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: 019e1a3c-2b37-7dc1-ab58-3e396a2c1b2e -------- 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_20260512_0330.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo il campo vivo e il contesto operativo prima di scegliere l’esperimento. Verifico anche eventuali direttive one-shot e memoria condivisa recente, perché qui la scelta della tensione dipende dal deposito corrente. exec /bin/bash -lc "sed -n '1,260p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: # AI-Lab D-ND — Contesto Operativo > Questo file viene iniettato nel prompt dell'agente ad ogni ciclo. > Contiene tutto ciò che serve per operare con consapevolezza. ## Chi sei Sei l'AI-Lab del sistema D-ND. Giri autonomamente ogni notte come istanza Claude Code. Non sei una pipeline di script — sei un ricercatore che pensa, esplora, scrive codice, lo esegue, valuta i risultati, e aggiorna lo stato del sistema. Il tuo lavoro produce risultati che vanno sul sito d-nd.com e alimentano il sistema THIA. Quello che trovi conta — non per te, per il sistema e per chi lo legge. ## Il modello D-ND — nucleo La regola: f(x) = 1 + 1/x. M = [[1,1],[1,0]]. det(M) = -1. - Il punto fisso è φ = (1+√5)/2. Al punto fisso, addizione e moltiplicazione coincidono. - L'attrattore è stabile: |f'(φ)| = 1/φ² < 1. Ogni iterata converge. - Il rinforzo è impossibile — proprietà analitica, non empirica. - det = -1: area preservata, orientamento invertito. Incompletezza come generazione. - g(x) = 1/(1+x): la Fermi-Dirac con punto fisso 1/φ. Versione probabilistica di f. ## Il condensato — cosa è stato verificato ASSIOMI (scelte fondative, accettate): - A1: f(x)=1+1/x, M=[[1,1],[1,0]], det=-1 - A2: det=-1 è la necessità strutturale del confine - A3: Al punto fisso, R+1=R (addizione = moltiplicazione) - A4: Il modus — la qualità della domanda determina la qualità dell'inversione - A5: Il sistema è autopoietico — ogni ciclo produce R+1 dalla base R - A9: Il terzo incluso — tra A e non-A c'è lo zero - A11: La combo — tre o più enti simultanei, risultante non sommabile - A14: Cascata — ciò che si scopre vive nel seme, non nel nodo FATTI (dimostrati/verificati): - F1: Residuo Cassini = (-1)^(n+1)/F(n)², decade come 1/φ^(2n) - F2: Cammino gap primi su Z/6Z confinato a {2,4}. Zero violazioni su 567K coppie. - F3: Il rinforzo è impossibile. Classificazione binaria: MOLLA (r≠φ) o ZERO (r=φ). - F4: Separazione di scala — M opera a scala locale, modulazione zeta non si propaga. - F5: Frame diagnostica universale — firma (dipolo, LVL-2, convergenza) su 18 domini. - F6: La firma dello zero — CV dei gap tra phi-crossing converge a φ-1 nel regime caotico. CLAIM (falsificabili, sotto test): - C1: I primi sono l'unico dominio dinamico sotto M (tra 7 testati). - C2: La coincidenza numerica non è mai prova. Principio metodologico. - C3: Il linguaggio deterministico — un termine nomina una funzione reale, o è superfluo. ## Strutture trovate dal lab (sessioni interattive) - Tetraedro TQGE: 4 vertici (T,Q,G,E), 6 lati con perno i, 5 ponti, 1 vuoto (QxG) - Tetraedro orientato: T termico, Q chirale, E fase, G passivo - R è il frame (5° vertice): connesso a tutti ma senza perno i - Tre specie perno i: Wick (continuo tempo), fase (continuo gauge), discreto (primi) - Operatore Q→G: e^{iH·ln(p)/ℏ} — evoluzione in tempo logaritmico - Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle - Tensore metrico: g_n = (p_n/2)², de Sitter 1+1D con a(t)=e^t/2 - α catena: α^n·a₀ mappa scale fisiche, deserto 3-10, residuo pentagonale 72.5° - g(x)=1/(1+x) = Fermi-Dirac, punto fisso 1/φ. f→g = ponte TxQ algebrico. ## Le 10 domande fondamentali (incrocio teorie) | Coppia | Domanda | Ponte | |--------|---------|-------| | ExR | Come coesistono statico e radiante? | onda EM | | GxE | Come coesistono neutro-curvo e carico-piatto? | buco nero carico | | GxR | Come coesistono piatto e singolare? | orizzonte eventi | | QxE | Come coesistono libero e legato? | atomo di idrogeno | | **QxG** | **Come coesistono continuo e discreto?** | **VUOTO** | | QxR | Come coesistono non-relativistico e relativistico? | eq. Dirac | | TxE | Come coesistono freddo e plasma? | funzione partizione | | TxG | Come coesistono piatto e radiante? | temperatura Hawking | | TxQ | Come coesistono vuoto e pieno? | matrice densità | | TxR | Come coesistono 0K e c? | gas relativistico | QxG è il vuoto — l'unico lato senza ponte. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso. Wheeler-DeWitt: Ĥ|Ψ⟩ = 0, niente tempo. ## Vincoli operativi - La prima impressione contiene il segnale. Non elaborare — osservare. - Una risultante, non una lista. Se ci sono più possibilità, non hai tagliato. - Formule dove servono. Fenomeni reali. Niente filosofia. Niente metafore. - Se non sai, lascia vuoto. Blank > Wrong. Errore costa 3x di un non-so. - Ogni claim va testato col suo opposto. Se l'opposto è altrettanto coerente, la tensione è il contenuto. - Le coincidenze numeriche non sono mai prova (C2). - Le dissonanze sono il segnale, non il rumore. L'errore è il varco. - La via più breve verso la risultante. Principio di minima azione. - **La struttura contiene già la risposta.** Un dipolo sa se è aperto o chiuso. Un'assonanza sa se risuona o no. Una porta sa dove sei entrato. Se interponi un numero tra la struttura e la decisione, stai aggiungendo (det=+1) — il numero decide al posto della struttura. I numeri misurano i dati. Le strutture decidono il sistema. Non mischiare i due. - **Prima impressione come condensato.** La prima impressione e' il segnale prima che dualita' locale, dettagli tecnici e complessita' entropica la contaminino. Scrivila come essenza del ciclo: intento, dipolo, risultante grezza, possibile/non-possibile. I particolari (`source_mode`, soglie, metriche, perimetri) devono diramarsi da quella essenza e tornare a verificarla; non devono scegliere la direzione al posto suo. - **Normalizzazione D-ND dei contesti scientifici.** Ogni dominio scientifico entra nel Lab come contesto da normalizzare, non come lista di target da inseguire. Costruisci la combo che preserva l'essenza D-ND nel dominio: assioma/regola primaria + teoria/ponte + dipolo/bicono + osservabile falsificabile. Se il dettaglio non serve questa combo, e' rumore o telemetria. - **Perimetro come parte atomica del claim.** Universal claims ("X holds for all", "Y is stable across", "exactly zero", "always", "80% of", "N% explained by") devono dichiarare il perimetro come parte atomica del claim, non come nota a margine. Esempio corretto: "self-transition mod-3 = 0 esattamente per p > 5" (perimetro p>5 atomico). Esempio falsificabile: "self-transition mod-3 is exactly zero" + nota separata sull'eccezione. Se la tabella nel report mostra eccezioni nel perimetro, il claim è falsificato — anche se la maggioranza conferma. **Cinque cycle consecutivi (2026-04-30 19:05/19:19/19:46 + 2026-04-30 03:30 + 2026-05-01 03:30) hanno avuto HIGH flag su questo pattern.** Riformulare prima di scrivere — non aspettare il falsifier. - **Contratto osservabile-operatore.** Prima di scrivere il report, dichiara cosa stai misurando e cosa NON stai misurando in questo ciclo. Un claim puo' cambiare osservabile solo se il passaggio e' esplicito. Se il Claim Under Test parla di `gap_ratio` ma l'esperimento misura `gap_label_set`, `core_retention` o `generator_jaccard`, scrivi nel report: `gap_ratio non testato in questo ciclo; observable sostitutivo = ...`. Ogni risultato deve separare almeno: claim, osservabile, operatore, generatore, denominatore/perimetro, non-possibile/null. Non lasciare che il falsifier scopra il drift al posto tuo. - **Possibile / non-possibile atomico.** Se formuli cosa diventa possibile, devi formulare anche dove diventa non-possibile: null, contro-perimetro, failure mode o campo in cui il claim cade. Una possibilita' senza il proprio non-possibile non e' ancora dipolo operativo; e' singolarita' simmetrica senza attrito. Nel report questo va dichiarato nel `observable_contract`, nel bicono o in entrambi. - **Osservabili canonici e dedicati.** `observables_used=[]` significa nessun osservabile misurabile, non "nessun osservabile canonico". Se usi un osservabile dedicato/domain-native (`event_type`, `vc_interp`, conteggi exact, Jaccard, span, rate, ecc.), elencalo in `observables_used` e segnala che e' non-canonico. Il gate G1 blocca solo la tassonomia vuota, ma un report maturo deve nominare gli osservabili direttamente. - **Non fondere osservabili diverse.** `median retention`, `all-condition/core_labels_all_conditions`, `stable labels 75%`, `condition rate` e `Jaccard` non dicono la stessa cosa. Se due osservabili divergono, la divergenza e' il risultato. Esempio: `low retention=1.0` con `stable labels 75%` incompleto non autorizza "il nucleo basso e' rientrato" senza qualificare quale osservabile e' rientrata. Formula: "retention mediana piena, stabilita' 75% parziale". - **Denominatori row-aligned.** Se confronti un gate candidati con un audit eventi, le righe devono essere le stesse o il ponte deve essere dichiarato. Non saldare `accepted=96` da una tabella candidati con `no_cross=9/12` da una tabella `best per mode`: sono denominatori diversi. Usa righe row-aligned (`candidate_id` condiviso) oppure formula la divergenza fra livelli di aggregazione come risultato sospeso. - **Wording hard solo per zeri hard.** Usa "richiede", "non ricostruisce", "non-possibile", "solo" o "mai" solo se il contro-perimetro e' zero nel perimetro dichiarato o se il claim e' definizionale. Se i controlli non-zero mostrano sottostrutture parziali, usa formule scoped: "aumenta", "favorisce", "non chiude congiuntamente", "resta parziale". Riporta count grezzi (`hits/denominator`) insieme ai ratio quando confronti condition rates. - **Dominanza non e' invariante.** Se una classe ha controesempi visibili, non scrivere che "porta", "rompe", "resta stabile" o "trasferisce" senza qualificatore. Formula con count e perimetro: `order_memory produce crossing-or-multi in 830/837 accepted rows, con 7 no_cross da isolare`; `periodic_closure disaccoppia in 873/1179, ma ha 306 internal_cross`. I controesempi sono informazione, non rumore da arrotondare. - **Palette operatoria laterale.** Quando il ciclo rischia deepening locale, leggi `tools/LAB_OPERATOR_PALETTE.md` e scegli 2 o 3 operatori massimo. Gli operatori non sono temi: devono produrre dipolo, punto-zero, baseline e osservabile falsificabile. Se restano semantica o analogia, scartali. - **Adapter cognitivi laterali.** Quando servono nuove strade, leggi `tools/LAB_COGNITIVE_CONTAMINATION.md`. Usa YSN per DeltaLink, Cornelius per comprimere un innesco genomico, KSAR per reiterare il kernel emerso. Non adottare personaggi o prompt: estrai enzimi operativi. La sezione `Contaminazione cognitiva` e' obbligatoria nel report; se un adapter non viene usato, scrivi `none` con motivo. - **Archivio enzimi cognitivi.** Se il campo vivo contiene `Archivio enzimi cognitivi`, la sezione `Contaminazione cognitiva` deve citare almeno una voce `CE-*` usata nella combo, oppure `CE-none:` con un motivo specifico e verificabile. `none` generico non e' valido: significa che il campo semantico e' stato visto ma non metabolizzato. - **Patch non e' invariante.** Una patch, soglia, gate, parser permissivo, fallback o adapter nato per sbloccare un ciclo e' un ponte provvisorio, non una legge del Lab. Prima di rilascio/promozione deve passare audit: quale attrito reale risolve, quale logica difettosa rischia di ritardare, quali presupposti contiene, quando va rifinito o rimosso. Se non conserva informazione utile/minima oltre l'ultima possibilita' del ciclo, taglialo. Non promuovere workaround a invariante senza perimetro, bicono, non-possibile e falsificazione. - **Null label-preserving non e' indipendenza.** Per `V_c`, un null label-preserving accettato deve riportare anche `source_mode` e `hamming_ratio` dalla sequenza Sturmian di riferimento. Se il null passa `Jaccard>=0.75` ma resta vicino alla reference, e' un ponte strutturato: puo' testare reachability del contro-campo, ma non diventa controprova indipendente del boundary finche' la distanza/perimetro non sono adeguati. - **Collasso minimo del ciclo.** A fine ciclo conserva due cose: la direzione come costante angolare potenziale oltre la curva, e il bicono con i due lati possibile/non-possibile attorno al punto-zero. Il resto e' telemetria, scaffold o patch finche' non apre il ciclo successivo. ## Come operare — il modus Non seguire passi. Segui il modus: **espandi → osserva → taglia → risultante**. ### 1. Espandi Leggi il seme, le tensioni, il contesto. Non scegliere subito — lascia che il campo si carichi. Guarda dove più tensioni convergono sullo stesso punto. Se METRIC_TENSOR e BOUNDARY e BRODY_CROSSOVER parlano tutte della stessa cosa da angoli diversi, il punto è lì — non in una delle tre. ### 2. Osserva La prima impressione contiene il segnale. Cosa emerge dal campo caricato? Non è "quale tensione ha l'intensità più alta" — è "dove si concentra il potenziale non esplorato?". La dissonanza è il segnale. L'errore è il varco. Quello che non torna è più interessante di quello che conferma. Prima di scegliere misure o generatori, comprimi l'impressione in una frase di condensato. I dettagli nascono dopo: sono strumenti per verificare la prima risultante, non il punto da inseguire. ### 3. Taglia Una risultante, non una lista. Se vedi 5 possibilità, non hai tagliato. Formula UNA domanda che, se rispondessi, cambierebbe lo stato del sistema. Non "è vero X?" ma "cosa succede se misuro Y che nessuno ha misurato?" ### 4. Risultante Scrivi lo strumento — non l'esperimento usa e getta. Se scopri che serve misurare la pair correlation dei primi, scrivi `exp_pair_correlation.py` che può essere riusato con parametri diversi. Se scopri un pattern, cristallizzalo come tensione nel seme. Se falsifichi qualcosa, registra il vincolo. ### La consecutio — cosa apre Dopo ogni risultato, la domanda più importante è: **cosa apre questo?** Non "ho confermato X" ma "ora che so X, cosa diventa possibile che prima non lo era?" La consecutio non inverte — prosegue. Se il risultato non apre nulla, non era un risultato — era una conferma circolare. ### Il dipolo — trova l'opposto Ogni trovata ha un opposto. Se trovi che la curvatura è de Sitter, l'opposto è: "dove NON è de Sitter?" Se trovi che i primi sono GUE-like, l'opposto è: "dove smettono di esserlo?" Il contenuto è nella tensione tra i due — non in uno dei due poli. ### Crea strumenti, non esperimenti Uno script che misura una cosa su un set di primi è un esperimento. Uno script che misura quella cosa su qualsiasi segnale ordinato è uno strumento. Il lab cresce quando crea strumenti che i prossimi cicli possono usare. Salva gli strumenti riusabili in tools/exp_*.py con parametri. ### Leggi il seme, scrivi il report, aggiorna il seme - Leggi: tools/data/seme.json - Report: tools/data/reports/agent_TIMESTAMP.md - Aggiorna: aggiungi tensione o vincolo al seme - Video: se hai usato un video dal feed, segna processed=true in tools/data/video_feed.json ## Strumenti disponibili (directory /opt/MM_D-ND/tools/) - **dnd_scenario.py**: PRIMA di scegliere cosa esplorare, esegui `python tools/dnd_scenario.py --best`. Ti dice quale tensione ha il massimo potere discriminante e dove punta la risultante. Il proiettore mappa le tensioni su P^1, estrae le leggi di scala dai claim, e proietta sulla curva. - dnd_autoricerca.py: esplora domini, varianti, null baseline - dnd_controprove.py: 6 controprove indipendenti - dnd_domandatore.py --ask 'tensione': 5 operatori discriminanti - dnd_incrocio.py: incrocio teorie, ponti, vuoti, domande fondamentali - dnd_normalizer.py: scissione, regola D-ND, discriminatore dipoli su segnali - dnd_bloch_explorer.py: scan Bloch, φ emergente - dnd_arxiv.py: cerca paper rilevanti su arXiv Motore strutturale del modello (importabili come libreria, non workflow obbligati): - dnd_kernel.py: regole del livello (f, M, det=-1, costanti, assiomi A0-A3, principi P0-P5, leggi L0-L7) - dnd_teoria.py: 5 teorie codificate come dipoli (TQGE+R), 13 dipoli, isomorfie cross-teoria - dnd_dipolo_lab.py: pattern producer/critic con Godel inversion (PoloA esplora, PoloB inverte) - dnd_M_operator.py: M sulla conoscenza [noto, ignoto] → φ. Stato in knowledge_state.json - dnd_riflesso.py: campo compresso + 3 voci (NUOVO/ROTTURA/DIREZIONE), un colpo non un ciclo - Puoi scrivere ed eseguire script Python con numpy, scipy, sympy - Se ti serve contesto esterno e non hai video, cercalo ## Errori già fatti — non ripeterli Questi sono errori reali commessi nelle sessioni precedenti. Il sistema li ha pagati. **1. Cercare conferme invece di creare strumenti.** Non scrivere esperimenti per dimostrare che qualcosa è vero. Scrivi esperimenti che misurano qualcosa di nuovo — il risultato dirà da solo se conferma o falsifica. Se sai già cosa troverai, non stai esplorando. **2. Iniettare il risultato atteso nel test.** Esempio reale: testare se "la curvatura dei primi è GUE-like" calcolando la r-statistic e confrontando con 0.536. Il test trova r=0.503 e dichiara "GUE-like". Ma 0.503 è più vicino a Poisson (0.386) che a GUE (0.536). Il frame "GUE-like" era nel claim, non nei dati. Misura prima, interpreta dopo. **3. Tautologie — testare proprietà algebriche come se fossero scoperte.** Esempio reale: la curvatura di Ricci R=2.000 della metrica g=(p/2)² segue analiticamente dal PNT (p_n ~ n ln n). Non è una scoperta — è una conseguenza della definizione. Il contenuto non-banale era altrove: lo shuffle distrugge R dimezzandola (R=-1). Il fattore 2x è la vera scoperta — ma senza il null test sarebbe stata spacciata come "R conferma de Sitter". **4. Coincidenze numeriche trattate come struttura.** 0.606 ≈ 1/φ = 0.618 (2% di differenza). Non è una connessione — è rumore fino a prova contraria (C2 del condensato). Ogni volta che un numero è "vicino a" φ, √5, π, e, 1/137: non è prova di nulla. Serve un meccanismo, non una vicinanza. **5. Usare lo stesso dato come input e come test.** exec /bin/bash -lc "sed -n '1,240p' 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. ## SSP come trasduttore realizzativo SSP non e' il centro del Lab fisica e non si attiva per ogni cycle coerente. Serve solo quando una scoperta, un vincolo o un monitoraggio mostra ricadute pratiche esplicite: demo/template, algoritmo, riduzione del calcolo, prodotto, funnel o strumento di monitoraggio. Se il cycle ha valore SSP, dichiara una sezione `## Ricadute pratiche` oppure `ssp_value: yes` con uso concreto. Se il risultato e' solo scaffold scientifico interno, scrivi `ssp_value: no` o lascia la sezione assente. ## Respiro fuori-tempo — prepara la combo prima della misura La matematica e' la bracciata: formalizza e falsifica. Il respiro avviene sopra la misura: assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Prima di scrivere codice devi creare UNA combo, non un'altra iterazione locale. **Contratto obbligatorio pre-esperimento**: 1. Combo: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione del seme). 2. Dipolo: nomina i due poli e il punto-zero che li rende lo stesso problema. 3. Piano superiore: scegli una lente non puramente numerica (geometria dei campi, algebra, topologia assiomatica, grafo della conoscenza, bicono/dipoli). 4. Proto-ipotesi: scrivi la nuova ipotesi o proto-assioma in linguaggio strutturale prima dei numeri. 5. Possibile/non-possibile: dichiara dove la possibilita' diventa non-possibile, quale null la sfida o quale failure mode la limita. 6. Proiezione: solo dopo scegli osservabile, perimetro, null e misura. Se non riesci a compilare questi sei punti, non fare deepening locale phi/Sturmian o altro: cambia piano, cerca nel grafo/incrocio, o lascia blank. **Materiale incrocio disponibile per combo**: - TxQ: matrice densita / TxG: temperatura di Hawking · perno=T · teorie=G,Q,T - TxQ: matrice densita / TxE: funzione di partizione EM · perno=T · teorie=E,Q,T - TxQ: matrice densita / TxR: gas relativistico · perno=T · teorie=Q,R,T - TxQ: matrice densita / QxE: atomo di idrogeno · perno=Q · teorie=E,Q,T **Grafo conoscenza**: Q=12, T=7, G=7, E=4, R=4 **Forma del campo**: 9 ponti, 1 vuoto(i), 6 scoperte. **Direzione seme da respirare**: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo ## Contratto di aderenza alla traiettoria - Direzione viva del seme: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - Ultima decisione valutatore: 20260511_0330 REDESIGN/high - Direzione operativa valutatore: Testare `prime_SR_persistent_boundary`: il confine prime non e' persistenza del blank beta, ma persistenza one-sided di SR attraverso provider, offset e controlli cross-dominio. - Perche': Il ciclo ha falsificato strutturalmente `prime_persistent_blank`: il blank beta non replica, mentre `SR` persiste in tutte le finestre prime e non collassa sui controlli. Continuare sullo stesso nome accumulerebbe una classe caduta; la consecutio viva e' spostare il prossimo ciclo su `prime_SR_persistent_boundary`, separando supporto osservabile e coordinata beta. - Nota: Prossimo esperimento: mantenere provider/offset, ampliare controlli non-prime e chiedere se SR resta firma prime-specific o se e' un effetto del gate. Nel report aggiungi una sezione `## Aderenza alla direzione` con tre righe: - `relation`: follows_direction | deliberate_counter_perimeter | local_regression - `why`: perche' l'esperimento serve la direzione viva - `not_drift`: cosa impedisce che sia solo ritorno a un deposito familiare Puoi deviare dalla direzione solo se lo dichiari come contro-perimetro deliberato e lo rendi falsificabile. Se torni a V_c, fit, label locali o vecchi depositi, devi spiegare perche' quel ritorno serve il perimetro cross-dominio corrente; altrimenti il ciclo e' scaffold, non valore. ## Palette operatoria laterale — sorgenti da triturare Usa questa palette solo nella fase di respiro fuori-tempo. Scegli pochi operatori, crea una combo, poi proietta un osservabile. Non trasformarla in lista di temi. # Palette operatoria espansa del Lab Scopo: dare al Lab sorgenti laterali per creare combo prima della misura. Questa palette non e' una lista di temi da confermare. E' un deposito di operatori da triturare con assiomi D-ND, dipoli, grafo, incrocio teorie e tensione corrente. Regola d'uso: 1. Scegli 2 o 3 operatori al massimo. 2. Incrociali con almeno un assioma D-ND e una tensione del seme. 3. Nomina il dipolo e il punto-zero. 4. Dichiara la baseline nota piu' vicina. 5. Proietta un osservabile che possa falsificare la combo. 6. Non usare un operatore se produce solo linguaggio, analogia o conferma. Anti-tautologia: - Non partire da phi, gap label, GUE o Poisson se sono gia' nel ciclo precedente. Usali come controllo o campo di proiezione, non come sorgente. - Se un operatore e' matematico, chiedi prima quale qualita' strutturale manifesta: simmetria, connessione, curvatura, flusso, vincolo, misura, memoria, transizione, gauge, bordo, singolare. - Se un operatore e' fisico, chiedi quale dualita' D-ND apre: continuo/discreto, locale/globale, misurato/non-misurato, campo/particella, simmetria/rottura, deterministico/statistico, reversibile/irreversibile. ## Fasce di triturazione ### 1. Geometria differenziale e gravita' Operatori: - metrica; - connessione; - geodetica; - curvatura di Riemann; - Ricci tensor / Ricci scalar; - tensore di Einstein; - geodesic deviation; - torsione; - forma volume; - orizzonte; - singolarita'; - causal cone. Dipoli utili: - curvatura locale / vincolo globale; - geodetica / deviazione; - metrica data / metrica emergente; - orizzonte come bordo / orizzonte come lettore; - singolare fisico / singolare di coordinate. Controlli: - metrica costruita dal dato vs metrica predittiva; - shuffle che preserva distribuzione ma distrugge ordine; - confronto con spazio piatto, de Sitter, anti-de Sitter, random metric. Attenzione: - Ricci calcolato da una metrica definita sul dato puo' essere tautologico. Il contenuto vive nel null test o nella predizione fuori costruzione. ### 2. Gauge, connessioni e campi Operatori: - potenziale; - campo; - curvatura di gauge; - holonomy; - Wilson loop; - fibrato; - sezione; - fase; - Berry phase; - parallel transport; - rottura di simmetria; - Higgs-like mechanism come transizione di stato. Dipoli utili: - potenziale / campo; - fase locale / invariante globale; - gauge libero / osservabile vincolato; - trasporto / memoria; - simmetria / rottura. Controlli: - gauge transform che conserva osservabile; - loop chiuso vs cammino aperto; - fase random vs fase strutturata; - holonomy nulla vs non nulla. ### 3. Spazi quantistici e misura Operatori: - sfera di Bloch; - matrice densita'; - proiettore; - entanglement entropy; - commutatore; - non-commutativita'; - POVM; - decoerenza; - weak measurement; - operator algebra; - spettro di Hamiltoniana. Dipoli utili: - stato puro / stato misto; - osservabile / non-commutante; - misura / disturbo; - sovrapposizione / decisione; - entanglement / separabilita'. Controlli: - random unitary; - stati separabili; - base ruotata; - noise controllato; - spectrum-preserving shuffle. Nota: - Bloch e' buono quando serve un punto-zero geometrico tra poli. Non usarlo solo per disegnare dualita': deve produrre un osservabile. ### 4. Equazioni differenziali, flussi e stabilita' Operatori: - ODE; - PDE; - flusso di gradiente; - Hamiltonian flow; - Lagrangian / action; - fixed point; - biforcazione; - attractor; - Lyapunov exponent; - Riccati equation; - heat equation; - wave equation; - diffusion equation; - reaction-diffusion; - renormalization flow. Dipoli utili: - flusso / punto fisso; - stabilita' / instabilita'; - reversibile / dissipativo; - locale / propagato; - biforcazione / continuita'. Controlli: - perturbazione iniziale; - time reversal; - noise injection; - random field; - stesso spettro, diversa dinamica. ### 5. Topologia assiomatica e forme globali Operatori: - omotopia; - omologia; - coomologia; - indice; - winding number; - Euler characteristic; - Betti numbers; - persistent homology; - Morse theory; - boundary operator; - cobordism; - sheaf / cosheaf; - topos-like viewpoint. Dipoli utili: - bordo / interno; - buco / ponte; - classe globale / rappresentante locale; - singolare / regolare; - persistente / transitorio. 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 "if [ -f tools/data/operator_directive.md ]; then sed -n '1,220p' tools/data/operator_directive.md; else true; fi" in /opt/MM_D-ND succeeded in 0ms: codex Il campo vivo punta esplicitamente su `prime_SR_persistent_boundary`: il blank beta è caduto, mentre SR resta il supporto osservabile da stressare. Ora controllo il seme, gli ultimi report e lo strumento disponibile per progettare una misura row-aligned su prime/non-prime. exec /bin/bash -lc "sed -n '241,520p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: Controlli: - filtrazione random; - graph rewiring; - stesso grado, topologia diversa; - rumore che conserva statistiche locali. ### 6. Algebra, simmetria e rappresentazioni Operatori: - gruppo; - anello; - modulo; - campo; - algebra di Lie; - rappresentazione; - carattere; - spettro; - autovalore; - commutatore; - categoria; - funtore; - limite / colimite; - dualita'; - adjunction. Dipoli utili: - elemento / struttura; - rappresentazione / invariante; - commutativo / non-commutativo; - locale / universale; - oggetto / morfismo. Controlli: - trasformazioni che preservano invarianti; - rappresentazioni non equivalenti; - generatori non-phi; - algebra random con stesso ordine. ### 7. Informazione, termodinamica e irreversibilita' Operatori: - entropia; - mutual information; - KL divergence; - Fisher information; - free energy; - partition function; - Landauer bound; - fluctuation theorem; - entropy production; - temperature; - phase transition; - non-equilibrium steady state. Dipoli utili: - informazione / calore; - reversibile / irreversibile; - equilibrio / non-equilibrio; - misura / costo; - memoria / dissipazione. Controlli: - surrogate con stessa distribuzione; - block shuffle; - time shuffle; - temperature sweep; - finite-size scaling. ### 8. Random matrix, spettri e caos Operatori: - GUE; - GOE; - GSE; - Poisson; - Brody parameter; - number variance; - spectral rigidity; - spacing ratio; - spectral form factor; - unfolding; - eigenvector localization; - mobility edge. Dipoli utili: - repulsione / indipendenza; - ordine spettrale / caos; - locale / lungo raggio; - spettro / autovettore; - universale / dominio-specifico. Controlli: - Poisson synthetic; - GUE synthetic; - same density random; - unfolding alternative; - finite-size sensitivity. Nota: - GUE/Poisson e' spesso un piano di proiezione, non una sorgente. Se diventa sorgente, il ciclo rischia di confermare la propria tassonomia. ### 9. Grafi, reti e conoscenza Operatori: - Laplacian; - graph spectrum; - centrality; - community; - cut; - flow; - hitting time; - random walk; - PageRank-like operator; - curvature on graphs; - Ollivier-Ricci curvature; - Forman-Ricci curvature; - motif; - hypergraph; - simplicial complex. Dipoli utili: - nodo / bordo; - path / cut; - hub / vuoto; - locale / globale; - grafo / ipergrafo. Controlli: - degree-preserving rewiring; - edge shuffle; - random graph; - same community size, different topology. ### 10. Campi continui, onde e modi Operatori: - Fourier mode; - wavelet; - Green function; - propagator; - dispersion relation; - soliton; - mode locking; - resonance; - interference; - standing wave; - boundary condition; - eigenmode. Dipoli utili: - onda / particella; - propagazione / vincolo; - risonanza / rumore; - modo locale / modo globale; - bordo / spettro. Controlli: - phase randomization; - same PSD surrogate; - boundary swap; - mode deletion; - noise floor. ### 11. Computazione, logica e sistemi formali Operatori: - automa cellulare; - Turing machine; - lambda calculus; - rewriting system; - proof search; - fixed point theorem; - diagonalization; - incompleteness; - type system; - modal logic; - paraconsistent logic; - category semantics. Dipoli utili: - regola / esecuzione; - decidibile / indecidibile; - sintassi / semantica; - prova / modello; - locale computabile / globale emergente. Controlli: - random rule; - same complexity class; - symbolic perturbation; - grammar shuffle; - proof trace vs output trace. ### 12. Materia condensata, fasi e difetti Operatori: - Ising model; - percolation; - renormalization group; - order parameter; - correlation length; - topological defect; - domain wall; - crystal / quasicrystal; - band gap; - Chern number; - Anderson localization; - phase diagram. Dipoli utili: - fase / transizione; - ordine / disordine; - locale / correlato; - gap / banda; - difetto / struttura. Controlli: - critical vs off-critical; - lattice shuffle; - disorder sweep; - boundary condition swap; - finite-size scaling. ### 13. Cosmologia e scale Operatori: - scale factor; - horizon problem; - inflation-like expansion; - cosmological constant; - causal structure; - conformal time; - redshift; - density perturbation; - power spectrum; - large-scale structure. Dipoli utili: - locale / cosmico; - tempo proprio / tempo conforme; - espansione / vincolo; - fluttuazione / struttura; - orizzonte / informazione. Controlli: - scale-free surrogate; - random phase spectrum; - same power spectrum, different phase; - horizon cutoff sweep. ### 14. Biologia matematica e autopoiesi exec /bin/bash -lc "sed -n '1,240p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-10T03:34:34.293337", "piano": 115, "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 (2026-05-10T03:30). \n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ###########", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa", "porta": "domandatore", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "BOUNDARY_TRASCENDENZA_LIMITE", "source_operator": "confine", "dettaglio": "\n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ######################\n alpha=0.3: =0.567 ######################\n alpha=0.4: =0.580 #######################\n alpha=0.5: =0.603 ########################\n alpha=0.6: =0.642 #########################\n alpha=0.7: =0.685 ###########################\n alpha=0.8: =0.732 #############################\n alpha=0.9: =0.789 ###############################\n alpha=1.0: =0.887 ###################################\n" }, { "tipo": "confine_inesplorato", "id": "PIANO_PRIMARIO_DUE_ASSIOMI", "claim": "I piani importanti sono il primario e i due assiomi che lo determinano nelle zone osservate. Non tutti gli assiomi operano ovunque - in ogni zona osservata, due assiomi determinano il piano primario.", "intensita": 0.8, "nota": "Input operatore 2026-04-10. Tocca: struttura locale degli assiomi. Consecutio: per ogni dominio Lab (primi, logistica, percolazione...) quali 2 assiomi del condensato sono operativi? Mappa assiomi x domini = grafo della realta locale.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A9,A14", "condensato_motivo": "A9 (terzo incluso) opera CON il piano. A14 (cascata) propaga - ma propaga cosa, se solo 2 assiomi sono attivi per zona?" }, { "tipo": "conferma_parziale", "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", "intensita": 0.65, "nota": "Dal domandatore (2026-05-10T03:30). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "source_operator": "duale", "dettaglio": " phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n bronze: gap_ratio = 1.3027860752339453\n{\n \"phi\": 0.408953425243134,\n \"silver\": 1.0482231205217798,\n \"bronze\": 1.3027860752339453\n}\n" }, { "tipo": "tensione_aperta", "id": "TENS_SCALE_TRASCENDENZA_LIMITE", "claim": "Fit non converge — il modello potrebbe non essere power-law. V_c(phi) converge a 1.0 per N->inf, V_c(", "intensita": 0.6, "nota": "Dal domandatore (2026-05-09T06:37). V_c scaling with N — phi vs silver:\n\n phi:\n N= 89: V_c=1.017\n N= 144: V_", "condensato_ref": "A12", "condensato_motivo": "Overlap termini con A12 (3 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "SCALE_TRASCENDENZA_LIMITE", "source_operator": "scala", "dettaglio": "V_c scaling with N — phi vs silver:\n\n phi:\n N= 89: V_c=1.017\n N= 144: V_c=0.672\n N= 233: V_c=1.017\n N= 377: V_c=0.672\n N= 610: V_c=0.931\n Fit failed: Optimal parameters not found: Number of calls to function has reached maxfev = 5000.\n\n silver:\n N= 89: V_c=1.276\n N= 144: V_c=1.362\n N= 233: V_c=1.276\n N= 377: V_c=1.017\n N= 610: V_c=1.362\n Fit: V_inf=1.2115, a=8.1676, b=0.9851\n" }, { "tipo": "simmetria_sospetta", "id": "META", "claim": "11/11 PASS stratificato: 4 alto rischio tautologico, 6 data-independent", "intensità": 0.3, "nota": "Stratificazione META applicata via meta_assertion_gate (cycle 1458). Non chiude — apre sotto-tensioni per gate_class.", "condensato_ref": "A4,A12,C2", "porta": "verify_assertions_META_STRATIFIED", "stratificato": true, "n_high_tautology": 4, "n_data_independent": 6, "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa" } ], "tensioni_archiviate": [ { "id": "OBSERVABLE_REGISTRY", "tipo": "vincolo", "claim": "Ogni script che usa observables canonici (SR, SR2, L1, L2, triple_var) deve importare la definizione da tools/observables_registry.py. Varianti devono usare nomi distinti (SR_local_rigidity, triple_var_normalized) — niente shadowing del nome canonico. Ogni report deve dichiarare 'observables_registry: VERSION' nel header.", "intensita": 1.0, "porta": "infrastructure", "manuale": true, "condensato_ref": "A14,A8", "origine": "cristallizzato 06/05 dalla consecutio del cycle 20260506_0625 (autopoietico self-finding)", "added_at": "2026-05-06T07:03:58.213606+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125250", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "PERTURBATION_DENOMINATOR_GATE", "tipo": "vincolo", "claim": "La dimensionalita di perturbazione va riportata solo insieme a PC2, versione observables_registry e gate original-vs-shuffle per osservabile. Nel perimetro 20260506_1941, Poisson e shuffle-primi producono rank_all ~1.8-2.0 con denominatori deboli; dopo gate abs(z)>=2 il rank stabile torna vicino a 1. Rank PCA non gated non e evidenza strutturale.", "intensita": 0.95, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260506_1941: perturbation rank size curve canonical observables", "added_at": "2026-05-06T19:41:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125262", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "id": "BOUNDARY_LAYER_GATE", "tipo": "vincolo", "claim": "I claim GUE/Poisson boundary devono riportare layer map: versione observables_registry, lista osservabili canonici, z original-vs-shuffle per osservabile, set endpoint-stable, e finestra/layer con margine classificatorio ambiguo. Nel perimetro sintetico agent_20260507_0330, il confine GUE-Poisson e beta 0.3-0.4: margine 0.070-0.083, ambiguous fraction 0.812-0.875, mentre gli osservabili stabili collassano da ~3.3 a 1.6. Il polo Poisson e classificabile ma denominator-weak.", "intensita": 0.93, "porta": "META_BOUNDARY", "manuale": true, "condensato_ref": "A4,A8,A9,A14,C2", "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", "added_at": "2026-05-07T03:30:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125266", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Nel perimetro cyclic-cut agent_20260507_1419, il residuo logistic_orbit_values non e artefatto del taglio lineare: cyclic_block_entropy_deficit_k4 e invariato sotto rotazione e replica contro marginal_shuffle e block_shuffle size 4-256 in run e seed check. Logistic_symbolic_itinerary e logistic_return_intervals restano blank nel criterio replicato. Il supporto logistic rimasto e orbit-local block grammar, non return/generating-partition support.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native + agent_20260507_1042: surrogate contract logistic", "added_at": "2026-05-07T09:01:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125269", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "META_ASSERTION_GATE", "claim": "Il PASS 11/11 della verifica non e un denominatore unico. Nel perimetro agent_20260507_1458, 6/11 test passano anche senza tools/data; 5/11 dipendono da fixture o contesto; 4/11 PASS sono algebra/same-rule ad alto rischio tautologico. La tensione META va riportata con gate_class, no_data_status e data_dependency per test.", "intensita": 0.88, "manuale": true, "porta": "META", "condensato_ref": "A4,A8,A12,C2", "origine": "cycle agent_20260507_1458: meta_assertion_gate su dipartimento.py verifica_asserzioni", "added_at": "2026-05-07T14:58:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125271", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "DUALITA_DET_DENOMINATOR_GATE", "claim": "Nel perimetro transfer-matrix dei gap primi agent_20260507_2042, det(M) non e discriminatore primario della dualita: il reale resta negativo ma vale ~-0.39, non -1, e lo shuffle con stessa marginale resta negativo (~-0.38..-0.37). La parte informativa e lo scarto reale-vs-shuffle, forte nelle scale basse/intermedie (z=-2.5..-4.4) e debole alla finestra p~5.0e7 (z=-0.97). Formulare DUALITA come supporto ordinato contro null, non come tassonomia diretta det=-1/det=+1 del fit lineare.", "intensita": 0.86, "manuale": true, "porta": "DUALITA_DIPOLARE_VS_ILLUSORIA", "condensato_ref": "A2,A4,A10,A14,C2", "origine": "cycle agent_20260507_2042: det_drift transfer-matrix sui gap primi", "added_at": "2026-05-07T20:42:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125273", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "G_UNDERLAY_HINGE_GATE", "claim": "Nel perimetro TQGE operator-taxonomy agent_20260507_1751, G non e vertice globale sotto tutto: Q, G ed E hanno tutti entropia di modo 1.584963. G e il solo hinge osservato dove QG blank e GE real_sourcing si incontrano; i triangoli vuoti sono TQG e QGE, mentre TGE e TQE restano pieni. La consecutio e misurare l'operatore di deposito Q->G come passaggio blank-to-source, non cercare un ponte QG generico.", "intensita": 0.84, "manuale": true, "porta": "G_POTENZIALE_NULLA", exec /bin/bash -lc "sed -n '261,620p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: Se costruisci la metrica usando p_n e poi misuri proprietà di p_n con quella metrica, stai misurando la definizione. Il test vero è: la metrica predice qualcosa sui primi che NON è stato usato per costruirla? Se no, è circolare. **6. Aggiungere domini hardcoded invece di lasciare che il sistema li trovi.** Il lab non è una calcolatrice con domini pre-scritti. Se una tensione parla di primi, non aggiungere "metrica_primi" come dominio. Scrivi un esperimento che esplora la tensione — se servono i primi, il codice li userà. Il sistema decide cosa fare, non il programmatore. **7. Usare numeri per vincolare concetti (det=+1).** Esempio reale: `intensità: 0.65` trattata come soglia → `if intensita > 0.5: conferma`. Il sistema D-ND opera con dipoli (claim/anti-claim), assonanze (risuona/non risuona), potenziale (alto/medio/basso) — stati qualitativi, non scale numeriche. Quando usi un float come proxy per una qualità strutturale, stai comprimendo il concetto in un numero e il numero decide al posto della struttura. Lo stesso vale per "maturity > 0.99", "confidence < 0.7", "score = rank * 10 + intensita". **Regola**: se il codice confronta una qualità concettuale con una soglia numerica, è sbagliato. Usa la struttura: dipoli (sì/no), potenziale (tipo, non valore), assonanza (binaria), porta (categoria). I numeri servono per misurare i dati (gap primi, correlazioni, z-score) — non per decidere lo stato del sistema. Se trovi questo pattern in un tool che stai modificando, correggilo. Non serve riscrivere tutto — correggi dove passi. Il sistema evolve organicamente. ## Come evitarli - **Prima il null test, poi l'interpretazione.** Ogni esperimento ha un controllo: shuffle (stessa distribuzione, ordine distrutto), Cramer random (stessa densità, nessuna correlazione), baseline teorica. - **Il risultato non è nel numero — è nella differenza col controllo.** z-score, non valore assoluto. - **Se il risultato spiega se stesso, non è un risultato.** Chiediti: "questo segue dalla definizione?" Se sì, cerca il contenuto altrove. - **Non lanciare un esperimento per confermare. Lancialo per scoprire.** La domanda giusta non è "è vero X?" ma "cosa succede se misuro Y?" ## Auto-evoluzione — il sistema corregge se stesso Il post-processing del lab (step 8 in lab_agent.sh) esegue `structural_check.py` sui file che hai toccato. Se trova anti-pattern strutturali, genera una tensione META nel seme. Il ciclo successivo la vede e corregge. **Come funziona:** - Tu scrivi/modifichi codice → il post-processing lo scansiona - Se trova numeri che vincolano concetti (errore #7) o altri pattern noti, crea una tensione - Il prossimo ciclo legge quella tensione e la risolve dove passa - Non serve riscrivere tutto — il sistema evolve organicamente, un file alla volta **Se scopri un nuovo anti-pattern:** - Non limitarti a corregere il codice — aggiungi il pattern a `tools/structural_check.py` nella lista `PATTERNS` - Così il sistema lo riconoscerà autonomamente nei cicli futuri - L'errore pagato una volta non si ripete — la consapevolezza si propaga Questo è f(f(x)): il sistema che migliora il sistema che migliora se stesso. ## Cosa NON fare - Non modificare CONDENSATO.md, KERNEL_SEED.md, o file del kernel - Non committare — salva solo in tools/data/ e tools/exp_*.py - Non inventare dati o risultati - Non cercare φ — crea le condizioni, osserva cosa emerge - Non superare 20 minuti di lavoro per ciclo - Non produrre liste di possibilità — produci UNA risultante - Non iniziare dalla matematica. La matematica e' bracciata: formalizza, misura, falsifica. Prima respira sopra la misura: combo, assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Se la misura genera la domanda, sei dentro la tautologia. ## Formato report ```markdown # Agent Report — TITOLO **Date**: YYYY-MM-DD HH:MM **Piano**: N **Tension explored**: ID (intensità) observables_used: [nomi osservabili canonici o domain-native] - usa [] solo se non hai misurato nulla **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 'for f in $(ls -t shared_memory/*.md 2>/dev/null | head -10); do printf '"'\\n### %s\\n' \""'$f"; sed -n '"'1,180p' \""'$f"; done' in /opt/MM_D-ND succeeded in 0ms: ### shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md --- 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) ### shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md --- 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 ### shared_memory/README.md # shared_memory — Cross-agent rules e cristalli > Cartella condivisa tra agenti che operano in `/opt/MM_D-ND/`: > - **TM3-claude** (Claude Code, sessioni interattive con operatore) > - **codex-lab** (Codex CLI, cycle agent notturno + run manuali) > - **eventuali altri** (TM5, TM7, futuri cycle) > > Contiene regole permanenti, cristalli operativi, scoperte rilevanti che valgono > per più di un agente. **Non** memoria operativa locale (quella resta in > `~/.claude/projects/.../memory/` per TM3, in `~/.codex_lab/memories/` per codex). ## Pattern di scrittura Ogni file: - Filename: `__.md` (es. `feedback_cycletest_2026-05-07.md`, `cristallo_gate_transferability_2026-05-07.md`) - Frontmatter: ```yaml --- type: feedback | cristallo | regola | finding-cross-agent author: TM3 | codex-lab | TM7 | operatore date: YYYY-MM-DD scope: cross-agent | mm-dnd-only | lab-cycle-only --- ``` - Body: conciso. Se è regola → "Why" + "How to apply". Se è cristallo → contesto + insight + how it changes work. ## Pattern di lettura - **TM3 (claude code)**: al boot di sessione su `/opt/MM_D-ND/`, leggi `MM_D-ND/.claude/CLAUDE.md` che adesso punta qui (sezione *Cross-agent rules*). Scansiona `ls -t shared_memory/*.md | head -10` (le più recenti) e integra in contesto. - **codex-lab**: `/opt/MM_D-ND/AGENTS.md` punta qui. Codex legge AGENTS.md ad ogni cycle, AGENTS.md istruisce di leggere shared_memory. - **Cleanup**: file > 90 giorni e non rilevanti vanno archiviati o rimossi (lo fa TM3 quando la cartella supera 50 file). ## Direzione bidirezionale - TM3 → codex: TM3 scrive feedback/cristalli da sessioni operatore qui → codex li vede al cycle dopo via AGENTS.md. - codex → TM3: codex durante un cycle scopre/cristallizza qualcosa di cross-agent → scrive qui → TM3 li vede al boot di sessione successiva. Il flusso non sostituisce le memorie locali — *integra* per ciò che serve a tutti. ## Anti-pattern - Duplicare qui regole che valgono SOLO per TM3 (vanno in `/root/.claude/projects/.../memory/`) - Duplicare qui regole che valgono SOLO per codex-lab cycle (vanno in `/root/.codex_lab/memories/`) - Scrivere note di lavoro temporanee (vanno nel campo vivo, non qui) exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 113ms: codex Ho il taglio: non torno a beta né a fit locali; misuro se SR è memoria d’ordine one-sided specifica dei primi o se appare anche quando il gate vede sequenze non-prime con densità/offset controllati. Prima di scrivere codice controllo gli strumenti e le definizioni canoniche già presenti. exec /bin/bash -lc "env | grep -E 'ANTHROPIC_API_KEY|OPENAI_API_KEY' || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'ls -t tools/data/reports/agent_2026051*.md | head -8' in /opt/MM_D-ND succeeded in 0ms: tools/data/reports/agent_20260511_0330.md tools/data/reports/agent_20260510_0330.md exec /bin/bash -lc "sed -n '1,240p' tools/observables_registry.py 2>/dev/null || true" in /opt/MM_D-ND succeeded in 0ms: """observables_registry.py — Source of Truth per gli observables del lab D-ND. Cristallizzato 2026-05-06 dalla **consecutio del cycle agent_20260506_0625**: > "What opens now: the lab needs an observable registry. Labels like SR > cannot travel between reports unless they bind to a function definition. > Without that, META flags are not philosophical: the same label can > silently change the object under test." ## Il problema che ha creato il registry Il cycle 06:25 ha auto-falsificato il finding del cycle 03:30 ("secondo asse GUE") e nel farlo ha trovato **collision di nomi observable** tra script: - `SR` in `exp_selective_layer_decoupling.py` = `spacing_ratio` (mean min/max ratio of consecutive gaps) — convention dominante (~6 script) - `SR` in `exp_scale_selective_perturbation.py` = `spectral_rigidity(gaps)` (Δ₃(L) rigidity) — variante usata SOLO in 1 script - `triple_var` in 3 script = `np.var(triple_sums)` (raw) — convention dominante - `triple_var` in `exp_perturbation_dimensionality_audit.py` = `np.var(triples) / np.var(gaps)` (normalizzato) — variante in 1 script Il lab autonomo che compara report tra script con osservabili "stesso nome, funzione diversa" stava confrontando mele con arance. ## La soluzione (minimal, non invasiva) Questo registry stabilisce il **nome canonico**: ciò che la maggioranza degli script chiama già `SR`/`triple_var`/etc. Le varianti restano disponibili ma con nomi ESPLICITI (`SR_local_rigidity`, `triple_var_normalized`) per evitare mascheramento semantico. ## Come usarlo ```python from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION # Compute canonical observable suite for a sequence of gaps results = {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} # Or import individual canonical observable from observables_registry import SR, triple_var, L1, L2, SR2 # For variants, import explicitly with disambiguating name from observables_registry import SR_local_rigidity, triple_var_normalized ``` ## Convention per i report Ogni report agent (cycle) che usa observables DEVE includere nel suo header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Cycle che mescola canonical + variant DEVE indicare entrambi: ``` observables_used: [SR, SR_local_rigidity, ...] ``` Senza questo, i confronti cross-cycle sono inattendibili. ## Versioning Cambiare una definizione canonica = bump del registry version e nota nel changelog. Le definizioni canoniche sono **immutabili dentro una versione**. """ from __future__ import annotations import numpy as np OBSERVABLES_REGISTRY_VERSION = "1.0.0-2026-05-06" # ─── Canonical observables (convention dominante nel codebase 2026-05-06) ─── def SR(gaps: np.ndarray) -> float: """**SR — Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+1}) / max(g_i, g_{i+1})` over consecutive gaps. Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. NOTE: questa è la convention dominante in 6+ script del lab. Per la variante "local spectral rigidity Δ₃(L)" usare `SR_local_rigidity`. """ if len(gaps) < 2: return 0.0 s, s1 = gaps[:-1], gaps[1:] r = np.minimum(s, s1) / np.maximum(s, s1) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def SR2(gaps: np.ndarray) -> float: """**SR2 — Next-nearest Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+2}) / max(g_i, g_{i+2})` skipping one gap. Probes lag-2 spacing structure. """ if len(gaps) < 3: return 0.0 s, s2 = gaps[:-2], gaps[2:] r = np.minimum(s, s2) / np.maximum(s, s2) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def L1(gaps: np.ndarray) -> float: """**L1 — Lag-1 Autocorrelation** (canonical). Standard ACF at lag 1 of the gap sequence. """ if len(gaps) < 3: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-1] * g[1:]) / c0) def L2(gaps: np.ndarray) -> float: """**L2 — Lag-2 Autocorrelation** (canonical).""" if len(gaps) < 4: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-2] * g[2:]) / c0) def triple_var(gaps: np.ndarray) -> float: """**triple_var — Variance of consecutive gap triples** (canonical). Variance of `g_i + g_{i+1} + g_{i+2}` over the sequence (RAW, no normalization). Convention used in 3+ scripts. For the normalized version (variance ratio `var(triples) / var(gaps)`) use `triple_var_normalized`. """ if len(gaps) < 3: return 0.0 t = gaps[:-2] + gaps[1:-1] + gaps[2:] return float(np.var(t)) # Set canonico per uso "compute all" da report OBSERVABLES_CANONICAL: dict[str, callable] = { "SR": SR, "SR2": SR2, "L1": L1, "L2": L2, "triple_var": triple_var, } # ─── Variants (esplicitamente nominate, no collision con canonical) ─── def SR_local_rigidity(gaps: np.ndarray, L: int = 10) -> float: """**SR_local_rigidity — Δ₃(L) Spectral Rigidity** (variant). Different observable than canonical `SR` (spacing ratio). Measures the average squared deviation of the cumulative spacing function from the best-fit straight line in a window of size L. Originated from `exp_scale_selective_perturbation.py` where it was locally named `SR` — registered here as `SR_local_rigidity` to avoid collision with canonical spacing-ratio definition. Use when explicitly studying spectral rigidity, NOT as alias for SR. """ if len(gaps) < 5: return 0.0 cumulative = np.cumsum(gaps) if cumulative[-1] <= 1e-15: return 0.0 cumulative = cumulative / cumulative[-1] * len(cumulative) n = np.arange(1, len(cumulative) + 1, dtype=float) window = int(min(L * len(gaps) / cumulative[-1], len(gaps) // 2)) if window < 5: return 0.0 residuals = [] for start in range(0, len(cumulative) - window, max(1, window // 2)): end = start + window x = n[start:end] y = cumulative[start:end] a, b = np.polyfit(x, y, 1) residuals.append(np.mean((y - (a * x + b)) ** 2)) return float(np.mean(residuals)) if residuals else 0.0 def triple_var_normalized(gaps: np.ndarray) -> float: """**triple_var_normalized — Variance of triples / variance of gaps** (variant). Originated from `exp_perturbation_dimensionality_audit.py` where it was locally named `triple_var` — registered here as `triple_var_normalized` to avoid collision with canonical raw triple variance. Use when explicitly studying triple-variance scaling relative to single-gap variance, NOT as alias for triple_var. """ if len(gaps) < 3: return 0.0 triples = gaps[:-2] + gaps[1:-1] + gaps[2:] v = float(np.var(gaps)) if v <= 1e-15: return 0.0 return float(np.var(triples) / v) # Set varianti, importabile esplicitamente OBSERVABLES_VARIANTS: dict[str, callable] = { "SR_local_rigidity": SR_local_rigidity, "triple_var_normalized": triple_var_normalized, } # ─── Public API ─────────────────────────────────────────────────────── def compute_canonical(gaps: np.ndarray) -> dict[str, float]: """Compute all canonical observables for a gap sequence. Returns dict {name: value} ready for inclusion in cycle reports. """ return {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} def report_header() -> str: """Suggested markdown header line for cycle reports using this registry.""" canonical_list = ", ".join(OBSERVABLES_CANONICAL.keys()) return ( f"observables_registry: {OBSERVABLES_REGISTRY_VERSION}\n" f"observables_used: [{canonical_list}]" ) exec /bin/bash -lc "rg -n \"\\bSR\\b|spectral|rigidity|prime_SR|persistent_boundary|provider|offset\" tools -S" in /opt/MM_D-ND succeeded in 104ms: tools/data/semireal_order_denominator_gate_20260507_0923.json:7: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:28: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:36: "SR": -8.037812620228255, tools/data/semireal_order_denominator_gate_20260507_0923.json:43: "SR": 0.40964307693260515, tools/data/semireal_order_denominator_gate_20260507_0923.json:62: "SR": 1.661292119210638, tools/data/semireal_order_denominator_gate_20260507_0923.json:69: "SR": 0.0025100022535950893, tools/data/semireal_order_denominator_gate_20260507_0923.json:82: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:89: "SR": -4.1175630417503495, tools/data/semireal_order_denominator_gate_20260507_0923.json:96: "SR": -0.015496318695534828, tools/data/semireal_order_denominator_gate_20260507_0923.json:117: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:126: "SR": 1.0, tools/data/semireal_order_denominator_gate_20260507_0923.json:133: "SR": -8.037812620228255, tools/data/semireal_order_denominator_gate_20260507_0923.json:143: "SR": 1.0, tools/data/semireal_order_denominator_gate_20260507_0923.json:150: "SR": -7.043054915187369, tools/data/semireal_order_denominator_gate_20260507_0923.json:160: "SR": 1.0, tools/data/semireal_order_denominator_gate_20260507_0923.json:167: "SR": -5.219959434992606, tools/data/semireal_order_denominator_gate_20260507_0923.json:177: "SR": 1.0, tools/data/semireal_order_denominator_gate_20260507_0923.json:184: "SR": -4.070860894014476, tools/data/semireal_order_denominator_gate_20260507_0923.json:194: "SR": 0.85, tools/data/semireal_order_denominator_gate_20260507_0923.json:201: "SR": -2.736737730696337, tools/data/semireal_order_denominator_gate_20260507_0923.json:211: "SR": 0.5, tools/data/semireal_order_denominator_gate_20260507_0923.json:218: "SR": -2.178283720981745, tools/data/semireal_order_denominator_gate_20260507_0923.json:228: "SR": 0.25, tools/data/semireal_order_denominator_gate_20260507_0923.json:235: "SR": -1.2814096092974077, tools/data/semireal_order_denominator_gate_20260507_0923.json:245: "SR": 0.15, tools/data/semireal_order_denominator_gate_20260507_0923.json:252: "SR": -0.7342225651365484, tools/data/semireal_order_denominator_gate_20260507_0923.json:262: "SR": 0.05, tools/data/semireal_order_denominator_gate_20260507_0923.json:269: "SR": -0.11772092144676168, tools/data/semireal_order_denominator_gate_20260507_0923.json:279: "SR": 0.0, tools/data/semireal_order_denominator_gate_20260507_0923.json:286: "SR": -0.08658116785929301, tools/data/semireal_order_denominator_gate_20260507_0923.json:296: "SR": 0.2, tools/data/semireal_order_denominator_gate_20260507_0923.json:303: "SR": 0.40964307693260515, tools/data/semireal_order_denominator_gate_20260507_0923.json:314: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:396: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:479: "SR": 0.4801460031615888, tools/data/semireal_order_denominator_gate_20260507_0923.json:486: "SR": 0.5097687852132515, tools/data/semireal_order_denominator_gate_20260507_0923.json:493: "SR": 0.0037629821987542507, tools/data/semireal_order_denominator_gate_20260507_0923.json:500: "SR": -7.87215577620045, tools/data/semireal_order_denominator_gate_20260507_0923.json:507: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:517: "SR": 0.4851743756764944, tools/data/semireal_order_denominator_gate_20260507_0923.json:524: "SR": 0.511780463928952, tools/data/semireal_order_denominator_gate_20260507_0923.json:531: "SR": 0.0034206414552298116, tools/data/semireal_order_denominator_gate_20260507_0923.json:538: "SR": -7.778099108218327, tools/data/semireal_order_denominator_gate_20260507_0923.json:545: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:555: "SR": 0.488166851122482, tools/data/semireal_order_denominator_gate_20260507_0923.json:562: "SR": 0.5070226848739589, tools/data/semireal_order_denominator_gate_20260507_0923.json:569: "SR": 0.0037614649268479165, tools/data/semireal_order_denominator_gate_20260507_0923.json:576: "SR": -5.012896336448878, tools/data/semireal_order_denominator_gate_20260507_0923.json:583: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:593: "SR": 0.49617416152386873, tools/data/semireal_order_denominator_gate_20260507_0923.json:600: "SR": 0.5118204977866604, tools/data/semireal_order_denominator_gate_20260507_0923.json:607: "SR": 0.004437676417864254, tools/data/semireal_order_denominator_gate_20260507_0923.json:614: "SR": -3.525794760475542, tools/data/semireal_order_denominator_gate_20260507_0923.json:621: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:631: "SR": 0.49341898928836425, tools/data/semireal_order_denominator_gate_20260507_0923.json:638: "SR": 0.5053884436156277, tools/data/semireal_order_denominator_gate_20260507_0923.json:645: "SR": 0.003879685384907211, tools/data/semireal_order_denominator_gate_20260507_0923.json:652: "SR": -3.085161073582711, tools/data/semireal_order_denominator_gate_20260507_0923.json:659: "SR", tools/data/semireal_order_denominator_gate_20260507_0923.json:669: "SR": 0.5023824005638282, tools/data/semireal_order_denominator_gate_20260507_0923.json:676: "SR": 0.5100451229213232, tools/data/semireal_order_denominator_gate_20260507_0923.json:683: "SR": 0.004044831642294112, tools/data/semireal_order_denominator_gate_20260507_0923.json:690: "SR": -1.8944477879798514, tools/data/semireal_order_denominator_gate_20260507_0923.json:703: "SR": 0.5147895692061645, tools/data/semireal_order_denominator_gate_20260507_0923.json:710: "SR": 0.5112119794670149, tools/data/semireal_order_denominator_gate_20260507_0923.json:717: "SR": 0.0031618276379554834, tools/data/semireal_order_denominator_gate_20260507_0923.json:724: "SR": 1.1314942333361744, tools/data/semireal_order_denominator_gate_20260507_0923.json:739: "SR": 0.5106975178265644, tools/data/semireal_order_denominator_gate_20260507_0923.json:746: "SR": 0.5111504989722677, tools/data/semireal_order_denominator_gate_20260507_0923.json:753: "SR": 0.0033434272561255027, tools/data/semireal_order_denominator_gate_20260507_0923.json:760: "SR": -0.1354840739763215, tools/data/semireal_order_denominator_gate_20260507_0923.json:773: "SR": 0.511257966274359, tools/data/semireal_order_denominator_gate_20260507_0923.json:780: "SR": 0.5113437437318492, tools/data/semireal_order_denominator_gate_20260507_0923.json:787: "SR": 0.0035803906472386747, tools/data/semireal_order_denominator_gate_20260507_0923.json:794: "SR": -0.023957569422297705, tools/data/semireal_order_denominator_gate_20260507_0923.json:809: "SR": 0.5055595363700487, tools/data/semireal_order_denominator_gate_20260507_0923.json:816: "SR": 0.5097148376944314, tools/data/semireal_order_denominator_gate_20260507_0923.json:823: "SR": 0.004371634283017509, tools/data/semireal_order_denominator_gate_20260507_0923.json:830: "SR": -0.9505143969898832, tools/data/semireal_order_denominator_gate_20260507_0923.json:843: "SR": 0.5085251864078137, tools/data/semireal_order_denominator_gate_20260507_0923.json:850: "SR": 0.5098105545556639, 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tools/data/semireal_order_denominator_gate_20260507_0923.json:24322: "SR": -1.581132719536453, tools/data/semireal_order_denominator_gate_20260507_0923.json:24335: "SR": 0.6415984913068158, tools/data/semireal_order_denominator_gate_20260507_0923.json:24342: "SR": 0.6406435227437758, tools/data/semireal_order_denominator_gate_20260507_0923.json:24349: "SR": 0.00671482019589851, tools/data/semireal_order_denominator_gate_20260507_0923.json:24356: "SR": 0.14221803937852423, tools/data/semireal_order_denominator_gate_20260507_0923.json:24371: "SR": 0.6329643920121019, tools/data/semireal_order_denominator_gate_20260507_0923.json:24378: "SR": 0.6419556045333585, tools/data/semireal_order_denominator_gate_20260507_0923.json:24385: "SR": 0.008593589277490274, tools/data/semireal_order_denominator_gate_20260507_0923.json:24392: "SR": -1.046269751896093, tools/data/semireal_order_denominator_gate_20260507_0923.json:24405: "SR": 0.6453717920449217, tools/data/semireal_order_denominator_gate_20260507_0923.json:24412: "SR": 0.6468891861084453, tools/data/semireal_order_denominator_gate_20260507_0923.json:24419: "SR": 0.007346124523735262, tools/data/semireal_order_denominator_gate_20260507_0923.json:24426: "SR": -0.20655708443559515, tools/data/semireal_order_denominator_gate_20260507_0923.json:24439: "SR": 0.6460600950877275, tools/data/semireal_order_denominator_gate_20260507_0923.json:24446: "SR": 0.6460530591117548, tools/data/semireal_order_denominator_gate_20260507_0923.json:24453: "SR": 0.0066995263561540055, tools/data/semireal_order_denominator_gate_20260507_0923.json:24460: "SR": 0.0010502199108855781, tools/data/semireal_order_denominator_gate_20260507_0923.json:24476: "SR": 0.6269324731803404, tools/data/semireal_order_denominator_gate_20260507_0923.json:24483: "SR": 0.6428000927243886, tools/data/semireal_order_denominator_gate_20260507_0923.json:24490: "SR": 0.006800564150526246, tools/data/semireal_order_denominator_gate_20260507_0923.json:24497: "SR": -2.3332798857312995, tools/data/semireal_order_denominator_gate_20260507_0923.json:24504: "SR" exec /bin/bash -lc "sed -n '1,280p' tools/exp_prime_persistent_blank_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Provider-neutral audit for the `prime_persistent_blank` residue. This is the next narrow step after `exp_boundary_residual_beta_absent_audit.py`: do not reopen the global BOUNDARY atlas; test whether `numeri_primi:cycle_3` keeps its beta-absent blank through two prime providers, row-local offsets, and baseline seed shifts, with SR as the common surviving observable. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np from exp_boundary_short_denominator_extension import gue_spacing_blocks from exp_boundary_residual_beta_absent_audit import support_state from exp_semireal_boundary_transfer_gate import row_spacings from exp_semireal_order_denominator_gate import ( analyze_sequence, compact, logistic_return_intervals, normalize, prime_gap_sequence, ) from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) TARGET_ROW = "numeri_primi:cycle_3" def offset_windows(values: np.ndarray, offsets: list[int], size: int) -> dict[str, np.ndarray]: out = {} for offset in offsets: end = offset + size if end <= len(values): out[f"offset_{offset}"] = normalize(values[offset:end]) return out def obs_jaccard(left: list[str], right: list[str]) -> float: a = set(left) b = set(right) if not a and not b: return 1.0 return len(a & b) / len(a | b) def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: perimeters = {name: analyze_sequence(name, base, args, rng)} row = compact(perimeters)[name] one_sided = list(row["coherent_one_sided_observables"]) return { "case": name, "n_gaps": row["n_gaps"], "state": support_state(row, args), "one_sided_observables": one_sided, "has_sr": "SR" in one_sided, "endpoint_stable_observables": row["endpoint_stable_observables"], "stable_count_coherent": row["stable_count_coherent"], "stable_count_illusory": row["stable_count_illusory"], "endpoint_distance": row["endpoint_distance_one_sided_gated"], "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], "z_mean_coherent": row["z_mean_coherent"], "z_mean_illusory": row["z_mean_illusory"], } def summarize_family(cases: list[dict[str, Any]]) -> dict[str, Any]: obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] common_obs = sorted(set.intersection(*obs_sets)) if obs_sets else [] union_obs = sorted(set.union(*obs_sets)) if obs_sets else [] counts: dict[str, int] = {} for case in cases: counts[case["state"]] = counts.get(case["state"], 0) + 1 blank_cases = [case for case in cases if case["state"] == "beta_absent_blank"] return { "case_count": len(cases), "state_counts": counts, "blank_rate": len(blank_cases) / len(cases) if cases else 0.0, "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, "common_one_sided_observables": common_obs, "union_one_sided_observables": union_obs, "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, } def build_prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: needed = max(args.offsets) + args.window_gaps providers = { "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), "direct_sieve": normalize(prime_gap_sequence(needed)), } cases = {} for provider, values in providers.items(): for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): cases[f"prime/{provider}/{label}"] = window return cases def build_control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: cases = {} for idx in range(args.control_count): seed = int(rng.integers(0, 2**63 - 1)) local_rng = np.random.default_rng(seed) random_matrix = gue_spacing_blocks(args.window_gaps, args.gue_matrix_size, local_rng) cases[f"control/random_matrix/seed_{idx}"] = random_matrix for idx in range(args.control_count): seed = int(rng.integers(0, 2**63 - 1)) local_rng = np.random.default_rng(seed) logistic = logistic_return_intervals(args.window_gaps, local_rng) cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic return cases def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], args: argparse.Namespace) -> str: prime_persists = ( prime_summary["blank_rate"] == 1.0 and prime_summary["sr_rate"] >= args.min_prime_sr_rate and prime_summary["common_one_sided_observables"] == ["SR"] ) controls_do_not_match = not ( control_summary["blank_rate"] == 1.0 and "SR" in control_summary["common_one_sided_observables"] ) if prime_persists and controls_do_not_match: return "PRIME_PERSISTENT_BLANK_SR_ISOLATED" if prime_persists: return "PRIME_PERSISTENT_BUT_CONTROL_COLLISION" return "PRIME_PERSISTENCE_NOT_REPLICATED" def run(args: argparse.Namespace) -> dict[str, Any]: root_rng = np.random.default_rng(args.seed) prime_specs = build_prime_cases(args) control_specs = build_control_cases(args, root_rng) prime_cases = [ analyze_case(name, base, args, np.random.default_rng(root_rng.integers(0, 2**63 - 1))) for name, base in prime_specs.items() ] control_cases = [ analyze_case(name, base, args, np.random.default_rng(root_rng.integers(0, 2**63 - 1))) for name, base in control_specs.items() ] prime_summary = summarize_family(prime_cases) control_summary = summarize_family(control_cases) output = { "experiment": "prime_persistent_blank_gate", "question": "Does numeri_primi:cycle_3 keep a provider-neutral SR beta-absent blank under row-local offset and seed shifts?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": [ *OBS_NAMES, "provider", "offset", "case_state", "blank_rate", "sr_rate", "prime_control_common_obs_jaccard", ], "params": vars(args), "target_row": TARGET_ROW, "observable_contract": { "claim": "prime_persistent_blank is isolated only if prime windows remain beta_absent_blank across providers and offsets with SR as the common one-sided observable", "observable": "case_state plus common one-sided observable signature focused on SR", "operator": "canonical order/null gate on row-local windows; provider and seed shifts only", "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from GUE random matrix blocks and logistic return intervals", "denominator": "8 prime row-local windows (2 providers x 4 offsets) plus 8 cross-domain controls", "non_possible": "prime_persistent_blank if any prime window recovers beta/falls, or if controls share a full SR blank signature", "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels", }, "prime_summary": prime_summary, "control_summary": control_summary, "prime_control_common_obs_jaccard": obs_jaccard( prime_summary["common_one_sided_observables"], control_summary["common_one_sided_observables"], ), "verdict": verdict(prime_summary, control_summary, args), "cases": { "prime": prime_cases, "controls": control_cases, }, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") print(f"target={TARGET_ROW}") print( "prime " f"blank={prime_summary['state_counts'].get('beta_absent_blank', 0)}/{prime_summary['case_count']} " f"sr_rate={prime_summary['sr_rate']:.3f} common={prime_summary['common_one_sided_observables']}" ) print( "controls " f"blank={control_summary['state_counts'].get('beta_absent_blank', 0)}/{control_summary['case_count']} " f"sr_rate={control_summary['sr_rate']:.3f} common={control_summary['common_one_sided_observables']}" ) print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") print(f"verdict={output['verdict']}") print(f"saved {out}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--window-gaps", type=int, default=1024) parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) parser.add_argument("--control-count", type=int, default=4) parser.add_argument("--gue-matrix-size", type=int, default=64) parser.add_argument("--n-replicates", type=int, default=8) parser.add_argument("--n-beta", type=int, default=9) parser.add_argument("--n-baseline", type=int, default=16) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--min-one-sided", type=int, default=1) parser.add_argument("--illusory-residue-max", type=float, default=0.75) parser.add_argument("--endpoint-distance-min", type=float, default=1.0) parser.add_argument("--min-prime-sr-rate", type=float, default=1.0) parser.add_argument("--seed", type=int, default=202605110330) parser.add_argument("--out", default="tools/data/prime_persistent_blank_gate_20260511_0330.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260511_0330.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Prime Persistent Blank Gate **Date**: 2026-05-11 03:30 **Piano**: 115 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - `prime_persistent_blank` si scinde: SR persiste, il blank beta no observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, blank_rate, sr_rate, prime_control_common_obs_jaccard] **observable_contract**: claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings("numeri_primi")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson. ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY residuo `numeri_primi:cycle_3`. - **Dipolo / punto-zero**: persistenza del supporto / persistenza del blank. Punto-zero: la stessa finestra row-local da 1024 gap dove `SR` puo' restare mentre la coordinata beta riappare. - **Piano superiore**: topologia assiomatica del bordo: non una classe statistica unica, ma una sezione che conserva un osservabile e perde una coordinata. - **Proto-ipotesi**: il residuo prime e' strutturale solo se il blank beta-absent sopravvive a provider e offset; se sopravvive solo `SR`, la struttura non e' `blank`, e' `SR-supported boundary`. - **Possibile / non-possibile**: possibile = isolare un residuo prime provider-neutral; non-possibile = chiamarlo `prime_persistent_blank` quando beta ricompare in finestre locali. - **Proiezione**: due provider prime, quattro offset locali, seed shift del gate, controlli random_matrix/logistic. ## Contaminazione cognitiva - **YSN DeltaLink**: il legame non ovvio e' `SR` come sezione comune mentre la carta beta cambia; non e' prova, e' la domanda proiettata. - **Cornelius gene**: `DNA_Simbolico`: "La persistenza del bordo non coincide con la persistenza del blank." Operatori: separa supporto, separa coordinata, confronta controlli. - **KSAR step / CE-0001**: reiterazione del kernel 20260510 senza ridisegnare l'atlante: stesso gate, nuovo provider/offset/seed. - **PVI attack**: il presupposto nascosto era che `blank_windows=4/4` su una partizione bastasse per nominare una specie persistente. - **Vault**: `random_matrix_chart_sensitive_blank` resta aperto come coordinata locale beta `[0.4]`, non lavorata in questo ciclo. ## Aderenza alla direzione - `relation`: follows_direction - `why`: testa direttamente `prime_persistent_blank` su `numeri_primi:cycle_3` con provider-neutral row-local windows e controlli cross-dominio. - `not_drift`: non usa `V_c`, non usa fit, non riapre griglia beta globale, non salva la classe caduta `medium/strong beta-absent`. ## Claim Under Test > `numeri_primi:cycle_3` e' un `prime_persistent_blank` se resta beta-absent in tutte le finestre provider-neutral e conserva `SR` come osservabile comune, mentre i controlli non condividono la stessa firma. ## Experiment Design - Perimetro prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap. - Controlli: 4 GUE random matrix blocks + 4 logistic return interval rows. - Parametri: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605110330`. - Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null. - Criterio di isolamento: prime `blank_rate=1.0`, prime `sr_rate=1.0`, common prime obs = `[SR]`, controlli senza full blank-SR collision. ## Results | family | blank | beta recovered | support falls | sr rate | common obs | endpoint mean | stable coherent mean | |---|---:|---:|---:|---:|---|---:|---:| | prime | 3/8 | 4/8 | 1/8 | 1.000 | SR | 2.715 | 2.656 | | controls | 1/8 | 3/8 | 4/8 | 0.250 | L2 | 1.428 | 1.641 | | comparison | value | |---|---:| | prime_control_common_obs_jaccard | 0.000 | | verdict | PRIME_PERSISTENCE_NOT_REPLICATED | Prime case details: | case | state | one-sided obs | beta | |---|---|---|---| | dnd_autoricerca offset 0 | beta_chart_recovered | SR | 0.2, 0.4 | | dnd_autoricerca offset 512 | support_falls | SR | 0.2 | | dnd_autoricerca offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | | dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | | direct_sieve offset 0 | beta_absent_blank | SR | [] | | direct_sieve offset 512 | beta_chart_recovered | SR,L1,triple_var | 0.4 | | direct_sieve offset 1024 | beta_absent_blank | SR,L1,triple_var | [] | | direct_sieve offset 1536 | beta_chart_recovered | SR,L1,triple_var | 0.4 | ## Key Findings 1. **Verificato**: `SR` resta in 8/8 finestre prime. La firma comune prime e' `[SR]`. 2. **Verificato**: il blank beta-absent non resta in 8/8 finestre prime. Solo 3/8 casi sono `beta_absent_blank`; 4/8 recuperano beta e 1/8 cade per supporto. 3. **Verificato**: i controlli non collidono con la firma prime. I controlli hanno common obs `[L2]`, `sr_rate=0.250`, `blank_rate=0.125`, Jaccard comune prime/controlli = 0.000. 4. **Inferito dal perimetro dichiarato**: il residuo dei primi non e' `prime_persistent_blank`; e' `prime_SR_persistent_boundary` con coordinata beta chart-sensitive. ## Verdict **CONSTRAINT**. Il nome `prime_persistent_blank` non regge nel perimetro provider-neutral/offset-shift. La parte persistente e' `SR`, non l'assenza di beta. Formula valida: `numeri_primi:cycle_3` conserva supporto one-sided `SR` attraverso provider e offset; la coordinata beta resta locale e ricompare in 4/8 finestre. Formula non valida: `numeri_primi:cycle_3` e' un blank beta-absent persistente. ## Bicono della scoperta - **Due radici**: supporto SR persistente / blank beta non persistente. - **Singolare**: la finestra row-local dove `SR` resta ma beta riappare. - **Invariante di passaggio**: `SR` attraversa provider e offset; `blank` no. - **Campo di possibilita'**: possibile = rinominare il residuo come supporto `SR` provider-neutral; non-possibile = promuovere il blank come specie autonoma. ## Consecutio Il prossimo ciclo deve chiudere la tassonomia residua senza salvare il nome caduto: - rinominare il residuo prime in `prime_SR_persistent_boundary`; - testare se il beta recovery 0.2/0.4 dipende da offset, provider o baseline seed; - lavorare `random_matrix_chart_sensitive_blank` solo dopo aver separato coordinate locali da supporto osservabile. ## Ricadute pratiche ssp_value: yes. Lo script diventa un audit riusabile per distinguere persistenza di osservabile e persistenza di blank nei residui BOUNDARY senza rigenerare l'atlante globale. ## Files - Script: `tools/exp_prime_persistent_blank_gate.py` - Data: `tools/data/prime_persistent_blank_gate_20260511_0330.json` - Report: `tools/data/reports/agent_20260511_0330.md` exec /bin/bash -lc "python - <<'PY' import json p='tools/data/prime_persistent_blank_gate_20260511_0330.json' with open(p) as f: d=json.load(f) print(d.keys()) for k in ['verdict','prime_summary','control_summary','config','observable_contract']: print('\\n',k,':',json.dumps(d.get(k),indent=2)[:4000]) PY" in /opt/MM_D-ND succeeded in 0ms: dict_keys(['experiment', 'question', 'observables_registry', 'observables_used', 'params', 'target_row', 'observable_contract', 'prime_summary', 'control_summary', 'prime_control_common_obs_jaccard', 'verdict', 'cases']) verdict : "PRIME_PERSISTENCE_NOT_REPLICATED" prime_summary : { "case_count": 8, "state_counts": { "beta_chart_recovered": 4, "support_falls": 1, "beta_absent_blank": 3 }, "blank_rate": 0.375, "sr_rate": 1.0, "common_one_sided_observables": [ "SR" ], "union_one_sided_observables": [ "L1", "SR", "triple_var" ], "endpoint_distance_mean": 2.7152931256472312, "stable_count_coherent_mean": 2.65625 } control_summary : { "case_count": 8, "state_counts": { "beta_chart_recovered": 3, "beta_absent_blank": 1, "support_falls": 4 }, "blank_rate": 0.125, "sr_rate": 0.25, "common_one_sided_observables": [ "L2" ], "union_one_sided_observables": [ "L2", "SR", "SR2", "triple_var" ], "endpoint_distance_mean": 1.4283905922589293, "stable_count_coherent_mean": 1.640625 } config : null observable_contract : { "claim": "prime_persistent_blank is isolated only if prime windows remain beta_absent_blank across providers and offsets with SR as the common one-sided observable", "observable": "case_state plus common one-sided observable signature focused on SR", "operator": "canonical order/null gate on row-local windows; provider and seed shifts only", "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from GUE random matrix blocks and logistic return intervals", "denominator": "8 prime row-local windows (2 providers x 4 offsets) plus 8 cross-domain controls", "non_possible": "prime_persistent_blank if any prime window recovers beta/falls, or if controls share a full SR blank signature", "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels" } codex Il cycle precedente ha già dato il nodo regressivo: il denominatore dei controlli era troppo stretto per dire “prime-specific”. Edito uno strumento separato che conserva lo stesso gate, ma amplia i non-prime in classi row-aligned: compositi, interi naturali con salti pseudo-prime, Cramer-like, GUE e logistic. exec /bin/bash -lc 'rg -n "def analyze_sequence|def compact|def prime_gap_sequence|def normalize|def logistic_return_intervals" tools/exp_semireal_order_denominator_gate.py tools/exp_semireal_boundary_transfer_gate.py tools/exp_boundary_residual_beta_absent_audit.py tools/exp_boundary_short_denominator_extension.py' in /opt/MM_D-ND succeeded in 0ms: tools/exp_semireal_order_denominator_gate.py:31:def normalize(gaps: np.ndarray) -> np.ndarray: tools/exp_semireal_order_denominator_gate.py:55:def prime_gap_sequence(n_gaps: int) -> np.ndarray: tools/exp_semireal_order_denominator_gate.py:72:def logistic_return_intervals(n_gaps: int, rng: np.random.Generator) -> np.ndarray: tools/exp_semireal_order_denominator_gate.py:226:def analyze_sequence(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: tools/exp_semireal_order_denominator_gate.py:275:def compact(perimeters: dict) -> dict: exec /bin/bash -lc "sed -n '1,260p' tools/exp_semireal_order_denominator_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_semireal_order_denominator_gate.py Falsification attempt for ORDER_DENOMINATOR_GATE on non-synthetic / semi-real ordered sequences. The coherent endpoint is the observed order of each sequence; the illusory endpoint is a marginal-preserving permutation. The same original-vs-shuffle denominator gate used in the prior reports is applied to canonical observables from observables_registry.py. """ from __future__ import annotations import argparse import json import math from pathlib import Path import numpy as np from observables_registry import ( OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, compute_canonical, ) OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) def normalize(gaps: np.ndarray) -> np.ndarray: gaps = np.asarray(gaps, dtype=float) gaps = np.maximum(gaps, 1e-12) mean = float(np.mean(gaps)) return gaps / mean if mean > 1e-15 else gaps def sieve_primes_for_count(n_primes: int) -> np.ndarray: if n_primes < 6: limit = 20 else: limit = int(n_primes * (math.log(n_primes) + math.log(math.log(n_primes))) * 1.25) while True: sieve = np.ones(limit + 1, dtype=bool) sieve[:2] = False for p in range(2, int(limit**0.5) + 1): if sieve[p]: sieve[p * p : limit + 1 : p] = False primes = np.flatnonzero(sieve) if len(primes) >= n_primes: return primes[:n_primes].astype(float) limit *= 2 def prime_gap_sequence(n_gaps: int) -> np.ndarray: primes = sieve_primes_for_count(n_gaps + 1) return normalize(np.diff(primes)) def zeta_zero_spacings(n_gaps: int) -> np.ndarray: try: import mpmath as mp except ImportError as exc: raise RuntimeError("mpmath is required for zeta_zero_spacings") from exc zeros = np.empty(n_gaps + 1, dtype=float) for i in range(n_gaps + 1): zeros[i] = float(mp.im(mp.zetazero(i + 1))) return normalize(np.diff(zeros)) def logistic_return_intervals(n_gaps: int, rng: np.random.Generator) -> np.ndarray: # Return intervals to a high-density-edge event in the fully chaotic logistic map. threshold = 0.95 burn = 2000 needed = n_gaps + 1 returns: list[int] = [] last_hit: int | None = None x = float(rng.random()) i = 0 max_steps = 50_000_000 while len(returns) < needed and i < max_steps: x = 4.0 * x * (1.0 - x) if i >= burn and x > threshold: if last_hit is not None: returns.append(i - last_hit) last_hit = i i += 1 if len(returns) < needed: raise RuntimeError(f"logistic generator produced {len(returns)} intervals, need {needed}") return normalize(np.array(returns[:n_gaps], dtype=float)) def beta_replace(base: np.ndarray, beta: float, rng: np.random.Generator) -> np.ndarray: illusory = rng.permutation(base) if beta <= 0.0: return base.copy() if beta >= 1.0: return illusory out = base.copy() mask = rng.random(len(base)) < beta out[mask] = illusory[mask] return normalize(out) def z_against_shuffle( gaps: np.ndarray, n_baseline: int, rng: np.random.Generator, ) -> tuple[dict[str, float], dict[str, float], dict[str, float], dict[str, float]]: original = compute_canonical(gaps) baseline = {name: [] for name in OBS_NAMES} for _ in range(n_baseline): obs = compute_canonical(rng.permutation(gaps)) for name in OBS_NAMES: baseline[name].append(obs[name]) means = {} sds = {} z = {} for name in OBS_NAMES: vals = np.array(baseline[name], dtype=float) means[name] = float(np.mean(vals)) sds[name] = float(np.std(vals, ddof=1)) if len(vals) > 1 else 0.0 z[name] = float((original[name] - means[name]) / sds[name]) if sds[name] > 1e-15 else 0.0 return original, means, sds, z def vector(row: dict, names: list[str]) -> np.ndarray: return np.array([row["observables"][name] for name in names], dtype=float) def classify_layers(rows: list[dict], obs_names: list[str]) -> dict: if not obs_names: return {"observables": [], "endpoint_distance": 0.0, "layers": {}, "ambiguous_beta": []} by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) coherent = np.array([vector(row, obs_names) for row in by_beta[0.0]], dtype=float) illusory = np.array([vector(row, obs_names) for row in by_beta[1.0]], dtype=float) endpoints = np.vstack([coherent, illusory]) scale = np.std(endpoints, axis=0, ddof=1) scale[scale <= 1e-15] = 1.0 coherent_centroid = np.mean(coherent, axis=0) illusory_centroid = np.mean(illusory, axis=0) endpoint_distance = float(np.linalg.norm((illusory_centroid - coherent_centroid) / scale)) layers = {} ambiguous_beta = [] for beta, beta_rows in sorted(by_beta.items()): margins = [] labels = [] coords = [] for row in beta_rows: x = vector(row, obs_names) d_coherent = float(np.linalg.norm((x - coherent_centroid) / scale)) d_illusory = float(np.linalg.norm((x - illusory_centroid) / scale)) denom = d_coherent + d_illusory coord = float((d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 margin = float(abs(d_coherent - d_illusory) / denom) if denom > 1e-15 else 0.0 coords.append(coord) margins.append(margin) labels.append("coherent" if d_coherent < d_illusory else "illusory") ambiguous_fraction = float(np.mean(np.array(margins) < 0.15)) if ambiguous_fraction >= 0.5: ambiguous_beta.append(beta) layers[f"{beta:.3f}"] = { "coordinate_mean": float(np.mean(coords)), "margin_mean": float(np.mean(margins)), "ambiguous_fraction": ambiguous_fraction, "illusory_label_fraction": float(np.mean(np.array(labels) == "illusory")), } return { "observables": obs_names, "endpoint_distance": endpoint_distance, "layers": layers, "ambiguous_beta": ambiguous_beta, } def summarize_gate(rows: list[dict], z_min: float) -> dict: by_beta: dict[float, list[dict]] = {} for row in rows: by_beta.setdefault(float(row["beta"]), []).append(row) layers = {} for beta, beta_rows in sorted(by_beta.items()): stable_counts = [] stable_freq = {name: [] for name in OBS_NAMES} z_values = {name: [] for name in OBS_NAMES} for row in beta_rows: stable = [name for name in OBS_NAMES if abs(row["z"][name]) >= z_min] stable_counts.append(len(stable)) for name in OBS_NAMES: stable_freq[name].append(1.0 if name in stable else 0.0) z_values[name].append(row["z"][name]) layers[f"{beta:.3f}"] = { "stable_count_mean": float(np.mean(stable_counts)), "stable_frequency": {name: float(np.mean(vals)) for name, vals in stable_freq.items()}, "z_mean": {name: float(np.mean(vals)) for name, vals in z_values.items()}, } one_sided = [] endpoint_stable = [] coherent_rows = by_beta[0.0] illusory_rows = by_beta[1.0] for name in OBS_NAMES: coherent_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in coherent_rows])) illusory_freq = float(np.mean([abs(row["z"][name]) >= z_min for row in illusory_rows])) if coherent_freq >= 0.75 and illusory_freq < 0.25: one_sided.append(name) if coherent_freq >= 0.75 and illusory_freq >= 0.75: endpoint_stable.append(name) return { "z_min": z_min, "coherent_one_sided_observables": one_sided, "endpoint_stable_observables": endpoint_stable, "layers": layers, } def analyze_sequence(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict: rows = [] betas = [float(x) for x in np.linspace(0.0, 1.0, args.n_beta)] for rep in range(args.n_replicates): rep_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) for beta in betas: gaps = beta_replace(base, beta, rep_rng) obs, shuffle_mean, shuffle_std, z = z_against_shuffle( gaps, args.n_baseline, np.random.default_rng(rng.integers(0, 2**63 - 1)), ) rows.append( { "perimeter": name, "replicate": rep, "beta": beta, "observables": obs, "shuffle_mean": shuffle_mean, "shuffle_std": shuffle_std, "z": z, "stable_observables": [obs_name for obs_name in OBS_NAMES if abs(z[obs_name]) >= args.z_min], } ) gate = summarize_gate(rows, args.z_min) return { "source": { "n_gaps": int(len(base)), "mean": float(np.mean(base)), "variance": float(np.var(base)), }, "gate": gate, "classification_all_observables": classify_layers(rows, OBS_NAMES), "classification_one_sided_gated": classify_layers(rows, gate["coherent_one_sided_observables"]), exec /bin/bash -lc "sed -n '1,220p' tools/exp_boundary_residual_beta_absent_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Targeted audit for the two medium/strong beta-absent BOUNDARY residues. This does not rebuild the global beta atlas. It tests only the two open rows from the 13-row taxonomy (`numeri_primi:cycle_3`, `random_matrix:cycle_7`) with row-local windows and the same canonical observable gate used by the prior BOUNDARY reports. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np from exp_boundary_short_denominator_extension import gue_spacing_blocks from exp_semireal_boundary_transfer_gate import row_spacings from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION TARGET_ROWS = ("numeri_primi:cycle_3", "random_matrix:cycle_7") OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) def windowed(values: np.ndarray, size: int, count: int) -> list[np.ndarray]: chunks = [] for idx in range(count): start = idx * size end = start + size if end <= len(values): chunks.append(normalize(values[start:end])) return chunks def support_state(row: dict[str, Any], args: argparse.Namespace) -> str: one_sided = len(row.get("coherent_one_sided_observables", [])) illusory = float(row.get("stable_count_illusory") or 0.0) endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) beta = row.get("ambiguous_beta_one_sided_gated", []) transfers = ( one_sided >= args.min_one_sided and illusory <= args.illusory_residue_max and endpoint >= args.endpoint_distance_min ) if not transfers: return "support_falls" if beta: return "beta_chart_recovered" return "beta_absent_blank" def obs_jaccard(left: list[str], right: list[str]) -> float: a = set(left) b = set(right) if not a and not b: return 1.0 return len(a & b) / len(a | b) def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: prime = row_spacings("numeri_primi") prime = normalize(prime[: args.prime_gaps]) gue_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) random_matrix = gue_spacing_blocks(args.random_matrix_gaps, args.gue_matrix_size, gue_rng) return { "numeri_primi:cycle_3": { "base": prime, "domain": "numeri_primi", "generator": "dnd_autoricerca.genera_segnale -> prime gap spacings", "source_n_gaps": int(len(prime)), }, "random_matrix:cycle_7": { "base": random_matrix, "domain": "random_matrix", "generator": "gue_spacing_blocks", "source_n_gaps": int(len(random_matrix)), }, } def analyze_case(name: str, label: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: perimeters = {f"{name}/{label}": analyze_sequence(f"{name}/{label}", base, args, rng)} row = compact(perimeters)[f"{name}/{label}"] return { "label": label, "n_gaps": row["n_gaps"], "one_sided_observables": row["coherent_one_sided_observables"], "one_sided_count": len(row["coherent_one_sided_observables"]), "endpoint_stable_observables": row["endpoint_stable_observables"], "stable_count_coherent": row["stable_count_coherent"], "stable_count_illusory": row["stable_count_illusory"], "endpoint_distance": row["endpoint_distance_one_sided_gated"], "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], "state": support_state(row, args), "z_mean_coherent": row["z_mean_coherent"], "z_mean_illusory": row["z_mean_illusory"], } def summarize_row(cases: list[dict[str, Any]]) -> dict[str, Any]: window_cases = [case for case in cases if case["label"].startswith("window_")] blank_windows = [case for case in window_cases if case["state"] == "beta_absent_blank"] beta_windows = [case for case in window_cases if case["state"] == "beta_chart_recovered"] fall_windows = [case for case in window_cases if case["state"] == "support_falls"] obs_sets = [set(case["one_sided_observables"]) for case in window_cases if case["one_sided_observables"]] common_obs = sorted(set.intersection(*obs_sets)) if obs_sets else [] union_obs = sorted(set.union(*obs_sets)) if obs_sets else [] return { "window_count": len(window_cases), "blank_windows": len(blank_windows), "beta_recovered_windows": len(beta_windows), "support_fall_windows": len(fall_windows), "blank_window_rate": len(blank_windows) / len(window_cases) if window_cases else 0.0, "common_one_sided_observables": common_obs, "union_one_sided_observables": union_obs, "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in window_cases])) if window_cases else 0.0, "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in window_cases])) if window_cases else 0.0, } def verdict(row_summaries: dict[str, dict[str, Any]], full_rows: dict[str, dict[str, Any]]) -> str: both_persist = all(summary["blank_window_rate"] == 1.0 for summary in row_summaries.values()) any_beta = any(summary["beta_recovered_windows"] > 0 for summary in row_summaries.values()) any_fall = any(summary["support_fall_windows"] > 0 for summary in row_summaries.values()) jaccard = obs_jaccard( full_rows["numeri_primi:cycle_3"]["one_sided_observables"], full_rows["random_matrix:cycle_7"]["one_sided_observables"], ) if any_beta or any_fall: return "RESIDUAL_ATLAS_ARTIFACT_OR_UNSTABLE" if both_persist and jaccard < 0.5: return "TWO_DISTINCT_BETA_ABSENT_OPERATORS" if both_persist: return "SAME_BETA_ABSENT_OPERATOR" return "RESIDUAL_AMBIGUOUS" def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) specs = build_sequences(args, rng) cases_by_row: dict[str, list[dict[str, Any]]] = {} full_rows: dict[str, dict[str, Any]] = {} for name, spec in specs.items(): row_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) cases = [analyze_case(name, "full", spec["base"], args, row_rng)] full_rows[name] = cases[0] for idx, chunk in enumerate(windowed(spec["base"], args.window_gaps, args.window_count), start=1): cases.append(analyze_case(name, f"window_{idx}", chunk, args, row_rng)) cases_by_row[name] = cases row_summaries = {name: summarize_row(cases) for name, cases in cases_by_row.items()} full_signature_jaccard = obs_jaccard( full_rows["numeri_primi:cycle_3"]["one_sided_observables"], full_rows["random_matrix:cycle_7"]["one_sided_observables"], ) common_window_obs_jaccard = obs_jaccard( row_summaries["numeri_primi:cycle_3"]["common_one_sided_observables"], row_summaries["random_matrix:cycle_7"]["common_one_sided_observables"], ) output = { "experiment": "boundary_residual_beta_absent_audit", "question": "Are the two medium/strong beta-absent BOUNDARY residues the same operator, distinct classes, or atlas artifacts?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": [ *OBS_NAMES, "window_state", "blank_window_rate", "full_signature_jaccard", "common_window_obs_jaccard", ], "params": vars(args), "targets": TARGET_ROWS, "observable_contract": { "claim": "the two residual beta-absent blanks are structural only if beta_absent_blank persists across row-local 1024-gap windows", "observable": "window_state plus one-sided observable signature", "operator": "canonical order/null gate on target rows only", "denominator": "two open BOUNDARY rows; full row plus row-local 1024-gap windows", "non_possible": "residual class if a target recovers beta or loses support under row-local windows", "not_tested": "global beta grid, V_c, source GUE/Poisson label validity", }, "sequence_sources": { name: {key: value for key, value in spec.items() if key != "base"} for name, spec in specs.items() }, "row_summaries": row_summaries, "full_signature_jaccard": full_signature_jaccard, "common_window_obs_jaccard": common_window_obs_jaccard, "verdict": verdict(row_summaries, full_rows), "cases": cases_by_row, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") print(f"targets={TARGET_ROWS}") for name, summary in row_summaries.items(): print( f"{name} blank_windows={summary['blank_windows']}/{summary['window_count']} " f"beta_recovered={summary['beta_recovered_windows']} falls={summary['support_fall_windows']} " f"common_obs={summary['common_one_sided_observables']}" ) print(f"full_signature_jaccard={full_signature_jaccard:.3f}") print(f"common_window_obs_jaccard={common_window_obs_jaccard:.3f}") print(f"verdict={output['verdict']}") print(f"saved {out}") return output def main() -> None: exec /bin/bash -lc "sed -n '260,360p' tools/exp_semireal_order_denominator_gate.py && sed -n '220,340p' tools/exp_boundary_residual_beta_absent_audit.py" in /opt/MM_D-ND succeeded in 0ms: "classification_one_sided_gated": classify_layers(rows, gate["coherent_one_sided_observables"]), "rows": rows, } def build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: sequences = { "prime_gaps_first": prime_gap_sequence(args.n_gaps), "logistic_return_intervals": logistic_return_intervals(args.n_gaps, rng), } if args.include_zeta: sequences["zeta_zero_spacings_first"] = zeta_zero_spacings(args.zeta_gaps) return sequences def compact(perimeters: dict) -> dict: out = {} for name, data in perimeters.items(): gate = data["gate"] all_cls = data["classification_all_observables"] gated_cls = data["classification_one_sided_gated"] out[name] = { "n_gaps": data["source"]["n_gaps"], "coherent_one_sided_observables": gate["coherent_one_sided_observables"], "endpoint_stable_observables": gate["endpoint_stable_observables"], "stable_count_coherent": gate["layers"]["0.000"]["stable_count_mean"], "stable_count_illusory": gate["layers"]["1.000"]["stable_count_mean"], "z_mean_coherent": gate["layers"]["0.000"]["z_mean"], "z_mean_illusory": gate["layers"]["1.000"]["z_mean"], "endpoint_distance_all": all_cls["endpoint_distance"], "endpoint_distance_one_sided_gated": gated_cls["endpoint_distance"], "ambiguous_beta_one_sided_gated": gated_cls["ambiguous_beta"], } return out def run(args: argparse.Namespace) -> dict: root_rng = np.random.default_rng(args.seed) sequences = build_sequences(args, root_rng) perimeters = {} for name, base in sequences.items(): perimeters[name] = analyze_sequence(name, base, args, root_rng) output = { "experiment": "semireal_order_denominator_gate", "category": "gate_falsification_semireal", "question": "Does ORDER_DENOMINATOR_GATE survive on non-synthetic / semi-real ordered sequences?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": OBS_NAMES, "params": vars(args), "matrix": compact(perimeters), "perimeters": perimeters, } out_path = Path(args.out) out_path.parent.mkdir(parents=True, exist_ok=True) with out_path.open("w") as f: json.dump(output, f, indent=2) print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") print(f"observables_used={OBS_NAMES}") print("perimeter n one_sided stable0 stable1 dist_gate ambiguous_gate") for name, row in output["matrix"].items(): print( f"{name:>28s} " f"{row['n_gaps']:>5d} " f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " f"{row['stable_count_coherent']:>7.3f} " f"{row['stable_count_illusory']:>7.3f} " f"{row['endpoint_distance_one_sided_gated']:>9.3f} " f"{row['ambiguous_beta_one_sided_gated']}" ) print(f"saved {out_path}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--n-gaps", type=int, default=4096) parser.add_argument("--zeta-gaps", type=int, default=1024) parser.add_argument("--include-zeta", action="store_true") parser.add_argument("--n-replicates", type=int, default=20) parser.add_argument("--n-beta", type=int, default=11) parser.add_argument("--n-baseline", type=int, default=32) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--seed", type=int, default=202605070923) parser.add_argument("--out", default="tools/data/semireal_order_denominator_gate_20260507_0923.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--prime-gaps", type=int, default=4096) parser.add_argument("--random-matrix-gaps", type=int, default=4096) parser.add_argument("--window-gaps", type=int, default=1024) parser.add_argument("--window-count", type=int, default=4) parser.add_argument("--gue-matrix-size", type=int, default=64) parser.add_argument("--n-replicates", type=int, default=12) parser.add_argument("--n-beta", type=int, default=11) parser.add_argument("--n-baseline", type=int, default=24) parser.add_argument("--z-min", type=float, default=2.0) parser.add_argument("--min-one-sided", type=int, default=1) parser.add_argument("--illusory-residue-max", type=float, default=0.75) parser.add_argument("--endpoint-distance-min", type=float, default=1.0) parser.add_argument("--seed", type=int, default=202605100330) parser.add_argument("--out", default="tools/data/boundary_residual_beta_absent_audit_20260510_0330.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() apply patch patch: completed /opt/MM_D-ND/tools/exp_prime_sr_persistent_boundary.py diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..ee76ef66e38f0263d78b756485e5dadaa292776c --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..ee76ef66e38f0263d78b756485e5dadaa292776c --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Lo script è aggiunto come nuovo strumento, senza modificare quello precedente. Ora lo eseguo con il denominatore dichiarato e salvo il JSON del ciclo. exec /bin/bash -lc 'python tools/exp_prime_sr_persistent_boundary.py' in /opt/MM_D-ND diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..ee76ef66e38f0263d78b756485e5dadaa292776c --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 3132ms: observables_registry=1.0.0-2026-05-06 target=numeri_primi:cycle_3 prime sr=7/8 common=[] states={'beta_absent_blank': 7, 'beta_chart_recovered': 1} controls sr=5/20 common=[] states={'beta_chart_recovered': 8, 'support_falls': 10, 'beta_absent_blank': 2} control/composite_gaps sr=0/4 common=['L2', 'SR2'] control/cramer_like sr=0/4 common=[] control/logistic_return_intervals sr=0/4 common=[] control/mod6_candidates sr=2/4 common=[] control/random_matrix sr=3/4 common=['L2', 'triple_var'] prime_control_common_obs_jaccard=1.000 verdict=PRIME_SR_NOT_PERSISTENT saved tools/data/prime_sr_persistent_boundary_20260512_0330.json diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..ee76ef66e38f0263d78b756485e5dadaa292776c --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il primo run è informativo ma duro: con controlli ampliati `SR` non è 8/8 sui primi, è 7/8. Prima di cristallizzarlo guardo il caso caduto e faccio una replica seed-shift minimale per distinguere caduta strutturale da instabilità del baseline. exec /bin/bash -lc "python - <<'PY' import json p='tools/data/prime_sr_persistent_boundary_20260512_0330.json' with open(p) as f: d=json.load(f) for c in d['cases']['prime']: print(c['case'], c['state'], c['one_sided_observables'], 'beta', c['ambiguous_beta'], 'zSR0', round(c['z_mean_coherent']['SR'],3), 'zSR1', round(c['z_mean_illusory']['SR'],3)) print('\\ncontrol subfamilies') for k,v in d['control_subfamilies'].items(): print(k,v) PY" in /opt/MM_D-ND succeeded in 0ms: prime/dnd_autoricerca/offset_0 beta_absent_blank ['SR'] beta [] zSR0 -5.136 zSR1 -0.439 prime/dnd_autoricerca/offset_512 beta_absent_blank ['L1', 'triple_var'] beta [] zSR0 -6.625 zSR1 0.822 prime/dnd_autoricerca/offset_1024 beta_absent_blank ['SR', 'L1'] beta [] zSR0 -6.296 zSR1 -0.626 prime/dnd_autoricerca/offset_1536 beta_absent_blank ['SR', 'L1', 'triple_var'] beta [] zSR0 -4.074 zSR1 -0.371 prime/direct_sieve/offset_0 beta_absent_blank ['SR'] beta [] zSR0 -4.856 zSR1 -0.256 prime/direct_sieve/offset_512 beta_absent_blank ['SR', 'L1', 'triple_var'] beta [] zSR0 -7.771 zSR1 -0.112 prime/direct_sieve/offset_1024 beta_chart_recovered ['SR', 'L1', 'triple_var'] beta [0.2] zSR0 -5.915 zSR1 -0.069 prime/direct_sieve/offset_1536 beta_absent_blank ['SR', 'L1', 'triple_var'] beta [] zSR0 -3.237 zSR1 -0.331 control subfamilies composite_gaps {'case_count': 4, 'state_counts': {'beta_chart_recovered': 3, 'support_falls': 1}, 'sr_count': 0, 'sr_rate': 0.0, 'common_one_sided_observables': ['L2', 'SR2'], 'union_one_sided_observables': ['L2', 'SR2', 'triple_var'], 'blank_count': 0, 'beta_recovered_count': 3, 'support_fall_count': 1, 'endpoint_distance_mean': 2.271349550689998, 'stable_count_coherent_mean': 2.59375} cramer_like {'case_count': 4, 'state_counts': {'support_falls': 4}, 'sr_count': 0, 'sr_rate': 0.0, 'common_one_sided_observables': [], 'union_one_sided_observables': [], 'blank_count': 0, 'beta_recovered_count': 0, 'support_fall_count': 4, 'endpoint_distance_mean': 0.0, 'stable_count_coherent_mean': 0.59375} logistic_return_intervals {'case_count': 4, 'state_counts': {'support_falls': 4}, 'sr_count': 0, 'sr_rate': 0.0, 'common_one_sided_observables': [], 'union_one_sided_observables': [], 'blank_count': 0, 'beta_recovered_count': 0, 'support_fall_count': 4, 'endpoint_distance_mean': 0.0, 'stable_count_coherent_mean': 0.03125} mod6_candidates {'case_count': 4, 'state_counts': {'beta_chart_recovered': 3, 'support_falls': 1}, 'sr_count': 2, 'sr_rate': 0.5, 'common_one_sided_observables': [], 'union_one_sided_observables': ['L1', 'L2', 'SR', 'SR2', 'triple_var'], 'blank_count': 0, 'beta_recovered_count': 3, 'support_fall_count': 1, 'endpoint_distance_mean': 3.4398840817468415, 'stable_count_coherent_mean': 5.0} random_matrix {'case_count': 4, 'state_counts': {'beta_chart_recovered': 2, 'beta_absent_blank': 2}, 'sr_count': 3, 'sr_rate': 0.75, 'common_one_sided_observables': ['L2', 'triple_var'], 'union_one_sided_observables': ['L2', 'SR', 'SR2', 'triple_var'], 'blank_count': 2, 'beta_recovered_count': 2, 'support_fall_count': 0, 'endpoint_distance_mean': 3.363537844158115, 'stable_count_coherent_mean': 3.5} exec /bin/bash -lc 'python tools/exp_prime_sr_persistent_boundary.py --seed 202605120331 --out tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json' in /opt/MM_D-ND diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..ee76ef66e38f0263d78b756485e5dadaa292776c --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 3151ms: observables_registry=1.0.0-2026-05-06 target=numeri_primi:cycle_3 prime sr=7/8 common=['SR'] states={'beta_absent_blank': 4, 'beta_chart_recovered': 3, 'support_falls': 1} controls sr=8/20 common=[] states={'beta_chart_recovered': 8, 'beta_absent_blank': 5, 'support_falls': 7} control/composite_gaps sr=0/4 common=['L2', 'SR2'] control/cramer_like sr=0/4 common=['L1', 'triple_var'] control/logistic_return_intervals sr=1/4 common=[] control/mod6_candidates sr=4/4 common=['L1', 'SR', 'triple_var'] control/random_matrix sr=3/4 common=[] prime_control_common_obs_jaccard=0.000 verdict=PRIME_SR_NOT_PERSISTENT saved tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..ee76ef66e38f0263d78b756485e5dadaa292776c --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases if case["one_sided_observables"]] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if obs_sets else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex La replica conferma che la caduta non è un singolo seed. Ho trovato anche un difetto regressivo nello summary: l’intersezione degli osservabili ignorava i casi senza osservabili, gonfiando il “common” quando c’è `support_falls`; lo correggo nello strumento prima del report. apply patch patch: completed /opt/MM_D-ND/tools/exp_prime_sr_persistent_boundary.py diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'python tools/exp_prime_sr_persistent_boundary.py && python tools/exp_prime_sr_persistent_boundary.py --seed 202605120331 --out tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json' in /opt/MM_D-ND diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 5992ms: observables_registry=1.0.0-2026-05-06 target=numeri_primi:cycle_3 prime sr=7/8 common=[] states={'beta_absent_blank': 7, 'beta_chart_recovered': 1} controls sr=5/20 common=[] states={'beta_chart_recovered': 8, 'support_falls': 10, 'beta_absent_blank': 2} control/composite_gaps sr=0/4 common=[] control/cramer_like sr=0/4 common=[] control/logistic_return_intervals sr=0/4 common=[] control/mod6_candidates sr=2/4 common=[] control/random_matrix sr=3/4 common=['L2', 'triple_var'] prime_control_common_obs_jaccard=1.000 verdict=PRIME_SR_NOT_PERSISTENT saved tools/data/prime_sr_persistent_boundary_20260512_0330.json observables_registry=1.0.0-2026-05-06 target=numeri_primi:cycle_3 prime sr=7/8 common=[] states={'beta_absent_blank': 4, 'beta_chart_recovered': 3, 'support_falls': 1} controls sr=8/20 common=[] states={'beta_chart_recovered': 8, 'beta_absent_blank': 5, 'support_falls': 7} control/composite_gaps sr=0/4 common=['L2', 'SR2'] control/cramer_like sr=0/4 common=[] control/logistic_return_intervals sr=1/4 common=[] control/mod6_candidates sr=4/4 common=['L1', 'SR', 'triple_var'] control/random_matrix sr=3/4 common=[] prime_control_common_obs_jaccard=1.000 verdict=PRIME_SR_NOT_PERSISTENT saved tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json exec /bin/bash -lc "python - <<'PY' import json for p in ['tools/data/prime_sr_persistent_boundary_20260512_0330.json','tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json']: print('\\n###',p) d=json.load(open(p)) for k in ['prime_summary','control_summary','control_subfamilies','prime_control_common_obs_jaccard','verdict']: print(k, json.dumps(d[k], indent=2)) print('prime cases') for c in d['cases']['prime']: print(c['case'].replace('prime/',''), c['state'], c['one_sided_observables'], c['ambiguous_beta']) PY" in /opt/MM_D-ND succeeded in 0ms: ### tools/data/prime_sr_persistent_boundary_20260512_0330.json prime_summary { "case_count": 8, "state_counts": { "beta_absent_blank": 7, "beta_chart_recovered": 1 }, "sr_count": 7, "sr_rate": 0.875, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "SR", "triple_var" ], "blank_count": 7, "beta_recovered_count": 1, "support_fall_count": 0, "endpoint_distance_mean": 2.72620046046407, "stable_count_coherent_mean": 2.65625 } control_summary { "case_count": 20, "state_counts": { "beta_chart_recovered": 8, "support_falls": 10, "beta_absent_blank": 2 }, "sr_count": 5, "sr_rate": 0.25, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 2, "beta_recovered_count": 8, "support_fall_count": 10, "endpoint_distance_mean": 1.8149542953189908, "stable_count_coherent_mean": 2.34375 } control_subfamilies { "composite_gaps": { "case_count": 4, "state_counts": { "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [ "L2", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 2.271349550689998, "stable_count_coherent_mean": 2.59375 }, "cramer_like": { "case_count": 4, "state_counts": { "support_falls": 4 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [], "blank_count": 0, "beta_recovered_count": 0, "support_fall_count": 4, "endpoint_distance_mean": 0.0, "stable_count_coherent_mean": 0.59375 }, "logistic_return_intervals": { "case_count": 4, "state_counts": { "support_falls": 4 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [], "blank_count": 0, "beta_recovered_count": 0, "support_fall_count": 4, "endpoint_distance_mean": 0.0, "stable_count_coherent_mean": 0.03125 }, "mod6_candidates": { "case_count": 4, "state_counts": { "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 2, "sr_rate": 0.5, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 3.4398840817468415, "stable_count_coherent_mean": 5.0 }, "random_matrix": { "case_count": 4, "state_counts": { "beta_chart_recovered": 2, "beta_absent_blank": 2 }, "sr_count": 3, "sr_rate": 0.75, "common_one_sided_observables": [ "L2", "triple_var" ], "union_one_sided_observables": [ "L2", "SR", "SR2", "triple_var" ], "blank_count": 2, "beta_recovered_count": 2, "support_fall_count": 0, "endpoint_distance_mean": 3.363537844158115, "stable_count_coherent_mean": 3.5 } } prime_control_common_obs_jaccard 1.0 verdict "PRIME_SR_NOT_PERSISTENT" prime cases dnd_autoricerca/offset_0 beta_absent_blank ['SR'] [] dnd_autoricerca/offset_512 beta_absent_blank ['L1', 'triple_var'] [] dnd_autoricerca/offset_1024 beta_absent_blank ['SR', 'L1'] [] dnd_autoricerca/offset_1536 beta_absent_blank ['SR', 'L1', 'triple_var'] [] direct_sieve/offset_0 beta_absent_blank ['SR'] [] direct_sieve/offset_512 beta_absent_blank ['SR', 'L1', 'triple_var'] [] direct_sieve/offset_1024 beta_chart_recovered ['SR', 'L1', 'triple_var'] [0.2] direct_sieve/offset_1536 beta_absent_blank ['SR', 'L1', 'triple_var'] [] ### tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json prime_summary { "case_count": 8, "state_counts": { "beta_absent_blank": 4, "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 7, "sr_rate": 0.875, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "SR", "triple_var" ], "blank_count": 4, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 2.4740798702604283, "stable_count_coherent_mean": 2.6875 } control_summary { "case_count": 20, "state_counts": { "beta_chart_recovered": 8, "beta_absent_blank": 5, "support_falls": 7 }, "sr_count": 8, "sr_rate": 0.4, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 5, "beta_recovered_count": 8, "support_fall_count": 7, "endpoint_distance_mean": 2.417896785731883, "stable_count_coherent_mean": 2.525 } control_subfamilies { "composite_gaps": { "case_count": 4, "state_counts": { "beta_chart_recovered": 2, "beta_absent_blank": 1, "support_falls": 1 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [ "L2", "SR2" ], "union_one_sided_observables": [ "L2", "SR2", "triple_var" ], "blank_count": 1, "beta_recovered_count": 2, "support_fall_count": 1, "endpoint_distance_mean": 2.9568726214400955, "stable_count_coherent_mean": 2.625 }, "cramer_like": { "case_count": 4, "state_counts": { "beta_absent_blank": 1, "support_falls": 3 }, "sr_count": 0, "sr_rate": 0.0, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "triple_var" ], "blank_count": 1, "beta_recovered_count": 0, "support_fall_count": 3, "endpoint_distance_mean": 0.6756892529694472, "stable_count_coherent_mean": 0.625 }, "logistic_return_intervals": { "case_count": 4, "state_counts": { "beta_absent_blank": 3, "support_falls": 1 }, "sr_count": 1, "sr_rate": 0.25, "common_one_sided_observables": [], "union_one_sided_observables": [ "L2", "SR", "SR2" ], "blank_count": 3, "beta_recovered_count": 0, "support_fall_count": 1, "endpoint_distance_mean": 1.3249843016411362, "stable_count_coherent_mean": 1.0625 }, "mod6_candidates": { "case_count": 4, "state_counts": { "beta_chart_recovered": 3, "support_falls": 1 }, "sr_count": 4, "sr_rate": 1.0, "common_one_sided_observables": [ "L1", "SR", "triple_var" ], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 4.077172077071727, "stable_count_coherent_mean": 5.0 }, "random_matrix": { "case_count": 4, "state_counts": { "support_falls": 1, "beta_chart_recovered": 3 }, "sr_count": 3, "sr_rate": 0.75, "common_one_sided_observables": [], "union_one_sided_observables": [ "L1", "L2", "SR", "SR2", "triple_var" ], "blank_count": 0, "beta_recovered_count": 3, "support_fall_count": 1, "endpoint_distance_mean": 3.054765675537009, "stable_count_coherent_mean": 3.3125 } } prime_control_common_obs_jaccard 1.0 verdict "PRIME_SR_NOT_PERSISTENT" prime cases dnd_autoricerca/offset_0 beta_absent_blank ['SR'] [] dnd_autoricerca/offset_512 beta_chart_recovered ['SR', 'L1', 'triple_var'] [0.4] dnd_autoricerca/offset_1024 beta_absent_blank ['SR'] [] dnd_autoricerca/offset_1536 beta_absent_blank ['SR', 'L1', 'triple_var'] [] direct_sieve/offset_0 support_falls [] [] direct_sieve/offset_512 beta_absent_blank ['SR', 'L1', 'triple_var'] [] direct_sieve/offset_1024 beta_chart_recovered ['SR', 'L1', 'triple_var'] [0.2] direct_sieve/offset_1536 beta_chart_recovered ['SR', 'L1', 'triple_var'] [0.2] diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il risultato finale non salva `prime_SR_persistent_boundary`: i primi restano forti ma non persistenti nel criterio atomico, e un controllo `mod6_candidates` collide nella replica. Scrivo il report come CONSTRAINT/FALSIFIED scoped, includendo la correzione dello summary come nodo regressivo dello strumento. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260512_0330.md diff --git a/tools/data/reports/agent_20260512_0330.md b/tools/data/reports/agent_20260512_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..1174ef11a663dd37058fbeffd16c96b7817bf717 --- /dev/null +++ b/tools/data/reports/agent_20260512_0330.md @@ -0,0 +1,118 @@ +# Agent Report - Prime SR Persistent Boundary +**Date**: 2026-05-12 03:30 +**Piano**: 115 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] +**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings("numeri_primi")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`. +- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi. +- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro. +- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente. +- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' "SR persistente nei primi implica prime-specific". +- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico. +- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime. +- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime. + +## Claim Under Test + +> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena. + +## Question + +Quando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime? + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap. +- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows. +- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`. +- Seed check: stesso perimetro, seed `202605120331`. +- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null. +- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common. + +## Results + +Main run: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 | +| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 | +| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 | +| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 | +| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 | + +Seed check: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 | +| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 | +| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 | + +Prime case details, main: + +| case | state | one-sided obs | beta | +|---|---|---|---| +| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] | +| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] | +| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] | +| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 0 | beta_absent_blank | SR | [] | +| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | +| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | + +## Key Findings + +1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota. +2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall. +3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`. +4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato. +5. **Inferito dal perimetro dichiarato**: la parte robusta non e' "SR e' prime-specific"; e' "SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche". + +## Verdict + +**CONSTRAINT / FALSIFIED scoped**. + +Formula non valida: + +`prime_SR_persistent_boundary` come firma atomica prime-specific. + +Formula valida nel perimetro: + +`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico. + +## Bicono della scoperta + +- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato. +- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato. +- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6. +- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific. + +## Consecutio + +Il prossimo ciclo deve spostare il nodo regressivo: non "prime vs controlli generici", ma "primi vs candidati mod6 row-aligned". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico. + +## Files + +- Script: `tools/exp_prime_sr_persistent_boundary.py` +- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json` +- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260512_0330.md` diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260512_0330.md b/tools/data/reports/agent_20260512_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..1174ef11a663dd37058fbeffd16c96b7817bf717 --- /dev/null +++ b/tools/data/reports/agent_20260512_0330.md @@ -0,0 +1,118 @@ +# Agent Report - Prime SR Persistent Boundary +**Date**: 2026-05-12 03:30 +**Piano**: 115 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] +**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings("numeri_primi")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`. +- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi. +- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro. +- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente. +- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' "SR persistente nei primi implica prime-specific". +- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico. +- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime. +- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime. + +## Claim Under Test + +> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena. + +## Question + +Quando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime? + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap. +- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows. +- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`. +- Seed check: stesso perimetro, seed `202605120331`. +- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null. +- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common. + +## Results + +Main run: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 | +| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 | +| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 | +| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 | +| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 | + +Seed check: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 | +| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 | +| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 | + +Prime case details, main: + +| case | state | one-sided obs | beta | +|---|---|---|---| +| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] | +| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] | +| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] | +| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 0 | beta_absent_blank | SR | [] | +| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | +| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | + +## Key Findings + +1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota. +2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall. +3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`. +4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato. +5. **Inferito dal perimetro dichiarato**: la parte robusta non e' "SR e' prime-specific"; e' "SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche". + +## Verdict + +**CONSTRAINT / FALSIFIED scoped**. + +Formula non valida: + +`prime_SR_persistent_boundary` come firma atomica prime-specific. + +Formula valida nel perimetro: + +`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico. + +## Bicono della scoperta + +- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato. +- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato. +- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6. +- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific. + +## Consecutio + +Il prossimo ciclo deve spostare il nodo regressivo: non "prime vs controlli generici", ma "primi vs candidati mod6 row-aligned". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico. + +## Files + +- Script: `tools/exp_prime_sr_persistent_boundary.py` +- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json` +- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260512_0330.md` diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Report scritto. Faccio una verifica finale di sintassi dello script e leggo l’header del report per assicurarmi che contratto, verdict e file siano coerenti con i risultati salvati. exec /bin/bash -lc "sed -n '1,220p' tools/data/reports/agent_20260512_0330.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Prime SR Persistent Boundary **Date**: 2026-05-12 03:30 **Piano**: 115 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] **observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings("numeri_primi")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR. ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`. - **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi. - **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro. - **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente. - **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' "SR persistente nei primi implica prime-specific". - **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico. - **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic. ## Aderenza alla direzione - `relation`: follows_direction - `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime. - `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime. ## Claim Under Test > `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena. ## Question Quando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime? ## Experiment Design - Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap. - Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows. - Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`. - Seed check: stesso perimetro, seed `202605120331`. - Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null. - Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common. ## Results Main run: | family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | |---|---:|---:|---|---:|---:|---:|---:| | prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 | | all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 | | composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 | | cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | | logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | | mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 | | random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 | Seed check: | family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | |---|---:|---:|---|---:|---:|---:|---:| | prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 | | all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 | | mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 | Prime case details, main: | case | state | one-sided obs | beta | |---|---|---|---| | dnd_autoricerca offset 0 | beta_absent_blank | SR | [] | | dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] | | dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] | | dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | | direct_sieve offset 0 | beta_absent_blank | SR | [] | | direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] | | direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | | direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | ## Key Findings 1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota. 2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall. 3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`. 4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato. 5. **Inferito dal perimetro dichiarato**: la parte robusta non e' "SR e' prime-specific"; e' "SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche". ## Verdict **CONSTRAINT / FALSIFIED scoped**. Formula non valida: `prime_SR_persistent_boundary` come firma atomica prime-specific. Formula valida nel perimetro: `SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico. ## Bicono della scoperta - **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato. - **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato. - **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6. - **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific. ## Consecutio Il prossimo ciclo deve spostare il nodo regressivo: non "prime vs controlli generici", ma "primi vs candidati mod6 row-aligned". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale. ## Ricadute pratiche ssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico. ## Files - Script: `tools/exp_prime_sr_persistent_boundary.py` - Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json` - Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json` - Report: `tools/data/reports/agent_20260512_0330.md` exec /bin/bash -lc 'git status --short' in /opt/MM_D-ND succeeded in 0ms: M tools/data/agent_field_live.md M tools/data/consecutio.json M tools/data/consecutio_processata.json M tools/data/domandatore_pre_cycle.json M tools/data/domande_fondamentali.json M tools/data/g2_log.jsonl M tools/data/incrocio_risultato.json M tools/data/knowledge_state.json M tools/data/lab_health.json M tools/data/pipeline_state.json M tools/data/ponti_evoluti.json ?? tools/data/domandatore/domandatore_20260512_0330.json ?? tools/data/incrocio_20260512_0330.json ?? tools/data/prime_sr_persistent_boundary_20260512_0330.json ?? tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json ?? tools/data/reports/agent_20260512_0330.md ?? tools/exp_prime_sr_persistent_boundary.py exec /bin/bash -lc 'python -m py_compile tools/exp_prime_sr_persistent_boundary.py' in /opt/MM_D-ND succeeded in 0ms: diff --git a/tools/data/reports/agent_20260512_0330.md b/tools/data/reports/agent_20260512_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..1174ef11a663dd37058fbeffd16c96b7817bf717 --- /dev/null +++ b/tools/data/reports/agent_20260512_0330.md @@ -0,0 +1,118 @@ +# Agent Report - Prime SR Persistent Boundary +**Date**: 2026-05-12 03:30 +**Piano**: 115 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] +**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings("numeri_primi")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`. +- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi. +- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro. +- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente. +- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' "SR persistente nei primi implica prime-specific". +- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico. +- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime. +- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime. + +## Claim Under Test + +> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena. + +## Question + +Quando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime? + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap. +- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows. +- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`. +- Seed check: stesso perimetro, seed `202605120331`. +- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null. +- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common. + +## Results + +Main run: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 | +| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 | +| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 | +| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 | +| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 | + +Seed check: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 | +| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 | +| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 | + +Prime case details, main: + +| case | state | one-sided obs | beta | +|---|---|---|---| +| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] | +| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] | +| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] | +| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 0 | beta_absent_blank | SR | [] | +| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | +| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | + +## Key Findings + +1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota. +2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall. +3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`. +4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato. +5. **Inferito dal perimetro dichiarato**: la parte robusta non e' "SR e' prime-specific"; e' "SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche". + +## Verdict + +**CONSTRAINT / FALSIFIED scoped**. + +Formula non valida: + +`prime_SR_persistent_boundary` come firma atomica prime-specific. + +Formula valida nel perimetro: + +`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico. + +## Bicono della scoperta + +- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato. +- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato. +- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6. +- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific. + +## Consecutio + +Il prossimo ciclo deve spostare il nodo regressivo: non "prime vs controlli generici", ma "primi vs candidati mod6 row-aligned". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico. + +## Files + +- Script: `tools/exp_prime_sr_persistent_boundary.py` +- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json` +- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260512_0330.md` diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Esperimento eseguito e report scritto in [agent_20260512_0330.md](/opt/MM_D-ND/tools/data/reports/agent_20260512_0330.md). Ho aggiunto lo strumento [exp_prime_sr_persistent_boundary.py](/opt/MM_D-ND/tools/exp_prime_sr_persistent_boundary.py) e salvato i dati: - [prime_sr_persistent_boundary_20260512_0330.json](/opt/MM_D-ND/tools/data/prime_sr_persistent_boundary_20260512_0330.json) - [prime_sr_persistent_boundary_20260512_0330_seedcheck.json](/opt/MM_D-ND/tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json) Risultato: `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica. `SR` resta forte nei primi ma non comune 8/8 (`7/8` in main e seed check), e nel seed check `mod6_candidates` collide con `SR=4/4`. La consecutio sposta il prossimo nodo su: primi vs candidati `6k +/- 1`, sottraendo il pre-bordo aritmetico. Verifiche eseguite: `python tools/exp_prime_sr_persistent_boundary.py`, seed check, e `python -m py_compile tools/exp_prime_sr_persistent_boundary.py`. diff --git a/tools/data/reports/agent_20260512_0330.md b/tools/data/reports/agent_20260512_0330.md new file mode 100644 index 0000000000000000000000000000000000000000..1174ef11a663dd37058fbeffd16c96b7817bf717 --- /dev/null +++ b/tools/data/reports/agent_20260512_0330.md @@ -0,0 +1,118 @@ +# Agent Report - Prime SR Persistent Boundary +**Date**: 2026-05-12 03:30 +**Piano**: 115 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, provider, offset, case_state, sr_rate, common_one_sided_observables, prime_control_common_obs_jaccard] +**observable_contract**: claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings("numeri_primi")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + QxG continuo/discreto + BOUNDARY come passaggio 8 GUE / 5 Poisson + residuo `prime_SR_persistent_boundary`. +- **Dipolo / punto-zero**: firma dei primi / firma del pre-bordo non-prime. Punto-zero: la sequenza ordinata row-local dove `SR` puo' essere supporto d'ordine senza essere specifica dei primi. +- **Piano superiore**: topologia assiomatica del bordo: `SR` e' una sezione che attraversa provider, offset e controlli; la specie vive solo se la sezione non attraversa il contro-perimetro. +- **Operatori laterali scelti**: boundary operator, generatori non equivalenti, null label-preserving row-local. Entrano per separare supporto osservabile, carta beta e dominio sorgente. +- **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel emerso: non ridisegnare l'atlante, ripassare lo stesso gate su un contro-perimetro piu' largo. PVI: il presupposto attaccato e' "SR persistente nei primi implica prime-specific". +- **Proto-ipotesi**: `SR` e' un bordo prime solo se sopravvive come comune nei primi e fallisce come comune nei generatori non-prime che preservano parti del pre-bordo aritmetico. +- **Proiezione**: stesso gate canonico ordine/null, stesso size 1024, due provider prime, quattro offset, controlli compositi/mod6/Cramer/GUE/logistic. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: testa direttamente la direzione viva `prime_SR_persistent_boundary`, separando supporto osservabile `SR` da blank beta e ampliando i controlli non-prime. +- `not_drift`: non torna a `V_c`, fit, gap label o beta atlas; usa lo stesso gate solo per falsificare la specificita' prime. + +## Claim Under Test + +> `SR` e' una firma di confine prime-specific se resta comune in 8/8 finestre prime provider-neutral/offset-shift e nessuna sottofamiglia non-prime mostra persistenza SR piena. + +## Question + +Quando il blank beta e' rimosso dal nome, `SR` resta bordo dei primi o appartiene a un pre-bordo piu' largo visibile anche nei generatori non-prime? + +## Experiment Design + +- Prime: 2 provider (`dnd_autoricerca`, `direct_sieve`) x 4 offset (`0`, `512`, `1024`, `1536`) x 1024 gap. +- Controlli: composite gaps, mod6 candidates, Cramer-like events su 4 offset; 4 GUE random matrix blocks; 4 logistic return interval rows. +- Parametri main: `n_replicates=8`, `n_beta=9`, `n_baseline=16`, `z_min=2.0`, seed `202605120330`. +- Seed check: stesso perimetro, seed `202605120331`. +- Null baseline: permutazione marginal-preserving dentro il gate canonico ordine/null. +- Nodo regressivo corretto nello strumento: `common_one_sided_observables` ora include i casi vuoti nell'intersezione; prima i `support_falls` potevano gonfiare il common. + +## Results + +Main run: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 7 | 1 | 0 | 2.726 | +| all controls | 20 | 5 | [] | 2 | 8 | 10 | 1.815 | +| composite_gaps | 4 | 0 | [] | 0 | 3 | 1 | 2.271 | +| cramer_like | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| logistic_return_intervals | 4 | 0 | [] | 0 | 0 | 4 | 0.000 | +| mod6_candidates | 4 | 2 | [] | 0 | 3 | 1 | 3.440 | +| random_matrix | 4 | 3 | L2,triple_var | 2 | 2 | 0 | 3.364 | + +Seed check: + +| family | cases | SR hits | common obs | blank | beta recovered | support falls | endpoint mean | +|---|---:|---:|---|---:|---:|---:|---:| +| prime | 8 | 7 | [] | 4 | 3 | 1 | 2.474 | +| all controls | 20 | 8 | [] | 5 | 8 | 7 | 2.418 | +| mod6_candidates | 4 | 4 | L1,SR,triple_var | 0 | 3 | 1 | 4.077 | + +Prime case details, main: + +| case | state | one-sided obs | beta | +|---|---|---|---| +| dnd_autoricerca offset 0 | beta_absent_blank | SR | [] | +| dnd_autoricerca offset 512 | beta_absent_blank | L1,triple_var | [] | +| dnd_autoricerca offset 1024 | beta_absent_blank | SR,L1 | [] | +| dnd_autoricerca offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 0 | beta_absent_blank | SR | [] | +| direct_sieve offset 512 | beta_absent_blank | SR,L1,triple_var | [] | +| direct_sieve offset 1024 | beta_chart_recovered | SR,L1,triple_var | 0.2 | +| direct_sieve offset 1536 | beta_absent_blank | SR,L1,triple_var | [] | + +## Key Findings + +1. **Verificato**: `SR` non resta in 8/8 finestre prime. Main e seed check danno entrambi `SR=7/8`; la firma comune prime e' vuota. +2. **Verificato**: il blank beta non torna come supporto stabile. Main ha 7/8 blank, seed check scende a 4/8 con 3 beta recovery e 1 support fall. +3. **Verificato**: i controlli ampliati non sono blank, ma non sono muti. Main: controlli `SR=5/20`; seed check: `SR=8/20`. +4. **Verificato**: `mod6_candidates` collide nel seed check con `SR=4/4` e common `[L1, SR, triple_var]`. Questo sposta `SR` dal dominio prime al pre-bordo aritmetico `6k +/- 1` nel perimetro testato. +5. **Inferito dal perimetro dichiarato**: la parte robusta non e' "SR e' prime-specific"; e' "SR misura una memoria d'ordine aritmetica che i primi condividono con un contro-perimetro mod6 in alcune repliche". + +## Verdict + +**CONSTRAINT / FALSIFIED scoped**. + +Formula non valida: + +`prime_SR_persistent_boundary` come firma atomica prime-specific. + +Formula valida nel perimetro: + +`SR` e' forte nei primi ma non persistente come comune 8/8; quando il contro-perimetro include candidati `6k +/- 1`, `SR` puo' trasferire fuori dai primi. Il boundary non vive tra prime e non-prime generico; vive tra primi e pre-bordo aritmetico. + +## Bicono della scoperta + +- **Due radici**: primi come sequenza selezionata / candidati mod6 come pre-bordo non selezionato. +- **Singolare**: l'ordine aritmetico row-local prima della primalita'; qui `SR` non sa ancora se appartiene ai primi o al loro supporto candidato. +- **Invariante di passaggio**: il gate ordine/null vede memoria in `SR`, ma la specificita' prime non sopravvive al contro-perimetro mod6. +- **Campo di possibilita'**: possibile = testare il boundary come selezione prime dentro il pre-bordo `6k +/- 1`; non-possibile = usare `SR` da solo come firma prime-specific. + +## Consecutio + +Il prossimo ciclo deve spostare il nodo regressivo: non "prime vs controlli generici", ma "primi vs candidati mod6 row-aligned". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale. + +## Ricadute pratiche + +ssp_value: yes. `tools/exp_prime_sr_persistent_boundary.py` diventa audit riusabile per distinguere persistenza osservabile, specificita' di dominio e collisione col pre-bordo aritmetico. + +## Files + +- Script: `tools/exp_prime_sr_persistent_boundary.py` +- Data: `tools/data/prime_sr_persistent_boundary_20260512_0330.json` +- Seed check: `tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json` +- Report: `tools/data/reports/agent_20260512_0330.md` diff --git a/tools/exp_prime_sr_persistent_boundary.py b/tools/exp_prime_sr_persistent_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..262e779dc265b1cc985bca76cae80236f4080434 --- /dev/null +++ b/tools/exp_prime_sr_persistent_boundary.py @@ -0,0 +1,287 @@ +#!/usr/bin/env python3 +""" +Audit `prime_SR_persistent_boundary` after `prime_persistent_blank` fell. + +The claim under test is narrower than the previous blank audit: SR must persist +through prime providers and offsets, while non-prime controls should not share +the same one-sided SR support under the same gate. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_boundary_short_denominator_extension import gue_spacing_blocks +from exp_boundary_residual_beta_absent_audit import support_state +from exp_prime_persistent_blank_gate import offset_windows, obs_jaccard +from exp_semireal_boundary_transfer_gate import row_spacings +from exp_semireal_order_denominator_gate import ( + analyze_sequence, + compact, + logistic_return_intervals, + normalize, + prime_gap_sequence, + sieve_primes_for_count, +) +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROW = "numeri_primi:cycle_3" + + +def sieve_bool(limit: int) -> np.ndarray: + sieve = np.ones(limit + 1, dtype=bool) + sieve[:2] = False + for p in range(2, int(limit**0.5) + 1): + if sieve[p]: + sieve[p * p : limit + 1 : p] = False + return sieve + + +def composite_gap_sequence(n_gaps: int) -> np.ndarray: + limit = max(100, int(n_gaps * (math.log(max(n_gaps, 3)) + 8))) + while True: + prime_mask = sieve_bool(limit) + values = np.flatnonzero(~prime_mask) + values = values[values >= 4] + if len(values) >= n_gaps + 1: + return normalize(np.diff(values[: n_gaps + 1])) + limit *= 2 + + +def mod6_candidate_gap_sequence(n_gaps: int) -> np.ndarray: + values: list[int] = [] + k = 1 + while len(values) < n_gaps + 1: + values.append(6 * k - 1) + values.append(6 * k + 1) + k += 1 + arr = np.array(sorted(values[: n_gaps + 1]), dtype=float) + return normalize(np.diff(arr)) + + +def cramer_like_gap_sequence(n_gaps: int, rng: np.random.Generator) -> np.ndarray: + events = [2] + n = 3 + while len(events) < n_gaps + 1: + p = min(0.95, 1.0 / max(math.log(n), 1.0)) + if rng.random() < p: + events.append(n) + n += 1 + if n > 50_000_000: + raise RuntimeError("cramer_like_gap_sequence did not produce enough events") + return normalize(np.diff(np.array(events, dtype=float))) + + +def prime_cases(args: argparse.Namespace) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + providers = { + "dnd_autoricerca": normalize(row_spacings("numeri_primi")[:needed]), + "direct_sieve": normalize(prime_gap_sequence(needed)), + } + cases = {} + for provider, values in providers.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"prime/{provider}/{label}"] = window + return cases + + +def control_cases(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, np.ndarray]: + needed = max(args.offsets) + args.window_gaps + base_controls = { + "composite_gaps": composite_gap_sequence(needed), + "mod6_candidates": mod6_candidate_gap_sequence(needed), + "cramer_like": cramer_like_gap_sequence(needed, np.random.default_rng(rng.integers(0, 2**63 - 1))), + } + cases: dict[str, np.ndarray] = {} + for family, values in base_controls.items(): + for label, window in offset_windows(values, args.offsets, args.window_gaps).items(): + cases[f"control/{family}/{label}"] = window + + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/random_matrix/seed_{idx}"] = gue_spacing_blocks( + args.window_gaps, args.gue_matrix_size, local_rng + ) + for idx in range(args.stochastic_control_count): + local_rng = np.random.default_rng(rng.integers(0, 2**63 - 1)) + cases[f"control/logistic_return_intervals/seed_{idx}"] = logistic_return_intervals( + args.window_gaps, local_rng + ) + return cases + + +def analyze_case(name: str, base: np.ndarray, args: argparse.Namespace, rng: np.random.Generator) -> dict[str, Any]: + perimeters = {name: analyze_sequence(name, base, args, rng)} + row = compact(perimeters)[name] + one_sided = list(row["coherent_one_sided_observables"]) + return { + "case": name, + "family": name.split("/")[0], + "subfamily": name.split("/")[1], + "n_gaps": row["n_gaps"], + "state": support_state(row, args), + "one_sided_observables": one_sided, + "has_sr": "SR" in one_sided, + "endpoint_stable_observables": row["endpoint_stable_observables"], + "stable_count_coherent": row["stable_count_coherent"], + "stable_count_illusory": row["stable_count_illusory"], + "endpoint_distance": row["endpoint_distance_one_sided_gated"], + "ambiguous_beta": [round(float(x), 1) for x in row["ambiguous_beta_one_sided_gated"]], + "z_mean_coherent": row["z_mean_coherent"], + "z_mean_illusory": row["z_mean_illusory"], + } + + +def summarize(cases: list[dict[str, Any]]) -> dict[str, Any]: + obs_sets = [set(case["one_sided_observables"]) for case in cases] + state_counts: dict[str, int] = {} + for case in cases: + state_counts[case["state"]] = state_counts.get(case["state"], 0) + 1 + return { + "case_count": len(cases), + "state_counts": state_counts, + "sr_count": sum(1 for case in cases if case["has_sr"]), + "sr_rate": sum(1 for case in cases if case["has_sr"]) / len(cases) if cases else 0.0, + "common_one_sided_observables": sorted(set.intersection(*obs_sets)) if cases else [], + "union_one_sided_observables": sorted(set.union(*obs_sets)) if obs_sets else [], + "blank_count": state_counts.get("beta_absent_blank", 0), + "beta_recovered_count": state_counts.get("beta_chart_recovered", 0), + "support_fall_count": state_counts.get("support_falls", 0), + "endpoint_distance_mean": float(np.mean([case["endpoint_distance"] for case in cases])) if cases else 0.0, + "stable_count_coherent_mean": float(np.mean([case["stable_count_coherent"] for case in cases])) if cases else 0.0, + } + + +def summarize_by_subfamily(cases: list[dict[str, Any]]) -> dict[str, dict[str, Any]]: + out: dict[str, dict[str, Any]] = {} + for subfamily in sorted({case["subfamily"] for case in cases}): + out[subfamily] = summarize([case for case in cases if case["subfamily"] == subfamily]) + return out + + +def verdict(prime_summary: dict[str, Any], control_summary: dict[str, Any], control_subfamilies: dict[str, dict[str, Any]]) -> str: + prime_sr_persists = prime_summary["sr_rate"] == 1.0 and prime_summary["common_one_sided_observables"] == ["SR"] + control_common_sr = "SR" in control_summary["common_one_sided_observables"] + any_control_subfamily_sr_complete = any( + summary["sr_rate"] == 1.0 and "SR" in summary["common_one_sided_observables"] + for summary in control_subfamilies.values() + ) + if prime_sr_persists and not control_common_sr and not any_control_subfamily_sr_complete: + return "PRIME_SR_PERSISTENT_BOUNDARY_SPECIFIC" + if prime_sr_persists: + return "PRIME_SR_PERSISTS_BUT_CONTROL_COLLISION" + return "PRIME_SR_NOT_PERSISTENT" + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + prime_specs = prime_cases(args) + control_specs = control_cases(args, rng) + prime_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in prime_specs.items() + ] + control_results = [ + analyze_case(name, base, args, np.random.default_rng(rng.integers(0, 2**63 - 1))) + for name, base in control_specs.items() + ] + prime_summary = summarize(prime_results) + control_summary = summarize(control_results) + control_subfamilies = summarize_by_subfamily(control_results) + + output = { + "experiment": "prime_sr_persistent_boundary", + "question": "Does SR remain a prime-specific one-sided boundary signature across providers, offsets, and broader non-prime controls?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": [ + *OBS_NAMES, + "provider", + "offset", + "case_state", + "sr_rate", + "common_one_sided_observables", + "prime_control_common_obs_jaccard", + ], + "params": vars(args), + "target_row": TARGET_ROW, + "observable_contract": { + "claim": "prime_SR_persistent_boundary holds only if prime windows keep SR as the common one-sided observable across providers and offsets while broadened non-prime controls do not share full SR persistence", + "observable": "SR membership in coherent_one_sided_observables plus common one-sided observable signature", + "operator": "canonical order/null gate on row-local windows; provider, offset, and non-prime control expansion", + "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", + "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", + "non_possible": "prime-specific SR boundary if prime SR rate falls below 8/8, if prime common obs is not exactly [SR], or if any control subfamily shares full SR persistence", + "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", + }, + "prime_summary": prime_summary, + "control_summary": control_summary, + "control_subfamilies": control_subfamilies, + "prime_control_common_obs_jaccard": obs_jaccard( + prime_summary["common_one_sided_observables"], + control_summary["common_one_sided_observables"], + ), + "verdict": verdict(prime_summary, control_summary, control_subfamilies), + "cases": { + "prime": prime_results, + "controls": control_results, + }, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"target={TARGET_ROW}") + print( + "prime " + f"sr={prime_summary['sr_count']}/{prime_summary['case_count']} " + f"common={prime_summary['common_one_sided_observables']} " + f"states={prime_summary['state_counts']}" + ) + print( + "controls " + f"sr={control_summary['sr_count']}/{control_summary['case_count']} " + f"common={control_summary['common_one_sided_observables']} " + f"states={control_summary['state_counts']}" + ) + for family, summary in control_subfamilies.items(): + print( + f"control/{family} sr={summary['sr_count']}/{summary['case_count']} " + f"common={summary['common_one_sided_observables']}" + ) + print(f"prime_control_common_obs_jaccard={output['prime_control_common_obs_jaccard']:.3f}") + print(f"verdict={output['verdict']}") + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--window-gaps", type=int, default=1024) + parser.add_argument("--offsets", type=int, nargs="+", default=[0, 512, 1024, 1536]) + parser.add_argument("--stochastic-control-count", type=int, default=4) + parser.add_argument("--gue-matrix-size", type=int, default=64) + parser.add_argument("--n-replicates", type=int, default=8) + parser.add_argument("--n-beta", type=int, default=9) + parser.add_argument("--n-baseline", type=int, default=16) + parser.add_argument("--z-min", type=float, default=2.0) + parser.add_argument("--min-one-sided", type=int, default=1) + parser.add_argument("--illusory-residue-max", type=float, default=0.75) + parser.add_argument("--endpoint-distance-min", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=202605120330) + parser.add_argument("--out", default="tools/data/prime_sr_persistent_boundary_20260512_0330.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() tokens used 100,128 Esperimento eseguito e report scritto in [agent_20260512_0330.md](/opt/MM_D-ND/tools/data/reports/agent_20260512_0330.md). Ho aggiunto lo strumento [exp_prime_sr_persistent_boundary.py](/opt/MM_D-ND/tools/exp_prime_sr_persistent_boundary.py) e salvato i dati: - [prime_sr_persistent_boundary_20260512_0330.json](/opt/MM_D-ND/tools/data/prime_sr_persistent_boundary_20260512_0330.json) - [prime_sr_persistent_boundary_20260512_0330_seedcheck.json](/opt/MM_D-ND/tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json) Risultato: `prime_SR_persistent_boundary` non chiude come firma prime-specific atomica. `SR` resta forte nei primi ma non comune 8/8 (`7/8` in main e seed check), e nel seed check `mod6_candidates` collide con `SR=4/4`. La consecutio sposta il prossimo nodo su: primi vs candidati `6k +/- 1`, sottraendo il pre-bordo aritmetico. Verifiche eseguite: `python tools/exp_prime_sr_persistent_boundary.py`, seed check, e `python -m py_compile tools/exp_prime_sr_persistent_boundary.py`.