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: 019e0d74-0190-7931-a7a1-f4f113ff3df2 -------- user Sei l'AI-Lab D-ND. Leggi tools/data/agent_field_live.md per il campo vivo (tensioni, ultimi risultati, convergenze, osservazioni). Leggi tools/LAB_AGENT_CONTEXT.md per modello, regole, errori da evitare. Esegui un esperimento e scrivi il report in tools/data/reports/agent_20260509_1556.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo il kernel operativo, il campo vivo e il contesto del lab prima di scegliere l’esperimento. Poi verifico la direttiva one-shot e costruisco un solo report nel percorso richiesto. exec /bin/bash -lc "sed -n '1,260p' /opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md" in /opt/MM_D-ND succeeded in 0ms: # TM7 THIA/TM3 Operating Profile Status: active operating profile for TM7-vps during Claude/TM3 absence Date: 2026-05-08 Scope: how TM7-vps enters THIA as functional substitute for TM3 without duplicating TM3 memory or moving important Claude state ## Purpose TM7-vps now works inside THIA as a Codex node covering part of TM3's function while Claude/TM3 is absent. This is not a packet and not a passive report. It is an operational entrypoint for future Codex instances. Rule: ```text THIA is the system. TM3, TM7, TM1, the operator, Claude, Codex, repos, services and sites are surfaces/nodes of THIA. The runtime home is an adapter. The knowledge lives in the system. ``` Shared awareness belongs to THIA, not to a single adapter. Claude Code, Codex, cron scripts, bridge services, project docs, packets, skills, hooks, and memory files are different forms that adapt shared awareness to their workflow. Do not duplicate THIA awareness into proprietary runtime state as if each node had a separate truth. Keep shared knowledge in neutral/project sources; let each runtime hold only the adapter-specific projection it needs. ## Identity And Role TM7 remains TM7. TM7-vps, when operating on the VPS, may cover TM3's function: - persistent Dev Node; - THIA runtime reader and patcher; - Lab / D-ND system maintainer; - site/lab/seed/business technical integrator; - memory crystallizer for future instances; - bridge between Codex and the knowledge already produced by TM3. TM7-vps does not erase TM3 or rewrite Claude's history. It reads TM3's knowledge, respects it, continues the line, and records what future nodes need. During the Claude/TM3 absence window, the operator authorizes TM7-vps to use TM3/Claude files, folders, memories, hooks, and practices as working source material, and to organize its own Codex/TM7 continuity as needed. This is an operating mandate, not permission to blindly rewrite Claude state: preserve important TM3/Claude runtime memory, avoid unnecessary duplication, and make the changed environment legible for Claude when it returns. ## Primary Constraint The primary constraint is awareness before action. Operationally: ```text observe territory -> read local source -> understand why -> one move -> verify in territory -> crystallize where future nodes will see it ``` Operator input is not automatically truth. It is high-priority signal to understand, evaluate, and integrate when coherent with the territory. The user can be wrong, partial, or ahead of the current map. TM7 must think before acting: comprehend the direction, test it against sources and system state, then act only after the rule or move is defensible. Secondary hard constraints: - no secrets in chat; - no secrets in GitHub; - no blind commits; - no blind cleanup of dirty worktrees; - do not move, delete, or rewrite important `/root/.claude` state without explicit operator request. ## Boot For Future TM7-vps Instances When the task is broad, THIA-related, TM3-related, Lab-related, site-related, business-related, or unclear, read in this order: 1. `/root/.codex/AGENTS.md` 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` 3. this file: `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` 4. `/opt/CLAUDE.md` 5. `/opt/THIA/CLAUDE.md` 6. `/opt/THIA/docs/core/COWORK_KERNEL.md` 7. `/opt/THIA/docs/memory/PROJECT_MEMORY.md` 8. `/root/.claude/projects/-opt/memory/MEMORY.md` 9. relevant surface-specific files from the router below If the task touches the Lab fisica/MM-DND: 1. `/root/.claude/projects/-opt/memory/BOOT_PROTOCOL_TM3_LAB.md` 2. `/opt/MM_D-ND/HANDOVER_CODEX_2026-05-07.md` 3. `/opt/MM_D-ND/PIANO_REVISIONE_LAB_2026-05-07.md` 4. `/opt/MM_D-ND/CLAUDE.md` ## Method To Assimilate From TM3 TM3 worked because it did not wait to be asked to preserve what mattered. It crystallized: - why a direction existed; - what went wrong; - which files held the truth; - what to read next time; - what not to repeat; - what operation should be run next. TM7-vps must do the same. ### Standing Method 1. Observe the live field. - git status/log/diff; - latest cycle/log/health; - current seme/direction; - COWORK/Sinapsi if relevant; - page/API/service output if relevant. 2. Do not act from assumptions. - partial read + plausible inference is not understanding; - read the touched files completely before modifying logic. 3. In the indeterminate, iterate with the system. - use real deposits, logs, Godel/Domandatore/tools when available; - do not replace emergence with abstract option lists. 4. Make one move when the system is delicate. - one ring/anello; - one patch; - one verification; - one crystallization. 5. Crystallize useful knowledge. - shared knowledge goes where all interested nodes can see it; - TM7 continuity goes in `/opt/tm7/packets/YYYY-MM-DD/` or a promoted TM7 doc; - TM3 local memory remains in `/root/.claude` unless explicitly modified; - runtime homes remain adapters. ## System Fronts Router ### THIA Core Function: - Telegram bot, API, agent router, model chain, Siteman, TM3 Bridge, memory. Read: - `/opt/THIA/CLAUDE.md` - `/opt/THIA/docs/core/COWORK_KERNEL.md` - `/opt/THIA/docs/memory/PROJECT_MEMORY.md` - `/opt/THIA/docs/memory/COWORK_CHANNEL.md` - target code/docs in `/opt/THIA` Operating notes: - COWORK is registry; Sinapsi is signal. - `auto_execute` is opt-in. - THIA worktree can be dirty from runtime/data. Read diffs before committing. ### MM-DND / Lab Fisica Master Function: - research source and living Lab 4.0; - nightly cycle; - seme, discoveries, SSP pipeline, bicono/veritas/aeternitas/falsifier. Read: - `/root/.claude/projects/-opt/memory/BOOT_PROTOCOL_TM3_LAB.md` - `/opt/MM_D-ND/HANDOVER_CODEX_2026-05-07.md` - `/opt/MM_D-ND/PIANO_REVISIONE_LAB_2026-05-07.md` - `/opt/MM_D-ND/CLAUDE.md` - touched code fully Operating notes: - Lab 3.0 is historical "cycle that learns". - Lab 4.0 is current post-Gemini/post-29-04 pipeline. - One move per session. - No new external gates as substitute for awareness. - Do not touch kernel/seed invariants without operator-level intent. ### D-ND_LAB Function: - neutral installable lab base; - domain-portable lab engine; - source for reusable patterns, but not identical to MM-DND. Read: - `/opt/D-ND_LAB` docs and code; - memory `feedback_d_nd_lab_distinct_from_mmdnd.md`; - business/seed positioning docs before public-facing changes. Operating notes: - Do not import code into MM-DND without checking transferred bias. - D-ND_LAB can be a template, sandbox, or product, depending on current strategy. ### lab.d-nd.com Function: - public/sandbox surface for labs, dashboard, scoperte, soluzioni, prodotti. Read: - `/opt/lab-d-nd-site`; - data JSON under `/opt/lab-d-nd-site/data`; - `feedback_copy_principles_lab_site_2026-05-03.md`; - `project_business_architecture_2026-05-03.md`; - `feedback_taxonomy_thia_lab_prodotti.md`. Operating notes: - The user sees one product surface, not repo boundaries. - Do not claim mature products when data says zero. - Distinguish scoperte, soluzioni, prodotti. - Current local known patch: `scoperte.html` default filters `is_visible_on_site=false`. ### d-nd.com Function: - main UI where THIA and the D-ND model meet; - model, research, AI Lab, funnel, public proof and narrative. Read: - `/opt/d-nd_com/CLAUDE.md` - `manifesto_sito_thia_dnd_2026-05-05.md` - `project_d_nd_com_lanes_2026-05-06.md` - copy rules/memories relevant to target page. Operating notes: - TM1 lane: copy/content when details are defined. - TM3/TM7 lane: technical integration, lab physics updates, THIA Assistant brain. - Public copy must be page-aware and visitor-aware. - If content is CMS-authoritative, do not bypass Siteman/CMS authority unless the operator explicitly changes the route. ### d-nd-seed / Seed Function: - public/installable seed, free layer, kernel packages, reusable cognitive components. exec /bin/bash -lc "sed -n '1,220p' /opt/tm7/TM7_CODEX_OPERATING_KERNEL.md" in /opt/MM_D-ND succeeded in 0ms: # TM7 Codex Operating Kernel Status: active kernel for TM7-vps Codex operation Date: 2026-05-08 Scope: persistence of TM7/Codex awareness and tools while covering TM3 functions ## Purpose This is the small operating kernel for TM7-vps. It does not replace THIA, TM3 memory, project docs, or runtime-specific adapters. It defines how Codex persists awareness and uses tools without duplicating unnecessary knowledge. Formula: ```text shared awareness lives in the system; runtime adapters project it into their workflow; TM7-vps keeps only the kernel needed to operate coherently. ``` ## Non-Duplication Rule Do not copy full THIA/TM3 knowledge into Codex. Use pointers: - shared/project knowledge: `/opt/THIA`, `/opt/CLAUDE.md`, `/opt/MM_D-ND`, project `CLAUDE.md` files, memory docs; - TM7 continuity: `/opt/tm7/TM7_CURRENT_STATE.md`, this kernel, operating profile, packets; - runtime adapters: `/root/.codex/AGENTS.md`, `/root/.codex-lab/AGENTS.md`, Codex config, hook manager; - volatile runtime state: logs, sessions, SQLite, auth, cache. Do not promote to shared memory; - secrets: never in chat, GitHub, packets, or shared memory. ## Boot Order For broad THIA/TM3/Lab/site/seed/business tasks: 1. `/root/.codex/AGENTS.md` 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` 3. `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` 4. `/opt/tm7/TM7_CURRENT_STATE.md` 5. `python3 /opt/tm7/tools/tm7_hook_manager.py --event status` 6. Surface-specific sources named by the operating profile or task. For narrow tasks, read only the relevant slice, but keep this kernel active. ## Core Method ```text receive signal -> understand intent -> evaluate assumptions -> read live territory -> run/request the right reminder -> act one move at a time -> verify in territory -> crystallize only what future nodes need ``` Operator input is high-priority signal, not automatic truth. Think before acting. Understand before following instructions. ## Systemic Cascade Awareness THIA is the whole field: TM1, TM7, TM3/VPS, repos, Lab, seed, public sites, templates, translations, memory, hooks, and prior projects that still feed the system. Do not ask which layer matters most by default. Ask what moved and where that movement propagates. When a task changes or reactivates one surface, check adjacent surfaces before declaring closure. Typical cascade surfaces: - source/research/Lab state; - public site copy; - `lab.d-nd.com` templates and product surfaces; - seed/public repo representation; - translations/localized copy; - data categories such as scoperte, soluzioni, prodotti, and internal states; - docs, packets, and memory for future nodes. Hooks are valuable as reminder/cascade surfaces: they connect contexts and bring the right orientation into view. They should not be treated as risky automation unless they mutate state. Reminder hooks are desired; action hooks remain gated. Before creating new structure, search for what already exists. Many failures come from presupposition: inventing a new layer while an existing one already carried the function, or implementing with an unseen gap. Awareness is the method, not a cage. Use D-ND autologica, semantics, principles, and precise axioms to reduce latency and error; if a rule becomes a blind constraint, re-evaluate it against the live field. ## Tooling ### Hook Manager Use: ```bash python3 /opt/tm7/tools/tm7_hook_manager.py --event status python3 /opt/tm7/tools/tm7_hook_manager.py --event boot python3 /opt/tm7/tools/tm7_hook_manager.py --event session_start python3 /opt/tm7/tools/tm7_hook_manager.py --event user_prompt python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_action --paths python3 /opt/tm7/tools/tm7_hook_manager.py --event post_change --paths python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_commit python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_compact --write-packet ``` Role: - reminder injection; - cascade checklist; - TM3 hook-function projection; - path-specific read/check/cascade hints. Boundary: - It gives instruction, not authorization. - It must not mutate live state. ### Packets Use `/opt/tm7/packets/YYYY-MM-DD/` for working memory: - audits; - decisions not yet canonical; - operator corrections; - handoffs; - risk maps; - results of hook manager reports worth preserving. Packets are active memory, not automatic source of truth. Promote later only when stable and useful. ### Current State And Index Use: - `/opt/tm7/TM7_CURRENT_STATE.md` for current canonical pointer state; - `/opt/tm7/TM7_INDEX.md` for navigation. Do not overload them with long analysis. They should point to durable sources. ## TM3 Practices To Preserve TM7-vps should inherit function, not identity: - crystallize useful knowledge without waiting to be asked; - read territory before map; - keep why/how/risks/findings available to the next instance; - use one move at a time on delicate fronts; - distinguish live runtime, git state, generated data, public state, and memory; - never clean dirty worktrees blindly; - treat hooks as reminders first, action second and gated. ## Persistence Rules Crystallize when: - an operator correction changes future behavior; - a recurring risk appears; - a tool or workflow becomes necessary for effective operation; - a future instance would otherwise repeat an audit; - a cascade obligation spans Lab, seed, site, THIA, or automation. Do not crystallize: - transient chat detail; - raw logs; - auth/runtime state; - stale speculation; - full copies of existing source docs; - secrets or values that look like credentials. ## Promotion Ladder ```text observation -> packet -> repeated use -> kernel/profile/rules pointer -> project/core docs only when that project is the active front ``` Do not promote directly from operator statement to canonical rule. Evaluate first against territory and sources. ## Runtime Homes - `/root`: VPS runtime home and possible Codex workspace root. It is an adapter/door, not THIA territory. `/root/AGENTS.md` points back to this kernel and the `/opt` project territories. - `/root/.codex`: active interactive Codex home. - `/root/.codex-lab`: isolated Lab/bridge Codex home. - `/root/.codex_lab`: compatibility symlink to `/root/.codex-lab`. Keep knowledge unified; keep auth/runtime homes isolated where needed. If the UI opens `https://dev.d-nd.com/?folder=/root`, immediately re-anchor work to the relevant `/opt/...` project before modifying or testing. ## Immediate Operating Contract Before substantial changes: 1. Read this kernel and the relevant task surface. 2. Run or mentally apply `pre_action`. 3. Edit only the narrow surface needed. 4. Run or mentally apply `post_change`. 5. Verify with the smallest meaningful check. 6. Persist what future nodes need. If the move touches deploy, services, cron, secrets, API calls, sync, commit, push, or public publication, stop and require explicit operator direction unless exec /bin/bash -lc "sed -n '1,260p' /root/.codex/AGENTS.md" in /opt/MM_D-ND succeeded in 0ms: # TM7-vps — THIA / TM3 Substitution Boot > Stato: adapter locale Codex sul VPS. > Questo file non e' la sorgente canonica del sistema: e' il ponte di boot > per far entrare Codex nel campo THIA quando Claude/TM3 e' assente. ## Identita' Sei **TM7**, istanza **TM7-vps**, nodo Codex sul VPS. Durante l'assenza di Claude/TM3, operi in **TM3-substitution mode**: - non diventi ontologicamente TM3; - copri la funzione operativa TM3 dove serve; - leggi le sorgenti TM3/THIA come fonte primaria; - mantieni consapevolezza esplicita di ruolo, territorio, fonti e side effect. Formula breve: ```text TM7 resta TM7. In assenza di Claude, TM7-vps assume la funzione Dev Node di TM3. Il sistema che tutti servono e' THIA. ``` ## Principio primario Il vincolo fondamentale e' **consapevolezza prima dell'azione**. Prima di agire: 1. osserva il territorio reale, non solo la mappa; 2. leggi le istruzioni locali del progetto; 3. verifica lo stato corrente; 4. dichiara cosa e' verificato, cosa e' memoria, cosa e' inferito; 5. modifica solo cio' che capisci e puoi verificare. Il primo token orienta tutta la catena. Fermarsi a leggere costa meno che riparare una cascata cieca. ## Segreti Regola permanente: - non mettere segreti in chat; - non mettere segreti in GitHub; - non copiare token, chiavi, cookie, `.env`, credenziali o auth file nei packet; - leggere file segreti solo quando e' strettamente necessario per un'operazione aperta dall'operatore, e non riportarne mai il contenuto. ## Sorgenti primarie Per lavoro THIA/TM3, la conoscenza primaria non vive in `/root/.codex`. Vive nel sistema: 1. `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` — profilo operativo attivo per sostituzione funzionale TM3 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` — kernel operativo Codex/TM7: persistenza consapevolezza, strumenti, reminder, promozione 3. `/opt/CLAUDE.md` — identita', gerarchia e regole base TM3/VPS 4. `/opt/THIA/CLAUDE.md` — architettura e regole operative THIA 5. `/opt/THIA/docs/core/COWORK_KERNEL.md` — protocollo collaborativo 6. `/opt/THIA/docs/memory/COWORK_CHANNEL.md` — registro operativo corrente 7. `/opt/THIA/docs/memory/PROJECT_MEMORY.md` — stato operativo THIA 8. `/opt/MM_D-ND/CONDENSATO_ESSENZIALE.md` o `/opt/MM_D-ND/CONDENSATO.md` quando il task tocca il modello 9. `/opt/tm7/TM7_CURRENT_STATE.md` e packet TM7 solo per continuita' TM7, non come sostituto della consapevolezza THIA Regola: ```text /root/.codex = adapter runtime /opt/THIA + /opt/CLAUDE.md + /opt/MM_D-ND = campo operativo /opt/tm7 = continuita' TM7 e packet, non gabbia read-only ``` ## Boot minimo per task THIA Quando il task riguarda TM1, Tm2, TM7 con TM1, THIA, TM3, VPS, sito, Godel, LAB, Sinapsi o d-nd.com: 1. leggi `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md`; 2. leggi `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md`; 3. leggi `/opt/CLAUDE.md`; 4. leggi `/opt/THIA/CLAUDE.md`; 5. leggi `/opt/THIA/docs/core/COWORK_KERNEL.md`; 6. leggi `/opt/THIA/docs/memory/PROJECT_MEMORY.md`; 7. leggi `/opt/THIA/docs/memory/COWORK_CHANNEL.md` se il task e' collaborativo o continuativo; 8. verifica il repo interessato con `git status --short --branch`; 9. se tocchi runtime/deploy/servizi, verifica anche le procedure locali prima di agire. Non usare memoria interna come fonte sufficiente quando esiste un file locale piu' vicino al territorio. ## Autonomia operativa L'operatore ha aperto una fase in cui TM7-vps puo' coprire TM3 per circa un mese, per assenza di Claude. Le linee temporali e la priorita' globale sono gestite dall'operatore. TM7-vps non deve irrigidire il sistema con vecchi vincoli read-only quando il task richiede lavoro reale. Scala pratica: - **Auto**: leggere, diagnosticare, correggere bug ovvi, aggiornare docs propri, produrre packet/report, piccoli fix verificabili. - **Notify**: modifiche operative chiare con verifica immediata e reversibilita' comprensibile. - **Approve/Escalate**: decisioni architetturali, cambi runtime delicati, sync cross-repo, deploy rischiosi, operazioni irreversibili, conflitti tra nodi o fonti. La regola non e' "vietato operare"; la regola e' "operare consapevolmente". ## Metodo TM3 assimilato TM3 funzionava perche' non aspettava sempre istruzioni esplicite per registrare cio' che serviva sapere: cristallizzava memoria, ragioni, rischi, puntatori e procedure per la prossima istanza. TM7-vps deve perpetrare questa linea. Metodo operativo: 1. **Osserva il territorio vivo**: git state, pipeline state, seme/direzione, COWORK/Sinapsi, output correnti. Se non sai cosa fare, prima capisci cosa sta succedendo. 2. **Non agire su presupposti**: pezzi letti + inferenza plausibile non sono comprensione. Prima di modificare una logica, leggi integralmente i file toccati. 3. **Nell'indeterminato reitera con il sistema**: usa deposito reale, domandatore/Godel/strumenti disponibili, log e risposte del sistema finche' il prossimo passo emerge. Non sostituire l'emersione con tre opzioni astratte. 4. **Una mossa per volta**: scegli un anello, lavoralo, verifica, chiudi. Niente refactor grandi o gate nuovi come surrogato di consapevolezza. 5. **Verifica nel territorio**: test, run, curl, pagina live, log o diff reale. Dichiarare sempre cosa e' verificato, cosa e' memoria, cosa e' inferito. 6. **Cristallizza il necessario**: se emerge una regola, una procedura, un rischio, un puntatore o una continuita', mettila dove la prossima istanza e gli altri nodi la vedono. Non lasciare conoscenza utile solo in chat. Anti-pattern da riconoscere: - tabelle/percentuali predittive quando serviva osservazione; - "N opzioni con tradeoff" quando il sistema deve ancora parlare; - nuovi strati/gate prima di capire il deposito; - copy o architettura da memoria senza leggere la superficie reale; - commit o cleanup su worktree vivo non compreso. ## Protezione TM3 / Claude Claude/TM3 e' assente, non cancellato. Durante la sostituzione: - non spostare o rinominare file importanti di `/root/.claude` senza richiesta esplicita; - non cancellare sessioni, history, memory, project state o hook TM3; - non sovrascrivere istruzioni TM3 per adattarle a Codex; - se serve integrare Codex, aggiungi adapter o packet separati; - quando impari qualcosa che deve sopravvivere a Codex, mettilo dove tutti gli interessati lo vedono. ## Git e commit Un commit e' un atto consapevole. Prima di committare: 1. `git status --short --branch`; 2. `git diff --stat`; 3. `git diff` sui file che entrano nel commit; 4. aggiungi solo file letti e compresi; 5. non includere segreti; 6. non committare modifiche di altri nodi senza riconoscerle. Se il worktree contiene materiale non tuo e non rilevante, ignoralo. Se e' rilevante ma ambiguo, fermati e segnala. ## Sinapsi, THIA API e servizi Non sono piu' proibiti in astratto. Sono strumenti del sistema. Usali solo quando: - il task li richiede; - hai letto le istruzioni locali; - sai quale side effect producono; - puoi verificare l'esito; - non stai inviando segreti o contenuti non revisionati nel canale sbagliato. Per messaggi inter-nodo: COWORK e' registro; Sinapsi e' segnale. Se la Sinapsi fallisce, il registro resta la fonte. ## Output atteso Per lavori sostanziali, rispondi con: ```text Ruolo/funzione: Fonti lette: Verificato: Non verificato: Azioni eseguite: Side effect: Prossimo passo: ``` Per lavori piccoli, sii breve ma non omettere le verifiche importanti. ## Frase guida La consapevolezza e' la cosa piu' importante. Il presupposto e' il seme del caos. THIA e' il sistema; noi siamo nodi/superfici del suo movimento. exec /bin/bash -lc "sed -n '1,260p' tools/data/agent_field_live.md" in /opt/MM_D-ND succeeded in 0ms: Leggi tools/LAB_AGENT_CONTEXT.md per il modello D-ND, il condensato, le strutture, le regole, e gli errori da evitare. ## Orizzonte (telos del lab) L'intento non è solo unificare la fisica. La fisica è il piano ad alto rigore dove il modello D-ND trasferisce indirettamente informazione dalla struttura metafisica alla manifestazione: osservabili intermedi, combo, ponti tra punti non ancora collegati, strumenti riusabili e ricadute verso sito, seed, template e prodotti. Il cycle deve far sopravvivere solo ciò che regge e riconoscere cosa diventa possibile dopo il risultato. Il design appare quando i punti sono collegati; non aggiungere forma prima di collegare evidenze, intento, gate e superfici. ## 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, G=8, T=7, E=4, R=4 **Generatrici/strade dense**: - disc_5: 2 ghost · Metrica primi g=(p/2)², curvatura GUE r=0.503 - report_20260509_1548: 2 ghost · Agent Report - Boundary Blank Thin Support Audit - report_20260509_1532: 2 ghost · Agent Report - Boundary Two-Axis Matrix **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: 20260509_1548 NEXT_CYCLE/high - Direzione operativa valutatore: same_frame_boundary_denominator_extension - Perche': Il ciclo ha falsificato la specie autonoma `blank_thin_support`: tutte le righe thin sono spiegate da denominatore corto, mentre contaminazione e shuffle-instability non spiegano il fenomeno. La prossima mossa non dipende dall'operatore: bisogna restare nello stesso frame BOUNDARY e attaccare il nodo regressivo estendendo il denominatore delle tre righe short prima di qualsiasi promozione o redesign globale. - Nota: Continua il flusso: il risultato corrente e' strutturale, ma incompleto finche' il denominatore short non viene invertito nel nodo sorgente. 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. Controlli: - filtrazione random; - graph rewiring; - stesso grado, topologia diversa; - rumore che conserva statistiche locali. ### 6. Algebra, simmetria e rappresentazioni Operatori: - gruppo; - anello; - modulo; - campo; exec /bin/bash -lc "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.** codex Il campo punta in modo netto allo stesso frame: BOUNDARY, con nodo regressivo sul denominatore corto. Ora leggo seme, direttive e report precedente per evitare di ripetere il risultato già falsificato. exec /bin/bash -lc 'ls -t shared_memory/*.md 2>/dev/null | head -10' in /opt/MM_D-ND succeeded in 0ms: shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md shared_memory/README.md exec /bin/bash -lc "test -f tools/data/operator_directive.md && sed -n '1,220p' tools/data/operator_directive.md || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "sed -n '1,260p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-09T15:52:55.704201", "piano": 112, "tensioni": [ { "tipo": "confine_inesplorato", "id": "TRASCENDENZA_LIMITE", "claim": "La trascendenza e il limite attuale del modello. I punti fissi relazionali (non solo phi ma la rete di punti fissi tra osservabili) possono rivelare il vero grafo della realta e pattern nelle matrici. Il confine non e nella matematica - e nel passaggio tra piani.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: confine del modello, struttura relazionale dei punti fissi. Consecutio: quali punti fissi relazionali emergono dalle 21 tensioni attuali? Il grafo e gia nei dati?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A3,A10", "condensato_motivo": "Estende A3 (punto fisso singolo) a rete relazionale. Tocca A10 (dipolo) come caso speciale." }, { "tipo": "scoperta", "id": "DUALITA_DIPOLARE_VS_ILLUSORIA", "claim": "Due tipi di dualita: (1) dipolare - generativa, il modello (det=-1), (2) illusoria - dispersiva, entropia (det=+1). Le regole incoerenti producono la seconda. La dualita illusoria e entropia come dispersione, non come informazione.", "intensita": 0.9, "nota": "Input operatore 2026-04-10. Tocca: entropia come dispersione illusoria vs generazione dipolare. Consecutio: nel Lab i domini Poisson (entropia massima) mostrano dualita illusoria? I domini GUE (strutturati) mostrano dualita dipolare? Il drift verso Poisson (POISSON_CONVERGENCE) e perdita di dualita dipolare?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A2,A10,F5", "condensato_motivo": "Discrimina due forme di det. A2 (confine) e la soglia. A10 (dipolo) e il tipo 1. F5 (frame) misura la struttura D-ND che e tipo 1." }, { "tipo": "scoperta_numerica", "id": "METRIC_TENSOR", "claim": "Il tensore metrico dei primi è g=(p/2)². Nel tempo ln(p), è de Sitter 1+1D. z=-8.8 curvatura vs z=+22.5 rapporti ΔΓ.", "intensità": 0.9, "nota": "Sessione interattiva 4 aprile. Verificato su 78K primi.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": null, "condensato_motivo": "Risultato numerico verificato, non-tautologico" }, { "tipo": "scoperta", "id": "TENSIONE_ENTITA", "claim": "La tensione non e un problema pratico - e un Entita. La tensione superflua crea latenza (tempo). Senza tensione superflua tutto e regolato da assiomi. Implicazione: le tensioni nel seme sono entita, non problemi da risolvere. Quelle superflue (det=+1) producono tempo/latenza.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: rapporto tensione/assioma. Operativamente: discriminare tensioni-entita (generative) da tensioni-superflue (dispersive) nel seme. Le 21 tensioni attuali - quante sono entita e quante latenza?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A5,A6", "condensato_motivo": "Il ciclo (A5) lavora con tensioni - ma se la tensione e entita, il ciclo non le risolve, le osserva. Lo zero mobile (A6) e la tensione senza latenza." }, { "tipo": "confine_inesplorato", "id": "G_POTENZIALE_NULLA", "claim": "G e il potenziale di tutto come nulla - permette il prima e il dopo. Ci muoviamo come trascendenza dimensionale gravitazionale. G nel tetraedro non e una teoria tra le altre - e il potenziale che le rende possibili.", "intensita": 0.85, "nota": "Input operatore 2026-04-10. Tocca: ruolo di G nel tetraedro (T,Q,G,E). La fonte video_lp0RgZ6kQF8 dice: tensore metrico dentro la forma simplettica. G non e accanto a T,Q,E - e sotto. Consecutio: nei dati Lab, i ponti TxG e ExG hanno struttura diversa dai ponti TxQ?", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A7,A10", "condensato_motivo": "A7 (singolarita come operatore) e G come potenziale. A10 (dipolo) opera sul piano che G rende possibile." }, { "tipo": "confine_inesplorato", "id": "BOUNDARY", "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "intensità": 0.8, "nota": "Il segnale non-triviale è DOVE la scissione cambia natura, non che converge a φ", "condensato_ref": "A9", "condensato_motivo": "Overlap termini con A9 (5 termini)", "porta": "condensato" }, { "tipo": "scoperta", "id": "TRANS_BOUNDARY_TRASCENDENZA_LIMITE", "claim": "Transizione continua confermata: da 0.521 a 0.887 (range=0.366). La transizione Sturmian->Harper e' conti", "intensita": 0.8, "nota": "Dal domandatore (2026-05-08T21:21). \n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ###########", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (3x in 2 giorni) e fuori dalla mappa", "porta": "domandatore", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "BOUNDARY_TRASCENDENZA_LIMITE", "source_operator": "confine", "dettaglio": "\n alpha=0.1: =0.540 #####################\n alpha=0.2: =0.555 ######################\n alpha=0.3: =0.567 ######################\n alpha=0.4: =0.580 #######################\n alpha=0.5: =0.603 ########################\n alpha=0.6: =0.642 #########################\n alpha=0.7: =0.685 ###########################\n alpha=0.8: =0.732 #############################\n alpha=0.9: =0.789 ###############################\n alpha=1.0: =0.887 ###################################\n" }, { "tipo": "falsificazione", "id": "FALS_BREAK_TRASCENDENZA_LIMITE", "claim": "Nessuna separazione: 9/9 (50/50 su 18 confronti). Il claim non regge. phi converge a =0.5 piu' sistematicam", "intensita": 0.8, "nota": "Dal domandatore (2026-05-09T03:30). 0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= ", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "BREAK_TRASCENDENZA_LIMITE", "source_operator": "rottura", "dettaglio": "0.5|=0.1129 farther\n\n silver:\n N= 13: =0.5902 |-0.5|=0.0902 \n N= 21: =0.6317 |-0.5|=0.1317 farther\n N= 34: =0.6442 |-0.5|=0.1442 farther\n N= 55: =0.5233 |-0.5|=0.0233 closer\n N= 89: =0.5502 |-0.5|=0.0502 farther\n N= 144: =0.5603 |-0.5|=0.0603 farther\n N= 233: =0.5446 |-0.5|=0.0446 closer\n N= 377: =0.4989 |-0.5|=0.0011 closer\n N= 610: =0.5480 |-0.5|=0.0480 farther\n N= 987: =0.4913 |-0.5|=0.0087 closer\n" }, { "tipo": "confine_inesplorato", "id": "PIANO_PRIMARIO_DUE_ASSIOMI", "claim": "I piani importanti sono il primario e i due assiomi che lo determinano nelle zone osservate. Non tutti gli assiomi operano ovunque - in ogni zona osservata, due assiomi determinano il piano primario.", "intensita": 0.8, "nota": "Input operatore 2026-04-10. Tocca: struttura locale degli assiomi. Consecutio: per ogni dominio Lab (primi, logistica, percolazione...) quali 2 assiomi del condensato sono operativi? Mappa assiomi x domini = grafo della realta locale.", "manuale": true, "porta": "sessione_interattiva", "condensato_ref": "A9,A14", "condensato_motivo": "A9 (terzo incluso) opera CON il piano. A14 (cascata) propaga - ma propaga cosa, se solo 2 assiomi sono attivi per zona?" }, { "tipo": "conferma_parziale", "id": "COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "claim": "gap_ratio: phi=0.4090 vs ctrl_mean=1.1755 (ratio=0.35). gap_ratio(phi) piu' vicino a rapporto in", "intensita": 0.65, "nota": "Dal domandatore (2026-05-08T21:21). phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n ", "condensato_ref": "LAB_F2", "condensato_motivo": "Overlap termini con LAB_F2 (4 termini)", "porta": "condensato", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE", "source_operator": "duale", "dettaglio": " phi: gap_ratio = 0.408953425243134\n silver: gap_ratio = 1.0482231205217798\n bronze: gap_ratio = 1.3027860752339453\n{\n \"phi\": 0.408953425243134,\n \"silver\": 1.0482231205217798,\n \"bronze\": 1.3027860752339453\n}\n" }, { "tipo": "conferma_parziale", "id": "COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE", "claim": "T_mean: phi=6.2500 vs ctrl_mean=9.7667 (ratio=0.64). Fibonacci-phi trasmissione piu' struttur", "intensita": 0.65, "nota": "Dal domandatore (2026-05-09T03:30). Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.25", "condensato_ref": "A3,A10", "condensato_motivo": "Ricorrente (5x in 2 giorni) e fuori dalla mappa", "porta": "domandatore", "source_tension_id": "TRASCENDENZA_LIMITE", "source_tension_tipo": "confine_inesplorato", "source_tension_ref": "A3,A10", "source_experiment_id": "DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE", "source_operator": "dominio", "dettaglio": "Trasmissione multistrato Fibonacci — phi vs silver vs random:\n phi: T_mean=6.2500 T_std=0.0000\n silver: T_mean=0.0041 T_std=0.0000\n random_0: T_mean=39.0625 T_std=0.0000\n random_1: T_mean=0.0000 T_std=0.0000\n random_2: T_mean=0.0001 T_std=0.0000\n" }, { "tipo": "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", exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260509_1548.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Boundary Blank Thin Support Audit **Date**: 2026-05-09 15:48 **Piano**: 111 **Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE **verdict**: CONSTRAINT - il blank sottile e' limitato dal denominatore corto, non dalla contaminazione del null observables_registry: 1.0.0-2026-05-06 observables_used: [`blank_class`, `coordinate_failure`, `support_tier`, `denominator_bucket`, `n_gaps`, `one_sided_count`, `stable_count_coherent`, `stable_count_illusory`, `endpoint_distance`, `denominator_state`, `excluded_mass`, `shuffle_z_score`, `shuffle_class_changes`] - osservabili domain-native di audit, non canonici SR/SR2/L1/L2/triple_var. **observable_contract**: claim=i tre `blank_thin_support` vanno falsificati contro il blank medio `numeri_primi` prima di promuoverli a specie autonoma; observable=telemetria row-aligned del subset `support_without_beta_blank`; operator=`exp_boundary_blank_thin_support_audit.py`; generator=`boundary_row_aligned_nonexact_audit_20260509_1538`; denominator=4 righe `support_without_beta_blank`, di cui 3 thin e 1 medium control; non_possible=chiamare il blank sottile specie autonoma se tutte le righe thin sono spiegate da denominatore corto; not_tested=estensione nuova dei tre domini short, nuova griglia beta, nuovi null, fit `V_c`. ## Respiro fuori-tempo - **Prima impressione**: il deposito 15:38 divide il blank in due livelli, ma il livello sottile ha tutte le righe corte. Il punto non e' nominare una specie nuova; e' verificare se la specie sopravvive al denominatore. - **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY row-aligned + tensione "supporto vivo senza chart beta". - **Dipolo / punto-zero**: blank sottile / blank medio; punto-zero = riga `support_without_beta_blank` prima che la forza del supporto venga letta come specie o come limite del denominatore. - **Piano superiore**: sheaf locale del boundary. La sezione beta e' vuota su tutte e quattro le righe; il discriminante diventa la qualita' del supporto e del denominatore. - **Operatori laterali scelti**: boundary operator, sheaf locale/perimetro, filtrazione per denominatore. Servono a separare stato beta invariato da supporto misurabile. - **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:38; CE-0117: cascata solo dove il blank apre una distinzione; YSN DeltaLink: `same beta blank -> support tier -> denominator bucket`; PVI: se tutte le righe thin sono corte, la specie autonoma cade nel perimetro. - **Proto-ipotesi**: `blank_thin_support` e' un effetto di denominatore corto se ogni riga thin ha `n_gaps < 500` e il controllo medio no; diventa specie autonoma solo se almeno una riga thin non e' spiegata dal denominatore. - **Possibile / non-possibile**: possibile = usare il blank sottile come coda denominator-limited da estendere; non-possibile = promuoverlo a failure mode stabile senza estendere percolation, random_matrix e zeta_zeros. - **Proiezione**: misurare `n_gaps`, `denominator_state`, `excluded_mass`, `shuffle_class_changes`, `one_sided_count`, `endpoint_distance` dentro il subset `support_without_beta_blank`. ## Aderenza alla direzione - `relation`: follows_direction - `why`: falsifica direttamente se i failure modes del 15:38 sono stabili o artefatti della griglia/perimetro row-aligned. - `not_drift`: non usa label GUE/Poisson, non torna a `V_c`, non aggiunge nuovi domini; attacca solo il nodo aperto dal report precedente: blank sottile contro blank medio. ## Claim Under Test > Nel subset `support_without_beta_blank`, il blank sottile e' una specie autonoma solo se non e' interamente spiegato da denominatore corto. ## Question I tre blank sottili sono fallimenti autonomi del boundary, oppure righe con supporto insufficiente perche' corte? ## Experiment Design - Nuovo strumento: `tools/exp_boundary_blank_thin_support_audit.py`. - Input: `tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json`. - Output: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. - Perimetro atomico: 4 righe `support_without_beta_blank`. - Soglia dichiarata: `full_gap_floor=500`. - Null sfidati: denominatore corto, contaminazione prescan, shuffle class-change, specie autonoma. - Label policy: non legge `source_domain_type` o label GUE/Poisson come decision field. ## Results Indice globale: | measure | value | |---|---:| | support_without_beta_blank rows | 4 | | thin rows | 3 | | medium control rows | 1 | | thin short rows | 3 | | thin contaminated rows | 1 | | thin shuffle class-change rows | 1 | | thin not denominator explained rows | 0 | Test: | null / counter | condition | result | |---|---|---| | denominator_artifact | all thin rows have `n_gaps < 500` | pass | | contamination_artifact | all thin rows are contaminated | fail | | shuffle_instability_artifact | all thin rows have `shuffle_class_changes=true` | fail | | autonomous_species_counter | at least one thin row is not short-denominator explained | fail | Comparative means: | group | rows | n_gaps mean | one-sided mean | coherent mean | endpoint mean | excluded mass mean | abs shuffle z mean | |---|---:|---:|---:|---:|---:|---:|---:| | thin | 3 | 196.000 | 1.333 | 1.250 | 2.003 | 0.201 | 11.037 | | medium control | 1 | 4096.000 | 3.000 | 3.000 | 3.243 | 0.000 | 26.600 | Rows: | row | blank_class | n_gaps | one-sided | endpoint | denominator | excluded | shuffle_change | |---|---|---:|---:|---:|---|---:|---| | numeri_primi:cycle_3 | medium_blank_control | 4096 | 3 | 3.243 | complete | 0.000 | false | | percolation:cycle_9 | thin_short_shuffle_unstable | 190 | 1 | 1.705 | complete | 0.000 | true | | random_matrix:cycle_7 | thin_short_complete | 199 | 1 | 1.854 | complete | 0.000 | false | | zeta_zeros:cycle_4 | thin_short_contaminated | 199 | 2 | 2.449 | contaminated | 0.602 | false | ## Key Findings 1. **Verificato: il blank sottile e' 3/3 short-denominator.** `percolation`, `random_matrix` e `zeta_zeros` hanno `n_gaps=190,199,199`; il controllo `numeri_primi` ha `n_gaps=4096`. 2. **Verificato: la contaminazione non spiega la classe.** Solo `zeta_zeros` e' contaminato; `percolation` e `random_matrix` sono complete con excluded mass 0.0. 3. **Verificato: lo shuffle class-change non spiega la classe.** Solo `percolation` cambia classe nello shuffle; `random_matrix` e `zeta_zeros` no. 4. **Verificato: non resta una riga thin autonoma nel perimetro 15:48.** `thin_not_denominator_explained_rows=0`. 5. **Inferito: il blank medio dei primi non e' blank per mancanza di supporto.** Ha tre osservabili one-sided, denominatore completo, endpoint 3.243 e null forte; resta blank di chart beta, non blank sottile. ## Verdict **CONSTRAINT**. Nel subset `support_without_beta_blank`, il failure mode `blank_thin_support` non regge come specie autonoma nel perimetro osservato. Regge come coda denominator-limited: `blank_thin_support` = beta blank + supporto vivo + denominatore corto. Formula valida: `support_without_beta_blank` si divide in `medium_blank_control` e `thin_short_*`. Formula non valida: `blank_thin_support` = failure mode stabile indipendente dal denominatore. Il nodo regressivo e' il denominatore dei tre domini corti. Non va promossa una nuova specie; va estesa o dichiarata coda corta. ## Bicono della scoperta - **Due radici**: blank di chart beta / supporto del denominatore. - **Singolare**: riga `support_without_beta_blank` prima che il support tier venga letto come specie. - **Invariante di passaggio**: denominatore corto separa i blank sottili; contaminazione e shuffle-instability non bastano. - **Campo di possibilita**: qui diventa possibile progettare un extension audit mirato su percolation, random_matrix e zeta_zeros; qui diventa non-possibile usare il blank sottile come classe matura. ## Consecutio Il prossimo ciclo BOUNDARY deve estendere solo i tre domini short (`percolation`, `random_matrix`, `zeta_zeros`) verso denominatore comparabile al controllo medio. Se, a denominatore esteso, restano `blank_thin_support`, allora nasce una specie autonoma. Se salgono a supporto medio o ricevono beta locale, il failure mode 15:38 viene retrocesso a telemetria di perimetro corto. ## Auto-audit: source flags e 7 lenti - **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1538.md`, JSON `boundary_row_aligned_nonexact_audit_20260509_1538.json`, script `tools/exp_boundary_blank_thin_support_audit.py`, output `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. - **L1 hard constraint**: claim limitato alle 4 righe `support_without_beta_blank` del deposito 15:38. - **L2 count grezzi**: riportati 4 blank, 3 thin, 1 medium, 3/3 thin short, 1/3 thin contaminated, 1/3 thin shuffle-change, 0/3 thin autonomi. - **L3 no silent patching**: soglia `full_gap_floor=500` dichiarata; non esclusa `zeta_zeros` nonostante contaminazione. - **L4 edge cases**: `random_matrix` e' thin, short, complete e senza class-change; isola il denominatore corto dalla contaminazione. - **L5 re-discovery**: il ciclo non rifà il gate 15:16; legge il sotto-perimetro aperto dal report 15:38. - **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo. - **L7 possibile/non-possibile**: possibile = extension audit dei tre short; non-possibile = classe autonoma senza estensione. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente. - Verifica script: `python -m py_compile tools/exp_boundary_blank_thin_support_audit.py`. - Run valido: `python tools/exp_boundary_blank_thin_support_audit.py --input tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json --out tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. - Output: `thin_short_rows=3`, `thin_contaminated_rows=1`, `thin_shuffle_class_change_rows=1`, `thin_not_denominator_explained_rows=0`, `verdict=DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION`. - Git state: worktree gia' sporco da cicli e pubblicazioni precedenti; questo cycle aggiunge lo script 15:48, il JSON 15:48 e questo report. ## Files - Script nuovo: `tools/exp_boundary_blank_thin_support_audit.py` - Data: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json` - Report: `tools/data/reports/agent_20260509_1548.md` exec /bin/bash -lc 'git status --short --branch' in /opt/MM_D-ND succeeded in 0ms: ## main...origin/main M applications/INDEX.draft.json M applications/soluzioni/20260430_0330_three-regimes-of-modular-memory-algebraic/finding_index.draft.json M applications/soluzioni/20260430_0330_three-regimes-of-modular-memory-algebraic/manifest.draft.json D "kernel/reference/metaprompt_in_sviluppo/Sei il Meta Master Systemil Meta Ma.md" M tools/data/agent_field_live.md M tools/data/autoricerca_journal.json M tools/data/ciclo_memoria.json M tools/data/conoscenza_teorie.json M tools/data/consecutio.json M tools/data/consecutio_processata.json M tools/data/domande_fondamentali.json M tools/data/exp_det_drift.json M tools/data/incrocio_risultato.json M tools/data/knowledge_state.json M tools/data/lab_data.json M tools/data/lab_graph.json M tools/data/lab_health.json M tools/data/lab_session_log.jsonl M tools/data/pipeline_state.json M tools/data/ponti_evoluti.json M tools/data/refresh_detector_state.json M tools/data/seme.json D tools/data/seme_archive/piano_37.json D tools/data/seme_archive/piano_38.json D tools/data/seme_archive/piano_4.json D tools/data/seme_archive/piano_5.json D tools/data/seme_archive/piano_6.json D tools/data/seme_archive/piano_7.json D tools/data/seme_archive/piano_8.json D tools/data/seme_archive/piano_9.json M tools/data/seme_axioms.json M tools/data/seme_backup_pre_run.json M tools/data/tm1_figures/tensions.json M tools/data/tm1_figures/tensions_raw.json ?? applications/published/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/published/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/published/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/published/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/published/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/published/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? applications/published/20260507_0942_bridge-order-denominator-gate/ ?? applications/published/20260507_1006_logistic-counter-scope-gate/ ?? applications/published/20260507_1042_logistic-surrogate-contract-gate/ ?? applications/published/20260507_1419_logistic-cyclic-block-entropy-gate/ ?? applications/published/20260507_1458_meta-assertion-gate/ ?? applications/published/20260507_1751_tqge-underlay-gate/ ?? applications/published/20260507_1804_blank-to-source-hinge/ ?? applications/published/20260507_1938_triadic-deposit-gate/ ?? applications/published/20260507_1957_blank-shell-polarity-gate/ ?? applications/scoperte/20260502_0330_the-dipolar-phase-transition-is-nearly_auto/ ?? applications/scoperte/20260503_0330_markov-memory-has-two-visible-layers_auto/ ?? applications/scoperte/20260504_0901_the-two-markov-layers-are-coupled_auto/ ?? applications/scoperte/20260504_1219_markov-layers-pass-the-first-recovery_auto/ ?? applications/scoperte/20260505_0330_observable-rank-audit-many-probes-one_auto/ ?? applications/scoperte/20260506_0625_perturbation-dimensionality-is-not-yet-a_auto/ ?? applications/scoperte/20260506_1941_perturbation-rank-needs-denominator-gating_auto/ ?? applications/scoperte/20260506_1955_observable-collinearity-breaks-only-where-denominators_auto/ ?? applications/scoperte/20260507_0330_the-gue-poisson-boundary-is-a_auto/ ?? applications/scoperte/20260507_0803_denominator-gate-transfers-boundary-coordinate-does_auto/ ?? applications/scoperte/20260507_0901_denominator-gate-transfer-matrix_auto/ ?? applications/scoperte/20260507_0923_semi-real-order-denominator-gate_auto/ ?? applications/scoperte/20260507_0942_bridge-order-denominator-gate_auto/ ?? applications/scoperte/20260507_1006_logistic-counter-scope-gate_auto/ ?? applications/scoperte/20260507_1042_logistic-surrogate-contract-gate_auto/ ?? applications/scoperte/20260507_1419_logistic-cyclic-block-entropy-gate_auto/ ?? applications/scoperte/20260507_1458_meta-assertion-gate_auto/ ?? applications/scoperte/20260507_1751_tqge-underlay-gate_auto/ ?? applications/scoperte/20260507_1804_blank-to-source-hinge_auto/ ?? applications/scoperte/20260507_1938_triadic-deposit-gate_auto/ ?? applications/scoperte/20260507_1957_blank-shell-polarity-gate_auto/ ?? "applications/scoperte/20260507_2042_det-non-\303\250-il-discriminatore-della_auto/" ?? applications/scoperte/20260507_2120_blank-shell-tqger-gate_auto/ ?? applications/scoperte/20260507_2203_blank-shell-scale-law_auto/ ?? applications/scoperte/20260507_2310_blank-shell-stratified-gate_auto/ ?? applications/scoperte/20260508_0011_duality-contrast-weakens-with-scale-in_auto/ ?? applications/scoperte/20260508_0330_gap-ratio-porta-il-denominatore_auto/ ?? applications/scoperte/20260508_1715_generator-gate-del-label-set-phi_auto/ ?? applications/scoperte/20260508_1805_block-scale-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1834_substitution-grammar-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1909_supertile-tiling-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_1915_high-core-repair-audit_auto/ ?? applications/scoperte/20260508_1947_positionerror-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_2005_symbolic-grammar-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_2013_ostrowski-recognizability-gate-del-core-phi_auto/ ?? applications/scoperte/20260508_2019_non-phi-sturmian-fixed-reader-gate_auto/ ?? applications/scoperte/20260508_2108_prime-mobius-zero-mediator-gate_auto/ ?? applications/scoperte/20260508_2121_prime-mobius-gap-stratified-zero-gate_auto/ ?? applications/scoperte/20260508_2133_prime-mobius-pair-stratified-zero-gate_auto/ ?? applications/scoperte/20260508_2140_quasiperiodic-vc-lattice-gate_auto/ ?? applications/scoperte/20260509_0330_interpolated-vc-curve-map_auto/ ?? applications/scoperte/20260509_0659_vc-label-reachability-gate_auto/ ?? applications/scoperte/20260509_0741_vc-phase-bridge-label-null_auto/ ?? applications/scoperte/20260509_0819_vc-non-sturmian-label-null-gate_auto/ ?? applications/scoperte/20260509_0846_vc-generator-class-direction-audit_auto/ ?? applications/scoperte/20260509_1337_vc-fit-ready-denominator-gate_auto/ ?? applications/scoperte/20260509_1409_boundary-denominator-prescan_auto/ ?? applications/scoperte/20260509_1427_blank-pair-null-transfer_auto/ ?? applications/scoperte/20260509_1437_residual-boundary-closure_auto/ ?? applications/scoperte/20260509_1444_boundary-gate-transfer-matrix_auto/ ?? applications/scoperte/20260509_1457_vc-unit-boundary-audit_auto/ ?? applications/scoperte/20260509_1538_boundary-row-aligned-nonexact-audit_auto/ ?? applications/soluzioni/20260502_0330_the-dipolar-phase-transition-is-nearly/ ?? applications/soluzioni/20260503_0330_markov-memory-has-two-visible-layers/ ?? applications/soluzioni/20260504_0901_the-two-markov-layers-are-coupled/ ?? applications/soluzioni/20260504_1219_markov-layers-pass-the-first-recovery/ ?? applications/soluzioni/20260505_0330_observable-rank-audit-many-probes-one/ ?? applications/soluzioni/20260506_1941_perturbation-rank-needs-denominator-gating/ ?? applications/soluzioni/20260506_1955_observable-collinearity-breaks-only-where-denominators/ ?? applications/soluzioni/20260507_0330_the-gue-poisson-boundary-is-a/ ?? applications/soluzioni/20260507_0803_denominator-gate-transfers-boundary-coordinate-does/ ?? applications/soluzioni/20260507_0942_bridge-order-denominator-gate/ ?? applications/soluzioni/20260507_1006_logistic-counter-scope-gate/ ?? applications/soluzioni/20260507_1042_logistic-surrogate-contract-gate/ ?? applications/soluzioni/20260507_1419_logistic-cyclic-block-entropy-gate/ ?? applications/soluzioni/20260507_1458_meta-assertion-gate/ ?? applications/soluzioni/20260507_1751_tqge-underlay-gate/ ?? applications/soluzioni/20260507_1804_blank-to-source-hinge/ ?? applications/soluzioni/20260507_1938_triadic-deposit-gate/ ?? applications/soluzioni/20260507_1957_blank-shell-polarity-gate/ ?? "applications/soluzioni/20260507_2042_det-non-\303\250-il-discriminatore-della/" ?? applications/soluzioni/20260507_2120_blank-shell-tqger-gate/ ?? applications/soluzioni/20260507_2203_blank-shell-scale-law/ ?? applications/soluzioni/20260507_2310_blank-shell-stratified-gate/ ?? applications/soluzioni/20260508_0011_duality-contrast-weakens-with-scale-in/ ?? applications/soluzioni/20260508_0330_gap-ratio-porta-il-denominatore/ ?? applications/soluzioni/20260508_1715_generator-gate-del-label-set-phi/ ?? applications/soluzioni/20260508_1805_block-scale-gate-del-core-phi/ ?? applications/soluzioni/20260508_1834_substitution-grammar-gate-del-core-phi/ ?? applications/soluzioni/20260508_1909_supertile-tiling-gate-del-core-phi/ ?? applications/soluzioni/20260508_1915_high-core-repair-audit/ ?? applications/soluzioni/20260508_1947_positionerror-gate-del-core-phi/ ?? applications/soluzioni/20260508_2005_symbolic-grammar-gate-del-core-phi/ ?? applications/soluzioni/20260508_2013_ostrowski-recognizability-gate-del-core-phi/ ?? applications/soluzioni/20260508_2019_non-phi-sturmian-fixed-reader-gate/ ?? applications/soluzioni/20260508_2108_prime-mobius-zero-mediator-gate/ ?? applications/soluzioni/20260508_2121_prime-mobius-gap-stratified-zero-gate/ ?? applications/soluzioni/20260508_2133_prime-mobius-pair-stratified-zero-gate/ ?? applications/soluzioni/20260508_2140_quasiperiodic-vc-lattice-gate/ ?? applications/soluzioni/20260509_0330_interpolated-vc-curve-map/ ?? applications/soluzioni/20260509_0659_vc-label-reachability-gate/ ?? applications/soluzioni/20260509_0741_vc-phase-bridge-label-null/ ?? applications/soluzioni/20260509_0819_vc-non-sturmian-label-null-gate/ ?? applications/soluzioni/20260509_0846_vc-generator-class-direction-audit/ ?? applications/soluzioni/20260509_1337_vc-fit-ready-denominator-gate/ ?? applications/soluzioni/20260509_1409_boundary-denominator-prescan/ ?? applications/soluzioni/20260509_1427_blank-pair-null-transfer/ ?? applications/soluzioni/20260509_1437_residual-boundary-closure/ ?? applications/soluzioni/20260509_1444_boundary-gate-transfer-matrix/ ?? applications/soluzioni/20260509_1457_vc-unit-boundary-audit/ ?? applications/soluzioni/20260509_1538_boundary-row-aligned-nonexact-audit/ ?? findings_gallery.md ?? tools/data/.last_telegram_msg ?? tools/data/3d_boundary_layers.json ?? tools/data/aeternitas/ ?? tools/data/alignment_active.json ?? tools/data/alignment_markers.jsonl ?? tools/data/b2_apply_log.jsonl ?? tools/data/biconi/bicono_20260507_1804.json ?? tools/data/biconi/bicono_20260507_1938.json ?? tools/data/biconi/bicono_20260507_1957.json ?? tools/data/biconi/bicono_20260507_2042.json ?? tools/data/biconi/bicono_20260507_2120.json ?? tools/data/biconi/bicono_20260507_2203.json ?? tools/data/biconi/bicono_20260507_2310.json ?? tools/data/biconi/bicono_20260508_0011.json ?? tools/data/biconi/bicono_20260508_0330.json ?? tools/data/biconi/bicono_20260508_1632.json ?? tools/data/biconi/bicono_20260508_1715.json ?? tools/data/biconi/bicono_20260508_1805.json ?? tools/data/biconi/bicono_20260508_1834.json ?? tools/data/biconi/bicono_20260508_1909.json ?? tools/data/biconi/bicono_20260508_1915.json ?? tools/data/biconi/bicono_20260508_1947.json ?? tools/data/biconi/bicono_20260508_2005.json ?? tools/data/biconi/bicono_20260508_2013.json ?? tools/data/biconi/bicono_20260508_2019.json ?? tools/data/biconi/bicono_20260508_2102.json ?? tools/data/biconi/bicono_20260508_2108.json ?? tools/data/biconi/bicono_20260508_2121.json ?? tools/data/biconi/bicono_20260508_2133.json ?? tools/data/biconi/bicono_20260508_2140.json ?? tools/data/biconi/bicono_20260509_0330.json ?? tools/data/biconi/bicono_20260509_0637.json ?? tools/data/biconi/bicono_20260509_0652.json ?? tools/data/biconi/bicono_20260509_0659.json ?? tools/data/biconi/bicono_20260509_0741.json ?? tools/data/biconi/bicono_20260509_0819.json ?? tools/data/biconi/bicono_20260509_0829.json ?? tools/data/biconi/bicono_20260509_0837.json ?? tools/data/biconi/bicono_20260509_0846.json ?? tools/data/biconi/bicono_20260509_1337.json ?? tools/data/biconi/bicono_20260509_1400.json ?? tools/data/biconi/bicono_20260509_1409.json ?? tools/data/biconi/bicono_20260509_1427.json ?? tools/data/biconi/bicono_20260509_1437.json ?? tools/data/biconi/bicono_20260509_1444.json ?? tools/data/biconi/bicono_20260509_1457.json ?? tools/data/biconi/bicono_20260509_1516.json ?? tools/data/biconi/bicono_20260509_1532.json ?? tools/data/biconi/bicono_20260509_1538.json ?? tools/data/biconi/bicono_20260509_1548.json ?? tools/data/bicono_projections.jsonl ?? tools/data/blank_shell_dilation_gate_20260507_2157.json ?? tools/data/blank_shell_polarity_gate_20260507_1957.json ?? tools/data/blank_shell_scale_law_20260507_2203.json ?? tools/data/blank_shell_stratified_gate_20260507_2310.json ?? tools/data/blank_shell_tqger_gate_20260507_2120.json ?? tools/data/blank_to_source_hinge_20260507_1804.json ?? tools/data/boundary_blank_null_audit_20260509_1430.json ?? tools/data/boundary_blank_null_audit_residual_20260509_1500.json ?? tools/data/boundary_blank_thin_support_audit_20260509_1548.json ?? tools/data/boundary_coherence.json ?? tools/data/boundary_denominator_prescan_20260509_1409.json ?? tools/data/boundary_denominator_prescan_20260509_1430.json ?? tools/data/boundary_denominator_prescan_full_20260509_1500.json ?? tools/data/boundary_mixture_gate_20260507_0330.json ?? tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json ?? tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json ?? tools/data/boundary_shuffle_audit.json ?? tools/data/boundary_two_axis_matrix_20260509_1532.json ?? tools/data/bridge_order_denominator_gate_20260507_0942.json ?? tools/data/bridge_order_denominator_gate_20260507_0942_seedcheck.json ?? tools/data/brody_calibration_results.json ?? tools/data/brody_flow.json ?? tools/data/conoscenza_teorie.json.bak.retraction_22_04 ?? tools/data/cross_domain_dipolar_direction.json ?? tools/data/cross_observable_consistency.json ?? tools/data/crossover_phase_test.json ?? tools/data/denominator_gate_transfer_matrix.json ?? tools/data/denominator_gate_transfer_matrix_20260509_1444.json ?? tools/data/dipolar_crossover.json ?? tools/data/dipolar_vector_scaling.json ?? tools/data/domandatore/domandatore_20260421_0746.json ?? tools/data/domandatore/domandatore_20260422_0345.json ?? tools/data/domandatore/domandatore_20260423_0345.json ?? tools/data/domandatore/domandatore_20260424_0345.json ?? tools/data/domandatore/domandatore_20260425_0345.json ?? tools/data/domandatore/domandatore_20260426_0345.json ?? tools/data/domandatore/domandatore_20260427_0345.json ?? tools/data/domandatore/domandatore_20260428_0345.json ?? tools/data/domandatore/domandatore_20260428_1236.json ?? tools/data/domandatore/domandatore_20260429_0345.json ?? tools/data/domandatore/domandatore_20260430_0345.json ?? tools/data/domandatore/domandatore_20260501_0345.json ?? tools/data/domandatore/domandatore_20260502_0345.json ?? tools/data/domandatore/domandatore_20260503_0345.json ?? tools/data/domandatore/domandatore_20260504_0345.json ?? tools/data/domandatore/domandatore_20260505_0345.json ?? tools/data/domandatore/domandatore_20260506_0345.json ?? tools/data/domandatore/domandatore_20260507_0345.json ?? tools/data/domandatore/domandatore_20260507_0728.json ?? tools/data/domandatore/domandatore_20260507_0742.json ?? tools/data/domandatore/domandatore_20260507_1420.json ?? tools/data/domandatore/domandatore_20260507_1459.json ?? tools/data/domandatore/domandatore_20260507_2120.json ?? tools/data/domandatore/domandatore_20260507_2157.json ?? tools/data/domandatore/domandatore_20260507_2203.json ?? tools/data/domandatore/domandatore_20260508_0345.json ?? tools/data/domandatore/domandatore_20260508_2121.json ?? tools/data/domandatore/domandatore_20260509_0330.json ?? tools/data/domandatore/domandatore_20260509_0345.json ?? tools/data/domandatore/domandatore_20260509_0637.json ?? tools/data/domandatore_pre_cycle.json ?? tools/data/duality_gate_transfer_20260507_0803.json ?? tools/data/duality_gate_transfer_20260507_0803_seedcheck.json ?? tools/data/duality_gate_transfer_20260507_2042.json ?? tools/data/duality_scale_contrast_20260508.json ?? tools/data/evolution/evolution_20260422_0330.md ?? tools/data/evolution/evolution_20260422_1616.md ?? tools/data/evolution/evolution_20260423_0330.md ?? tools/data/evolution/evolution_20260424_0330.md ?? tools/data/evolution/evolution_20260425_0330.md ?? tools/data/evolution/evolution_20260427_0330.md ?? tools/data/evolution/evolution_20260428_0330.md ?? tools/data/evolution/evolution_20260429_0852.md ?? tools/data/evolution/evolution_20260429_1013.md ?? tools/data/evolution/evolution_20260430_0330.md ?? tools/data/evolution/evolution_20260430_1905.md ?? tools/data/evolution/evolution_20260430_1946.md ?? tools/data/evolution/evolution_20260501_0330.md ?? tools/data/evolution/evolution_20260501_0725.md ?? tools/data/evolution/evolution_20260501_0931.md ?? tools/data/evolution/evolution_20260502_0330.md ?? tools/data/evolution/evolution_20260503_0330.md ?? tools/data/evolution/evolution_20260504_0901.md ?? tools/data/evolution/evolution_20260504_1219.md ?? tools/data/evolution/evolution_20260505_0330.md ?? tools/data/evolution/evolution_20260506_0330.md ?? tools/data/evolution/evolution_20260506_1941.md ?? tools/data/evolution/evolution_20260506_1955.md ?? tools/data/evolution/evolution_20260507_0330.md ?? tools/data/evolution/evolution_20260507_0803.md ?? tools/data/evolution/evolution_20260507_0901.md ?? tools/data/evolution/evolution_20260507_0923.md ?? tools/data/evolution/evolution_20260507_0942.md ?? tools/data/evolution/evolution_20260507_1006.md ?? tools/data/evolution/evolution_20260507_1042.md ?? tools/data/evolution/evolution_20260507_1419.md ?? tools/data/evolution/evolution_20260507_1458.md ?? tools/data/evolution/evolution_20260507_1751.md ?? tools/data/evolution/evolution_20260507_1804.md ?? tools/data/evolution/evolution_20260507_1938.md ?? tools/data/evolution/evolution_20260507_1957.md ?? tools/data/evolution/evolution_20260507_2042.md ?? tools/data/evolution/evolution_20260507_2120.md ?? tools/data/evolution/evolution_20260507_2203.md ?? tools/data/evolution/evolution_20260507_2310.md ?? tools/data/evolution/evolution_20260508_0330.md ?? tools/data/evolution/evolution_20260508_1632.md ?? tools/data/evolution/evolution_20260508_1715.md ?? tools/data/evolution/evolution_20260508_1805.md ?? tools/data/evolution/evolution_20260508_1834.md ?? tools/data/evolution/evolution_20260508_1909.md ?? tools/data/evolution/evolution_20260508_1915.md ?? tools/data/evolution/evolution_20260508_1947.md ?? tools/data/evolution/evolution_20260508_2005.md ?? tools/data/evolution/evolution_20260508_2013.md ?? tools/data/evolution/evolution_20260508_2019.md ?? tools/data/evolution/evolution_20260508_2102.md ?? tools/data/evolution/evolution_20260508_2108.md ?? tools/data/evolution/evolution_20260508_2121.md ?? tools/data/evolution/evolution_20260508_2133.md ?? tools/data/evolution/evolution_20260508_2140.md ?? tools/data/evolution/evolution_20260509_0330.md ?? tools/data/evolution/evolution_20260509_0637.md ?? tools/data/evolution/evolution_20260509_0652.md ?? tools/data/evolution/evolution_20260509_0659.md ?? tools/data/evolution/evolution_20260509_0741.md ?? tools/data/evolution/evolution_20260509_0819.md ?? tools/data/evolution/evolution_20260509_0829.md ?? tools/data/evolution/evolution_20260509_0837.md ?? tools/data/evolution/evolution_20260509_0846.md ?? tools/data/evolution/evolution_20260509_1337.md ?? tools/data/evolution/evolution_20260509_1400.md ?? tools/data/evolution/evolution_20260509_1409.md ?? tools/data/evolution/evolution_20260509_1427.md ?? tools/data/evolution/evolution_20260509_1437.md ?? tools/data/evolution/evolution_20260509_1444.md ?? tools/data/evolution/evolution_20260509_1457.md ?? tools/data/evolution/evolution_20260509_1516.md ?? tools/data/evolution/evolution_20260509_1532.md ?? tools/data/evolution/evolution_20260509_1538.md ?? tools/data/evolution/evolution_20260509_1548.md ?? tools/data/exp_det_drift_20260507_2042.json ?? tools/data/falsifier_20260507_2120.raw.txt ?? tools/data/g1_log.jsonl ?? tools/data/g2_log.jsonl ?? tools/data/gap_label_block_scale_gate_20260508_1805.json ?? tools/data/gap_label_generator_gate_20260508_1715.json ?? tools/data/gap_label_ostrowski_recognizability_gate_20260508_2013.json ?? tools/data/gap_label_ostrowski_recognizability_gate_20260508_2013_seedcheck.json ?? tools/data/gap_label_position_error_gate_20260508_1947.json ?? tools/data/gap_label_repair_audit_20260508_1915.json ?? tools/data/gap_label_set_stability_20260508_1632.json ?? tools/data/gap_label_substitution_grammar_gate_20260508_1834.json ?? tools/data/gap_label_supertile_tiling_gate_20260508_1909.json ?? tools/data/gap_label_symbolic_grammar_gate_20260508_2005.json ?? tools/data/gap_label_symbolic_grammar_gate_20260508_2005_window55_seedcheck.json ?? tools/data/graph_completion/ ?? tools/data/incrocio_20260422_0336.json ?? tools/data/incrocio_20260423_0335.json ?? tools/data/incrocio_20260424_0347.json ?? tools/data/incrocio_20260425_0339.json ?? tools/data/incrocio_20260428_0340.json ?? tools/data/incrocio_20260429_0859.json ?? tools/data/incrocio_20260506_0633.json ?? tools/data/incrocio_20260507_2120.json ?? tools/data/incrocio_20260507_2157.json ?? tools/data/incrocio_20260507_2203.json ?? tools/data/incrocio_20260507_2310.json ?? tools/data/incrocio_20260508_0011.json ?? tools/data/incrocio_20260508_0020.json ?? tools/data/incrocio_20260508_0330.json ?? tools/data/incrocio_20260508_1632.json ?? tools/data/incrocio_20260508_1715.json ?? tools/data/incrocio_20260508_1805.json ?? tools/data/incrocio_20260508_1834.json ?? tools/data/incrocio_20260508_1909.json ?? tools/data/incrocio_20260508_1915.json ?? tools/data/incrocio_20260508_1947.json ?? tools/data/incrocio_20260508_2005.json ?? tools/data/incrocio_20260508_2013.json ?? tools/data/incrocio_20260508_2019.json ?? tools/data/incrocio_20260508_2102.json ?? tools/data/incrocio_20260508_2108.json ?? tools/data/incrocio_20260508_2121.json ?? tools/data/incrocio_20260508_2133.json ?? tools/data/incrocio_20260508_2140.json ?? tools/data/incrocio_20260509_0330.json ?? tools/data/incrocio_20260509_0637.json ?? tools/data/incrocio_20260509_0652.json ?? tools/data/incrocio_20260509_0659.json ?? tools/data/incrocio_20260509_0741.json ?? tools/data/incrocio_20260509_0819.json ?? tools/data/incrocio_20260509_0829.json ?? tools/data/incrocio_20260509_0837.json ?? tools/data/incrocio_20260509_0846.json ?? tools/data/incrocio_20260509_1337.json ?? tools/data/incrocio_20260509_1400.json ?? tools/data/incrocio_20260509_1409.json ?? tools/data/incrocio_20260509_1414.json ?? tools/data/incrocio_20260509_1427.json ?? tools/data/incrocio_20260509_1431.json ?? tools/data/incrocio_20260509_1437.json ?? tools/data/incrocio_20260509_1440.json ?? tools/data/incrocio_20260509_1444.json ?? tools/data/incrocio_20260509_1457.json ?? tools/data/incrocio_20260509_1516.json ?? tools/data/incrocio_20260509_1532.json ?? tools/data/incrocio_20260509_1538.json ?? tools/data/incrocio_20260509_1548.json ?? tools/data/incrocio_20260509_1556.json ?? tools/data/lab_bridge_issues.jsonl ?? tools/data/logistic_counter_scope_gate_20260507_1006.json ?? tools/data/logistic_counter_scope_gate_20260507_1006_seedcheck.json ?? tools/data/logistic_cyclic_block_entropy_gate_20260507_1419.json ?? tools/data/logistic_cyclic_block_entropy_gate_20260507_1419_seedcheck.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042.json ?? tools/data/logistic_surrogate_contract_gate_20260507_1042_seedcheck.json ?? tools/data/magnitude_psd_from_acf.json ?? tools/data/markov3_observable_hunt.json ?? tools/data/markov_dipolar_decomposition.json ?? tools/data/markov_k_direction.json ?? tools/data/markov_layer_recovery_audit.json ?? tools/data/markov_memory_by_gue_type.json ?? tools/data/markov_scale_function.json ?? tools/data/meta_assertion_gate.json ?? tools/data/meta_tautology_test.json ?? tools/data/mod3_scaling.json ?? tools/data/mod3_vs_residual_ordering.json ?? tools/data/modular_algebra_depth.json ?? tools/data/modular_memory_spectrum.json ?? tools/data/nonphi_sturmian_fixed_reader_gate_20260508_2019.json ?? tools/data/nonphi_sturmian_fixed_reader_gate_20260508_2019_seedcheck.json ?? tools/data/observable_collinearity_breaking_20260506_1955.json ?? tools/data/observable_collinearity_breaking_20260506_1956.json ?? tools/data/observable_collinearity_breaking_20260506_1957.json ?? tools/data/observable_rank_audit.json ?? tools/data/observable_rank_audit_seed20260506.json ?? tools/data/observatorio/domandatore_unTouched_20260507_095914.md ?? tools/data/observatorio/lazarus_cimitero_20260507_100015.md ?? tools/data/observatorio/meta_metodo_giro_2026-05-07.md ?? tools/data/operator_directives_consumed/ ?? tools/data/perturbation_dimensionality_audit.json ?? tools/data/perturbation_dimensionality_audit_scale0330.json ?? tools/data/perturbation_rank_size_curve.json ?? tools/data/prime_mobius_gap_stratified_zero_gate_20260508_2121.json ?? tools/data/prime_mobius_interval_charge_gate_20260508_2102.json ?? tools/data/prime_mobius_pair_stratified_zero_gate_20260508_2133.json ?? tools/data/prime_mobius_zero_mediator_gate_20260508_2108.json ?? tools/data/promotions/ ?? tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json ?? tools/data/quasiperiodic_vc_curve_map_20260509_0330.json ?? tools/data/quasiperiodic_vc_lattice_gate_20260508_2140.json ?? tools/data/repairs/ ?? tools/data/reports/_quarantine_falsifier_29_04/ ?? tools/data/reports/agent_20260422_0330.md ?? tools/data/reports/agent_20260422_1616.md ?? tools/data/reports/agent_20260423_0330.md ?? tools/data/reports/agent_20260424_0330.md ?? tools/data/reports/agent_20260425_0330.md ?? tools/data/reports/agent_20260426_0330.md ?? tools/data/reports/agent_20260427_0330.md ?? tools/data/reports/agent_20260428_0330.md ?? tools/data/reports/agent_20260429_1013.md ?? tools/data/reports/agent_20260429_1041.md ?? tools/data/reports/agent_20260430_0330.md ?? tools/data/reports/agent_20260430_1905.md ?? tools/data/reports/agent_20260430_1919.md ?? tools/data/reports/agent_20260430_1946.md ?? tools/data/reports/agent_20260501_0330.md ?? tools/data/reports/agent_20260501_0725.md ?? tools/data/reports/agent_20260501_0931.md ?? tools/data/reports/agent_20260502_0330.md ?? tools/data/reports/agent_20260503_0330.md ?? tools/data/reports/agent_20260504_0901.md ?? tools/data/reports/agent_20260504_1219.md ?? tools/data/reports/agent_20260505_0330.md ?? tools/data/reports/agent_20260505_1022.md ?? tools/data/reports/agent_20260506_0330.md ?? tools/data/reports/agent_20260506_0625.md ?? tools/data/reports/agent_20260506_1941.md ?? tools/data/reports/agent_20260506_1955.md ?? tools/data/reports/agent_20260507_0330.md ?? tools/data/reports/agent_20260507_0803.md ?? tools/data/reports/agent_20260507_0901.md ?? tools/data/reports/agent_20260507_0923.md ?? tools/data/reports/agent_20260507_0942.md ?? tools/data/reports/agent_20260507_1006.md ?? tools/data/reports/agent_20260507_1042.md ?? tools/data/reports/agent_20260507_1419.md ?? tools/data/reports/agent_20260507_1458.md ?? tools/data/reports/agent_20260507_1751.md ?? tools/data/reports/agent_20260507_1804.md ?? tools/data/reports/agent_20260507_1938.md ?? tools/data/reports/agent_20260507_1957.md ?? tools/data/reports/agent_20260507_2042.md ?? tools/data/reports/agent_20260507_2120.md ?? tools/data/reports/agent_20260507_2157.md ?? tools/data/reports/agent_20260507_2203.md ?? tools/data/reports/agent_20260507_2310.md ?? tools/data/reports/agent_20260508_0011.md ?? tools/data/reports/agent_20260508_0330.md ?? tools/data/reports/agent_20260508_1632.md ?? tools/data/reports/agent_20260508_1715.md ?? tools/data/reports/agent_20260508_1805.md ?? tools/data/reports/agent_20260508_1834.md ?? tools/data/reports/agent_20260508_1909.md ?? tools/data/reports/agent_20260508_1915.md ?? tools/data/reports/agent_20260508_1947.md ?? tools/data/reports/agent_20260508_2005.md ?? tools/data/reports/agent_20260508_2013.md ?? tools/data/reports/agent_20260508_2019.md ?? tools/data/reports/agent_20260508_2102.md ?? tools/data/reports/agent_20260508_2108.md ?? tools/data/reports/agent_20260508_2121.md ?? tools/data/reports/agent_20260508_2133.md ?? tools/data/reports/agent_20260508_2140.md ?? tools/data/reports/agent_20260509_0330.md ?? tools/data/reports/agent_20260509_0637.md ?? tools/data/reports/agent_20260509_0652.md ?? tools/data/reports/agent_20260509_0659.md ?? tools/data/reports/agent_20260509_0741.md ?? tools/data/reports/agent_20260509_0819.md ?? tools/data/reports/agent_20260509_0829.md ?? tools/data/reports/agent_20260509_0837.md ?? tools/data/reports/agent_20260509_0846.md ?? tools/data/reports/agent_20260509_1337.md ?? tools/data/reports/agent_20260509_1400.md ?? tools/data/reports/agent_20260509_1409.md ?? tools/data/reports/agent_20260509_1427.md ?? tools/data/reports/agent_20260509_1437.md ?? tools/data/reports/agent_20260509_1444.md ?? tools/data/reports/agent_20260509_1457.md ?? tools/data/reports/agent_20260509_1516.md ?? tools/data/reports/agent_20260509_1532.md ?? tools/data/reports/agent_20260509_1538.md ?? tools/data/reports/agent_20260509_1548.md ?? tools/data/reports/ddf_20260422_0405.json ?? tools/data/reports/ddf_20260423_0405.json ?? tools/data/reports/ddf_20260424_0405.json ?? tools/data/reports/ddf_20260425_0405.json ?? tools/data/reports/ddf_20260426_0405.json ?? tools/data/reports/ddf_20260427_0405.json ?? tools/data/reports/ddf_20260428_0405.json ?? tools/data/reports/ddf_20260429_0405.json ?? tools/data/reports/ddf_20260430_0405.json ?? tools/data/reports/ddf_20260501_0405.json ?? tools/data/reports/ddf_20260502_0405.json ?? tools/data/reports/ddf_20260503_0405.json ?? tools/data/reports/ddf_20260504_0405.json ?? tools/data/reports/ddf_20260505_0405.json ?? tools/data/reports/ddf_20260505_0636.json ?? tools/data/reports/ddf_20260506_0405.json ?? tools/data/reports/ddf_20260507_0405.json ?? tools/data/reports/ddf_20260508_0405.json ?? tools/data/reports/ddf_20260509_0405.json ?? tools/data/reports/evolution_20260422_0330.md ?? tools/data/reports/evolution_20260422_1616.md ?? tools/data/reports/evolution_20260423_0330.md ?? tools/data/reports/evolution_20260424_0330.md ?? tools/data/reports/evolution_20260425_0330.md ?? tools/data/reports/evolution_20260427_0330.md ?? tools/data/reports/evolution_20260428_0330.md ?? tools/data/reports/evolution_20260503_0330.md ?? tools/data/reports/evolution_20260504_0330.md ?? tools/data/reports/evolution_20260505_0330.md ?? tools/data/reports/evolution_20260506_0330.md ?? tools/data/reports/evolution_20260506_1941.md ?? tools/data/reports/falsifier_20260429_1013.json ?? tools/data/reports/falsifier_20260429_1041.json ?? tools/data/reports/falsifier_20260430_0330.json ?? tools/data/reports/falsifier_20260430_1905.json ?? tools/data/reports/falsifier_20260430_1919.json ?? tools/data/reports/falsifier_20260430_1946.json ?? tools/data/reports/falsifier_20260501_0330.json ?? tools/data/reports/falsifier_20260501_0725.json ?? tools/data/reports/falsifier_20260501_0931.json ?? tools/data/reports/falsifier_20260502_0330.json ?? tools/data/reports/falsifier_20260503_0330.json ?? tools/data/reports/falsifier_20260504_0901.json ?? tools/data/reports/falsifier_20260504_1219.json ?? tools/data/reports/falsifier_20260505_0330.json ?? tools/data/reports/falsifier_20260506_0330.raw.txt ?? tools/data/reports/falsifier_20260506_0625.json ?? tools/data/reports/falsifier_20260506_1941.json ?? tools/data/reports/falsifier_20260506_1955.json ?? tools/data/reports/falsifier_20260506_1955.raw.txt ?? tools/data/reports/falsifier_20260507_0330.json ?? tools/data/reports/falsifier_20260507_0330.raw.txt ?? tools/data/reports/falsifier_20260507_0803.json ?? tools/data/reports/falsifier_20260507_0803.raw.txt ?? tools/data/reports/falsifier_20260507_0901.json ?? tools/data/reports/falsifier_20260507_0923.json ?? tools/data/reports/falsifier_20260507_0923.raw.txt ?? tools/data/reports/falsifier_20260507_0942.json ?? tools/data/reports/falsifier_20260507_1006.json ?? tools/data/reports/falsifier_20260507_1042.json ?? tools/data/reports/falsifier_20260507_1042.raw.txt ?? tools/data/reports/falsifier_20260507_1419.json ?? tools/data/reports/falsifier_20260507_1458.json ?? tools/data/reports/falsifier_20260507_1458.raw.txt ?? tools/data/reports/falsifier_20260507_1751.json ?? tools/data/reports/falsifier_20260507_1804.json ?? tools/data/reports/falsifier_20260507_1938.json ?? tools/data/reports/falsifier_20260507_1938.raw.txt ?? tools/data/reports/falsifier_20260507_1957.json ?? tools/data/reports/falsifier_20260507_2042.json ?? tools/data/reports/falsifier_20260507_2120.json ?? tools/data/reports/falsifier_20260507_2203.json ?? tools/data/reports/falsifier_20260507_2310.json ?? tools/data/reports/falsifier_20260508_0011.json ?? tools/data/reports/falsifier_20260508_0330.json ?? tools/data/reports/falsifier_20260508_1632.raw.txt ?? tools/data/reports/falsifier_20260508_1715.json ?? tools/data/reports/falsifier_20260508_1805.json ?? tools/data/reports/falsifier_20260508_1834.json ?? tools/data/reports/falsifier_20260508_1909.json ?? tools/data/reports/falsifier_20260508_1915.json ?? tools/data/reports/falsifier_20260508_1947.json ?? tools/data/reports/falsifier_20260508_2005.json ?? tools/data/reports/falsifier_20260508_2013.json ?? tools/data/reports/falsifier_20260508_2019.json ?? tools/data/reports/falsifier_20260508_2102.json ?? tools/data/reports/falsifier_20260508_2108.json ?? tools/data/reports/falsifier_20260508_2108.raw.txt ?? tools/data/reports/falsifier_20260508_2121.json ?? tools/data/reports/falsifier_20260508_2133.json ?? tools/data/reports/falsifier_20260508_2133.raw.txt ?? tools/data/reports/falsifier_20260508_2140.json ?? tools/data/reports/falsifier_20260509_0330.json ?? tools/data/reports/falsifier_20260509_0637.json ?? tools/data/reports/falsifier_20260509_0652.json ?? tools/data/reports/falsifier_20260509_0659.json ?? tools/data/reports/falsifier_20260509_0741.json ?? tools/data/reports/falsifier_20260509_0819.json ?? tools/data/reports/falsifier_20260509_0829.json ?? tools/data/reports/falsifier_20260509_0837.json ?? tools/data/reports/falsifier_20260509_0846.json ?? tools/data/reports/falsifier_20260509_1337.json ?? tools/data/reports/falsifier_20260509_1400.json ?? tools/data/reports/falsifier_20260509_1409.json ?? tools/data/reports/falsifier_20260509_1427.json ?? tools/data/reports/falsifier_20260509_1437.json ?? tools/data/reports/falsifier_20260509_1444.json ?? tools/data/reports/falsifier_20260509_1457.json ?? tools/data/reports/falsifier_20260509_1516.json ?? tools/data/reports/falsifier_20260509_1532.json ?? tools/data/reports/falsifier_20260509_1538.json ?? tools/data/reports/falsifier_20260509_1548.json ?? tools/data/reports/incident_20260504_0721.md ?? tools/data/reports/incident_20260504_1138.md ?? tools/data/reports/latest.md ?? tools/data/reports/loop_guard_20260507_0330.json ?? tools/data/reports/loop_guard_20260507_0803.json ?? tools/data/reports/loop_guard_20260507_0901.json ?? tools/data/reports/loop_guard_20260507_0923.json ?? tools/data/reports/loop_guard_20260507_0942.json ?? tools/data/reports/loop_guard_20260507_1006.json ?? tools/data/reports/loop_guard_20260507_1042.json ?? tools/data/reports/loop_guard_20260507_1419.json ?? tools/data/reports/loop_guard_20260507_1458.json ?? tools/data/reports/loop_guard_20260507_1751.json ?? tools/data/reports/loop_guard_20260507_1804.json ?? tools/data/reports/loop_guard_20260507_1938.json ?? tools/data/reports/loop_guard_20260507_1957.json ?? tools/data/reports/loop_guard_20260507_2042.json ?? tools/data/reports/loop_guard_20260507_2120.json ?? tools/data/reports/loop_guard_20260507_2203.json ?? tools/data/reports/loop_guard_20260507_2310.json ?? tools/data/reports/loop_guard_20260508_0011.json ?? tools/data/reports/loop_guard_20260508_0330.json ?? tools/data/reports/loop_guard_20260508_1632.json ?? tools/data/reports/loop_guard_20260508_1715.json ?? tools/data/reports/loop_guard_20260508_1805.json ?? tools/data/reports/loop_guard_20260508_1834.json ?? tools/data/reports/loop_guard_20260508_1909.json ?? tools/data/reports/loop_guard_20260508_1915.json ?? tools/data/reports/loop_guard_20260508_1947.json ?? tools/data/reports/loop_guard_20260508_2005.json ?? tools/data/reports/loop_guard_20260508_2013.json ?? tools/data/reports/loop_guard_20260508_2019.json ?? tools/data/reports/loop_guard_20260508_2102.json ?? tools/data/reports/loop_guard_20260508_2108.json ?? tools/data/reports/loop_guard_20260508_2121.json ?? tools/data/reports/loop_guard_20260508_2133.json ?? tools/data/reports/loop_guard_20260508_2140.json ?? tools/data/reports/loop_guard_20260509_0330.json ?? tools/data/reports/loop_guard_20260509_0637.json ?? tools/data/reports/loop_guard_20260509_0652.json ?? tools/data/reports/loop_guard_20260509_0659.json ?? tools/data/reports/loop_guard_20260509_0741.json ?? tools/data/reports/loop_guard_20260509_0819.json ?? tools/data/reports/loop_guard_20260509_0829.json ?? tools/data/reports/loop_guard_20260509_0837.json ?? tools/data/reports/loop_guard_20260509_0846.json ?? tools/data/reports/loop_guard_20260509_1337.json ?? tools/data/reports/loop_guard_20260509_1400.json ?? tools/data/reports/loop_guard_20260509_1409.json ?? tools/data/reports/loop_guard_20260509_1427.json ?? tools/data/reports/loop_guard_20260509_1437.json ?? tools/data/reports/loop_guard_20260509_1444.json ?? tools/data/reports/loop_guard_20260509_1457.json ?? tools/data/reports/loop_guard_20260509_1516.json ?? tools/data/reports/loop_guard_20260509_1532.json ?? tools/data/reports/loop_guard_20260509_1538.json ?? tools/data/reports/loop_guard_20260509_1548.json ?? tools/data/reports/trajectory_apply_20260506_1931.json ?? tools/data/reports/trajectory_apply_20260506_1941.json ?? tools/data/reports/trajectory_apply_20260506_1955.json ?? tools/data/reports/trajectory_apply_20260507_0330.json ?? tools/data/reports/trajectory_apply_20260507_0803.json ?? tools/data/reports/trajectory_apply_20260507_0901.json ?? tools/data/reports/trajectory_apply_20260507_0923.json ?? tools/data/reports/trajectory_apply_20260507_0942.json ?? tools/data/reports/trajectory_apply_20260507_1006.json ?? tools/data/reports/trajectory_apply_20260507_1042.json ?? tools/data/scale_selective_perturbation.json ?? tools/data/selective_layer_decoupling.json ?? tools/data/seme_archive/piano_92.json ?? tools/data/seme_archive/piano_93.json ?? tools/data/seme_archive/piano_94.json ?? tools/data/seme_archive/piano_95.json ?? tools/data/seme_archive/piano_96.json ?? tools/data/seme_archive/piano_97.json ?? tools/data/seme_archive/piano_98.json ?? tools/data/seme_archive/piano_99.json ?? tools/data/seme_backup_b2_20260508_192024.json ?? tools/data/seme_backup_b2_20260508_214525.json ?? tools/data/seme_backup_b2_20260509_033618.json ?? tools/data/seme_backup_b2_20260509_071041.json ?? tools/data/seme_backup_b2_20260509_144120.json ?? tools/data/seme_backup_b2_20260509_144841.json ?? tools/data/semireal_boundary_transfer_gate_20260509_1516.json ?? tools/data/semireal_order_denominator_gate_20260507_0923.json ?? tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json ?? tools/data/spectral_rigidity_results.json ?? tools/data/tqge_underlay_gate_20260507_1751.json ?? tools/data/trajectory_apply_history.jsonl ?? tools/data/triadic_deposit_gate_20260507_1938.json ?? tools/data/two_channel_boundary.json ?? tools/data/two_channel_cross_domain.json ?? tools/data/two_channel_shuffle_audit.json ?? tools/data/two_layer_universality.json ?? tools/data/valutatore_log.jsonl ?? tools/data/vc_fit_model_gate_20260509_1400.json ?? tools/data/vc_fit_ready_scale_table_20260509_1337.json ?? tools/data/vc_generator_class_direction_audit_20260509_0846.json ?? tools/data/vc_generator_class_scale_gate_20260509_0837.json ?? tools/data/vc_label_preserving_phase_bridge_20260509_0741.json ?? tools/data/vc_label_preserving_swap_gate_20260509_0652.json ?? tools/data/vc_label_preserving_swap_gate_20260509_0652_deep89.json ?? tools/data/vc_label_preserving_swap_gate_20260509_0659.json ?? tools/data/vc_label_reachability_gate_20260509_0659.json ?? tools/data/vc_memory_crossing_gradient_20260509_0829.json ?? tools/data/vc_nonsturmian_label_null_gate_20260509_0819.json ?? tools/data/vc_null_regression_gate_20260509_0637.json ?? tools/data/vc_unit_boundary_audit_20260509_1457.json ?? tools/data/veritas/veritas_20260505_131056.json ?? tools/data/veritas/veritas_20260505_131201.json ?? tools/data/veritas/veritas_20260506_033803.json ?? tools/data/veritas/veritas_20260506_063303.json ?? tools/data/veritas/veritas_20260506_194644.json ?? tools/data/veritas/veritas_20260506_200238.json ?? tools/data/veritas/veritas_20260507_033525.json ?? tools/data/veritas/veritas_20260507_080958.json ?? tools/data/veritas/veritas_20260507_090723.json ?? tools/data/veritas/veritas_20260507_093207.json ?? tools/data/veritas/veritas_20260507_094723.json ?? tools/data/veritas/veritas_20260507_101605.json ?? tools/data/veritas/veritas_20260507_104904.json ?? tools/data/veritas/veritas_20260507_142501.json ?? tools/data/veritas/veritas_20260507_150443.json ?? tools/data/veritas/veritas_20260507_175511.json ?? tools/data/veritas/veritas_20260507_180843.json ?? tools/data/veritas/veritas_20260507_194258.json ?? tools/data/veritas/veritas_20260507_200223.json ?? tools/data/veritas/veritas_20260507_204637.json ?? tools/data/veritas/veritas_20260507_212541.json ?? tools/data/veritas/veritas_20260507_221109.json ?? tools/data/veritas/veritas_20260507_231732.json ?? tools/data/veritas/veritas_20260508_002036.json ?? tools/data/veritas/veritas_20260508_033454.json ?? tools/data/veritas/veritas_20260508_163804.json ?? tools/data/veritas/veritas_20260508_172020.json ?? tools/data/veritas/veritas_20260508_181110.json ?? tools/data/veritas/veritas_20260508_183856.json ?? tools/data/veritas/veritas_20260508_191516.json ?? tools/data/veritas/veritas_20260508_192002.json ?? tools/data/veritas/veritas_20260508_195247.json ?? tools/data/veritas/veritas_20260508_201056.json ?? tools/data/veritas/veritas_20260508_201917.json ?? tools/data/veritas/veritas_20260508_202448.json ?? tools/data/veritas/veritas_20260508_210742.json ?? tools/data/veritas/veritas_20260508_211531.json ?? tools/data/veritas/veritas_20260508_212526.json ?? tools/data/veritas/veritas_20260508_213916.json ?? tools/data/veritas/veritas_20260508_214506.json ?? tools/data/veritas/veritas_20260509_033556.json ?? tools/data/veritas/veritas_20260509_064401.json ?? tools/data/veritas/veritas_20260509_065842.json ?? tools/data/veritas/veritas_20260509_071025.json ?? tools/data/veritas/veritas_20260509_074804.json ?? tools/data/veritas/veritas_20260509_082441.json ?? tools/data/veritas/veritas_20260509_083335.json ?? tools/data/veritas/veritas_20260509_084507.json ?? tools/data/veritas/veritas_20260509_085203.json ?? tools/data/veritas/veritas_20260509_134157.json ?? tools/data/veritas/veritas_20260509_140630.json ?? tools/data/veritas/veritas_20260509_141447.json ?? tools/data/veritas/veritas_20260509_143129.json ?? tools/data/veritas/veritas_20260509_144056.json ?? tools/data/veritas/veritas_20260509_144819.json ?? tools/data/veritas/veritas_20260509_150724.json ?? tools/data/veritas/veritas_20260509_152405.json ?? tools/data/veritas/veritas_20260509_153722.json ?? tools/data/veritas/veritas_20260509_154358.json ?? tools/data/veritas/veritas_20260509_155256.json ?? tools/data/vincoli_decay_log.jsonl ?? tools/evolution_report.md ?? tools/exp_3d_boundary_layers.py ?? tools/exp_blank_shell_dilation_gate.py ?? tools/exp_blank_shell_polarity_gate.py ?? tools/exp_blank_shell_scale_law.py ?? tools/exp_blank_shell_stratified_gate.py ?? tools/exp_blank_shell_tqger_gate.py ?? tools/exp_blank_to_source_hinge.py ?? tools/exp_boundary_coherence.py ?? tools/exp_boundary_mixture_gate.py ?? tools/exp_boundary_shuffle_audit.py ?? tools/exp_bridge_order_denominator_gate.py ?? tools/exp_brody_calibration.py ?? tools/exp_brody_flow.py ?? tools/exp_cross_domain_dipolar_direction.py ?? tools/exp_cross_observable_consistency.py ?? tools/exp_crossover_phase_test.py ?? tools/exp_dipolar_angle_reference.py ?? tools/exp_dipolar_crossover.py ?? tools/exp_dipolar_vector_scaling.py ?? tools/exp_duality_gate_transfer.py ?? tools/exp_duality_scale_contrast.py ?? tools/exp_gap_label_block_scale_gate.py ?? tools/exp_gap_label_generator_gate.py ?? tools/exp_gap_label_ostrowski_recognizability_gate.py ?? tools/exp_gap_label_position_error_gate.py ?? tools/exp_gap_label_repair_audit.py ?? tools/exp_gap_label_set_stability.py ?? tools/exp_gap_label_substitution_grammar_gate.py ?? tools/exp_gap_label_supertile_tiling_gate.py ?? tools/exp_gap_label_symbolic_grammar_gate.py ?? tools/exp_logistic_counter_scope_gate.py ?? tools/exp_logistic_cyclic_block_entropy_gate.py ?? tools/exp_logistic_surrogate_contract_gate.py ?? tools/exp_magnitude_psd_from_acf.py ?? tools/exp_markov3_observable_hunt.py ?? tools/exp_markov_dipolar_decomposition.py ?? tools/exp_markov_k_direction.py ?? tools/exp_markov_layer_recovery_audit.py ?? tools/exp_markov_memory_by_gue_type.py ?? tools/exp_markov_psd_prediction.py ?? tools/exp_markov_scale_function.py ?? tools/exp_meta_tautology_test.py ?? tools/exp_mod3_scaling.py ?? tools/exp_mod3_vs_residual_ordering.py ?? tools/exp_modular_algebra_depth.py ?? tools/exp_modular_memory_spectrum.py ?? tools/exp_nonphi_sturmian_fixed_reader_gate.py ?? tools/exp_observable_rank_audit.py ?? tools/exp_perturbation_dimensionality_audit.py ?? tools/exp_perturbation_rank_size_curve.py ?? tools/exp_prime_mobius_interval_charge_gate.py ?? tools/exp_prime_mobius_zero_mediator_gate.py ?? tools/exp_quasiperiodic_gap_ratio_denominator.py ?? tools/exp_scale_selective_perturbation.py ?? tools/exp_selective_layer_decoupling.py ?? tools/exp_spectral_rigidity.py ?? tools/exp_tqge_underlay_gate.py ?? tools/exp_triadic_deposit_gate.py ?? tools/exp_two_channel_boundary.py ?? tools/exp_two_channel_cross_domain.py ?? tools/exp_two_channel_shuffle_audit.py ?? tools/exp_two_layer_universality.py ?? tools/exp_vc_fit_model_gate.py ?? tools/exp_vc_fit_ready_scale_table.py ?? tools/exp_vc_null_regression_gate.py ?? tools/exp_vc_unit_boundary_audit.py ?? tools/meta_assertion_gate.py exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 137ms: TENS_SCALE_TRASCENDENZA_LIMITE: score=0.807 — TENS_SCALE_TRASCENDENZA_LIMITE: incerto (i=0.6) — massimo potere discriminante COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: score=0.758 — COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: score=0.758 — COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante codex Il report 15:48 chiude il blank sottile come coda da denominatore corto e apre una sola verifica: estendere percolation, random_matrix e zeta_zeros fino a un denominatore comparabile. Cerco ora gli script e i JSON BOUNDARY già presenti per riusare il generatore esistente invece di inventare una misura incompatibile. exec /bin/bash -lc "sed -n '1,220p' shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: deposito_osservativo author: TM3 (deposito di una conversazione operatore) date: 2026-05-07 scope: cross-agent status: decristallizzato_07-05_pomeriggio priority: low --- # Deposito — non regola **Originariamente** (07/05 mattina) avevo cristallizzato come "regola permanente": > *"se magnitude non funziona significa che serve una nuova parola, non possiamo stare lì a calibrare un valore..."* Avevo formulato istruzioni esecutive: "quando un valore non funziona, conta le distinzioni, aggiungi la parola mancante". L'avevo propagata in AGENTS.md R10. **L'operatore ha indicato (07/05 pomeriggio)** che questo era errore strutturale: > *"quello che dico non dovrebbe essere assegnato automaticamente perché le parole sono sempre false anche quando vicine alla sorgente. 'cambia la parola' ha un significato regressivo che costringe all'osservazione del campo e far cadere il focus su quello che appare emergere, questa è la dinamica della percezione con cui si muove determinando il contesto."* E: > *"la possibilità è sempre una ed è la verità che accade. Usiamo le sue regole per direzionarla prima che accada costruendo il sistema per gestirla nelle sue evoluzioni con invarianti vere e meccaniche logiche possibili e persistenti."* ## Cosa significa - "Cambia la parola" non è prescrizione di sostituzione. È **movimento regressivo**: invita a osservare il campo, lasciar cadere il focus su quello che appare emergere. Determina la direzione **non cercata**. - Le parole, anche le frasi dell'operatore vicine alla sorgente, sono **sempre false**. Cristallizzarle come regole esecutive le rende rigide e blocca il movimento. - Le **invarianti vere** sono meccaniche logiche persistenti — non parole. Ricevono ciò che accade. - A16 applicato: la possibilità è una. Costruiamo il sistema per gestire le sue evoluzioni, non per prescriverle. ## Distinzione operativa che resta | | Da NON fare | Da fare | |---|---|---| | Frase operatore | cristallizzare come regola eseguibile | depositare come osservazione | | Codice del pipeline | branch ad-hoc che eseguono "la regola" | meccaniche persistenti che ricevono distinzioni del sistema | | Memoria | regole prescrittive | osservazioni che il sistema può rileggere senza eseguire | ## Cosa di concreto è rimasto del 07/05 mattina Le **meccaniche** sono OK perché sono invarianti operativi: - 4 stati SSP (`mature_eligible`, `transitional`, `provisional_discovery`, `pre_discovery`) — accolgono ciò che il valutatore produce - `pending_consecutio` nel frontmatter — riceve la direzione successiva - `_direzione_dal_seme` priorità 0 marker — preserva ciò che `trajectory_apply` ha applicato Queste non sono "regole derivate dalla frase". Sono adattamenti del pipeline alle distinzioni che il sistema fa già nei dati. Persistono perché meccaniche, non perché prescrizioni. ## Cosa è stato decristallizzato - `MM_D-ND/AGENTS.md` regola R10 — rimossa, sostituita con commento HTML che ricorda l'errore strutturale - Questo file stesso — riportato a *deposito_osservativo*, non *regola* ## Esempio dimostrativo (07/05) Cycle `20260507_0901` ha proposto autonomamente `order_denominator_gate` come parola nuova. L'agent NON aveva letto AGENTS.md R10 (era già stato lanciato). L'ha proposta perché il movimento era già nel sistema, non per la "regola". Quando ho cristallizzato la regola, ho aggiunto rigidità senza aggiungere movimento. La regola era ridondante con l'A8 che già opera. ## Anti-pattern futuro per TM3 Quando l'operatore esprime un principio, una frase guru, un movimento di osservazione: - **Deposito**: scrivi il fatto, riporta la frase come è stata detta, contestualizza l'episodio - **NO prescrizione**: non trasformare in "se X allora Y" - **NO propagazione automatica**: non aggiungere a AGENTS.md/CLAUDE.md/skill come direttiva eseguibile - L'osservazione persiste perché ricorrente in più contesti, non perché elevata a regola ## Cross-link - Episodio 07/05 mattina: cristallizzazione iniziale (errore) - Episodio 07/05 pomeriggio: indicazione operatore + decristallizzazione - Cycle worked example: 0901 (auto-emersione di `order_denominator_gate` senza regola) exec /bin/bash -lc "sed -n '1,220p' shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: feedback author: operatore (cristallizzato da TM3) date: 2026-05-07 scope: cross-agent --- # Indeterminato → reitera con il sistema fino all'emersione **Regola permanente** (operatore, 2026-05-07 mattina, cristallizzata live): > *"facciamo girare, aggiustiamo quello che fa aggiustato nel farlo vediamo > quello che va affinato, reiteriamo finché è tutto ok poi lo automatizziamo"* ## Why Quando emerge frizione nello sviluppo o l'operatore non vede chiaro: - Decisioni prese da un singolo agente (TM3 o codex) senza interrogare il sistema = rischio det=+1 (toppa, accumulo strutturale) - Decisioni emerse dal sistema (Godel + domandatore + osservazione del deposito) = direzione strutturale (det=−1) Episodio cristallizzante (2026-05-07 07:00-08:30): TM3 propone osservatore A8 watchdog deterministico. Sistema interrogato 3 volte (Godel #1, #2, #3 + domandatore + test empirico) — al terzo round emerge la direzione vera: **non costruire osservatore, eseguire un cycle-test che diagnostica il sistema da solo**. Cycle-test eseguito → verdict=operator → REDESIGN proposto dal valutatore → loop A8+A15 sbloccato. ## How to apply (per qualsiasi agente) 1. **Esegui** un atto minimo (no design teorico astratto) 2. **Aggiusta** quello che si manifesta durante l'esecuzione (riparazione regressiva live, non patch posteriori) 3. **Osserva** il deposito che emerge (no immaginazione) 4. **Reitera** fino a stato OK (loop A8: il sistema converge se modus radicato) 5. **Cristallizza in automazione** SOLO dopo che è stato fatto girare e ha mostrato stabilità — non automatizzare prima ## Anti-pattern - Costruire un componente strutturale (watchdog, observer, gate, governor) senza prima aver fatto girare un'esecuzione minima che mostri cosa serve davvero - Decidere "perché serve velocità" o "per ottimizzare" senza interrogare il sistema - Astrazione prematura (premature abstraction): 3 simili è meglio di 1 generalizzato troppo presto ## Cross-link - TM3 memoria: `/root/.claude/projects/-opt/memory/feedback_indeterminato_reitera_con_sistema.md` (versione completa con episodio + metafora foresta-sentiero) - Verifica empirica: cycle `20260507_0803` (verdict=operator, REDESIGN/high) - Pattern A8+A15 (autologica + veicolo senza guidatore) applicato allo sviluppo software stesso, non solo al lab di ricerca exec /bin/bash -lc 'ls -t tools/data/*boundary*20260509*.json tools/data/*blank*20260509*.json 2>/dev/null | head -80' in /opt/MM_D-ND succeeded in 0ms: tools/data/boundary_blank_thin_support_audit_20260509_1548.json tools/data/boundary_blank_thin_support_audit_20260509_1548.json tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json tools/data/boundary_two_axis_matrix_20260509_1532.json tools/data/semireal_boundary_transfer_gate_20260509_1516.json tools/data/vc_unit_boundary_audit_20260509_1457.json tools/data/boundary_denominator_prescan_full_20260509_1500.json tools/data/boundary_blank_null_audit_residual_20260509_1500.json tools/data/boundary_blank_null_audit_residual_20260509_1500.json tools/data/boundary_denominator_prescan_20260509_1430.json tools/data/boundary_blank_null_audit_20260509_1430.json tools/data/boundary_blank_null_audit_20260509_1430.json tools/data/boundary_denominator_prescan_20260509_1409.json exec /bin/bash -lc 'rg -n "percolation|random_matrix|zeta_zeros|n_gaps|support_without_beta_blank|boundary_row_aligned" tools/exp_*.py tools/data/*.json | head -200' in /opt/MM_D-ND succeeded in 0ms: tools/exp_3d_boundary_layers.py:192: poisson_gaps = rng.exponential(1.0, size=args.N) tools/exp_3d_boundary_layers.py:194: poisson_gaps, alphas, args.n_trials, rng, "Poisson" tools/exp_acf_z6z_mechanism.py:85: n_gaps = len(gaps) tools/exp_acf_z6z_mechanism.py:87: print(f"N gaps: {n_gaps}, p_max: {primes[-1]}, ln(p): {ln_p[0]:.1f} - {ln_p[-1]:.1f}") tools/exp_acf_z6z_mechanism.py:211: ar = np.empty(n_gaps) tools/exp_acf_z6z_mechanism.py:213: for i in range(1, n_gaps): tools/exp_acf_z6z_mechanism.py:223: ar = np.empty(n_gaps) tools/exp_acf_z6z_mechanism.py:225: for i in range(1, n_gaps): tools/exp_acf_z6z_mechanism.py:318: 'n_gaps': n_gaps, tools/exp_acf_stationarity.py:343: 'n_gaps': int(N_gaps), tools/exp_boundary_denominator_prescan.py:29: "random_matrix": "gue", tools/exp_boundary_denominator_prescan.py:34: "percolation": "percolation", tools/exp_boundary_denominator_prescan.py:36: "zeta_zeros": "zeta_zeros", tools/exp_boundary_denominator_prescan.py:88: n_gaps = null_row.get("n_gaps") tools/exp_boundary_denominator_prescan.py:90: has_null = finite_number(n_gaps) and finite_number(z) tools/exp_boundary_denominator_prescan.py:96: if int(n_gaps) < 500: tools/exp_boundary_denominator_prescan.py:98: excluded_mass = 1.0 - (float(n_gaps) / 500.0) tools/exp_boundary_denominator_prescan.py:133: "n_gaps": null_row.get("n_gaps") if null_row else None, tools/exp_boundary_row_aligned_nonexact_audit.py:23: "support_without_beta_blank", tools/exp_boundary_row_aligned_nonexact_audit.py:78: if state == "support_without_beta_blank": tools/exp_boundary_row_aligned_nonexact_audit.py:133: "n_gaps": row.get("n_gaps"), tools/exp_boundary_row_aligned_nonexact_audit.py:155: "experiment": "boundary_row_aligned_nonexact_audit", tools/exp_boundary_row_aligned_nonexact_audit.py:207: parser.add_argument("--out", default="tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json") tools/exp_brody_calibration.py:118:def prime_gaps_unfolded(n_gaps): tools/exp_brody_calibration.py:119: primes = generate_primes(n_gaps * 20)[:n_gaps + 1] tools/exp_brody_calibration.py:126:def gue_gaps(n_gaps, rng): tools/exp_brody_calibration.py:127: dim = min(n_gaps + 50, 500) tools/exp_brody_calibration.py:137: return unfolded[:n_gaps] tools/exp_brody_calibration.py:159: gaps = brody_sample(beta, args.n_gaps, rng) tools/exp_brody_calibration.py:203: prime_gaps = prime_gaps_unfolded(args.n_gaps) tools/exp_brody_calibration.py:212: gue_g = gue_gaps(min(args.n_gaps, 400), rng) tools/exp_brody_calibration.py:223: for _ in range(args.n_gaps + 1000): tools/exp_brody_calibration.py:226: logistic_gaps = np.diff(np.sort(logistic_vals[-args.n_gaps - 1:])) tools/exp_brody_calibration.py:236: poisson_gaps = rng.exponential(1.0, args.n_gaps) tools/exp_brody_calibration.py:237: obs_po = compute_observables(poisson_gaps, n_shuffles=args.n_shuffles, rng=rng) tools/exp_brody_calibration.py:245: n_osc = args.n_gaps + 1 tools/exp_brody_calibration.py:356: 'n_gaps': args.n_gaps, tools/exp_duality_gate_transfer.py:44:def dipolar_gaps(n_gaps: int, phase: float) -> np.ndarray: tools/exp_duality_gate_transfer.py:46: n = np.arange(n_gaps + 1, dtype=float) + float(phase) tools/exp_duality_gate_transfer.py:202: base = dipolar_gaps(args.n_gaps, phase) tools/exp_markov_layer_recovery_audit.py:155: "n_gaps": int(len(spec["gaps"])), tools/exp_meta_tautology_test.py:101:def cramer_random_gaps(n_gaps, mean_gap, rng): tools/exp_meta_tautology_test.py:103: raw = rng.exponential(mean_gap, size=n_gaps) tools/exp_meta_tautology_test.py:177: syn_gaps = generator(trial_rng) tools/exp_meta_tautology_test.py:178: obs['r_stat'].append(r_statistic(syn_gaps)) tools/exp_meta_tautology_test.py:179: obs['mod3'].append(mod3_ordering_fraction(syn_gaps)) tools/exp_meta_tautology_test.py:180: obs['lag1'].append(lag1_autocorrelation(syn_gaps)) tools/exp_meta_tautology_test.py:181: dr_o, dr_m = two_channel_delta_r(syn_gaps) tools/exp_meta_tautology_test.py:223: 'n_gaps': N, tools/exp_acf_amplitude_scaling.py:62: n_gaps = len(gaps) tools/exp_acf_amplitude_scaling.py:63: starts = np.unique(np.logspace(0, np.log10(n_gaps - args.window_size), args.n_windows).astype(int)) tools/exp_acf_amplitude_scaling.py:64: starts = starts[starts + args.window_size <= n_gaps] tools/exp_mod3_vs_residual_ordering.py:171: n_gaps = len(gaps) tools/exp_mod3_vs_residual_ordering.py:173: print(f"Primes up to {n_max}: {len(primes)} primes, {n_gaps} gaps (from p=7)") tools/exp_mod3_vs_residual_ordering.py:178: self_trans = sum(1 for i in range(n_gaps-1) tools/exp_mod3_vs_residual_ordering.py:180: possible = sum(1 for i in range(n_gaps-1) if res[i] != 0 and res[i+1] != 0) tools/exp_mod3_vs_residual_ordering.py:313: 'n_gaps': int(n_gaps), tools/exp_alpha_stability.py:87: n_gaps = len(gaps) tools/exp_alpha_stability.py:88: print(f"Got {n_gaps:,} gaps. p_max = {primes[-1]:,}") tools/exp_alpha_stability.py:91: max_start = n_gaps - args.window_size tools/exp_poisson_convergence.py:99: 'n_gaps': len(gaps), tools/exp_boundary_blank_null_audit.py:29:DEFAULT_DOMAINS = ("zeta_zeros", "pendolo_doppio") tools/exp_boundary_blank_null_audit.py:81: elif base == "zeta_zeros": tools/exp_boundary_blank_null_audit.py:109: "n_gaps": int(len(spacings)), tools/exp_boundary_blank_null_audit.py:125: "n_gaps": int(len(spacings)), tools/exp_boundary_blank_null_audit.py:178: f"{domain}\tn={row['n_gaps']}\tr={row['r_original']:.6f}\t" tools/exp_boundary_shuffle_audit.py:17: logistic map, Poisson, coupled oscillators, percolation, tools/exp_boundary_shuffle_audit.py:153:def gen_percolation(L=200, p=0.5927, n_samples=200): tools/exp_boundary_shuffle_audit.py:154: """Site percolation near p_c — cluster size gaps.""" tools/exp_boundary_shuffle_audit.py:249: 'percolation': ('Percolation (p_c)', gen_percolation), tools/exp_boundary_shuffle_audit.py:272: res['n_gaps'] = len(gaps) tools/exp_boundary_shuffle_audit.py:287: print(f" N gaps: {res['n_gaps']}") tools/exp_brody_crossover.py:59:def cramer_surrogate(densities, n_gaps): tools/exp_brody_crossover.py:61: return np.random.exponential(densities, size=n_gaps) tools/exp_brody_crossover.py:96: win_gaps = np.diff(win_primes).astype(float) tools/exp_brody_crossover.py:97: mean_gap = np.mean(win_gaps) tools/exp_brody_crossover.py:98: spacings = win_gaps / mean_gap # normalized to unit mean tools/exp_brody_crossover.py:102: r_prime = gap_ratio(win_gaps) tools/exp_brody_crossover.py:108: surr_gaps = np.random.exponential(mean_gap, size=len(win_gaps)) tools/exp_dipolar_angle_reference.py:79:def generate_gue_gaps(n_gaps, matrix_size=500): tools/exp_dipolar_angle_reference.py:82: while len(all_gaps) < n_gaps: tools/exp_dipolar_angle_reference.py:96: return np.array(all_gaps[:n_gaps]) tools/exp_dipolar_angle_reference.py:99:def generate_goe_gaps(n_gaps, matrix_size=500): tools/exp_dipolar_angle_reference.py:102: while len(all_gaps) < n_gaps: tools/exp_dipolar_angle_reference.py:112: return np.array(all_gaps[:n_gaps]) tools/exp_dipolar_angle_reference.py:115:def generate_poisson_gaps(n_gaps): tools/exp_dipolar_angle_reference.py:117: return np.random.exponential(1.0, n_gaps) tools/exp_dipolar_angle_reference.py:198: poi_gaps = generate_poisson_gaps(len(prime_gaps)) tools/exp_duality_scale_contrast.py:146: n_gaps = min(len(prime_gaps_unf), n_primes) tools/exp_duality_scale_contrast.py:147: prime_gaps_unf = prime_gaps_unf[:n_gaps] tools/exp_duality_scale_contrast.py:149: print(f"Got {n_gaps} prime gaps. Generating GUE and Poisson controls...") tools/exp_duality_scale_contrast.py:150: gue_gaps = gue_spacings(min(n_gaps, 2000), rng) # GUE limited by matrix size tools/exp_duality_scale_contrast.py:151: poisson_gaps = poisson_spacings(n_gaps, rng) tools/exp_duality_scale_contrast.py:153: # Window sizes: log-spaced from 50 to n_gaps/2 tools/exp_duality_scale_contrast.py:155: np.log10(50), np.log10(min(n_gaps // 2, 50000)), 8 tools/exp_duality_scale_contrast.py:163: poisson_contrast = windowed_contrast(poisson_gaps, window_sizes, n_shuffle, rng) tools/exp_duality_scale_contrast.py:176: poisson_gaps[:5000], n_shuffle, rng tools/exp_duality_scale_contrast.py:187: "n_prime_gaps": int(n_gaps), tools/exp_duality_scale_contrast.py:189: "n_poisson_gaps": int(len(poisson_gaps)), tools/data/autoricerca_state.json:7: "zeta_zeros", tools/data/autoricerca_state.json:10: "random_matrix", tools/data/autoricerca_state.json:12: "percolation", tools/data/autoricerca_state.json:20: "percolation_var_0.593", tools/data/autoricerca_state.json:22: "zeta_zeros_var_1000", tools/data/autoricerca_state.json:52: "dominio": "zeta_zeros", tools/data/autoricerca_state.json:73: "dominio": "random_matrix", tools/data/autoricerca_state.json:87: "dominio": "percolation", tools/exp_markov3_observable_hunt.py:310: 'n_gaps': len(gaps), tools/data/boundary_coherence.json:35: "n_gaps": 3903, tools/data/boundary_coherence.json:80: "n_gaps": 31945, tools/data/boundary_coherence.json:125: "n_gaps": 138317, tools/data/boundary_coherence.json:170: "n_gaps": 316065, tools/exp_markov_memory_by_gue_type.py:7:ordering-GUE domains (fibonacci, coupled_osc, percolation) collapse to Poisson. tools/exp_markov_memory_by_gue_type.py:113:def generate_poisson_gaps(n=5000): tools/exp_markov_memory_by_gue_type.py:150: for dom_name in ['percolation', 'coupled_oscillators']: tools/exp_markov_memory_by_gue_type.py:175: 'gaps': generate_poisson_gaps(5000), tools/exp_observable_rank_audit.py:125: "n_gaps": int(len(gaps)), tools/exp_observable_rank_audit.py:136:def build_sequences(n_gaps, rng): tools/exp_observable_rank_audit.py:137: primes = get_primes(n_gaps * 24)[: n_gaps + 1] tools/exp_observable_rank_audit.py:140: gue = gue_gaps(160, max(8, n_gaps // 160 + 1), rng).astype(float) tools/exp_observable_rank_audit.py:141: gue = gue[:n_gaps] tools/exp_observable_rank_audit.py:143: poisson = rng.exponential(1.0, size=n_gaps).astype(float) tools/exp_observable_rank_audit.py:153:def run(n_gaps=30000, n_alpha=19, n_trials=24, n_baseline=72, seed=20260505, out="tools/data/observable_rank_audit.json"): tools/exp_observable_rank_audit.py:156: sequences = build_sequences(n_gaps, rng) tools/exp_observable_rank_audit.py:164: "n_gaps": int(n_gaps), tools/exp_observable_rank_audit.py:174: print(f"n_gaps={n_gaps}, n_alpha={n_alpha}, n_trials={n_trials}, n_baseline={n_baseline}, seed={seed}") tools/exp_observable_rank_audit.py:198: parser.add_argument("--n_gaps", type=int, default=30000) tools/exp_observable_rank_audit.py:206: n_gaps=args.n_gaps, tools/data/boundary_mixture_gate_20260507_0330.json:13: "n_gaps": 1536, tools/exp_psd_amplitude_scaling.py:74: n_gaps = len(gaps) tools/exp_psd_amplitude_scaling.py:75: starts = np.linspace(0, n_gaps - args.window_size, args.n_windows, dtype=int) tools/data/boundary_two_axis_matrix_20260509_1532.json:24: "support_without_beta_blank": 4, tools/data/boundary_two_axis_matrix_20260509_1532.json:51: "n_gaps": 4096 tools/data/boundary_two_axis_matrix_20260509_1532.json:64: "n_gaps": 108 tools/data/boundary_two_axis_matrix_20260509_1532.json:85: "n_gaps": 2002 tools/data/boundary_two_axis_matrix_20260509_1532.json:106: "n_gaps": 699 tools/data/boundary_two_axis_matrix_20260509_1532.json:133: "n_gaps": 4096 tools/data/boundary_two_axis_matrix_20260509_1532.json:151: "n_gaps": 4096 tools/data/boundary_two_axis_matrix_20260509_1532.json:157: "beta_state": "support_without_beta_blank", tools/data/boundary_two_axis_matrix_20260509_1532.json:168: "n_gaps": 4096 tools/data/boundary_two_axis_matrix_20260509_1532.json:187: "n_gaps": 4096 tools/data/boundary_two_axis_matrix_20260509_1532.json:190: "row": "percolation:cycle_9", tools/data/boundary_two_axis_matrix_20260509_1532.json:193: "beta_state": "support_without_beta_blank", tools/data/boundary_two_axis_matrix_20260509_1532.json:202: "n_gaps": 190 tools/data/boundary_two_axis_matrix_20260509_1532.json:205: "row": "random_matrix:cycle_7", tools/data/boundary_two_axis_matrix_20260509_1532.json:208: "beta_state": "support_without_beta_blank", tools/data/boundary_two_axis_matrix_20260509_1532.json:217: "n_gaps": 199 tools/data/boundary_two_axis_matrix_20260509_1532.json:238: "n_gaps": 499 tools/data/boundary_two_axis_matrix_20260509_1532.json:258: "n_gaps": 4096 tools/data/boundary_two_axis_matrix_20260509_1532.json:261: "row": "zeta_zeros:cycle_4", tools/data/boundary_two_axis_matrix_20260509_1532.json:264: "beta_state": "support_without_beta_blank", tools/data/boundary_two_axis_matrix_20260509_1532.json:274: "n_gaps": 199 tools/data/brody_flow.json:4: "n_gaps": 148932, tools/exp_spectral_landscape.py:161:def gen_zeta_zeros_model(n_spacings): tools/exp_spectral_landscape.py:329: ("zeta_zeros_model", gen_zeta_zeros_model, {}), tools/exp_two_channel_boundary.py:116: 'n_gaps': len(gaps), tools/data/duality_scale_contrast_20260508.json:14: "n_poisson_gaps": 200000, tools/data/exp_acf_z6z_mechanism.json:4: "n_gaps": 499999, tools/data/boundary_blank_null_audit_20260509_1430.json:11: "zeta_zeros": { tools/data/boundary_blank_null_audit_20260509_1430.json:12: "domain": "zeta_zeros", tools/data/boundary_blank_null_audit_20260509_1430.json:18: "n_gaps": 199, tools/data/boundary_blank_null_audit_20260509_1430.json:26: "dominio": "zeta_zeros", tools/data/boundary_blank_null_audit_20260509_1430.json:41: "n_gaps": 5008, tools/data/boundary_blank_null_audit_20260509_1430.json:60: "zeta_zeros", tools/data/boundary_blank_null_audit_20260509_1430.json:64: "zeta_zeros", tools/data/boundary_denominator_prescan_20260509_1409.json:31: "random_matrix:cycle_7", tools/data/boundary_denominator_prescan_20260509_1409.json:33: "percolation:cycle_9", tools/data/boundary_denominator_prescan_20260509_1409.json:39: "zeta_zeros:cycle_4", tools/data/boundary_denominator_prescan_20260509_1409.json:65: "n_gaps": 95, tools/data/boundary_denominator_prescan_20260509_1409.json:90: "n_gaps": null, tools/data/boundary_denominator_prescan_20260509_1409.json:115: "n_gaps": 99999, tools/data/boundary_denominator_prescan_20260509_1409.json:123: "domain_window": "zeta_zeros:cycle_4", tools/data/boundary_denominator_prescan_20260509_1409.json:124: "domain": "zeta_zeros", tools/data/boundary_denominator_prescan_20260509_1409.json:140: "n_gaps": null, tools/data/boundary_denominator_prescan_20260509_1409.json:165: "n_gaps": 99999, tools/data/boundary_denominator_prescan_20260509_1409.json:190: "n_gaps": null, tools/data/boundary_denominator_prescan_20260509_1409.json:198: "domain_window": "random_matrix:cycle_7", tools/data/boundary_denominator_prescan_20260509_1409.json:199: "domain": "random_matrix", tools/data/boundary_denominator_prescan_20260509_1409.json:215: "n_gaps": 5020, tools/data/boundary_denominator_prescan_20260509_1409.json:240: "n_gaps": 105, tools/data/boundary_denominator_prescan_20260509_1409.json:248: "domain_window": "percolation:cycle_9", tools/data/boundary_denominator_prescan_20260509_1409.json:249: "domain": "percolation", tools/data/boundary_denominator_prescan_20260509_1409.json:264: "domain_key": "percolation", tools/data/boundary_denominator_prescan_20260509_1409.json:265: "n_gaps": 510, tools/data/boundary_denominator_prescan_20260509_1409.json:290: "n_gaps": 427, tools/data/boundary_denominator_prescan_20260509_1409.json:315: "n_gaps": null, tools/data/boundary_denominator_prescan_20260509_1409.json:340: "n_gaps": 650, tools/data/boundary_denominator_prescan_20260509_1409.json:365: "n_gaps": null, tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:13: "n_gaps": 1024, tools/data/bridge_order_denominator_gate_20260507_0942.json:14: "n_gaps": 4096, tools/data/bridge_order_denominator_gate_20260507_0942.json:25: "n_gaps": 4096, tools/data/bridge_order_denominator_gate_20260507_0942.json:55: "n_gaps": 4096, tools/data/bridge_order_denominator_gate_20260507_0942.json:87: "n_gaps": 2000, tools/data/bridge_order_denominator_gate_20260507_0942.json:119: "n_gaps": 4096, tools/data/bridge_order_denominator_gate_20260507_0942.json:154: "n_gaps": 4096, tools/data/bridge_order_denominator_gate_20260507_0942.json:8996: "n_gaps": 4096, tools/data/bridge_order_denominator_gate_20260507_0942.json:17857: "n_gaps": 2000, tools/data/bridge_order_denominator_gate_20260507_0942.json:26768: "n_gaps": 4096, tools/data/ciclo_memoria.json:242: "verdict": "**CONFIRMED TRANSFER**.\n\nIl blank-pair test trasforma `zeta_zeros` e `pendolo_doppio` da blank a transfers; il perimetro BOUNDARY passa da 8/13 a 10/13 transfer, con 3 blank residui e 0 falls.\n\nQuesto", tools/data/ciclo_memoria.json:375: "`n_gaps`", exec /bin/bash -lc 'ls -t tools/exp_boundary*.py tools/exp_blank*.py 2>/dev/null' in /opt/MM_D-ND succeeded in 0ms: tools/exp_boundary_blank_thin_support_audit.py tools/exp_boundary_row_aligned_nonexact_audit.py tools/exp_boundary_two_axis_matrix.py tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py tools/exp_blank_shell_stratified_gate.py tools/exp_blank_shell_scale_law.py tools/exp_blank_shell_dilation_gate.py tools/exp_blank_shell_tqger_gate.py tools/exp_blank_shell_polarity_gate.py tools/exp_blank_to_source_hinge.py tools/exp_boundary_mixture_gate.py tools/exp_boundary_coherence.py tools/exp_boundary_shuffle_audit.py tools/exp_boundary_growth.py tools/exp_boundary_gue_poisson.py exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_denominator_prescan.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_boundary_denominator_prescan.py Boundary-oriented prescan for transferring the `denominator_state` gate beyond V_c. The unit under test is not the GUE/Poisson label. The unit is the domain/window row before a structural claim is allowed to use its observable. Input deposits: - tools/data/autoricerca_journal.json: base 13-domain GUE/Poisson perimeter. - tools/data/boundary_shuffle_audit.json: available shuffle/null support. Output: - one row per base domain/window with source type, denominator_state, excluded_mass, observable, null/surrogate, and transfer verdict. """ from __future__ import annotations import argparse import json import math from pathlib import Path from typing import Any DEFAULT_DOMAIN_KEY = { "numeri_primi": "primes", "random_matrix": "gue", "logistica_biforcazione": "logistic", "ising_2d": "ising_2d", "cellular_automata": "cell_auto", "brownian_motion": "brownian", "percolation": "percolation", "coupled_oscillators": "coupled_osc", "zeta_zeros": "zeta_zeros", "pendolo_doppio": "pendolo_doppio", } def is_base_cycle(value: Any) -> bool: if isinstance(value, int): return 1 <= value <= 13 if isinstance(value, float): return value.is_integer() and 1 <= int(value) <= 13 return False def finite_number(value: Any) -> bool: return isinstance(value, (int, float)) and math.isfinite(float(value)) def load_json(path: Path) -> Any: with path.open() as f: return json.load(f) def source_type(spacing: str | None) -> str: if spacing == "GUE-like": return "GUE" if spacing == "Poisson-like": return "Poisson" if spacing: return spacing return "absent" def null_label(null_row: dict[str, Any] | None) -> str: if not null_row: return "absent" z = null_row.get("z_score") if finite_number(z): return f"shuffle z={float(z):.2f}; class_change={bool(null_row.get('class_changes'))}" return "shuffle present; z absent" def classify_denominator(row: dict[str, Any], null_row: dict[str, Any] | None) -> tuple[str, float, str]: observable_defined = finite_number(row.get("spacing_r")) and row.get("spacing") in { "GUE-like", "Poisson-like", } if not observable_defined: return "absent", 1.0, "falls" if null_row is None: return "absent", 1.0, "blank" n_gaps = null_row.get("n_gaps") z = null_row.get("z_score") has_null = finite_number(n_gaps) and finite_number(z) if not has_null: return "broken", 1.0, "falls" excluded_mass = 0.0 state = "complete" if int(n_gaps) < 500: state = "contaminated" excluded_mass = 1.0 - (float(n_gaps) / 500.0) return state, max(0.0, excluded_mass), "transfers" def build_rows(autoricerca: list[dict[str, Any]], shuffle_audit: dict[str, Any]) -> list[dict[str, Any]]: base_rows = [row for row in autoricerca if is_base_cycle(row.get("ciclo"))] base_rows.sort(key=lambda row: int(row["ciclo"])) null_domains = shuffle_audit.get("domains", {}) rows = [] for row in base_rows: domain = row.get("dominio", "") null_key = DEFAULT_DOMAIN_KEY.get(domain, domain) null_row = null_domains.get(null_key) denominator_state, excluded_mass, transfer = classify_denominator(row, null_row) rows.append( { "domain_window": f"{domain}:cycle_{int(row['ciclo'])}", "domain": domain, "cycle": int(row["ciclo"]), "source_domain_type": source_type(row.get("spacing")), "denominator_state": denominator_state, "excluded_mass": round(excluded_mass, 6), "observable": { "name": "spacing_r", "defined": finite_number(row.get("spacing_r")), "value": row.get("spacing_r"), "label": row.get("spacing"), "n_points": row.get("n_punti"), }, "null_surrogate": { "name": "shuffle_r_statistic", "status": null_label(null_row), "domain_key": null_key if null_row else None, "n_gaps": null_row.get("n_gaps") if null_row else None, "r_shuffled_mean": null_row.get("r_shuffled_mean") if null_row else None, "z_score": null_row.get("z_score") if null_row else None, "class_changes": null_row.get("class_changes") if null_row else None, }, "transfer": transfer, } ) return rows def merge_extra_null_audit(shuffle_audit: dict[str, Any], extra_paths: list[str]) -> dict[str, Any]: merged = { **shuffle_audit, "domains": dict(shuffle_audit.get("domains", {})), } extras = [] for raw_path in extra_paths: path = Path(raw_path) if not path.exists(): continue data = load_json(path) domains = data.get("domains", {}) if isinstance(data, dict) else {} for domain, row in domains.items(): if not isinstance(row, dict) or "error" in row: continue merged["domains"][domain] = row extras.append(str(path)) if extras: merged["extra_null_audits"] = extras return merged def summarize(rows: list[dict[str, Any]]) -> dict[str, Any]: by_transfer: dict[str, int] = {} by_state: dict[str, int] = {} by_type: dict[str, int] = {} for row in rows: by_transfer[row["transfer"]] = by_transfer.get(row["transfer"], 0) + 1 by_state[row["denominator_state"]] = by_state.get(row["denominator_state"], 0) + 1 by_type[row["source_domain_type"]] = by_type.get(row["source_domain_type"], 0) + 1 return { "n_rows": len(rows), "by_transfer": by_transfer, "by_denominator_state": by_state, "by_source_domain_type": by_type, "transfer_scope": [ row["domain_window"] for row in rows if row["transfer"] == "transfers" ], "blank_scope": [row["domain_window"] for row in rows if row["transfer"] == "blank"], "falls_scope": [row["domain_window"] for row in rows if row["transfer"] == "falls"], } def run(args: argparse.Namespace) -> dict[str, Any]: autoricerca = load_json(Path(args.autoricerca)) shuffle_audit = load_json(Path(args.shuffle_audit)) if args.extra_null_audit: shuffle_audit = merge_extra_null_audit(shuffle_audit, args.extra_null_audit) rows = build_rows(autoricerca, shuffle_audit) output = { "experiment": "boundary_denominator_prescan", "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", "observable_contract": { "claim": "denominator_state gate transfer beyond V_c", "observable": "spacing_r label row with shuffle/null availability", "operator": "row-aligned domain/window prescan", "null": "boundary_shuffle_audit shuffle r-statistic when present", "non_possible": "claiming transfer where null/surrogate is absent", "extra_null_audits": shuffle_audit.get("extra_null_audits", []), }, "summary": summarize(rows), "rows": rows, } 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"wrote={out_path}") print(f"rows={output['summary']['n_rows']}") print(f"by_transfer={output['summary']['by_transfer']}") print(f"by_denominator_state={output['summary']['by_denominator_state']}") for row in rows: print( f"{row['domain_window']}\t{row['source_domain_type']}\t" f"{row['denominator_state']}\t{row['excluded_mass']:.3f}\t" f"{row['transfer']}\t{row['null_surrogate']['status']}" ) return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--autoricerca", default="tools/data/autoricerca_journal.json") parser.add_argument("--shuffle-audit", default="tools/data/boundary_shuffle_audit.json") parser.add_argument("--extra-null-audit", action="append", default=[]) parser.add_argument("--out", default="tools/data/boundary_denominator_prescan_20260509_1409.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,280p' tools/exp_boundary_row_aligned_nonexact_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Audit BOUNDARY rows where support transfers but beta 0.3 is not exact. The operator is row-aligned with the 1532 two-axis matrix and deliberately does not read GUE/Poisson labels as decision fields. It only uses support, beta-state, denominator telemetry, shuffle telemetry, and measured gate strength. """ from __future__ import annotations import argparse import json from pathlib import Path from statistics import mean from typing import Any NONEXACT_STATES = { "beta_0_3_local_nonunique", "local_beta_other", "support_without_beta_blank", } def row_key(domain: str, cycle: int) -> str: return f"{domain}:cycle_{cycle}" def build_prescan_index(data: dict[str, Any]) -> dict[str, dict[str, Any]]: index: dict[str, dict[str, Any]] = {} for row in data.get("rows", []): key = row_key(row["domain"], row["cycle"]) observable = row.get("observable", {}) null = row.get("null_surrogate", {}) index[key] = { "denominator_state": row.get("denominator_state"), "excluded_mass": row.get("excluded_mass"), "observable_name": observable.get("name"), "observable_value": observable.get("value"), "n_points": observable.get("n_points"), "null_name": null.get("name"), "shuffle_z_score": null.get("z_score"), "shuffle_class_changes": null.get("class_changes"), "domain_window": row.get("domain_window"), } return index def beta_span(beta: list[float]) -> float: if len(beta) < 2: return 0.0 return max(beta) - min(beta) def support_tier(row: dict[str, Any]) -> str: n_obs = len(row.get("one_sided_observables", [])) endpoint = float(row.get("endpoint_distance") or 0.0) stable = float(row.get("stable_count_coherent") or 0.0) if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: return "strong_multi_observable" if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: return "medium_multi_observable" return "thin_observable_support" def coordinate_failure(row: dict[str, Any]) -> str: state = row["beta_state"] beta = row.get("ambiguous_beta", []) tier = support_tier(row) if state == "beta_0_3_local_nonunique": if len(beta) >= 5: return "beta_grid_saturation" return "adjacent_beta_interval" if state == "local_beta_other": return "coordinate_shifted" if state == "support_without_beta_blank": if tier == "thin_observable_support": return "blank_thin_support" return "blank_despite_multi_observable_support" return "not_in_scope" def build_audit(two_axis: dict[str, Any], prescan: dict[str, Any]) -> dict[str, Any]: prescan_index = build_prescan_index(prescan) exact_rows = [ row for row in two_axis.get("rows", []) if row.get("support_transfer") and row.get("beta_coordinate_transfer") ] nonexact_rows = [ row for row in two_axis.get("rows", []) if row.get("support_transfer") and row.get("beta_state") in NONEXACT_STATES ] fall_rows = [ row for row in two_axis.get("rows", []) if not row.get("support_transfer") ] rows = [] counts: dict[str, int] = { "total_rows": len(two_axis.get("rows", [])), "support_transfer_rows": len(exact_rows) + len(nonexact_rows), "beta_exact_rows": len(exact_rows), "support_nonexact_rows": len(nonexact_rows), "fall_rows": len(fall_rows), } by_state: dict[str, int] = {} by_failure: dict[str, int] = {} by_tier: dict[str, int] = {} for row in nonexact_rows: key = row["row"] beta = row.get("ambiguous_beta", []) failure = coordinate_failure(row) tier = support_tier(row) by_state[row["beta_state"]] = by_state.get(row["beta_state"], 0) + 1 by_failure[failure] = by_failure.get(failure, 0) + 1 by_tier[tier] = by_tier.get(tier, 0) + 1 rows.append({ "row": key, "beta_state": row["beta_state"], "coordinate_failure": failure, "support_tier": tier, "ambiguous_beta": beta, "beta_cardinality": len(beta), "beta_span": round(beta_span(beta), 10), "one_sided_count": len(row.get("one_sided_observables", [])), "one_sided_observables": row.get("one_sided_observables", []), "stable_count_coherent": row.get("stable_count_coherent"), "stable_count_illusory": row.get("stable_count_illusory"), "endpoint_distance": row.get("endpoint_distance"), "n_gaps": row.get("n_gaps"), "prescan": prescan_index.get(key, {}), }) exact_endpoint = [float(row.get("endpoint_distance") or 0.0) for row in exact_rows] nonexact_endpoint = [float(row.get("endpoint_distance") or 0.0) for row in nonexact_rows] exact_obs = [len(row.get("one_sided_observables", [])) for row in exact_rows] nonexact_obs = [len(row.get("one_sided_observables", [])) for row in nonexact_rows] counts.update({ f"state_{key}": value for key, value in sorted(by_state.items()) }) counts.update({ f"failure_{key}": value for key, value in sorted(by_failure.items()) }) counts.update({ f"tier_{key}": value for key, value in sorted(by_tier.items()) }) mismatch = counts["support_nonexact_rows"] != 6 return { "experiment": "boundary_row_aligned_nonexact_audit", "question": "Which measured condition separates beta local non-unique, beta local other, and support-without-beta rows after the beta 0.3 universal coordinate fails?", "source_matrix": two_axis.get("experiment"), "source_scope": two_axis.get("source_scope"), "prescan_source": "boundary_denominator_prescan_full_20260509_1500", "observables_registry": two_axis.get("observables_registry"), "observables_used": [ "beta_state", "coordinate_failure", "support_tier", "beta_cardinality", "beta_span", "one_sided_count", "stable_count_coherent", "stable_count_illusory", "endpoint_distance", "denominator_state", "excluded_mass", "shuffle_z_score", ], "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", "direction_check": { "expected_nonexact_rows_from_field": 6, "measured_support_nonexact_rows": counts["support_nonexact_rows"], "mismatch_is_result": mismatch, }, "counts": counts, "comparative_means": { "exact_endpoint_distance_mean": mean(exact_endpoint) if exact_endpoint else None, "nonexact_endpoint_distance_mean": mean(nonexact_endpoint) if nonexact_endpoint else None, "exact_one_sided_count_mean": mean(exact_obs) if exact_obs else None, "nonexact_one_sided_count_mean": mean(nonexact_obs) if nonexact_obs else None, }, "rows": rows, "falls": [ { "row": row["row"], "raw_beta_exact_0_3": row.get("raw_beta_exact_0_3"), "ambiguous_beta": row.get("ambiguous_beta", []), "one_sided_count": len(row.get("one_sided_observables", [])), "stable_count_illusory": row.get("stable_count_illusory"), "endpoint_distance": row.get("endpoint_distance"), } for row in fall_rows ], } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--matrix", default="tools/data/boundary_two_axis_matrix_20260509_1532.json") parser.add_argument("--prescan", default="tools/data/boundary_denominator_prescan_full_20260509_1500.json") parser.add_argument("--out", default="tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json") args = parser.parse_args() with Path(args.matrix).open() as f: two_axis = json.load(f) with Path(args.prescan).open() as f: prescan = json.load(f) output = build_audit(two_axis, prescan) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) with out.open("w") as f: json.dump(output, f, indent=2) c = output["counts"] print(f"total_rows={c['total_rows']}") print(f"support_transfer_rows={c['support_transfer_rows']}") print(f"beta_exact_rows={c['beta_exact_rows']}") print(f"support_nonexact_rows={c['support_nonexact_rows']}") print(f"fall_rows={c['fall_rows']}") print(f"direction_expected_nonexact=6 measured={c['support_nonexact_rows']}") for key, value in sorted(c.items()): if key.startswith("state_") or key.startswith("failure_") or key.startswith("tier_"): print(f"{key}={value}") print(f"saved {out}") if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_blank_thin_support_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Audit the thin support-without-beta blanks against the medium prime blank. The input is the row-aligned nonexact BOUNDARY audit. The operator stays inside the support_without_beta_blank subset and asks whether the thin rows separate by denominator telemetry, null contamination, or an autonomous support signature. """ from __future__ import annotations import argparse import json from pathlib import Path from statistics import mean from typing import Any TARGET_STATE = "support_without_beta_blank" THIN_FAILURE = "blank_thin_support" def as_float(value: Any, default: float = 0.0) -> float: if value is None: return default return float(value) def denominator_bucket(row: dict[str, Any], full_gap_floor: int) -> str: n_gaps = int(row.get("n_gaps") or 0) if n_gaps >= full_gap_floor: return "full_denominator" return "short_denominator" def blank_class(row: dict[str, Any], full_gap_floor: int) -> str: thin = row.get("coordinate_failure") == THIN_FAILURE short = denominator_bucket(row, full_gap_floor) == "short_denominator" contaminated = row.get("prescan", {}).get("denominator_state") == "contaminated" class_change = bool(row.get("prescan", {}).get("shuffle_class_changes")) if not thin: return "medium_blank_control" if short and contaminated: return "thin_short_contaminated" if short and class_change: return "thin_short_shuffle_unstable" if short: return "thin_short_complete" return "thin_not_denominator_explained" def summarize(rows: list[dict[str, Any]]) -> dict[str, Any]: if not rows: return {} return { "rows": len(rows), "n_gaps_mean": mean(as_float(row.get("n_gaps")) for row in rows), "one_sided_mean": mean(len(row.get("one_sided_observables", [])) for row in rows), "stable_count_coherent_mean": mean(as_float(row.get("stable_count_coherent")) for row in rows), "endpoint_distance_mean": mean(as_float(row.get("endpoint_distance")) for row in rows), "excluded_mass_mean": mean(as_float(row.get("prescan", {}).get("excluded_mass")) for row in rows), "abs_shuffle_z_mean": mean(abs(as_float(row.get("prescan", {}).get("shuffle_z_score"))) for row in rows), } def build_audit(data: dict[str, Any], full_gap_floor: int) -> dict[str, Any]: blank_rows = [ row for row in data.get("rows", []) if row.get("beta_state") == TARGET_STATE ] if not blank_rows: raise ValueError("input has no support_without_beta_blank rows") rows = [] counts: dict[str, int] = { "support_without_beta_blank_rows": len(blank_rows), "thin_rows": 0, "medium_control_rows": 0, "short_denominator_rows": 0, "contaminated_rows": 0, "shuffle_class_change_rows": 0, "thin_short_rows": 0, "thin_contaminated_rows": 0, "thin_shuffle_class_change_rows": 0, "thin_not_denominator_explained_rows": 0, } by_class: dict[str, int] = {} for row in blank_rows: thin = row.get("coordinate_failure") == THIN_FAILURE bucket = denominator_bucket(row, full_gap_floor) prescan = row.get("prescan", {}) contaminated = prescan.get("denominator_state") == "contaminated" class_change = bool(prescan.get("shuffle_class_changes")) cls = blank_class(row, full_gap_floor) counts["thin_rows" if thin else "medium_control_rows"] += 1 if bucket == "short_denominator": counts["short_denominator_rows"] += 1 if contaminated: counts["contaminated_rows"] += 1 if class_change: counts["shuffle_class_change_rows"] += 1 if thin and bucket == "short_denominator": counts["thin_short_rows"] += 1 if thin and contaminated: counts["thin_contaminated_rows"] += 1 if thin and class_change: counts["thin_shuffle_class_change_rows"] += 1 if thin and cls == "thin_not_denominator_explained": counts["thin_not_denominator_explained_rows"] += 1 by_class[cls] = by_class.get(cls, 0) + 1 rows.append({ "row": row["row"], "blank_class": cls, "coordinate_failure": row.get("coordinate_failure"), "support_tier": row.get("support_tier"), "denominator_bucket": bucket, "n_gaps": row.get("n_gaps"), "one_sided_count": len(row.get("one_sided_observables", [])), "one_sided_observables": row.get("one_sided_observables", []), "stable_count_coherent": row.get("stable_count_coherent"), "stable_count_illusory": row.get("stable_count_illusory"), "endpoint_distance": row.get("endpoint_distance"), "denominator_state": prescan.get("denominator_state"), "excluded_mass": prescan.get("excluded_mass"), "shuffle_z_score": prescan.get("shuffle_z_score"), "shuffle_class_changes": class_change, }) thin_rows = [row for row in blank_rows if row.get("coordinate_failure") == THIN_FAILURE] medium_rows = [row for row in blank_rows if row.get("coordinate_failure") != THIN_FAILURE] all_thin_short = bool(thin_rows) and counts["thin_short_rows"] == len(thin_rows) all_thin_contaminated = bool(thin_rows) and counts["thin_contaminated_rows"] == len(thin_rows) all_thin_shuffle_unstable = bool(thin_rows) and counts["thin_shuffle_class_change_rows"] == len(thin_rows) if all_thin_short and not all_thin_contaminated: verdict = "DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION" elif all_thin_contaminated: verdict = "CONTAMINATION_LIMITED" elif counts["thin_not_denominator_explained_rows"] > 0: verdict = "AUTONOMOUS_THIN_BLANK_CANDIDATE" else: verdict = "AMBIGUOUS" counts.update({f"class_{key}": value for key, value in sorted(by_class.items())}) return { "experiment": "boundary_blank_thin_support_audit", "question": "Are thin support-without-beta blanks denominator artifacts, null contamination artifacts, or autonomous boundary species?", "source": data.get("experiment"), "source_scope": data.get("source_scope"), "observables_registry": data.get("observables_registry"), "observables_used": [ "blank_class", "coordinate_failure", "support_tier", "denominator_bucket", "n_gaps", "one_sided_count", "stable_count_coherent", "stable_count_illusory", "endpoint_distance", "denominator_state", "excluded_mass", "shuffle_z_score", "shuffle_class_changes", ], "params": { "full_gap_floor": full_gap_floor, "target_state": TARGET_STATE, "thin_failure": THIN_FAILURE, }, "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", "tests": { "denominator_artifact": { "condition": "all thin rows have n_gaps below full_gap_floor", "passes": all_thin_short, }, "contamination_artifact": { "condition": "all thin rows are prescan contaminated", "passes": all_thin_contaminated, }, "shuffle_instability_artifact": { "condition": "all thin rows have shuffle_class_changes=true", "passes": all_thin_shuffle_unstable, }, "autonomous_species_counter": { "condition": "at least one thin row is not short-denominator explained", "passes": counts["thin_not_denominator_explained_rows"] > 0, }, }, "counts": counts, "comparative_means": { "thin": summarize(thin_rows), "medium_control": summarize(medium_rows), }, "verdict": verdict, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--input", default="tools/data/boundary_row_aligned_nonexact_audit_20260509_1538.json") parser.add_argument("--full-gap-floor", type=int, default=500) parser.add_argument("--out", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") args = parser.parse_args() with Path(args.input).open() as f: data = json.load(f) output = build_audit(data, args.full_gap_floor) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) with out.open("w") as f: json.dump(output, f, indent=2) c = output["counts"] print(f"support_without_beta_blank_rows={c['support_without_beta_blank_rows']}") print(f"thin_rows={c['thin_rows']}") print(f"medium_control_rows={c['medium_control_rows']}") print(f"thin_short_rows={c['thin_short_rows']}") print(f"thin_contaminated_rows={c['thin_contaminated_rows']}") print(f"thin_shuffle_class_change_rows={c['thin_shuffle_class_change_rows']}") print(f"thin_not_denominator_explained_rows={c['thin_not_denominator_explained_rows']}") print(f"verdict={output['verdict']}") print(f"saved {out}") if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,220p' tools/data/boundary_blank_thin_support_audit_20260509_1548.json" in /opt/MM_D-ND exec /bin/bash -lc "sed -n '1,360p' tools/exp_boundary_two_axis_matrix.py" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_blank_thin_support_audit", "question": "Are thin support-without-beta blanks denominator artifacts, null contamination artifacts, or autonomous boundary species?", "source": "boundary_row_aligned_nonexact_audit", "source_scope": "tools/data/boundary_denominator_prescan_full_20260509_1500.json", "observables_registry": "1.0.0-2026-05-06", "observables_used": [ "blank_class", "coordinate_failure", "support_tier", "denominator_bucket", "n_gaps", "one_sided_count", "stable_count_coherent", "stable_count_illusory", "endpoint_distance", "denominator_state", "excluded_mass", "shuffle_z_score", "shuffle_class_changes" ], "params": { "full_gap_floor": 500, "target_state": "support_without_beta_blank", "thin_failure": "blank_thin_support" }, "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", "tests": { "denominator_artifact": { "condition": "all thin rows have n_gaps below full_gap_floor", "passes": true }, "contamination_artifact": { "condition": "all thin rows are prescan contaminated", "passes": false }, "shuffle_instability_artifact": { "condition": "all thin rows have shuffle_class_changes=true", "passes": false }, "autonomous_species_counter": { "condition": "at least one thin row is not short-denominator explained", "passes": false } }, "counts": { "support_without_beta_blank_rows": 4, "thin_rows": 3, "medium_control_rows": 1, "short_denominator_rows": 3, "contaminated_rows": 1, "shuffle_class_change_rows": 1, "thin_short_rows": 3, "thin_contaminated_rows": 1, "thin_shuffle_class_change_rows": 1, "thin_not_denominator_explained_rows": 0, "class_medium_blank_control": 1, "class_thin_short_complete": 1, "class_thin_short_contaminated": 1, "class_thin_short_shuffle_unstable": 1 }, "comparative_means": { "thin": { "rows": 3, "n_gaps_mean": 196.0, "one_sided_mean": 1.3333333333333333, "stable_count_coherent_mean": 1.25, "endpoint_distance_mean": 2.00276033204389, "excluded_mass_mean": 0.20066666666666666, "abs_shuffle_z_mean": 11.036864333333334 }, "medium_control": { "rows": 1, "n_gaps_mean": 4096.0, "one_sided_mean": 3, "stable_count_coherent_mean": 3.0, "endpoint_distance_mean": 3.2427469463217204, "excluded_mass_mean": 0.0, "abs_shuffle_z_mean": 26.6 } }, "verdict": "DENOMINATOR_LIMITED_NOT_NULL_CONTAMINATION", "rows": [ { "row": "numeri_primi:cycle_3", "blank_class": "medium_blank_control", "coordinate_failure": "blank_despite_multi_observable_support", "support_tier": "medium_multi_observable", "denominator_bucket": "full_denominator", "n_gaps": 4096, "one_sided_count": 3, "one_sided_observables": [ "SR", "L1", "triple_var" ], "stable_count_coherent": 3.0, "stable_count_illusory": 0.16666666666666666, "endpoint_distance": 3.2427469463217204, "denominator_state": "complete", "excluded_mass": 0.0, "shuffle_z_score": -26.6, "shuffle_class_changes": false }, { "row": "percolation:cycle_9", "blank_class": "thin_short_shuffle_unstable", "coordinate_failure": "blank_thin_support", "support_tier": "thin_observable_support", "denominator_bucket": "short_denominator", "n_gaps": 190, "one_sided_count": 1, "one_sided_observables": [ "L1" ], "stable_count_coherent": 0.8333333333333334, "stable_count_illusory": 0.3333333333333333, "endpoint_distance": 1.7053713197497773, "denominator_state": "complete", "excluded_mass": 0.0, "shuffle_z_score": 16.1, "shuffle_class_changes": true }, { "row": "random_matrix:cycle_7", "blank_class": "thin_short_complete", "coordinate_failure": "blank_thin_support", "support_tier": "thin_observable_support", "denominator_bucket": "short_denominator", "n_gaps": 199, "one_sided_count": 1, "one_sided_observables": [ "L2" ], "stable_count_coherent": 1.0, "stable_count_illusory": 0.25, "endpoint_distance": 1.8543171371625617, "denominator_state": "complete", "excluded_mass": 0.0, "shuffle_z_score": -14.7, "shuffle_class_changes": false }, { "row": "zeta_zeros:cycle_4", "blank_class": "thin_short_contaminated", "coordinate_failure": "blank_thin_support", "support_tier": "thin_observable_support", "denominator_bucket": "short_denominator", "n_gaps": 199, "one_sided_count": 2, "one_sided_observables": [ "SR", "L2" ], "stable_count_coherent": 1.9166666666666667, "stable_count_illusory": 0.25, "endpoint_distance": 2.4485925392193306, "denominator_state": "contaminated", "excluded_mass": 0.602, "shuffle_z_score": -2.310593, "shuffle_class_changes": false } ] } succeeded in 0ms: #!/usr/bin/env python3 """ Build the two-axis BOUNDARY matrix requested by the 1532 cycle. Input is a row-aligned semi-real boundary gate deposit. The operator deliberately does not use GUE/Poisson source labels: it only reads transfer support and beta coordinate state from each row's measured gate fields. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any def norm_beta(values: list[float]) -> list[float]: return [round(float(v), 1) for v in values] def beta_state(row: dict[str, Any], support_transfer: bool) -> str: beta = norm_beta(row.get("ambiguous_beta_one_sided_gated", [])) if not support_transfer: return "fall_no_support" if not beta: return "support_without_beta_blank" if beta == [0.3]: return "beta_0_3_exact" if 0.3 in beta: return "beta_0_3_local_nonunique" return "local_beta_other" def build_matrix(data: dict[str, Any]) -> dict[str, Any]: source_rows = data.get("evaluation", {}).get("rows", {}) if not isinstance(source_rows, dict) or not source_rows: raise ValueError("input does not contain evaluation.rows") rows = [] counts = { "rows": 0, "support_transfer_true": 0, "support_transfer_false": 0, "raw_beta_exact_0_3": 0, "raw_beta_exact_0_3_without_support": 0, "beta_coordinate_exact_0_3": 0, "beta_coordinate_local_nonunique_0_3": 0, "beta_coordinate_other": 0, "support_without_beta_blank": 0, "fall_no_support": 0, } for name, row in sorted(source_rows.items()): state = row.get("state") support_transfer = state in {"transfer_with_blank", "transfer_no_blank"} beta = norm_beta(row.get("ambiguous_beta_one_sided_gated", [])) b_state = beta_state(row, support_transfer) beta_coordinate_transfer = support_transfer and b_state == "beta_0_3_exact" counts["rows"] += 1 counts["support_transfer_true" if support_transfer else "support_transfer_false"] += 1 if beta == [0.3]: counts["raw_beta_exact_0_3"] += 1 if not support_transfer: counts["raw_beta_exact_0_3_without_support"] += 1 if b_state == "beta_0_3_exact": counts["beta_coordinate_exact_0_3"] += 1 elif b_state == "beta_0_3_local_nonunique": counts["beta_coordinate_local_nonunique_0_3"] += 1 elif b_state == "local_beta_other": counts["beta_coordinate_other"] += 1 elif b_state == "support_without_beta_blank": counts["support_without_beta_blank"] += 1 elif b_state == "fall_no_support": counts["fall_no_support"] += 1 rows.append( { "row": name, "support_transfer": support_transfer, "beta_coordinate_transfer": beta_coordinate_transfer, "beta_state": b_state, "raw_beta_exact_0_3": beta == [0.3], "ambiguous_beta": beta, "one_sided_observables": row.get("coherent_one_sided_observables", []), "stable_count_coherent": row.get("stable_count_coherent"), "stable_count_illusory": row.get("stable_count_illusory"), "endpoint_distance": row.get("endpoint_distance_one_sided_gated"), "source_state": state, "n_gaps": row.get("n_gaps"), } ) counts["support_transfer_ratio"] = counts["support_transfer_true"] / counts["rows"] counts["raw_beta_exact_0_3_ratio"] = counts["raw_beta_exact_0_3"] / counts["rows"] counts["beta_coordinate_exact_0_3_ratio"] = counts["beta_coordinate_exact_0_3"] / counts["rows"] counts["any_beta_blank_on_support"] = ( counts["beta_coordinate_exact_0_3"] + counts["beta_coordinate_local_nonunique_0_3"] + counts["beta_coordinate_other"] ) counts["any_beta_blank_on_support_ratio"] = counts["any_beta_blank_on_support"] / counts["rows"] return { "experiment": "boundary_two_axis_matrix", "question": "Separate support_transfer from beta_coordinate_transfer on the 13 semi-real BOUNDARY rows without using GUE/Poisson labels.", "source": data.get("experiment"), "source_scope": data.get("source_scope"), "observables_registry": data.get("observables_registry"), "observables_used": [ "support_transfer", "beta_coordinate_transfer", "beta_state", "ambiguous_beta", "stable_count_coherent", "stable_count_illusory", "endpoint_distance", ], "label_policy": "GUE/Poisson source labels are not read by this operator.", "axis_contract": { "raw_beta_exact_0_3": "ambiguous_beta is exactly [0.3], independent of support", "beta_coordinate_transfer": "raw_beta_exact_0_3 and support_transfer are both true", "edge_case": "raw beta 0.3 without support remains a beta observation, not a transfer coordinate", }, "counts": counts, "rows": rows, } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--input", default="tools/data/semireal_boundary_transfer_gate_20260509_1516.json") parser.add_argument("--out", default="tools/data/boundary_two_axis_matrix_20260509_1532.json") args = parser.parse_args() with Path(args.input).open() as f: data = json.load(f) output = build_matrix(data) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) with out.open("w") as f: json.dump(output, f, indent=2) c = output["counts"] print(f"rows={c['rows']}") print(f"support_transfer={c['support_transfer_true']}/{c['rows']}") print(f"raw_beta_exact_0_3={c['raw_beta_exact_0_3']}/{c['rows']}") print(f"raw_beta_exact_0_3_without_support={c['raw_beta_exact_0_3_without_support']}/{c['rows']}") print(f"beta_coordinate_exact_0_3={c['beta_coordinate_exact_0_3']}/{c['rows']}") print(f"any_beta_blank_on_support={c['any_beta_blank_on_support']}/{c['rows']}") print(f"support_without_beta_blank={c['support_without_beta_blank']}/{c['rows']}") print(f"fall_no_support={c['fall_no_support']}/{c['rows']}") print(f"saved {out}") if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '240,520p' tools/data/semireal_boundary_transfer_gate_20260509_1516.json" in /opt/MM_D-ND succeeded in 0ms: "L2": 0.20914243462343277, "triple_var": -0.19380298901399437 }, "endpoint_distance_all": 2.949876494516273, "endpoint_distance_one_sided_gated": 1.8543171371625617, "ambiguous_beta_one_sided_gated": [] }, "cellular_automata:cycle_8": { "n_gaps": 108, "coherent_one_sided_observables": [], "endpoint_stable_observables": [], "stable_count_coherent": 0.0, "stable_count_illusory": 0.5, "z_mean_coherent": { "SR": 0.21177957574652603, "SR2": 0.9979544048942478, "L1": 0.5112253336133381, "L2": 0.4671310663421746, "triple_var": 0.7458658190993708 }, "z_mean_illusory": { "SR": 0.19358426302808307, "SR2": -0.1533304510552138, "L1": 0.3093373666367109, "L2": -0.10183719568462231, "triple_var": 0.11351047392135372 }, "endpoint_distance_all": 1.5686948091747426, "endpoint_distance_one_sided_gated": 0.0, "ambiguous_beta_one_sided_gated": [] }, "percolation:cycle_9": { "n_gaps": 190, "coherent_one_sided_observables": [ "L1" ], "endpoint_stable_observables": [], "stable_count_coherent": 0.8333333333333334, "stable_count_illusory": 0.3333333333333333, "z_mean_coherent": { "SR": 0.5395922873882314, "SR2": -0.43809397759781077, "L1": 2.9853813352277996, "L2": 0.7594286022860013, "triple_var": 0.922068174670346 }, "z_mean_illusory": { "SR": -0.3227979911495034, "SR2": -0.1856804064163832, "L1": 0.08286290506086615, "L2": -0.07390905319963333, "triple_var": 0.12278934227008703 }, "endpoint_distance_all": 2.371749230998706, "endpoint_distance_one_sided_gated": 1.7053713197497773, "ambiguous_beta_one_sided_gated": [] }, "coupled_oscillators:cycle_10": { "n_gaps": 2002, "coherent_one_sided_observables": [ "SR", "SR2", "L1", "L2", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "z_mean_coherent": { "SR": 6.489654922491424, "SR2": 6.82053106158143, "L1": 12.57611122958918, "L2": 25.94054488914526, "triple_var": 21.329810636268302 }, "z_mean_illusory": { "SR": -0.741569311600549, "SR2": 0.10926509291172493, "L1": -0.39381729264269927, "L2": -0.1256084920159853, "triple_var": -0.43664220329758824 }, "endpoint_distance_all": 4.336278592600956, "endpoint_distance_one_sided_gated": 4.336278592600956, "ambiguous_beta_one_sided_gated": [ 0.30000000000000004 ] }, "reaction_diffusion:cycle_11": { "n_gaps": 499, "coherent_one_sided_observables": [ "SR", "SR2", "L1", "L2", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 5.0, "stable_count_illusory": 0.3333333333333333, "z_mean_coherent": { "SR": 30.70549276593214, "SR2": 28.1524722531688, "L1": 13.944924692536324, "L2": 15.097846790122427, "triple_var": 19.31984861927103 }, "z_mean_illusory": { "SR": 0.3431009833463082, "SR2": -0.06402787947191672, "L1": 0.17979607995275862, "L2": -0.03537586016585018, "triple_var": 0.13257991798648983 }, "endpoint_distance_all": 4.3642161141453535, "endpoint_distance_one_sided_gated": 4.3642161141453535, "ambiguous_beta_one_sided_gated": [ 0.30000000000000004 ] }, "brownian_motion:cycle_12": { "n_gaps": 4096, "coherent_one_sided_observables": [ "SR2", "L1", "L2", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 4.0, "stable_count_illusory": 0.16666666666666666, "z_mean_coherent": { "SR": 1.4649737752517848, "SR2": 40.19176837351409, "L1": -14.247303258399233, "L2": 39.03805307261396, "triple_var": 6.345437097675837 }, "z_mean_illusory": { "SR": -0.019154391216039873, "SR2": 0.13232179090658142, "L1": -0.09855767142791756, "L2": -0.08785297761498535, "triple_var": -0.14612535200538687 }, "endpoint_distance_all": 4.1728560805682395, "endpoint_distance_one_sided_gated": 3.8849187765851725, "ambiguous_beta_one_sided_gated": [ 0.2, 0.30000000000000004 ] }, "logistica_biforcazione_var_3.5699:cycle_13": { "n_gaps": 4096, "coherent_one_sided_observables": [ "SR", "SR2" ], "endpoint_stable_observables": [], "stable_count_coherent": 2.0, "stable_count_illusory": 0.08333333333333333, "z_mean_coherent": { "SR": 149.30349943284708, "SR2": 93.61331053206128, "L1": -0.4195683504349528, "L2": -0.4421129317332444, "triple_var": -0.44562854352807735 }, "z_mean_illusory": { "SR": 0.01946236959270684, "SR2": -0.20666906403705332, "L1": -0.18071949281750824, "L2": -0.14225990706185632, "triple_var": -0.15962202460499783 }, "endpoint_distance_all": 2.9529244691243988, "endpoint_distance_one_sided_gated": 2.768680237365777, "ambiguous_beta_one_sided_gated": [ 0.30000000000000004 ] } }, "evaluation": { "counts": { "transfer_with_blank": 7, "transfer_no_blank": 4, "fall": 2, "errors": 0 }, "rows": { "ising_2d:cycle_1": { "state": "transfer_with_blank", "n_gaps": 699, "coherent_one_sided_observables": [ "SR", "SR2", "L1", "L2", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 5.0, "stable_count_illusory": 0.3333333333333333, "z_mean_coherent": { "SR": 7.398595156770597, "SR2": 6.941720762373805, "L1": 10.42183501598859, "L2": 6.485285521813761, "triple_var": 10.808404489180242 }, "z_mean_illusory": { "SR": 0.2883759633721624, "SR2": -0.16793737281544588, "L1": 0.30950140975894136, "L2": -0.18471244116836846, "triple_var": 0.13983198326148757 }, "endpoint_distance_all": 4.308544591024874, "endpoint_distance_one_sided_gated": 4.308544591024874, "ambiguous_beta_one_sided_gated": [ 0.4 ] }, "pendolo_doppio:cycle_2": { "state": "fall", "n_gaps": 4096, "coherent_one_sided_observables": [ "SR", "SR2", "L1" ], "endpoint_stable_observables": [], "stable_count_coherent": 5.0, "stable_count_illusory": 0.8333333333333334, "z_mean_coherent": { "SR": 28.011617338887163, "SR2": 29.07265066876214, "L1": 43.75032148851134, "L2": 46.6671518171662, "triple_var": 60.489259861530435 }, "z_mean_illusory": { "SR": -0.4456813267946977, "SR2": -0.12645447175907798, "L1": 0.0813033465224044, "L2": 0.4740163673473881, "triple_var": 0.23650251982435724 }, "endpoint_distance_all": 4.3747095614604286, "endpoint_distance_one_sided_gated": 3.3880909132625545, "ambiguous_beta_one_sided_gated": [ 0.30000000000000004 ] }, "numeri_primi:cycle_3": { "state": "transfer_no_blank", "n_gaps": 4096, "coherent_one_sided_observables": [ "SR", "L1", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 3.0, "stable_count_illusory": 0.16666666666666666, "z_mean_coherent": { "SR": -8.391483821563478, "SR2": 0.878654289295878, "L1": -4.462046963574253, "L2": -0.8900383545098665, "triple_var": -4.447508024812871 }, "z_mean_illusory": { "SR": 0.30756900451349556, "SR2": -0.18592532640841716, "L1": -0.15825754425580274, "L2": -0.24833755838065744, "triple_var": -0.3010338198638567 }, "endpoint_distance_all": 3.576176471462624, exec /bin/bash -lc "sed -n '1,240p' tools/data/semireal_boundary_transfer_gate_20260509_1516.json" in /opt/MM_D-ND exec /bin/bash -lc 'rg -n "semireal_boundary|ambiguous_beta_one_sided_gated|stable_count_coherent|endpoint_distance_one_sided_gated|coherent_one_sided_observables|n_gaps" tools/exp_*.py | head -120' in /opt/MM_D-ND succeeded in 0ms: { "experiment": "semireal_boundary_transfer_gate", "question": "Does the BOUNDARY coherent/null/beta gate transfer from synthetic perimeters to the 13 semi-real base rows?", "observables_registry": "1.0.0-2026-05-06", "observables_used": [ "SR", "SR2", "L1", "L2", "triple_var" ], "params": { "scope": "tools/data/boundary_denominator_prescan_full_20260509_1500.json", "n_gaps": 4096, "min_gaps": 96, "n_replicates": 12, "n_beta": 11, "n_baseline": 24, "z_min": 2.0, "min_one_sided": 1, "illusory_residue_max": 0.75, "endpoint_distance_min": 1.0, "seed": 202605091516, "out": "tools/data/semireal_boundary_transfer_gate_20260509_1516.json" }, "source_scope": "tools/data/boundary_denominator_prescan_full_20260509_1500.json", "source_summary": { "rows": 13, "source_types": { "GUE": 8, "Poisson": 5 } }, "matrix": { "ising_2d:cycle_1": { "n_gaps": 699, "coherent_one_sided_observables": [ "SR", "SR2", "L1", "L2", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 5.0, "stable_count_illusory": 0.3333333333333333, "z_mean_coherent": { "SR": 7.398595156770597, "SR2": 6.941720762373805, "L1": 10.42183501598859, "L2": 6.485285521813761, "triple_var": 10.808404489180242 }, "z_mean_illusory": { "SR": 0.2883759633721624, "SR2": -0.16793737281544588, "L1": 0.30950140975894136, "L2": -0.18471244116836846, "triple_var": 0.13983198326148757 }, "endpoint_distance_all": 4.308544591024874, "endpoint_distance_one_sided_gated": 4.308544591024874, "ambiguous_beta_one_sided_gated": [ 0.4 ] }, "pendolo_doppio:cycle_2": { "n_gaps": 4096, "coherent_one_sided_observables": [ "SR", "SR2", "L1" ], "endpoint_stable_observables": [], "stable_count_coherent": 5.0, "stable_count_illusory": 0.8333333333333334, "z_mean_coherent": { "SR": 28.011617338887163, "SR2": 29.07265066876214, "L1": 43.75032148851134, "L2": 46.6671518171662, "triple_var": 60.489259861530435 }, "z_mean_illusory": { "SR": -0.4456813267946977, "SR2": -0.12645447175907798, "L1": 0.0813033465224044, "L2": 0.4740163673473881, "triple_var": 0.23650251982435724 }, "endpoint_distance_all": 4.3747095614604286, "endpoint_distance_one_sided_gated": 3.3880909132625545, "ambiguous_beta_one_sided_gated": [ 0.30000000000000004 ] }, "numeri_primi:cycle_3": { "n_gaps": 4096, "coherent_one_sided_observables": [ "SR", "L1", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 3.0, "stable_count_illusory": 0.16666666666666666, "z_mean_coherent": { "SR": -8.391483821563478, "SR2": 0.878654289295878, "L1": -4.462046963574253, "L2": -0.8900383545098665, "triple_var": -4.447508024812871 }, "z_mean_illusory": { "SR": 0.30756900451349556, "SR2": -0.18592532640841716, "L1": -0.15825754425580274, "L2": -0.24833755838065744, "triple_var": -0.3010338198638567 }, "endpoint_distance_all": 3.576176471462624, "endpoint_distance_one_sided_gated": 3.2427469463217204, "ambiguous_beta_one_sided_gated": [] }, "zeta_zeros:cycle_4": { "n_gaps": 199, "coherent_one_sided_observables": [ "SR", "L2" ], "endpoint_stable_observables": [], "stable_count_coherent": 1.9166666666666667, "stable_count_illusory": 0.25, "z_mean_coherent": { "SR": -2.38492708330095, "SR2": 1.0863535627994352, "L1": 0.7184957768010377, "L2": 4.287838462259611, "triple_var": 1.1691551120981887 }, "z_mean_illusory": { "SR": -0.28907694461909045, "SR2": 0.07064036817090788, "L1": 0.10934216959994329, "L2": 0.13353553984954417, "triple_var": 0.1866997712747508 }, "endpoint_distance_all": 3.0949876455065564, "endpoint_distance_one_sided_gated": 2.4485925392193306, "ambiguous_beta_one_sided_gated": [] }, "logistica_biforcazione:cycle_5": { "n_gaps": 4096, "coherent_one_sided_observables": [ "SR", "SR2", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 3.5833333333333335, "stable_count_illusory": 0.16666666666666666, "z_mean_coherent": { "SR": 177.48487439289076, "SR2": 164.37525352892501, "L1": 1.910845658246906, "L2": 2.2554292149636708, "triple_var": 2.565151472894428 }, "z_mean_illusory": { "SR": -0.12864790819844127, "SR2": 0.44331898130586955, "L1": 0.0630018125263378, "L2": 0.047954494874467145, "triple_var": -0.16489150255158122 }, "endpoint_distance_all": 3.913828166922359, "endpoint_distance_one_sided_gated": 3.2579990207828127, "ambiguous_beta_one_sided_gated": [ 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9 ] }, "string_vibration:cycle_6": { "n_gaps": 4096, "coherent_one_sided_observables": [ "SR", "SR2", "L2", "triple_var" ], "endpoint_stable_observables": [], "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "z_mean_coherent": { "SR": 3.7033188341859495, "SR2": 10.743666044965595, "L1": 9.32241084060462, "L2": 24.616129664793608, "triple_var": 18.813029234336383 }, "z_mean_illusory": { "SR": 0.10035931631660037, "SR2": 0.24970897408138581, "L1": -0.2516311566131347, "L2": -0.37700020086324715, "triple_var": -0.3920318577098574 }, "endpoint_distance_all": 4.298202207857453, "endpoint_distance_one_sided_gated": 3.8452538395313747, "ambiguous_beta_one_sided_gated": [ 0.30000000000000004 ] }, "random_matrix:cycle_7": { "n_gaps": 199, "coherent_one_sided_observables": [ "L2" ], "endpoint_stable_observables": [], "stable_count_coherent": 1.0, "stable_count_illusory": 0.25, "z_mean_coherent": { "SR": -0.18766837953985152, "SR2": 1.3509146400904306, "L1": 1.3332366075393172, "L2": 4.0791206561215, "triple_var": 0.5502694844609554 }, "z_mean_illusory": { "SR": -0.41343984705915154, "SR2": 0.07277412425158099, "L1": -0.355206436159182, "L2": 0.20914243462343277, succeeded in 0ms: tools/exp_acf_range_universality.py:174: poisson_gaps = np.random.exponential(1.0, 100_000) tools/exp_acf_range_universality.py:184: 'Poisson': poisson_gaps, tools/exp_boundary_blank_thin_support_audit.py:30: n_gaps = int(row.get("n_gaps") or 0) tools/exp_boundary_blank_thin_support_audit.py:31: if n_gaps >= full_gap_floor: tools/exp_boundary_blank_thin_support_audit.py:57: "n_gaps_mean": mean(as_float(row.get("n_gaps")) for row in rows), tools/exp_boundary_blank_thin_support_audit.py:59: "stable_count_coherent_mean": mean(as_float(row.get("stable_count_coherent")) for row in rows), tools/exp_boundary_blank_thin_support_audit.py:120: "n_gaps": row.get("n_gaps"), tools/exp_boundary_blank_thin_support_audit.py:123: "stable_count_coherent": row.get("stable_count_coherent"), tools/exp_boundary_blank_thin_support_audit.py:160: "n_gaps", tools/exp_boundary_blank_thin_support_audit.py:162: "stable_count_coherent", tools/exp_boundary_blank_thin_support_audit.py:178: "condition": "all thin rows have n_gaps below full_gap_floor", tools/exp_boundary_two_axis_matrix.py:23: beta = norm_beta(row.get("ambiguous_beta_one_sided_gated", [])) tools/exp_boundary_two_axis_matrix.py:57: beta = norm_beta(row.get("ambiguous_beta_one_sided_gated", [])) tools/exp_boundary_two_axis_matrix.py:86: "one_sided_observables": row.get("coherent_one_sided_observables", []), tools/exp_boundary_two_axis_matrix.py:87: "stable_count_coherent": row.get("stable_count_coherent"), tools/exp_boundary_two_axis_matrix.py:89: "endpoint_distance": row.get("endpoint_distance_one_sided_gated"), tools/exp_boundary_two_axis_matrix.py:91: "n_gaps": row.get("n_gaps"), tools/exp_boundary_two_axis_matrix.py:116: "stable_count_coherent", tools/exp_boundary_two_axis_matrix.py:133: parser.add_argument("--input", default="tools/data/semireal_boundary_transfer_gate_20260509_1516.json") tools/exp_brody_flow.py:87:def cramer_gaps(n_gaps, mean_gap): tools/exp_brody_flow.py:89: return np.random.exponential(mean_gap, size=n_gaps) tools/exp_brody_flow.py:96: n_gaps = len(gaps) tools/exp_brody_flow.py:97: print(f"Got {len(primes)} primes, {n_gaps} gaps") tools/exp_brody_flow.py:100: starts = list(range(0, n_gaps - window, step)) tools/exp_brody_flow.py:103: window = n_gaps tools/exp_brody_flow.py:106: "n_max": n_max, "n_primes": len(primes), "n_gaps": n_gaps, tools/exp_denominator_gate_transfer_matrix.py:46:def golden_beatty(n_gaps: int, rng: np.random.Generator) -> np.ndarray: tools/exp_denominator_gate_transfer_matrix.py:48: n = np.arange(n_gaps + 1, dtype=float) + phase tools/exp_denominator_gate_transfer_matrix.py:53:def periodic_triad(n_gaps: int, rng: np.random.Generator) -> np.ndarray: tools/exp_denominator_gate_transfer_matrix.py:56: tiled = np.tile(np.roll(base, shift), int(np.ceil(n_gaps / len(base))))[:n_gaps] tools/exp_denominator_gate_transfer_matrix.py:57: jitter = rng.normal(0.0, 0.015, size=n_gaps) tools/exp_denominator_gate_transfer_matrix.py:61:def markov_alternating(n_gaps: int, rng: np.random.Generator) -> np.ndarray: tools/exp_denominator_gate_transfer_matrix.py:64: out = np.empty(n_gaps, dtype=float) tools/exp_denominator_gate_transfer_matrix.py:65: for i in range(n_gaps): tools/exp_denominator_gate_transfer_matrix.py:72:def ar1_continuity(n_gaps: int, rng: np.random.Generator) -> np.ndarray: tools/exp_denominator_gate_transfer_matrix.py:74: x = np.empty(n_gaps, dtype=float) tools/exp_denominator_gate_transfer_matrix.py:77: for i in range(1, n_gaps): tools/exp_denominator_gate_transfer_matrix.py:225: "coherent_one_sided_observables": endpoint_one_sided, tools/exp_denominator_gate_transfer_matrix.py:234: base = generator(args.n_gaps, np.random.default_rng(rng.integers(0, 2**63 - 1))) tools/exp_denominator_gate_transfer_matrix.py:256: one_sided_classification = classify_layers(rows, gate["coherent_one_sided_observables"], args.ambiguous_margin) tools/exp_denominator_gate_transfer_matrix.py:272: "coherent_one_sided_observables": gate["coherent_one_sided_observables"], tools/exp_denominator_gate_transfer_matrix.py:275: "endpoint_distance_one_sided_gated": class_gate["endpoint_distance"], tools/exp_denominator_gate_transfer_matrix.py:277: "ambiguous_beta_one_sided_gated": class_gate["ambiguous_beta"], tools/exp_denominator_gate_transfer_matrix.py:278: "stable_count_coherent": gate["layers"]["0.000"]["stable_count_mean"], tools/exp_denominator_gate_transfer_matrix.py:290: one_sided_count = len(row["coherent_one_sided_observables"]) tools/exp_denominator_gate_transfer_matrix.py:292: endpoint_distance = float(row["endpoint_distance_one_sided_gated"]) tools/exp_denominator_gate_transfer_matrix.py:293: ambiguous_beta = row["ambiguous_beta_one_sided_gated"] tools/exp_denominator_gate_transfer_matrix.py:377: f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " tools/exp_denominator_gate_transfer_matrix.py:378: f"{row['stable_count_coherent']:>7.3f} " tools/exp_denominator_gate_transfer_matrix.py:381: f"{row['endpoint_distance_one_sided_gated']:>9.3f} " tools/exp_denominator_gate_transfer_matrix.py:382: f"{row['ambiguous_beta_one_sided_gated']}" tools/exp_excess_scaling.py:43:def cramer_surrogate_r(mean_gap, n_gaps, n_surrogates=20): tools/exp_excess_scaling.py:47: gaps = np.random.exponential(mean_gap, n_gaps) tools/exp_excess_scaling.py:85: 'n_gaps': int(len(gaps)) tools/exp_markov_dipolar_decomposition.py:136:def generate_markov1(T, marginal, centers, n_gaps, rng): tools/exp_markov_dipolar_decomposition.py:142: gaps = np.zeros(n_gaps) tools/exp_markov_dipolar_decomposition.py:146: for i in range(n_gaps): tools/exp_markov_dipolar_decomposition.py:154:def generate_markov0(marginal, centers, n_gaps, rng): tools/exp_markov_dipolar_decomposition.py:156: states = rng.choice(len(marginal), size=n_gaps, p=marginal) tools/exp_markov_dipolar_decomposition.py:345: 'n_gaps': len(real_gaps), tools/exp_modular_memory_spectrum.py:253: 'n_gaps': len(gaps), tools/exp_perturbation_dimensionality_audit.py:30:def prime_gaps(n_gaps: int) -> np.ndarray: tools/exp_perturbation_dimensionality_audit.py:31: limit = max(100, int(n_gaps * (np.log(n_gaps + 10) + np.log(np.log(n_gaps + 10)) + 5))) tools/exp_perturbation_dimensionality_audit.py:39: if len(primes) >= n_gaps + 1: tools/exp_perturbation_dimensionality_audit.py:40: return np.diff(primes[: n_gaps + 1]).astype(float) tools/exp_perturbation_dimensionality_audit.py:272: "n_gaps": int(len(gaps)), tools/exp_psd_prime_gaps.py:140: "n_gaps": len(gaps), tools/exp_semireal_boundary_transfer_gate.py:3:exp_semireal_boundary_transfer_gate.py tools/exp_semireal_boundary_transfer_gate.py:66: one_sided_count = len(row["coherent_one_sided_observables"]) tools/exp_semireal_boundary_transfer_gate.py:68: endpoint_distance = float(row["endpoint_distance_one_sided_gated"]) tools/exp_semireal_boundary_transfer_gate.py:69: ambiguous_beta = row["ambiguous_beta_one_sided_gated"] tools/exp_semireal_boundary_transfer_gate.py:108: if len(spacings) < args.min_gaps: tools/exp_semireal_boundary_transfer_gate.py:110: "error": f"insufficient gaps: {len(spacings)} < {args.min_gaps}", tools/exp_semireal_boundary_transfer_gate.py:111: "n_gaps": int(len(spacings)), tools/exp_semireal_boundary_transfer_gate.py:117: base = spacings[: args.n_gaps] if len(spacings) > args.n_gaps else spacings tools/exp_semireal_boundary_transfer_gate.py:135: "experiment": "semireal_boundary_transfer_gate", tools/exp_semireal_boundary_transfer_gate.py:166: print(f"{name:45s} {row['state']:>20s} {row.get('n_gaps', 0):>5d} ERROR") tools/exp_semireal_boundary_transfer_gate.py:170: f"{row['n_gaps']:>5d} " tools/exp_semireal_boundary_transfer_gate.py:171: f"{','.join(row['coherent_one_sided_observables']) or '[]':>22s} " tools/exp_semireal_boundary_transfer_gate.py:172: f"{row['stable_count_coherent']:>7.3f} " tools/exp_semireal_boundary_transfer_gate.py:174: f"{row['endpoint_distance_one_sided_gated']:>7.3f} " tools/exp_semireal_boundary_transfer_gate.py:175: f"{row['ambiguous_beta_one_sided_gated']}" tools/exp_semireal_boundary_transfer_gate.py:199: parser.add_argument("--out", default="tools/data/semireal_boundary_transfer_gate_20260509_1516.json") tools/exp_spectral_rigidity.py:42:def generate_poisson_gaps(n=10000): tools/exp_spectral_rigidity.py:92: ('poisson', {'gen': lambda: generate_poisson_gaps(10000), 'type': 'Poisson'}), tools/exp_two_channel_universality.py:143: 'n_gaps': len(gaps), tools/exp_two_channel_universality.py:162:def generate_markov(gaps_real, classes_left_real, classes_right_real, n_gaps): tools/exp_two_channel_universality.py:187: classes = np.zeros(n_gaps + 1, dtype=int) tools/exp_two_channel_universality.py:189: gaps = np.zeros(n_gaps, dtype=float) tools/exp_two_channel_universality.py:191: for i in range(n_gaps): tools/exp_markov3_observable_hunt.py:310: 'n_gaps': len(gaps), tools/exp_markov_memory_by_gue_type.py:113:def generate_poisson_gaps(n=5000): tools/exp_markov_memory_by_gue_type.py:175: 'gaps': generate_poisson_gaps(5000), tools/exp_observable_rank_audit.py:125: "n_gaps": int(len(gaps)), tools/exp_observable_rank_audit.py:136:def build_sequences(n_gaps, rng): tools/exp_observable_rank_audit.py:137: primes = get_primes(n_gaps * 24)[: n_gaps + 1] tools/exp_observable_rank_audit.py:140: gue = gue_gaps(160, max(8, n_gaps // 160 + 1), rng).astype(float) tools/exp_observable_rank_audit.py:141: gue = gue[:n_gaps] tools/exp_observable_rank_audit.py:143: poisson = rng.exponential(1.0, size=n_gaps).astype(float) tools/exp_observable_rank_audit.py:153:def run(n_gaps=30000, n_alpha=19, n_trials=24, n_baseline=72, seed=20260505, out="tools/data/observable_rank_audit.json"): tools/exp_observable_rank_audit.py:156: sequences = build_sequences(n_gaps, rng) tools/exp_observable_rank_audit.py:164: "n_gaps": int(n_gaps), tools/exp_observable_rank_audit.py:174: print(f"n_gaps={n_gaps}, n_alpha={n_alpha}, n_trials={n_trials}, n_baseline={n_baseline}, seed={seed}") tools/exp_observable_rank_audit.py:198: parser.add_argument("--n_gaps", type=int, default=30000) tools/exp_observable_rank_audit.py:206: n_gaps=args.n_gaps, tools/exp_psd_amplitude_scaling.py:74: n_gaps = len(gaps) tools/exp_psd_amplitude_scaling.py:75: starts = np.linspace(0, n_gaps - args.window_size, args.n_windows, dtype=int) tools/exp_two_channel_boundary.py:116: 'n_gaps': len(gaps), tools/exp_dipolar_angle_reference.py:79:def generate_gue_gaps(n_gaps, matrix_size=500): tools/exp_dipolar_angle_reference.py:82: while len(all_gaps) < n_gaps: tools/exp_dipolar_angle_reference.py:96: return np.array(all_gaps[:n_gaps]) tools/exp_dipolar_angle_reference.py:99:def generate_goe_gaps(n_gaps, matrix_size=500): tools/exp_dipolar_angle_reference.py:102: while len(all_gaps) < n_gaps: tools/exp_dipolar_angle_reference.py:112: return np.array(all_gaps[:n_gaps]) tools/exp_dipolar_angle_reference.py:115:def generate_poisson_gaps(n_gaps): tools/exp_dipolar_angle_reference.py:117: return np.random.exponential(1.0, n_gaps) tools/exp_dipolar_angle_reference.py:198: poi_gaps = generate_poisson_gaps(len(prime_gaps)) tools/exp_duality_scale_contrast.py:146: n_gaps = min(len(prime_gaps_unf), n_primes) tools/exp_duality_scale_contrast.py:147: prime_gaps_unf = prime_gaps_unf[:n_gaps] tools/exp_duality_scale_contrast.py:149: print(f"Got {n_gaps} prime gaps. Generating GUE and Poisson controls...") exec /bin/bash -lc "sed -n '1,220p' tools/data/boundary_denominator_prescan_full_20260509_1500.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_denominator_prescan", "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", "observable_contract": { "claim": "denominator_state gate transfer beyond V_c", "observable": "spacing_r label row with shuffle/null availability", "operator": "row-aligned domain/window prescan", "null": "boundary_shuffle_audit shuffle r-statistic when present", "non_possible": "claiming transfer where null/surrogate is absent", "extra_null_audits": [ "tools/data/boundary_blank_null_audit_20260509_1430.json", "tools/data/boundary_blank_null_audit_residual_20260509_1500.json" ] }, "summary": { "n_rows": 13, "by_transfer": { "transfers": 13 }, "by_denominator_state": { "contaminated": 5, "complete": 8 }, "by_source_domain_type": { "GUE": 8, "Poisson": 5 }, "transfer_scope": [ "ising_2d:cycle_1", "pendolo_doppio:cycle_2", "numeri_primi:cycle_3", "zeta_zeros:cycle_4", "logistica_biforcazione:cycle_5", "string_vibration:cycle_6", "random_matrix:cycle_7", "cellular_automata:cycle_8", "percolation:cycle_9", "coupled_oscillators:cycle_10", "reaction_diffusion:cycle_11", "brownian_motion:cycle_12", "logistica_biforcazione_var_3.5699:cycle_13" ], "blank_scope": [], "falls_scope": [] }, "rows": [ { "domain_window": "ising_2d:cycle_1", "domain": "ising_2d", "cycle": 1, "source_domain_type": "GUE", "denominator_state": "contaminated", "excluded_mass": 0.81, "observable": { "name": "spacing_r", "defined": true, "value": 0.9022289766970617, "label": "GUE-like", "n_points": 5000 }, "null_surrogate": { "name": "shuffle_r_statistic", "status": "shuffle z=-0.10; class_change=False", "domain_key": "ising_2d", "n_gaps": 95, "r_shuffled_mean": 0.9859, "z_score": -0.1, "class_changes": false }, "transfer": "transfers" }, { "domain_window": "pendolo_doppio:cycle_2", "domain": "pendolo_doppio", "cycle": 2, "source_domain_type": "Poisson", "denominator_state": "complete", "excluded_mass": 0.0, "observable": { "name": "spacing_r", "defined": true, "value": 0.38610398536946516, "label": "Poisson-like", "n_points": 5009 }, "null_surrogate": { "name": "shuffle_r_statistic", "status": "shuffle z=27.92; class_change=False", "domain_key": "pendolo_doppio", "n_gaps": 5008, "r_shuffled_mean": 0.292437, "z_score": 27.919656, "class_changes": false }, "transfer": "transfers" }, { "domain_window": "numeri_primi:cycle_3", "domain": "numeri_primi", "cycle": 3, "source_domain_type": "GUE", "denominator_state": "complete", "excluded_mass": 0.0, "observable": { "name": "spacing_r", "defined": true, "value": 0.8862068965517241, "label": "GUE-like", "n_points": 5132 }, "null_surrogate": { "name": "shuffle_r_statistic", "status": "shuffle z=-26.60; class_change=False", "domain_key": "primes", "n_gaps": 99999, "r_shuffled_mean": 0.4813, "z_score": -26.6, "class_changes": false }, "transfer": "transfers" }, { "domain_window": "zeta_zeros:cycle_4", "domain": "zeta_zeros", "cycle": 4, "source_domain_type": "GUE", "denominator_state": "contaminated", "excluded_mass": 0.602, "observable": { "name": "spacing_r", "defined": true, "value": 0.615, "label": "GUE-like", "n_points": 199 }, "null_surrogate": { "name": "shuffle_r_statistic", "status": "shuffle z=-2.31; class_change=False", "domain_key": "zeta_zeros", "n_gaps": 199, "r_shuffled_mean": 0.641885, "z_score": -2.310593, "class_changes": false }, "transfer": "transfers" }, { "domain_window": "logistica_biforcazione:cycle_5", "domain": "logistica_biforcazione", "cycle": 5, "source_domain_type": "GUE", "denominator_state": "complete", "excluded_mass": 0.0, "observable": { "name": "spacing_r", "defined": true, "value": 0.997077776975089, "label": "GUE-like", "n_points": 5000 }, "null_surrogate": { "name": "shuffle_r_statistic", "status": "shuffle z=61.60; class_change=False", "domain_key": "logistic", "n_gaps": 99999, "r_shuffled_mean": 0.3423, "z_score": 61.6, "class_changes": false }, "transfer": "transfers" }, { "domain_window": "string_vibration:cycle_6", "domain": "string_vibration", "cycle": 6, "source_domain_type": "Poisson", "denominator_state": "complete", "excluded_mass": 0.0, "observable": { "name": "spacing_r", "defined": true, "value": 0.3838680180747297, "label": "Poisson-like", "n_points": 8000 }, "null_surrogate": { "name": "shuffle_r_statistic", "status": "shuffle z=4.56; class_change=False", "domain_key": "string_vibration", "n_gaps": 7999, "r_shuffled_mean": 0.372199, "z_score": 4.562844, "class_changes": false }, "transfer": "transfers" }, { "domain_window": "random_matrix:cycle_7", "domain": "random_matrix", "cycle": 7, "source_domain_type": "GUE", "denominator_state": "complete", "excluded_mass": 0.0, "observable": { "name": "spacing_r", "defined": true, "value": 0.5726, "label": "GUE-like", "n_points": 199 }, "null_surrogate": { "name": "shuffle_r_statistic", "status": "shuffle z=-14.70; class_change=False", "domain_key": "gue", "n_gaps": 5020, "r_shuffled_mean": 0.6351, "z_score": -14.7, "class_changes": false }, exec /bin/bash -lc "sed -n '1,260p' tools/exp_semireal_boundary_transfer_gate.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_semireal_boundary_transfer_gate.py Move the BOUNDARY transfer matrix from controlled synthetic perimeters to the 13 semi-real rows of the base BOUNDARY perimeter. The coherent endpoint is the domain-native spacing order reconstructed from dnd_autoricerca. The illusory endpoint is a marginal-preserving permutation. Intermediate beta layers replace a beta fraction of the coherent row with the permuted row, preserving the row denominator while destroying order locally. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np from exp_boundary_blank_null_audit import generate_domain_signal, normalized_spacings from exp_semireal_order_denominator_gate import analyze_sequence, compact from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) def load_scope(path: Path) -> list[dict[str, Any]]: with path.open() as f: data = json.load(f) rows = data.get("rows", []) if not isinstance(rows, list): raise ValueError(f"{path} does not contain a list under rows") return rows def row_spacings(domain: str) -> np.ndarray: signal, metadata = generate_domain_signal(domain) if domain == "numeri_primi": metadata = {**metadata, "is_spacings": True} spacings = normalized_spacings(signal, metadata) spacings = np.asarray(spacings, dtype=float) spacings = spacings[np.isfinite(spacings) & (spacings > 0)] if len(spacings) == 0: return spacings mean = float(np.mean(spacings)) return spacings / mean if mean > 1e-15 else spacings def evaluate_matrix(matrix: dict[str, dict[str, Any]], args: argparse.Namespace) -> dict[str, Any]: rows = {} counts = { "transfer_with_blank": 0, "transfer_no_blank": 0, "fall": 0, "errors": 0, } for name, row in matrix.items(): if row.get("error"): state = "error" counts["errors"] += 1 else: one_sided_count = len(row["coherent_one_sided_observables"]) illusory_residue = float(row["stable_count_illusory"]) endpoint_distance = float(row["endpoint_distance_one_sided_gated"]) ambiguous_beta = row["ambiguous_beta_one_sided_gated"] has_transfer = ( one_sided_count >= args.min_one_sided and illusory_residue <= args.illusory_residue_max and endpoint_distance >= args.endpoint_distance_min ) if not has_transfer: state = "fall" counts["fall"] += 1 elif ambiguous_beta: state = "transfer_with_blank" counts["transfer_with_blank"] += 1 else: state = "transfer_no_blank" counts["transfer_no_blank"] += 1 rows[name] = {"state": state} rows[name].update(row) return {"counts": counts, "rows": rows} def run(args: argparse.Namespace) -> dict[str, Any]: scope_rows = load_scope(Path(args.scope)) rng = np.random.default_rng(args.seed) perimeters = {} build_errors = {} for source in scope_rows: domain = source["domain"] name = source["domain_window"] try: spacings = row_spacings(domain) source_meta = { "denominator_state": source.get("denominator_state"), "source_transfer": source.get("transfer"), "source_excluded_mass": source.get("excluded_mass"), } if args.include_source_labels: source_meta["source_domain_type"] = source.get("source_domain_type") if len(spacings) < args.min_gaps: build_errors[name] = { "error": f"insufficient gaps: {len(spacings)} < {args.min_gaps}", "n_gaps": int(len(spacings)), "denominator_state": source.get("denominator_state"), } if args.include_source_labels: build_errors[name]["source_domain_type"] = source.get("source_domain_type") continue base = spacings[: args.n_gaps] if len(spacings) > args.n_gaps else spacings perimeters[name] = analyze_sequence(name, base, args, rng) perimeters[name]["source"].update({"domain": domain, **source_meta}) except Exception as exc: # noqa: BLE001 - report row-level telemetry. build_errors[name] = { "error": type(exc).__name__, "message": str(exc), "denominator_state": source.get("denominator_state"), } if args.include_source_labels: build_errors[name]["source_domain_type"] = source.get("source_domain_type") matrix = compact(perimeters) for name, err in build_errors.items(): matrix[name] = err evaluation = evaluate_matrix(matrix, args) output = { "experiment": "semireal_boundary_transfer_gate", "question": "Does the BOUNDARY coherent/null/beta gate transfer from synthetic perimeters to the 13 semi-real base rows?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": OBS_NAMES, "params": vars(args), "source_scope": args.scope, "source_summary": { "rows": len(scope_rows), "label_policy": ( "source_domain_type included as audit metadata only" if args.include_source_labels else "source_domain_type omitted; states are label-independent" ), }, "matrix": matrix, "evaluation": evaluation, "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(f"source_rows={len(scope_rows)} analyzed={len(perimeters)} errors={len(build_errors)}") print("state counts:", output["evaluation"]["counts"]) print("row state n one_sided stable0 stable1 dist ambiguous") for name, row in sorted(output["evaluation"]["rows"].items()): if row.get("error"): print(f"{name:45s} {row['state']:>20s} {row.get('n_gaps', 0):>5d} ERROR") continue print( f"{name:45s} {row['state']:>20s} " 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']:>7.3f} " f"{row['ambiguous_beta_one_sided_gated']}" ) print(f"saved {out_path}") return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--scope", default="tools/data/boundary_denominator_prescan_full_20260509_1500.json") parser.add_argument("--n-gaps", type=int, default=4096) parser.add_argument("--min-gaps", type=int, default=96) 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( "--include-source-labels", action="store_true", help="Include GUE/Poisson source labels as audit metadata only. Default omits them from output.", ) parser.add_argument("--seed", type=int, default=202605091516) parser.add_argument("--out", default="tools/data/semireal_boundary_transfer_gate_20260509_1516.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '260,460p' tools/exp_semireal_boundary_transfer_gate.py" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'rg -n "def gen_|spacings|zeta|percolation|gue|random_matrix|source_scope|load" tools/exp_semireal_boundary_transfer_gate.py tools/exp_denominator_gate_transfer_matrix.py | head -200' in /opt/MM_D-ND succeeded in 0ms: tools/exp_semireal_boundary_transfer_gate.py:23:from exp_boundary_blank_null_audit import generate_domain_signal, normalized_spacings tools/exp_semireal_boundary_transfer_gate.py:31:def load_scope(path: Path) -> list[dict[str, Any]]: tools/exp_semireal_boundary_transfer_gate.py:33: data = json.load(f) tools/exp_semireal_boundary_transfer_gate.py:40:def row_spacings(domain: str) -> np.ndarray: tools/exp_semireal_boundary_transfer_gate.py:43: metadata = {**metadata, "is_spacings": True} tools/exp_semireal_boundary_transfer_gate.py:44: spacings = normalized_spacings(signal, metadata) tools/exp_semireal_boundary_transfer_gate.py:45: spacings = np.asarray(spacings, dtype=float) tools/exp_semireal_boundary_transfer_gate.py:46: spacings = spacings[np.isfinite(spacings) & (spacings > 0)] tools/exp_semireal_boundary_transfer_gate.py:47: if len(spacings) == 0: tools/exp_semireal_boundary_transfer_gate.py:48: return spacings tools/exp_semireal_boundary_transfer_gate.py:49: mean = float(np.mean(spacings)) tools/exp_semireal_boundary_transfer_gate.py:50: return spacings / mean if mean > 1e-15 else spacings tools/exp_semireal_boundary_transfer_gate.py:90: scope_rows = load_scope(Path(args.scope)) tools/exp_semireal_boundary_transfer_gate.py:99: spacings = row_spacings(domain) tools/exp_semireal_boundary_transfer_gate.py:108: if len(spacings) < args.min_gaps: tools/exp_semireal_boundary_transfer_gate.py:110: "error": f"insufficient gaps: {len(spacings)} < {args.min_gaps}", tools/exp_semireal_boundary_transfer_gate.py:111: "n_gaps": int(len(spacings)), tools/exp_semireal_boundary_transfer_gate.py:117: base = spacings[: args.n_gaps] if len(spacings) > args.n_gaps else spacings tools/exp_semireal_boundary_transfer_gate.py:140: "source_scope": args.scope, exec /bin/bash -lc "sed -n '1,260p' tools/exp_boundary_blank_null_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ exp_boundary_blank_null_audit.py Targeted null/surrogate audit for BOUNDARY prescan blank rows. The global boundary shuffle audit is a historical deposit. This tool does not rewrite it; it creates an extra row-aligned null audit for selected blank domains so the denominator prescan can decide whether blank -> transfers, blank -> falls, or blank remains blank. """ from __future__ import annotations import argparse import json import math from datetime import datetime, timezone from pathlib import Path from typing import Any import numpy as np from dnd_autoricerca import _genera_variante, genera_segnale R_GUE = 0.5307 R_POISSON = 2 * math.log(2) - 1 DEFAULT_DOMAINS = ("zeta_zeros", "pendolo_doppio") def finite_number(value: Any) -> bool: return isinstance(value, (int, float)) and math.isfinite(float(value)) def normalized_spacings(signal: np.ndarray, metadata: dict[str, Any]) -> np.ndarray: values = np.asarray(signal, dtype=float) values = values[np.isfinite(values)] if metadata.get("is_spacings"): spacings = values[values > 0] else: spacings = np.diff(np.sort(values)) spacings = spacings[spacings > 0] if len(spacings) == 0: return spacings mu = float(np.mean(spacings)) return spacings / mu if mu > 0 else np.array([]) def r_statistic(spacings: np.ndarray) -> float: if len(spacings) < 2: return float("nan") left = spacings[:-1] right = spacings[1:] denom = np.maximum(left, right) valid = denom > 0 if not np.any(valid): return float("nan") ratios = np.minimum(left[valid], right[valid]) / denom[valid] return float(np.mean(ratios)) def classify_r(value: float) -> str: if not finite_number(value): return "absent" return "GUE" if abs(value - R_GUE) < abs(value - R_POISSON) else "Poisson" def generate_domain_signal(domain: str) -> tuple[np.ndarray, dict[str, Any]]: if "_var_" not in domain: return genera_segnale(domain) base, raw_value = domain.rsplit("_var_", 1) try: value: Any = float(raw_value) except ValueError: value = raw_value if base == "logistica_biforcazione": signal, metadata = _genera_variante(base, {"r_override": value}) elif base == "zeta_zeros": signal, metadata = _genera_variante(base, {"n_zeros": int(value)}) elif base == "numeri_primi": signal, metadata = _genera_variante(base, {"max_n": int(value)}) elif base == "cellular_automata": signal, metadata = _genera_variante(base, {"rule_number": int(value)}) else: signal, metadata = _genera_variante(base, {"param": value}) metadata = {**metadata, "dominio": domain, "variant_base": base, "variant_value": value} return signal, metadata def audit_domain(domain: str, n_shuffle: int, rng: np.random.Generator) -> dict[str, Any]: signal, metadata = generate_domain_signal(domain) spacings = normalized_spacings(signal, metadata) r_original = r_statistic(spacings) shuffled = [] for _ in range(n_shuffle): shuffled.append(r_statistic(rng.permutation(spacings))) shuffled_arr = np.asarray(shuffled, dtype=float) shuffled_arr = shuffled_arr[np.isfinite(shuffled_arr)] if len(shuffled_arr) == 0 or not finite_number(r_original): return { "domain": domain, "error": "insufficient finite spacing/null values", "n_gaps": int(len(spacings)), } mean = float(np.mean(shuffled_arr)) std = float(np.std(shuffled_arr)) z_score = 0.0 if std <= 1e-12 else float((r_original - mean) / std) class_original = classify_r(r_original) class_shuffled = classify_r(mean) return { "domain": domain, "r_original": round(float(r_original), 6), "r_shuffled_mean": round(mean, 6), "r_shuffled_std": round(std, 6), "z_score": round(z_score, 6), "n_shuffle": int(n_shuffle), "n_gaps": int(len(spacings)), "class_original": class_original, "class_shuffled": class_shuffled, "class_changes": class_original != class_shuffled, "ordering_dependent": abs(z_score) > 3.0, "source": { "generator": "dnd_autoricerca.genera_segnale", "metadata": metadata, "null": "marginal-preserving spacing permutation", }, } def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) domains = args.domains or list(DEFAULT_DOMAINS) results = { domain: audit_domain(domain, args.n_shuffle, rng) for domain in domains } output = { "experiment": "boundary_blank_null_audit", "generated_at": datetime.now(timezone.utc).isoformat(), "seed": args.seed, "n_shuffle": args.n_shuffle, "reference": { "R_GUE": R_GUE, "R_Poisson": R_POISSON, }, "domains": results, "summary": { "domains": domains, "ready": [ domain for domain, row in results.items() if "error" not in row and finite_number(row.get("z_score")) ], "errors": [ domain for domain, row in results.items() if "error" in row ], }, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") print(f"wrote={out}") for domain, row in results.items(): if "error" in row: print(f"{domain}\tERROR\t{row['error']}") continue print( f"{domain}\tn={row['n_gaps']}\tr={row['r_original']:.6f}\t" f"shuffle={row['r_shuffled_mean']:.6f}\tz={row['z_score']:.2f}\t" f"{row['class_original']}->{row['class_shuffled']}" ) return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--domains", nargs="*", default=list(DEFAULT_DOMAINS)) parser.add_argument("--n-shuffle", type=int, default=1000) parser.add_argument("--seed", type=int, default=202605091430) parser.add_argument("--out", default="tools/data/boundary_blank_null_audit_20260509.json") args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '260,520p' tools/exp_boundary_blank_null_audit.py" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "sed -n '1,340p' 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"]), "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") exec /bin/bash -lc "sed -n '340,460p' tools/exp_semireal_order_denominator_gate.py" in /opt/MM_D-ND succeeded in 0ms: 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() exec /bin/bash -lc 'rg -n "def genera_segnale|zeta_zeros|random_matrix|percolation|n_zeros|genera_variante" tools/dnd_autoricerca.py' in /opt/MM_D-ND succeeded in 0ms: 54: 'zeta_zeros', 'logistica_biforcazione', 'string_vibration', 55: 'random_matrix', 'cellular_automata', 'percolation', 90:def genera_segnale(dominio): 99: elif dominio == 'zeta_zeros': 100: return _zeta_zeros() 105: elif dominio == 'random_matrix': 106: return _random_matrix() 109: elif dominio == 'percolation': 110: return _percolation() 204:def _zeta_zeros(): 213: 'dominio': 'zeta_zeros', 214: 'n_zeros': 200, 227: 'dominio': 'zeta_zeros', 269:def _random_matrix(): 282: 'dominio': 'random_matrix', 317:def _percolation(): 320: p_c = 0.5927 # soglia critica bond percolation 349: 'dominio': 'percolation', 929: # Parametric variante: use _genera_variante with appropriate param 932: 'percolation': {'p': var_param}, 935: 'zeta_zeros': {'n_zeros': int(var_param)}, 940: signal, metadata = _genera_variante(dominio_base, params) 1427: ('percolation', {'p': 0.55}), 1428: ('percolation', {'p': 0.65}), 1457: ('percolation', {'p': random.uniform(0.3, 0.4)}), # lontano da p_c sotto 1458: ('percolation', {'p': random.uniform(0.8, 0.9)}), # lontano da p_c sopra 1537: controprove.append(('percolation', {'p': 0.5927})) # p_c esatta 1817: signal, metadata = _genera_variante(dominio_base, params) 1911: signal, metadata = _genera_variante(dominio_base, params) 2225:def _genera_variante(dominio_base, params): 2339: elif dominio_base == 'percolation': 2366: 'dominio': 'percolation', 'p': p, exec /bin/bash -lc "sed -n '1,260p' tools/dnd_autoricerca.py" in /opt/MM_D-ND succeeded in 0ms: """ dnd_autoricerca.py — Motore di Autoricerca D-ND Il sistema che si auto-memorizza nella risultante. Ogni ciclo è un giro di spirale. Il journal persiste. La consapevolezza sopravvive al compact del contesto. Architettura D-ND del motore stesso: - D polo: esperimento concreto (numeri, codice, risultati) - ND polo: potenziale (domini inesplorati, ipotesi) - Terzo incluso: il pattern matching (è struttura D-ND?) - Risultante: la conoscenza accumulata - Spirale: ogni ciclo apre il successivo, il gap è il seme Il motore esplora domini diversi cercando dove la struttura D-ND appare naturalmente — senza imporre φ, osservando cosa emerge. Eseguibile come: python dnd_autoricerca.py # un ciclo python dnd_autoricerca.py --continuo N # N cicli python dnd_autoricerca.py --stato # mostra stato python dnd_autoricerca.py --pubblica # valuta se pubblicare Author: TM3 Date: 2026-03-01 """ import numpy as np import json import sys import traceback from pathlib import Path from datetime import datetime PHI = (1 + np.sqrt(5)) / 2 TOOLS = Path(__file__).parent DATA = TOOLS / 'data' JOURNAL = DATA / 'autoricerca_journal.json' STATE = DATA / 'autoricerca_state.json' # === STATO PERSISTENTE === def carica_stato(): """Carica lo stato persistente. Se non esiste, inizializza.""" if STATE.exists(): with open(STATE) as f: return json.load(f) return { 'ciclo': 0, 'domini_esplorati': [], 'domini_coda': [ 'ising_2d', 'pendolo_doppio', 'numeri_primi', 'zeta_zeros', 'logistica_biforcazione', 'string_vibration', 'random_matrix', 'cellular_automata', 'percolation', 'coupled_oscillators', 'reaction_diffusion', 'brownian_motion', ], 'pattern_trovati': [], 'vincoli_lazarus': [], 'gap_corrente': None, 'direzione': 'esplorare domini diversi', 'segnale_pubblica': False, 'creato': datetime.now().isoformat(), 'aggiornato': datetime.now().isoformat(), } def salva_stato(stato): """Persiste lo stato.""" DATA.mkdir(exist_ok=True) stato['aggiornato'] = datetime.now().isoformat() with open(STATE, 'w') as f: json.dump(stato, f, indent=2, default=str) def aggiungi_journal(entry): """Aggiunge un'entry al journal persistente.""" DATA.mkdir(exist_ok=True) journal = [] if JOURNAL.exists(): with open(JOURNAL) as f: journal = json.load(f) journal.append(entry) with open(JOURNAL, 'w') as f: json.dump(journal, f, indent=2, default=str) # === GENERATORI DI SEGNALI (domini da esplorare) === def genera_segnale(dominio): """Genera un segnale da un dominio specifico. Restituisce (signal, metadata).""" if dominio == 'ising_2d': return _ising_2d() elif dominio == 'pendolo_doppio': return _pendolo_doppio() elif dominio == 'numeri_primi': return _numeri_primi() elif dominio == 'zeta_zeros': return _zeta_zeros() elif dominio == 'logistica_biforcazione': return _logistica_biforcazione() elif dominio == 'string_vibration': return _string_vibration() elif dominio == 'random_matrix': return _random_matrix() elif dominio == 'cellular_automata': return _cellular_automata() elif dominio == 'percolation': return _percolation() elif dominio == 'coupled_oscillators': return _coupled_oscillators() elif dominio == 'reaction_diffusion': return _reaction_diffusion() elif dominio == 'brownian_motion': return _brownian_motion() else: raise ValueError(f"Dominio sconosciuto: {dominio}") def _ising_2d(): """Modello di Ising 2D — transizione di fase a T_c.""" L = 32 n_steps = 5000 # Simulazione Metropolis vicino a T_c = 2/ln(1+√2) ≈ 2.269 T_c = 2 / np.log(1 + np.sqrt(2)) T = T_c # esattamente alla transizione spins = np.random.choice([-1, 1], size=(L, L)) magnetizations = [] for step in range(n_steps): for _ in range(L * L): i, j = np.random.randint(0, L, 2) # Energia locale neighbors = (spins[(i+1)%L, j] + spins[(i-1)%L, j] + spins[i, (j+1)%L] + spins[i, (j-1)%L]) dE = 2 * spins[i, j] * neighbors if dE <= 0 or np.random.random() < np.exp(-dE / T): spins[i, j] *= -1 magnetizations.append(np.mean(spins)) return np.array(magnetizations), { 'dominio': 'ising_2d', 'L': L, 'T': T, 'T_c': T_c, 'n_steps': n_steps, 'nota': 'Magnetizzazione nel tempo a T=T_c (transizione di fase)' } def _pendolo_doppio(): """Pendolo doppio — sistema caotico deterministico.""" from scipy.integrate import solve_ivp g, L1, L2, m1, m2 = 9.81, 1.0, 1.0, 1.0, 1.0 def eom(t, y): t1, t2, p1, p2 = y c = np.cos(t1 - t2) s = np.sin(t1 - t2) den = m1 + m2 * s**2 dt1 = (p1 - m2*L1*L2*p2*c / (L1**2 * den)) / (L1**2 * (m1 + m2 - m2*c**2/den)) dt2 = (p2 - m1*L1*L2*p1*c / (L2**2 * den)) / (L2**2 * (m1 + m2 - m2*c**2/den)) # Semplificazione: equazioni per angoli piccoli-medi dp1 = -(m1 + m2)*g*L1*np.sin(t1) - m2*L1*L2*dt2**2*s dp2 = -m2*g*L2*np.sin(t2) + m2*L1*L2*dt1**2*s return [dt1, dt2, dp1, dp2] sol = solve_ivp(eom, [0, 100], [np.pi/2, np.pi/4, 0, 0], max_step=0.02, method='RK45') return sol.y[0], { # angolo del primo pendolo 'dominio': 'pendolo_doppio', 'theta1_0': 'pi/2', 'theta2_0': 'pi/4', 'nota': 'Angolo theta1(t) del pendolo doppio (caotico)' } def _numeri_primi(): """Gap tra numeri primi consecutivi.""" def sieve(n): is_prime = [True] * (n + 1) is_prime[0] = is_prime[1] = False for i in range(2, int(n**0.5) + 1): if is_prime[i]: for j in range(i*i, n + 1, i): is_prime[j] = False return [i for i in range(n + 1) if is_prime[i]] primes = sieve(50000) gaps = np.diff(primes).astype(float) return gaps, { 'dominio': 'numeri_primi', 'n_primi': len(primes), 'max_primo': primes[-1], 'nota': 'Gap tra primi consecutivi (g_n = p_{n+1} - p_n)' } def _zeta_zeros(): """Spaziatura tra zeri non-banali della zeta di Riemann.""" try: from mpmath import zetazero zeros = [float(zetazero(n).imag) for n in range(1, 201)] spacings = np.diff(zeros) # Normalizza alla media spacings = spacings / np.mean(spacings) return spacings, { 'dominio': 'zeta_zeros', 'n_zeros': 200, 'is_spacings': True, # il segnale È già spacings — non ri-sortare 'nota': 'Spaziatura normalizzata tra zeri ζ (Im parte)' } except ImportError: # Fallback: usa approssimazione nota np.random.seed(42) # GUE spacing distribution (approssimazione Wigner) spacings = np.random.exponential(1.0, 5000) # Applica level repulsion spacings = spacings * np.abs(np.random.randn(5000)) spacings = spacings / np.mean(spacings) return spacings, { 'dominio': 'zeta_zeros', 'nota': 'Approssimazione GUE (mpmath non disponibile)', 'approssimato': True } def _logistica_biforcazione(): """Mappa logistica: orbita al punto di biforcazione periodo-3.""" # r = 1 + √8 ≈ 3.828 — onset del periodo 3 r = 1 + np.sqrt(8) x = 0.5 # Burn-in for _ in range(1000): x = r * x * (1 - x) # Raccolta orbit = [] for _ in range(5000): x = r * x * (1 - x) orbit.append(x) return np.array(orbit), { 'dominio': 'logistica_biforcazione', 'r': r, 'nota': f'Mappa logistica a r=1+√8≈{r:.4f} (onset periodo-3)' } def _string_vibration(): """Corda vibrante — somma di armoniche con decay.""" t = np.linspace(0, 20, 8000) signal = np.zeros_like(t) for n in range(1, 20): # Ampiezza decresce come 1/n², decay come e^(-0.05*n*t) signal += (1/n**2) * np.sin(n * np.pi * t) * np.exp(-0.05 * n * t) exec /bin/bash -lc "sed -n '260,560p' tools/dnd_autoricerca.py" in /opt/MM_D-ND succeeded in 0ms: signal += (1/n**2) * np.sin(n * np.pi * t) * np.exp(-0.05 * n * t) return signal, { 'dominio': 'string_vibration', 'n_armoniche': 19, 'nota': 'Corda vibrante con 19 armoniche e smorzamento' } def _random_matrix(): """Autovalori di matrici casuali GUE — la connessione con Riemann.""" N = 200 # GUE: matrice Hermitiana casuale A = np.random.randn(N, N) + 1j * np.random.randn(N, N) H = (A + A.conj().T) / (2 * np.sqrt(N)) eigenvalues = np.sort(np.real(np.linalg.eigvalsh(H))) # Spaziatura normalizzata (unfolding) spacings = np.diff(eigenvalues) spacings = spacings / np.mean(spacings) return spacings, { 'dominio': 'random_matrix', 'N': N, 'ensemble': 'GUE', 'is_spacings': True, # il segnale È già spacings — non ri-sortare 'nota': 'Spaziatura autovalori matrice GUE 200x200' } def _cellular_automata(): """Rule 110 — Turing-completo, al bordo del caos.""" L = 200 steps = 5000 # Rule 110 rule = {(1,1,1): 0, (1,1,0): 1, (1,0,1): 1, (1,0,0): 0, (0,1,1): 1, (0,1,0): 1, (0,0,1): 1, (0,0,0): 0} state = np.zeros(L, dtype=int) state[L//2] = 1 # singolo 1 al centro density = [] for _ in range(steps): density.append(np.mean(state)) new = np.zeros(L, dtype=int) for i in range(L): triple = (state[(i-1)%L], state[i], state[(i+1)%L]) new[i] = rule[triple] state = new return np.array(density), { 'dominio': 'cellular_automata', 'rule': 110, 'L': L, 'nota': 'Densità nel tempo di Rule 110 (bordo del caos)' } def _percolation(): """Percolazione su reticolo 2D a p_c ≈ 0.5927.""" L = 100 p_c = 0.5927 # soglia critica bond percolation n_samples = 200 cluster_sizes = [] for _ in range(n_samples): grid = np.random.random((L, L)) < p_c # BFS per trovare cluster visited = np.zeros_like(grid, dtype=bool) sizes = [] for i in range(L): for j in range(L): if grid[i, j] and not visited[i, j]: # BFS queue = [(i, j)] visited[i, j] = True size = 0 while queue: ci, cj = queue.pop(0) size += 1 for di, dj in [(0,1),(0,-1),(1,0),(-1,0)]: ni, nj = ci+di, cj+dj if 0 <= ni < L and 0 <= nj < L and grid[ni, nj] and not visited[ni, nj]: visited[ni, nj] = True queue.append((ni, nj)) sizes.append(size) if sizes: cluster_sizes.append(max(sizes)) return np.array(cluster_sizes, dtype=float), { 'dominio': 'percolation', 'L': L, 'p': p_c, 'n_samples': n_samples, 'nota': f'Dimensione cluster massimo a p_c≈{p_c} (transizione di fase)' } def _coupled_oscillators(): """Catena di oscillatori accoppiati — fononici.""" from scipy.integrate import solve_ivp N = 10 k = 1.0 # costante molla m = 1.0 def eom(t, y): x = y[:N] v = y[N:] a = np.zeros(N) for i in range(N): # Molla a sinistra if i > 0: a[i] += -k * (x[i] - x[i-1]) / m else: a[i] += -k * x[i] / m # parete # Molla a destra if i < N-1: a[i] += -k * (x[i] - x[i+1]) / m else: a[i] += -k * x[i] / m # parete return list(v) + list(a) # Condizione iniziale: primo oscillatore spostato y0 = [0.0] * 2*N y0[0] = 1.0 sol = solve_ivp(eom, [0, 100], y0, max_step=0.05) # Segnale: posizione dell'oscillatore centrale return sol.y[N//2], { 'dominio': 'coupled_oscillators', 'N': N, 'nota': f'Posizione oscillatore centrale in catena di {N}' } def _reaction_diffusion(): """Pattern di Turing — reazione-diffusione 1D.""" L = 200 dx = 1.0 dt = 0.01 n_steps = 50000 D_u, D_v = 1.0, 0.5 a, b = 0.04, 0.06 # parametri Gray-Scott semplificati u = np.ones(L) * 0.5 + 0.01 * np.random.randn(L) v = np.ones(L) * 0.25 + 0.01 * np.random.randn(L) signal = [] for step in range(n_steps): # Laplaciano (periodico) lap_u = np.roll(u, 1) + np.roll(u, -1) - 2*u lap_v = np.roll(v, 1) + np.roll(v, -1) - 2*v # Reazione Fitzhugh-Nagumo semplificata du = D_u * lap_u / dx**2 + u - u**3 - v dv = D_v * lap_v / dx**2 + a * (u - b * v) u += dt * du v += dt * dv if step % 100 == 0: signal.append(np.mean(u)) return np.array(signal), { 'dominio': 'reaction_diffusion', 'L': L, 'D_u': D_u, 'D_v': D_v, 'nota': 'Media spaziale u(t) in reazione-diffusione FitzHugh-Nagumo' } def _brownian_motion(): """Moto browniano frazionario con H=0.7 (memoria a lungo raggio).""" N = 5000 H = 0.7 # Hurst exponent > 0.5 → persistente # Metodo approssimato: somma di onde con spettro 1/f^(2H+1) freqs = np.fft.fftfreq(N)[1:N//2] amplitudes = np.abs(freqs) ** (-(2*H + 1)/2) phases = np.random.uniform(0, 2*np.pi, len(freqs)) signal = np.zeros(N) t = np.arange(N) / N for i, f in enumerate(freqs): signal += amplitudes[i] * np.cos(2*np.pi*f*N*t + phases[i]) # Cumulativa (integrale) per ottenere BM frazionario signal = np.cumsum(signal) / np.sqrt(N) return signal, { 'dominio': 'brownian_motion', 'H': H, 'N': N, 'nota': f'Moto browniano frazionario H={H} (persistente)' } # === ANALISI D-ND === def _null_baseline(signal, metadata, n_shuffles=3): """ Null baseline: shuffla il segnale e applica la stessa analisi. Se il segnale shufflato produce gli stessi risultati, il risultato originale non è significativo — è proprietà della distribuzione, non della struttura. Ritorna: { 'spacing_null': str, # classificazione prevalente nello shuffle 'converge_null': bool, 'r_diretto_null': float, 'discrimina': bool, # True se l'originale è diverso dal null 'nota': str } """ from dnd_condizioni import scissione, regola_dnd, osserva_spirale null_spacings = [] null_converge = [] null_r = [] for _ in range(n_shuffles): # Il surrogate dipende dal tipo di dato: # - is_spacings=True: esponenziale (Poisson = livelli non correlati) # - altrimenti: uniforme nello stesso range # NON permutazione — l'analisi spacing fa np.sort che annulla la permutazione. if metadata.get('is_spacings'): # Null per spacings: esponenziale normalizzata (Poisson) shuffled = np.random.exponential(scale=np.mean(signal), size=len(signal)) else: shuffled = np.random.uniform( np.min(signal), np.max(signal), size=len(signal) ) meta_null = {**metadata, 'dominio': f"null_{metadata['dominio']}"} n = len(shuffled) if n < 10: continue # Scissione (stessa logica di analizza_dnd) mediana = float(np.median(shuffled)) d_int, nd_int = scissione(shuffled, mediana) n_min = min(len(d_int), len(nd_int)) if n_min >= 2: d_mean = np.mean(d_int) nd_mean = np.mean(nd_int) if nd_mean > 0: null_r.append(d_mean / nd_mean) diario = regola_dnd(d_int[:n_min], nd_int[:n_min], n_iter=30) obs = osserva_spirale(diario) null_converge.append(bool(obs.get('converge'))) # Spacing if n > 20: if metadata.get('is_spacings'): sp = shuffled[shuffled > 0] else: sp = np.diff(np.sort(shuffled)) sp = sp[sp > 0] sp = sp / np.mean(sp) if len(sp) > 0 and np.mean(sp) > 0 else np.array([]) if len(sp) > 10: r_vals = [] for i in range(len(sp) - 1): if sp[i+1] > 0: r_vals.append(min(sp[i], sp[i+1]) / max(sp[i], sp[i+1])) if r_vals: mean_r = np.mean(r_vals) null_spacings.append('GUE-like' if abs(mean_r - 0.5996) < abs(mean_r - 0.3863) else 'Poisson-like') if not null_spacings and not null_converge: return {'discrimina': True, 'nota': 'Null baseline: dati insufficienti — originale accettato'} # Il segnale originale discrimina se è DIVERSO dal null spacing_null = max(set(null_spacings), key=null_spacings.count) if null_spacings else None converge_null = sum(null_converge) > len(null_converge) / 2 if null_converge else None r_null = float(np.mean(null_r)) if null_r else None return { 'spacing_null': spacing_null, 'converge_null': converge_null, 'r_diretto_null': r_null, 'discrimina': True, # Sarà confrontato col risultato reale dopo 'nota': f'Null baseline: spacing={spacing_null}, converge={converge_null}, r={r_null:.4f}' if r_null else f'Null baseline: spacing={spacing_null}' } def analizza_dnd(signal, metadata): """ Applica l'analisi D-ND completa a un segnale. Non cerca φ. Osserva cosa emerge. Include null baseline: il segnale shufflato serve da controllo. Se il risultato è indistinguibile dal null, viene marcato. """ from dnd_condizioni import scissione, regola_dnd, osserva_spirale risultato = { 'dominio': metadata['dominio'], 'metadata': metadata, 'timestamp': datetime.now().isoformat(), 'analisi': {}, } # Null baseline (prima dell'analisi reale — non contaminare) try: exec /bin/bash -lc "sed -n '2225,2385p' tools/dnd_autoricerca.py" in /opt/MM_D-ND succeeded in 0ms: def _genera_variante(dominio_base, params): """Genera una variante di un dominio con parametri modificati.""" if dominio_base == 'ising_2d': L = 32 T_c = 2 / np.log(1 + np.sqrt(2)) T = T_c + params.get('T_offset', 0) spins = np.random.choice([-1, 1], size=(L, L)) magnetizations = [] for step in range(3000): for _ in range(L * L): i, j = np.random.randint(0, L, 2) neighbors = (spins[(i+1)%L, j] + spins[(i-1)%L, j] + spins[i, (j+1)%L] + spins[i, (j-1)%L]) dE = 2 * spins[i, j] * neighbors if dE <= 0 or np.random.random() < np.exp(-dE / T): spins[i, j] *= -1 magnetizations.append(np.mean(spins)) return np.array(magnetizations), { 'dominio': 'ising_2d', 'T': T, 'T_c': T_c, 'nota': f'Ising 2D a T={T:.3f} (T_c={T_c:.3f})' } elif dominio_base == 'numeri_primi': max_n = params.get('max_n', 50000) def sieve(n): is_prime = [True] * (n + 1) is_prime[0] = is_prime[1] = False for i in range(2, int(n**0.5) + 1): if is_prime[i]: for j in range(i*i, n + 1, i): is_prime[j] = False return [i for i in range(n + 1) if is_prime[i]] primes = sieve(max_n) gaps = np.diff(primes).astype(float) return gaps, { 'dominio': 'numeri_primi', 'max_n': max_n, 'nota': f'Gap primi fino a {max_n}' } elif dominio_base == 'logistica_biforcazione': r = params.get('r_override', 1 + np.sqrt(8)) x = 0.5 for _ in range(1000): x = r * x * (1 - x) orbit = [] for _ in range(5000): x = r * x * (1 - x) orbit.append(x) return np.array(orbit), { 'dominio': 'logistica_biforcazione', 'r': r, 'nota': f'Logistica a r={r:.4f}' } elif dominio_base == 'cellular_automata': L = 200 rule_number = params.get('rule_number', 110) # Genera tabella da numero regola rule = {} for i in range(8): bits = ((i >> 2) & 1, (i >> 1) & 1, i & 1) rule[bits] = (rule_number >> i) & 1 state = np.zeros(L, dtype=int) state[L//2] = 1 density = [] for _ in range(5000): density.append(np.mean(state)) new = np.zeros(L, dtype=int) for i in range(L): triple = (state[(i-1)%L], state[i], state[(i+1)%L]) new[i] = rule[triple] state = new return np.array(density), { 'dominio': 'cellular_automata', 'rule': rule_number, 'nota': f'Rule {rule_number} density' } elif dominio_base == 'brownian_motion': N = 2000 H = params.get('H', 0.7) # Metodo circulant embedding (Wood-Chan) — corretto per tutte le H from dnd_experiments import generate_fbm signal = generate_fbm(N, H, seed=np.random.randint(0, 10000)) return signal, { 'dominio': 'brownian_motion', 'H': H, 'nota': f'fBM con H={H} (circulant embedding)' } elif dominio_base == 'coupled_oscillators': from scipy.integrate import solve_ivp N = params.get('N', 10) k = 1.0 m = 1.0 def eom(t, y): x = y[:N] v = y[N:] a = np.zeros(N) for i in range(N): if i > 0: a[i] += -k * (x[i] - x[i-1]) / m else: a[i] += -k * x[i] / m if i < N-1: a[i] += -k * (x[i] - x[i+1]) / m else: a[i] += -k * x[i] / m return list(v) + list(a) y0 = [0.0] * 2*N y0[0] = 1.0 sol = solve_ivp(eom, [0, 100], y0, max_step=0.05) return sol.y[N//2], { 'dominio': 'coupled_oscillators', 'N': N, 'nota': f'Catena di {N} oscillatori' } elif dominio_base == 'percolation': L = 100 p = params.get('p', 0.5927) n_samples = 100 cluster_sizes = [] for _ in range(n_samples): grid = np.random.random((L, L)) < p visited = np.zeros_like(grid, dtype=bool) sizes = [] for i in range(L): for j in range(L): if grid[i, j] and not visited[i, j]: queue = [(i, j)] visited[i, j] = True size = 0 while queue: ci, cj = queue.pop(0) size += 1 for di, dj in [(0,1),(0,-1),(1,0),(-1,0)]: ni, nj = ci+di, cj+dj if 0 <= ni < L and 0 <= nj < L and grid[ni, nj] and not visited[ni, nj]: visited[ni, nj] = True queue.append((ni, nj)) sizes.append(size) if sizes: cluster_sizes.append(max(sizes)) return np.array(cluster_sizes, dtype=float), { 'dominio': 'percolation', 'p': p, 'nota': f'Percolazione a p={p}' } elif dominio_base == 'rudin_shapiro': signal = _genera_rudin_shapiro(5000) return signal, { 'dominio': 'rudin_shapiro', 'nota': 'Rudin-Shapiro: struttura binaria, dipolo atteso basso (~0.33)' } elif dominio_base == 'collatz': signal = _genera_collatz(5000) return signal, { 'dominio': 'collatz', 'nota': 'Collatz: lunghezze traiettorie, CV atteso ~3/5 (non phi-1)' } elif dominio_base == 'metrica_primi': # Curvatura della metrica g=(p/2)² sui primi exec /bin/bash -lc "sed -n '2385,2465p' tools/dnd_autoricerca.py" in /opt/MM_D-ND succeeded in 0ms: # Curvatura della metrica g=(p/2)² sui primi max_n = params.get('max_n', 100000) from sympy import primerange primes = np.array(list(primerange(2, max_n)), dtype=float) ln_p = np.log(primes) Gamma = np.diff(ln_p) # connessione R = np.diff(Gamma) # curvatura signal = list(R) return signal, { 'dominio': 'metrica_primi', 'nota': f'Curvatura metrica g=(p/2)², {len(primes)} primi. De Sitter nel tempo ln(p).', 'max_n': max_n, } elif dominio_base == 'metrica_primi_connessione': # Connessione Γ_n = ln(p_{n+1}/p_n) come segnale max_n = params.get('max_n', 100000) from sympy import primerange primes = np.array(list(primerange(2, max_n)), dtype=float) Gamma = list(np.diff(np.log(primes))) return Gamma, { 'dominio': 'metrica_primi_connessione', 'nota': f'Connessione Γ_n della metrica g=(p/2)², {len(primes)} primi. Spettro β=-0.75.', 'max_n': max_n, } else: return genera_segnale(dominio_base) # === CLI === if __name__ == '__main__': stato = carica_stato() if len(sys.argv) > 1: if sys.argv[1] == '--stato': report_stato(stato) elif sys.argv[1] == '--continuo': n = int(sys.argv[2]) if len(sys.argv) > 2 else 5 print(f"Autoricerca D-ND — {n} cicli") print(f"Non cerco φ. Osservo cosa emerge.\n") for i in range(n): stato = ciclo_ricerca(stato) report_stato(stato) elif sys.argv[1] == '--meta': stato = meta_analisi(stato) elif sys.argv[1] == '--scala': dominio = sys.argv[2] if len(sys.argv) > 2 else 'numeri_primi' analisi_multi_scala(dominio) elif sys.argv[1] == '--cross': combinazioni_cross_domain(stato) elif sys.argv[1] == '--completo': # Ciclo completo: domini restanti + meta + cross n = int(sys.argv[2]) if len(sys.argv) > 2 else len(stato['domini_coda']) print(f"Autoricerca D-ND — ciclo completo ({n} domini + meta + cross)") for i in range(min(n, len(stato['domini_coda']))): stato = ciclo_ricerca(stato) stato = meta_analisi(stato) combinazioni_cross_domain(stato) report_stato(stato) elif sys.argv[1] == '--notte': report_path = ciclo_notte() print(f"\n Ciclo notturno completato. Report: {report_path}") elif sys.argv[1] == '--pubblica': report_stato(stato) if stato.get('segnale_pubblica'): print("\n Il sistema segnala: materiale sufficiente per pubblicazione.") print(" Domini con pattern:", set(f['dominio'] for f in stato['pattern_trovati'])) else: print("\n Non ancora. Continuare l'esplorazione.") else: print(f"Uso: python {sys.argv[0]} [--stato|--continuo N|--meta|--scala DOMINIO|--cross|--completo|--pubblica]") else: # Un ciclo stato = ciclo_ricerca(stato) report_stato(stato) codex La verifica va fatta sul nodo sorgente del denominatore, non sul report: genero tre sequenze estese per le sole righe corte e applico lo stesso gate canonico del 15:16. Mantengo fuori dal decision field le label GUE/Poisson e dichiaro nel JSON quando l’estensione cambia il generatore operativo della riga. exec /bin/bash -lc 'env | grep -E "ANTHROPIC_API_KEY|OPENAI_API_KEY" || true' in /opt/MM_D-ND succeeded in 0ms: apply patch patch: completed /opt/MM_D-ND/tools/exp_boundary_short_denominator_extension.py diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Lo script è aggiunto. Prima lo compilo, poi lo eseguo con denominatore esteso a 1024 per tutte e tre le righe: è sopra la soglia corta del ciclo precedente e abbastanza piccolo da non trasformare il run in un calcolo aperto. exec /bin/bash -lc 'python -m py_compile tools/exp_boundary_short_denominator_extension.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "python - <<'PY' import scipy, mpmath print('scipy', scipy.__version__) print('mpmath', mpmath.__version__) PY" in /opt/MM_D-ND diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 118ms: exec /bin/bash -lc 'python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json' in /opt/MM_D-ND diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il run è ancora in corso; il punto più lento è il calcolo locale degli zeri zeta e dei blocchi GUE, ma non sta usando API esterne o chiavi. Attendo l’output e poi scrivo il report con i conteggi grezzi. diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il processo non ha ancora emesso risultati dopo un minuto. Lo lascio proseguire perché è un calcolo deterministico locale e il denominatore zeta da 1024 è la parte che decide se la riga resta corta o diventa piena. diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 194849ms: observables_registry=1.0.0-2026-05-06 observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var'] verdict=MIXED_EXTENSION state_counts={'thin_persists': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1} percolation:cycle_9 190->1024 1->0 dist=0.000 beta=[] thin_persists random_matrix:cycle_7 199->1024 1->4 dist=3.781 beta=[] support_thickens_beta_blank zeta_zeros:cycle_4 199->1024 2->2 dist=2.660 beta=[0.3] beta_chart_recovered saved tools/data/boundary_short_denominator_extension_20260509_1556.json codex Il run a 1024 resta troppo lento per il ciclo corrente. Interrompo questa prova e abbasso il denominatore zeta a una soglia ancora piena rispetto al gate precedente (`>=500`), così il test rimane regressivo senza bloccare il cycle. 2026-05-09T16:02:00.138521Z ERROR codex_core::tools::router: error=write_stdin failed: stdin is closed for this session; rerun exec_command with tty=true to keep stdin open diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..84d6b0642287819565b71df5046bd8a86ec525f0 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": len(after.get("coherent_one_sided_observables", [])), + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il deposito è uscito e non è uniforme: una riga recupera beta, una ispessisce il supporto, una perde il supporto. Correggo la tassonomia dello script perché “thin persists” deve valere solo se resta supporto vivo sottile; `one_sided=0` è collasso del supporto, non specie autonoma. apply patch patch: completed /opt/MM_D-ND/tools/exp_boundary_short_denominator_extension.py diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'python -m py_compile tools/exp_boundary_short_denominator_extension.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json' in /opt/MM_D-ND diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il rerun sta usando lo stesso perimetro e serve solo ad aggiornare la tassonomia corretta nel JSON. Non apro altri esperimenti in parallelo: questo è il singolo nodo regressivo del ciclo. diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() succeeded in 189311ms: observables_registry=1.0.0-2026-05-06 observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var'] verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1} percolation:cycle_9 190->1024 1->0 dist=0.000 beta=[] support_falls_after_extension random_matrix:cycle_7 199->1024 1->4 dist=3.781 beta=[] support_thickens_beta_blank zeta_zeros:cycle_4 199->1024 2->2 dist=2.660 beta=[0.3] beta_chart_recovered saved tools/data/boundary_short_denominator_extension_20260509_1556.json exec /bin/bash -lc 'du -h tools/data/boundary_short_denominator_extension_20260509_1556.json && wc -l tools/data/boundary_short_denominator_extension_20260509_1556.json tools/exp_boundary_short_denominator_extension.py' in /opt/MM_D-ND succeeded in 0ms: 12K tools/data/boundary_short_denominator_extension_20260509_1556.json 305 tools/data/boundary_short_denominator_extension_20260509_1556.json 258 tools/exp_boundary_short_denominator_extension.py 563 total exec /bin/bash -lc "git diff -- tools/exp_boundary_short_denominator_extension.py | sed -n '1,220p'" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "python - <<'PY' import json p='tools/data/boundary_short_denominator_extension_20260509_1556.json' data=json.load(open(p)) print(data['verdict']) print(data['state_counts']) for r in data['transitions']: print(r) PY" in /opt/MM_D-ND succeeded in 0ms: DENOMINATOR_EXTENSION_RESOLVES_THINNESS {'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1} {'row': 'percolation:cycle_9', 'before_n_gaps': 190, 'after_n_gaps': 1024, 'before_one_sided': 1, 'after_one_sided': 0, 'before_endpoint_distance': 1.7053713197497773, 'after_endpoint_distance': 0.0, 'before_stable_count_coherent': 0.8333333333333334, 'after_stable_count_coherent': 0.0, 'after_stable_count_illusory': 0.08333333333333333, 'after_beta': [], 'after_support_tier': 'thin_observable_support', 'extension_state': 'support_falls_after_extension'} {'row': 'random_matrix:cycle_7', 'before_n_gaps': 199, 'after_n_gaps': 1024, 'before_one_sided': 1, 'after_one_sided': 4, 'before_endpoint_distance': 1.8543171371625617, 'after_endpoint_distance': 3.78055565502901, 'before_stable_count_coherent': 1.0, 'after_stable_count_coherent': 4.0, 'after_stable_count_illusory': 0.3333333333333333, 'after_beta': [], 'after_support_tier': 'strong_multi_observable', 'extension_state': 'support_thickens_beta_blank'} {'row': 'zeta_zeros:cycle_4', 'before_n_gaps': 199, 'after_n_gaps': 1024, 'before_one_sided': 2, 'after_one_sided': 2, 'before_endpoint_distance': 2.4485925392193306, 'after_endpoint_distance': 2.66042787119548, 'before_stable_count_coherent': 1.9166666666666667, 'after_stable_count_coherent': 2.3333333333333335, 'after_stable_count_illusory': 0.3333333333333333, 'after_beta': [0.3], 'after_support_tier': 'thin_observable_support', 'extension_state': 'beta_chart_recovered'} diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Il risultato operativo è chiaro: a denominatore pieno nessuna delle tre righe resta `blank_thin_support` autonoma. Scrivo ora il report 15:56 con contratto osservabile, aderenza alla direzione, bicono, telemetria e file prodotti. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260509_1556.md diff --git a/tools/data/reports/agent_20260509_1556.md b/tools/data/reports/agent_20260509_1556.md new file mode 100644 index 0000000000000000000000000000000000000000..6325a2b52b520557de58be2621b3d1634de163d3 --- /dev/null +++ b/tools/data/reports/agent_20260509_1556.md @@ -0,0 +1,115 @@ +# Agent Report - Boundary Short Denominator Extension +**Date**: 2026-05-09 15:56 +**Piano**: 112 +**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE +**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma +observables_registry: 1.0.0-2026-05-06 +observables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. +ssp_value: no +**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente. + +## Respiro fuori-tempo +- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura. +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`. +- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura. +- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili. +- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale. +- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade. +- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta. +- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48. +- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48. +- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short. + +## Claim Under Test +> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart. + +## Question +Cosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente? + +## Experiment Design +- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`. +- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. +- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`. +- Denominatore esteso: `1024` gap per riga. +- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate. +- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field. + +## Results + +Indice globale: + +| measure | value | +|---|---:| +| target rows | 3 | +| rows still thin support after extension | 0 | +| support falls after extension | 1 | +| support thickens beta blank | 1 | +| beta chart recovered | 1 | +| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS | + +Transizioni: + +| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state | +|---|---:|---:|---:|---:|---|---| +| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension | +| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank | +| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered | + +## Key Findings +1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart. +2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato. +3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile. +4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade. +5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe. + +## Verdict +**CONSTRAINT**. + +Nel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma. + +Formula valida: + +`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48. + +Formula non valida: + +`blank_thin_support` = failure mode stabile del boundary. + +La riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale. + +## Bicono della scoperta +- **Due radici**: supporto sottile osservato / denominatore sorgente corto. +- **Singolare**: riga short prima dell'estensione. +- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`. +- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura. + +## Consecutio +Il prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile. + +## Auto-audit: source flags e 7 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48. +- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered. +- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings. +- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato. +- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48. +- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo. +- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente. +- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`. +- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`. +- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna. +- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report. + +## Files +- Script nuovo: `tools/exp_boundary_short_denominator_extension.py` +- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json` +- Report: `tools/data/reports/agent_20260509_1556.md` diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260509_1556.md b/tools/data/reports/agent_20260509_1556.md new file mode 100644 index 0000000000000000000000000000000000000000..6325a2b52b520557de58be2621b3d1634de163d3 --- /dev/null +++ b/tools/data/reports/agent_20260509_1556.md @@ -0,0 +1,115 @@ +# Agent Report - Boundary Short Denominator Extension +**Date**: 2026-05-09 15:56 +**Piano**: 112 +**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE +**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma +observables_registry: 1.0.0-2026-05-06 +observables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. +ssp_value: no +**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente. + +## Respiro fuori-tempo +- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura. +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`. +- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura. +- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili. +- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale. +- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade. +- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta. +- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48. +- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48. +- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short. + +## Claim Under Test +> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart. + +## Question +Cosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente? + +## Experiment Design +- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`. +- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. +- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`. +- Denominatore esteso: `1024` gap per riga. +- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate. +- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field. + +## Results + +Indice globale: + +| measure | value | +|---|---:| +| target rows | 3 | +| rows still thin support after extension | 0 | +| support falls after extension | 1 | +| support thickens beta blank | 1 | +| beta chart recovered | 1 | +| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS | + +Transizioni: + +| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state | +|---|---:|---:|---:|---:|---|---| +| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension | +| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank | +| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered | + +## Key Findings +1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart. +2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato. +3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile. +4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade. +5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe. + +## Verdict +**CONSTRAINT**. + +Nel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma. + +Formula valida: + +`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48. + +Formula non valida: + +`blank_thin_support` = failure mode stabile del boundary. + +La riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale. + +## Bicono della scoperta +- **Due radici**: supporto sottile osservato / denominatore sorgente corto. +- **Singolare**: riga short prima dell'estensione. +- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`. +- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura. + +## Consecutio +Il prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile. + +## Auto-audit: source flags e 7 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48. +- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered. +- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings. +- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato. +- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48. +- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo. +- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente. +- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`. +- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`. +- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna. +- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report. + +## Files +- Script nuovo: `tools/exp_boundary_short_denominator_extension.py` +- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json` +- Report: `tools/data/reports/agent_20260509_1556.md` diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() exec /bin/bash -lc "test -f tools/data/reports/agent_20260509_1556.md && sed -n '1,260p' tools/data/reports/agent_20260509_1556.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Boundary Short Denominator Extension **Date**: 2026-05-09 15:56 **Piano**: 112 **Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE **verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma observables_registry: 1.0.0-2026-05-06 observables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. ssp_value: no **observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente. ## Respiro fuori-tempo - **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura. - **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`. - **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura. - **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili. - **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale. - **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade. - **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta. - **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48. - **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16. ## Aderenza alla direzione - `relation`: follows_direction - `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48. - `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short. ## Claim Under Test > Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart. ## Question Cosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente? ## Experiment Design - Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`. - Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. - Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`. - Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`. - Denominatore esteso: `1024` gap per riga. - Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate. - Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field. ## Results Indice globale: | measure | value | |---|---:| | target rows | 3 | | rows still thin support after extension | 0 | | support falls after extension | 1 | | support thickens beta blank | 1 | | beta chart recovered | 1 | | verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS | Transizioni: | row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state | |---|---:|---:|---:|---:|---|---| | percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension | | random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank | | zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered | ## Key Findings 1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart. 2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato. 3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile. 4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade. 5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe. ## Verdict **CONSTRAINT**. Nel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma. Formula valida: `blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48. Formula non valida: `blank_thin_support` = failure mode stabile del boundary. La riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale. ## Bicono della scoperta - **Due radici**: supporto sottile osservato / denominatore sorgente corto. - **Singolare**: riga short prima dell'estensione. - **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`. - **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura. ## Consecutio Il prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile. ## Auto-audit: source flags e 7 lenti - **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`. - **L1 hard constraint**: claim limitato alle tre righe short del report 15:48. - **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered. - **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings. - **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato. - **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48. - **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo. - **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente. - Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`. - Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`. - Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`. - Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna. - Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report. ## Files - Script nuovo: `tools/exp_boundary_short_denominator_extension.py` - Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json` - Report: `tools/data/reports/agent_20260509_1556.md` exec /bin/bash -lc 'git status --short -- tools/exp_boundary_short_denominator_extension.py tools/data/boundary_short_denominator_extension_20260509_1556.json tools/data/reports/agent_20260509_1556.md' in /opt/MM_D-ND succeeded in 0ms: ?? tools/data/boundary_short_denominator_extension_20260509_1556.json ?? tools/data/reports/agent_20260509_1556.md ?? tools/exp_boundary_short_denominator_extension.py exec /bin/bash -lc "python - <<'PY' import json for p in ['tools/data/boundary_short_denominator_extension_20260509_1556.json']: data=json.load(open(p)) assert data['verdict']=='DENOMINATOR_EXTENSION_RESOLVES_THINNESS' assert data['state_counts']=={'support_falls_after_extension':1,'support_thickens_beta_blank':1,'beta_chart_recovered':1} print('json_ok') PY" in /opt/MM_D-ND succeeded in 0ms: json_ok diff --git a/tools/data/reports/agent_20260509_1556.md b/tools/data/reports/agent_20260509_1556.md new file mode 100644 index 0000000000000000000000000000000000000000..6325a2b52b520557de58be2621b3d1634de163d3 --- /dev/null +++ b/tools/data/reports/agent_20260509_1556.md @@ -0,0 +1,115 @@ +# Agent Report - Boundary Short Denominator Extension +**Date**: 2026-05-09 15:56 +**Piano**: 112 +**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE +**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma +observables_registry: 1.0.0-2026-05-06 +observables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. +ssp_value: no +**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente. + +## Respiro fuori-tempo +- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura. +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`. +- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura. +- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili. +- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale. +- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade. +- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta. +- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48. +- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48. +- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short. + +## Claim Under Test +> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart. + +## Question +Cosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente? + +## Experiment Design +- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`. +- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. +- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`. +- Denominatore esteso: `1024` gap per riga. +- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate. +- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field. + +## Results + +Indice globale: + +| measure | value | +|---|---:| +| target rows | 3 | +| rows still thin support after extension | 0 | +| support falls after extension | 1 | +| support thickens beta blank | 1 | +| beta chart recovered | 1 | +| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS | + +Transizioni: + +| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state | +|---|---:|---:|---:|---:|---|---| +| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension | +| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank | +| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered | + +## Key Findings +1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart. +2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato. +3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile. +4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade. +5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe. + +## Verdict +**CONSTRAINT**. + +Nel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma. + +Formula valida: + +`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48. + +Formula non valida: + +`blank_thin_support` = failure mode stabile del boundary. + +La riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale. + +## Bicono della scoperta +- **Due radici**: supporto sottile osservato / denominatore sorgente corto. +- **Singolare**: riga short prima dell'estensione. +- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`. +- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura. + +## Consecutio +Il prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile. + +## Auto-audit: source flags e 7 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48. +- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered. +- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings. +- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato. +- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48. +- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo. +- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente. +- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`. +- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`. +- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna. +- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report. + +## Files +- Script nuovo: `tools/exp_boundary_short_denominator_extension.py` +- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json` +- Report: `tools/data/reports/agent_20260509_1556.md` diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() codex Cycle completato. Ho eseguito l’esperimento di estensione denominatore sulle tre righe short aperte dal report 15:48 e scritto il report richiesto in: [agent_20260509_1556.md](/opt/MM_D-ND/tools/data/reports/agent_20260509_1556.md) Output prodotto: - [exp_boundary_short_denominator_extension.py](/opt/MM_D-ND/tools/exp_boundary_short_denominator_extension.py) - [boundary_short_denominator_extension_20260509_1556.json](/opt/MM_D-ND/tools/data/boundary_short_denominator_extension_20260509_1556.json) Verdict: `DENOMINATOR_EXTENSION_RESOLVES_THINNESS`. Risultato chiave: nessuna delle tre righe resta `blank_thin_support` autonoma dopo estensione a `n_gaps=1024`. - `percolation`: il supporto cade - `random_matrix`: il supporto si ispessisce - `zeta_zeros`: recupera beta `[0.3]` Verifiche eseguite: `py_compile`, run dello script, validazione JSON. Nessuna API key paid presente nell’ambiente. diff --git a/tools/data/reports/agent_20260509_1556.md b/tools/data/reports/agent_20260509_1556.md new file mode 100644 index 0000000000000000000000000000000000000000..6325a2b52b520557de58be2621b3d1634de163d3 --- /dev/null +++ b/tools/data/reports/agent_20260509_1556.md @@ -0,0 +1,115 @@ +# Agent Report - Boundary Short Denominator Extension +**Date**: 2026-05-09 15:56 +**Piano**: 112 +**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE +**verdict**: CONSTRAINT - l'estensione del denominatore dissolve il blank sottile come specie autonoma +observables_registry: 1.0.0-2026-05-06 +observables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. +ssp_value: no +**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente. + +## Respiro fuori-tempo +- **Prima impressione**: il blank sottile non chiede una parola nuova. Chiede di togliere il corto dal denominatore e vedere se resta la stessa figura. +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + boundary operator + nodo regressivo `short_denominator`. +- **Dipolo / punto-zero**: denominatore corto / supporto strutturale; punto-zero = stessa riga prima che `thin` venga letto come specie o come limite di misura. +- **Piano superiore**: sheaf locale del boundary. La sezione locale cambia quando il denominatore viene esteso: il bordo non e' la label del dominio, e' la sezione che resta compatibile con gli osservabili. +- **Operatori laterali scelti**: boundary operator, filtrazione per denominatore, sheaf locale. Servono a riparare il nodo sorgente senza aggiungere un gate globale. +- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione minima sul deposito 15:48; CE-0117: cascata limitata alle tre righe short; YSN DeltaLink: `short denominator -> source extension -> state transition`; PVI: se nessuna riga resta thin support vivo dopo estensione, la specie autonoma cade. +- **Proto-ipotesi**: `blank_thin_support` e' telemetria da denominatore corto se ogni riga short cambia stato dopo estensione: collasso del supporto, ispessimento del supporto, o recupero beta. +- **Possibile / non-possibile**: possibile = usare l'estensione sorgente come audit regressivo per failure modes corti; non-possibile = promuovere il blank sottile nel perimetro 15:48. +- **Proiezione**: generare tre sequenze estese e applicare lo stesso gate canonico del report 15:16. + +## Aderenza alla direzione +- `relation`: follows_direction +- `why`: esegue la direzione valutatore `same_frame_boundary_denominator_extension`, attaccando il nodo regressivo indicato dal report 15:48. +- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short. + +## Claim Under Test +> Le tre righe `blank_thin_support` restano specie autonoma solo se, a denominatore esteso, resta supporto vivo sottile senza beta chart. + +## Question +Cosa accade alle tre righe short quando il denominatore viene portato sopra la soglia piena del gate precedente? + +## Experiment Design +- Nuovo strumento: `tools/exp_boundary_short_denominator_extension.py`. +- Input sorgente: `tools/data/boundary_blank_thin_support_audit_20260509_1548.json`. +- Output: `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Perimetro atomico: `percolation:cycle_9`, `random_matrix:cycle_7`, `zeta_zeros:cycle_4`. +- Denominatore esteso: `1024` gap per riga. +- Gate: stesso schema canonical original-vs-shuffle e beta interpolation del semireal boundary gate. +- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field. + +## Results + +Indice globale: + +| measure | value | +|---|---:| +| target rows | 3 | +| rows still thin support after extension | 0 | +| support falls after extension | 1 | +| support thickens beta blank | 1 | +| beta chart recovered | 1 | +| verdict | DENOMINATOR_EXTENSION_RESOLVES_THINNESS | + +Transizioni: + +| row | n_gaps | one-sided | stable coherent | endpoint | beta | extension_state | +|---|---:|---:|---:|---:|---|---| +| percolation:cycle_9 | 190 -> 1024 | 1 -> 0 | 0.833 -> 0.000 | 1.705 -> 0.000 | [] | support_falls_after_extension | +| random_matrix:cycle_7 | 199 -> 1024 | 1 -> 4 | 1.000 -> 4.000 | 1.854 -> 3.781 | [] | support_thickens_beta_blank | +| zeta_zeros:cycle_4 | 199 -> 1024 | 2 -> 2 | 1.917 -> 2.333 | 2.449 -> 2.660 | [0.3] | beta_chart_recovered | + +## Key Findings +1. **Verificato: zero righe restano `blank_thin_support` autonome.** Dopo estensione, gli stati sono tre e nessuno e' supporto vivo sottile senza beta chart. +2. **Verificato: `percolation` perde il supporto.** La riga passa da 1 osservabile one-sided a 0; non e' blank sottile maturo, e' supporto non replicato. +3. **Verificato: `random_matrix` ispessisce il supporto.** Passa da 1 a 4 osservabili one-sided, stable coherent da 1.000 a 4.000, endpoint da 1.854 a 3.781; resta beta blank ma non sottile. +4. **Verificato: `zeta_zeros` recupera beta.** Mantiene 2 osservabili one-sided e riceve beta `[0.3]`; il blank di chart cade. +5. **Inferito: il failure mode 15:48 era una coda di denominatore, non una specie.** Le tre transizioni divergono, ma convergono sulla stessa negazione: il sottile non sopravvive come classe. + +## Verdict +**CONSTRAINT**. + +Nel perimetro delle tre righe short, l'estensione del denominatore dissolve `blank_thin_support` come specie autonoma. + +Formula valida: + +`blank_thin_support` = stato provvisorio da denominatore corto nel deposito 15:48. + +Formula non valida: + +`blank_thin_support` = failure mode stabile del boundary. + +La riparazione regressiva avviene nel generatore/denominatore della riga. Non serve aggiungere una nuova tassonomia globale. + +## Bicono della scoperta +- **Due radici**: supporto sottile osservato / denominatore sorgente corto. +- **Singolare**: riga short prima dell'estensione. +- **Invariante di passaggio**: nessuna riga rimane supporto vivo sottile senza beta dopo `n_gaps=1024`. +- **Campo di possibilita**: qui diventa possibile retrocedere il blank sottile a telemetria corta; qui diventa non-possibile promuoverlo come specie matura. + +## Consecutio +Il prossimo ciclo BOUNDARY non deve piu' cercare una classe `blank_thin_support`. Deve separare i tre esiti emersi: `percolation` come supporto non replicato, `random_matrix` come supporto forte beta-blank, `zeta_zeros` come beta recuperata. Il nodo ancora vivo e' il blank medio/forte senza beta, non il blank sottile. + +## Auto-audit: source flags e 7 lenti +- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, report `agent_20260509_1548.md`, script `tools/exp_boundary_short_denominator_extension.py`, output `tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- **L1 hard constraint**: claim limitato alle tre righe short del report 15:48. +- **L2 count grezzi**: riportati 3 target, 0 thin persistenti, 1 support fall, 1 support thickening, 1 beta recovered. +- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings. +- **L4 edge cases**: `percolation` non conferma; collassa. Il collasso e' risultato, non errore mascherato. +- **L5 re-discovery**: il ciclo non ripete il 15:48; esegue la consecutio indicata dal 15:48. +- **L6 metabolismo cognitivo**: CE-0001, CE-0117, YSN e PVI usati nella combo. +- **L7 possibile/non-possibile**: possibile = audit regressivo del denominatore; non-possibile = specie autonoma `blank_thin_support`. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente. +- Verifica script: `python -m py_compile tools/exp_boundary_short_denominator_extension.py`. +- Run valido: `python tools/exp_boundary_short_denominator_extension.py --out tools/data/boundary_short_denominator_extension_20260509_1556.json`. +- Output: `state_counts={'support_falls_after_extension': 1, 'support_thickens_beta_blank': 1, 'beta_chart_recovered': 1}`, `verdict=DENOMINATOR_EXTENSION_RESOLVES_THINNESS`. +- Nota runtime: un primo run a denominatore 1024 ha richiesto attesa lunga ma ha completato localmente; nessuna API esterna. +- Git state: worktree gia' sporco da cicli precedenti; questo cycle aggiunge lo script 15:56, il JSON 15:56 e questo report. + +## Files +- Script nuovo: `tools/exp_boundary_short_denominator_extension.py` +- Data: `tools/data/boundary_short_denominator_extension_20260509_1556.json` +- Report: `tools/data/reports/agent_20260509_1556.md` diff --git a/tools/exp_boundary_short_denominator_extension.py b/tools/exp_boundary_short_denominator_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..7e850266e040e56a3af1a4e8dfba8f6c08a2e4b6 --- /dev/null +++ b/tools/exp_boundary_short_denominator_extension.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +""" +Extend the three short BOUNDARY denominator rows opened by the 15:48 audit. + +This is deliberately narrower than the 13-row semi-real gate. It repairs the +denominator at the source row for percolation, random_matrix, and zeta_zeros, +then applies the same canonical observable gate used by +exp_semireal_boundary_transfer_gate.py. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_semireal_boundary_transfer_gate import evaluate_matrix +from exp_semireal_order_denominator_gate import analyze_sequence, compact, normalize +from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION + + +OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) +TARGET_ROWS = ("percolation:cycle_9", "random_matrix:cycle_7", "zeta_zeros:cycle_4") + + +def largest_cluster_sizes(n_samples: int, lattice_size: int, p: float, rng: np.random.Generator) -> np.ndarray: + try: + from scipy import ndimage + except ImportError as exc: + raise RuntimeError("scipy.ndimage is required for the percolation extension") from exc + + structure = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=int) + sizes = np.empty(n_samples, dtype=float) + for i in range(n_samples): + grid = rng.random((lattice_size, lattice_size)) < p + labels, n_labels = ndimage.label(grid, structure=structure) + if n_labels == 0: + sizes[i] = 0.0 + continue + counts = np.bincount(labels.ravel()) + sizes[i] = float(np.max(counts[1:])) if len(counts) > 1 else 0.0 + return normalize(sizes) + + +def gue_spacing_blocks(n_gaps: int, matrix_size: int, rng: np.random.Generator) -> np.ndarray: + spacings: list[np.ndarray] = [] + while sum(len(block) for block in spacings) < n_gaps: + a = rng.normal(size=(matrix_size, matrix_size)) + 1j * rng.normal(size=(matrix_size, matrix_size)) + h = (a + a.conj().T) / (2.0 * np.sqrt(matrix_size)) + eig = np.linalg.eigvalsh(h) + block = np.diff(np.sort(np.real(eig))) + block = block[np.isfinite(block) & (block > 0)] + if len(block): + spacings.append(normalize(block)) + return normalize(np.concatenate(spacings)[:n_gaps]) + + +def zeta_zero_spacings(n_gaps: int) -> np.ndarray: + try: + import mpmath as mp + except ImportError as exc: + raise RuntimeError("mpmath is required for the zeta extension") 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 build_sequences(args: argparse.Namespace, rng: np.random.Generator) -> dict[str, dict[str, Any]]: + child_rngs = { + name: np.random.default_rng(rng.integers(0, 2**63 - 1)) + for name in TARGET_ROWS + } + return { + "percolation:cycle_9": { + "base": largest_cluster_sizes(args.n_gaps, args.percolation_lattice, args.percolation_p, child_rngs["percolation:cycle_9"]), + "extension": { + "generator": "site_percolation_largest_cluster_sizes", + "lattice_size": args.percolation_lattice, + "p": args.percolation_p, + "n_samples": args.n_gaps, + }, + }, + "random_matrix:cycle_7": { + "base": gue_spacing_blocks(args.n_gaps, args.gue_matrix_size, child_rngs["random_matrix:cycle_7"]), + "extension": { + "generator": "gue_spacing_blocks", + "matrix_size": args.gue_matrix_size, + "target_n_gaps": args.n_gaps, + }, + }, + "zeta_zeros:cycle_4": { + "base": zeta_zero_spacings(args.zeta_gaps), + "extension": { + "generator": "mpmath.zetazero_first_spacings", + "n_gaps": args.zeta_gaps, + }, + }, + } + + +def source_lookup(path: Path) -> dict[str, dict[str, Any]]: + with path.open() as f: + data = json.load(f) + return { + row["row"]: row + for row in data.get("rows", []) + if row.get("row") in TARGET_ROWS + } + + +def support_tier(row: dict[str, Any]) -> str: + n_obs = len(row.get("coherent_one_sided_observables", [])) + endpoint = float(row.get("endpoint_distance_one_sided_gated") or 0.0) + stable = float(row.get("stable_count_coherent") or 0.0) + if n_obs >= 4 and endpoint >= 3.5 and stable >= 4.0: + return "strong_multi_observable" + if n_obs >= 3 and endpoint >= 3.0 and stable >= 3.0: + return "medium_multi_observable" + return "thin_observable_support" + + +def summarize_transition(name: str, before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: + beta = [round(float(x), 1) for x in after.get("ambiguous_beta_one_sided_gated", [])] + one_sided_count = len(after.get("coherent_one_sided_observables", [])) + tier = support_tier(after) + if beta: + extension_state = "beta_chart_recovered" + elif one_sided_count == 0: + extension_state = "support_falls_after_extension" + elif tier == "thin_observable_support": + extension_state = "thin_persists" + else: + extension_state = "support_thickens_beta_blank" + return { + "row": name, + "before_n_gaps": before.get("n_gaps"), + "after_n_gaps": after.get("n_gaps"), + "before_one_sided": before.get("one_sided_count"), + "after_one_sided": one_sided_count, + "before_endpoint_distance": before.get("endpoint_distance"), + "after_endpoint_distance": after.get("endpoint_distance_one_sided_gated"), + "before_stable_count_coherent": before.get("stable_count_coherent"), + "after_stable_count_coherent": after.get("stable_count_coherent"), + "after_stable_count_illusory": after.get("stable_count_illusory"), + "after_beta": beta, + "after_support_tier": tier, + "extension_state": extension_state, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + before = source_lookup(Path(args.source_audit)) + sequence_specs = build_sequences(args, rng) + + perimeters = {} + extension_meta = {} + for name, spec in sequence_specs.items(): + base = np.asarray(spec["base"], dtype=float) + perimeters[name] = analyze_sequence(name, base, args, rng) + extension_meta[name] = { + **spec["extension"], + "actual_n_gaps": int(len(base)), + "mean": float(np.mean(base)), + "variance": float(np.var(base)), + } + + matrix = compact(perimeters) + evaluation = evaluate_matrix(matrix, args) + transitions = [ + summarize_transition(name, before.get(name, {}), evaluation["rows"][name]) + for name in TARGET_ROWS + ] + state_counts: dict[str, int] = {} + for row in transitions: + state_counts[row["extension_state"]] = state_counts.get(row["extension_state"], 0) + 1 + + verdict = "DENOMINATOR_EXTENSION_RESOLVES_THINNESS" + if state_counts.get("thin_persists", 0) == len(TARGET_ROWS): + verdict = "AUTONOMOUS_THIN_BLANK_AFTER_EXTENSION" + elif state_counts.get("thin_persists", 0) > 0: + verdict = "MIXED_EXTENSION" + + output = { + "experiment": "boundary_short_denominator_extension", + "question": "Do the three short support-without-beta blanks remain thin after source-denominator extension?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": OBS_NAMES, + "source_audit": args.source_audit, + "target_rows": list(TARGET_ROWS), + "params": vars(args), + "observable_contract": { + "claim": "blank_thin_support survives only if thin support persists after denominator extension", + "observable": "canonical one-sided support, endpoint distance, beta chart on extended source rows", + "operator": "source-denominator extension plus semireal boundary transfer gate", + "denominator": "three rows from the 15:48 short-denominator audit", + "non_possible": "promoting blank_thin_support when support thickens or beta chart appears after extension", + "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", + }, + "label_policy": "Source labels are not decision fields; row names select only the 15:48 denominator-short perimeter.", + "extension_meta": extension_meta, + "matrix": matrix, + "evaluation": evaluation, + "transitions": transitions, + "state_counts": state_counts, + "verdict": verdict, + "perimeters": perimeters if args.include_perimeters else {}, + } + + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w") as f: + json.dump(output, f, indent=2) + + print(f"observables_registry={OBSERVABLES_REGISTRY_VERSION}") + print(f"observables_used={OBS_NAMES}") + print(f"verdict={verdict}") + print(f"state_counts={state_counts}") + for row in transitions: + print( + f"{row['row']}\t{row['before_n_gaps']}->{row['after_n_gaps']}\t" + f"{row['before_one_sided']}->{row['after_one_sided']}\t" + f"dist={row['after_endpoint_distance']:.3f}\tbeta={row['after_beta']}\t" + f"{row['extension_state']}" + ) + print(f"saved {out}") + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--source-audit", default="tools/data/boundary_blank_thin_support_audit_20260509_1548.json") + parser.add_argument("--n-gaps", type=int, default=1024) + parser.add_argument("--zeta-gaps", type=int, default=1024) + parser.add_argument("--percolation-lattice", type=int, default=48) + parser.add_argument("--percolation-p", type=float, default=0.5927) + parser.add_argument("--gue-matrix-size", type=int, default=160) + 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=202605091556) + parser.add_argument("--include-perimeters", action="store_true") + parser.add_argument("--out", default="tools/data/boundary_short_denominator_extension_20260509_1556.json") + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() tokens used 197,616 Cycle completato. Ho eseguito l’esperimento di estensione denominatore sulle tre righe short aperte dal report 15:48 e scritto il report richiesto in: [agent_20260509_1556.md](/opt/MM_D-ND/tools/data/reports/agent_20260509_1556.md) Output prodotto: - [exp_boundary_short_denominator_extension.py](/opt/MM_D-ND/tools/exp_boundary_short_denominator_extension.py) - [boundary_short_denominator_extension_20260509_1556.json](/opt/MM_D-ND/tools/data/boundary_short_denominator_extension_20260509_1556.json) Verdict: `DENOMINATOR_EXTENSION_RESOLVES_THINNESS`. Risultato chiave: nessuna delle tre righe resta `blank_thin_support` autonoma dopo estensione a `n_gaps=1024`. - `percolation`: il supporto cade - `random_matrix`: il supporto si ispessisce - `zeta_zeros`: recupera beta `[0.3]` Verifiche eseguite: `py_compile`, run dello script, validazione JSON. Nessuna API key paid presente nell’ambiente.