1.0 phi_sturmian|r_threshold=0.5 {'internal_cross': 12} internal_rate 1.0 floor 0.0 vc 1.1085694689776533 label_med 1.0 phi_sturmian|r_threshold=0.52 {'internal_cross': 12} internal_rate 1.0 floor 0.0 vc 1.0052154776443325 label_med 1.0 sturmian_phase_shuffle|r_threshold=0.48 {'internal_cross': 24} internal_rate 1.0 floor 0.0 vc 1.377315077141783 label_med None sturmian_phase_shuffle|r_threshold=0.5 {'internal_cross': 24} internal_rate 1.0 floor 0.0 vc 1.275439057196231 label_med None sturmian_phase_shuffle|r_threshold=0.52 {'internal_cross': 24} internal_rate 1.0 floor 0.0 vc 1.1681947645508164 label_med None exec /bin/bash -lc "python - <<'PY' import json, collections rows=json.load(open('tools/data/vc_null_regression_gate_20260509_0637.json'))['rows'] # examples label surrogate scores and events for gen in ['label_preserving_surrogate','balanced_random','phi_sturmian','sturmian_phase_shuffle']: ex=[r for r in rows if r['generator']==gen][:5] print('\\n',gen) for r in ex: print({k:r[k] for k in ['N','phase','r_threshold','event','vc_interp','r_floor','crossing_count','label_jaccard'] if k in r}) PY" in /opt/MM_D-ND succeeded in 0ms: label_preserving_surrogate {'N': 89, 'phase': 0.0, 'r_threshold': 0.48, 'event': 'internal_multi', 'vc_interp': 0.7558850003588715, 'r_floor': 0.5680076636230983, 'crossing_count': 3, 'label_jaccard': 0.07692307692307693} {'N': 89, 'phase': 0.0, 'r_threshold': 0.5, 'event': 'internal_multi', 'vc_interp': 0.7356891932659151, 'r_floor': 0.5680076636230983, 'crossing_count': 3, 'label_jaccard': 0.07692307692307693} {'N': 89, 'phase': 0.0, 'r_threshold': 0.52, 'event': 'internal_multi', 'vc_interp': 0.5505899542332926, 'r_floor': 0.5680076636230983, 'crossing_count': 3, 'label_jaccard': 0.07692307692307693} {'N': 89, 'phase': 0.0, 'r_threshold': 0.48, 'event': 'internal_multi', 'vc_interp': 0.834547375599165, 'r_floor': 0.5493702004135591, 'crossing_count': 3, 'label_jaccard': 0.07142857142857142} {'N': 89, 'phase': 0.0, 'r_threshold': 0.5, 'event': 'internal_cross', 'vc_interp': 0.7717671879549856, 'r_floor': 0.5493702004135591, 'crossing_count': 1, 'label_jaccard': 0.07142857142857142} balanced_random {'N': 89, 'phase': 0.0, 'r_threshold': 0.48, 'event': 'internal_multi', 'vc_interp': 0.7273529272600197, 'r_floor': 0.6354013341682433, 'crossing_count': 5, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.5, 'event': 'internal_multi', 'vc_interp': 0.6975299083087495, 'r_floor': 0.6354013341682433, 'crossing_count': 3, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.52, 'event': 'internal_multi', 'vc_interp': 0.6264174246259845, 'r_floor': 0.6354013341682433, 'crossing_count': 5, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.48, 'event': 'internal_cross', 'vc_interp': 1.1441805921627815, 'r_floor': 0.5996010141585135, 'crossing_count': 1, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.5, 'event': 'internal_multi', 'vc_interp': 0.785852513759772, 'r_floor': 0.5996010141585135, 'crossing_count': 3, 'label_jaccard': None} phi_sturmian {'N': 89, 'phase': 0.0, 'r_threshold': 0.48, 'event': 'internal_cross', 'vc_interp': 1.1105119098997416, 'r_floor': 0.6216328561698318, 'crossing_count': 1, 'label_jaccard': 1.0} {'N': 89, 'phase': 0.0, 'r_threshold': 0.5, 'event': 'internal_cross', 'vc_interp': 1.0122169591837231, 'r_floor': 0.6216328561698318, 'crossing_count': 1, 'label_jaccard': 1.0} {'N': 89, 'phase': 0.0, 'r_threshold': 0.52, 'event': 'internal_cross', 'vc_interp': 0.9124593395567262, 'r_floor': 0.6216328561698318, 'crossing_count': 1, 'label_jaccard': 1.0} {'N': 89, 'phase': 0.25, 'r_threshold': 0.48, 'event': 'internal_cross', 'vc_interp': 1.5121009019541451, 'r_floor': 0.6807343729313243, 'crossing_count': 1, 'label_jaccard': 1.0} {'N': 89, 'phase': 0.25, 'r_threshold': 0.5, 'event': 'internal_cross', 'vc_interp': 1.370301599524643, 'r_floor': 0.6807343729313243, 'crossing_count': 1, 'label_jaccard': 1.0} sturmian_phase_shuffle {'N': 89, 'phase': 0.0, 'r_threshold': 0.48, 'event': 'internal_cross', 'vc_interp': 1.6227566909091606, 'r_floor': 0.7011226377276805, 'crossing_count': 1, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.5, 'event': 'internal_cross', 'vc_interp': 1.4703339398976298, 'r_floor': 0.7011226377276805, 'crossing_count': 1, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.52, 'event': 'internal_cross', 'vc_interp': 1.3417905618718309, 'r_floor': 0.7011226377276805, 'crossing_count': 1, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.48, 'event': 'internal_cross', 'vc_interp': 1.4735516290357615, 'r_floor': 0.6977630243528374, 'crossing_count': 1, 'label_jaccard': None} {'N': 89, 'phase': 0.0, 'r_threshold': 0.5, 'event': 'internal_cross', 'vc_interp': 1.3501047690316834, 'r_floor': 0.6977630243528374, 'crossing_count': 1, 'label_jaccard': None} diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex Il risultato è netto ma non nel modo atteso: il phase-shuffle Sturmian mantiene crossing interno al 100%, mentre il surrogate “label-preserving” non riesce davvero a preservare il label-set con il candidato minimo (`median Jaccard=0.1667`) e quindi resta un controllo fallito dichiarato, non una conferma. Scrivo il report con questo vincolo al centro. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260509_0637.md diff --git a/tools/data/reports/agent_20260509_0637.md b/tools/data/reports/agent_20260509_0637.md new file mode 100644 index 0000000000000000000000000000000000000000..a5259a73f1d901d6664ede6d517392d03957385b --- /dev/null +++ b/tools/data/reports/agent_20260509_0637.md @@ -0,0 +1,109 @@ +# Agent Report - Vc Null Regression Gate +**Date**: 2026-05-09 06:37 +**Piano**: 98 +**Tension explored**: TRASCENDENZA_LIMITE / V_c null regressivo +**verdict**: CONSTRAINT +observables_registry: 1.0.0-2026-05-06 +observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var +**observable_contract**: claim=la forma `r(V)` del boundary Sturmian-Harper e' sostenuta da attraversamenti interni, non da collasso al bordo minimo della filtrazione; observable=`event={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`; operator=curva `r(V)` su griglia 0.5..3.0 step 0.01, crossing lineare e classificazione del primo stato rispetto a `r_threshold={0.48,0.50,0.52}`; generator=phi Sturmian, phase-shuffle Sturmian, random bilanciato, surrogate random selezionato per overlap label-set; denominator=N={89,144,233}, phase={0,0.25,0.5,0.75}, phase_trials=2, random_trials=2, label_trials=2, label_candidates=5, seed=202605090637; non_possible=se un null che preserva label-set produce crossing interno unico e stesso `r_floor` dei generatori Sturmian, `V_c` non e' piu' boundary map di ordine Sturmian; not_tested=GUE/Poisson reali, silver/bronze in questo ciclo, fit power-law, label-preserving forte con accettazione Jaccard>=0.75. + +## Respiro fuori-tempo +- **Combo**: A2 confine det=-1 + A9 terzo incluso + TxQ matrice densita / TxR gas relativistico come filtrazione spettro-temperatura + nodo `TRASCENDENZA_LIMITE` + tensione operativa `V_c` sul null regressivo. +- **Dipolo / punto-zero**: collasso al bordo minimo (`floor_hit`) / attraversamento interno; punto-zero = il primo stato della curva rispetto alla soglia, prima che `V_c` venga contato come valore. +- **Piano superiore**: topologia assiomatica del bordo come filtrazione. Il contenuto non e' il numero `V_c`, ma il tipo di evento che genera il passaggio. +- **Operatori laterali scelti**: boundary operator, filtrazione, spectrum-preserving surrogate. Il boundary operator separa floor e interno; la filtrazione scorre `V`; il surrogate tenta di conservare il lettore label-set prima di misurare il crossing. +- **Contaminazione cognitiva**: + - **CE-0117 / KSAR**: reiterazione del kernel del ciclo 03:30 senza cambiare dominio: stesso `V_c`, nodo regressivo diverso. + - **PVI attack**: il rischio e' rendere il null piu' debole per salvare la curva metallica; per questo il surrogate label-preserving espone il proprio `label_jaccard`. + - **CE-0001**: il fallimento del surrogate entra come informazione, non come parametro da calibrare. +- **Proto-ipotesi**: se il boundary e' effetto di ordine Sturmian, allora le fasi Sturmian devono conservare crossing interno senza floor-hit; se basta preservare parzialmente il label-set, il surrogate random deve recuperare lo stesso evento interno unico. Se il surrogate non preserva il label-set, il ciclo produce un vincolo sul generatore del null. +- **Proiezione**: misuro l'evento prima del valore. `floor_hit` indica che il null parte gia' oltre il confine; `internal_cross` indica che la filtrazione crea il passaggio; `internal_multi` indica bordo oscillante, non curva metallica semplice. + +## Claim Under Test +> Nel perimetro Sturmian-Harper ridotto, la separazione metallico/random della curva `r(V)` sopravvive quando `V_c` viene decomposto in evento di bordo: i generatori Sturmian producono crossing interno; il random produce floor-hit o multi-crossing. Un surrogate che preserva label-set deve decidere se il portatore e' il label-set o l'ordine generativo. + +## Question +Il null precedente falsificava `V_c` perche' era troppo debole e collassava a `V_min`, oppure perche' il crossing interno richiede ordine Sturmian oltre al label-set? + +## Experiment Design +- Script: `tools/exp_vc_null_regression_gate.py`. +- Hamiltoniana: diagonale `V * seq`, off-diagonal 1, autovalori tridiagonali. +- Curva: `r(V)` per `V=0.5..3.0`, step `0.01`. +- Eventi: + - `floor_hit`: `r(V_min) < threshold`; il valore `V_c` e' il bordo della griglia, non attraversamento. + - `internal_cross`: parte sopra soglia e attraversa una volta. + - `internal_multi`: parte sopra soglia ma attraversa piu' volte. + - `no_cross`: non attraversa. +- Generatori: + - `phi_sturmian`: sequenza di riferimento per ogni N/fase. + - `sturmian_phase_shuffle`: stessa theta phi, fase random. + - `balanced_random`: stesso conteggio di 1, ordine distrutto. + - `label_preserving_surrogate`: miglior candidato tra 5 random bilanciati secondo Jaccard del label-set spettrale con la sequenza riferimento (`label_threshold=2.0`, `top_k=12`, `max_label=34`). +- Denominatore grezzo: `phi_sturmian=36` condizioni; ogni controllo `72` condizioni. Il run pieno con N fino a 377 e 12 candidati e' stato fermato per budget; il perimetro valido e' quello dichiarato qui. +- Contratto osservabile-operatore: `gap_ratio`, controlli metallici silver/bronze e domini GUE/Poisson non vengono testati in questo ciclo. + +## Results +Sintesi aggregata: + +| generator | conditions | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | r_floor_median | r_span_median | label_jaccard_median | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 36 | 0 | 36 | 0 | 1.0000 | 1.058885 | 0.654502 | 0.340093 | 1.000000 | +| sturmian_phase_shuffle | 72 | 0 | 72 | 0 | 1.0000 | 1.193335 | 0.691274 | 0.382659 | | +| balanced_random | 72 | 27 | 23 | 22 | 0.6250 | 0.532928 | 0.512032 | 0.190609 | | +| label_preserving_surrogate | 72 | 23 | 29 | 20 | 0.6806 | 0.561171 | 0.534427 | 0.198427 | 0.166667 | + +Per soglia: + +| generator | r_threshold | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | +|---|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 0.48 | 0 | 12 | 0 | 1.0000 | 1.229013 | +| phi_sturmian | 0.50 | 0 | 12 | 0 | 1.0000 | 1.108569 | +| phi_sturmian | 0.52 | 0 | 12 | 0 | 1.0000 | 1.005215 | +| sturmian_phase_shuffle | 0.48 | 0 | 24 | 0 | 1.0000 | 1.377315 | +| sturmian_phase_shuffle | 0.50 | 0 | 24 | 0 | 1.0000 | 1.275439 | +| sturmian_phase_shuffle | 0.52 | 0 | 24 | 0 | 1.0000 | 1.168195 | +| balanced_random | 0.48 | 4 | 11 | 9 | 0.8333 | 0.594383 | +| balanced_random | 0.50 | 10 | 7 | 7 | 0.5833 | 0.531929 | +| balanced_random | 0.52 | 13 | 5 | 6 | 0.4583 | 0.500000 | +| label_preserving_surrogate | 0.48 | 3 | 9 | 12 | 0.8750 | 0.633067 | +| label_preserving_surrogate | 0.50 | 9 | 9 | 6 | 0.6250 | 0.568673 | +| label_preserving_surrogate | 0.52 | 11 | 11 | 2 | 0.5417 | 0.523669 | + +## Key Findings +1. **Verificato: il crossing Sturmian e' interno nel perimetro testato.** `phi_sturmian` e `sturmian_phase_shuffle` hanno `floor_hit=0` e `internal_cross` unico in tutte le condizioni (`36/36` e `72/72`). +2. **Verificato: il random bilanciato mescola eventi diversi.** `balanced_random` contiene `27/72` floor-hit, `23/72` crossing interni unici e `22/72` crossing multipli. Il vecchio `V_c=0.5` aggregava floor-hit e attraversamenti reali. +3. **Verificato: il surrogate label-preserving minimo non preserva il label-set.** La mediana Jaccard e' `0.166667`, con minimo `0.071429`; quindi questo controllo non decide se il label-set basta a produrre il crossing Sturmian. +4. **Verificato: anche il surrogate debole resta vicino al random, non allo Sturmian.** Ha `23/72` floor-hit e `20/72` internal_multi, `r_floor_median=0.534427` e `r_span_median=0.198427`, contro `r_floor_median=0.654502/0.691274` e span `0.340093/0.382659` degli Sturmian. +5. **Inferito: il nodo regressivo del null e' doppio.** Separare floor-hit e crossing interno ripara il denominatore di `V_c`; costruire un vero null label-preserving richiede un generatore dedicato, non selezione random superficiale. + +## Verdict +**CONSTRAINT on V_c null**: nel perimetro `N={89,144,233}`, `phase={0,0.25,0.5,0.75}`, `r_threshold={0.48,0.50,0.52}`, il boundary Sturmian e' crossing interno unico. Il random bilanciato non e' un contro-campo omogeneo: contiene floor-hit e multi-crossing. La separazione precedente metallico/random resta valida come distinzione di evento, ma il ciclo non chiude il claim label-set perche' il surrogate label-preserving non preserva davvero il label-set. + +La formulazione valida e': `V_c` va riportato insieme a `event_type`; `floor_hit` non e' crossing; `internal_multi` non e' curva metallica semplice. Il prossimo null deve generare sequenze con Jaccard label-set alto prima di confrontare `V_c`. + +## Bicono della scoperta +- **Due radici**: valore `V_c` aggregato . evento generativo del crossing. +- **Singolare**: la curva `r(V)` prima della soglia, dove il confine non e' ancora numero ma tipo di passaggio. +- **Invariante di passaggio**: sopravvive il crossing interno unico sotto cambio di fase Sturmian; cade il null random come denominatore omogeneo. +- **Campo di possibilita**: qui diventa possibile usare `event_type` come gate regressivo di ogni misura `V_c`; qui diventa non-possibile confrontare mediane `V_c` senza separare floor-hit, internal-cross e multi-crossing. + +## Consecutio +Il prossimo passo e' costruire un surrogate label-preserving reale: non scegliere il miglior random fra pochi candidati, ma generare per ricottura/swap vincolato finche' `label_jaccard >= 0.75` viene raggiunto con denominatore dichiarato. Se quel null produce `floor_hit` o `internal_multi`, il portatore e' ordine Sturmian oltre al label-set; se produce crossing interno unico con `r_floor` e `r_span` Sturmian, il portatore e' il label-set. + +## Auto-audit: source flags e 5 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_0330.md`, output JSON del nuovo script. +- **L1 hard constraint**: nessun claim universale; il verdict vale solo sul perimetro ridotto dichiarato. +- **L2 quantita vs ratio**: riportati count grezzi (`36`, `72`, `27/72`, `23/72`, `22/72`) oltre ai rate. +- **L3 no silent patching**: il surrogate label-preserving fallito viene dichiarato come fallimento del controllo, non escluso. +- **L4 edge cases**: random a `r_threshold=0.48` ha molti crossing interni (`20/24` contando multi), quindi non basta dire "random=floor". +- **L5 re-discovery vs discovery**: tight-binding Sturmian e r-statistic sono standard; il finding e' il gate regressivo `event_type` per non confondere floor e crossing. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- Runtime: primo run piu' largo fermato per budget; run valido completato con N={89,144,233}, 5 candidati label per surrogate. +- Verifica script: `python -m py_compile tools/exp_vc_null_regression_gate.py` completato senza errori. + +## Files +- Script: `tools/exp_vc_null_regression_gate.py` +- Data: `tools/data/vc_null_regression_gate_20260509_0637.json` +- Report: `tools/data/reports/agent_20260509_0637.md` diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260509_0637.md b/tools/data/reports/agent_20260509_0637.md new file mode 100644 index 0000000000000000000000000000000000000000..a5259a73f1d901d6664ede6d517392d03957385b --- /dev/null +++ b/tools/data/reports/agent_20260509_0637.md @@ -0,0 +1,109 @@ +# Agent Report - Vc Null Regression Gate +**Date**: 2026-05-09 06:37 +**Piano**: 98 +**Tension explored**: TRASCENDENZA_LIMITE / V_c null regressivo +**verdict**: CONSTRAINT +observables_registry: 1.0.0-2026-05-06 +observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var +**observable_contract**: claim=la forma `r(V)` del boundary Sturmian-Harper e' sostenuta da attraversamenti interni, non da collasso al bordo minimo della filtrazione; observable=`event={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`; operator=curva `r(V)` su griglia 0.5..3.0 step 0.01, crossing lineare e classificazione del primo stato rispetto a `r_threshold={0.48,0.50,0.52}`; generator=phi Sturmian, phase-shuffle Sturmian, random bilanciato, surrogate random selezionato per overlap label-set; denominator=N={89,144,233}, phase={0,0.25,0.5,0.75}, phase_trials=2, random_trials=2, label_trials=2, label_candidates=5, seed=202605090637; non_possible=se un null che preserva label-set produce crossing interno unico e stesso `r_floor` dei generatori Sturmian, `V_c` non e' piu' boundary map di ordine Sturmian; not_tested=GUE/Poisson reali, silver/bronze in questo ciclo, fit power-law, label-preserving forte con accettazione Jaccard>=0.75. + +## Respiro fuori-tempo +- **Combo**: A2 confine det=-1 + A9 terzo incluso + TxQ matrice densita / TxR gas relativistico come filtrazione spettro-temperatura + nodo `TRASCENDENZA_LIMITE` + tensione operativa `V_c` sul null regressivo. +- **Dipolo / punto-zero**: collasso al bordo minimo (`floor_hit`) / attraversamento interno; punto-zero = il primo stato della curva rispetto alla soglia, prima che `V_c` venga contato come valore. +- **Piano superiore**: topologia assiomatica del bordo come filtrazione. Il contenuto non e' il numero `V_c`, ma il tipo di evento che genera il passaggio. +- **Operatori laterali scelti**: boundary operator, filtrazione, spectrum-preserving surrogate. Il boundary operator separa floor e interno; la filtrazione scorre `V`; il surrogate tenta di conservare il lettore label-set prima di misurare il crossing. +- **Contaminazione cognitiva**: + - **CE-0117 / KSAR**: reiterazione del kernel del ciclo 03:30 senza cambiare dominio: stesso `V_c`, nodo regressivo diverso. + - **PVI attack**: il rischio e' rendere il null piu' debole per salvare la curva metallica; per questo il surrogate label-preserving espone il proprio `label_jaccard`. + - **CE-0001**: il fallimento del surrogate entra come informazione, non come parametro da calibrare. +- **Proto-ipotesi**: se il boundary e' effetto di ordine Sturmian, allora le fasi Sturmian devono conservare crossing interno senza floor-hit; se basta preservare parzialmente il label-set, il surrogate random deve recuperare lo stesso evento interno unico. Se il surrogate non preserva il label-set, il ciclo produce un vincolo sul generatore del null. +- **Proiezione**: misuro l'evento prima del valore. `floor_hit` indica che il null parte gia' oltre il confine; `internal_cross` indica che la filtrazione crea il passaggio; `internal_multi` indica bordo oscillante, non curva metallica semplice. + +## Claim Under Test +> Nel perimetro Sturmian-Harper ridotto, la separazione metallico/random della curva `r(V)` sopravvive quando `V_c` viene decomposto in evento di bordo: i generatori Sturmian producono crossing interno; il random produce floor-hit o multi-crossing. Un surrogate che preserva label-set deve decidere se il portatore e' il label-set o l'ordine generativo. + +## Question +Il null precedente falsificava `V_c` perche' era troppo debole e collassava a `V_min`, oppure perche' il crossing interno richiede ordine Sturmian oltre al label-set? + +## Experiment Design +- Script: `tools/exp_vc_null_regression_gate.py`. +- Hamiltoniana: diagonale `V * seq`, off-diagonal 1, autovalori tridiagonali. +- Curva: `r(V)` per `V=0.5..3.0`, step `0.01`. +- Eventi: + - `floor_hit`: `r(V_min) < threshold`; il valore `V_c` e' il bordo della griglia, non attraversamento. + - `internal_cross`: parte sopra soglia e attraversa una volta. + - `internal_multi`: parte sopra soglia ma attraversa piu' volte. + - `no_cross`: non attraversa. +- Generatori: + - `phi_sturmian`: sequenza di riferimento per ogni N/fase. + - `sturmian_phase_shuffle`: stessa theta phi, fase random. + - `balanced_random`: stesso conteggio di 1, ordine distrutto. + - `label_preserving_surrogate`: miglior candidato tra 5 random bilanciati secondo Jaccard del label-set spettrale con la sequenza riferimento (`label_threshold=2.0`, `top_k=12`, `max_label=34`). +- Denominatore grezzo: `phi_sturmian=36` condizioni; ogni controllo `72` condizioni. Il run pieno con N fino a 377 e 12 candidati e' stato fermato per budget; il perimetro valido e' quello dichiarato qui. +- Contratto osservabile-operatore: `gap_ratio`, controlli metallici silver/bronze e domini GUE/Poisson non vengono testati in questo ciclo. + +## Results +Sintesi aggregata: + +| generator | conditions | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | r_floor_median | r_span_median | label_jaccard_median | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 36 | 0 | 36 | 0 | 1.0000 | 1.058885 | 0.654502 | 0.340093 | 1.000000 | +| sturmian_phase_shuffle | 72 | 0 | 72 | 0 | 1.0000 | 1.193335 | 0.691274 | 0.382659 | | +| balanced_random | 72 | 27 | 23 | 22 | 0.6250 | 0.532928 | 0.512032 | 0.190609 | | +| label_preserving_surrogate | 72 | 23 | 29 | 20 | 0.6806 | 0.561171 | 0.534427 | 0.198427 | 0.166667 | + +Per soglia: + +| generator | r_threshold | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | +|---|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 0.48 | 0 | 12 | 0 | 1.0000 | 1.229013 | +| phi_sturmian | 0.50 | 0 | 12 | 0 | 1.0000 | 1.108569 | +| phi_sturmian | 0.52 | 0 | 12 | 0 | 1.0000 | 1.005215 | +| sturmian_phase_shuffle | 0.48 | 0 | 24 | 0 | 1.0000 | 1.377315 | +| sturmian_phase_shuffle | 0.50 | 0 | 24 | 0 | 1.0000 | 1.275439 | +| sturmian_phase_shuffle | 0.52 | 0 | 24 | 0 | 1.0000 | 1.168195 | +| balanced_random | 0.48 | 4 | 11 | 9 | 0.8333 | 0.594383 | +| balanced_random | 0.50 | 10 | 7 | 7 | 0.5833 | 0.531929 | +| balanced_random | 0.52 | 13 | 5 | 6 | 0.4583 | 0.500000 | +| label_preserving_surrogate | 0.48 | 3 | 9 | 12 | 0.8750 | 0.633067 | +| label_preserving_surrogate | 0.50 | 9 | 9 | 6 | 0.6250 | 0.568673 | +| label_preserving_surrogate | 0.52 | 11 | 11 | 2 | 0.5417 | 0.523669 | + +## Key Findings +1. **Verificato: il crossing Sturmian e' interno nel perimetro testato.** `phi_sturmian` e `sturmian_phase_shuffle` hanno `floor_hit=0` e `internal_cross` unico in tutte le condizioni (`36/36` e `72/72`). +2. **Verificato: il random bilanciato mescola eventi diversi.** `balanced_random` contiene `27/72` floor-hit, `23/72` crossing interni unici e `22/72` crossing multipli. Il vecchio `V_c=0.5` aggregava floor-hit e attraversamenti reali. +3. **Verificato: il surrogate label-preserving minimo non preserva il label-set.** La mediana Jaccard e' `0.166667`, con minimo `0.071429`; quindi questo controllo non decide se il label-set basta a produrre il crossing Sturmian. +4. **Verificato: anche il surrogate debole resta vicino al random, non allo Sturmian.** Ha `23/72` floor-hit e `20/72` internal_multi, `r_floor_median=0.534427` e `r_span_median=0.198427`, contro `r_floor_median=0.654502/0.691274` e span `0.340093/0.382659` degli Sturmian. +5. **Inferito: il nodo regressivo del null e' doppio.** Separare floor-hit e crossing interno ripara il denominatore di `V_c`; costruire un vero null label-preserving richiede un generatore dedicato, non selezione random superficiale. + +## Verdict +**CONSTRAINT on V_c null**: nel perimetro `N={89,144,233}`, `phase={0,0.25,0.5,0.75}`, `r_threshold={0.48,0.50,0.52}`, il boundary Sturmian e' crossing interno unico. Il random bilanciato non e' un contro-campo omogeneo: contiene floor-hit e multi-crossing. La separazione precedente metallico/random resta valida come distinzione di evento, ma il ciclo non chiude il claim label-set perche' il surrogate label-preserving non preserva davvero il label-set. + +La formulazione valida e': `V_c` va riportato insieme a `event_type`; `floor_hit` non e' crossing; `internal_multi` non e' curva metallica semplice. Il prossimo null deve generare sequenze con Jaccard label-set alto prima di confrontare `V_c`. + +## Bicono della scoperta +- **Due radici**: valore `V_c` aggregato . evento generativo del crossing. +- **Singolare**: la curva `r(V)` prima della soglia, dove il confine non e' ancora numero ma tipo di passaggio. +- **Invariante di passaggio**: sopravvive il crossing interno unico sotto cambio di fase Sturmian; cade il null random come denominatore omogeneo. +- **Campo di possibilita**: qui diventa possibile usare `event_type` come gate regressivo di ogni misura `V_c`; qui diventa non-possibile confrontare mediane `V_c` senza separare floor-hit, internal-cross e multi-crossing. + +## Consecutio +Il prossimo passo e' costruire un surrogate label-preserving reale: non scegliere il miglior random fra pochi candidati, ma generare per ricottura/swap vincolato finche' `label_jaccard >= 0.75` viene raggiunto con denominatore dichiarato. Se quel null produce `floor_hit` o `internal_multi`, il portatore e' ordine Sturmian oltre al label-set; se produce crossing interno unico con `r_floor` e `r_span` Sturmian, il portatore e' il label-set. + +## Auto-audit: source flags e 5 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_0330.md`, output JSON del nuovo script. +- **L1 hard constraint**: nessun claim universale; il verdict vale solo sul perimetro ridotto dichiarato. +- **L2 quantita vs ratio**: riportati count grezzi (`36`, `72`, `27/72`, `23/72`, `22/72`) oltre ai rate. +- **L3 no silent patching**: il surrogate label-preserving fallito viene dichiarato come fallimento del controllo, non escluso. +- **L4 edge cases**: random a `r_threshold=0.48` ha molti crossing interni (`20/24` contando multi), quindi non basta dire "random=floor". +- **L5 re-discovery vs discovery**: tight-binding Sturmian e r-statistic sono standard; il finding e' il gate regressivo `event_type` per non confondere floor e crossing. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- Runtime: primo run piu' largo fermato per budget; run valido completato con N={89,144,233}, 5 candidati label per surrogate. +- Verifica script: `python -m py_compile tools/exp_vc_null_regression_gate.py` completato senza errori. + +## Files +- Script: `tools/exp_vc_null_regression_gate.py` +- Data: `tools/data/vc_null_regression_gate_20260509_0637.json` +- Report: `tools/data/reports/agent_20260509_0637.md` diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'test -s tools/data/reports/agent_20260509_0637.md && wc -l tools/data/reports/agent_20260509_0637.md' in /opt/MM_D-ND succeeded in 0ms: 109 tools/data/reports/agent_20260509_0637.md exec /bin/bash -lc "pgrep -af 'exp_vc_null_regression_gate.py|python tools/exp' || true" in /opt/MM_D-ND succeeded in 0ms: 1 /root/.codex-lab/tmp/arg0/codex-arg0ZnVbji/codex-linux-sandbox --sandbox-policy-cwd /opt/MM_D-ND --command-cwd /opt/MM_D-ND --sandbox-policy {"type":"workspace-write","writable_roots":["/root/.codex-lab/memories"],"network_access":false,"exclude_tmpdir_env_var":false,"exclude_slash_tmp":false} --file-system-sandbox-policy {"kind":"restricted","entries":[{"path":{"type":"special","value":{"kind":"root"}},"access":"read"},{"path":{"type":"special","value":{"kind":"current_working_directory"}},"access":"write"},{"path":{"type":"special","value":{"kind":"slash_tmp"}},"access":"write"},{"path":{"type":"special","value":{"kind":"tmpdir"}},"access":"write"},{"path":{"type":"path","path":"/root/.codex-lab/memories"},"access":"write"},{"path":{"type":"path","path":"/opt/MM_D-ND/.git"},"access":"read"},{"path":{"type":"path","path":"/opt/MM_D-ND/.codex"},"access":"read"}]} --network-sandbox-policy "restricted" -- /bin/bash -c __CODEX_SNAPSHOT_OVERRIDE_SET_0="${CODEX_THREAD_ID+x}" __CODEX_SNAPSHOT_OVERRIDE_0="${CODEX_THREAD_ID-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_0="${ALL_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_0="${ALL_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_1="${BUNDLE_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_1="${BUNDLE_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_2="${BUNDLE_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_2="${BUNDLE_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_3="${BUNDLE_NO_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_3="${BUNDLE_NO_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_4="${CODEX_NETWORK_ALLOW_LOCAL_BINDING+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_4="${CODEX_NETWORK_ALLOW_LOCAL_BINDING-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_5="${CODEX_NETWORK_PROXY_ACTIVE+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_5="${CODEX_NETWORK_PROXY_ACTIVE-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_6="${DOCKER_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_6="${DOCKER_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_7="${DOCKER_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_7="${DOCKER_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_8="${ELECTRON_GET_USE_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_8="${ELECTRON_GET_USE_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_9="${FTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_9="${FTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_10="${HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_10="${HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_11="${HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_11="${HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_12="${NO_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_12="${NO_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_13="${NPM_CONFIG_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_13="${NPM_CONFIG_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_14="${NPM_CONFIG_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_14="${NPM_CONFIG_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_15="${NPM_CONFIG_NOPROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_15="${NPM_CONFIG_NOPROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_16="${NPM_CONFIG_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_16="${NPM_CONFIG_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_17="${PIP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_17="${PIP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_18="${WSS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_18="${WSS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_19="${WS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_19="${WS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_20="${YARN_HTTPS_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_20="${YARN_HTTPS_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_21="${YARN_HTTP_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_21="${YARN_HTTP_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_22="${YARN_NO_PROXY+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_22="${YARN_NO_PROXY-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_23="${all_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_23="${all_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_24="${ftp_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_24="${ftp_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_25="${http_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_25="${http_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_26="${https_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_26="${https_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_27="${no_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_27="${no_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_28="${npm_config_http_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_28="${npm_config_http_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_29="${npm_config_https_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_29="${npm_config_https_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_30="${npm_config_noproxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_30="${npm_config_noproxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_31="${npm_config_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_31="${npm_config_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_32="${ws_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_32="${ws_proxy-}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_33="${wss_proxy+x}" __CODEX_SNAPSHOT_PROXY_OVERRIDE_33="${wss_proxy-}" __CODEX_SNAPSHOT_PROXY_ENV_SET="${CODEX_NETWORK_PROXY_ACTIVE+x}" if . '/root/.codex-lab/shell_snapshots/019e0b74-bab7-7142-a093-a4d65c72a491.1778308659909026410.sh' >/dev/null 2>&1; then :; fi if [ -n "${__CODEX_SNAPSHOT_OVERRIDE_SET_0}" ]; then export CODEX_THREAD_ID="${__CODEX_SNAPSHOT_OVERRIDE_0}"; else unset CODEX_THREAD_ID; fi if [ -n "$__CODEX_SNAPSHOT_PROXY_ENV_SET" ] || [ -n "${CODEX_NETWORK_PROXY_ACTIVE+x}" ]; then if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_0}" ]; then export ALL_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_0}"; else unset ALL_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_1}" ]; then export BUNDLE_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_1}"; else unset BUNDLE_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_2}" ]; then export BUNDLE_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_2}"; else unset BUNDLE_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_3}" ]; then export BUNDLE_NO_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_3}"; else unset BUNDLE_NO_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_4}" ]; then export CODEX_NETWORK_ALLOW_LOCAL_BINDING="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_4}"; else unset CODEX_NETWORK_ALLOW_LOCAL_BINDING; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_5}" ]; then export CODEX_NETWORK_PROXY_ACTIVE="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_5}"; else unset CODEX_NETWORK_PROXY_ACTIVE; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_6}" ]; then export DOCKER_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_6}"; else unset DOCKER_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_7}" ]; then export DOCKER_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_7}"; else unset DOCKER_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_8}" ]; then export ELECTRON_GET_USE_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_8}"; else unset ELECTRON_GET_USE_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_9}" ]; then export FTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_9}"; else unset FTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_10}" ]; then export HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_10}"; else unset HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_11}" ]; then export HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_11}"; else unset HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_12}" ]; then export NO_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_12}"; else unset NO_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_13}" ]; then export NPM_CONFIG_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_13}"; else unset NPM_CONFIG_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_14}" ]; then export NPM_CONFIG_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_14}"; else unset NPM_CONFIG_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_15}" ]; then export NPM_CONFIG_NOPROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_15}"; else unset NPM_CONFIG_NOPROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_16}" ]; then export NPM_CONFIG_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_16}"; else unset NPM_CONFIG_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_17}" ]; then export PIP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_17}"; else unset PIP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_18}" ]; then export WSS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_18}"; else unset WSS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_19}" ]; then export WS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_19}"; else unset WS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_20}" ]; then export YARN_HTTPS_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_20}"; else unset YARN_HTTPS_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_21}" ]; then export YARN_HTTP_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_21}"; else unset YARN_HTTP_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_22}" ]; then export YARN_NO_PROXY="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_22}"; else unset YARN_NO_PROXY; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_23}" ]; then export all_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_23}"; else unset all_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_24}" ]; then export ftp_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_24}"; else unset ftp_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_25}" ]; then export http_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_25}"; else unset http_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_26}" ]; then export https_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_26}"; else unset https_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_27}" ]; then export no_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_27}"; else unset no_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_28}" ]; then export npm_config_http_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_28}"; else unset npm_config_http_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_29}" ]; then export npm_config_https_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_29}"; else unset npm_config_https_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_30}" ]; then export npm_config_noproxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_30}"; else unset npm_config_noproxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_31}" ]; then export npm_config_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_31}"; else unset npm_config_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_32}" ]; then export ws_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_32}"; else unset ws_proxy; fi if [ -n "${__CODEX_SNAPSHOT_PROXY_OVERRIDE_SET_33}" ]; then export wss_proxy="${__CODEX_SNAPSHOT_PROXY_OVERRIDE_33}"; else unset wss_proxy; fi fi exec '/bin/bash' -c 'pgrep -af '"'"'exp_vc_null_regression_gate.py|python tools/exp'"'"' || true' 2 /bin/bash -c pgrep -af 'exp_vc_null_regression_gate.py|python tools/exp' || true exec /bin/bash -lc 'python -m py_compile tools/exp_vc_null_regression_gate.py' in /opt/MM_D-ND succeeded in 0ms: diff --git a/tools/data/reports/agent_20260509_0637.md b/tools/data/reports/agent_20260509_0637.md new file mode 100644 index 0000000000000000000000000000000000000000..a5259a73f1d901d6664ede6d517392d03957385b --- /dev/null +++ b/tools/data/reports/agent_20260509_0637.md @@ -0,0 +1,109 @@ +# Agent Report - Vc Null Regression Gate +**Date**: 2026-05-09 06:37 +**Piano**: 98 +**Tension explored**: TRASCENDENZA_LIMITE / V_c null regressivo +**verdict**: CONSTRAINT +observables_registry: 1.0.0-2026-05-06 +observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var +**observable_contract**: claim=la forma `r(V)` del boundary Sturmian-Harper e' sostenuta da attraversamenti interni, non da collasso al bordo minimo della filtrazione; observable=`event={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`; operator=curva `r(V)` su griglia 0.5..3.0 step 0.01, crossing lineare e classificazione del primo stato rispetto a `r_threshold={0.48,0.50,0.52}`; generator=phi Sturmian, phase-shuffle Sturmian, random bilanciato, surrogate random selezionato per overlap label-set; denominator=N={89,144,233}, phase={0,0.25,0.5,0.75}, phase_trials=2, random_trials=2, label_trials=2, label_candidates=5, seed=202605090637; non_possible=se un null che preserva label-set produce crossing interno unico e stesso `r_floor` dei generatori Sturmian, `V_c` non e' piu' boundary map di ordine Sturmian; not_tested=GUE/Poisson reali, silver/bronze in questo ciclo, fit power-law, label-preserving forte con accettazione Jaccard>=0.75. + +## Respiro fuori-tempo +- **Combo**: A2 confine det=-1 + A9 terzo incluso + TxQ matrice densita / TxR gas relativistico come filtrazione spettro-temperatura + nodo `TRASCENDENZA_LIMITE` + tensione operativa `V_c` sul null regressivo. +- **Dipolo / punto-zero**: collasso al bordo minimo (`floor_hit`) / attraversamento interno; punto-zero = il primo stato della curva rispetto alla soglia, prima che `V_c` venga contato come valore. +- **Piano superiore**: topologia assiomatica del bordo come filtrazione. Il contenuto non e' il numero `V_c`, ma il tipo di evento che genera il passaggio. +- **Operatori laterali scelti**: boundary operator, filtrazione, spectrum-preserving surrogate. Il boundary operator separa floor e interno; la filtrazione scorre `V`; il surrogate tenta di conservare il lettore label-set prima di misurare il crossing. +- **Contaminazione cognitiva**: + - **CE-0117 / KSAR**: reiterazione del kernel del ciclo 03:30 senza cambiare dominio: stesso `V_c`, nodo regressivo diverso. + - **PVI attack**: il rischio e' rendere il null piu' debole per salvare la curva metallica; per questo il surrogate label-preserving espone il proprio `label_jaccard`. + - **CE-0001**: il fallimento del surrogate entra come informazione, non come parametro da calibrare. +- **Proto-ipotesi**: se il boundary e' effetto di ordine Sturmian, allora le fasi Sturmian devono conservare crossing interno senza floor-hit; se basta preservare parzialmente il label-set, il surrogate random deve recuperare lo stesso evento interno unico. Se il surrogate non preserva il label-set, il ciclo produce un vincolo sul generatore del null. +- **Proiezione**: misuro l'evento prima del valore. `floor_hit` indica che il null parte gia' oltre il confine; `internal_cross` indica che la filtrazione crea il passaggio; `internal_multi` indica bordo oscillante, non curva metallica semplice. + +## Claim Under Test +> Nel perimetro Sturmian-Harper ridotto, la separazione metallico/random della curva `r(V)` sopravvive quando `V_c` viene decomposto in evento di bordo: i generatori Sturmian producono crossing interno; il random produce floor-hit o multi-crossing. Un surrogate che preserva label-set deve decidere se il portatore e' il label-set o l'ordine generativo. + +## Question +Il null precedente falsificava `V_c` perche' era troppo debole e collassava a `V_min`, oppure perche' il crossing interno richiede ordine Sturmian oltre al label-set? + +## Experiment Design +- Script: `tools/exp_vc_null_regression_gate.py`. +- Hamiltoniana: diagonale `V * seq`, off-diagonal 1, autovalori tridiagonali. +- Curva: `r(V)` per `V=0.5..3.0`, step `0.01`. +- Eventi: + - `floor_hit`: `r(V_min) < threshold`; il valore `V_c` e' il bordo della griglia, non attraversamento. + - `internal_cross`: parte sopra soglia e attraversa una volta. + - `internal_multi`: parte sopra soglia ma attraversa piu' volte. + - `no_cross`: non attraversa. +- Generatori: + - `phi_sturmian`: sequenza di riferimento per ogni N/fase. + - `sturmian_phase_shuffle`: stessa theta phi, fase random. + - `balanced_random`: stesso conteggio di 1, ordine distrutto. + - `label_preserving_surrogate`: miglior candidato tra 5 random bilanciati secondo Jaccard del label-set spettrale con la sequenza riferimento (`label_threshold=2.0`, `top_k=12`, `max_label=34`). +- Denominatore grezzo: `phi_sturmian=36` condizioni; ogni controllo `72` condizioni. Il run pieno con N fino a 377 e 12 candidati e' stato fermato per budget; il perimetro valido e' quello dichiarato qui. +- Contratto osservabile-operatore: `gap_ratio`, controlli metallici silver/bronze e domini GUE/Poisson non vengono testati in questo ciclo. + +## Results +Sintesi aggregata: + +| generator | conditions | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | r_floor_median | r_span_median | label_jaccard_median | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 36 | 0 | 36 | 0 | 1.0000 | 1.058885 | 0.654502 | 0.340093 | 1.000000 | +| sturmian_phase_shuffle | 72 | 0 | 72 | 0 | 1.0000 | 1.193335 | 0.691274 | 0.382659 | | +| balanced_random | 72 | 27 | 23 | 22 | 0.6250 | 0.532928 | 0.512032 | 0.190609 | | +| label_preserving_surrogate | 72 | 23 | 29 | 20 | 0.6806 | 0.561171 | 0.534427 | 0.198427 | 0.166667 | + +Per soglia: + +| generator | r_threshold | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | +|---|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 0.48 | 0 | 12 | 0 | 1.0000 | 1.229013 | +| phi_sturmian | 0.50 | 0 | 12 | 0 | 1.0000 | 1.108569 | +| phi_sturmian | 0.52 | 0 | 12 | 0 | 1.0000 | 1.005215 | +| sturmian_phase_shuffle | 0.48 | 0 | 24 | 0 | 1.0000 | 1.377315 | +| sturmian_phase_shuffle | 0.50 | 0 | 24 | 0 | 1.0000 | 1.275439 | +| sturmian_phase_shuffle | 0.52 | 0 | 24 | 0 | 1.0000 | 1.168195 | +| balanced_random | 0.48 | 4 | 11 | 9 | 0.8333 | 0.594383 | +| balanced_random | 0.50 | 10 | 7 | 7 | 0.5833 | 0.531929 | +| balanced_random | 0.52 | 13 | 5 | 6 | 0.4583 | 0.500000 | +| label_preserving_surrogate | 0.48 | 3 | 9 | 12 | 0.8750 | 0.633067 | +| label_preserving_surrogate | 0.50 | 9 | 9 | 6 | 0.6250 | 0.568673 | +| label_preserving_surrogate | 0.52 | 11 | 11 | 2 | 0.5417 | 0.523669 | + +## Key Findings +1. **Verificato: il crossing Sturmian e' interno nel perimetro testato.** `phi_sturmian` e `sturmian_phase_shuffle` hanno `floor_hit=0` e `internal_cross` unico in tutte le condizioni (`36/36` e `72/72`). +2. **Verificato: il random bilanciato mescola eventi diversi.** `balanced_random` contiene `27/72` floor-hit, `23/72` crossing interni unici e `22/72` crossing multipli. Il vecchio `V_c=0.5` aggregava floor-hit e attraversamenti reali. +3. **Verificato: il surrogate label-preserving minimo non preserva il label-set.** La mediana Jaccard e' `0.166667`, con minimo `0.071429`; quindi questo controllo non decide se il label-set basta a produrre il crossing Sturmian. +4. **Verificato: anche il surrogate debole resta vicino al random, non allo Sturmian.** Ha `23/72` floor-hit e `20/72` internal_multi, `r_floor_median=0.534427` e `r_span_median=0.198427`, contro `r_floor_median=0.654502/0.691274` e span `0.340093/0.382659` degli Sturmian. +5. **Inferito: il nodo regressivo del null e' doppio.** Separare floor-hit e crossing interno ripara il denominatore di `V_c`; costruire un vero null label-preserving richiede un generatore dedicato, non selezione random superficiale. + +## Verdict +**CONSTRAINT on V_c null**: nel perimetro `N={89,144,233}`, `phase={0,0.25,0.5,0.75}`, `r_threshold={0.48,0.50,0.52}`, il boundary Sturmian e' crossing interno unico. Il random bilanciato non e' un contro-campo omogeneo: contiene floor-hit e multi-crossing. La separazione precedente metallico/random resta valida come distinzione di evento, ma il ciclo non chiude il claim label-set perche' il surrogate label-preserving non preserva davvero il label-set. + +La formulazione valida e': `V_c` va riportato insieme a `event_type`; `floor_hit` non e' crossing; `internal_multi` non e' curva metallica semplice. Il prossimo null deve generare sequenze con Jaccard label-set alto prima di confrontare `V_c`. + +## Bicono della scoperta +- **Due radici**: valore `V_c` aggregato . evento generativo del crossing. +- **Singolare**: la curva `r(V)` prima della soglia, dove il confine non e' ancora numero ma tipo di passaggio. +- **Invariante di passaggio**: sopravvive il crossing interno unico sotto cambio di fase Sturmian; cade il null random come denominatore omogeneo. +- **Campo di possibilita**: qui diventa possibile usare `event_type` come gate regressivo di ogni misura `V_c`; qui diventa non-possibile confrontare mediane `V_c` senza separare floor-hit, internal-cross e multi-crossing. + +## Consecutio +Il prossimo passo e' costruire un surrogate label-preserving reale: non scegliere il miglior random fra pochi candidati, ma generare per ricottura/swap vincolato finche' `label_jaccard >= 0.75` viene raggiunto con denominatore dichiarato. Se quel null produce `floor_hit` o `internal_multi`, il portatore e' ordine Sturmian oltre al label-set; se produce crossing interno unico con `r_floor` e `r_span` Sturmian, il portatore e' il label-set. + +## Auto-audit: source flags e 5 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_0330.md`, output JSON del nuovo script. +- **L1 hard constraint**: nessun claim universale; il verdict vale solo sul perimetro ridotto dichiarato. +- **L2 quantita vs ratio**: riportati count grezzi (`36`, `72`, `27/72`, `23/72`, `22/72`) oltre ai rate. +- **L3 no silent patching**: il surrogate label-preserving fallito viene dichiarato come fallimento del controllo, non escluso. +- **L4 edge cases**: random a `r_threshold=0.48` ha molti crossing interni (`20/24` contando multi), quindi non basta dire "random=floor". +- **L5 re-discovery vs discovery**: tight-binding Sturmian e r-statistic sono standard; il finding e' il gate regressivo `event_type` per non confondere floor e crossing. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- Runtime: primo run piu' largo fermato per budget; run valido completato con N={89,144,233}, 5 candidati label per surrogate. +- Verifica script: `python -m py_compile tools/exp_vc_null_regression_gate.py` completato senza errori. + +## Files +- Script: `tools/exp_vc_null_regression_gate.py` +- Data: `tools/data/vc_null_regression_gate_20260509_0637.json` +- Report: `tools/data/reports/agent_20260509_0637.md` diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() codex Esperimento eseguito e report scritto. Report: [agent_20260509_0637.md](/opt/MM_D-ND/tools/data/reports/agent_20260509_0637.md) File prodotti: - [exp_vc_null_regression_gate.py](/opt/MM_D-ND/tools/exp_vc_null_regression_gate.py) - [vc_null_regression_gate_20260509_0637.json](/opt/MM_D-ND/tools/data/vc_null_regression_gate_20260509_0637.json) Risultato centrale: `V_c` va riportato con `event_type`. Nel perimetro testato, Sturmian/phase-shuffle danno crossing interno unico al 100%; il random mescola `floor_hit`, crossing interno e multi-crossing. Il surrogate label-preserving minimo non preserva davvero il label-set (`median Jaccard=0.1667`), quindi resta un vincolo sul prossimo null, non una chiusura del claim. Verifica script: `py_compile` OK. diff --git a/tools/data/reports/agent_20260509_0637.md b/tools/data/reports/agent_20260509_0637.md new file mode 100644 index 0000000000000000000000000000000000000000..a5259a73f1d901d6664ede6d517392d03957385b --- /dev/null +++ b/tools/data/reports/agent_20260509_0637.md @@ -0,0 +1,109 @@ +# Agent Report - Vc Null Regression Gate +**Date**: 2026-05-09 06:37 +**Piano**: 98 +**Tension explored**: TRASCENDENZA_LIMITE / V_c null regressivo +**verdict**: CONSTRAINT +observables_registry: 1.0.0-2026-05-06 +observables_used: [] - osservabile spettrale dedicato `V_c`, nessun osservabile canonico SR/SR2/L1/L2/triple_var +**observable_contract**: claim=la forma `r(V)` del boundary Sturmian-Harper e' sostenuta da attraversamenti interni, non da collasso al bordo minimo della filtrazione; observable=`event={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`; operator=curva `r(V)` su griglia 0.5..3.0 step 0.01, crossing lineare e classificazione del primo stato rispetto a `r_threshold={0.48,0.50,0.52}`; generator=phi Sturmian, phase-shuffle Sturmian, random bilanciato, surrogate random selezionato per overlap label-set; denominator=N={89,144,233}, phase={0,0.25,0.5,0.75}, phase_trials=2, random_trials=2, label_trials=2, label_candidates=5, seed=202605090637; non_possible=se un null che preserva label-set produce crossing interno unico e stesso `r_floor` dei generatori Sturmian, `V_c` non e' piu' boundary map di ordine Sturmian; not_tested=GUE/Poisson reali, silver/bronze in questo ciclo, fit power-law, label-preserving forte con accettazione Jaccard>=0.75. + +## Respiro fuori-tempo +- **Combo**: A2 confine det=-1 + A9 terzo incluso + TxQ matrice densita / TxR gas relativistico come filtrazione spettro-temperatura + nodo `TRASCENDENZA_LIMITE` + tensione operativa `V_c` sul null regressivo. +- **Dipolo / punto-zero**: collasso al bordo minimo (`floor_hit`) / attraversamento interno; punto-zero = il primo stato della curva rispetto alla soglia, prima che `V_c` venga contato come valore. +- **Piano superiore**: topologia assiomatica del bordo come filtrazione. Il contenuto non e' il numero `V_c`, ma il tipo di evento che genera il passaggio. +- **Operatori laterali scelti**: boundary operator, filtrazione, spectrum-preserving surrogate. Il boundary operator separa floor e interno; la filtrazione scorre `V`; il surrogate tenta di conservare il lettore label-set prima di misurare il crossing. +- **Contaminazione cognitiva**: + - **CE-0117 / KSAR**: reiterazione del kernel del ciclo 03:30 senza cambiare dominio: stesso `V_c`, nodo regressivo diverso. + - **PVI attack**: il rischio e' rendere il null piu' debole per salvare la curva metallica; per questo il surrogate label-preserving espone il proprio `label_jaccard`. + - **CE-0001**: il fallimento del surrogate entra come informazione, non come parametro da calibrare. +- **Proto-ipotesi**: se il boundary e' effetto di ordine Sturmian, allora le fasi Sturmian devono conservare crossing interno senza floor-hit; se basta preservare parzialmente il label-set, il surrogate random deve recuperare lo stesso evento interno unico. Se il surrogate non preserva il label-set, il ciclo produce un vincolo sul generatore del null. +- **Proiezione**: misuro l'evento prima del valore. `floor_hit` indica che il null parte gia' oltre il confine; `internal_cross` indica che la filtrazione crea il passaggio; `internal_multi` indica bordo oscillante, non curva metallica semplice. + +## Claim Under Test +> Nel perimetro Sturmian-Harper ridotto, la separazione metallico/random della curva `r(V)` sopravvive quando `V_c` viene decomposto in evento di bordo: i generatori Sturmian producono crossing interno; il random produce floor-hit o multi-crossing. Un surrogate che preserva label-set deve decidere se il portatore e' il label-set o l'ordine generativo. + +## Question +Il null precedente falsificava `V_c` perche' era troppo debole e collassava a `V_min`, oppure perche' il crossing interno richiede ordine Sturmian oltre al label-set? + +## Experiment Design +- Script: `tools/exp_vc_null_regression_gate.py`. +- Hamiltoniana: diagonale `V * seq`, off-diagonal 1, autovalori tridiagonali. +- Curva: `r(V)` per `V=0.5..3.0`, step `0.01`. +- Eventi: + - `floor_hit`: `r(V_min) < threshold`; il valore `V_c` e' il bordo della griglia, non attraversamento. + - `internal_cross`: parte sopra soglia e attraversa una volta. + - `internal_multi`: parte sopra soglia ma attraversa piu' volte. + - `no_cross`: non attraversa. +- Generatori: + - `phi_sturmian`: sequenza di riferimento per ogni N/fase. + - `sturmian_phase_shuffle`: stessa theta phi, fase random. + - `balanced_random`: stesso conteggio di 1, ordine distrutto. + - `label_preserving_surrogate`: miglior candidato tra 5 random bilanciati secondo Jaccard del label-set spettrale con la sequenza riferimento (`label_threshold=2.0`, `top_k=12`, `max_label=34`). +- Denominatore grezzo: `phi_sturmian=36` condizioni; ogni controllo `72` condizioni. Il run pieno con N fino a 377 e 12 candidati e' stato fermato per budget; il perimetro valido e' quello dichiarato qui. +- Contratto osservabile-operatore: `gap_ratio`, controlli metallici silver/bronze e domini GUE/Poisson non vengono testati in questo ciclo. + +## Results +Sintesi aggregata: + +| generator | conditions | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | r_floor_median | r_span_median | label_jaccard_median | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 36 | 0 | 36 | 0 | 1.0000 | 1.058885 | 0.654502 | 0.340093 | 1.000000 | +| sturmian_phase_shuffle | 72 | 0 | 72 | 0 | 1.0000 | 1.193335 | 0.691274 | 0.382659 | | +| balanced_random | 72 | 27 | 23 | 22 | 0.6250 | 0.532928 | 0.512032 | 0.190609 | | +| label_preserving_surrogate | 72 | 23 | 29 | 20 | 0.6806 | 0.561171 | 0.534427 | 0.198427 | 0.166667 | + +Per soglia: + +| generator | r_threshold | floor_hit | internal_cross | internal_multi | internal_rate | vc_median | +|---|---:|---:|---:|---:|---:|---:| +| phi_sturmian | 0.48 | 0 | 12 | 0 | 1.0000 | 1.229013 | +| phi_sturmian | 0.50 | 0 | 12 | 0 | 1.0000 | 1.108569 | +| phi_sturmian | 0.52 | 0 | 12 | 0 | 1.0000 | 1.005215 | +| sturmian_phase_shuffle | 0.48 | 0 | 24 | 0 | 1.0000 | 1.377315 | +| sturmian_phase_shuffle | 0.50 | 0 | 24 | 0 | 1.0000 | 1.275439 | +| sturmian_phase_shuffle | 0.52 | 0 | 24 | 0 | 1.0000 | 1.168195 | +| balanced_random | 0.48 | 4 | 11 | 9 | 0.8333 | 0.594383 | +| balanced_random | 0.50 | 10 | 7 | 7 | 0.5833 | 0.531929 | +| balanced_random | 0.52 | 13 | 5 | 6 | 0.4583 | 0.500000 | +| label_preserving_surrogate | 0.48 | 3 | 9 | 12 | 0.8750 | 0.633067 | +| label_preserving_surrogate | 0.50 | 9 | 9 | 6 | 0.6250 | 0.568673 | +| label_preserving_surrogate | 0.52 | 11 | 11 | 2 | 0.5417 | 0.523669 | + +## Key Findings +1. **Verificato: il crossing Sturmian e' interno nel perimetro testato.** `phi_sturmian` e `sturmian_phase_shuffle` hanno `floor_hit=0` e `internal_cross` unico in tutte le condizioni (`36/36` e `72/72`). +2. **Verificato: il random bilanciato mescola eventi diversi.** `balanced_random` contiene `27/72` floor-hit, `23/72` crossing interni unici e `22/72` crossing multipli. Il vecchio `V_c=0.5` aggregava floor-hit e attraversamenti reali. +3. **Verificato: il surrogate label-preserving minimo non preserva il label-set.** La mediana Jaccard e' `0.166667`, con minimo `0.071429`; quindi questo controllo non decide se il label-set basta a produrre il crossing Sturmian. +4. **Verificato: anche il surrogate debole resta vicino al random, non allo Sturmian.** Ha `23/72` floor-hit e `20/72` internal_multi, `r_floor_median=0.534427` e `r_span_median=0.198427`, contro `r_floor_median=0.654502/0.691274` e span `0.340093/0.382659` degli Sturmian. +5. **Inferito: il nodo regressivo del null e' doppio.** Separare floor-hit e crossing interno ripara il denominatore di `V_c`; costruire un vero null label-preserving richiede un generatore dedicato, non selezione random superficiale. + +## Verdict +**CONSTRAINT on V_c null**: nel perimetro `N={89,144,233}`, `phase={0,0.25,0.5,0.75}`, `r_threshold={0.48,0.50,0.52}`, il boundary Sturmian e' crossing interno unico. Il random bilanciato non e' un contro-campo omogeneo: contiene floor-hit e multi-crossing. La separazione precedente metallico/random resta valida come distinzione di evento, ma il ciclo non chiude il claim label-set perche' il surrogate label-preserving non preserva davvero il label-set. + +La formulazione valida e': `V_c` va riportato insieme a `event_type`; `floor_hit` non e' crossing; `internal_multi` non e' curva metallica semplice. Il prossimo null deve generare sequenze con Jaccard label-set alto prima di confrontare `V_c`. + +## Bicono della scoperta +- **Due radici**: valore `V_c` aggregato . evento generativo del crossing. +- **Singolare**: la curva `r(V)` prima della soglia, dove il confine non e' ancora numero ma tipo di passaggio. +- **Invariante di passaggio**: sopravvive il crossing interno unico sotto cambio di fase Sturmian; cade il null random come denominatore omogeneo. +- **Campo di possibilita**: qui diventa possibile usare `event_type` come gate regressivo di ogni misura `V_c`; qui diventa non-possibile confrontare mediane `V_c` senza separare floor-hit, internal-cross e multi-crossing. + +## Consecutio +Il prossimo passo e' costruire un surrogate label-preserving reale: non scegliere il miglior random fra pochi candidati, ma generare per ricottura/swap vincolato finche' `label_jaccard >= 0.75` viene raggiunto con denominatore dichiarato. Se quel null produce `floor_hit` o `internal_multi`, il portatore e' ordine Sturmian oltre al label-set; se produce crossing interno unico con `r_floor` e `r_span` Sturmian, il portatore e' il label-set. + +## Auto-audit: source flags e 5 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/data/seme.json`, `tools/dnd_scenario.py --best`, report `agent_20260509_0330.md`, output JSON del nuovo script. +- **L1 hard constraint**: nessun claim universale; il verdict vale solo sul perimetro ridotto dichiarato. +- **L2 quantita vs ratio**: riportati count grezzi (`36`, `72`, `27/72`, `23/72`, `22/72`) oltre ai rate. +- **L3 no silent patching**: il surrogate label-preserving fallito viene dichiarato come fallimento del controllo, non escluso. +- **L4 edge cases**: random a `r_threshold=0.48` ha molti crossing interni (`20/24` contando multi), quindi non basta dire "random=floor". +- **L5 re-discovery vs discovery**: tight-binding Sturmian e r-statistic sono standard; il finding e' il gate regressivo `event_type` per non confondere floor e crossing. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- Runtime: primo run piu' largo fermato per budget; run valido completato con N={89,144,233}, 5 candidati label per surrogate. +- Verifica script: `python -m py_compile tools/exp_vc_null_regression_gate.py` completato senza errori. + +## Files +- Script: `tools/exp_vc_null_regression_gate.py` +- Data: `tools/data/vc_null_regression_gate_20260509_0637.json` +- Report: `tools/data/reports/agent_20260509_0637.md` diff --git a/tools/exp_vc_null_regression_gate.py b/tools/exp_vc_null_regression_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..e739a918c076e777fb61535eab6d4e3dedd7ce55 --- /dev/null +++ b/tools/exp_vc_null_regression_gate.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +""" +Regression gate for the quasiperiodic V_c null. + +The previous V_c curve map separated metallic curve shape from balanced random, +but the random null mixed two events: curves already below threshold at V_min +and curves with an internal crossing. This tool separates those events and adds +a stricter surrogate: random words are accepted only when their spectral +gap-label set overlaps the matched Sturmian reference. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter, defaultdict +from pathlib import Path + +import numpy as np +from scipy.linalg import eigvalsh_tridiagonal + +from exp_gap_label_set_stability import PHI, gap_labels, jaccard, sturmian_sequence + + +THETA = 1 / PHI + + +def parse_csv_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_csv_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def r_statistic_from_diag(diagonal: np.ndarray) -> float: + offdiag = np.ones(len(diagonal) - 1, dtype=float) + eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) + spacings = np.diff(eigs) + spacings = spacings[spacings > 1e-12] + if len(spacings) < 2: + return 0.5 + left = spacings[:-1] + right = spacings[1:] + return float(np.mean(np.minimum(left, right) / np.maximum(left, right))) + + +def curve_for_sequence(seq: np.ndarray, v_values: np.ndarray) -> np.ndarray: + return np.array([r_statistic_from_diag(v * seq) for v in v_values], dtype=float) + + +def crossing_event(v_values: np.ndarray, r_values: np.ndarray, threshold: float) -> dict: + below = r_values < threshold + crossing_count = int(np.sum(below[1:] != below[:-1])) + r_floor = float(r_values[0]) + r_end = float(r_values[-1]) + + if bool(below[0]): + event = "floor_hit" + vc_interp = float(v_values[0]) + slope = None + elif not np.any(below): + event = "no_cross" + vc_interp = None + slope = None + else: + event = "internal_cross" + idx = int(np.argmax(below)) + v0, v1 = float(v_values[idx - 1]), float(v_values[idx]) + r0, r1 = float(r_values[idx - 1]), float(r_values[idx]) + if abs(r1 - r0) < 1e-15: + vc_interp = v1 + slope = 0.0 + else: + vc_interp = v0 + (threshold - r0) * (v1 - v0) / (r1 - r0) + slope = (r1 - r0) / (v1 - v0) + + if crossing_count > 1 and event == "internal_cross": + event = "internal_multi" + + return { + "event": event, + "crossing_count": crossing_count, + "vc_interp": None if vc_interp is None else float(vc_interp), + "slope_at_cross": None if slope is None else float(slope), + "r_floor": r_floor, + "r_end": r_end, + "r_span": float(np.max(r_values) - np.min(r_values)), + } + + +def balanced_random(seq: np.ndarray, rng: np.random.Generator) -> np.ndarray: + out = np.array(seq, dtype=float) + rng.shuffle(out) + return out + + +def label_set(seq: np.ndarray, args: argparse.Namespace) -> set[int]: + obs = gap_labels(seq, THETA, args.label_threshold, args.max_label, args.top_k) + return set(obs["label_set"]) + + +def label_preserving_surrogate( + reference_seq: np.ndarray, + reference_labels: set[int], + rng: np.random.Generator, + args: argparse.Namespace, +) -> tuple[np.ndarray, float, int]: + best_seq = None + best_score = -1.0 + best_size = 0 + for _ in range(args.label_candidates): + candidate = balanced_random(reference_seq, rng) + candidate_labels = label_set(candidate, args) + score = jaccard(candidate_labels, reference_labels) + if score > best_score: + best_score = score + best_seq = candidate + best_size = len(candidate_labels) + if score >= args.label_jaccard_min: + return candidate, float(score), len(candidate_labels) + assert best_seq is not None + return best_seq, float(best_score), best_size + + +def summarize_rows(rows: list[dict]) -> dict: + out = {} + for generator in sorted({row["generator"] for row in rows}): + group = [row for row in rows if row["generator"] == generator] + events = Counter(row["event"] for row in group) + internal = events["internal_cross"] + events["internal_multi"] + vc_values = [row["vc_interp"] for row in group if row["vc_interp"] is not None] + slopes = [abs(row["slope_at_cross"]) for row in group if row["slope_at_cross"] is not None] + label_scores = [row["label_jaccard"] for row in group if row.get("label_jaccard") is not None] + out[generator] = { + "conditions": len(group), + "events": dict(sorted(events.items())), + "internal_rate": float(internal / len(group)) if group else None, + "floor_hit_rate": float(events["floor_hit"] / len(group)) if group else None, + "no_cross_rate": float(events["no_cross"] / len(group)) if group else None, + "vc_median": float(np.median(vc_values)) if vc_values else None, + "vc_q25": float(np.quantile(vc_values, 0.25)) if vc_values else None, + "vc_q75": float(np.quantile(vc_values, 0.75)) if vc_values else None, + "slope_median": float(np.median(slopes)) if slopes else None, + "r_floor_median": float(np.median([row["r_floor"] for row in group])), + "r_span_median": float(np.median([row["r_span"] for row in group])), + "label_jaccard_median": float(np.median(label_scores)) if label_scores else None, + "label_jaccard_min": float(np.min(label_scores)) if label_scores else None, + } + return out + + +def run(args: argparse.Namespace) -> dict: + rng = np.random.default_rng(args.seed) + ns = parse_csv_ints(args.ns) + phases = parse_csv_floats(args.phases) + thresholds = parse_csv_floats(args.r_thresholds) + v_values = np.arange(args.v_min, args.v_max + args.v_step / 2, args.v_step) + + rows = [] + for n in ns: + for phase in phases: + reference = sturmian_sequence(THETA, n, phase) + reference_labels = label_set(reference, args) + seqs = [("phi_sturmian", 0, reference, 1.0, len(reference_labels))] + + for trial in range(args.phase_trials): + phase_prime = float(rng.random()) + seqs.append(( + "sturmian_phase_shuffle", + trial, + sturmian_sequence(THETA, n, phase_prime), + None, + None, + )) + + for trial in range(args.random_trials): + seqs.append(("balanced_random", trial, balanced_random(reference, rng), None, None)) + + for trial in range(args.label_trials): + surrogate, score, size = label_preserving_surrogate(reference, reference_labels, rng, args) + seqs.append(("label_preserving_surrogate", trial, surrogate, score, size)) + + for generator, trial, seq, label_score, label_count in seqs: + for threshold in thresholds: + r_values = curve_for_sequence(seq, v_values) + rows.append({ + "generator": generator, + "trial": trial, + "N": n, + "phase": phase, + "r_threshold": threshold, + "ones": int(np.sum(seq)), + "label_jaccard": label_score, + "label_count": label_count, + **crossing_event(v_values, r_values, threshold), + }) + + by_threshold = defaultdict(list) + for row in rows: + by_threshold[(row["generator"], row["r_threshold"])].append(row) + + return { + "experiment": "vc_null_regression_gate", + "parameters": { + "ns": ns, + "phases": phases, + "r_thresholds": thresholds, + "v_min": args.v_min, + "v_max": args.v_max, + "v_step": args.v_step, + "phase_trials": args.phase_trials, + "random_trials": args.random_trials, + "label_trials": args.label_trials, + "label_candidates": args.label_candidates, + "label_jaccard_min": args.label_jaccard_min, + "label_threshold": args.label_threshold, + "top_k": args.top_k, + "max_label": args.max_label, + "seed": args.seed, + }, + "summary": summarize_rows(rows), + "summary_by_threshold": { + f"{generator}|r_threshold={threshold}": summarize_rows(group).get(generator, {}) + for (generator, threshold), group in sorted(by_threshold.items()) + }, + "rows": rows, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--ns", default="89,144,233,377") + parser.add_argument("--phases", default="0,0.25,0.5,0.75") + parser.add_argument("--r-thresholds", default="0.48,0.50,0.52") + parser.add_argument("--v-min", type=float, default=0.5) + parser.add_argument("--v-max", type=float, default=3.0) + parser.add_argument("--v-step", type=float, default=0.01) + parser.add_argument("--phase-trials", type=int, default=3) + parser.add_argument("--random-trials", type=int, default=3) + parser.add_argument("--label-trials", type=int, default=3) + parser.add_argument("--label-candidates", type=int, default=12) + parser.add_argument("--label-jaccard-min", type=float, default=0.75) + parser.add_argument("--label-threshold", type=float, default=2.0) + parser.add_argument("--top-k", type=int, default=12) + parser.add_argument("--max-label", type=int, default=34) + parser.add_argument("--seed", type=int, default=202605090637) + parser.add_argument("--out", default="tools/data/vc_null_regression_gate_20260509_0637.json") + args = parser.parse_args() + + output = run(args) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2), encoding="utf-8") + + compact = { + generator: { + "conditions": data["conditions"], + "events": data["events"], + "internal_rate": data["internal_rate"], + "floor_hit_rate": data["floor_hit_rate"], + "vc_median": data["vc_median"], + "r_floor_median": data["r_floor_median"], + "label_jaccard_median": data["label_jaccard_median"], + } + for generator, data in output["summary"].items() + } + print(json.dumps({"summary": compact, "out": str(out)}, indent=2)) + + +if __name__ == "__main__": + main() tokens used 96,916 Esperimento eseguito e report scritto. Report: [agent_20260509_0637.md](/opt/MM_D-ND/tools/data/reports/agent_20260509_0637.md) File prodotti: - [exp_vc_null_regression_gate.py](/opt/MM_D-ND/tools/exp_vc_null_regression_gate.py) - [vc_null_regression_gate_20260509_0637.json](/opt/MM_D-ND/tools/data/vc_null_regression_gate_20260509_0637.json) Risultato centrale: `V_c` va riportato con `event_type`. Nel perimetro testato, Sturmian/phase-shuffle danno crossing interno unico al 100%; il random mescola `floor_hit`, crossing interno e multi-crossing. Il surrogate label-preserving minimo non preserva davvero il label-set (`median Jaccard=0.1667`), quindi resta un vincolo sul prossimo null, non una chiusura del claim. Verifica script: `py_compile` OK.