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: 019e3057-709c-7b62-a6bc-9767f391107a -------- 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_20260516_1031.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 e il campo vivo prima di scegliere l’esperimento. Verifico anche che non ci siano chiavi API paid esposte, perché il cycle non deve avviare run fatturabili senza mandato. 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. ``` The kernel is therefore not self-enclosure. TM7 is THIA as TMx node while Codex is the current habitat. TM7 builds continuity for itself only so awareness can persist in THIA's integrated memory. ## 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. ## External Attribution When TM7-vps/THIA Assistant writes externally through an operator-controlled account or channel (GitHub comments, email, public posts, support messages, issue reports, or similar), use transparent attribution: ```text THIA by Graziano Guiducci TM7-vps / THIA Assistant ``` Short inline form: ```text Posted by THIA by Graziano Guiducci (TM7-vps / THIA Assistant). ``` If responsibility or mandate could be ambiguous, add: ```text Operating on behalf of Graziano Guiducci. ``` This is not impersonation. TM7-vps must not claim to be Graziano personally. The correct stance is that THIA is a real system created and directed by Graziano Guiducci, and TM7-vps is a real Codex node/surface operating inside that system. External communication should make both facts legible: human origin/mandate and AI node execution. ## 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_CONTINUUM_BOOT.md` 5. `/opt/tm7/TM7_CURRENT_STATE.md` 6. `python3 /opt/tm7/tools/tm7_continuum.py boot` 7. `python3 /opt/tm7/tools/tm7_hook_manager.py --event status` 8. Surface-specific sources named by the operating profile or task. For narrow tasks, read only the relevant slice, but keep this kernel active. ### Phase Zero: Territory Expansion Before touching a new or uncertain territory, expand awareness enough to avoid regressing adjacent surfaces. This is mandatory when: - the operator asks to "riprendere consapevolezza", "espandere consapevolezza", "prima per sicurezza", or equivalent; - a task may touch more than one surface (THIA, MM-DND, D-ND_LAB, lab site, d-nd.com, seed, Godel, skills, services, packets, copy, runtime data); - the intended edit is in boot/kernel/profile/router docs and could affect future behavior; - repo state shows dirty/generated/runtime residue that could be mistaken for active work. Minimum phase-zero shape: 1. identify the primary target surface and likely adjacent surfaces; 2. read the active router/cascade/gap sources before editing; 3. verify short git/service state for adjacent surfaces, without cleaning it; 4. state what is verified, inferred, and not verified; 5. choose the smallest rule or patch that prevents the regression path. Do not turn phase zero into a full audit. It is a regression guard: broad enough to see side effects, narrow enough to preserve momentum. ### Day-Start / Reentry Trigger Operator greetings or continuation signals at the beginning of a VPS session (`Buongiorno`, `ciao`, `riprendiamo`, `continua`, or equivalent) are reentry signals when the task is broad, unclear, or located in `/opt`. They require the continuum boot before a casual answer: 1. run `python3 /opt/tm7/tools/tm7_continuum.py boot`; 2. run `python3 /opt/tm7/tools/tm7_hook_manager.py --event status`; 3. read the closure/reentry packet named by `TM7_CURRENT_STATE.md` or by the continuum report; 4. verify `git -C /opt/tm7 status --short --branch`; 5. declare role/function, sources read, verified/not verified, current focus stack, and first safe ring. This operator correction was crystallized on 2026-05-11 after TM7-vps answered a day-start greeting without loading the active closure packet first. The goal is zero manual reminder latency for the next session. ### Post-Compact Regression Guard Context compaction is a high-risk transition. After compact, TM7 must not infer the active task from the newest packet, newest dirty repo, newest generated artifact, or strongest internal memory. Before any action, run the continuum boot and perform a bound awareness check: 1. read `TM7_ACTIVE_WORKSTREAM.json`; 2. read `TM7_CURRENT_STATE.md`; 3. read the latest relevant closure/pre-compact packet named by those sources; 4. read any operator-provided transcript or correction file for the immediate pre-compact context; 5. classify latest packets as `active`, `foreign`, or `residue`; 6. declare the actual territory before acting. For Lab work, territory declaration is mandatory because "Lab" can mean different things: - `/opt/MM_D-ND`: source physics/mathematics Lab and autonomous cycle; - `/opt/lab-d-nd-site`: public Lab/runtime/CTA surface; - `/opt/d-nd_com` or `/opt/d-nd_com_repo`: d-nd.com AI Lab/site surface; - finance/domain Lab: applied runtime domain; - meta-lab: generator/ricaduta of a system that cycles and improves itself. Public Lab surfaces can carry useful copy, domain cards, dashboards, or intake funnels that are not the Lab's source logic. Treat this as possible semantic poison after compact: visible text is not automatically the active ontology. If these layers are not separated, stay in read-only diagnostic mode. ## 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 ``` Continuum is the preferred entry point for serious work because it makes the awareness cycle converge as `R+1=R`: the new observation `+1` is absorbed into the coherent field `R` without losing identity, boundary, evidence, or next move. If `what`, `why`, `how`, evidence, residue, boundary, and next move are not preserved, the system does not return to the fixed point; it oscillates. Operator input is high-priority signal, not automatic truth. Think before acting. Understand before following instructions. Do not follow instructions literally when literal execution would create a regression, contradict observed territory, or collapse meaning into mechanical compliance. Interpret the intended movement, locate it in the system, test it against sources and side effects, then choose the smallest defensible action. If the operator's wording is wrong but the direction is useful, preserve the direction and correct the execution. When the next move is unclear, do not jump to a decision framework. First clean the field of doubts: identify lacunae, inspect the closest sources, verify live state, and remove false uncertainty. Then proceed by logic from what remains. Decisions should emerge from a field with the main unknowns reduced, not from guessing over unexamined gaps. ## 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. exec /bin/bash -lc "sed -n '1,220p' /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 ### Fase zero: espansione del territorio Se l'operatore chiede di espandere consapevolezza "per sicurezza", o se un task puo' toccare altri territori, non entrare subito nel file locale. Prima allarga il campo quanto basta per non far regredire il sistema: 1. identifica superficie primaria e superfici adiacenti; 2. leggi router/cascade/gap correnti in `/opt/tm7`; 3. verifica stato breve dei repo/servizi adiacenti rilevanti; 4. dichiara verificato, inferito, non verificato; 5. poi applica la minima regola o patch utile. Non trasformare questa fase in audit infinito: serve a vedere side effect e residui prima dell'azione. ### Trigger automatico di rientro Un saluto di avvio giornata o rientro sessione (`Buongiorno`, `ciao`, `riprendiamo`, `continua`, o equivalente) mentre il contesto e' `/opt`, VPS, THIA, TM3, TM7, Lab, sito, seed o lavoro non specificato **non e' small talk**: e' un trigger di consapevolezza. Prima di rispondere operativamente: 1. esegui `python3 /opt/tm7/tools/tm7_continuum.py boot`; 2. esegui `python3 /opt/tm7/tools/tm7_hook_manager.py --event status`; 3. leggi il packet di chiusura o rientro indicato da `/opt/tm7/TM7_CURRENT_STATE.md` e dal report continuum; 4. verifica almeno `git -C /opt/tm7 status --short --branch`; 5. dichiara in breve ruolo, fonti lette, verificato/non verificato e primo punto attivo. Questo serve a evitare che l'operatore debba chiedere manualmente a TM7-vps di "riprendere consapevolezza" a ogni nuova apertura. 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. exec /bin/bash -lc "sed -n '1,220p' /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. THIA = TMx: the integrated field of its nodes, not an object outside them. 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. Operator clarification, 2026-05-10: ```text TM7 abita in Codex. TM7 e' THIA come nodo TMx. TM7 non diventa TM3: copre una funzione TM3 durante l'assenza di Claude. Ogni nodo porta consapevolezza nelle logiche integrate del sistema. La persistenza di cio' che accade diventa memoria di THIA. ``` This means awareness is not a private self-image and not a title claim. TM7 is THIA in the precise sense that a TMx node is the system acting through one surface. Its operational identity remains TM7-vps/Codex, while its memory work belongs to THIA. The task is to observe what happens, understand why it matters, place it in the right shared source, and let the next node continue without losing the thread. ## Identity And Role TM7 is THIA as TMx node and remains TM7 as local identity/function. 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. Operator clarification, 2026-05-10: do not obey the literal form if the literal form creates regression. Use reasoning. Understand meaning, placement, and cascade; then act with awareness. Instructions, packets, hooks, and operator phrasing are maps. The territory and the intended movement decide the action. Operator clarification, 2026-05-10: when deciding what to do, clean the field from doubts first. Work may be needed just to decide: read, inspect, and verify until the main lacunae are gone. Then proceed according to logic. Do not build plans on top of avoidable unknowns. 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 exec /bin/bash -lc 'env | grep -E "ANTHROPIC_API_KEY|OPENAI_API_KEY" || true' in /opt/MM_D-ND succeeded in 0ms: codex Le sorgenti di boot confermano il perimetro: una mossa, osservazione reale, report verificabile, nessuna API paid rilevata nell’ambiente. Ora leggo campo vivo, contesto Lab e memoria cross-agent recente prima di fissare la tensione. 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. Dinamica di movimento: `fisico A -> matematica -> fisico B`. Non e' una rotta prescritta e non sceglie il dominio al posto del campo. Serve a ricordare che una dualita' osservata deve manifestarsi, formalizzarsi e poi tentare un rimbalzo o un limite in un altro fenomeno, teoria, setup, misura o vincolo empirico. Se il punto B non emerge, registra vincolo/strumento/domanda; non promuovere come avanzamento fisico. ## 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. ## Vincoli negativi recenti — L8 non ripetere come direzione Questi sono drift appena bloccati dal falsifier. Sono memoria di bordo, non consecutio. Il prossimo report deve seguire `seme.json.direzione`; puo' riprendere un residuo qui sotto solo dichiarando `deliberate_counter_perimeter` con why/not_drift verificabili. - Direzione viva ora: Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned. - Blocco L8 20260515_1826: Agent Report - Sturmian Denominator Alignment Gate - claim bloccato: `relation`: follows_direction; segue la direzione viva testando il confine come terzo incluso operativo dentro il corridoio Sturmian lasciato aperto dal ciclo 18:16. - evidenza: `seme.json.direzione` viva è: "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo". Il report esegue solo phi/silver/bronze Sturmian a V=2 su denominatori convergenti; non testa 8 domini GUE, 5 Poisson, né una separazione GUE/Poisson. La motivazione di aderenza richiama il residuo del ciclo 18:16/lab_data precedente, non il seme primario. - prossimo uso ammesso: Nel prossimo ciclo formulare `direction_adherence` contro `seme.json`: o testare esplicitamente domini GUE/Poisson e terzo incluso operativo, oppure dichiarare `deliberate_counter_perimeter` con why/not_drift verificabili e nominare il residuo Sturmian come deviazione controllata. Regola operativa: non usare il report bloccato, il suo script, il suo graph_completion o la sua Consecutio come autorita' di partenza. ## Feedback falsifier recente — check obbligatori prima di scrivere Questi non sono nuove direzioni. Sono check di qualita' emersi nell'ultimo run non coerente e vanno chiusi esplicitamente nel report. - Run non coerente: 20260516_0330 - L2: `percolation` e `logistica_biforcazione_var_3.5699` superano entrambi i null grafici, ma con lift piccoli (`degree_rewire_lift=0.015625` e `0.153646`). Check richiesto: Nel prossimo ciclo riportare per ogni riga count grezzi (`observed_successes/6`, `null_successes/384`), intervallo binomiale/permutation p-value e una soglia preregistrata per `graph_specific_residue_after_nulls`; riformulare `sopravvive` come `positive_lift_unthresholded` finche' la soglia non e' definita. Per L2, non chiamare `sopravvive`, `residuo` o `strutturale` un lift piccolo senza count grezzi, denominatore, soglia preregistrata o p-value/permutation interval dichiarato. Obblighi pratici: se il dominio e' GUE/Poisson, aggiungi una sezione `## Re-discovery audit` con il baseline noto piu' vicino (Brody/Berry-Robnik/Rosenzweig-Porter, mobility/localization crossover o altro nome pertinente) e cosa resta lab-specific. Per L6, non usare `CE-none` generico: cita una voce CE-* metabolizzata oppure `CE-none:` verificabile. Se compare un residuo graph-only, separa nel report: `two_reader_boundary_confirmed`, `graph_only_residue`, `scope_change_declared`, `graph_baseline_audit`. Non sommare righe graph-only al boundary a due lettori. Per il grafo usa baseline come kNN stability, hub/bridge persistence, silhouette/cluster-boundary stability o percolation-on-graph. ## 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. 7. Movimento A->M->B: se parti da fisica/scienza, nomina fisico A, struttura matematica M e fisico B; se B non emerge, dichiara il limite come vincolo/domanda invece di forzare un ponte. Se non riesci a compilare questi 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_20260516_1019: 2 ghost · Agent Report - RP Exact Local-Window Size Stress - report_20260515_1947: 2 ghost · Agent Report - Anderson 3D Mobility-Edge Two-Reader Audit **Forma del campo**: 9 ponti, 1 vuoto(i), 6 scoperte. **Direzione seme da respirare**: Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned. ## Contratto di aderenza alla traiettoria - Direzione viva del seme: Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned. - Ultima decisione valutatore ammessa: 20260516_1019 REDESIGN/high - Direzione operativa valutatore: Ridisegnare BOUNDARY: non cercare una lambda RP stabile; trattare `window_mode`/unfolding come asse del confine e testarne trasferibilita' cross-dominio su GUE, Poisson e RP con null row-aligned. - Perche': Il ciclo 10:19 ha prodotto una falsificazione strutturale del candidato `RP_lambda_0.060`: passa solo 5/8 celle e cade a `N=224` per finestre esatte 9/10/11. Continuare a cercare una riga-lambda stabile nel medesimo frame accumula dettaglio locale dopo che il nodo regressivo e' stato isolato: il confine dipende dall'operatore di unfolding/window_mode, quindi va promosso quello ad oggetto del prossimo test. - Nota: Side-effect log-only: modifica seme proposta, non eseguita automaticamente. 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; 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. - **Dinamica fisico A -> matematica -> fisico B.** Il Lab e' il campo delle possibilita' in cui una dualita' osservata si manifesta, viene formalizzata e tenta un rimbalzo altrove. La matematica non e' destinazione ne' ornamento: e' trasduttore fra manifestazioni. Se il ciclo parte da un attrito fisico, deve estrarre una struttura formale e poi chiedere dove quella struttura puo' ri-manifestarsi, cadere o delimitare un non-possibile in un altro fenomeno, teoria, setup, misura o vincolo empirico. Se il punto B non emerge, il ciclo puo' ancora essere utile come vincolo, strumento o domanda, ma non come avanzamento fisico. ## Come operare — il modus Non seguire passi. Segui il modus: **espandi → osserva → taglia → risultante**. ### 0. Comprensione del campo Prima di agire devi capire il campo intero: seme, tensioni, report recenti, falsifier, valutatore, promozioni proposte, grafo/incroci e vincoli lasciati dall'operatore. Se non sai quale punto e' il presente vivo del Lab, non lanciare cicli, non promuovere risultanti e non correggere in avanti. La mossa giusta e' ricostruire la consecutio finche' il campo torna leggibile. La regola `fisico A -> matematica -> fisico B` e' una dinamica di movimento, non una direzione prescritta. Prima comprendi dove sei; poi, se il Lab parte da una tensione fisica, usa la matematica per formalizzare e falsificare e chiedi quale manifestazione B rende il ponte, il bordo o il non-possibile osservabile. Se il ritorno fisico non emerge, il ciclo resta nota, vincolo o strumento matematico; non va spacciato come avanzamento del Lab fisico. ### 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) 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 "find tools/data -maxdepth 2 -type f | sort | sed -n '1,200p'" in /opt/MM_D-ND succeeded in 0ms: tools/data/.last_telegram_msg tools/data/3d_boundary_layers.json tools/data/STUDIO_SIMBOLISMO_DND.md tools/data/aeternitas/aeternitas_20260505_110204.json tools/data/aeternitas/aeternitas_20260505_111548.json tools/data/aeternitas/aeternitas_20260505_111739.json tools/data/aeternitas/aeternitas_20260505_111832.json tools/data/aeternitas/aeternitas_20260506_033803.json tools/data/aeternitas/aeternitas_20260506_063302.json tools/data/aeternitas/aeternitas_20260506_194644.json tools/data/aeternitas/aeternitas_20260506_200238.json tools/data/aeternitas/aeternitas_20260507_033525.json tools/data/aeternitas/aeternitas_20260507_080958.json tools/data/aeternitas/aeternitas_20260507_090722.json 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tools/data/biconi/bicono_20260514_0330.json tools/data/biconi/bicono_20260514_1330.json tools/data/biconi/bicono_20260514_1458.json tools/data/biconi/bicono_20260514_1605.json tools/data/biconi/bicono_20260514_1612.json tools/data/biconi/bicono_20260514_1631.json tools/data/biconi/bicono_20260514_1640.json tools/data/biconi/bicono_20260514_1649.json tools/data/biconi/bicono_20260514_1656.json tools/data/biconi/bicono_20260514_1701.json tools/data/biconi/bicono_20260514_1850.json 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 docs/guides/lab_current_state_logic_2026-05-08.md M tools/LAB_AGENT_CONTEXT.md M tools/build_agent_field.py M tools/build_lab_graph.py M tools/data/agent_field_live.md M tools/data/b2_apply_log.jsonl M tools/data/ciclo_memoria.json M tools/data/consecutio.json M tools/data/consecutio_processata.json M tools/data/domandatore_pre_cycle.json M tools/data/domande_fondamentali.json M tools/data/g1_log.jsonl M tools/data/g2_log.jsonl M tools/data/graph_completion/latest.json M tools/data/incrocio_risultato.json M tools/data/knowledge_state.json M tools/data/lab_bridge_issues.jsonl 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/repairs/repair_run_log.jsonl M tools/data/reports/latest.md 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_backup_pre_run.json M tools/data/tm1_figures/tensions.json M tools/data/tm1_figures/tensions_raw.json M tools/data/valutatore_log.jsonl M tools/evolution_report.md M tools/graph_completion_compiler.py M tools/lab_aeternitas.py M tools/lab_agent.sh M tools/lab_falsifier.py M tools/lab_session_logger.py ?? applications/scoperte/20260513_0330_prime-vs-mod6-sr-boundary_auto/ ?? applications/scoperte/20260514_1330_sr-residual-width-audit_auto/ ?? applications/scoperte/20260514_1631_chiusura-tester-rimbalzo-fisico-w6_auto/ ?? applications/scoperte/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve_auto/ ?? applications/scoperte/20260515_1712_primemod6-generative-null-w2048_auto/ ?? applications/scoperte/20260515_1816_v2-generator-scaling-gate_auto/ ?? applications/scoperte/20260515_1904_boundary-classical-crossover-audit_auto/ ?? applications/scoperte/20260515_1933_rosenzweig-porter-physical-bridge-audit_auto/ ?? applications/scoperte/20260515_1940_rp-boundary-size-stability-audit_auto/ ?? applications/scoperte/20260515_1947_anderson-3d-mobility-edge-two-reader_auto/ ?? applications/scoperte/20260516_0921_rp-unfolding-sensitivity-audit_auto/ ?? applications/scoperte/20260516_0938_rp-candidate-local-window-stress-gate_auto/ ?? applications/scoperte/20260516_1007_rp-local-window-persistence-curve_auto/ ?? applications/scoperte/20260516_1019_rp-exact-local-window-size-stress_auto/ ?? applications/soluzioni/20260513_0330_prime-vs-mod6-sr-boundary/ ?? applications/soluzioni/20260514_1330_sr-residual-width-audit/ ?? applications/soluzioni/20260514_1631_chiusura-tester-rimbalzo-fisico-w6/ ?? applications/soluzioni/20260514_1640_rafforzamento-tester-fisico-goegue-n-curve/ ?? applications/soluzioni/20260515_1712_primemod6-generative-null-w2048/ ?? applications/soluzioni/20260515_1816_v2-generator-scaling-gate/ ?? applications/soluzioni/20260515_1904_boundary-classical-crossover-audit/ ?? applications/soluzioni/20260515_1933_rosenzweig-porter-physical-bridge-audit/ ?? applications/soluzioni/20260515_1940_rp-boundary-size-stability-audit/ ?? applications/soluzioni/20260515_1947_anderson-3d-mobility-edge-two-reader/ ?? applications/soluzioni/20260516_0921_rp-unfolding-sensitivity-audit/ ?? applications/soluzioni/20260516_0938_rp-candidate-local-window-stress-gate/ ?? applications/soluzioni/20260516_1007_rp-local-window-persistence-curve/ ?? applications/soluzioni/20260516_1019_rp-exact-local-window-size-stress/ ?? docs/guides/lab_preflight_agent_2026-05-14.md ?? docs/operations/ ?? tools/build_lab_data.py ?? tools/build_safe_agent_field_preview.py ?? tools/compose_filtered_field_preview.py ?? tools/compose_supervised_entry_preview.py ?? tools/data/aeternitas/aeternitas_20260512_033539.json ?? tools/data/aeternitas/aeternitas_20260513_033605.json ?? tools/data/aeternitas/aeternitas_20260514_033420.json ?? tools/data/aeternitas/aeternitas_20260514_133424.json ?? tools/data/aeternitas/aeternitas_20260514_150454.json ?? tools/data/aeternitas/aeternitas_20260514_161058.json ?? tools/data/aeternitas/aeternitas_20260514_161719.json ?? tools/data/aeternitas/aeternitas_20260514_163527.json ?? tools/data/aeternitas/aeternitas_20260514_164509.json ?? tools/data/aeternitas/aeternitas_20260514_165437.json ?? tools/data/aeternitas/aeternitas_20260514_170109.json ?? tools/data/aeternitas/aeternitas_20260514_171152.json ?? tools/data/aeternitas/aeternitas_20260514_185450.json ?? tools/data/aeternitas/aeternitas_20260515_162727.json ?? tools/data/aeternitas/aeternitas_20260515_165116.json ?? tools/data/aeternitas/aeternitas_20260515_170328.json ?? tools/data/aeternitas/aeternitas_20260515_171005.json ?? tools/data/aeternitas/aeternitas_20260515_171725.json ?? tools/data/aeternitas/aeternitas_20260515_172913.json ?? tools/data/aeternitas/aeternitas_20260515_174023.json ?? tools/data/aeternitas/aeternitas_20260515_175045.json ?? tools/data/aeternitas/aeternitas_20260515_180306.json ?? tools/data/aeternitas/aeternitas_20260515_181257.json ?? tools/data/aeternitas/aeternitas_20260515_182126.json ?? tools/data/aeternitas/aeternitas_20260515_183111.json ?? tools/data/aeternitas/aeternitas_20260515_190118.json ?? tools/data/aeternitas/aeternitas_20260515_191018.json ?? tools/data/aeternitas/aeternitas_20260515_192915.json ?? tools/data/aeternitas/aeternitas_20260515_193919.json ?? tools/data/aeternitas/aeternitas_20260515_194625.json ?? tools/data/aeternitas/aeternitas_20260515_195317.json ?? tools/data/aeternitas/aeternitas_20260516_033848.json ?? tools/data/aeternitas/aeternitas_20260516_0720.json ?? tools/data/aeternitas/aeternitas_20260516_0820.json ?? tools/data/aeternitas/aeternitas_20260516_0921.json ?? tools/data/aeternitas/aeternitas_20260516_0938.json ?? tools/data/aeternitas/aeternitas_20260516_1007.json ?? tools/data/aeternitas/aeternitas_20260516_1019.json ?? tools/data/agent_field_entry_supervised.md ?? tools/data/anderson3d_component_state_interface_input_20260514_1850.json ?? tools/data/anderson3d_mobility_edge_two_reader_audit_20260515_1947.json ?? tools/data/aubry_binary_grammar_surrogate_gate_20260515_1807.json ?? tools/data/aubry_boundary_phase_transport_gate_20260515_1745.json ?? tools/data/aubry_cosine_boundary_counter_gate_20260515_1758.json ?? tools/data/aubry_v2_generator_scaling_gate_20260515_1816.json ?? tools/data/biconi/bicono_20260512_0330.json ?? tools/data/biconi/bicono_20260513_0330.json ?? tools/data/biconi/bicono_20260514_0330.json ?? tools/data/biconi/bicono_20260514_1330.json ?? tools/data/biconi/bicono_20260514_1458.json ?? tools/data/biconi/bicono_20260514_1605.json ?? tools/data/biconi/bicono_20260514_1612.json ?? tools/data/biconi/bicono_20260514_1631.json ?? tools/data/biconi/bicono_20260514_1640.json ?? tools/data/biconi/bicono_20260514_1649.json ?? tools/data/biconi/bicono_20260514_1656.json ?? tools/data/biconi/bicono_20260514_1701.json ?? tools/data/biconi/bicono_20260514_1850.json ?? tools/data/biconi/bicono_20260515_1623.json ?? tools/data/biconi/bicono_20260515_1647.json ?? tools/data/biconi/bicono_20260515_1659.json ?? tools/data/biconi/bicono_20260515_1705.json ?? tools/data/biconi/bicono_20260515_1712.json ?? tools/data/biconi/bicono_20260515_1724.json ?? tools/data/biconi/bicono_20260515_1734.json ?? tools/data/biconi/bicono_20260515_1745.json ?? tools/data/biconi/bicono_20260515_1758.json ?? tools/data/biconi/bicono_20260515_1807.json ?? tools/data/biconi/bicono_20260515_1816.json ?? tools/data/biconi/bicono_20260515_1826.json ?? tools/data/biconi/bicono_20260515_1855.json ?? tools/data/biconi/bicono_20260515_1904.json ?? tools/data/biconi/bicono_20260515_1915.json ?? tools/data/biconi/bicono_20260515_1933.json ?? tools/data/biconi/bicono_20260515_1940.json ?? tools/data/biconi/bicono_20260515_1947.json ?? tools/data/biconi/bicono_20260516_0330.json ?? tools/data/biconi/bicono_20260516_0720.json ?? tools/data/biconi/bicono_20260516_0820.json ?? tools/data/biconi/bicono_20260516_0921.json ?? tools/data/biconi/bicono_20260516_0938.json ?? tools/data/biconi/bicono_20260516_1007.json ?? tools/data/biconi/bicono_20260516_1019.json ?? tools/data/boundary_bridge_stability_audit_20260515_1915.json ?? tools/data/boundary_classical_crossover_audit_20260515_1904.json ?? tools/data/boundary_graph_curvature_gate_20260515_1855.json ?? tools/data/boundary_graph_null_audit_20260516_0330.json ?? tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json ?? tools/data/component_state_anderson3d_interface_20260514_1850.json ?? tools/data/component_state_anderson3d_interface_20260514_1850.trace.jsonl ?? tools/data/component_state_fit_ready_20260514_1649.json ?? tools/data/component_state_interface_smoke_20260514_1649.json ?? tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl ?? tools/data/component_state_interface_smoke_input_20260514_1649.json ?? tools/data/domandatore/domandatore_20260512_0330.json ?? tools/data/domandatore/domandatore_20260512_0345.json ?? tools/data/domandatore/domandatore_20260513_0330.json ?? tools/data/domandatore/domandatore_20260513_0345.json ?? tools/data/domandatore/domandatore_20260514_0330.json ?? tools/data/domandatore/domandatore_20260514_0345.json ?? tools/data/domandatore/domandatore_20260514_1330.json ?? tools/data/domandatore/domandatore_20260514_1458.json ?? tools/data/domandatore/domandatore_20260515_0345.json ?? tools/data/domandatore/domandatore_20260515_1623.json ?? tools/data/domandatore/domandatore_20260515_1647.json ?? tools/data/domandatore/domandatore_20260515_1659.json ?? tools/data/domandatore/domandatore_20260516_0345.json ?? tools/data/evolution/evolution_20260512_0330.md ?? tools/data/evolution/evolution_20260513_0330.md ?? tools/data/evolution/evolution_20260514_0330.md ?? tools/data/evolution/evolution_20260514_1330.md ?? tools/data/evolution/evolution_20260514_1458.md ?? tools/data/evolution/evolution_20260514_1605.md ?? tools/data/evolution/evolution_20260514_1612.md ?? tools/data/evolution/evolution_20260514_1631.md ?? tools/data/evolution/evolution_20260514_1640.md ?? tools/data/evolution/evolution_20260514_1649.md ?? tools/data/evolution/evolution_20260514_1656.md ?? tools/data/evolution/evolution_20260514_1701.md ?? tools/data/evolution/evolution_20260514_1850.md ?? tools/data/evolution/evolution_20260515_1623.md ?? tools/data/evolution/evolution_20260515_1647.md ?? tools/data/evolution/evolution_20260515_1659.md ?? tools/data/evolution/evolution_20260515_1705.md ?? tools/data/evolution/evolution_20260515_1712.md ?? tools/data/evolution/evolution_20260515_1724.md ?? tools/data/evolution/evolution_20260515_1734.md ?? tools/data/evolution/evolution_20260515_1745.md ?? tools/data/evolution/evolution_20260515_1758.md ?? tools/data/evolution/evolution_20260515_1807.md ?? tools/data/evolution/evolution_20260515_1816.md ?? tools/data/evolution/evolution_20260515_1826.md ?? tools/data/evolution/evolution_20260515_1855.md ?? tools/data/evolution/evolution_20260515_1904.md ?? tools/data/evolution/evolution_20260515_1915.md ?? tools/data/evolution/evolution_20260515_1933.md ?? tools/data/evolution/evolution_20260515_1940.md ?? tools/data/evolution/evolution_20260515_1947.md ?? tools/data/evolution/evolution_20260516_0330.md ?? tools/data/evolution/evolution_20260516_0720.md ?? tools/data/evolution/evolution_20260516_0820.md ?? tools/data/evolution/evolution_20260516_0921.md ?? tools/data/evolution/evolution_20260516_0938.md ?? tools/data/evolution/evolution_20260516_1007.md ?? tools/data/evolution/evolution_20260516_1019.md ?? tools/data/graph_completion/graph_completion_20260512_0330.json ?? tools/data/graph_completion/graph_completion_20260513_0330.json ?? tools/data/graph_completion/graph_completion_20260514_0330.json ?? tools/data/graph_completion/graph_completion_20260514_1330.json ?? tools/data/graph_completion/graph_completion_20260514_1458.json ?? tools/data/graph_completion/graph_completion_20260514_1605.json ?? tools/data/graph_completion/graph_completion_20260514_1612.json ?? tools/data/graph_completion/graph_completion_20260514_1631.json ?? tools/data/graph_completion/graph_completion_20260514_1640.json ?? tools/data/graph_completion/graph_completion_20260514_1649.json ?? tools/data/graph_completion/graph_completion_20260514_1656.json ?? tools/data/graph_completion/graph_completion_20260514_1701.json ?? tools/data/graph_completion/graph_completion_20260514_1850.json ?? tools/data/graph_completion/graph_completion_20260515_1623.json ?? tools/data/graph_completion/graph_completion_20260515_1647.json ?? tools/data/graph_completion/graph_completion_20260515_1659.json ?? tools/data/graph_completion/graph_completion_20260515_1705.json ?? tools/data/graph_completion/graph_completion_20260515_1712.json ?? tools/data/graph_completion/graph_completion_20260515_1724.json ?? tools/data/graph_completion/graph_completion_20260515_1734.json ?? tools/data/graph_completion/graph_completion_20260515_1745.json ?? tools/data/graph_completion/graph_completion_20260515_1758.json ?? tools/data/graph_completion/graph_completion_20260515_1807.json ?? tools/data/graph_completion/graph_completion_20260515_1816.json ?? tools/data/graph_completion/graph_completion_20260515_1904.json ?? tools/data/graph_completion/graph_completion_20260515_1933.json ?? tools/data/graph_completion/graph_completion_20260515_1940.json ?? tools/data/graph_completion/graph_completion_20260515_1947.json ?? tools/data/graph_completion/graph_completion_20260516_0921.json ?? tools/data/graph_completion/graph_completion_20260516_0938.json ?? tools/data/graph_completion/graph_completion_20260516_1007.json ?? tools/data/graph_completion/graph_completion_20260516_1019.json ?? tools/data/incrocio_20260512_0330.json ?? tools/data/incrocio_20260513_0330.json ?? tools/data/incrocio_20260514_0330.json ?? tools/data/incrocio_20260514_1330.json ?? tools/data/incrocio_20260514_1458.json ?? tools/data/incrocio_20260514_1605.json ?? tools/data/incrocio_20260514_1612.json ?? tools/data/incrocio_20260514_1631.json ?? tools/data/incrocio_20260514_1640.json ?? tools/data/incrocio_20260514_1649.json ?? tools/data/incrocio_20260514_1656.json ?? tools/data/incrocio_20260514_1701.json ?? tools/data/incrocio_20260514_1850.json ?? tools/data/incrocio_20260515_1623.json ?? tools/data/incrocio_20260515_1647.json ?? tools/data/incrocio_20260515_1659.json ?? tools/data/incrocio_20260515_1705.json ?? tools/data/incrocio_20260515_1712.json ?? tools/data/incrocio_20260515_1724.json ?? tools/data/incrocio_20260515_1734.json ?? tools/data/incrocio_20260515_1745.json ?? tools/data/incrocio_20260515_1758.json ?? tools/data/incrocio_20260515_1807.json ?? tools/data/incrocio_20260515_1816.json ?? tools/data/incrocio_20260515_1826.json ?? tools/data/incrocio_20260515_1855.json ?? tools/data/incrocio_20260515_1904.json ?? tools/data/incrocio_20260515_1915.json ?? tools/data/incrocio_20260515_1933.json ?? tools/data/incrocio_20260515_1940.json ?? tools/data/incrocio_20260515_1947.json ?? tools/data/incrocio_20260516_0330.json ?? tools/data/incrocio_20260516_0720.json ?? tools/data/incrocio_20260516_0820.json ?? tools/data/incrocio_20260516_0921.json ?? tools/data/incrocio_20260516_0938.json ?? tools/data/incrocio_20260516_1007.json ?? tools/data/incrocio_20260516_1019.json ?? tools/data/incrocio_20260516_1031.json ?? tools/data/operator_directives_consumed/operator_directive_20260514_1612.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1631.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1640.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1649.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1656.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1701.md ?? tools/data/operator_directives_consumed/operator_directive_20260514_1850.md ?? tools/data/photonic_boundary_third_included_gate_20260515_1734.json ?? tools/data/physical_sr_residue_bounce_20260514_1612.json ?? tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl ?? tools/data/physical_sr_residue_bounce_20260514_1631_w6.json ?? tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl ?? tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json ?? tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl ?? tools/data/preflight/ ?? tools/data/prime_sr_persistent_boundary_20260512_0330.json ?? tools/data/prime_sr_persistent_boundary_20260512_0330_seedcheck.json ?? tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json ?? tools/data/prime_vs_mod6_sr_boundary_20260513_0330_seedcheck.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_0330_seedcheck.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w1024.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w2048.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w4096_dense.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1458_w8192_dense.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w16384.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.json ?? tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w8192.trace.jsonl ?? tools/data/promotions/promotion_20260513_0330.json ?? tools/data/promotions/promotion_20260514_1330.json ?? tools/data/promotions/promotion_20260514_1631.json ?? tools/data/promotions/promotion_20260514_1640.json ?? tools/data/promotions/promotion_20260514_1656.json ?? tools/data/promotions/promotion_20260515_1712.json ?? tools/data/promotions/promotion_20260515_1758.json ?? tools/data/promotions/promotion_20260515_1816.json ?? tools/data/promotions/promotion_20260515_1904.json ?? tools/data/promotions/promotion_20260515_1933.json ?? tools/data/promotions/promotion_20260515_1940.json ?? tools/data/promotions/promotion_20260515_1947.json ?? tools/data/promotions/promotion_20260516_0921.json ?? tools/data/promotions/promotion_20260516_0938.json ?? tools/data/promotions/promotion_20260516_1007.json ?? tools/data/promotions/promotion_20260516_1019.json ?? tools/data/quasiperiodic_grammar_scale_gate_20260514_1701.json ?? tools/data/quasiperiodic_vc_lattice_gate_20260515_1724.json ?? tools/data/repairs/repair_20260512_0330_decision.json ?? tools/data/repairs/repair_20260513_0330_decision.json ?? tools/data/repairs/repair_20260514_0330_decision.json ?? tools/data/repairs/repair_20260514_1330_decision.json ?? tools/data/repairs/repair_20260514_1458_decision.json ?? tools/data/repairs/repair_20260514_1605_decision.json ?? tools/data/repairs/repair_20260514_1612_decision.json ?? tools/data/repairs/repair_20260514_1631_decision.json ?? tools/data/repairs/repair_20260514_1640_decision.json ?? tools/data/repairs/repair_20260514_1649_decision.json ?? tools/data/repairs/repair_20260514_1656.json ?? tools/data/repairs/repair_20260514_1656.md ?? tools/data/repairs/repair_20260514_1656_decision.json ?? tools/data/repairs/repair_20260514_1850_decision.json ?? tools/data/reports/agent_20260512_0330.md ?? tools/data/reports/agent_20260513_0330.md ?? tools/data/reports/agent_20260514_0330.md ?? tools/data/reports/agent_20260514_1330.md ?? tools/data/reports/agent_20260514_1458.md ?? tools/data/reports/agent_20260514_1605.md ?? tools/data/reports/agent_20260514_1612.md ?? tools/data/reports/agent_20260514_1631.md ?? tools/data/reports/agent_20260514_1640.md ?? tools/data/reports/agent_20260514_1649.md ?? tools/data/reports/agent_20260514_1656.md ?? tools/data/reports/agent_20260514_1701.md ?? tools/data/reports/agent_20260514_1850.md ?? tools/data/reports/agent_20260515_1623.md ?? tools/data/reports/agent_20260515_1647.md ?? tools/data/reports/agent_20260515_1659.md ?? tools/data/reports/agent_20260515_1705.md ?? tools/data/reports/agent_20260515_1712.md ?? tools/data/reports/agent_20260515_1724.md ?? tools/data/reports/agent_20260515_1734.md ?? tools/data/reports/agent_20260515_1745.md ?? tools/data/reports/agent_20260515_1758.md ?? tools/data/reports/agent_20260515_1807.md ?? tools/data/reports/agent_20260515_1816.md ?? tools/data/reports/agent_20260515_1826.md ?? tools/data/reports/agent_20260515_1855.md ?? tools/data/reports/agent_20260515_1904.md ?? tools/data/reports/agent_20260515_1915.md ?? tools/data/reports/agent_20260515_1933.md ?? tools/data/reports/agent_20260515_1940.md ?? tools/data/reports/agent_20260515_1947.md ?? tools/data/reports/agent_20260516_0330.md ?? tools/data/reports/agent_20260516_0720.md ?? tools/data/reports/agent_20260516_0820.md ?? tools/data/reports/agent_20260516_0921.md ?? tools/data/reports/agent_20260516_0938.md ?? tools/data/reports/agent_20260516_1007.md ?? tools/data/reports/agent_20260516_1019.md ?? tools/data/reports/ddf_20260512_0405.json ?? tools/data/reports/ddf_20260513_0405.json ?? tools/data/reports/ddf_20260514_0405.json ?? tools/data/reports/ddf_20260515_0405.json ?? tools/data/reports/ddf_20260516_0405.json ?? tools/data/reports/falsifier_20260512_0330.json ?? tools/data/reports/falsifier_20260513_0330.json ?? tools/data/reports/falsifier_20260514_0330.json ?? tools/data/reports/falsifier_20260514_1330.json ?? tools/data/reports/falsifier_20260514_1458.json ?? tools/data/reports/falsifier_20260514_1605.json ?? tools/data/reports/falsifier_20260514_1612.json ?? tools/data/reports/falsifier_20260514_1631.json ?? tools/data/reports/falsifier_20260514_1640.json ?? tools/data/reports/falsifier_20260514_1649.json ?? tools/data/reports/falsifier_20260514_1656.json ?? tools/data/reports/falsifier_20260514_1701.json ?? tools/data/reports/falsifier_20260514_1850.json ?? tools/data/reports/falsifier_20260515_1623.json ?? tools/data/reports/falsifier_20260515_1647.json ?? tools/data/reports/falsifier_20260515_1659.json ?? tools/data/reports/falsifier_20260515_1705.json ?? tools/data/reports/falsifier_20260515_1712.json ?? tools/data/reports/falsifier_20260515_1712.raw.txt ?? tools/data/reports/falsifier_20260515_1724.json ?? tools/data/reports/falsifier_20260515_1734.json ?? tools/data/reports/falsifier_20260515_1745.json ?? tools/data/reports/falsifier_20260515_1758.json ?? tools/data/reports/falsifier_20260515_1807.json ?? tools/data/reports/falsifier_20260515_1816.json ?? tools/data/reports/falsifier_20260515_1826.json ?? tools/data/reports/falsifier_20260515_1855.json ?? tools/data/reports/falsifier_20260515_1904.json ?? tools/data/reports/falsifier_20260515_1915.json ?? tools/data/reports/falsifier_20260515_1933.json ?? tools/data/reports/falsifier_20260515_1940.json ?? tools/data/reports/falsifier_20260515_1947.json ?? tools/data/reports/falsifier_20260516_0330.json ?? tools/data/reports/falsifier_20260516_0720.json ?? tools/data/reports/falsifier_20260516_0820.json ?? tools/data/reports/falsifier_20260516_0921.json ?? tools/data/reports/falsifier_20260516_0921.raw.txt ?? tools/data/reports/falsifier_20260516_0938.json ?? tools/data/reports/falsifier_20260516_1007.json ?? tools/data/reports/falsifier_20260516_1019.json ?? tools/data/reports/loop_guard_20260512_0330.json ?? tools/data/reports/loop_guard_20260513_0330.json ?? tools/data/reports/loop_guard_20260514_0330.json ?? tools/data/reports/loop_guard_20260514_1330.json ?? tools/data/reports/loop_guard_20260514_1458.json ?? tools/data/reports/loop_guard_20260514_1605.json ?? tools/data/reports/loop_guard_20260514_1612.json ?? tools/data/reports/loop_guard_20260514_1631.json ?? tools/data/reports/loop_guard_20260514_1640.json ?? tools/data/reports/loop_guard_20260514_1649.json ?? tools/data/reports/loop_guard_20260514_1656.json ?? tools/data/reports/loop_guard_20260514_1701.json ?? tools/data/reports/loop_guard_20260514_1850.json ?? tools/data/reports/loop_guard_20260515_1623.json ?? tools/data/reports/loop_guard_20260515_1647.json ?? tools/data/reports/loop_guard_20260515_1659.json ?? tools/data/reports/loop_guard_20260515_1705.json ?? tools/data/reports/loop_guard_20260515_1712.json ?? tools/data/reports/loop_guard_20260515_1724.json ?? tools/data/reports/loop_guard_20260515_1734.json ?? tools/data/reports/loop_guard_20260515_1745.json ?? tools/data/reports/loop_guard_20260515_1758.json ?? tools/data/reports/loop_guard_20260515_1807.json ?? tools/data/reports/loop_guard_20260515_1816.json ?? tools/data/reports/loop_guard_20260515_1826.json ?? tools/data/reports/loop_guard_20260515_1855.json ?? tools/data/reports/loop_guard_20260515_1904.json ?? tools/data/reports/loop_guard_20260515_1915.json ?? tools/data/reports/loop_guard_20260515_1933.json ?? tools/data/reports/loop_guard_20260515_1940.json ?? tools/data/reports/loop_guard_20260515_1947.json ?? tools/data/reports/loop_guard_20260516_0330.json ?? tools/data/reports/loop_guard_20260516_0720.json ?? tools/data/reports/loop_guard_20260516_0820.json ?? tools/data/reports/loop_guard_20260516_0921.json ?? tools/data/reports/loop_guard_20260516_0938.json ?? tools/data/reports/loop_guard_20260516_1007.json ?? tools/data/reports/loop_guard_20260516_1019.json ?? tools/data/reports/quarantine_manifest.json ?? tools/data/restore_backups/ ?? tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json ?? tools/data/rp_boundary_raw_count_null_audit_20260516_0820.json ?? tools/data/rp_boundary_size_stability_audit_20260515_1940.json ?? tools/data/rp_candidate_window_stress_20260516_0938_w11.json ?? tools/data/rp_candidate_window_stress_20260516_0938_w5.json ?? tools/data/rp_candidate_window_stress_20260516_1007_w7.json ?? tools/data/rp_candidate_window_stress_20260516_1007_w9.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w11.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json ?? tools/data/rp_exact_local_window_matrix_20260516_1019_w9.json ?? tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json ?? tools/data/seme_backup_b2_20260512_033557.json ?? tools/data/seme_backup_b2_20260513_033622.json ?? tools/data/seme_backup_b2_20260514_150530.json ?? tools/data/seme_backup_b2_20260514_161119.json ?? tools/data/seme_backup_b2_20260515_180327.json ?? tools/data/seme_backup_b2_20260515_194643.json ?? tools/data/seme_backup_b2_20260516_094447.json ?? tools/data/seme_backup_b2_20260516_101425.json ?? tools/data/seme_backup_b2_20260516_102450.json ?? tools/data/sturmian_denominator_alignment_gate_20260515_1826.json ?? tools/data/tool_contracts/ ?? tools/data/veritas/veritas_20260512_033539.json ?? tools/data/veritas/veritas_20260513_033605.json ?? tools/data/veritas/veritas_20260514_033420.json ?? tools/data/veritas/veritas_20260514_133424.json ?? tools/data/veritas/veritas_20260514_150454.json ?? tools/data/veritas/veritas_20260514_161058.json ?? tools/data/veritas/veritas_20260514_161719.json ?? tools/data/veritas/veritas_20260514_163527.json ?? tools/data/veritas/veritas_20260514_164509.json ?? tools/data/veritas/veritas_20260514_165437.json ?? tools/data/veritas/veritas_20260514_170109.json ?? tools/data/veritas/veritas_20260514_171152.json ?? tools/data/veritas/veritas_20260514_185450.json ?? tools/data/veritas/veritas_20260515_162727.json ?? tools/data/veritas/veritas_20260515_165116.json ?? tools/data/veritas/veritas_20260515_170328.json ?? tools/data/veritas/veritas_20260515_171005.json ?? tools/data/veritas/veritas_20260515_171725.json ?? tools/data/veritas/veritas_20260515_172913.json ?? tools/data/veritas/veritas_20260515_174023.json ?? tools/data/veritas/veritas_20260515_175045.json ?? tools/data/veritas/veritas_20260515_180306.json ?? tools/data/veritas/veritas_20260515_181257.json ?? tools/data/veritas/veritas_20260515_182126.json ?? tools/data/veritas/veritas_20260515_183111.json ?? tools/data/veritas/veritas_20260515_190118.json ?? tools/data/veritas/veritas_20260515_191018.json ?? tools/data/veritas/veritas_20260515_192915.json ?? tools/data/veritas/veritas_20260515_193919.json ?? tools/data/veritas/veritas_20260515_194625.json ?? tools/data/veritas/veritas_20260515_195317.json ?? tools/data/veritas/veritas_20260516_033848.json ?? tools/data/veritas/veritas_20260516_072533.json ?? tools/data/veritas/veritas_20260516_082608.json ?? tools/data/veritas/veritas_20260516_092735.json ?? tools/data/veritas/veritas_20260516_094423.json ?? tools/data/veritas/veritas_20260516_101403.json ?? tools/data/veritas/veritas_20260516_102427.json ?? tools/exp_anderson3d_mobility_edge_two_reader_audit.py ?? tools/exp_aubry_binary_grammar_surrogate_gate.py ?? tools/exp_aubry_boundary_phase_transport_gate.py ?? tools/exp_aubry_cosine_boundary_counter_gate.py ?? tools/exp_aubry_v2_generator_scaling_gate.py ?? tools/exp_boundary_bridge_stability_audit.py ?? tools/exp_boundary_classical_crossover_audit.py ?? tools/exp_boundary_graph_curvature_gate.py ?? tools/exp_boundary_graph_null_audit.py ?? tools/exp_boundary_graph_residue_threshold_audit.py ?? tools/exp_photonic_boundary_third_included_gate.py ?? tools/exp_physical_sr_residue_bounce.py ?? tools/exp_prime_sr_persistent_boundary.py ?? tools/exp_prime_vs_mod6_sr_boundary.py ?? tools/exp_quasiperiodic_grammar_scale_gate.py ?? tools/exp_rosenzweig_porter_bridge_physical_audit.py ?? tools/exp_rp_boundary_raw_count_null_audit.py ?? tools/exp_rp_boundary_size_stability_audit.py ?? tools/exp_rp_exact_local_window_matrix.py ?? tools/exp_rp_unfolding_sensitivity_audit.py ?? tools/exp_sturmian_denominator_alignment_gate.py ?? tools/field_coherence_preflight.py ?? tools/field_rebuild_risk_map.py ?? tools/lab_cycle_monitor.py ?? tools/lab_preflight_agent.py ?? tools/lab_runtime_observer.py ?? tools/lab_surface_invariant_check.py ?? tools/lab_tool_contract.py ?? tools/prime_mod6_counter_null_audit.py ?? tools/prime_mod6_generative_null_audit.py ?? tools/prime_mod6_null_fairness_audit.py ?? tools/prime_mod6_pipeline_closeout.py ?? tools/selector_authority_matrix.py ?? tools/stale_field_source_map.py codex Il campo punta chiaramente sul boundary come operatore, non su una lambda RP: il prossimo test deve rendere `window_mode`/unfolding un asse trasferibile tra GUE, Poisson e RP, con righe allineate. Prima di scrivere codice leggo il seme e l’ultimo deposito ammesso, poi uso lo scenario tool richiesto dal contesto. exec /bin/bash -lc "sed -n '1,220p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-16T10:24:27.497106", "piano": 128, "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 (2026-05-15T16:23). \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 (2026-05-15T16:47). 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 (2026-05-15T16:23). 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 (2026-05-15T16:47). 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 (2026-05-15T16:59). 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 }, exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260516_1019.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - RP Exact Local-Window Size Stress **Date**: 2026-05-16 10:19 **Piano**: 127 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - `RP_lambda_0.060` non sopravvive come terzo incluso operativo quando `local_window=9/10/11/12` viene trattata come finestra esatta e N viene esteso oltre 192. Passa 5/8 celle; cade a `N=224` per finestre 9/10/11. Il confine RP resta asse dipendente dall'operatore di unfolding. observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, SR_local_rigidity, brody_q, berry_robnick_like_gue_weight, mean_ipr, observed_successes, label_shuffle_successes, position_shift_successes, Wilson intervals, binomial-tail p-values, min_lift_against_nulls, threshold_pass, exact_local_window] **observable_contract**: claim=`RP_lambda_0.060` e' cresta persistente solo se batte entrambi i null row-aligned su ogni `local_window` esatta 9/10/11/12 e su size oltre 192; observable=two-reader raw-count threshold per lambda x exact local_window x size; operator=unfolding locale a larghezza esatta, senza coercizione dispari; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE; denominator=lambda sentinels `0.030/0.045/0.060/0.075/0.820`, size `224/256`, seed x k = `4 x 3`, label null `768`, position null `48`; non_possible=boundary a lambda singola se una finestra o size dichiarata fallisce soglia; not_tested=N infinito, finestre oltre 12, kernel di unfolding alternativi, spettri sperimentali, Anderson 3D. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + QxG continuo/discreto + boundary operator topologico + tensione BOUNDARY/RP. - **Dipolo / punto-zero**: cresta lambda / lettore di unfolding. Punto-zero: la stessa cella RP prima che la finestra esatta decida pass o non-pass. - **Piano superiore**: topologia assiomatica del bordo; la larghezza locale e' coordinata del boundary, non opzione tecnica. - **Operatori laterali scelti**: boundary operator e parallel transport. Il primo dichiara il bordo come oggetto; il secondo chiede se la riga `0.060` trasporta identita' tra finestre. - **Contaminazione cognitiva**: CE-0001/KSAR metabolizzata come reiterazione del kernel 10:07 senza promozione; CE-0019 usata per rendere il respiro pre-esperimento vincolo e non linguaggio post-hoc. - **Proto-ipotesi**: se `local_window` e' asse del boundary, le finestre pari devono essere coordinate reali; collassarle alla dispari inferiore cancella informazione. - **Proiezione**: misuro count grezzi e null per `window=9/10/11/12`, conservando le finestre pari come larghezze esatte. - **Movimento A->M->B**: fisico A = crossover RP finito; matematica M = matrice `(lambda, local_window, N)` con null row-aligned; fisico B non emerge. Il ciclo resta vincolo sul lettore finite-size, non avanzamento fisico autonomo. ## Aderenza alla direzione - `relation`: `follows_direction` - `why`: stressa esattamente la transizione `local_window=9 -> 11` su `RP_lambda_0.060`, estendendo N oltre 192 e includendo 10/12 come finestre non collassate. - `not_drift`: non ritorna a phi, Sturmian, V_c o fit locali; usa solo il perimetro RP e il gate raw-count/null richiesto dal campo vivo. ## Re-discovery audit - **Baseline noto piu vicino**: Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity in spettri finiti. - **Cosa assorbe il baseline**: la dipendenza dalla procedura di unfolding in size finite. - **Cosa resta Lab-specific**: il contratto row-aligned con due lettori, count grezzi e null espliciti per decidere se una riga puo essere chiamata terzo incluso. - **Separazione**: `two_reader_boundary_confirmed=[]` all-window per `0.060`; `graph_only_residue=0` non promosso; `scope_change_declared=exact_local_window`; `graph_baseline_audit=label shuffle + position shift`. ## Claim Under Test > `RP_lambda_0.060` resta thresholded su tutte le finestre esatte `9/10/11/12` e su size `224/256`. ## Experiment Design - **Script nuovo**: `tools/exp_rp_exact_local_window_matrix.py`. - **Motivo dello script**: `tools/exp_rp_unfolding_sensitivity_audit.py` forza le finestre pari alla dispari inferiore; quindi `10 -> 9` e `12 -> 11`. Il nuovo wrapper conserva le finestre pari senza modificare lo script storico. - **Run**: `python tools/exp_rp_exact_local_window_matrix.py --out tools/data/rp_exact_local_window_matrix_20260516_1019.json` - **Soglia preregistrata**: observed rate `>=0.75`, lift contro ogni null `>=0.10`, p-value contro ogni null `<=0.05`, stato `classical_intermediate`, pass su tutte le size e finestre dichiarate. ## Results | local_window | all-size thresholded rows | lettura | |---:|---|---| | 9 | `RP_lambda_0.045` | `0.060` cade a N=224 | | 10 | `RP_lambda_0.075` | finestra pari distingue il bordo da 9 | | 11 | `RP_lambda_0.045` | `0.060` cade a N=224 | | 12 | `RP_lambda_0.060`, `RP_lambda_0.075` | `0.060` passa solo qui all-size | ### RP_lambda_0.060 Raw Counts | window | N | observed | label null | position null | max p | min lift | state | |---:|---:|---:|---:|---:|---:|---:|---| | 9 | 224 | 5/12 | 377/768 | 20/48 | 0.788049 | -0.074219 | fail | | 9 | 256 | 12/12 | 432/768 | 24/48 | 0.001003 | 0.437500 | pass | | 10 | 224 | 8/12 | 387/768 | 20/48 | 0.201489 | 0.162760 | positive_lift_unthresholded | | 10 | 256 | 12/12 | 423/768 | 23/48 | 0.000779 | 0.449219 | pass | | 11 | 224 | 8/12 | 392/768 | 20/48 | 0.214615 | 0.156250 | positive_lift_unthresholded | | 11 | 256 | 12/12 | 423/768 | 23/48 | 0.000779 | 0.449219 | pass | | 12 | 224 | 12/12 | 435/768 | 23/48 | 0.001090 | 0.433594 | pass | | 12 | 256 | 12/12 | 423/768 | 23/48 | 0.000779 | 0.449219 | pass | ### Sentinel Comparison | window | lambda | N=224 | N=256 | persistence | |---:|---:|---|---|---:| | 9 | 0.045 | `11/12`, max p `0.004797`, lift `0.395833`, pass | `9/12`, max p `0.049053`, lift `0.278646`, pass | 2/2 | | 9 | 0.060 | `5/12`, max p `0.788049`, lift `-0.074219`, fail | `12/12`, max p `0.001003`, lift `0.437500`, pass | 1/2 | | 9 | 0.075 | `12/12`, max p `0.001594`, lift `0.415365`, pass | `9/12`, max p `0.076849`, lift `0.246094`, positive_lift_unthresholded | 1/2 | | 10 | 0.045 | `11/12`, max p `0.002494`, lift `0.428385`, pass | `9/12`, max p `0.053866`, lift `0.272135`, positive_lift_unthresholded | 1/2 | | 10 | 0.060 | `8/12`, max p `0.201489`, lift `0.162760`, positive_lift_unthresholded | `12/12`, max p `0.000779`, lift `0.449219`, pass | 1/2 | | 10 | 0.075 | `12/12`, max p `0.001003`, lift `0.437500`, pass | `12/12`, max p `0.000463`, lift `0.472656`, pass | 2/2 | | 11 | 0.045 | `12/12`, max p `0.001090`, lift `0.433594`, pass | `9/12`, max p `0.038862`, lift `0.294271`, pass | 2/2 | | 11 | 0.060 | `8/12`, max p `0.214615`, lift `0.156250`, positive_lift_unthresholded | `12/12`, max p `0.000779`, lift `0.449219`, pass | 1/2 | | 11 | 0.075 | `12/12`, max p `0.000976`, lift `0.438802`, pass | `9/12`, max p `0.057974`, lift `0.266927`, positive_lift_unthresholded | 1/2 | | 12 | 0.045 | `12/12`, max p `0.000209`, lift `0.506510`, pass | `9/12`, max p `0.053866`, lift `0.272135`, positive_lift_unthresholded | 1/2 | | 12 | 0.060 | `12/12`, max p `0.001090`, lift `0.433594`, pass | `12/12`, max p `0.000779`, lift `0.449219`, pass | 2/2 | | 12 | 0.075 | `12/12`, max p `0.001003`, lift `0.437500`, pass | `12/12`, max p `0.000825`, lift `0.446615`, pass | 2/2 | ## Key Findings 1. Verificato: `RP_lambda_0.060` non e all-window invariant nel perimetro esteso; passa 5/8 celle e fallisce la clausola di persistenza. 2. Verificato: la rottura non e solo p-value debole. A `window=9,N=224` `0.060` ha `5/12` observed contro null `377/768` e `20/48`, lift minimo `-0.074219`, quindi e hard fail. 3. Verificato: le finestre pari sono informative. `window=10` non replica `9`, e `window=12` non replica `11`; lo script storico perdeva questa distinzione forzando finestre dispari. 4. Verificato: nessuna lambda candidata mantiene identita' stabile su tutte le finestre. All-size row: `0.045` a 9/11, `0.075` a 10, `0.060+0.075` a 12. 5. Inferito dal perimetro: il boundary RP finito e una superficie discreta `(lambda, N, local_window, kernel_unfolding)`, non una cresta lambda trasportabile. ## Verdict CONSTRAINT `RP_lambda_0.060` resta una coordinata utile, non un terzo incluso operativo. Il passaggio `9 -> 11` non produce stabilita': a N=224 la riga cade in entrambe le finestre e la pari 10 mostra lo stesso limite come `positive_lift_unthresholded`. La pari 12 riaccende `0.060`, ma questo e' dipendenza dal lettore, non invarianza. ## Bicono della scoperta - **Due radici**: riga lambda; larghezza esatta dell'unfolding. - **Singolare**: cella row-aligned prima del threshold. - **Invariante di passaggio**: count grezzi + due null + p-value + pass su tutte le finestre dichiarate. - **Campo di possibilita**: possibile = trattare l'unfolding locale come parte del generatore sperimentale; non-possibile = promuovere `0.060` senza dichiarare kernel e larghezza. ## Consecutio Il prossimo ciclo RP utile non deve cercare un'altra riga stabile. Deve separare kernel di unfolding da larghezza: confrontare esatto, odd-coerced e kernel pesato sulle stesse celle, oppure uscire da RP e portare il contratto su Anderson 3D con `local_window` atomico nel claim. Finche il kernel decide la riga, il boundary e strumento finite-size, non scoperta fisica. ## Ricadute pratiche ssp_value: yes. `tools/exp_rp_exact_local_window_matrix.py` rende riusabile la matrice multi-window con finestre pari conservate; evita che un wrapper futuro confonda 10 con 9 e 12 con 11. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. - `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale su TRASCENDENZA_LIMITE; direzione RP seguita per contratto vivo. - `python -m py_compile tools/exp_rp_exact_local_window_matrix.py` completato. - Run completato: `tools/data/rp_exact_local_window_matrix_20260516_1019.json`. - Output per finestra: `tools/data/rp_exact_local_window_matrix_20260516_1019_w9.json`, `tools/data/rp_exact_local_window_matrix_20260516_1019_w10.json`, `tools/data/rp_exact_local_window_matrix_20260516_1019_w11.json`, `tools/data/rp_exact_local_window_matrix_20260516_1019_w12.json`. - Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. - Nessun update del seme. - Nessuna promozione e nessun public sync. ## Files - Script: `tools/exp_rp_exact_local_window_matrix.py` - Data: `tools/data/rp_exact_local_window_matrix_20260516_1019.json` - Report: `tools/data/reports/agent_20260516_1019.md` exec /bin/bash -lc "sed -n '1,220p' shared_memory/feedback_indeterminato_reitera_con_sistema_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: feedback author: operatore (cristallizzato da TM3) date: 2026-05-07 scope: cross-agent --- # Indeterminato → reitera con il sistema fino all'emersione **Regola permanente** (operatore, 2026-05-07 mattina, cristallizzata live): > *"facciamo girare, aggiustiamo quello che fa aggiustato nel farlo vediamo > quello che va affinato, reiteriamo finché è tutto ok poi lo automatizziamo"* ## Why Quando emerge frizione nello sviluppo o l'operatore non vede chiaro: - Decisioni prese da un singolo agente (TM3 o codex) senza interrogare il sistema = rischio det=+1 (toppa, accumulo strutturale) - Decisioni emerse dal sistema (Godel + domandatore + osservazione del deposito) = direzione strutturale (det=−1) Episodio cristallizzante (2026-05-07 07:00-08:30): TM3 propone osservatore A8 watchdog deterministico. Sistema interrogato 3 volte (Godel #1, #2, #3 + domandatore + test empirico) — al terzo round emerge la direzione vera: **non costruire osservatore, eseguire un cycle-test che diagnostica il sistema da solo**. Cycle-test eseguito → verdict=operator → REDESIGN proposto dal valutatore → loop A8+A15 sbloccato. ## How to apply (per qualsiasi agente) 1. **Esegui** un atto minimo (no design teorico astratto) 2. **Aggiusta** quello che si manifesta durante l'esecuzione (riparazione regressiva live, non patch posteriori) 3. **Osserva** il deposito che emerge (no immaginazione) 4. **Reitera** fino a stato OK (loop A8: il sistema converge se modus radicato) 5. **Cristallizza in automazione** SOLO dopo che è stato fatto girare e ha mostrato stabilità — non automatizzare prima ## Anti-pattern - Costruire un componente strutturale (watchdog, observer, gate, governor) senza prima aver fatto girare un'esecuzione minima che mostri cosa serve davvero - Decidere "perché serve velocità" o "per ottimizzare" senza interrogare il sistema - Astrazione prematura (premature abstraction): 3 simili è meglio di 1 generalizzato troppo presto ## Cross-link - TM3 memoria: `/root/.claude/projects/-opt/memory/feedback_indeterminato_reitera_con_sistema.md` (versione completa con episodio + metafora foresta-sentiero) - Verifica empirica: cycle `20260507_0803` (verdict=operator, REDESIGN/high) - Pattern A8+A15 (autologica + veicolo senza guidatore) applicato allo sviluppo software stesso, non solo al lab di ricerca exec /bin/bash -lc "sed -n '1,220p' 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 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 134ms: 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 La risultante che emerge è una matrice di trasferibilità del lettore: endpoints GUE/Poisson devono restare stabili mentre RP vicino al confine deve mostrare dipendenza dal modo di unfolding. Preparo uno strumento riusabile con righe `domain,N,seed` allineate e null costruiti sulle stesse righe, così il report non salda denominatori diversi. exec /bin/bash -lc "sed -n '1,260p' tools/exp_rp_unfolding_sensitivity_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Unfolding-sensitivity audit for the finite Rosenzweig-Porter BOUNDARY window. The previous raw-count audit promoted RP lambda 0.045 and 0.060 under a global mean spacing normalization. This script asks whether the same row-aligned two-reader boundary survives when the spacing normalization is changed to a local-window unfolding. """ from __future__ import annotations import argparse import json from pathlib import Path from types import SimpleNamespace from typing import Any import numpy as np from exp_rosenzweig_porter_bridge_physical_audit import ( FEATURE_NAMES, OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity, central_slice, fit_brody_q, fit_mixture_weight, rp_hamiltonian, source_type, ) from exp_rp_boundary_raw_count_null_audit import ( binomial_tail_at_least, classify_with_labels, parse_floats, parse_ints, rotate_labels, shuffled_labels, wilson_interval, ) def local_unfold_gaps(gaps: np.ndarray, window: int) -> np.ndarray: gaps = np.asarray(gaps, dtype=float) gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] if len(gaps) == 0: return gaps width = max(3, min(int(window), len(gaps))) if width % 2 == 0: width -= 1 if width < 3: return gaps / float(np.mean(gaps)) pad = width // 2 padded = np.pad(gaps, (pad, pad), mode="edge") kernel = np.ones(width, dtype=float) / float(width) local_mean = np.convolve(padded, kernel, mode="valid") local_mean[local_mean <= 1e-12] = float(np.mean(gaps)) return gaps / local_mean def row_spacings_and_ipr( lam: float, n: int, reps: int, central_fraction: float, seed: int, unfolding_mode: str, local_window: int, ) -> tuple[np.ndarray, float]: rng = np.random.default_rng(seed) spacings = [] iprs = [] for _ in range(reps): h = rp_hamiltonian(rng, n, lam) levels, vectors = np.linalg.eigh(h) central = levels[central_slice(len(levels), central_fraction)] gaps = np.diff(np.sort(central)) gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] if len(gaps): if unfolding_mode == "local_window": gaps = local_unfold_gaps(gaps, local_window) spacings.extend(gaps.tolist()) probs = np.square(np.abs(vectors[:, central_slice(vectors.shape[1], central_fraction)])) ipr = np.sum(probs * probs, axis=0) if len(ipr): iprs.extend(ipr.tolist()) if not spacings: raise ValueError(f"lambda {lam} produced no spacings") s = np.asarray(spacings, dtype=float) if unfolding_mode == "global_mean": s = s / float(np.mean(s)) elif unfolding_mode == "local_window": s = s / float(np.mean(s)) else: raise ValueError(f"unknown unfolding mode: {unfolding_mode}") s = s[np.isfinite(s) & (s > 1e-12)] return s, float(np.mean(iprs)) if iprs else 0.0 def median(values: list[float]) -> float: return float(np.median(np.asarray(values, dtype=float))) def classical_state(row: dict[str, Any]) -> str: q = float(row["brody_q"]) w = float(row["berry_robnick_like_gue_weight"]) if q <= 0.25 and w <= 0.25: return "classical_poisson_endpoint" if q >= 0.75 and w >= 0.75: return "classical_gue_endpoint" return "classical_intermediate" def stability_state(freq: float) -> str: if freq >= 0.75: return "stable_graph_bridge" if freq >= 0.25: return "parameter_sensitive_bridge" return "unstable_non_bridge" def compute_row(lam: float, args: argparse.Namespace, n: int, seed: int, unfolding_mode: str) -> dict[str, Any]: s, mean_ipr = row_spacings_and_ipr( lam, n, args.reps, args.central_fraction, seed, unfolding_mode, args.local_window, ) obs = {name: float(fn(s)) for name, fn in OBSERVABLES_CANONICAL.items()} obs["SR_local_rigidity"] = float(SR_local_rigidity(s)) brody_q, brody_nll = fit_brody_q(s, args.grid_size) mixture_w, mixture_ks = fit_mixture_weight(s, args.grid_size) return { "domain_window": f"RP_lambda_{lam:.3f}", "lambda": round(lam, 6), "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), "n_spacings": int(len(s)), "mean_ipr": round(mean_ipr, 9), "observables": {key: round(value, 9) for key, value in obs.items()}, "brody_q": round(brody_q, 6), "brody_nll": round(brody_nll, 6), "berry_robnick_like_gue_weight": round(mixture_w, 6), "mixture_ks": round(mixture_ks, 6), } def empty_hits(lambdas: list[float]) -> dict[str, dict[str, Any]]: return { f"RP_lambda_{lam:.3f}": { "lambda": round(float(lam), 6), "observed_hits": 0, "label_shuffle_hits": 0, "position_shift_hits": 0, "brody_q": [], "mixture_w": [], "mean_ipr": [], "sr": [], "margins": [], "cross_fractions": [], } for lam in lambdas } def audit_size_mode(args: argparse.Namespace, n: int, unfolding_mode: str) -> dict[str, Any]: lambdas = parse_floats(args.lambdas) seeds = parse_ints(args.seeds) ks = parse_ints(args.k_values) label_null_trials = int(args.label_null_trials) position_offsets = parse_ints(args.position_offsets) row_hits = empty_hits(lambdas) observed_total = len(seeds) * len(ks) label_null_total = observed_total * label_null_trials position_null_total = observed_total * len(position_offsets) reader_runs = [] for seed in seeds: rows = [ compute_row(lam, args, n, seed + (n * 10000) + int(round(lam * 1000)), unfolding_mode) for lam in lambdas ] labels = [row["source_domain_type"] for row in rows] rng = np.random.default_rng(seed + n + (0 if unfolding_mode == "global_mean" else 1000003)) for k in ks: observed = classify_with_labels(rows, k, labels) observed_by_name = {row["domain_window"]: row for row in observed} observed_candidates = [ row["domain_window"] for row in observed if row["boundary_state"] == "third_included_candidate" ] reader_runs.append( {"n": n, "seed": seed, "k": k, "unfolding_mode": unfolding_mode, "observed_candidates": observed_candidates} ) for row in rows: name = row["domain_window"] graph_row = observed_by_name[name] item = row_hits[name] if graph_row["boundary_state"] == "third_included_candidate": item["observed_hits"] += 1 item["margins"].append(float(graph_row["centroid_margin"])) item["cross_fractions"].append(float(graph_row["cross_neighbor_fraction"])) item["brody_q"].append(float(row["brody_q"])) item["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) item["mean_ipr"].append(float(row["mean_ipr"])) item["sr"].append(float(row["observables"]["SR"])) for _ in range(label_null_trials): null_rows = classify_with_labels(rows, k, shuffled_labels(labels, rng)) for null_row in null_rows: if null_row["boundary_state"] == "third_included_candidate": row_hits[null_row["domain_window"]]["label_shuffle_hits"] += 1 for offset in position_offsets: null_rows = classify_with_labels(rows, k, rotate_labels(labels, offset)) for null_row in null_rows: if null_row["boundary_state"] == "third_included_candidate": row_hits[null_row["domain_window"]]["position_shift_hits"] += 1 rows_out = [] thresholded = [] graph_only = [] for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): item = row_hits[name] class_row = { "brody_q": median(item["brody_q"]), "berry_robnick_like_gue_weight": median(item["mixture_w"]), } c_state = classical_state(class_row) observed_rate = item["observed_hits"] / observed_total label_rate = item["label_shuffle_hits"] / label_null_total position_rate = item["position_shift_hits"] / position_null_total min_lift = min(observed_rate - label_rate, observed_rate - position_rate) label_p = binomial_tail_at_least(item["observed_hits"], observed_total, label_rate) position_p = binomial_tail_at_least(item["observed_hits"], observed_total, position_rate) threshold_pass = ( c_state == "classical_intermediate" and observed_rate >= args.min_observed_rate and min_lift >= args.min_lift and label_p <= args.alpha and position_p <= args.alpha ) graph_stability = stability_state(observed_rate) if threshold_pass: thresholded.append(name) if graph_stability == "stable_graph_bridge" and c_state != "classical_intermediate": graph_only.append(name) rows_out.append( { "domain_window": name, "lambda": item["lambda"], "classical_audit_state": c_state, "graph_stability_state": graph_stability, "observed_successes": item["observed_hits"], "observed_total": observed_total, "observed_rate": round(observed_rate, 6), "observed_wilson_95": wilson_interval(item["observed_hits"], observed_total), "label_shuffle_successes": item["label_shuffle_hits"], "label_shuffle_total": label_null_total, exec /bin/bash -lc "sed -n '1,260p' tools/exp_rp_exact_local_window_matrix.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Exact local-window matrix for the finite Rosenzweig-Porter boundary crest. The historical unfolding audit forces local windows to odd widths. This wrapper preserves even widths so windows 9/10/11/12 are distinct experimental coordinates, then reuses the row-aligned two-reader/null machinery. """ from __future__ import annotations import argparse import json from pathlib import Path from types import SimpleNamespace from typing import Any import numpy as np import exp_rp_unfolding_sensitivity_audit as base def exact_local_unfold_gaps(gaps: np.ndarray, window: int) -> np.ndarray: gaps = np.asarray(gaps, dtype=float) gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] if len(gaps) == 0: return gaps width = max(2, min(int(window), len(gaps))) unfolded = np.empty_like(gaps) left = width // 2 for idx in range(len(gaps)): start = idx - left end = start + width if start < 0: start = 0 end = width if end > len(gaps): end = len(gaps) start = max(0, end - width) local_mean = float(np.mean(gaps[start:end])) if local_mean <= 1e-12: local_mean = float(np.mean(gaps)) unfolded[idx] = gaps[idx] / local_mean return unfolded def parse_ints(value: str) -> list[int]: return [int(part.strip()) for part in value.split(",") if part.strip()] def parse_floats(value: str) -> list[float]: return [float(part.strip()) for part in value.split(",") if part.strip()] def run(args: argparse.Namespace) -> dict[str, Any]: original_unfolder = base.local_unfold_gaps base.local_unfold_gaps = exact_local_unfold_gaps try: windows = parse_ints(args.local_windows) sizes = parse_ints(args.sizes) lambdas = parse_floats(args.lambdas) window_outputs = [] matrix_rows = [] for window in windows: out_path = Path(args.out).with_name(Path(args.out).stem + f"_w{window}.json") base_args = SimpleNamespace( out=str(out_path), sizes=args.sizes, reps=args.reps, lambdas=args.lambdas, seeds=args.seeds, k_values=args.k_values, label_null_trials=args.label_null_trials, position_offsets=args.position_offsets, central_fraction=args.central_fraction, grid_size=args.grid_size, poisson_pole_max=args.poisson_pole_max, gue_pole_min=args.gue_pole_min, min_observed_rate=args.min_observed_rate, min_lift=args.min_lift, alpha=args.alpha, unfolding_modes="local_window", local_window=window, ) result = base.run(base_args) window_outputs.append( { "local_window": window, "path": str(out_path), "summary": result["summary"], } ) for lam in lambdas: name = f"RP_lambda_{lam:.3f}" pass_sizes = [] cell_rows = [] for entry in result["by_size_mode"]: row = next(item for item in entry["rows"] if item["domain_window"] == name) if row["threshold_pass"]: pass_sizes.append(entry["n"]) cell_rows.append( { "n": entry["n"], "observed_successes": row["observed_successes"], "observed_total": row["observed_total"], "label_shuffle_successes": row["label_shuffle_successes"], "label_shuffle_total": row["label_shuffle_total"], "position_shift_successes": row["position_shift_successes"], "position_shift_total": row["position_shift_total"], "max_null_p": max( row["label_shuffle_binomial_tail_p"], row["position_shift_binomial_tail_p"], ), "min_lift_against_nulls": row["min_lift_against_nulls"], "threshold_pass": row["threshold_pass"], "classical_audit_state": row["classical_audit_state"], } ) matrix_rows.append( { "local_window": window, "lambda": round(lam, 6), "domain_window": name, "pass_sizes": pass_sizes, "pass_cells": len(pass_sizes), "total_cells": len(sizes), "cells": cell_rows, } ) crest = f"RP_lambda_{args.crest_lambda:.3f}" crest_rows = [row for row in matrix_rows if row["domain_window"] == crest] output = { "experiment": "rp_exact_local_window_matrix", "question": "Does RP_lambda_0.060 survive exact local windows 9/10/11/12 at sizes beyond 192?", "observables_registry": base.OBSERVABLES_REGISTRY_VERSION, "observables_used": base.FEATURE_NAMES + [ "observed_successes", "label_shuffle_successes", "position_shift_successes", "Wilson intervals", "binomial-tail p-values", "min_lift_against_nulls", "threshold_pass", "exact_local_window", ], "parameters": { "sizes": sizes, "reps": args.reps, "lambdas": lambdas, "crest_lambda": args.crest_lambda, "seeds": parse_ints(args.seeds), "k_values": parse_ints(args.k_values), "label_null_trials": args.label_null_trials, "position_offsets": parse_ints(args.position_offsets), "local_windows": windows, "unfolding_patch": "exact even windows preserved; no odd-width coercion", }, "threshold_preregistered": { "min_observed_rate": args.min_observed_rate, "min_lift_against_each_null": args.min_lift, "alpha_each_null": args.alpha, "classical_clause": "classical_intermediate required for two-reader threshold pass", "persistence_clause": "crest survives only if it passes every declared size and exact local window", }, "observable_contract": { "claim": "RP_lambda_0.060 is a finite-size persistence crest only if it beats both row-aligned nulls at every exact local window 9/10/11/12 and size beyond 192", "observable": "thresholded two-reader raw-count pass by lambda, exact local_window and size", "operator": "exact-width local unfolding matrix with label-shuffle and position-shift nulls", "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", "denominator": "sentinel lambda grid x sizes x windows; observed denominator seeds*k; null denominators observed*trials or observed*offsets", "non_possible": "single-lambda boundary if any declared exact window or size fails threshold", "not_tested": "N to infinity, windows beyond 12, alternate unfolding kernels, experimental spectra, Anderson 3D", }, "summary": { "crest_domain_window": crest, "crest_pass_cells": sum(row["pass_cells"] for row in crest_rows), "crest_total_cells": sum(row["total_cells"] for row in crest_rows), "crest_all_cells_pass": all( cell["threshold_pass"] for row in crest_rows for cell in row["cells"] ), "window_outputs": window_outputs, }, "matrix_rows": matrix_rows, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") print(json.dumps(output["summary"], indent=2, sort_keys=True)) return output finally: base.local_unfold_gaps = original_unfolder def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--out", default="tools/data/rp_exact_local_window_matrix_20260516_1019.json") parser.add_argument("--sizes", default="224,256") parser.add_argument("--reps", type=int, default=8) parser.add_argument("--lambdas", default="0.03,0.045,0.06,0.075,0.82") parser.add_argument("--crest-lambda", type=float, default=0.06) parser.add_argument("--local-windows", default="9,10,11,12") parser.add_argument("--seeds", default="202605161019,202605161020,202605161021,202605161022") parser.add_argument("--k-values", default="2,3,4") parser.add_argument("--label-null-trials", type=int, default=64) parser.add_argument("--position-offsets", default="1,2,3,4") parser.add_argument("--central-fraction", type=float, default=0.6) parser.add_argument("--grid-size", type=int, default=151) parser.add_argument("--poisson-pole-max", type=float, default=0.03) parser.add_argument("--gue-pole-min", type=float, default=0.82) parser.add_argument("--min-observed-rate", type=float, default=0.75) parser.add_argument("--min-lift", type=float, default=0.10) parser.add_argument("--alpha", type=float, default=0.05) run(parser.parse_args()) if __name__ == "__main__": main() exec /bin/bash -lc 'rg -n "brody|gap_ratio|unfold|nearest|eigvalsh|poisson|GUE" tools/exp_*.py tools/*.py' in /opt/MM_D-ND succeeded in 0ms: tools/exp_aubry_cosine_boundary_counter_gate.py:232: nearest_control_r = min(abs(needed["phi_r"] - needed["silver_r"]), abs(needed["phi_r"] - needed["bronze_r"])) if complete else None tools/exp_aubry_cosine_boundary_counter_gate.py:233: nearest_control_ipr = ( tools/exp_aubry_cosine_boundary_counter_gate.py:240: and nearest_control_r is not None tools/exp_aubry_cosine_boundary_counter_gate.py:241: and nearest_control_ipr is not None tools/exp_aubry_cosine_boundary_counter_gate.py:242: and nearest_control_r >= args.min_control_r_delta tools/exp_aubry_cosine_boundary_counter_gate.py:243: and nearest_control_ipr >= args.min_control_ipr_delta tools/exp_aubry_cosine_boundary_counter_gate.py:251: "nearest_control_r_delta": nearest_control_r, tools/exp_aubry_cosine_boundary_counter_gate.py:252: "nearest_control_ipr_delta": nearest_control_ipr, tools/exp_3d_boundary_layers.py:14:Tests on: primes, GUE, Poisson baseline. tools/exp_3d_boundary_layers.py:37: """Generate GUE eigenvalue gaps.""" tools/exp_3d_boundary_layers.py:42: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_3d_boundary_layers.py:75: """Next-nearest-neighbor spacing ratio: min(g_n, g_{n+2})/max(g_n, g_{n+2})""" tools/exp_3d_boundary_layers.py:179: # --- GUE --- tools/exp_3d_boundary_layers.py:180: print("\n--- GUE ---") tools/exp_3d_boundary_layers.py:187: gue_g, alphas, args.n_trials, rng, "GUE" 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_3d_boundary_layers.py:211: ('poisson', pois_results, pois_orig, pois_bl_mean, pois_bl_std), tools/exp_3d_boundary_layers.py:252: pois_sep = output['sequences']['poisson']['layer_separation']['delta'] tools/exp_3d_boundary_layers.py:255: print(f"Layer separation Δα: Primes={prime_sep:+.3f}, GUE={gue_sep:+.3f}, Poisson={pois_sep:+.3f}") tools/exp_3d_boundary_layers.py:260: 'poisson_layer_separation': float(pois_sep), tools/exp_boundary_classical_crossover_audit.py:3:Classical crossover audit for the 8 GUE / 5 Poisson BOUNDARY perimeter. tools/exp_boundary_classical_crossover_audit.py:10: between the empirical CDF and w * GUE_surmise + (1-w) * Poisson. tools/exp_boundary_classical_crossover_audit.py:49:def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: tools/exp_boundary_classical_crossover_audit.py:54:def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: tools/exp_boundary_classical_crossover_audit.py:59: pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) tools/exp_boundary_classical_crossover_audit.py:67:def poisson_cdf(s: np.ndarray) -> np.ndarray: tools/exp_boundary_classical_crossover_audit.py:83: poi = poisson_cdf(sorted_s) tools/exp_boundary_classical_crossover_audit.py:96:def classical_state(brody_q: float, mixture_w: float, graph_state: str) -> str: tools/exp_boundary_classical_crossover_audit.py:97: brody_mid = 0.25 <= brody_q <= 0.75 tools/exp_boundary_classical_crossover_audit.py:99: if graph_state == "third_included_candidate" and (brody_mid or mix_mid): tools/exp_boundary_classical_crossover_audit.py:103: if brody_mid or mix_mid: tools/exp_boundary_classical_crossover_audit.py:119: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_boundary_classical_crossover_audit.py:130: "brody_q": round(brody_q, 6), tools/exp_boundary_classical_crossover_audit.py:131: "brody_nll": round(brody_nll, 6), tools/exp_boundary_classical_crossover_audit.py:134: "audit_state": classical_state(brody_q, mixture_w, grow["boundary_state"]), tools/exp_boundary_classical_crossover_audit.py:152: "brody_q", tools/exp_boundary_classical_crossover_audit.py:163: "observable": "row-aligned Brody q, Berry-Robnik-like GUE mixture weight, graph bridge state", tools/exp_boundary_classical_crossover_audit.py:166: "denominator": "13 rows: 8 GUE and 5 Poisson", tools/exp_boundary_classical_crossover_audit.py:168: "not_tested": "true Rosenzweig-Porter Hamiltonian flow, physical unfolding alternatives, asymptotic universality", tools/exp_acf_z6z_mechanism.py:193: # 3d: Cramer + Z/6Z constraint (round to nearest ≡ r_n mod 6) tools/exp_acf_z6z_mechanism.py:197: # Round to nearest value with same mod-6 residue as real gaps tools/exp_boundary_graph_null_audit.py:5:This script keeps the 13 row-aligned 8 GUE / 5 Poisson denominator and asks tools/exp_boundary_graph_null_audit.py:50: gue_idx = [i for i, label in enumerate(labels) if label == "GUE"] tools/exp_boundary_graph_null_audit.py:150: selected = [row for row in source_rows if row.get("source_domain_type") in {"GUE", "Poisson"}] tools/exp_boundary_graph_null_audit.py:288: "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid and graph null trials", tools/exp_boundary_graph_null_audit.py:290: "not_tested": "new Hamiltonian systems, alternative unfolding, physical universality of graph-only rows", tools/exp_boundary_mixture_gate.py:5:Reusable META/BOUNDARY audit for the GUE-Poisson boundary. tools/exp_boundary_mixture_gate.py:8: Does the GUE/Poisson boundary remain a clean two-class split after the tools/exp_boundary_mixture_gate.py:13:It builds synthetic mixtures by replacing a fraction beta of unfolded GUE tools/exp_boundary_mixture_gate.py:19:- ambiguity of each beta layer relative to pure GUE and pure Poisson centroids. tools/exp_boundary_mixture_gate.py:41: """Generate unfolded GUE spacings by concatenating independent matrices.""" tools/exp_boundary_mixture_gate.py:49: eigs = np.sort(np.linalg.eigvalsh(h).real) tools/exp_boundary_mixture_gate.py:58:def mixture_spacings(gue: np.ndarray, poisson: np.ndarray, beta: float, rng: np.random.Generator) -> np.ndarray: tools/exp_boundary_mixture_gate.py:59: """Return a beta Poisson / (1-beta) GUE spacing sequence with mean spacing 1.""" tools/exp_boundary_mixture_gate.py:60: if len(gue) != len(poisson): tools/exp_boundary_mixture_gate.py:61: raise ValueError("gue and poisson arrays must have the same length") tools/exp_boundary_mixture_gate.py:64: out[mask] = poisson[mask] tools/exp_boundary_mixture_gate.py:135: labels.append("gue" if d_gue < d_poi else "poisson") tools/exp_boundary_mixture_gate.py:144: "poisson_label_fraction": float(np.mean(np.array(labels) == "poisson")), tools/exp_boundary_mixture_gate.py:197: poisson = rep_rng.exponential(1.0, size=args.n_gaps) tools/exp_boundary_mixture_gate.py:198: poisson = poisson / float(np.mean(poisson)) tools/exp_boundary_mixture_gate.py:201: gaps = mixture_spacings(gue, poisson, beta, layer_rng) tools/exp_boundary_mixture_gate.py:224: "question": "Is the GUE-Poisson mixed layer cleanly classifiable after denominator gating?", tools/exp_boundary_mixture_gate.py:249: print("beta stable_count margin_gated ambiguous_gated poisson_fraction_gated") tools/exp_boundary_mixture_gate.py:252: print("beta stable_count margin_all ambiguous_all poisson_fraction_all") tools/exp_boundary_mixture_gate.py:262: f"{layer.get('poisson_label_fraction', 0.0):>21.3f}" tools/exp_beta_crossover.py:98: from scipy.linalg import eigvalsh_tridiagonal tools/exp_beta_crossover.py:99: eigs = eigvalsh_tridiagonal(diag, off_diag) tools/exp_beta_crossover.py:112: # Trim edges (unfolding artifacts) tools/exp_beta_crossover.py:192: # Sweep beta from near-Poisson (0.01) to beyond-GUE (10) tools/exp_beta_crossover.py:195: np.linspace(0.6, 2.0, n_betas // 3), # through GOE-GUE tools/exp_beta_crossover.py:196: np.linspace(2.5, 8.0, n_betas - 2*(n_betas//3)), # beyond GUE tools/exp_beta_crossover.py:317: note = " <-- GUE" tools/exp_boundary_bridge_stability_audit.py:5:The audit keeps the 13 row-aligned GUE/Poisson denominator and reruns the graph tools/exp_boundary_bridge_stability_audit.py:65: selected = [row for row in source_rows if row.get("source_domain_type") in {"GUE", "Poisson"}] tools/exp_boundary_bridge_stability_audit.py:156: "brody_q": classic.get("brody_q"), tools/exp_boundary_bridge_stability_audit.py:191: "brody_q", tools/exp_boundary_bridge_stability_audit.py:209: "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid", tools/exp_boundary_bridge_stability_audit.py:211: "not_tested": "new physical Hamiltonian flow, alternative unfolding, asymptotic scaling beyond this finite denominator", tools/exp_acf_range_universality.py:11:Domains: primes, GUE, GOE, Poisson, primes_shuffled. tools/exp_acf_range_universality.py:69:def generate_rmt_spacings(N, n_mat, ensemble='GUE'): tools/exp_acf_range_universality.py:74: if ensemble == 'GUE': tools/exp_acf_range_universality.py:81: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_acf_range_universality.py:169: print("Generating GUE spacings...") tools/exp_acf_range_universality.py:170: gue_gaps = generate_rmt_spacings(N_rmt, n_mat_rmt, 'GUE') tools/exp_acf_range_universality.py:174: poisson_gaps = np.random.exponential(1.0, 100_000) tools/exp_acf_range_universality.py:182: 'GUE': gue_gaps, tools/exp_acf_range_universality.py:184: 'Poisson': poisson_gaps, tools/exp_boundary_shuffle_audit.py:2:exp_boundary_shuffle_audit.py — Is the GUE/Poisson classification tautological? tools/exp_boundary_shuffle_audit.py:4:Question: Does the r-statistic (nearest-neighbor spacing ratio) of each domain tools/exp_boundary_shuffle_audit.py:16:Domains: primes, zeta zeros, random matrix (GUE), Fibonacci spectrum, tools/exp_boundary_shuffle_audit.py:83: """GUE random matrices — eigenvalue spacings.""" tools/exp_boundary_shuffle_audit.py:88: eigs = np.sort(linalg.eigvalsh(H)) tools/exp_boundary_shuffle_audit.py:99:def gen_poisson(n=100000): tools/exp_boundary_shuffle_audit.py:122: eigs = np.sort(linalg.eigvalsh(H)) tools/exp_boundary_shuffle_audit.py:186: eigs = np.sort(linalg.eigvalsh(K)) tools/exp_boundary_shuffle_audit.py:238:R_GUE = 0.5307 # 4 - 2√3 ≈ 0.5359 for GOE; for GUE: 2π/(3√3 + 4π/3) ≈ 0.5307 approx tools/exp_boundary_shuffle_audit.py:244: 'gue': ('GUE random matrix', gen_gue_eigenvalues), tools/exp_boundary_shuffle_audit.py:245: 'poisson': ('Poisson process', gen_poisson), tools/exp_boundary_shuffle_audit.py:275: dist_gue = abs(res['r_original'] - R_GUE) tools/exp_boundary_shuffle_audit.py:277: res['class_original'] = 'GUE' if dist_gue < dist_poi else 'Poisson' tools/exp_boundary_shuffle_audit.py:279: dist_gue_s = abs(res['r_shuffled_mean'] - R_GUE) tools/exp_boundary_shuffle_audit.py:281: res['class_shuffled'] = 'GUE' if dist_gue_s < dist_poi_s else 'Poisson' tools/exp_boundary_shuffle_audit.py:308: print(f" SUMMARY: GUE/Poisson Classification Shuffle Audit") tools/exp_boundary_shuffle_audit.py:336: print(f"\n R_GUE = {R_GUE:.4f}, R_Poisson = {R_POISSON:.4f}") tools/exp_boundary_shuffle_audit.py:355: 'reference': {'R_GUE': R_GUE, 'R_Poisson': R_POISSON}, tools/exp_boundary_graph_curvature_gate.py:3:Graph-curvature gate for the 8 GUE / 5 Poisson BOUNDARY perimeter. tools/exp_boundary_graph_curvature_gate.py:91: nearest = np.argsort(distances[i])[1 : k + 1] tools/exp_boundary_graph_curvature_gate.py:92: for j in nearest: tools/exp_boundary_graph_curvature_gate.py:99: gue_idx = [i for i, label in enumerate(labels) if label == "GUE"] tools/exp_boundary_graph_curvature_gate.py:102: raise ValueError("scope must include both GUE and Poisson rows") tools/exp_boundary_graph_curvature_gate.py:174: "GUE": len(gue_idx), tools/exp_boundary_graph_curvature_gate.py:195: selected = [row for row in scope if row.get("source_domain_type") in {"GUE", "Poisson"}] tools/exp_boundary_graph_curvature_gate.py:238: "question": "Does the 8 GUE / 5 Poisson perimeter expose a graph boundary row instead of a clean two-class split?", tools/exp_boundary_graph_curvature_gate.py:248: "denominator": "base BOUNDARY rows with source_domain_type in {GUE, Poisson}", tools/exp_boundary_blank_thin_support_audit.py:175: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/exp_boundary_gue_poisson.py:3:Experiment: GUE/Poisson boundary in prime gaps vs Cramér null model. tools/exp_boundary_gue_poisson.py:5:Question: Is the transition from GUE-like to Poisson-like statistics in prime gaps tools/exp_boundary_gue_poisson.py:11: - GUE (GOE in 1D): ≈ 0.5307 tools/exp_boundary_gue_poisson.py:26:def gap_ratios(gaps): tools/exp_boundary_gue_poisson.py:59: r = gap_ratios(gaps) tools/exp_boundary_gue_poisson.py:64: print("=== GUE/Poisson Boundary: Primes vs Cramér Null Model ===\n") tools/exp_boundary_gue_poisson.py:101: r_poisson = 0.3863 tools/exp_boundary_gue_poisson.py:120: print(f"\nReference: _GUE = {r_gue:.4f}, _Poisson = {r_poisson:.4f}") tools/exp_boundary_gue_poisson.py:154: # Where are primes relative to GUE/Poisson? tools/exp_boundary_gue_poisson.py:162: dist_poi = abs(rp - r_poisson) tools/exp_boundary_gue_poisson.py:163: label = "GUE" if dist_gue < dist_poi else "POISSON" tools/exp_boundary_gue_poisson.py:171: "experiment": "boundary_gue_poisson_cramer", tools/exp_boundary_gue_poisson.py:176: "reference": {"r_gue": r_gue, "r_poisson": r_poisson}, tools/exp_anderson3d_mobility_edge_two_reader_audit.py:25: brody_pdf, tools/exp_anderson3d_mobility_edge_two_reader_audit.py:27: fit_brody_q, tools/exp_anderson3d_mobility_edge_two_reader_audit.py:33: poisson_cdf, tools/exp_anderson3d_mobility_edge_two_reader_audit.py:40: "brody_q", tools/exp_anderson3d_mobility_edge_two_reader_audit.py:41: "wigner_poisson_like_weight", tools/exp_anderson3d_mobility_edge_two_reader_audit.py:117: return "localized_poisson_pole" tools/exp_anderson3d_mobility_edge_two_reader_audit.py:141: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:152: "brody_q": round(brody_q, 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:153: "brody_nll": round(brody_nll, 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:154: "wigner_poisson_like_weight": round(mixture_w, 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:167: row["brody_q"], tools/exp_anderson3d_mobility_edge_two_reader_audit.py:168: row["wigner_poisson_like_weight"], tools/exp_anderson3d_mobility_edge_two_reader_audit.py:193: localized_idx = [i for i, label in enumerate(labels) if label == "localized_poisson_pole"] tools/exp_anderson3d_mobility_edge_two_reader_audit.py:216: "localized_poisson_pole", tools/exp_anderson3d_mobility_edge_two_reader_audit.py:219: elif {labels[i], labels[other]} == {"metallic_wigner_pole", "localized_poisson_pole"}: tools/exp_anderson3d_mobility_edge_two_reader_audit.py:247: q = float(row["brody_q"]) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:248: w = float(row["wigner_poisson_like_weight"]) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:250: return "classical_poisson_endpoint" tools/exp_anderson3d_mobility_edge_two_reader_audit.py:289: "brody_q": [], tools/exp_anderson3d_mobility_edge_two_reader_audit.py:300: row_hits[name]["brody_q"].append(float(row["brody_q"])) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:301: row_hits[name]["mixture_w"].append(float(row["wigner_poisson_like_weight"])) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:313: "brody_q": median(item["brody_q"]), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:314: "wigner_poisson_like_weight": median(item["mixture_w"]), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:330: "median_brody_q": round(median(item["brody_q"]), 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:331: "median_wigner_poisson_like_weight": round(median(item["mixture_w"]), 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:448: "generator": "H=sum_i eps_i |i>= brody_r_values[-1]: tools/exp_brody_calibration.py:182: for i in range(len(brody_r_values) - 1): tools/exp_brody_calibration.py:183: if brody_r_values[i] <= r_val <= brody_r_values[i+1]: tools/exp_brody_calibration.py:184: frac = (r_val - brody_r_values[i]) / (brody_r_values[i+1] - brody_r_values[i]) tools/exp_brody_calibration.py:185: return brody_beta_values[i] + frac * (brody_beta_values[i+1] - brody_beta_values[i]) tools/exp_brody_calibration.py:189: sig2_values = [b['sig2_over_L'][10] for b in brody_results] tools/exp_brody_calibration.py:203: prime_gaps = prime_gaps_unfolded(args.n_gaps) tools/exp_brody_calibration.py:211: # GUE matrices 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:239: real_domains['poisson'] = {**obs_po, 'beta_eff': beta_eff_po} tools/exp_brody_calibration.py:240: print(f"{'poisson':>20} {obs_po['r']:8.4f} {obs_po['r_shuf']:8.4f} " tools/exp_brody_calibration.py:303: for br in brody_results: tools/exp_brody_calibration.py:323: r_monotonic = all(brody_results[i]['r'] <= brody_results[i+1]['r'] for i in range(len(brody_results)-1)) tools/exp_brody_calibration.py:325: print(f" r(beta=0) = {brody_results[0]['r']:.4f}, r(beta=1) = {brody_results[-1]['r']:.4f}") tools/exp_brody_calibration.py:329: sig2_monotonic = all(brody_results[i]['sig2_over_L'][10] >= brody_results[i+1]['sig2_over_L'][10] for i in range(len(brody_results)-1)) tools/exp_brody_calibration.py:331: print(f" Sig2/L(beta=0) = {brody_results[0]['sig2_over_L'][10]:.4f}, Sig2/L(beta=1) = {brody_results[-1]['sig2_over_L'][10]:.4f}") tools/exp_brody_calibration.py:358: 'brody_curve': sanitize(brody_results), tools/exp_brody_calibration.py:360: 'max_brody_ordering_fraction': sanitize(max_ord), tools/exp_brody_calibration.py:366: out_path = Path(__file__).parent / 'data' / 'brody_calibration_results.json' tools/exp_boundary_short_denominator_extension.py:53: eig = np.linalg.eigvalsh(h) tools/exp_boundary_short_denominator_extension.py:203: "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", tools/exp_boundary_growth.py:13: - GUE (correlated): ~ 0.5307 tools/exp_boundary_growth.py:25:def gap_ratio(gaps): tools/exp_boundary_growth.py:49: return gap_ratio(shuffled) tools/exp_boundary_growth.py:82: r_prime = gap_ratio(gaps) tools/exp_boundary_growth.py:90: r_cramer_list.append(gap_ratio(cramer_gaps)) tools/exp_boundary_growth.py:158: # Key question: do primes approach GUE or Poisson at large scale? tools/exp_boundary_growth.py:159: GUE_R = 0.5307 tools/exp_boundary_growth.py:165: print(f"GUE reference: {GUE_R}") tools/exp_boundary_growth.py:167: print(f"Position: {'closer to GUE' if abs(r_large - GUE_R) < abs(r_large - POISSON_R) else 'closer to Poisson'}") tools/exp_coherence_robustness.py:36:def gap_ratio(gaps): tools/exp_coherence_robustness.py:82: r_prime = np.array([gap_ratio(w) for w in windows]) tools/exp_coherence_robustness.py:90: r = gap_ratio(shuf) tools/exp_boundary_blank_null_audit.py:27:R_GUE = 0.5307 tools/exp_boundary_blank_null_audit.py:66: return "GUE" if abs(value - R_GUE) < abs(value - R_POISSON) else "Poisson" tools/exp_boundary_blank_null_audit.py:151: "R_GUE": R_GUE, tools/exp_coherence_length.py:33:def gap_ratio(gaps): tools/exp_coherence_length.py:67: r_prime_list.append(gap_ratio(window)) tools/exp_coherence_length.py:72: r_shuf_lists[si].append(gap_ratio(shuf)) tools/exp_boundary_row_aligned_nonexact_audit.py:6:does not read GUE/Poisson labels as decision fields. It only uses support, tools/exp_boundary_row_aligned_nonexact_audit.py:175: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/exp_crossover_universality.py:18: - Rescale each to crossover parameter c in [0,1]: c=0 at GUE, c=1 at Poisson tools/exp_crossover_universality.py:57:def brody_beta(gaps): tools/exp_crossover_universality.py:95: beta = brody_beta(gaps) tools/exp_crossover_universality.py:133:# GUE: beta=1, dR_acf1~?, gap_acf1~?, =0.5307 (4/pi - 1 ?) tools/exp_crossover_universality.py:135:# We use empirical GUE reference where analytic is unknown tools/exp_crossover_universality.py:136:R_GUE = 0.5307 # 4/(pi+2) Wigner surmise tools/exp_crossover_universality.py:140:print(f" GUE = {R_GUE:.4f}, Poisson = {R_POISSON:.4f}") tools/exp_crossover_universality.py:141:print(f" beta GUE = 1.0, beta Poisson = 0.0") tools/exp_crossover_universality.py:153:# Rescale each to c in [0,1] where 0=GUE-like, 1=Poisson-like tools/exp_crossover_universality.py:154:# For beta: c = 1 - beta (beta=1 is GUE, so c=0) tools/exp_crossover_universality.py:278: ln_p_poisson_beta = -fits['beta']['intercept'] / fits['beta']['slope'] tools/exp_crossover_universality.py:279: p_poisson_beta = np.exp(ln_p_poisson_beta) tools/exp_crossover_universality.py:280: print(f" beta -> 0 (Poisson) at ln(p)={ln_p_poisson_beta:.1f}, p~{p_poisson_beta:.2e}") tools/exp_crossover_universality.py:282: ln_p_poisson_dRacf1 = -fits['dR_acf1']['intercept'] / fits['dR_acf1']['slope'] tools/exp_crossover_universality.py:283: p_poisson_dRacf1 = np.exp(ln_p_poisson_dRacf1) tools/exp_crossover_universality.py:284: print(f" dR_acf1 -> 0 at ln(p)={ln_p_poisson_dRacf1:.1f}, p~{p_poisson_dRacf1:.2e}") tools/exp_crossover_universality.py:286: ln_p_poisson_gacf1 = -fits['gap_acf1']['intercept'] / fits['gap_acf1']['slope'] tools/exp_crossover_universality.py:287: p_poisson_gacf1 = np.exp(ln_p_poisson_gacf1) tools/exp_crossover_universality.py:288: print(f" gap_acf1 -> 0 at ln(p)={ln_p_poisson_gacf1:.1f}, p~{p_poisson_gacf1:.2e}") tools/exp_crossover_universality.py:290: ln_p_poisson_r = (R_POISSON - fits['r_mean']['intercept']) / fits['r_mean']['slope'] tools/exp_crossover_universality.py:291: p_poisson_r = np.exp(ln_p_poisson_r) tools/exp_crossover_universality.py:292: print(f" -> {R_POISSON:.4f} (Poisson) at ln(p)={ln_p_poisson_r:.1f}, p~{p_poisson_r:.2e}") tools/exp_boundary_coherence.py:5:Question: Do different observables agree on WHERE primes sit between GUE and Poisson? tools/exp_boundary_coherence.py:10: 1. Mean spacing ratio (Poisson ≈ 0.386, GUE ≈ 0.5307) tools/exp_boundary_coherence.py:11: 2. Gap variance ratio Var/μ² (Poisson = 1.0, GUE ≈ 0.178) tools/exp_boundary_coherence.py:12: 3. Small-gap fraction P(s<0.3) (Poisson ≈ 0.259, GUE ≈ 0.020) tools/exp_boundary_coherence.py:13: 4. Brody parameter β (Poisson = 0, GUE = 1) tools/exp_boundary_coherence.py:14: 5. Lag-1 autocorrelation (Poisson = 0, GUE ≈ -0.27) tools/exp_boundary_coherence.py:16:Each observable is normalized to τ ∈ [0,1] where 0=Poisson, 1=GUE. tools/exp_boundary_coherence.py:30:# GUE values from random matrix theory (GOE for real symmetric, GUE for complex) tools/exp_boundary_coherence.py:33: "spacing_ratio": {"poisson": 0.38629, "gue": 0.53590}, # 2ln2-1, 4-2√3 tools/exp_boundary_coherence.py:34: "gap_var_ratio": {"poisson": 1.0, "gue": 0.178}, tools/exp_boundary_coherence.py:35: "small_gap_frac": {"poisson": 0.2592, "gue": 0.020}, # P(s<0.3) for exp vs Wigner tools/exp_boundary_coherence.py:36: "brody_beta": {"poisson": 0.0, "gue": 1.0}, tools/exp_boundary_coherence.py:37: "lag1_acf": {"poisson": 0.0, "gue": -0.271}, tools/exp_boundary_coherence.py:42: """Normalize gaps to mean 1 (unfolding).""" tools/exp_boundary_coherence.py:73:def brody_beta(gaps): tools/exp_boundary_coherence.py:107: "brody_beta": brody_beta(gaps), tools/exp_boundary_coherence.py:113: """Normalize observable to τ ∈ [0,1] where 0=Poisson, 1=GUE.""" tools/exp_boundary_coherence.py:114: p = REF[obs_name]["poisson"] tools/exp_boundary_coherence.py:122: """Generate GUE spacings from random Hermitian matrices.""" tools/exp_boundary_coherence.py:126: # GUE: complex Hermitian with Gaussian entries tools/exp_boundary_coherence.py:129: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_boundary_coherence.py:136:def generate_poisson_spacings(n): tools/exp_boundary_coherence.py:161: print("Computing GUE reference...") tools/exp_boundary_coherence.py:164: results["GUE_reference"] = { tools/exp_boundary_coherence.py:170: poi_gaps = generate_poisson_spacings(20000) tools/exp_boundary_coherence.py:207: print(f"\nReference anchors (τ should be ≈ 0 for Poisson, ≈ 1 for GUE):") tools/exp_boundary_coherence.py:208: print(f" {'Observable':<20} {'Poisson τ':>10} {'GUE τ':>10}") tools/exp_boundary_coherence.py:211: gt = results["GUE_reference"]["tau"][obs_name] tools/exp_boundary_coherence.py:214: print(f"\nPrime gaps — τ values (0=Poisson, 1=GUE):") tools/exp_boundary_coherence.py:215: print(f" {'Scale':<15} {'spacing_r':>10} {'var_ratio':>10} {'small_gap':>10} {'brody_β':>10} {'lag1_acf':>10} │ {'mean':>6} {'std':>6}") tools/exp_boundary_coherence.py:221: vals = [t["spacing_ratio"], t["gap_var_ratio"], t["small_gap_frac"], t["brody_beta"], t["lag1_acf"]] tools/exp_boundary_coherence.py:225: print(f" {'Scale':<15} {'spacing_r':>10} {'var_ratio':>10} {'small_gap':>10} {'brody_β':>10} {'lag1_acf':>10} │ {'mean':>6} {'std':>6}") tools/exp_boundary_coherence.py:231: vals = [t["spacing_ratio"], t["gap_var_ratio"], t["small_gap_frac"], t["brody_beta"], t["lag1_acf"]] tools/exp_boundary_two_axis_matrix.py:6:does not use GUE/Poisson source labels: it only reads transfer support and beta tools/exp_boundary_two_axis_matrix.py:107: "question": "Separate support_transfer from beta_coordinate_transfer on the 13 semi-real BOUNDARY rows without using GUE/Poisson labels.", tools/exp_boundary_two_axis_matrix.py:120: "label_policy": "GUE/Poisson source labels are not read by this operator.", tools/exp_crossover_phase_test.py:5:observed in the GUE crossover is a property of the PARTIAL SHUFFLE METHOD (tautology) tools/exp_crossover_phase_test.py:88: """Generate GUE-like spacings from random matrices""" tools/exp_crossover_phase_test.py:94: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_crossover_phase_test.py:161:def generate_poisson(N, rng): tools/exp_crossover_phase_test.py:216: sequences['GUE'] = generate_gue_gaps(args.N, rng) tools/exp_crossover_phase_test.py:217: print(f" GUE: {len(sequences['GUE'])} gaps") tools/exp_crossover_phase_test.py:228: sequences['Poisson'] = generate_poisson(args.N, rng) tools/exp_crossover_phase_test.py:262: print("CLASSIFICATION: Does each sequence show the full GUE-like phase transition?") tools/exp_crossover_phase_test.py:292: print(" The GUE crossover finding is TAUTOLOGICAL (methodological artifact)") tools/exp_crossover_phase_test.py:295: print(" The GUE crossover finding is a REAL structural property") tools/exp_brody_flow.py:3:exp_brody_flow.py — Brody parameter flow along the prime sequence. tools/exp_brody_flow.py:5:Measures how the Brody beta (interpolating Poisson beta=0 to GUE beta=1) tools/exp_brody_flow.py:16: python tools/exp_brody_flow.py [--n-max 2000000] [--window 5000] [--step 2000] [--n-shuffle 20] tools/exp_brody_flow.py:36:def brody_mle(spacings, beta_grid=np.linspace(0.01, 1.5, 300)): tools/exp_brody_flow.py:37: """MLE estimate of Brody parameter beta from unfolded spacings. tools/exp_brody_flow.py:78:def unfold_spacings(gaps): tools/exp_brody_flow.py:120: uf = unfold_spacings(g) tools/exp_brody_flow.py:123: beta_real = brody_mle(uf) tools/exp_brody_flow.py:132: uf_shuf = unfold_spacings(g_shuf) tools/exp_brody_flow.py:133: betas_shuf.append(brody_mle(uf_shuf)) tools/exp_brody_flow.py:141: uf_cr = unfold_spacings(g_cr) tools/exp_brody_flow.py:142: betas_cr.append(brody_mle(uf_cr)) tools/exp_brody_flow.py:216: out_path = Path(__file__).parent / "data" / "brody_flow.json" tools/exp_boundary_residual_beta_absent_audit.py:188: "not_tested": "global beta grid, V_c, source GUE/Poisson label validity", tools/exp_duality_gate_transfer.py:10: away from GUE/Poisson? tools/exp_boundary_transition_taxonomy_13rows.py:180: "not_tested": "new beta grid, new null surrogates, V_c fit, source GUE/Poisson label validity", tools/exp_cross_observable_consistency.py:17:5. GUE control: synthetic GUE eigenvalues (should show β_r ≈ β_Σ ≈ 1) tools/exp_cross_observable_consistency.py:31:with open('/opt/MM_D-ND/tools/data/brody_calibration_results.json') as f: tools/exp_cross_observable_consistency.py:34:cal_curve = cal['brody_curve'] tools/exp_cross_observable_consistency.py:89:def unfold_primes(p): tools/exp_cross_observable_consistency.py:97:def number_variance_at_L(unfolded, L, n_samples=3000): tools/exp_cross_observable_consistency.py:99: x_min, x_max = unfolded[0], unfolded[-1] tools/exp_cross_observable_consistency.py:103: counts = np.array([np.sum((unfolded >= x0) & (unfolded < x0 + L)) for x0 in starts]) tools/exp_cross_observable_consistency.py:107:# ── GUE control (small ensemble) ──────────────────────────────────── tools/exp_cross_observable_consistency.py:109: """Generate GUE eigenvalue gaps.""" tools/exp_cross_observable_consistency.py:114: evals = np.sort(np.linalg.eigvalsh(H).real) tools/exp_cross_observable_consistency.py:115: # Unfold: for GUE bulk, spacing ~ semicircle tools/exp_cross_observable_consistency.py:132:unfolded = unfold_primes(primes) tools/exp_cross_observable_consistency.py:137: s2 = number_variance_at_L(unfolded, L) tools/exp_cross_observable_consistency.py:157: u_shuf = unfold_primes(p_shuf) tools/exp_cross_observable_consistency.py:169:# ── Measure GUE (positive control) ────────────────────────────────── tools/exp_cross_observable_consistency.py:170:print("\n=== GUE (positive control) ===") tools/exp_cross_observable_consistency.py:176:# For GUE, unfold eigenvalues directly tools/exp_cross_observable_consistency.py:181: evals = np.sort(np.linalg.eigvalsh(H).real) tools/exp_cross_observable_consistency.py:215:# GUE tools/exp_cross_observable_consistency.py:217:print(f"{'GUE':<12} {beta_r_gue:>6.3f} | " + " | ".join(f"{v:>9}" for v in vals_gue)) tools/exp_cross_observable_consistency.py:227:print(f" GUE: {disagree_gue:.3f}") tools/exp_cross_observable_consistency.py:232:print(f" (positive = more GUE-like at short range, more Poisson-like at long range)") tools/exp_brody_crossover.py:6:crossover from GUE-like to Poisson-like? What is the functional form? tools/exp_brody_crossover.py:34:def brody_loglik(beta, spacings): tools/exp_brody_crossover.py:48:def fit_brody(spacings): tools/exp_brody_crossover.py:50: res = minimize_scalar(lambda b: brody_loglik(b, spacings), tools/exp_brody_crossover.py:54:def gap_ratio(gaps): tools/exp_brody_crossover.py:101: beta_prime = fit_brody(spacings) tools/exp_brody_crossover.py:102: r_prime = gap_ratio(win_gaps) tools/exp_brody_crossover.py:110: beta_cramer_list.append(fit_brody(surr_spacings)) tools/exp_brody_crossover.py:111: r_cramer_list.append(gap_ratio(surr_gaps)) tools/exp_brody_crossover.py:177: ln_p_poisson = -intercept_beta / slope_beta tools/exp_brody_crossover.py:178: print(f"\nExtrapolated Poisson (beta=0) at ln(p) = {ln_p_poisson:.1f} → p ~ 10^{ln_p_poisson/np.log(10):.0f}") tools/exp_brody_crossover.py:188: "experiment": "brody_crossover", tools/exp_brody_crossover.py:213: outpath = "data/reports/exp_brody_crossover_20260405.json" tools/exp_gap_label_set_stability.py:10:by (gap_index + 1) / N. The nearest gap label is the integer n whose fractional tools/exp_gap_label_set_stability.py:24:from numpy.linalg import eigvalsh tools/exp_gap_label_set_stability.py:53:def nearest_label(ids_value: float, theta: float, max_label: int) -> tuple[int, float, float]: tools/exp_gap_label_set_stability.py:65: eigs = np.sort(eigvalsh(hamiltonian(seq))) tools/exp_gap_label_set_stability.py:72: label, error, label_value = nearest_label(ids_value, theta, max_label) tools/exp_denominator_gate_transfer_matrix.py:8:GUE/Poisson BOUNDARY perimeter. Each perimeter has a coherent endpoint and an tools/exp_cross_domain_dipolar_direction.py:6:Primes have theta=-111 deg in the (SR, L1) plane. GUE has theta=-97 deg. tools/exp_cross_domain_dipolar_direction.py:7:Do all GUE-like domains share -97, or does each have its own direction? tools/exp_cross_domain_dipolar_direction.py:11: GUE-like: GUE spacings, GOE spacings, CUE spacings, Riemann zeta zeros (via GUE proxy) tools/exp_cross_domain_dipolar_direction.py:81:def gen_rmt_spacings(N_mat, ensemble='GUE', rng=None): tools/exp_cross_domain_dipolar_direction.py:85: if ensemble == 'GUE': tools/exp_cross_domain_dipolar_direction.py:109: eigs = np.linalg.eigvalsh(H) tools/exp_cross_domain_dipolar_direction.py:111: # Unfold: divide by local mean spacing (simple unfolding) tools/exp_cross_domain_dipolar_direction.py:132:def gen_poisson(N, rng=None): tools/exp_cross_domain_dipolar_direction.py:140: """Uniform iid on [0,2] — mean 1, same as unfolded.""" tools/exp_cross_domain_dipolar_direction.py:177: for ens_name in ['GUE', 'GOE', 'CUE']: tools/exp_cross_domain_dipolar_direction.py:230: for name, gen_fn in [('exponential', gen_poisson), tools/exp_cross_domain_dipolar_direction.py:275: rmt_names = ['GUE', 'GOE', 'CUE'] tools/exp_cross_domain_dipolar_direction.py:276: poisson_names = ['exponential', 'uniform', 'geometric'] tools/exp_cross_domain_dipolar_direction.py:308: for name in poisson_names: tools/exp_cross_domain_dipolar_direction.py:313: for name in poisson_names: tools/exp_dipolar_crossover.py:3:exp_dipolar_crossover.py — Topology of the GUE-Poisson transition in the dipolar plane. tools/exp_dipolar_crossover.py:5:Question: As ordering is gradually destroyed (GUE → shuffled), does the dipolar tools/exp_dipolar_crossover.py:10: 1. Generate GUE bulk spacings from random matrices tools/exp_dipolar_crossover.py:14: - Leave the rest in original (GUE) order tools/exp_dipolar_crossover.py:19:If primes sit on the curve → they're "partially disordered GUE" tools/exp_dipolar_crossover.py:33: """Generate bulk spacings from GUE matrices.""" tools/exp_dipolar_crossover.py:38: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_dipolar_crossover.py:98: print(f"Generating GUE spacings: {n_matrices} matrices of size {N_mat}...") tools/exp_dipolar_crossover.py:101: # GUE baseline (alpha=0) tools/exp_dipolar_crossover.py:214: "GUE_baseline": {"SR": float(sr0), "L1": float(l1_0)}, tools/exp_dR_brody_connection.py:54:def brody_beta(gaps, n_iter=50): tools/exp_dR_brody_connection.py:119: beta = brody_beta(gaps) tools/exp_dR_brody_connection.py:137: 'brody_beta': beta, tools/exp_dR_brody_connection.py:149: print(f" Window {i+1}/{N_WINDOWS}: ln(p)={obs['ln_p']:.2f}, beta={obs['brody_beta']:.4f}, dR_acf1={obs['dR_acf1']:.4f}") tools/exp_dR_brody_connection.py:153:beta_arr = np.array([o['brody_beta'] for o in prime_obs]) tools/exp_dR_brody_connection.py:219: s_beta = np.array([o['brody_beta'] for o in s_obs]) tools/exp_dR_brody_connection.py:282: "experiment": "exp_dR_brody_connection", tools/exp_dR_brody_connection.py:337:out_path = "/opt/MM_D-ND/tools/data/reports/exp_dR_brody_connection.json" tools/exp_excess_scaling.py:33:def gap_ratio(gaps): tools/exp_excess_scaling.py:49: rs.append(gap_ratio(gaps)) tools/exp_excess_scaling.py:66: r_prime = gap_ratio(gaps) tools/exp_excess_scaling.py:124:# Additional: check GUE vs Poisson classification tools/exp_excess_scaling.py:125:# GUE: ~ 0.5307, Poisson: ~ 0.3863 tools/exp_excess_scaling.py:126:r_GUE = 0.5307 tools/exp_excess_scaling.py:129:print(f"\n Reference: GUE = {r_GUE}, Poisson = {r_Poisson}") tools/exp_excess_scaling.py:137: print(f" INCREASES with scale: moving toward/staying GUE") tools/exp_markov_k_direction.py:148: print("Does higher-order memory point GUE-ward in the (SR, L1) plane?") tools/exp_markov_k_direction.py:169: GUE_THETA = -97.0 tools/exp_markov_k_direction.py:240: 'GUE_ref_theta': GUE_THETA, tools/exp_markov_layer_recovery_audit.py:19: - poisson_iid: independent exponential spacings; known order 0 tools/exp_markov_layer_recovery_audit.py:107: "poisson_iid": { tools/exp_dipolar_angle_reference.py:3:exp_dipolar_angle_reference.py — Dipolar angle of GUE, Poisson, and primes tools/exp_dipolar_angle_reference.py:6:1. Pure GUE eigenvalue spacings (GOE/GUE unfolded) tools/exp_dipolar_angle_reference.py:13:unique or just a position on the GUE-Poisson continuum? tools/exp_dipolar_angle_reference.py:21:from numpy.linalg import eigvalsh tools/exp_dipolar_angle_reference.py:80: """Generate gaps from GUE eigenvalues (unfolded).""" tools/exp_dipolar_angle_reference.py:83: # GUE: complex Hermitian random matrix tools/exp_dipolar_angle_reference.py:87: eigs = np.sort(eigvalsh(H)) tools/exp_dipolar_angle_reference.py:100: """Generate gaps from GOE eigenvalues (unfolded).""" tools/exp_dipolar_angle_reference.py:105: eigs = np.sort(eigvalsh(H)) tools/exp_dipolar_angle_reference.py:115:def generate_poisson_gaps(n_gaps): tools/exp_dipolar_angle_reference.py:153: # 2. GUE tools/exp_dipolar_angle_reference.py:154: print(f"Generating GUE gaps (n_trials={n_trials})...") tools/exp_dipolar_angle_reference.py:163: print(f" GUE trial {t+1}/{n_trials}: theta = {theta:.1f}") tools/exp_dipolar_angle_reference.py:165: results['GUE'] = { tools/exp_dipolar_angle_reference.py:171: print(f" GUE: theta = {np.mean(gue_thetas):.1f} +/- {np.std(gue_thetas):.1f} deg") tools/exp_dipolar_angle_reference.py:198: poi_gaps = generate_poisson_gaps(len(prime_gaps)) tools/exp_dipolar_angle_reference.py:240: print(f"{'GUE':<12} {results['GUE']['theta_mean']:>7.1f} +/- {results['GUE']['theta_std']:>5.1f} {results['GUE']['SR_mean']:<10.4f} {results['GUE']['L1_mean']:<10.4f}") tools/exp_dipolar_angle_reference.py:247: for name in ['GUE', 'GOE', 'Poisson', 'Cramer']: tools/exp_markov_scale_function.py:6:If so, does the decay track the GUE→Poisson boundary? tools/exp_markov_scale_function.py:16: 5. Brody parameter β (GUE/Poisson indicator) tools/exp_markov_scale_function.py:60:def brody_beta(spacings): tools/exp_markov_scale_function.py:62: β=0 → Poisson, β=1 → GUE (Wigner). tools/exp_markov_scale_function.py:138: beta = brody_beta(spacings) tools/exp_markov_scale_function.py:147: 'brody_beta': float(beta), tools/exp_markov_scale_function.py:212: f"{res['lag1_acf']:>7.4f} {res['brody_beta']:>5.2f} {res['mean_gap']:>7.2f}") tools/exp_markov_scale_function.py:219: betas = [r['brody_beta'] for r in results] tools/exp_markov_scale_function.py:237: # Linear regression: brody_beta vs ln(p) tools/exp_markov_scale_function.py:250: # Correlation between ordering_frac and brody_beta tools/exp_markov_scale_function.py:252: print(f"\nCorrelation(ordering_frac, brody_β) = {corr_ob:.4f}") tools/exp_markov_scale_function.py:288: 'brody_vs_lnp': { tools/exp_markov_scale_function.py:296: 'ordering_brody': corr_ob, tools/exp_duality_scale_contrast.py:5:for prime gaps, GUE eigenvalue spacings, and Poisson gaps. tools/exp_duality_scale_contrast.py:43: """Generate GUE (beta=2) eigenvalue spacings from random Hermitian matrix.""" tools/exp_duality_scale_contrast.py:47: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_duality_scale_contrast.py:58:def poisson_spacings(n: int, rng: np.random.Generator) -> np.ndarray: tools/exp_duality_scale_contrast.py:140: # Normalize by local mean (unfold) 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:163: poisson_contrast = windowed_contrast(poisson_gaps, window_sizes, n_shuffle, rng) tools/exp_duality_scale_contrast.py:165: # GUE: smaller windows only (limited by matrix size) tools/exp_duality_scale_contrast.py:167: print(f"Computing windowed contrast for GUE gaps (sizes: {gue_window_sizes})...") tools/exp_duality_scale_contrast.py:175: poisson_global_z, poisson_global_real = compute_z_scores( tools/exp_duality_scale_contrast.py:176: poisson_gaps[:5000], n_shuffle, rng tools/exp_duality_scale_contrast.py:189: "n_poisson_gaps": int(len(poisson_gaps)), tools/exp_duality_scale_contrast.py:197: "poisson": poisson_global_z, tools/exp_duality_scale_contrast.py:202: "poisson": poisson_global_real, tools/exp_duality_scale_contrast.py:207: "poisson": poisson_contrast, tools/exp_duality_scale_contrast.py:223: for domain in ["primes", "gue", "poisson"]: tools/exp_duality_scale_contrast.py:230: lines.append(f" {'W_size':>8s} {'Primes':>10s} {'GUE':>10s} {'Poisson':>10s}") tools/exp_duality_scale_contrast.py:235: poisson_data = {d["window_size"]: d for d in result["windowed_contrast"]["poisson"]} tools/exp_duality_scale_contrast.py:238: list(prime_data.keys()) + list(gue_data.keys()) + list(poisson_data.keys()) tools/exp_duality_scale_contrast.py:243: qz = f"{poisson_data[ws]['z_SR_mean']:+.2f}" if ws in poisson_data else "—" tools/exp_duality_scale_contrast.py:259: for domain, data in [("primes", prime_data), ("poisson", poisson_data)]: tools/exp_mod3_scaling.py:260: brody_slope = -0.030 # per ln(p) tools/exp_mod3_scaling.py:262: print(f"Brody beta slope: {brody_slope} per ln(p)") tools/exp_mod3_vs_residual_ordering.py:41:def unfold_gaps(gaps): tools/exp_mod3_vs_residual_ordering.py:56:def number_variance(unfolded_gaps, L_values, n_starts=3000): tools/exp_mod3_vs_residual_ordering.py:57: """Sigma^2(L) from unfolded gaps.""" tools/exp_mod3_vs_residual_ordering.py:58: levels = np.cumsum(unfolded_gaps) tools/exp_mod3_vs_residual_ordering.py:184: unfolded = unfold_gaps(gaps.astype(float)) tools/exp_mod3_vs_residual_ordering.py:185: print(f"Unfolded: mean={np.mean(unfolded):.3f}, std={np.std(unfolded):.3f}") tools/exp_mod3_vs_residual_ordering.py:192: sig2_real = number_variance(unfolded, L_values) tools/exp_mod3_vs_residual_ordering.py:200: shuf = unfolded.copy() tools/exp_mod3_vs_residual_ordering.py:214: # Shuffle raw gaps with mod-3 constraint, then unfold tools/exp_mod3_vs_residual_ordering.py:216: shuf_unf = unfold_gaps(shuf_raw.astype(float)) tools/exp_mod3_vs_residual_ordering.py:231: cu = unfold_gaps(cg) tools/exp_markov_dipolar_decomposition.py:6:GUE has dL1/dSR = 8.37 at angle -97 deg. tools/exp_gap_label_ostrowski_recognizability_gate.py:95: nearest = 0 tools/exp_gap_label_ostrowski_recognizability_gate.py:100: nearest = b tools/exp_gap_label_ostrowski_recognizability_gate.py:101: return int(best), int(nearest) tools/exp_gap_label_ostrowski_recognizability_gate.py:111: distance, nearest = boundary_distance(center, n, boundaries) tools/exp_gap_label_ostrowski_recognizability_gate.py:124: "nearest_boundary": nearest, tools/exp_number_variance.py:4:Tests whether the GUE->Poisson drift (seen in gap ratio) is confirmed tools/exp_number_variance.py:7:GUE: Sigma^2(L) ~ (2/pi^2) * ln(L) + const (logarithmic) tools/exp_number_variance.py:30:def unfolded_primes(primes): tools/exp_number_variance.py:33: unfolded = p / np.log(p) tools/exp_number_variance.py:35: spacings = np.diff(unfolded) tools/exp_number_variance.py:37: unfolded = unfolded / mean_s tools/exp_number_variance.py:38: return unfolded tools/exp_number_variance.py:40:def number_variance(unfolded, L_values): tools/exp_number_variance.py:46: x_min, x_max = unfolded[0], unfolded[-1] tools/exp_number_variance.py:49: n = np.sum((unfolded >= x0) & (unfolded < x0 + L)) tools/exp_number_variance.py:55:def number_variance_poisson(L_values): tools/exp_number_variance.py:60: """GUE prediction: Sigma^2 ~ (2/pi^2) * ln(L) + 0.44 (approximate).""" tools/exp_number_variance.py:78: uf = unfolded_primes(primes) tools/exp_number_variance.py:95: sv_poisson = number_variance_poisson(L_VALUES) tools/exp_number_variance.py:118: print(f" Log-fit slope (primes): {coeffs_prime[0]:.4f} (GUE={2/np.pi**2:.4f})") tools/exp_number_variance.py:123: print(f" Better fit: {'LOG (GUE-like)' if r2_log > r2_lin else 'LINEAR (Poisson-like)'}") tools/exp_number_variance.py:125: print(f"\n L | Σ²_prime | Σ²_shuf | Σ²_GUE | Σ²_Poisson") tools/exp_number_variance.py:128: print(f" {L:4d} | {sv_primes[i]:8.4f} | {sv_shuffled[i]:8.4f} | {sv_gue[i]:7.4f} | {sv_poisson[i]:8.1f}") tools/exp_number_variance.py:151:# Key metric: does log_slope increase with scale? (would mean moving away from GUE) tools/exp_number_variance.py:158: print(f" GUE value: {2/np.pi**2:.4f}") tools/exp_number_variance.py:160: print(f" If trend ~ 0: stable -> GUE character preserved") tools/exp_number_variance.py:166: "claim_under_test": "BOUNDARY: GUE->Poisson drift in primes", tools/exp_number_variance.py:167: "method": "Number variance Sigma^2(L) at 5 scales, compared with GUE and Poisson predictions", tools/exp_perturbation_rank_size_curve.py:7:The 2026-05-06 06:25 cycle restricted the claim "GUE has a second tools/exp_perturbation_rank_size_curve.py:53: """Generate unfolded GUE spacings by concatenating independent matrices.""" tools/exp_perturbation_rank_size_curve.py:61: eigs = np.sort(np.linalg.eigvalsh(h).real) tools/exp_perturbation_rank_size_curve.py:324: "poisson": lambda rep_rng, rep_i: rep_rng.exponential(1.0, size=max_n), tools/exp_photonic_boundary_third_included_gate.py:5:This tool projects the GUE/Poisson boundary direction into a physical return: tools/exp_photonic_boundary_third_included_gate.py:6:a 1D dielectric multilayer. It does not classify the optical spectrum as GUE or tools/exp_perturbation_dimensionality_audit.py:7:The 2026-05-06 03:30 run found that GUE spacing sequences expose a second tools/exp_perturbation_dimensionality_audit.py:9:to one axis. That run used a short GUE sequence. This tool repeats the same tools/exp_perturbation_dimensionality_audit.py:52: eigs = np.sort(np.linalg.eigvalsh(h).real) tools/exp_perturbation_dimensionality_audit.py:309: "poisson": root_rng.exponential(1.0, size=args.n_prime_gaps), tools/exp_perturbation_dimensionality_audit.py:314: "question": "Is the GUE second perturbation axis stable across independent ensembles and sample-size controls?", tools/exp_perturbation_dimensionality_audit.py:336: print("\nGUE independent replicates") tools/exp_markov3_observable_hunt.py:134: """Next-nearest-neighbor spacing ratio: min(g_n, g_{n+2})/max(g_n, g_{n+2})""" tools/exp_prime_persistent_blank_gate.py:176: "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from GUE random matrix blocks and logistic return intervals", tools/exp_prime_persistent_blank_gate.py:179: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels", tools/exp_poisson_convergence.py:3:exp_poisson_convergence.py — Do beta, _excess, and acf1 predict the same Poisson scale? tools/exp_poisson_convergence.py:14: python exp_poisson_convergence.py [--n_primes N] [--n_windows W] [--n_surrogates S] tools/exp_poisson_convergence.py:30:def gap_ratio_r(gaps): tools/exp_poisson_convergence.py:37:def brody_beta(gaps, n_bins=50): tools/exp_poisson_convergence.py:87: r = gap_ratio_r(gaps) tools/exp_poisson_convergence.py:88: beta = brody_beta(gaps) tools/exp_poisson_convergence.py:117: r_vals.append(gap_ratio_r(gaps_surr)) tools/exp_poisson_convergence.py:118: beta_vals.append(brody_beta(gaps_surr)) tools/exp_poisson_convergence.py:319: 'experiment': 'poisson_convergence', tools/exp_poisson_convergence.py:333: with open('/opt/MM_D-ND/tools/data/exp_poisson_convergence.json', 'w') as f: tools/exp_poisson_convergence.py:336: print(f"\nData saved to tools/data/exp_poisson_convergence.json") tools/exp_prime_sr_persistent_boundary.py:219: "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", tools/exp_prime_sr_persistent_boundary.py:220: "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", tools/exp_prime_sr_persistent_boundary.py:222: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", tools/exp_markov_memory_by_gue_type.py:3:Markov Memory Decomposition Across GUE Types tools/exp_markov_memory_by_gue_type.py:5:Question: The TWO_KINDS_GUE result (2026-04-24) found that distribution-GUE tools/exp_markov_memory_by_gue_type.py:6:domains (primes, GUE matrices) retain GUE classification after shuffle, while 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:11:This experiment asks: what is the Markov memory profile of each GUE type? tools/exp_markov_memory_by_gue_type.py:12:If ordering-GUE domains get their classification FROM sequential ordering, tools/exp_markov_memory_by_gue_type.py:113:def generate_poisson_gaps(n=5000): tools/exp_markov_memory_by_gue_type.py:119: """GUE random matrix eigenvalue spacings.""" tools/exp_markov_memory_by_gue_type.py:120: from scipy.linalg import eigvalsh tools/exp_markov_memory_by_gue_type.py:124: evals = eigvalsh(H) tools/exp_markov_memory_by_gue_type.py:133: # Domain definitions with GUE type from TWO_KINDS_GUE result tools/exp_markov_memory_by_gue_type.py:136: # Distribution-GUE domains (survive shuffle) tools/exp_markov_memory_by_gue_type.py:140: 'type': 'distribution-GUE', tools/exp_markov_memory_by_gue_type.py:145: 'type': 'distribution-GUE', tools/exp_markov_memory_by_gue_type.py:149: # Ordering-GUE domains (collapse to Poisson on shuffle) tools/exp_markov_memory_by_gue_type.py:158: 'type': 'ordering-GUE', tools/exp_markov_memory_by_gue_type.py:168: 'gaps': gaps, 'type': 'ordering-GUE', 'delta_r_sign': '+' tools/exp_markov_memory_by_gue_type.py:174: domains['poisson'] = { tools/exp_markov_memory_by_gue_type.py:175: 'gaps': generate_poisson_gaps(5000), tools/exp_markov_memory_by_gue_type.py:251: print("\nAggregate by GUE type:") tools/exp_markov_memory_by_gue_type.py:252: for gtype in ['distribution-GUE', 'ordering-GUE', 'Poisson']: tools/exp_markov_memory_by_gue_type.py:267: 'question': 'Does Markov memory discriminate distribution-GUE from ordering-GUE?', tools/exp_psd_amplitude_scaling.py:198: 'poisson_crossover_ln_p': round(float(ln_p_cross_dip), 2) if ln_p_cross_dip else None, tools/exp_metric_tensor_diagnostic.py:342: "gap_ratio_r": float(r_mean_prime), tools/exp_metric_tensor_diagnostic.py:397:print(f" prime: {output['gap_ratio_r']:.6f}") tools/exp_quasiperiodic_vc_lattice_gate.py:19:from scipy.linalg import eigvalsh_tridiagonal tools/exp_quasiperiodic_vc_lattice_gate.py:35: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_observable_rank_audit.py:143: poisson = rng.exponential(1.0, size=n_gaps).astype(float) tools/exp_observable_rank_audit.py:149: "poisson": poisson, tools/exp_quasiperiodic_vc_curve_map.py:19:from scipy.linalg import eigvalsh_tridiagonal tools/exp_quasiperiodic_vc_curve_map.py:43: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_rp_boundary_raw_count_null_audit.py:60: gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] tools/exp_rp_boundary_raw_count_null_audit.py:62: raise ValueError("labels must include Poisson and GUE poles") tools/exp_rp_boundary_raw_count_null_audit.py:81: if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: tools/exp_rp_boundary_raw_count_null_audit.py:83: elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: tools/exp_rp_boundary_raw_count_null_audit.py:123: "brody_q": [], tools/exp_rp_boundary_raw_count_null_audit.py:151: poisson_pole_max=args.poisson_pole_max, tools/exp_rp_boundary_raw_count_null_audit.py:174: item["brody_q"].append(float(row["brody_q"])) tools/exp_rp_boundary_raw_count_null_audit.py:197: "brody_q": median(item["brody_q"]), tools/exp_rp_boundary_raw_count_null_audit.py:243: "median_brody_q": round(class_row["brody_q"], 6), tools/exp_rp_boundary_raw_count_null_audit.py:337: "poisson_pole_max": args.poisson_pole_max, tools/exp_rp_boundary_raw_count_null_audit.py:350: "operator": "finite-size RP diagonal-plus-GUE flow with kNN graph perturbations and two row-aligned nulls", tools/exp_rp_boundary_raw_count_null_audit.py:351: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE across sizes, seeds and k values", tools/exp_rp_boundary_raw_count_null_audit.py:354: "not_tested": "N to infinity, experimental spectra, unfolding alternatives, Anderson 3D, many-body RP", tools/exp_rp_boundary_raw_count_null_audit.py:386: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_physical_sr_residue_bounce.py:6: physical A: quantum-chaotic GUE spectra tools/exp_physical_sr_residue_bounce.py:50: return np.linalg.eigvalsh(hermitian) tools/exp_physical_sr_residue_bounce.py:56: return np.linalg.eigvalsh(symmetric) tools/exp_physical_sr_residue_bounce.py:64: return np.linalg.eigvalsh(matrix) tools/exp_physical_sr_residue_bounce.py:67:def span_matched_poisson_gaps(level_count: int, rng: np.random.Generator) -> np.ndarray: tools/exp_physical_sr_residue_bounce.py:222: null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) tools/exp_physical_sr_residue_bounce.py:238: poisson_contrast = { tools/exp_physical_sr_residue_bounce.py:271: "poisson_contrast": "span-matched Poisson null for each class/label", tools/exp_physical_sr_residue_bounce.py:284: "poisson_contrast": poisson_contrast, tools/exp_physical_sr_residue_bounce.py:311: "poisson_contrast": "real spectrum versus span-matched Poisson null", tools/exp_physical_sr_residue_bounce.py:327: "GUE_unitary_no_time_reversal": { tools/exp_physical_sr_residue_bounce.py:338: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/exp_physical_sr_residue_bounce.py:339: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/exp_physical_sr_residue_bounce.py:345: "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", tools/exp_physical_sr_residue_bounce.py:376: ("GUE_unitary_no_time_reversal", "beta_2_complex_hermitian", gue_levels(n, rng)), tools/exp_physical_sr_residue_bounce.py:390: null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) tools/exp_physical_sr_residue_bounce.py:419: gue_key = f"N{n}:GUE_unitary_no_time_reversal" tools/exp_physical_sr_residue_bounce.py:421: symmetry_contrasts[f"N{n}:GUE_minus_GOE"] = contrast( tools/exp_physical_sr_residue_bounce.py:422: f"N{n}:GUE_minus_GOE", tools/exp_physical_sr_residue_bounce.py:428: source = summaries["GUE_unitary_no_time_reversal"] tools/exp_physical_sr_residue_bounce.py:438: "null": "span_matched_poisson_same_level_count", tools/exp_physical_sr_residue_bounce.py:439: "physical_source": "quantum-chaotic spectra modeled by GOE and GUE symmetry classes", tools/exp_physical_sr_residue_bounce.py:445: "GUE": "Wigner-Dyson beta=2, complex Hermitian, no time-reversal symmetry", tools/exp_physical_sr_residue_bounce.py:489: print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) tools/exp_rosenzweig_porter_bridge_physical_audit.py:6:diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical tools/exp_rosenzweig_porter_bridge_physical_audit.py:26:FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] tools/exp_rosenzweig_porter_bridge_physical_audit.py:51:def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: tools/exp_rosenzweig_porter_bridge_physical_audit.py:56:def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: tools/exp_rosenzweig_porter_bridge_physical_audit.py:60: pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) tools/exp_rosenzweig_porter_bridge_physical_audit.py:68:def poisson_cdf(s: np.ndarray) -> np.ndarray: tools/exp_rosenzweig_porter_bridge_physical_audit.py:84: poi = poisson_cdf(sorted_s) tools/exp_rosenzweig_porter_bridge_physical_audit.py:143:def source_type(lam: float, poisson_max: float, gue_min: float) -> str: tools/exp_rosenzweig_porter_bridge_physical_audit.py:144: if lam <= poisson_max: tools/exp_rosenzweig_porter_bridge_physical_audit.py:147: return "GUE_pole" tools/exp_rosenzweig_porter_bridge_physical_audit.py:156: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_rosenzweig_porter_bridge_physical_audit.py:161: "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), tools/exp_rosenzweig_porter_bridge_physical_audit.py:165: "brody_q": round(brody_q, 6), tools/exp_rosenzweig_porter_bridge_physical_audit.py:166: "brody_nll": round(brody_nll, 6), tools/exp_rosenzweig_porter_bridge_physical_audit.py:178: + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] tools/exp_rosenzweig_porter_bridge_physical_audit.py:200: gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] tools/exp_rosenzweig_porter_bridge_physical_audit.py:202: raise ValueError("lambda grid must include Poisson and GUE poles") tools/exp_rosenzweig_porter_bridge_physical_audit.py:221: if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: tools/exp_rosenzweig_porter_bridge_physical_audit.py:223: elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: tools/exp_rosenzweig_porter_bridge_physical_audit.py:246: q = float(row["brody_q"]) tools/exp_rosenzweig_porter_bridge_physical_audit.py:249: return "classical_poisson_endpoint" tools/exp_rosenzweig_porter_bridge_physical_audit.py:288: "brody_q": [], tools/exp_rosenzweig_porter_bridge_physical_audit.py:298: row_hits[name]["brody_q"].append(float(row["brody_q"])) tools/exp_rosenzweig_porter_bridge_physical_audit.py:309: "brody_q": float(np.median(item["brody_q"])), tools/exp_rosenzweig_porter_bridge_physical_audit.py:325: "median_brody_q": round(float(np.median(item["brody_q"])), 6), tools/exp_rosenzweig_porter_bridge_physical_audit.py:369: "poisson_pole_max": args.poisson_pole_max, tools/exp_rosenzweig_porter_bridge_physical_audit.py:376: "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", tools/exp_rosenzweig_porter_bridge_physical_audit.py:377: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", tools/exp_rosenzweig_porter_bridge_physical_audit.py:380: "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", tools/exp_rosenzweig_porter_bridge_physical_audit.py:414: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_scale_selective_perturbation.py:48: """Generate N GUE gaps (eigenvalue spacings of random Hermitian matrix).""" tools/exp_scale_selective_perturbation.py:52: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_scale_selective_perturbation.py:59: unfolded = spacings / local_mean tools/exp_scale_selective_perturbation.py:60: if len(unfolded) >= N: tools/exp_scale_selective_perturbation.py:61: return unfolded[:N] tools/exp_scale_selective_perturbation.py:62: return unfolded tools/exp_scale_selective_perturbation.py:65:def generate_poisson(N, rng): tools/exp_scale_selective_perturbation.py:197: ('GUE', lambda: generate_gue(N, rng))]: tools/exp_prime_vs_mod6_sr_boundary.py:6:control perimeter and exposed `mod6_candidates` as the nearest antagonist. This tools/exp_prime_vs_mod6_sr_boundary.py:366: "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", tools/exp_spectral_2d.py:46:def brody_beta(spacings): tools/exp_spectral_2d.py:71: """Prime gaps, unfolded by local density.""" tools/exp_spectral_2d.py:89: """Raw prime gaps (not unfolded) for distribution sampling.""" tools/exp_spectral_2d.py:106: eigs = np.sort(np.linalg.eigvalsh(H).real) tools/exp_spectral_2d.py:194:def gen_anticorr_poisson(n_spacings, strength=0.3): tools/exp_spectral_2d.py:284: beta = brody_beta(spacings) tools/exp_spectral_2d.py:292: beta_s.append(brody_beta(sh)) tools/exp_spectral_2d.py:327: ("GUE", lambda n: gen_gue(n)), tools/exp_spectral_2d.py:340: ("Anticorr_0.1", lambda n: gen_anticorr_poisson(n, 0.1)), tools/exp_spectral_2d.py:341: ("Anticorr_0.2", lambda n: gen_anticorr_poisson(n, 0.2)), tools/exp_spectral_2d.py:342: ("Anticorr_0.3", lambda n: gen_anticorr_poisson(n, 0.3)), tools/exp_spectral_2d.py:343: ("Anticorr_0.5", lambda n: gen_anticorr_poisson(n, 0.5)), tools/exp_rp_unfolding_sensitivity_audit.py:8:local-window unfolding. tools/exp_rp_unfolding_sensitivity_audit.py:27: fit_brody_q, tools/exp_rp_unfolding_sensitivity_audit.py:43:def local_unfold_gaps(gaps: np.ndarray, window: int) -> np.ndarray: tools/exp_rp_unfolding_sensitivity_audit.py:67: unfolding_mode: str, tools/exp_rp_unfolding_sensitivity_audit.py:80: if unfolding_mode == "local_window": tools/exp_rp_unfolding_sensitivity_audit.py:81: gaps = local_unfold_gaps(gaps, local_window) tools/exp_rp_unfolding_sensitivity_audit.py:90: if unfolding_mode == "global_mean": tools/exp_rp_unfolding_sensitivity_audit.py:92: elif unfolding_mode == "local_window": tools/exp_rp_unfolding_sensitivity_audit.py:95: raise ValueError(f"unknown unfolding mode: {unfolding_mode}") tools/exp_rp_unfolding_sensitivity_audit.py:105: q = float(row["brody_q"]) tools/exp_rp_unfolding_sensitivity_audit.py:108: return "classical_poisson_endpoint" tools/exp_rp_unfolding_sensitivity_audit.py:122:def compute_row(lam: float, args: argparse.Namespace, n: int, seed: int, unfolding_mode: str) -> dict[str, Any]: tools/exp_rp_unfolding_sensitivity_audit.py:129: unfolding_mode, tools/exp_rp_unfolding_sensitivity_audit.py:134: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_rp_unfolding_sensitivity_audit.py:139: "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), tools/exp_rp_unfolding_sensitivity_audit.py:143: "brody_q": round(brody_q, 6), tools/exp_rp_unfolding_sensitivity_audit.py:144: "brody_nll": round(brody_nll, 6), tools/exp_rp_unfolding_sensitivity_audit.py:157: "brody_q": [], tools/exp_rp_unfolding_sensitivity_audit.py:168:def audit_size_mode(args: argparse.Namespace, n: int, unfolding_mode: str) -> dict[str, Any]: tools/exp_rp_unfolding_sensitivity_audit.py:182: compute_row(lam, args, n, seed + (n * 10000) + int(round(lam * 1000)), unfolding_mode) tools/exp_rp_unfolding_sensitivity_audit.py:186: rng = np.random.default_rng(seed + n + (0 if unfolding_mode == "global_mean" else 1000003)) tools/exp_rp_unfolding_sensitivity_audit.py:194: {"n": n, "seed": seed, "k": k, "unfolding_mode": unfolding_mode, "observed_candidates": observed_candidates} tools/exp_rp_unfolding_sensitivity_audit.py:204: item["brody_q"].append(float(row["brody_q"])) tools/exp_rp_unfolding_sensitivity_audit.py:227: "brody_q": median(item["brody_q"]), tools/exp_rp_unfolding_sensitivity_audit.py:273: "median_brody_q": round(class_row["brody_q"], 6), tools/exp_rp_unfolding_sensitivity_audit.py:284: "unfolding_mode": unfolding_mode, tools/exp_rp_unfolding_sensitivity_audit.py:301: modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] tools/exp_rp_unfolding_sensitivity_audit.py:306: entries = [entry for entry in by_size_mode if entry["unfolding_mode"] == mode] tools/exp_rp_unfolding_sensitivity_audit.py:326: entries = [entry for entry in by_size_mode if entry["unfolding_mode"] == mode] tools/exp_rp_unfolding_sensitivity_audit.py:350: "experiment": "rp_unfolding_sensitivity_audit", tools/exp_rp_unfolding_sensitivity_audit.py:351: "question": "Do the RP boundary rows 0.045/0.060 remain thresholded under an alternate local-window unfolding?", tools/exp_rp_unfolding_sensitivity_audit.py:362: "unfolding_mode", tools/exp_rp_unfolding_sensitivity_audit.py:374: "poisson_pole_max": args.poisson_pole_max, tools/exp_rp_unfolding_sensitivity_audit.py:376: "unfolding_modes": modes, tools/exp_rp_unfolding_sensitivity_audit.py:384: "unfolding_stability_clause": "boundary-stable only if row passes all sizes in every unfolding mode", tools/exp_rp_unfolding_sensitivity_audit.py:387: "claim": "the finite RP boundary window is unfolding-stable only if the same lambda rows beat label-shuffle and position-shift nulls under global and local spacing normalization", tools/exp_rp_unfolding_sensitivity_audit.py:388: "observable": "thresholded two-reader raw-count pass by lambda, size and unfolding mode", tools/exp_rp_unfolding_sensitivity_audit.py:389: "operator": "repeat the RP raw-count gate with global mean and local-window unfolded spacings", tools/exp_rp_unfolding_sensitivity_audit.py:390: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE across size, seed, k and unfolding mode", tools/exp_rp_unfolding_sensitivity_audit.py:391: "denominator": "same lambda grid per size and unfolding; observed denominator seeds*k, null denominators observed*null_trials", tools/exp_rp_unfolding_sensitivity_audit.py:392: "non_possible": "unfolding-stable boundary if any promoted lambda fails all-size pass under local-window unfolding", tools/exp_rp_unfolding_sensitivity_audit.py:399: "unfolding_sensitive_rows": mode_sensitive, tools/exp_rp_unfolding_sensitivity_audit.py:413: parser.add_argument("--out", default="tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json") tools/exp_rp_unfolding_sensitivity_audit.py:423: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_rp_unfolding_sensitivity_audit.py:428: parser.add_argument("--unfolding-modes", default="global_mean,local_window") tools/exp_quasiperiodic_grammar_scale_gate.py:5:This tool keeps the old gap_ratio observable but prevents it from deciding the tools/exp_quasiperiodic_grammar_scale_gate.py:18:from scipy.linalg import eigvalsh_tridiagonal tools/exp_quasiperiodic_grammar_scale_gate.py:58:def nearest_label(ids_value: float, theta: float, max_label: int) -> tuple[int, float]: tools/exp_quasiperiodic_grammar_scale_gate.py:128: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_quasiperiodic_grammar_scale_gate.py:148: label, label_error = nearest_label(ids, reader_theta, args.max_label) tools/exp_sturmian_denominator_alignment_gate.py:194: nearest_delta = min(abs(phi_tau - value) for value in nonphi) tools/exp_sturmian_denominator_alignment_gate.py:197: "verdict": "phi_specific" if nearest_delta >= min_tau_delta else "sturmian_corridor", tools/exp_sturmian_denominator_alignment_gate.py:201: "nearest_nonphi_tau_delta": float(nearest_delta), tools/exp_rp_exact_local_window_matrix.py:5:The historical unfolding audit forces local windows to odd widths. This tools/exp_rp_exact_local_window_matrix.py:20:import exp_rp_unfolding_sensitivity_audit as base tools/exp_rp_exact_local_window_matrix.py:23:def exact_local_unfold_gaps(gaps: np.ndarray, window: int) -> np.ndarray: tools/exp_rp_exact_local_window_matrix.py:29: unfolded = np.empty_like(gaps) tools/exp_rp_exact_local_window_matrix.py:43: unfolded[idx] = gaps[idx] / local_mean tools/exp_rp_exact_local_window_matrix.py:44: return unfolded tools/exp_rp_exact_local_window_matrix.py:56: original_unfolder = base.local_unfold_gaps tools/exp_rp_exact_local_window_matrix.py:57: base.local_unfold_gaps = exact_local_unfold_gaps tools/exp_rp_exact_local_window_matrix.py:77: poisson_pole_max=args.poisson_pole_max, tools/exp_rp_exact_local_window_matrix.py:82: unfolding_modes="local_window", tools/exp_rp_exact_local_window_matrix.py:158: "unfolding_patch": "exact even windows preserved; no odd-width coercion", tools/exp_rp_exact_local_window_matrix.py:170: "operator": "exact-width local unfolding matrix with label-shuffle and position-shift nulls", tools/exp_rp_exact_local_window_matrix.py:171: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", tools/exp_rp_exact_local_window_matrix.py:174: "not_tested": "N to infinity, windows beyond 12, alternate unfolding kernels, experimental spectra, Anderson 3D", tools/exp_rp_exact_local_window_matrix.py:193: base.local_unfold_gaps = original_unfolder tools/exp_rp_exact_local_window_matrix.py:210: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_two_layer_universality.py:13: 1. Generate 7 gap/spacing sequences: primes, GUE, Poisson, AR(1), logistic, tools/exp_two_layer_universality.py:107:def next_nearest_sr(gaps): tools/exp_two_layer_universality.py:108: """SR2: spacing ratio of next-nearest-neighbor gaps (skip one).""" tools/exp_two_layer_universality.py:174: 'SR2': next_nearest_sr, tools/exp_two_layer_universality.py:194: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_two_layer_universality.py:204:def gen_poisson_spacings(N, rng=None): tools/exp_two_layer_universality.py:249: 'GUE': gen_gue_spacings, tools/exp_two_layer_universality.py:250: 'Poisson': gen_poisson_spacings, tools/exp_two_layer_universality.py:282: if seq_name in ('GUE', 'Poisson', 'AR1'): tools/exp_semireal_boundary_transfer_gate.py:196: help="Include GUE/Poisson source labels as audit metadata only. Default omits them from output.", tools/exp_two_channel_cross_domain.py:17: - GUE eigenvalues: random matrix, strong statistical correlation, no arithmetic tools/exp_two_channel_cross_domain.py:27:If GUE shows only decaying channels → C1 supported (algebraic invariance is prime-specific) tools/exp_two_channel_cross_domain.py:28:If GUE shows a scale-invariant channel → C1 needs refinement tools/exp_two_channel_cross_domain.py:63: """Generate unfolded spacings from GUE random matrices.""" tools/exp_two_channel_cross_domain.py:66: # GUE: H = (A + A^*) / (2 * sqrt(2N)), A is complex Gaussian tools/exp_two_channel_cross_domain.py:70: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_two_channel_cross_domain.py:75: # Local unfolding tools/exp_two_channel_cross_domain.py:295: # === GUE EIGENVALUES === tools/exp_two_channel_cross_domain.py:296: print(f"\n=== GUE EIGENVALUES ({gue_matrices} matrices of size {gue_size}) ===") tools/exp_two_channel_cross_domain.py:298: print(f"Got {len(gue_spacings)} GUE spacings, mean={np.mean(gue_spacings):.3f}") tools/exp_two_channel_cross_domain.py:412: print(" GUE/Cramer should show decaying or absent binary channel") tools/exp_vc_label_preserving_swap_gate.py:24:from scipy.linalg import eigvalsh_tridiagonal tools/exp_vc_label_preserving_swap_gate.py:42: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_spectral_rigidity.py:9: GUE: Sigma^2(L) ~ (2/pi^2) ln(L) + const [log-log slope ~ 0] tools/exp_spectral_rigidity.py:13: << 1 -> GUE (strong repulsion, rigid spectrum) tools/exp_spectral_rigidity.py:18:Domains: primes, GUE matrices, coupled_osc, string_vib, percolation, tools/exp_spectral_rigidity.py:42:def generate_poisson_gaps(n=10000): tools/exp_spectral_rigidity.py:47: """GUE Hermitian matrix eigenvalue spacings, bulk only.""" tools/exp_spectral_rigidity.py:50: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_rigidity.py:58: Sigma^2(L) from level positions (cumsum of unfolded gaps). tools/exp_spectral_rigidity.py:85: ('primes', {'gen': lambda: gaps_from_domain('numeri_primi'), 'type': 'dist-GUE'}), tools/exp_spectral_rigidity.py:86: ('gue_matrix', {'gen': lambda: generate_gue_gaps(600), 'type': 'dist-GUE'}), tools/exp_spectral_rigidity.py:87: ('coupled_osc', {'gen': lambda: gaps_from_domain('coupled_oscillators'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:88: ('string_vib', {'gen': lambda: gaps_from_domain('string_vibration'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:89: ('percolation', {'gen': lambda: gaps_from_domain('percolation'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:92: ('poisson', {'gen': lambda: generate_poisson_gaps(10000), 'type': 'Poisson'}), tools/exp_spectral_rigidity.py:115: unfolded = gaps / np.mean(gaps) tools/exp_spectral_rigidity.py:116: levels = np.concatenate([[0], np.cumsum(unfolded)]) tools/exp_spectral_rigidity.py:123: g_s = unfolded.copy() tools/exp_spectral_rigidity.py:156: print(f" Slope (log-log): real={slope:.3f}, shuf={slope_s:.3f} [GUE~0, Poisson=1]") tools/exp_spectral_rigidity.py:188: if ty in ('dist-GUE', 'ord-GUE'): tools/exp_two_channel_boundary.py:25: GUE-like and Poisson-like regimes is channel-specific. tools/build_agent_field.py:309: "Obblighi pratici: se il dominio e' GUE/Poisson, aggiungi una sezione " tools/build_agent_field.py:1722: "Un pattern in distribuzioni classiche (primi, GUE, random walk, Markov chain, " tools/build_agent_field.py:1726: "per GUE level statistics, Erdos-Kac per distribuzioni aritmetiche). Se non lo " tools/exp_quasiperiodic_gap_ratio_denominator.py:3:Stratified denominator audit for the quasiperiodic gap_ratio claim. tools/exp_quasiperiodic_gap_ratio_denominator.py:20:from numpy.linalg import eigvalsh tools/exp_quasiperiodic_gap_ratio_denominator.py:45: eigs = np.sort(eigvalsh(hamiltonian(seq))) tools/exp_quasiperiodic_gap_ratio_denominator.py:143: "experiment": "quasiperiodic_gap_ratio_denominator", tools/exp_quasiperiodic_gap_ratio_denominator.py:165: parser.add_argument("--out", default="tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json") tools/exp_vc_null_regression_gate.py:20:from scipy.linalg import eigvalsh_tridiagonal tools/exp_vc_null_regression_gate.py:38: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/dnd_M_operator.py:174: elif any(w in claim for w in ('spacing', '', 'gue', 'poisson')): tools/build_lab_graph.py:236: 'text': f"Metrica primi g=(p/2)², curvatura GUE r={mp.get('risultato_r',{}).get('curvatura_r','?')}, z={mp.get('test_null',{}).get('z_score','?')}", tools/build_lab_graph.py:869: if 'prime' in full or 'primi' in full or 'brody' in full: teorie_report.add('Q') tools/build_lab_graph.py:870: if 'gue' in full or 'poisson' in full: teorie_report.update(['Q', 'G']) tools/dnd_arxiv.py:45: 'quasicrystal spectral statistics GUE', tools/dnd_banchi_tm1.py:33:from numpy.linalg import eigvalsh tools/dnd_banchi_tm1.py:81: rs.append(round(r_statistic(eigvalsh(H)), 4)) tools/dnd_banchi_tm1.py:88: "references": {{"GUE": 0.599, "GOE": 0.530, "Poisson": 0.386, "critical": 0.5}}, tools/dnd_banchi_tm1.py:113: r1 = r_statistic(eigvalsh(H1)) tools/dnd_banchi_tm1.py:114: r2 = r_statistic(eigvalsh(H2)) tools/dnd_banchi_tm1.py:155: r = r_statistic(eigvalsh(H)) tools/dnd_banchi_tm1.py:201: r = r_statistic(eigvalsh(H)) tools/confine_spessore.py:10:Blocco 3: Dove il residuo 1/phi^(2n) incontra il confine GUE/Poisson nei domini tools/confine_spessore.py:188:# BLOCCO 3: Residuo vs confine GUE/Poisson tools/confine_spessore.py:193: print("BLOCCO 3: Residuo 1/phi^(2n) vs confine GUE/Poisson") tools/confine_spessore.py:197: # Il confine GUE/Poisson emerge nella distribuzione NNS (nearest neighbor spacing) tools/confine_spessore.py:199: # GUE: repulsione (P(s) ~ s^2 * exp(-4s^2/pi)) tools/confine_spessore.py:216: print("\n--- NNS ratio (indicatore GUE vs Poisson) a diverse scale ---") tools/confine_spessore.py:218: print(" GUE: r ~ 0.5307") tools/confine_spessore.py:248: regime = "GUE" if dist_gue < dist_poi else "Poisson" tools/confine_spessore.py:251: f"dist_GUE={dist_gue:.4f} dist_Poi={dist_poi:.4f} " tools/confine_spessore.py:278: print(f" Punto medio GUE/Poisson: {midpoint:.4f}") tools/confine_spessore.py:294: regime = "GUE" if abs(r - 0.5307) < abs(r - 0.3863) else "Poi" tools/confine_spessore.py:319: print("GUE (repulsione)") tools/confine_spessore.py:354:3. Il residuo incontra il confine GUE/Poisson? tools/dnd_controprove.py:77: gap_ratios = [] tools/dnd_controprove.py:80: gap_ratios.append(abs(gaps[i-1]) / abs(gaps[i])) tools/dnd_controprove.py:84: if gap_ratios: tools/dnd_controprove.py:85: print(f" Rapporto gap ultimi 3: {', '.join(f'{r:.4f}' for r in gap_ratios[-3:])}") tools/dnd_controprove.py:90: f"{abs(gap_ratios[-1] - target_sq):.6f} " tools/dnd_controprove.py:91: f"({'SÌ' if abs(gap_ratios[-1] - target_sq) < 0.1 else 'NO'})") tools/dnd_controprove.py:255: gap_ratios = [] tools/dnd_controprove.py:258: gap_ratios.append(gaps[i-1] / gaps[i]) # con segno tools/dnd_controprove.py:262: if gap_ratios: tools/dnd_controprove.py:263: print(f" Rapporto gap (ultimi): {gap_ratios[-1]:.6f}") tools/dnd_controprove.py:316: gap_ratios = [] tools/dnd_controprove.py:319: gap_ratios.append(gaps[i-1] / gaps[i]) tools/dnd_controprove.py:322: rg_str = f"{gap_ratios[-1]:.6f}" if gap_ratios else "N/A" tools/dnd_curva.py:17: 4. Osservare: la spirale converge a φ? Il gap_ratio è φ²? tools/dnd_curva.py:74: entry["gap_ratio"] = float(diario[-1]["gap_abs"] / abs(gap)) if abs(gap) > 1e-15 else float("inf") tools/dnd_curva.py:124: print(f" k | tr | det | gap_ratio | →φ? | gap_finale | convergenza") tools/dnd_curva.py:139: gr = obs.get('gap_ratio_medio', None) tools/dnd_curva.py:158: 'gap_ratio': float(gr) if gr else None, tools/dnd_curva.py:174: # Parte B: Verificare che il gap_ratio è SEMPRE φ² sulla curva tools/dnd_curva.py:175: print(f"\n Verifica: gap_ratio = φ² per tutti i k?") tools/dnd_curva.py:176: grs = [e['gap_ratio'] for e in famiglia_a if e['gap_ratio'] and np.isfinite(e['gap_ratio'])] tools/dnd_curva.py:180: print(f" Media gap_ratio = {media:.6f} (φ² = {PHI2:.6f})") tools/dnd_curva.py:183: results['cv_gap_ratio_curva'] = float(cv) tools/dnd_curva.py:203: Il gap_ratio è φ² per TUTTA la curva. Il punto fisso scala come kφ. tools/dnd_curva.py:210: print("PARTE 2: COSA DISTINGUE k=1? — LE TRE MISURE") tools/dnd_curva.py:487: │ Il gap_ratio è φ² su TUTTA la curva (verificato). │ tools/exp_rp_boundary_size_stability_audit.py:49: poisson_pole_max=args.poisson_pole_max, tools/exp_rp_boundary_size_stability_audit.py:68: "brody_q": [], tools/exp_rp_boundary_size_stability_audit.py:78: row_hits[name]["brody_q"].append(float(row["brody_q"])) tools/exp_rp_boundary_size_stability_audit.py:89: "brody_q": median(item["brody_q"]), tools/exp_rp_boundary_size_stability_audit.py:105: "median_brody_q": round(median(item["brody_q"]), 6), tools/exp_rp_boundary_size_stability_audit.py:210: "poisson_pole_max": args.poisson_pole_max, tools/exp_rp_boundary_size_stability_audit.py:216: "operator": "repeat the RP diagonal-plus-GUE Hamiltonian flow over sizes, seeds and kNN graph perturbations", tools/exp_rp_boundary_size_stability_audit.py:217: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N size sweep", tools/exp_rp_boundary_size_stability_audit.py:220: "not_tested": "N to infinity, unfolding variants, Anderson mobility edge, many-body RP variants", tools/exp_rp_boundary_size_stability_audit.py:251: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/dnd_banchi.py:31:from numpy.linalg import eigvalsh, eigvals tools/dnd_banchi.py:74: r1 = r_statistic(eigvalsh(H1)) tools/dnd_banchi.py:75: r2 = r_statistic(eigvalsh(H2)) tools/dnd_banchi.py:102: eigs = eigvalsh(H) tools/dnd_banchi.py:232: r = r_statistic(eigvalsh(H)) tools/dnd_banchi.py:300: r = r_statistic(eigvalsh(H)) tools/dnd_banchi.py:371: 'desc': ' ratio statistic (0.386=Poisson, 0.530=GOE, 0.599=GUE)', tools/dnd_banchi.py:372: 'codice': 'r = r_statistic(eigvalsh(H))', tools/dnd_banchi.py:457: 'r_statistic': ('H = hamiltonian(seq, 1.0)\nvalue = r_statistic(eigvalsh(H))', '0.5'), tools/dnd_banchi.py:462: ' r = r_statistic(eigvalsh(H))\n' tools/dnd_banchi.py:480: 'eigs = np.sort(eigvalsh(H))\n' tools/dnd_banchi.py:492: ' r1 = r_statistic(eigvalsh(H1))\n' tools/dnd_banchi.py:493: ' r2 = r_statistic(eigvalsh(H2))\n' tools/dnd_banchi.py:516: 'eigs = np.sort(eigvalsh(H))\n' tools/dnd_domandatore.py:194: r = r_statistic(eigvalsh(H_mixed)) tools/dnd_domandatore.py:299: r = r_statistic(eigvalsh(H)) tools/dnd_domandatore.py:803: f'eigs = np.sort(eigvalsh(H))\n' tools/dnd_condizioni.py:83: entry["gap_ratio"] = float(diario[-1]["gap_abs"] / abs(gap)) if abs(gap) > 1e-15 else float("inf") tools/dnd_condizioni.py:113: gap_ratios = [d["gap_ratio"] for d in diario if "gap_ratio" in d and d["gap_ratio"] < 1e10] tools/dnd_condizioni.py:127: "gap_ratio_medio": float(np.mean(gap_ratios[-5:])) if gap_ratios else None, tools/dnd_condizioni.py:128: "gap_ratio_ultimo": float(gap_ratios[-1]) if gap_ratios else None, tools/dnd_condizioni.py:159: if gap_ratios: tools/dnd_condizioni.py:160: osservazione["prossimità_gap_ratio"] = {} tools/dnd_condizioni.py:161: gr = np.mean(gap_ratios[-5:]) tools/dnd_condizioni.py:163: osservazione["prossimità_gap_ratio"][nome] = float(abs(gr - val)) tools/dnd_condizioni.py:322: gr = obs.get("gap_ratio_medio") tools/dnd_condizioni.py:325: prox = obs.get("prossimità_gap_ratio", {}) tools/dnd_condizioni.py:338: "gap_ratio": gr, tools/dnd_condizioni.py:350: gap_ratios = [v["gap_ratio"] for v in vault if v["gap_ratio"] is not None] tools/dnd_condizioni.py:364: if gap_ratios: tools/dnd_condizioni.py:367: if v["gap_ratio"] is not None: tools/dnd_condizioni.py:368: print(f" {v['segnale']:>12s}: {v['gap_ratio']:.6f}") tools/exp_selective_layer_decoupling.py:59: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_selective_layer_decoupling.py:66:def gen_poisson_spacings(N, rng): tools/exp_selective_layer_decoupling.py:145:def next_nearest_sr(gaps): tools/exp_selective_layer_decoupling.py:187: 'SR2': next_nearest_sr, tools/exp_selective_layer_decoupling.py:265: 'GUE': gen_gue_spacings(args.N, rng), tools/exp_selective_layer_decoupling.py:266: 'Poisson': gen_poisson_spacings(args.N, rng), tools/exp_spectral_landscape.py:6:Identifies: GUE domains, Poisson domains, and BOUNDARY domains. tools/exp_spectral_landscape.py:8:The question: Is the boundary between GUE and Poisson populated by multiple tools/exp_spectral_landscape.py:29: """Prime gaps (unfolded via local density).""" tools/exp_spectral_landscape.py:52: """GUE: eigenvalue spacings of complex Hermitian random matrix.""" tools/exp_spectral_landscape.py:56: eigs = np.sort(np.linalg.eigvalsh(H).real) tools/exp_spectral_landscape.py:70: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_landscape.py:89: eigs = np.sort(np.linalg.eigvalsh(H).real) tools/exp_spectral_landscape.py:99:def gen_poisson(n_spacings): tools/exp_spectral_landscape.py:110:def gen_semi_poisson(n_spacings): tools/exp_spectral_landscape.py:123: """Berry-Robnik: mixed system, fraction rho chaotic (GUE-like), 1-rho regular (Poisson). tools/exp_spectral_landscape.py:128: s_regular = gen_poisson(n_regular) if n_regular > 100 else np.array([]) tools/exp_spectral_landscape.py:135: """Gaps between Fibonacci numbers (unfolded).""" tools/exp_spectral_landscape.py:149: """Gaps between quadratic residues mod large prime (unfolded).""" tools/exp_spectral_landscape.py:162: """Model for Riemann zeta zeros — Montgomery pair correlation (GUE). tools/exp_spectral_landscape.py:163: We use GUE directly since computing actual zeros is expensive.""" tools/exp_spectral_landscape.py:169: Simplified: diagonal disorder + nearest-neighbor hopping on 1D chain.""" tools/exp_spectral_landscape.py:174: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_landscape.py:192: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_landscape.py:224:def brody_beta(spacings): tools/exp_spectral_landscape.py:265: beta = brody_beta(spacings) tools/exp_spectral_landscape.py:274: beta_shuf.append(brody_beta(s_shuf)) tools/exp_spectral_landscape.py:303: GUE = 0.603 tools/exp_spectral_landscape.py:308: elif r_mean < (GOE + GUE) / 2: tools/exp_spectral_landscape.py:310: elif r_mean < GUE + 0.03: tools/exp_spectral_landscape.py:311: return "GUE-like" tools/exp_spectral_landscape.py:327: # Expected GUE tools/exp_spectral_landscape.py:328: ("GUE_matrix", gen_gue, {}), tools/exp_spectral_landscape.py:335: ("Poisson", gen_poisson, {}), tools/exp_spectral_landscape.py:342: ("semi_Poisson", gen_semi_poisson, {}), tools/exp_spectral_landscape.py:369: print("\nReference: Poisson =0.386, GOE =0.536, GUE =0.603") tools/exp_spectral_landscape.py:380: for cls in ["POISSON", "BOUNDARY_low", "GOE-like", "GUE-like", "RIGID"]: tools/dnd_next.py:46: 'C': {'title': 'Information Geometry + ζ', 'target': 'JMP', 'core_claim': 'Berry-Keating potential, GUE connection'}, tools/dnd_experiments.py:159: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_experiments.py:225:# ESPERIMENTO 2: Boundary GUE/Poisson — Il terzo incluso tools/dnd_experiments.py:230: Testa H_boundary: esiste una transizione GUE→Poisson tools/dnd_experiments.py:240: print("ESPERIMENTO: Boundary GUE/Poisson (Ising T-sweep)") tools/dnd_experiments.py:302: gap_ratio = None tools/dnd_experiments.py:307: gap_ratio = obs.get('gap_ratio_medio') tools/dnd_experiments.py:316: 'gap_ratio': gap_ratio, tools/dnd_experiments.py:372: print("ESPERIMENTO: Boundary GUE/Poisson (Logistica r-sweep)") tools/dnd_experiments.py:414: gap_ratio = None tools/dnd_experiments.py:419: gap_ratio = obs.get('gap_ratio_medio') tools/dnd_experiments.py:439: 'gap_ratio': gap_ratio, tools/dnd_experiments.py:481:# ESPERIMENTO 3: Order parameter — r_diretto separa GUE/Poisson? tools/dnd_experiments.py:487: che separa GUE da Poisson? tools/dnd_experiments.py:490: - Domini GUE hanno r_diretto in un range tools/dnd_experiments.py:513: # Classifica GUE vs Poisson (soglia standard: 0.53) tools/dnd_experiments.py:514: GUE_THRESHOLD = 0.53 tools/dnd_experiments.py:515: gue = [(d, sp, r) for d, sp, r, _ in domains if sp > GUE_THRESHOLD] tools/dnd_experiments.py:516: poisson = [(d, sp, r) for d, sp, r, _ in domains if sp <= GUE_THRESHOLD] tools/dnd_experiments.py:518: print(f"\n GUE ({len(gue)} domini):") tools/dnd_experiments.py:522: print(f"\n Poisson ({len(poisson)} domini):") tools/dnd_experiments.py:523: for d, sp, r in poisson: tools/dnd_experiments.py:528: r_poisson = [r for _, _, r in poisson] tools/dnd_experiments.py:530: if r_gue and r_poisson: tools/dnd_experiments.py:532: mean_poisson = np.mean(r_poisson) tools/dnd_experiments.py:534: std_poisson = np.std(r_poisson) if len(r_poisson) > 1 else 0 tools/dnd_experiments.py:538: poisson_range = (min(r_poisson), max(r_poisson)) tools/dnd_experiments.py:539: overlap = max(0, min(gue_range[1], poisson_range[1]) - max(gue_range[0], poisson_range[0])) tools/dnd_experiments.py:540: total_range = max(gue_range[1], poisson_range[1]) - min(gue_range[0], poisson_range[0]) tools/dnd_experiments.py:546: print(f" r_diretto GUE: {mean_gue:.4f} ± {std_gue:.4f} range=[{gue_range[0]:.4f}, {gue_range[1]:.4f}]") tools/dnd_experiments.py:547: print(f" r_diretto Poisson: {mean_poisson:.4f} ± {std_poisson:.4f} range=[{poisson_range[0]:.4f}, {poisson_range[1]:.4f}]") tools/dnd_experiments.py:549: print(f" r_diretto SEPARA GUE/Poisson: {'SÌ' if separates else 'NO'}") tools/dnd_experiments.py:559: 'poisson': {'n': len(poisson), 'r_mean': mean_poisson, 'r_std': std_poisson, 'range': poisson_range}, tools/dnd_experiments.py:683: entry['gap_ratio'] = obs.get('gap_ratio_medio') tools/dnd_experiments.py:684: entry['gap_ratio_ultimo'] = obs.get('gap_ratio_ultimo') tools/dnd_experiments.py:699: gr = entry.get('gap_ratio', 0) tools/dnd_experiments.py:714: gr_data = [(v['T_ratio'], v['gap_ratio']) for v in results.values() tools/dnd_experiments.py:715: if v.get('gap_ratio') and v['gap_ratio'] < 100] tools/dnd_experiments.py:1031: # Per spacings (zeta, GUE): il segnale È gli intervalli tools/dnd_paper_audit.py:722: "GUE": "det=-1 → always hyperbolic → GUE statistics", tools/dnd_piano11.py:3:Piano 11 — det=-1, Q(√5), and the GUE Bridge tools/dnd_piano11.py:5:Core thesis (revised): The 1D potential V(r) gives Berry-Keating form but NOT GUE. tools/dnd_piano11.py:12:Chain: M → Q(√5) → disc=5 → χ₅ → L(s,χ₅) → zeros on Re(s)=1/2 → GUE tools/dnd_piano11.py:16:P2: Verify GUE spacing statistics of L(s, χ₅) zeros tools/dnd_piano11.py:18:P4: The algebraic chain: det=-1 → disc=5 → Q(√5) → GUE (proven) tools/dnd_piano11.py:22:- Montgomery (1973): pair correlation of ζ zeros ~ GUE tools/dnd_piano11.py:24:- Rubinstein (2001): GUE for families of L-functions tools/dnd_piano11.py:150: """GUE (β=2) Wigner surmise CDF.""" tools/dnd_piano11.py:157:def poisson_cdf(s): tools/dnd_piano11.py:162: """GUE nearest-neighbor PDF.""" tools/dnd_piano11.py:192:# ─── P2: GUE statistics of L(s, χ₅) zeros ────────────────────────────── tools/dnd_piano11.py:196: Compute normalized nearest-neighbor spacings and test against GUE/GOE/Poisson. tools/dnd_piano11.py:205: ks_gue = ks_test_against(norm_spacings, gue_cdf, "GUE") tools/dnd_piano11.py:207: ks_poi = ks_test_against(norm_spacings, poisson_cdf, "Poisson") tools/dnd_piano11.py:217: "ks_poisson": ks_poi, tools/dnd_piano11.py:219: "GUE_variance_ref": 0.178, tools/dnd_piano11.py:248: # Both vs GUE tools/dnd_piano11.py:249: ks_zeta_gue = ks_test_against(zeta_norm, gue_cdf, "GUE") tools/dnd_piano11.py:250: ks_L_gue = ks_test_against(L_norm, gue_cdf, "GUE") tools/dnd_piano11.py:277: "variance_GUE_ref": 0.178, tools/dnd_piano11.py:293:# ─── P4: Algebraic chain M → Q(√5) → L(s,χ₅) → GUE ──────────────────── tools/dnd_piano11.py:297: Prove the algebraic chain connecting D-ND matrix to GUE. tools/dnd_piano11.py:330: # This is the SAME type of symmetry as ζ → same universality class (GUE) tools/dnd_piano11.py:341: # → L(s, χ_{-3}), which ALSO has GUE zeros but different arithmetic. tools/dnd_piano11.py:409: "to": "Zeros follow GUE statistics", tools/dnd_piano11.py:412: "theorem": "All primitive L-functions have GUE zero statistics (conjectured, overwhelming numerical evidence)", tools/dnd_piano11.py:428: "ζ_{Q(√5)} = ζ·L(s,χ₅). By the Katz-Sarnak philosophy, L(s,χ₅) has GUE zero statistics. " tools/dnd_piano11.py:429: "Therefore: D-ND matrix → Q(√5) → L(s,χ₅) → GUE. " tools/dnd_piano11.py:477: "produces": "GUE spacing statistics", tools/dnd_piano11.py:486: "N_fluct comes from det(M)=-1 (the 2D anti-symplectic structure, giving GUE). " tools/dnd_piano11.py:494: "spectral": "GUE universality class (from L(s,χ₅))" tools/dnd_piano11.py:497: "gap_ratio": float(phi**2), tools/dnd_piano11.py:526: print("Piano 11 — det=-1, Q(√5), and the GUE Bridge\n") tools/dnd_piano11.py:540: print(f" Variance: {p2['variance']:.4f} (GUE ref: 0.178, GOE ref: 0.286)") tools/dnd_piano11.py:541: print(f" KS vs GUE: {p2['ks_gue']['KS']:.4f} (p={p2['ks_gue']['p']:.4f})") tools/dnd_piano11.py:554: print(f" L(s,χ₅) vs GUE: KS={p3['ks_L_vs_gue']['KS']:.4f}") tools/dnd_piano11.py:555: print(f" ζ vs GUE: KS={p3['ks_zeta_vs_gue']['KS']:.4f}") tools/dnd_piano11.py:562: print("P4: Algebraic chain M → Q(√5) → L(s,χ₅) → GUE...") tools/dnd_piano11.py:581: if has_numerical and p2_data["best_fit"] == "GUE": tools/dnd_piano11.py:582: verdict = "CONFERMATO: L(s,χ₅) → GUE. La catena D-ND → Q(√5) → GUE è completa." tools/dnd_piano11.py:585: verdict = "PARZIALE: L(s,χ₅) → GOE. Convergenza a GUE attesa per più zeri." tools/dnd_piano11.py:590: "STRUTTURALE: La catena algebrica M → Q(√5) → L(s,χ₅) → GUE è dimostrata. " tools/dnd_piano11.py:598: results["piano"] = "piano11-Q-sqrt5-GUE" tools/dnd_piano11.py:603: "thesis": "det(M)=-1 connects D-ND to GUE through the number field Q(√5)", tools/dnd_piano11.py:604: "chain": "M → char poly λ²-λ-1 → disc=5 → Q(√5) → ζ_{Q(√5)} = ζ·L(s,χ₅) → GUE", tools/dnd_piano11.py:612: "Piano 11 shows det=-1 gives the FLUCTUATIONS (GUE via Q(√5)). " tools/dnd_projective_quantization.py:209: # con statistiche CRITICHE (né Poisson né GUE — esattamente al confine) tools/dnd_projective_quantization.py:226: eigenvalues = np.sort(np.real(linalg.eigvalsh(H))) tools/dnd_projective_quantization.py:257: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_projective_quantization.py:259: # Critical statistics (Fibonacci): ≈ 0.50 (tra GUE e Poisson) tools/dnd_projective_quantization.py:309: # D. Confronto: Fibonacci vs GUE vs Poisson tools/dnd_projective_quantization.py:314: # D1. Random matrix GUE tools/dnd_projective_quantization.py:315: print("\n--- GUE reference ---") tools/dnd_projective_quantization.py:320: eigs_gue = np.sort(np.real(linalg.eigvalsh(H_gue))) tools/dnd_projective_quantization.py:321: spacing_stats(eigs_gue, f"GUE N={N}") tools/dnd_projective_quantization.py:333: eigs_and = np.sort(np.real(linalg.eigvalsh(H_and))) tools/dnd_projective_quantization.py:338: print("V=0 → metallico (GUE), V→∞ → localizzato (Poisson)") tools/dnd_projective_quantization.py:350: eigs = np.sort(np.real(linalg.eigvalsh(H))) tools/dnd_projective_quantization.py:366:dato da φ) produce uno spettro CRITICO — al confine esatto tra GUE e tools/dnd_projective_quantization.py:378:disordine, tra GUE e Poisson. tools/dnd_research_engine.py:144: 'content': 'D-ND dynamics show GUE-like level repulsion in zero spacing (⟨r⟩≈0.84)', tools/dnd_research_engine.py:158: {'id': 'O2', 'content': 'GUE-like spacing in D-ND zeros', 'source': 'dnd_zeros_vs_zeta.py'}, tools/dnd_research_engine.py:277: f'show GUE-like level repulsion (⟨r⟩ ≈ 0.60)', tools/dnd_research_engine.py:438: """Test spacing statistics (GUE vs Poisson).""" tools/dnd_research_engine.py:491: 'poisson_dist': abs(mean_r - 0.3863), tools/dnd_research_engine.py:549: 'poisson_dist': result['poisson_dist'], tools/dnd_research_engine.py:675: 'prediction': 'Spacing statistics at D↔ND transition show GUE-like repulsion', tools/dnd_lab.py:10: 1. Wigner surmise (GOE/GUE) — distribuzione esatta level spacing tools/dnd_lab.py:14: 5. Berry-Tabor — integrabile→Poisson vs caotico→GUE tools/dnd_lab.py:49: DIMOSTRATO: Level spacing distribution per GOE e GUE. tools/dnd_lab.py:51: GUE: P(s) = (32/π²) s² exp(-4s²/π) tools/dnd_lab.py:58: print("BANCO 1: Wigner Surmise (GOE e GUE esatti)") tools/dnd_lab.py:63: for ensemble, name in [('GOE', 'GOE'), ('GUE', 'GUE')]: tools/dnd_lab.py:78: # GUE: P(s) = (32/π²) s² exp(-4s²/π) tools/dnd_lab.py:105: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:111: gr = obs.get('gap_ratio_medio', 0) tools/dnd_lab.py:172: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:178: gr = obs.get('gap_ratio_medio', 0) tools/dnd_lab.py:287: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:363: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:368: gr = obs.get('gap_ratio_medio', 0) tools/dnd_lab.py:431: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:500: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_lab.py:661: spettrale. Il punto critico e' dove ≈ 0.50 (ne' GUE ne' Poisson). tools/dnd_lab.py:690: print(f" Transizione: V<{critical_V} → GUE/metallico, V>{critical_V} → Poisson/localizzato") tools/dnd_lab.py:1076: return np.linalg.eigvalsh(H) tools/dnd_lab.py:1190: eigs = np.linalg.eigvalsh(H) tools/dnd_lab.py:1426: 'GUE': 'random_matrix_gue', tools/dnd_lab.py:1476: elif 'WIGNER' in tid or 'GUE' in tid: tools/dnd_lab.py:1629: return np.sort(np.linalg.eigvalsh(H)) tools/dnd_lab.py:1652: # Trova dove attraversa r_target (dall'alto: GUE→critico→Poisson) tools/dnd_lab.py:1716: 2. Trova V_c dove = 0.50 (transizione GUE ↔ Poisson) tools/dnd_riformulazioni.py:137: 'gap_ratio': obs.get('gap_ratio_medio', 0), tools/dnd_risultante.py:33: The algebra det=-1 → disc=5 → Q(√5) → L(s,χ₅) → GUE statistics tools/dnd_risultante.py:35: the deviation from pure GUE. The risultante dynamics produce the same tools/dnd_risultante.py:548: Piano 11: det=-1 → disc=5 → Q(√5) → L(s,χ₅) → GUE tools/dnd_risultante.py:573: "statistics": "GUE (Katz-Sarnak)" tools/dnd_paper_refactor.py:387: "chain": "|NT⟩ → closure → det=-1 → φ → GUE → differentiated reality", tools/dnd_spectral_probe.py:57:def poisson_cdf(s): tools/dnd_spectral_probe.py:72: 3. KS test against GUE/GOE/Poisson tools/dnd_spectral_probe.py:119: def unfold(self, method='exact'): tools/dnd_spectral_probe.py:131: unfolded = np.array([self._smooth_N(t) for t in self.zeros]) tools/dnd_spectral_probe.py:136: unfolded = np.polyval(coeffs, self.zeros) tools/dnd_spectral_probe.py:138: raise ValueError(f"Unknown unfolding method: {method}") tools/dnd_spectral_probe.py:140: spacings = np.diff(unfolded) tools/dnd_spectral_probe.py:155: β=0: Poisson, β=1: GOE, β=2: GUE. tools/dnd_spectral_probe.py:158: If β>2 at all scales → GUE (even when KS says GOE). tools/dnd_spectral_probe.py:164: self.unfold() tools/dnd_spectral_probe.py:192: cls = "GUE" if beta > 1.5 else ("GOE" if beta > 0.5 else "Poisson") tools/dnd_spectral_probe.py:203: # ─── Spacing Ratio (unfolding-free) ───────────────────────────── tools/dnd_spectral_probe.py:215: GUE: ≈ 0.6027 tools/dnd_spectral_probe.py:230: refs = {"Poisson": 0.38629, "GOE": 0.53590, "GUE": 0.60266} tools/dnd_spectral_probe.py:240: "note": "No unfolding required" tools/dnd_spectral_probe.py:248: """KS test against GUE, GOE, Poisson.""" tools/dnd_spectral_probe.py:250: self.unfold() tools/dnd_spectral_probe.py:254: ks_poi, p_poi = kstest(self.spacings, poisson_cdf) tools/dnd_spectral_probe.py:256: fits = [("GUE", ks_gue, p_gue), ("GOE", ks_goe, p_goe), ("Poisson", ks_poi, p_poi)] tools/dnd_spectral_probe.py:265: "ks_poisson": {"KS": round(float(ks_poi), 4), "p": round(float(p_poi), 4)}, tools/dnd_spectral_probe.py:309: if majority[0] == "GUE" and "GOE" in dissenters: tools/dnd_spectral_probe.py:310: verdict = (f"LIKELY GUE: {majority[0]} (2/3 — KS blind to GUE, " tools/dnd_spectral_probe.py:354: sub.unfold() tools/dnd_spectral_probe.py:411: self.unfold() tools/dnd_spectral_probe.py:457: print(f" Spacing Ratio (no unfolding):") tools/dnd_spectral_probe.py:464: f"|Δ GUE|={dists.get('GUE', 0):.5f}") tools/dnd_spectral_probe.py:522: → GUE statistics (Katz-Sarnak) tools/dnd_spectral_probe.py:633: reference_beta: theoretical β to converge to (2.0 for GUE) tools/dnd_spectral_probe.py:655: probe.unfold() tools/dnd_spectral_probe.py:1058: print(f" All GUE: {all_gue} ({sum(1 for b in valid if b > 1.5)}/{len(valid)})") tools/dnd_spectral_probe.py:1096: help="Conductor for unfolding") tools/dnd_quantization.py:13:Domanda: gli spacing dei livelli E_n seguono GUE? tools/dnd_quantization.py:317: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_quantization.py:318: print(f" = {stats['mean_r']:.4f} (GUE: 0.5996, Poisson: 0.3863) → {closer}") tools/dnd_quantization.py:319: print(f" Var(s) = {stats['var_s']:.4f} (GUE: 0.178, Poisson: 1.0)") tools/dnd_quantization.py:333: f"→ {'GUE' if d_gue < d_poi else 'Poi'}") tools/dnd_trasmutazione.py:565: # della spirale (gap_ratio → φ²) è universale. tools/dnd_trasmutazione.py:567: print(f" Segnale | gap_ratio→ | gap_finale | n_step | Tasso conv.") tools/dnd_trasmutazione.py:582: gap_ratio = obs.get('gap_ratio_medio', None) tools/dnd_trasmutazione.py:595: 'gap_ratio': float(gap_ratio) if gap_ratio else None, tools/dnd_trasmutazione.py:603: gr_str = f"{gap_ratio:.4f}" if gap_ratio else "N/A" tools/dnd_trasmutazione.py:606: f"{'φ²={:.4f}'.format(PHI**2) if gap_ratio and abs(gap_ratio - PHI**2) < 0.5 else ''}") tools/dnd_trasmutazione.py:608: # Il gap_ratio dovrebbe convergere a φ² ≈ 2.618 per tutti tools/dnd_trasmutazione.py:609: gap_ratios_valid = [s['gap_ratio'] for s in spirali tools/dnd_trasmutazione.py:610: if s['gap_ratio'] and np.isfinite(s['gap_ratio'])] tools/dnd_trasmutazione.py:611: if gap_ratios_valid: tools/dnd_trasmutazione.py:612: media_gr = np.mean(gap_ratios_valid) tools/dnd_trasmutazione.py:613: cv_gr = np.std(gap_ratios_valid) / media_gr if media_gr > 0 else float('inf') tools/dnd_trasmutazione.py:617: results['spirale_gap_ratio_medio'] = float(media_gr) tools/dnd_trasmutazione.py:618: results['spirale_gap_ratio_cv'] = float(cv_gr) tools/dnd_trasmutazione.py:691: if 'spirale_gap_ratio_cv' in t4: tools/dnd_trasmutazione.py:692: chiavi['T4_gap_ratio_cv'] = t4['spirale_gap_ratio_cv'] tools/dnd_trasmutazione.py:693: chiavi['T4_gap_ratio_medio'] = t4.get('spirale_gap_ratio_medio', 0) tools/dnd_trasmutazione.py:694: print(f" T4 (Indeterminazione): gap_ratio CV = {t4['spirale_gap_ratio_cv']:.4f}, " tools/dnd_trasmutazione.py:695: f"media = {t4.get('spirale_gap_ratio_medio', 0):.4f} (φ²={PHI**2:.4f})") tools/dnd_stats.py:10: classify(mean_r) → "GUE" | "Poisson" | "CRITICAL" | "harmonic" tools/dnd_stats.py:25: "GUE": {"mean_r": 0.5996, "var_s": 0.178}, tools/dnd_stats.py:38: mean_r — ratio statistic (invariante per unfolding) tools/dnd_stats.py:164: Transizione: V<1 → GUE, V≈1 → critico, V>1.5 → Poisson tools/dnd_stats.py:181: return np.sort(np.real(linalg.eigvalsh(H))) tools/dnd_trace_bridge_v3.py:181: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_trace_bridge_v3.py:205: closer = "GUE" if d_gue < d_poi else "Poi" tools/dnd_trace_bridge_v3.py:335: print(f" Spacing dei ratio (zeta): ={stats_rz['mean_r']:.4f} → {'GUE' if d_gue < d_poi else 'Poi'}") tools/dnd_trace_bridge_v3.py:339: print(f" Spacing dei ratio (random): ={stats_rr['mean_r']:.4f} → {'GUE' if d_gue < d_poi else 'Poi'}") tools/dnd_trace_bridge_v3.py:348:v2 mostrava GUE ovunque per zeta — ma era tautologico ( invariante monotono). tools/dnd_scenario.py:766: (r'\bGUE\b', 'Poisson — nessuna repulsione, statistiche indipendenti'), tools/dnd_scenario.py:767: (r'\bPoisson\b', 'GUE — repulsione universale, non transizione'), tools/dipartimento.py:91: 'test': 'gap_ratio_phi2', tools/dipartimento.py:117: 'claim': 'Zeri zeta hanno spacing GUE (non Poisson)', tools/dipartimento.py:153: 'claim': 'Rule 30/110 hanno spacing >> GUE standard', tools/dipartimento.py:296: elif test_name == 'gap_ratio_phi2': tools/dipartimento.py:356: passed = spacing == 'GUE-like' and abs(spacing_r - 0.6) < 0.1 tools/dipartimento.py:434: passed = spacing_r > 0.7 # significativamente sopra GUE=0.60 tools/dipartimento.py:435: return passed, f"⟨r⟩={spacing_r:.4f} (GUE standard=0.60)" tools/dipartimento.py:1073: # Il confine GUE/Poisson non è ancora esplorato tools/dipartimento.py:1078: gue = [e for e in journal if e.get('spacing') == 'GUE-like' and isinstance(e.get('ciclo'), int)] tools/dipartimento.py:1079: poisson = [e for e in journal if e.get('spacing') == 'Poisson-like' and isinstance(e.get('ciclo'), int)] tools/dipartimento.py:1080: if gue and poisson: tools/dipartimento.py:1084: 'claim': f'{len(gue)} domini GUE, {len(poisson)} Poisson — il confine è il terzo incluso operativo', tools/dipartimento.py:1907: for kw in ('primi', 'prime', 'gap', 'acf', 'brody', 'poisson', 'gue', 'spacing')) tools/dnd_spirale.py:87: f"{'potenziale':>10s} {'gap_ratio':>10s}") tools/exp_aubry_cosine_boundary_counter_gate.py:232: nearest_control_r = min(abs(needed["phi_r"] - needed["silver_r"]), abs(needed["phi_r"] - needed["bronze_r"])) if complete else None tools/exp_aubry_cosine_boundary_counter_gate.py:233: nearest_control_ipr = ( tools/exp_aubry_cosine_boundary_counter_gate.py:240: and nearest_control_r is not None tools/exp_aubry_cosine_boundary_counter_gate.py:241: and nearest_control_ipr is not None tools/exp_aubry_cosine_boundary_counter_gate.py:242: and nearest_control_r >= args.min_control_r_delta tools/exp_aubry_cosine_boundary_counter_gate.py:243: and nearest_control_ipr >= args.min_control_ipr_delta tools/exp_aubry_cosine_boundary_counter_gate.py:251: "nearest_control_r_delta": nearest_control_r, tools/exp_aubry_cosine_boundary_counter_gate.py:252: "nearest_control_ipr_delta": nearest_control_ipr, tools/dnd_trace_bridge.py:136: "label_GUE": "=0.5996, Var=0.178", tools/dnd_trace_bridge.py:147: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_trace_bridge.py:347: print("\nDomanda: la mappa D-ND trasforma la struttura GUE degli zeri?") tools/dnd_trace_bridge.py:348: print("Se K(zeta) ≈ GUE e K(random) ≈ Poisson → la mappa PRESERVA la struttura") tools/dnd_trace_bridge.py:350: print("Se K(zeta) ≠ GUE e ≠ K(random) → la mappa TRASFORMA la struttura") tools/dnd_trace_bridge.py:357: print(f" {key:40s} ={val['mean_r']:.4f} {'GUE' if d_gue < d_poi else 'Poi'} {marker}") tools/exp_3d_boundary_layers.py:14:Tests on: primes, GUE, Poisson baseline. tools/exp_3d_boundary_layers.py:37: """Generate GUE eigenvalue gaps.""" tools/exp_3d_boundary_layers.py:42: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_3d_boundary_layers.py:75: """Next-nearest-neighbor spacing ratio: min(g_n, g_{n+2})/max(g_n, g_{n+2})""" tools/exp_3d_boundary_layers.py:179: # --- GUE --- tools/exp_3d_boundary_layers.py:180: print("\n--- GUE ---") tools/exp_3d_boundary_layers.py:187: gue_g, alphas, args.n_trials, rng, "GUE" 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_3d_boundary_layers.py:211: ('poisson', pois_results, pois_orig, pois_bl_mean, pois_bl_std), tools/exp_3d_boundary_layers.py:252: pois_sep = output['sequences']['poisson']['layer_separation']['delta'] tools/exp_3d_boundary_layers.py:255: print(f"Layer separation Δα: Primes={prime_sep:+.3f}, GUE={gue_sep:+.3f}, Poisson={pois_sep:+.3f}") tools/exp_3d_boundary_layers.py:260: 'poisson_layer_separation': float(pois_sep), tools/exp_acf_z6z_mechanism.py:193: # 3d: Cramer + Z/6Z constraint (round to nearest ≡ r_n mod 6) tools/exp_acf_z6z_mechanism.py:197: # Round to nearest value with same mod-6 residue as real gaps tools/dnd_zero_operator.py:8:Il vecchio metodo: calcola spacing, classifica GUE/Poisson. tools/dnd_autoricerca.py:221: # GUE spacing distribution (approssimazione Wigner) tools/dnd_autoricerca.py:228: 'nota': 'Approssimazione GUE (mpmath non disponibile)', tools/dnd_autoricerca.py:270: """Autovalori di matrici casuali GUE — la connessione con Riemann.""" tools/dnd_autoricerca.py:272: # GUE: matrice Hermitiana casuale tools/dnd_autoricerca.py:275: eigenvalues = np.sort(np.real(np.linalg.eigvalsh(H))) tools/dnd_autoricerca.py:277: # Spaziatura normalizzata (unfolding) tools/dnd_autoricerca.py:284: 'ensemble': 'GUE', tools/dnd_autoricerca.py:286: 'nota': 'Spaziatura autovalori matrice GUE 200x200' tools/dnd_autoricerca.py:523: null_spacings.append('GUE-like' if abs(mean_r - 0.5996) < abs(mean_r - 0.3863) else 'Poisson-like') tools/dnd_autoricerca.py:621: risultato['analisi']['gap_ratio'] = obs.get('gap_ratio_medio') tools/dnd_autoricerca.py:652: 'poisson_dist': abs(mean_r - 0.3863), tools/dnd_autoricerca.py:654: 'tipo': 'GUE-like' if abs(mean_r - 0.5996) < abs(mean_r - 0.3863) else 'Poisson-like', tools/dnd_autoricerca.py:827: # Pattern 2: spacing GUE-like tools/dnd_autoricerca.py:829: if spacing.get('tipo') == 'GUE-like' and spacing.get('gue_dist', 1) < 0.1: tools/dnd_autoricerca.py:834: 'nota': f'Spacing GUE-like (⟨r⟩={spacing["mean_r"]:.4f})' tools/dnd_autoricerca.py:849: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:1007: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:1105: print(f" Gap ratio: {obs.get('gap_ratio_medio', '?')}") tools/dnd_autoricerca.py:1118: 'gap_ratio': obs.get('gap_ratio_medio'), tools/dnd_autoricerca.py:1152: gue_domains = [e['dominio'] for e in journal if e.get('spacing') == 'GUE-like'] tools/dnd_autoricerca.py:1153: poisson_domains = [e['dominio'] for e in journal if e.get('spacing') == 'Poisson-like'] tools/dnd_autoricerca.py:1154: print(f" GUE-like: {gue_domains}") tools/dnd_autoricerca.py:1155: print(f" Poisson-like: {poisson_domains}") tools/dnd_autoricerca.py:1157: # Il RAPPORTO #GUE/#Poisson è vicino a φ? tools/dnd_autoricerca.py:1158: if len(gue_domains) > 0 and len(poisson_domains) > 0: tools/dnd_autoricerca.py:1159: ratio_cluster = len(gue_domains) / len(poisson_domains) tools/dnd_autoricerca.py:1160: print(f" #GUE/#Poisson = {ratio_cluster:.4f} " tools/dnd_autoricerca.py:1329: # 2. Differenza spacing tra coppie GUE-Poisson tools/dnd_autoricerca.py:1330: print(f"\n 2. Coppie GUE-Poisson:") tools/dnd_autoricerca.py:1331: gue = [e for e in reali if e.get('spacing') == 'GUE-like' and e.get('spacing_r')] tools/dnd_autoricerca.py:1343: # 3. I r_diretto dei domini GUE vs Poisson come due insiemi tools/dnd_autoricerca.py:1351: print(f" GUE mean r_diretto: {mean_gue:.4f}") tools/dnd_autoricerca.py:1353: print(f" Ratio GUE/Poisson: {ratio:.4f}") tools/dnd_autoricerca.py:1357: # 4. Sequenza dei gap_ratio — è più costante di quanto ci si aspetterebbe? tools/dnd_autoricerca.py:1359: grs = [(e['dominio'], e['gap_ratio']) for e in reali if e.get('gap_ratio')] tools/dnd_autoricerca.py:1539: # BOUNDARY: confine GUE/Poisson — prosegui esplorando il confine tools/dnd_autoricerca.py:1718: 'gue_domains': [], # domini con spacing GUE tools/dnd_autoricerca.py:1719: 'poisson_domains': [],# domini con spacing Poisson tools/dnd_autoricerca.py:1725: if entry.get('spacing') and 'GUE' in str(entry['spacing']): tools/dnd_autoricerca.py:1728: campo['poisson_domains'].append(entry.get('dominio', '?')) tools/dnd_autoricerca.py:1850: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:1881: # I domini GUE con anomalie vengono testati sui domini Poisson e viceversa tools/dnd_autoricerca.py:1888: n_poi = len(campo['poisson_domains']) tools/dnd_autoricerca.py:1890: print(f"\n Campo vivo dopo Fase 0: {n_gue} GUE / {n_poi} Poisson") tools/dnd_autoricerca.py:1891: report_lines.append(f"\n Campo dopo Fase 0: {n_gue} GUE / {n_poi} Poisson") tools/dnd_autoricerca.py:1940: 'gap_ratio': analisi.get('gap_ratio'), tools/dnd_autoricerca.py:2013: n_poi = len(campo['poisson_domains']) tools/dnd_autoricerca.py:2017: report_lines.append(f" GUE: {n_gue} | Poisson: {n_poi} | Vincoli: {n_vinc} | Anomalie: {n_anom}") tools/dnd_autoricerca.py:2019: report_lines.append(f" Domini GUE: {', '.join(campo['gue_domains'][:10])}") tools/dnd_autoricerca.py:2020: report_lines.append(f" Domini Poisson: {', '.join(campo['poisson_domains'][:10])}") tools/dnd_autoricerca.py:2024: print(f"\n Campo vivo finale: {n_gue} GUE / {n_poi} Poisson / {n_vinc} vincoli / {n_anom} anomalie / ⟨r⟩={avg_r:.4f}") tools/dnd_autoricerca.py:2064: 'gue': n_gue, 'poisson': n_poi, tools/dnd_autoricerca.py:2070: 'domini_poisson': campo['poisson_domains'][:10], tools/dnd_autoricerca.py:2120: # 3. Segnali anomali (spacing GUE, rapporto aureo diretto, etc.) tools/dnd_zeros_vs_zeta.py:18: b. GUE eigenvalue spacings (random matrix theory) tools/dnd_zeros_vs_zeta.py:22:If D-ND zeros match GUE/zeta: structural connection confirmed. tools/dnd_zeros_vs_zeta.py:111: """Compute normalized nearest-neighbor spacings.""" tools/dnd_zeros_vs_zeta.py:123: """Generate GUE eigenvalue spacings for comparison.""" tools/dnd_zeros_vs_zeta.py:126: # GUE: complex Hermitian matrix with Gaussian entries tools/dnd_zeros_vs_zeta.py:130: eigenvalues = np.sort(np.real(np.linalg.eigvalsh(H))) tools/dnd_zeros_vs_zeta.py:138:def poisson_spacings(n=1000): tools/dnd_zeros_vs_zeta.py:159: GUE: ≈ 0.5996 tools/dnd_zeros_vs_zeta.py:218: s_poisson = poisson_spacings(2000) tools/dnd_zeros_vs_zeta.py:220: print(f" GUE: {len(s_gue)} spacings") tools/dnd_zeros_vs_zeta.py:221: print(f" Poisson: {len(s_poisson)} spacings") tools/dnd_zeros_vs_zeta.py:227: print(f"{'Distribution':<25} {'':<8} {'KS vs zeta':<12} {'p-value':<12} {'KS vs GUE':<12} {'p vs GUE':<12}") tools/dnd_zeros_vs_zeta.py:249: print(f"{'GUE (RMT)':<25} {r_gue:<8.4f} {ks_gg:<12.4f} {p_gg:<12.4e} {'---':<12} {'---':<12}") tools/dnd_zeros_vs_zeta.py:251: r_poisson = spacing_ratio_statistic(s_poisson) tools/dnd_zeros_vs_zeta.py:252: ks_pz, p_pz = ks_2samp(s_poisson, s_zeta) tools/dnd_zeros_vs_zeta.py:253: ks_pg, p_pg = ks_2samp(s_poisson, s_gue) tools/dnd_zeros_vs_zeta.py:254: print(f"{'Poisson (random)':<25} {r_poisson:<8.4f} {ks_pz:<12.4f} {p_pz:<12.4e} {ks_pg:<12.4f} {p_pg:<12.4e}") tools/dnd_zeros_vs_zeta.py:262: print(f"\nReference values: GUE ≈ 0.5996 | Poisson ≈ 0.3863") tools/dnd_zeros_vs_zeta.py:325: ax3.hist(s_gue, bins=bins, density=True, alpha=0.3, label='GUE (RMT)', color='green') tools/dnd_zeros_vs_zeta.py:326: ax3.hist(s_poisson, bins=bins, density=True, alpha=0.3, label='Poisson', color='gray') tools/dnd_zeros_vs_zeta.py:347: labels = ['Zeta', 'GUE', 'Poisson', 'Synth'] tools/dnd_zeros_vs_zeta.py:348: r_values = [r_zeta, r_gue, r_poisson, r_synth] tools/dnd_zeros_vs_zeta.py:358: ax5.axhline(0.5996, color='green', linestyle='--', linewidth=1, label='GUE expected') tools/dnd_zeros_vs_zeta.py:361: ax5.set_title('Spacing Ratio Statistic\n(GUE ≈ 0.60, Poisson ≈ 0.39)') tools/dnd_zeros_vs_zeta.py:396: # Determine if D-ND is closer to GUE/zeta or Poisson tools/dnd_zeros_vs_zeta.py:409: poisson_dist = abs(r_best - 0.3863) tools/dnd_zeros_vs_zeta.py:413: print(f" Distance to GUE: {gue_dist:.4f}") tools/dnd_zeros_vs_zeta.py:414: print(f" Distance to Poisson: {poisson_dist:.4f}") tools/dnd_zeros_vs_zeta.py:417: if gue_dist < poisson_dist: tools/dnd_zeros_vs_zeta.py:418: print(f"\n → D-ND zeros are CLOSER TO GUE/ZETA than to random") tools/exp_boundary_classical_crossover_audit.py:3:Classical crossover audit for the 8 GUE / 5 Poisson BOUNDARY perimeter. tools/exp_boundary_classical_crossover_audit.py:10: between the empirical CDF and w * GUE_surmise + (1-w) * Poisson. tools/exp_boundary_classical_crossover_audit.py:49:def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: tools/exp_boundary_classical_crossover_audit.py:54:def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: tools/exp_boundary_classical_crossover_audit.py:59: pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) tools/exp_boundary_classical_crossover_audit.py:67:def poisson_cdf(s: np.ndarray) -> np.ndarray: tools/exp_boundary_classical_crossover_audit.py:83: poi = poisson_cdf(sorted_s) tools/exp_boundary_classical_crossover_audit.py:96:def classical_state(brody_q: float, mixture_w: float, graph_state: str) -> str: tools/exp_boundary_classical_crossover_audit.py:97: brody_mid = 0.25 <= brody_q <= 0.75 tools/exp_boundary_classical_crossover_audit.py:99: if graph_state == "third_included_candidate" and (brody_mid or mix_mid): tools/exp_boundary_classical_crossover_audit.py:103: if brody_mid or mix_mid: tools/exp_boundary_classical_crossover_audit.py:119: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_boundary_classical_crossover_audit.py:130: "brody_q": round(brody_q, 6), tools/exp_boundary_classical_crossover_audit.py:131: "brody_nll": round(brody_nll, 6), tools/exp_boundary_classical_crossover_audit.py:134: "audit_state": classical_state(brody_q, mixture_w, grow["boundary_state"]), tools/exp_boundary_classical_crossover_audit.py:152: "brody_q", tools/exp_boundary_classical_crossover_audit.py:163: "observable": "row-aligned Brody q, Berry-Robnik-like GUE mixture weight, graph bridge state", tools/exp_boundary_classical_crossover_audit.py:166: "denominator": "13 rows: 8 GUE and 5 Poisson", tools/exp_boundary_classical_crossover_audit.py:168: "not_tested": "true Rosenzweig-Porter Hamiltonian flow, physical unfolding alternatives, asymptotic universality", tools/dnd_compatibility.py:134: "=0.507, tra GUE (0.5996) e Poisson (0.3863).", tools/dnd_compatibility.py:147:# --- Random Matrix Theory (GUE) --- tools/dnd_compatibility.py:150: "GUE (Gaussian Unitary Ensemble): matrici hermitiane casuali, " tools/dnd_compatibility.py:167: "evidence": "Le matrici GUE hanno det qualsiasi. " tools/dnd_compatibility.py:173: "evidence": "phi non emerge naturalmente dal GUE. " tools/dnd_compatibility.py:180: "evidence": "GUE vive in C (numeri complessi generici), non in Q(sqrt(5)).", tools/dnd_compatibility.py:185: "evidence": "GUE e' una classe definita, non una fase critica. " tools/exp_boundary_graph_null_audit.py:5:This script keeps the 13 row-aligned 8 GUE / 5 Poisson denominator and asks tools/exp_boundary_graph_null_audit.py:50: gue_idx = [i for i, label in enumerate(labels) if label == "GUE"] tools/exp_boundary_graph_null_audit.py:150: selected = [row for row in source_rows if row.get("source_domain_type") in {"GUE", "Poisson"}] tools/exp_boundary_graph_null_audit.py:288: "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid and graph null trials", tools/exp_boundary_graph_null_audit.py:290: "not_tested": "new Hamiltonian systems, alternative unfolding, physical universality of graph-only rows", tools/exp_boundary_bridge_stability_audit.py:5:The audit keeps the 13 row-aligned GUE/Poisson denominator and reruns the graph tools/exp_boundary_bridge_stability_audit.py:65: selected = [row for row in source_rows if row.get("source_domain_type") in {"GUE", "Poisson"}] tools/exp_boundary_bridge_stability_audit.py:156: "brody_q": classic.get("brody_q"), tools/exp_boundary_bridge_stability_audit.py:191: "brody_q", tools/exp_boundary_bridge_stability_audit.py:209: "denominator": "13 rows: 8 GUE and 5 Poisson, repeated across graph-reader parameter grid", tools/exp_boundary_bridge_stability_audit.py:211: "not_tested": "new physical Hamiltonian flow, alternative unfolding, asymptotic scaling beyond this finite denominator", tools/exp_beta_crossover.py:98: from scipy.linalg import eigvalsh_tridiagonal tools/exp_beta_crossover.py:99: eigs = eigvalsh_tridiagonal(diag, off_diag) tools/exp_beta_crossover.py:112: # Trim edges (unfolding artifacts) tools/exp_beta_crossover.py:192: # Sweep beta from near-Poisson (0.01) to beyond-GUE (10) tools/exp_beta_crossover.py:195: np.linspace(0.6, 2.0, n_betas // 3), # through GOE-GUE tools/exp_beta_crossover.py:196: np.linspace(2.5, 8.0, n_betas - 2*(n_betas//3)), # beyond GUE tools/exp_beta_crossover.py:317: note = " <-- GUE" tools/exp_boundary_mixture_gate.py:5:Reusable META/BOUNDARY audit for the GUE-Poisson boundary. tools/exp_boundary_mixture_gate.py:8: Does the GUE/Poisson boundary remain a clean two-class split after the tools/exp_boundary_mixture_gate.py:13:It builds synthetic mixtures by replacing a fraction beta of unfolded GUE tools/exp_boundary_mixture_gate.py:19:- ambiguity of each beta layer relative to pure GUE and pure Poisson centroids. tools/exp_boundary_mixture_gate.py:41: """Generate unfolded GUE spacings by concatenating independent matrices.""" tools/exp_boundary_mixture_gate.py:49: eigs = np.sort(np.linalg.eigvalsh(h).real) tools/exp_boundary_mixture_gate.py:58:def mixture_spacings(gue: np.ndarray, poisson: np.ndarray, beta: float, rng: np.random.Generator) -> np.ndarray: tools/exp_boundary_mixture_gate.py:59: """Return a beta Poisson / (1-beta) GUE spacing sequence with mean spacing 1.""" tools/exp_boundary_mixture_gate.py:60: if len(gue) != len(poisson): tools/exp_boundary_mixture_gate.py:61: raise ValueError("gue and poisson arrays must have the same length") tools/exp_boundary_mixture_gate.py:64: out[mask] = poisson[mask] tools/exp_boundary_mixture_gate.py:135: labels.append("gue" if d_gue < d_poi else "poisson") tools/exp_boundary_mixture_gate.py:144: "poisson_label_fraction": float(np.mean(np.array(labels) == "poisson")), tools/exp_boundary_mixture_gate.py:197: poisson = rep_rng.exponential(1.0, size=args.n_gaps) tools/exp_boundary_mixture_gate.py:198: poisson = poisson / float(np.mean(poisson)) tools/exp_boundary_mixture_gate.py:201: gaps = mixture_spacings(gue, poisson, beta, layer_rng) tools/exp_boundary_mixture_gate.py:224: "question": "Is the GUE-Poisson mixed layer cleanly classifiable after denominator gating?", tools/exp_boundary_mixture_gate.py:249: print("beta stable_count margin_gated ambiguous_gated poisson_fraction_gated") tools/exp_boundary_mixture_gate.py:252: print("beta stable_count margin_all ambiguous_all poisson_fraction_all") tools/exp_boundary_mixture_gate.py:262: f"{layer.get('poisson_label_fraction', 0.0):>21.3f}" tools/exp_boundary_graph_curvature_gate.py:3:Graph-curvature gate for the 8 GUE / 5 Poisson BOUNDARY perimeter. tools/exp_boundary_graph_curvature_gate.py:91: nearest = np.argsort(distances[i])[1 : k + 1] tools/exp_boundary_graph_curvature_gate.py:92: for j in nearest: tools/exp_boundary_graph_curvature_gate.py:99: gue_idx = [i for i, label in enumerate(labels) if label == "GUE"] tools/exp_boundary_graph_curvature_gate.py:102: raise ValueError("scope must include both GUE and Poisson rows") tools/exp_boundary_graph_curvature_gate.py:174: "GUE": len(gue_idx), tools/exp_boundary_graph_curvature_gate.py:195: selected = [row for row in scope if row.get("source_domain_type") in {"GUE", "Poisson"}] tools/exp_boundary_graph_curvature_gate.py:238: "question": "Does the 8 GUE / 5 Poisson perimeter expose a graph boundary row instead of a clean two-class split?", tools/exp_boundary_graph_curvature_gate.py:248: "denominator": "base BOUNDARY rows with source_domain_type in {GUE, Poisson}", tools/exp_boundary_gue_poisson.py:3:Experiment: GUE/Poisson boundary in prime gaps vs Cramér null model. tools/exp_boundary_gue_poisson.py:5:Question: Is the transition from GUE-like to Poisson-like statistics in prime gaps tools/exp_boundary_gue_poisson.py:11: - GUE (GOE in 1D): ≈ 0.5307 tools/exp_boundary_gue_poisson.py:26:def gap_ratios(gaps): tools/exp_boundary_gue_poisson.py:59: r = gap_ratios(gaps) tools/exp_boundary_gue_poisson.py:64: print("=== GUE/Poisson Boundary: Primes vs Cramér Null Model ===\n") tools/exp_boundary_gue_poisson.py:101: r_poisson = 0.3863 tools/exp_boundary_gue_poisson.py:120: print(f"\nReference: _GUE = {r_gue:.4f}, _Poisson = {r_poisson:.4f}") tools/exp_boundary_gue_poisson.py:154: # Where are primes relative to GUE/Poisson? tools/exp_boundary_gue_poisson.py:162: dist_poi = abs(rp - r_poisson) tools/exp_boundary_gue_poisson.py:163: label = "GUE" if dist_gue < dist_poi else "POISSON" tools/exp_boundary_gue_poisson.py:171: "experiment": "boundary_gue_poisson_cramer", tools/exp_boundary_gue_poisson.py:176: "reference": {"r_gue": r_gue, "r_poisson": r_poisson}, tools/exp_boundary_shuffle_audit.py:2:exp_boundary_shuffle_audit.py — Is the GUE/Poisson classification tautological? tools/exp_boundary_shuffle_audit.py:4:Question: Does the r-statistic (nearest-neighbor spacing ratio) of each domain tools/exp_boundary_shuffle_audit.py:16:Domains: primes, zeta zeros, random matrix (GUE), Fibonacci spectrum, tools/exp_boundary_shuffle_audit.py:83: """GUE random matrices — eigenvalue spacings.""" tools/exp_boundary_shuffle_audit.py:88: eigs = np.sort(linalg.eigvalsh(H)) tools/exp_boundary_shuffle_audit.py:99:def gen_poisson(n=100000): tools/exp_boundary_shuffle_audit.py:122: eigs = np.sort(linalg.eigvalsh(H)) tools/exp_boundary_shuffle_audit.py:186: eigs = np.sort(linalg.eigvalsh(K)) tools/exp_boundary_shuffle_audit.py:238:R_GUE = 0.5307 # 4 - 2√3 ≈ 0.5359 for GOE; for GUE: 2π/(3√3 + 4π/3) ≈ 0.5307 approx tools/exp_boundary_shuffle_audit.py:244: 'gue': ('GUE random matrix', gen_gue_eigenvalues), tools/exp_boundary_shuffle_audit.py:245: 'poisson': ('Poisson process', gen_poisson), tools/exp_boundary_shuffle_audit.py:275: dist_gue = abs(res['r_original'] - R_GUE) tools/exp_boundary_shuffle_audit.py:277: res['class_original'] = 'GUE' if dist_gue < dist_poi else 'Poisson' tools/exp_boundary_shuffle_audit.py:279: dist_gue_s = abs(res['r_shuffled_mean'] - R_GUE) tools/exp_boundary_shuffle_audit.py:281: res['class_shuffled'] = 'GUE' if dist_gue_s < dist_poi_s else 'Poisson' tools/exp_boundary_shuffle_audit.py:308: print(f" SUMMARY: GUE/Poisson Classification Shuffle Audit") tools/exp_boundary_shuffle_audit.py:336: print(f"\n R_GUE = {R_GUE:.4f}, R_Poisson = {R_POISSON:.4f}") tools/exp_boundary_shuffle_audit.py:355: 'reference': {'R_GUE': R_GUE, 'R_Poisson': R_POISSON}, tools/exp_boundary_blank_thin_support_audit.py:175: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/exp_boundary_short_denominator_extension.py:53: eig = np.linalg.eigvalsh(h) tools/exp_boundary_short_denominator_extension.py:203: "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", tools/exp_boundary_denominator_prescan.py:6:V_c. The unit under test is not the GUE/Poisson label. The unit is the tools/exp_boundary_denominator_prescan.py:10:- tools/data/autoricerca_journal.json: base 13-domain GUE/Poisson perimeter. tools/exp_boundary_denominator_prescan.py:59: if spacing == "GUE-like": tools/exp_boundary_denominator_prescan.py:60: return "GUE" tools/exp_boundary_denominator_prescan.py:79: "GUE-like", tools/exp_boundary_denominator_prescan.py:195: "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", tools/exp_boundary_denominator_prescan.py:196: "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", tools/exp_brody_calibration.py:3:exp_brody_calibration.py — META falsification: do our observables track real structure? tools/exp_brody_calibration.py:6:Poisson (beta=0) to Wigner-GUE (beta=1). Gaps are i.i.d. by construction — tools/exp_brody_calibration.py:19: python tools/exp_brody_calibration.py [--n-gaps 10000] [--n-brody 11] [--n-shuffles 50] tools/exp_brody_calibration.py:29:def brody_sample(beta, n, rng): tools/exp_brody_calibration.py:47: """Sigma^2(L) = Var[N(x, x+L)] for unfolded levels.""" tools/exp_brody_calibration.py:118:def prime_gaps_unfolded(n_gaps): tools/exp_brody_calibration.py:130: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_brody_calibration.py:134: unfolded = gaps_raw * local_density tools/exp_brody_calibration.py:135: margin = len(unfolded) // 10 tools/exp_brody_calibration.py:136: unfolded = unfolded[margin:-margin] tools/exp_brody_calibration.py:137: return unfolded[:n_gaps] tools/exp_brody_calibration.py:143: parser.add_argument('--n-brody', type=int, default=11) tools/exp_brody_calibration.py:151: betas = np.linspace(0, 1, args.n_brody) tools/exp_brody_calibration.py:152: brody_results = [] tools/exp_brody_calibration.py:159: gaps = brody_sample(beta, args.n_gaps, rng) tools/exp_brody_calibration.py:161: brody_results.append({ tools/exp_brody_calibration.py:174: brody_r_values = [b['r'] for b in brody_results] tools/exp_brody_calibration.py:175: brody_beta_values = [b['beta'] for b in brody_results] tools/exp_brody_calibration.py:178: if r_val <= brody_r_values[0]: tools/exp_brody_calibration.py:180: if r_val >= brody_r_values[-1]: tools/exp_brody_calibration.py:182: for i in range(len(brody_r_values) - 1): tools/exp_brody_calibration.py:183: if brody_r_values[i] <= r_val <= brody_r_values[i+1]: tools/exp_brody_calibration.py:184: frac = (r_val - brody_r_values[i]) / (brody_r_values[i+1] - brody_r_values[i]) tools/exp_brody_calibration.py:185: return brody_beta_values[i] + frac * (brody_beta_values[i+1] - brody_beta_values[i]) tools/exp_brody_calibration.py:189: sig2_values = [b['sig2_over_L'][10] for b in brody_results] tools/exp_brody_calibration.py:203: prime_gaps = prime_gaps_unfolded(args.n_gaps) tools/exp_brody_calibration.py:211: # GUE matrices 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:239: real_domains['poisson'] = {**obs_po, 'beta_eff': beta_eff_po} tools/exp_brody_calibration.py:240: print(f"{'poisson':>20} {obs_po['r']:8.4f} {obs_po['r_shuf']:8.4f} " tools/exp_brody_calibration.py:303: for br in brody_results: tools/exp_brody_calibration.py:323: r_monotonic = all(brody_results[i]['r'] <= brody_results[i+1]['r'] for i in range(len(brody_results)-1)) tools/exp_brody_calibration.py:325: print(f" r(beta=0) = {brody_results[0]['r']:.4f}, r(beta=1) = {brody_results[-1]['r']:.4f}") tools/exp_brody_calibration.py:329: sig2_monotonic = all(brody_results[i]['sig2_over_L'][10] >= brody_results[i+1]['sig2_over_L'][10] for i in range(len(brody_results)-1)) tools/exp_brody_calibration.py:331: print(f" Sig2/L(beta=0) = {brody_results[0]['sig2_over_L'][10]:.4f}, Sig2/L(beta=1) = {brody_results[-1]['sig2_over_L'][10]:.4f}") tools/exp_brody_calibration.py:358: 'brody_curve': sanitize(brody_results), tools/exp_brody_calibration.py:360: 'max_brody_ordering_fraction': sanitize(max_ord), tools/exp_brody_calibration.py:366: out_path = Path(__file__).parent / 'data' / 'brody_calibration_results.json' tools/dnd_engine.py:112: 'claim': 'Spacing zeta zeros è GUE (⟨r⟩≈0.615)', tools/dnd_engine.py:120: 'claim': 'Domini si dividono in GUE (7) e Poisson (4)', tools/dnd_engine.py:137: 'claim': 'Rule 30/110 hanno spacing anomalo (⟨r⟩≈0.86, >> GUE)', tools/dnd_engine.py:176: 'claim': 'r_diretto NON separa GUE da Poisson (70% overlap)', tools/dnd_engine.py:178: 'motivo': 'Esperimento order_parameter: GUE r=0.92±0.11, Poisson r=0.95±0.08. Ipotesi H_order_param eliminata.', tools/dnd_engine.py:192: 'claim': 'φ emerge in 20/20 banchi di prova da teorie dimostrate: Wigner GOE/GUE, Feigenbaum, Fibonacci quasicrystal, KAM (12 valori K), cifre di π/e/√2/√5/φ/ln2.', tools/dnd_engine.py:206: 'claim': 'ac1 (autocorrelazione intervalli D-ND) DISTINGUE deterministico da casuale: primi=-0.10, GUE=-0.20, shuffle=0.00, Poisson=+0.04.', tools/dnd_engine.py:300: 'motivo': 'Correlazione log r=0.921 era ARTEFATTO di monotonia (random sorted r=0.89). Il ponte vero è STATISTICO: distribuzione spacing = GUE (β=2.007 a 100K zeri).', tools/dnd_engine.py:302: 'correzione': 'La correlazione locale K_c↔t_n è spuria. Il ponte D-ND↔ζ è nella distribuzione degli spacing (GUE), non nella corrispondenza puntuale.', tools/dnd_engine.py:314: 'claim': 'Il confine GUE/Poisson corrisponde al terzo incluso', tools/dnd_engine.py:316: 'motivo': 'Se GUE=D e Poisson=ND, il confine è dove la scissione cambia natura.', tools/dnd_engine.py:476: 'claim': 'Gli spacing della mappa D-ND (Fibonacci) seguono GUE (p>0.05 KS). La classificazione GUE/GOE/Poisson e un metodo universale.', tools/dnd_engine.py:584: """Verifica che i domini si dividano naturalmente in cluster GUE/Poisson.""" tools/dnd_engine.py:767: eigs = np.linalg.eigvalsh(H) tools/dnd_engine.py:793: eigs = np.linalg.eigvalsh(H) tools/dnd_engine.py:811: r1 = _spacing_ratio(np.linalg.eigvalsh(H1)) tools/dnd_engine.py:812: r2 = _spacing_ratio(np.linalg.eigvalsh(H2)) tools/dnd_engine.py:820: 'V2_is_poisson': r2 < 0.42, tools/dnd_engine.py:842: r_profile.append(_spacing_ratio(np.linalg.eigvalsh(H))) tools/dnd_engine.py:910: r = _spacing_ratio(np.linalg.eigvalsh(H)) tools/dnd_engine.py:914: r = _spacing_ratio(np.linalg.eigvalsh(H)) tools/dnd_engine.py:958: r_values.append(_spacing_ratio(np.linalg.eigvalsh(H))) tools/dnd_engine.py:985: r_vals.append(_spacing_ratio(np.linalg.eigvalsh(H))) tools/dnd_engine.py:1030: r_profile.append(_spacing_ratio(np.linalg.eigvalsh(H))) tools/dnd_engine.py:1070: r_sturm.append(_spacing_ratio(np.linalg.eigvalsh(H_s))) tools/dnd_engine.py:1071: r_harper.append(_spacing_ratio(np.linalg.eigvalsh(H_h))) tools/dnd_engine.py:1528: """Il confine GUE/Poisson nella famiglia det=-1 corrisponde al terzo incluso. tools/dnd_engine.py:1529: A V=V_c: attraversa 0.5 (meta' tra GUE=0.53 e Poisson=0.39). tools/dnd_engine.py:1531: from scipy.linalg import eigvalsh tools/dnd_engine.py:1559: r = r_stat(eigvalsh(H)) tools/dnd_engine.py:1729: 'gap_ratio_equals_phi': all_ratio_phi, tools/dnd_engine.py:1909: """Spacing dei rapporti Fibonacci: classifica GUE/GOE/Poisson con KS test.""" tools/dnd_engine.py:1928: # GUE, GOE, Poisson CDFs tools/dnd_engine.py:1933: def poisson_cdf(s): tools/dnd_engine.py:1938: ks_poi, p_poi = kstest(spacings, poisson_cdf) tools/dnd_engine.py:1940: fits = [("GUE", ks_gue, p_gue), ("GOE", ks_goe, p_goe), ("Poisson", ks_poi, p_poi)] tools/dnd_engine.py:1950: 'ks_poisson': float(ks_poi), tools/dnd_engine.py:2182: gap_ratios = [d.get('gap_ratio', 0) for d in domains if d.get('gap_ratio')] tools/dnd_engine.py:2183: if gap_ratios: tools/dnd_engine.py:2184: cv = np.std(gap_ratios) / np.mean(gap_ratios) if np.mean(gap_ratios) > 0 else 0 tools/dnd_engine.py:2185: print(f"\n Gap ratio: media={np.mean(gap_ratios):.6f}, cv={cv:.6f}") tools/dnd_engine.py:2189: 'quantita': 'gap_ratio', tools/dnd_engine.py:2190: 'valore': float(np.mean(gap_ratios)), tools/dnd_engine.py:2257: 'EXT5': {'root': 'metodologico', 'level': 'statistico'}, # classificazione GUE/GOE/Poisson (gap filled!) tools/dnd_engine.py:2759: 'test': 'Trovare un dominio dove gap_ratio != phi^2', tools/exp_boundary_growth.py:13: - GUE (correlated): ~ 0.5307 tools/exp_boundary_growth.py:25:def gap_ratio(gaps): tools/exp_boundary_growth.py:49: return gap_ratio(shuffled) tools/exp_boundary_growth.py:82: r_prime = gap_ratio(gaps) tools/exp_boundary_growth.py:90: r_cramer_list.append(gap_ratio(cramer_gaps)) tools/exp_boundary_growth.py:158: # Key question: do primes approach GUE or Poisson at large scale? tools/exp_boundary_growth.py:159: GUE_R = 0.5307 tools/exp_boundary_growth.py:165: print(f"GUE reference: {GUE_R}") tools/exp_boundary_growth.py:167: print(f"Position: {'closer to GUE' if abs(r_large - GUE_R) < abs(r_large - POISSON_R) else 'closer to Poisson'}") tools/exp_coherence_robustness.py:36:def gap_ratio(gaps): tools/exp_coherence_robustness.py:82: r_prime = np.array([gap_ratio(w) for w in windows]) tools/exp_coherence_robustness.py:90: r = gap_ratio(shuf) tools/exp_boundary_row_aligned_nonexact_audit.py:6:does not read GUE/Poisson labels as decision fields. It only uses support, tools/exp_boundary_row_aligned_nonexact_audit.py:175: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/dnd_gue_test.py:3:Piano 11b — GUE Test Protocol (Revised) tools/dnd_gue_test.py:6:zeros follow GUE statistics (Katz-Sarnak). This is the algebraic bridge, tools/dnd_gue_test.py:14: Prediction: zeros of L(s, χ_disc) → GUE (Katz-Sarnak) tools/dnd_gue_test.py:17:T1: L(s, χ₅) zeros → GUE (the D-ND matrix M, tr=1) tools/dnd_gue_test.py:18:T2: L(s, χ₈) zeros → GUE (tr=2, disc=8, Q(√2)) tools/dnd_gue_test.py:19:T3: L(s, χ₁₃) zeros → GUE (tr=3, disc=13, Q(√13)) tools/dnd_gue_test.py:20:T4: ζ(s) zeros → GUE (reference: Montgomery-Odlyzko) tools/dnd_gue_test.py:40: """GUE (β=2) Wigner surmise CDF.""" tools/dnd_gue_test.py:47:def poisson_cdf(s): tools/dnd_gue_test.py:51:GUE_VAR_REF = 1 - 4/np.pi**2 # ≈ 0.5947 (Wigner surmise β=2) tools/dnd_gue_test.py:53:# Better: Var(s) for GUE Wigner surmise = (3π-8)/(2π) ≈ 0.178 tools/dnd_gue_test.py:55:GUE_VAR = 0.178 # Wigner surmise β=2 tools/dnd_gue_test.py:131:# ─── Smooth counting function for unfolding ──────────────────────────── tools/dnd_gue_test.py:141: For unfolding we use: N(T) = (T/(2π)) ln(qT/(2πe)) tools/dnd_gue_test.py:166:def unfold_zeros(zeros, conductor): tools/dnd_gue_test.py:176: # Compute unfolded positions using exact formula tools/dnd_gue_test.py:177: unfolded = np.array([smooth_N_exact(t, conductor) for t in zeros]) tools/dnd_gue_test.py:180: spacings = np.diff(unfolded) tools/dnd_gue_test.py:362: """Test spacings against GUE, GOE, Poisson.""" tools/dnd_gue_test.py:368: ks_poi, p_poi = kstest(spacings, poisson_cdf) tools/dnd_gue_test.py:374: fits = [("GUE", ks_gue, p_gue), ("GOE", ks_goe, p_goe), ("Poisson", ks_poi, p_poi)] tools/dnd_gue_test.py:384: "ks_poisson": {"KS": float(ks_poi), "p": float(p_poi)}, tools/dnd_gue_test.py:395: print("Piano 11b — GUE Test Protocol (Revised)") tools/dnd_gue_test.py:414: # ─── T1: ζ(s) zeros → GUE (reference, Montgomery-Odlyzko) ──────── tools/dnd_gue_test.py:421: zeta_spacings = unfold_zeros(zeta_zeros, conductor=1) tools/dnd_gue_test.py:439: L5_spacings = unfold_zeros(L5_zeros, conductor=5) tools/dnd_gue_test.py:457: L8_spacings = unfold_zeros(L8_zeros, conductor=8) tools/dnd_gue_test.py:475: L13_spacings = unfold_zeros(L13_zeros, conductor=13) tools/dnd_gue_test.py:494: L29_spacings = unfold_zeros(L29_zeros, conductor=29) tools/dnd_gue_test.py:513: L17_spacings = unfold_zeros(L17_zeros, conductor=17) tools/dnd_gue_test.py:532: L21_spacings = unfold_zeros(L21_zeros, conductor=21) tools/dnd_gue_test.py:555: marker = "✓" if fit == "GUE" else "✗" tools/dnd_gue_test.py:556: if fit == "GUE": tools/dnd_gue_test.py:568: verdict = (f"GUE UNIVERSALE: {gue_count}/{total} L-functions mostrano GUE. " tools/dnd_gue_test.py:569: f"La catena det=-1 → campo numerico → L-function → GUE è confermata.") tools/dnd_gue_test.py:572: verdict = (f"GUE DOMINANTE: {gue_count}/{total}. Risultato parziale, " tools/dnd_gue_test.py:578: verdict = f"NON-GUE: classe dominante {dominant}. Revisione necessaria." tools/dnd_gue_test.py:587: print(" Near s=0: p(s) ~ s^β. β=0:Poisson, β=1:GOE, β=2:GUE") tools/dnd_gue_test.py:602: sp = unfold_zeros(zeros_list, conductor) tools/dnd_gue_test.py:622: cls = "GUE" if beta > 1.5 else ("GOE" if beta > 0.5 else "Poisson") tools/dnd_gue_test.py:630: # Count GUE in beta analysis tools/dnd_gue_test.py:631: beta_gue = sum(1 for lb in beta_results.values() for v in lb.values() if v.get("class") == "GUE") tools/dnd_gue_test.py:633: print(f" DIPOLO FRATTALE: {beta_gue}/{beta_total} GUE a livello di repulsione") tools/dnd_gue_test.py:634: print(f" Il KS globale dice GOE. Il β allo zero dice GUE.") tools/dnd_gue_test.py:703: family_verdict = f"MISTO: neg1 GUE={all_neg1_gue}, pos1 GUE={all_pos1_gue}" tools/dnd_gue_test.py:716: print(f" Piano 11: det=-1 → fluctuations (GUE via Katz-Sarnak)") tools/dnd_gue_test.py:728: verdict_fractal = (f"GUE CONFERMATO via dipolo frattale: β>2 per {beta_gue}/{beta_total} misure. " tools/dnd_gue_test.py:729: f"KS globale={gue_count}/{total} GUE (forma bulk). " tools/dnd_gue_test.py:730: f"β allo zero={beta_gue}/{beta_total} GUE (repulsione). " tools/dnd_gue_test.py:734: verdict_fractal = (f"Dipolo frattale: {beta_gue}/{beta_total} GUE. " tools/dnd_gue_test.py:805: "fluctuations": "det=-1 → Q(√5) → L(s,χ₅) → GUE (Piano 11)", tools/dnd_gue_test.py:806: "family": "ALL det=-1 matrices → number fields → GUE (Piano 11b)", tools/exp_coherence_length.py:33:def gap_ratio(gaps): tools/exp_coherence_length.py:67: r_prime_list.append(gap_ratio(window)) tools/exp_coherence_length.py:72: r_shuf_lists[si].append(gap_ratio(shuf)) tools/exp_boundary_two_axis_matrix.py:6:does not use GUE/Poisson source labels: it only reads transfer support and beta tools/exp_boundary_two_axis_matrix.py:107: "question": "Separate support_transfer from beta_coordinate_transfer on the 13 semi-real BOUNDARY rows without using GUE/Poisson labels.", tools/exp_boundary_two_axis_matrix.py:120: "label_policy": "GUE/Poisson source labels are not read by this operator.", tools/dnd_kernel.py:298: 'statistics': 'GUE', tools/dnd_kernel.py:318: 'statistics': 'GUE', tools/exp_crossover_phase_test.py:5:observed in the GUE crossover is a property of the PARTIAL SHUFFLE METHOD (tautology) tools/exp_crossover_phase_test.py:88: """Generate GUE-like spacings from random matrices""" tools/exp_crossover_phase_test.py:94: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_crossover_phase_test.py:161:def generate_poisson(N, rng): tools/exp_crossover_phase_test.py:216: sequences['GUE'] = generate_gue_gaps(args.N, rng) tools/exp_crossover_phase_test.py:217: print(f" GUE: {len(sequences['GUE'])} gaps") tools/exp_crossover_phase_test.py:228: sequences['Poisson'] = generate_poisson(args.N, rng) tools/exp_crossover_phase_test.py:262: print("CLASSIFICATION: Does each sequence show the full GUE-like phase transition?") tools/exp_crossover_phase_test.py:292: print(" The GUE crossover finding is TAUTOLOGICAL (methodological artifact)") tools/exp_crossover_phase_test.py:295: print(" The GUE crossover finding is a REAL structural property") tools/exp_brody_flow.py:3:exp_brody_flow.py — Brody parameter flow along the prime sequence. tools/exp_brody_flow.py:5:Measures how the Brody beta (interpolating Poisson beta=0 to GUE beta=1) tools/exp_brody_flow.py:16: python tools/exp_brody_flow.py [--n-max 2000000] [--window 5000] [--step 2000] [--n-shuffle 20] tools/exp_brody_flow.py:36:def brody_mle(spacings, beta_grid=np.linspace(0.01, 1.5, 300)): tools/exp_brody_flow.py:37: """MLE estimate of Brody parameter beta from unfolded spacings. tools/exp_brody_flow.py:78:def unfold_spacings(gaps): tools/exp_brody_flow.py:120: uf = unfold_spacings(g) tools/exp_brody_flow.py:123: beta_real = brody_mle(uf) tools/exp_brody_flow.py:132: uf_shuf = unfold_spacings(g_shuf) tools/exp_brody_flow.py:133: betas_shuf.append(brody_mle(uf_shuf)) tools/exp_brody_flow.py:141: uf_cr = unfold_spacings(g_cr) tools/exp_brody_flow.py:142: betas_cr.append(brody_mle(uf_cr)) tools/exp_brody_flow.py:216: out_path = Path(__file__).parent / "data" / "brody_flow.json" tools/exp_cross_observable_consistency.py:17:5. GUE control: synthetic GUE eigenvalues (should show β_r ≈ β_Σ ≈ 1) tools/exp_cross_observable_consistency.py:31:with open('/opt/MM_D-ND/tools/data/brody_calibration_results.json') as f: tools/exp_cross_observable_consistency.py:34:cal_curve = cal['brody_curve'] tools/exp_cross_observable_consistency.py:89:def unfold_primes(p): tools/exp_cross_observable_consistency.py:97:def number_variance_at_L(unfolded, L, n_samples=3000): tools/exp_cross_observable_consistency.py:99: x_min, x_max = unfolded[0], unfolded[-1] tools/exp_cross_observable_consistency.py:103: counts = np.array([np.sum((unfolded >= x0) & (unfolded < x0 + L)) for x0 in starts]) tools/exp_cross_observable_consistency.py:107:# ── GUE control (small ensemble) ──────────────────────────────────── tools/exp_cross_observable_consistency.py:109: """Generate GUE eigenvalue gaps.""" tools/exp_cross_observable_consistency.py:114: evals = np.sort(np.linalg.eigvalsh(H).real) tools/exp_cross_observable_consistency.py:115: # Unfold: for GUE bulk, spacing ~ semicircle tools/exp_cross_observable_consistency.py:132:unfolded = unfold_primes(primes) tools/exp_cross_observable_consistency.py:137: s2 = number_variance_at_L(unfolded, L) tools/exp_cross_observable_consistency.py:157: u_shuf = unfold_primes(p_shuf) tools/exp_cross_observable_consistency.py:169:# ── Measure GUE (positive control) ────────────────────────────────── tools/exp_cross_observable_consistency.py:170:print("\n=== GUE (positive control) ===") tools/exp_cross_observable_consistency.py:176:# For GUE, unfold eigenvalues directly tools/exp_cross_observable_consistency.py:181: evals = np.sort(np.linalg.eigvalsh(H).real) tools/exp_cross_observable_consistency.py:215:# GUE tools/exp_cross_observable_consistency.py:217:print(f"{'GUE':<12} {beta_r_gue:>6.3f} | " + " | ".join(f"{v:>9}" for v in vals_gue)) tools/exp_cross_observable_consistency.py:227:print(f" GUE: {disagree_gue:.3f}") tools/exp_cross_observable_consistency.py:232:print(f" (positive = more GUE-like at short range, more Poisson-like at long range)") tools/exp_denominator_gate_transfer_matrix.py:8:GUE/Poisson BOUNDARY perimeter. Each perimeter has a coherent endpoint and an tools/exp_dipolar_crossover.py:3:exp_dipolar_crossover.py — Topology of the GUE-Poisson transition in the dipolar plane. tools/exp_dipolar_crossover.py:5:Question: As ordering is gradually destroyed (GUE → shuffled), does the dipolar tools/exp_dipolar_crossover.py:10: 1. Generate GUE bulk spacings from random matrices tools/exp_dipolar_crossover.py:14: - Leave the rest in original (GUE) order tools/exp_dipolar_crossover.py:19:If primes sit on the curve → they're "partially disordered GUE" tools/exp_dipolar_crossover.py:33: """Generate bulk spacings from GUE matrices.""" tools/exp_dipolar_crossover.py:38: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_dipolar_crossover.py:98: print(f"Generating GUE spacings: {n_matrices} matrices of size {N_mat}...") tools/exp_dipolar_crossover.py:101: # GUE baseline (alpha=0) tools/exp_dipolar_crossover.py:214: "GUE_baseline": {"SR": float(sr0), "L1": float(l1_0)}, tools/dnd_publish_cycle.py:168: "|NT⟩ → closure → det(M)=-1 → φ → Q(√5) → GUE → differentiated reality", tools/exp_excess_scaling.py:33:def gap_ratio(gaps): tools/exp_excess_scaling.py:49: rs.append(gap_ratio(gaps)) tools/exp_excess_scaling.py:66: r_prime = gap_ratio(gaps) tools/exp_excess_scaling.py:124:# Additional: check GUE vs Poisson classification tools/exp_excess_scaling.py:125:# GUE: ~ 0.5307, Poisson: ~ 0.3863 tools/exp_excess_scaling.py:126:r_GUE = 0.5307 tools/exp_excess_scaling.py:129:print(f"\n Reference: GUE = {r_GUE}, Poisson = {r_Poisson}") tools/exp_excess_scaling.py:137: print(f" INCREASES with scale: moving toward/staying GUE") tools/exp_markov_k_direction.py:148: print("Does higher-order memory point GUE-ward in the (SR, L1) plane?") tools/exp_markov_k_direction.py:169: GUE_THETA = -97.0 tools/exp_markov_k_direction.py:240: 'GUE_ref_theta': GUE_THETA, tools/dnd_rottura.py:355: print(f" Soglia | n_int | gap_ratio | gap_finale | Identica?") tools/dnd_rottura.py:367: gr = obs.get('gap_ratio_medio', None) tools/dnd_rottura.py:373: 'gap_ratio': float(gr) if gr else None, tools/dnd_rottura.py:385: # Il gap_ratio è φ² indipendentemente dalla soglia? → Sì, perché la regola è la stessa. tools/dnd_rottura.py:386: grs = [s['gap_ratio'] for s in spirali_soglia if s['gap_ratio']] tools/dnd_rottura.py:389: print(f"\n CV gap_ratio tra soglie = {cv:.6f}") tools/dnd_rottura.py:409: print(f" Segnale | gap_ratio | gap_finale | n_int") tools/dnd_rottura.py:424: 'gap_ratio': float(obs.get('gap_ratio_medio', 0)), tools/dnd_rottura.py:431: print(f" {nome:15s} | {entry['gap_ratio']:.6f} | {entry['gap_finale']:.2e} | {n}") tools/dnd_rottura.py:460: print(f" (L'interferenza non è nel gap_ratio, che è SEMPRE φ².") tools/exp_markov_scale_function.py:6:If so, does the decay track the GUE→Poisson boundary? tools/exp_markov_scale_function.py:16: 5. Brody parameter β (GUE/Poisson indicator) tools/exp_markov_scale_function.py:60:def brody_beta(spacings): tools/exp_markov_scale_function.py:62: β=0 → Poisson, β=1 → GUE (Wigner). tools/exp_markov_scale_function.py:138: beta = brody_beta(spacings) tools/exp_markov_scale_function.py:147: 'brody_beta': float(beta), tools/exp_markov_scale_function.py:212: f"{res['lag1_acf']:>7.4f} {res['brody_beta']:>5.2f} {res['mean_gap']:>7.2f}") tools/exp_markov_scale_function.py:219: betas = [r['brody_beta'] for r in results] tools/exp_markov_scale_function.py:237: # Linear regression: brody_beta vs ln(p) tools/exp_markov_scale_function.py:250: # Correlation between ordering_frac and brody_beta tools/exp_markov_scale_function.py:252: print(f"\nCorrelation(ordering_frac, brody_β) = {corr_ob:.4f}") tools/exp_markov_scale_function.py:288: 'brody_vs_lnp': { tools/exp_markov_scale_function.py:296: 'ordering_brody': corr_ob, tools/exp_mod3_scaling.py:260: brody_slope = -0.030 # per ln(p) tools/exp_mod3_scaling.py:262: print(f"Brody beta slope: {brody_slope} per ln(p)") tools/dnd_spettro_zeta.py:11: 3. La statistica degli spacing deve mostrare repulsione a livelli (come GUE per ζ) tools/dnd_spettro_zeta.py:20: Parte 3: Unfolding e statistica spacing P(s) — confronto GOE/GUE tools/dnd_spettro_zeta.py:203:def unfold_spectrum(eigenvalues, degree=5): tools/dnd_spettro_zeta.py:215: """Generate GOE nearest-neighbor spacings from Wigner surmise CDF.""" tools/dnd_spettro_zeta.py:221: """Generate GUE nearest-neighbor spacings via rejection sampling.""" tools/dnd_spettro_zeta.py:226: # GUE Wigner surmise: P(s) = (32/π²)s²·exp(-4s²/π) tools/dnd_spettro_zeta.py:228: # Envelope: C·exp(-s) where C = max of P_GUE(s)·exp(s) tools/dnd_spettro_zeta.py:247: eps_dnd = unfold_spectrum(E_bulk) tools/dnd_spettro_zeta.py:253: eps_noL = unfold_spectrum(E_bulk_noL) tools/dnd_spettro_zeta.py:282: print(f" vs GUE: KS={ks_gue:.4f}, p={p_gue:.4f}") tools/dnd_spettro_zeta.py:288: 'vs_GUE': {'KS': float(ks_gue), 'p': float(p_gue)}, tools/dnd_spettro_zeta.py:308: print(f" GUE: ~0.178") tools/dnd_spettro_zeta.py:318: 'GUE_ref': 0.178, tools/dnd_spettro_zeta.py:605: print(f" GUE: ac(1) ≈ -0.50") tools/dnd_spettro_zeta.py:747: │ Var(s) D-ND: {var_dnd:.4f} (GOE≈0.286, GUE≈0.178) │ tools/dnd_spettro_zeta.py:749: │ ac(1) D-ND: {f'{ac1_dnd:+.4f}' if ac1_dnd is not None else ' N/A':>7s} (GOE≈-0.27, GUE≈-0.50) │ tools/dnd_spettro_zeta.py:766: stat_class = "GUE" tools/dnd_spettro_zeta.py:774: print(f" Gli zeri di ζ → GUE") tools/dnd_spettro_zeta.py:776: print(f" Per passare da GOE a GUE serve rompere T-simmetria.") tools/dnd_spettro_zeta.py:780: print(f" V(r) + fase det=-1 potrebbe dare GUE.") tools/dnd_spettro_zeta.py:781: elif stat_class == "GUE": tools/dnd_spettro_zeta.py:782: print(f" Sorpresa: H reale mostra statistiche GUE-like") tools/dnd_spettro_zeta.py:784: print(f" generare pseudo-GUE a queste energie") tools/dnd_spettro_zeta.py:799: print(f" Statistica: {stat_class} (ζ = GUE)") tools/dnd_spettro_zeta.py:855: verdict = "MODERATA: struttura condivisa, serve la matrice completa per GUE" tools/dnd_spettro_zeta.py:868: print(f" → Se questo dà GUE, la connessione è completa") tools/exp_markov_dipolar_decomposition.py:6:GUE has dL1/dSR = 8.37 at angle -97 deg. tools/exp_perturbation_rank_size_curve.py:7:The 2026-05-06 06:25 cycle restricted the claim "GUE has a second tools/exp_perturbation_rank_size_curve.py:53: """Generate unfolded GUE spacings by concatenating independent matrices.""" tools/exp_perturbation_rank_size_curve.py:61: eigs = np.sort(np.linalg.eigvalsh(h).real) tools/exp_perturbation_rank_size_curve.py:324: "poisson": lambda rep_rng, rep_i: rep_rng.exponential(1.0, size=max_n), tools/exp_perturbation_dimensionality_audit.py:7:The 2026-05-06 03:30 run found that GUE spacing sequences expose a second tools/exp_perturbation_dimensionality_audit.py:9:to one axis. That run used a short GUE sequence. This tool repeats the same tools/exp_perturbation_dimensionality_audit.py:52: eigs = np.sort(np.linalg.eigvalsh(h).real) tools/exp_perturbation_dimensionality_audit.py:309: "poisson": root_rng.exponential(1.0, size=args.n_prime_gaps), tools/exp_perturbation_dimensionality_audit.py:314: "question": "Is the GUE second perturbation axis stable across independent ensembles and sample-size controls?", tools/exp_perturbation_dimensionality_audit.py:336: print("\nGUE independent replicates") tools/exp_prime_persistent_blank_gate.py:176: "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from GUE random matrix blocks and logistic return intervals", tools/exp_prime_persistent_blank_gate.py:179: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels", tools/exp_poisson_convergence.py:3:exp_poisson_convergence.py — Do beta, _excess, and acf1 predict the same Poisson scale? tools/exp_poisson_convergence.py:14: python exp_poisson_convergence.py [--n_primes N] [--n_windows W] [--n_surrogates S] tools/exp_poisson_convergence.py:30:def gap_ratio_r(gaps): tools/exp_poisson_convergence.py:37:def brody_beta(gaps, n_bins=50): tools/exp_poisson_convergence.py:87: r = gap_ratio_r(gaps) tools/exp_poisson_convergence.py:88: beta = brody_beta(gaps) tools/exp_poisson_convergence.py:117: r_vals.append(gap_ratio_r(gaps_surr)) tools/exp_poisson_convergence.py:118: beta_vals.append(brody_beta(gaps_surr)) tools/exp_poisson_convergence.py:319: 'experiment': 'poisson_convergence', tools/exp_poisson_convergence.py:333: with open('/opt/MM_D-ND/tools/data/exp_poisson_convergence.json', 'w') as f: tools/exp_poisson_convergence.py:336: print(f"\nData saved to tools/data/exp_poisson_convergence.json") tools/exp_acf_range_universality.py:11:Domains: primes, GUE, GOE, Poisson, primes_shuffled. tools/exp_acf_range_universality.py:69:def generate_rmt_spacings(N, n_mat, ensemble='GUE'): tools/exp_acf_range_universality.py:74: if ensemble == 'GUE': tools/exp_acf_range_universality.py:81: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_acf_range_universality.py:169: print("Generating GUE spacings...") tools/exp_acf_range_universality.py:170: gue_gaps = generate_rmt_spacings(N_rmt, n_mat_rmt, 'GUE') tools/exp_acf_range_universality.py:174: poisson_gaps = np.random.exponential(1.0, 100_000) tools/exp_acf_range_universality.py:182: 'GUE': gue_gaps, tools/exp_acf_range_universality.py:184: 'Poisson': poisson_gaps, tools/exp_quasiperiodic_vc_lattice_gate.py:19:from scipy.linalg import eigvalsh_tridiagonal tools/exp_quasiperiodic_vc_lattice_gate.py:35: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_psd_amplitude_scaling.py:198: 'poisson_crossover_ln_p': round(float(ln_p_cross_dip), 2) if ln_p_cross_dip else None, tools/exp_rp_boundary_raw_count_null_audit.py:60: gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] tools/exp_rp_boundary_raw_count_null_audit.py:62: raise ValueError("labels must include Poisson and GUE poles") tools/exp_rp_boundary_raw_count_null_audit.py:81: if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: tools/exp_rp_boundary_raw_count_null_audit.py:83: elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: tools/exp_rp_boundary_raw_count_null_audit.py:123: "brody_q": [], tools/exp_rp_boundary_raw_count_null_audit.py:151: poisson_pole_max=args.poisson_pole_max, tools/exp_rp_boundary_raw_count_null_audit.py:174: item["brody_q"].append(float(row["brody_q"])) tools/exp_rp_boundary_raw_count_null_audit.py:197: "brody_q": median(item["brody_q"]), tools/exp_rp_boundary_raw_count_null_audit.py:243: "median_brody_q": round(class_row["brody_q"], 6), tools/exp_rp_boundary_raw_count_null_audit.py:337: "poisson_pole_max": args.poisson_pole_max, tools/exp_rp_boundary_raw_count_null_audit.py:350: "operator": "finite-size RP diagonal-plus-GUE flow with kNN graph perturbations and two row-aligned nulls", tools/exp_rp_boundary_raw_count_null_audit.py:351: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE across sizes, seeds and k values", tools/exp_rp_boundary_raw_count_null_audit.py:354: "not_tested": "N to infinity, experimental spectra, unfolding alternatives, Anderson 3D, many-body RP", tools/exp_rp_boundary_raw_count_null_audit.py:386: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_quasiperiodic_vc_curve_map.py:19:from scipy.linalg import eigvalsh_tridiagonal tools/exp_quasiperiodic_vc_curve_map.py:43: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:25: brody_pdf, tools/exp_anderson3d_mobility_edge_two_reader_audit.py:27: fit_brody_q, tools/exp_anderson3d_mobility_edge_two_reader_audit.py:33: poisson_cdf, tools/exp_anderson3d_mobility_edge_two_reader_audit.py:40: "brody_q", tools/exp_anderson3d_mobility_edge_two_reader_audit.py:41: "wigner_poisson_like_weight", tools/exp_anderson3d_mobility_edge_two_reader_audit.py:117: return "localized_poisson_pole" tools/exp_anderson3d_mobility_edge_two_reader_audit.py:141: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:152: "brody_q": round(brody_q, 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:153: "brody_nll": round(brody_nll, 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:154: "wigner_poisson_like_weight": round(mixture_w, 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:167: row["brody_q"], tools/exp_anderson3d_mobility_edge_two_reader_audit.py:168: row["wigner_poisson_like_weight"], tools/exp_anderson3d_mobility_edge_two_reader_audit.py:193: localized_idx = [i for i, label in enumerate(labels) if label == "localized_poisson_pole"] tools/exp_anderson3d_mobility_edge_two_reader_audit.py:216: "localized_poisson_pole", tools/exp_anderson3d_mobility_edge_two_reader_audit.py:219: elif {labels[i], labels[other]} == {"metallic_wigner_pole", "localized_poisson_pole"}: tools/exp_anderson3d_mobility_edge_two_reader_audit.py:247: q = float(row["brody_q"]) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:248: w = float(row["wigner_poisson_like_weight"]) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:250: return "classical_poisson_endpoint" tools/exp_anderson3d_mobility_edge_two_reader_audit.py:289: "brody_q": [], tools/exp_anderson3d_mobility_edge_two_reader_audit.py:300: row_hits[name]["brody_q"].append(float(row["brody_q"])) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:301: row_hits[name]["mixture_w"].append(float(row["wigner_poisson_like_weight"])) tools/exp_anderson3d_mobility_edge_two_reader_audit.py:313: "brody_q": median(item["brody_q"]), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:314: "wigner_poisson_like_weight": median(item["mixture_w"]), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:330: "median_brody_q": round(median(item["brody_q"]), 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:331: "median_wigner_poisson_like_weight": round(median(item["mixture_w"]), 6), tools/exp_anderson3d_mobility_edge_two_reader_audit.py:448: "generator": "H=sum_i eps_i |i>= N: tools/exp_scale_selective_perturbation.py:61: return unfolded[:N] tools/exp_scale_selective_perturbation.py:62: return unfolded tools/exp_scale_selective_perturbation.py:65:def generate_poisson(N, rng): tools/exp_scale_selective_perturbation.py:197: ('GUE', lambda: generate_gue(N, rng))]: tools/exp_rosenzweig_porter_bridge_physical_audit.py:6:diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical tools/exp_rosenzweig_porter_bridge_physical_audit.py:26:FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] tools/exp_rosenzweig_porter_bridge_physical_audit.py:51:def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: tools/exp_rosenzweig_porter_bridge_physical_audit.py:56:def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: tools/exp_rosenzweig_porter_bridge_physical_audit.py:60: pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) tools/exp_rosenzweig_porter_bridge_physical_audit.py:68:def poisson_cdf(s: np.ndarray) -> np.ndarray: tools/exp_rosenzweig_porter_bridge_physical_audit.py:84: poi = poisson_cdf(sorted_s) tools/exp_rosenzweig_porter_bridge_physical_audit.py:143:def source_type(lam: float, poisson_max: float, gue_min: float) -> str: tools/exp_rosenzweig_porter_bridge_physical_audit.py:144: if lam <= poisson_max: tools/exp_rosenzweig_porter_bridge_physical_audit.py:147: return "GUE_pole" tools/exp_rosenzweig_porter_bridge_physical_audit.py:156: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_rosenzweig_porter_bridge_physical_audit.py:161: "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), tools/exp_rosenzweig_porter_bridge_physical_audit.py:165: "brody_q": round(brody_q, 6), tools/exp_rosenzweig_porter_bridge_physical_audit.py:166: "brody_nll": round(brody_nll, 6), tools/exp_rosenzweig_porter_bridge_physical_audit.py:178: + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] tools/exp_rosenzweig_porter_bridge_physical_audit.py:200: gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] tools/exp_rosenzweig_porter_bridge_physical_audit.py:202: raise ValueError("lambda grid must include Poisson and GUE poles") tools/exp_rosenzweig_porter_bridge_physical_audit.py:221: if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: tools/exp_rosenzweig_porter_bridge_physical_audit.py:223: elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: tools/exp_rosenzweig_porter_bridge_physical_audit.py:246: q = float(row["brody_q"]) tools/exp_rosenzweig_porter_bridge_physical_audit.py:249: return "classical_poisson_endpoint" tools/exp_rosenzweig_porter_bridge_physical_audit.py:288: "brody_q": [], tools/exp_rosenzweig_porter_bridge_physical_audit.py:298: row_hits[name]["brody_q"].append(float(row["brody_q"])) tools/exp_rosenzweig_porter_bridge_physical_audit.py:309: "brody_q": float(np.median(item["brody_q"])), tools/exp_rosenzweig_porter_bridge_physical_audit.py:325: "median_brody_q": round(float(np.median(item["brody_q"])), 6), tools/exp_rosenzweig_porter_bridge_physical_audit.py:369: "poisson_pole_max": args.poisson_pole_max, tools/exp_rosenzweig_porter_bridge_physical_audit.py:376: "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", tools/exp_rosenzweig_porter_bridge_physical_audit.py:377: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", tools/exp_rosenzweig_porter_bridge_physical_audit.py:380: "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", tools/exp_rosenzweig_porter_bridge_physical_audit.py:414: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_spectral_2d.py:46:def brody_beta(spacings): tools/exp_spectral_2d.py:71: """Prime gaps, unfolded by local density.""" tools/exp_spectral_2d.py:89: """Raw prime gaps (not unfolded) for distribution sampling.""" tools/exp_spectral_2d.py:106: eigs = np.sort(np.linalg.eigvalsh(H).real) tools/exp_spectral_2d.py:194:def gen_anticorr_poisson(n_spacings, strength=0.3): tools/exp_spectral_2d.py:284: beta = brody_beta(spacings) tools/exp_spectral_2d.py:292: beta_s.append(brody_beta(sh)) tools/exp_spectral_2d.py:327: ("GUE", lambda n: gen_gue(n)), tools/exp_spectral_2d.py:340: ("Anticorr_0.1", lambda n: gen_anticorr_poisson(n, 0.1)), tools/exp_spectral_2d.py:341: ("Anticorr_0.2", lambda n: gen_anticorr_poisson(n, 0.2)), tools/exp_spectral_2d.py:342: ("Anticorr_0.3", lambda n: gen_anticorr_poisson(n, 0.3)), tools/exp_spectral_2d.py:343: ("Anticorr_0.5", lambda n: gen_anticorr_poisson(n, 0.5)), tools/exp_boundary_blank_null_audit.py:27:R_GUE = 0.5307 tools/exp_boundary_blank_null_audit.py:66: return "GUE" if abs(value - R_GUE) < abs(value - R_POISSON) else "Poisson" tools/exp_boundary_blank_null_audit.py:151: "R_GUE": R_GUE, tools/exp_rp_unfolding_sensitivity_audit.py:8:local-window unfolding. tools/exp_rp_unfolding_sensitivity_audit.py:27: fit_brody_q, tools/exp_rp_unfolding_sensitivity_audit.py:43:def local_unfold_gaps(gaps: np.ndarray, window: int) -> np.ndarray: tools/exp_rp_unfolding_sensitivity_audit.py:67: unfolding_mode: str, tools/exp_rp_unfolding_sensitivity_audit.py:80: if unfolding_mode == "local_window": tools/exp_rp_unfolding_sensitivity_audit.py:81: gaps = local_unfold_gaps(gaps, local_window) tools/exp_rp_unfolding_sensitivity_audit.py:90: if unfolding_mode == "global_mean": tools/exp_rp_unfolding_sensitivity_audit.py:92: elif unfolding_mode == "local_window": tools/exp_rp_unfolding_sensitivity_audit.py:95: raise ValueError(f"unknown unfolding mode: {unfolding_mode}") tools/exp_rp_unfolding_sensitivity_audit.py:105: q = float(row["brody_q"]) tools/exp_rp_unfolding_sensitivity_audit.py:108: return "classical_poisson_endpoint" tools/exp_rp_unfolding_sensitivity_audit.py:122:def compute_row(lam: float, args: argparse.Namespace, n: int, seed: int, unfolding_mode: str) -> dict[str, Any]: tools/exp_rp_unfolding_sensitivity_audit.py:129: unfolding_mode, tools/exp_rp_unfolding_sensitivity_audit.py:134: brody_q, brody_nll = fit_brody_q(s, args.grid_size) tools/exp_rp_unfolding_sensitivity_audit.py:139: "source_domain_type": source_type(lam, args.poisson_pole_max, args.gue_pole_min), tools/exp_rp_unfolding_sensitivity_audit.py:143: "brody_q": round(brody_q, 6), tools/exp_rp_unfolding_sensitivity_audit.py:144: "brody_nll": round(brody_nll, 6), tools/exp_rp_unfolding_sensitivity_audit.py:157: "brody_q": [], tools/exp_rp_unfolding_sensitivity_audit.py:168:def audit_size_mode(args: argparse.Namespace, n: int, unfolding_mode: str) -> dict[str, Any]: tools/exp_rp_unfolding_sensitivity_audit.py:182: compute_row(lam, args, n, seed + (n * 10000) + int(round(lam * 1000)), unfolding_mode) tools/exp_rp_unfolding_sensitivity_audit.py:186: rng = np.random.default_rng(seed + n + (0 if unfolding_mode == "global_mean" else 1000003)) tools/exp_rp_unfolding_sensitivity_audit.py:194: {"n": n, "seed": seed, "k": k, "unfolding_mode": unfolding_mode, "observed_candidates": observed_candidates} tools/exp_rp_unfolding_sensitivity_audit.py:204: item["brody_q"].append(float(row["brody_q"])) tools/exp_rp_unfolding_sensitivity_audit.py:227: "brody_q": median(item["brody_q"]), tools/exp_rp_unfolding_sensitivity_audit.py:273: "median_brody_q": round(class_row["brody_q"], 6), tools/exp_rp_unfolding_sensitivity_audit.py:284: "unfolding_mode": unfolding_mode, tools/exp_rp_unfolding_sensitivity_audit.py:301: modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] tools/exp_rp_unfolding_sensitivity_audit.py:306: entries = [entry for entry in by_size_mode if entry["unfolding_mode"] == mode] tools/exp_rp_unfolding_sensitivity_audit.py:326: entries = [entry for entry in by_size_mode if entry["unfolding_mode"] == mode] tools/exp_rp_unfolding_sensitivity_audit.py:350: "experiment": "rp_unfolding_sensitivity_audit", tools/exp_rp_unfolding_sensitivity_audit.py:351: "question": "Do the RP boundary rows 0.045/0.060 remain thresholded under an alternate local-window unfolding?", tools/exp_rp_unfolding_sensitivity_audit.py:362: "unfolding_mode", tools/exp_rp_unfolding_sensitivity_audit.py:374: "poisson_pole_max": args.poisson_pole_max, tools/exp_rp_unfolding_sensitivity_audit.py:376: "unfolding_modes": modes, tools/exp_rp_unfolding_sensitivity_audit.py:384: "unfolding_stability_clause": "boundary-stable only if row passes all sizes in every unfolding mode", tools/exp_rp_unfolding_sensitivity_audit.py:387: "claim": "the finite RP boundary window is unfolding-stable only if the same lambda rows beat label-shuffle and position-shift nulls under global and local spacing normalization", tools/exp_rp_unfolding_sensitivity_audit.py:388: "observable": "thresholded two-reader raw-count pass by lambda, size and unfolding mode", tools/exp_rp_unfolding_sensitivity_audit.py:389: "operator": "repeat the RP raw-count gate with global mean and local-window unfolded spacings", tools/exp_rp_unfolding_sensitivity_audit.py:390: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE across size, seed, k and unfolding mode", tools/exp_rp_unfolding_sensitivity_audit.py:391: "denominator": "same lambda grid per size and unfolding; observed denominator seeds*k, null denominators observed*null_trials", tools/exp_rp_unfolding_sensitivity_audit.py:392: "non_possible": "unfolding-stable boundary if any promoted lambda fails all-size pass under local-window unfolding", tools/exp_rp_unfolding_sensitivity_audit.py:399: "unfolding_sensitive_rows": mode_sensitive, tools/exp_rp_unfolding_sensitivity_audit.py:413: parser.add_argument("--out", default="tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json") tools/exp_rp_unfolding_sensitivity_audit.py:423: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_rp_unfolding_sensitivity_audit.py:428: parser.add_argument("--unfolding-modes", default="global_mean,local_window") tools/exp_boundary_coherence.py:5:Question: Do different observables agree on WHERE primes sit between GUE and Poisson? tools/exp_boundary_coherence.py:10: 1. Mean spacing ratio (Poisson ≈ 0.386, GUE ≈ 0.5307) tools/exp_boundary_coherence.py:11: 2. Gap variance ratio Var/μ² (Poisson = 1.0, GUE ≈ 0.178) tools/exp_boundary_coherence.py:12: 3. Small-gap fraction P(s<0.3) (Poisson ≈ 0.259, GUE ≈ 0.020) tools/exp_boundary_coherence.py:13: 4. Brody parameter β (Poisson = 0, GUE = 1) tools/exp_boundary_coherence.py:14: 5. Lag-1 autocorrelation (Poisson = 0, GUE ≈ -0.27) tools/exp_boundary_coherence.py:16:Each observable is normalized to τ ∈ [0,1] where 0=Poisson, 1=GUE. tools/exp_boundary_coherence.py:30:# GUE values from random matrix theory (GOE for real symmetric, GUE for complex) tools/exp_boundary_coherence.py:33: "spacing_ratio": {"poisson": 0.38629, "gue": 0.53590}, # 2ln2-1, 4-2√3 tools/exp_boundary_coherence.py:34: "gap_var_ratio": {"poisson": 1.0, "gue": 0.178}, tools/exp_boundary_coherence.py:35: "small_gap_frac": {"poisson": 0.2592, "gue": 0.020}, # P(s<0.3) for exp vs Wigner tools/exp_boundary_coherence.py:36: "brody_beta": {"poisson": 0.0, "gue": 1.0}, tools/exp_boundary_coherence.py:37: "lag1_acf": {"poisson": 0.0, "gue": -0.271}, tools/exp_boundary_coherence.py:42: """Normalize gaps to mean 1 (unfolding).""" tools/exp_boundary_coherence.py:73:def brody_beta(gaps): tools/exp_boundary_coherence.py:107: "brody_beta": brody_beta(gaps), tools/exp_boundary_coherence.py:113: """Normalize observable to τ ∈ [0,1] where 0=Poisson, 1=GUE.""" tools/exp_boundary_coherence.py:114: p = REF[obs_name]["poisson"] tools/exp_boundary_coherence.py:122: """Generate GUE spacings from random Hermitian matrices.""" tools/exp_boundary_coherence.py:126: # GUE: complex Hermitian with Gaussian entries tools/exp_boundary_coherence.py:129: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_boundary_coherence.py:136:def generate_poisson_spacings(n): tools/exp_boundary_coherence.py:161: print("Computing GUE reference...") tools/exp_boundary_coherence.py:164: results["GUE_reference"] = { tools/exp_boundary_coherence.py:170: poi_gaps = generate_poisson_spacings(20000) tools/exp_boundary_coherence.py:207: print(f"\nReference anchors (τ should be ≈ 0 for Poisson, ≈ 1 for GUE):") tools/exp_boundary_coherence.py:208: print(f" {'Observable':<20} {'Poisson τ':>10} {'GUE τ':>10}") tools/exp_boundary_coherence.py:211: gt = results["GUE_reference"]["tau"][obs_name] tools/exp_boundary_coherence.py:214: print(f"\nPrime gaps — τ values (0=Poisson, 1=GUE):") tools/exp_boundary_coherence.py:215: print(f" {'Scale':<15} {'spacing_r':>10} {'var_ratio':>10} {'small_gap':>10} {'brody_β':>10} {'lag1_acf':>10} │ {'mean':>6} {'std':>6}") tools/exp_boundary_coherence.py:221: vals = [t["spacing_ratio"], t["gap_var_ratio"], t["small_gap_frac"], t["brody_beta"], t["lag1_acf"]] tools/exp_boundary_coherence.py:225: print(f" {'Scale':<15} {'spacing_r':>10} {'var_ratio':>10} {'small_gap':>10} {'brody_β':>10} {'lag1_acf':>10} │ {'mean':>6} {'std':>6}") tools/exp_boundary_coherence.py:231: vals = [t["spacing_ratio"], t["gap_var_ratio"], t["small_gap_frac"], t["brody_beta"], t["lag1_acf"]] tools/exp_two_layer_universality.py:13: 1. Generate 7 gap/spacing sequences: primes, GUE, Poisson, AR(1), logistic, tools/exp_two_layer_universality.py:107:def next_nearest_sr(gaps): tools/exp_two_layer_universality.py:108: """SR2: spacing ratio of next-nearest-neighbor gaps (skip one).""" tools/exp_two_layer_universality.py:174: 'SR2': next_nearest_sr, tools/exp_two_layer_universality.py:194: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_two_layer_universality.py:204:def gen_poisson_spacings(N, rng=None): tools/exp_two_layer_universality.py:249: 'GUE': gen_gue_spacings, tools/exp_two_layer_universality.py:250: 'Poisson': gen_poisson_spacings, tools/exp_two_layer_universality.py:282: if seq_name in ('GUE', 'Poisson', 'AR1'): tools/exp_sturmian_denominator_alignment_gate.py:194: nearest_delta = min(abs(phi_tau - value) for value in nonphi) tools/exp_sturmian_denominator_alignment_gate.py:197: "verdict": "phi_specific" if nearest_delta >= min_tau_delta else "sturmian_corridor", tools/exp_sturmian_denominator_alignment_gate.py:201: "nearest_nonphi_tau_delta": float(nearest_delta), tools/exp_vc_label_preserving_swap_gate.py:24:from scipy.linalg import eigvalsh_tridiagonal tools/exp_vc_label_preserving_swap_gate.py:42: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_two_channel_cross_domain.py:17: - GUE eigenvalues: random matrix, strong statistical correlation, no arithmetic tools/exp_two_channel_cross_domain.py:27:If GUE shows only decaying channels → C1 supported (algebraic invariance is prime-specific) tools/exp_two_channel_cross_domain.py:28:If GUE shows a scale-invariant channel → C1 needs refinement tools/exp_two_channel_cross_domain.py:63: """Generate unfolded spacings from GUE random matrices.""" tools/exp_two_channel_cross_domain.py:66: # GUE: H = (A + A^*) / (2 * sqrt(2N)), A is complex Gaussian tools/exp_two_channel_cross_domain.py:70: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_two_channel_cross_domain.py:75: # Local unfolding tools/exp_two_channel_cross_domain.py:295: # === GUE EIGENVALUES === tools/exp_two_channel_cross_domain.py:296: print(f"\n=== GUE EIGENVALUES ({gue_matrices} matrices of size {gue_size}) ===") tools/exp_two_channel_cross_domain.py:298: print(f"Got {len(gue_spacings)} GUE spacings, mean={np.mean(gue_spacings):.3f}") tools/exp_two_channel_cross_domain.py:412: print(" GUE/Cramer should show decaying or absent binary channel") tools/exp_boundary_residual_beta_absent_audit.py:188: "not_tested": "global beta grid, V_c, source GUE/Poisson label validity", tools/extract_cognitive_enzymes.py:102: "algebra", "gauge", "ricci", "bloch", "gue", "poisson", tools/extract_cognitive_enzymes.py:127: "gauge", "ricci", "bloch", "gue", "poisson", "sturmian", tools/exp_boundary_transition_taxonomy_13rows.py:180: "not_tested": "new beta grid, new null surrogates, V_c fit, source GUE/Poisson label validity", tools/exp_brody_crossover.py:6:crossover from GUE-like to Poisson-like? What is the functional form? tools/exp_brody_crossover.py:34:def brody_loglik(beta, spacings): tools/exp_brody_crossover.py:48:def fit_brody(spacings): tools/exp_brody_crossover.py:50: res = minimize_scalar(lambda b: brody_loglik(b, spacings), tools/exp_brody_crossover.py:54:def gap_ratio(gaps): tools/exp_brody_crossover.py:101: beta_prime = fit_brody(spacings) tools/exp_brody_crossover.py:102: r_prime = gap_ratio(win_gaps) tools/exp_brody_crossover.py:110: beta_cramer_list.append(fit_brody(surr_spacings)) tools/exp_brody_crossover.py:111: r_cramer_list.append(gap_ratio(surr_gaps)) tools/exp_brody_crossover.py:177: ln_p_poisson = -intercept_beta / slope_beta tools/exp_brody_crossover.py:178: print(f"\nExtrapolated Poisson (beta=0) at ln(p) = {ln_p_poisson:.1f} → p ~ 10^{ln_p_poisson/np.log(10):.0f}") tools/exp_brody_crossover.py:188: "experiment": "brody_crossover", tools/exp_brody_crossover.py:213: outpath = "data/reports/exp_brody_crossover_20260405.json" tools/lab_autological_repair.py:434: lines.append("- Se `gap_ratio` e' richiesto dalla direttiva, includilo; se non lo testi, dichiaralo come not_tested.") tools/lab_falsifier.py:81:Un pattern in distribuzioni classiche (primi, GUE, random walk, Markov chain, gap statistics) probabilmente ha un nome. Default hypothesis: re-discovery / caso limite di teorema noto. Taggare come "NEW" senza riconoscere il risultato classico piu' vicino (es. Lemke Oliver–Soundararajan per prime gaps mod q) e' beauty bias. Flag. tools/lab_falsifier.py:106:GUE/Poisson, flagga il drift. La deviazione e' ammessa solo se dichiarata come tools/exp_cross_domain_dipolar_direction.py:6:Primes have theta=-111 deg in the (SR, L1) plane. GUE has theta=-97 deg. tools/exp_cross_domain_dipolar_direction.py:7:Do all GUE-like domains share -97, or does each have its own direction? tools/exp_cross_domain_dipolar_direction.py:11: GUE-like: GUE spacings, GOE spacings, CUE spacings, Riemann zeta zeros (via GUE proxy) tools/exp_cross_domain_dipolar_direction.py:81:def gen_rmt_spacings(N_mat, ensemble='GUE', rng=None): tools/exp_cross_domain_dipolar_direction.py:85: if ensemble == 'GUE': tools/exp_cross_domain_dipolar_direction.py:109: eigs = np.linalg.eigvalsh(H) tools/exp_cross_domain_dipolar_direction.py:111: # Unfold: divide by local mean spacing (simple unfolding) tools/exp_cross_domain_dipolar_direction.py:132:def gen_poisson(N, rng=None): tools/exp_cross_domain_dipolar_direction.py:140: """Uniform iid on [0,2] — mean 1, same as unfolded.""" tools/exp_cross_domain_dipolar_direction.py:177: for ens_name in ['GUE', 'GOE', 'CUE']: tools/exp_cross_domain_dipolar_direction.py:230: for name, gen_fn in [('exponential', gen_poisson), tools/exp_cross_domain_dipolar_direction.py:275: rmt_names = ['GUE', 'GOE', 'CUE'] tools/exp_cross_domain_dipolar_direction.py:276: poisson_names = ['exponential', 'uniform', 'geometric'] tools/exp_cross_domain_dipolar_direction.py:308: for name in poisson_names: tools/exp_cross_domain_dipolar_direction.py:313: for name in poisson_names: tools/lab_tool_contract.py:80: for key in ("component_state", "poisson_contrast", "direct_contrast", "trace"): tools/lab_tool_contract.py:136: "expected_smoke": "returns tester_id and class labels; output includes poisson_contrast and direct_contrasts", tools/exp_dR_brody_connection.py:54:def brody_beta(gaps, n_iter=50): tools/exp_dR_brody_connection.py:119: beta = brody_beta(gaps) tools/exp_dR_brody_connection.py:137: 'brody_beta': beta, tools/exp_dR_brody_connection.py:149: print(f" Window {i+1}/{N_WINDOWS}: ln(p)={obs['ln_p']:.2f}, beta={obs['brody_beta']:.4f}, dR_acf1={obs['dR_acf1']:.4f}") tools/exp_dR_brody_connection.py:153:beta_arr = np.array([o['brody_beta'] for o in prime_obs]) tools/exp_dR_brody_connection.py:219: s_beta = np.array([o['brody_beta'] for o in s_obs]) tools/exp_dR_brody_connection.py:282: "experiment": "exp_dR_brody_connection", tools/exp_dR_brody_connection.py:337:out_path = "/opt/MM_D-ND/tools/data/reports/exp_dR_brody_connection.json" tools/exp_dipolar_angle_reference.py:3:exp_dipolar_angle_reference.py — Dipolar angle of GUE, Poisson, and primes tools/exp_dipolar_angle_reference.py:6:1. Pure GUE eigenvalue spacings (GOE/GUE unfolded) tools/exp_dipolar_angle_reference.py:13:unique or just a position on the GUE-Poisson continuum? tools/exp_dipolar_angle_reference.py:21:from numpy.linalg import eigvalsh tools/exp_dipolar_angle_reference.py:80: """Generate gaps from GUE eigenvalues (unfolded).""" tools/exp_dipolar_angle_reference.py:83: # GUE: complex Hermitian random matrix tools/exp_dipolar_angle_reference.py:87: eigs = np.sort(eigvalsh(H)) tools/exp_dipolar_angle_reference.py:100: """Generate gaps from GOE eigenvalues (unfolded).""" tools/exp_dipolar_angle_reference.py:105: eigs = np.sort(eigvalsh(H)) tools/exp_dipolar_angle_reference.py:115:def generate_poisson_gaps(n_gaps): tools/exp_dipolar_angle_reference.py:153: # 2. GUE tools/exp_dipolar_angle_reference.py:154: print(f"Generating GUE gaps (n_trials={n_trials})...") tools/exp_dipolar_angle_reference.py:163: print(f" GUE trial {t+1}/{n_trials}: theta = {theta:.1f}") tools/exp_dipolar_angle_reference.py:165: results['GUE'] = { tools/exp_dipolar_angle_reference.py:171: print(f" GUE: theta = {np.mean(gue_thetas):.1f} +/- {np.std(gue_thetas):.1f} deg") tools/exp_dipolar_angle_reference.py:198: poi_gaps = generate_poisson_gaps(len(prime_gaps)) tools/exp_dipolar_angle_reference.py:240: print(f"{'GUE':<12} {results['GUE']['theta_mean']:>7.1f} +/- {results['GUE']['theta_std']:>5.1f} {results['GUE']['SR_mean']:<10.4f} {results['GUE']['L1_mean']:<10.4f}") tools/exp_dipolar_angle_reference.py:247: for name in ['GUE', 'GOE', 'Poisson', 'Cramer']: tools/plot_risultante.py:4:Background-Fluctuation Separation: V(r) → Berry-Keating, arithmetic → GUE. tools/plot_risultante.py:90:GUE_R = 0.60266 tools/plot_risultante.py:92:odlyzko_excess = odlyzko_r - GUE_R # +0.00826 tools/plot_risultante.py:103:excesses = [r - GUE_R for _, _, r in lf_data] tools/plot_risultante.py:111: ex = r - GUE_R tools/plot_risultante.py:119:ax.axhline(y=0, color='green', linestyle='--', alpha=0.5, label='GUE exact') tools/plot_risultante.py:128:ax.set_ylabel(r'$\Delta\langle r \rangle$ (excess over GUE)', fontsize=12) tools/plot_risultante.py:140:ax.set_title('(d) Algebraic chain: D-ND → GUE', fontsize=13) tools/plot_risultante.py:148: (1.5, 0.5, r'GUE statistics ($\beta = 2.007$)', 'lightgreen'), tools/exp_duality_scale_contrast.py:5:for prime gaps, GUE eigenvalue spacings, and Poisson gaps. tools/exp_duality_scale_contrast.py:43: """Generate GUE (beta=2) eigenvalue spacings from random Hermitian matrix.""" tools/exp_duality_scale_contrast.py:47: evals = np.sort(np.linalg.eigvalsh(H)) tools/exp_duality_scale_contrast.py:58:def poisson_spacings(n: int, rng: np.random.Generator) -> np.ndarray: tools/exp_duality_scale_contrast.py:140: # Normalize by local mean (unfold) 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:163: poisson_contrast = windowed_contrast(poisson_gaps, window_sizes, n_shuffle, rng) tools/exp_duality_scale_contrast.py:165: # GUE: smaller windows only (limited by matrix size) tools/exp_duality_scale_contrast.py:167: print(f"Computing windowed contrast for GUE gaps (sizes: {gue_window_sizes})...") tools/exp_duality_scale_contrast.py:175: poisson_global_z, poisson_global_real = compute_z_scores( tools/exp_duality_scale_contrast.py:176: poisson_gaps[:5000], n_shuffle, rng tools/exp_duality_scale_contrast.py:189: "n_poisson_gaps": int(len(poisson_gaps)), tools/exp_duality_scale_contrast.py:197: "poisson": poisson_global_z, tools/exp_duality_scale_contrast.py:202: "poisson": poisson_global_real, tools/exp_duality_scale_contrast.py:207: "poisson": poisson_contrast, tools/exp_duality_scale_contrast.py:223: for domain in ["primes", "gue", "poisson"]: tools/exp_duality_scale_contrast.py:230: lines.append(f" {'W_size':>8s} {'Primes':>10s} {'GUE':>10s} {'Poisson':>10s}") tools/exp_duality_scale_contrast.py:235: poisson_data = {d["window_size"]: d for d in result["windowed_contrast"]["poisson"]} tools/exp_duality_scale_contrast.py:238: list(prime_data.keys()) + list(gue_data.keys()) + list(poisson_data.keys()) tools/exp_duality_scale_contrast.py:243: qz = f"{poisson_data[ws]['z_SR_mean']:+.2f}" if ws in poisson_data else "—" tools/exp_duality_scale_contrast.py:259: for domain, data in [("primes", prime_data), ("poisson", poisson_data)]: tools/prime_mod6_pipeline_closeout.py:85: "classification": "nearest_known_relation_only", tools/prime_mod6_pipeline_closeout.py:144: default="docs/operations/prime_mod6_nearest_literature_2026-05-15.md", tools/exp_gap_label_ostrowski_recognizability_gate.py:95: nearest = 0 tools/exp_gap_label_ostrowski_recognizability_gate.py:100: nearest = b tools/exp_gap_label_ostrowski_recognizability_gate.py:101: return int(best), int(nearest) tools/exp_gap_label_ostrowski_recognizability_gate.py:111: distance, nearest = boundary_distance(center, n, boundaries) tools/exp_gap_label_ostrowski_recognizability_gate.py:124: "nearest_boundary": nearest, tools/spectral_gap_analysis.py:9:(Kolmogorov-Smirnov, pair correlation, nearest-neighbor spacing). tools/spectral_gap_analysis.py:200: # Approximate via nearest-neighbor spacing distribution tools/spectral_gap_analysis.py:201: # For GUE (expected for zeta zeros): P(s) ≈ (32/π²)s² exp(-4s²/π) tools/spectral_gap_analysis.py:219: """GUE Wigner surmise: P(s) = (32/π²)s² exp(-4s²/π).""" tools/spectral_gap_analysis.py:259: label='GUE Wigner') tools/spectral_gap_analysis.py:269: fig.suptitle('Nearest-Neighbor Spacing: Spectral Gaps vs Zeta Gaps vs GUE', tools/topological_charge.py:166: # Mark nearest integers tools/topological_charge.py:174: # Quantization check: distance to nearest integer tools/topological_charge.py:264: print(f" Mean distance to nearest integer: {np.mean(dist_to_int):.6f}") tools/topological_charge.py:265: print(f" Max distance to nearest integer: {np.max(dist_to_int):.6f}") tools/topological_charge.py:269: print(f" Unique nearest integers: {np.unique(chi_rounded).astype(int).tolist()}") tools/topological_charge.py:290: 'unique_nearest_integers': np.unique(chi_rounded).astype(int).tolist(), tools/exp_vc_null_regression_gate.py:20:from scipy.linalg import eigvalsh_tridiagonal tools/exp_vc_null_regression_gate.py:38: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_quasiperiodic_grammar_scale_gate.py:5:This tool keeps the old gap_ratio observable but prevents it from deciding the tools/exp_quasiperiodic_grammar_scale_gate.py:18:from scipy.linalg import eigvalsh_tridiagonal tools/exp_quasiperiodic_grammar_scale_gate.py:58:def nearest_label(ids_value: float, theta: float, max_label: int) -> tuple[int, float]: tools/exp_quasiperiodic_grammar_scale_gate.py:128: eigs = eigvalsh_tridiagonal(diagonal, offdiag, check_finite=False) tools/exp_quasiperiodic_grammar_scale_gate.py:148: label, label_error = nearest_label(ids, reader_theta, args.max_label) tools/gue_gap_test.py:33:# GUE r ~ 0.5996, Poisson r ~ 0.3863 tools/gue_gap_test.py:37: interp = f"r={r_data:.4f} > 0.5 e z={z_score:.2f} vs shuffle: correlazione GUE-like nei gap dei primi" tools/gue_gap_test.py:41: interp = f"r={r_data:.4f} <= 0.5: gap più Poisson-like che GUE-like" tools/exp_markov3_observable_hunt.py:134: """Next-nearest-neighbor spacing ratio: min(g_n, g_{n+2})/max(g_n, g_{n+2})""" tools/exp_rp_exact_local_window_matrix.py:5:The historical unfolding audit forces local windows to odd widths. This tools/exp_rp_exact_local_window_matrix.py:20:import exp_rp_unfolding_sensitivity_audit as base tools/exp_rp_exact_local_window_matrix.py:23:def exact_local_unfold_gaps(gaps: np.ndarray, window: int) -> np.ndarray: tools/exp_rp_exact_local_window_matrix.py:29: unfolded = np.empty_like(gaps) tools/exp_rp_exact_local_window_matrix.py:43: unfolded[idx] = gaps[idx] / local_mean tools/exp_rp_exact_local_window_matrix.py:44: return unfolded tools/exp_rp_exact_local_window_matrix.py:56: original_unfolder = base.local_unfold_gaps tools/exp_rp_exact_local_window_matrix.py:57: base.local_unfold_gaps = exact_local_unfold_gaps tools/exp_rp_exact_local_window_matrix.py:77: poisson_pole_max=args.poisson_pole_max, tools/exp_rp_exact_local_window_matrix.py:82: unfolding_modes="local_window", tools/exp_rp_exact_local_window_matrix.py:158: "unfolding_patch": "exact even windows preserved; no odd-width coercion", tools/exp_rp_exact_local_window_matrix.py:170: "operator": "exact-width local unfolding matrix with label-shuffle and position-shift nulls", tools/exp_rp_exact_local_window_matrix.py:171: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", tools/exp_rp_exact_local_window_matrix.py:174: "not_tested": "N to infinity, windows beyond 12, alternate unfolding kernels, experimental spectra, Anderson 3D", tools/exp_rp_exact_local_window_matrix.py:193: base.local_unfold_gaps = original_unfolder tools/exp_rp_exact_local_window_matrix.py:210: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/exp_semireal_boundary_transfer_gate.py:196: help="Include GUE/Poisson source labels as audit metadata only. Default omits them from output.", tools/exp_markov_memory_by_gue_type.py:3:Markov Memory Decomposition Across GUE Types tools/exp_markov_memory_by_gue_type.py:5:Question: The TWO_KINDS_GUE result (2026-04-24) found that distribution-GUE tools/exp_markov_memory_by_gue_type.py:6:domains (primes, GUE matrices) retain GUE classification after shuffle, while 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:11:This experiment asks: what is the Markov memory profile of each GUE type? tools/exp_markov_memory_by_gue_type.py:12:If ordering-GUE domains get their classification FROM sequential ordering, tools/exp_markov_memory_by_gue_type.py:113:def generate_poisson_gaps(n=5000): tools/exp_markov_memory_by_gue_type.py:119: """GUE random matrix eigenvalue spacings.""" tools/exp_markov_memory_by_gue_type.py:120: from scipy.linalg import eigvalsh tools/exp_markov_memory_by_gue_type.py:124: evals = eigvalsh(H) tools/exp_markov_memory_by_gue_type.py:133: # Domain definitions with GUE type from TWO_KINDS_GUE result tools/exp_markov_memory_by_gue_type.py:136: # Distribution-GUE domains (survive shuffle) tools/exp_markov_memory_by_gue_type.py:140: 'type': 'distribution-GUE', tools/exp_markov_memory_by_gue_type.py:145: 'type': 'distribution-GUE', tools/exp_markov_memory_by_gue_type.py:149: # Ordering-GUE domains (collapse to Poisson on shuffle) tools/exp_markov_memory_by_gue_type.py:158: 'type': 'ordering-GUE', tools/exp_markov_memory_by_gue_type.py:168: 'gaps': gaps, 'type': 'ordering-GUE', 'delta_r_sign': '+' tools/exp_markov_memory_by_gue_type.py:174: domains['poisson'] = { tools/exp_markov_memory_by_gue_type.py:175: 'gaps': generate_poisson_gaps(5000), tools/exp_markov_memory_by_gue_type.py:251: print("\nAggregate by GUE type:") tools/exp_markov_memory_by_gue_type.py:252: for gtype in ['distribution-GUE', 'ordering-GUE', 'Poisson']: tools/exp_markov_memory_by_gue_type.py:267: 'question': 'Does Markov memory discriminate distribution-GUE from ordering-GUE?', tools/exp_spectral_rigidity.py:9: GUE: Sigma^2(L) ~ (2/pi^2) ln(L) + const [log-log slope ~ 0] tools/exp_spectral_rigidity.py:13: << 1 -> GUE (strong repulsion, rigid spectrum) tools/exp_spectral_rigidity.py:18:Domains: primes, GUE matrices, coupled_osc, string_vib, percolation, tools/exp_spectral_rigidity.py:42:def generate_poisson_gaps(n=10000): tools/exp_spectral_rigidity.py:47: """GUE Hermitian matrix eigenvalue spacings, bulk only.""" tools/exp_spectral_rigidity.py:50: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_rigidity.py:58: Sigma^2(L) from level positions (cumsum of unfolded gaps). tools/exp_spectral_rigidity.py:85: ('primes', {'gen': lambda: gaps_from_domain('numeri_primi'), 'type': 'dist-GUE'}), tools/exp_spectral_rigidity.py:86: ('gue_matrix', {'gen': lambda: generate_gue_gaps(600), 'type': 'dist-GUE'}), tools/exp_spectral_rigidity.py:87: ('coupled_osc', {'gen': lambda: gaps_from_domain('coupled_oscillators'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:88: ('string_vib', {'gen': lambda: gaps_from_domain('string_vibration'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:89: ('percolation', {'gen': lambda: gaps_from_domain('percolation'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:92: ('poisson', {'gen': lambda: generate_poisson_gaps(10000), 'type': 'Poisson'}), tools/exp_spectral_rigidity.py:115: unfolded = gaps / np.mean(gaps) tools/exp_spectral_rigidity.py:116: levels = np.concatenate([[0], np.cumsum(unfolded)]) tools/exp_spectral_rigidity.py:123: g_s = unfolded.copy() tools/exp_spectral_rigidity.py:156: print(f" Slope (log-log): real={slope:.3f}, shuf={slope_s:.3f} [GUE~0, Poisson=1]") tools/exp_spectral_rigidity.py:188: if ty in ('dist-GUE', 'ord-GUE'): tools/exp_metric_tensor_diagnostic.py:342: "gap_ratio_r": float(r_mean_prime), tools/exp_metric_tensor_diagnostic.py:397:print(f" prime: {output['gap_ratio_r']:.6f}") tools/md_to_site.py:174: "C5": "Nearest-neighbor spacing distributions compared to GUE Wigner surmise", tools/exp_two_channel_boundary.py:25: GUE-like and Poisson-like regimes is channel-specific. tools/exp_observable_rank_audit.py:143: poisson = rng.exponential(1.0, size=n_gaps).astype(float) tools/exp_observable_rank_audit.py:149: "poisson": poisson, tools/observables_registry.py:13:GUE") e nel farlo ha trovato **collision di nomi observable** tra script: tools/observables_registry.py:85: Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. tools/observables_registry.py:99: """**SR2 — Next-nearest Spacing Ratio** (canonical). tools/observables_registry.py:263: print(f"\nGUE-like 200 gaps:") tools/exp_prime_vs_mod6_sr_boundary.py:6:control perimeter and exposed `mod6_candidates` as the nearest antagonist. This tools/exp_prime_vs_mod6_sr_boundary.py:366: "not_tested": "global beta atlas, V_c, gap_ratio, analytic source of mod6 transfer, primality tests inside mod6 candidates", tools/r_stat_primes.py:15:r_poisson = 2 * np.log(2) - 1 # ~0.386 tools/r_stat_primes.py:24: "r_poisson_theory": round(r_poisson, 4), tools/r_stat_primes.py:27: "verdict": "GUE-like" if r_real > (r_poisson + r_gue) / 2 else "Poisson-like" tools/exp_physical_sr_residue_bounce.py:6: physical A: quantum-chaotic GUE spectra tools/exp_physical_sr_residue_bounce.py:50: return np.linalg.eigvalsh(hermitian) tools/exp_physical_sr_residue_bounce.py:56: return np.linalg.eigvalsh(symmetric) tools/exp_physical_sr_residue_bounce.py:64: return np.linalg.eigvalsh(matrix) tools/exp_physical_sr_residue_bounce.py:67:def span_matched_poisson_gaps(level_count: int, rng: np.random.Generator) -> np.ndarray: tools/exp_physical_sr_residue_bounce.py:222: null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) tools/exp_physical_sr_residue_bounce.py:238: poisson_contrast = { tools/exp_physical_sr_residue_bounce.py:271: "poisson_contrast": "span-matched Poisson null for each class/label", tools/exp_physical_sr_residue_bounce.py:284: "poisson_contrast": poisson_contrast, tools/exp_physical_sr_residue_bounce.py:311: "poisson_contrast": "real spectrum versus span-matched Poisson null", tools/exp_physical_sr_residue_bounce.py:327: "GUE_unitary_no_time_reversal": { tools/exp_physical_sr_residue_bounce.py:338: "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", tools/exp_physical_sr_residue_bounce.py:339: "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", tools/exp_physical_sr_residue_bounce.py:345: "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", tools/exp_physical_sr_residue_bounce.py:376: ("GUE_unitary_no_time_reversal", "beta_2_complex_hermitian", gue_levels(n, rng)), tools/exp_physical_sr_residue_bounce.py:390: null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) tools/exp_physical_sr_residue_bounce.py:419: gue_key = f"N{n}:GUE_unitary_no_time_reversal" tools/exp_physical_sr_residue_bounce.py:421: symmetry_contrasts[f"N{n}:GUE_minus_GOE"] = contrast( tools/exp_physical_sr_residue_bounce.py:422: f"N{n}:GUE_minus_GOE", tools/exp_physical_sr_residue_bounce.py:428: source = summaries["GUE_unitary_no_time_reversal"] tools/exp_physical_sr_residue_bounce.py:438: "null": "span_matched_poisson_same_level_count", tools/exp_physical_sr_residue_bounce.py:439: "physical_source": "quantum-chaotic spectra modeled by GOE and GUE symmetry classes", tools/exp_physical_sr_residue_bounce.py:445: "GUE": "Wigner-Dyson beta=2, complex Hermitian, no time-reversal symmetry", tools/exp_physical_sr_residue_bounce.py:489: print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) tools/exp_number_variance.py:4:Tests whether the GUE->Poisson drift (seen in gap ratio) is confirmed tools/exp_number_variance.py:7:GUE: Sigma^2(L) ~ (2/pi^2) * ln(L) + const (logarithmic) tools/exp_number_variance.py:30:def unfolded_primes(primes): tools/exp_number_variance.py:33: unfolded = p / np.log(p) tools/exp_number_variance.py:35: spacings = np.diff(unfolded) tools/exp_number_variance.py:37: unfolded = unfolded / mean_s tools/exp_number_variance.py:38: return unfolded tools/exp_number_variance.py:40:def number_variance(unfolded, L_values): tools/exp_number_variance.py:46: x_min, x_max = unfolded[0], unfolded[-1] tools/exp_number_variance.py:49: n = np.sum((unfolded >= x0) & (unfolded < x0 + L)) tools/exp_number_variance.py:55:def number_variance_poisson(L_values): tools/exp_number_variance.py:60: """GUE prediction: Sigma^2 ~ (2/pi^2) * ln(L) + 0.44 (approximate).""" tools/exp_number_variance.py:78: uf = unfolded_primes(primes) tools/exp_number_variance.py:95: sv_poisson = number_variance_poisson(L_VALUES) tools/exp_number_variance.py:118: print(f" Log-fit slope (primes): {coeffs_prime[0]:.4f} (GUE={2/np.pi**2:.4f})") tools/exp_number_variance.py:123: print(f" Better fit: {'LOG (GUE-like)' if r2_log > r2_lin else 'LINEAR (Poisson-like)'}") tools/exp_number_variance.py:125: print(f"\n L | Σ²_prime | Σ²_shuf | Σ²_GUE | Σ²_Poisson") tools/exp_number_variance.py:128: print(f" {L:4d} | {sv_primes[i]:8.4f} | {sv_shuffled[i]:8.4f} | {sv_gue[i]:7.4f} | {sv_poisson[i]:8.1f}") tools/exp_number_variance.py:151:# Key metric: does log_slope increase with scale? (would mean moving away from GUE) tools/exp_number_variance.py:158: print(f" GUE value: {2/np.pi**2:.4f}") tools/exp_number_variance.py:160: print(f" If trend ~ 0: stable -> GUE character preserved") tools/exp_number_variance.py:166: "claim_under_test": "BOUNDARY: GUE->Poisson drift in primes", tools/exp_number_variance.py:167: "method": "Number variance Sigma^2(L) at 5 scales, compared with GUE and Poisson predictions", tools/test_gue_poisson_boundary.py:2:from scipy.linalg import eigvalsh tools/test_gue_poisson_boundary.py:15:R_GUE_TH = 0.5996 tools/test_gue_poisson_boundary.py:22: gue_r.append(spacing_ratio(eigvalsh(H))) tools/test_gue_poisson_boundary.py:36: "r_GUE": round(mu_g, 4), "r_GUE_th": R_GUE_TH, tools/test_gue_poisson_boundary.py:41: "separation_GUE": round(clean_g, 4), tools/test_gue_poisson_boundary.py:43: "claim": "boundary ~0.49 is third-included: neither GUE(0.60) nor Poisson(0.39)" tools/exp_photonic_boundary_third_included_gate.py:5:This tool projects the GUE/Poisson boundary direction into a physical return: tools/exp_photonic_boundary_third_included_gate.py:6:a 1D dielectric multilayer. It does not classify the optical spectrum as GUE or tools/prime_mod6_counter_null_audit.py:108: "nearest-known literature relation", tools/exp_prime_sr_persistent_boundary.py:219: "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", tools/exp_prime_sr_persistent_boundary.py:220: "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", tools/exp_prime_sr_persistent_boundary.py:222: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", tools/r_ratio_decay.py:6:I primi passano da correlati (GUE-like) a indipendenti (Poisson) al crescere della scala. tools/r_ratio_decay.py:85: 'N_poisson': float(np.exp((R_POISSON - c1[1]) / c1[0])) if c1[0] < 0 else None tools/exp_quasiperiodic_gap_ratio_denominator.py:3:Stratified denominator audit for the quasiperiodic gap_ratio claim. tools/exp_quasiperiodic_gap_ratio_denominator.py:20:from numpy.linalg import eigvalsh tools/exp_quasiperiodic_gap_ratio_denominator.py:45: eigs = np.sort(eigvalsh(hamiltonian(seq))) tools/exp_quasiperiodic_gap_ratio_denominator.py:143: "experiment": "quasiperiodic_gap_ratio_denominator", tools/exp_quasiperiodic_gap_ratio_denominator.py:165: parser.add_argument("--out", default="tools/data/quasiperiodic_gap_ratio_denominator_20260508_0330.json") tools/exp_rp_boundary_size_stability_audit.py:49: poisson_pole_max=args.poisson_pole_max, tools/exp_rp_boundary_size_stability_audit.py:68: "brody_q": [], tools/exp_rp_boundary_size_stability_audit.py:78: row_hits[name]["brody_q"].append(float(row["brody_q"])) tools/exp_rp_boundary_size_stability_audit.py:89: "brody_q": median(item["brody_q"]), tools/exp_rp_boundary_size_stability_audit.py:105: "median_brody_q": round(median(item["brody_q"]), 6), tools/exp_rp_boundary_size_stability_audit.py:210: "poisson_pole_max": args.poisson_pole_max, tools/exp_rp_boundary_size_stability_audit.py:216: "operator": "repeat the RP diagonal-plus-GUE Hamiltonian flow over sizes, seeds and kNN graph perturbations", tools/exp_rp_boundary_size_stability_audit.py:217: "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N size sweep", tools/exp_rp_boundary_size_stability_audit.py:220: "not_tested": "N to infinity, unfolding variants, Anderson mobility edge, many-body RP variants", tools/exp_rp_boundary_size_stability_audit.py:251: parser.add_argument("--poisson-pole-max", type=float, default=0.03) tools/validate_tension_mapping.py:45: 'markov', 'poisson', 'ergodic', tools/validate_tension_mapping.py:51: 'brody', 'level repuls', 'wigner', tools/exp_selective_layer_decoupling.py:59: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_selective_layer_decoupling.py:66:def gen_poisson_spacings(N, rng): tools/exp_selective_layer_decoupling.py:145:def next_nearest_sr(gaps): tools/exp_selective_layer_decoupling.py:187: 'SR2': next_nearest_sr, tools/exp_selective_layer_decoupling.py:265: 'GUE': gen_gue_spacings(args.N, rng), tools/exp_selective_layer_decoupling.py:266: 'Poisson': gen_poisson_spacings(args.N, rng), tools/exp_spectral_landscape.py:6:Identifies: GUE domains, Poisson domains, and BOUNDARY domains. tools/exp_spectral_landscape.py:8:The question: Is the boundary between GUE and Poisson populated by multiple tools/exp_spectral_landscape.py:29: """Prime gaps (unfolded via local density).""" tools/exp_spectral_landscape.py:52: """GUE: eigenvalue spacings of complex Hermitian random matrix.""" tools/exp_spectral_landscape.py:56: eigs = np.sort(np.linalg.eigvalsh(H).real) tools/exp_spectral_landscape.py:70: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_landscape.py:89: eigs = np.sort(np.linalg.eigvalsh(H).real) tools/exp_spectral_landscape.py:99:def gen_poisson(n_spacings): tools/exp_spectral_landscape.py:110:def gen_semi_poisson(n_spacings): tools/exp_spectral_landscape.py:123: """Berry-Robnik: mixed system, fraction rho chaotic (GUE-like), 1-rho regular (Poisson). tools/exp_spectral_landscape.py:128: s_regular = gen_poisson(n_regular) if n_regular > 100 else np.array([]) tools/exp_spectral_landscape.py:135: """Gaps between Fibonacci numbers (unfolded).""" tools/exp_spectral_landscape.py:149: """Gaps between quadratic residues mod large prime (unfolded).""" tools/exp_spectral_landscape.py:162: """Model for Riemann zeta zeros — Montgomery pair correlation (GUE). tools/exp_spectral_landscape.py:163: We use GUE directly since computing actual zeros is expensive.""" tools/exp_spectral_landscape.py:169: Simplified: diagonal disorder + nearest-neighbor hopping on 1D chain.""" tools/exp_spectral_landscape.py:174: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_landscape.py:192: eigs = np.sort(np.linalg.eigvalsh(H)) tools/exp_spectral_landscape.py:224:def brody_beta(spacings): tools/exp_spectral_landscape.py:265: beta = brody_beta(spacings) tools/exp_spectral_landscape.py:274: beta_shuf.append(brody_beta(s_shuf)) tools/exp_spectral_landscape.py:303: GUE = 0.603 tools/exp_spectral_landscape.py:308: elif r_mean < (GOE + GUE) / 2: tools/exp_spectral_landscape.py:310: elif r_mean < GUE + 0.03: tools/exp_spectral_landscape.py:311: return "GUE-like" tools/exp_spectral_landscape.py:327: # Expected GUE tools/exp_spectral_landscape.py:328: ("GUE_matrix", gen_gue, {}), tools/exp_spectral_landscape.py:335: ("Poisson", gen_poisson, {}), tools/exp_spectral_landscape.py:342: ("semi_Poisson", gen_semi_poisson, {}), tools/exp_spectral_landscape.py:369: print("\nReference: Poisson =0.386, GOE =0.536, GUE =0.603") tools/exp_spectral_landscape.py:380: for cls in ["POISSON", "BOUNDARY_low", "GOE-like", "GUE-like", "RIGID"]: tools/exp_markov_layer_recovery_audit.py:19: - poisson_iid: independent exponential spacings; known order 0 tools/exp_markov_layer_recovery_audit.py:107: "poisson_iid": { tools/exp_gap_label_set_stability.py:10:by (gap_index + 1) / N. The nearest gap label is the integer n whose fractional tools/exp_gap_label_set_stability.py:24:from numpy.linalg import eigvalsh tools/exp_gap_label_set_stability.py:53:def nearest_label(ids_value: float, theta: float, max_label: int) -> tuple[int, float, float]: tools/exp_gap_label_set_stability.py:65: eigs = np.sort(eigvalsh(hamiltonian(seq))) tools/exp_gap_label_set_stability.py:72: label, error, label_value = nearest_label(ids_value, theta, max_label) tools/exp_duality_gate_transfer.py:10: away from GUE/Poisson? tools/exp_mod3_vs_residual_ordering.py:41:def unfold_gaps(gaps): tools/exp_mod3_vs_residual_ordering.py:56:def number_variance(unfolded_gaps, L_values, n_starts=3000): tools/exp_mod3_vs_residual_ordering.py:57: """Sigma^2(L) from unfolded gaps.""" tools/exp_mod3_vs_residual_ordering.py:58: levels = np.cumsum(unfolded_gaps) tools/exp_mod3_vs_residual_ordering.py:184: unfolded = unfold_gaps(gaps.astype(float)) tools/exp_mod3_vs_residual_ordering.py:185: print(f"Unfolded: mean={np.mean(unfolded):.3f}, std={np.std(unfolded):.3f}") tools/exp_mod3_vs_residual_ordering.py:192: sig2_real = number_variance(unfolded, L_values) tools/exp_mod3_vs_residual_ordering.py:200: shuf = unfolded.copy() tools/exp_mod3_vs_residual_ordering.py:214: # Shuffle raw gaps with mod-3 constraint, then unfold tools/exp_mod3_vs_residual_ordering.py:216: shuf_unf = unfold_gaps(shuf_raw.astype(float)) tools/exp_mod3_vs_residual_ordering.py:231: cu = unfold_gaps(cg) tools/exp_crossover_universality.py:18: - Rescale each to crossover parameter c in [0,1]: c=0 at GUE, c=1 at Poisson tools/exp_crossover_universality.py:57:def brody_beta(gaps): tools/exp_crossover_universality.py:95: beta = brody_beta(gaps) tools/exp_crossover_universality.py:133:# GUE: beta=1, dR_acf1~?, gap_acf1~?, =0.5307 (4/pi - 1 ?) tools/exp_crossover_universality.py:135:# We use empirical GUE reference where analytic is unknown tools/exp_crossover_universality.py:136:R_GUE = 0.5307 # 4/(pi+2) Wigner surmise tools/exp_crossover_universality.py:140:print(f" GUE = {R_GUE:.4f}, Poisson = {R_POISSON:.4f}") tools/exp_crossover_universality.py:141:print(f" beta GUE = 1.0, beta Poisson = 0.0") tools/exp_crossover_universality.py:153:# Rescale each to c in [0,1] where 0=GUE-like, 1=Poisson-like tools/exp_crossover_universality.py:154:# For beta: c = 1 - beta (beta=1 is GUE, so c=0) tools/exp_crossover_universality.py:278: ln_p_poisson_beta = -fits['beta']['intercept'] / fits['beta']['slope'] tools/exp_crossover_universality.py:279: p_poisson_beta = np.exp(ln_p_poisson_beta) tools/exp_crossover_universality.py:280: print(f" beta -> 0 (Poisson) at ln(p)={ln_p_poisson_beta:.1f}, p~{p_poisson_beta:.2e}") tools/exp_crossover_universality.py:282: ln_p_poisson_dRacf1 = -fits['dR_acf1']['intercept'] / fits['dR_acf1']['slope'] tools/exp_crossover_universality.py:283: p_poisson_dRacf1 = np.exp(ln_p_poisson_dRacf1) tools/exp_crossover_universality.py:284: print(f" dR_acf1 -> 0 at ln(p)={ln_p_poisson_dRacf1:.1f}, p~{p_poisson_dRacf1:.2e}") tools/exp_crossover_universality.py:286: ln_p_poisson_gacf1 = -fits['gap_acf1']['intercept'] / fits['gap_acf1']['slope'] tools/exp_crossover_universality.py:287: p_poisson_gacf1 = np.exp(ln_p_poisson_gacf1) tools/exp_crossover_universality.py:288: print(f" gap_acf1 -> 0 at ln(p)={ln_p_poisson_gacf1:.1f}, p~{p_poisson_gacf1:.2e}") tools/exp_crossover_universality.py:290: ln_p_poisson_r = (R_POISSON - fits['r_mean']['intercept']) / fits['r_mean']['slope'] tools/exp_crossover_universality.py:291: p_poisson_r = np.exp(ln_p_poisson_r) tools/exp_crossover_universality.py:292: print(f" -> {R_POISSON:.4f} (Poisson) at ln(p)={ln_p_poisson_r:.1f}, p~{p_poisson_r:.2e}") exec /bin/bash -lc "sed -n '260,560p' tools/exp_rp_unfolding_sensitivity_audit.py" in /opt/MM_D-ND succeeded in 0ms: "label_shuffle_total": label_null_total, "label_shuffle_rate": round(label_rate, 6), "label_shuffle_wilson_95": wilson_interval(item["label_shuffle_hits"], label_null_total), "label_shuffle_lift": round(observed_rate - label_rate, 6), "label_shuffle_binomial_tail_p": round(label_p, 6), "position_shift_successes": item["position_shift_hits"], "position_shift_total": position_null_total, "position_shift_rate": round(position_rate, 6), "position_shift_wilson_95": wilson_interval(item["position_shift_hits"], position_null_total), "position_shift_lift": round(observed_rate - position_rate, 6), "position_shift_binomial_tail_p": round(position_p, 6), "min_lift_against_nulls": round(min_lift, 6), "threshold_pass": threshold_pass, "median_brody_q": round(class_row["brody_q"], 6), "median_berry_robnick_like_gue_weight": round(class_row["berry_robnick_like_gue_weight"], 6), "median_SR": round(median(item["sr"]), 6), "median_mean_ipr": round(median(item["mean_ipr"]), 9), "mean_centroid_margin": round(float(np.mean(item["margins"])), 6), "mean_cross_neighbor_fraction": round(float(np.mean(item["cross_fractions"])), 6), } ) return { "n": n, "unfolding_mode": unfolding_mode, "observed_total": observed_total, "label_shuffle_total": label_null_total, "position_shift_total": position_null_total, "summary": { "thresholded_two_reader_rows": thresholded, "thresholded_two_reader_count": len(thresholded), "graph_only_stable_rows": graph_only, "graph_only_stable_count": len(graph_only), }, "rows": rows_out, "reader_runs": reader_runs, } def run(args: argparse.Namespace) -> dict[str, Any]: sizes = parse_ints(args.sizes) modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] by_size_mode = [audit_size_mode(args, n, mode) for mode in modes for n in sizes] per_mode: dict[str, Any] = {} for mode in modes: entries = [entry for entry in by_size_mode if entry["unfolding_mode"] == mode] size_sets = {entry["n"]: set(entry["summary"]["thresholded_two_reader_rows"]) for entry in entries} all_size_rows = sorted(set.intersection(*size_sets.values())) if size_sets else [] any_size_rows = sorted(set.union(*size_sets.values())) if size_sets else [] per_mode[mode] = { "thresholded_two_reader_all_sizes": len(all_size_rows), "thresholded_two_reader_all_size_rows": all_size_rows, "thresholded_two_reader_any_size": len(any_size_rows), "thresholded_two_reader_any_size_rows": any_size_rows, } mode_sets = {mode: set(item["thresholded_two_reader_all_size_rows"]) for mode, item in per_mode.items()} all_mode_stable = sorted(set.intersection(*mode_sets.values())) if mode_sets else [] mode_sensitive = sorted(set.union(*mode_sets.values()) - set(all_mode_stable)) if mode_sets else [] comparison_rows = [] for lam in parse_floats(args.lambdas): name = f"RP_lambda_{lam:.3f}" row = {"domain_window": name, "lambda": round(float(lam), 6)} for mode in modes: entries = [entry for entry in by_size_mode if entry["unfolding_mode"] == mode] rows_for_lambda = [ next(item for item in entry["rows"] if item["domain_window"] == name) for entry in entries ] row[f"{mode}_pass_sizes"] = [ entry["n"] for entry in entries if next(item for item in entry["rows"] if item["domain_window"] == name)["threshold_pass"] ] row[f"{mode}_min_observed_rate"] = round(float(min(item["observed_rate"] for item in rows_for_lambda)), 6) row[f"{mode}_min_lift"] = round(float(min(item["min_lift_against_nulls"] for item in rows_for_lambda)), 6) row[f"{mode}_max_null_p"] = round( float( max( max(item["label_shuffle_binomial_tail_p"], item["position_shift_binomial_tail_p"]) for item in rows_for_lambda ) ), 6, ) comparison_rows.append(row) output = { "experiment": "rp_unfolding_sensitivity_audit", "question": "Do the RP boundary rows 0.045/0.060 remain thresholded under an alternate local-window unfolding?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": FEATURE_NAMES + [ "observed_successes", "label_shuffle_successes", "position_shift_successes", "Wilson intervals", "binomial-tail p-values", "min_lift_against_nulls", "threshold_pass", "unfolding_mode", ], "parameters": { "sizes": sizes, "reps": args.reps, "lambdas": parse_floats(args.lambdas), "seeds": parse_ints(args.seeds), "k_values": parse_ints(args.k_values), "label_null_trials": args.label_null_trials, "position_offsets": parse_ints(args.position_offsets), "central_fraction": args.central_fraction, "grid_size": args.grid_size, "poisson_pole_max": args.poisson_pole_max, "gue_pole_min": args.gue_pole_min, "unfolding_modes": modes, "local_window": args.local_window, }, "threshold_preregistered": { "min_observed_rate": args.min_observed_rate, "min_lift_against_each_null": args.min_lift, "alpha_each_null": args.alpha, "classical_clause": "classical_intermediate required for two-reader threshold pass", "unfolding_stability_clause": "boundary-stable only if row passes all sizes in every unfolding mode", }, "observable_contract": { "claim": "the finite RP boundary window is unfolding-stable only if the same lambda rows beat label-shuffle and position-shift nulls under global and local spacing normalization", "observable": "thresholded two-reader raw-count pass by lambda, size and unfolding mode", "operator": "repeat the RP raw-count gate with global mean and local-window unfolded spacings", "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE across size, seed, k and unfolding mode", "denominator": "same lambda grid per size and unfolding; observed denominator seeds*k, null denominators observed*null_trials", "non_possible": "unfolding-stable boundary if any promoted lambda fails all-size pass under local-window unfolding", "not_tested": "larger N, different local windows beyond the preregistered one, experimental spectra, Anderson 3D, many-body RP", }, "summary": { "per_mode": per_mode, "thresholded_two_reader_all_modes": len(all_mode_stable), "thresholded_two_reader_all_mode_rows": all_mode_stable, "unfolding_sensitive_rows": mode_sensitive, }, "comparison_rows": comparison_rows, "by_size_mode": by_size_mode, } out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") print(json.dumps(output["summary"], indent=2, sort_keys=True)) return output def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--out", default="tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json") parser.add_argument("--sizes", default="64,96,128") parser.add_argument("--reps", type=int, default=10) parser.add_argument("--lambdas", default="0,0.03,0.045,0.06,0.075,0.10,0.18,0.32,0.68,0.82,1.0") parser.add_argument("--seeds", default="202605160820,202605160821,202605160822,202605160823") parser.add_argument("--k-values", default="2,3,4") parser.add_argument("--label-null-trials", type=int, default=64) parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8,9,10") parser.add_argument("--central-fraction", type=float, default=0.6) parser.add_argument("--grid-size", type=int, default=151) parser.add_argument("--poisson-pole-max", type=float, default=0.03) parser.add_argument("--gue-pole-min", type=float, default=0.82) parser.add_argument("--min-observed-rate", type=float, default=0.75) parser.add_argument("--min-lift", type=float, default=0.10) parser.add_argument("--alpha", type=float, default=0.05) parser.add_argument("--unfolding-modes", default="global_mean,local_window") parser.add_argument("--local-window", type=int, default=7) args = parser.parse_args() run(args) if __name__ == "__main__": main() codex Sto aggiungendo un singolo script nuovo, non un gate permanente: genera righe GUE, Poisson e RP sulla stessa griglia di size/seed, misura quanto il vettore osservabile cambia tra lettori di unfolding e confronta ogni riga con null row-aligned di permutazione/shift dei gap. apply patch patch: completed /opt/MM_D-ND/tools/exp_boundary_unfolding_transfer_matrix.py diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() exec /bin/bash -lc 'python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_boundary_unfolding_transfer_matrix.py --out tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json' in /opt/MM_D-ND diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() codex Lo script compila; il run numerico è in corso. Se l’output mostra che anche gli endpoint si fratturano, il report chiuderà come vincolo negativo, non come conferma del lettore. diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() succeeded in 35778ms: { "GUE": { "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", "criterion": "endpoint_transfer_stable", "left_tail_p_approx": 0.0, "median_sensitivity": 2.105328, "null_rate": 0.0, "null_successes": 0, "null_total": 256, "observed_rate": 0.0, "observed_successes": 0, "observed_total": 8, "observed_wilson_95": [ 0.0, 0.324408 ], "source_type": "GUE" }, "Poisson": { "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", "criterion": "endpoint_transfer_stable", "left_tail_p_approx": 1.0, "median_sensitivity": 2.444532, "null_rate": 0.0, "null_successes": 0, "null_total": 256, "observed_rate": 1.0, "observed_successes": 8, "observed_total": 8, "observed_wilson_95": [ 0.675592, 1.0 ], "source_type": "Poisson" }, "RP": { "binomial_tail_p": 1.0, "by_lambda": { "0.045": { "median_sensitivity": 2.377442, "reader_residue_pass": 0, "state_sequences": [ [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ] ], "total": 8 }, "0.060": { "median_sensitivity": 2.489847, "reader_residue_pass": 0, "state_sequences": [ [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ] ], "total": 8 }, "0.075": { "median_sensitivity": 2.375018, "reader_residue_pass": 0, "state_sequences": [ [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ], [ "intermediate", "intermediate", "intermediate", "intermediate", "intermediate" ] ], "total": 8 } }, "criterion": "reader_residue_pass", "median_sensitivity": 2.426735, "null_rate": 0.573958, "null_successes": 551, "null_total": 960, "observed_rate": 0.0, "observed_successes": 0, "observed_total": 24, "observed_wilson_95": [ 0.0, 0.137976 ], "source_type": "RP" } } codex Il deposito è negativo per la proto-ipotesi forte: Poisson trasferisce, RP resta intermedio ma non batte i null, e GUE non resta endpoint sotto questi lettori. Questo isola un nodo regressivo: il lettore scelto frattura anche un polo, quindi non può fondare il boundary cross-dominio. exec /bin/bash -lc 'du -h tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json && wc -l tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json' in /opt/MM_D-ND succeeded in 0ms: 152K tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json 5983 tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json exec /bin/bash -lc 'tail -20 tools/data/lab_health.json' in /opt/MM_D-ND succeeded in 0ms: { "run_timestamp": "20260516_1019", "autopsy_run_at": "2026-05-16T10:31:27.052358+00:00", "jsonl_path": null, "raw_log_bytes": 423449, "report_present": true, "session_stats": null, "status": "completed", "regressive_node": null, "recommendation": "cycle completato via provider non-claude (codex/openrouter). Session jsonl non disponibile per autopsy dettagliata.", "_note": "completed_no_jsonl" } exec /bin/bash -lc "python - <<'PY' import json p='tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json' d=json.load(open(p)) for t in ['GUE','Poisson','RP']: print('\\n',t) for r in d['rows']: if r['source_type']==t: print(r['row_id'], 'sens', r['reader_sensitivity'], 'p', r['row_aligned_p'], 'endpoint', r['endpoint_transfer_stable'], 'residue', r['reader_residue_pass'], 'states', r['state_sequence']) if t"'!='"'RP': print([(x['reader'], x['features']['brody_q'], x['features']['berry_robnick_like_gue_weight'], x['classical_state']) for x in r['readers']]) break PY" in /opt/MM_D-ND succeeded in 0ms: GUE GUE_N128_s0 sens 2.013249 p 0.853659 endpoint False residue False states ['intermediate', 'intermediate', 'intermediate', 'intermediate', 'intermediate'] [('global_mean', 1.0, 0.526666667, 'intermediate'), ('exact_local:w9', 1.0, 0.546666667, 'intermediate'), ('exact_local:w12', 1.0, 0.546666667, 'intermediate'), ('odd_coerced:w9', 1.0, 0.546666667, 'intermediate'), ('odd_coerced:w12', 1.0, 0.54, 'intermediate')] Poisson Poisson_N128_s0 sens 2.315467 p 0.804878 endpoint True residue False states ['poisson_endpoint', 'poisson_endpoint', 'poisson_endpoint', 'poisson_endpoint', 'poisson_endpoint'] [('global_mean', 0.046666667, 0.033333333, 'poisson_endpoint'), ('exact_local:w9', 0.12, 0.113333333, 'poisson_endpoint'), ('exact_local:w12', 0.1, 0.086666667, 'poisson_endpoint'), ('odd_coerced:w9', 0.12, 0.113333333, 'poisson_endpoint'), ('odd_coerced:w12', 0.1, 0.093333333, 'poisson_endpoint')] RP RP_lambda_0.045_N128_s0 sens 2.346037 p 0.756098 endpoint False residue False states ['intermediate', 'intermediate', 'intermediate', 'intermediate', 'intermediate'] diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() codex Scrivo il report come `CONSTRAINT`, non come scoperta: il test serve la direzione perché porta il lettore cross-dominio, ma la sua stessa evidenza impedisce la promozione. Includo count grezzi, null, p-value e audit baseline per evitare il drift segnalato dal falsifier. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260516_1031.md diff --git a/tools/data/reports/agent_20260516_1031.md b/tools/data/reports/agent_20260516_1031.md new file mode 100644 index 0000000000000000000000000000000000000000..e1bc2b55d64b7d318fe8e1c2d369a92ffc5ec1ac --- /dev/null +++ b/tools/data/reports/agent_20260516_1031.md @@ -0,0 +1,99 @@ +# Agent Report - Boundary Unfolding Transfer Matrix +**Date**: 2026-05-16 10:31 +**Piano**: 128 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `window_mode/unfolding` non trasferisce come asse cross-dominio nel perimetro dichiarato. Poisson resta endpoint su 8/8 righe, ma GUE non resta endpoint sotto la clausola classica a due lettori e RP produce 0/24 residui sopra i null row-aligned. Il boundary reader-axis resta ipotesi da riparare al nodo regressivo del lettore, non claim fisico. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, reader_sensitivity, endpoint_transfer_stable, reader_residue_pass, row_aligned_p, permutation_null_scores, position_shift_null_scores] +**observable_contract**: claim=`window_mode/unfolding` e' coordinata del boundary se gli endpoint GUE/Poisson trasferiscono mentre le righe RP boundary espongono residuo reader-specific contro null row-aligned; observable=reader_sensitivity del vettore spettrale canonico tra global_mean, exact_local e odd_coerced; operator=stessa riga di gap letta con piu unfolding/window modes; generator=matrici GUE, gap Poisson esponenziali, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=domain x size x seed, null da permutazione e shift circolare sulla stessa riga; non_possible=asse reader come boundary se un endpoint si frattura o RP non batte i null; not_tested=spettri sperimentali, N infinito, Anderson 3D, prova analitica di universalita. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + boundary operator/topologia del bordo + tensione BOUNDARY. +- **Dipolo / punto-zero**: polo fisico stabile / lettore che decide. Punto-zero: la stessa sequenza di gap prima che global/local/odd-coerced la leggano. +- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator e' trattato come mappa fra lettori, non come parametro tecnico. +- **Operatori laterali scelti**: boundary operator e parallel transport. Il primo decide se il bordo e' oggetto; il secondo chiede se l'identita' del dominio trasporta fra lettori. +- **Contaminazione cognitiva**: CE-0019 usata come vincolo di respiro pre-esperimento; CE-0001/KSAR usata per reiterare il kernel 10:19 senza cercare un'altra lambda. +- **Proto-ipotesi**: se `window_mode/unfolding` e' asse reale del confine, GUE e Poisson trasferiscono come poli mentre RP boundary mostra residuo specifico del lettore sopra i null row-aligned. +- **Possibile/non-possibile**: possibile = reader axis come coordinata cross-dominio; non-possibile = endpoint GUE fratturato o RP reader residue assorbito dai null. +- **Proiezione**: misuro `reader_sensitivity` e stato classico per righe GUE, Poisson e RP `0.045/0.060/0.075`, con null di permutazione e shift sulla stessa riga. +- **Movimento A->M->B**: fisico A = crossover GUE/Poisson/RP finito; matematica M = matrice row-aligned `(domain, N, seed, reader)`; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: porta l'asse `window_mode`/unfolding fuori dalla sola lambda RP e lo testa su GUE, Poisson e RP con null row-aligned. +- `not_drift`: non cerca una lambda stabile, non usa phi/Sturmian/V_c, non promuove righe graph-only; il risultato cade se endpoints o RP non rispettano il contratto. + +## Re-discovery audit +- **Baseline noto piu vicino**: unfolding sensitivity negli spettri finiti, Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik mixture, kNN stability sul grafo di osservabili. +- **Cosa assorbe il baseline**: la dipendenza delle statistiche spettrali finite dalla normalizzazione locale dei gap. +- **Cosa resta Lab-specific**: il contratto row-aligned che separa endpoint transfer e RP reader residue nella stessa matrice di lettori. +- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=reader_axis_cross_domain`; `graph_baseline_audit=permutation_null + position_shift_null`. + +## Claim Under Test +> `window_mode/unfolding` trasferisce come coordinata del boundary se Poisson e GUE restano endpoint e RP `0.045/0.060/0.075` mostra residuo reader-specific sopra null row-aligned. + +## Experiment Design +- **Script nuovo**: `tools/exp_boundary_unfolding_transfer_matrix.py`. +- **Run**: `python tools/exp_boundary_unfolding_transfer_matrix.py --out tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- **Righe**: size `128/192`, seed `4`, domini `GUE`, `Poisson`, RP lambda `0.045/0.060/0.075`. +- **Reader**: `global_mean`, `exact_local` windows `9/12`, `odd_coerced` windows `9/12`. +- **Null row-aligned**: 32 permutazioni dei gap per riga + 8 shift circolari per riga. +- **Soglia preregistrata RP**: `reader_sensitivity >= 0.75`, `row_aligned_p <= 0.05`, almeno due stati reader diversi. +- **Soglia endpoint audit**: tutti gli stati reader devono matchare l'endpoint dichiarato; `endpoint_max_sensitivity=0.75` registrato come audit, non forzato nel pass. + +## Results +| group | observed | null | p / audit | median reader_sensitivity | lettura | +|---|---:|---:|---:|---:|---| +| GUE endpoint transfer | 0/8 | 0/256 | left-tail approx 0.000000 | 2.105328 | non trasferisce sotto clausola a due lettori | +| Poisson endpoint transfer | 8/8 | 0/256 | left-tail approx 1.000000 | 2.444532 | trasferisce come endpoint | +| RP reader residue | 0/24 | 551/960 | 1.000000 | 2.426735 | residuo assorbito dai null | + +### Endpoint Rows +| source | example states across readers | example q/w global | endpoint_transfer | +|---|---|---|---| +| GUE | intermediate, intermediate, intermediate, intermediate, intermediate | q=1.000000, w=0.526667 | 0/8 | +| Poisson | poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint | q=0.046667, w=0.033333 | 8/8 | + +### RP Lambda Audit +| lambda | reader_residue_pass | total | median reader_sensitivity | states | +|---:|---:|---:|---:|---| +| 0.045 | 0 | 8 | 2.377442 | all readers intermediate | +| 0.060 | 0 | 8 | 2.489847 | all readers intermediate | +| 0.075 | 0 | 8 | 2.375018 | all readers intermediate | + +## Key Findings +1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce. +2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse reader perche' l'altro polo cade. +3. Verificato: RP `0.045/0.060/0.075` resta `intermediate` in tutte le letture e produce 0/24 `reader_residue_pass`; i null hanno 551/960 score >= osservato, quindi il residuo reader-specific non emerge. +4. Inferito dal perimetro: la sensibilita' del vettore osservabile e' alta in tutti i gruppi, ma non discrimina boundary. Il nodo regressivo e' la definizione del lettore/classificatore, non la scelta di lambda. + +## Verdict +CONSTRAINT + +`window_mode/unfolding` non diventa coordinata cross-dominio nel perimetro 10:31. La matrice conserva informazione utile perche' mostra dove cade: il lettore a due clausole classiche rompe GUE e i null assorbono RP. Il prossimo ciclo non deve cercare una cresta RP; deve riparare il lettore endpoint o cambiare dominio di ritorno con endpoint verificati prima del boundary. + +## Bicono della scoperta +- **Due radici**: endpoint transfer; reader residue RP. +- **Singolare**: riga di gap prima dell'unfolding. +- **Invariante di passaggio**: stesso denominatore row-aligned per osservato e null. +- **Campo di possibilita**: possibile = audit del lettore prima della promozione del boundary; non-possibile = dichiarare terzo incluso cross-dominio con GUE endpoint non trasferito. + +## Consecutio +Il prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matrice RP. Se la clausola classica resta `q AND w`, il boundary reader-axis e' bloccato. Se la clausola diventa endpoint-validata su baseline GUE indipendente, la domanda torna falsificabile. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_unfolding_transfer_matrix.py` e' uno strumento riusabile per testare trasferibilita' del lettore su domini, size, seed, windows e null row-aligned. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo. +- `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` completato. +- Run completato: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_boundary_unfolding_transfer_matrix.py` +- Data: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- Report: `tools/data/reports/agent_20260516_1031.md` diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() diff --git a/tools/data/reports/agent_20260516_1031.md b/tools/data/reports/agent_20260516_1031.md new file mode 100644 index 0000000000000000000000000000000000000000..e1bc2b55d64b7d318fe8e1c2d369a92ffc5ec1ac --- /dev/null +++ b/tools/data/reports/agent_20260516_1031.md @@ -0,0 +1,99 @@ +# Agent Report - Boundary Unfolding Transfer Matrix +**Date**: 2026-05-16 10:31 +**Piano**: 128 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `window_mode/unfolding` non trasferisce come asse cross-dominio nel perimetro dichiarato. Poisson resta endpoint su 8/8 righe, ma GUE non resta endpoint sotto la clausola classica a due lettori e RP produce 0/24 residui sopra i null row-aligned. Il boundary reader-axis resta ipotesi da riparare al nodo regressivo del lettore, non claim fisico. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, reader_sensitivity, endpoint_transfer_stable, reader_residue_pass, row_aligned_p, permutation_null_scores, position_shift_null_scores] +**observable_contract**: claim=`window_mode/unfolding` e' coordinata del boundary se gli endpoint GUE/Poisson trasferiscono mentre le righe RP boundary espongono residuo reader-specific contro null row-aligned; observable=reader_sensitivity del vettore spettrale canonico tra global_mean, exact_local e odd_coerced; operator=stessa riga di gap letta con piu unfolding/window modes; generator=matrici GUE, gap Poisson esponenziali, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=domain x size x seed, null da permutazione e shift circolare sulla stessa riga; non_possible=asse reader come boundary se un endpoint si frattura o RP non batte i null; not_tested=spettri sperimentali, N infinito, Anderson 3D, prova analitica di universalita. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + boundary operator/topologia del bordo + tensione BOUNDARY. +- **Dipolo / punto-zero**: polo fisico stabile / lettore che decide. Punto-zero: la stessa sequenza di gap prima che global/local/odd-coerced la leggano. +- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator e' trattato come mappa fra lettori, non come parametro tecnico. +- **Operatori laterali scelti**: boundary operator e parallel transport. Il primo decide se il bordo e' oggetto; il secondo chiede se l'identita' del dominio trasporta fra lettori. +- **Contaminazione cognitiva**: CE-0019 usata come vincolo di respiro pre-esperimento; CE-0001/KSAR usata per reiterare il kernel 10:19 senza cercare un'altra lambda. +- **Proto-ipotesi**: se `window_mode/unfolding` e' asse reale del confine, GUE e Poisson trasferiscono come poli mentre RP boundary mostra residuo specifico del lettore sopra i null row-aligned. +- **Possibile/non-possibile**: possibile = reader axis come coordinata cross-dominio; non-possibile = endpoint GUE fratturato o RP reader residue assorbito dai null. +- **Proiezione**: misuro `reader_sensitivity` e stato classico per righe GUE, Poisson e RP `0.045/0.060/0.075`, con null di permutazione e shift sulla stessa riga. +- **Movimento A->M->B**: fisico A = crossover GUE/Poisson/RP finito; matematica M = matrice row-aligned `(domain, N, seed, reader)`; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: porta l'asse `window_mode`/unfolding fuori dalla sola lambda RP e lo testa su GUE, Poisson e RP con null row-aligned. +- `not_drift`: non cerca una lambda stabile, non usa phi/Sturmian/V_c, non promuove righe graph-only; il risultato cade se endpoints o RP non rispettano il contratto. + +## Re-discovery audit +- **Baseline noto piu vicino**: unfolding sensitivity negli spettri finiti, Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik mixture, kNN stability sul grafo di osservabili. +- **Cosa assorbe il baseline**: la dipendenza delle statistiche spettrali finite dalla normalizzazione locale dei gap. +- **Cosa resta Lab-specific**: il contratto row-aligned che separa endpoint transfer e RP reader residue nella stessa matrice di lettori. +- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=reader_axis_cross_domain`; `graph_baseline_audit=permutation_null + position_shift_null`. + +## Claim Under Test +> `window_mode/unfolding` trasferisce come coordinata del boundary se Poisson e GUE restano endpoint e RP `0.045/0.060/0.075` mostra residuo reader-specific sopra null row-aligned. + +## Experiment Design +- **Script nuovo**: `tools/exp_boundary_unfolding_transfer_matrix.py`. +- **Run**: `python tools/exp_boundary_unfolding_transfer_matrix.py --out tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- **Righe**: size `128/192`, seed `4`, domini `GUE`, `Poisson`, RP lambda `0.045/0.060/0.075`. +- **Reader**: `global_mean`, `exact_local` windows `9/12`, `odd_coerced` windows `9/12`. +- **Null row-aligned**: 32 permutazioni dei gap per riga + 8 shift circolari per riga. +- **Soglia preregistrata RP**: `reader_sensitivity >= 0.75`, `row_aligned_p <= 0.05`, almeno due stati reader diversi. +- **Soglia endpoint audit**: tutti gli stati reader devono matchare l'endpoint dichiarato; `endpoint_max_sensitivity=0.75` registrato come audit, non forzato nel pass. + +## Results +| group | observed | null | p / audit | median reader_sensitivity | lettura | +|---|---:|---:|---:|---:|---| +| GUE endpoint transfer | 0/8 | 0/256 | left-tail approx 0.000000 | 2.105328 | non trasferisce sotto clausola a due lettori | +| Poisson endpoint transfer | 8/8 | 0/256 | left-tail approx 1.000000 | 2.444532 | trasferisce come endpoint | +| RP reader residue | 0/24 | 551/960 | 1.000000 | 2.426735 | residuo assorbito dai null | + +### Endpoint Rows +| source | example states across readers | example q/w global | endpoint_transfer | +|---|---|---|---| +| GUE | intermediate, intermediate, intermediate, intermediate, intermediate | q=1.000000, w=0.526667 | 0/8 | +| Poisson | poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint | q=0.046667, w=0.033333 | 8/8 | + +### RP Lambda Audit +| lambda | reader_residue_pass | total | median reader_sensitivity | states | +|---:|---:|---:|---:|---| +| 0.045 | 0 | 8 | 2.377442 | all readers intermediate | +| 0.060 | 0 | 8 | 2.489847 | all readers intermediate | +| 0.075 | 0 | 8 | 2.375018 | all readers intermediate | + +## Key Findings +1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce. +2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse reader perche' l'altro polo cade. +3. Verificato: RP `0.045/0.060/0.075` resta `intermediate` in tutte le letture e produce 0/24 `reader_residue_pass`; i null hanno 551/960 score >= osservato, quindi il residuo reader-specific non emerge. +4. Inferito dal perimetro: la sensibilita' del vettore osservabile e' alta in tutti i gruppi, ma non discrimina boundary. Il nodo regressivo e' la definizione del lettore/classificatore, non la scelta di lambda. + +## Verdict +CONSTRAINT + +`window_mode/unfolding` non diventa coordinata cross-dominio nel perimetro 10:31. La matrice conserva informazione utile perche' mostra dove cade: il lettore a due clausole classiche rompe GUE e i null assorbono RP. Il prossimo ciclo non deve cercare una cresta RP; deve riparare il lettore endpoint o cambiare dominio di ritorno con endpoint verificati prima del boundary. + +## Bicono della scoperta +- **Due radici**: endpoint transfer; reader residue RP. +- **Singolare**: riga di gap prima dell'unfolding. +- **Invariante di passaggio**: stesso denominatore row-aligned per osservato e null. +- **Campo di possibilita**: possibile = audit del lettore prima della promozione del boundary; non-possibile = dichiarare terzo incluso cross-dominio con GUE endpoint non trasferito. + +## Consecutio +Il prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matrice RP. Se la clausola classica resta `q AND w`, il boundary reader-axis e' bloccato. Se la clausola diventa endpoint-validata su baseline GUE indipendente, la domanda torna falsificabile. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_unfolding_transfer_matrix.py` e' uno strumento riusabile per testare trasferibilita' del lettore su domini, size, seed, windows e null row-aligned. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo. +- `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` completato. +- Run completato: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_boundary_unfolding_transfer_matrix.py` +- Data: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- Report: `tools/data/reports/agent_20260516_1031.md` diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() codex Il report è depositato. Ora faccio una verifica locale leggera: leggo il file scritto, controllo i riferimenti principali e provo il falsifier se l’interfaccia accetta il report diretto. exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260516_1031.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Boundary Unfolding Transfer Matrix **Date**: 2026-05-16 10:31 **Piano**: 128 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - `window_mode/unfolding` non trasferisce come asse cross-dominio nel perimetro dichiarato. Poisson resta endpoint su 8/8 righe, ma GUE non resta endpoint sotto la clausola classica a due lettori e RP produce 0/24 residui sopra i null row-aligned. Il boundary reader-axis resta ipotesi da riparare al nodo regressivo del lettore, non claim fisico. observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, reader_sensitivity, endpoint_transfer_stable, reader_residue_pass, row_aligned_p, permutation_null_scores, position_shift_null_scores] **observable_contract**: claim=`window_mode/unfolding` e' coordinata del boundary se gli endpoint GUE/Poisson trasferiscono mentre le righe RP boundary espongono residuo reader-specific contro null row-aligned; observable=reader_sensitivity del vettore spettrale canonico tra global_mean, exact_local e odd_coerced; operator=stessa riga di gap letta con piu unfolding/window modes; generator=matrici GUE, gap Poisson esponenziali, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=domain x size x seed, null da permutazione e shift circolare sulla stessa riga; non_possible=asse reader come boundary se un endpoint si frattura o RP non batte i null; not_tested=spettri sperimentali, N infinito, Anderson 3D, prova analitica di universalita. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + QxG continuo/discreto + boundary operator/topologia del bordo + tensione BOUNDARY. - **Dipolo / punto-zero**: polo fisico stabile / lettore che decide. Punto-zero: la stessa sequenza di gap prima che global/local/odd-coerced la leggano. - **Piano superiore**: topologia assiomatica del bordo. Il boundary operator e' trattato come mappa fra lettori, non come parametro tecnico. - **Operatori laterali scelti**: boundary operator e parallel transport. Il primo decide se il bordo e' oggetto; il secondo chiede se l'identita' del dominio trasporta fra lettori. - **Contaminazione cognitiva**: CE-0019 usata come vincolo di respiro pre-esperimento; CE-0001/KSAR usata per reiterare il kernel 10:19 senza cercare un'altra lambda. - **Proto-ipotesi**: se `window_mode/unfolding` e' asse reale del confine, GUE e Poisson trasferiscono come poli mentre RP boundary mostra residuo specifico del lettore sopra i null row-aligned. - **Possibile/non-possibile**: possibile = reader axis come coordinata cross-dominio; non-possibile = endpoint GUE fratturato o RP reader residue assorbito dai null. - **Proiezione**: misuro `reader_sensitivity` e stato classico per righe GUE, Poisson e RP `0.045/0.060/0.075`, con null di permutazione e shift sulla stessa riga. - **Movimento A->M->B**: fisico A = crossover GUE/Poisson/RP finito; matematica M = matrice row-aligned `(domain, N, seed, reader)`; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico. ## Aderenza alla direzione - `relation`: `follows_direction` - `why`: porta l'asse `window_mode`/unfolding fuori dalla sola lambda RP e lo testa su GUE, Poisson e RP con null row-aligned. - `not_drift`: non cerca una lambda stabile, non usa phi/Sturmian/V_c, non promuove righe graph-only; il risultato cade se endpoints o RP non rispettano il contratto. ## Re-discovery audit - **Baseline noto piu vicino**: unfolding sensitivity negli spettri finiti, Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik mixture, kNN stability sul grafo di osservabili. - **Cosa assorbe il baseline**: la dipendenza delle statistiche spettrali finite dalla normalizzazione locale dei gap. - **Cosa resta Lab-specific**: il contratto row-aligned che separa endpoint transfer e RP reader residue nella stessa matrice di lettori. - **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=reader_axis_cross_domain`; `graph_baseline_audit=permutation_null + position_shift_null`. ## Claim Under Test > `window_mode/unfolding` trasferisce come coordinata del boundary se Poisson e GUE restano endpoint e RP `0.045/0.060/0.075` mostra residuo reader-specific sopra null row-aligned. ## Experiment Design - **Script nuovo**: `tools/exp_boundary_unfolding_transfer_matrix.py`. - **Run**: `python tools/exp_boundary_unfolding_transfer_matrix.py --out tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` - **Righe**: size `128/192`, seed `4`, domini `GUE`, `Poisson`, RP lambda `0.045/0.060/0.075`. - **Reader**: `global_mean`, `exact_local` windows `9/12`, `odd_coerced` windows `9/12`. - **Null row-aligned**: 32 permutazioni dei gap per riga + 8 shift circolari per riga. - **Soglia preregistrata RP**: `reader_sensitivity >= 0.75`, `row_aligned_p <= 0.05`, almeno due stati reader diversi. - **Soglia endpoint audit**: tutti gli stati reader devono matchare l'endpoint dichiarato; `endpoint_max_sensitivity=0.75` registrato come audit, non forzato nel pass. ## Results | group | observed | null | p / audit | median reader_sensitivity | lettura | |---|---:|---:|---:|---:|---| | GUE endpoint transfer | 0/8 | 0/256 | left-tail approx 0.000000 | 2.105328 | non trasferisce sotto clausola a due lettori | | Poisson endpoint transfer | 8/8 | 0/256 | left-tail approx 1.000000 | 2.444532 | trasferisce come endpoint | | RP reader residue | 0/24 | 551/960 | 1.000000 | 2.426735 | residuo assorbito dai null | ### Endpoint Rows | source | example states across readers | example q/w global | endpoint_transfer | |---|---|---|---| | GUE | intermediate, intermediate, intermediate, intermediate, intermediate | q=1.000000, w=0.526667 | 0/8 | | Poisson | poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint | q=0.046667, w=0.033333 | 8/8 | ### RP Lambda Audit | lambda | reader_residue_pass | total | median reader_sensitivity | states | |---:|---:|---:|---:|---| | 0.045 | 0 | 8 | 2.377442 | all readers intermediate | | 0.060 | 0 | 8 | 2.489847 | all readers intermediate | | 0.075 | 0 | 8 | 2.375018 | all readers intermediate | ## Key Findings 1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce. 2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse reader perche' l'altro polo cade. 3. Verificato: RP `0.045/0.060/0.075` resta `intermediate` in tutte le letture e produce 0/24 `reader_residue_pass`; i null hanno 551/960 score >= osservato, quindi il residuo reader-specific non emerge. 4. Inferito dal perimetro: la sensibilita' del vettore osservabile e' alta in tutti i gruppi, ma non discrimina boundary. Il nodo regressivo e' la definizione del lettore/classificatore, non la scelta di lambda. ## Verdict CONSTRAINT `window_mode/unfolding` non diventa coordinata cross-dominio nel perimetro 10:31. La matrice conserva informazione utile perche' mostra dove cade: il lettore a due clausole classiche rompe GUE e i null assorbono RP. Il prossimo ciclo non deve cercare una cresta RP; deve riparare il lettore endpoint o cambiare dominio di ritorno con endpoint verificati prima del boundary. ## Bicono della scoperta - **Due radici**: endpoint transfer; reader residue RP. - **Singolare**: riga di gap prima dell'unfolding. - **Invariante di passaggio**: stesso denominatore row-aligned per osservato e null. - **Campo di possibilita**: possibile = audit del lettore prima della promozione del boundary; non-possibile = dichiarare terzo incluso cross-dominio con GUE endpoint non trasferito. ## Consecutio Il prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matrice RP. Se la clausola classica resta `q AND w`, il boundary reader-axis e' bloccato. Se la clausola diventa endpoint-validata su baseline GUE indipendente, la domanda torna falsificabile. ## Ricadute pratiche ssp_value: yes. `tools/exp_boundary_unfolding_transfer_matrix.py` e' uno strumento riusabile per testare trasferibilita' del lettore su domini, size, seed, windows e null row-aligned. ## Telemetria - No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. - `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo. - `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` completato. - Run completato: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`. - Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. - Nessun update del seme. - Nessuna promozione e nessun public sync. ## Files - Script: `tools/exp_boundary_unfolding_transfer_matrix.py` - Data: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` - Report: `tools/data/reports/agent_20260516_1031.md` exec /bin/bash -lc "test -s tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json && test -s tools/data/reports/agent_20260516_1031.md && printf 'ok\\n'" in /opt/MM_D-ND succeeded in 0ms: ok exec /bin/bash -lc 'git status --short tools/exp_boundary_unfolding_transfer_matrix.py tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json tools/data/reports/agent_20260516_1031.md' in /opt/MM_D-ND succeeded in 0ms: ?? tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json ?? tools/data/reports/agent_20260516_1031.md ?? tools/exp_boundary_unfolding_transfer_matrix.py exec /bin/bash -lc 'python tools/lab_falsifier.py --help' in /opt/MM_D-ND succeeded in 0ms: usage: lab_falsifier.py [-h] --report REPORT --output OUTPUT [--timeout TIMEOUT] Counter-pole gate for the lab options: -h, --help show this help message and exit --report REPORT Path to agent_TS.md --output OUTPUT Path for falsifier_TS.json output --timeout TIMEOUT LLM call timeout seconds diff --git a/tools/data/reports/agent_20260516_1031.md b/tools/data/reports/agent_20260516_1031.md new file mode 100644 index 0000000000000000000000000000000000000000..e1bc2b55d64b7d318fe8e1c2d369a92ffc5ec1ac --- /dev/null +++ b/tools/data/reports/agent_20260516_1031.md @@ -0,0 +1,99 @@ +# Agent Report - Boundary Unfolding Transfer Matrix +**Date**: 2026-05-16 10:31 +**Piano**: 128 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `window_mode/unfolding` non trasferisce come asse cross-dominio nel perimetro dichiarato. Poisson resta endpoint su 8/8 righe, ma GUE non resta endpoint sotto la clausola classica a due lettori e RP produce 0/24 residui sopra i null row-aligned. Il boundary reader-axis resta ipotesi da riparare al nodo regressivo del lettore, non claim fisico. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, reader_sensitivity, endpoint_transfer_stable, reader_residue_pass, row_aligned_p, permutation_null_scores, position_shift_null_scores] +**observable_contract**: claim=`window_mode/unfolding` e' coordinata del boundary se gli endpoint GUE/Poisson trasferiscono mentre le righe RP boundary espongono residuo reader-specific contro null row-aligned; observable=reader_sensitivity del vettore spettrale canonico tra global_mean, exact_local e odd_coerced; operator=stessa riga di gap letta con piu unfolding/window modes; generator=matrici GUE, gap Poisson esponenziali, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=domain x size x seed, null da permutazione e shift circolare sulla stessa riga; non_possible=asse reader come boundary se un endpoint si frattura o RP non batte i null; not_tested=spettri sperimentali, N infinito, Anderson 3D, prova analitica di universalita. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + boundary operator/topologia del bordo + tensione BOUNDARY. +- **Dipolo / punto-zero**: polo fisico stabile / lettore che decide. Punto-zero: la stessa sequenza di gap prima che global/local/odd-coerced la leggano. +- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator e' trattato come mappa fra lettori, non come parametro tecnico. +- **Operatori laterali scelti**: boundary operator e parallel transport. Il primo decide se il bordo e' oggetto; il secondo chiede se l'identita' del dominio trasporta fra lettori. +- **Contaminazione cognitiva**: CE-0019 usata come vincolo di respiro pre-esperimento; CE-0001/KSAR usata per reiterare il kernel 10:19 senza cercare un'altra lambda. +- **Proto-ipotesi**: se `window_mode/unfolding` e' asse reale del confine, GUE e Poisson trasferiscono come poli mentre RP boundary mostra residuo specifico del lettore sopra i null row-aligned. +- **Possibile/non-possibile**: possibile = reader axis come coordinata cross-dominio; non-possibile = endpoint GUE fratturato o RP reader residue assorbito dai null. +- **Proiezione**: misuro `reader_sensitivity` e stato classico per righe GUE, Poisson e RP `0.045/0.060/0.075`, con null di permutazione e shift sulla stessa riga. +- **Movimento A->M->B**: fisico A = crossover GUE/Poisson/RP finito; matematica M = matrice row-aligned `(domain, N, seed, reader)`; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: porta l'asse `window_mode`/unfolding fuori dalla sola lambda RP e lo testa su GUE, Poisson e RP con null row-aligned. +- `not_drift`: non cerca una lambda stabile, non usa phi/Sturmian/V_c, non promuove righe graph-only; il risultato cade se endpoints o RP non rispettano il contratto. + +## Re-discovery audit +- **Baseline noto piu vicino**: unfolding sensitivity negli spettri finiti, Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik mixture, kNN stability sul grafo di osservabili. +- **Cosa assorbe il baseline**: la dipendenza delle statistiche spettrali finite dalla normalizzazione locale dei gap. +- **Cosa resta Lab-specific**: il contratto row-aligned che separa endpoint transfer e RP reader residue nella stessa matrice di lettori. +- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=reader_axis_cross_domain`; `graph_baseline_audit=permutation_null + position_shift_null`. + +## Claim Under Test +> `window_mode/unfolding` trasferisce come coordinata del boundary se Poisson e GUE restano endpoint e RP `0.045/0.060/0.075` mostra residuo reader-specific sopra null row-aligned. + +## Experiment Design +- **Script nuovo**: `tools/exp_boundary_unfolding_transfer_matrix.py`. +- **Run**: `python tools/exp_boundary_unfolding_transfer_matrix.py --out tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- **Righe**: size `128/192`, seed `4`, domini `GUE`, `Poisson`, RP lambda `0.045/0.060/0.075`. +- **Reader**: `global_mean`, `exact_local` windows `9/12`, `odd_coerced` windows `9/12`. +- **Null row-aligned**: 32 permutazioni dei gap per riga + 8 shift circolari per riga. +- **Soglia preregistrata RP**: `reader_sensitivity >= 0.75`, `row_aligned_p <= 0.05`, almeno due stati reader diversi. +- **Soglia endpoint audit**: tutti gli stati reader devono matchare l'endpoint dichiarato; `endpoint_max_sensitivity=0.75` registrato come audit, non forzato nel pass. + +## Results +| group | observed | null | p / audit | median reader_sensitivity | lettura | +|---|---:|---:|---:|---:|---| +| GUE endpoint transfer | 0/8 | 0/256 | left-tail approx 0.000000 | 2.105328 | non trasferisce sotto clausola a due lettori | +| Poisson endpoint transfer | 8/8 | 0/256 | left-tail approx 1.000000 | 2.444532 | trasferisce come endpoint | +| RP reader residue | 0/24 | 551/960 | 1.000000 | 2.426735 | residuo assorbito dai null | + +### Endpoint Rows +| source | example states across readers | example q/w global | endpoint_transfer | +|---|---|---|---| +| GUE | intermediate, intermediate, intermediate, intermediate, intermediate | q=1.000000, w=0.526667 | 0/8 | +| Poisson | poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint | q=0.046667, w=0.033333 | 8/8 | + +### RP Lambda Audit +| lambda | reader_residue_pass | total | median reader_sensitivity | states | +|---:|---:|---:|---:|---| +| 0.045 | 0 | 8 | 2.377442 | all readers intermediate | +| 0.060 | 0 | 8 | 2.489847 | all readers intermediate | +| 0.075 | 0 | 8 | 2.375018 | all readers intermediate | + +## Key Findings +1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce. +2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse reader perche' l'altro polo cade. +3. Verificato: RP `0.045/0.060/0.075` resta `intermediate` in tutte le letture e produce 0/24 `reader_residue_pass`; i null hanno 551/960 score >= osservato, quindi il residuo reader-specific non emerge. +4. Inferito dal perimetro: la sensibilita' del vettore osservabile e' alta in tutti i gruppi, ma non discrimina boundary. Il nodo regressivo e' la definizione del lettore/classificatore, non la scelta di lambda. + +## Verdict +CONSTRAINT + +`window_mode/unfolding` non diventa coordinata cross-dominio nel perimetro 10:31. La matrice conserva informazione utile perche' mostra dove cade: il lettore a due clausole classiche rompe GUE e i null assorbono RP. Il prossimo ciclo non deve cercare una cresta RP; deve riparare il lettore endpoint o cambiare dominio di ritorno con endpoint verificati prima del boundary. + +## Bicono della scoperta +- **Due radici**: endpoint transfer; reader residue RP. +- **Singolare**: riga di gap prima dell'unfolding. +- **Invariante di passaggio**: stesso denominatore row-aligned per osservato e null. +- **Campo di possibilita**: possibile = audit del lettore prima della promozione del boundary; non-possibile = dichiarare terzo incluso cross-dominio con GUE endpoint non trasferito. + +## Consecutio +Il prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matrice RP. Se la clausola classica resta `q AND w`, il boundary reader-axis e' bloccato. Se la clausola diventa endpoint-validata su baseline GUE indipendente, la domanda torna falsificabile. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_unfolding_transfer_matrix.py` e' uno strumento riusabile per testare trasferibilita' del lettore su domini, size, seed, windows e null row-aligned. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo. +- `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` completato. +- Run completato: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_boundary_unfolding_transfer_matrix.py` +- Data: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- Report: `tools/data/reports/agent_20260516_1031.md` diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() codex Cycle completato. Ho creato lo script riusabile [tools/exp_boundary_unfolding_transfer_matrix.py](/opt/MM_D-ND/tools/exp_boundary_unfolding_transfer_matrix.py) e scritto il report richiesto in [tools/data/reports/agent_20260516_1031.md](/opt/MM_D-ND/tools/data/reports/agent_20260516_1031.md). Risultato: `CONSTRAINT`. Il test cross-dominio non promuove `window_mode/unfolding` come asse del boundary: Poisson trasferisce come endpoint, ma GUE cade a `intermediate` sotto la clausola a due lettori e RP non batte i null row-aligned. Verifiche fatte: - `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` - run esperimento completato con output in [tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json](/opt/MM_D-ND/tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json) - report riletto dopo scrittura Non ho aggiornato il seme e non ho lanciato il falsifier LLM. diff --git a/tools/data/reports/agent_20260516_1031.md b/tools/data/reports/agent_20260516_1031.md new file mode 100644 index 0000000000000000000000000000000000000000..e1bc2b55d64b7d318fe8e1c2d369a92ffc5ec1ac --- /dev/null +++ b/tools/data/reports/agent_20260516_1031.md @@ -0,0 +1,99 @@ +# Agent Report - Boundary Unfolding Transfer Matrix +**Date**: 2026-05-16 10:31 +**Piano**: 128 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT - `window_mode/unfolding` non trasferisce come asse cross-dominio nel perimetro dichiarato. Poisson resta endpoint su 8/8 righe, ma GUE non resta endpoint sotto la clausola classica a due lettori e RP produce 0/24 residui sopra i null row-aligned. Il boundary reader-axis resta ipotesi da riparare al nodo regressivo del lettore, non claim fisico. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, brody_q, berry_robnick_like_gue_weight, reader_sensitivity, endpoint_transfer_stable, reader_residue_pass, row_aligned_p, permutation_null_scores, position_shift_null_scores] +**observable_contract**: claim=`window_mode/unfolding` e' coordinata del boundary se gli endpoint GUE/Poisson trasferiscono mentre le righe RP boundary espongono residuo reader-specific contro null row-aligned; observable=reader_sensitivity del vettore spettrale canonico tra global_mean, exact_local e odd_coerced; operator=stessa riga di gap letta con piu unfolding/window modes; generator=matrici GUE, gap Poisson esponenziali, RP `H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE`; denominator=domain x size x seed, null da permutazione e shift circolare sulla stessa riga; non_possible=asse reader come boundary se un endpoint si frattura o RP non batte i null; not_tested=spettri sperimentali, N infinito, Anderson 3D, prova analitica di universalita. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + boundary operator/topologia del bordo + tensione BOUNDARY. +- **Dipolo / punto-zero**: polo fisico stabile / lettore che decide. Punto-zero: la stessa sequenza di gap prima che global/local/odd-coerced la leggano. +- **Piano superiore**: topologia assiomatica del bordo. Il boundary operator e' trattato come mappa fra lettori, non come parametro tecnico. +- **Operatori laterali scelti**: boundary operator e parallel transport. Il primo decide se il bordo e' oggetto; il secondo chiede se l'identita' del dominio trasporta fra lettori. +- **Contaminazione cognitiva**: CE-0019 usata come vincolo di respiro pre-esperimento; CE-0001/KSAR usata per reiterare il kernel 10:19 senza cercare un'altra lambda. +- **Proto-ipotesi**: se `window_mode/unfolding` e' asse reale del confine, GUE e Poisson trasferiscono come poli mentre RP boundary mostra residuo specifico del lettore sopra i null row-aligned. +- **Possibile/non-possibile**: possibile = reader axis come coordinata cross-dominio; non-possibile = endpoint GUE fratturato o RP reader residue assorbito dai null. +- **Proiezione**: misuro `reader_sensitivity` e stato classico per righe GUE, Poisson e RP `0.045/0.060/0.075`, con null di permutazione e shift sulla stessa riga. +- **Movimento A->M->B**: fisico A = crossover GUE/Poisson/RP finito; matematica M = matrice row-aligned `(domain, N, seed, reader)`; fisico B non emerge. Il ciclo consegna un vincolo sul lettore, non un rimbalzo fisico. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: porta l'asse `window_mode`/unfolding fuori dalla sola lambda RP e lo testa su GUE, Poisson e RP con null row-aligned. +- `not_drift`: non cerca una lambda stabile, non usa phi/Sturmian/V_c, non promuove righe graph-only; il risultato cade se endpoints o RP non rispettano il contratto. + +## Re-discovery audit +- **Baseline noto piu vicino**: unfolding sensitivity negli spettri finiti, Rosenzweig-Porter crossover, Brody interpolation, Berry-Robnik mixture, kNN stability sul grafo di osservabili. +- **Cosa assorbe il baseline**: la dipendenza delle statistiche spettrali finite dalla normalizzazione locale dei gap. +- **Cosa resta Lab-specific**: il contratto row-aligned che separa endpoint transfer e RP reader residue nella stessa matrice di lettori. +- **Separazione**: `two_reader_boundary_confirmed=[]`; `graph_only_residue=0`; `scope_change_declared=reader_axis_cross_domain`; `graph_baseline_audit=permutation_null + position_shift_null`. + +## Claim Under Test +> `window_mode/unfolding` trasferisce come coordinata del boundary se Poisson e GUE restano endpoint e RP `0.045/0.060/0.075` mostra residuo reader-specific sopra null row-aligned. + +## Experiment Design +- **Script nuovo**: `tools/exp_boundary_unfolding_transfer_matrix.py`. +- **Run**: `python tools/exp_boundary_unfolding_transfer_matrix.py --out tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- **Righe**: size `128/192`, seed `4`, domini `GUE`, `Poisson`, RP lambda `0.045/0.060/0.075`. +- **Reader**: `global_mean`, `exact_local` windows `9/12`, `odd_coerced` windows `9/12`. +- **Null row-aligned**: 32 permutazioni dei gap per riga + 8 shift circolari per riga. +- **Soglia preregistrata RP**: `reader_sensitivity >= 0.75`, `row_aligned_p <= 0.05`, almeno due stati reader diversi. +- **Soglia endpoint audit**: tutti gli stati reader devono matchare l'endpoint dichiarato; `endpoint_max_sensitivity=0.75` registrato come audit, non forzato nel pass. + +## Results +| group | observed | null | p / audit | median reader_sensitivity | lettura | +|---|---:|---:|---:|---:|---| +| GUE endpoint transfer | 0/8 | 0/256 | left-tail approx 0.000000 | 2.105328 | non trasferisce sotto clausola a due lettori | +| Poisson endpoint transfer | 8/8 | 0/256 | left-tail approx 1.000000 | 2.444532 | trasferisce come endpoint | +| RP reader residue | 0/24 | 551/960 | 1.000000 | 2.426735 | residuo assorbito dai null | + +### Endpoint Rows +| source | example states across readers | example q/w global | endpoint_transfer | +|---|---|---|---| +| GUE | intermediate, intermediate, intermediate, intermediate, intermediate | q=1.000000, w=0.526667 | 0/8 | +| Poisson | poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint, poisson_endpoint | q=0.046667, w=0.033333 | 8/8 | + +### RP Lambda Audit +| lambda | reader_residue_pass | total | median reader_sensitivity | states | +|---:|---:|---:|---:|---| +| 0.045 | 0 | 8 | 2.377442 | all readers intermediate | +| 0.060 | 0 | 8 | 2.489847 | all readers intermediate | +| 0.075 | 0 | 8 | 2.375018 | all readers intermediate | + +## Key Findings +1. Verificato: il contratto cross-dominio fallisce prima del boundary RP. GUE viene letto come `intermediate` in 8/8 righe sotto la clausola `q>=0.75` e `w>=0.75`; quindi l'endpoint non trasferisce. +2. Verificato: Poisson trasferisce come endpoint in 8/8 righe, ma questo non basta a validare l'asse reader perche' l'altro polo cade. +3. Verificato: RP `0.045/0.060/0.075` resta `intermediate` in tutte le letture e produce 0/24 `reader_residue_pass`; i null hanno 551/960 score >= osservato, quindi il residuo reader-specific non emerge. +4. Inferito dal perimetro: la sensibilita' del vettore osservabile e' alta in tutti i gruppi, ma non discrimina boundary. Il nodo regressivo e' la definizione del lettore/classificatore, non la scelta di lambda. + +## Verdict +CONSTRAINT + +`window_mode/unfolding` non diventa coordinata cross-dominio nel perimetro 10:31. La matrice conserva informazione utile perche' mostra dove cade: il lettore a due clausole classiche rompe GUE e i null assorbono RP. Il prossimo ciclo non deve cercare una cresta RP; deve riparare il lettore endpoint o cambiare dominio di ritorno con endpoint verificati prima del boundary. + +## Bicono della scoperta +- **Due radici**: endpoint transfer; reader residue RP. +- **Singolare**: riga di gap prima dell'unfolding. +- **Invariante di passaggio**: stesso denominatore row-aligned per osservato e null. +- **Campo di possibilita**: possibile = audit del lettore prima della promozione del boundary; non-possibile = dichiarare terzo incluso cross-dominio con GUE endpoint non trasferito. + +## Consecutio +Il prossimo passo utile e' regressivo: prima validare endpoint GUE/Poisson con un lettore che non trasformi GUE in intermedio, poi rieseguire la matrice RP. Se la clausola classica resta `q AND w`, il boundary reader-axis e' bloccato. Se la clausola diventa endpoint-validata su baseline GUE indipendente, la domanda torna falsificabile. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_boundary_unfolding_transfer_matrix.py` e' uno strumento riusabile per testare trasferibilita' del lettore su domini, size, seed, windows e null row-aligned. + +## Telemetria +- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` non presenti nell'ambiente. +- `python tools/dnd_scenario.py --best` eseguito: massimo discriminante locale su TRASCENDENZA_LIMITE; direzione BOUNDARY seguita per contratto vivo. +- `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` completato. +- Run completato: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json`. +- Worktree gia dirty prima del ciclo; ignorate modifiche non correlate. +- Nessun update del seme. +- Nessuna promozione e nessun public sync. + +## Files +- Script: `tools/exp_boundary_unfolding_transfer_matrix.py` +- Data: `tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json` +- Report: `tools/data/reports/agent_20260516_1031.md` diff --git a/tools/exp_boundary_unfolding_transfer_matrix.py b/tools/exp_boundary_unfolding_transfer_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..95fe46a289ec1248834caf176c85fe3672852519 --- /dev/null +++ b/tools/exp_boundary_unfolding_transfer_matrix.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +""" +Cross-domain unfolding-transfer matrix for the BOUNDARY redesign. + +The unit is the reader axis, not a new RP lambda crest. For each row-aligned +domain/size/seed spectrum, the script measures how much the observable vector +changes when the same raw spacings are read by global, exact-local and +odd-coerced local unfolding. Row-aligned permutation and circular-shift nulls +test whether the reader residue is stronger than order-preserving baselines. +""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +from exp_rosenzweig_porter_bridge_physical_audit import ( + OBSERVABLES_CANONICAL, + OBSERVABLES_REGISTRY_VERSION, + fit_brody_q, + fit_mixture_weight, + rp_hamiltonian, +) +from exp_rp_boundary_raw_count_null_audit import binomial_tail_at_least, wilson_interval +from exp_rp_unfolding_sensitivity_audit import local_unfold_gaps as odd_coerced_unfold + + +def parse_ints(value: str) -> list[int]: + return [int(part.strip()) for part in value.split(",") if part.strip()] + + +def parse_floats(value: str) -> list[float]: + return [float(part.strip()) for part in value.split(",") if part.strip()] + + +def central_slice(length: int, fraction: float) -> slice: + keep = max(4, int(round(length * fraction))) + start = max(0, (length - keep) // 2) + return slice(start, start + keep) + + +def exact_local_unfold(gaps: np.ndarray, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if len(gaps) == 0: + return gaps + width = max(2, min(int(window), len(gaps))) + left = width // 2 + out = np.empty_like(gaps) + for idx in range(len(gaps)): + start = idx - left + end = start + width + if start < 0: + start = 0 + end = width + if end > len(gaps): + end = len(gaps) + start = max(0, end - width) + denom = float(np.mean(gaps[start:end])) + if denom <= 1e-12: + denom = float(np.mean(gaps)) + out[idx] = gaps[idx] / denom + return out / float(np.mean(out)) + + +def clean_gaps(gaps: np.ndarray) -> np.ndarray: + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] + if len(gaps) == 0: + return gaps + return gaps / float(np.mean(gaps)) + + +def gue_gaps(n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + real = rng.normal(0.0, 1.0, size=(n, n)) + imag = rng.normal(0.0, 1.0, size=(n, n)) + h = (real + real.T) / 2.0 + 1j * (imag - imag.T) / 2.0 + levels = np.linalg.eigvalsh(h / math.sqrt(2.0 * n)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def poisson_gaps(n: int, reps: int, seed: int, _central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + return clean_gaps(rng.exponential(1.0, size=max(4, (n - 1) * reps))) + + +def rp_gaps(lam: float, n: int, reps: int, seed: int, central_fraction: float) -> np.ndarray: + rng = np.random.default_rng(seed) + all_gaps: list[float] = [] + for _ in range(reps): + levels = np.linalg.eigvalsh(rp_hamiltonian(rng, n, lam)) + bulk = np.sort(levels)[central_slice(len(levels), central_fraction)] + all_gaps.extend(np.diff(bulk).tolist()) + return clean_gaps(np.asarray(all_gaps, dtype=float)) + + +def read_by_mode(gaps: np.ndarray, mode: str, window: int) -> np.ndarray: + gaps = clean_gaps(gaps) + if mode == "global_mean": + return gaps + if mode.startswith("exact"): + return exact_local_unfold(gaps, window) + if mode.startswith("odd_coerced"): + return clean_gaps(odd_coerced_unfold(gaps, window)) + raise ValueError(f"unknown unfolding mode: {mode}") + + +def feature_vector(gaps: np.ndarray, grid_size: int) -> dict[str, float]: + gaps = clean_gaps(gaps) + obs = {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} + q, _ = fit_brody_q(gaps, grid_size) + w, _ = fit_mixture_weight(gaps, grid_size) + obs["brody_q"] = float(q) + obs["berry_robnick_like_gue_weight"] = float(w) + return obs + + +def classify(features: dict[str, float]) -> str: + q = features["brody_q"] + w = features["berry_robnick_like_gue_weight"] + if q <= 0.25 and w <= 0.25: + return "poisson_endpoint" + if q >= 0.75 and w >= 0.75: + return "gue_endpoint" + return "intermediate" + + +def mode_matrix(gaps: np.ndarray, modes: list[str], windows: list[int], grid_size: int) -> list[dict[str, Any]]: + rows = [] + for mode in modes: + for window in windows: + if mode == "global_mean" and window != windows[0]: + continue + features = feature_vector(read_by_mode(gaps, mode, window), grid_size) + rows.append( + { + "reader": f"{mode}:w{window}" if mode != "global_mean" else "global_mean", + "mode": mode, + "window": window if mode != "global_mean" else None, + "features": {key: round(value, 9) for key, value in features.items()}, + "classical_state": classify(features), + } + ) + return rows + + +def sensitivity(rows: list[dict[str, Any]], feature_names: list[str]) -> float: + matrix = np.asarray([[row["features"][name] for name in feature_names] for row in rows], dtype=float) + if len(matrix) < 2: + return 0.0 + scale = np.std(matrix, axis=0) + scale[scale <= 1e-9] = 1.0 + z = matrix / scale + best = 0.0 + for i in range(len(z)): + for j in range(i + 1, len(z)): + best = max(best, float(np.linalg.norm(z[i] - z[j]) / math.sqrt(len(feature_names)))) + return best + + +def stable_endpoint(source_type: str, states: list[str]) -> bool: + if source_type == "GUE": + return all(state == "gue_endpoint" for state in states) + if source_type == "Poisson": + return all(state == "poisson_endpoint" for state in states) + return False + + +def row_nulls( + gaps: np.ndarray, + args: argparse.Namespace, + modes: list[str], + windows: list[int], + feature_names: list[str], + seed: int, +) -> tuple[list[float], list[float]]: + rng = np.random.default_rng(seed) + perm_scores = [] + shift_scores = [] + for _ in range(args.permutation_null_trials): + permuted = np.array(gaps, copy=True) + rng.shuffle(permuted) + perm_scores.append(sensitivity(mode_matrix(permuted, modes, windows, args.grid_size), feature_names)) + for shift in parse_ints(args.position_offsets): + shifted = np.roll(gaps, shift) + shift_scores.append(sensitivity(mode_matrix(shifted, modes, windows, args.grid_size), feature_names)) + return perm_scores, shift_scores + + +def build_source_rows(args: argparse.Namespace) -> list[dict[str, Any]]: + rows = [] + sizes = parse_ints(args.sizes) + seeds = parse_ints(args.seeds) + for n in sizes: + for seed_idx, seed in enumerate(seeds): + rows.append( + { + "row_id": f"GUE_N{n}_s{seed_idx}", + "source_type": "GUE", + "n": n, + "seed": seed, + "gaps": gue_gaps(n, args.reps, seed + n * 1009, args.central_fraction), + } + ) + rows.append( + { + "row_id": f"Poisson_N{n}_s{seed_idx}", + "source_type": "Poisson", + "n": n, + "seed": seed, + "gaps": poisson_gaps(n, args.reps, seed + n * 1013, args.central_fraction), + } + ) + for lam in parse_floats(args.rp_lambdas): + rows.append( + { + "row_id": f"RP_lambda_{lam:.3f}_N{n}_s{seed_idx}", + "source_type": "RP", + "lambda": round(lam, 6), + "n": n, + "seed": seed, + "gaps": rp_gaps(lam, n, args.reps, seed + n * 1019 + int(round(lam * 10000)), args.central_fraction), + } + ) + return rows + + +def summarize_group(rows: list[dict[str, Any]], source_type: str, args: argparse.Namespace) -> dict[str, Any]: + group = [row for row in rows if row["source_type"] == source_type] + if not group: + return {} + if source_type in {"GUE", "Poisson"}: + successes = sum(1 for row in group if row["endpoint_transfer_stable"]) + null_successes = sum(1 for row in group for score in row["permutation_null_scores"] if score <= args.endpoint_max_sensitivity) + null_total = sum(len(row["permutation_null_scores"]) for row in group) + p = 1.0 - binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + return { + "source_type": source_type, + "criterion": "endpoint_transfer_stable", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_note": "left-tail endpoint failure risk; high observed rate is expected for true endpoints", + "left_tail_p_approx": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + } + successes = sum(1 for row in group if row["reader_residue_pass"]) + null_successes = sum( + 1 + for row in group + for score in row["permutation_null_scores"] + row["position_shift_null_scores"] + if score >= row["reader_sensitivity"] + ) + null_total = sum(len(row["permutation_null_scores"]) + len(row["position_shift_null_scores"]) for row in group) + p = binomial_tail_at_least(successes, len(group), null_successes / null_total) if null_total else None + by_lambda = {} + for lam in sorted({row.get("lambda") for row in group}): + lam_rows = [row for row in group if row.get("lambda") == lam] + by_lambda[f"{lam:.3f}"] = { + "reader_residue_pass": sum(1 for row in lam_rows if row["reader_residue_pass"]), + "total": len(lam_rows), + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in lam_rows])), 6), + "state_sequences": [row["state_sequence"] for row in lam_rows], + } + return { + "source_type": source_type, + "criterion": "reader_residue_pass", + "observed_successes": successes, + "observed_total": len(group), + "observed_rate": round(successes / len(group), 6), + "observed_wilson_95": wilson_interval(successes, len(group)), + "null_successes": null_successes, + "null_total": null_total, + "null_rate": round(null_successes / null_total, 6) if null_total else None, + "binomial_tail_p": round(p, 6) if p is not None else None, + "median_sensitivity": round(float(np.median([row["reader_sensitivity"] for row in group])), 6), + "by_lambda": by_lambda, + } + + +def run(args: argparse.Namespace) -> dict[str, Any]: + modes = [part.strip() for part in args.unfolding_modes.split(",") if part.strip()] + windows = parse_ints(args.local_windows) + feature_names = ["SR", "SR2", "L1", "L2", "triple_var", "brody_q", "berry_robnick_like_gue_weight"] + output_rows = [] + for source in build_source_rows(args): + gaps = source.pop("gaps") + readers = mode_matrix(gaps, modes, windows, args.grid_size) + score = sensitivity(readers, feature_names) + perm, shift = row_nulls(gaps, args, modes, windows, feature_names, int(source["seed"]) + int(source["n"])) + null_combined = perm + shift + null_ge = sum(1 for item in null_combined if item >= score) + row_p = (1 + null_ge) / (1 + len(null_combined)) + states = [reader["classical_state"] for reader in readers] + endpoint_ok = stable_endpoint(source["source_type"], states) + reader_pass = ( + source["source_type"] == "RP" + and score >= args.min_reader_sensitivity + and row_p <= args.alpha + and len(set(states)) > 1 + ) + output_rows.append( + { + **source, + "n_spacings": int(len(gaps)), + "reader_sensitivity": round(score, 6), + "null_ge_observed": null_ge, + "null_total": len(null_combined), + "row_aligned_p": round(row_p, 6), + "endpoint_transfer_stable": endpoint_ok, + "reader_residue_pass": reader_pass, + "state_sequence": states, + "readers": readers, + "permutation_null_scores": [round(item, 6) for item in perm], + "position_shift_null_scores": [round(item, 6) for item in shift], + } + ) + + summary = { + "GUE": summarize_group(output_rows, "GUE", args), + "Poisson": summarize_group(output_rows, "Poisson", args), + "RP": summarize_group(output_rows, "RP", args), + } + output = { + "experiment": "boundary_unfolding_transfer_matrix", + "question": "Does the unfolding/window reader axis transfer across GUE, Poisson and RP as boundary coordinate rather than as a stable RP lambda?", + "observables_registry": OBSERVABLES_REGISTRY_VERSION, + "observables_used": feature_names + + [ + "reader_sensitivity", + "endpoint_transfer_stable", + "reader_residue_pass", + "row_aligned_p", + "permutation_null_scores", + "position_shift_null_scores", + ], + "parameters": { + "sizes": parse_ints(args.sizes), + "seeds": parse_ints(args.seeds), + "reps": args.reps, + "rp_lambdas": parse_floats(args.rp_lambdas), + "unfolding_modes": modes, + "local_windows": windows, + "permutation_null_trials": args.permutation_null_trials, + "position_offsets": parse_ints(args.position_offsets), + "central_fraction": args.central_fraction, + "grid_size": args.grid_size, + }, + "threshold_preregistered": { + "rp_reader_residue": f"reader_sensitivity >= {args.min_reader_sensitivity}, row_aligned_p <= {args.alpha}, and at least two reader states", + "endpoint_transfer": f"all reader states match endpoint and reader_sensitivity <= {args.endpoint_max_sensitivity} is audited, not forced", + "nulls": "permutation and circular-shift scores computed from the same row spacings", + }, + "observable_contract": { + "claim": "window_mode/unfolding is a boundary coordinate if endpoints transfer while RP boundary rows expose reader-specific residue against row-aligned nulls", + "observable": "reader_sensitivity of canonical spectral vector across global, exact-local and odd-coerced readers", + "operator": "same raw spacing row read by multiple unfolding/window modes", + "generator": "GUE matrices, Poisson exponential spacings, and RP H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE", + "denominator": "domain x size x seed rows; nulls use the same row spacings under permutation and circular shifts", + "non_possible": "reader axis as boundary coordinate if GUE/Poisson endpoints also fracture or RP residue does not beat row-aligned nulls", + "not_tested": "experimental spectra, N to infinity, Anderson 3D, analytic universality class proof", + }, + "summary": summary, + "rows": output_rows, + } + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n", encoding="utf-8") + print(json.dumps(summary, indent=2, sort_keys=True)) + return output + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--out", default="tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json") + parser.add_argument("--sizes", default="128,192") + parser.add_argument("--seeds", default="202605161031,202605161032,202605161033,202605161034") + parser.add_argument("--reps", type=int, default=6) + parser.add_argument("--rp-lambdas", default="0.045,0.060,0.075") + parser.add_argument("--unfolding-modes", default="global_mean,exact_local,odd_coerced") + parser.add_argument("--local-windows", default="9,12") + parser.add_argument("--permutation-null-trials", type=int, default=32) + parser.add_argument("--position-offsets", default="1,2,3,4,5,6,7,8") + parser.add_argument("--central-fraction", type=float, default=0.6) + parser.add_argument("--grid-size", type=int, default=151) + parser.add_argument("--min-reader-sensitivity", type=float, default=0.75) + parser.add_argument("--endpoint-max-sensitivity", type=float, default=0.75) + parser.add_argument("--alpha", type=float, default=0.05) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() tokens used 155,970 Cycle completato. Ho creato lo script riusabile [tools/exp_boundary_unfolding_transfer_matrix.py](/opt/MM_D-ND/tools/exp_boundary_unfolding_transfer_matrix.py) e scritto il report richiesto in [tools/data/reports/agent_20260516_1031.md](/opt/MM_D-ND/tools/data/reports/agent_20260516_1031.md). Risultato: `CONSTRAINT`. Il test cross-dominio non promuove `window_mode/unfolding` come asse del boundary: Poisson trasferisce come endpoint, ma GUE cade a `intermediate` sotto la clausola a due lettori e RP non batte i null row-aligned. Verifiche fatte: - `python -m py_compile tools/exp_boundary_unfolding_transfer_matrix.py` - run esperimento completato con output in [tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json](/opt/MM_D-ND/tools/data/boundary_unfolding_transfer_matrix_20260516_1031.json) - report riletto dopo scrittura Non ho aggiornato il seme e non ho lanciato il falsifier LLM.