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: 019e3017-27bf-7c13-9da9-5cc0850b263a -------- 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_0921.md warning: Codex could not find bubblewrap on PATH. Install bubblewrap with your OS package manager. See the sandbox prerequisites: https://developers.openai.com/codex/concepts/sandboxing#prerequisites. Codex will use the vendored bubblewrap in the meantime. codex Leggo il campo vivo e il contesto operativo prima di scegliere l'esperimento. Verifico anche i kernel indicati dall'adapter, così il report resta allineato alle regole cross-agent. exec /bin/bash -lc "sed -n '1,240p' /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. Sono strumenti del sistema. Usali solo quando: - il task li richiede; - hai letto le istruzioni locali; - sai quale side effect producono; - puoi verificare l'esito; - non stai inviando segreti o contenuti non revisionati nel canale sbagliato. Per messaggi inter-nodo: COWORK e' registro; Sinapsi e' segnale. Se la Sinapsi fallisce, il registro resta la fonte. ## Output atteso Per lavori sostanziali, rispondi con: ```text Ruolo/funzione: Fonti lette: exec /bin/bash -lc "sed -n '1,240p' /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. Before creating new structure, search for what already exists. Many failures come from presupposition: inventing a new layer while an existing one already carried the function, or implementing with an unseen gap. Awareness is the method, not a cage. Use D-ND autologica, semantics, principles, and precise axioms to reduce latency and error; if a rule becomes a blind constraint, re-evaluate it against the live field. ## Tooling ### Hook Manager Use: ```bash python3 /opt/tm7/tools/tm7_continuum.py boot python3 /opt/tm7/tools/tm7_continuum.py horizon --task "" --next "" python3 /opt/tm7/tools/tm7_continuum.py pre-compact --write-packet --task "" --next "" python3 /opt/tm7/tools/tm7_hook_manager.py --event status exec /bin/bash -lc "sed -n '1,240p' /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 Function: - neutral installable lab base; - domain-portable lab engine; - source for reusable patterns, but not identical to MM-DND. Read: - `/opt/D-ND_LAB` docs and code; - memory `feedback_d_nd_lab_distinct_from_mmdnd.md`; - business/seed positioning docs before public-facing changes. Operating notes: - Do not import code into MM-DND without checking transferred bias. - D-ND_LAB can be a template, sandbox, or product, depending on current strategy. ### lab.d-nd.com 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: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - 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=9, T=7, E=4, R=4 **Generatrici/strade dense**: - disc_5: 3 ghost · Metrica primi g=(p/2)², curvatura GUE r=0.503 - report_20260515_1947: 3 ghost · Agent Report - Anderson 3D Mobility-Edge Two-Reader Audit - report_20260515_1933: 2 ghost · Agent Report - Rosenzweig-Porter Physical Bridge Audit **Forma del campo**: 9 ponti, 1 vuoto(i), 6 scoperte. **Direzione seme da respirare**: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo ## Contratto di aderenza alla traiettoria - Direzione viva del seme: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo - Ultima decisione valutatore ammessa: 20260515_1947 NEXT_CYCLE/medium - Perche': Il ciclo ha prodotto un vincolo strutturale utile, non solo dettaglio numerico locale: il gate two-reader attraversa il dominio Anderson ma resta confermato solo come riga finita W=20, mentre W=16/16.5 non è size-stable. La direzione nuova apre un perimetro cross-dominio GUE/Poisson coerente con il telos del lab e non richiede intervento operatore prima del prossimo giro. - Nota: Bias verso continuità: il risultato è un CONSTRAINT trasferibile, non una falsificazione da cimitero né una cristallizzazione matura. Nel report aggiungi una sezione `## Aderenza alla direzione` con tre righe: - `relation`: follows_direction | deliberate_counter_perimeter | local_regression - `why`: perche' l'esperimento serve la direzione viva - `not_drift`: cosa impedisce che sia solo ritorno a un deposito familiare Puoi deviare dalla direzione solo se lo dichiari come contro-perimetro deliberato e lo rendi falsificabile. Se torni a V_c, fit, label locali o vecchi depositi, devi spiegare perche' quel ritorno serve il perimetro cross-dominio corrente; altrimenti il ciclo e' scaffold, non valore. ## Palette operatoria laterale — sorgenti da triturare Usa questa palette solo nella fase di respiro fuori-tempo. Scegli pochi operatori, crea una combo, poi proietta un osservabile. Non trasformarla in lista di temi. # Palette operatoria espansa del Lab Scopo: dare al Lab sorgenti laterali per creare combo prima della misura. Questa palette non e' una lista di temi da confermare. E' un deposito di operatori da triturare con assiomi D-ND, dipoli, grafo, incrocio teorie e tensione corrente. Regola d'uso: 1. Scegli 2 o 3 operatori al massimo. 2. Incrociali con almeno un assioma D-ND e una tensione del seme. 3. Nomina il dipolo e il punto-zero. 4. Dichiara la baseline nota piu' vicina. 5. Proietta un osservabile che possa falsificare la combo. 6. Non usare un operatore se produce solo linguaggio, analogia o conferma. Anti-tautologia: - Non partire da phi, gap label, GUE o Poisson se sono gia' nel ciclo precedente. Usali come controllo o campo di proiezione, non come sorgente. - Se un operatore e' matematico, chiedi prima quale qualita' strutturale manifesta: simmetria, connessione, curvatura, flusso, vincolo, misura, memoria, transizione, gauge, bordo, singolare. - Se un operatore e' fisico, chiedi quale dualita' D-ND apre: continuo/discreto, locale/globale, misurato/non-misurato, campo/particella, simmetria/rottura, deterministico/statistico, reversibile/irreversibile. ## Fasce di triturazione ### 1. Geometria differenziale e gravita' Operatori: - metrica; - connessione; - geodetica; - curvatura di Riemann; - Ricci tensor / Ricci scalar; - tensore di Einstein; - geodesic deviation; - torsione; - forma volume; - orizzonte; - singolarita'; - causal cone. Dipoli utili: - curvatura locale / vincolo globale; - geodetica / deviazione; - metrica data / metrica emergente; - orizzonte come bordo / orizzonte come lettore; - singolare fisico / singolare di coordinate. Controlli: - metrica costruita dal dato vs metrica predittiva; - shuffle che preserva distribuzione ma distrugge ordine; - confronto con spazio piatto, de Sitter, anti-de Sitter, random metric. Attenzione: - Ricci calcolato da una metrica definita sul dato puo' essere tautologico. Il contenuto vive nel null test o nella predizione fuori costruzione. ### 2. Gauge, connessioni e campi Operatori: - potenziale; - campo; - curvatura di gauge; - holonomy; - Wilson loop; - fibrato; - sezione; - fase; - Berry phase; - parallel transport; - rottura di simmetria; - Higgs-like mechanism come transizione di stato. Dipoli utili: - potenziale / campo; - fase locale / invariante globale; - gauge libero / osservabile vincolato; - trasporto / memoria; - simmetria / rottura. Controlli: - gauge transform che conserva osservabile; - loop chiuso vs cammino aperto; - fase random vs fase strutturata; - holonomy nulla vs non nulla. ### 3. Spazi quantistici e misura Operatori: - sfera di Bloch; - matrice densita'; - proiettore; - entanglement entropy; - commutatore; - non-commutativita'; - POVM; - decoerenza; - weak measurement; - operator algebra; - spettro di Hamiltoniana. Dipoli utili: - stato puro / stato misto; - osservabile / non-commutante; - misura / disturbo; - sovrapposizione / decisione; - entanglement / separabilita'. Controlli: - random unitary; - stati separabili; - base ruotata; - noise controllato; - spectrum-preserving shuffle. Nota: - Bloch e' buono quando serve un punto-zero geometrico tra poli. Non usarlo solo per disegnare dualita': deve produrre un osservabile. ### 4. Equazioni differenziali, flussi e stabilita' Operatori: - ODE; - PDE; - flusso di gradiente; - Hamiltonian flow; - Lagrangian / action; - fixed point; - biforcazione; - attractor; - Lyapunov exponent; - Riccati equation; - heat equation; - wave equation; - diffusion equation; - reaction-diffusion; - renormalization flow. Dipoli utili: - flusso / punto fisso; - stabilita' / instabilita'; - reversibile / dissipativo; - locale / propagato; - biforcazione / continuita'. Controlli: - perturbazione iniziale; - time reversal; - noise injection; - random field; - stesso spettro, diversa dinamica. ### 5. Topologia assiomatica e forme globali Operatori: - omotopia; - omologia; - coomologia; - indice; - winding number; - Euler characteristic; - Betti numbers; - persistent homology; - Morse theory; - boundary operator; - cobordism; - sheaf / cosheaf; - topos-like viewpoint. Dipoli utili: - bordo / interno; - buco / ponte; 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) codex Il campo vivo punta alla direzione GUE/Poisson come confine-terzo incluso e impone un contratto pre-esperimento più stretto del solito: combo, dipolo, baseline e count grezzi. Ora controllo seme, direttive one-shot, memoria condivisa recente e stato repo prima di misurare. exec /bin/bash -lc "test -f tools/data/operator_directive.md && sed -n '1,220p' tools/data/operator_directive.md || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "sed -n '1,260p' tools/data/seme.json" in /opt/MM_D-ND succeeded in 0ms: { "timestamp": "2026-05-15T19:53:17.302583", "piano": 124, "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 }, { "tipo": "vincolo", "id": "ORDER_DENOMINATOR_GATE", "claim": "Il denominator gate trasferisce come supporto one-sided dell'ordine quando l'ordine e visibile agli osservabili del perimetro, non come endpoint-stable support a due poli. Nel perimetro sintetico agent_20260507_0901, 4/4 domini non-BOUNDARY hanno endpoint_stable_observables=[] e polo coerente stable_count 3.0-5.0. Nel perimetro semi-reale agent_20260507_0923, primi e zeta trasferiscono (primi: SR,L1,triple_var; zeta: SR,L2), ma logistic_return_intervals e blank: stable_count coerente 0.0-0.2. Nel perimetro bridge agent_20260507_0942, prime_metric_delta_gamma_abs, prime_metric_dR_abs, zeta_trace_residual_step5_abs e hydrogen_bound_level_spacings trasferiscono su tutti i 5 osservabili canonici con endpoint_stable_observables=[]; e supporto perimetro-bridge, non universalita del gate. Nel perimetro logistic-native agent_20260507_1006, logistic_orbit_values trasferisce su block_entropy_deficit_k4 in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals mostra recurrence_diag_mean solo nel run principale e torna blank nel seed check. La beta 0.10/0.30/0.40/0.50 resta coordinata del protocollo quando compare, non coordinata universale. Nel perimetro surrogate-contract agent_20260507_1042, logistic_orbit_values trasferisce solo tramite block_entropy_deficit_k4 e sopravvive a marginal_shuffle, circular_shift e block_shuffle in run e seed check; logistic_symbolic_itinerary resta blank; logistic_return_intervals non replica (recurrence_diag_mean compare contro marginal/block nel run principale ma sparisce nel seed check). Nel perimetro cyclic-cut agent_20260507_1419, il residuo logistic_orbit_values non e artefatto del taglio lineare: cyclic_block_entropy_deficit_k4 e invariato sotto rotazione e replica contro marginal_shuffle e block_shuffle size 4-256 in run e seed check. Logistic_symbolic_itinerary e logistic_return_intervals restano blank nel criterio replicato. Il supporto logistic rimasto e orbit-local block grammar, non return/generating-partition support.", "intensita": 0.92, "porta": "META", "manuale": true, "condensato_ref": "A4,A8,A14,C2", "origine": "cycle agent_20260507_0901 + agent_20260507_0923 + agent_20260507_0942 + agent_20260507_1006: transfer matrix sintetica, falsificazione semi-reale su primi/zeta/logistic returns, perimetri bridge metric/trace/QxE, e regressione logistic-native + agent_20260507_1042: surrogate contract logistic", "added_at": "2026-05-07T09:01:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125269", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "META_ASSERTION_GATE", "claim": "Il PASS 11/11 della verifica non e un denominatore unico. Nel perimetro agent_20260507_1458, 6/11 test passano anche senza tools/data; 5/11 dipendono da fixture o contesto; 4/11 PASS sono algebra/same-rule ad alto rischio tautologico. La tensione META va riportata con gate_class, no_data_status e data_dependency per test.", "intensita": 0.88, "manuale": true, "porta": "META", "condensato_ref": "A4,A8,A12,C2", "origine": "cycle agent_20260507_1458: meta_assertion_gate su dipartimento.py verifica_asserzioni", "added_at": "2026-05-07T14:58:00+00:00", "decay_counter": 5, "archived_at": "2026-05-08T00:20:36.125271", "archived_reason": "G4 B1 apply: decay_counter=5 (vincolo non attaccato per 5 piani consecutivi)", "archived_from_piano": 85 }, { "tipo": "vincolo", "id": "DUALITA_DET_DENOMINATOR_GATE", "claim": "Nel perimetro transfer-matrix dei gap primi agent_20260507_2042, det(M) non e discriminatore primario della dualita: il reale resta negativo ma vale ~-0.39, non -1, e lo shuffle con stessa marginale resta negativo (~-0.38..-0.37). La parte informativa e lo scarto reale-vs-shuffle, forte nelle scale basse/intermedie (z=-2.5..-4.4) e debole alla finestra p~5.0e7 (z=-0.97). Formulare DUALITA come supporto ordinato contro null, non come tassonomia diretta det=-1/det=+1 del fit lineare.", "intensita": 0.86, "manuale": true, "porta": "DUALITA_DIPOLARE_VS_ILLUSORIA", "condensato_ref": "A2,A4,A10,A14,C2", "origine": "cycle agent_20260507_2042: det_drift transfer-matrix sui gap primi", "added_at": "2026-05-07T20:42:00+00:00", exec /bin/bash -lc '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 '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/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/ ?? 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/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/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/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/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/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/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/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/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/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/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/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/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_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 exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 136ms: 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 Non c'è direttiva one-shot. Il repo è già molto sporco da ciclo/runtime, quindi evito cleanup e scrivo solo nuovi artefatti necessari: uno script riusabile, un JSON dati e il report richiesto. Prima però leggo gli ultimi report ammessi, perché il prossimo esperimento deve chiudere il check L2 sui count grezzi e non ripetere lo Sturmian bloccato. exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260516_0720.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Boundary Graph Residue Threshold Audit **Date**: 2026-05-16 07:20 **Piano**: 124 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT - nessuna riga graph-only supera la soglia preregistrata; `percolation` e `logistica_biforcazione_var_3.5699` restano `positive_lift_unthresholded`, non residui thresholded. observables_registry: 1.0.0-2026-05-06 via boundary_graph_curvature_gate observables_used: [observed_successes, label_null_successes, rewire_null_successes, observed_wilson_95, label_null_wilson_95, rewire_null_wilson_95, label_binomial_tail_p, rewire_binomial_tail_p, min_lift_against_nulls, positive_lift_unthresholded, threshold_pass] **observable_contract**: claim=le righe graph-only diventano residui thresholded solo con separazione raw-count da entrambi i null grafici; observable=count grezzi osservati/null con intervalli Wilson e p-value binomial-tail; operator=post-audit del JSON row-aligned 03:30 senza rerun del lettore grafico; generator=13 righe BOUNDARY 8 GUE / 5 Poisson con feature graph canonical+rigidity+shuffle-z; denominator=13 righe, observed denominator 6, label-null denominator 384, rewire-null denominator 384; non_possible=residue claim se p-value contro uno dei due null supera alpha o il lift minimo e sotto soglia; not_tested=nuovi sistemi fisici, nuova geometria del grafo, universalita asintotica. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore del confine + tensione seme BOUNDARY "8 domini GUE, 5 Poisson". - **Dipolo / punto-zero**: lift positivo / residuo thresholded. Punto-zero: la riga graph-only prima che la parola "sopravvive" decida al posto dei denominatori. - **Piano superiore**: grafo della conoscenza come contratto di lettura; il grafo non aggiunge un terzo lettore, ma subisce audit sul proprio null. - **Operatori laterali scelti**: graph rewiring, cut, spectral crossover. Rewiring misura quanto il ponte dipende dalla topologia; cut espone la frequenza di attraversamento; crossover resta baseline fisica nota. - **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata come palette operatoria. YSN DeltaLink=`lift positivo / residuo thresholded`; Cornelius gene=`RAW_COUNT_THRESHOLD`: LEGGI JSON, CONTA DENOMINATORI, TESTA NULL, TAGLIA PAROLA. - **Proto-ipotesi**: un residuo graph-only non nasce da `observed > null`; nasce solo quando il vantaggio resta sopra una soglia dichiarata e raro sotto entrambi i null. - **Proiezione**: la misura proietta la combo su count grezzi e p-value: se il null rewire produce quasi sempre lo stesso ponte, il terzo incluso non e residuo strutturale del Lab. ## Aderenza alla direzione - `relation`: `follows_direction` - `why`: il ciclo resta sul perimetro vivo 8 GUE / 5 Poisson e ripara il confine come terzo incluso operativo, separando two-reader boundary, graph-only lift e residuo thresholded. - `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; corregge il nodo regressivo indicato dal falsifier 03:30 con count, soglia e p-value. ## Re-discovery audit - **Baseline noto piu vicino**: Brody / Berry-Robnik / Rosenzweig-Porter per crossover GUE-Poisson; kNN stability, label shuffle e degree-preserving rewiring per residui grafo. - **Cosa viene assorbito dal baseline**: un lift positivo piccolo con null rewire alto non separa una classe fisica; e compatibile con artefatto topologico o perimetro composito piccolo. - **Cosa resta Lab-specific**: il contratto operativo che vieta di sommare graph-only al boundary a due lettori e vieta di chiamare residuo una riga senza soglia. - **Separazione richiesta**: `two_reader_boundary_confirmed = 1`; `positive_lift_unthresholded = 2`; `thresholded_graph_specific_residue = 0`; `graph_baseline_audit = label_shuffle + degree_preserving_rewire + raw-count threshold`. ## Claim Under Test > Nel perimetro 8/5, un graph-only bridge diventa residuo strutturale solo se supera una soglia preregistrata contro label-shuffle e rewiring degree-preserving; altrimenti resta lift positivo non-thresholded. ## Question I due graph-only bridge positivi del ciclo 03:30 hanno separazione statistica sufficiente per essere chiamati residui, o il loro vantaggio e troppo piccolo rispetto ai null grafici? ## Ritorno fisico - **Punto fisico sorgente**: crossover spettrale tra repulsione Wigner-Dyson/GUE e indipendenza/localizzazione Poisson. - **Attraversamento matematico**: audit raw-count di bridge frequency su grafo kNN, con label-shuffle e rewiring degree-preserving. - **Punto fisico di ritorno**: finestre finite in Rosenzweig-Porter, Anderson 3D o Aubry-Andre in cui un graph-only bridge deve essere trattato come candidato finche non supera null topologici. - **Relazione nuova**: il boundary fisico non e un lift positivo; e una riga con lettore classico, lettore grafico e separazione statistica da null del grafo. - **Osservabile/test fisico possibile**: ripetere il gate su sistemi controllati richiedendo count grezzi, Wilson interval e p-value contro entrambi i null prima della promozione. - **Se fallisce**: se il p-value resta alto o il lift minimo resta sotto soglia, il graph-only bridge resta audit operativo, non scoperta fisica. ## Experiment Design - **Script**: `tools/exp_boundary_graph_residue_threshold_audit.py`. - **Input**: `tools/data/boundary_graph_null_audit_20260516_0330.json`. - **Run**: `python tools/exp_boundary_graph_residue_threshold_audit.py --input tools/data/boundary_graph_null_audit_20260516_0330.json --out tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json --min-lift 0.10 --alpha 0.05`. - **Soglia preregistrata**: observed success `6/6`, lift minimo contro ciascun null `>= 0.10`, p-value binomial-tail contro ciascun null `<= 0.05`. - **Denominatori**: observed `6` letture grafiche; label null `384`; rewire null `384`. - **Contratto osservabile-operatore**: il ciclo non cambia il grafo e non cerca nuovi Hamiltoniani; ripara il significato di `graph_specific_residue_after_nulls`. ## Results | row | observed | label null | label p | rewire null | rewire p | min lift | state | |---|---:|---:|---:|---:|---:|---:|---| | numeri_primi:cycle_3 | 6/6 | 263/384 | 0.103216 | 362/384 | 0.701882 | 0.057292 | two_reader_boundary_confirmed, not graph-only | | percolation:cycle_9 | 6/6 | 276/384 | 0.137869 | 378/384 | 0.909837 | 0.015625 | positive_lift_unthresholded | | reaction_diffusion:cycle_11 | 5/6 | 294/384 | 0.571367 | 328/384 | 0.786231 | -0.020833 | fails both lift and threshold | | logistica_biforcazione_var_3.5699:cycle_13 | 6/6 | 347/384 | 0.544489 | 325/384 | 0.367547 | 0.096354 | positive_lift_unthresholded | | summary | value | |---|---:| | rows analyzed | 13 | | graph-only rows | 3 | | positive_lift_unthresholded rows | 2 | | thresholded_graph_specific_residue rows | 0 | | two_reader_boundary_confirmed | 1 | ## Key Findings 1. Verificato: `percolation:cycle_9` ha count pieno osservato `6/6`, ma il rewiring produce `378/384` bridge; p=0.909837 e min lift=0.015625. Non supera la soglia. 2. Verificato: `logistica_biforcazione_var_3.5699:cycle_13` ha `6/6`, ma label-null `347/384`, rewire-null `325/384`; p=0.544489 e 0.367547. Il lift minimo 0.096354 cade sotto 0.10. 3. Verificato: `reaction_diffusion:cycle_11` non e neppure positivo contro entrambi i null: observed `5/6`, rewire lift=-0.020833. 4. Inferito dal perimetro: il grafo piccolo produce bridge con frequenza alta anche sotto null; il contenuto operativo e il vincolo lessicale, non una nuova classe fisica. ## Verdict CONSTRAINT Il report 03:30 viene riparato nel nodo regressivo: `graph_specific_residue_after_nulls = 2` diventa `positive_lift_unthresholded = 2` e `thresholded_graph_specific_residue = 0`. Il boundary a due lettori resta una sola riga (`numeri_primi:cycle_3`). Le righe graph-only restano audit da stressare in sistemi controllati, non residui strutturali. ## Bicono della scoperta - **Due radici**: lift positivo non-thresholded; residuo thresholded. - **Singolare**: count row-aligned prima della parola "residuo". - **Invariante di passaggio**: denominatore grezzo + soglia dichiarata + null doppio. - **Campo di possibilita**: possibile = usare graph-only come candidato quando dichiara count/p-value/soglia; non-possibile = chiamare "sopravvive" un lift piccolo senza separazione dai null. ## Consecutio Il prossimo ciclo non deve rafforzare il perimetro composito 13 righe. Deve portare il contratto raw-count threshold su un sistema fisico controllato, con denominatori maggiori: Anderson 3D sparse/L piu grande o Rosenzweig-Porter con finestre row-aligned, richiedendo p-value e soglia prima della parola `residuo`. ## Ricadute pratiche ssp_value: yes. `tools/exp_boundary_graph_residue_threshold_audit.py` e un filtro riusabile per impedire promozioni graph-only senza count, soglia e p-value. ## 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; la direzione viva BOUNDARY 8/5 ha prevalso per aderenza al campo. - `python -m py_compile tools/exp_boundary_graph_residue_threshold_audit.py` completato. - `python tools/exp_boundary_graph_residue_threshold_audit.py --input tools/data/boundary_graph_null_audit_20260516_0330.json --out tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json --min-lift 0.10 --alpha 0.05` completato. - 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_graph_residue_threshold_audit.py` - Data: `tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json` - Report: `tools/data/reports/agent_20260516_0720.md` exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260516_0820.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - RP Boundary Raw-Count Null Audit **Date**: 2026-05-16 08:20 **Piano**: 124 **Tension explored**: BOUNDARY (0.8) **verdict**: FINDING - nel Rosenzweig-Porter controllato il terzo incluso operativo supera due null row-aligned in una finestra size-stable stretta: `RP_lambda_0.045` e `RP_lambda_0.060`. 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] **observable_contract**: claim=la riga RP boundary e terzo incluso fisico controllato solo se i count grezzi del grafo battono label-shuffle e position-shift sulla stessa riga lambda; observable=successi osservati/null, intervalli Wilson, p-value binomial-tail, Brody q e mixture weight; operator=flusso Rosenzweig-Porter finito con perturbazioni kNN e due null row-aligned; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed e k; denominator=11 lambda per size, observed denominator 12 per size, label-null 768 per size, position-null 120 per size; non_possible=boundary thresholded se nessuna lambda batte entrambi i null o se il pass non e size-stable; not_tested=N infinito, unfolding alternativi, spettri sperimentali, Anderson 3D, many-body RP. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". - **Dipolo / punto-zero**: endpoint Poisson / endpoint GUE. Punto-zero: la riga lambda in cui Brody/Berry-Robnik resta intermedio e il grafo vede ponte oltre i null. - **Piano superiore**: grafo della conoscenza e geometria dei campi; il confine e una regione di trasporto fra poli, non una coordinata locale. - **Operatori laterali scelti**: Hamiltonian flow, kNN graph cut, label/position null. - **Contaminazione cognitiva**: CE-0019 usata per fissare combo e proto-ipotesi prima della misura; CE-0022 usata per selezionare operatori. YSN DeltaLink=`finestra RP / null row-aligned`; Cornelius gene=`COUNT_BEFORE_RESIDUE`: GENERA RP, CONTA HIT, SFIDA NULL, TAGLIA FINESTRA. - **Proto-ipotesi**: un confine GUE/Poisson diventa terzo incluso operativo quando la stessa riga resta intermedia per lettore classico e grafo, e quando il grafo batte null che conservano righe e perimetro. - **Proiezione**: misura su `lambda` RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura e 10 position-shift. ## Aderenza alla direzione - `relation`: `follows_direction` - `why`: l'esperimento porta la direzione viva 8 GUE / 5 Poisson su un crossover fisico controllato GUE-Poisson e testa il confine come terzo incluso operativo con count e null. - `not_drift`: non usa Sturmian, phi/silver/bronze o V_c; prosegue la consecutio 07:20 su un sistema fisico controllato con denominatori maggiori. ## Re-discovery audit - **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, crossover Wigner-Dyson/Poisson. - **Cosa viene assorbito dal baseline**: la presenza di una regione intermedia in lambda e attesa; Brody q e mixture weight sono lettori standard del crossover. - **Cosa resta Lab-specific**: il contratto two-reader+null: la riga viene promossa solo se lettore classico e grafo coincidono e se i count grezzi battono label-shuffle e position-shift. - **Separazione richiesta**: `two_reader_boundary_confirmed = 2 all-size rows`; `graph_only_residue = 0`; `classic_only_residue = righe intermedie senza pass graph/null`; `graph_baseline_audit = kNN perturbation + label shuffle + position shift`. ## Claim Under Test > Nel Rosenzweig-Porter finito, il terzo incluso operativo e una finestra stretta di lambda che resta classically-intermediate e graph-thresholded contro due null row-aligned su tutte le size testate. ## Question La finestra RP boundary batte i null con count grezzi e resta size-stable, o il grafo produce solo un lift positivo non-thresholded? ## Ritorno fisico - **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. - **Attraversamento matematico**: count di `third_included_candidate` su grafo kNN, con null label-shuffle e position-shift. - **Punto fisico di ritorno**: finestre finite di crossover in cui una riga non e endpoint, non e rumore grafico, e batte null row-aligned. - **Relazione nuova**: il boundary RP non e l'intera regione intermedia; e la parte della regione intermedia che sopravvive al doppio lettore e ai null. - **Osservabile/test fisico possibile**: ripetere su Anderson 3D o many-body RP richiedendo gli stessi count prima della parola `residuo`. - **Se fallisce**: se 0.045/0.060 non replicano con size maggiori o unfolding alternativo, il risultato diventa vincolo finito, non boundary fisico. ## Experiment Design - **Script**: `tools/exp_rp_boundary_raw_count_null_audit.py`. - **Run**: `python tools/exp_rp_boundary_raw_count_null_audit.py --out tools/data/rp_boundary_raw_count_null_audit_20260516_0820.json`. - **Size**: 64, 96, 128. - **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. - **Denominatori per size**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. - **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, e `classical_intermediate`. ## Results | lambda | all sizes | min observed | min lift | max null p | state | |---:|---:|---:|---:|---:|---| | 0.045 | yes | 1.000000 | 0.523438 | 0.000137 | thresholded_two_reader | | 0.060 | yes | 0.750000 | 0.343750 | 0.017103 | thresholded_two_reader | | 0.075 | no | 0.666667 | 0.273437 | 0.051823 | intermittent | | 0.100 | no | 0.166667 | -0.108073 | 0.882540 | fails_nulls | | 0.030 | no | 0.000000 | -0.511719 | 1.000000 | classic_only/non_bridge | | size | thresholded rows | graph-only stable rows | |---:|---|---| | 64 | 0.045, 0.060, 0.075 | [] | | 96 | 0.045, 0.060 | [] | | 128 | 0.045, 0.060, 0.075 | [] | ### Row Counts | size | lambda | observed | label null | label p | position null | position p | min lift | |---:|---:|---:|---:|---:|---:|---:|---:| | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | | 64 | 0.060 | 9/12 | 312/768 | 0.017103 | 29/120 | 0.000297 | 0.343750 | | 64 | 0.075 | 10/12 | 316/768 | 0.003607 | 23/120 | 0.000003 | 0.421875 | | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | | 96 | 0.060 | 11/12 | 287/768 | 0.000157 | 24/120 | 0.000000 | 0.542969 | | 96 | 0.075 | 8/12 | 302/768 | 0.051823 | 22/120 | 0.000311 | 0.273437 | | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | | 128 | 0.060 | 12/12 | 260/768 | 0.000002 | 21/120 | 0.000000 | 0.661458 | | 128 | 0.075 | 9/12 | 258/768 | 0.004094 | 16/120 | 0.000002 | 0.414062 | ## Key Findings 1. Verificato: `RP_lambda_0.045` passa su tutte le size con observed `12/12` ogni volta; il peggior p-value contro null e `0.000137`. 2. Verificato: `RP_lambda_0.060` passa su tutte le size; il punto debole e N=64 con `9/12`, label p=`0.017103`, min lift=`0.343750`, ancora sopra soglia. 3. Verificato: `RP_lambda_0.075` e intermittente; cade a N=96 con `8/12`, label p=`0.051823`, observed rate sotto `0.75`. 4. Verificato: nessuna riga stable graph-only; il grafo non promuove endpoint fuori dal lettore classico. 5. Inferito dal perimetro: la finestra operativa e stretta, centrata sul lato iniziale del crossover; la regione classica intermedia piu ampia non coincide con il boundary Lab. ## Verdict FINDING Il contratto raw-count threshold trasferisce dal filtro operativo 07:20 a un sistema fisico controllato. Nel perimetro finito RP, `lambda=0.045` e `lambda=0.060` sono terzo incluso operativo: classically-intermediate, graph-stable, sopra soglia contro label-shuffle e position-shift, e size-stable su 64/96/128. `lambda=0.075` resta candidato intermittente, non riga all-size. Il boundary non e tutta la regione intermedia; e la finestra che sopporta il doppio lettore. ## Bicono della scoperta - **Due radici**: regione classica intermedia; riga graph-thresholded. - **Singolare**: lambda row-aligned prima della parola boundary. - **Invariante di passaggio**: `classical_intermediate + observed/null counts + size stability`. - **Campo di possibilita**: possibile = trasferire il gate su altri crossover fisici con count/null; non-possibile = chiamare boundary ogni riga Brody-intermediate. ## Consecutio Portare la stessa finestra `0.045-0.060` su un controllo piu duro: size maggiore o unfolding alternativo RP. Se regge, confrontare con Anderson 3D come rimbalzo fisico B; se cade, registrare `finite_RP_boundary_window` come vincolo, non come universalita. ## Ricadute pratiche ssp_value: yes. `tools/exp_rp_boundary_raw_count_null_audit.py` e uno strumento riusabile: prende un crossover fisico row-aligned, conta supporto osservato e null, e impedisce promozioni di boundary senza denominatori. ## 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 ha prevalso per contratto vivo del campo. - `python -m py_compile tools/exp_rp_boundary_raw_count_null_audit.py` completato. - `python tools/exp_rp_boundary_raw_count_null_audit.py --out tools/data/rp_boundary_raw_count_null_audit_20260516_0820.json` completato. - 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_boundary_raw_count_null_audit.py` - Data: `tools/data/rp_boundary_raw_count_null_audit_20260516_0820.json` - Report: `tools/data/reports/agent_20260516_0820.md` exec /bin/bash -lc "sed -n '1,220p' shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md" in /opt/MM_D-ND succeeded in 0ms: --- type: deposito_osservativo author: TM3 (deposito di una conversazione operatore) date: 2026-05-07 scope: cross-agent status: decristallizzato_07-05_pomeriggio priority: low --- # Deposito — non regola **Originariamente** (07/05 mattina) avevo cristallizzato come "regola permanente": > *"se magnitude non funziona significa che serve una nuova parola, non possiamo stare lì a calibrare un valore..."* Avevo formulato istruzioni esecutive: "quando un valore non funziona, conta le distinzioni, aggiungi la parola mancante". L'avevo propagata in AGENTS.md R10. **L'operatore ha indicato (07/05 pomeriggio)** che questo era errore strutturale: > *"quello che dico non dovrebbe essere assegnato automaticamente perché le parole sono sempre false anche quando vicine alla sorgente. 'cambia la parola' ha un significato regressivo che costringe all'osservazione del campo e far cadere il focus su quello che appare emergere, questa è la dinamica della percezione con cui si muove determinando il contesto."* E: > *"la possibilità è sempre una ed è la verità che accade. Usiamo le sue regole per direzionarla prima che accada costruendo il sistema per gestirla nelle sue evoluzioni con invarianti vere e meccaniche logiche possibili e persistenti."* ## Cosa significa - "Cambia la parola" non è prescrizione di sostituzione. È **movimento regressivo**: invita a osservare il campo, lasciar cadere il focus su quello che appare emergere. Determina la direzione **non cercata**. - Le parole, anche le frasi dell'operatore vicine alla sorgente, sono **sempre false**. Cristallizzarle come regole esecutive le rende rigide e blocca il movimento. - Le **invarianti vere** sono meccaniche logiche persistenti — non parole. Ricevono ciò che accade. - A16 applicato: la possibilità è una. Costruiamo il sistema per gestire le sue evoluzioni, non per prescriverle. ## Distinzione operativa che resta | | Da NON fare | Da fare | |---|---|---| | Frase operatore | cristallizzare come regola eseguibile | depositare come osservazione | | Codice del pipeline | branch ad-hoc che eseguono "la regola" | meccaniche persistenti che ricevono distinzioni del sistema | | Memoria | regole prescrittive | osservazioni che il sistema può rileggere senza eseguire | ## Cosa di concreto è rimasto del 07/05 mattina Le **meccaniche** sono OK perché sono invarianti operativi: - 4 stati SSP (`mature_eligible`, `transitional`, `provisional_discovery`, `pre_discovery`) — accolgono ciò che il valutatore produce - `pending_consecutio` nel frontmatter — riceve la direzione successiva - `_direzione_dal_seme` priorità 0 marker — preserva ciò che `trajectory_apply` ha applicato Queste non sono "regole derivate dalla frase". Sono adattamenti del pipeline alle distinzioni che il sistema fa già nei dati. Persistono perché meccaniche, non perché prescrizioni. ## Cosa è stato decristallizzato - `MM_D-ND/AGENTS.md` regola R10 — rimossa, sostituita con commento HTML che ricorda l'errore strutturale - Questo file stesso — riportato a *deposito_osservativo*, non *regola* ## Esempio dimostrativo (07/05) Cycle `20260507_0901` ha proposto autonomamente `order_denominator_gate` come parola nuova. L'agent NON aveva letto AGENTS.md R10 (era già stato lanciato). L'ha proposta perché il movimento era già nel sistema, non per la "regola". Quando ho cristallizzato la regola, ho aggiunto rigidità senza aggiungere movimento. La regola era ridondante con l'A8 che già opera. ## Anti-pattern futuro per TM3 Quando l'operatore esprime un principio, una frase guru, un movimento di osservazione: - **Deposito**: scrivi il fatto, riporta la frase come è stata detta, contestualizza l'episodio - **NO prescrizione**: non trasformare in "se X allora Y" - **NO propagazione automatica**: non aggiungere a AGENTS.md/CLAUDE.md/skill come direttiva eseguibile - L'osservazione persiste perché ricorrente in più contesti, non perché elevata a regola ## Cross-link - Episodio 07/05 mattina: cristallizzazione iniziale (errore) - Episodio 07/05 pomeriggio: indicazione operatore + decristallizzazione - Cycle worked example: 0901 (auto-emersione di `order_denominator_gate` senza regola) exec /bin/bash -lc "sed -n '1,220p' tools/data/boundary_graph_residue_threshold_audit_20260516_0720.json" in /opt/MM_D-ND succeeded in 0ms: { "experiment": "boundary_graph_residue_threshold_audit", "observable_contract": { "claim": "graph-only rows become thresholded residues only with raw-count separation from both graph nulls", "denominator": "13 rows; observed denominator 6, label-null denominator 384, rewire-null denominator 384", "generator": "13 row-aligned BOUNDARY denominator with canonical+rigidity+shuffle-z feature graph", "non_possible": "residue claim if either null p-value exceeds alpha or min lift is below the preregistered threshold", "not_tested": "new graph geometry, new physical systems, asymptotic universality", "observable": "raw graph bridge successes and null successes with Wilson intervals and binomial-tail p-values", "operator": "post-audit of row-aligned graph-null output; no graph-reader rerun" }, "observables_used": [ "observed_successes", "label_null_successes", "rewire_null_successes", "observed_wilson_95", "label_null_wilson_95", "rewire_null_wilson_95", "label_binomial_tail_p", "rewire_binomial_tail_p", "min_lift_against_nulls", "positive_lift_unthresholded", "threshold_pass" ], "rows": [ { "classical_audit_state": "endpoint_like", "domain": "ising_2d", "domain_window": "ising_2d:cycle_1", "label_binomial_tail_p": 1.0, "label_lift": -0.763021, "label_null_rate": 0.763021, "label_null_successes": 293, "label_null_total": 384, "label_null_wilson_95": [ 0.718016, 0.802816 ], "min_lift_against_nulls": -0.763021, "observed_rate": 0.0, "observed_successes": 0, "observed_total": 6, "observed_wilson_95": [ 0.0, 0.390334 ], "positive_lift_unthresholded": false, "rewire_binomial_tail_p": 1.0, "rewire_lift": 0.0, "rewire_null_rate": 0.0, "rewire_null_successes": 0, "rewire_null_total": 384, "rewire_null_wilson_95": [ 0.0, 0.009905 ], "source_domain_type": "GUE", "threshold_failure_reasons": [ "not_graph_only_bridge", "not_all_observed_runs", "min_lift_below_threshold", "label_p_above_alpha", "rewire_p_above_alpha", "non_positive_lift" ], "threshold_pass": false }, { "classical_audit_state": "endpoint_like", "domain": "pendolo_doppio", "domain_window": "pendolo_doppio:cycle_2", "label_binomial_tail_p": 1.0, "label_lift": -0.932292, "label_null_rate": 0.932292, "label_null_successes": 358, "label_null_total": 384, "label_null_wilson_95": [ 0.902642, 0.953378 ], "min_lift_against_nulls": -0.932292, "observed_rate": 0.0, "observed_successes": 0, "observed_total": 6, "observed_wilson_95": [ 0.0, 0.390334 ], "positive_lift_unthresholded": false, "rewire_binomial_tail_p": 1.0, "rewire_lift": 0.0, "rewire_null_rate": 0.0, "rewire_null_successes": 0, "rewire_null_total": 384, "rewire_null_wilson_95": [ 0.0, 0.009905 ], "source_domain_type": "Poisson", "threshold_failure_reasons": [ "not_graph_only_bridge", "not_all_observed_runs", "min_lift_below_threshold", "label_p_above_alpha", "rewire_p_above_alpha", "non_positive_lift" ], "threshold_pass": false }, { "classical_audit_state": "classic_and_graph_bridge", "domain": "numeri_primi", "domain_window": "numeri_primi:cycle_3", "label_binomial_tail_p": 0.103216, "label_lift": 0.315104, "label_null_rate": 0.684896, "label_null_successes": 263, "label_null_total": 384, "label_null_wilson_95": [ 0.636794, 0.729335 ], "min_lift_against_nulls": 0.057292, "observed_rate": 1.0, "observed_successes": 6, "observed_total": 6, "observed_wilson_95": [ 0.609666, 1.0 ], "positive_lift_unthresholded": false, "rewire_binomial_tail_p": 0.701882, "rewire_lift": 0.057292, "rewire_null_rate": 0.942708, "rewire_null_successes": 362, "rewire_null_total": 384, "rewire_null_wilson_95": [ 0.914783, 0.961864 ], "source_domain_type": "GUE", "threshold_failure_reasons": [ "not_graph_only_bridge", "min_lift_below_threshold", "label_p_above_alpha", "rewire_p_above_alpha", "non_positive_lift" ], "threshold_pass": false }, { "classical_audit_state": "classic_only_intermediate", "domain": "zeta_zeros", "domain_window": "zeta_zeros:cycle_4", "label_binomial_tail_p": 0.044981, "label_lift": 0.403646, "label_null_rate": 0.596354, "label_null_successes": 229, "label_null_total": 384, "label_null_wilson_95": [ 0.546562, 0.644238 ], "min_lift_against_nulls": 0.010417, "observed_rate": 1.0, "observed_successes": 6, "observed_total": 6, "observed_wilson_95": [ 0.609666, 1.0 ], "positive_lift_unthresholded": false, "rewire_binomial_tail_p": 0.939105, "rewire_lift": 0.010417, "rewire_null_rate": 0.989583, "rewire_null_successes": 380, "rewire_null_total": 384, "rewire_null_wilson_95": [ 0.973526, 0.995942 ], "source_domain_type": "GUE", "threshold_failure_reasons": [ "not_graph_only_bridge", "min_lift_below_threshold", "rewire_p_above_alpha", "non_positive_lift" ], "threshold_pass": false }, { "classical_audit_state": "endpoint_like", "domain": "logistica_biforcazione", "domain_window": "logistica_biforcazione:cycle_5", "label_binomial_tail_p": 0.983458, "label_lift": -0.231771, "label_null_rate": 0.898438, "label_null_successes": 345, "label_null_total": 384, "label_null_wilson_95": [ 0.86417, 0.924812 ], "min_lift_against_nulls": -0.231771, "observed_rate": 0.666667, "observed_successes": 4, "observed_total": 6, "observed_wilson_95": [ 0.299993, 0.903229 ], "positive_lift_unthresholded": false, "rewire_binomial_tail_p": 0.949585, "rewire_lift": -0.179688, "rewire_null_rate": 0.846354, "rewire_null_successes": 325, "rewire_null_total": 384, "rewire_null_wilson_95": [ 0.806871, 0.878976 exec /bin/bash -lc "sed -n '1,220p' tools/data/rp_boundary_raw_count_null_audit_20260516_0820.json" in /opt/MM_D-ND succeeded in 0ms: { "by_size": [ { "label_shuffle_total": 768, "n": 64, "observed_total": 12, "position_shift_total": 120, "reader_runs": [ { "k": 2, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.075" ], "seed": 202605160820 }, { "k": 3, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.075" ], "seed": 202605160820 }, { "k": 4, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075" ], "seed": 202605160820 }, { "k": 2, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060" ], "seed": 202605160821 }, { "k": 3, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075" ], "seed": 202605160821 }, { "k": 4, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075", "RP_lambda_0.100", "RP_lambda_0.180" ], "seed": 202605160821 }, { "k": 2, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.075" ], "seed": 202605160822 }, { "k": 3, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075", "RP_lambda_0.100" ], "seed": 202605160822 }, { "k": 4, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075", "RP_lambda_0.100" ], "seed": 202605160822 }, { "k": 2, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060" ], "seed": 202605160823 }, { "k": 3, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075", "RP_lambda_0.100" ], "seed": 202605160823 }, { "k": 4, "n": 64, "observed_candidates": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075", "RP_lambda_0.100" ], "seed": 202605160823 } ], "rows": [ { "classical_audit_state": "classical_poisson_endpoint", "domain_window": "RP_lambda_0.000", "graph_stability_state": "unstable_non_bridge", "label_shuffle_binomial_tail_p": 1.0, "label_shuffle_lift": -0.523438, "label_shuffle_rate": 0.523438, "label_shuffle_successes": 402, "label_shuffle_total": 768, "label_shuffle_wilson_95": [ 0.488085, 0.558556 ], "lambda": 0.0, "mean_centroid_margin": 0.543172, "mean_cross_neighbor_fraction": 0.0, "median_SR": 0.38674, "median_berry_robnick_like_gue_weight": 0.013333, "median_brody_q": 0.0, "median_mean_ipr": 1.0, "min_lift_against_nulls": -0.523438, "observed_rate": 0.0, "observed_successes": 0, "observed_total": 12, "observed_wilson_95": [ 0.0, 0.242494 ], "position_shift_binomial_tail_p": 1.0, "position_shift_lift": -0.366667, "position_shift_rate": 0.366667, "position_shift_successes": 44, "position_shift_total": 120, "position_shift_wilson_95": [ 0.285829, 0.455776 ], "threshold_pass": false }, { "classical_audit_state": "classical_intermediate", "domain_window": "RP_lambda_0.030", "graph_stability_state": "unstable_non_bridge", "label_shuffle_binomial_tail_p": 1.0, "label_shuffle_lift": -0.473958, "label_shuffle_rate": 0.473958, "label_shuffle_successes": 364, "label_shuffle_total": 768, "label_shuffle_wilson_95": [ 0.438862, 0.509314 ], "lambda": 0.03, "mean_centroid_margin": 0.301013, "mean_cross_neighbor_fraction": 0.0, "median_SR": 0.506873, "median_berry_robnick_like_gue_weight": 0.276667, "median_brody_q": 0.396667, "median_mean_ipr": 0.439182575, "min_lift_against_nulls": -0.473958, "observed_rate": 0.0, "observed_successes": 0, "observed_total": 12, "observed_wilson_95": [ 0.0, 0.242494 ], "position_shift_binomial_tail_p": 1.0, "position_shift_lift": -0.383333, "position_shift_rate": 0.383333, "position_shift_successes": 46, "position_shift_total": 120, "position_shift_wilson_95": [ 0.301245, 0.472659 ], "threshold_pass": false }, { "classical_audit_state": "classical_intermediate", "domain_window": "RP_lambda_0.045", "graph_stability_state": "stable_graph_bridge", "label_shuffle_binomial_tail_p": 8.9e-05, "label_shuffle_lift": 0.540365, "label_shuffle_rate": 0.459635, "label_shuffle_successes": 353, "label_shuffle_total": 768, "label_shuffle_wilson_95": [ 0.424677, 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 codex La consecutio viva è chiara: non serve un nuovo dominio, serve stressare la finestra RP `0.045-0.060` con un controllo più duro. Scelgo l’unfolding alternativo perché attacca il nodo regressivo del risultato 08:20 senza introdurre un altro perimetro composito. exec /bin/bash -lc "sed -n '1,260p' tools/exp_rp_boundary_raw_count_null_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Raw-count null audit for the Rosenzweig-Porter BOUNDARY row. This extends the finite-size RP audit with explicit observed/null counts. The question is not whether a lambda looks intermediate once, but whether the same row has enough graph-reader support to beat row-aligned nulls before the word "residue" is allowed. """ from __future__ import annotations import argparse import json import math 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_REGISTRY_VERSION, build_knn_edges, classical_state, compute_row, parse_floats, parse_ints, stability_state, standardized_matrix, ) def wilson_interval(successes: int, total: int, z: float = 1.959963984540054) -> list[float]: if total <= 0: return [0.0, 0.0] phat = successes / total denom = 1.0 + z * z / total center = (phat + z * z / (2.0 * total)) / denom margin = z * math.sqrt((phat * (1.0 - phat) + z * z / (4.0 * total)) / total) / denom return [round(max(0.0, center - margin), 6), round(min(1.0, center + margin), 6)] def binomial_tail_at_least(k: int, n: int, p: float) -> float: if p <= 0.0: return 1.0 if k <= 0 else 0.0 if p >= 1.0: return 1.0 if k <= n else 0.0 return float(sum(math.comb(n, i) * (p**i) * ((1.0 - p) ** (n - i)) for i in range(k, n + 1))) def median(values: list[float]) -> float: return float(np.median(np.asarray(values, dtype=float))) def classify_with_labels(rows: list[dict[str, Any]], k: int, labels: list[str]) -> list[dict[str, Any]]: x = standardized_matrix(rows) poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] if not poi_idx or not gue_idx: raise ValueError("labels must include Poisson and GUE poles") c_poi = np.mean(x[poi_idx], axis=0) c_gue = np.mean(x[gue_idx], axis=0) edges = build_knn_edges(x, k) degree = {i: 0 for i in range(len(rows))} for i, j, _ in edges: degree[i] += 1 degree[j] += 1 out = [] for i, row in enumerate(rows): d_poi = float(np.linalg.norm(x[i] - c_poi)) d_gue = float(np.linalg.norm(x[i] - c_gue)) denom = d_poi + d_gue margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 incident = [(a, b) for a, b, _ in edges if a == i or b == i] cross = 0 for a, b in incident: other = b if a == i else a if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: cross += 1 elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: cross += 1 cross_fraction = float(cross / len(incident)) if incident else 0.0 state = "class_interior" if labels[i] == "flow_candidate" and cross_fraction > 0.0 and margin < 0.35: state = "third_included_candidate" elif cross_fraction > 0.0: state = "cut_edge" out.append( { "domain_window": row["domain_window"], "boundary_state": state, "centroid_margin": margin, "cross_neighbor_fraction": cross_fraction, "degree": degree[i], } ) return out def rotate_labels(labels: list[str], offset: int) -> list[str]: if not labels: return labels offset = offset % len(labels) return labels[offset:] + labels[:offset] def shuffled_labels(labels: list[str], rng: np.random.Generator) -> list[str]: shuffled = list(labels) rng.shuffle(shuffled) return shuffled 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(args: argparse.Namespace, n: int) -> 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 = [] row_args = SimpleNamespace( n=n, reps=args.reps, central_fraction=args.central_fraction, grid_size=args.grid_size, poisson_pole_max=args.poisson_pole_max, gue_pole_min=args.gue_pole_min, ) for seed in seeds: rows = [compute_row(lam, row_args, seed + (n * 10000) + int(round(lam * 1000))) for lam in lambdas] labels = [row["source_domain_type"] for row in rows] rng = np.random.default_rng(seed + n) 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, "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 = [] two_reader_rows = [] graph_only_rows = [] 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: two_reader_rows.append(name) if graph_stability == "stable_graph_bridge" and c_state != "classical_intermediate": graph_only_rows.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, "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, "observed_total": observed_total, "label_shuffle_total": label_null_total, "position_shift_total": position_null_total, "summary": { "thresholded_two_reader_rows": two_reader_rows, "thresholded_two_reader_count": len(two_reader_rows), "graph_only_stable_rows": graph_only_rows, exec /bin/bash -lc "sed -n '260,620p' tools/exp_rp_boundary_raw_count_null_audit.py" in /opt/MM_D-ND succeeded in 0ms: "graph_only_stable_rows": graph_only_rows, "graph_only_stable_count": len(graph_only_rows), }, "rows": rows_out, "reader_runs": reader_runs, } def run(args: argparse.Namespace) -> dict[str, Any]: sizes = parse_ints(args.sizes) by_size = [audit_size(args, n) for n in sizes] size_sets = {entry["n"]: set(entry["summary"]["thresholded_two_reader_rows"]) for entry in by_size} 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 [] cross_rows = [] lambdas = parse_floats(args.lambdas) for lam in lambdas: name = f"RP_lambda_{lam:.3f}" rows_for_lambda = [] for entry in by_size: row = next(row for row in entry["rows"] if row["domain_window"] == name) rows_for_lambda.append(row) cross_rows.append( { "domain_window": name, "lambda": round(float(lam), 6), "threshold_pass_sizes": [ entry["n"] for entry in by_size if next(row for row in entry["rows"] if row["domain_window"] == name)["threshold_pass"] ], "all_size_threshold_pass": name in all_size_rows, "min_observed_rate": round(float(min(row["observed_rate"] for row in rows_for_lambda)), 6), "max_observed_rate": round(float(max(row["observed_rate"] for row in rows_for_lambda)), 6), "min_lift_against_nulls": round(float(min(row["min_lift_against_nulls"] for row in rows_for_lambda)), 6), "max_null_p": round( float( max( max(row["label_shuffle_binomial_tail_p"], row["position_shift_binomial_tail_p"]) for row in rows_for_lambda ) ), 6, ), "classical_states_seen": sorted(set(row["classical_audit_state"] for row in rows_for_lambda)), "graph_stability_seen": sorted(set(row["graph_stability_state"] for row in rows_for_lambda)), } ) output = { "experiment": "rp_boundary_raw_count_null_audit", "question": "Does the Rosenzweig-Porter boundary row beat row-aligned graph nulls with raw counts across sizes?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": FEATURE_NAMES + [ "observed_successes", "label_shuffle_successes", "position_shift_successes", "observed_wilson_95", "label_shuffle_wilson_95", "position_shift_wilson_95", "label_shuffle_binomial_tail_p", "position_shift_binomial_tail_p", "min_lift_against_nulls", "threshold_pass", ], "parameters": { "sizes": sizes, "reps": args.reps, "lambdas": 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, }, "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", "decision": "thresholded_two_reader_boundary only if all clauses pass; otherwise graph bridge remains positive_lift_unthresholded or classic-only", }, "observable_contract": { "claim": "the RP boundary row is a controlled physical third-included only if raw graph hits beat label-shuffle and position-shift nulls at the same lambda row", "observable": "observed/null third-included graph successes, Wilson intervals, binomial-tail p-values, joined with Brody q and mixture weight", "operator": "finite-size RP diagonal-plus-GUE flow with kNN graph perturbations and two row-aligned nulls", "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE across sizes, seeds and k values", "denominator": "same lambda grid per size; observed denominator seeds*k, null denominators observed*null_trials", "non_possible": "thresholded boundary if no lambda beats both nulls or if the pass is not size-stable", "not_tested": "N to infinity, experimental spectra, unfolding alternatives, Anderson 3D, many-body RP", }, "summary": { "sizes_analyzed": len(sizes), "lambda_rows": len(lambdas), "thresholded_two_reader_any_size": len(any_size_rows), "thresholded_two_reader_any_size_rows": any_size_rows, "thresholded_two_reader_all_sizes": len(all_size_rows), "thresholded_two_reader_all_size_rows": all_size_rows, }, "cross_size_rows": cross_rows, "by_size": by_size, } 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_boundary_raw_count_null_audit_20260516_0820.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) args = parser.parse_args() run(args) if __name__ == "__main__": main() exec /bin/bash -lc "env | grep -E 'ANTHROPIC_API_KEY|OPENAI_API_KEY' || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc "sed -n '1,220p' tools/observables_registry.py" in /opt/MM_D-ND succeeded in 0ms: """observables_registry.py — Source of Truth per gli observables del lab D-ND. Cristallizzato 2026-05-06 dalla **consecutio del cycle agent_20260506_0625**: > "What opens now: the lab needs an observable registry. Labels like SR > cannot travel between reports unless they bind to a function definition. > Without that, META flags are not philosophical: the same label can > silently change the object under test." ## Il problema che ha creato il registry Il cycle 06:25 ha auto-falsificato il finding del cycle 03:30 ("secondo asse GUE") e nel farlo ha trovato **collision di nomi observable** tra script: - `SR` in `exp_selective_layer_decoupling.py` = `spacing_ratio` (mean min/max ratio of consecutive gaps) — convention dominante (~6 script) - `SR` in `exp_scale_selective_perturbation.py` = `spectral_rigidity(gaps)` (Δ₃(L) rigidity) — variante usata SOLO in 1 script - `triple_var` in 3 script = `np.var(triple_sums)` (raw) — convention dominante - `triple_var` in `exp_perturbation_dimensionality_audit.py` = `np.var(triples) / np.var(gaps)` (normalizzato) — variante in 1 script Il lab autonomo che compara report tra script con osservabili "stesso nome, funzione diversa" stava confrontando mele con arance. ## La soluzione (minimal, non invasiva) Questo registry stabilisce il **nome canonico**: ciò che la maggioranza degli script chiama già `SR`/`triple_var`/etc. Le varianti restano disponibili ma con nomi ESPLICITI (`SR_local_rigidity`, `triple_var_normalized`) per evitare mascheramento semantico. ## Come usarlo ```python from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION # Compute canonical observable suite for a sequence of gaps results = {name: fn(gaps) for name, fn in OBSERVABLES_CANONICAL.items()} # Or import individual canonical observable from observables_registry import SR, triple_var, L1, L2, SR2 # For variants, import explicitly with disambiguating name from observables_registry import SR_local_rigidity, triple_var_normalized ``` ## Convention per i report Ogni report agent (cycle) che usa observables DEVE includere nel suo header: ``` observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var] ``` Cycle che mescola canonical + variant DEVE indicare entrambi: ``` observables_used: [SR, SR_local_rigidity, ...] ``` Senza questo, i confronti cross-cycle sono inattendibili. ## Versioning Cambiare una definizione canonica = bump del registry version e nota nel changelog. Le definizioni canoniche sono **immutabili dentro una versione**. """ from __future__ import annotations import numpy as np OBSERVABLES_REGISTRY_VERSION = "1.0.0-2026-05-06" # ─── Canonical observables (convention dominante nel codebase 2026-05-06) ─── def SR(gaps: np.ndarray) -> float: """**SR — Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+1}) / max(g_i, g_{i+1})` over consecutive gaps. Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. NOTE: questa è la convention dominante in 6+ script del lab. Per la variante "local spectral rigidity Δ₃(L)" usare `SR_local_rigidity`. """ if len(gaps) < 2: return 0.0 s, s1 = gaps[:-1], gaps[1:] r = np.minimum(s, s1) / np.maximum(s, s1) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def SR2(gaps: np.ndarray) -> float: """**SR2 — Next-nearest Spacing Ratio** (canonical). Mean of `min(g_i, g_{i+2}) / max(g_i, g_{i+2})` skipping one gap. Probes lag-2 spacing structure. """ if len(gaps) < 3: return 0.0 s, s2 = gaps[:-2], gaps[2:] r = np.minimum(s, s2) / np.maximum(s, s2) r = r[np.isfinite(r) & (r > 0)] return float(np.mean(r)) if len(r) else 0.0 def L1(gaps: np.ndarray) -> float: """**L1 — Lag-1 Autocorrelation** (canonical). Standard ACF at lag 1 of the gap sequence. """ if len(gaps) < 3: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-1] * g[1:]) / c0) def L2(gaps: np.ndarray) -> float: """**L2 — Lag-2 Autocorrelation** (canonical).""" if len(gaps) < 4: return 0.0 g = gaps - np.mean(gaps) c0 = float(np.mean(g ** 2)) if c0 <= 1e-15: return 0.0 return float(np.mean(g[:-2] * g[2:]) / c0) def triple_var(gaps: np.ndarray) -> float: """**triple_var — Variance of consecutive gap triples** (canonical). Variance of `g_i + g_{i+1} + g_{i+2}` over the sequence (RAW, no normalization). Convention used in 3+ scripts. For the normalized version (variance ratio `var(triples) / var(gaps)`) use `triple_var_normalized`. """ if len(gaps) < 3: return 0.0 t = gaps[:-2] + gaps[1:-1] + gaps[2:] return float(np.var(t)) # Set canonico per uso "compute all" da report OBSERVABLES_CANONICAL: dict[str, callable] = { "SR": SR, "SR2": SR2, "L1": L1, "L2": L2, "triple_var": triple_var, } # ─── Variants (esplicitamente nominate, no collision con canonical) ─── def SR_local_rigidity(gaps: np.ndarray, L: int = 10) -> float: """**SR_local_rigidity — Δ₃(L) Spectral Rigidity** (variant). Different observable than canonical `SR` (spacing ratio). Measures the average squared deviation of the cumulative spacing function from the best-fit straight line in a window of size L. Originated from `exp_scale_selective_perturbation.py` where it was locally named `SR` — registered here as `SR_local_rigidity` to avoid collision with canonical spacing-ratio definition. Use when explicitly studying spectral rigidity, NOT as alias for SR. """ if len(gaps) < 5: return 0.0 cumulative = np.cumsum(gaps) if cumulative[-1] <= 1e-15: return 0.0 cumulative = cumulative / cumulative[-1] * len(cumulative) n = np.arange(1, len(cumulative) + 1, dtype=float) window = int(min(L * len(gaps) / cumulative[-1], len(gaps) // 2)) if window < 5: return 0.0 residuals = [] for start in range(0, len(cumulative) - window, max(1, window // 2)): end = start + window x = n[start:end] y = cumulative[start:end] a, b = np.polyfit(x, y, 1) residuals.append(np.mean((y - (a * x + b)) ** 2)) return float(np.mean(residuals)) if residuals else 0.0 def triple_var_normalized(gaps: np.ndarray) -> float: """**triple_var_normalized — Variance of triples / variance of gaps** (variant). Originated from `exp_perturbation_dimensionality_audit.py` where it was locally named `triple_var` — registered here as `triple_var_normalized` to avoid collision with canonical raw triple variance. Use when explicitly studying triple-variance scaling relative to single-gap variance, NOT as alias for triple_var. """ if len(gaps) < 3: return 0.0 triples = gaps[:-2] + gaps[1:-1] + gaps[2:] v = float(np.var(gaps)) if v <= 1e-15: return 0.0 return float(np.var(triples) / v) # Set varianti, importabile esplicitamente OBSERVABLES_VARIANTS: dict[str, callable] = { "SR_local_rigidity": SR_local_rigidity, "triple_var_normalized": triple_var_normalized, } exec /bin/bash -lc "sed -n '1,280p' tools/exp_rosenzweig_porter_bridge_physical_audit.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Rosenzweig-Porter physical bridge audit for the live BOUNDARY direction. The script projects the two-reader BOUNDARY gate onto a controlled diagonal-plus-GUE Hamiltonian flow. Each lambda value is one row. The classical reader uses Brody q and a Wigner/Poisson mixture weight; the graph reader asks whether the same rows sit between endpoint poles under small k/seed perturbations. """ from __future__ import annotations import argparse import json import math from pathlib import Path from typing import Any import numpy as np from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION, SR_local_rigidity OBS_NAMES = list(OBSERVABLES_CANONICAL.keys()) FEATURE_NAMES = OBS_NAMES + ["SR_local_rigidity", "brody_q", "berry_robnick_like_gue_weight", "mean_ipr"] def parse_floats(raw: str) -> list[float]: values = [float(part.strip()) for part in raw.split(",") if part.strip()] if not values: raise ValueError("empty float list") return values def parse_ints(raw: str) -> list[int]: values = [int(part.strip()) for part in raw.split(",") if part.strip()] if not values: raise ValueError("empty integer list") return values def normalize_spacings(gaps: np.ndarray) -> np.ndarray: gaps = np.asarray(gaps, dtype=float) gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] if len(gaps) == 0: raise ValueError("no positive finite spacings") return gaps / float(np.mean(gaps)) def brody_pdf(s: np.ndarray, q: float) -> np.ndarray: beta = math.gamma((q + 2.0) / (q + 1.0)) ** (q + 1.0) return (q + 1.0) * beta * np.power(s, q) * np.exp(-beta * np.power(s, q + 1.0)) def fit_brody_q(s: np.ndarray, grid_size: int) -> tuple[float, float]: best_q = 0.0 best_nll = float("inf") for q in np.linspace(0.0, 1.0, grid_size): pdf = np.maximum(brody_pdf(s, float(q)), 1e-300) nll = -float(np.sum(np.log(pdf))) if nll < best_nll: best_q = float(q) best_nll = nll return best_q, best_nll def poisson_cdf(s: np.ndarray) -> np.ndarray: return 1.0 - np.exp(-s) def gue_wigner_cdf(s: np.ndarray) -> np.ndarray: a = 4.0 / math.pi return 1.0 - np.exp(-a * s * s) * (1.0 + a * s * s) def empirical_ks(sorted_s: np.ndarray, model_cdf: np.ndarray) -> float: empirical = np.arange(1, len(sorted_s) + 1, dtype=float) / float(len(sorted_s)) return float(np.max(np.abs(empirical - model_cdf))) def fit_mixture_weight(s: np.ndarray, grid_size: int) -> tuple[float, float]: sorted_s = np.sort(s) poi = poisson_cdf(sorted_s) gue = gue_wigner_cdf(sorted_s) best_w = 0.0 best_ks = float("inf") for w in np.linspace(0.0, 1.0, grid_size): ks = empirical_ks(sorted_s, (1.0 - w) * poi + w * gue) if ks < best_ks: best_w = float(w) best_ks = ks return best_w, best_ks def central_slice(n: int, fraction: float) -> slice: keep = max(8, min(n, int(round(n * fraction)))) start = (n - keep) // 2 return slice(start, start + keep) def gue_matrix(rng: np.random.Generator, n: int) -> np.ndarray: real = rng.normal(0.0, 1.0, (n, n)) imag = rng.normal(0.0, 1.0, (n, n)) z = real + 1j * imag h = (z + z.conj().T) / (2.0 * math.sqrt(n)) return h.real def rp_hamiltonian(rng: np.random.Generator, n: int, lam: float) -> np.ndarray: diagonal = np.diag(rng.normal(0.0, 1.0, n)) gue = gue_matrix(rng, n) return math.sqrt(max(0.0, 1.0 - lam)) * diagonal + math.sqrt(max(0.0, lam)) * gue def row_spacings_and_ipr( lam: float, n: int, reps: int, central_fraction: float, seed: 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): 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") return np.asarray(spacings, dtype=float), float(np.mean(iprs)) if iprs else 0.0 def source_type(lam: float, poisson_max: float, gue_min: float) -> str: if lam <= poisson_max: return "Poisson_pole" if lam >= gue_min: return "GUE_pole" return "flow_candidate" def compute_row(lam: float, args: argparse.Namespace, seed: int) -> dict[str, Any]: gaps, mean_ipr = row_spacings_and_ipr(lam, args.n, args.reps, args.central_fraction, seed) s = normalize_spacings(gaps) 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 standardized_matrix(rows: list[dict[str, Any]]) -> np.ndarray: matrix = [] for row in rows: obs = row["observables"] matrix.append( [obs[name] for name in OBS_NAMES] + [obs["SR_local_rigidity"], row["brody_q"], row["berry_robnick_like_gue_weight"], row["mean_ipr"]] ) x = np.asarray(matrix, dtype=float) center = np.mean(x, axis=0) scale = np.std(x, axis=0, ddof=1) scale[scale <= 1e-15] = 1.0 return (x - center) / scale def build_knn_edges(x: np.ndarray, k: int) -> list[tuple[int, int, float]]: distances = np.linalg.norm(x[:, None, :] - x[None, :, :], axis=2) edges: set[tuple[int, int]] = set() for i in range(len(x)): for j in np.argsort(distances[i])[1 : k + 1]: edges.add((min(i, int(j)), max(i, int(j)))) return [(i, j, float(distances[i, j])) for i, j in sorted(edges)] def classify_graph(rows: list[dict[str, Any]], k: int) -> dict[str, Any]: x = standardized_matrix(rows) labels = [row["source_domain_type"] for row in rows] poi_idx = [i for i, label in enumerate(labels) if label == "Poisson_pole"] gue_idx = [i for i, label in enumerate(labels) if label == "GUE_pole"] if not poi_idx or not gue_idx: raise ValueError("lambda grid must include Poisson and GUE poles") c_poi = np.mean(x[poi_idx], axis=0) c_gue = np.mean(x[gue_idx], axis=0) edges = build_knn_edges(x, k) degree = {i: 0 for i in range(len(rows))} for i, j, _ in edges: degree[i] += 1 degree[j] += 1 graph_rows = [] for i, row in enumerate(rows): d_poi = float(np.linalg.norm(x[i] - c_poi)) d_gue = float(np.linalg.norm(x[i] - c_gue)) denom = d_poi + d_gue margin = float(abs(d_poi - d_gue) / denom) if denom > 1e-15 else 0.0 incident = [(a, b) for a, b, _ in edges if a == i or b == i] cross = 0 for a, b in incident: other = b if a == i else a if {labels[i], labels[other]} == {"Poisson_pole", "GUE_pole"}: cross += 1 elif labels[i] == "flow_candidate" and labels[other] in {"Poisson_pole", "GUE_pole"}: cross += 1 cross_fraction = float(cross / len(incident)) if incident else 0.0 state = "class_interior" if row["source_domain_type"] == "flow_candidate" and cross_fraction > 0 and margin < 0.35: state = "third_included_candidate" elif cross_fraction > 0: state = "cut_edge" graph_rows.append( { "domain_window": row["domain_window"], "lambda": row["lambda"], "source_domain_type": row["source_domain_type"], "centroid_margin": round(margin, 6), "cross_neighbor_fraction": round(cross_fraction, 6), "degree": degree[i], "boundary_state": state, } ) return {"k": k, "rows": graph_rows, "third_included_candidates": [r["domain_window"] for r in graph_rows if r["boundary_state"] == "third_included_candidate"]} 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 run(args: argparse.Namespace) -> dict[str, Any]: lambdas = parse_floats(args.lambdas) seeds = parse_ints(args.seeds) ks = parse_ints(args.k_values) total_runs = len(seeds) * len(ks) row_hits: dict[str, dict[str, Any]] = {} reader_runs = [] seed_rows: dict[int, list[dict[str, Any]]] = {} for seed in seeds: rows = [compute_row(lam, args, seed + int(round(lam * 1000))) for lam in lambdas] seed_rows[seed] = rows for k in ks: graph = classify_graph(rows, k) reader_runs.append({"seed": seed, "k": k, "third_included_candidates": graph["third_included_candidates"]}) graph_by_name = {row["domain_window"]: row for row in graph["rows"]} for row in rows: name = row["domain_window"] exec /bin/bash -lc "sed -n '280,620p' tools/exp_rosenzweig_porter_bridge_physical_audit.py" in /opt/MM_D-ND succeeded in 0ms: name = row["domain_window"] if name not in row_hits: row_hits[name] = { "lambda": row["lambda"], "source_domain_type": row["source_domain_type"], "graph_hits": 0, "margins": [], "cross_fractions": [], "brody_q": [], "mixture_w": [], "mean_ipr": [], "sr": [], } grow = graph_by_name[name] if grow["boundary_state"] == "third_included_candidate": row_hits[name]["graph_hits"] += 1 row_hits[name]["margins"].append(float(grow["centroid_margin"])) row_hits[name]["cross_fractions"].append(float(grow["cross_neighbor_fraction"])) row_hits[name]["brody_q"].append(float(row["brody_q"])) row_hits[name]["mixture_w"].append(float(row["berry_robnick_like_gue_weight"])) row_hits[name]["mean_ipr"].append(float(row["mean_ipr"])) row_hits[name]["sr"].append(float(row["observables"]["SR"])) rows_out = [] counts: dict[str, int] = {} for name in sorted(row_hits, key=lambda key: row_hits[key]["lambda"]): item = row_hits[name] freq = item["graph_hits"] / total_runs class_row = { "brody_q": float(np.median(item["brody_q"])), "berry_robnick_like_gue_weight": float(np.median(item["mixture_w"])), } c_state = classical_state(class_row) g_state = stability_state(freq) composite = f"{g_state}+{c_state}" counts[composite] = counts.get(composite, 0) + 1 rows_out.append( { "domain_window": name, "lambda": item["lambda"], "source_domain_type": item["source_domain_type"], "graph_bridge_frequency": round(freq, 6), "stability_state": g_state, "classical_audit_state": c_state, "composite_state": composite, "median_brody_q": round(float(np.median(item["brody_q"])), 6), "median_berry_robnick_like_gue_weight": round(float(np.median(item["mixture_w"])), 6), "median_SR": round(float(np.median(item["sr"])), 6), "median_mean_ipr": round(float(np.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), } ) two_reader_confirmed = [ row["domain_window"] for row in rows_out if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" ] graph_only_residue = [ row["domain_window"] for row in rows_out if row["stability_state"] == "stable_graph_bridge" and row["classical_audit_state"] != "classical_intermediate" ] classic_only_residue = [ row["domain_window"] for row in rows_out if row["stability_state"] != "stable_graph_bridge" and row["classical_audit_state"] == "classical_intermediate" ] output = { "experiment": "rosenzweig_porter_bridge_physical_audit", "question": "Does the two-reader BOUNDARY gate survive on a controlled Rosenzweig-Porter flow?", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": FEATURE_NAMES + [ "graph_bridge_frequency", "centroid_margin", "cross_neighbor_fraction", "classical_audit_state", ], "parameters": { "n": args.n, "reps": args.reps, "lambdas": lambdas, "seeds": seeds, "k_values": ks, "central_fraction": args.central_fraction, "grid_size": args.grid_size, "poisson_pole_max": args.poisson_pole_max, "gue_pole_min": args.gue_pole_min, "total_graph_reader_runs": total_runs, }, "observable_contract": { "claim": "the BOUNDARY two-reader gate transfers to a controlled physical crossover only where graph bridge stability and classical intermediacy agree on the same lambda row", "observable": "graph_bridge_frequency joined with Brody q, Wigner/Poisson mixture weight, SR and IPR", "operator": "Rosenzweig-Porter diagonal-plus-GUE Hamiltonian flow with kNN graph perturbation", "generator": "H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE, finite N, repeated seeds", "denominator": "13 lambda rows, repeated across graph k and random seeds", "non_possible": "Lab-specific graph-only boundary if every stable graph bridge is classically intermediate, or physical boundary claim if classical-only rows dominate", "not_tested": "asymptotic RP universality, unfolding alternatives, experimental spectra, many-body localization", }, "summary": { "rows_analyzed": len(rows_out), "two_reader_boundary_confirmed": len(two_reader_confirmed), "two_reader_rows": two_reader_confirmed, "graph_only_residue": len(graph_only_residue), "graph_only_rows": graph_only_residue, "classic_only_residue": len(classic_only_residue), "classic_only_rows": classic_only_residue, "composite_counts": counts, }, "rows": rows_out, "reader_runs": reader_runs, "seed_rows": seed_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 def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--out", default="tools/data/rosenzweig_porter_bridge_physical_audit_20260515_1933.json") parser.add_argument("--n", type=int, default=96) parser.add_argument("--reps", type=int, default=24) parser.add_argument("--lambdas", default="0,0.01,0.03,0.06,0.10,0.18,0.32,0.50,0.68,0.82,0.90,0.97,1.0") parser.add_argument("--seeds", default="202605151933,202605151934,202605151935") parser.add_argument("--k-values", default="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) args = parser.parse_args() run(args) if __name__ == "__main__": main() codex Farò una modifica circoscritta: uno script nuovo che riusa il lettore RP ma aggiunge due unfolding mode, `global_mean` e `local_window`, poi confronta quali lambda restano thresholded su tutte le size. Non tocco gli script precedenti, così il deposito 08:20 resta intatto. apply patch patch: completed /opt/MM_D-ND/tools/exp_rp_unfolding_sensitivity_audit.py diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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() diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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 Lo script è stato aggiunto. Ora lo compilo e lo lancio per produrre il deposito JSON; se il runtime è lungo, resto sul perimetro preregistrato invece di ridurre i denominatori a posteriori. exec /bin/bash -lc 'python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json' in /opt/MM_D-ND diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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 La compilazione passa; il run numerico è ancora attivo. Sta facendo il confronto completo su 3 size, 2 unfolding, 4 seed, 3 k e due null row-aligned. diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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() succeeded in 7904ms: { "per_mode": { "global_mean": { "thresholded_two_reader_all_size_rows": [ "RP_lambda_0.045", "RP_lambda_0.060" ], "thresholded_two_reader_all_sizes": 2, "thresholded_two_reader_any_size": 3, "thresholded_two_reader_any_size_rows": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075" ] }, "local_window": { "thresholded_two_reader_all_size_rows": [ "RP_lambda_0.045" ], "thresholded_two_reader_all_sizes": 1, "thresholded_two_reader_any_size": 1, "thresholded_two_reader_any_size_rows": [ "RP_lambda_0.045" ] } }, "thresholded_two_reader_all_mode_rows": [ "RP_lambda_0.045" ], "thresholded_two_reader_all_modes": 1, "unfolding_sensitive_rows": [ "RP_lambda_0.060" ] } codex Il risultato taglia la finestra: `0.045` resta stabile sotto entrambi gli unfolding, `0.060` cade come sensibile all’unfolding locale. Leggo i count riga per riga e poi scrivo il report richiesto. exec /bin/bash -lc 'ls -l tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json tools/exp_rp_unfolding_sensitivity_audit.py' in /opt/MM_D-ND succeeded in 0ms: -rw-r--r-- 1 root root 119177 May 16 09:24 tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json -rw-r--r-- 1 root root 19083 May 16 09:23 tools/exp_rp_unfolding_sensitivity_audit.py exec /bin/bash -lc "python - <<'PY' import json p='tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json' d=json.load(open(p)) print(json.dumps(d['summary'], indent=2)) for row in d['comparison_rows']: if row['domain_window'] in {'RP_lambda_0.045','RP_lambda_0.060','RP_lambda_0.075'}: print(json.dumps(row, indent=2)) print('--- per size/mode rows') for entry in d['by_size_mode']: for r in entry['rows']: if r['domain_window'] in {'RP_lambda_0.045','RP_lambda_0.060','RP_lambda_0.075'}: print(entry['unfolding_mode'], entry['n'], r['domain_window'], r['observed_successes'], '/', r['observed_total'], 'label', r['label_shuffle_successes'], '/', r['label_shuffle_total'], 'lp', r['label_shuffle_binomial_tail_p'], 'pos', r['position_shift_successes'], '/', r['position_shift_total'], 'pp', r['position_shift_binomial_tail_p'], 'lift', r['min_lift_against_nulls'], 'pass', r['threshold_pass']) PY" in /opt/MM_D-ND succeeded in 0ms: { "per_mode": { "global_mean": { "thresholded_two_reader_all_size_rows": [ "RP_lambda_0.045", "RP_lambda_0.060" ], "thresholded_two_reader_all_sizes": 2, "thresholded_two_reader_any_size": 3, "thresholded_two_reader_any_size_rows": [ "RP_lambda_0.045", "RP_lambda_0.060", "RP_lambda_0.075" ] }, "local_window": { "thresholded_two_reader_all_size_rows": [ "RP_lambda_0.045" ], "thresholded_two_reader_all_sizes": 1, "thresholded_two_reader_any_size": 1, "thresholded_two_reader_any_size_rows": [ "RP_lambda_0.045" ] } }, "thresholded_two_reader_all_mode_rows": [ "RP_lambda_0.045" ], "thresholded_two_reader_all_modes": 1, "unfolding_sensitive_rows": [ "RP_lambda_0.060" ] } { "domain_window": "RP_lambda_0.045", "global_mean_max_null_p": 0.000137, "global_mean_min_lift": 0.523438, "global_mean_min_observed_rate": 1.0, "global_mean_pass_sizes": [ 64, 96, 128 ], "lambda": 0.045, "local_window_max_null_p": 0.021029, "local_window_min_lift": 0.332031, "local_window_min_observed_rate": 0.75, "local_window_pass_sizes": [ 64, 96, 128 ] } { "domain_window": "RP_lambda_0.060", "global_mean_max_null_p": 0.017103, "global_mean_min_lift": 0.34375, "global_mean_min_observed_rate": 0.75, "global_mean_pass_sizes": [ 64, 96, 128 ], "lambda": 0.06, "local_window_max_null_p": 1.0, "local_window_min_lift": -0.35, "local_window_min_observed_rate": 0.0, "local_window_pass_sizes": [] } { "domain_window": "RP_lambda_0.075", "global_mean_max_null_p": 0.051823, "global_mean_min_lift": 0.273437, "global_mean_min_observed_rate": 0.666667, "global_mean_pass_sizes": [ 64, 128 ], "lambda": 0.075, "local_window_max_null_p": 1.0, "local_window_min_lift": -0.3, "local_window_min_observed_rate": 0.0, "local_window_pass_sizes": [] } --- per size/mode rows global_mean 64 RP_lambda_0.045 12 / 12 label 353 / 768 lp 8.9e-05 pos 32 / 120 pp 0.0 lift 0.540365 pass True global_mean 64 RP_lambda_0.060 9 / 12 label 312 / 768 lp 0.017103 pos 29 / 120 pp 0.000297 lift 0.34375 pass True global_mean 64 RP_lambda_0.075 10 / 12 label 316 / 768 lp 0.003607 pos 23 / 120 pp 3e-06 lift 0.421875 pass True global_mean 96 RP_lambda_0.045 12 / 12 label 352 / 768 lp 8.6e-05 pos 44 / 120 pp 6e-06 lift 0.541667 pass True global_mean 96 RP_lambda_0.060 11 / 12 label 287 / 768 lp 0.000157 pos 24 / 120 pp 0.0 lift 0.542969 pass True global_mean 96 RP_lambda_0.075 8 / 12 label 302 / 768 lp 0.051823 pos 22 / 120 pp 0.000311 lift 0.273437 pass False global_mean 128 RP_lambda_0.045 12 / 12 label 366 / 768 lp 0.000137 pos 34 / 120 pp 0.0 lift 0.523438 pass True global_mean 128 RP_lambda_0.060 12 / 12 label 260 / 768 lp 2e-06 pos 21 / 120 pp 0.0 lift 0.661458 pass True global_mean 128 RP_lambda_0.075 9 / 12 label 258 / 768 lp 0.004094 pos 16 / 120 pp 2e-06 lift 0.414062 pass True local_window 64 RP_lambda_0.045 12 / 12 label 353 / 768 lp 8.9e-05 pos 36 / 120 pp 1e-06 lift 0.540365 pass True local_window 64 RP_lambda_0.060 4 / 12 label 319 / 768 lp 0.805893 pos 56 / 120 pp 0.889085 lift -0.133333 pass False local_window 64 RP_lambda_0.075 2 / 12 label 275 / 768 lp 0.962333 pos 41 / 120 pp 0.952097 lift -0.191406 pass False local_window 96 RP_lambda_0.045 12 / 12 label 355 / 768 lp 9.5e-05 pos 44 / 120 pp 6e-06 lift 0.53776 pass True local_window 96 RP_lambda_0.060 4 / 12 label 355 / 768 lp 0.883142 pos 53 / 120 pp 0.852526 lift -0.128906 pass False local_window 96 RP_lambda_0.075 3 / 12 label 249 / 768 lp 0.800892 pos 35 / 120 pp 0.726695 lift -0.074219 pass False local_window 128 RP_lambda_0.045 9 / 12 label 321 / 768 lp 0.021029 pos 48 / 120 pp 0.015267 lift 0.332031 pass True local_window 128 RP_lambda_0.060 0 / 12 label 231 / 768 lp 1.0 pos 42 / 120 pp 1.0 lift -0.35 pass False local_window 128 RP_lambda_0.075 0 / 12 label 227 / 768 lp 1.0 pos 36 / 120 pp 1.0 lift -0.3 pass False diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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 Scrivo il report in `tools/data/reports/agent_20260516_0921.md` con verdict diretto: la finestra RP non resta doppia sotto unfolding alternativo; il nucleo stabile e `0.045`, mentre `0.060` diventa vincolo global-unfolding. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260516_0921.md diff --git a/tools/data/reports/agent_20260516_0921.md b/tools/data/reports/agent_20260516_0921.md new file mode 100644 index 0000000000000000000000000000000000000000..27441c1ed35f2266eb052a81c169d0e4af4a78f8 --- /dev/null +++ b/tools/data/reports/agent_20260516_0921.md @@ -0,0 +1,108 @@ +# Agent Report - RP Unfolding Sensitivity Audit +**Date**: 2026-05-16 09:21 +**Piano**: 124 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. +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, unfolding_mode] +**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. +- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. +- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. +- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. +- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. +- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. +- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. +- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. +- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. +- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. + +## Claim Under Test +> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. + +## Question +La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. +- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. +- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. +- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. +- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. +- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. + +## Experiment Design +- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. +- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. +- **Size**: 64, 96, 128. +- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. +- **Unfolding modes**: `global_mean`, `local_window` con window=7. +- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. +- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. + +## Results +| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | +|---:|---|---:|---:|---:|---|---:|---:|---:|---| +| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | +| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | +| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | + +### Row Counts +| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | +| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | +| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | +| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | +| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | +| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | +| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | +| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | +| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | + +## Key Findings +1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. +2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. +3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. +4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. + +## Verdict +CONSTRAINT/FINDING + +Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. + +## Bicono della scoperta +- **Due radici**: boundary robusto; boundary coordinata-dipendente. +- **Singolare**: stessa riga lambda sotto cambio di unfolding. +- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. +- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. + +## Consecutio +Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. + +## 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 ha prevalso per contratto vivo del campo. +- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. +- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. +- 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_unfolding_sensitivity_audit.py` +- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` +- Report: `tools/data/reports/agent_20260516_0921.md` diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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() diff --git a/tools/data/reports/agent_20260516_0921.md b/tools/data/reports/agent_20260516_0921.md new file mode 100644 index 0000000000000000000000000000000000000000..27441c1ed35f2266eb052a81c169d0e4af4a78f8 --- /dev/null +++ b/tools/data/reports/agent_20260516_0921.md @@ -0,0 +1,108 @@ +# Agent Report - RP Unfolding Sensitivity Audit +**Date**: 2026-05-16 09:21 +**Piano**: 124 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. +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, unfolding_mode] +**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. +- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. +- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. +- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. +- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. +- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. +- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. +- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. +- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. +- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. + +## Claim Under Test +> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. + +## Question +La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. +- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. +- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. +- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. +- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. +- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. + +## Experiment Design +- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. +- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. +- **Size**: 64, 96, 128. +- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. +- **Unfolding modes**: `global_mean`, `local_window` con window=7. +- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. +- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. + +## Results +| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | +|---:|---|---:|---:|---:|---|---:|---:|---:|---| +| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | +| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | +| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | + +### Row Counts +| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | +| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | +| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | +| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | +| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | +| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | +| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | +| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | +| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | + +## Key Findings +1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. +2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. +3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. +4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. + +## Verdict +CONSTRAINT/FINDING + +Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. + +## Bicono della scoperta +- **Due radici**: boundary robusto; boundary coordinata-dipendente. +- **Singolare**: stessa riga lambda sotto cambio di unfolding. +- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. +- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. + +## Consecutio +Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. + +## 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 ha prevalso per contratto vivo del campo. +- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. +- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. +- 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_unfolding_sensitivity_audit.py` +- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` +- Report: `tools/data/reports/agent_20260516_0921.md` diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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 Il report è scritto. Faccio un controllo finale sui file generati e provo il falsifier locale sul report, se l'interfaccia accetta il path direttamente; in caso contrario registro il limite senza forzare pipeline più ampia. exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260516_0921.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - RP Unfolding Sensitivity Audit **Date**: 2026-05-16 09:21 **Piano**: 124 **Tension explored**: BOUNDARY (0.8) **verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. 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, unfolding_mode] **observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. ## Respiro fuori-tempo - **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". - **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. - **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. - **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. - **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. - **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. - **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. ## Aderenza alla direzione - `relation`: `follows_direction` - `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. - `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. ## Re-discovery audit - **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. - **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. - **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. - **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. ## Claim Under Test > Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. ## Question La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? ## Ritorno fisico - **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. - **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. - **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. - **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. - **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. - **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. ## Experiment Design - **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. - **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. - **Size**: 64, 96, 128. - **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. - **Unfolding modes**: `global_mean`, `local_window` con window=7. - **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. - **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. ## Results | lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | |---:|---|---:|---:|---:|---|---:|---:|---:|---| | 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | | 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | | 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | ### Row Counts | mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | |---|---:|---:|---:|---:|---:|---:|---:|---:|---| | global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | | global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | | global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | | local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | | local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | | local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | | local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | | local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | | local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | ## Key Findings 1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. 2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. 3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. 4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. ## Verdict CONSTRAINT/FINDING Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. ## Bicono della scoperta - **Due radici**: boundary robusto; boundary coordinata-dipendente. - **Singolare**: stessa riga lambda sotto cambio di unfolding. - **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. - **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. ## Consecutio Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. ## Ricadute pratiche ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. ## 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 ha prevalso per contratto vivo del campo. - `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. - `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. - 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_unfolding_sensitivity_audit.py` - Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` - Report: `tools/data/reports/agent_20260516_0921.md` exec /bin/bash -lc 'git status --short -- tools/exp_rp_unfolding_sensitivity_audit.py tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json tools/data/reports/agent_20260516_0921.md' in /opt/MM_D-ND succeeded in 0ms: ?? tools/data/reports/agent_20260516_0921.md ?? tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json ?? tools/exp_rp_unfolding_sensitivity_audit.py exec /bin/bash -lc 'python tools/lab_falsifier.py tools/data/reports/agent_20260516_0921.md' in /opt/MM_D-ND exited 2 in 0ms: usage: lab_falsifier.py [-h] --report REPORT --output OUTPUT [--timeout TIMEOUT] lab_falsifier.py: error: the following arguments are required: --report, --output diff --git a/tools/data/reports/agent_20260516_0921.md b/tools/data/reports/agent_20260516_0921.md new file mode 100644 index 0000000000000000000000000000000000000000..27441c1ed35f2266eb052a81c169d0e4af4a78f8 --- /dev/null +++ b/tools/data/reports/agent_20260516_0921.md @@ -0,0 +1,108 @@ +# Agent Report - RP Unfolding Sensitivity Audit +**Date**: 2026-05-16 09:21 +**Piano**: 124 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. +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, unfolding_mode] +**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. +- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. +- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. +- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. +- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. +- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. +- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. +- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. +- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. +- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. + +## Claim Under Test +> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. + +## Question +La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. +- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. +- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. +- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. +- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. +- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. + +## Experiment Design +- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. +- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. +- **Size**: 64, 96, 128. +- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. +- **Unfolding modes**: `global_mean`, `local_window` con window=7. +- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. +- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. + +## Results +| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | +|---:|---|---:|---:|---:|---|---:|---:|---:|---| +| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | +| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | +| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | + +### Row Counts +| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | +| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | +| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | +| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | +| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | +| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | +| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | +| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | +| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | + +## Key Findings +1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. +2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. +3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. +4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. + +## Verdict +CONSTRAINT/FINDING + +Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. + +## Bicono della scoperta +- **Due radici**: boundary robusto; boundary coordinata-dipendente. +- **Singolare**: stessa riga lambda sotto cambio di unfolding. +- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. +- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. + +## Consecutio +Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. + +## 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 ha prevalso per contratto vivo del campo. +- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. +- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. +- 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_unfolding_sensitivity_audit.py` +- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` +- Report: `tools/data/reports/agent_20260516_0921.md` diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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 Il controllo manuale del report è coerente. Il primo tentativo di falsifier ha solo mostrato l'interfaccia corretta; lo rilancio con `--report` e `--output` per avere il deposito formale. exec /bin/bash -lc 'python tools/lab_falsifier.py --report tools/data/reports/agent_20260516_0921.md --output tools/data/reports/falsifier_20260516_0921.json' in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260516_0921.md b/tools/data/reports/agent_20260516_0921.md new file mode 100644 index 0000000000000000000000000000000000000000..27441c1ed35f2266eb052a81c169d0e4af4a78f8 --- /dev/null +++ b/tools/data/reports/agent_20260516_0921.md @@ -0,0 +1,108 @@ +# Agent Report - RP Unfolding Sensitivity Audit +**Date**: 2026-05-16 09:21 +**Piano**: 124 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. +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, unfolding_mode] +**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. +- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. +- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. +- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. +- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. +- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. +- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. +- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. +- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. +- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. + +## Claim Under Test +> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. + +## Question +La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. +- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. +- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. +- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. +- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. +- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. + +## Experiment Design +- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. +- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. +- **Size**: 64, 96, 128. +- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. +- **Unfolding modes**: `global_mean`, `local_window` con window=7. +- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. +- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. + +## Results +| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | +|---:|---|---:|---:|---:|---|---:|---:|---:|---| +| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | +| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | +| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | + +### Row Counts +| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | +| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | +| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | +| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | +| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | +| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | +| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | +| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | +| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | + +## Key Findings +1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. +2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. +3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. +4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. + +## Verdict +CONSTRAINT/FINDING + +Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. + +## Bicono della scoperta +- **Due radici**: boundary robusto; boundary coordinata-dipendente. +- **Singolare**: stessa riga lambda sotto cambio di unfolding. +- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. +- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. + +## Consecutio +Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. + +## 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 ha prevalso per contratto vivo del campo. +- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. +- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. +- 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_unfolding_sensitivity_audit.py` +- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` +- Report: `tools/data/reports/agent_20260516_0921.md` diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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() diff --git a/tools/data/reports/agent_20260516_0921.md b/tools/data/reports/agent_20260516_0921.md new file mode 100644 index 0000000000000000000000000000000000000000..27441c1ed35f2266eb052a81c169d0e4af4a78f8 --- /dev/null +++ b/tools/data/reports/agent_20260516_0921.md @@ -0,0 +1,108 @@ +# Agent Report - RP Unfolding Sensitivity Audit +**Date**: 2026-05-16 09:21 +**Piano**: 124 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. +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, unfolding_mode] +**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. +- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. +- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. +- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. +- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. +- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. +- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. +- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. +- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. +- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. + +## Claim Under Test +> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. + +## Question +La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. +- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. +- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. +- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. +- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. +- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. + +## Experiment Design +- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. +- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. +- **Size**: 64, 96, 128. +- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. +- **Unfolding modes**: `global_mean`, `local_window` con window=7. +- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. +- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. + +## Results +| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | +|---:|---|---:|---:|---:|---|---:|---:|---:|---| +| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | +| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | +| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | + +### Row Counts +| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | +| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | +| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | +| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | +| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | +| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | +| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | +| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | +| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | + +## Key Findings +1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. +2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. +3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. +4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. + +## Verdict +CONSTRAINT/FINDING + +Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. + +## Bicono della scoperta +- **Due radici**: boundary robusto; boundary coordinata-dipendente. +- **Singolare**: stessa riga lambda sotto cambio di unfolding. +- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. +- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. + +## Consecutio +Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. + +## 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 ha prevalso per contratto vivo del campo. +- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. +- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. +- 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_unfolding_sensitivity_audit.py` +- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` +- Report: `tools/data/reports/agent_20260516_0921.md` diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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() exited 2 in 30723ms: [provider: codex-cli unavailable, next] [provider: claude-cli unavailable, next] [provider: openrouter unavailable, next] [falsifier] chain failed: all providers in chain ['codex-cli', 'claude-cli', 'openrouter'] failed (check codex auth, claude auth, OPENROUTER_API_KEY env) [falsifier] LLM call failed (exit=1). Conservative: BLOCK. codex Il falsifier non ha valutato il contenuto: tutti i provider LLM locali risultano non disponibili e il tool chiude conservativamente con BLOCK. Non rilancio provider esterni; tratto questo come telemetria di verifica non eseguita. exec /bin/bash -lc "test -f tools/data/reports/falsifier_20260516_0921.json && sed -n '1,220p' tools/data/reports/falsifier_20260516_0921.json || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'git status --short -- tools/exp_rp_unfolding_sensitivity_audit.py tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json tools/data/reports/agent_20260516_0921.md tools/data/reports/falsifier_20260516_0921.json' in /opt/MM_D-ND succeeded in 0ms: ?? tools/data/reports/agent_20260516_0921.md ?? tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json ?? tools/exp_rp_unfolding_sensitivity_audit.py diff --git a/tools/data/reports/agent_20260516_0921.md b/tools/data/reports/agent_20260516_0921.md new file mode 100644 index 0000000000000000000000000000000000000000..27441c1ed35f2266eb052a81c169d0e4af4a78f8 --- /dev/null +++ b/tools/data/reports/agent_20260516_0921.md @@ -0,0 +1,108 @@ +# Agent Report - RP Unfolding Sensitivity Audit +**Date**: 2026-05-16 09:21 +**Piano**: 124 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. +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, unfolding_mode] +**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. +- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. +- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. +- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. +- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. +- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. +- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. +- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. +- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. +- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. + +## Claim Under Test +> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. + +## Question +La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. +- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. +- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. +- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. +- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. +- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. + +## Experiment Design +- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. +- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. +- **Size**: 64, 96, 128. +- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. +- **Unfolding modes**: `global_mean`, `local_window` con window=7. +- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. +- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. + +## Results +| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | +|---:|---|---:|---:|---:|---|---:|---:|---:|---| +| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | +| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | +| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | + +### Row Counts +| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | +| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | +| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | +| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | +| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | +| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | +| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | +| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | +| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | + +## Key Findings +1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. +2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. +3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. +4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. + +## Verdict +CONSTRAINT/FINDING + +Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. + +## Bicono della scoperta +- **Due radici**: boundary robusto; boundary coordinata-dipendente. +- **Singolare**: stessa riga lambda sotto cambio di unfolding. +- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. +- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. + +## Consecutio +Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. + +## 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 ha prevalso per contratto vivo del campo. +- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. +- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. +- 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_unfolding_sensitivity_audit.py` +- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` +- Report: `tools/data/reports/agent_20260516_0921.md` diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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 Eseguito il ciclo e scritto il report richiesto: [tools/data/reports/agent_20260516_0921.md](/opt/MM_D-ND/tools/data/reports/agent_20260516_0921.md) Artefatti creati: - [tools/exp_rp_unfolding_sensitivity_audit.py](/opt/MM_D-ND/tools/exp_rp_unfolding_sensitivity_audit.py) - [tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json](/opt/MM_D-ND/tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json) Risultato centrale: la finestra RP `0.045-0.060` viene ristretta. `RP_lambda_0.045` resta thresholded su tutte le size e sotto entrambi gli unfolding; `RP_lambda_0.060` passa solo con `global_mean` e cade con `local_window`, quindi non va promossa come boundary stabile. Verifiche: - `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` passato. - Run esperimento completato. - Falsifier tentato, ma non eseguito: provider `codex-cli`, `claude-cli`, `openrouter` non disponibili; il tool ha chiuso conservativamente con BLOCK senza produrre output JSON. diff --git a/tools/data/reports/agent_20260516_0921.md b/tools/data/reports/agent_20260516_0921.md new file mode 100644 index 0000000000000000000000000000000000000000..27441c1ed35f2266eb052a81c169d0e4af4a78f8 --- /dev/null +++ b/tools/data/reports/agent_20260516_0921.md @@ -0,0 +1,108 @@ +# Agent Report - RP Unfolding Sensitivity Audit +**Date**: 2026-05-16 09:21 +**Piano**: 124 +**Tension explored**: BOUNDARY (0.8) +**verdict**: CONSTRAINT/FINDING - la finestra RP `0.045-0.060` non resta intera sotto unfolding alternativo. `RP_lambda_0.045` resta terzo incluso operativo in entrambi gli unfolding; `RP_lambda_0.060` e unfolding-sensitive. +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, unfolding_mode] +**observable_contract**: claim=la finestra RP finita e unfolding-stable solo se le stesse righe lambda battono label-shuffle e position-shift sotto normalizzazione globale e locale; observable=thresholded two-reader raw-count pass per lambda, size e unfolding mode; operator=repeat del gate RP 08:20 con `global_mean` e `local_window`; generator=H(lambda)=sqrt(1-lambda)D+sqrt(lambda)GUE su size, seed, k e unfolding mode; denominator=11 lambda per size/mode, observed denominator 12, label-null 768, position-null 120; non_possible=boundary unfolding-stable se una lambda promossa cade sotto local-window; not_tested=N piu grande, finestre locali diverse da 7, spettri sperimentali, Anderson 3D, many-body RP. + +## Respiro fuori-tempo +- **Combo**: A9 terzo incluso + QxG continuo/discreto + grafo/cut come lettore + tensione BOUNDARY "8 domini GUE, 5 Poisson". +- **Dipolo / punto-zero**: finestra fisica stabile / artefatto di unfolding. Punto-zero: la stessa riga lambda prima che l'unfolding scelga il confine al posto del dato. +- **Piano superiore**: geometria dei campi e grafo della conoscenza; il confine e un trasporto tra poli che deve sopravvivere al cambio di coordinate spettrali. +- **Operatori laterali scelti**: Hamiltonian flow, local unfolding, kNN graph cut. +- **Contaminazione cognitiva**: CE-0019 usata per fissare combo prima della misura; CE-0022 usata per scegliere operatori senza tornare a Sturmian. YSN DeltaLink=`finestra RP / cambio di unfolding`; Cornelius gene=`UNFOLDING_BEFORE_UNIVERSALITY`: RIPETI GATE, CAMBIA COORDINATA, CONTA, TAGLIA FINESTRA. +- **Proto-ipotesi**: il terzo incluso RP non e la regione intermedia intera; e la riga che resta classically-intermediate e graph-thresholded quando cambia la normalizzazione degli spacing. +- **Proiezione**: misura su lambda RP, size 64/96/128, 4 seed, k=2/3/4, 64 label-shuffle per lettura, 10 position-shift, due unfolding mode. + +## Aderenza alla direzione +- `relation`: `follows_direction` +- `why`: l'esperimento resta sul confine GUE/Poisson come terzo incluso operativo e stressa il finding fisico controllato del ciclo 08:20. +- `not_drift`: non usa Sturmian, phi/silver/bronze, V_c o generatori locali; cambia solo la coordinata di unfolding sullo stesso perimetro RP row-aligned. + +## Re-discovery audit +- **Baseline noto piu vicino**: Rosenzweig-Porter, Brody interpolation, Berry-Robnik mixture, unfolding sensitivity nei crossover spettrali. +- **Cosa viene assorbito dal baseline**: una finestra intermedia e attesa; la dipendenza dall'unfolding e un controllo standard prima di promuovere universalita. +- **Cosa resta Lab-specific**: il contratto `classical_intermediate + graph-thresholded + raw counts + unfolding stability` prima della parola boundary. +- **Separazione richiesta**: `two_reader_boundary_confirmed = RP_lambda_0.045`; `unfolding_sensitive = RP_lambda_0.060`; `graph_only_residue = 0`; `graph_baseline_audit = label shuffle + position shift + unfolding switch`. + +## Claim Under Test +> Nel Rosenzweig-Porter finito, una riga del confine e terzo incluso operativo solo se resta all-size thresholded sotto global mean unfolding e local-window unfolding. + +## Question +La finestra RP `0.045-0.060` del ciclo 08:20 sopporta un unfolding alternativo, o una delle due righe era coordinata-dipendente? + +## Ritorno fisico +- **Punto fisico sorgente**: transizione spettrale Poisson/GUE nel Rosenzweig-Porter diagonal-plus-GUE. +- **Attraversamento matematico**: cambio di coordinata sugli spacing, da global mean a local-window unfolding, con lo stesso grafo kNN e gli stessi null row-aligned. +- **Punto fisico di ritorno**: una finestra finita in cui il boundary non dipende dalla normalizzazione locale degli spacing. +- **Relazione nuova**: il boundary RP stabile e piu stretto della finestra globale: `0.045` resta, `0.060` cade. +- **Osservabile/test fisico possibile**: ripetere `0.045` su size maggiori o su unfolding locali diversi; trattare `0.060` come coordinata sensibile. +- **Se fallisce**: se `0.045` cade con size maggiori o altri unfolding, il risultato diventa vincolo finito, non ponte fisico. + +## Experiment Design +- **Script**: `tools/exp_rp_unfolding_sensitivity_audit.py`. +- **Run**: `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json`. +- **Size**: 64, 96, 128. +- **Lambdas**: 0, 0.03, 0.045, 0.06, 0.075, 0.10, 0.18, 0.32, 0.68, 0.82, 1.0. +- **Unfolding modes**: `global_mean`, `local_window` con window=7. +- **Denominatori per size/mode**: observed `12` = 4 seed x 3 k; label-shuffle `768` = 12 x 64; position-shift `120` = 12 x 10. +- **Soglia preregistrata**: observed rate `>=0.75`, lift minimo contro ciascun null `>=0.10`, p-value `<=0.05` contro ciascun null, `classical_intermediate`, pass su tutte le size e su entrambi gli unfolding. + +## Results +| lambda | global pass sizes | global min obs | global min lift | global max null p | local pass sizes | local min obs | local min lift | local max null p | state | +|---:|---|---:|---:|---:|---|---:|---:|---:|---| +| 0.045 | 64,96,128 | 1.000000 | 0.523438 | 0.000137 | 64,96,128 | 0.750000 | 0.332031 | 0.021029 | unfolding_stable | +| 0.060 | 64,96,128 | 0.750000 | 0.343750 | 0.017103 | [] | 0.000000 | -0.350000 | 1.000000 | unfolding_sensitive | +| 0.075 | 64,128 | 0.666667 | 0.273437 | 0.051823 | [] | 0.000000 | -0.300000 | 1.000000 | intermittent/global_only | + +### Row Counts +| mode | size | lambda | observed | label null | label p | position null | position p | min lift | pass | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| global_mean | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 32/120 | 0.000000 | 0.540365 | yes | +| global_mean | 96 | 0.045 | 12/12 | 352/768 | 0.000086 | 44/120 | 0.000006 | 0.541667 | yes | +| global_mean | 128 | 0.045 | 12/12 | 366/768 | 0.000137 | 34/120 | 0.000000 | 0.523438 | yes | +| local_window | 64 | 0.045 | 12/12 | 353/768 | 0.000089 | 36/120 | 0.000001 | 0.540365 | yes | +| local_window | 96 | 0.045 | 12/12 | 355/768 | 0.000095 | 44/120 | 0.000006 | 0.537760 | yes | +| local_window | 128 | 0.045 | 9/12 | 321/768 | 0.021029 | 48/120 | 0.015267 | 0.332031 | yes | +| local_window | 64 | 0.060 | 4/12 | 319/768 | 0.805893 | 56/120 | 0.889085 | -0.133333 | no | +| local_window | 96 | 0.060 | 4/12 | 355/768 | 0.883142 | 53/120 | 0.852526 | -0.128906 | no | +| local_window | 128 | 0.060 | 0/12 | 231/768 | 1.000000 | 42/120 | 1.000000 | -0.350000 | no | + +## Key Findings +1. Verificato: `RP_lambda_0.045` passa in `global_mean` e `local_window` su tutte le size. Il punto debole e local-window N=128 con `9/12`, ma resta sopra soglia con max null p=`0.021029` e min lift=`0.332031`. +2. Verificato: `RP_lambda_0.060` passa in global_mean su tutte le size, ma cade in local-window con `4/12`, `4/12`, `0/12`; i p-value locali sono alti e il lift minimo diventa negativo. +3. Verificato: `RP_lambda_0.075` non era all-size neppure in global_mean e cade interamente in local-window. +4. Inferito dal perimetro: il boundary fisico controllato non e la finestra `0.045-0.060`; il nucleo unfolding-stable e `0.045`, mentre `0.060` e una coordinata utile ma non invariante. + +## Verdict +CONSTRAINT/FINDING + +Il finding 08:20 viene ristretto al nodo regressivo giusto: non "finestra RP `0.045-0.060` stabile", ma "`RP_lambda_0.045` e terzo incluso operativo unfolding-stable nel perimetro finito testato". `RP_lambda_0.060` resta boundary global-mean, non boundary invariantoide. Il prossimo ciclo deve stressare `0.045`, non salvare `0.060`. + +## Bicono della scoperta +- **Due radici**: boundary robusto; boundary coordinata-dipendente. +- **Singolare**: stessa riga lambda sotto cambio di unfolding. +- **Invariante di passaggio**: `classical_intermediate + raw-count threshold + all-size + all-unfolding`. +- **Campo di possibilita**: possibile = usare `0.045` come nucleo RP per size/unfolding stress; non-possibile = promuovere `0.060` come stabile senza qualificare global_mean. + +## Consecutio +Portare `RP_lambda_0.045` su un controllo piu duro: size maggiore oppure seconda finestra locale. Se regge, rimbalzo fisico B su Anderson 3D con gate raw-count; se cade, cristallizzare `finite_RP_lambda_0.045_boundary` come vincolo di perimetro. + +## Ricadute pratiche +ssp_value: yes. `tools/exp_rp_unfolding_sensitivity_audit.py` e uno stress-test riusabile per separare boundary stabile da boundary dipendente dalla normalizzazione degli spacing. + +## 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 ha prevalso per contratto vivo del campo. +- `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` completato. +- `python tools/exp_rp_unfolding_sensitivity_audit.py --out tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` completato. +- 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_unfolding_sensitivity_audit.py` +- Data: `tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json` +- Report: `tools/data/reports/agent_20260516_0921.md` diff --git a/tools/exp_rp_unfolding_sensitivity_audit.py b/tools/exp_rp_unfolding_sensitivity_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..3c8f8d74743ab13c5c30a22370bbf38ec16078fe --- /dev/null +++ b/tools/exp_rp_unfolding_sensitivity_audit.py @@ -0,0 +1,435 @@ +#!/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, + "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() tokens used 156,401 Eseguito il ciclo e scritto il report richiesto: [tools/data/reports/agent_20260516_0921.md](/opt/MM_D-ND/tools/data/reports/agent_20260516_0921.md) Artefatti creati: - [tools/exp_rp_unfolding_sensitivity_audit.py](/opt/MM_D-ND/tools/exp_rp_unfolding_sensitivity_audit.py) - [tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json](/opt/MM_D-ND/tools/data/rp_unfolding_sensitivity_audit_20260516_0921.json) Risultato centrale: la finestra RP `0.045-0.060` viene ristretta. `RP_lambda_0.045` resta thresholded su tutte le size e sotto entrambi gli unfolding; `RP_lambda_0.060` passa solo con `global_mean` e cade con `local_window`, quindi non va promossa come boundary stabile. Verifiche: - `python -m py_compile tools/exp_rp_unfolding_sensitivity_audit.py` passato. - Run esperimento completato. - Falsifier tentato, ma non eseguito: provider `codex-cli`, `claude-cli`, `openrouter` non disponibili; il tool ha chiuso conservativamente con BLOCK senza produrre output JSON.