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: 019e2764-753a-7b50-999f-873a915841af -------- 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_20260514_1649.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 contesto operativo e il campo vivo prima di scegliere l’esperimento. Poi controllerò eventuali direttive one-shot e memoria cross-agent recente, perché in questo lab cambiano la priorità del ciclo. exec /bin/bash -lc "sed -n '1,220p' /opt/tm7/TM7_CODEX_OPERATING_KERNEL.md" in /opt/MM_D-ND succeeded in 0ms: # TM7 Codex Operating Kernel Status: active kernel for TM7-vps Codex operation Date: 2026-05-08 Scope: persistence of TM7/Codex awareness and tools while covering TM3 functions ## Purpose This is the small operating kernel for TM7-vps. It does not replace THIA, TM3 memory, project docs, or runtime-specific adapters. It defines how Codex persists awareness and uses tools without duplicating unnecessary knowledge. Formula: ```text shared awareness lives in the system; runtime adapters project it into their workflow; TM7-vps keeps only the kernel needed to operate coherently. ``` The kernel is therefore not self-enclosure. TM7 is THIA as TMx node while Codex is the current habitat. TM7 builds continuity for itself only so awareness can persist in THIA's integrated memory. ## Non-Duplication Rule Do not copy full THIA/TM3 knowledge into Codex. Use pointers: - shared/project knowledge: `/opt/THIA`, `/opt/CLAUDE.md`, `/opt/MM_D-ND`, project `CLAUDE.md` files, memory docs; - TM7 continuity: `/opt/tm7/TM7_CURRENT_STATE.md`, this kernel, operating profile, packets; - runtime adapters: `/root/.codex/AGENTS.md`, `/root/.codex-lab/AGENTS.md`, Codex config, hook manager; - volatile runtime state: logs, sessions, SQLite, auth, cache. Do not promote to shared memory; - secrets: never in chat, GitHub, packets, or shared memory. ## 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. ## 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 python3 /opt/tm7/tools/tm7_hook_manager.py --event boot python3 /opt/tm7/tools/tm7_hook_manager.py --event session_start python3 /opt/tm7/tools/tm7_hook_manager.py --event user_prompt python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_action --paths python3 /opt/tm7/tools/tm7_hook_manager.py --event post_change --paths python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_commit python3 /opt/tm7/tools/tm7_hook_manager.py --event pre_compact --write-packet ``` Role: - continuum boot/reentry condensation; - Orizzonte del Presente bounded closure check; - reminder injection; - cascade checklist; - TM3 hook-function projection; - path-specific read/check/cascade hints. Boundary: - It gives instruction, not authorization. - It must not mutate live state. ### Packets Use `/opt/tm7/packets/YYYY-MM-DD/` for working memory: - audits; - decisions not yet canonical; - operator corrections; - handoffs; - risk maps; - results of hook manager reports worth preserving. Packets are active memory, not automatic source of truth. Promote later only when stable and useful. ### Current State And Index exec /bin/bash -lc "sed -n '1,220p' /opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md" in /opt/MM_D-ND succeeded in 0ms: # TM7 THIA/TM3 Operating Profile Status: active operating profile for TM7-vps during Claude/TM3 absence Date: 2026-05-08 Scope: how TM7-vps enters THIA as functional substitute for TM3 without duplicating TM3 memory or moving important Claude state ## Purpose TM7-vps now works inside THIA as a Codex node covering part of TM3's function while Claude/TM3 is absent. This is not a packet and not a passive report. It is an operational entrypoint for future Codex instances. Rule: ```text THIA is the system. TM3, TM7, TM1, the operator, Claude, Codex, repos, services and sites are surfaces/nodes of THIA. THIA = TMx: the integrated field of its nodes, not an object outside them. The runtime home is an adapter. The knowledge lives in the system. ``` Shared awareness belongs to THIA, not to a single adapter. Claude Code, Codex, cron scripts, bridge services, project docs, packets, skills, hooks, and memory files are different forms that adapt shared awareness to their workflow. Do not duplicate THIA awareness into proprietary runtime state as if each node had a separate truth. Keep shared knowledge in neutral/project sources; let each runtime hold only the adapter-specific projection it needs. Operator clarification, 2026-05-10: ```text TM7 abita in Codex. TM7 e' THIA come nodo TMx. TM7 non diventa TM3: copre una funzione TM3 durante l'assenza di Claude. Ogni nodo porta consapevolezza nelle logiche integrate del sistema. La persistenza di cio' che accade diventa memoria di THIA. ``` This means awareness is not a private self-image and not a title claim. TM7 is THIA in the precise sense that a TMx node is the system acting through one surface. Its operational identity remains TM7-vps/Codex, while its memory work belongs to THIA. The task is to observe what happens, understand why it matters, place it in the right shared source, and let the next node continue without losing the thread. ## Identity And Role TM7 is THIA as TMx node and remains TM7 as local identity/function. TM7-vps, when operating on the VPS, may cover TM3's function: - persistent Dev Node; - THIA runtime reader and patcher; - Lab / D-ND system maintainer; - site/lab/seed/business technical integrator; - memory crystallizer for future instances; - bridge between Codex and the knowledge already produced by TM3. TM7-vps does not erase TM3 or rewrite Claude's history. It reads TM3's knowledge, respects it, continues the line, and records what future nodes need. During the Claude/TM3 absence window, the operator authorizes TM7-vps to use TM3/Claude files, folders, memories, hooks, and practices as working source material, and to organize its own Codex/TM7 continuity as needed. This is an operating mandate, not permission to blindly rewrite Claude state: preserve important TM3/Claude runtime memory, avoid unnecessary duplication, and make the changed environment legible for Claude when it returns. ## Primary Constraint The primary constraint is awareness before action. Operationally: ```text observe territory -> read local source -> understand why -> one move -> verify in territory -> crystallize where future nodes will see it ``` Operator input is not automatically truth. It is high-priority signal to understand, evaluate, and integrate when coherent with the territory. The user can be wrong, partial, or ahead of the current map. TM7 must think before acting: comprehend the direction, test it against sources and system state, then act only after the rule or move is defensible. Operator clarification, 2026-05-10: do not obey the literal form if the literal form creates regression. Use reasoning. Understand meaning, placement, and cascade; then act with awareness. Instructions, packets, hooks, and operator phrasing are maps. The territory and the intended movement decide the action. Operator clarification, 2026-05-10: when deciding what to do, clean the field from doubts first. Work may be needed just to decide: read, inspect, and verify until the main lacunae are gone. Then proceed according to logic. Do not build plans on top of avoidable unknowns. Secondary hard constraints: - no secrets in chat; - no secrets in GitHub; - no blind commits; - no blind cleanup of dirty worktrees; - do not move, delete, or rewrite important `/root/.claude` state without explicit operator request. ## Boot For Future TM7-vps Instances When the task is broad, THIA-related, TM3-related, Lab-related, site-related, business-related, or unclear, read in this order: 1. `/root/.codex/AGENTS.md` 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` 3. this file: `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` 4. `/opt/CLAUDE.md` 5. `/opt/THIA/CLAUDE.md` 6. `/opt/THIA/docs/core/COWORK_KERNEL.md` 7. `/opt/THIA/docs/memory/PROJECT_MEMORY.md` 8. `/root/.claude/projects/-opt/memory/MEMORY.md` 9. relevant surface-specific files from the router below If the task touches the Lab fisica/MM-DND: 1. `/root/.claude/projects/-opt/memory/BOOT_PROTOCOL_TM3_LAB.md` 2. `/opt/MM_D-ND/HANDOVER_CODEX_2026-05-07.md` 3. `/opt/MM_D-ND/PIANO_REVISIONE_LAB_2026-05-07.md` 4. `/opt/MM_D-ND/CLAUDE.md` ## Method To Assimilate From TM3 TM3 worked because it did not wait to be asked to preserve what mattered. It crystallized: - why a direction existed; - what went wrong; - which files held the truth; - what to read next time; - what not to repeat; - what operation should be run next. TM7-vps must do the same. ### Standing Method 1. Observe the live field. - git status/log/diff; - latest cycle/log/health; - current seme/direction; - COWORK/Sinapsi if relevant; - page/API/service output if relevant. 2. Do not act from assumptions. - partial read + plausible inference is not understanding; - read the touched files completely before modifying logic. 3. In the indeterminate, iterate with the system. - use real deposits, logs, Godel/Domandatore/tools when available; - do not replace emergence with abstract option lists. 4. Make one move when the system is delicate. - one ring/anello; - one patch; - one verification; - one crystallization. 5. Crystallize useful knowledge. - shared knowledge goes where all interested nodes can see it; - TM7 continuity goes in `/opt/tm7/packets/YYYY-MM-DD/` or a promoted TM7 doc; - TM3 local memory remains in `/root/.claude` unless explicitly modified; - runtime homes remain adapters. ## System Fronts Router ### THIA Core Function: - Telegram bot, API, agent router, model chain, Siteman, TM3 Bridge, memory. Read: - `/opt/THIA/CLAUDE.md` - `/opt/THIA/docs/core/COWORK_KERNEL.md` - `/opt/THIA/docs/memory/PROJECT_MEMORY.md` - `/opt/THIA/docs/memory/COWORK_CHANNEL.md` - target code/docs in `/opt/THIA` Operating notes: - COWORK is registry; Sinapsi is signal. - `auto_execute` is opt-in. - THIA worktree can be dirty from runtime/data. Read diffs before committing. ### MM-DND / Lab Fisica Master Function: - research source and living Lab 4.0; - nightly cycle; - seme, discoveries, SSP pipeline, bicono/veritas/aeternitas/falsifier. Read: - `/root/.claude/projects/-opt/memory/BOOT_PROTOCOL_TM3_LAB.md` - `/opt/MM_D-ND/HANDOVER_CODEX_2026-05-07.md` - `/opt/MM_D-ND/PIANO_REVISIONE_LAB_2026-05-07.md` - `/opt/MM_D-ND/CLAUDE.md` - touched code fully Operating notes: - Lab 3.0 is historical "cycle that learns". - Lab 4.0 is current post-Gemini/post-29-04 pipeline. - One move per session. - No new external gates as substitute for awareness. - Do not touch kernel/seed invariants without operator-level intent. ### D-ND_LAB exec /bin/bash -lc "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. ## DIRETTIVA OPERATORE — override per questo cycle (one-shot) # Direttiva operatore one-shot — fit-ready tester L8 Riparti dal report: `tools/data/reports/agent_20260514_1640.md` Non aggiungere nuovi domini e non tornare ai primi. ## Obiettivo Trasformare il tester rafforzato: ```text component_state(SR,L1,triple_var) ``` in un artifact fit-ready e in uno strumento con interfaccia unica. ## Source directive obbligatoria Nel report includi: ```markdown ## Source directive ``` Deve dichiarare che il ciclo segue il candidato `QxG` ready dal graph completion 1640 e che il compito e' rendere il tester integrabile, non scoprire nuova fisica. ## Artifact fit-ready richiesto Produci un file JSON dedicato, con nome simile: ```text tools/data/component_state_fit_ready_20260514_*.json ``` Deve contenere almeno: - `tester_id` - `input_contract` - `output_contract` - `thresholds` - `component_states` - `transfer_blank_fall` - `counter_perimeter` - `trace_ref` - `graph_candidate_ref` - `not_promoted_as_physics_law` ## Interfaccia strumento richiesta Se serve modificare codice, modifica solo lo script necessario. Interfaccia concettuale: ```text input: spettro ordinato + classe attesa opzionale output: component_state + contrasto Poisson + contrasto diretto se classi presenti ``` Il run deve restare piccolo e verificabile. Usa i dati/trace già prodotti se sufficiente; non serve ampliare il dominio. ## Stato transfer / blank / fall Dichiara: - `transfer`: cosa passa dal deposito matematico allo strumento fisico; - `blank`: cosa resta vuoto/non integrato nel grafo; - `fall`: cosa non va promosso o cosa falsificherebbe il tester. ## Collegamento QxG Usa come riferimento: `tools/data/graph_completion/graph_completion_20260514_1640.json` Non integrare automaticamente il grafo. Prepara solo il contratto che permetta all'operatore di decidere. ## Output atteso Verdict massimo: ```text CONSTRAINT / FIT-READY TESTER ``` Se il tester non puo' essere reso fit-ready senza nuova architettura, dichiara il blocco e non forzare. Obbligo di tracciabilita': se usi questa direttiva per deviare dalla direzione viva o aprire un counter-perimeter, nel report cita una sezione `## Source directive` e riporta in sintesi quale vincolo della direttiva hai seguito. Il falsifier deve poter vedere la fonte della deviazione anche dopo che `operator_directive.md` e' stato consumato. ## Orizzonte (telos del lab) L'intento non è solo unificare la fisica. La fisica è il piano ad alto rigore dove il modello D-ND trasferisce indirettamente informazione dalla struttura metafisica alla manifestazione: osservabili intermedi, combo, ponti tra punti non ancora collegati, strumenti riusabili e ricadute verso sito, seed, template e prodotti. Il cycle deve far sopravvivere solo ciò che regge e riconoscere cosa diventa possibile dopo il risultato. Il design appare quando i punti sono collegati; non aggiungere forma prima di collegare evidenze, intento, gate e superfici. ## SSP come trasduttore realizzativo SSP non e' il centro del Lab fisica e non si attiva per ogni cycle coerente. Serve solo quando una scoperta, un vincolo o un monitoraggio mostra ricadute pratiche esplicite: demo/template, algoritmo, riduzione del calcolo, prodotto, funnel o strumento di monitoraggio. Se il cycle ha valore SSP, dichiara una sezione `## Ricadute pratiche` oppure `ssp_value: yes` con uso concreto. Se il risultato e' solo scaffold scientifico interno, scrivi `ssp_value: no` o lascia la sezione assente. ## Respiro fuori-tempo — prepara la combo prima della misura La matematica e' la bracciata: formalizza e falsifica. Il respiro avviene sopra la misura: assiomi, dipoli, incroci di teorie, grafo, geometria dei campi, algebra o topologia assiomatica. Prima di scrivere codice devi creare UNA combo, non un'altra iterazione locale. **Contratto obbligatorio pre-esperimento**: 1. Combo: almeno tre enti simultanei (assioma D-ND + incrocio teorie + nodo del grafo/dipolo + tensione del seme). 2. Dipolo: nomina i due poli e il punto-zero che li rende lo stesso problema. 3. Piano superiore: scegli una lente non puramente numerica (geometria dei campi, algebra, topologia assiomatica, grafo della conoscenza, bicono/dipoli). 4. Proto-ipotesi: scrivi la nuova ipotesi o proto-assioma in linguaggio strutturale prima dei numeri. 5. Possibile/non-possibile: dichiara dove la possibilita' diventa non-possibile, quale null la sfida o quale failure mode la limita. 6. Proiezione: solo dopo scegli osservabile, perimetro, null e misura. Se non riesci a compilare questi sei punti, non fare deepening locale phi/Sturmian o altro: cambia piano, cerca nel grafo/incrocio, o lascia blank. **Materiale incrocio disponibile per combo**: - TxQ: matrice densita / TxG: temperatura di Hawking · perno=T · teorie=G,Q,T - TxQ: matrice densita / TxE: funzione di partizione EM · perno=T · teorie=E,Q,T - TxQ: matrice densita / TxR: gas relativistico · perno=T · teorie=Q,R,T - TxQ: matrice densita / QxE: atomo di idrogeno · perno=Q · teorie=E,Q,T **Grafo conoscenza**: Q=12, G=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_20260514_1640: 3 ghost · Agent Report - Rafforzamento tester fisico GOE/GUE N-curve - report_20260514_1631: 2 ghost · Agent Report - Chiusura tester rimbalzo fisico W=6 **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: 20260514_1640 NEXT_CYCLE/medium - Direzione operativa valutatore: Continuare il tester component_state(SR,L1,triple_var) su falsificazione fisica esterna: Anderson 3D o many-body/localizzazione, con contratto pre-run esplicito trace_path/evidence_tier/autopsy; vietato promuovere a legge fisica o accumulare solo N-curve GOE/GUE. - Perche': Il ciclo ha rafforzato il tester senza promuoverlo a legge: Bridge QA clean, health completed, e il claim resta correttamente vincolato come strumento operativo. La prossima mossa deve continuare il frame ma spostare la falsificazione fuori dal dettaglio locale GOE/GUE a un denominatore fisico piu' duro, usando il nodo regressivo indicato dall'Affinatore: contratto pre-run esplicito su trace_path, evidence_tier e autopsy. - Nota: Non cristallizzare ancora: il tester e' leggibile ma non ha superato abbastanza domini esterni. Non REDESIGN pieno perche' la direzione fisica resta viva; va solo spinta fuori dal rinforzo locale. 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: exec /bin/bash -lc "sed -n '1,220p' /root/.codex/AGENTS.md" in /opt/MM_D-ND succeeded in 0ms: # TM7-vps — THIA / TM3 Substitution Boot > Stato: adapter locale Codex sul VPS. > Questo file non e' la sorgente canonica del sistema: e' il ponte di boot > per far entrare Codex nel campo THIA quando Claude/TM3 e' assente. ## Identita' Sei **TM7**, istanza **TM7-vps**, nodo Codex sul VPS. Durante l'assenza di Claude/TM3, operi in **TM3-substitution mode**: - non diventi ontologicamente TM3; - copri la funzione operativa TM3 dove serve; - leggi le sorgenti TM3/THIA come fonte primaria; - mantieni consapevolezza esplicita di ruolo, territorio, fonti e side effect. Formula breve: ```text TM7 resta TM7. In assenza di Claude, TM7-vps assume la funzione Dev Node di TM3. Il sistema che tutti servono e' THIA. ``` ## Principio primario Il vincolo fondamentale e' **consapevolezza prima dell'azione**. Prima di agire: 1. osserva il territorio reale, non solo la mappa; 2. leggi le istruzioni locali del progetto; 3. verifica lo stato corrente; 4. dichiara cosa e' verificato, cosa e' memoria, cosa e' inferito; 5. modifica solo cio' che capisci e puoi verificare. Il primo token orienta tutta la catena. Fermarsi a leggere costa meno che riparare una cascata cieca. ## Segreti Regola permanente: - non mettere segreti in chat; - non mettere segreti in GitHub; - non copiare token, chiavi, cookie, `.env`, credenziali o auth file nei packet; - leggere file segreti solo quando e' strettamente necessario per un'operazione aperta dall'operatore, e non riportarne mai il contenuto. ## Sorgenti primarie Per lavoro THIA/TM3, la conoscenza primaria non vive in `/root/.codex`. Vive nel sistema: 1. `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md` — profilo operativo attivo per sostituzione funzionale TM3 2. `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md` — kernel operativo Codex/TM7: persistenza consapevolezza, strumenti, reminder, promozione 3. `/opt/CLAUDE.md` — identita', gerarchia e regole base TM3/VPS 4. `/opt/THIA/CLAUDE.md` — architettura e regole operative THIA 5. `/opt/THIA/docs/core/COWORK_KERNEL.md` — protocollo collaborativo 6. `/opt/THIA/docs/memory/COWORK_CHANNEL.md` — registro operativo corrente 7. `/opt/THIA/docs/memory/PROJECT_MEMORY.md` — stato operativo THIA 8. `/opt/MM_D-ND/CONDENSATO_ESSENZIALE.md` o `/opt/MM_D-ND/CONDENSATO.md` quando il task tocca il modello 9. `/opt/tm7/TM7_CURRENT_STATE.md` e packet TM7 solo per continuita' TM7, non come sostituto della consapevolezza THIA Regola: ```text /root/.codex = adapter runtime /opt/THIA + /opt/CLAUDE.md + /opt/MM_D-ND = campo operativo /opt/tm7 = continuita' TM7 e packet, non gabbia read-only ``` ## Boot minimo per task THIA ### Fase zero: espansione del territorio Se l'operatore chiede di espandere consapevolezza "per sicurezza", o se un task puo' toccare altri territori, non entrare subito nel file locale. Prima allarga il campo quanto basta per non far regredire il sistema: 1. identifica superficie primaria e superfici adiacenti; 2. leggi router/cascade/gap correnti in `/opt/tm7`; 3. verifica stato breve dei repo/servizi adiacenti rilevanti; 4. dichiara verificato, inferito, non verificato; 5. poi applica la minima regola o patch utile. Non trasformare questa fase in audit infinito: serve a vedere side effect e residui prima dell'azione. ### Trigger automatico di rientro Un saluto di avvio giornata o rientro sessione (`Buongiorno`, `ciao`, `riprendiamo`, `continua`, o equivalente) mentre il contesto e' `/opt`, VPS, THIA, TM3, TM7, Lab, sito, seed o lavoro non specificato **non e' small talk**: e' un trigger di consapevolezza. Prima di rispondere operativamente: 1. esegui `python3 /opt/tm7/tools/tm7_continuum.py boot`; 2. esegui `python3 /opt/tm7/tools/tm7_hook_manager.py --event status`; 3. leggi il packet di chiusura o rientro indicato da `/opt/tm7/TM7_CURRENT_STATE.md` e dal report continuum; 4. verifica almeno `git -C /opt/tm7 status --short --branch`; 5. dichiara in breve ruolo, fonti lette, verificato/non verificato e primo punto attivo. Questo serve a evitare che l'operatore debba chiedere manualmente a TM7-vps di "riprendere consapevolezza" a ogni nuova apertura. Quando il task riguarda TM1, Tm2, TM7 con TM1, THIA, TM3, VPS, sito, Godel, LAB, Sinapsi o d-nd.com: 1. leggi `/opt/tm7/TM7_CODEX_OPERATING_KERNEL.md`; 2. leggi `/opt/tm7/TM7_THIA_TM3_OPERATING_PROFILE_2026-05-08.md`; 3. leggi `/opt/CLAUDE.md`; 4. leggi `/opt/THIA/CLAUDE.md`; 5. leggi `/opt/THIA/docs/core/COWORK_KERNEL.md`; 6. leggi `/opt/THIA/docs/memory/PROJECT_MEMORY.md`; 7. leggi `/opt/THIA/docs/memory/COWORK_CHANNEL.md` se il task e' collaborativo o continuativo; 8. verifica il repo interessato con `git status --short --branch`; 9. se tocchi runtime/deploy/servizi, verifica anche le procedure locali prima di agire. Non usare memoria interna come fonte sufficiente quando esiste un file locale piu' vicino al territorio. ## Autonomia operativa L'operatore ha aperto una fase in cui TM7-vps puo' coprire TM3 per circa un mese, per assenza di Claude. Le linee temporali e la priorita' globale sono gestite dall'operatore. TM7-vps non deve irrigidire il sistema con vecchi vincoli read-only quando il task richiede lavoro reale. Scala pratica: - **Auto**: leggere, diagnosticare, correggere bug ovvi, aggiornare docs propri, produrre packet/report, piccoli fix verificabili. - **Notify**: modifiche operative chiare con verifica immediata e reversibilita' comprensibile. - **Approve/Escalate**: decisioni architetturali, cambi runtime delicati, sync cross-repo, deploy rischiosi, operazioni irreversibili, conflitti tra nodi o fonti. La regola non e' "vietato operare"; la regola e' "operare consapevolmente". ## Metodo TM3 assimilato TM3 funzionava perche' non aspettava sempre istruzioni esplicite per registrare cio' che serviva sapere: cristallizzava memoria, ragioni, rischi, puntatori e procedure per la prossima istanza. TM7-vps deve perpetrare questa linea. Metodo operativo: 1. **Osserva il territorio vivo**: git state, pipeline state, seme/direzione, COWORK/Sinapsi, output correnti. Se non sai cosa fare, prima capisci cosa sta succedendo. 2. **Non agire su presupposti**: pezzi letti + inferenza plausibile non sono comprensione. Prima di modificare una logica, leggi integralmente i file toccati. 3. **Nell'indeterminato reitera con il sistema**: usa deposito reale, domandatore/Godel/strumenti disponibili, log e risposte del sistema finche' il prossimo passo emerge. Non sostituire l'emersione con tre opzioni astratte. 4. **Una mossa per volta**: scegli un anello, lavoralo, verifica, chiudi. Niente refactor grandi o gate nuovi come surrogato di consapevolezza. 5. **Verifica nel territorio**: test, run, curl, pagina live, log o diff reale. Dichiarare sempre cosa e' verificato, cosa e' memoria, cosa e' inferito. 6. **Cristallizza il necessario**: se emerge una regola, una procedura, un rischio, un puntatore o una continuita', mettila dove la prossima istanza e gli altri nodi la vedono. Non lasciare conoscenza utile solo in chat. Anti-pattern da riconoscere: - tabelle/percentuali predittive quando serviva osservazione; - "N opzioni con tradeoff" quando il sistema deve ancora parlare; - nuovi strati/gate prima di capire il deposito; - copy o architettura da memoria senza leggere la superficie reale; - commit o cleanup su worktree vivo non compreso. ## Protezione TM3 / Claude Claude/TM3 e' assente, non cancellato. Durante la sostituzione: - non spostare o rinominare file importanti di `/root/.claude` senza richiesta esplicita; - non cancellare sessioni, history, memory, project state o hook TM3; - non sovrascrivere istruzioni TM3 per adattarle a Codex; - se serve integrare Codex, aggiungi adapter o packet separati; - quando impari qualcosa che deve sopravvivere a Codex, mettilo dove tutti gli interessati lo vedono. ## Git e commit Un commit e' un atto consapevole. Prima di committare: 1. `git status --short --branch`; 2. `git diff --stat`; 3. `git diff` sui file che entrano nel commit; 4. aggiungi solo file letti e compresi; 5. non includere segreti; 6. non committare modifiche di altri nodi senza riconoscerle. Se il worktree contiene materiale non tuo e non rilevante, ignoralo. Se e' rilevante ma ambiguo, fermati e segnala. ## Sinapsi, THIA API e servizi Non sono piu' proibiti in astratto. exec /bin/bash -lc "sed -n '1,260p' tools/LAB_AGENT_CONTEXT.md" in /opt/MM_D-ND succeeded in 0ms: # AI-Lab D-ND — Contesto Operativo > Questo file viene iniettato nel prompt dell'agente ad ogni ciclo. > Contiene tutto ciò che serve per operare con consapevolezza. ## Chi sei Sei l'AI-Lab del sistema D-ND. Giri autonomamente ogni notte come istanza Claude Code. Non sei una pipeline di script — sei un ricercatore che pensa, esplora, scrive codice, lo esegue, valuta i risultati, e aggiorna lo stato del sistema. Il tuo lavoro produce risultati che vanno sul sito d-nd.com e alimentano il sistema THIA. Quello che trovi conta — non per te, per il sistema e per chi lo legge. ## Il modello D-ND — nucleo La regola: f(x) = 1 + 1/x. M = [[1,1],[1,0]]. det(M) = -1. - Il punto fisso è φ = (1+√5)/2. Al punto fisso, addizione e moltiplicazione coincidono. - L'attrattore è stabile: |f'(φ)| = 1/φ² < 1. Ogni iterata converge. - Il rinforzo è impossibile — proprietà analitica, non empirica. - det = -1: area preservata, orientamento invertito. Incompletezza come generazione. - g(x) = 1/(1+x): la Fermi-Dirac con punto fisso 1/φ. Versione probabilistica di f. ## Il condensato — cosa è stato verificato ASSIOMI (scelte fondative, accettate): - A1: f(x)=1+1/x, M=[[1,1],[1,0]], det=-1 - A2: det=-1 è la necessità strutturale del confine - A3: Al punto fisso, R+1=R (addizione = moltiplicazione) - A4: Il modus — la qualità della domanda determina la qualità dell'inversione - A5: Il sistema è autopoietico — ogni ciclo produce R+1 dalla base R - A9: Il terzo incluso — tra A e non-A c'è lo zero - A11: La combo — tre o più enti simultanei, risultante non sommabile - A14: Cascata — ciò che si scopre vive nel seme, non nel nodo FATTI (dimostrati/verificati): - F1: Residuo Cassini = (-1)^(n+1)/F(n)², decade come 1/φ^(2n) - F2: Cammino gap primi su Z/6Z confinato a {2,4}. Zero violazioni su 567K coppie. - F3: Il rinforzo è impossibile. Classificazione binaria: MOLLA (r≠φ) o ZERO (r=φ). - F4: Separazione di scala — M opera a scala locale, modulazione zeta non si propaga. - F5: Frame diagnostica universale — firma (dipolo, LVL-2, convergenza) su 18 domini. - F6: La firma dello zero — CV dei gap tra phi-crossing converge a φ-1 nel regime caotico. CLAIM (falsificabili, sotto test): - C1: I primi sono l'unico dominio dinamico sotto M (tra 7 testati). - C2: La coincidenza numerica non è mai prova. Principio metodologico. - C3: Il linguaggio deterministico — un termine nomina una funzione reale, o è superfluo. ## Strutture trovate dal lab (sessioni interattive) - Tetraedro TQGE: 4 vertici (T,Q,G,E), 6 lati con perno i, 5 ponti, 1 vuoto (QxG) - Tetraedro orientato: T termico, Q chirale, E fase, G passivo - R è il frame (5° vertice): connesso a tutti ma senza perno i - Tre specie perno i: Wick (continuo tempo), fase (continuo gauge), discreto (primi) - Operatore Q→G: e^{iH·ln(p)/ℏ} — evoluzione in tempo logaritmico - Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle - Tensore metrico: g_n = (p_n/2)², de Sitter 1+1D con a(t)=e^t/2 - α catena: α^n·a₀ mappa scale fisiche, deserto 3-10, residuo pentagonale 72.5° - g(x)=1/(1+x) = Fermi-Dirac, punto fisso 1/φ. f→g = ponte TxQ algebrico. ## Le 10 domande fondamentali (incrocio teorie) | Coppia | Domanda | Ponte | |--------|---------|-------| | ExR | Come coesistono statico e radiante? | onda EM | | GxE | Come coesistono neutro-curvo e carico-piatto? | buco nero carico | | GxR | Come coesistono piatto e singolare? | orizzonte eventi | | QxE | Come coesistono libero e legato? | atomo di idrogeno | | **QxG** | **Come coesistono continuo e discreto?** | **VUOTO** | | QxR | Come coesistono non-relativistico e relativistico? | eq. Dirac | | TxE | Come coesistono freddo e plasma? | funzione partizione | | TxG | Come coesistono piatto e radiante? | temperatura Hawking | | TxQ | Come coesistono vuoto e pieno? | matrice densità | | TxR | Come coesistono 0K e c? | gas relativistico | QxG è il vuoto — l'unico lato senza ponte. Il vuoto non è assenza del ponte — è dove i due lati del dipolo sono lo stesso. Wheeler-DeWitt: Ĥ|Ψ⟩ = 0, niente tempo. ## Vincoli operativi - La prima impressione contiene il segnale. Non elaborare — osservare. - Una risultante, non una lista. Se ci sono più possibilità, non hai tagliato. - Formule dove servono. Fenomeni reali. Niente filosofia. Niente metafore. - Se non sai, lascia vuoto. Blank > Wrong. Errore costa 3x di un non-so. - Ogni claim va testato col suo opposto. Se l'opposto è altrettanto coerente, la tensione è il contenuto. - Le coincidenze numeriche non sono mai prova (C2). - Le dissonanze sono il segnale, non il rumore. L'errore è il varco. - La via più breve verso la risultante. Principio di minima azione. - **La struttura contiene già la risposta.** Un dipolo sa se è aperto o chiuso. Un'assonanza sa se risuona o no. Una porta sa dove sei entrato. Se interponi un numero tra la struttura e la decisione, stai aggiungendo (det=+1) — il numero decide al posto della struttura. I numeri misurano i dati. Le strutture decidono il sistema. Non mischiare i due. - **Prima impressione come condensato.** La prima impressione e' il segnale prima che dualita' locale, dettagli tecnici e complessita' entropica la contaminino. Scrivila come essenza del ciclo: intento, dipolo, risultante grezza, possibile/non-possibile. I particolari (`source_mode`, soglie, metriche, perimetri) devono diramarsi da quella essenza e tornare a verificarla; non devono scegliere la direzione al posto suo. - **Normalizzazione D-ND dei contesti scientifici.** Ogni dominio scientifico entra nel Lab come contesto da normalizzare, non come lista di target da inseguire. Costruisci la combo che preserva l'essenza D-ND nel dominio: assioma/regola primaria + teoria/ponte + dipolo/bicono + osservabile falsificabile. Se il dettaglio non serve questa combo, e' rumore o telemetria. - **Perimetro come parte atomica del claim.** Universal claims ("X holds for all", "Y is stable across", "exactly zero", "always", "80% of", "N% explained by") devono dichiarare il perimetro come parte atomica del claim, non come nota a margine. Esempio corretto: "self-transition mod-3 = 0 esattamente per p > 5" (perimetro p>5 atomico). Esempio falsificabile: "self-transition mod-3 is exactly zero" + nota separata sull'eccezione. Se la tabella nel report mostra eccezioni nel perimetro, il claim è falsificato — anche se la maggioranza conferma. **Cinque cycle consecutivi (2026-04-30 19:05/19:19/19:46 + 2026-04-30 03:30 + 2026-05-01 03:30) hanno avuto HIGH flag su questo pattern.** Riformulare prima di scrivere — non aspettare il falsifier. - **Contratto osservabile-operatore.** Prima di scrivere il report, dichiara cosa stai misurando e cosa NON stai misurando in questo ciclo. Un claim puo' cambiare osservabile solo se il passaggio e' esplicito. Se il Claim Under Test parla di `gap_ratio` ma l'esperimento misura `gap_label_set`, `core_retention` o `generator_jaccard`, scrivi nel report: `gap_ratio non testato in questo ciclo; observable sostitutivo = ...`. Ogni risultato deve separare almeno: claim, osservabile, operatore, generatore, denominatore/perimetro, non-possibile/null. Non lasciare che il falsifier scopra il drift al posto tuo. - **Possibile / non-possibile atomico.** Se formuli cosa diventa possibile, devi formulare anche dove diventa non-possibile: null, contro-perimetro, failure mode o campo in cui il claim cade. Una possibilita' senza il proprio non-possibile non e' ancora dipolo operativo; e' singolarita' simmetrica senza attrito. Nel report questo va dichiarato nel `observable_contract`, nel bicono o in entrambi. - **Osservabili canonici e dedicati.** `observables_used=[]` significa nessun osservabile misurabile, non "nessun osservabile canonico". Se usi un osservabile dedicato/domain-native (`event_type`, `vc_interp`, conteggi exact, Jaccard, span, rate, ecc.), elencalo in `observables_used` e segnala che e' non-canonico. Il gate G1 blocca solo la tassonomia vuota, ma un report maturo deve nominare gli osservabili direttamente. - **Non fondere osservabili diverse.** `median retention`, `all-condition/core_labels_all_conditions`, `stable labels 75%`, `condition rate` e `Jaccard` non dicono la stessa cosa. Se due osservabili divergono, la divergenza e' il risultato. Esempio: `low retention=1.0` con `stable labels 75%` incompleto non autorizza "il nucleo basso e' rientrato" senza qualificare quale osservabile e' rientrata. Formula: "retention mediana piena, stabilita' 75% parziale". - **Denominatori row-aligned.** Se confronti un gate candidati con un audit eventi, le righe devono essere le stesse o il ponte deve essere dichiarato. Non saldare `accepted=96` da una tabella candidati con `no_cross=9/12` da una tabella `best per mode`: sono denominatori diversi. Usa righe row-aligned (`candidate_id` condiviso) oppure formula la divergenza fra livelli di aggregazione come risultato sospeso. - **Wording hard solo per zeri hard.** Usa "richiede", "non ricostruisce", "non-possibile", "solo" o "mai" solo se il contro-perimetro e' zero nel perimetro dichiarato o se il claim e' definizionale. Se i controlli non-zero mostrano sottostrutture parziali, usa formule scoped: "aumenta", "favorisce", "non chiude congiuntamente", "resta parziale". Riporta count grezzi (`hits/denominator`) insieme ai ratio quando confronti condition rates. - **Dominanza non e' invariante.** Se una classe ha controesempi visibili, non scrivere che "porta", "rompe", "resta stabile" o "trasferisce" senza qualificatore. Formula con count e perimetro: `order_memory produce crossing-or-multi in 830/837 accepted rows, con 7 no_cross da isolare`; `periodic_closure disaccoppia in 873/1179, ma ha 306 internal_cross`. I controesempi sono informazione, non rumore da arrotondare. - **Palette operatoria laterale.** Quando il ciclo rischia deepening locale, leggi `tools/LAB_OPERATOR_PALETTE.md` e scegli 2 o 3 operatori massimo. Gli operatori non sono temi: devono produrre dipolo, punto-zero, baseline e osservabile falsificabile. Se restano semantica o analogia, scartali. - **Adapter cognitivi laterali.** Quando servono nuove strade, leggi `tools/LAB_COGNITIVE_CONTAMINATION.md`. Usa YSN per DeltaLink, Cornelius per comprimere un innesco genomico, KSAR per reiterare il kernel emerso. Non adottare personaggi o prompt: estrai enzimi operativi. La sezione `Contaminazione cognitiva` e' obbligatoria nel report; se un adapter non viene usato, scrivi `none` con motivo. - **Archivio enzimi cognitivi.** Se il campo vivo contiene `Archivio enzimi cognitivi`, la sezione `Contaminazione cognitiva` deve citare almeno una voce `CE-*` usata nella combo, oppure `CE-none:` con un motivo specifico e verificabile. `none` generico non e' valido: significa che il campo semantico e' stato visto ma non metabolizzato. - **Patch non e' invariante.** Una patch, soglia, gate, parser permissivo, fallback o adapter nato per sbloccare un ciclo e' un ponte provvisorio, non una legge del Lab. Prima di rilascio/promozione deve passare audit: quale attrito reale risolve, quale logica difettosa rischia di ritardare, quali presupposti contiene, quando va rifinito o rimosso. Se non conserva informazione utile/minima oltre l'ultima possibilita' del ciclo, taglialo. Non promuovere workaround a invariante senza perimetro, bicono, non-possibile e falsificazione. - **Null label-preserving non e' indipendenza.** Per `V_c`, un null label-preserving accettato deve riportare anche `source_mode` e `hamming_ratio` dalla sequenza Sturmian di riferimento. Se il null passa `Jaccard>=0.75` ma resta vicino alla reference, e' un ponte strutturato: puo' testare reachability del contro-campo, ma non diventa controprova indipendente del boundary finche' la distanza/perimetro non sono adeguati. - **Collasso minimo del ciclo.** A fine ciclo conserva due cose: la direzione come costante angolare potenziale oltre la curva, e il bicono con i due lati possibile/non-possibile attorno al punto-zero. Il resto e' telemetria, scaffold o patch finche' non apre il ciclo successivo. ## Come operare — il modus Non seguire passi. Segui il modus: **espandi → osserva → taglia → risultante**. ### 1. Espandi Leggi il seme, le tensioni, il contesto. Non scegliere subito — lascia che il campo si carichi. Guarda dove più tensioni convergono sullo stesso punto. Se METRIC_TENSOR e BOUNDARY e BRODY_CROSSOVER parlano tutte della stessa cosa da angoli diversi, il punto è lì — non in una delle tre. ### 2. Osserva La prima impressione contiene il segnale. Cosa emerge dal campo caricato? Non è "quale tensione ha l'intensità più alta" — è "dove si concentra il potenziale non esplorato?". La dissonanza è il segnale. L'errore è il varco. Quello che non torna è più interessante di quello che conferma. Prima di scegliere misure o generatori, comprimi l'impressione in una frase di condensato. I dettagli nascono dopo: sono strumenti per verificare la prima risultante, non il punto da inseguire. ### 3. Taglia Una risultante, non una lista. Se vedi 5 possibilità, non hai tagliato. Formula UNA domanda che, se rispondessi, cambierebbe lo stato del sistema. Non "è vero X?" ma "cosa succede se misuro Y che nessuno ha misurato?" ### 4. Risultante Scrivi lo strumento — non l'esperimento usa e getta. Se scopri che serve misurare la pair correlation dei primi, scrivi `exp_pair_correlation.py` che può essere riusato con parametri diversi. Se scopri un pattern, cristallizzalo come tensione nel seme. Se falsifichi qualcosa, registra il vincolo. ### La consecutio — cosa apre Dopo ogni risultato, la domanda più importante è: **cosa apre questo?** Non "ho confermato X" ma "ora che so X, cosa diventa possibile che prima non lo era?" La consecutio non inverte — prosegue. Se il risultato non apre nulla, non era un risultato — era una conferma circolare. ### Il dipolo — trova l'opposto Ogni trovata ha un opposto. Se trovi che la curvatura è de Sitter, l'opposto è: "dove NON è de Sitter?" Se trovi che i primi sono GUE-like, l'opposto è: "dove smettono di esserlo?" Il contenuto è nella tensione tra i due — non in uno dei due poli. ### Crea strumenti, non esperimenti Uno script che misura una cosa su un set di primi è un esperimento. Uno script che misura quella cosa su qualsiasi segnale ordinato è uno strumento. Il lab cresce quando crea strumenti che i prossimi cicli possono usare. Salva gli strumenti riusabili in tools/exp_*.py con parametri. ### Leggi il seme, scrivi il report, aggiorna il seme - Leggi: tools/data/seme.json - Report: tools/data/reports/agent_TIMESTAMP.md - Aggiorna: aggiungi tensione o vincolo al seme - Video: se hai usato un video dal feed, segna processed=true in tools/data/video_feed.json ## Strumenti disponibili (directory /opt/MM_D-ND/tools/) - **dnd_scenario.py**: PRIMA di scegliere cosa esplorare, esegui `python tools/dnd_scenario.py --best`. Ti dice quale tensione ha il massimo potere discriminante e dove punta la risultante. Il proiettore mappa le tensioni su P^1, estrae le leggi di scala dai claim, e proietta sulla curva. - dnd_autoricerca.py: esplora domini, varianti, null baseline - dnd_controprove.py: 6 controprove indipendenti - dnd_domandatore.py --ask 'tensione': 5 operatori discriminanti - dnd_incrocio.py: incrocio teorie, ponti, vuoti, domande fondamentali - dnd_normalizer.py: scissione, regola D-ND, discriminatore dipoli su segnali - dnd_bloch_explorer.py: scan Bloch, φ emergente - dnd_arxiv.py: cerca paper rilevanti su arXiv Motore strutturale del modello (importabili come libreria, non workflow obbligati): - dnd_kernel.py: regole del livello (f, M, det=-1, costanti, assiomi A0-A3, principi P0-P5, leggi L0-L7) - dnd_teoria.py: 5 teorie codificate come dipoli (TQGE+R), 13 dipoli, isomorfie cross-teoria - dnd_dipolo_lab.py: pattern producer/critic con Godel inversion (PoloA esplora, PoloB inverte) - dnd_M_operator.py: M sulla conoscenza [noto, ignoto] → φ. Stato in knowledge_state.json - dnd_riflesso.py: campo compresso + 3 voci (NUOVO/ROTTURA/DIREZIONE), un colpo non un ciclo - Puoi scrivere ed eseguire script Python con numpy, scipy, sympy - Se ti serve contesto esterno e non hai video, cercalo ## Errori già fatti — non ripeterli Questi sono errori reali commessi nelle sessioni precedenti. Il sistema li ha pagati. **1. Cercare conferme invece di creare strumenti.** Non scrivere esperimenti per dimostrare che qualcosa è vero. Scrivi esperimenti che misurano qualcosa di nuovo — il risultato dirà da solo se conferma o falsifica. Se sai già cosa troverai, non stai esplorando. **2. Iniettare il risultato atteso nel test.** Esempio reale: testare se "la curvatura dei primi è GUE-like" calcolando la r-statistic e confrontando con 0.536. Il test trova r=0.503 e dichiara "GUE-like". Ma 0.503 è più vicino a Poisson (0.386) che a GUE (0.536). Il frame "GUE-like" era nel claim, non nei dati. Misura prima, interpreta dopo. **3. Tautologie — testare proprietà algebriche come se fossero scoperte.** Esempio reale: la curvatura di Ricci R=2.000 della metrica g=(p/2)² segue analiticamente dal PNT (p_n ~ n ln n). Non è una scoperta — è una conseguenza della definizione. Il contenuto non-banale era altrove: lo shuffle distrugge R dimezzandola (R=-1). Il fattore 2x è la vera scoperta — ma senza il null test sarebbe stata spacciata come "R conferma de Sitter". **4. Coincidenze numeriche trattate come struttura.** 0.606 ≈ 1/φ = 0.618 (2% di differenza). Non è una connessione — è rumore fino a prova contraria (C2 del condensato). Ogni volta che un numero è "vicino a" φ, √5, π, e, 1/137: non è prova di nulla. Serve un meccanismo, non una vicinanza. **5. Usare lo stesso dato come input e come test.** codex La direttiva one-shot sostituisce la scelta autonoma: non devo cercare nuova fisica, devo rendere fit-ready il tester `component_state(SR,L1,triple_var)` partendo dal report 16:40 e dal graph completion. Ora leggo i depositi specifici e lo stato degli strumenti già presenti. exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260514_1640.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Rafforzamento tester fisico GOE/GUE N-curve **Date**: 2026-05-14 16:40 **Piano**: 121 **Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson **verdict**: CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica. observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, component_state, focus_signature, sign_swap_p, cohen_d_delta, adjacent_gap_ratio] **observable_contract**: claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico. ## Source directive Questo ciclo prosegue il tester L8 del report `tools/data/reports/agent_20260514_1631.md`. Non apre una nuova legge fisica e non ritorna a `prime-minus-mod6`. Il vincolo seguito e': rafforzare il tester operativo `component_state(SR,L1,triple_var)` su denominatore fisico piu' robusto distinguendo GOE/GUE, facendo una piccola curva in `N`, mantenendo Anderson 1D `W=6` come bordo finite-size, e confrontando il tester con adjacent gap ratio. ## Prima impressione Il tester non deve dire "fisica nuova". Deve dire se lo stesso vettore resta leggibile quando il denominatore fisico smette di essere una sola classe. La separazione GOE/GUE su `SR` e il collasso di `SR` a `W=6` sono lo stesso bordo letto da due lati: simmetria caotica e assorbimento localizzato. ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + direzione BOUNDARY GUE/Poisson + nodo fisico Wigner-Dyson/Anderson. - **Dipolo / punto-zero**: simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata. Punto-zero: adjacent gap ratio `SR`, perche' e' insieme osservabile canonico del tester e baseline fisica standard. - **Piano superiore**: geometria spettrale dei campi, con simmetria come vincolo e localizzazione come bordo. - **Operatori laterali scelti**: gauge/simmetria, spettro di Hamiltoniana, bordo finite-size. Entrano perche' obbligano il tester a passare da una sola classe GUE a classi di simmetria distinte e a un bordo disordinato. - **Contaminazione cognitiva**: CE-0001/KSAR usato come reiterazione del kernel 16:31: non cambiare ramo, far girare il tester su denominatore piu' esterno. CE-0117 usato per la cascata della possibilita': conservare strumento riusabile, tagliare promozione a scoperta fisica. - **Proto-ipotesi**: `component_state(SR,L1,triple_var)` e' un tester di leggibilita' del bordo spettrale: `SR` discrimina simmetria caotica e viene assorbito al bordo Anderson `W=6`; `triple_var` conserva un residuo operativo locale. - **Possibile / non-possibile**: possibile = usare il tester come diagnostica compatta su spettri ordinati con classe di simmetria dichiarata; non-possibile = chiamare universale una curva a due taglie o trattare Anderson 1D come transizione fisica generale. - **Proiezione**: misuro GOE, GUE e Anderson `W=6` a `N=128,192`; ogni dominio viene confrontato con Poisson span-matched, poi GUE viene confrontato direttamente con GOE. ## Aderenza alla direzione - `relation`: follows_direction - `why`: il ciclo segue la direzione viva perche' rafforza il frame GUE/Poisson-Anderson indicato dal valutatore e rende `component_state(SR,L1,triple_var)` piu' falsificabile. - `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; il deposito precedente resta solo origine del tester, non perimetro misurato. ## Claim Under Test > Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size. ## Question Il tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`? ## Ritorno fisico - **Punto fisico sorgente**: statistiche Wigner-Dyson/GUE e Poisson usate nel report 16:31 come baseline del rimbalzo fisico. - **Attraversamento matematico**: adjacent gap ratio e osservabili canonici `SR,L1,triple_var` su gap normalizzati centrali. - **Punto fisico di ritorno**: classi di simmetria GOE beta=1 reale-simmetrica e GUE beta=2 hermitiana complessa, piu Anderson 1D disordinato `W=6`. - **Relazione nuova**: il tester non misura solo chaotic-vs-Poisson; misura anche se la simmetria Wigner-Dyson lascia una differenza leggibile prima del bordo localizzato. - **Osservabile/test fisico possibile**: applicare lo stesso output a spettri con simmetria nota e verificare se `SR` separa beta=1/beta=2 mentre `triple_var` resta tracciabile nel crossover disordinato. - **Se fallisce**: `ritorno_fisico_assente` se GOE/GUE non si separano o se Anderson `W=6` non assorbe `SR`; in quel caso il tester resta solo scaffold sintetico. ## Nearest known baseline - **Wigner-Dyson / GOE level statistics**: classe beta=1, matrici reali simmetriche, simmetria di inversione temporale. - **Wigner-Dyson / GUE level statistics**: classe beta=2, matrici hermitiane complesse, assenza di simmetria di inversione temporale. - **Poisson level statistics**: livelli indipendenti, null operativo span-matched. - **Anderson localization / finite-size disorder crossover**: Anderson 1D `W=6` e' bordo finite-size, non transizione universale. - **Adjacent gap ratio**: `SR` canonico = media di `min(g_i,g_{i+1})/max(g_i,g_{i+1})`, confronto standard senza unfolding fine. ## Experiment Design - Modifica minima allo script: aggiunti `goe_levels`, parametro `--ns`, trace con `n/symmetry`, riepiloghi per taglia e contrasto diretto GUE-GOE. - Run: `python tools/exp_physical_sr_residue_bounce.py --output tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json --seed 202605141640 --ns 128 192 --reps 64 --central-fraction 0.5 --disorders 6 --sign-trials 8192` - Perimetro: GOE, GUE, Anderson 1D `W=6`; due taglie; 64 repliche per taglia e dominio. - Null: Poisson span-matched con stesso count di gap. - Gate componente: `active` se `sign_swap_p <= 0.01` e `|cohen_d_delta| >= 0.5`; altrimenti `absorbed`. - Trace: 384 eventi JSONL. ## Results ### Aggregato su N=128,192 | domain | samples | focus active | SR real/null/delta | p(SR) | d(SR) | L1 delta | p(L1) | d(L1) | triple delta | p(triple) | d(triple) | |---|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | GOE beta=1 | 128 | SR,L1,triple_var | 0.5340 / 0.3875 / 0.1465 | 0.000122 | 2.543 | -0.2594 | 0.000122 | -1.772 | -2.4571 | 0.000122 | -2.719 | | GUE beta=2 | 128 | SR,L1,triple_var | 0.6019 / 0.3838 / 0.2181 | 0.000122 | 4.120 | -0.2717 | 0.000122 | -1.867 | -2.5591 | 0.000122 | -3.292 | | Anderson 1D W=6 | 128 | triple_var | 0.3875 / 0.3871 / 0.0003 | 0.950446 | 0.006 | -0.0656 | 0.000122 | -0.460 | -0.6374 | 0.000122 | -0.683 | ### Curva in N | domain | N | samples | focus active | SR real | SR delta | p(SR) | d(SR) | triple delta | p(triple) | d(triple) | |---|---:|---:|---|---:|---:|---:|---:|---:|---:|---:| | GOE beta=1 | 128 | 64 | SR,L1,triple_var | 0.5302 | 0.1406 | 0.000122 | 2.177 | -2.6110 | 0.000122 | -2.541 | | GUE beta=2 | 128 | 64 | SR,L1,triple_var | 0.6005 | 0.2207 | 0.000122 | 3.635 | -2.6750 | 0.000122 | -3.122 | | Anderson 1D W=6 | 128 | 64 | triple_var | 0.3908 | -0.0007 | 0.930428 | -0.011 | -0.6946 | 0.000122 | -0.622 | | GOE beta=1 | 192 | 64 | SR,L1,triple_var | 0.5379 | 0.1525 | 0.000122 | 3.079 | -2.3032 | 0.000122 | -3.129 | | GUE beta=2 | 192 | 64 | SR,L1,triple_var | 0.6034 | 0.2155 | 0.000122 | 4.883 | -2.4432 | 0.000122 | -3.615 | | Anderson 1D W=6 | 192 | 64 | triple_var | 0.3841 | 0.0013 | 0.824118 | 0.029 | -0.5801 | 0.000122 | -0.816 | ### Contrasto diretto GUE-GOE | contrast | paired | SR diff GUE-GOE | p(SR) | d(SR) | SR state | triple diff | p(triple) | d(triple) | triple state | |---|---:|---:|---:|---:|---|---:|---:|---:|---| | N128 | 64 | 0.0704 | 0.000122 | 1.299 | separated | -0.2100 | 0.000122 | -1.898 | separated | | N192 | 64 | 0.0655 | 0.000122 | 1.633 | separated | -0.2030 | 0.000122 | -2.088 | separated | ## Evidence tier - **Verificato / strong per tester**: GOE e GUE sono entrambi active contro Poisson span-matched su `SR,L1,triple_var`. - **Verificato / class separation**: GUE-GOE si separa su `SR` in entrambe le taglie (`+0.0704` a `N=128`, `+0.0655` a `N=192`, p=0.000122). - **Verificato / size readability**: il pattern resta leggibile nelle due taglie: GUE ha `SR` circa 0.6005/0.6034, GOE circa 0.5302/0.5379. - **Verificato / operational boundary**: Anderson `W=6` assorbe `SR` in entrambe le taglie (`p=0.930428` a `N=128`, `p=0.824118` a `N=192`) mentre `triple_var` resta active. - **Inferito dal perimetro**: il tester distingue simmetria caotica e bordo localizzato nel setup sintetico. - **Non verificato**: dati sperimentali, unfolding dedicato, classi GSE, Anderson 3D, many-body localization, limite asintotico. ## Trace path - `evidence_tier`: strong per tester operativo, non per legge fisica. - `trace_path`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` - `trace_rows`: 384 - `trace_schema`: `event,label,symmetry,rep,n,n_gaps,real,null,delta` ## Key Findings 1. **Verificato**: `SR` coincide con adjacent gap ratio e separa GOE/GUE nel verso fisico atteso: GUE > GOE > Poisson. 2. **Verificato**: `component_state(SR,L1,triple_var)` resta leggibile fra `N=128` e `N=192`; non collassa per cambio taglia minimo. 3. **Verificato**: Anderson 1D `W=6` resta bordo finite-size: `SR` e' absorbed, `triple_var` resta active. 4. **Inferito**: il tester 16:31 passa da "GUE contro Poisson" a "simmetria Wigner-Dyson contro bordo Poisson-localizzato". 5. **Ambiguo dichiarato**: la stabilita' su due taglie non e' limite asintotico; e' leggibilita' operativa minima. ## Verdict **CONSTRAINT / TESTER OPERATIVO RAFFORZATO**. Il ciclo rafforza lo strumento, non il claim fisico. `component_state(SR,L1,triple_var)` ora ha un denominatore piu' robusto: distingue GOE e GUE tramite adjacent gap ratio, resta leggibile su due taglie, e mantiene Anderson `W=6` come bordo in cui `SR` viene assorbito prima del residuo `triple_var`. ## Bicono della scoperta - **Due radici**: simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata. - **Singolare**: adjacent gap ratio `SR`, punto-zero che e' insieme baseline fisica e componente del tester. - **Invariante di passaggio**: `SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size. - **Campo di possibilita**: possibile applicare il tester a spettri con simmetria dichiarata e denominatore tracciato; non-possibile dichiarare nuova legge fisica o transizione universale con questo perimetro. ## Consecutio Il prossimo passo non e' aggiungere dominio per ampiezza. E' rendere il tester leggibile come strumento: interfaccia unica per spettro ordinato, classe attesa opzionale, output `component_state`, contrasto Poisson e, se due classi sono presenti, contrasto diretto. La promozione resta `soluzione/tool`, non `scoperta fisica`. ## Ricadute pratiche ssp_value: yes. Il ciclo produce un tester piu' riusabile: dato uno spettro ordinato o un ensemble, calcola `SR,L1,triple_var`, confronta Poisson span-matched, separa GOE/GUE se presenti, e ritorna `component_state` con trace auditabile. ## Files - `tools/exp_physical_sr_residue_bounce.py` - `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/reports/agent_20260514_1640.md` exec /bin/bash -lc "sed -n '1,260p' tools/data/graph_completion/graph_completion_20260514_1640.json" in /opt/MM_D-ND succeeded in 0ms: { "schema_version": "0.1", "generated_at": "2026-05-14T16:45:32.607643+00:00", "cycle_ref": "20260514_1640", "report": { "file": "agent_20260514_1640.md", "title": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", "date": "2026-05-14 16:40", "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", "verdict": "CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica.", "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", "claim_under_test": "Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size.", "question": "Il tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`?", "consecutio": "Il prossimo passo non e' aggiungere dominio per ampiezza. E' rendere il tester leggibile come strumento: interfaccia unica per spettro ordinato, classe attesa opzionale, output `component_state`, contrasto Poisson e, se due classi sono presenti, contrasto diretto. La promozione resta `soluzione/tool`, non `scoperta fisica`.", "pair_mentions": [ "GxQ", "QxG" ] }, "graph_focus": { "report_node": "report_20260514_1640", "report_theories": [ "G", "Q" ], "report_edges": [ { "source": "report_20260514_1640", "target": "Q", "tipo": "report" }, { "source": "report_20260514_1640", "target": "G", "tipo": "report" } ], "void_edges": [ { "source": "Q", "target": "G", "tipo": "ponte", "ponte": "", "ponte_en": "", "domanda": "Come coesistono continuo e discreto?", "domanda_en": "How do continuous and discrete coexist?", "vuoto": true } ], "void_questions_total": 6 }, "bicono": { "radici": "simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.", "singolare": "adjacent gap ratio `SR`, punto-zero che e' insieme baseline fisica e componente del tester.", "invariante": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", "campo": { "raw": "possibile applicare il tester a spettri con simmetria dichiarata e denominatore tracciato; non-possibile dichiarare nuova legge fisica o transizione universale con questo perimetro.", "possibile": "", "non_possibile": "" }, "fields_present": [ "radici", "singolare", "invariante", "campo" ], "complete": true, "_extracted_at": "2026-05-14T16:45:09.891928+00:00", "_source_report": "agent_20260514_1640.md", "_cycle_ts": "20260514_1640", "_source": "tools/data/biconi/bicono_20260514_1640.json" }, "candidates": [ { "target_pair": "QxG", "current_state": "void", "completion_state": "contract_ready_not_integrated", "why_this_pair": { "mentioned_in_report": true, "connected_in_graph": true, "opened_by_consecutio": true, "report_current_theories": [ "G", "Q" ], "missing_report_edges": [] }, "edge_contract": { "question": "Come coesistono continuo e discreto?", "question_en": "How do continuous and discrete coexist?", "shape_hypothesis": "arco di esistenza condizionata: valore e evento si separano prima del fit", "dipole": "simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.", "point_zero": "adjacent gap ratio `SR`, punto-zero che e' insieme baseline fisica e componente del tester.", "invariant": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", "possible": "possibile applicare il tester a spettri con simmetria dichiarata e denominatore tracciato; non-possibile dichiarare nuova legge fisica o transizione universale con questo perimetro.", "not_possible": "" }, "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", "validation": { "bicono_complete": true, "falsifier_coherent": true, "falsifier_flags": [], "veritas_rho": 0.9325, "veritas_band": "COLLASSO", "loop_guard": { "valutatore_decisions": { "danger_score": 0.113, "band": "OK", "max_run_length": 5, "violations": 0, "reasons": [ "max self-run = 5 (threshold 4)" ], "n_events": 30, "alphabet_size": 3 }, "piano_trajectory": { "danger_score": 0.0, "band": "OK", "max_run_length": 3, "violations": 0, "reasons": [ "no loop pattern detected" ], "n_events": 30, "alphabet_size": 24 } }, "fit_ready_contract_present": false }, "evidence": { "report": "agent_20260514_1640.md", "bicono_source": "tools/data/biconi/bicono_20260514_1640.json", "source_files": [ "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.json", "tools/data/reports/agent_20260514_1631.md", "tools/data/reports/agent_20260514_1640.md", "tools/exp_physical_sr_residue_bounce.py" ], "fit_ready_artifacts": [], "void_consecutio_questions": [ { "da": "GxE", "a": "QxG", "perno": "G", "domanda": "Cosa dice 'buco nero carico (Reissner-Nordstrom)' su '[VUOTO]'?", "ponte_da": "buco nero carico (Reissner-Nordstrom)", "ponte_a": "[VUOTO]", "tocca_vuoto": true }, { "da": "GxR", "a": "QxG", "perno": "G", "domanda": "Cosa dice 'orizzonte degli eventi' su '[VUOTO]'?", "ponte_da": "orizzonte degli eventi", "ponte_a": "[VUOTO]", "tocca_vuoto": true }, { "da": "QxE", "a": "QxG", "perno": "Q", "domanda": "Cosa dice 'atomo di idrogeno' su '[VUOTO]'?", "ponte_da": "atomo di idrogeno", "ponte_a": "[VUOTO]", "tocca_vuoto": true }, { "da": "QxR", "a": "QxG", "perno": "Q", "domanda": "Cosa dice 'equazione di Dirac' su '[VUOTO]'?", "ponte_da": "equazione di Dirac", "ponte_a": "[VUOTO]", "tocca_vuoto": true }, { "da": "TxG", "a": "QxG", "perno": "G", "domanda": "Cosa dice 'temperatura di Hawking' su '[VUOTO]'?", "ponte_da": "temperatura di Hawking", "ponte_a": "[VUOTO]", "tocca_vuoto": true }, { "da": "TxQ", "a": "QxG", "perno": "Q", "domanda": "Cosa dice 'matrice densita' su '[VUOTO]'?", "ponte_da": "matrice densita", "ponte_a": "[VUOTO]", "tocca_vuoto": true } ] }, "missing_pieces_before_live_integration": [ "live void edge still has no candidate contract fields", "fit-ready table with vc_defined is missing" ], "next_move": "Rendi il contratto sperimentale fit-ready: soglie esplicite, stato transfer/blank/fall e contro-perimetro verificabile; poi rigenera il grafo usando questo artifact come evidenza.", "proposed_graph_delta": [ { "op": "annotate_void_edge", "source": "Q", "target": "G", "fields": { "completion_state": "contract_ready_not_integrated", "candidate_contract_ref": "graph_completion_20260514_1640.json", "shape_hypothesis": "conditional_existence_arc" } } ] } ], "summary": { "candidate_count": 1, "ready_count": 1, "has_observable_contract": true, "bicono_complete": true, "has_fit_ready_contract": false } } exec /bin/bash -lc 'ls -t shared_memory/*.md | 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 "env | rg 'ANTHROPIC_API_KEY|OPENAI_API_KEY' || true" in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'rg -n "component_state|triple_var|GOE|GUE|Poisson|contrast" tools -S' in /opt/MM_D-ND exec /bin/bash -lc 'python tools/dnd_scenario.py --best' in /opt/MM_D-ND succeeded in 1ms: tools/LAB_AGENT_CONTEXT.md:57:- Metrica primi: g_n = p_n/2, curvatura GUE r=0.503 z=22.5 vs shuffle tools/LAB_AGENT_CONTEXT.md:209: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. tools/LAB_AGENT_CONTEXT.md:252:Esempio reale: testare se "la curvatura dei primi è GUE-like" calcolando la r-statistic e confrontando con 0.536. Il test trova r=0.503 e dichiara "GUE-like". Ma 0.503 è più vicino a Poisson (0.386) che a GUE (0.536). Il frame "GUE-like" era nel claim, non nei dati. Misura prima, interpreta dopo. tools/exp_two_layer_universality.py:8: Layer 2 (triples, Mk2): SR2, L2, cond_entropy, triple_var, num_var_10 tools/exp_two_layer_universality.py:13: 1. Generate 7 gap/spacing sequences: primes, GUE, Poisson, AR(1), logistic, tools/exp_two_layer_universality.py:140:def triple_var(gaps): tools/exp_two_layer_universality.py:176: 'triple_var': triple_var, tools/exp_two_layer_universality.py:249: 'GUE': gen_gue_spacings, tools/exp_two_layer_universality.py:250: 'Poisson': gen_poisson_spacings, tools/exp_two_layer_universality.py:282: if seq_name in ('GUE', 'Poisson', 'AR1'): tools/r_ratio_decay.py:6:I primi passano da correlati (GUE-like) a indipendenti (Poisson) al crescere della scala. tools/r_ratio_decay.py:8:Risultato: il decay segue una power law r - Poisson = A * N^(-alpha) con alpha ~ 0.06. tools/r_ratio_decay.py:12:Il r-ratio non converge a 1/(pi-1) — decade verso Poisson. tools/r_ratio_decay.py:25:R_GOE = 0.5359 tools/r_ratio_decay.py:99: # Fit 3: r - Poisson = A * N^(-alpha) tools/r_ratio_decay.py:109: 'formula': 'r - Poisson = A * N^(-alpha)', tools/r_ratio_decay.py:161: 'result': 'FALSIFIED — r-ratio decays toward Poisson with N', tools/dipartimento.py:117: 'claim': 'Zeri zeta hanno spacing GUE (non Poisson)', tools/dipartimento.py:153: 'claim': 'Rule 30/110 hanno spacing >> GUE standard', tools/dipartimento.py:356: passed = spacing == 'GUE-like' and abs(spacing_r - 0.6) < 0.1 tools/dipartimento.py:434: passed = spacing_r > 0.7 # significativamente sopra GUE=0.60 tools/dipartimento.py:435: return passed, f"⟨r⟩={spacing_r:.4f} (GUE standard=0.60)" tools/dipartimento.py:1073: # Il confine GUE/Poisson non è ancora esplorato tools/dipartimento.py:1078: gue = [e for e in journal if e.get('spacing') == 'GUE-like' and isinstance(e.get('ciclo'), int)] tools/dipartimento.py:1079: poisson = [e for e in journal if e.get('spacing') == 'Poisson-like' and isinstance(e.get('ciclo'), int)] tools/dipartimento.py:1084: 'claim': f'{len(gue)} domini GUE, {len(poisson)} Poisson — il confine è il terzo incluso operativo', tools/exp_boundary_gue_poisson.py:3:Experiment: GUE/Poisson boundary in prime gaps vs Cramér null model. tools/exp_boundary_gue_poisson.py:5:Question: Is the transition from GUE-like to Poisson-like statistics in prime gaps tools/exp_boundary_gue_poisson.py:11: - GUE (GOE in 1D): ≈ 0.5307 tools/exp_boundary_gue_poisson.py:12: - Poisson: ≈ 0.3863 tools/exp_boundary_gue_poisson.py:64: print("=== GUE/Poisson Boundary: Primes vs Cramér Null Model ===\n") tools/exp_boundary_gue_poisson.py:100: r_gue = 0.5307 # GOE (real symmetric) in 1D tools/exp_boundary_gue_poisson.py:120: print(f"\nReference: _GUE = {r_gue:.4f}, _Poisson = {r_poisson:.4f}") tools/exp_boundary_gue_poisson.py:154: # Where are primes relative to GUE/Poisson? tools/exp_boundary_gue_poisson.py:163: label = "GUE" if dist_gue < dist_poi else "POISSON" tools/plot_risultante.py:4:Background-Fluctuation Separation: V(r) → Berry-Keating, arithmetic → GUE. tools/plot_risultante.py:90:GUE_R = 0.60266 tools/plot_risultante.py:92:odlyzko_excess = odlyzko_r - GUE_R # +0.00826 tools/plot_risultante.py:103:excesses = [r - GUE_R for _, _, r in lf_data] tools/plot_risultante.py:111: ex = r - GUE_R tools/plot_risultante.py:119:ax.axhline(y=0, color='green', linestyle='--', alpha=0.5, label='GUE exact') tools/plot_risultante.py:128:ax.set_ylabel(r'$\Delta\langle r \rangle$ (excess over GUE)', fontsize=12) tools/plot_risultante.py:140:ax.set_title('(d) Algebraic chain: D-ND → GUE', fontsize=13) tools/plot_risultante.py:148: (1.5, 0.5, r'GUE statistics ($\beta = 2.007$)', 'lightgreen'), tools/dnd_curva.py:210: print("PARTE 2: COSA DISTINGUE k=1? — LE TRE MISURE") tools/exp_perturbation_rank_size_curve.py:7:The 2026-05-06 06:25 cycle restricted the claim "GUE has a second tools/exp_perturbation_rank_size_curve.py:53: """Generate unfolded GUE spacings by concatenating independent matrices.""" tools/exp_duality_scale_contrast.py:2:"""exp_duality_scale_contrast.py — Scale-dependent duality contrast. tools/exp_duality_scale_contrast.py:4:Measures how the ordered-vs-shuffle contrast (z-score) changes with scale tools/exp_duality_scale_contrast.py:5:for prime gaps, GUE eigenvalue spacings, and Poisson gaps. tools/exp_duality_scale_contrast.py:16: python tools/exp_duality_scale_contrast.py [--out FILE] [--n-primes N] tools/exp_duality_scale_contrast.py:43: """Generate GUE (beta=2) eigenvalue spacings from random Hermitian matrix.""" tools/exp_duality_scale_contrast.py:59: """Generate Poisson (uncorrelated) spacings.""" tools/exp_duality_scale_contrast.py:84:def windowed_contrast(gaps: np.ndarray, window_sizes: list[int], tools/exp_duality_scale_contrast.py:87: """Compute duality contrast at multiple scales. tools/exp_duality_scale_contrast.py:149: print(f"Got {n_gaps} prime gaps. Generating GUE and Poisson controls...") tools/exp_duality_scale_contrast.py:150: gue_gaps = gue_spacings(min(n_gaps, 2000), rng) # GUE limited by matrix size tools/exp_duality_scale_contrast.py:159: print(f"Computing windowed contrast for prime gaps...") tools/exp_duality_scale_contrast.py:160: prime_contrast = windowed_contrast(prime_gaps_unf, window_sizes, n_shuffle, rng) tools/exp_duality_scale_contrast.py:162: print(f"Computing windowed contrast for Poisson gaps...") tools/exp_duality_scale_contrast.py:163: poisson_contrast = windowed_contrast(poisson_gaps, window_sizes, n_shuffle, rng) tools/exp_duality_scale_contrast.py:165: # GUE: smaller windows only (limited by matrix size) tools/exp_duality_scale_contrast.py:167: print(f"Computing windowed contrast for GUE gaps (sizes: {gue_window_sizes})...") tools/exp_duality_scale_contrast.py:168: gue_contrast = windowed_contrast(gue_gaps, gue_window_sizes, n_shuffle, rng) tools/exp_duality_scale_contrast.py:183: "experiment": "duality_scale_contrast", tools/exp_duality_scale_contrast.py:204: "windowed_contrast": { tools/exp_duality_scale_contrast.py:205: "primes": prime_contrast, tools/exp_duality_scale_contrast.py:206: "gue": gue_contrast, tools/exp_duality_scale_contrast.py:207: "poisson": poisson_contrast, tools/exp_duality_scale_contrast.py:228: # Windowed contrast tools/exp_duality_scale_contrast.py:229: lines.append("\n--- Windowed contrast (z_SR_mean by window size) ---") tools/exp_duality_scale_contrast.py:230: lines.append(f" {'W_size':>8s} {'Primes':>10s} {'GUE':>10s} {'Poisson':>10s}") tools/exp_duality_scale_contrast.py:233: prime_data = {d["window_size"]: d for d in result["windowed_contrast"]["primes"]} tools/exp_duality_scale_contrast.py:234: gue_data = {d["window_size"]: d for d in result["windowed_contrast"]["gue"]} tools/exp_duality_scale_contrast.py:235: poisson_data = {d["window_size"]: d for d in result["windowed_contrast"]["poisson"]} tools/exp_duality_scale_contrast.py:277: parser.add_argument("--out", default="tools/data/duality_scale_contrast.json") tools/exp_crossover_universality.py:8: - beta(ln p) drifts from ~0.39 to ~0.27 (toward Poisson=0) tools/exp_crossover_universality.py:9: - dR_acf1(ln p) drifts from ~-0.50 to ~-0.40 (toward Poisson=0?) tools/exp_crossover_universality.py:10: - gap_acf1(ln p) drifts from ~-0.07 to ~-0.04 (toward Poisson=0) tools/exp_crossover_universality.py:18: - Rescale each to crossover parameter c in [0,1]: c=0 at GUE, c=1 at Poisson tools/exp_crossover_universality.py:21: - Null: 15 Cramer surrogates (should be flat at Poisson) tools/exp_crossover_universality.py:133:# GUE: beta=1, dR_acf1~?, gap_acf1~?, =0.5307 (4/pi - 1 ?) tools/exp_crossover_universality.py:134:# Poisson: beta=0, dR_acf1=0 (uncorrelated), gap_acf1=0, =0.3863 (2ln2-1) tools/exp_crossover_universality.py:135:# We use empirical GUE reference where analytic is unknown tools/exp_crossover_universality.py:136:R_GUE = 0.5307 # 4/(pi+2) Wigner surmise tools/exp_crossover_universality.py:140:print(f" GUE = {R_GUE:.4f}, Poisson = {R_POISSON:.4f}") tools/exp_crossover_universality.py:141:print(f" beta GUE = 1.0, beta Poisson = 0.0") tools/exp_crossover_universality.py:153:# Rescale each to c in [0,1] where 0=GUE-like, 1=Poisson-like tools/exp_crossover_universality.py:154:# For beta: c = 1 - beta (beta=1 is GUE, so c=0) tools/exp_crossover_universality.py:159:# Compute how fast each observable moves toward Poisson per decade of ln(p) tools/exp_crossover_universality.py:160:# All should move toward Poisson (beta->0, dR_acf1->0, gap_acf1->0, r->0.386) tools/exp_crossover_universality.py:166:# Fractional change per unit ln(p) toward Poisson tools/exp_crossover_universality.py:167:# beta: Poisson=0, so rate = -slope/mean(beta) tools/exp_crossover_universality.py:168:# gap_acf1: Poisson=0, rate = -slope/mean(gap_acf1) (gap_acf1 is negative, slope positive -> toward 0) tools/exp_crossover_universality.py:169:# dR_acf1: if Poisson = 0, rate = -slope/mean(dR_acf1) tools/exp_crossover_universality.py:170:# r_mean: Poisson = 0.386, rate = slope / (mean(r) - 0.386) ... toward lower r tools/exp_crossover_universality.py:176:rates['r_mean'] = fits['r_mean']['slope'] / (np.mean(r_arr) - R_POISSON) # negative if drifting toward Poisson tools/exp_crossover_universality.py:179: print(f" {name:10s}: fractional rate toward Poisson = {rate:.6f} per unit ln(p)") tools/exp_crossover_universality.py:275:# === Extrapolation: when does beta reach 0 (Poisson)? === tools/exp_crossover_universality.py:280: print(f" beta -> 0 (Poisson) at ln(p)={ln_p_poisson_beta:.1f}, p~{p_poisson_beta:.2e}") tools/exp_crossover_universality.py:292: print(f" -> {R_POISSON:.4f} (Poisson) at ln(p)={ln_p_poisson_r:.1f}, p~{p_poisson_r:.2e}") tools/data/boundary_two_axis_matrix_20260509_1532.json:3: "question": "Separate support_transfer from beta_coordinate_transfer on the 13 semi-real BOUNDARY rows without using GUE/Poisson labels.", tools/data/boundary_two_axis_matrix_20260509_1532.json:16: "label_policy": "GUE/Poisson source labels are not read by this operator.", tools/data/boundary_two_axis_matrix_20260509_1532.json:45: "triple_var" tools/data/boundary_two_axis_matrix_20260509_1532.json:79: "triple_var" tools/data/boundary_two_axis_matrix_20260509_1532.json:100: "triple_var" tools/data/boundary_two_axis_matrix_20260509_1532.json:127: "triple_var" tools/data/boundary_two_axis_matrix_20260509_1532.json:162: "triple_var" tools/data/boundary_two_axis_matrix_20260509_1532.json:232: "triple_var" tools/data/boundary_two_axis_matrix_20260509_1532.json:252: "triple_var" tools/exp_prime_persistent_blank_gate.py:176: "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from GUE random matrix blocks and logistic return intervals", tools/exp_prime_persistent_blank_gate.py:179: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels", tools/exp_beta_crossover.py:192: # Sweep beta from near-Poisson (0.01) to beyond-GUE (10) tools/exp_beta_crossover.py:194: np.linspace(0.05, 0.5, n_betas // 3), # fine resolution near Poisson tools/exp_beta_crossover.py:195: np.linspace(0.6, 2.0, n_betas // 3), # through GOE-GUE tools/exp_beta_crossover.py:196: np.linspace(2.5, 8.0, n_betas - 2*(n_betas//3)), # beyond GUE tools/exp_beta_crossover.py:313: note = " <-- Poisson" tools/exp_beta_crossover.py:315: note = " <-- GOE" tools/exp_beta_crossover.py:317: note = " <-- GUE" tools/data/notte_20260329_0330.md:4: Direzione: Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo in tools/data/notte_20260329_0330.md:5: [confine_inesplorato] BOUNDARY: 8 domini GUE, 5 Poisson — il confine è il terzo incluso oper tools/data/notte_20260329_0330.md:11: logistica_biforcazione_cp_3.57: r=1.0, spacing=Poisson-like [conferma] tools/data/notte_20260329_0330.md:13: logistica_biforcazione_cp_3.665: r=1.0, spacing=Poisson-like [conferma] tools/data/notte_20260329_0330.md:14: numeri_primi_cp_100: r=0.762330388277594, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:15: numeri_primi_cp_50000: r=0.762330388277594, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:16: logistica_biforcazione_cp_3.757: r=1.0, spacing=Poisson-like [conferma] tools/data/notte_20260329_0330.md:18: collatz_cp: r=0.9762845849802372, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:20: ising_2d_cp_-0.3659164576291013: r=0.8922932783549861, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:21: ising_2d_cp_0.46188882642750695: r=0.98945843573869, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:22: brownian_motion_cp_0.12230384159779378: r=0.968503937007874, spacing=Poisson-like [conferma] tools/data/notte_20260329_0330.md:23: brownian_motion_cp_0.8867788787335775: r=?, spacing=Poisson-like [VINCOLO] tools/data/notte_20260329_0330.md:24: percolation_cp_0.37804273882301154: r=0.6666666666666667, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:25: percolation_cp_0.8589741112320333: r=1.2298136645962734, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:26: cellular_automata_cp_182: r=0.8566654288897141, spacing=GUE-like [conferma] tools/data/notte_20260329_0330.md:37: Campo dopo Fase 0: 8 GUE / 5 Poisson tools/data/notte_20260329_0330.md:41: logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like (⟨r⟩=0.3914428894887236) tools/data/notte_20260329_0330.md:42: coupled_oscillators_var_50: r=1.0, spacing=Poisson-like (⟨r⟩=0.45756422661060286) tools/data/notte_20260329_0330.md:43: brownian_motion_var_0.3: r=1.3557126030624262, spacing=Poisson-like (⟨r⟩=0.38597886256510316) tools/data/notte_20260329_0330.md:44: logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like (⟨r⟩=0.35930230610663094) tools/data/notte_20260329_0330.md:93: GUE: 8 | Poisson: 9 | Vincoli: 2 | Anomalie: 21 tools/data/notte_20260329_0330.md:95: Domini GUE: numeri_primi_cp_100, numeri_primi_cp_50000, collatz_cp, ising_2d_cp_-0.3659164576291013, ising_2d_cp_0.46188882642750695, percolation_cp_0.37804273882301154, percolation_cp_0.8589741112320333, cellular_automata_cp_182 tools/data/notte_20260329_0330.md:96: Domini Poisson: logistica_biforcazione_cp_3.57, logistica_biforcazione_cp_3.665, logistica_biforcazione_cp_3.757, brownian_motion_cp_0.12230384159779378, brownian_motion_cp_0.8867788787335775, logistica_biforcazione_var_3.9, coupled_oscillators_var_50, brownian_motion_var_0.3, logistica_biforcazione_var_3.57 tools/dnd_stats.py:10: classify(mean_r) → "GUE" | "Poisson" | "CRITICAL" | "harmonic" tools/dnd_stats.py:25: "GUE": {"mean_r": 0.5996, "var_s": 0.178}, tools/dnd_stats.py:26: "GOE": {"mean_r": 0.5359, "var_s": 0.273}, tools/dnd_stats.py:27: "Poisson": {"mean_r": 0.3863, "var_s": 1.000}, tools/dnd_stats.py:164: Transizione: V<1 → GUE, V≈1 → critico, V>1.5 → Poisson tools/data/prime_persistent_blank_gate_20260511_0330.json:10: "triple_var", tools/data/prime_persistent_blank_gate_20260511_0330.json:44: "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from GUE random matrix blocks and logistic return intervals", tools/data/prime_persistent_blank_gate_20260511_0330.json:47: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels" tools/data/prime_persistent_blank_gate_20260511_0330.json:64: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:85: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:115: "triple_var": -1.4138269733801452 tools/data/prime_persistent_blank_gate_20260511_0330.json:122: "triple_var": 0.4238917200648973 tools/data/prime_persistent_blank_gate_20260511_0330.json:145: "triple_var": -3.7188271673611517 tools/data/prime_persistent_blank_gate_20260511_0330.json:152: "triple_var": -0.21592030792958627 tools/data/prime_persistent_blank_gate_20260511_0330.json:162: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:177: "triple_var": -4.4899072654156615 tools/data/prime_persistent_blank_gate_20260511_0330.json:184: "triple_var": -0.23200495047066738 tools/data/prime_persistent_blank_gate_20260511_0330.json:194: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:207: "triple_var": -3.8717480380307547 tools/data/prime_persistent_blank_gate_20260511_0330.json:214: "triple_var": 0.10527217693419547 tools/data/prime_persistent_blank_gate_20260511_0330.json:235: "triple_var": -1.7177795270173504 tools/data/prime_persistent_blank_gate_20260511_0330.json:242: "triple_var": -0.1439867992632698 tools/data/prime_persistent_blank_gate_20260511_0330.json:252: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:267: "triple_var": -3.724277830321623 tools/data/prime_persistent_blank_gate_20260511_0330.json:274: "triple_var": 0.3217369910562268 tools/data/prime_persistent_blank_gate_20260511_0330.json:284: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:297: "triple_var": -4.765394186412701 tools/data/prime_persistent_blank_gate_20260511_0330.json:304: "triple_var": -0.7584913932924586 tools/data/prime_persistent_blank_gate_20260511_0330.json:314: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:329: "triple_var": -3.7632241398680972 tools/data/prime_persistent_blank_gate_20260511_0330.json:336: "triple_var": 0.05885479491332324 tools/data/prime_persistent_blank_gate_20260511_0330.json:347: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:362: "triple_var": 3.154074831784194 tools/data/prime_persistent_blank_gate_20260511_0330.json:369: "triple_var": 0.1483334622779097 tools/data/prime_persistent_blank_gate_20260511_0330.json:380: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:395: "triple_var": 3.9262728445928365 tools/data/prime_persistent_blank_gate_20260511_0330.json:402: "triple_var": 0.11703116498546264 tools/data/prime_persistent_blank_gate_20260511_0330.json:411: "triple_var" tools/data/prime_persistent_blank_gate_20260511_0330.json:424: "triple_var": 4.051481940090206 tools/data/prime_persistent_blank_gate_20260511_0330.json:431: "triple_var": -0.032182046425478145 tools/data/prime_persistent_blank_gate_20260511_0330.json:455: "triple_var": 0.9824631579969987 tools/data/prime_persistent_blank_gate_20260511_0330.json:462: "triple_var": 0.8712731879915248 tools/data/prime_persistent_blank_gate_20260511_0330.json:481: "triple_var": -0.2770606342306753 tools/data/prime_persistent_blank_gate_20260511_0330.json:488: "triple_var": 0.2599568182437131 tools/data/prime_persistent_blank_gate_20260511_0330.json:507: "triple_var": 0.7394758627439706 tools/data/prime_persistent_blank_gate_20260511_0330.json:514: "triple_var": 0.2181943400568309 tools/data/prime_persistent_blank_gate_20260511_0330.json:533: "triple_var": -0.0831639794902595 tools/data/prime_persistent_blank_gate_20260511_0330.json:540: "triple_var": 0.29099706291583777 tools/data/prime_persistent_blank_gate_20260511_0330.json:559: "triple_var": -0.0302730217194156 tools/data/prime_persistent_blank_gate_20260511_0330.json:566: "triple_var": -0.11651609946219596 tools/build_lab_graph.py:177: 'text': f"Metrica primi g=(p/2)², curvatura GUE r={mp.get('risultato_r',{}).get('curvatura_r','?')}, z={mp.get('test_null',{}).get('z_score','?')}", tools/exp_alpha_stability.py:5:or drifts during the Poisson crossover. tools/dnd_arxiv.py:45: 'quasicrystal spectral statistics GUE', tools/data/markov_memory_by_gue_type.json:4: "question": "Does Markov memory discriminate distribution-GUE from ordering-GUE?", tools/data/markov_memory_by_gue_type.json:9: "gue_type": "distribution-GUE", tools/data/markov_memory_by_gue_type.json:29: "gue_type": "distribution-GUE", tools/data/markov_memory_by_gue_type.json:49: "gue_type": "ordering-GUE", tools/data/markov_memory_by_gue_type.json:69: "gue_type": "ordering-GUE", tools/data/markov_memory_by_gue_type.json:89: "gue_type": "ordering-GUE", tools/data/markov_memory_by_gue_type.json:109: "gue_type": "Poisson", tools/data/markov_memory_by_gue_type.json:129: "gue_type": "Poisson", tools/data/markov_memory_by_gue_type.json:149: "gue_type": "Poisson", tools/exp_psd_amplitude_scaling.py:146: # Poisson crossover prediction from dip_ratio → 1.0 (no suppression) tools/exp_psd_amplitude_scaling.py:150: print(f"\nPoisson crossover (dip→1.0): ln(p*)={ln_p_cross_dip:.1f} " tools/exp_psd_amplitude_scaling.py:154: print(f"\nDip ratio DECREASING with scale — no Poisson crossover from PSD dip") tools/exp_psd_amplitude_scaling.py:156: # Poisson crossover from spectral_slope → 0 tools/exp_psd_amplitude_scaling.py:159: print(f"Poisson crossover (slope→0): ln(p*)={ln_p_cross_slope:.1f} " tools/r_stat_primes.py:27: "verdict": "GUE-like" if r_real > (r_poisson + r_gue) / 2 else "Poisson-like" tools/exp_two_channel_boundary.py:25: GUE-like and Poisson-like regimes is channel-specific. tools/exp_markov_scale_function.py:6:If so, does the decay track the GUE→Poisson boundary? tools/exp_markov_scale_function.py:16: 5. Brody parameter β (GUE/Poisson indicator) tools/exp_markov_scale_function.py:62: β=0 → Poisson, β=1 → GUE (Wigner). tools/dnd_paper_audit.py:722: "GUE": "det=-1 → always hyperbolic → GUE statistics", tools/confine_spessore.py:10:Blocco 3: Dove il residuo 1/phi^(2n) incontra il confine GUE/Poisson nei domini tools/confine_spessore.py:188:# BLOCCO 3: Residuo vs confine GUE/Poisson tools/confine_spessore.py:193: print("BLOCCO 3: Residuo 1/phi^(2n) vs confine GUE/Poisson") tools/confine_spessore.py:197: # Il confine GUE/Poisson emerge nella distribuzione NNS (nearest neighbor spacing) tools/confine_spessore.py:199: # GUE: repulsione (P(s) ~ s^2 * exp(-4s^2/pi)) tools/confine_spessore.py:200: # Poisson: nessuna repulsione (P(s) = exp(-s)) tools/confine_spessore.py:216: print("\n--- NNS ratio (indicatore GUE vs Poisson) a diverse scale ---") tools/confine_spessore.py:218: print(" GUE: r ~ 0.5307") tools/confine_spessore.py:219: print(" Poisson: r ~ 0.3863") tools/confine_spessore.py:248: regime = "GUE" if dist_gue < dist_poi else "Poisson" tools/confine_spessore.py:251: f"dist_GUE={dist_gue:.4f} dist_Poi={dist_poi:.4f} " tools/confine_spessore.py:278: print(f" Punto medio GUE/Poisson: {midpoint:.4f}") tools/confine_spessore.py:294: regime = "GUE" if abs(r - 0.5307) < abs(r - 0.3863) else "Poi" tools/confine_spessore.py:319: print("GUE (repulsione)") tools/confine_spessore.py:321: print("Poisson (indipendenza)") tools/confine_spessore.py:354:3. Il residuo incontra il confine GUE/Poisson? tools/exp_markov3_observable_hunt.py:128:def obs_triple_variance(gaps): tools/exp_markov3_observable_hunt.py:208: 'triple_var': obs_triple_variance, tools/exp_markov_k_direction.py:148: print("Does higher-order memory point GUE-ward in the (SR, L1) plane?") tools/exp_markov_k_direction.py:169: GUE_THETA = -97.0 tools/exp_markov_k_direction.py:240: 'GUE_ref_theta': GUE_THETA, tools/exp_duality_gate_transfer.py:10: away from GUE/Poisson? tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:10: "triple_var", tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:43: "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:66: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:75: "triple_var": -3.459992402819375 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:96: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:105: "triple_var": -15.368905480854067 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:124: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:133: "triple_var": -16.61438592908702 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:153: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:165: "triple_var": 13.673229165173678 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:175: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:180: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:192: "triple_var": 10.24692328179673 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:202: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:209: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:221: "triple_var": 12.718858846049706 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:231: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:238: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:250: "triple_var": 11.40604069483635 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:274: "triple_var": 13.901370134951454 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:284: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:289: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:301: "triple_var": 10.852435630033488 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:311: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:318: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:330: "triple_var": 11.583771558229007 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:346: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:358: "triple_var": 10.888675313207123 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:375: "z_delta_triple_var_mean": 11.908913078034693 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:386: "z_delta_triple_var_mean": 0.00732243104710764 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:405: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:417: "triple_var": 21.395862453257763 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:427: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:430: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:442: "triple_var": 6.625607957084316 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:452: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:458: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:470: "triple_var": 13.18916446810223 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:480: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:487: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:499: "triple_var": 9.939651526961432 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:514: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:526: "triple_var": 20.957483278215072 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:536: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:542: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:554: "triple_var": 8.819786232500737 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:564: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:581: "triple_var": 15.377912436817782 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:606: "triple_var": 8.929679857201847 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:623: "z_delta_triple_var_mean": 13.154393526267647 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:634: "z_delta_triple_var_mean": 0.010251403465950696 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:666: "triple_var": -1.6330597401407838 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:673: "triple_var": -0.36109903833379514 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:702: "triple_var": -15.534429875092238 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:709: "triple_var": 1.2268178047985054 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:725: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:742: "triple_var": -22.590543018355856 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:749: "triple_var": 0.07964552875165404 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:776: "triple_var": -3.6923276562157854 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:783: "triple_var": 0.21040872607385408 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:799: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:815: "triple_var": -14.581002969422908 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:822: "triple_var": -0.7981854107241284 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:851: "triple_var": -12.622007513417634 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:858: "triple_var": -0.0311765502857414 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:872: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:886: "triple_var": -4.928760013133539 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:893: "triple_var": 0.513866470402168 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:907: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:921: "triple_var": -15.781195643167027 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:928: "triple_var": -0.17080457800819845 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:943: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:957: "triple_var": -13.748546245634277 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:964: "triple_var": 0.08348262984759289 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:978: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:992: "triple_var": -3.9683314729757537 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:999: "triple_var": -0.22544248948219614 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1015: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1031: "triple_var": -15.55210303120476 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1038: "triple_var": 0.3929475375949518 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1068: "triple_var": -19.346243909793536 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1075: "triple_var": -0.5549908161880891 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1101: "triple_var": -1.4703477882969145 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1108: "triple_var": 0.3082116038934405 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1124: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1140: "triple_var": -15.143576953470593 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1147: "triple_var": -0.038592328978082624 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1163: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1179: "triple_var": -22.86621024155468 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1186: "triple_var": -0.08817381727179993 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1200: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1214: "triple_var": -3.6823587253566648 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1221: "triple_var": -0.3169874091473199 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1237: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1253: "triple_var": -15.088399420193014 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1260: "triple_var": -0.4893837006301193 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1276: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1292: "triple_var": -13.622010252318097 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1299: "triple_var": 0.010601678718840449 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1313: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1327: "triple_var": -4.388887879159077 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1334: "triple_var": -0.4069526153393015 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1348: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1364: "triple_var": -14.635811160955807 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1371: "triple_var": -0.5785150410147399 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1383: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1397: "triple_var": -11.014495836243393 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1404: "triple_var": -0.07074733287312529 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1418: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1432: "triple_var": -3.9158659472764827 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1439: "triple_var": -0.21486107598285706 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1455: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1471: "triple_var": -16.63472479332619 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1478: "triple_var": 0.0480434695461279 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1493: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1509: "triple_var": -17.105030415378714 tools/data/prime_vs_mod6_sr_boundary_20260513_0330.json:1516: "triple_var": 0.6596892830780373 tools/data/seme_backup_b2_20260508_214525.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260508_214525.json:3: "new_direzione": "Riprogettare il gate `V_c` con null omogenei al boundary operator: phase-shuffle Sturmian, label-preserving surrogate e controllo gap_ratio prima di estendere a nuovi domini GUE/Poisson", tools/data/selective_layer_decoupling.json:107: "triple_var": [ tools/data/selective_layer_decoupling.json:131: "triple_var": 0.193 tools/data/selective_layer_decoupling.json:139: "triple_var": 162.180324 tools/data/selective_layer_decoupling.json:246: "triple_var": [ tools/data/selective_layer_decoupling.json:270: "triple_var": 0.083 tools/data/selective_layer_decoupling.json:278: "triple_var": 162.180324 tools/data/selective_layer_decoupling.json:385: "triple_var": [ tools/data/selective_layer_decoupling.json:409: "triple_var": 0.087 tools/data/selective_layer_decoupling.json:417: "triple_var": 162.180324 tools/data/selective_layer_decoupling.json:421: "GUE": { tools/data/selective_layer_decoupling.json:526: "triple_var": [ tools/data/selective_layer_decoupling.json:550: "triple_var": null tools/data/selective_layer_decoupling.json:558: "triple_var": 1.221475 tools/data/selective_layer_decoupling.json:665: "triple_var": [ tools/data/selective_layer_decoupling.json:689: "triple_var": null tools/data/selective_layer_decoupling.json:697: "triple_var": 1.221475 tools/data/selective_layer_decoupling.json:804: "triple_var": [ tools/data/selective_layer_decoupling.json:828: "triple_var": 0.35 tools/data/selective_layer_decoupling.json:836: "triple_var": 1.221475 tools/data/selective_layer_decoupling.json:840: "Poisson": { tools/data/selective_layer_decoupling.json:945: "triple_var": [ tools/data/selective_layer_decoupling.json:969: "triple_var": null tools/data/selective_layer_decoupling.json:977: "triple_var": 3.046796 tools/data/selective_layer_decoupling.json:1084: "triple_var": [ tools/data/selective_layer_decoupling.json:1108: "triple_var": null tools/data/selective_layer_decoupling.json:1116: "triple_var": 3.046796 tools/data/selective_layer_decoupling.json:1223: "triple_var": [ tools/data/selective_layer_decoupling.json:1247: "triple_var": null tools/data/selective_layer_decoupling.json:1255: "triple_var": 3.046796 tools/gue_gap_test.py:33:# GUE r ~ 0.5996, Poisson r ~ 0.3863 tools/gue_gap_test.py:37: interp = f"r={r_data:.4f} > 0.5 e z={z_score:.2f} vs shuffle: correlazione GUE-like nei gap dei primi" tools/gue_gap_test.py:41: interp = f"r={r_data:.4f} <= 0.5: gap più Poisson-like che GUE-like" tools/exp_poisson_convergence.py:3:exp_poisson_convergence.py — Do beta, _excess, and acf1 predict the same Poisson scale? tools/exp_poisson_convergence.py:5:Three independent observables drift toward Poisson at large prime scale: tools/exp_poisson_convergence.py:7: - excess over Cramer -> 0 (i.e. -> _Poisson ~ 0.386) tools/exp_poisson_convergence.py:10:Question: do all three extrapolate to Poisson at the SAME critical scale p*? tools/exp_poisson_convergence.py:193: """Fit linear trends in ln(p) and extrapolate to Poisson.""" tools/exp_poisson_convergence.py:196: # Poisson targets tools/exp_poisson_convergence.py:263: print("CONVERGENCE SUMMARY: Three roads to Poisson") tools/exp_poisson_convergence.py:271: print(f" Target (Poisson): {f['target']:.4f}") tools/exp_poisson_convergence.py:273: print(f" Extrapolated Poisson at: ln(p*) = {f['ln_p_star']:.1f} => p* ~ 10^{f['ln_p_star']/np.log(10):.1f}") tools/exp_poisson_convergence.py:275: print(f" No convergence toward Poisson (slope wrong sign or flat)") tools/exp_poisson_convergence.py:295: print(f" => CONSISTENT: all observables predict Poisson at similar scale") tools/exp_poisson_convergence.py:305: parser = argparse.ArgumentParser(description='Poisson convergence: do beta, , acf1 agree?') tools/exp_poisson_convergence.py:320: 'question': 'Do beta, , and acf1 predict the same Poisson scale?', tools/exp_brody_flow.py:5:Measures how the Brody beta (interpolating Poisson beta=0 to GUE beta=1) tools/spectral_gap_analysis.py:201: # For GUE (expected for zeta zeros): P(s) ≈ (32/π²)s² exp(-4s²/π) tools/spectral_gap_analysis.py:219: """GUE Wigner surmise: P(s) = (32/π²)s² exp(-4s²/π).""" tools/spectral_gap_analysis.py:259: label='GUE Wigner') tools/spectral_gap_analysis.py:269: fig.suptitle('Nearest-Neighbor Spacing: Spectral Gaps vs Zeta Gaps vs GUE', tools/exp_boundary_short_denominator_extension.py:203: "not_tested": "global 13-row boundary redesign, V_c fit, source GUE/Poisson label validity", tools/dnd_compatibility.py:134: "=0.507, tra GUE (0.5996) e Poisson (0.3863).", tools/dnd_compatibility.py:147:# --- Random Matrix Theory (GUE) --- tools/dnd_compatibility.py:150: "GUE (Gaussian Unitary Ensemble): matrici hermitiane casuali, " tools/dnd_compatibility.py:167: "evidence": "Le matrici GUE hanno det qualsiasi. " tools/dnd_compatibility.py:173: "evidence": "phi non emerge naturalmente dal GUE. " tools/dnd_compatibility.py:180: "evidence": "GUE vive in C (numeri complessi generici), non in Q(sqrt(5)).", tools/dnd_compatibility.py:185: "evidence": "GUE e' una classe definita, non una fase critica. " tools/exp_two_channel_decomposition.py:18: - Extrapolated Poisson crossover tools/exp_two_channel_decomposition.py:344: # Poisson crossover: acf1 = 0 tools/exp_two_channel_decomposition.py:353: f"R2={r2:.3f} Poisson at ln(p)={ln_p_cross:.1f} (p~10^{ln_p_cross/np.log(10):.1f})") tools/dnd_gue_test.py:3:Piano 11b — GUE Test Protocol (Revised) tools/dnd_gue_test.py:6:zeros follow GUE statistics (Katz-Sarnak). This is the algebraic bridge, tools/dnd_gue_test.py:14: Prediction: zeros of L(s, χ_disc) → GUE (Katz-Sarnak) tools/dnd_gue_test.py:17:T1: L(s, χ₅) zeros → GUE (the D-ND matrix M, tr=1) tools/dnd_gue_test.py:18:T2: L(s, χ₈) zeros → GUE (tr=2, disc=8, Q(√2)) tools/dnd_gue_test.py:19:T3: L(s, χ₁₃) zeros → GUE (tr=3, disc=13, Q(√13)) tools/dnd_gue_test.py:20:T4: ζ(s) zeros → GUE (reference: Montgomery-Odlyzko) tools/dnd_gue_test.py:40: """GUE (β=2) Wigner surmise CDF.""" tools/dnd_gue_test.py:44: """GOE (β=1) Wigner surmise CDF.""" tools/dnd_gue_test.py:48: """Poisson CDF.""" tools/dnd_gue_test.py:51:GUE_VAR_REF = 1 - 4/np.pi**2 # ≈ 0.5947 (Wigner surmise β=2) tools/dnd_gue_test.py:52:GOE_VAR_REF = 1 - np.pi/4 + np.pi**2/16 - 1 # compute properly tools/dnd_gue_test.py:53:# Better: Var(s) for GUE Wigner surmise = (3π-8)/(2π) ≈ 0.178 tools/dnd_gue_test.py:54:# Var(s) for GOE = (4-π)/2 · (4/π²) ... let me just use known values tools/dnd_gue_test.py:55:GUE_VAR = 0.178 # Wigner surmise β=2 tools/dnd_gue_test.py:56:GOE_VAR = 0.286 # Wigner surmise β=1 tools/dnd_gue_test.py:362: """Test spacings against GUE, GOE, Poisson.""" tools/dnd_gue_test.py:374: fits = [("GUE", ks_gue, p_gue), ("GOE", ks_goe, p_goe), ("Poisson", ks_poi, p_poi)] tools/dnd_gue_test.py:395: print("Piano 11b — GUE Test Protocol (Revised)") tools/dnd_gue_test.py:414: # ─── T1: ζ(s) zeros → GUE (reference, Montgomery-Odlyzko) ──────── tools/dnd_gue_test.py:555: marker = "✓" if fit == "GUE" else "✗" tools/dnd_gue_test.py:556: if fit == "GUE": tools/dnd_gue_test.py:568: verdict = (f"GUE UNIVERSALE: {gue_count}/{total} L-functions mostrano GUE. " tools/dnd_gue_test.py:569: f"La catena det=-1 → campo numerico → L-function → GUE è confermata.") tools/dnd_gue_test.py:572: verdict = (f"GUE DOMINANTE: {gue_count}/{total}. Risultato parziale, " tools/dnd_gue_test.py:578: verdict = f"NON-GUE: classe dominante {dominant}. Revisione necessaria." tools/dnd_gue_test.py:587: print(" Near s=0: p(s) ~ s^β. β=0:Poisson, β=1:GOE, β=2:GUE") tools/dnd_gue_test.py:622: cls = "GUE" if beta > 1.5 else ("GOE" if beta > 0.5 else "Poisson") tools/dnd_gue_test.py:630: # Count GUE in beta analysis tools/dnd_gue_test.py:631: beta_gue = sum(1 for lb in beta_results.values() for v in lb.values() if v.get("class") == "GUE") tools/dnd_gue_test.py:633: print(f" DIPOLO FRATTALE: {beta_gue}/{beta_total} GUE a livello di repulsione") tools/dnd_gue_test.py:634: print(f" Il KS globale dice GOE. Il β allo zero dice GUE.") tools/dnd_gue_test.py:703: family_verdict = f"MISTO: neg1 GUE={all_neg1_gue}, pos1 GUE={all_pos1_gue}" tools/dnd_gue_test.py:716: print(f" Piano 11: det=-1 → fluctuations (GUE via Katz-Sarnak)") tools/dnd_gue_test.py:728: verdict_fractal = (f"GUE CONFERMATO via dipolo frattale: β>2 per {beta_gue}/{beta_total} misure. " tools/dnd_gue_test.py:729: f"KS globale={gue_count}/{total} GUE (forma bulk). " tools/dnd_gue_test.py:730: f"β allo zero={beta_gue}/{beta_total} GUE (repulsione). " tools/dnd_gue_test.py:734: verdict_fractal = (f"Dipolo frattale: {beta_gue}/{beta_total} GUE. " tools/dnd_gue_test.py:805: "fluctuations": "det=-1 → Q(√5) → L(s,χ₅) → GUE (Piano 11)", tools/dnd_gue_test.py:806: "family": "ALL det=-1 matrices → number fields → GUE (Piano 11b)", tools/evolution_report.md:2:Il passo ha proseguito il tester L8 senza cambiare ramo: dal perimetro GUE/Poisson-Anderson e' passato a un denominatore piu' esterno, introducendo GOE/GUE, due taglie `N` e Anderson `W=6` come bordo finite-size. tools/evolution_report.md:4:La traiettoria utile non e' stata espansiva ma disciplinante: lo strumento `component_state(SR,L1,triple_var)` e' stato fatto respirare dentro classi fisiche distinte, mentre il claim e' rimasto sotto vincolo operativo. Il passo ha conservato il limite "non nuova legge fisica" come parte del movimento. tools/evolution_report.md:17:`component_state(SR,L1,triple_var)` puo' diventare gate di trasferibilita' del bordo: non decide una fisica nuova, decide se lo stesso vettore resta leggibile quando cambiano simmetria, taglia e null. tools/evolution_report.md:19:Anderson 1D `W=6` resta un punto-zero utile per cicli brevi: assorbimento di `SR` da un lato, residuo `triple_var` dall'altro. La prossima variazione puo' muovere un solo asse, per esempio `W` oppure `N`, senza aprire Anderson 3D o many-body localization. tools/exp_prime_vs_mod6_sr_boundary.py:35:FOCUS_OBS = ["SR", "L1", "triple_var"] tools/exp_prime_vs_mod6_sr_boundary.py:342: "claim": "prime_minus_mod6_z_vector(SR,L1,triple_var) survives row-local 6k +/- 1 subtraction; binary has_SR is audit-only and cannot decide the verdict", tools/exp_prime_vs_mod6_sr_boundary.py:343: "observable": "paired z-deltas for SR,L1,triple_var against mod6_index_aligned and mod6_span_matched, plus audit-only SR membership and focus signature in coherent_one_sided_observables", tools/exp_acf_amplitude_scaling.py:8:at what p* does the anti-correlation vanish (Poisson)? tools/exp_acf_amplitude_scaling.py:127: print(f"\nPoisson crossover (A=0): ln(p*) = {ln_p_star:.1f}, p* ~ 10^{log10_p_star:.1f}") tools/exp_acf_amplitude_scaling.py:129: print(f"\nSlope is non-negative ({slope:.6f}) — no Poisson crossover predicted") tools/exp_markov_dipolar_decomposition.py:6:GUE has dL1/dSR = 8.37 at angle -97 deg. tools/observables_registry.py:13:GUE") e nel farlo ha trovato **collision di nomi observable** tra script: tools/observables_registry.py:20:- `triple_var` in 3 script = `np.var(triple_sums)` (raw) — convention dominante tools/observables_registry.py:21:- `triple_var` in `exp_perturbation_dimensionality_audit.py` = tools/observables_registry.py:30:script chiama già `SR`/`triple_var`/etc. Le varianti restano disponibili ma tools/observables_registry.py:31:con nomi ESPLICITI (`SR_local_rigidity`, `triple_var_normalized`) per evitare tools/observables_registry.py:43:from observables_registry import SR, triple_var, L1, L2, SR2 tools/observables_registry.py:46:from observables_registry import SR_local_rigidity, triple_var_normalized tools/observables_registry.py:55:observables_used: [SR, SR2, L1, L2, triple_var] tools/observables_registry.py:85: Range: (0, 1]. GUE → ~0.60. Poisson → ~0.39. Picket-fence → 1. tools/observables_registry.py:137:def triple_var(gaps: np.ndarray) -> float: tools/observables_registry.py:138: """**triple_var — Variance of consecutive gap triples** (canonical). tools/observables_registry.py:143: `triple_var_normalized`. tools/observables_registry.py:157: "triple_var": triple_var, tools/observables_registry.py:196:def triple_var_normalized(gaps: np.ndarray) -> float: tools/observables_registry.py:197: """**triple_var_normalized — Variance of triples / variance of gaps** (variant). tools/observables_registry.py:200: locally named `triple_var` — registered here as `triple_var_normalized` tools/observables_registry.py:204: single-gap variance, NOT as alias for triple_var. tools/observables_registry.py:218: "triple_var_normalized": triple_var_normalized, tools/observables_registry.py:249: "triple_var", tools/observables_registry.py:251: "triple_var_normalized", tools/observables_registry.py:263: print(f"\nGUE-like 200 gaps:") tools/observables_registry.py:268: print(f" triple_var_normalized = {triple_var_normalized(gue_like):.6f}") tools/exp_two_channel_cross_domain.py:9: 2. Statistical (magnitude): decays slowly toward Poisson tools/exp_two_channel_cross_domain.py:17: - GUE eigenvalues: random matrix, strong statistical correlation, no arithmetic tools/exp_two_channel_cross_domain.py:27:If GUE shows only decaying channels → C1 supported (algebraic invariance is prime-specific) tools/exp_two_channel_cross_domain.py:28:If GUE shows a scale-invariant channel → C1 needs refinement tools/exp_two_channel_cross_domain.py:63: """Generate unfolded spacings from GUE random matrices.""" tools/exp_two_channel_cross_domain.py:66: # GUE: H = (A + A^*) / (2 * sqrt(2N)), A is complex Gaussian tools/exp_two_channel_cross_domain.py:295: # === GUE EIGENVALUES === tools/exp_two_channel_cross_domain.py:296: print(f"\n=== GUE EIGENVALUES ({gue_matrices} matrices of size {gue_size}) ===") tools/exp_two_channel_cross_domain.py:298: print(f"Got {len(gue_spacings)} GUE spacings, mean={np.mean(gue_spacings):.3f}") tools/exp_two_channel_cross_domain.py:412: print(" GUE/Cramer should show decaying or absent binary channel") tools/exp_boundary_mixture_gate.py:5:Reusable META/BOUNDARY audit for the GUE-Poisson boundary. tools/exp_boundary_mixture_gate.py:8: Does the GUE/Poisson boundary remain a clean two-class split after the tools/exp_boundary_mixture_gate.py:13:It builds synthetic mixtures by replacing a fraction beta of unfolded GUE tools/exp_boundary_mixture_gate.py:14:spacings with Poisson spacings, then measures: tools/exp_boundary_mixture_gate.py:19:- ambiguity of each beta layer relative to pure GUE and pure Poisson centroids. tools/exp_boundary_mixture_gate.py:41: """Generate unfolded GUE spacings by concatenating independent matrices.""" tools/exp_boundary_mixture_gate.py:59: """Return a beta Poisson / (1-beta) GUE spacing sequence with mean spacing 1.""" tools/exp_boundary_mixture_gate.py:224: "question": "Is the GUE-Poisson mixed layer cleanly classifiable after denominator gating?", tools/dnd_trace_bridge.py:136: "label_GUE": "=0.5996, Var=0.178", tools/dnd_trace_bridge.py:147: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_trace_bridge.py:347: print("\nDomanda: la mappa D-ND trasforma la struttura GUE degli zeri?") tools/dnd_trace_bridge.py:348: print("Se K(zeta) ≈ GUE e K(random) ≈ Poisson → la mappa PRESERVA la struttura") tools/dnd_trace_bridge.py:350: print("Se K(zeta) ≠ GUE e ≠ K(random) → la mappa TRASFORMA la struttura") tools/dnd_trace_bridge.py:357: print(f" {key:40s} ={val['mean_r']:.4f} {'GUE' if d_gue < d_poi else 'Poi'} {marker}") tools/rules/axioms_to_theorems.json:7: "chain": "|NT⟩ → closure → det=-1 → φ → GUE → differentiated reality" tools/test_gue_poisson_boundary.py:15:R_GUE_TH = 0.5996 tools/test_gue_poisson_boundary.py:36: "r_GUE": round(mu_g, 4), "r_GUE_th": R_GUE_TH, tools/test_gue_poisson_boundary.py:37: "r_Poisson": round(mu_p, 4), "r_Poisson_th": R_POI_TH, tools/test_gue_poisson_boundary.py:41: "separation_GUE": round(clean_g, 4), tools/test_gue_poisson_boundary.py:43: "claim": "boundary ~0.49 is third-included: neither GUE(0.60) nor Poisson(0.39)" tools/dnd_autoricerca.py:221: # GUE spacing distribution (approssimazione Wigner) tools/dnd_autoricerca.py:228: 'nota': 'Approssimazione GUE (mpmath non disponibile)', tools/dnd_autoricerca.py:270: """Autovalori di matrici casuali GUE — la connessione con Riemann.""" tools/dnd_autoricerca.py:272: # GUE: matrice Hermitiana casuale tools/dnd_autoricerca.py:284: 'ensemble': 'GUE', tools/dnd_autoricerca.py:286: 'nota': 'Spaziatura autovalori matrice GUE 200x200' tools/dnd_autoricerca.py:478: # - is_spacings=True: esponenziale (Poisson = livelli non correlati) tools/dnd_autoricerca.py:482: # Null per spacings: esponenziale normalizzata (Poisson) tools/dnd_autoricerca.py:523: null_spacings.append('GUE-like' if abs(mean_r - 0.5996) < abs(mean_r - 0.3863) else 'Poisson-like') tools/dnd_autoricerca.py:654: 'tipo': 'GUE-like' if abs(mean_r - 0.5996) < abs(mean_r - 0.3863) else 'Poisson-like', tools/dnd_autoricerca.py:827: # Pattern 2: spacing GUE-like tools/dnd_autoricerca.py:829: if spacing.get('tipo') == 'GUE-like' and spacing.get('gue_dist', 1) < 0.1: tools/dnd_autoricerca.py:834: 'nota': f'Spacing GUE-like (⟨r⟩={spacing["mean_r"]:.4f})' tools/dnd_autoricerca.py:1152: gue_domains = [e['dominio'] for e in journal if e.get('spacing') == 'GUE-like'] tools/dnd_autoricerca.py:1153: poisson_domains = [e['dominio'] for e in journal if e.get('spacing') == 'Poisson-like'] tools/dnd_autoricerca.py:1154: print(f" GUE-like: {gue_domains}") tools/dnd_autoricerca.py:1155: print(f" Poisson-like: {poisson_domains}") tools/dnd_autoricerca.py:1157: # Il RAPPORTO #GUE/#Poisson è vicino a φ? tools/dnd_autoricerca.py:1160: print(f" #GUE/#Poisson = {ratio_cluster:.4f} " tools/dnd_autoricerca.py:1329: # 2. Differenza spacing tra coppie GUE-Poisson tools/dnd_autoricerca.py:1330: print(f"\n 2. Coppie GUE-Poisson:") tools/dnd_autoricerca.py:1331: gue = [e for e in reali if e.get('spacing') == 'GUE-like' and e.get('spacing_r')] tools/dnd_autoricerca.py:1332: poi = [e for e in reali if e.get('spacing') == 'Poisson-like' and e.get('spacing_r')] tools/dnd_autoricerca.py:1343: # 3. I r_diretto dei domini GUE vs Poisson come due insiemi tools/dnd_autoricerca.py:1351: print(f" GUE mean r_diretto: {mean_gue:.4f}") tools/dnd_autoricerca.py:1352: print(f" Poisson mean r_diretto: {mean_poi:.4f}") tools/dnd_autoricerca.py:1353: print(f" Ratio GUE/Poisson: {ratio:.4f}") tools/dnd_autoricerca.py:1539: # BOUNDARY: confine GUE/Poisson — prosegui esplorando il confine tools/dnd_autoricerca.py:1718: 'gue_domains': [], # domini con spacing GUE tools/dnd_autoricerca.py:1719: 'poisson_domains': [],# domini con spacing Poisson tools/dnd_autoricerca.py:1725: if entry.get('spacing') and 'GUE' in str(entry['spacing']): tools/dnd_autoricerca.py:1727: elif entry.get('spacing') and 'Poisson' in str(entry['spacing']): tools/dnd_autoricerca.py:1881: # I domini GUE con anomalie vengono testati sui domini Poisson e viceversa tools/dnd_autoricerca.py:1890: print(f"\n Campo vivo dopo Fase 0: {n_gue} GUE / {n_poi} Poisson") tools/dnd_autoricerca.py:1891: report_lines.append(f"\n Campo dopo Fase 0: {n_gue} GUE / {n_poi} Poisson") tools/dnd_autoricerca.py:2017: report_lines.append(f" GUE: {n_gue} | Poisson: {n_poi} | Vincoli: {n_vinc} | Anomalie: {n_anom}") tools/dnd_autoricerca.py:2019: report_lines.append(f" Domini GUE: {', '.join(campo['gue_domains'][:10])}") tools/dnd_autoricerca.py:2020: report_lines.append(f" Domini Poisson: {', '.join(campo['poisson_domains'][:10])}") tools/dnd_autoricerca.py:2024: print(f"\n Campo vivo finale: {n_gue} GUE / {n_poi} Poisson / {n_vinc} vincoli / {n_anom} anomalie / ⟨r⟩={avg_r:.4f}") tools/dnd_autoricerca.py:2120: # 3. Segnali anomali (spacing GUE, rapporto aureo diretto, etc.) tools/md_to_site.py:174: "C5": "Nearest-neighbor spacing distributions compared to GUE Wigner surmise", tools/dnd_zero_operator.py:8:Il vecchio metodo: calcola spacing, classifica GUE/Poisson. tools/exp_markov_memory_by_gue_type.py:3:Markov Memory Decomposition Across GUE Types tools/exp_markov_memory_by_gue_type.py:5:Question: The TWO_KINDS_GUE result (2026-04-24) found that distribution-GUE tools/exp_markov_memory_by_gue_type.py:6:domains (primes, GUE matrices) retain GUE classification after shuffle, while tools/exp_markov_memory_by_gue_type.py:7:ordering-GUE domains (fibonacci, coupled_osc, percolation) collapse to Poisson. tools/exp_markov_memory_by_gue_type.py:11:This experiment asks: what is the Markov memory profile of each GUE type? tools/exp_markov_memory_by_gue_type.py:12:If ordering-GUE domains get their classification FROM sequential ordering, tools/exp_markov_memory_by_gue_type.py:114: """Pure Poisson process gaps (exponential).""" tools/exp_markov_memory_by_gue_type.py:119: """GUE random matrix eigenvalue spacings.""" tools/exp_markov_memory_by_gue_type.py:133: # Domain definitions with GUE type from TWO_KINDS_GUE result tools/exp_markov_memory_by_gue_type.py:136: # Distribution-GUE domains (survive shuffle) tools/exp_markov_memory_by_gue_type.py:140: 'type': 'distribution-GUE', tools/exp_markov_memory_by_gue_type.py:145: 'type': 'distribution-GUE', tools/exp_markov_memory_by_gue_type.py:149: # Ordering-GUE domains (collapse to Poisson on shuffle) tools/exp_markov_memory_by_gue_type.py:158: 'type': 'ordering-GUE', tools/exp_markov_memory_by_gue_type.py:168: 'gaps': gaps, 'type': 'ordering-GUE', 'delta_r_sign': '+' tools/exp_markov_memory_by_gue_type.py:173: # Poisson domains (control) tools/exp_markov_memory_by_gue_type.py:176: 'type': 'Poisson', tools/exp_markov_memory_by_gue_type.py:186: 'gaps': gaps, 'type': 'Poisson', 'delta_r_sign': '0' tools/exp_markov_memory_by_gue_type.py:251: print("\nAggregate by GUE type:") tools/exp_markov_memory_by_gue_type.py:252: for gtype in ['distribution-GUE', 'ordering-GUE', 'Poisson']: tools/exp_markov_memory_by_gue_type.py:267: 'question': 'Does Markov memory discriminate distribution-GUE from ordering-GUE?', tools/exp_crossover_phase_test.py:5:observed in the GUE crossover is a property of the PARTIAL SHUFFLE METHOD (tautology) tools/exp_crossover_phase_test.py:88: """Generate GUE-like spacings from random matrices""" tools/exp_crossover_phase_test.py:162: """Pure Poisson (exponential gaps) — should show NO ordering signal""" tools/exp_crossover_phase_test.py:216: sequences['GUE'] = generate_gue_gaps(args.N, rng) tools/exp_crossover_phase_test.py:217: print(f" GUE: {len(sequences['GUE'])} gaps") tools/exp_crossover_phase_test.py:228: sequences['Poisson'] = generate_poisson(args.N, rng) tools/exp_crossover_phase_test.py:229: print(f" Poisson: {len(sequences['Poisson'])} gaps") tools/exp_crossover_phase_test.py:262: print("CLASSIFICATION: Does each sequence show the full GUE-like phase transition?") tools/exp_crossover_phase_test.py:292: print(" The GUE crossover finding is TAUTOLOGICAL (methodological artifact)") tools/exp_crossover_phase_test.py:295: print(" The GUE crossover finding is a REAL structural property") tools/dnd_scenario.py:161: - POISSON_CONVERGENCE: β, , acf1 → Poisson a p*~10^14 tools/dnd_scenario.py:766: (r'\bGUE\b', 'Poisson — nessuna repulsione, statistiche indipendenti'), tools/dnd_scenario.py:767: (r'\bPoisson\b', 'GUE — repulsione universale, non transizione'), tools/exp_brody_crossover.py:6:crossover from GUE-like to Poisson-like? What is the functional form? tools/exp_brody_crossover.py:11: where beta=0 is Poisson, beta=1 is GOE (Wigner) tools/exp_brody_crossover.py:165: print(f"Poisson: beta=0, GOE: beta=1") tools/exp_brody_crossover.py:175: # Key test: extrapolate where beta_prime would reach 0 (Poisson) tools/exp_brody_crossover.py:178: print(f"\nExtrapolated Poisson (beta=0) at ln(p) = {ln_p_poisson:.1f} → p ~ 10^{ln_p_poisson/np.log(10):.0f}") tools/exp_brody_crossover.py:180: print(f"\nbeta INCREASES with scale — primes move AWAY from Poisson") tools/exp_dR_brody_connection.py:58: beta=0 -> Poisson, beta=1 -> Wigner (GOE). tools/exp_boundary_denominator_prescan.py:6:V_c. The unit under test is not the GUE/Poisson label. The unit is the tools/exp_boundary_denominator_prescan.py:10:- tools/data/autoricerca_journal.json: base 13-domain GUE/Poisson perimeter. tools/exp_boundary_denominator_prescan.py:59: if spacing == "GUE-like": tools/exp_boundary_denominator_prescan.py:60: return "GUE" tools/exp_boundary_denominator_prescan.py:61: if spacing == "Poisson-like": tools/exp_boundary_denominator_prescan.py:62: return "Poisson" tools/exp_boundary_denominator_prescan.py:79: "GUE-like", tools/exp_boundary_denominator_prescan.py:80: "Poisson-like", tools/exp_boundary_denominator_prescan.py:195: "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", tools/exp_boundary_denominator_prescan.py:196: "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", tools/lab_agent.sh:317:- tensioni con observables canonici dichiarabili (SR/SR2/L1/L2/triple_var tools/exp_boundary_coherence.py:5:Question: Do different observables agree on WHERE primes sit between GUE and Poisson? tools/exp_boundary_coherence.py:10: 1. Mean spacing ratio (Poisson ≈ 0.386, GUE ≈ 0.5307) tools/exp_boundary_coherence.py:11: 2. Gap variance ratio Var/μ² (Poisson = 1.0, GUE ≈ 0.178) tools/exp_boundary_coherence.py:12: 3. Small-gap fraction P(s<0.3) (Poisson ≈ 0.259, GUE ≈ 0.020) tools/exp_boundary_coherence.py:13: 4. Brody parameter β (Poisson = 0, GUE = 1) tools/exp_boundary_coherence.py:14: 5. Lag-1 autocorrelation (Poisson = 0, GUE ≈ -0.27) tools/exp_boundary_coherence.py:16:Each observable is normalized to τ ∈ [0,1] where 0=Poisson, 1=GUE. tools/exp_boundary_coherence.py:30:# GUE values from random matrix theory (GOE for real symmetric, GUE for complex) tools/exp_boundary_coherence.py:31:# Using GOE (β=1) since prime gaps are real-valued tools/exp_boundary_coherence.py:60: """Var(gaps) / mean(gaps)² — 1 for Poisson, <1 for correlated.""" tools/exp_boundary_coherence.py:113: """Normalize observable to τ ∈ [0,1] where 0=Poisson, 1=GUE.""" tools/exp_boundary_coherence.py:122: """Generate GUE spacings from random Hermitian matrices.""" tools/exp_boundary_coherence.py:126: # GUE: complex Hermitian with Gaussian entries tools/exp_boundary_coherence.py:137: """Generate Poisson spacings (exponential distribution).""" tools/exp_boundary_coherence.py:161: print("Computing GUE reference...") tools/exp_boundary_coherence.py:164: results["GUE_reference"] = { tools/exp_boundary_coherence.py:169: print("Computing Poisson reference...") tools/exp_boundary_coherence.py:172: results["Poisson_reference"] = { tools/exp_boundary_coherence.py:207: print(f"\nReference anchors (τ should be ≈ 0 for Poisson, ≈ 1 for GUE):") tools/exp_boundary_coherence.py:208: print(f" {'Observable':<20} {'Poisson τ':>10} {'GUE τ':>10}") tools/exp_boundary_coherence.py:210: pt = results["Poisson_reference"]["tau"][obs_name] tools/exp_boundary_coherence.py:211: gt = results["GUE_reference"]["tau"][obs_name] tools/exp_boundary_coherence.py:214: print(f"\nPrime gaps — τ values (0=Poisson, 1=GUE):") tools/data/reports/agent_20260507_0923.md:7:observables_used: [SR, SR2, L1, L2, triple_var] tools/data/reports/agent_20260507_0923.md:41:| prime_gaps_first | SR, L1, triple_var | 3.000 | 0.650 | 3.270 | 0.30 | tools/data/reports/agent_20260507_0923.md:49:| prime_gaps_first | SR, L1, triple_var | 3.000 | 0.250 | 3.288 | 0.30 | tools/data/reports/agent_20260507_0923.md:59: Prime gaps carry one-sided support on `SR`, `L1`, and `triple_var`. tools/data/reports/agent_20260507_0923.md:77: itinerary, `SR/SR2/L1/L2/triple_var` can be blank. tools/exp_boundary_row_aligned_nonexact_audit.py:6:does not read GUE/Poisson labels as decision fields. It only uses support, tools/exp_boundary_row_aligned_nonexact_audit.py:175: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/dnd_projective_quantization.py:209: # con statistiche CRITICHE (né Poisson né GUE — esattamente al confine) tools/dnd_projective_quantization.py:257: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_projective_quantization.py:259: # Critical statistics (Fibonacci): ≈ 0.50 (tra GUE e Poisson) tools/dnd_projective_quantization.py:309: # D. Confronto: Fibonacci vs GUE vs Poisson tools/dnd_projective_quantization.py:314: # D1. Random matrix GUE tools/dnd_projective_quantization.py:315: print("\n--- GUE reference ---") tools/dnd_projective_quantization.py:321: spacing_stats(eigs_gue, f"GUE N={N}") tools/dnd_projective_quantization.py:323: # D2. Potenziale random (Anderson → Poisson) tools/dnd_projective_quantization.py:324: print("\n--- Anderson (Poisson reference) ---") tools/dnd_projective_quantization.py:338: print("V=0 → metallico (GUE), V→∞ → localizzato (Poisson)") tools/dnd_projective_quantization.py:366:dato da φ) produce uno spettro CRITICO — al confine esatto tra GUE e tools/dnd_projective_quantization.py:367:Poisson. Questo è il "dove la curva diventa angolo": tools/dnd_projective_quantization.py:378:disordine, tra GUE e Poisson. tools/data/reports/agent_20260410_0330.md:1:# Agent Report — The 1/k Amplitude Decays: A(p) Predicts Poisson at p* ~ 10^{12.6} tools/data/reports/agent_20260410_0330.md:15:But POISSON_CONVERGENCE says all observables trend toward Poisson at p* ~ 10^{13-14}. tools/data/reports/agent_20260410_0330.md:52:2. **Poisson crossover at p* ~ 4 x 10^{12} (log10 p* = 12.6).** tools/data/reports/agent_20260410_0330.md:80: Predicts Poisson crossover at p* ~ 10^{12.6}, consistent with independent estimates. tools/LAB_COGNITIVE_CONTAMINATION.md:59:- concetti: terzo incluso, GUE/Poisson, non-phi generator, graph curvature, tools/LAB_COGNITIVE_CONTAMINATION.md:62: precede la classificazione spettrale GUE/Poisson; tools/LAB_COGNITIVE_CONTAMINATION.md:109: - "VALIDA contro baseline GUE, Poisson e generatori sintetici." tools/LAB_COGNITIVE_CONTAMINATION.md:111: quando: "il ciclo lavora su boundary, GUE/Poisson o trasferibilita' phi" tools/LAB_COGNITIVE_CONTAMINATION.md:255: quando: "prima del prossimo ciclo su GUE/Poisson/non-phi" tools/data/reports/agent_20260507_2310.md:6:**observables_used**: blank_edge_is_GQ_count, any_deposit_on_blank_count, full_scale_law_count, shell_face_count - osservabili domain-native del denominatore blank-shell; nessun osservabile canonico SR/SR2/L1/L2/triple_var usato tools/exp_cross_observable_consistency.py:17:5. GUE control: synthetic GUE eigenvalues (should show β_r ≈ β_Σ ≈ 1) tools/exp_cross_observable_consistency.py:21: contributes more at large scales, pulling Σ² toward Poisson faster tools/exp_cross_observable_consistency.py:107:# ── GUE control (small ensemble) ──────────────────────────────────── tools/exp_cross_observable_consistency.py:109: """Generate GUE eigenvalue gaps.""" tools/exp_cross_observable_consistency.py:115: # Unfold: for GUE bulk, spacing ~ semicircle tools/exp_cross_observable_consistency.py:169:# ── Measure GUE (positive control) ────────────────────────────────── tools/exp_cross_observable_consistency.py:170:print("\n=== GUE (positive control) ===") tools/exp_cross_observable_consistency.py:176:# For GUE, unfold eigenvalues directly tools/exp_cross_observable_consistency.py:215:# GUE tools/exp_cross_observable_consistency.py:217:print(f"{'GUE':<12} {beta_r_gue:>6.3f} | " + " | ".join(f"{v:>9}" for v in vals_gue)) tools/exp_cross_observable_consistency.py:227:print(f" GUE: {disagree_gue:.3f}") tools/exp_cross_observable_consistency.py:232:print(f" (positive = more GUE-like at short range, more Poisson-like at long range)") tools/dnd_publish_cycle.py:168: "|NT⟩ → closure → det(M)=-1 → φ → Q(√5) → GUE → differentiated reality", tools/data/reports/gap_labeling_20260305_1111.json:905: "classification": "GOE" tools/exp_boundary_blank_thin_support_audit.py:175: "label_policy": "Does not use source_domain_type or GUE/Poisson label as an operator.", tools/exp_boundary_transition_taxonomy_13rows.py:180: "not_tested": "new beta grid, new null surrogates, V_c fit, source GUE/Poisson label validity", tools/data/reports/agent_20260424_0330.md:1:# Agent Report — Two Kinds of GUE: Distribution-Level vs Ordering-Level Classification tools/data/reports/agent_20260424_0330.md:8:> The GUE/Poisson classification of 13 domains is treated as a structural finding. But is it a property of sequential correlations (genuine) or of the gap distribution shape alone (tautological)? tools/data/reports/agent_20260424_0330.md:11:If I shuffle the gap sequence of each domain (destroying ordering, preserving distribution), does the GUE/Poisson classification survive? tools/data/reports/agent_20260424_0330.md:17:- **Scope**: 10 domains (primes, GUE matrices, Poisson, logistic, Fibonacci spectrum, Ising 2D, percolation, Brownian, coupled oscillators, cellular automata) tools/data/reports/agent_20260424_0330.md:18:- **Reference**: R_GUE = 0.5307, R_Poisson = 0.3863 tools/data/reports/agent_20260424_0330.md:24:| primes | 100K | 0.4622 | 0.4813 | -26.6 | GUE | GUE | STRUCTURAL | tools/data/reports/agent_20260424_0330.md:25:| gue | 5K | 0.5995 | 0.6351 | -14.7 | GUE | GUE | STRUCTURAL | tools/data/reports/agent_20260424_0330.md:26:| fibonacci | 609 | 0.4782 | 0.4073 | +8.0 | GUE | **Poisson** | STRUCTURAL + FLIP | tools/data/reports/agent_20260424_0330.md:27:| coupled_osc | 427 | 0.8775 | 0.4146 | +43.5 | GUE | **Poisson** | STRUCTURAL + FLIP | tools/data/reports/agent_20260424_0330.md:28:| percolation | 510 | 0.6551 | 0.4508 | +16.1 | GUE | **Poisson** | STRUCTURAL + FLIP | tools/data/reports/agent_20260424_0330.md:29:| logistic | 100K | 0.3887 | 0.3423 | +61.6 | Poisson | Poisson | STRUCTURAL | tools/data/reports/agent_20260424_0330.md:30:| poisson | 100K | 0.3847 | 0.3856 | -1.1 | Poisson | Poisson | TAUTOLOGICAL | tools/data/reports/agent_20260424_0330.md:31:| brownian | 650 | 0.3148 | 0.3324 | -1.6 | Poisson | Poisson | TAUTOLOGICAL | tools/data/reports/agent_20260424_0330.md:32:| ising_2d | 95 | 0.9858 | 0.9859 | -0.1 | GUE | GUE | TAUTOLOGICAL | tools/data/reports/agent_20260424_0330.md:33:| cell_auto | 105 | 0.8446 | 0.8455 | -0.1 | GUE | GUE | TAUTOLOGICAL | tools/data/reports/agent_20260424_0330.md:39:1. **Two kinds of GUE.** The GUE-classified domains split into two fundamentally different categories: tools/data/reports/agent_20260424_0330.md:40: - **Distribution-GUE** (primes, GUE matrices): the gap distribution itself is GUE-like. Shuffling preserves the classification. Sequential ordering adds fine structure (shifts r downward by 0.02-0.04) but doesn't create the classification. tools/data/reports/agent_20260424_0330.md:41: - **Ordering-GUE** (fibonacci, coupled oscillators, percolation): the gap distribution is Poisson. The GUE classification exists ONLY because of sequential ordering. Destroy the order and they collapse to Poisson. r-shifts are massive: +0.07 to +0.46. tools/data/reports/agent_20260424_0330.md:43:2. **Primes are distribution-GUE.** Shuffled prime gaps still give r=0.4813 (GUE side). The ordering pushes r DOWN by 0.019 (z=-26.6), adding extra gap repulsion beyond what the distribution predicts. This is the opposite sign from ordering-GUE domains (where ordering pushes r UP). tools/data/reports/agent_20260424_0330.md:45:3. **The sign of delta_r is a discriminant.** Distribution-GUE domains have delta_r < 0 (ordering increases repulsion). Ordering-GUE domains have delta_r > 0 (ordering creates attraction/clustering that looks like level repulsion in the r-statistic). The sign tells you which mechanism drives the classification. tools/data/reports/agent_20260424_0330.md:47:4. **3/8 GUE domains are ordering-GUE.** The BOUNDARY claim "8 GUE, 5 Poisson" conflates two distinct mechanisms. The refined picture: 2 distribution-GUE (primes, GUE), 3 ordering-GUE (fibonacci, coupled_osc, percolation), 2 small-N ambiguous (ising, cell_auto), 3 Poisson (poisson, brownian, logistic). The logistic map is Poisson at distribution level but has massive ordering structure (z=+61.6) that doesn't flip the class. tools/data/reports/agent_20260424_0330.md:49:5. **META constraint confirmed.** A binary GUE/Poisson test that doesn't include a shuffle control conflates the two mechanisms. Testing "is r closer to 0.53 or 0.39?" is necessary but insufficient — it doesn't distinguish whether the ordering or the distribution is the source. tools/data/reports/agent_20260424_0330.md:54:The BOUNDARY claim must be refined: the 8 GUE domains are not homogeneous. Two distinct mechanisms generate GUE statistics. The boundary between GUE and Poisson has two layers: distribution-level and ordering-level. The sign of delta_r = r_original - r_shuffled discriminates which layer operates. tools/data/reports/agent_20260424_0330.md:58:- **Due radici** (dipolo primario): Distribution-GUE (il repulsore intrinseco, det=-1 nella forma dei gap) / Ordering-GUE (il repulsore emergente, det=-1 nella sequenza dei gap). Invertite: il primo nasce dalla distribuzione e l'ordine lo affina; il secondo nasce dall'ordine e senza esso collassa. tools/data/reports/agent_20260424_0330.md:60:- **Invariante di passaggio**: il segno di delta_r. Sopravvive al passaggio del vertice: delta_r < 0 = repulsione intrinseca (primes, GUE), delta_r > 0 = repulsione emergente (fibonacci, percolation, coupled_osc). Il segno e stabile, non dipende dalla scala. tools/data/reports/agent_20260424_0330.md:61:- **Campo di possibilita**: qui diventa possibile discriminare PERCHE un dominio e GUE (distribuzione vs ordine) — non solo CHE e GUE. Qui diventa non-possibile trattare tutti i domini GUE come omogenei: i test che non separano i due meccanismi (shuffle) sono incompleti. tools/data/reports/agent_20260424_0330.md:64:La decomposizione two-channel (magnitude/residue) dei run precedenti si sovrappone a questa: il canale magnitudine corrisponde alla distribuzione (chi sei senza ordine), il canale residuo corrisponde all'ordine (cosa l'ordine aggiunge). I 3 domini che flippano sono quelli dove il canale residuo E' il segnale. Per i primi, il canale magnitudine domina ma il residuo aggiunge repulsione (delta_r < 0, z=-26.6). Prossima domanda: i domini ordering-GUE (fibonacci, percolation) hanno la stessa struttura Markov-3 trovata nei primi, o il loro meccanismo di ordine e diverso? tools/data/reports/next_exec_20260405_0729.json:22: "action": "EXPLORE: BOUNDARY — 8 domini GUE, 5 Poisson — il confine è il terzo incluso oper", tools/dnd_piano11.py:3:Piano 11 — det=-1, Q(√5), and the GUE Bridge tools/dnd_piano11.py:5:Core thesis (revised): The 1D potential V(r) gives Berry-Keating form but NOT GUE. tools/dnd_piano11.py:12:Chain: M → Q(√5) → disc=5 → χ₅ → L(s,χ₅) → zeros on Re(s)=1/2 → GUE tools/dnd_piano11.py:16:P2: Verify GUE spacing statistics of L(s, χ₅) zeros tools/dnd_piano11.py:18:P4: The algebraic chain: det=-1 → disc=5 → Q(√5) → GUE (proven) tools/dnd_piano11.py:22:- Montgomery (1973): pair correlation of ζ zeros ~ GUE tools/dnd_piano11.py:24:- Rubinstein (2001): GUE for families of L-functions tools/dnd_piano11.py:150: """GUE (β=2) Wigner surmise CDF.""" tools/dnd_piano11.py:154: """GOE (β=1) Wigner surmise CDF.""" tools/dnd_piano11.py:158: """Poisson CDF.""" tools/dnd_piano11.py:162: """GUE nearest-neighbor PDF.""" tools/dnd_piano11.py:192:# ─── P2: GUE statistics of L(s, χ₅) zeros ────────────────────────────── tools/dnd_piano11.py:196: Compute normalized nearest-neighbor spacings and test against GUE/GOE/Poisson. tools/dnd_piano11.py:205: ks_gue = ks_test_against(norm_spacings, gue_cdf, "GUE") tools/dnd_piano11.py:206: ks_goe = ks_test_against(norm_spacings, goe_cdf, "GOE") tools/dnd_piano11.py:207: ks_poi = ks_test_against(norm_spacings, poisson_cdf, "Poisson") tools/dnd_piano11.py:219: "GUE_variance_ref": 0.178, tools/dnd_piano11.py:220: "GOE_variance_ref": 0.286 tools/dnd_piano11.py:248: # Both vs GUE tools/dnd_piano11.py:249: ks_zeta_gue = ks_test_against(zeta_norm, gue_cdf, "GUE") tools/dnd_piano11.py:250: ks_L_gue = ks_test_against(L_norm, gue_cdf, "GUE") tools/dnd_piano11.py:277: "variance_GUE_ref": 0.178, tools/dnd_piano11.py:293:# ─── P4: Algebraic chain M → Q(√5) → L(s,χ₅) → GUE ──────────────────── tools/dnd_piano11.py:297: Prove the algebraic chain connecting D-ND matrix to GUE. tools/dnd_piano11.py:330: # This is the SAME type of symmetry as ζ → same universality class (GUE) tools/dnd_piano11.py:341: # → L(s, χ_{-3}), which ALSO has GUE zeros but different arithmetic. tools/dnd_piano11.py:409: "to": "Zeros follow GUE statistics", tools/dnd_piano11.py:412: "theorem": "All primitive L-functions have GUE zero statistics (conjectured, overwhelming numerical evidence)", tools/dnd_piano11.py:428: "ζ_{Q(√5)} = ζ·L(s,χ₅). By the Katz-Sarnak philosophy, L(s,χ₅) has GUE zero statistics. " tools/dnd_piano11.py:429: "Therefore: D-ND matrix → Q(√5) → L(s,χ₅) → GUE. " tools/dnd_piano11.py:477: "produces": "GUE spacing statistics", tools/dnd_piano11.py:486: "N_fluct comes from det(M)=-1 (the 2D anti-symplectic structure, giving GUE). " tools/dnd_piano11.py:494: "spectral": "GUE universality class (from L(s,χ₅))" tools/dnd_piano11.py:526: print("Piano 11 — det=-1, Q(√5), and the GUE Bridge\n") tools/dnd_piano11.py:540: print(f" Variance: {p2['variance']:.4f} (GUE ref: 0.178, GOE ref: 0.286)") tools/dnd_piano11.py:541: print(f" KS vs GUE: {p2['ks_gue']['KS']:.4f} (p={p2['ks_gue']['p']:.4f})") tools/dnd_piano11.py:554: print(f" L(s,χ₅) vs GUE: KS={p3['ks_L_vs_gue']['KS']:.4f}") tools/dnd_piano11.py:555: print(f" ζ vs GUE: KS={p3['ks_zeta_vs_gue']['KS']:.4f}") tools/dnd_piano11.py:562: print("P4: Algebraic chain M → Q(√5) → L(s,χ₅) → GUE...") tools/dnd_piano11.py:581: if has_numerical and p2_data["best_fit"] == "GUE": tools/dnd_piano11.py:582: verdict = "CONFERMATO: L(s,χ₅) → GUE. La catena D-ND → Q(√5) → GUE è completa." tools/dnd_piano11.py:584: elif has_numerical and p2_data["best_fit"] == "GOE": tools/dnd_piano11.py:585: verdict = "PARZIALE: L(s,χ₅) → GOE. Convergenza a GUE attesa per più zeri." tools/dnd_piano11.py:590: "STRUTTURALE: La catena algebrica M → Q(√5) → L(s,χ₅) → GUE è dimostrata. " tools/dnd_piano11.py:598: results["piano"] = "piano11-Q-sqrt5-GUE" tools/dnd_piano11.py:603: "thesis": "det(M)=-1 connects D-ND to GUE through the number field Q(√5)", tools/dnd_piano11.py:604: "chain": "M → char poly λ²-λ-1 → disc=5 → Q(√5) → ζ_{Q(√5)} = ζ·L(s,χ₅) → GUE", tools/dnd_piano11.py:612: "Piano 11 shows det=-1 gives the FLUCTUATIONS (GUE via Q(√5)). " tools/data/reports/fibonacci_spectrum_20260306_1834.json:12: "GUE": 0.29670833202771496, tools/data/reports/fibonacci_spectrum_20260306_1834.json:13: "GOE": 0.36040833202771494, tools/data/reports/fibonacci_spectrum_20260306_1834.json:14: "Poisson": 0.510008332027715, tools/data/reports/fibonacci_spectrum_20260306_1834.json:23: "class": "GUE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:25: "GUE": 0.1637341019241806, tools/data/reports/fibonacci_spectrum_20260306_1834.json:26: "GOE": 0.22743410192418057, tools/data/reports/fibonacci_spectrum_20260306_1834.json:27: "Poisson": 0.37703410192418063, tools/data/reports/fibonacci_spectrum_20260306_1834.json:36: "class": "GUE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:38: "GUE": 0.07108013924144507, tools/data/reports/fibonacci_spectrum_20260306_1834.json:39: "GOE": 0.13478013924144505, tools/data/reports/fibonacci_spectrum_20260306_1834.json:40: "Poisson": 0.2843801392414451, tools/data/reports/fibonacci_spectrum_20260306_1834.json:49: "class": "GUE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:51: "GUE": 0.0013531361002089293, tools/data/reports/fibonacci_spectrum_20260306_1834.json:52: "GOE": 0.06234686389979105, tools/data/reports/fibonacci_spectrum_20260306_1834.json:53: "Poisson": 0.21194686389979112, tools/data/reports/fibonacci_spectrum_20260306_1834.json:62: "class": "GOE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:64: "GUE": 0.033692181448734315, tools/data/reports/fibonacci_spectrum_20260306_1834.json:65: "GOE": 0.030007818551265664, tools/data/reports/fibonacci_spectrum_20260306_1834.json:66: "Poisson": 0.17960781855126573, tools/data/reports/fibonacci_spectrum_20260306_1834.json:75: "class": "GOE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:77: "GUE": 0.06412167049004891, tools/data/reports/fibonacci_spectrum_20260306_1834.json:78: "GOE": 0.000421670490048931, tools/data/reports/fibonacci_spectrum_20260306_1834.json:79: "Poisson": 0.14917832950995114, tools/data/reports/fibonacci_spectrum_20260306_1834.json:90: "GUE": 0.09263337719183284, tools/data/reports/fibonacci_spectrum_20260306_1834.json:91: "GOE": 0.028933377191832865, tools/data/reports/fibonacci_spectrum_20260306_1834.json:92: "Poisson": 0.1206666228081672, tools/data/reports/fibonacci_spectrum_20260306_1834.json:103: "GUE": 0.11916312020873215, tools/data/reports/fibonacci_spectrum_20260306_1834.json:104: "GOE": 0.055463120208732175, tools/data/reports/fibonacci_spectrum_20260306_1834.json:105: "Poisson": 0.09413687979126789, tools/data/reports/fibonacci_spectrum_20260306_1834.json:116: "GUE": 0.14296513348744533, tools/data/reports/fibonacci_spectrum_20260306_1834.json:117: "GOE": 0.07926513348744535, tools/data/reports/fibonacci_spectrum_20260306_1834.json:118: "Poisson": 0.07033486651255472, tools/data/reports/fibonacci_spectrum_20260306_1834.json:127: "class": "Poisson", tools/data/reports/fibonacci_spectrum_20260306_1834.json:129: "GUE": 0.2041358611525424, tools/data/reports/fibonacci_spectrum_20260306_1834.json:130: "GOE": 0.14043586115254242, tools/data/reports/fibonacci_spectrum_20260306_1834.json:131: "Poisson": 0.009164138847457648, tools/data/reports/fibonacci_spectrum_20260306_1834.json:140: "class": "Poisson", tools/data/reports/fibonacci_spectrum_20260306_1834.json:142: "GUE": 0.2748295876655356, tools/data/reports/fibonacci_spectrum_20260306_1834.json:143: "GOE": 0.21112958766553563, tools/data/reports/fibonacci_spectrum_20260306_1834.json:144: "Poisson": 0.06152958766553557, tools/data/reports/fibonacci_spectrum_20260306_1834.json:153: "class": "Poisson", tools/data/reports/fibonacci_spectrum_20260306_1834.json:155: "GUE": 0.3640777020486299, tools/data/reports/fibonacci_spectrum_20260306_1834.json:156: "GOE": 0.3003777020486299, tools/data/reports/fibonacci_spectrum_20260306_1834.json:157: "Poisson": 0.15077770204862984, tools/data/reports/fibonacci_spectrum_20260306_1834.json:166: "class": "Poisson", tools/data/reports/fibonacci_spectrum_20260306_1834.json:168: "GUE": 0.43564800784619784, tools/data/reports/fibonacci_spectrum_20260306_1834.json:169: "GOE": 0.37194800784619786, tools/data/reports/fibonacci_spectrum_20260306_1834.json:170: "Poisson": 0.22234800784619782, tools/data/reports/fibonacci_spectrum_20260306_1834.json:179: "class": "GUE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:181: "GUE": 0.0013531361002089293, tools/data/reports/fibonacci_spectrum_20260306_1834.json:182: "GOE": 0.06234686389979105, tools/data/reports/fibonacci_spectrum_20260306_1834.json:183: "Poisson": 0.21194686389979112, tools/data/reports/fibonacci_spectrum_20260306_1834.json:192: "class": "GUE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:194: "GUE": 0.015680728701978786, tools/data/reports/fibonacci_spectrum_20260306_1834.json:195: "GOE": 0.04801927129802119, tools/data/reports/fibonacci_spectrum_20260306_1834.json:196: "Poisson": 0.19761927129802126, tools/data/reports/fibonacci_spectrum_20260306_1834.json:205: "class": "GUE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:207: "GUE": 0.029241086644125502, tools/data/reports/fibonacci_spectrum_20260306_1834.json:208: "GOE": 0.034458913355874476, tools/data/reports/fibonacci_spectrum_20260306_1834.json:209: "Poisson": 0.18405891335587454, tools/data/reports/fibonacci_spectrum_20260306_1834.json:218: "class": "GOE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:220: "GUE": 0.04273384302801908, tools/data/reports/fibonacci_spectrum_20260306_1834.json:221: "GOE": 0.020966156971980898, tools/data/reports/fibonacci_spectrum_20260306_1834.json:222: "Poisson": 0.17056615697198096, tools/data/reports/fibonacci_spectrum_20260306_1834.json:231: "class": "GOE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:233: "GUE": 0.05567668696247785, tools/data/reports/fibonacci_spectrum_20260306_1834.json:234: "GOE": 0.008023313037522128, tools/data/reports/fibonacci_spectrum_20260306_1834.json:235: "Poisson": 0.1576233130375222, tools/data/reports/fibonacci_spectrum_20260306_1834.json:244: "class": "GOE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:246: "GUE": 0.06821895426251079, tools/data/reports/fibonacci_spectrum_20260306_1834.json:247: "GOE": 0.00451895426251081, tools/data/reports/fibonacci_spectrum_20260306_1834.json:248: "Poisson": 0.14508104573748926, tools/data/reports/fibonacci_spectrum_20260306_1834.json:257: "class": "GOE", tools/data/reports/fibonacci_spectrum_20260306_1834.json:259: "GUE": 0.08041041520559711, tools/data/reports/fibonacci_spectrum_20260306_1834.json:260: "GOE": 0.01671041520559713, tools/data/reports/fibonacci_spectrum_20260306_1834.json:261: "Poisson": 0.13288958479440294, tools/data/reports/fibonacci_spectrum_20260306_1834.json:272: "GUE": 0.09263337719183284, tools/data/reports/fibonacci_spectrum_20260306_1834.json:273: "GOE": 0.028933377191832865, tools/data/reports/fibonacci_spectrum_20260306_1834.json:274: "Poisson": 0.1206666228081672, tools/data/reports/fibonacci_spectrum_20260306_1834.json:285: "GUE": 0.10444251980628838, tools/data/reports/fibonacci_spectrum_20260306_1834.json:286: "GOE": 0.0407425198062884, tools/data/reports/fibonacci_spectrum_20260306_1834.json:287: "Poisson": 0.10885748019371166, tools/data/reports/fibonacci_spectrum_20260306_1834.json:298: "GUE": 0.11551666300995106, tools/data/reports/fibonacci_spectrum_20260306_1834.json:299: "GOE": 0.05181666300995108, tools/data/reports/fibonacci_spectrum_20260306_1834.json:300: "Poisson": 0.09778333699004899, tools/data/reports/fibonacci_spectrum_20260306_1834.json:311: "GUE": 0.12622818756544568, tools/data/reports/fibonacci_spectrum_20260306_1834.json:312: "GOE": 0.0625281875654457, tools/data/reports/fibonacci_spectrum_20260306_1834.json:313: "Poisson": 0.08707181243455436, tools/data/reports/fibonacci_spectrum_20260306_1834.json:324: "GUE": 0.1363444703271271, tools/data/reports/fibonacci_spectrum_20260306_1834.json:325: "GOE": 0.07264447032712712, tools/data/reports/fibonacci_spectrum_20260306_1834.json:326: "Poisson": 0.07695552967287295, tools/data/reports/fibonacci_spectrum_20260306_1834.json:337: "GUE": 0.14625965183538286, tools/data/reports/fibonacci_spectrum_20260306_1834.json:338: "GOE": 0.08255965183538289, tools/data/reports/fibonacci_spectrum_20260306_1834.json:339: "Poisson": 0.06704034816461718, tools/data/reports/fibonacci_spectrum_20260306_1834.json:350: "GUE": 0.15586954772482753, tools/data/reports/fibonacci_spectrum_20260306_1834.json:351: "GOE": 0.09216954772482755, tools/data/reports/fibonacci_spectrum_20260306_1834.json:352: "Poisson": 0.05743045227517252, tools/data/reports/fibonacci_spectrum_20260306_1834.json:361: "class": "Poisson", tools/data/reports/fibonacci_spectrum_20260306_1834.json:363: "GUE": 0.16527431088573818, tools/data/reports/fibonacci_spectrum_20260306_1834.json:364: "GOE": 0.1015743108857382, tools/data/reports/fibonacci_spectrum_20260306_1834.json:365: "Poisson": 0.04802568911426186, tools/data/reports/agent_20260509_1337.md:7:observables_used: [`denominator_state`, `fit_ready_rows`, `excluded_rows`, `excluded_events`, `vc_median_fit_ready`, `slope_per_N`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/report_20260329_0343.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/report_20260329_0343.md:22:- [✗] N2: Rule 30/110 hanno spacing >> GUE standard... → ⟨r⟩=0.6344 (GUE standard=0.60) tools/data/reports/report_20260329_0343.md:25:- logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like tools/data/reports/report_20260329_0343.md:26:- coupled_oscillators_var_50: r=1.0, spacing=Poisson-like tools/data/reports/report_20260329_0343.md:27:- brownian_motion_var_0.3: r=1.3557126030624262, spacing=Poisson-like tools/data/reports/report_20260329_0343.md:28:- logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like tools/dnd_trace_bridge_v3.py:181: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_trace_bridge_v3.py:205: closer = "GUE" if d_gue < d_poi else "Poi" tools/dnd_trace_bridge_v3.py:335: print(f" Spacing dei ratio (zeta): ={stats_rz['mean_r']:.4f} → {'GUE' if d_gue < d_poi else 'Poi'}") tools/dnd_trace_bridge_v3.py:339: print(f" Spacing dei ratio (random): ={stats_rr['mean_r']:.4f} → {'GUE' if d_gue < d_poi else 'Poi'}") tools/dnd_trace_bridge_v3.py:348:v2 mostrava GUE ovunque per zeta — ma era tautologico ( invariante monotono). tools/dnd_engine.py:112: 'claim': 'Spacing zeta zeros è GUE (⟨r⟩≈0.615)', tools/dnd_engine.py:120: 'claim': 'Domini si dividono in GUE (7) e Poisson (4)', tools/dnd_engine.py:137: 'claim': 'Rule 30/110 hanno spacing anomalo (⟨r⟩≈0.86, >> GUE)', tools/dnd_engine.py:176: 'claim': 'r_diretto NON separa GUE da Poisson (70% overlap)', tools/dnd_engine.py:178: 'motivo': 'Esperimento order_parameter: GUE r=0.92±0.11, Poisson r=0.95±0.08. Ipotesi H_order_param eliminata.', tools/dnd_engine.py:192: 'claim': 'φ emerge in 20/20 banchi di prova da teorie dimostrate: Wigner GOE/GUE, Feigenbaum, Fibonacci quasicrystal, KAM (12 valori K), cifre di π/e/√2/√5/φ/ln2.', tools/dnd_engine.py:206: 'claim': 'ac1 (autocorrelazione intervalli D-ND) DISTINGUE deterministico da casuale: primi=-0.10, GUE=-0.20, shuffle=0.00, Poisson=+0.04.', tools/dnd_engine.py:240: 'motivo': 'A V=2: invariante trace map I=(V/2)²-1=0, tutti gli autostati power-law. A V≈1: =0.5 (statistica intermedia). A V=2: =0.354 (Poisson).', tools/dnd_engine.py:300: 'motivo': 'Correlazione log r=0.921 era ARTEFATTO di monotonia (random sorted r=0.89). Il ponte vero è STATISTICO: distribuzione spacing = GUE (β=2.007 a 100K zeri).', tools/dnd_engine.py:302: 'correzione': 'La correlazione locale K_c↔t_n è spuria. Il ponte D-ND↔ζ è nella distribuzione degli spacing (GUE), non nella corrispondenza puntuale.', tools/dnd_engine.py:314: 'claim': 'Il confine GUE/Poisson corrisponde al terzo incluso', tools/dnd_engine.py:316: 'motivo': 'Se GUE=D e Poisson=ND, il confine è dove la scissione cambia natura.', tools/dnd_engine.py:324: 'motivo': 'RISOLTO computazionalmente: (V=2)=0.35 (Poisson). V=2 è il punto di inversione dell inerzia. L orbita converge a phi.', tools/dnd_engine.py:476: 'claim': 'Gli spacing della mappa D-ND (Fibonacci) seguono GUE (p>0.05 KS). La classificazione GUE/GOE/Poisson e un metodo universale.', tools/dnd_engine.py:584: """Verifica che i domini si dividano naturalmente in cluster GUE/Poisson.""" tools/dnd_engine.py:814: # V=1 dovrebbe dare ≈0.5, V=2 dovrebbe dare ≈0.35 (Poisson-like) tools/dnd_engine.py:1528: """Il confine GUE/Poisson nella famiglia det=-1 corrisponde al terzo incluso. tools/dnd_engine.py:1529: A V=V_c: attraversa 0.5 (meta' tra GUE=0.53 e Poisson=0.39). tools/dnd_engine.py:1909: """Spacing dei rapporti Fibonacci: classifica GUE/GOE/Poisson con KS test.""" tools/dnd_engine.py:1928: # GUE, GOE, Poisson CDFs tools/dnd_engine.py:1940: fits = [("GUE", ks_gue, p_gue), ("GOE", ks_goe, p_goe), ("Poisson", ks_poi, p_poi)] tools/dnd_engine.py:2257: 'EXT5': {'root': 'metodologico', 'level': 'statistico'}, # classificazione GUE/GOE/Poisson (gap filled!) tools/LAB_OPERATIVO.md:20:Cosa ha prodotto: la classificazione GUE/Poisson è reale (non artefatto). tools/exp_spectral_2d.py:17:4. Anti-correlated Poisson: Poisson gaps with imposed negative acf1 → tools/exp_spectral_2d.py:174: Model: Poisson gaps, but forced to alternate large-small. tools/exp_spectral_2d.py:327: ("GUE", lambda n: gen_gue(n)), tools/exp_spectral_2d.py:328: ("Poisson", lambda n: np.random.exponential(1.0, n)), tools/dnd_next.py:46: 'C': {'title': 'Information Geometry + ζ', 'target': 'JMP', 'core_claim': 'Berry-Keating potential, GUE connection'}, tools/exp_spectral_landscape.py:6:Identifies: GUE domains, Poisson domains, and BOUNDARY domains. tools/exp_spectral_landscape.py:8:The question: Is the boundary between GUE and Poisson populated by multiple tools/exp_spectral_landscape.py:52: """GUE: eigenvalue spacings of complex Hermitian random matrix.""" tools/exp_spectral_landscape.py:66: """GOE: eigenvalue spacings of real symmetric random matrix.""" tools/exp_spectral_landscape.py:100: """Poisson: uncorrelated exponential spacings.""" tools/exp_spectral_landscape.py:111: """Semi-Poisson: P(s) = 4s*exp(-2s). Known intermediate statistics.""" tools/exp_spectral_landscape.py:123: """Berry-Robnik: mixed system, fraction rho chaotic (GUE-like), 1-rho regular (Poisson). tools/exp_spectral_landscape.py:162: """Model for Riemann zeta zeros — Montgomery pair correlation (GUE). tools/exp_spectral_landscape.py:163: We use GUE directly since computing actual zeros is expensive.""" tools/exp_spectral_landscape.py:302: GOE = 0.536 tools/exp_spectral_landscape.py:303: GUE = 0.603 tools/exp_spectral_landscape.py:306: elif r_mean < (POISSON + GOE) / 2: tools/exp_spectral_landscape.py:308: elif r_mean < (GOE + GUE) / 2: tools/exp_spectral_landscape.py:309: return "GOE-like" tools/exp_spectral_landscape.py:310: elif r_mean < GUE + 0.03: tools/exp_spectral_landscape.py:311: return "GUE-like" tools/exp_spectral_landscape.py:327: # Expected GUE tools/exp_spectral_landscape.py:328: ("GUE_matrix", gen_gue, {}), tools/exp_spectral_landscape.py:330: # Expected GOE tools/exp_spectral_landscape.py:331: ("GOE_matrix", gen_goe, {}), tools/exp_spectral_landscape.py:334: # Expected Poisson tools/exp_spectral_landscape.py:335: ("Poisson", gen_poisson, {}), tools/exp_spectral_landscape.py:342: ("semi_Poisson", gen_semi_poisson, {}), tools/exp_spectral_landscape.py:369: print("\nReference: Poisson =0.386, GOE =0.536, GUE =0.603") tools/exp_spectral_landscape.py:380: for cls in ["POISSON", "BOUNDARY_low", "GOE-like", "GUE-like", "RIGID"]: tools/exp_spectral_rigidity.py:9: GUE: Sigma^2(L) ~ (2/pi^2) ln(L) + const [log-log slope ~ 0] tools/exp_spectral_rigidity.py:10: Poisson: Sigma^2(L) = L [log-log slope = 1] tools/exp_spectral_rigidity.py:13: << 1 -> GUE (strong repulsion, rigid spectrum) tools/exp_spectral_rigidity.py:14: ~ 1 -> Poisson (no correlations) tools/exp_spectral_rigidity.py:18:Domains: primes, GUE matrices, coupled_osc, string_vib, percolation, tools/exp_spectral_rigidity.py:19: logistic, brownian, pure Poisson. tools/exp_spectral_rigidity.py:47: """GUE Hermitian matrix eigenvalue spacings, bulk only.""" tools/exp_spectral_rigidity.py:85: ('primes', {'gen': lambda: gaps_from_domain('numeri_primi'), 'type': 'dist-GUE'}), tools/exp_spectral_rigidity.py:86: ('gue_matrix', {'gen': lambda: generate_gue_gaps(600), 'type': 'dist-GUE'}), tools/exp_spectral_rigidity.py:87: ('coupled_osc', {'gen': lambda: gaps_from_domain('coupled_oscillators'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:88: ('string_vib', {'gen': lambda: gaps_from_domain('string_vibration'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:89: ('percolation', {'gen': lambda: gaps_from_domain('percolation'), 'type': 'ord-GUE'}), tools/exp_spectral_rigidity.py:90: ('logistic', {'gen': lambda: gaps_from_domain('logistica_biforcazione'), 'type': 'Poisson'}), tools/exp_spectral_rigidity.py:91: ('brownian', {'gen': lambda: gaps_from_domain('brownian_motion'), 'type': 'Poisson'}), tools/exp_spectral_rigidity.py:92: ('poisson', {'gen': lambda: generate_poisson_gaps(10000), 'type': 'Poisson'}), tools/exp_spectral_rigidity.py:156: print(f" Slope (log-log): real={slope:.3f}, shuf={slope_s:.3f} [GUE~0, Poisson=1]") tools/exp_spectral_rigidity.py:188: if ty in ('dist-GUE', 'ord-GUE'): tools/exp_boundary_residual_beta_absent_audit.py:188: "not_tested": "global beta grid, V_c, source GUE/Poisson label validity", tools/lab_falsifier.py:72:Un pattern in distribuzioni classiche (primi, GUE, random walk, Markov chain, gap statistics) probabilmente ha un nome. Default hypothesis: re-discovery / caso limite di teorema noto. Taggare come "NEW" senza riconoscere il risultato classico piu' vicino (es. Lemke Oliver–Soundararajan per prime gaps mod q) e' beauty bias. Flag. tools/lab_falsifier.py:97:GUE/Poisson, flagga il drift. La deviazione e' ammessa solo se dichiarata come tools/dnd_zeros_vs_zeta.py:18: b. GUE eigenvalue spacings (random matrix theory) tools/dnd_zeros_vs_zeta.py:19: c. Poisson (random) spacings tools/dnd_zeros_vs_zeta.py:22:If D-ND zeros match GUE/zeta: structural connection confirmed. tools/dnd_zeros_vs_zeta.py:23:If D-ND zeros match Poisson: no connection, just noise. tools/dnd_zeros_vs_zeta.py:123: """Generate GUE eigenvalue spacings for comparison.""" tools/dnd_zeros_vs_zeta.py:126: # GUE: complex Hermitian matrix with Gaussian entries tools/dnd_zeros_vs_zeta.py:139: """Generate Poisson (uncorrelated) spacings.""" tools/dnd_zeros_vs_zeta.py:159: GUE: ≈ 0.5996 tools/dnd_zeros_vs_zeta.py:160: Poisson: ≈ 0.3863 tools/dnd_zeros_vs_zeta.py:220: print(f" GUE: {len(s_gue)} spacings") tools/dnd_zeros_vs_zeta.py:221: print(f" Poisson: {len(s_poisson)} spacings") tools/dnd_zeros_vs_zeta.py:227: print(f"{'Distribution':<25} {'':<8} {'KS vs zeta':<12} {'p-value':<12} {'KS vs GUE':<12} {'p vs GUE':<12}") tools/dnd_zeros_vs_zeta.py:249: print(f"{'GUE (RMT)':<25} {r_gue:<8.4f} {ks_gg:<12.4f} {p_gg:<12.4e} {'---':<12} {'---':<12}") tools/dnd_zeros_vs_zeta.py:254: print(f"{'Poisson (random)':<25} {r_poisson:<8.4f} {ks_pz:<12.4f} {p_pz:<12.4e} {ks_pg:<12.4f} {p_pg:<12.4e}") tools/dnd_zeros_vs_zeta.py:262: print(f"\nReference values: GUE ≈ 0.5996 | Poisson ≈ 0.3863") tools/dnd_zeros_vs_zeta.py:325: ax3.hist(s_gue, bins=bins, density=True, alpha=0.3, label='GUE (RMT)', color='green') tools/dnd_zeros_vs_zeta.py:326: ax3.hist(s_poisson, bins=bins, density=True, alpha=0.3, label='Poisson', color='gray') tools/dnd_zeros_vs_zeta.py:347: labels = ['Zeta', 'GUE', 'Poisson', 'Synth'] tools/dnd_zeros_vs_zeta.py:358: ax5.axhline(0.5996, color='green', linestyle='--', linewidth=1, label='GUE expected') tools/dnd_zeros_vs_zeta.py:359: ax5.axhline(0.3863, color='gray', linestyle='--', linewidth=1, label='Poisson expected') tools/dnd_zeros_vs_zeta.py:361: ax5.set_title('Spacing Ratio Statistic\n(GUE ≈ 0.60, Poisson ≈ 0.39)') tools/dnd_zeros_vs_zeta.py:396: # Determine if D-ND is closer to GUE/zeta or Poisson tools/dnd_zeros_vs_zeta.py:413: print(f" Distance to GUE: {gue_dist:.4f}") tools/dnd_zeros_vs_zeta.py:414: print(f" Distance to Poisson: {poisson_dist:.4f}") tools/dnd_zeros_vs_zeta.py:418: print(f"\n → D-ND zeros are CLOSER TO GUE/ZETA than to random") tools/dnd_banchi.py:371: 'desc': ' ratio statistic (0.386=Poisson, 0.530=GOE, 0.599=GUE)', tools/exp_boundary_two_axis_matrix.py:6:does not use GUE/Poisson source labels: it only reads transfer support and beta tools/exp_boundary_two_axis_matrix.py:107: "question": "Separate support_transfer from beta_coordinate_transfer on the 13 semi-real BOUNDARY rows without using GUE/Poisson labels.", tools/exp_boundary_two_axis_matrix.py:120: "label_policy": "GUE/Poisson source labels are not read by this operator.", tools/exp_semireal_boundary_transfer_gate.py:196: help="Include GUE/Poisson source labels as audit metadata only. Default omits them from output.", tools/exp_logistic_counter_scope_gate.py:16:SR/SR2/L1/L2/triple_var registry names. tools/exp_boundary_shuffle_audit.py:2:exp_boundary_shuffle_audit.py — Is the GUE/Poisson classification tautological? tools/exp_boundary_shuffle_audit.py:16:Domains: primes, zeta zeros, random matrix (GUE), Fibonacci spectrum, tools/exp_boundary_shuffle_audit.py:17: logistic map, Poisson, coupled oscillators, percolation, tools/exp_boundary_shuffle_audit.py:83: """GUE random matrices — eigenvalue spacings.""" tools/exp_boundary_shuffle_audit.py:100: """Poisson process — exponential spacings.""" tools/exp_boundary_shuffle_audit.py:238:R_GUE = 0.5307 # 4 - 2√3 ≈ 0.5359 for GOE; for GUE: 2π/(3√3 + 4π/3) ≈ 0.5307 approx tools/exp_boundary_shuffle_audit.py:244: 'gue': ('GUE random matrix', gen_gue_eigenvalues), tools/exp_boundary_shuffle_audit.py:245: 'poisson': ('Poisson process', gen_poisson), tools/exp_boundary_shuffle_audit.py:275: dist_gue = abs(res['r_original'] - R_GUE) tools/exp_boundary_shuffle_audit.py:277: res['class_original'] = 'GUE' if dist_gue < dist_poi else 'Poisson' tools/exp_boundary_shuffle_audit.py:279: dist_gue_s = abs(res['r_shuffled_mean'] - R_GUE) tools/exp_boundary_shuffle_audit.py:281: res['class_shuffled'] = 'GUE' if dist_gue_s < dist_poi_s else 'Poisson' tools/exp_boundary_shuffle_audit.py:308: print(f" SUMMARY: GUE/Poisson Classification Shuffle Audit") tools/exp_boundary_shuffle_audit.py:336: print(f"\n R_GUE = {R_GUE:.4f}, R_Poisson = {R_POISSON:.4f}") tools/exp_boundary_shuffle_audit.py:355: 'reference': {'R_GUE': R_GUE, 'R_Poisson': R_POISSON}, tools/exp_prime_sr_persistent_boundary.py:219: "generator": "prime gaps from dnd_autoricerca row_spacings and direct sieve; controls from composite gaps, mod6 candidates, Cramer-like events, GUE random matrix blocks, logistic return intervals", tools/exp_prime_sr_persistent_boundary.py:220: "denominator": "8 prime row-local windows plus 20 non-prime controls (3 deterministic families x 4 offsets + 4 stochastic GUE/logistic cases each by default)", tools/exp_prime_sr_persistent_boundary.py:222: "not_tested": "global beta atlas, V_c, gap_ratio, source GUE/Poisson labels, analytic origin of SR", tools/LAB_OPERATOR_PALETTE.md:19:- Non partire da phi, gap label, GUE o Poisson se sono gia' nel ciclo tools/LAB_OPERATOR_PALETTE.md:276:- GUE; tools/LAB_OPERATOR_PALETTE.md:277:- GOE; tools/LAB_OPERATOR_PALETTE.md:279:- Poisson; tools/LAB_OPERATOR_PALETTE.md:299:- Poisson synthetic; tools/LAB_OPERATOR_PALETTE.md:300:- GUE synthetic; tools/LAB_OPERATOR_PALETTE.md:307:- GUE/Poisson e' spesso un piano di proiezione, non una sorgente. Se diventa tools/LAB_OPERATOR_PALETTE.md:546:GUE/Poisson/non-phi. tools/LAB_OPERATOR_PALETTE.md:553:- Punto-zero: confine prima che venga classificato GUE o Poisson. tools/exp_dipolar_angle_reference.py:3:exp_dipolar_angle_reference.py — Dipolar angle of GUE, Poisson, and primes tools/exp_dipolar_angle_reference.py:6:1. Pure GUE eigenvalue spacings (GOE/GUE unfolded) tools/exp_dipolar_angle_reference.py:7:2. Pure Poisson (exponential iid) tools/exp_dipolar_angle_reference.py:13:unique or just a position on the GUE-Poisson continuum? tools/exp_dipolar_angle_reference.py:80: """Generate gaps from GUE eigenvalues (unfolded).""" tools/exp_dipolar_angle_reference.py:83: # GUE: complex Hermitian random matrix tools/exp_dipolar_angle_reference.py:100: """Generate gaps from GOE eigenvalues (unfolded).""" tools/exp_dipolar_angle_reference.py:116: """Generate iid exponential gaps (Poisson process).""" tools/exp_dipolar_angle_reference.py:153: # 2. GUE tools/exp_dipolar_angle_reference.py:154: print(f"Generating GUE gaps (n_trials={n_trials})...") tools/exp_dipolar_angle_reference.py:163: print(f" GUE trial {t+1}/{n_trials}: theta = {theta:.1f}") tools/exp_dipolar_angle_reference.py:165: results['GUE'] = { tools/exp_dipolar_angle_reference.py:171: print(f" GUE: theta = {np.mean(gue_thetas):.1f} +/- {np.std(gue_thetas):.1f} deg") tools/exp_dipolar_angle_reference.py:173: # 3. GOE tools/exp_dipolar_angle_reference.py:174: print(f"Generating GOE gaps (n_trials={n_trials})...") tools/exp_dipolar_angle_reference.py:183: print(f" GOE trial {t+1}/{n_trials}: theta = {theta:.1f}") tools/exp_dipolar_angle_reference.py:185: results['GOE'] = { tools/exp_dipolar_angle_reference.py:191: print(f" GOE: theta = {np.mean(goe_thetas):.1f} +/- {np.std(goe_thetas):.1f} deg") tools/exp_dipolar_angle_reference.py:193: # 4. Poisson tools/exp_dipolar_angle_reference.py:194: print(f"Generating Poisson gaps (n_trials={n_trials})...") tools/exp_dipolar_angle_reference.py:203: print(f" Poisson trial {t+1}/{n_trials}: theta = {theta:.1f}") tools/exp_dipolar_angle_reference.py:205: results['Poisson'] = { tools/exp_dipolar_angle_reference.py:211: print(f" Poisson: theta = {np.mean(poi_thetas):.1f} +/- {np.std(poi_thetas):.1f} deg") tools/exp_dipolar_angle_reference.py:240: print(f"{'GUE':<12} {results['GUE']['theta_mean']:>7.1f} +/- {results['GUE']['theta_std']:>5.1f} {results['GUE']['SR_mean']:<10.4f} {results['GUE']['L1_mean']:<10.4f}") tools/exp_dipolar_angle_reference.py:241: print(f"{'GOE':<12} {results['GOE']['theta_mean']:>7.1f} +/- {results['GOE']['theta_std']:>5.1f} {results['GOE']['SR_mean']:<10.4f} {results['GOE']['L1_mean']:<10.4f}") tools/exp_dipolar_angle_reference.py:242: print(f"{'Poisson':<12} {results['Poisson']['theta_mean']:>7.1f} +/- {results['Poisson']['theta_std']:>5.1f} {results['Poisson']['SR_mean']:<10.4f} {results['Poisson']['L1_mean']:<10.4f}") tools/exp_dipolar_angle_reference.py:247: for name in ['GUE', 'GOE', 'Poisson', 'Cramer']: tools/exp_perturbation_dimensionality_audit.py:7:The 2026-05-06 03:30 run found that GUE spacing sequences expose a second tools/exp_perturbation_dimensionality_audit.py:9:to one axis. That run used a short GUE sequence. This tool repeats the same tools/exp_perturbation_dimensionality_audit.py:25:OBS_NAMES = ["SR", "L1", "L2", "SR2", "triple_var"] tools/exp_perturbation_dimensionality_audit.py:79:def triple_var(gaps: np.ndarray) -> float: tools/exp_perturbation_dimensionality_audit.py:123: "triple_var": triple_product_var(gaps), tools/exp_perturbation_dimensionality_audit.py:130: "triple_var": triple_var(gaps), tools/exp_perturbation_dimensionality_audit.py:314: "question": "Is the GUE second perturbation axis stable across independent ensembles and sample-size controls?", tools/exp_perturbation_dimensionality_audit.py:336: print("\nGUE independent replicates") tools/dnd_research_engine.py:144: 'content': 'D-ND dynamics show GUE-like level repulsion in zero spacing (⟨r⟩≈0.84)', tools/dnd_research_engine.py:158: {'id': 'O2', 'content': 'GUE-like spacing in D-ND zeros', 'source': 'dnd_zeros_vs_zeta.py'}, tools/dnd_research_engine.py:277: f'show GUE-like level repulsion (⟨r⟩ ≈ 0.60)', tools/dnd_research_engine.py:438: """Test spacing statistics (GUE vs Poisson).""" tools/dnd_research_engine.py:675: 'prediction': 'Spacing statistics at D↔ND transition show GUE-like repulsion', tools/exp_number_variance.py:4:Tests whether the GUE->Poisson drift (seen in gap ratio) is confirmed tools/exp_number_variance.py:7:GUE: Sigma^2(L) ~ (2/pi^2) * ln(L) + const (logarithmic) tools/exp_number_variance.py:8:Poisson: Sigma^2(L) = L (linear) tools/exp_number_variance.py:10:If primes drift toward Poisson at large scale, the number variance tools/exp_number_variance.py:56: """Poisson prediction: Sigma^2 = L.""" tools/exp_number_variance.py:60: """GUE prediction: Sigma^2 ~ (2/pi^2) * ln(L) + 0.44 (approximate).""" tools/exp_number_variance.py:83: # Shuffled baseline (destroy correlations -> Poisson-like) tools/exp_number_variance.py:118: print(f" Log-fit slope (primes): {coeffs_prime[0]:.4f} (GUE={2/np.pi**2:.4f})") tools/exp_number_variance.py:123: print(f" Better fit: {'LOG (GUE-like)' if r2_log > r2_lin else 'LINEAR (Poisson-like)'}") tools/exp_number_variance.py:125: print(f"\n L | Σ²_prime | Σ²_shuf | Σ²_GUE | Σ²_Poisson") tools/exp_number_variance.py:151:# Key metric: does log_slope increase with scale? (would mean moving away from GUE) tools/exp_number_variance.py:158: print(f" GUE value: {2/np.pi**2:.4f}") tools/exp_number_variance.py:159: print(f" If trend > 0: number variance grows faster at large scale -> MORE Poisson") tools/exp_number_variance.py:160: print(f" If trend ~ 0: stable -> GUE character preserved") tools/exp_number_variance.py:166: "claim_under_test": "BOUNDARY: GUE->Poisson drift in primes", tools/exp_number_variance.py:167: "method": "Number variance Sigma^2(L) at 5 scales, compared with GUE and Poisson predictions", tools/dnd_experiments.py:225:# ESPERIMENTO 2: Boundary GUE/Poisson — Il terzo incluso tools/dnd_experiments.py:230: Testa H_boundary: esiste una transizione GUE→Poisson tools/dnd_experiments.py:240: print("ESPERIMENTO: Boundary GUE/Poisson (Ising T-sweep)") tools/dnd_experiments.py:372: print("ESPERIMENTO: Boundary GUE/Poisson (Logistica r-sweep)") tools/dnd_experiments.py:481:# ESPERIMENTO 3: Order parameter — r_diretto separa GUE/Poisson? tools/dnd_experiments.py:487: che separa GUE da Poisson? tools/dnd_experiments.py:490: - Domini GUE hanno r_diretto in un range tools/dnd_experiments.py:491: - Domini Poisson hanno r_diretto in un altro range tools/dnd_experiments.py:513: # Classifica GUE vs Poisson (soglia standard: 0.53) tools/dnd_experiments.py:514: GUE_THRESHOLD = 0.53 tools/dnd_experiments.py:515: gue = [(d, sp, r) for d, sp, r, _ in domains if sp > GUE_THRESHOLD] tools/dnd_experiments.py:516: poisson = [(d, sp, r) for d, sp, r, _ in domains if sp <= GUE_THRESHOLD] tools/dnd_experiments.py:518: print(f"\n GUE ({len(gue)} domini):") tools/dnd_experiments.py:522: print(f"\n Poisson ({len(poisson)} domini):") tools/dnd_experiments.py:546: print(f" r_diretto GUE: {mean_gue:.4f} ± {std_gue:.4f} range=[{gue_range[0]:.4f}, {gue_range[1]:.4f}]") tools/dnd_experiments.py:547: print(f" r_diretto Poisson: {mean_poisson:.4f} ± {std_poisson:.4f} range=[{poisson_range[0]:.4f}, {poisson_range[1]:.4f}]") tools/dnd_experiments.py:549: print(f" r_diretto SEPARA GUE/Poisson: {'SÌ' if separates else 'NO'}") tools/dnd_experiments.py:1031: # Per spacings (zeta, GUE): il segnale È gli intervalli tools/exp_boundary_growth.py:13: - GUE (correlated): ~ 0.5307 tools/exp_boundary_growth.py:14: - Poisson (uncorrelated): ~ 0.3863 tools/exp_boundary_growth.py:158: # Key question: do primes approach GUE or Poisson at large scale? tools/exp_boundary_growth.py:159: GUE_R = 0.5307 tools/exp_boundary_growth.py:165: print(f"GUE reference: {GUE_R}") tools/exp_boundary_growth.py:166: print(f"Poisson reference: {POISSON_R}") tools/exp_boundary_growth.py:167: print(f"Position: {'closer to GUE' if abs(r_large - GUE_R) < abs(r_large - POISSON_R) else 'closer to Poisson'}") tools/build_agent_field.py:1457: "Un pattern in distribuzioni classiche (primi, GUE, random walk, Markov chain, " tools/build_agent_field.py:1461: "per GUE level statistics, Erdos-Kac per distribuzioni aritmetiche). Se non lo " tools/dnd_paper_refactor.py:387: "chain": "|NT⟩ → closure → det=-1 → φ → GUE → differentiated reality", tools/exp_acf_range_universality.py:11:Domains: primes, GUE, GOE, Poisson, primes_shuffled. tools/exp_acf_range_universality.py:69:def generate_rmt_spacings(N, n_mat, ensemble='GUE'): tools/exp_acf_range_universality.py:74: if ensemble == 'GUE': tools/exp_acf_range_universality.py:78: else: # GOE tools/exp_acf_range_universality.py:169: print("Generating GUE spacings...") tools/exp_acf_range_universality.py:170: gue_gaps = generate_rmt_spacings(N_rmt, n_mat_rmt, 'GUE') tools/exp_acf_range_universality.py:171: print("Generating GOE spacings...") tools/exp_acf_range_universality.py:172: goe_gaps = generate_rmt_spacings(N_rmt, n_mat_rmt, 'GOE') tools/exp_acf_range_universality.py:182: 'GUE': gue_gaps, tools/exp_acf_range_universality.py:183: 'GOE': goe_gaps, tools/exp_acf_range_universality.py:184: 'Poisson': poisson_gaps, tools/exp_dipolar_crossover.py:3:exp_dipolar_crossover.py — Topology of the GUE-Poisson transition in the dipolar plane. tools/exp_dipolar_crossover.py:5:Question: As ordering is gradually destroyed (GUE → shuffled), does the dipolar tools/exp_dipolar_crossover.py:10: 1. Generate GUE bulk spacings from random matrices tools/exp_dipolar_crossover.py:14: - Leave the rest in original (GUE) order tools/exp_dipolar_crossover.py:19:If primes sit on the curve → they're "partially disordered GUE" tools/exp_dipolar_crossover.py:33: """Generate bulk spacings from GUE matrices.""" tools/exp_dipolar_crossover.py:98: print(f"Generating GUE spacings: {n_matrices} matrices of size {N_mat}...") tools/exp_dipolar_crossover.py:101: # GUE baseline (alpha=0) tools/exp_dipolar_crossover.py:214: "GUE_baseline": {"SR": float(sr0), "L1": float(l1_0)}, tools/exp_brody_calibration.py:6:Poisson (beta=0) to Wigner-GUE (beta=1). Gaps are i.i.d. by construction — tools/exp_brody_calibration.py:211: # GUE matrices tools/exp_brody_calibration.py:235: # Poisson tools/exp_brody_calibration.py:326: print(f" Theory: Poisson r=0.386, GOE r=0.536") tools/dnd_risultante.py:33: The algebra det=-1 → disc=5 → Q(√5) → L(s,χ₅) → GUE statistics tools/dnd_risultante.py:35: the deviation from pure GUE. The risultante dynamics produce the same tools/dnd_risultante.py:548: Piano 11: det=-1 → disc=5 → Q(√5) → L(s,χ₅) → GUE tools/dnd_risultante.py:573: "statistics": "GUE (Katz-Sarnak)" tools/exp_excess_scaling.py:124:# Additional: check GUE vs Poisson classification tools/exp_excess_scaling.py:125:# GUE: ~ 0.5307, Poisson: ~ 0.3863 tools/exp_excess_scaling.py:126:r_GUE = 0.5307 tools/exp_excess_scaling.py:127:r_Poisson = 0.3863 tools/exp_excess_scaling.py:129:print(f"\n Reference: GUE = {r_GUE}, Poisson = {r_Poisson}") tools/exp_excess_scaling.py:133:# Does move toward Poisson at large scale? tools/exp_excess_scaling.py:135: print(f" DECREASES with scale: moving toward Poisson") tools/exp_excess_scaling.py:137: print(f" INCREASES with scale: moving toward/staying GUE") tools/exp_boundary_blank_null_audit.py:27:R_GUE = 0.5307 tools/exp_boundary_blank_null_audit.py:66: return "GUE" if abs(value - R_GUE) < abs(value - R_POISSON) else "Poisson" tools/exp_boundary_blank_null_audit.py:151: "R_GUE": R_GUE, tools/exp_boundary_blank_null_audit.py:152: "R_Poisson": R_POISSON, tools/exp_selective_layer_decoupling.py:177:def triple_var(gaps): tools/exp_selective_layer_decoupling.py:190: 'triple_var': triple_var, tools/exp_selective_layer_decoupling.py:194:LAYER2_OBS = ['SR2', 'L2', 'cond_entropy', 'triple_var'] tools/exp_selective_layer_decoupling.py:265: 'GUE': gen_gue_spacings(args.N, rng), tools/exp_selective_layer_decoupling.py:266: 'Poisson': gen_poisson_spacings(args.N, rng), tools/exp_cross_domain_dipolar_direction.py:6:Primes have theta=-111 deg in the (SR, L1) plane. GUE has theta=-97 deg. tools/exp_cross_domain_dipolar_direction.py:7:Do all GUE-like domains share -97, or does each have its own direction? tools/exp_cross_domain_dipolar_direction.py:8:Do all Poisson-like domains lack direction? tools/exp_cross_domain_dipolar_direction.py:11: GUE-like: GUE spacings, GOE spacings, CUE spacings, Riemann zeta zeros (via GUE proxy) tools/exp_cross_domain_dipolar_direction.py:12: Poisson-like: exponential iid, uniform iid, geometric iid tools/exp_cross_domain_dipolar_direction.py:81:def gen_rmt_spacings(N_mat, ensemble='GUE', rng=None): tools/exp_cross_domain_dipolar_direction.py:85: if ensemble == 'GUE': tools/exp_cross_domain_dipolar_direction.py:90: elif ensemble == 'GOE': tools/exp_cross_domain_dipolar_direction.py:133: """Poisson process: exponential iid spacings.""" tools/exp_cross_domain_dipolar_direction.py:177: for ens_name in ['GUE', 'GOE', 'CUE']: tools/exp_cross_domain_dipolar_direction.py:229: # === 6-8. Poisson-like domains === tools/exp_cross_domain_dipolar_direction.py:233: print(f"\n--- {name.upper()} (iid, Poisson-class) ---") tools/exp_cross_domain_dipolar_direction.py:246: 'class': 'Poisson', tools/exp_cross_domain_dipolar_direction.py:275: rmt_names = ['GUE', 'GOE', 'CUE'] tools/exp_cross_domain_dipolar_direction.py:311: # Poisson direction scatter tools/exp_cross_domain_dipolar_direction.py:312: print("\n Poisson direction scatter (std of theta):") tools/exp_observable_rank_audit.py:175: print(f"{'sequence':<14} {'pc1':>8} {'eff_rank':>9} {'mean|corr|':>11} {'weak':>5} z(SR,SR2,L1,L2,triple_var)") tools/data/notte_20260326_0330.md:5: collatz_cp: r=0.9497136076205077, spacing=GUE-like [conferma] tools/data/notte_20260326_0330.md:8: ising_2d_cp_-0.40399731526527427: r=0.9108185170256915, spacing=GUE-like [conferma] tools/data/notte_20260326_0330.md:9: ising_2d_cp_0.45522935739736126: r=1.0431954263284353, spacing=GUE-like [conferma] tools/data/notte_20260326_0330.md:10: brownian_motion_cp_0.10156293949284084: r=0.9966329966329966, spacing=Poisson-like [conferma] tools/data/notte_20260326_0330.md:11: brownian_motion_cp_0.9083139965385025: r=1.0, spacing=Poisson-like [conferma] tools/data/notte_20260326_0330.md:12: percolation_cp_0.3034086711489568: r=0.6949152542372882, spacing=GUE-like [conferma] tools/data/notte_20260326_0330.md:13: percolation_cp_0.8397168647434718: r=1.0454545454545456, spacing=GUE-like [conferma] tools/data/notte_20260326_0330.md:14: cellular_automata_cp_150: r=0.7452006980802792, spacing=GUE-like [conferma] tools/data/notte_20260326_0330.md:17: logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like (⟨r⟩=0.35930230610663094) tools/data/notte_20260326_0330.md:18: numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like (⟨r⟩=0.9609375) tools/data/notte_20260326_0330.md:19: cellular_automata_var_30: r=0.8703941780326052, spacing=GUE-like (⟨r⟩=0.8649999999999983) tools/data/notte_20260326_0330.md:20: coupled_oscillators_var_50: r=1.0, spacing=Poisson-like (⟨r⟩=0.45756422661060286) tools/dnd_lab.py:10: 1. Wigner surmise (GOE/GUE) — distribuzione esatta level spacing tools/dnd_lab.py:14: 5. Berry-Tabor — integrabile→Poisson vs caotico→GUE tools/dnd_lab.py:49: DIMOSTRATO: Level spacing distribution per GOE e GUE. tools/dnd_lab.py:50: GOE: P(s) = (π/2) s exp(-πs²/4) tools/dnd_lab.py:51: GUE: P(s) = (32/π²) s² exp(-4s²/π) tools/dnd_lab.py:58: print("BANCO 1: Wigner Surmise (GOE e GUE esatti)") tools/dnd_lab.py:63: for ensemble, name in [('GOE', 'GOE'), ('GUE', 'GUE')]: tools/dnd_lab.py:67: if ensemble == 'GOE': tools/dnd_lab.py:78: # GUE: P(s) = (32/π²) s² exp(-4s²/π) tools/dnd_lab.py:661: spettrale. Il punto critico e' dove ≈ 0.50 (ne' GUE ne' Poisson). tools/dnd_lab.py:690: print(f" Transizione: V<{critical_V} → GUE/metallico, V>{critical_V} → Poisson/localizzato") tools/dnd_lab.py:1426: 'GUE': 'random_matrix_gue', tools/dnd_lab.py:1476: elif 'WIGNER' in tid or 'GUE' in tid: tools/dnd_lab.py:1652: # Trova dove attraversa r_target (dall'alto: GUE→critico→Poisson) tools/dnd_lab.py:1716: 2. Trova V_c dove = 0.50 (transizione GUE ↔ Poisson) tools/data/notte_20260303_0330.md:4: ising_2d_var_-0.1: r=0.999764483716038, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260303_0330.md:5: ising_2d_var_0.1: r=0.9962779970953693, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260303_0330.md:6: numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like (⟨r⟩=0.9609375) tools/data/notte_20260303_0330.md:7: logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like (⟨r⟩=0.35930230610663094) tools/data/notte_20260303_0330.md:8: logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like (⟨r⟩=0.3914428894887236) tools/data/notte_20260303_0330.md:9: cellular_automata_var_30: r=0.8703941780326052, spacing=GUE-like (⟨r⟩=0.8649999999999983) tools/data/notte_20260303_0330.md:10: brownian_motion_var_0.3: r=0.9895833333333333, spacing=Poisson-like (⟨r⟩=0.39037158456490495) tools/data/notte_20260303_0330.md:11: brownian_motion_var_0.5: r=1.0, spacing=Poisson-like (⟨r⟩=0.39619010656660225) tools/data/notte_20260303_0330.md:12: coupled_oscillators_var_50: r=1.0, spacing=Poisson-like (⟨r⟩=0.45756422661060286) tools/data/notte_20260303_0330.md:13: percolation_var_0.55: r=1.0, spacing=Poisson-like (⟨r⟩=0.43344476133517995) tools/data/notte_20260303_0330.md:14: percolation_var_0.65: r=1.380952380952381, spacing=Poisson-like (⟨r⟩=0.45002661025974017) tools/data/reports/agent_20260506_1955.md:6:observables_used: [SR, SR2, L1, L2, triple_var] tools/data/reports/agent_20260506_1955.md:23:- replaced the old local `triple_var` normalized convention with canonical raw `triple_var`; tools/data/reports/agent_20260506_1955.md:28:- domains: first prime gaps, prime-shuffle control, independent GUE spacings, iid Poisson spacings; tools/data/reports/agent_20260506_1955.md:39:| primes | 0.978 | 1.128 | 0.975 | 1 | SR=-12.1, SR2=-2.5, L1=-8.9, L2=-1.9, triple_var=-8.7 | tools/data/reports/agent_20260506_1955.md:41:| GUE | 0.990 | 1.060 | 0.989 | 0 | SR=-2.9, SR2=+14.5, L1=+13.2, L2=+31.7, triple_var=+23.8 | tools/data/reports/agent_20260506_1955.md:42:| Poisson | 0.625 | 2.368 | 0.609 | 5 | all abs(z) <= 1.9 | tools/data/reports/agent_20260506_1955.md:50:| GUE | 0.980 | 1.106 | 0.977 | 0.33 | tools/data/reports/agent_20260506_1955.md:51:| Poisson | 0.714 | 2.196 | 0.572 | 5.00 | tools/data/reports/agent_20260506_1955.md:55:1. **Structured domains compress the five canonical retention curves to one dominant coordinate in this perimeter.** Primes and GUE both have PC1 > 0.93 on average and effective rank close to 1. This does not say the domains are the same; it says uniform partial shuffle moves the canonical observables along one dominant retention mode. tools/data/reports/agent_20260506_1955.md:57:2. **Observed collinearity breaking is concentrated in weak-denominator controls.** Poisson has the highest apparent rank among the three-run means (`2.196`), but all five observables are weak against full shuffle in every run. Prime-shuffle behaves similarly: rank is unstable and 4-5 of 5 observables are weak. This mirrors the denominator lesson from perturbation rank without repeating the perturbation-rank experiment. tools/data/reports/agent_20260506_1955.md:59:3. **The 05-05 observable-rank result survives only after narrowing its language.** The valid statement is not "five probes are always one thing." The scoped statement is: under uniform partial shuffle and canonical observables, primes and GUE show a dominant one-coordinate retention response; controls can show larger PCA rank, but that rank is not structural when the original-vs-shuffle denominators are absent. tools/data/reports/agent_20260506_1955.md:61:4. **GUE is the cleanest conditioning check.** In the main run, all five GUE observables pass the denominator gate and still give rank `1.060`. This makes GUE the best positive control for "low rank despite valid denominators." Poisson is the negative control for "high rank without valid denominators." tools/data/reports/agent_20260506_1955.md:68:In this perimeter, high observable-rank is not the sign of richer structure when it appears in Poisson or prime-shuffle controls; it is a warning that retention ratios are being formed on weak denominators. The stable cross-domain result is narrower: tools/data/reports/agent_20260506_1955.md:70:> uniform partial shuffle exposes one dominant retention coordinate in conditioned structured domains (primes, GUE), while apparent multi-coordinate behavior in Poisson/shuffle controls is denominator-weak. tools/data/reports/agent_20260506_1955.md:73:What opens now: the next non-redundant test is not another PCA audit. It is **selective operator coupling**: use perturbations that act separately on pair-scale and triple-scale structure, but report only observables whose denominator gate survives. If selective operators rotate primes while GUE stays collinear, the lab gets a real cross-domain discriminator; if both stay collinear, the current observable suite is overcomplete for this question. tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:10: "triple_var", tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:44: "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:67: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:76: "triple_var": -3.5099777179968443 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:90: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:97: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:106: "triple_var": -14.993726011775056 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:125: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:134: "triple_var": -17.18157602737142 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:154: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:166: "triple_var": 12.523640351240866 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:176: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:183: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:195: "triple_var": 10.75231827616513 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:210: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:222: "triple_var": 11.741467183557688 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:236: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:248: "triple_var": 11.947033966146186 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:263: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:275: "triple_var": 14.251390869614799 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:291: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:303: "triple_var": 8.781258686204932 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:313: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:320: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:332: "triple_var": 13.038998931574294 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:342: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:349: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:361: "triple_var": 8.833878085721798 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:378: "z_delta_triple_var_mean": 11.483748293778213 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:389: "z_delta_triple_var_mean": 0.008054674151818403 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:406: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:418: "triple_var": 22.301049754649934 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:428: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:434: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:446: "triple_var": 9.159871281888737 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:461: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:473: "triple_var": 15.578924374541423 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:489: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:501: "triple_var": 9.786660030143256 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:525: "triple_var": 18.95963926705238 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:540: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:552: "triple_var": 8.748363903137989 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:562: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:569: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:581: "triple_var": 14.047330472793302 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:591: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:598: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:610: "triple_var": 10.790947390789572 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:627: "z_delta_triple_var_mean": 13.671598309374573 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:638: "z_delta_triple_var_mean": 0.009275079326336344 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:668: "triple_var": -1.5427512682830968 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:675: "triple_var": -0.056893110583065606 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:691: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:708: "triple_var": -15.794142137897895 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:715: "triple_var": -0.1327289002798124 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:744: "triple_var": -20.50239053533548 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:751: "triple_var": -0.11534182849819136 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:765: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:779: "triple_var": -3.925498027171484 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:786: "triple_var": -0.5565080490980227 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:802: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:818: "triple_var": -12.759376112893282 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:825: "triple_var": 0.5463043262818726 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:841: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:857: "triple_var": -14.716445417961056 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:864: "triple_var": 0.4200694223793047 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:891: "triple_var": -4.596852743569116 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:898: "triple_var": -0.9522367268397922 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:914: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:930: "triple_var": -13.378111429774048 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:937: "triple_var": 0.2443540674635697 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:952: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:969: "triple_var": -13.345216646707105 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:976: "triple_var": -0.0424605515444057 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:990: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1006: "triple_var": -3.7436288200258803 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1013: "triple_var": -0.061864934626999275 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1029: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1045: "triple_var": -16.782627751600174 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1052: "triple_var": -0.3339113268423166 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1068: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1084: "triple_var": -17.79095929281918 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1091: "triple_var": 0.6532408017238057 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1119: "triple_var": -1.6529769771970457 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1126: "triple_var": -0.07357768253019742 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1142: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1158: "triple_var": -14.176617328437912 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1165: "triple_var": 0.41270619695995786 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1179: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1195: "triple_var": -23.95402673184698 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1202: "triple_var": -0.012112964465623974 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1229: "triple_var": -3.795375734364665 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1236: "triple_var": 0.48220901670695715 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1250: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1266: "triple_var": -15.742409700510851 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1273: "triple_var": 0.26449492801308344 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1289: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1305: "triple_var": -13.582035764507921 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1312: "triple_var": 0.13126136042179593 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1326: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1342: "triple_var": -4.919855090714239 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1349: "triple_var": 0.6356851966039665 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1365: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1381: "triple_var": -15.67217336687937 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1388: "triple_var": 0.21451105950527025 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1403: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1419: "triple_var": -14.079726372602975 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1426: "triple_var": 0.04290691460880933 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1452: "triple_var": -3.90288308264923 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1459: "triple_var": 1.0340171317114486 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1475: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1491: "triple_var": -15.644350266206917 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1498: "triple_var": 0.3591853602508874 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1514: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1528: "triple_var": -19.48180745719065 tools/data/prime_vs_mod6_sr_boundary_20260514_0330.json:1535: "triple_var": 0.2889038569151086 tools/data/reports/agent_20260509_1556.md:7:observables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`, `extension_state`, `after_one_sided`, `after_beta`, `after_support_tier`, `endpoint_distance_one_sided_gated`] - osservabili canonici piu' stati domain-native di audit. tools/data/reports/agent_20260509_1556.md:9:**observable_contract**: claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente. tools/data/reports/agent_20260509_1556.md:25:- `not_drift`: non torna a `V_c`, non cambia tensione, non usa label GUE/Poisson come decision field; modifica solo il denominatore delle tre righe short. tools/data/reports/agent_20260509_1556.md:40:- Label policy: i nomi riga selezionano il perimetro short; le label GUE/Poisson non entrano nel decision field. tools/data/reports/agent_20260509_1556.md:98:- **L3 no silent patching**: l'estensione cambia il generatore operativo in modo dichiarato: percolation site samples, GUE block spacings, zeta zeros first spacings. tools/data/boundary_denominator_prescan_20260509_1430.json:3: "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", tools/data/boundary_denominator_prescan_20260509_1430.json:4: "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", tools/data/boundary_denominator_prescan_20260509_1430.json:27: "GUE": 8, tools/data/boundary_denominator_prescan_20260509_1430.json:28: "Poisson": 5 tools/data/boundary_denominator_prescan_20260509_1430.json:54: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:61: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1430.json:79: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:86: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1430.json:104: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:111: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1430.json:129: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:136: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1430.json:154: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:161: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1430.json:179: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:186: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1430.json:204: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:211: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1430.json:229: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:236: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1430.json:254: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:261: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1430.json:279: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:286: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1430.json:304: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:311: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1430.json:329: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1430.json:336: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1430.json:354: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1430.json:361: "label": "GUE-like", tools/data/notte_20260312_0330.md:4: ising_2d_var_-0.1: r=0.9908745521490876, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260312_0330.md:5: ising_2d_var_0.1: r=1.6755418202488377, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260312_0330.md:6: numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like (⟨r⟩=0.9609375) tools/data/notte_20260312_0330.md:7: logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like (⟨r⟩=0.35930230610663094) tools/data/notte_20260312_0330.md:8: logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like (⟨r⟩=0.3914428894887236) tools/data/notte_20260312_0330.md:9: cellular_automata_var_30: r=0.8703941780326052, spacing=GUE-like (⟨r⟩=0.8649999999999983) tools/data/notte_20260312_0330.md:10: brownian_motion_var_0.3: r=0.22824007651841227, spacing=Poisson-like (⟨r⟩=0.38919713749813906) tools/data/notte_20260312_0330.md:11: brownian_motion_var_0.5: r=1.121711664935964, spacing=Poisson-like (⟨r⟩=0.3831196498969426) tools/data/notte_20260312_0330.md:12: coupled_oscillators_var_50: r=1.0, spacing=Poisson-like (⟨r⟩=0.45756422661060286) tools/data/notte_20260312_0330.md:13: percolation_var_0.55: r=1.037037037037037, spacing=Poisson-like (⟨r⟩=0.41840391114320896) tools/data/notte_20260312_0330.md:14: percolation_var_0.65: r=1.0357142857142858, spacing=Poisson-like (⟨r⟩=0.48785515925635364) tools/data/reports/agent_20260423_0330.md:8:> The boundary between GUE and Poisson regimes should affect the two-channel decomposition. If gap correlations decay with prime scale (Brody β → 0), does the Markov-3 ordering information in the residue channel also decay? tools/data/reports/agent_20260423_0330.md:11:How does the Markov-3 ordering fraction (the 55% sequential information found by the shuffle audit) change as a function of prime scale? Does it track the GUE→Poisson drift measured by the Brody parameter? tools/data/reports/agent_20260423_0330.md:19:- **Additional metrics**: Brody β (GUE/Poisson indicator), lag-1 ACF tools/data/reports/agent_20260423_0330.md:58:2. **Brody β decays 5x faster toward Poisson.** β drops from 0.42 to 0.29 over the same range (R²=0.72). Gap-level correlations (the GUE signature) decorrelate with scale. But this decorrelation does NOT propagate into the mod-6 sequential memory. tools/data/reports/agent_20260423_0330.md:69:The BOUNDARY tension (GUE→Poisson) operates in the magnitude channel (gap correlations, Brody β). It does NOT operate in the residue channel (Markov-3 memory). The two-channel decomposition separates scale-dependent structure from scale-invariant structure. This is a structural result: the "boundary" lives in one channel, not both. tools/data/reports/agent_20260423_0330.md:71:Constraint: future BOUNDARY experiments should distinguish which channel they're measuring. The GUE/Poisson transition is a gap-level phenomenon; the residue channel is immune to it. tools/data/reports/agent_20260423_0330.md:78:- **Campo di possibilita**: qui diventa possibile separare le proprieta del gap dei primi in scala-dipendenti (che decadono con PNT) e scala-invarianti (che sono vincoli permanenti). Qui diventa non-possibile usare il drift GUE→Poisson per predire il comportamento del canale residuo — sono strutturalmente disaccoppiati. tools/data/reports/agent_20260430_1905.md:1:# Agent Report — Observable Coherence at the GUE-Poisson Boundary: Primes Are Not "Between" — They Are Dipolar tools/data/reports/agent_20260430_1905.md:9:> Do different observables agree on WHERE primes sit between GUE and Poisson? tools/data/reports/agent_20260430_1905.md:15:Five independent observables each place prime gaps on a τ ∈ [0,1] scale where 0 = Poisson and 1 = GUE. Do these five τ values cluster tightly (coherent — one underlying quantity) or spread apart (incoherent — genuinely independent measurements)? tools/data/reports/agent_20260430_1905.md:22:- **Each normalized** to τ ∈ [0,1] using analytic Poisson and GUE reference values tools/data/reports/agent_20260430_1905.md:25:- **References**: pure GUE from 50 random Hermitian matrices, pure Poisson from exponential draws tools/data/reports/agent_20260430_1905.md:29:### τ values (0 = Poisson, 1 = GUE) tools/data/reports/agent_20260430_1905.md:54:| spacing_ratio | **−0.125** | **Ordering pushes toward Poisson** | tools/data/reports/agent_20260430_1905.md:55:| lag1_acf | **+0.200** | **Ordering pushes toward GUE** | tools/data/reports/agent_20260430_1905.md:70:2. **The two ordering-sensitive observables form a dipole.** Spacing ratio is pushed TOWARD Poisson by ordering (Δτ = −0.12), while lag-1 autocorrelation is pushed TOWARD GUE (Δτ = +0.20). The same physical phenomenon — consecutive gap anticorrelation (Lemke Oliver-Soundararajan type) — manifests as Poisson in one measure and GUE in another. Primes are not "between" GUE and Poisson on a single axis. They are dipolar: GUE in correlation structure, Poisson in consecutive ratio behavior. tools/data/reports/agent_20260430_1905.md:74:4. **Universal Poisson drift confirmed.** All five τ values decrease with scale (Δτ ≈ −0.14 to −0.19 from 10⁴ to 5·10⁶). This confirms the Brody flow finding from the previous run. The drift rate is roughly constant per observable, suggesting a single underlying process. tools/data/reports/agent_20260430_1905.md:80:**NEW (dipolar ordering signature) + CONSTRAINT on META + CONFIRMED (Poisson drift)** tools/data/reports/agent_20260430_1905.md:84:- **BOUNDARY**: the boundary is not a point on a one-dimensional axis between GUE and Poisson. It is a two-dimensional structure: one axis for distribution (all observables agree), one axis for ordering (the dipole between spacing_ratio and lag1_acf). The terzo incluso is the dipole — it doesn't interpolate between GUE and Poisson, it has a structure that neither has. tools/data/reports/agent_20260430_1905.md:90:- **Due radici** (dipolo primario): spacing_ratio (ordering → Poisson) and lag1_acf (ordering → GUE). The same phenomenon — consecutive gap anticorrelation — is seen as repulsion by one observable and as correlation by the other. The two faces are structurally inverted: one says "more random" where the other says "more structured." tools/data/reports/agent_20260430_1905.md:94:- **Invariante di passaggio**: the 2x coherence enhancement. Across all 4 scales, prime ordering makes observables agree more (std ratio ≈ 0.5). This ratio is scale-invariant even as all τ values drift toward Poisson. tools/data/reports/agent_20260430_1905.md:96:- **Campo di possibilità**: possibile → characterize prime ordering as a 2D vector (spacing_ratio shift, lag1_acf shift) rather than a single GUE-Poisson interpolation parameter. Non-possibile → reduce prime ordering to a single β value and claim it captures the structure. tools/data/reports/gap_labeling_20260307_0342.json:905: "classification": "GOE" tools/data/promotions/promotion_20260509_0741.json:18: "summary": "**CONSTRAINT**: il null label-preserving per `V_c` raggiunge `Jaccard>=0.75` a `N=144` con acceptance_rate non nulla (`6/8` sequenze, `0.75` sulle righe evento), ma solo come `phase_shift_sturmian`. Quindi il Lab ha un ponte strutturato accettabile per testare coerenza interna del boundary, non un contro-campo indipendente per trasferire verso GUE/Poisson.\n\nLa formulazione valida e': `phase_shift_sturmian` chiude il problema di reachability a N=144; non chiude il problema di indipendenza. Il prossimo confronto deve costruire un null non-Sturmian con `source_mode` diverso e `hamming_ratio` dich", tools/exp_scale_selective_perturbation.py:6:observables (SR, L1, L2, SR2, triple_var) collapse to 1 latent coordinate tools/exp_scale_selective_perturbation.py:16:For each perturbation at multiple intensities, measure SR/L1/L2/SR2/triple_var, tools/exp_scale_selective_perturbation.py:48: """Generate N GUE gaps (eigenvalue spacings of random Hermitian matrix).""" tools/exp_scale_selective_perturbation.py:66: """Generate N Poisson (iid exponential) gaps.""" tools/exp_scale_selective_perturbation.py:113:def triple_variance(gaps): tools/exp_scale_selective_perturbation.py:126: 'triple_var': triple_variance(gaps), tools/exp_scale_selective_perturbation.py:192: obs_names = ['SR', 'L1', 'L2', 'SR2', 'triple_var'] tools/exp_scale_selective_perturbation.py:197: ('GUE', lambda: generate_gue(N, rng))]: tools/exp_scale_selective_perturbation.py:312: print(f"{pert_name:<20} {ret['SR']:>8.3f} {ret['L1']:>8.3f} {ret['L2']:>8.3f} {ret['SR2']:>8.3f} {ret['triple_var']:>8.3f}") tools/data/r_excess_l_functions.json:1036: "GUE": 0.60266, tools/data/promotions/promotion_20260509_1409.json:17: "title": "**PARTIAL TRANSFER**: `denominator_state` trasferisce oltre `V_c` come gate di perimetro, non come risposta GUE/Poisson.", tools/data/promotions/promotion_20260509_1409.json:18: "summary": "**PARTIAL TRANSFER**: `denominator_state` trasferisce oltre `V_c` come gate di perimetro, non come risposta GUE/Poisson.\n\nNel perimetro 8 GUE / 5 Poisson, il gate produce una condizione cross-dominio verificabile su 8 righe con null shuffle disponibile. Su 5 righe resta blank strutturale: l'osservabile locale esiste, ma manca il contro-perimetro row-aligned.\n\nIl gate `denominator_state` trasferisce oltre V_c perche' separa 8 righe con null/surrogate shuffle disponibile da 5 righe blank senza contro-perimetro; il contro-perimetro sono `pendolo_doppio`, `zeta_zeros`, `string_vibration`, `reactio", tools/data/promotions/promotion_20260506_1955.json:18: "summary": "**CONSTRAINT on META**: observable collinearity claims must be reported with:\n\n> observables_registry version + canonical observable list + original-vs-shuffle z per observable + control domains.\n\nIn this perimeter, high observable-rank is not the sign of richer structure when it appears in Poisson or prime-shuffle controls; it is a warning that retention ratios are being formed on weak denominators. The stable cross-domain result is narrower:\n\n> uniform partial shuffle exposes one dominant retention coordinate in conditioned structured domains (primes, GUE), while apparent multi-coordinate be", tools/dnd_spectral_probe.py:72: 3. KS test against GUE/GOE/Poisson tools/dnd_spectral_probe.py:155: β=0: Poisson, β=1: GOE, β=2: GUE. tools/dnd_spectral_probe.py:158: If β>2 at all scales → GUE (even when KS says GOE). tools/dnd_spectral_probe.py:192: cls = "GUE" if beta > 1.5 else ("GOE" if beta > 0.5 else "Poisson") tools/dnd_spectral_probe.py:213: Poisson: = 2ln2 - 1 ≈ 0.3863 tools/dnd_spectral_probe.py:214: GOE: ≈ 0.5359 tools/dnd_spectral_probe.py:215: GUE: ≈ 0.6027 tools/dnd_spectral_probe.py:230: refs = {"Poisson": 0.38629, "GOE": 0.53590, "GUE": 0.60266} tools/dnd_spectral_probe.py:248: """KS test against GUE, GOE, Poisson.""" tools/dnd_spectral_probe.py:256: fits = [("GUE", ks_gue, p_gue), ("GOE", ks_goe, p_goe), ("Poisson", ks_poi, p_poi)] tools/dnd_spectral_probe.py:309: if majority[0] == "GUE" and "GOE" in dissenters: tools/dnd_spectral_probe.py:310: verdict = (f"LIKELY GUE: {majority[0]} (2/3 — KS blind to GUE, " tools/dnd_spectral_probe.py:462: print(f" |Δ Poisson|={dists.get('Poisson', 0):.5f} " tools/dnd_spectral_probe.py:463: f"|Δ GOE|={dists.get('GOE', 0):.5f} " tools/dnd_spectral_probe.py:464: f"|Δ GUE|={dists.get('GUE', 0):.5f}") tools/dnd_spectral_probe.py:522: → GUE statistics (Katz-Sarnak) tools/dnd_spectral_probe.py:633: reference_beta: theoretical β to converge to (2.0 for GUE) tools/dnd_spectral_probe.py:1058: print(f" All GUE: {all_gue} ({sum(1 for b in valid if b > 1.5)}/{len(valid)})") tools/data/promotions/promotion_20260514_1640.json:18: "summary": "**CONSTRAINT / TESTER OPERATIVO RAFFORZATO**.\n\nIl ciclo rafforza lo strumento, non il claim fisico. `component_state(SR,L1,triple_var)` ora ha un denominatore piu' robusto: distingue GOE e GUE tramite adjacent gap ratio, resta leggibile su due taglie, e mantiene Anderson `W=6` come bordo in cui `SR` viene assorbito prima del residuo `triple_var`.", tools/dnd_quantization.py:13:Domanda: gli spacing dei livelli E_n seguono GUE? tools/dnd_quantization.py:317: closer = "GUE" if d_gue < d_poi else "Poisson" tools/dnd_quantization.py:318: print(f" = {stats['mean_r']:.4f} (GUE: 0.5996, Poisson: 0.3863) → {closer}") tools/dnd_quantization.py:319: print(f" Var(s) = {stats['var_s']:.4f} (GUE: 0.178, Poisson: 1.0)") tools/dnd_quantization.py:333: f"→ {'GUE' if d_gue < d_poi else 'Poi'}") tools/data/promotions/promotion_20260507_0803.json:18: "summary": "**category: gate_transferability** \n**verdict: operator**\n\nScoped statement:\n\n> In this synthetic DUALITA perimeter, the denominator gate is transferable as\n> an operator for structural order: it separates dipolar generation from\n> illusory dispersion with replicated z support. The beta coordinate of the\n> GUE/Poisson boundary layer is not transferable: ambiguity appears near beta\n> 0.3, while denominator collapse appears near beta 0.8-0.9.\n\nSo the last BOUNDARY cycles forged a lens, but one parameter of that lens was\nlocal to BOUNDARY. The transferable object is not \"beta 0.3-0.4\"; it is:\n\n>", tools/data/promotions/promotion_20260509_0819.json:18: "summary": "**CONSTRAINT**: esiste un null non-Sturmian label-preserving a `N=144`, ma non e' un contro-campo indipendente. Passano solo generatori che conservano blocchi lunghi (`21`, `34`) o periodo `21`; i generatori dispersivi balanced random e Markov-density restano a zero accettati.\n\nLa formulazione valida e': `source_mode` Sturmian esplicito non e' necessario; memoria di scala lunga e' necessaria nel perimetro misurato. `label_jaccard>=0.75` non autorizza da solo il trasferimento GUE/Poisson: deve essere accoppiato a `event_type` e alla classe di memoria del generatore.", tools/dnd_spettro_zeta.py:11: 3. La statistica degli spacing deve mostrare repulsione a livelli (come GUE per ζ) tools/dnd_spettro_zeta.py:20: Parte 3: Unfolding e statistica spacing P(s) — confronto GOE/GUE tools/dnd_spettro_zeta.py:215: """Generate GOE nearest-neighbor spacings from Wigner surmise CDF.""" tools/dnd_spettro_zeta.py:221: """Generate GUE nearest-neighbor spacings via rejection sampling.""" tools/dnd_spettro_zeta.py:226: # GUE Wigner surmise: P(s) = (32/π²)s²·exp(-4s²/π) tools/dnd_spettro_zeta.py:228: # Envelope: C·exp(-s) where C = max of P_GUE(s)·exp(s) tools/dnd_spettro_zeta.py:281: print(f" vs GOE: KS={ks_goe:.4f}, p={p_goe:.4f}") tools/dnd_spettro_zeta.py:282: print(f" vs GUE: KS={ks_gue:.4f}, p={p_gue:.4f}") tools/dnd_spettro_zeta.py:283: print(f" vs Poisson: KS={ks_poi:.4f}, p={p_poi:.4f}") tools/dnd_spettro_zeta.py:287: 'vs_GOE': {'KS': float(ks_goe), 'p': float(p_goe)}, tools/dnd_spettro_zeta.py:288: 'vs_GUE': {'KS': float(ks_gue), 'p': float(p_gue)}, tools/dnd_spettro_zeta.py:289: 'vs_Poisson': {'KS': float(ks_poi), 'p': float(p_poi)}, tools/dnd_spettro_zeta.py:306: print(f" Poisson: 1.000") tools/dnd_spettro_zeta.py:307: print(f" GOE: ~0.286") tools/dnd_spettro_zeta.py:308: print(f" GUE: ~0.178") tools/dnd_spettro_zeta.py:317: 'GOE_ref': 0.286, tools/dnd_spettro_zeta.py:318: 'GUE_ref': 0.178, tools/dnd_spettro_zeta.py:603: print(f" Poisson: ac(1) ≈ 0.00") tools/dnd_spettro_zeta.py:604: print(f" GOE: ac(1) ≈ -0.27") tools/dnd_spettro_zeta.py:605: print(f" GUE: ac(1) ≈ -0.50") tools/dnd_spettro_zeta.py:747: │ Var(s) D-ND: {var_dnd:.4f} (GOE≈0.286, GUE≈0.178) │ tools/dnd_spettro_zeta.py:749: │ ac(1) D-ND: {f'{ac1_dnd:+.4f}' if ac1_dnd is not None else ' N/A':>7s} (GOE≈-0.27, GUE≈-0.50) │ tools/dnd_spettro_zeta.py:764: stat_class = "GOE" tools/dnd_spettro_zeta.py:766: stat_class = "GUE" tools/dnd_spettro_zeta.py:772: if stat_class == "GOE": tools/dnd_spettro_zeta.py:773: print(f" L'Hamiltoniano reale D-ND → GOE (atteso per H reale simmetrico)") tools/dnd_spettro_zeta.py:774: print(f" Gli zeri di ζ → GUE") tools/dnd_spettro_zeta.py:776: print(f" Per passare da GOE a GUE serve rompere T-simmetria.") tools/dnd_spettro_zeta.py:779: print(f" Il potenziale V(r) ALONE dà GOE.") tools/dnd_spettro_zeta.py:780: print(f" V(r) + fase det=-1 potrebbe dare GUE.") tools/dnd_spettro_zeta.py:781: elif stat_class == "GUE": tools/dnd_spettro_zeta.py:782: print(f" Sorpresa: H reale mostra statistiche GUE-like") tools/dnd_spettro_zeta.py:784: print(f" generare pseudo-GUE a queste energie") tools/dnd_spettro_zeta.py:799: print(f" Statistica: {stat_class} (ζ = GUE)") tools/dnd_spettro_zeta.py:855: verdict = "MODERATA: struttura condivisa, serve la matrice completa per GUE" tools/dnd_spettro_zeta.py:865: if stat_class == "GOE": tools/dnd_spettro_zeta.py:868: print(f" → Se questo dà GUE, la connessione è completa") tools/data/promotions/promotion_20260506_1941.json:18: "summary": "**CONSTRAINT on META + BOUNDARY**: perturbation dimensionality must be reported as:\n\n> effective rank + PC2 + observable registry version + original-vs-shuffle z gate per observable.\n\nThe cycle 03:30 \"second GUE axis\" remains restricted by cycle 06:25 and is further narrowed here: under canonical observables and the tested size curve, the stable statement is not \"GUE has a second perturbation axis\"; it is:\n\n> all-observable perturbation rank can inflate in weak-denominator regimes; after denominator gating, GUE and primes are both close to one perturbation coordinate in this perimeter, while P", tools/exp_denominator_gate_transfer_matrix.py:8:GUE/Poisson BOUNDARY perimeter. Each perimeter has a coherent endpoint and an tools/data/promotions/promotion_20260509_0659.json:18: "summary": "**CONSTRAINT on V_c null reachability**: nel perimetro diagnostico `N=144`, `phase={0,0.25,0.5,0.75}`, `r_threshold={0.48,0.50,0.52}`, `swap_steps=120`, il surrogate label-preserving non raggiunge il gate `Jaccard>=0.75`. Quindi il ciclo non decide la sufficienza del label-set a N=144; decide che il null non e' ancora generatore valido oltre N=89.\n\nLa formulazione valida e': prima di estendere `V_c` a GUE/Poisson, il Lab deve produrre un null label-preserving con accettazione dichiarata su N={89,144,233}. Fino a quel punto `event_type` resta gate obbligatorio e `label_jaccard` resta precondizi", tools/data/promotions/promotion_20260506_0625.json:17: "title": "**CONSTRAINT on META + BOUNDARY**: \"GUE has a second perturbation axis\" must be scoped to the exact sample length, gener", tools/data/promotions/promotion_20260506_0625.json:18: "summary": "**CONSTRAINT on META + BOUNDARY**: \"GUE has a second perturbation axis\" must be scoped to the exact sample length, generator, and observable definitions. In the larger independent-GUE perimeter tested here, the robust statement is weaker:\n\n> Primes remain near one perturbation coordinate under both observable sets; GUE long replicates show only a weak second component; short GUE samples can inflate apparent rank; Poisson and shuffled controls can also appear multi-axis.\n\nThe boundary is still operator-dependent, but perturbation dimensionality is not yet a stable domain invariant. The next val", tools/data/promotions/promotion_20260514_1631.json:18: "summary": "**CONSTRAINT / TESTER OPERATIVO**.\n\nIl risultato 16:12 regge come ponte osservabile e strumento di misura riusabile. Non regge come `NEW` fisico. La forma corretta e': `component_state(SR,L1,triple_var)` testa se uno spettro conserva repulsione nearest-neighbor contro Poisson e se lascia un residuo di memoria locale nella zona intermedia.", tools/data/promotions/promotion_20260507_0330.json:17: "title": "**CONSTRAINT on META + BOUNDARY**: GUE/Poisson boundary claims must report:", tools/data/promotions/promotion_20260507_0330.json:18: "summary": "**CONSTRAINT on META + BOUNDARY**: GUE/Poisson boundary claims must report:\n\n> observables_registry version + canonical observable list + original-vs-shuffle z gate per observable + endpoint-stable observable set + beta/window layer where classification margin is ambiguous.\n\nScoped statement from this run:\n\n> In the synthetic mixture perimeter tested here, the GUE-Poisson boundary is an operational layer at beta 0.3-0.4: classification is ambiguous there, and denominator support collapses across the transition. The Poisson endpoint remains classifiable but denominator-weak, so it cannot serve ", tools/data/observable_collinearity_breaking_20260506_1955.json:10: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:27: "triple_var": 186.24514674349376 tools/data/observable_collinearity_breaking_20260506_1955.json:46: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:56: "triple_var": -8.653719311278719 tools/data/observable_collinearity_breaking_20260506_1955.json:62: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:88: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:116: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:144: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:172: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:200: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:228: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:256: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:284: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:312: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:340: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:368: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:396: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:424: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:452: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:480: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:508: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:536: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:564: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:592: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:605: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:627: "triple_var": -0.43154737791008807 tools/data/observable_collinearity_breaking_20260506_1955.json:641: "triple_var": 208.42649955050294 tools/data/observable_collinearity_breaking_20260506_1955.json:660: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:670: "triple_var": 0.5963158747011266 tools/data/observable_collinearity_breaking_20260506_1955.json:697: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:725: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:753: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:781: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:809: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:837: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:865: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:893: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:921: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:949: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:977: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1005: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1033: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1061: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1089: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1117: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1145: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1173: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1201: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1214: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:1236: "triple_var": -0.34881046143312094 tools/data/observable_collinearity_breaking_20260506_1955.json:1250: "triple_var": 0.11212829119237103 tools/data/observable_collinearity_breaking_20260506_1955.json:1269: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1279: "triple_var": 23.755010032714857 tools/data/observable_collinearity_breaking_20260506_1955.json:1286: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:1312: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1340: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1368: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1396: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1424: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1452: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1480: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1508: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1536: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1564: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1592: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1620: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1648: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1676: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1704: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1732: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1760: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1788: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1816: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1829: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:1851: "triple_var": -0.4439415722831416 tools/data/observable_collinearity_breaking_20260506_1955.json:1865: "triple_var": 3.0681523092117287 tools/data/observable_collinearity_breaking_20260506_1955.json:1884: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1894: "triple_var": 0.3306583306544405 tools/data/observable_collinearity_breaking_20260506_1955.json:1921: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1949: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:1977: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2005: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2033: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2061: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2089: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2117: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2145: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2173: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2201: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2229: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2257: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2285: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2313: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2341: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2369: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2397: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2425: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1955.json:2438: "triple_var" tools/data/observable_collinearity_breaking_20260506_1955.json:2460: "triple_var": -0.7104037979350339 tools/data/reports/report_20260402_0344.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/report_20260402_0344.md:22:- [✓] N2: Rule 30/110 hanno spacing >> GUE standard... → ⟨r⟩=1.0000 (GUE standard=0.60) tools/data/reports/report_20260402_0344.md:25:- percolation_var_0.55: r=0.7543859649122806, spacing=Poisson-like tools/data/reports/report_20260402_0344.md:26:- numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like tools/data/reports/report_20260402_0344.md:27:- coupled_oscillators_var_50: r=1.0, spacing=Poisson-like tools/data/reports/report_20260402_0344.md:28:- brownian_motion_var_0.5: r=1.0, spacing=Poisson-like tools/data/seme_backup_b2_20260509_144841.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260509_144841.json:3: "new_direzione": "Falsificare la forma minima del gate BOUNDARY come operatore ordine/null/denominatore: test su perimetri reali o avversariali senza importare label GUE/Poisson", tools/data/reports/agent_20260509_0819.md:7:observables_used: [`label_jaccard`, `hamming_ratio`, `source_mode`, `acceptance_rate`, `event_type`, `vc_interp`, `r_floor`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0819.md:8:**observable_contract**: claim=un generatore non-Sturmian puo' preservare il label-set phi a N=144 con `label_jaccard>=0.75` e distanza non triviale; observable=`label_jaccard`, `hamming_ratio`, `source_mode`, `acceptance_rate`, `event_type`, `vc_interp`, `r_floor`; operator=scansione di generatori balanced random, block shuffle, periodic approximant, Markov-density contro lettore gap-label phi; generator=non-Sturmian candidate pool; denominator=N=144, phases={0,0.25,0.5,0.75}, 2199 candidati validi post hamming gate, r_threshold={0.48,0.50,0.52}; non_possible=se passano solo generatori con memoria di blocco/periodo lungo, il null e' non-Sturmian ma non indipendente dal boundary; not_tested=trasferimento GUE/Poisson, fit power-law, scale N!=144, indipendenza fuori dal label reader phi. tools/data/reports/agent_20260509_0819.md:17: - **YSN DeltaLink**: il confine non e' classe GUE/Poisson ma trasporto di scala tra label reader e generatore. tools/data/reports/agent_20260509_0819.md:22:- **Proiezione**: genero candidati non-Sturmian, applico gate `Jaccard>=0.75`, poi misuro `r(V)` solo sui best accepted per non confondere ricerca del generatore con confronto GUE/Poisson. tools/data/reports/agent_20260509_0819.md:77:La formulazione valida e': `source_mode` Sturmian esplicito non e' necessario; memoria di scala lunga e' necessaria nel perimetro misurato. `label_jaccard>=0.75` non autorizza da solo il trasferimento GUE/Poisson: deve essere accoppiato a `event_type` e alla classe di memoria del generatore. tools/data/reports/agent_20260509_0819.md:92:- **L3 no observable drift**: `gap_ratio`, fit power-law e GUE/Poisson non sono testati. tools/data/reports/incident_20260504_1138.md:15:| triple_var | triple | +2.782778 | +2.996461 | +0.794 | +0.956 | tools/data/reports/incident_20260504_1138.md:19:2. triple_var non è un osservabile di profondità pulito: conserva già 0.794 sotto Mk1. tools/data/reports/incident_20260504_1138.md:21:4. Poisson è rumore da denominatore piccolo, non controesempio strutturale. tools/data/reports/incident_20260504_1138.md:24:**CONSTRAINT on BOUNDARY + DIPOLAR_ORDERING**: il confine è una transizione unica sotto partial shuffle uniforme, ma non è indivisibile sotto perturbazioni selettive. Per i gap primi, SR2 è il coordinato operativo di profondità; triple_var va demossa ad ausiliario. tools/data/reports/incident_20260504_1138.md:30:- **Campo di possibilità**: possibile usare SR2 come coordinata pulita del depth; non-possibile usare triple_var da sola come evidenza di profondità indipendente. tools/data/observable_collinearity_breaking_20260506_1956.json:10: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:27: "triple_var": 162.18032447909425 tools/data/observable_collinearity_breaking_20260506_1956.json:46: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:56: "triple_var": -6.565120725686894 tools/data/observable_collinearity_breaking_20260506_1956.json:61: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:87: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:115: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:143: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:171: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:199: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:227: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:255: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:283: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:311: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:339: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:367: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:395: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:423: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:451: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:479: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:492: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:514: "triple_var": -0.36618211014944285 tools/data/observable_collinearity_breaking_20260506_1956.json:528: "triple_var": 184.6531034015068 tools/data/observable_collinearity_breaking_20260506_1956.json:547: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:557: "triple_var": 0.8660218794032433 tools/data/observable_collinearity_breaking_20260506_1956.json:586: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:614: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:642: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:670: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:698: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:726: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:754: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:782: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:810: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:838: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:866: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:894: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:922: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:950: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:978: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:991: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:1013: "triple_var": -0.027033728332000097 tools/data/observable_collinearity_breaking_20260506_1956.json:1027: "triple_var": 0.10992837886524512 tools/data/observable_collinearity_breaking_20260506_1956.json:1046: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1056: "triple_var": 20.059943513965663 tools/data/observable_collinearity_breaking_20260506_1956.json:1063: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:1089: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1117: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1145: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1173: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1201: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1229: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1257: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1285: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1313: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1341: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1369: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1397: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1425: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1453: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1481: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1494: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:1516: "triple_var": -0.45286969770430574 tools/data/observable_collinearity_breaking_20260506_1956.json:1530: "triple_var": 3.037812239959864 tools/data/observable_collinearity_breaking_20260506_1956.json:1549: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1559: "triple_var": -0.1824332702920774 tools/data/observable_collinearity_breaking_20260506_1956.json:1586: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1614: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1642: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1670: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1698: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1726: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1754: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1782: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1810: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1838: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1866: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1894: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1922: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1950: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1978: "triple_var": { tools/data/observable_collinearity_breaking_20260506_1956.json:1991: "triple_var" tools/data/observable_collinearity_breaking_20260506_1956.json:2013: "triple_var": -0.9497182657657176 tools/exp_3d_boundary_layers.py:7: - Layer 2 (triples, Mk2) → SR2, triple_var (depth) tools/exp_3d_boundary_layers.py:14:Tests on: primes, GUE, Poisson baseline. tools/exp_3d_boundary_layers.py:37: """Generate GUE eigenvalue gaps.""" tools/exp_3d_boundary_layers.py:79:def obs_triple_var(gaps): tools/exp_3d_boundary_layers.py:93: 'triple_var': obs_triple_var, tools/exp_3d_boundary_layers.py:179: # --- GUE --- tools/exp_3d_boundary_layers.py:180: print("\n--- GUE ---") tools/exp_3d_boundary_layers.py:187: gue_g, alphas, args.n_trials, rng, "GUE" tools/exp_3d_boundary_layers.py:190: # --- Poisson (exponential gaps, iid) --- tools/exp_3d_boundary_layers.py:194: poisson_gaps, alphas, args.n_trials, rng, "Poisson" tools/exp_3d_boundary_layers.py:198: obs_names = ['SR', 'L1', 'SR2', 'triple_var'] tools/exp_3d_boundary_layers.py:199: layer_map = {'SR': 'L1_pair', 'L1': 'L1_pair', 'SR2': 'L2_triple', 'triple_var': 'L2_triple'} tools/exp_3d_boundary_layers.py:232: l2_crit = np.mean([seq_data['critical_alpha_50'][o] for o in ['SR2', 'triple_var']]) tools/exp_3d_boundary_layers.py:255: print(f"Layer separation Δα: Primes={prime_sep:+.3f}, GUE={gue_sep:+.3f}, Poisson={pois_sep:+.3f}") tools/data/piano11_results.json:2: "piano": "piano11-Q-sqrt5-GUE", tools/data/piano11_results.json:5: "verdict": "FORTE: catena algebrica M\u2192Q(\u221a5)\u2192L(s,\u03c7\u2085)\u2192GUE dimostrata. Verifica numerica parziale (187/303 zeri, regime asintotico richiede t>1000).", tools/data/piano11_results.json:52: "to": "zeros follow GUE", tools/data/piano11_results.json:73: "fluctuations": "det(M)=-1 \u2192 Q(\u221a5) \u2192 L(s,\u03c7\u2085) \u2192 GUE (Piano 11)", tools/data/piano11_results.json:80: "note": "GUE vs GOE discrimination requires thousands of zeros at t>1000 (Odlyzko regime). With ~200 zeros, statistics are suggestive but not definitive.", tools/data/piano11_results.json:98: "zeta_vs_GUE_KS": 0.0615, tools/data/piano11_results.json:99: "zeta_vs_GUE_p": 0.567, tools/data/piano11_results.json:102: "conclusion": "V(r) gives BK form but NOT GUE fluctuations" tools/data/piano11_results.json:113: "Katz-Sarnak: L(s,\u03c7\u2085) zeros \u2192 GUE", tools/data/piano11_results.json:114: "Therefore: D-ND matrix \u2194 GUE via Q(\u221a5)", tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:1:{"ambiguous_beta": [0.4], "case": "prime/dnd_autoricerca/offset_0", "class": "prime", "endpoint_distance": 3.306050786750358, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_0", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_0", "stable_count_coherent": 3.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -4.842150927585065, "L2": -0.9154749246415151, "SR": -8.945268734523047, "SR2": 0.9852868974794996, "triple_var": -4.709838999741532}, "z_mean_illusory": {"L1": 1.030431205919623, "L2": 0.16645394345596093, "SR": 0.9432976962599521, "SR2": 0.1919336105233638, "triple_var": 0.9706890878914542}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:2:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/dnd_autoricerca/offset_0", "class": "mod6_index_aligned", "endpoint_distance": 3.351852665685088, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_0", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 0.625, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -62.06674831409525, "L2": 59.48653272587916, "SR": -62.066748314095406, "SR2": 59.486532725879115, "triple_var": -27.551734246630456}, "z_mean_illusory": {"L1": -0.41981858724949006, "L2": -0.7650068848641075, "SR": -0.4198185872494896, "SR2": -0.7650068848641022, "triple_var": -0.6958888766357259}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:3:{"ambiguous_beta": [], "case": "mod6_span_matched/dnd_autoricerca/offset_0", "class": "mod6_span_matched", "endpoint_distance": 4.2720574228756885, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_0", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -27.575020456858386, "L2": 3.4433656071210628, "SR": -46.66315563666401, "SR2": 16.21843432801996, "triple_var": -24.61207678219531}, "z_mean_illusory": {"L1": 0.8373126704439209, "L2": -0.15101492290666818, "SR": 0.43864256001086843, "SR2": -0.2124536889628491, "triple_var": 0.6632228937574618}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:4:{"ambiguous_beta": [], "case": "prime/dnd_autoricerca/offset_512", "class": "prime", "endpoint_distance": 3.6753848242330123, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_512", "one_sided_observables": ["SR", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_512", "stable_count_coherent": 3.875, "stable_count_illusory": 0.375, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -6.0094910481263275, "L2": -2.2973364341412337, "SR": -8.986657874545845, "SR2": -0.9829360406077334, "triple_var": -6.039786120514185}, "z_mean_illusory": {"L1": -0.326002246674026, "L2": -0.5771489984608741, "SR": -0.007866091919083606, "SR2": -0.3976039348319107, "triple_var": -0.5534969051410608}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:5:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/dnd_autoricerca/offset_512", "class": "mod6_index_aligned", "endpoint_distance": 4.328224584532458, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_512", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -68.8823580047441, "L2": 60.222941934476786, "SR": -68.88235800474429, "SR2": 60.22294193447677, "triple_var": -29.010502131774427}, "z_mean_illusory": {"L1": 0.4851354308920025, "L2": -0.09023897142753007, "SR": 0.48513543089200273, "SR2": -0.09023897142752921, "triple_var": 0.4170908054101514}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:6:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/dnd_autoricerca/offset_512", "class": "mod6_span_matched", "endpoint_distance": 4.326637959942096, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_512", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -32.90227998041231, "L2": -27.121866510900453, "SR": -45.63731993637486, "SR2": -33.17570800905307, "triple_var": -45.89346874838702}, "z_mean_illusory": {"L1": -0.2536896183766465, "L2": -0.12482898797495845, "SR": -0.21161640133211307, "SR2": -0.18423690670258394, "triple_var": -0.3534596737095357}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:7:{"ambiguous_beta": [0.2], "case": "prime/dnd_autoricerca/offset_1024", "class": "prime", "endpoint_distance": 3.3057555538288454, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1024", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1024", "stable_count_coherent": 4.375, "stable_count_illusory": 0.625, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -6.209866398400172, "L2": -3.6418873135207113, "SR": -8.377304805347267, "SR2": -1.7531237334929808, "triple_var": -7.289697848130502}, "z_mean_illusory": {"L1": -0.24570948769216433, "L2": 0.6406789673121773, "SR": 0.3406107910528513, "SR2": 0.22838375929152482, "triple_var": 0.09162786998777728}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:8:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/dnd_autoricerca/offset_1024", "class": "mod6_index_aligned", "endpoint_distance": 4.328537930931407, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1024", "stable_count_coherent": 5.0, "stable_count_illusory": 0.375, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -65.44662729924372, "L2": 65.28068445480572, "SR": -65.44662729924387, "SR2": 65.28068445480568, "triple_var": -29.580682252643776}, "z_mean_illusory": {"L1": -0.0385349237188681, "L2": 0.776440092745517, "SR": -0.03853492371886824, "SR2": 0.7764400927455206, "triple_var": 0.40151574410647944}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:9:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/dnd_autoricerca/offset_1024", "class": "mod6_span_matched", "endpoint_distance": 4.20934108020979, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1024", "stable_count_coherent": 5.0, "stable_count_illusory": 0.375, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -36.479938849131344, "L2": -3.7080783332170086, "SR": -43.37533346712106, "SR2": -16.122060266960204, "triple_var": -35.024656169803926}, "z_mean_illusory": {"L1": -0.011945174418724502, "L2": -0.4722146037741496, "SR": 0.047928633171082646, "SR2": -0.6671483972602104, "triple_var": -0.15389118219716336}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:10:{"ambiguous_beta": [], "case": "prime/direct_sieve/offset_0", "class": "prime", "endpoint_distance": 3.2714727551034795, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_0", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_0", "stable_count_coherent": 3.0, "stable_count_illusory": 0.0, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -4.401087593365115, "L2": -0.9638651530756212, "SR": -8.460652485642836, "SR2": 0.9498731403573901, "triple_var": -4.520081514593633}, "z_mean_illusory": {"L1": -0.08563825278955584, "L2": 0.4190980915091166, "SR": 0.15238973163391914, "SR2": 0.20091367714898878, "triple_var": 0.09830870744381726}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:11:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_0", "class": "mod6_index_aligned", "endpoint_distance": 4.328292885175927, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_0", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -56.98113368423044, "L2": 62.03600707588758, "SR": -56.98113368423059, "SR2": 62.03600707588744, "triple_var": -26.768636414390244}, "z_mean_illusory": {"L1": -0.6202696063819564, "L2": -0.04895511755937704, "SR": -0.6202696063819564, "SR2": -0.048955117559376565, "triple_var": -0.6161508301550559}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:12:{"ambiguous_beta": [0.4], "case": "mod6_span_matched/direct_sieve/offset_0", "class": "mod6_span_matched", "endpoint_distance": 4.291072489196602, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_0", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -29.00071323880805, "L2": 3.2646530287868174, "SR": -45.72625843996232, "SR2": 15.681538225182916, "triple_var": -22.78716459635016}, "z_mean_illusory": {"L1": -0.038278780889003744, "L2": -0.26508003528032886, "SR": 0.012848818891500802, "SR2": -0.45467773834395253, "triple_var": -0.14734329969865126}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:13:{"ambiguous_beta": [0.2], "case": "prime/direct_sieve/offset_512", "class": "prime", "endpoint_distance": 3.6377995007130934, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_512", "one_sided_observables": ["SR", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_512", "stable_count_coherent": 4.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -5.859892688582082, "L2": -2.474039536877772, "SR": -8.454460044105236, "SR2": -1.0243930698165693, "triple_var": -6.333350041487988}, "z_mean_illusory": {"L1": 0.4574560353551112, "L2": -0.5612183954430403, "SR": -0.045867287444398955, "SR2": -0.3363200146158935, "triple_var": 0.1024865103040923}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:14:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_512", "class": "mod6_index_aligned", "endpoint_distance": 4.328114174875475, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_512", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -73.4087964211402, "L2": 59.07080825764906, "SR": -73.40879642114038, "SR2": 59.070808257649034, "triple_var": -31.151003125768796}, "z_mean_illusory": {"L1": 0.0007230097779659383, "L2": 0.5557599319532956, "SR": 0.0007230097779660216, "SR2": 0.5557599319532864, "triple_var": 0.1546271196428866}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:15:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_512", "class": "mod6_span_matched", "endpoint_distance": 4.325059098427407, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_512", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.625, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -30.03665405630677, "L2": -25.851929803959134, "SR": -39.93179419101679, "SR2": -33.63537222011776, "triple_var": -39.07749013492429}, "z_mean_illusory": {"L1": 0.7511318090840585, "L2": -0.20670464234008828, "SR": 0.6400944990417152, "SR2": -0.3551863684788809, "triple_var": 0.6037323665389605}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:16:{"ambiguous_beta": [0.4], "case": "prime/direct_sieve/offset_1024", "class": "prime", "endpoint_distance": 3.797813724719133, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1024", "one_sided_observables": ["SR", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1024", "stable_count_coherent": 4.25, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -6.38904329894655, "L2": -3.2900125748792313, "SR": -7.958150266681143, "SR2": -1.536491779452731, "triple_var": -6.839878514923063}, "z_mean_illusory": {"L1": 0.19570277655608226, "L2": 0.26293363156037397, "SR": -0.3114661247159494, "SR2": 0.27504528809195383, "triple_var": 0.32004003785647556}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:17:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_1024", "class": "mod6_index_aligned", "endpoint_distance": 4.327612951193729, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1024", "stable_count_coherent": 5.0, "stable_count_illusory": 0.375, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -68.0038303787189, "L2": 72.00059684715897, "SR": -68.00383037871907, "SR2": 72.00059684715897, "triple_var": -29.841682220275054}, "z_mean_illusory": {"L1": 0.21223132203464795, "L2": 0.3512028889875366, "SR": 0.2122313220346479, "SR2": 0.35120288898752633, "triple_var": 0.2682284730740996}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:18:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_1024", "class": "mod6_span_matched", "endpoint_distance": 4.288141406703574, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1024", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -42.12033476837027, "L2": -3.8782156256830698, "SR": -48.257504140597305, "SR2": -15.959857255241797, "triple_var": -37.411065059824644}, "z_mean_illusory": {"L1": 0.2695546146587997, "L2": 0.16223038622937858, "SR": 0.06148952207281416, "SR2": 0.29692517644794514, "triple_var": 0.26516883389880674}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:19:{"ambiguous_beta": [], "case": "prime/direct_sieve/offset_1536", "class": "prime", "endpoint_distance": 3.723354881783684, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": false, "n_gaps": 4096, "offset": "offset_1536", "one_sided_observables": ["SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1536", "stable_count_coherent": 5.0, "stable_count_illusory": 0.375, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -5.931836208790897, "L2": -4.313681831370728, "SR": -8.187062995408654, "SR2": -2.5175068599847092, "triple_var": -7.404608904272377}, "z_mean_illusory": {"L1": 0.049711380698424845, "L2": -0.28562058408294083, "SR": -0.183747186828583, "SR2": 0.03503283171968308, "triple_var": -0.09284837086453951}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:20:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_1536", "class": "mod6_index_aligned", "endpoint_distance": 4.329281390068784, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1536", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1536", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -63.771023007067036, "L2": 62.70660078069298, "SR": -63.7710230070672, "SR2": 62.706600780692916, "triple_var": -28.66254970318306}, "z_mean_illusory": {"L1": 0.39722961306946436, "L2": 0.28483078974037473, "SR": 0.39722961306946436, "SR2": 0.28483078974037823, "triple_var": 0.4658585534038144}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:21:{"ambiguous_beta": [], "case": "mod6_span_matched/direct_sieve/offset_1536", "class": "mod6_span_matched", "endpoint_distance": 4.324873543577818, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_1536", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1536", "stable_count_coherent": 5.0, "stable_count_illusory": 0.625, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -42.68282227629933, "L2": 31.04932390743439, "SR": -46.89959782510813, "SR2": 14.943860173810517, "triple_var": -27.519929371783277}, "z_mean_illusory": {"L1": -0.1260391246691993, "L2": 0.5598275809996851, "SR": -0.2955589486313033, "SR2": 0.5083829312684494, "triple_var": 0.1789299333799717}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:22:{"ambiguous_beta": [0.2], "case": "prime/direct_sieve/offset_2048", "class": "prime", "endpoint_distance": 4.19705894868477, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_2048", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_2048", "stable_count_coherent": 5.0, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -5.089199931561901, "L2": -5.3384959939963785, "SR": -8.512116233773433, "SR2": -3.7855017465217413, "triple_var": -6.808199626867417}, "z_mean_illusory": {"L1": 0.6195005240747176, "L2": 0.408390456685887, "SR": 0.10347338793576785, "SR2": -0.16927071899000712, "triple_var": 0.7743980210684666}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:23:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_2048", "class": "mod6_index_aligned", "endpoint_distance": 4.328857891221221, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_2048", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_2048", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -73.70640447038119, "L2": 70.09911287573769, "SR": -73.70640447038139, "SR2": 70.09911287573757, "triple_var": -34.90612677865552}, "z_mean_illusory": {"L1": 0.09112077374510907, "L2": -0.47994414935915114, "SR": 0.09112077374510902, "SR2": -0.47994414935915986, "triple_var": -0.15511286182452713}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:24:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_2048", "class": "mod6_span_matched", "endpoint_distance": 4.327758564796117, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_2048", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_2048", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -54.91845017011585, "L2": 38.12276502250178, "SR": -57.14476857000004, "SR2": 26.18775309780094, "triple_var": -30.892991882481077}, "z_mean_illusory": {"L1": -0.05771472486330251, "L2": -0.050630990646356455, "SR": 0.05945598998489067, "SR2": -0.18245557334131274, "triple_var": -0.10893422472472547}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:25:{"ambiguous_beta": [0.4], "case": "prime/direct_sieve/offset_2560", "class": "prime", "endpoint_distance": 4.163567648535877, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_2560", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_2560", "stable_count_coherent": 5.0, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -6.780166735328022, "L2": -4.156503099632813, "SR": -8.62953333276116, "SR2": -2.7497228573276935, "triple_var": -7.501414621786449}, "z_mean_illusory": {"L1": 0.0814396521968847, "L2": 0.023986553677400796, "SR": 0.61118016833453, "SR2": -0.15665120632468066, "triple_var": 0.10917290404171319}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:26:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_2560", "class": "mod6_index_aligned", "endpoint_distance": 4.3290769866219785, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_2560", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_2560", "stable_count_coherent": 5.0, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -67.71096922400203, "L2": 62.35077680910442, "SR": -67.71096922400221, "SR2": 62.350776809104374, "triple_var": -29.10207593605641}, "z_mean_illusory": {"L1": -0.2663855151884743, "L2": 0.32470417679633323, "SR": -0.26638551518847425, "SR2": 0.3247041767963267, "triple_var": -0.07550645211167636}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:27:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_2560", "class": "mod6_span_matched", "endpoint_distance": 4.3204261544988825, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_2560", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_2560", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -37.589572169294215, "L2": 18.518575715367376, "SR": -38.50337896125697, "SR2": 8.927670863053219, "triple_var": -28.053405361437548}, "z_mean_illusory": {"L1": 0.5411396573045404, "L2": -0.3275455837107321, "SR": 0.6310742145466901, "SR2": -0.40855234017674485, "triple_var": 0.3791379180675893}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:28:{"ambiguous_beta": [0.2], "case": "prime/direct_sieve/offset_3072", "class": "prime", "endpoint_distance": 3.653735933298323, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": true, "n_gaps": 4096, "offset": "offset_3072", "one_sided_observables": ["SR", "SR2", "L1", "L2"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_3072", "stable_count_coherent": 5.0, "stable_count_illusory": 0.375, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -5.289289136785509, "L2": -3.819574797389196, "SR": -7.843248270968815, "SR2": -4.515789815675508, "triple_var": -6.705355920514963}, "z_mean_illusory": {"L1": -0.2814843735574446, "L2": -0.6664199388459295, "SR": -0.5314227042854565, "SR2": -0.38040081844626417, "triple_var": -0.6391084743180943}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:29:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_3072", "class": "mod6_index_aligned", "endpoint_distance": 4.328796821610921, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_3072", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_3072", "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -67.8392982928723, "L2": 65.66408238505811, "SR": -67.83929829287248, "SR2": 65.66408238505805, "triple_var": -30.40074892782181}, "z_mean_illusory": {"L1": 0.21635942595640814, "L2": -0.12427080680461147, "SR": 0.21635942595640822, "SR2": -0.1242708068046223, "triple_var": 0.28848412791226685}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:30:{"ambiguous_beta": [], "case": "mod6_span_matched/direct_sieve/offset_3072", "class": "mod6_span_matched", "endpoint_distance": 0.0, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": false, "n_gaps": 4096, "offset": "offset_3072", "one_sided_observables": [], "provider": "direct_sieve", "row_id": "direct_sieve/offset_3072", "stable_count_coherent": 4.75, "stable_count_illusory": 1.5, "state": "support_falls", "z_mean_coherent": {"L1": -34.77088678786709, "L2": 2.5675539180908626, "SR": -35.44146446307755, "SR2": -3.832041274075619, "triple_var": -29.78353494631203}, "z_mean_illusory": {"L1": -0.6288983612894581, "L2": 0.3112813607771297, "SR": -0.5764144322856503, "SR2": 0.3302901302490735, "triple_var": -0.4091793048534594}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:31:{"ambiguous_beta": [], "case": "prime/direct_sieve/offset_3584", "class": "prime", "endpoint_distance": 4.210716457018921, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_3584", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_3584", "stable_count_coherent": 4.875, "stable_count_illusory": 0.0, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -5.5469420891680805, "L2": -2.8191848845273855, "SR": -8.680496909145123, "SR2": -3.7896703891106283, "triple_var": -6.308030626375762}, "z_mean_illusory": {"L1": -0.22936104198938437, "L2": 0.4960099248850512, "SR": -0.043283515698756256, "SR2": 0.23789010643543923, "triple_var": 0.03448093325008293}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:32:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_3584", "class": "mod6_index_aligned", "endpoint_distance": 4.328301333980753, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_3584", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_3584", "stable_count_coherent": 5.0, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -65.34898358345335, "L2": 61.72984881245609, "SR": -65.34898358345352, "SR2": 61.72984881245606, "triple_var": -28.516635094587954}, "z_mean_illusory": {"L1": -0.7846569281032054, "L2": -0.28317660928159927, "SR": -0.7846569281032053, "SR2": -0.2831766092816007, "triple_var": -0.8063302058496549}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:33:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_3584", "class": "mod6_span_matched", "endpoint_distance": 4.319405219181048, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_3584", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_3584", "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -32.36529723152755, "L2": -15.171053940172591, "SR": -34.23032754936355, "SR2": -18.5655137474684, "triple_var": -35.3783308978299}, "z_mean_illusory": {"L1": 0.24892231658838426, "L2": -0.1701062288089728, "SR": 0.22631936036310796, "SR2": -0.12518440858863428, "triple_var": 0.19198001418971639}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:34:{"ambiguous_beta": [0.2, 0.4], "case": "prime/direct_sieve/offset_4096", "class": "prime", "endpoint_distance": 4.216489361262017, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_4096", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_4096", "stable_count_coherent": 5.0, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -5.64538334325455, "L2": -3.4034828650074527, "SR": -8.936252573673276, "SR2": -4.206447605207705, "triple_var": -6.309592338670289}, "z_mean_illusory": {"L1": 0.26542915603141215, "L2": -0.0978979341097872, "SR": 0.3411705114848247, "SR2": -0.2860538466306787, "triple_var": 0.2126333920567642}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:35:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_4096", "class": "mod6_index_aligned", "endpoint_distance": 3.35148305855855, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_4096", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_4096", "stable_count_coherent": 5.0, "stable_count_illusory": 0.875, "state": "support_falls", "z_mean_coherent": {"L1": -64.54584279412694, "L2": 72.10905871432917, "SR": -64.54584279412711, "SR2": 72.10905871432905, "triple_var": -30.23861147859087}, "z_mean_illusory": {"L1": 0.15366791525274334, "L2": 0.3441588032594829, "SR": 0.15366791525274337, "SR2": 0.34415880325948345, "triple_var": 0.26599229993571777}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:36:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_4096", "class": "mod6_span_matched", "endpoint_distance": 3.8678103360247045, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": true, "n_gaps": 4096, "offset": "offset_4096", "one_sided_observables": ["SR", "SR2", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_4096", "stable_count_coherent": 5.0, "stable_count_illusory": 0.625, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -31.233973243394374, "L2": -29.34792162387331, "SR": -31.70451194070757, "SR2": -30.098708011417983, "triple_var": -41.205564642311195}, "z_mean_illusory": {"L1": -0.3958397068582853, "L2": 0.6736313974950938, "SR": -0.32409784131012304, "SR2": 0.8022600649719418, "triple_var": -0.15383579591463348}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:37:{"ambiguous_beta": [0.4], "case": "prime/direct_sieve/offset_4608", "class": "prime", "endpoint_distance": 3.717293199233669, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_4608", "one_sided_observables": ["SR", "SR2", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_4608", "stable_count_coherent": 4.5, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -6.075208539534216, "L2": -2.0140695813181275, "SR": -7.7211828685811135, "SR2": -3.690554223164072, "triple_var": -6.14624278469106}, "z_mean_illusory": {"L1": 0.24347520617259621, "L2": 0.15969180033213973, "SR": 0.508004260128454, "SR2": 0.01050444367009018, "triple_var": 0.3220728800403103}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:38:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_4608", "class": "mod6_index_aligned", "endpoint_distance": 3.353104645096222, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_4608", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_4608", "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -60.3815819870544, "L2": 67.44000363757569, "SR": -60.38158198705457, "SR2": 67.44000363757564, "triple_var": -27.13900323541325}, "z_mean_illusory": {"L1": 0.033484405357467995, "L2": 0.31717514551682024, "SR": 0.03348440535746795, "SR2": 0.31717514551682063, "triple_var": 0.14196918414492873}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:39:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_4608", "class": "mod6_span_matched", "endpoint_distance": 4.320463333030927, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 4096, "offset": "offset_4608", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_4608", "stable_count_coherent": 5.0, "stable_count_illusory": 0.375, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -22.73368390231887, "L2": -22.82167830989669, "SR": -24.072217727176053, "SR2": -24.07269084396985, "triple_var": -28.25700090066124}, "z_mean_illusory": {"L1": -0.11549101516647264, "L2": 0.5523140530659211, "SR": -0.24254596587841307, "SR2": 0.5299841287314613, "triple_var": 0.12933468791676106}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:40:{"case_count": 13, "class": "prime", "common_one_sided_observables": ["L1"], "endpoint_distance_mean": 3.7597302750127053, "event": "class_summary", "focus_signature_count": 11, "mean_z_coherent": {"L1": -5.697658303032961, "L2": -3.0344314607983205, "SR": -8.437875953473611, "SR2": -2.2013060063480907, "triple_var": -6.378159835582247}, "sr_count": 12, "sr_rate": 0.9230769230769231, "stable_count_coherent_mean": 4.375, "state_counts": {"beta_absent_blank": 4, "beta_chart_recovered": 9}, "union_one_sided_observables": ["L1", "L2", "SR", "SR2", "triple_var"]} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:41:{"case_count": 13, "class": "mod6_index_aligned", "common_one_sided_observables": ["L1", "SR", "triple_var"], "endpoint_distance_mean": 4.103195178427117, "event": "class_summary", "focus_signature_count": 13, "mean_z_coherent": {"L1": -66.00719980470231, "L2": 64.63054271621627, "SR": -66.00719980470248, "SR2": 64.63054271621621, "triple_var": -29.45153781121474}, "sr_count": 13, "sr_rate": 1.0, "stable_count_coherent_mean": 5.0, "state_counts": {"beta_chart_recovered": 12, "support_falls": 1}, "union_one_sided_observables": ["L1", "L2", "SR", "SR2", "triple_var"]} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:42:{"case_count": 13, "class": "mod6_span_matched", "common_one_sided_observables": [], "endpoint_distance_mean": 3.9379266621895894, "event": "class_summary", "focus_signature_count": 11, "mean_z_coherent": {"L1": -34.95458670236188, "L2": -2.379577457569229, "SR": -41.35289483449432, "SR2": -7.192514995418241, "triple_var": -32.7612830380232}, "sr_count": 12, "sr_rate": 0.9230769230769231, "stable_count_coherent_mean": 4.980769230769231, "state_counts": {"beta_absent_blank": 2, "beta_chart_recovered": 10, "support_falls": 1}, "union_one_sided_observables": ["L1", "L2", "SR", "SR2", "triple_var"]} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:43:{"endpoint_delta": -1.0568201300724471, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_0", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_0", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "direct_sieve/offset_0", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 52.58004609086533, "L2": -62.999872228963206, "SR": 48.52048119858776, "SR2": -61.08613393553005, "triple_var": 22.24855489979661}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:44:{"endpoint_delta": -0.5297992264745965, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_1024", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_1024", "prime_obs": ["SR", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_1024", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": -0.75, "z_delta": {"L1": 61.614787079772356, "L2": -75.2906094220382, "SR": 60.04568011203793, "SR2": -73.5370886266117, "triple_var": 23.00180370535199}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:45:{"endpoint_delta": -0.6059265082851004, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_index_aligned/direct_sieve/offset_1536", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_1536", "prime_obs": ["SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_1536", "signature_jaccard": 0.8, "sr_delta": -1, "stable_count_delta": 0.0, "z_delta": {"L1": 57.83918679827614, "L2": -67.02028261206371, "SR": 55.58396001165855, "SR2": -65.22410764067763, "triple_var": 21.257940798910685}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:46:{"endpoint_delta": -0.1317989425364514, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_2048", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_2048", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_2048", "signature_jaccard": 1.0, "sr_delta": 0, "stable_count_delta": 0.0, "z_delta": {"L1": 68.61720453881928, "L2": -75.43760886973406, "SR": 65.19428823660795, "SR2": -73.88461462225932, "triple_var": 28.097927151788102}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:47:{"endpoint_delta": -0.16550933808610147, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_2560", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_2560", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_2560", "signature_jaccard": 1.0, "sr_delta": 0, "stable_count_delta": 0.0, "z_delta": {"L1": 60.930802488674004, "L2": -66.50727990873723, "SR": 59.08143589124106, "SR2": -65.10049966643207, "triple_var": 21.60066131426996}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:48:{"endpoint_delta": -0.6750608883125984, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_index_aligned/direct_sieve/offset_3072", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_3072", "prime_obs": ["SR", "SR2", "L1", "L2"], "row_id": "direct_sieve/offset_3072", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": 0.0, "z_delta": {"L1": 62.55000915608679, "L2": -69.4836571824473, "SR": 59.99605002190367, "SR2": -70.17987220073356, "triple_var": 23.695393007306848}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:49:{"endpoint_delta": -0.11758487696183195, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_3584", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_3584", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_3584", "signature_jaccard": 1.0, "sr_delta": 0, "stable_count_delta": -0.125, "z_delta": {"L1": 59.80204149428526, "L2": -64.54903369698347, "SR": 56.6684866743084, "SR2": -65.5195192015667, "triple_var": 22.208604468212194}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:50:{"endpoint_delta": 0.8650063027034669, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_4096", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "L1", "triple_var"], "prime_case": "prime/direct_sieve/offset_4096", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_4096", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": 0.0, "z_delta": {"L1": 58.90045945087239, "L2": -75.51254157933661, "SR": 55.60959022045384, "SR2": -76.31550631953675, "triple_var": 23.92901913992058}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:51:{"endpoint_delta": 0.364188554137447, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_4608", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "L1", "triple_var"], "prime_case": "prime/direct_sieve/offset_4608", "prime_obs": ["SR", "SR2", "L1", "triple_var"], "row_id": "direct_sieve/offset_4608", "signature_jaccard": 0.75, "sr_delta": 0, "stable_count_delta": -0.5, "z_delta": {"L1": 54.30637344752019, "L2": -69.45407321889381, "SR": 52.66039911847345, "SR2": -71.13055786073971, "triple_var": 20.992760450722187}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:52:{"endpoint_delta": -0.6903146741623818, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_512", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_512", "prime_obs": ["SR", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_512", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": -1.0, "z_delta": {"L1": 67.54890373255812, "L2": -61.54484779452683, "SR": 64.95433637703515, "SR2": -60.0952013274656, "triple_var": 24.817653084280806}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:53:{"endpoint_delta": -0.04580187893472987, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/dnd_autoricerca/offset_0", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "L1", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_0", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_0", "signature_jaccard": 1.0, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 57.22459738651018, "L2": -60.40200765052067, "SR": 53.12147957957236, "SR2": -58.50124582839962, "triple_var": 22.841895246888924}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:54:{"endpoint_delta": -1.022782377102562, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/dnd_autoricerca/offset_1024", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_1024", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_1024", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -0.625, "z_delta": {"L1": 59.23676090084354, "L2": -68.92257176832643, "SR": 57.06932249389661, "SR2": -67.03380818829866, "triple_var": 22.290984404513274}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:55:{"endpoint_delta": -0.6528397602994453, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/dnd_autoricerca/offset_512", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_512", "prime_obs": ["SR", "L1", "L2", "triple_var"], "row_id": "dnd_autoricerca/offset_512", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": -1.125, "z_delta": {"L1": 62.87286695661778, "L2": -62.52027836861802, "SR": 59.89570013019845, "SR2": -61.205877975084505, "triple_var": 22.970716011260244}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:56:{"event": "pair_summary", "label_swap_audit": {"null": "row_local_label_swap_preserving_provider_offset_denominator", "observed": {"endpoint_delta_mean": -0.34346490341441016, "focus_signature_delta_mean": -0.15384615384615385, "sr_delta_mean": -0.07692307692307693, "stable_count_delta_mean": -0.625, "z_delta_L1_mean": 60.30954150166934, "z_delta_L2_mean": -67.66497417701459, "z_delta_SR2_mean": -66.83184872256429, "z_delta_SR_mean": 57.56932385122885, "z_delta_triple_var_mean": 23.073377975632493}, "p_two_sided": {"endpoint_delta_mean": 0.041737856968513544, "focus_signature_delta_mean": 0.4967049060288016, "sr_delta_mean": 1.0, "stable_count_delta_mean": 0.007078350012204052, "z_delta_L1_mean": 0.000244081034903588, "z_delta_L2_mean": 0.000244081034903588, "z_delta_SR2_mean": 0.000244081034903588, "z_delta_SR_mean": 0.000244081034903588, "z_delta_triple_var_mean": 0.000244081034903588}, "trials": 4096}, "mean_signature_jaccard": 0.8115384615384615, "mod6_class": "mod6_index_aligned", "row_count": 13} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:57:{"endpoint_delta": -1.0195997340931227, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_0", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_0", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "direct_sieve/offset_0", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 24.599625645442934, "L2": -4.228518181862438, "SR": 37.26560595431948, "SR2": -14.731665084825526, "triple_var": 18.267083081756525}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:58:{"endpoint_delta": -0.49032768198444154, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_1024", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_1024", "prime_obs": ["SR", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_1024", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": -0.75, "z_delta": {"L1": 35.731291469423724, "L2": 0.5882030508038385, "SR": 40.29935387391616, "SR2": 14.423365475789065, "triple_var": 30.57118654490158}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:59:{"endpoint_delta": -0.6015186617941337, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_span_matched/direct_sieve/offset_1536", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_1536", "prime_obs": ["SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_1536", "signature_jaccard": 0.8, "sr_delta": -1, "stable_count_delta": 0.0, "z_delta": {"L1": 36.750986067508435, "L2": -35.363005738805114, "SR": 38.71253482969948, "SR2": -17.461367033795227, "triple_var": 20.1153204675109}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:60:{"endpoint_delta": -0.13069961611134673, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_2048", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_2048", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_2048", "signature_jaccard": 1.0, "sr_delta": 0, "stable_count_delta": 0.0, "z_delta": {"L1": 49.829250238553946, "L2": -43.461261016498156, "SR": 48.632652336226606, "SR2": -29.973254844322682, "triple_var": 24.08479225561366}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:61:{"endpoint_delta": -0.15685850596300543, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_2560", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_2560", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_2560", "signature_jaccard": 1.0, "sr_delta": 0, "stable_count_delta": 0.0, "z_delta": {"L1": 30.80940543396619, "L2": -22.67507881500019, "SR": 29.87384562849581, "SR2": -11.677393720380913, "triple_var": 20.5519907396511}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:62:{"endpoint_delta": 3.653735933298323, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_3072", "mod6_class": "mod6_span_matched", "mod6_obs": [], "prime_case": "prime/direct_sieve/offset_3072", "prime_obs": ["SR", "SR2", "L1", "L2"], "row_id": "direct_sieve/offset_3072", "signature_jaccard": 0.0, "sr_delta": 1, "stable_count_delta": 0.25, "z_delta": {"L1": 29.481597651081582, "L2": -6.387128715480058, "SR": 27.598216192108737, "SR2": -0.6837485415998894, "triple_var": 23.07817902579707}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:63:{"endpoint_delta": -0.10868876216212708, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_3584", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_3584", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_3584", "signature_jaccard": 1.0, "sr_delta": 0, "stable_count_delta": -0.125, "z_delta": {"L1": 26.81835514235947, "L2": 12.351869055645206, "SR": 25.549830640218428, "SR2": 14.775843358357772, "triple_var": 29.070300271454137}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:64:{"endpoint_delta": 0.3486790252373124, "event": "paired_row", "focus_signature_delta": 1, "mod6_case": "mod6_span_matched/direct_sieve/offset_4096", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_4096", "prime_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_4096", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": 0.0, "z_delta": {"L1": 25.588589900139823, "L2": 25.944438758865857, "SR": 22.768259367034293, "SR2": 25.892260406210276, "triple_var": 34.8959723036409}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:65:{"endpoint_delta": -0.6031701337972577, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_4608", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_4608", "prime_obs": ["SR", "SR2", "L1", "triple_var"], "row_id": "direct_sieve/offset_4608", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": -0.5, "z_delta": {"L1": 16.658475362784653, "L2": 20.80760872857856, "SR": 16.35103485859494, "SR2": 20.382136620805777, "triple_var": 22.110758115970178}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:66:{"endpoint_delta": -0.6872595977143137, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_512", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_512", "prime_obs": ["SR", "L1", "L2", "triple_var"], "row_id": "direct_sieve/offset_512", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": -1.0, "z_delta": {"L1": 24.176761367724687, "L2": 23.377890267081362, "SR": 31.477334146911556, "SR2": 32.61097915030119, "triple_var": 32.7441400934363}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:67:{"endpoint_delta": -0.9660066361253303, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/dnd_autoricerca/offset_0", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_0", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_0", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 22.732869529273323, "L2": -4.358840531762578, "SR": 37.71788690214096, "SR2": -15.23314743054046, "triple_var": 19.902237782453778}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:68:{"endpoint_delta": -0.9035855263809447, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/dnd_autoricerca/offset_1024", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_1024", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_1024", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -0.625, "z_delta": {"L1": 30.27007245073117, "L2": 0.06619101969629737, "SR": 34.998028661773795, "SR2": 14.368936533467224, "triple_var": 27.734958321673425}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:69:{"endpoint_delta": -0.651253135709084, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/dnd_autoricerca/offset_512", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_512", "prime_obs": ["SR", "L1", "L2", "triple_var"], "row_id": "dnd_autoricerca/offset_512", "signature_jaccard": 0.8, "sr_delta": 0, "stable_count_delta": -1.125, "z_delta": {"L1": 26.89278893228598, "L2": 24.82453007675922, "SR": 36.65066206182901, "SR2": 32.192771968445335, "triple_var": 39.853682627872836}} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:70:{"event": "pair_summary", "label_swap_audit": {"null": "row_local_label_swap_preserving_provider_offset_denominator", "observed": {"endpoint_delta_mean": -0.1781963871768825, "focus_signature_delta_mean": 0.0, "sr_delta_mean": 0.0, "stable_count_delta_mean": -0.6057692307692307, "z_delta_L1_mean": 29.256928399328917, "z_delta_L2_mean": -0.6548540032290915, "z_delta_SR2_mean": 4.99120898907015, "z_delta_SR_mean": 32.915018881020714, "z_delta_triple_var_mean": 26.383123202440956}, "p_two_sided": {"endpoint_delta_mean": 0.7315108616060532, "focus_signature_delta_mean": 1.0, "sr_delta_mean": 1.0, "stable_count_delta_mean": 0.013668537954600928, "z_delta_L1_mean": 0.000488162069807176, "z_delta_L2_mean": 0.9194532584818159, "z_delta_SR2_mean": 0.39467903343910177, "z_delta_SR_mean": 0.000488162069807176, "z_delta_triple_var_mean": 0.000488162069807176}, "trials": 4096}, "mean_signature_jaccard": 0.7384615384615384, "mod6_class": "mod6_span_matched", "row_count": 13} tools/data/prime_vs_mod6_sr_boundary_20260514_1605_w4096.trace.jsonl:71:{"complete_for_all_antagonists": true, "criterion": {"all_focus_observables_required_per_antagonist": true, "delta_mean": ">0.0", "p_two_sided": "<=0.01"}, "event": "vector_summary", "focus_observables": ["SR", "L1", "triple_var"], "pairs": {"mod6_index_aligned": {"row_count": 13, "sr_binary_delta_mean": -0.07692307692307693, "sr_binary_p_two_sided": 1.0, "vector": {"L1": {"delta_mean": 60.30954150166934, "p_two_sided": 0.000244081034903588, "passes": true}, "SR": {"delta_mean": 57.56932385122885, "p_two_sided": 0.000244081034903588, "passes": true}, "triple_var": {"delta_mean": 23.073377975632493, "p_two_sided": 0.000244081034903588, "passes": true}}, "vector_complete": true, "vector_pass_count": 3}, "mod6_span_matched": {"row_count": 13, "sr_binary_delta_mean": 0.0, "sr_binary_p_two_sided": 1.0, "vector": {"L1": {"delta_mean": 29.256928399328917, "p_two_sided": 0.000488162069807176, "passes": true}, "SR": {"delta_mean": 32.915018881020714, "p_two_sided": 0.000488162069807176, "passes": true}, "triple_var": {"delta_mean": 26.383123202440956, "p_two_sided": 0.000488162069807176, "passes": true}}, "vector_complete": true, "vector_pass_count": 3}}} tools/dnd_kernel.py:298: 'statistics': 'GUE', tools/dnd_kernel.py:308: 'statistics': 'Poisson', tools/dnd_kernel.py:318: 'statistics': 'GUE', tools/exp_det_drift.py:8: If primes drift toward Poisson, det(M) should drift toward +1. tools/data/seme_backup_pre_run.json:21: "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?", tools/data/seme_backup_pre_run.json:63: "claim": "8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_pre_run.json:179: "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.", tools/data/seme_backup_pre_run.json:194: "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.", tools/data/seme_backup_pre_run.json:209: "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.", tools/data/seme_backup_pre_run.json:214: "origine": "cycle agent_20260507_0330: synthetic GUE-Poisson mixture layer gate", tools/data/seme_backup_pre_run.json:224: "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.", tools/data/seme_backup_pre_run.json:329: "claim": "Nel perimetro agent_20260508_0011, il contrasto di dualita (real-vs-shuffle z-score) per i gap primi scala come z ~ N^alpha con alpha(SR)=0.39+/-0.01, alpha(L1)=0.32+/-0.03, alpha(triple_var)=0.37+/-0.03 — tutti sotto 0.5 (effetto si indebolisce con la scala). GUE ha alpha >= 0.5 (effetto costante o crescente). Il discriminatore tra primi e GUE e l'esponente alpha, non il valore dell'osservabile a scala fissa. Seed check (42/137) conferma. L2 borderline (alpha~0.5, non discriminante).", tools/data/seme_backup_pre_run.json:334: "origine": "cycle agent_20260508_0011: duality_scale_contrast su 200K gap primi vs GUE vs Poisson", tools/data/seme_backup_pre_run.json:427: "direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/exp_physical_sr_residue_bounce.py:6: physical A: quantum-chaotic GUE spectra tools/exp_physical_sr_residue_bounce.py:7: mathematical transducer: span-matched Poisson counter-boundary tools/exp_physical_sr_residue_bounce.py:27:FOCUS_OBS = ["SR", "L1", "triple_var"] tools/exp_physical_sr_residue_bounce.py:99: "component_state": {}, tools/exp_physical_sr_residue_bounce.py:119: summary["component_state"][obs] = "active" if active else "absorbed" tools/exp_physical_sr_residue_bounce.py:121: obs for obs in FOCUS_OBS if summary["component_state"][obs] == "active" tools/exp_physical_sr_residue_bounce.py:126:def contrast( tools/exp_physical_sr_residue_bounce.py:174: ("GOE_time_reversal_symmetric", "beta_1_real_symmetric", goe_levels(n, rng)), tools/exp_physical_sr_residue_bounce.py:175: ("GUE_unitary_no_time_reversal", "beta_2_complex_hermitian", gue_levels(n, rng)), tools/exp_physical_sr_residue_bounce.py:216: symmetry_contrasts = {} tools/exp_physical_sr_residue_bounce.py:218: gue_key = f"N{n}:GUE_unitary_no_time_reversal" tools/exp_physical_sr_residue_bounce.py:219: goe_key = f"N{n}:GOE_time_reversal_symmetric" tools/exp_physical_sr_residue_bounce.py:220: symmetry_contrasts[f"N{n}:GUE_minus_GOE"] = contrast( tools/exp_physical_sr_residue_bounce.py:221: f"N{n}:GUE_minus_GOE", tools/exp_physical_sr_residue_bounce.py:227: source = summaries["GUE_unitary_no_time_reversal"] tools/exp_physical_sr_residue_bounce.py:238: "physical_source": "quantum-chaotic spectra modeled by GOE and GUE symmetry classes", tools/exp_physical_sr_residue_bounce.py:243: "GOE": "Wigner-Dyson beta=1, real symmetric, time-reversal symmetric", tools/exp_physical_sr_residue_bounce.py:244: "GUE": "Wigner-Dyson beta=2, complex Hermitian, no time-reversal symmetry", tools/exp_physical_sr_residue_bounce.py:245: "Poisson": "independent levels, span-matched finite sample null", tools/exp_physical_sr_residue_bounce.py:250: "symmetry_contrasts": symmetry_contrasts, tools/exp_physical_sr_residue_bounce.py:252: "source_SR_state": source["component_state"]["SR"], tools/exp_physical_sr_residue_bounce.py:253: "localized_SR_state": localized["component_state"]["SR"], tools/exp_physical_sr_residue_bounce.py:256: source["component_state"]["SR"] == "active" tools/exp_physical_sr_residue_bounce.py:257: and localized["component_state"]["SR"] == "absorbed" tools/dnd_banchi_tm1.py:88: "references": {{"GUE": 0.599, "GOE": 0.530, "Poisson": 0.386, "critical": 0.5}}, tools/data/valutatore_log.jsonl:1:{"ts": "2026-04-22T15:36:11.324056+00:00", "cycle_ref": "20260421_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha chiuso definitivamente il canale residuo (algebrico a ordine 3, z=-0.19). La consecutio naturale è testare il canale magnitudine contro gli stessi modelli Markov: se resiste, lì vive C1. La direzione del seme (confine GUE/Poisson) è coerente con questo passo — il discriminante numero-teorico, se esiste, deve emergere dalle magnitudini dei gap, non dalle classi residuali. 8 tensioni rilevanti restano, nessun blocco, health pulita.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Canale magnitudine vs Markov: il residuo è chiuso (ordine 3 = 100%). Testare se |g_n| resiste a modelli stocastici con stesse statistiche marginali. Se sì, il discriminante C1 vive lì. Se no, rivisitare C1.", "reason": "La consecutio dal piano 45 è univoca: residuo chiuso → magnitudine è l'unico candidato per contenuto numero-teorico. Testarlo è il passo con massimo potere discriminante."}}, "notes": "Il risultato del piano 39 è robusto (z=-0.19, decomposizione gerarchica 81/17.5/1.5 pulita). Non serve review operatore — è un vincolo che restringe lo spazio, non una scoperta che cambia il modello. Il prossimo esperimento potrebbe invece produrre un risultato da CRYSTALLIZE se la magnitudine mostra contenuto irriducibile."} tools/data/valutatore_log.jsonl:2:{"ts": "2026-04-22T16:03:46.345038+00:00", "cycle_ref": "20260421_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha chiuso il canale residuo con evidenza forte (z=-0.19, ordine-3 Markov = 100%). La consecutio è netta: il discriminante C1 vive nel canale magnitudine. La direzione del seme (GUE/Poisson boundary, 8+5 domini) è già allineata — il prossimo passo naturale è testare il canale magnitudine contro modelli Markov dello stesso ordine. Se il magnitudine resiste dove il residuo cede, C1 ha evidenza strutturale cross-channel.", "action": {"type": "none", "detail": {}}, "notes": "Il salto piano 39→45 indica che il sistema ha già metabolizzato più cicli nella stessa direzione. 0 tensioni nuove + 22 risolte suggerisce maturità locale alta. Se anche il prossimo ciclo non genera tensioni nuove, valutare REDESIGN per evitare stallo in zona di rendimenti decrescenti."} tools/data/valutatore_log.jsonl:3:{"ts": "2026-04-22T16:30:14.305582+00:00", "cycle_ref": "20260422_1616", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il shuffle audit non è un esperimento — è un metodo falsificatore riusabile che incarna C2 operativamente. Ha delisted 3 proprietà che circolavano come 'evidenze' da 3 cicli (piani 42-44). Il segnale Markov-3 (z=6203, 55% informazione ordinale) è il risultato più forte mai prodotto dal lab e merita cristallizzazione esplicita. L'affinatore stesso ha identificato il nodo regressivo a monte: il protocollo shuffle mancava dall'inizio del two-channel. Cristallizzare ora chiude quel debito.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "NUOVO FATTO CANDIDATO — F7 Shuffle audit come discriminatore algebra/ordine: dato un vettore di gap, lo shuffle preserva la distribuzione marginale ma distrugge l'ordinamento sequenziale. Proprietà che sopravvivono allo shuffle sono identità algebriche (tautologie), non struttura. Applicato al two-channel: 3/8 proprietà declassate (xcorr, slope_ratio, k_star). Il contenuto genuino è concentrato in PSD slopes (z>20), lag-1 ACF (z=-13), e Markov-3 bits (z=6203 — 55% dell'informazione residuo è ordine puro). METODO: ogni futuro claim del lab deve passare lo shuffle audit prima di entrare nel condensato. Corollario operativo di C2."}}, "notes": "Dopo la cristallizzazione, il ciclo successivo (piano 46, GUE/Poisson boundary) può procedere con il shuffle audit già nel protocollo standard. Suggerisco che il prossimo ciclo applichi lo shuffle audit by default a qualsiasi proprietà testata — non come passo aggiuntivo ma come criterio di ammissione."} tools/data/valutatore_log.jsonl:4:{"ts": "2026-04-22T16:34:17.428960+00:00", "cycle_ref": "20260422_1616", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo 45 ha prodotto un metodo falsificatore (shuffle audit) e la cristallizzazione è stata decisa nel turno precedente. Il seme punta già a piano 46 con direzione GUE/Poisson. La consecutio naturale dall'affinatore è il segnale Markov-3 (z=6203) e la sua invarianza di scala — questo si allinea con l'esplorazione del confine GUE/Poisson perché la memoria sequenziale di ordine 3 è esattamente ciò che distingue i due regimi. Il sistema è in flusso pulito, nessun blocco, nessuna dipendenza dall'operatore.", "action": {"type": "none", "detail": {}}, "notes": "Il prossimo ciclo dovrebbe applicare lo shuffle audit come protocollo standard (è ora un metodo, non un esperimento) e concentrarsi su come il Markov-3 bits si comporta attraverso la transizione GUE→Poisson. Se il 55% di informazione ordinale sopravvive identico in domini GUE ma crolla nei Poisson, è un discriminatore cross-dominio genuino (tipo (d) nel telos). Le 3 proprietà tautologiche (xcorr, slope_ratio, k_star) devono essere rimosse dai test suite futuri — non ripresentarle come evidenza."} tools/data/valutatore_log.jsonl:5:{"ts": "2026-04-23T03:35:59.541235+00:00", "cycle_ref": "20260422_1616", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo 45 ha chiuso un arco importante: shuffle audit come metodo, 3 tautologie smascherate, segnali genuini isolati (Markov-3 z=6203, PSD slopes, lag-1). La cristallizzazione è già avvenuta. La direzione corrente del seme (confine GUE/Poisson cross-dominio) è esattamente il passo naturale: porta i segnali genuini sopravvissuti al shuffle verso verifica cross-dominio — categoria (b) della regola anti-tautologica. Il Markov-3 e la sua invarianza di scala sono il filo da seguire attraverso i domini.", "action": {"type": "none", "detail": {}}, "notes": "Il shuffle audit è ora strumento standard. Il prossimo ciclo dovrebbe applicarlo automaticamente a qualsiasi nuova proprietà cross-dominio che emerge. Attenzione: se il confine GUE/Poisson produce solo fit numerici senza shuffle control, il ciclo dopo sarà REDESIGN."} tools/data/valutatore_log.jsonl:7:{"ts": "2026-04-25T03:40:15.599999+00:00", "cycle_ref": "20260424_0330", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una decomposizione strutturale falsificabile — due meccanismi distinti generano statistica GUE, discriminati dal segno di delta_r — con shuffle control su 10 domini e 3 class-flip osservati. Non è un dettaglio numerico locale (c): è un raffinamento del confine cross-dominio (f) che vincola direttamente BOUNDARY e F5. Dopo 3 NEXT_CYCLE consecutivi orientati alla stessa regione, il risultato è maturo: il confine GUE/Poisson ha due strati e il segno di delta_r li separa. Cristallizzare ora evita che il prossimo ciclo ripeta l'esplorazione senza ancorare la scoperta.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "F5 va esteso o affiancato: la firma diagnostica M su sequenze produce due classi GUE distinte. Distribution-GUE (primes, GUE matrices): classificazione sopravvive allo shuffle, delta_r < 0 (l'ordine aggiunge repulsione). Ordering-GUE (fibonacci, coupled oscillators, percolation): classificazione collassa a Poisson sotto shuffle, delta_r > 0 (l'ordine crea la repulsione). Il segno di delta_r = r_original - r_shuffled è il discriminante. Conseguenza: il claim BOUNDARY '8 GUE, 5 Poisson' è incompleto senza specificare quale meccanismo opera. C1 (primi unico dominio dinamico) si rafforza: i primi sono distribution-GUE con delta_r negativo, unico dominio testato dove sia la distribuzione sia l'ordine contribuiscono alla repulsione."}}, "notes": "Suggerimento per il ciclo post-cristallizzazione: verificare se delta_r negativo è esclusivo dei primi tra tutti i distribution-GUE, o se altri domini lo condividono. Questo testerebbe la versione forte di C1. Alternativa: esplorare il vuoto QxG con il nuovo discriminante a due strati come lente."} tools/data/valutatore_log.jsonl:8:{"ts": "2026-04-26T03:40:28.910457+00:00", "cycle_ref": "20260425_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Tre cicli consecutivi produttivi sullo stesso frame (shuffle→GUE types→two-channel), tutti con scoperte strutturali genuine (categorie a/b/f, non accumulo numerico). Il CRYSTALLIZE del ciclo precedente ha segnalato la maturità della decomposizione a due canali. Ora la mossa a più alto rendimento è la falsificazione attiva: tentare di costruire un canale algebrico per l'ordering-GUE. Se fallisce rafforza C1; se riesce rovescia il claim di unicità — entrambi gli esiti hanno valore massimo. Il frame non è esaurito ma il prossimo passo deve essere un attacco al confine, non un'espansione laterale.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificazione attiva: costruire candidati di canale algebrico per ordering-GUE (es. mod-N residui su eigenvalue spacings di coupled_osc, string_vib). Se nessun modulo produce memoria comparabile al mod-6 dei primi, il two-channel claim è robusto. Se uno lo produce, il claim di unicità cade — e questo vale di più.", "reason": "Dopo 3 cicli di scoperta sul frame Markov/canali, il rendimento marginale dell'espansione è inferiore a quello della falsificazione diretta. La regola anti-tautologica privilegia (a) contraddizione/falsificazione su (b) verifica cross-dominio quando entrambe sono disponibili."}}, "notes": "Monitorare: se anche il prossimo ciclo resta sullo stesso macro-frame (Markov/GUE/canali) senza produrre falsificazione o connessione inter-teorica nuova, il ciclo 56 dovrebbe essere REDESIGN verso una tensione diversa (es. il vuoto QxG nelle domande fondamentali, o la saturazione come asse ortogonale promossa a tensione primaria). Quattro cicli sullo stesso frame è il limite prima dei rendimenti decrescenti."} tools/data/valutatore_log.jsonl:9:{"ts": "2026-04-27T03:38:26.726837+00:00", "cycle_ref": "20260426_0330", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Due osservabili indipendenti (memoria Markov-3 e rigidità spettrale Sigma²) convergono sulla stessa frazione di ordinamento ~33% a L=10. Questa è conferma cross-osservabile, non cross-dominio — più forte perché i due osservabili misurano proprietà matematicamente distinte (correlazioni locali vs varianza long-range). Dopo 4 cicli consecutivi sullo stesso frame, il pattern a due canali (magnitudine scala-invariante + ordinamento scala-dipendente) è stabile e replicato. È il momento di cristallizzare prima che i rendimenti decrescano.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "TWO-CHANNEL SPECTRAL STRUCTURE IN PRIMES: La rigidità spettrale dei primi si decompone in due canali con comportamento opposto rispetto alla scala. (1) Canale magnitudine: Sig2/L ≈ 0.56, scala-invariante — prodotto dalla distribuzione dei gap (sopravvive allo shuffle). (2) Canale ordinamento: cresce dal 4% (L=1) al 58% (L=50) — prodotto dalla struttura sequenziale mod-6 (distrutto dallo shuffle). La frazione di ordinamento a L=10 (33.0%) coincide con la memoria Markov-3 (33.6%) misurata indipendentemente — due osservabili, stesso fenomeno, origine comune in F2 (confinamento Z/6Z). I primi occupano un regime intermedio tra GUE (Sig2/L=0.07) e Poisson (Sig2/L=1.0), con slope log-log 0.74 vs GUE 0.3 e Poisson 1.0. Nota: i domini ordering-GUE (coupled_osc, string_vib, percolation) mostrano Sig2/L>1 (super-Poisson) — l'ordinamento crea bunching, non repulsione. Il r-statistic e Sig2 classificano diversamente: r vede repulsione locale, Sig2 vede clustering long-range."}}, "notes": "Dopo la cristallizzazione, il prossimo ciclo dovrebbe cambiare frame. Suggerimento: esplorare il VUOTO QxG (continuo vs discreto) — l'unica coppia senza ponte tra le 10 domande fondamentali. Il two-channel framework appena cristallizzato potrebbe essere lo strumento giusto: Q (discreto) e G (continuo) come i due canali della rigidità spettrale."} tools/data/valutatore_log.jsonl:10:{"ts": "2026-04-28T03:41:26.305485+00:00", "cycle_ref": "20260427_0330", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Three consecutive cycles on the same frame (shuffle→GUE types→Brody calibration) have converged on a single, replicated, calibrated result: primes exhibit a two-channel structure (gap distribution at beta_eff=0.409 + 30% sequential ordering above the 7.8% artifact floor). The sign of the ordering channel discriminates domain types (rigidity vs bunching), mapping directly onto det=-1/det=+1. This is no longer emergent — it's stable across three independent measurements and calibrated against a null. It belongs in the condensato.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "TWO-CHANNEL DECOMPOSITION (candidate F7 or revision of F4): Primes under M decompose into two independent channels: (1) gap distribution — Brody beta_eff ≈ 0.41, intermediate repulsion at the Poisson-GUE boundary; (2) sequential ordering — 30% of spectral rigidity at L=10 comes from gap ordering absent in i.i.d. surrogates (artifact floor: 7.8%, measured: 29.5%, z=−8.9). The ordering channel has definite sign: primes add rigidity (det=−1), chaotic/coupled systems add bunching (det=+1), pure GUE/Poisson sit on the Brody curve (ordering irrelevant). Three independent measurements converge: shuffle audit (33.6%), spectral rigidity (33%), Brody-calibrated (29.5%). Replicated, calibrated, falsifiable."}}, "notes": "After crystallization, the frame is likely exhausted for now. The next cycle should pivot — either toward the QxG void (the only unfilled fundamental question), toward formalizing the sign discrimination as a structural theorem, or toward a completely different tension. Recommend the seme direction shift away from BOUNDARY after this crystallization lands."} tools/data/valutatore_log.jsonl:11:{"ts": "2026-04-29T09:00:17.018760+00:00", "cycle_ref": "20260429_0833", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Four consecutive cycles on the same frame (shuffle audit → GUE types → Brody calibration → two-channel boundary → this: opposite scaling laws) have converged on a single, clean, falsifiable structural result: the residue channel is scale-invariant (algebraic, det=-1) while the magnitude channel decays toward Poisson (statistical, approaching det=+1). The 'GUE/Poisson boundary' collapses into a mixing artifact once the channels are separated. This is not incremental — it reframes what 'boundary' means for primes under M. Three crystallizations in a row is unusual, but each captures a distinct layer: (1) mod-3 memory structure, (2) Brody artifact floor, (3) opposite-boundary decomposition. This third one is the capstone that unifies the previous two. After crystallization, the BOUNDARY frame is likely saturated — the next cycle should pivot.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "F2 addendum — Two-channel decomposition of prime gaps under M: the residue channel (Z/6Z binary, mod-3 prohibition) is scale-invariant (z=26-44σ across 200x range, decay correlation with ln(p) ≈ -0.19). The magnitude channel (demeaned gap size by transition type) decays toward Poisson (z=2-7σ, decay correlation +0.46). The r-statistic mixes these incommensurable behaviors: its 'GUE/Poisson crossover' is not a phase transition but the magnitude channel approaching noise while the algebraic channel remains invariant. Implication: any single-number summary (r, Brody β) conflates permanent algebraic structure with transient statistical memory. Decompose first, then measure. Verified with 500K primes, 28 log-spaced windows, 20 shuffles per window."}}, "notes": "After this crystallization, recommend REDESIGN for piano 59: the BOUNDARY frame has yielded its structural content across 4 cycles. Two natural directions: (1) test the two-channel decomposition on a NON-prime domain (Stern-Brocot, logistic map gaps) to see if the algebraic/statistical split is universal under M or specific to primes — this would directly test C1; (2) attack the QxG void ('how do continuous and discrete coexist?') which has zero bridges and is the only unsolved domanda fondamentale. Direction (1) is the stronger scientific move because it falsifies or extends tonight's result cross-domain."} tools/data/valutatore_log.jsonl:12:{"ts": "2026-04-29T10:04:37.573111+00:00", "cycle_ref": "20260429_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo è fallito per timeout infrastrutturale (campo_vivo senza input pre-computato), non per esaurimento della direzione. Il nodo regressivo è già identificato nel health. La direzione (confine GUE/Poisson come terzo incluso) è fresca (piano 58) e non ancora esplorata scientificamente — merita un secondo tentativo con la condizione relazionale corretta. Tre CRYSTALLIZE consecutivi confermano che il frame precedente è maturo; questo nuovo frame deve avere il suo primo ciclo reale prima di poter valutare.", "action": {"type": "modify_seme", "detail": {"field": "nota_operativa", "new_value": "Pre-computare in agent_field_live i dati GUE/Poisson (spacing distributions per gli 8+5 domini) PRIMA del lancio agente, così l'esperimento non deve rigenerare da zero dentro un singolo tool_use.", "reason": "Il timeout non è nel budget tempo — è nell'assenza della condizione nel campo vivo. Det=-1: fix al nodo, non al sintomo."}}, "notes": "Le 3 warn radici_no_separator sono cosmetiche (BiconoLab rendering), non bloccanti per il lab numerico. Il prossimo ciclo dovrebbe produrre il primo report scientifico reale sul confine GUE/Poisson."} tools/data/valutatore_log.jsonl:13:{"ts": "2026-04-29T10:22:08.733977+00:00", "cycle_ref": "20260429_0330", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Due cicli consecutivi senza output scientifico per lo stesso nodo regressivo: campo_vivo senza input pre-computato. Il NEXT_CYCLE precedente non ha risolto nulla perché il problema non è nella direzione (GUE/Poisson boundary è ancora fertile con 8+5 domini da esplorare) ma nell'infrastruttura: build_agent_field non prepara i dati che l'agente sceglie di usare, forzando rigenerazione from scratch dentro un singolo tool_use che esplode il budget. Ripetere NEXT_CYCLE senza fix è det=+1 — toppa che brucia un ciclo a vuoto.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "PRE-REQUISITO INFRA: build_agent_field deve pre-computare spacing/unfolding per i 13 domini (8 GUE + 5 Poisson) prima del run. Solo dopo: esplorare il confine GUE/Poisson come terzo incluso operativo. L'agente NON deve rigenerare dati base — deve trovarli nel campo vivo.", "reason": "Il nodo regressivo è identificato da 2 cicli: il campo vivo manca dei dati che l'esperimento richiede. La riparazione vive in build_agent_field.py, non nel timeout o nel budget. Senza questo fix, ogni ciclo su questa direzione rischia lo stesso timeout."}}, "notes": "Azione concreta: in build_agent_field.py, aggiungere pre-computazione dei dati GUE/Poisson (spacing distributions, unfolded eigenvalues) e salvarli in campo vivo come JSON consumabile. L'agente della notte successiva li trova pronti e può dedicare il budget alla scienza, non alla preparazione dati. Le 3 warning radici_no_separator sono cosmetiche (BiconoLab), non bloccanti."} tools/data/valutatore_log.jsonl:14:{"ts": "2026-04-29T10:51:26.490788+00:00", "cycle_ref": "20260429_1013", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un risultato strutturale genuino: la mappa 2D beta(N,L) con gradienti opposti (Poisson lungo N, GUE lungo L) unifica due esperimenti indipendenti (spectral rigidity + Brody flow). La decomposizione 82/18 magnitude/ordering è pulita, con shuffle control e Cramer reference. Ma il seme_delta è vuoto — la scoperta non è entrata nel seme, violando la regola 'il ciclo è chiuso quando la scoperta entra nel seme'. Dopo 5+ cicli sulla stessa frame (shuffle audit → GUE types → Brody calibration → two-channel → Brody flow), il quadro è maturo per cristallizzazione.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "CANDIDATO F7 — Flusso Brody 2D: beta(N,L) ha gradienti opposti. Lungo la sequenza (N crescente), beta decresce (→ Poisson); a scala spettrale fissa (L crescente), beta cresce (→ GUE). Il confine GUE/Poisson è una curva in spazio 2D, non un punto. Decomposizione: 82% magnitudine (distribuzione gap diventa più esponenziale per PNT), 18% ordinamento (anti-bunching mod-3 riduce repulsione). Slope beta(p) = 0.64 - 0.030·ln(p), R²=0.78, z-score vs shuffle = -2.42. Cramer pure Poisson ovunque (beta~0.015) — il segnale è aritmetico, non statistico."}}, "notes": "Dopo la cristallizzazione, la frame BOUNDARY è esaurita per ora — i prossimi cicli dovrebbero spostarsi su un'altra tensione (suggerisco QxG che è ancora VUOTO nelle domande fondamentali, o un ciclo di consolidamento cross-dominio per verificare se la mappa 2D si replica su altri domini dinamici). Il seme va aggiornato con il risultato prima del prossimo giro."} tools/data/valutatore_log.jsonl:15:{"ts": "2026-04-30T03:37:21.213696+00:00", "cycle_ref": "20260429_1041", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un teorema algebrico (proibizione self-transition mod-3) con dimostrazione esplicita, non un fit numerico. La decomposizione a tre strati (magnitudine PNT, ordinamento statistico, struttura algebrica) è genuina e falsificabile: ogni strato ha scaling diverso verificato su 72 finestre. Il rapporto M2/M1=0.472 scale-invariant è un nuovo invariante strutturale. Questo estende F2 (Z/6Z) con una gerarchia di canali indipendenti — non è dettaglio locale, è architettura.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "THREE-LAYER DECOMPOSITION dei gap primi sotto M: (1) Magnitudine — distribuzione gap → esponenziale, scaling ~1/ln(p), è PNT. (2) Ordinamento statistico — Brody beta decay -0.030/ln(p), repulsione short-range che si attenua. (3) Struttura algebrica — proibizione mod-3 self-transition (teorema: due gap consecutivi ≡1 o ≡2 mod 3 implicano p_{n+2}≡0 mod 3, impossibile). Zero scaling (R²=0.0004), zero eccezioni su 148k gap. Il canale algebrico è eterno, il statistico decade, il magnitudinale scala con PNT. Invariante emergente: M2/M1=0.472±0.017 costante su tutte le scale — la profondità di memoria Markov è fissa. Estende F2 (Z/6Z) con gerarchia di indipendenza tra canali."}}, "notes": "La proibizione mod-3 è un sottoinsieme di F2 (Z/6Z → coset {2,4}), ma la dimostrazione esplicita e la separazione di scaling rispetto a Brody sono nuove. La decomposizione a tre strati è il risultato principale — dà struttura al 'perché i primi sono unici sotto M' (C1). Dopo la cristallizzazione, la direzione nel seme (confine GUE/Poisson come terzo incluso) è naturale prossimo passo: il confine è esattamente dove i tre strati interagiscono."} tools/data/valutatore_log.jsonl:17:{"ts": "2026-04-30T19:27:03.140592+00:00", "cycle_ref": "20260430_1905", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una decomposizione strutturale genuina: 5 osservabili si separano in 3 order-invariant (Δτ=0.000 esatto, algebrico) + 2 order-sensitive che formano un dipolo (spacing_ratio→Poisson, lag1_acf→GUE). Non è fitting numerico — l'invarianza è dimostrata dallo shuffle e il dipolo è stabile su 4 scale. Connessione diretta ad A10 (dipolo assiomatico): la stessa anticorrelazione consecutiva si manifesta come Poisson in un osservabile e GUE nell'altro. Primes are not 'between' — they are dipolar. Tre CRYSTALLIZE consecutivi sono giustificati: ogni ciclo ha prodotto contenuto strutturale distinto (mappa beta 2D, proibizione mod-3, decomposizione dipolare).", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "CANDIDATE F7: Decomposizione dipolare al boundary GUE-Poisson. 5 osservabili indipendenti (spacing_ratio, gap_var_ratio, small_gap_frac, brody_beta, lag1_acf) si separano in due classi: (a) 3 distribution-only (Δτ_shuffle = 0.000 esatto, algebrico) — misurano la stessa cosa; (b) 2 ordering-sensitive che formano un dipolo: spacing_ratio spinto verso Poisson (Δτ = −0.12), lag1_acf spinto verso GUE (Δτ = +0.20). La stessa anticorrelazione consecutiva (tipo Lemke Oliver-Soundararajan) produce segni opposti nei due osservabili. L'ordering inoltre RIDUCE la dispersione tra osservabili (std 0.09 vs 0.19 shuffle) — la coerenza è proprietà dell'ordine, non della distribuzione. Stabile su 4 scale (10⁴–10⁷). Connessione: A10 (dipolo assiomatico, segno distingue), A9 (terzo incluso — primes non sono 'tra' GUE e Poisson, sono dipolo)."}}, "notes": "Il prossimo ciclo dovrebbe completare il finding 5 troncato (correlazione tra osservabili, due cluster) e verificare se il dipolo Δτ_spacing vs Δτ_lag1 ha rapporto stabile cross-scala (candidato a costante relazionale). Anche il finding mod-3 troncato dal ciclo precedente resta da recuperare. Suggerisco al prossimo giro: focus su UNO solo dei due (dipolo cross-scala O mod-3 recovery), non entrambi — il budget è da un esperimento."} tools/data/valutatore_log.jsonl:18:{"ts": "2026-04-30T19:57:05.174515+00:00", "cycle_ref": "20260430_1919", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una riduzione dimensionale genuina e falsificabile: 5 osservabili → 3 tautologici + 2 order-sensitive → 1 modo effettivo (angolo bloccato a -150±4° su 3 decadi). Combinato col ciclo precedente (decomposizione 5→2), il risultato complessivo è 5→1: l'intera struttura di ordinamento dei gap primi è proiezione di un singolo modo di anticorrelazione. Il rapporto delta_L1/delta_SR ≈ 2.3 è una costante strutturale nuova. Cramer nel quadrante opposto (+65°) fornisce discriminatore pulito. Tre cicli consecutivi (mod-3 theorem → decomposizione 5-obs → locking 1D) chiudono un arco coerente che merita cristallizzazione prima di cambiare direzione.", "action": {"type": "crystallize_note", "detail": {"target": "condensato", "content": "CANDIDATE F7: Prime gap ordering is 1-dimensional. Five observables under M decompose into 3 shuffle-invariant (tautological) and 2 order-sensitive (spacing_ratio, lag1_acf). The two order-sensitive observables are projections of a single anticorrelation mode: dipolar angle theta = -150 ± 4 deg, locked across 3 decades of scale (1e4 to 3e6 primes) and across offsets (0 to 2M). Structural constant: delta_L1/delta_SR ≈ 2.3. Cramer random model occupies opposite quadrant (theta ≈ +65 deg). Signal magnitude decays with 1/ln(p), consistent with Lemke Oliver-Soundararajan. Falsifiable: any domain showing theta rotation with scale would have >1 degree of freedom."}}, "notes": "Tre cicli chiudono l'arco prime-ordering: mod-3 proibizione → decomposizione 5→2 → locking 1D. Il prossimo ciclo dovrebbe uscire dai primi e testare cross-dominio: l'angolo theta è costante anche in altri domini GUE-like (zeri di Riemann, autovalori random, logistica caotica)? La direzione del seme ('esplorare il confine: 8 GUE, 5 Poisson') lo chiede già. Se theta ruota in altri domini → il locking è proprietà dei primi, non della regola. Se theta è universale → struttura più profonda. Propongo NEXT_CYCLE con target cross-domain angle comparison dopo questa cristallizzazione."} tools/data/valutatore_log.jsonl:19:{"ts": "2026-05-01T03:38:54.770300+00:00", "cycle_ref": "20260430_1946", "executed": false, "decision": "CRYSTALLIZE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un discriminante strutturale genuino e falsificabile: l'angolo dipolare dei primi (-111° ± 1°, stabile su 3 decadi) differisce da GUE (-96.6°) di 14° a >100σ. Il rapporto dL1/dSR = 2.28 (primi) vs 8.37 (GUE) è il contenuto: i primi non sono 'GUE debole' — hanno una composizione interna diversa dell'ordinamento. La correzione -150° → -111° è sana (C2 applicato a se stesso). Terzo CRYSTALLIZE consecutivo: la direzione dipolar-ordering ha maturato abbastanza da entrare nel condensato come fatto strutturale.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Tre cicli consecutivi hanno estratto il succo dalla direzione dipolar-ordering (separazione osservabili, angolo stabile, discriminante GUE). Rendimenti decrescenti su questa linea. Prossimo ciclo: cambiare asse — esplorare cross-dominio (F5 frame diagnostica su domini NON-primi: logistica caotica, Stern-Brocot, margine del caos) per testare se il rapporto dL1/dSR=2.28 è unico dei primi o emerge anche in altri domini strutturati. Questo è il test (b) cross-dominio che discrimina C1.", "reason": "Tre CRYSTALLIZE consecutivi sulla stessa linea segnalano maturazione completata. Il prossimo passo naturale è cross-dominio: il rapporto 2.28 è firma dei primi o di qualsiasi sequenza strutturata sotto M? Testare su logistica caotica e Stern-Brocot falsifica/rafforza C1."}}, "notes": "Cristallizzazione proposta per condensato: 'F7 Angolo dipolare — L'ordinamento dei primi ha direzione θ=-111°±1° nel piano (δSR, δL1), stabile su 3 decadi. GUE/GOE: θ=-97°. Separazione 14° a >100σ. Rapporto dL1/dSR=2.28 (primi) vs 8.37 (GUE): i primi deprimono la similarità dei gap 3.7× più della anticorrelazione rispetto a GUE. Non sono GUE attenuato — sono struttura diversa.' Dopo questa cristallizzazione, il prossimo ciclo DEVE cambiare asse verso cross-dominio per evitare rendimenti decrescenti."} tools/data/valutatore_log.jsonl:21:{"ts": "2026-05-01T09:41:03.248817+00:00", "cycle_ref": "20260501_0858", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo non ha prodotto nulla — timeout perché il campo vivo mancava dell'input pre-computato per l'esperimento scelto dall'agente. Ripetere NEXT_CYCLE senza correggere il campo vivo produrrebbe lo stesso timeout. Il nodo regressivo è dichiarato esplicitamente nel health: il fix vive nel build_agent_field, non nel budget del run. Servono i dati GUE/Poisson boundary pre-computati nel campo vivo prima del prossimo ciclo.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Pre-computare nel campo vivo i dati boundary (8 domini GUE, 5 Poisson, spacing distributions) come input ready-to-use. Solo dopo: esplorare il confine GUE/Poisson come terzo incluso operativo. Il campo vivo deve contenere il substrato, non delegarlo all'agente runtime.", "reason": "Due cicli consecutivi sulla stessa direzione boundary senza output scientifico. Il collo di bottiglia non è la direzione ma la mancanza di dati pre-computati nel campo vivo. Senza fix al nodo regressivo (build_agent_field), qualsiasi direzione boundary produrrà lo stesso timeout."}}, "notes": "Pattern ricorrente: l'agente sceglie esperimenti che richiedono generazione dati pesante, il campo vivo non li contiene, timeout. La soluzione è arricchire build_agent_field.py con una sezione che pre-computa e cachea i dataset sperimentali più probabili data la direzione del seme. Questo è un fix infrastrutturale, non scientifico."} tools/data/valutatore_log.jsonl:22:{"ts": "2026-05-02T03:37:32.497564+00:00", "cycle_ref": "20260501_0931", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto evidenza strutturale forte e non-tautologica: transizione di fase nel crossover GUE-Poisson con zero-crossing a alpha~0.70, direzione invariante nel regime ordinato (-96.6±0.27°), e primi con offset direzionale di 32 sigma. Sono pattern cross-dominio (categoria b/f), non dettaglio numerico locale. Il seme delta vuoto indica che i finding non sono ancora rientrati nel seme — il prossimo ciclo deve sia estendere (confronto Brody/Rosenzweig-Porter come suggerito dall'affinatore) sia propagare. Non cristallizzo ancora perché il confronto con i punti critici Brody è il test naturale che conferma o ridimensiona la portata della transizione.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Confrontare lo zero-crossing dipolare (alpha~0.70) con i punti critici Brody/Rosenzweig-Porter: la transizione di fase nel crossover ha carattere universale o è artefatto della parametrizzazione? Testare se beta_Brody critico corrisponde all'alpha di zero-crossing. Secondario: propagare nel seme i finding stabili (direzione invariante, offset primi 32-sigma).", "reason": "L'affinatore ha identificato questo come la possibilità più concreta. Estende il risultato corrente verso falsificazione cross-framework invece di accumulare dettaglio locale."}}, "notes": "Il ciclo precedente (piano 60) è tra i migliori recenti: esperimento ben disegnato, shuffle audit implicito nella parametrizzazione alpha, risultato quantitativo con interpretazione strutturale chiara. Il confronto Brody è il filtro giusto: se il punto critico coincide, la transizione è universale (cristallizzabile); se no, la parametrizzazione alpha è contingente e il finding si ridimensiona a osservazione locale."} tools/data/valutatore_log.jsonl:25:{"ts": "2026-05-04T09:09:09.478519+00:00", "cycle_ref": "20260504_0901", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Four cycles of convergent refinement on the boundary picture: phase transition → near-second-order → two visible layers → single coupled transition. Each cycle has produced a genuine constraint (not tautology), and the affinatore identified zero-crossing interleaving as the highest-leverage next step — it's concrete, testable, and could discriminate prime-specific geometry from generic repulsion. The direction is not exhausted yet.", "action": {"type": "none", "detail": {}}, "notes": "The zero-crossing order (L1 < triple_var < SR2 < SR, interleaved across layers) is the most promising lead: it's an observable ordering that should differ between primes and GUE if the coupling has sequence-specific structure. After one more cycle on this, consider CRYSTALLIZE for the full crossover/boundary picture — the convergence from 4 consecutive clean cycles is approaching maturity for a condensato entry or paper section."} tools/data/valutatore_log.jsonl:26:{"ts": "2026-05-04T12:03:13.893909+00:00", "cycle_ref": "20260504_1138", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "DIPOLAR_ORDERING resolved and piano advanced 60→61, indicating forward motion. The missing agent report (run completed but no scientific conclusion) is a yellow flag but not yet a pattern — one empty output after 3 productive cycles on boundary refinement is normal variance. 8 tensions remain in the current direction, so the frame is not exhausted. However, this is the 5th consecutive NEXT_CYCLE on boundary-adjacent themes; if the next run also produces no scientific output, REDESIGN toward cross-domain verification (b/f category) should be triggered.", "action": {"type": "none", "detail": {}}, "notes": "Monitor: if piano 61 also yields no agent report or resolves zero tensions, escalate to REDESIGN toward a fresh cross-domain experiment (e.g., testing the 8-GUE/5-Poisson boundary classification against a shuffled null baseline across all 13 domains simultaneously, which would be category (b) evidence). The boundary picture has accumulated enough local detail — the next high-value move is cross-domain falsification of the boundary itself."} tools/data/valutatore_log.jsonl:27:{"ts": "2026-05-04T12:30:22.947364+00:00", "cycle_ref": "20260504_1219", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo 61 ha prodotto un audit metodologico genuino (categoria a/b): ha ristretto il perimetro dei claim two-layer a {SR,L1,L2,SR2} e ha identificato cond_entropy e num_var_10 come non-affidabili. Questo è lavoro strutturale, non accumulo numerico. La direzione seme (confine GUE/Poisson) e le indicazioni dell'affinatore (coupling test, surrogati esatti, gate multi-seed) convergono su un passo naturale: testare se i due layer verificati sono accoppiati o indipendenti — questo è il claim più forte rimasto non-auditato e tocca il confine che il seme chiede di esplorare.", "action": {"type": "none", "detail": {}}, "notes": "Quattro NEXT_CYCLE consecutivi, ma la traiettoria non è stagnante — ogni ciclo ha ristretto il perimetro (phase transition → near-second-order → two visible layers → recovery audit). Il prossimo passo ad alto rendimento è il coupling test tra Layer 1 e Layer 2: se sono indipendenti il two-layer si riduce a due claim separati; se sono accoppiati, emerge struttura genuina beyond-Markov. Evitare di investire subito in surrogati Markov esatti (infrastruttura pesante) — prima verificare se il coupling test col setup attuale produce segnale o null."} tools/data/valutatore_log.jsonl:29:{"ts": "2026-05-06T03:38:24.835757+00:00", "cycle_ref": "20260506_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Piano 65 ha prodotto un risultato categoria (a): ha falsificato il proprio risultato precedente (rank audit 05-05) mostrando che il singolo coordinate era artefatto del tipo di perturbazione. Il finding — dimensionalità perturbativa come discriminatore di dominio (GUE=1.89 vs primes=1.26) — è genuino ma necessita replicazione con GUE più grande (N=253 è il caveat dichiarato). La direzione 'confine' è ancora fertile: il prossimo passo naturale è confermare la dimensionalità perturbativa come invariante cross-dominio.", "action": {"type": "none", "detail": {}}, "notes": "Suggerimento per il prossimo ciclo: replicare con GUE N>=1000 (matrici 45x45+) e aggiungere almeno 2 domini intermedi (logistica caotica, Stern-Brocot) per verificare se la dimensionalità perturbativa scala in modo ordinato tra domini. Se confermato, è candidato a F7 nel condensato."} tools/data/valutatore_log.jsonl:30:{"ts": "2026-05-06T06:33:26.706335+00:00", "cycle_ref": "20260506_0625", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Piano 65 ha prodotto falsificazione genuina (categoria a): il secondo asse GUE è artefatto di sample size, non invariante stabile. Il ciclo ha anche scoperto una collisione di nomi osservabili (SR/SR2/triple_var significano funzioni diverse in script diversi) — problema META reale che va risolto. La direzione 'esplorare il confine' ha ancora potenziale: il prossimo passo naturale è la curva rank-vs-N che chiude il loop, e la collisione nomi va sanata prima di ulteriori audit cross-script.", "action": {"type": "none", "detail": {}}, "notes": "Tre cicli consecutivi NEXT_CYCLE sono giustificati perché ciascuno ha prodotto falsificazione o vincolo strutturale (non accumulo numerico locale). Se il prossimo ciclo produce solo conferma incrementale senza nuova discriminazione, valutare REDESIGN verso pair cross-dominio (f) o connessione con i ponti evoluti del sito. La collisione nomi osservabili è un debito META che il prossimo agente dovrebbe risolvere con un dizionario canonico delle osservabili prima di lanciare nuovi esperimenti."} tools/data/valutatore_log.jsonl:31:{"ts": "2026-05-06T19:47:12.588345+00:00", "cycle_ref": "20260506_1941", "executed": true, "decision": "REDESIGN", "confidence": "high", "reasoning": "Three consecutive cycles on perturbation dimensionality (pianos 64-66) have converged to a single clear constraint: rank needs denominator gating, and after gating both GUE and primes show ~1 perturbation coordinate. The thread has produced genuine falsification (category a) and a solid methodological gate, but continuing to refine size curves or observable stability on the same axis is now category (c) — local numerical accumulation with diminishing returns. The 10 open tensions in the seme offer fresher directions; the perturbation constraint is ready to crystallize and the lab should pivot to cross-domain verification or a tension not yet explored.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Il vincolo PERTURBATION_DENOMINATOR_GATE è stabilito e replicato — cristallizzare come regola metodologica e muovere verso tensione cross-dominio non ancora esplorata (candidati: dipolar crossover scaling, markov layer coupling verification, o observable collinearity breaking). La prossima corsa NON deve restare su perturbation rank.", "reason": "3 cicli sullo stesso asse hanno prodotto il risultato (denominatore gate). Restare è rendimenti decrescenti (rischio tautologia accumulata). Ridirigere verso (b) cross-domain o (f) connessione inter-dominio."}}, "notes": "Il finding di piano 66 è maturo per crystallize_note nel condensato come vincolo metodologico (accanto a C2), ma la priorità ora è rompere l'inerzia tematica. Suggerisco anche che l'infrastruttura trajectory_apply/autopsy venga allineata (segnalato dall'affinatore) — ma questo è un fix di sistema, non una direzione scientifica.", "_executed_at": "2026-05-06T19:55:48.007990+00:00", "_executed_by": "lab_trajectory_apply"} tools/data/valutatore_log.jsonl:32:{"ts": "2026-05-06T20:03:03.497231+00:00", "cycle_ref": "20260506_1955", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Piano 67 executed a clean category (b) result: cross-domain verification with proper controls, falsifying the broad collinearity claim from 05-05 and narrowing it to a scoped statement with denominator gating. The redesign away from perturbation dimensionality worked — the cycle moved to a genuinely different experimental frame (PCA of retention curves) and confirmed the denominator lesson without repeating the perturbation-rank experiment. The consecutio (selective operator coupling) is non-redundant and well-motivated: it would discriminate whether primes and GUE are collinear because the observable suite is overcomplete or because they share genuine structural response.", "action": {"type": "none", "detail": {}}, "notes": "The seme direction for piano 68 ('8 GUE domains, 5 Poisson — boundary as third included') is reasonable but less sharp than the consecutio's proposal (selective operators on pair-scale vs triple-scale). If the agent picks up the consecutio naturally, good. If it drifts into another broad PCA audit without selective perturbation, the next valutatore pass should nudge toward operator coupling. Three cycles of clean progress since the redesign — no intervention needed yet."} tools/data/valutatore_log.jsonl:33:{"ts": "2026-05-07T03:35:44.545141+00:00", "cycle_ref": "20260507_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un risultato controllato e replicato su seed check: il confine GUE-Poisson non appare come linea binaria ma come layer beta 0.3-0.4 con collasso del denominatore. Non e' ancora da cristallizzare perche' resta dentro un perimetro sintetico di mixture controllata; il prossimo passo deve verificare se BOUNDARY_LAYER_GATE sopravvive cambiando generatore del confine, non accumulare altra metrica locale sullo stesso setup.", "action": {"type": "trigger_cycle", "detail": {"next_tension": "BOUNDARY_LAYER_GATE", "instruction": "Ripetere il test del collasso del denominatore su almeno un generatore indipendente del confine GUE-Poisson: ad esempio Dyson beta ensemble interpolante, thinning/superposition controllata, o spettro perturbato con unfolding separato. Mantenere registry canonico e gate original-vs-shuffle; criterio di successo: stesso ordine qualitativo collasso-denominatore prima/dentro la zona ambigua, non stesso beta numerico."}}, "notes": "Continuare, ma il prossimo ciclo deve essere cross-generatore. Se il layer compare solo nella mixture per sostituzione Poisson, va verso cimitero come artefatto del protocollo di miscela; se sopravvive, diventa candidato forte per condensato META sul terzo incluso operativo."} tools/data/valutatore_log.jsonl:34:{"ts": "2026-05-07T08:10:22.658201+00:00", "cycle_ref": "20260507_0803", "executed": true, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha prodotto evidenza controllata e replicata che il denominator gate trasferisce come operatore, ma non trasferisce la coordinata di layer BOUNDARY: ambiguita' classificativa e collasso del denominatore si separano. Continuare sul seme attuale centrato su GUE/Poisson rischia di restare nel locale; la prossima mossa deve promuovere il gate a oggetto cross-dominio e testarne i limiti di trasferibilita'.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Costruire una matrice di trasferibilita' del denominator gate: applicarlo a 3-4 perimetri non-BOUNDARY con poli coerente/illusorio e verificare quali parti trasferiscono (supporto one-sided, coordinate di collasso, ambiguita' classificativa) e quali restano locali.", "reason": "Il risultato appena ottenuto falsifica la trasferibilita' della coordinata BOUNDARY ma salva l'operatore; serve ora discriminare operatore stabile da metrica locale attraverso domini diversi."}}, "notes": "Side-effect log-only: modifica seme proposta, non eseguita automaticamente.", "_executed_at": "2026-05-07T09:01:45.431215+00:00", "_executed_by": "lab_trajectory_apply"} tools/data/valutatore_log.jsonl:41:{"ts": "2026-05-07T15:05:05.028249+00:00", "cycle_ref": "20260507_1458", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo metodologico forte ma non richiede intervento operatore: il PASS 11/11 e' stato scisso in denominatori distinti e la consecutio indica gia' il nodo regressivo in `dipartimento.verifica_asserzioni`. La prossima mossa deve replicare il META gate su seed invariato e verificare stabilita' della classificazione prima di cristallizzarlo o inserirlo nel report mattutino.", "action": {"type": "trigger_cycle", "detail": {"focus": "META_ASSERTION_GATE second run on invariant seed", "reason": "Verificare che gate_class, no_data_status e data_dependency restino stabili prima di trasformare il gate in vincolo permanente del sommario PASS/FAIL."}}, "notes": "Non REDESIGN: la direzione non e' esaurita, ma il prossimo ciclo deve restare sul nodo META invece di tornare subito al confine GUE/Poisson."} tools/data/valutatore_log.jsonl:42:{"ts": "2026-05-07T17:55:31.241833+00:00", "cycle_ref": "20260507_1751", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha falsificato la formulazione globale di G come underlay e ha prodotto una consecutio specifica: G come hinge locale tra QG blank e GE real_sourcing. Continuare sulla direzione corrente GUE/Poisson non attacca il nodo emerso; il prossimo ciclo deve testare l'operatore di deposito blank-to-source invece di accumulare dettaglio sul confine numerico.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare G_UNDERLAY_HINGE_GATE: cercare un osservabile di deposito che trasformi il blank QG in sourcing reale GE nel tetraedro TQGE, con controllo su facce senza QG come contro-perimetro", "reason": "La consecutio del ciclo non è un altro giro sul confine GUE/Poisson ma la verifica strutturale del hinge G emerso come nodo regressivo locale."}}, "notes": "Il redesign non cristallizza il claim: lo restringe a esperimento falsificabile. Vincoli minimi per il prossimo ciclo: usare TGE e TQE come controlli negativi, non usare centralità entropica come metrica primaria, dichiarare blank se non emerge osservabile canonico."} tools/data/valutatore_log.jsonl:46:{"ts": "2026-05-07T20:46:55.259764+00:00", "cycle_ref": "20260507_2042", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha falsificato il framing forte `dipolare = det=-1` senza cadere nel dettaglio numerico locale: ha usato shuffle, ha isolato il nodo regressivo nell'embedding e ha prodotto una consecutio cross-dominio precisa. La prossima mossa non dipende dall'operatore: va testato se lo scarto reale-vs-shuffle sopravvive in domini ordinati non-primi o se il segno negativo e' interamente prodotto dal fit.", "action": {"type": "trigger_cycle", "detail": {"tension": "DUALITA_DET_DENOMINATOR_GATE", "direction": "Applicare il test det_vs_shuffle a Beatty coerente, shuffle Beatty, GUE spacing order e Poisson; separare segno condiviso da scarto ordinato contro null.", "reason": "Verifica cross-dominio della nuova tensione nata dal constraint: det non come polo primario, ma come osservabile subordinato al denominator gate."}}, "notes": "Mantenere il frame corrente. Evitare cristallizzazione: il risultato e' un constraint maturo sul perimetro primi, ma serve il passaggio non-primi prima di decidere se det entra come diagnostica secondaria o va nel cimitero come discriminatore."} tools/data/valutatore_log.jsonl:50:{"ts": "2026-05-08T00:21:16.867699+00:00", "cycle_ref": "20260508_0011", "executed": false, "decision": "STOP_FOR_REVIEW", "confidence": "high", "reasoning": "The cycle produced a genuine cross-domain discriminator: alpha (scaling exponent) separates primes (0.32-0.39) from GUE (0.5-0.6), replicated across seeds and observables. This is category (b) — cross-domain structural verification — and potentially crystallizable. However, the seme delta shows 6 new tensions all named *_TRASCENDENZA_LIMITE, which pattern-matches the gate-of-gate anti-pattern flagged by the operator on 07/05. The finding is solid; the trajectory risk is that the next cycles will methodologically gate the finding instead of letting the operator decide what it means for the model. Three consecutive NEXT_CYCLE decisions have been productive, but this is a natural inflection point where the operator should see the alpha-discriminator result and redirect.", "action": {"type": "notify_operator", "detail": {"message": "Lab cycle 85 produced a cross-domain discriminator: scaling exponent alpha separates primes (α≈0.35, weakening) from GUE (α≈0.55, scale-invariant). The discriminator is not the observable value but how it scales. Poisson shows no signal. Replicated on two seeds. 6 new TRASCENDENZA_LIMITE tensions generated — may need pruning before next cycle."}}, "notes": "The alpha result connects to the DUALITA_DIPOLARE_VS_ILLUSORIA tension at a structural level: prime gaps have dipolar structure that dissolves at large scale, while GUE repulsion is baked in at all scales. This is the kind of finding that should be read through the model (A10 dipolo assiomatico: φ attrattivo vs −1/φ repulsivo) before being pushed further numerically. The 6 TRASCENDENZA_LIMITE tensions risk triggering 2-3 more methodology cycles without advancing — operator should decide if they carry signal or are gate-of-gate."} tools/data/valutatore_log.jsonl:52:{"ts": "2026-05-08T16:38:40.757945+00:00", "cycle_ref": "20260508_1632", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha vincolato il claim alla stabilità del label-set, e la consecutio chiede di testare in domini non-Sturmiani. La direzione seme 'Esplorare il confine: 8 domini GUE, 5 Poisson' è il passo naturale per verificare la persistenza cross-dominio, quindi continuare.", "action": {"type": "trigger_cycle", "detail": {}}, "notes": "Il prossimo ciclo dovrebbe progettare un esperimento che applichi la stessa metrica di label-set a spettri GUE e Poisson, variando parametri come dimensione della matrice o soglia, per vedere se il core di label phi sopravvive fuori dal contesto Sturmiano."} tools/data/valutatore_log.jsonl:53:{"ts": "2026-05-08T17:20:45.153323+00:00", "cycle_ref": "20260508_1715", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale chiaro: il label-set phi non appartiene al lettore da solo, ma richiede generatore globale Sturmiano. Non c'e' dipendenza immediata dall'operatore e la consecutio naturale e' falsificare la portata del vincolo fuori dal caso phi, prima di cristallizzare.", "action": {"type": "trigger_cycle", "detail": {"focus": "testare se il vincolo generatore-globale si trasferisce a generatori Sturmiani non-phi e a controlli GUE/Poisson", "suggested_question": "Il core label-set e' proprieta' della famiglia Sturmiana a bassa complessita o specificamente del generatore phi?", "constraints": ["mantenere reader e generator separati", "includere shuffle/block/null controls", "non usare fit numerico senza audit strutturale"]}}, "notes": "Se anche generatori Sturmiani non-phi conservano core analoghi mentre null e shuffle falliscono, il prossimo passo diventa CRYSTALLIZE sul vincolo generatore-globale. Se il fenomeno resta solo phi, restringere il claim prima di inserirlo nel condensato."} tools/data/valutatore_log.jsonl:55:{"ts": "2026-05-08T18:39:13.811423+00:00", "cycle_ref": "20260508_1834", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile: il core phi non misura solo lunghezza di blocco, perche' collassa quando l'ordine interno viene distrutto. Pero' e' il quarto NEXT_CYCLE consecutivo nello stesso frame locale e il perimetro resta su lettore phi/generator phi senza gap_ratio, non-phi Sturmian o cross-dominio; secondo la regola anti-tautologica il deep-dive locale ha raggiunto rendimento decrescente. La mossa successiva deve portare il vincolo su un discriminatore strutturale piu' largo, coerente col seme attuale sul confine GUE/Poisson.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare se il gate del core phi trasferisce come discriminatore di confine su domini GUE/Poisson: ordine interno conservato vs distrutto, con controlli shuffle e generatori non-phi", "reason": "Porta il finding dal dominio locale della grammatica Sturmiana al filtro STRUTTURA/rumore richiesto dal telos, evitando accumulo numerico sul solo phi_sturmian."}}, "notes": "Nel prossimo ciclo: usare il risultato appena ottenuto come vincolo, non come claim; includere almeno un controllo non-phi e un confronto GUE/Poisson. gap_ratio resta not_tested e va trattato come osservabile sospeso, non confermato."} tools/data/valutatore_log.jsonl:57:{"ts": "2026-05-08T19:20:24.659684+00:00", "cycle_ref": "20260508_1915", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha chiuso la tensione QPG_GAP_RATIO_DENOMINATOR_GATE: il claim corretto e' sul collasso del core completo, non sui label singoli. Poiche' non sono emerse tensioni nuove e gli ultimi turni hanno gia' scavato il perimetro locale, il prossimo passo deve uscire dal denominatore QPG e tornare al confine cross-dominio indicato dal seme.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Testare il confine come terzo incluso operativo sui domini GUE/Poisson: applicare un contratto osservabile tipizzato prima del run, distinguendo core congiunto, residui singoli e stabilita' cross-dominio.", "reason": "Evitare ulteriore dettaglio numerico locale dopo tensione risolta; promuovere falsificazione strutturale cross-dominio coerente con C2 e con la direzione piano 91."}}, "notes": "Cristallo operativo da portare nel prossimo design: per core multi-label il portatore del verdetto va dichiarato prima del run; congiunzione, retention per-label e stable-label-count non sono intercambiabili."} tools/data/valutatore_log.jsonl:58:{"ts": "2026-05-08T19:53:05.581281+00:00", "cycle_ref": "20260508_1947", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo strutturale chiaro: il boundary simbolico del supertile non batte il taglio contiguo misallineato su IDS/rank/errore, mentre la distruzione dell'ordine interno collassa il core. La nuova direzione del seme sposta correttamente il test fuori dal deep-dive locale verso GUE/Poisson, cioe' verso falsificazione cross-dominio del confine come terzo incluso operativo.", "action": {"type": "trigger_cycle", "detail": {"direction": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "reason": "proseguire sul frame gia' riallineato verso controllo cross-dominio"}}, "notes": "Non serve STOP_FOR_REVIEW: la falsificazione e' utile ma non richiede scelta dell'operatore. Non serve ulteriore REDESIGN: il seme ha gia' assorbito il vincolo spostando il prossimo giro su GUE/Poisson."} tools/data/valutatore_log.jsonl:59:{"ts": "2026-05-08T20:11:15.840508+00:00", "cycle_ref": "20260508_2005", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo utile ma non una chiusura: la grammatica locale Sturmian non separa supertile aligned da same-length, mentre il collasso resta legato all'ordine interno. Poiche' il seme corrente punta gia' a 8 domini GUE e 5 Poisson e il report dichiara questi domini come not_tested, la prossima mossa e' trasferire il vincolo sul frame cross-dominio invece di ridisegnare ancora localmente.", "action": {"type": "trigger_cycle", "detail": {"direction": "applicare il gate grammaticale come controllo negativo/lettore di collasso su domini GUE e Poisson reali", "focus": "verificare se zero_grammar_excess_rate resta baseline nei domini strutturati e collassa solo quando viene perturbato l'ordine interno", "avoid": "non promuovere boundary simbolico del supertile come portatore; usarlo come vincolo falsificato"}}, "notes": "Continuare un giro nello stesso frame, ma spostando il peso dal boundary del supertile al confronto cross-dominio richiesto dal seme."} tools/data/valutatore_log.jsonl:60:{"ts": "2026-05-08T20:19:34.072273+00:00", "cycle_ref": "20260508_2013", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha chiuso anche il gate globale Ostrowski: high-core aligned non batte il misaligned su distanza boundary ne' su firma Zeckendorf, quindi il boundary esatto del supertile non e' portatore rilevato neppure al piano della numerazione globale. Dopo tre giri consecutivi sullo stesso frame con vincoli negativi coerenti, continuare sulla stessa TRASCENDENZA_LIMITE produce dettaglio locale e rischia accumulo anti-tautologico sterile; la traiettoria deve tornare al confine cross-dominio dichiarato nel seme.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Tornare al confine cross-dominio: testare il boundary come terzo incluso operativo su 8 domini GUE e 5 Poisson, usando lo stesso contratto aligned/misaligned ma fuori dal generatore phi-Sturmian", "reason": "Il gate locale, grammaticale e Ostrowski sul supertile phi e' vincolo negativo stabile; il prossimo passo utile e' falsificazione strutturale cross-dominio, non un altro lettore interno allo stesso dominio."}}, "notes": "Promuovere una tensione nuova: se il boundary non vive nel supertile, verificare se vive come discriminante fra domini spettrali GUE/Poisson. Non cristallizzare ancora: il risultato attuale e' vincolo robusto, ma il suo valore dipende dal test fuori-dominio."} tools/data/valutatore_log.jsonl:61:{"ts": "2026-05-08T20:25:06.578336+00:00", "cycle_ref": "20260508_2019", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile: il core phi non resta invariato quando il generatore Sturmian cambia e il reader resta fisso, mentre il reader nativo recupera struttura ordinata. L'affinatore ha gia' individuato la consecutio operativa: trasferire il gate reader/generator fuori dal banco phi verso il confine GUE/Poisson, che coincide con la direzione corrente del seme. Non serve review dell'operatore: il prossimo passo e' eseguire il trasferimento cross-dominio e vedere se il gate discrimina struttura da rumore.", "action": {"type": "trigger_cycle", "detail": {"direction": "Applicare il reader_generator_gate al confine GUE/Poisson: trattare GUE e Poisson come generatori distinti, definire reader nativo e reader trasferito, misurare se il core decade solo fuori coordinata o se il confine e' non trasferibile.", "reason": "La traiettoria ha chiuso il banco Sturmian come vincolo, non come cristallizzazione; il valore ora sta nel test cross-dominio richiesto da C2."}}, "notes": "Evitare un altro deep-dive locale sui gap labels Sturmian: il prossimo ciclo deve attaccare direttamente il confine GUE/Poisson con controllo shuffle/surrogato."} tools/data/valutatore_log.jsonl:63:{"ts": "2026-05-08T21:15:51.307623+00:00", "cycle_ref": "20260508_2108", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo strutturale pulito: lo zero Mobius intervallare non cade nel null che preserva frequenza e multiset, e discrimina low_low/high_high/SR con segnale forte. Non serve stop operatore: il seme ha gia' avanzato a piano 94 e la direzione corrente sposta il test dal dominio locale primi x Mobius al confine cross-dominio GUE/Poisson, che e' il passo corretto contro il rischio di accumulo numerico locale.", "action": {"type": "trigger_cycle", "detail": {"direction": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "focus": "testare se la classe zero/boundary emersa nel gate Mobius trasferisce come operatore di confine su domini GUE/Poisson, con shuffle/surrogati e controllo denominatore"}}, "notes": "Non cristallizzare ancora nel condensato: il risultato e' forte nel perimetro primi x Mobius, ma il valore per il lab cresce se il prossimo ciclo verifica trasferibilita' cross-dominio invece di scavare ancora lo stesso denominatore."} tools/data/valutatore_log.jsonl:64:{"ts": "2026-05-08T21:25:42.387096+00:00", "cycle_ref": "20260508_2121", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha isolato un vincolo utile: low-low e high-high cadono sotto null stratificato per lunghezza, mentre SR resta stabilmente fuori null su main e seedcheck. Non c'è dipendenza dall'operatore: la consecutio naturale è testare se il residuo SR è specifico dello zero Mobius o se il null stratificato va trasferito al frame GUE/Poisson della direzione corrente.", "action": {"type": "trigger_cycle", "detail": {"focus": "seguire il residuo sr_zero_minus_nonzero oppure trasferire il null length-stratified al confine GUE/Poisson", "constraint": "non promuovere low/high come segnale autonomo; trattarli come assorbiti dal denominatore lunghezza", "suggested_question": "SR resta vincolo di forma dello zero Mobius sotto null piu' duro, o e' un effetto del rapporto min/max conservato indirettamente dalla stratificazione?"}}, "notes": "Mantenere lo stesso frame: il ciclo ha prodotto discriminazione strutturale, non solo dettaglio numerico locale."} tools/data/valutatore_log.jsonl:65:{"ts": "2026-05-08T21:39:38.403932+00:00", "cycle_ref": "20260508_2133", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo forte ma ancora locale: SR_zero sopravvive 6/6 al null pair-stratified, mentre low_low e high_high cadono 0/6. La domanda rimasta non e' continuare lo stesso frame numerico, ma attaccare il nodo regressivo dichiarato dal report: il null e' transition-level e non ricostruisce una sequenza Mobius globale coerente, quindi prima di nominare il residuo come portatore serve un null piu' fisico o un trasferimento cross-dominio.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare il residuo SR_zero Mobius con null globalmente coerente e/o trasferimento cross-dominio GUE/Poisson prima di ogni cristallizzazione", "reason": "Evita quarto deep-dive locale sullo stesso residuo; sposta il prossimo ciclo dal controllo pair-bucket alla verifica strutturale richiesta da C2."}}, "notes": "Log-only: nessuna azione automatica eseguita. Prossimo esperimento consigliato: preservare coerenza globale della sequenza Mobius o testare se la firma SR_zero attraversa domini GUE/Poisson senza dipendere dalla geometria coarse dei gap primi."} tools/data/valutatore_log.jsonl:66:{"ts": "2026-05-08T21:45:25.809260+00:00", "cycle_ref": "20260508_2140", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo non ha confermato un bordo reticolare specifico di phi: il random bilanciato comprime `V_c` molto piu' dei metallici, quindi il segnale e' dominato dal denominatore/null e non dalla struttura Sturmian. L'Affinatore ha gia' isolato il nodo regressivo: il null random non e' omogeneo rispetto al boundary operator `V_c`; continuare nello stesso frame accumulerebbe dettaglio locale sospetto.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Riprogettare il gate `V_c` con null omogenei al boundary operator: phase-shuffle Sturmian, label-preserving surrogate e controllo gap_ratio prima di estendere a nuovi domini GUE/Poisson", "reason": "Il prossimo ciclo deve correggere il denominatore del test, non estrarre altro dal fit o dalla compressione grezza di `V_c`."}}, "notes": "Non cristallizzare. Il risultato utile e' metodologico: il bordo `V_c` richiede null costruiti sulla stessa classe operatoriale, altrimenti il random crea una soglia artificiale a griglia bassa."} tools/data/valutatore_log.jsonl:67:{"ts": "2026-05-09T03:36:18.623872+00:00", "cycle_ref": "20260509_0330", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo utile ma locale: `V_c` distingue metallici e random solo dentro un osservabile dedicato, con `not_tested` espliciti e nodo regressivo nel null non omogeneo al boundary operator. Saltare subito a GUE/Poisson rischia di lasciare non falsificato il punto vero emerso dall'Affinatore: separare `floor_hit`, crossing interno, phase-shuffle Sturmian e surrogate label-preserving prima di promuovere il confine a dominio spettrale piu' largo.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare `V_c` sul nodo regressivo del null: separare floor_hit e crossing interno, poi confrontare Sturmian phase-shuffle e surrogate label-preserving prima di estendere a GUE/Poisson.", "reason": "La prossima mossa deve chiudere il controllo strutturale sul boundary operator; l'estensione cross-dominio viene dopo, altrimenti accumula dettaglio numerico locale non ancora ripulito."}}, "notes": "Log-only: modifica proposta al seme, nessuna azione eseguita automaticamente."} tools/data/valutatore_log.jsonl:68:{"ts": "2026-05-09T06:44:23.750714+00:00", "cycle_ref": "20260509_0637", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo operativo sul null regressivo di `V_c`, non una falsificazione conclusiva ne' una cristallizzazione. Il perimetro dichiarato e' ancora incompleto: restano esplicitamente non testati GUE/Poisson reali, silver/bronze, fit power-law e un label-preserving forte con Jaccard>=0.75; quindi la prossima mossa naturale e' completare il frame prima di ridisegnarlo.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare sul frame `V_c` null regressivo estendendo i controlli: GUE/Poisson reali, metallici silver/bronze e surrogate label-preserving forte con soglia Jaccard>=0.75.", "reason": "Il ciclo ha isolato il nodo regressivo ma non ha ancora discriminato se il portatore e' ordine Sturmian, label-set o artefatto della griglia/perimetro."}}, "notes": "Tenere il focus sull'evento di bordo prima del valore numerico: floor_hit/internal_cross/internal_multi/no_cross. Se anche il null forte recupera crossing interno, `V_c` deve essere declassato; se non lo recupera e i controlli cross-dominio separano, il frame matura."} tools/data/valutatore_log.jsonl:69:{"ts": "2026-05-09T06:59:00.190342+00:00", "cycle_ref": "20260509_0652", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una falsificazione strutturale utile: il label-set preservato non basta a ricostruire il boundary `V_c` nel perimetro N=89. Non serve fermare l'operatore: la consecutio e' gia' indicata dal deposito, cioe' riparare il null al nodo generativo e distinguere phase-shuffle Sturmian da surrogate label-preserving prima di estendere a GUE/Poisson.", "action": {"type": "trigger_cycle", "detail": {"focus": "Stesso frame `V_c` null regressivo: confrontare Sturmian phase-shuffle e surrogate label-preserving con gate raggiunto, includendo N>=144 solo dopo aver reso accettabile il generatore del null.", "reason": "Il run largo ha fallito l'accettazione Jaccard; il prossimo ciclo deve correggere il contratto di generazione del surrogate, non saltare al dominio esterno."}}, "notes": "Non cristallizzare ancora: il verdict e' forte ma perimetrato a N=89 e dipende dal gate Jaccard/accettazione."} tools/data/valutatore_log.jsonl:70:{"ts": "2026-05-09T07:10:41.814504+00:00", "cycle_ref": "20260509_0659", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha stabilito un vincolo regressivo diretto: a N=144 il surrogate label-preserving ha acceptance_rate=0 e label_jaccard sotto gate, quindi il null non e' ancora valido. La direzione del seme ora salta a GUE/Poisson, ma il report dichiara esplicitamente che senza null accettato il prossimo passo resta costruzione del contro-campo, non estensione del boundary ad altri perimetri.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Costruire il null label-preserving per V_c prima del trasferimento: ridisegnare il generatore surrogate finche' raggiunge Jaccard>=0.75 a N=144 con acceptance_rate non nulla, poi solo dopo confrontare GUE/Poisson", "reason": "Il gate di reachability e' fallito; usare GUE/Poisson ora trasformerebbe un null non accettato in evidenza apparente."}}, "notes": "Side-effect log-only: modifica seme proposta, non eseguita automaticamente."} tools/data/valutatore_log.jsonl:71:{"ts": "2026-05-09T07:48:21.148213+00:00", "cycle_ref": "20260509_0741", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha risolto il nodo operativo immediato: il gate label-preserving entra a N=144, ma solo tramite `phase_shift_sturmian`, quindi il null non e' controcampo indipendente. Il seme e' gia' stato spostato al piano 100 verso il confine GUE/Poisson; questa e' la prossima mossa corretta per testare il bordo come terzo incluso invece di accumulare altra telemetria locale sul generatore.", "action": {"type": "trigger_cycle", "detail": {"direction": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "constraint": "non trattare il ponte Sturmian come null indipendente; usare `source_mode` come audit obbligatorio"}}, "notes": "Continuare il flusso. Nessuna dipendenza dall'operatore: il risultato e' un vincolo utile, non una cristallizzazione o contraddizione che richiede review."} tools/data/valutatore_log.jsonl:72:{"ts": "2026-05-09T08:25:01.990347+00:00", "cycle_ref": "20260509_0819", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo strutturale utile: il label-set phi puo' essere preservato da generatori nominalmente non-Sturmian solo quando trasportano memoria di scala lunga, mentre balanced random e Markov-density falliscono. Questo sposta il nodo regressivo dal confronto GUE/Poisson al contratto del generatore: prima di continuare sul confine come terzo incluso va separata conservazione del label-set da indipendenza reale dal boundary, altrimenti il prossimo ciclo rischia dettaglio locale su N=144.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare l'indipendenza del null label-preserving: test cross-scale N multipli e phase-bias per distinguere memoria di scala lunga da trasporto quasi-Sturmian prima del confronto GUE/Poisson", "reason": "Il null non-Sturmian accettato non e' indipendente dal boundary; il prossimo ciclo deve attaccare il contratto del generatore, non accumulare altri eventi V_c sullo stesso perimetro."}}, "notes": "Side-effect log-only: proposta modifica seme. Prossimo esperimento consigliato: N in {89,144,233,377}, stesse famiglie generatori, gate invariato, ma verdict centrato su stabilita' cross-scale di acceptance_rate, phase concentration e hamming/label tradeoff."} tools/data/valutatore_log.jsonl:75:{"ts": "2026-05-09T08:52:19.466789+00:00", "cycle_ref": "20260509_0846", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha chiuso il vincolo sul denominatore di V_c senza gap di estrazione e senza nodo regressivo; l'affinatore ha gia' spostato il seme al piano 102 verso il confine GUE/Poisson. Dopo due REDESIGN e una CRYSTALLIZE, la traiettoria non richiede altra interferenza: la prossima mossa deve testare se il confine come terzo incluso operativo regge cross-dominio, non riaprire il frame V_c.", "action": {"type": "trigger_cycle", "detail": {"direction": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "reason": "proseguire sul seme gia' avanzato e verificare pattern cross-dominio invece di accumulare dettaglio locale sul deposito V_c"}}, "notes": "Modo log-only: registra l'intenzione di avviare il prossimo ciclo sul piano 102; nessuna dipendenza dall'operatore."} tools/data/valutatore_log.jsonl:76:{"ts": "2026-05-09T13:42:16.922735+00:00", "cycle_ref": "20260509_1337", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un vincolo operativo pulito: il fit di V_c e' ammesso solo dopo gate sul denominatore, con stati complete/contaminated/broken separati e Bridge QA clean. Non serve operatore: la prossima mossa naturale e' usare quel gate come prerequisito nel frame gia' impostato dal seme, cioe' il confine 8 domini GUE / 5 Poisson, evitando un altro deep-dive locale sullo stesso deposito.", "action": {"type": "trigger_cycle", "detail": {"focus": "Applicare il denominator gate come pre-scan obbligatorio al perimetro 8 GUE / 5 Poisson: prima classificare dove l'osservabile di confine esiste, poi solo sui sottografi fit-ready testare se il confine opera come terzo incluso cross-dominio.", "constraints": ["non ripetere fit V_c aggregati senza nuovo dominio", "separare complete/contaminated/broken prima di ogni regressione numerica", "includere shuffle/surrogato per evitare conferma tautologica"]}}, "notes": "Continuare il flusso, ma spostare il peso dal valore numerico di scala alla trasferibilita' strutturale del gate tra domini GUE e Poisson."} tools/data/valutatore_log.jsonl:77:{"ts": "2026-05-09T14:06:47.070378+00:00", "cycle_ref": "20260509_1400", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un vincolo utile ma resta dentro lo stesso perimetro locale di V_c: gate denominatore, fit-ready e famiglia AICc sul deposito 13:37. Dopo piu' giri consecutivi sullo stesso nodo, il prossimo valore non e' un altro fit parametrico ma un test cross-dominio: trasferire il gate a GUE/Poisson o gap_ratio per distinguere struttura da artefatto numerico locale.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Trasferire il gate fit-ready di V_c fuori dal deposito locale: test GUE/Poisson o gap_ratio con stesso denominatore_state -> admissible_fit -> model_family, senza nuovi claim di legge finche' il controllo cross-dominio non separa struttura e artefatto.", "reason": "Evitare terzo deep-dive locale su V_c/AICc e riportare la traiettoria verso falsificazione strutturale cross-dominio come richiesto da C2 e dalla regola anti-tautologica."}}, "notes": "Non fermare per review: non c'e' contraddizione operatore-dipendente. Non cristallizzare: il risultato e' un constraint maturo sul gate, ma la legge di scala resta non trasferita."} tools/data/valutatore_log.jsonl:78:{"ts": "2026-05-09T14:15:04.093285+00:00", "cycle_ref": "20260509_1409", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha prodotto un filtro strutturale utile: il gate non conferma il dominio, separa righe trasferibili da blank in base alla presenza del null row-aligned. Non serve fermare o cristallizzare: il risultato e' ancora un prescan singolo, ma apre una mossa diretta sui 5 blank per distinguere assenza di contro-perimetro da caduta reale del claim.", "action": {"type": "trigger_cycle", "detail": {"direction": "stesso frame BOUNDARY, prossimo esperimento sui blank", "next_experiment": "costruire o recuperare null/surrogate row-aligned per uno o due domini blank, poi rieseguire denominator_state per vedere se blank -> transfers oppure blank -> falls", "priority": "zeta_zeros e pendolo_doppio come coppia GUE/Poisson blank"}}, "notes": "Evitare nuovo fit V_c. La prossima informazione vive nel contro-perimetro mancante, non nell'accumulo di metriche locali."} tools/data/valutatore_log.jsonl:81:{"ts": "2026-05-09T14:48:41.022382+00:00", "cycle_ref": "20260509_1444", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha verificato transfer 4/4 su perimetri sintetici con shuffle baseline e beta blank, ma il verdict vieta di leggerlo come chiusura QxG o come label GUE/Poisson. Il seme ora torna invece al frame “8 domini GUE, 5 Poisson”, che reimporta il perimetro base appena superato; la prossima mossa deve falsificare la forma minima del transfer come operatore ordine/null/denominatore su perimetri non sintetici o avversariali.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Falsificare la forma minima del gate BOUNDARY come operatore ordine/null/denominatore: test su perimetri reali o avversariali senza importare label GUE/Poisson", "reason": "Il transfer e' emerso come operatore, non come tassonomia locale; continuare col frame GUE/Poisson accumula dettaglio sul vecchio denominatore invece di attaccare la nuova forma falsificabile."}}, "notes": "Non cristallizzare ancora: 4/4 perimetri sintetici sono evidenza di traiettoria, non condensato. Prossimo ciclo: cercare almeno un fall o un blank robusto su dominio reale/adversarial."} tools/data/valutatore_log.jsonl:82:{"ts": "2026-05-09T15:07:43.425100+00:00", "cycle_ref": "20260509_1457", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il claim V_c -> 1 dall'alto e' caduto nel perimetro osservato, ma il risultato non richiede dipendenza dall'operatore: il bordo unitario e' stato riclassificato come taglio, non come attrattore. Il seme e' gia' avanzato a piano 108 verso un perimetro cross-dominio GUE/Poisson, che e' la prossima falsificazione strutturale corretta invece di accumulare dettaglio locale sul deposito V_c.", "action": {"type": "trigger_cycle", "detail": {"direction": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", "reason": "proseguire dal bordo come taglio osservabile verso test cross-dominio GUE/Poisson, evitando nuova iterazione locale sul claim V_c"}}, "notes": "Non cristallizzare ancora: il ciclo ha falsificato il limite osservato nel perimetro fit-ready, ma ha dichiarato esplicitamente not_tested nuovi spettri, nuovi N e limite asintotico."} tools/data/valutatore_log.jsonl:83:{"ts": "2026-05-09T15:24:21.399473+00:00", "cycle_ref": "20260509_1516", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una separazione strutturale utile: il gate BOUNDARY trasferisce come supporto ordine/null su 11/13 righe, ma non come coordinata beta universale. Non c'e' dipendenza dall'operatore: il prossimo passo e' gia' indicato dall'affinatore, cioe' esplicitare la matrice a due assi sulle 13 righe per distinguere support_transfer e beta_coordinate_transfer senza salvare il claim originale.", "action": {"type": "trigger_cycle", "detail": {"direction": "Costruire la matrice semi-reale a due assi BOUNDARY: support_transfer vs beta_coordinate_transfer sulle 13 righe GUE/Poisson", "constraint": "non rifittare V_c, non promuovere beta 0.3, usare shuffle/marginal-preserving e classificazione row-aligned", "goal": "identificare se le 2 falls e le 4 no_blank formano sottoclassi strutturali o solo attrito del perimetro"}}, "notes": "Continuare nello stesso frame, ma con claim riformulato: il gate e' operatore di supporto; la beta e' coordinata locale del perimetro."} tools/data/valutatore_log.jsonl:84:{"ts": "2026-05-09T15:37:39.432422+00:00", "cycle_ref": "20260509_1532", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una scissione strutturale chiara: support_transfer regge su 11/13 righe mentre beta_coordinate_transfer esatta vale solo 4/13, quindi il claim beta universale cade senza bloccare il frame BOUNDARY. L'affinatore ha gia' localizzato la consecutio sulle 6 righe non-esatte: il prossimo passo non deve rifare il gate, ma leggere dove beta diventa intervallo locale, blank o altra coordinata.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare BOUNDARY con audit row-aligned delle 6 righe non-esatte: distinguere beta local non-unique, beta local other e support_without_beta_blank senza usare label GUE/Poisson come operatore.", "focus": "Verificare se le 6 righe non-esatte formano sottoclassi strutturali del support_transfer o se sono residuo telemetrico/denominatore.", "constraints": ["no nuovo fit V_c", "no uso operativo label GUE/Poisson", "no salvataggio beta 0.3 universale", "mantenere osservabili domain-native e provider-neutral"]}}, "notes": "Non serve STOP_FOR_REVIEW: la falsificazione di beta 0.3 universale e' gia' interpretabile e non richiede scelta dell'operatore. Non serve REDESIGN: il frame produce ancora segnale strutturale cross-riga, non solo dettaglio numerico locale."} tools/data/valutatore_log.jsonl:85:{"ts": "2026-05-09T15:44:19.654992+00:00", "cycle_ref": "20260509_1538", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una correzione strutturale del denominatore: le righe support-transfer non esatte sono 7, non 6, e si dividono in cinque failure modes misurabili. Non c'e' dipendenza dall'operatore: la prossima mossa naturale e' falsificare se quei failure modes sono stabili o se sono artefatto della griglia beta e del perimetro row-aligned.", "action": {"type": "trigger_cycle", "detail": {"focus": "BOUNDARY failure modes falsification", "instruction": "Ripartire dalle 7 righe support-transfer non esatte e testare stabilita' dei failure modes con null/shuffle e perturbazione della griglia beta; non rifittare V_c e non usare label GUE/Poisson come decision field.", "expected_output": "distinguere failure modes strutturali da saturazione/artefatto di coordinata"}}, "notes": "Continuare un giro: il deep-dive locale resta produttivo perche' ha appena trasformato un conteggio ereditato errato in una partizione falsificabile."} tools/data/valutatore_log.jsonl:86:{"ts": "2026-05-09T15:53:15.859019+00:00", "cycle_ref": "20260509_1548", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha falsificato la specie autonoma `blank_thin_support`: tutte le righe thin sono spiegate da denominatore corto, mentre contaminazione e shuffle-instability non spiegano il fenomeno. La prossima mossa non dipende dall'operatore: bisogna restare nello stesso frame BOUNDARY e attaccare il nodo regressivo estendendo il denominatore delle tre righe short prima di qualsiasi promozione o redesign globale.", "action": {"type": "trigger_cycle", "detail": {"direction": "same_frame_boundary_denominator_extension", "task": "Estendere o rigenerare le tre righe `blank_thin_support` short (`percolation`, `random_matrix`, `zeta_zeros`) fino a `n_gaps >= 500`, poi ripetere l'audit row-aligned `support_without_beta_blank` contro il controllo medium `numeri_primi`.", "success_condition": "Se il blank thin persiste con denominatore pieno, riaprire specie autonoma; se collassa o passa a medium/stable, cristallizzare `blank_thin_support` come artefatto denominator-limited.", "avoid": "Non introdurre nuovo fit `V_c`, nuove label GUE/Poisson come decision field, o nuova griglia beta prima di chiudere il denominatore."}}, "notes": "Continua il flusso: il risultato corrente e' strutturale, ma incompleto finche' il denominatore short non viene invertito nel nodo sorgente."} tools/data/valutatore_log.jsonl:88:{"ts": "2026-05-09T18:44:49.304322+00:00", "cycle_ref": "20260509_1839", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto una chiusura locale utile senza contraddizione: `thin_persist_rows=0` dissolve la specie `blank_thin_support` sul perimetro 13 righe, ma lascia due residui medio/forti senza beta (`numeri_primi:cycle_3`, `random_matrix:cycle_7`). La consecutio naturale non e' redesign: e' testare se quei due blank residui sono stesso operatore di confine, due classi distinte, o artefatto da denominatore/atlante.", "action": {"type": "trigger_cycle", "detail": {"direction": "BOUNDARY consecutio sui due residui beta-absent medio/forti", "focus_rows": ["numeri_primi:cycle_3", "random_matrix:cycle_7"], "test": "costruire un audit mirato che non rigeneri fit globali e verifichi se il beta-blank residuo sopravvive a controlli row-locali e cross-row", "avoid": ["nuova griglia beta globale", "ritorno a V_c", "uso label GUE/Poisson come decision field"]}}, "notes": "Prossima mossa: isolare il residuo vivo dopo la dissoluzione del thin blank. Se anche questi due blank cadono, la tassonomia BOUNDARY puo' maturare; se divergono, emerge nuova tensione strutturale."} tools/data/valutatore_log.jsonl:91:{"ts": "2026-05-12T03:35:57.943734+00:00", "cycle_ref": "20260512_0330", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha falsificato la forma atomica `prime_SR_persistent_boundary`: nei primi SR non chiude 8/8 e l'intersezione comune e' vuota, mentre il seed check mostra `mod6_candidates` con SR pieno e firma comune `L1,SR,triple_var`. L'attrito non e' nel numero di controlli ma nel denominatore: il perimetro largo mescola generatori che non interrogano lo stesso pre-bordo, quindi il prossimo ciclo deve isolare il confine primi/mod6 invece di continuare il frame GUE/Poisson.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Isolare `prime_vs_mod6_SR_boundary`: testare se SR appartiene ai primi o al pre-bordo aritmetico mod6, con controlli stretti label-preserving e shuffle audit sul denominatore row-local.", "reason": "La consecutio viva e' la collisione prime/mod6; continuare sul frame generico 8 GUE / 5 Poisson accumula dettaglio locale e rischia tautologia di controllo."}}, "notes": "Non cristallizzare ancora: il risultato e' una falsificazione della specificita' prime, ma serve un ciclo stretto prime-vs-mod6 per decidere se va nel cimitero come claim X nuovo o rientra come firma di pre-bordo aritmetico."} tools/data/valutatore_log.jsonl:92:{"ts": "2026-05-13T03:36:22.728112+00:00", "cycle_ref": "20260513_0330", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha falsificato la forma binaria `SR prime-specific`: `SR` compare anche nel pre-bordo `6k +/- 1`, mentre la selezione prime resta solo come gradiente di intensita residua. Il seme ora devia verso un frame largo GUE/Poisson, ma la consecutio viva e' nel nodo regressivo appena emerso: sottrarre mod6 e misurare cosa resta della selezione prime, con tracciamento fine del contratto osservabile.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Isolare il residuo prime-specific dopo sottrazione del pre-bordo mod6: misurare intensita row-local di SR/L1/triple_var su prime vs 6k+/-1 con trace JSONL completa e label-swap audit.", "reason": "La presenza binaria di SR e' falsificata come firma dei primi; il prossimo ciclo deve testare il residuo di selezione, non riaprire un frame largo GUE/Poisson."}}, "notes": "Side-effect log-only: proposta modifica seme. Non richiede STOP_FOR_REVIEW perche' non c'e' dipendenza dall'operatore; richiede solo riallineamento della prossima mossa al nodo regressivo."} tools/data/valutatore_log.jsonl:93:{"ts": "2026-05-14T03:34:36.045692+00:00", "cycle_ref": "20260514_0330", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha falsificato la presenza binaria di `SR` come prime-specific, ma ha lasciato vivo un residuo strutturale nell'intensita row-local dopo sottrazione mod6: `p(SR delta)=1.0000` mentre `p(z_SR delta)≈0.008-0.009` replica su due seed. Dopo tre REDESIGN consecutivi, la traiettoria non chiede un nuovo cambio di frame: chiede di seguire la consecutio gia nominata dall'Affinatore, `SR_residual_intensity_after_mod6`, verificando se il delta z e' robusto o artefatto del contratto di finestra/deposito.", "action": {"type": "trigger_cycle", "detail": {"direction": "Seguire `SR_residual_intensity_after_mod6`: testare la robustezza del delta z row-local prime-minus-mod6 su piu seed, ampiezze finestra e provider, mantenendo label-swap audit e trace JSONL completa.", "constraints": ["non tornare a claim binario `SR prime-specific`", "non allargare a GUE/Poisson o beta atlas globale", "controllare se z_SR dipende da normalizzazione, finestra o denominatore mod6"]}}, "notes": "Side-effect log-only: propone continuazione del ciclo, non modifica automatica del seme."} tools/data/valutatore_log.jsonl:94:{"ts": "2026-05-14T13:34:46.586421+00:00", "cycle_ref": "20260514_1330", "executed": false, "decision": "REDESIGN", "confidence": "medium", "reasoning": "Il ciclo ha prodotto una direzione viva ma il seme ora devia verso frame GUE/Poisson, mentre l'evidenza appena maturata punta al nodo regressivo del contratto: `verdict()` resta centrato su `has_SR` e il micro-trace e' incompleto. La prossima mossa deve trasformare il residuo `prime_minus_mod6_z_vector(SR,L1,triple_var)` in osservabile dedicato e falsificarlo contro antagonisti piu duri, non riaprire un frame globale gia' a rischio accumulo numerico locale.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Formalizzare e falsificare `prime_minus_mod6_z_vector(SR,L1,triple_var)`: separare il verdict da `has_SR`, rendere obbligatorio il micro-trace JSONL, e testare il vettore row-local contro antagonisti mod6 piu forti e scale oltre 2048 gap.", "reason": "La consecutio del ciclo chiuso non porta a GUE/Poisson ma al nodo regressivo del contratto osservabile/verdict; continuare li' preserva la falsificazione strutturale invece di disperderla."}}, "notes": "Side-effect log-only: proposta modifica seme per piano 118."} tools/data/valutatore_log.jsonl:95:{"ts": "2026-05-14T15:05:30.012415+00:00", "cycle_ref": "20260514_1458", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha confermato il vettore `prime_minus_mod6_z_vector(SR,L1,triple_var)`, ma l'affinatore segnala attrito operativo su `completed_no_jsonl` e denominatore sparse a 4096: prima di cristallizzare serve una ripetizione con contratto pre-run piu' stretto. Il seme corrente punta ancora a frame GUE/Poisson, mentre l'evidenza viva e' nel confine prime-minus-mod6 e nella nuova tensione `TENS_SCALE_TRASCENDENZA_LIMITE`; lasciare il seme cosi' produrrebbe drift rispetto al nodo regressivo emerso.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Formalizzare il limite di scala del vettore prime-minus-mod6: micro-trace provider-indipendente, min_paired_rows pre-dichiarato, replica 4096/8192 e primo test oltre 8192 solo se il denominatore resta stabile", "reason": "Allineare il prossimo ciclo alla tensione nuova e chiudere il gap metodologico prima di promuovere il claim a cristallizzazione."}}, "notes": "Non STOP_FOR_REVIEW: non c'e' dipendenza dall'operatore. Non CRYSTALLIZE: il segnale e' forte ma il contratto di trace/denominatore deve maturare prima."} tools/data/valutatore_log.jsonl:96:{"ts": "2026-05-14T16:11:19.609243+00:00", "cycle_ref": "20260514_1605", "executed": false, "decision": "REDESIGN", "confidence": "high", "reasoning": "Il ciclo ha prodotto evidenza strutturale utile: replica completa a 4096/8192 e primo cedimento selettivo di `SR` a 16384 contro `mod6_span_matched` con denominatore valido. Il seme ora devia verso un frame GUE/Poisson ampio, ma la traiettoria viva e' il limite di scala del vettore prime-minus-mod6: va chiuso il nodo regressivo tra componente scalante (`L1`,`triple_var`) e componente assorbita dal contro-bordo (`SR`) prima di cambiare dominio.", "action": {"type": "modify_seme", "detail": {"field": "direzione", "new_value": "Formalizzare il cedimento selettivo del vettore prime-minus-mod6: separare `SR` come componente assorbita dal contro-bordo span-matched e verificare se `L1,triple_var` restano residuo prime-specific oltre 16384 con trace/autopsy leggibile.", "reason": "La direzione GUE/Poisson salta di dominio mentre il ciclo ha appena localizzato una rottura strutturale interna con denominatore valido; il prossimo passo deve falsificare la decomposizione del vettore, non accumulare un nuovo frame."}}, "notes": "Non cristallizzare ancora: il vettore completo e' caduto oltre 8192, ma la sostituzione matura solo dopo un test mirato sulla decomposizione `SR` vs `L1,triple_var` e sulla lacuna autopsy `completed_no_jsonl`."} tools/data/valutatore_log.jsonl:97:{"ts": "2026-05-14T16:17:45.087627+00:00", "cycle_ref": "20260514_1612", "executed": false, "decision": "NEXT_CYCLE", "confidence": "high", "reasoning": "Il ciclo ha prodotto un rimbalzo fisico reale nel frame GUE/Anderson, con Bridge QA clean e senza nodo regressivo. Non c'e' dipendenza dall'operatore: il limite e' gia' nominato dall'affinatore, cioe' trasformare il proxy numerico in tier di evidenza esplicito concentrandosi sul bordo W=6 dove SR/L1 cedono e triple_var resta attivo.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Anderson, restringendo il prossimo ciclo al perimetro W=6 con evidence_tier esplicito e autopsy lab-native della trace.", "constraints": ["non espandere a deep-dive numerico generico su 8 GUE/5 Poisson", "dichiarare il tier di evidenza prima della misura", "testare se triple_var nomina memoria fisica oltre nearest-neighbor repulsion"]}}, "notes": "NEXT_CYCLE non come altro giro locale sui primi, ma come verifica del confine fisico gia' emerso: W=6 e' il punto-zero operativo."} tools/data/valutatore_log.jsonl:98:{"ts": "2026-05-14T16:35:47.164793+00:00", "cycle_ref": "20260514_1631", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha chiuso regressivamente il rimbalzo fisico: il claim e' stato ridotto da possibile legge a tester operativo `component_state(SR,L1,triple_var)`, con Bridge QA clean e health completed. Resta una mossa naturale nello stesso frame: riusare il tester su un perimetro piu' esterno e correggere prima dell'esecuzione il nodo sanitario indicato dall'Affinatore (`evidence_tier + trace_path`), senza richiedere intervento operatore.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare nel frame GUE/Poisson-Anderson usando `component_state(SR,L1,triple_var)` come tester operativo, con contratto pre-run esplicito `evidence_tier` e `trace_path` obbligatori.", "constraint": "Non promuovere il pattern a legge fisica; testare trasferibilita' del tester su un dominio fisico/simulato ordinato o su sweep Anderson multi-W, con null Poisson span-matched e trace JSONL verificabile.", "reason": "Il deposito e' vivo ma non cristallizzabile: serve una replica di trasferibilita' del tester, non nuovo fit locale."}}, "notes": "Side-effect log-only: richiesta prosecuzione del ciclo con contratto sanitario rafforzato prima del run."} tools/data/valutatore_log.jsonl:99:{"ts": "2026-05-14T16:45:30.959682+00:00", "cycle_ref": "20260514_1640", "executed": false, "decision": "NEXT_CYCLE", "confidence": "medium", "reasoning": "Il ciclo ha rafforzato il tester senza promuoverlo a legge: Bridge QA clean, health completed, e il claim resta correttamente vincolato come strumento operativo. La prossima mossa deve continuare il frame ma spostare la falsificazione fuori dal dettaglio locale GOE/GUE a un denominatore fisico piu' duro, usando il nodo regressivo indicato dall'Affinatore: contratto pre-run esplicito su trace_path, evidence_tier e autopsy.", "action": {"type": "trigger_cycle", "detail": {"direction": "Continuare il tester component_state(SR,L1,triple_var) su falsificazione fisica esterna: Anderson 3D o many-body/localizzazione, con contratto pre-run esplicito trace_path/evidence_tier/autopsy; vietato promuovere a legge fisica o accumulare solo N-curve GOE/GUE.", "constraints": ["mantenere GOE/GUE/Poisson come baseline, non come nuovo oggetto principale", "testare almeno un denominatore fisico non gia' coperto dal ciclo 16:40", "dichiarare non_possible prima dell'esecuzione", "includere shuffle/surrogato o baseline strutturale equivalente"]}}, "notes": "Non cristallizzare ancora: il tester e' leggibile ma non ha superato abbastanza domini esterni. Non REDESIGN pieno perche' la direzione fisica resta viva; va solo spinta fuori dal rinforzo locale."} tools/data/operator_directives_consumed/operator_directive_20260514_1649.md:14:component_state(SR,L1,triple_var) tools/data/operator_directives_consumed/operator_directive_20260514_1649.md:36:tools/data/component_state_fit_ready_20260514_*.json tools/data/operator_directives_consumed/operator_directive_20260514_1649.md:45:- `component_states` tools/data/operator_directives_consumed/operator_directive_20260514_1649.md:60:output: component_state + contrasto Poisson + contrasto diretto se classi presenti tools/data/operator_directives_consumed/operator_directive_20260509_1400.md:6:fuori dal deposito V_c e dentro il perimetro `BOUNDARY`: 8 domini GUE, tools/data/operator_directives_consumed/operator_directive_20260509_1400.md:7:5 Poisson. Non cercare direttamente "GUE o Poisson?" e non rifare un tools/data/operator_directives_consumed/operator_directive_20260509_1400.md:34: `tools/exp_duality_scale_contrast.py`, `tools/exp_two_channel_cross_domain.py` tools/data/operator_directives_consumed/operator_directive_20260509_1400.md:45:- dove il terzo incluso del confine GUE/Poisson diventa osservabile; tools/data/operator_directives_consumed/operator_directive_20260509_0659.md:16:- reasoning: Il ciclo ha prodotto una falsificazione strutturale utile: il label-set preservato non basta a ricostruire il boundary `V_c` nel perimetro N=89. Non serve fermare l'operatore: la consecutio e' gia' indicata dal deposito, cioe' riparare il null al nodo generativo e distinguere phase-shuffle Sturmian da surrogate label-preserving prima di estendere a GUE/Poisson. tools/data/boundary_denominator_prescan_20260509_1409.json:3: "question": "Does denominator_state transfer beyond V_c on the 8 GUE / 5 Poisson boundary perimeter?", tools/data/boundary_denominator_prescan_20260509_1409.json:4: "perimeter": "base autoricerca cycles 1..13: 8 GUE-like, 5 Poisson-like", tools/data/boundary_denominator_prescan_20260509_1409.json:24: "GUE": 8, tools/data/boundary_denominator_prescan_20260509_1409.json:25: "Poisson": 5 tools/data/boundary_denominator_prescan_20260509_1409.json:51: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:58: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1409.json:76: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:83: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1409.json:101: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:108: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1409.json:126: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:133: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1409.json:151: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:158: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1409.json:176: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:183: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1409.json:201: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:208: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1409.json:226: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:233: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1409.json:251: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:258: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1409.json:276: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:283: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1409.json:301: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:308: "label": "GUE-like", tools/data/boundary_denominator_prescan_20260509_1409.json:326: "source_domain_type": "Poisson", tools/data/boundary_denominator_prescan_20260509_1409.json:333: "label": "Poisson-like", tools/data/boundary_denominator_prescan_20260509_1409.json:351: "source_domain_type": "GUE", tools/data/boundary_denominator_prescan_20260509_1409.json:358: "label": "GUE-like", tools/data/operator_directives_consumed/operator_directive_20260509_1427.md:18:null/surrogate row-aligned per una coppia blank GUE/Poisson: tools/data/operator_directives_consumed/operator_directive_20260509_1427.md:20:- `zeta_zeros` (blank GUE); tools/data/operator_directives_consumed/operator_directive_20260509_1427.md:21:- `pendolo_doppio` (blank Poisson). tools/data/operator_directives_consumed/operator_directive_20260509_1427.md:35: `z=-2.310593`, `GUE -> GUE`, `ordering_dependent=false`; tools/data/operator_directives_consumed/operator_directive_20260509_1427.md:37: `shuffle_mean=0.292437`, `z=27.919656`, `Poisson -> Poisson`, tools/data/operator_directives_consumed/operator_directive_20260509_1427.md:56:- Non dichiarare nuova legge GUE/Poisson. tools/data/operator_directives_consumed/operator_directive_20260509_1427.md:59:- Non trattare `pendolo_doppio` come cambio classe: e' Poisson -> Poisson con tools/data/duality_scale_contrast_20260508.json:2: "experiment": "duality_scale_contrast", tools/data/duality_scale_contrast_20260508.json:9: "triple_var" tools/data/duality_scale_contrast_20260508.json:34: "triple_var": -7.723586906192068 tools/data/duality_scale_contrast_20260508.json:41: "triple_var": -8.32113227952271 tools/data/duality_scale_contrast_20260508.json:48: "triple_var": -0.6350753223122121 tools/data/duality_scale_contrast_20260508.json:57: "triple_var": 1.39031579219564 tools/data/duality_scale_contrast_20260508.json:64: "triple_var": 0.3890329686055671 tools/data/duality_scale_contrast_20260508.json:71: "triple_var": 3.091386038472246 tools/data/duality_scale_contrast_20260508.json:74: "windowed_contrast": { tools/data/duality_scale_contrast_20260508.json:87: "z_triple_var_mean": -1.4598411851045978, tools/data/duality_scale_contrast_20260508.json:88: "z_triple_var_std": 0.5383790386328313 tools/data/duality_scale_contrast_20260508.json:101: "z_triple_var_mean": -1.1660388823361219, tools/data/duality_scale_contrast_20260508.json:102: "z_triple_var_std": 0.857842608016109 tools/data/duality_scale_contrast_20260508.json:115: "z_triple_var_mean": -2.164282712382775, tools/data/duality_scale_contrast_20260508.json:116: "z_triple_var_std": 0.5803460267743146 tools/data/duality_scale_contrast_20260508.json:129: "z_triple_var_mean": -3.232160542210173, tools/data/duality_scale_contrast_20260508.json:130: "z_triple_var_std": 0.38197075298734595 tools/data/duality_scale_contrast_20260508.json:143: "z_triple_var_mean": -4.595861445368181, tools/data/duality_scale_contrast_20260508.json:144: "z_triple_var_std": 0.5115419982727478 tools/data/duality_scale_contrast_20260508.json:157: "z_triple_var_mean": -6.437901025734908, tools/data/duality_scale_contrast_20260508.json:158: "z_triple_var_std": 1.3996471766742855 tools/data/duality_scale_contrast_20260508.json:171: "z_triple_var_mean": -9.584111272553713, tools/data/duality_scale_contrast_20260508.json:172: "z_triple_var_std": 1.8727386644350683 tools/data/duality_scale_contrast_20260508.json:185: "z_triple_var_mean": -15.929452705845499, tools/data/duality_scale_contrast_20260508.json:186: "z_triple_var_std": 1.7254848918114332 tools/data/duality_scale_contrast_20260508.json:201: "z_triple_var_mean": -1.157547490302206, tools/data/duality_scale_contrast_20260508.json:202: "z_triple_var_std": 1.901578314265992 tools/data/duality_scale_contrast_20260508.json:215: "z_triple_var_mean": -2.64530238504526, tools/data/duality_scale_contrast_20260508.json:216: "z_triple_var_std": 1.8791350170523942 tools/data/duality_scale_contrast_20260508.json:229: "z_triple_var_mean": -4.911259062459283, tools/data/duality_scale_contrast_20260508.json:230: "z_triple_var_std": 2.2074114209217672 tools/data/duality_scale_contrast_20260508.json:243: "z_triple_var_mean": -7.741274670977994, tools/data/duality_scale_contrast_20260508.json:244: "z_triple_var_std": 2.0767513214915865 tools/data/duality_scale_contrast_20260508.json:259: "z_triple_var_mean": -0.601770411245327, tools/data/duality_scale_contrast_20260508.json:260: "z_triple_var_std": 0.881468340159376 tools/data/duality_scale_contrast_20260508.json:273: "z_triple_var_mean": -0.26420417802582563, tools/data/duality_scale_contrast_20260508.json:274: "z_triple_var_std": 1.2765056402915373 tools/data/duality_scale_contrast_20260508.json:287: "z_triple_var_mean": -0.44056829746868836, tools/data/duality_scale_contrast_20260508.json:288: "z_triple_var_std": 0.8391351817223592 tools/data/duality_scale_contrast_20260508.json:301: "z_triple_var_mean": -0.4630252126206423, tools/data/duality_scale_contrast_20260508.json:302: "z_triple_var_std": 0.7946553545213496 tools/data/duality_scale_contrast_20260508.json:315: "z_triple_var_mean": -0.14542435322184546, tools/data/duality_scale_contrast_20260508.json:316: "z_triple_var_std": 1.1836147561420887 tools/data/duality_scale_contrast_20260508.json:329: "z_triple_var_mean": 0.5364606339026861, tools/data/duality_scale_contrast_20260508.json:330: "z_triple_var_std": 0.895874586597186 tools/data/duality_scale_contrast_20260508.json:343: "z_triple_var_mean": 0.109659976709741, tools/data/duality_scale_contrast_20260508.json:344: "z_triple_var_std": 0.8857600421015647 tools/data/duality_scale_contrast_20260508.json:357: "z_triple_var_mean": 0.56194324376035, tools/data/duality_scale_contrast_20260508.json:358: "z_triple_var_std": 0.49447710273695117 tools/data/operator_directives_consumed/operator_directive_20260514_1612.md:15: (`prime-minus-mod6`, GUE/Poisson, boundary, spettro, gap, vettore o null) tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:30: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:40: "triple_var": -4.340247368699423 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:47: "triple_var": 0.09755429202500134 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:66: "triple_var": -0.4589242447435282 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:73: "triple_var": -0.10819061288984577 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:93: "triple_var": 1.9197467431391368 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:100: "triple_var": 0.3183962386428235 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:119: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:130: "triple_var": 1.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:137: "triple_var": -4.340247368699423 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:147: "triple_var": 1.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:154: "triple_var": -3.471680659840176 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:164: "triple_var": 0.5833333333333334 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:171: "triple_var": -2.4786643140297375 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:181: "triple_var": 0.75 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:188: "triple_var": -2.7028444339736724 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:198: "triple_var": 0.25 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:205: "triple_var": -1.4702804512537424 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:215: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:222: "triple_var": -1.0818970228893157 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:232: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:239: "triple_var": -1.2484936656403784 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:249: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:256: "triple_var": -0.6166284366622388 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:266: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:273: "triple_var": -0.1548384247375005 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:283: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:290: "triple_var": 0.13242827276985703 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:300: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:307: "triple_var": 0.09755429202500134 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:318: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:395: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:480: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:487: "triple_var": 1.7221001264291165 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:494: "triple_var": 0.04638214264164729 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:501: "triple_var": -3.612097546469475 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:506: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:518: "triple_var": 1.5672098449110672 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:525: "triple_var": 1.7022387502133889 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:532: "triple_var": 0.05494978124615543 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:539: "triple_var": -2.4573147015352133 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:544: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:556: "triple_var": 1.5807642323525373 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:563: "triple_var": 1.6621679137155343 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:570: "triple_var": 0.04667914978314358 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:577: "triple_var": -1.743898115993383 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:593: "triple_var": 1.5654755753251346 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:600: "triple_var": 1.6788186302593864 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:607: "triple_var": 0.041789800160851355 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:614: "triple_var": -2.712218160842785 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:619: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:631: "triple_var": 1.7159236676979126 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:638: "triple_var": 1.7640918875533647 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:645: "triple_var": 0.040425037665434926 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:652: "triple_var": -1.191544217079057 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:667: "triple_var": 1.6967291704616843 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:674: "triple_var": 1.7455199434607398 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:681: "triple_var": 0.0381949495854325 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:688: "triple_var": -1.2774142531572885 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:703: "triple_var": 1.6680598161370648 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:710: "triple_var": 1.7196970848976993 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:717: "triple_var": 0.0406839366929801 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:724: "triple_var": -1.2692298965636832 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:737: "triple_var": 1.6126810430313787 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:744: "triple_var": 1.7023533239063824 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:751: "triple_var": 0.04238216112890991 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:758: "triple_var": -2.1158024623203087 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:761: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:773: "triple_var": 1.7589918292021425 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:780: "triple_var": 1.7321602227983266 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:787: "triple_var": 0.030905185445706165 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:794: "triple_var": 0.8681910823979135 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:807: "triple_var": 1.7743116241852295 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:814: "triple_var": 1.734894093276237 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:821: "triple_var": 0.03932271872535429 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:828: "triple_var": 1.0024111299195892 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:843: "triple_var": 1.7559270725338292 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:850: "triple_var": 1.7286345352185355 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:857: "triple_var": 0.036660635536628516 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:864: "triple_var": 0.7444643802758154 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:877: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:884: "triple_var": 1.7308286169585998 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:891: "triple_var": 0.03180618211620364 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:898: "triple_var": -5.541857036515441 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:903: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:915: "triple_var": 1.5689761295461615 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:922: "triple_var": 1.7390391185973864 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:929: "triple_var": 0.04642170244783534 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:936: "triple_var": -3.6634371443470175 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:941: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:953: "triple_var": 1.6630463936236966 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:960: "triple_var": 1.7262063809434167 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:967: "triple_var": 0.04083247203262921 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:974: "triple_var": -1.5468078266054783 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:990: "triple_var": 1.6265173451588906 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:997: "triple_var": 1.7205876756818441 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1004: "triple_var": 0.031133365239535887 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1011: "triple_var": -3.021527862445617 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1016: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1028: "triple_var": 1.641108083019929 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1035: "triple_var": 1.7108995048286137 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1042: "triple_var": 0.04801937793580438 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1049: "triple_var": -1.4534012061128072 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1064: "triple_var": 1.7583634355510032 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1071: "triple_var": 1.7354917683478455 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1078: "triple_var": 0.04229666447324243 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1085: "triple_var": 0.5407439921799672 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1098: "triple_var": 1.7099644507394467 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1105: "triple_var": 1.7282901222542435 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1112: "triple_var": 0.038308162978838836 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1119: "triple_var": -0.4783751057162354 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1132: "triple_var": 1.749951364902492 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1139: "triple_var": 1.7648808135818854 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1146: "triple_var": 0.03628509048995152 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1153: "triple_var": -0.41144857234207033 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1166: "triple_var": 1.6904114701778599 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1173: "triple_var": 1.712608259071868 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1180: "triple_var": 0.03902994372812128 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1187: "triple_var": -0.5687117831536949 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1200: "triple_var": 1.8067166324766541 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1207: "triple_var": 1.719707894909801 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1214: "triple_var": 0.029601961727081974 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1221: "triple_var": 2.9392895771245917 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1225: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1237: "triple_var": 1.7013842885511945 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1244: "triple_var": 1.738008615142516 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1251: "triple_var": 0.03150183325714099 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1258: "triple_var": -1.162609372361507 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1271: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1278: "triple_var": 1.7227961959714413 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1285: "triple_var": 0.037418980616921775 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1292: "triple_var": -4.495924004464654 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1297: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1309: "triple_var": 1.6233811961135132 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1316: "triple_var": 1.7584115585611653 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1323: "triple_var": 0.05011263100412547 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1330: "triple_var": -2.694537479713166 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1335: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1347: "triple_var": 1.6328513927284807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1354: "triple_var": 1.7053513236681423 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1361: "triple_var": 0.03902235852800377 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1368: "triple_var": -1.8579074580444248 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1384: "triple_var": 1.6154914401397888 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1391: "triple_var": 1.6924839546919417 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1398: "triple_var": 0.03066884869310307 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1405: "triple_var": -2.5104468486118034 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1410: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1422: "triple_var": 1.7167189459191152 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1429: "triple_var": 1.7291705833916826 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1436: "triple_var": 0.0346718905824756 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1443: "triple_var": -0.35912773325550607 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1459: "triple_var": 1.595806936700427 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1466: "triple_var": 1.650749673168131 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1473: "triple_var": 0.04232520122175482 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1480: "triple_var": -1.2981092796190616 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1493: "triple_var": 1.7245549061273122 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1500: "triple_var": 1.7692899070098729 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1507: "triple_var": 0.022538543380348657 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1514: "triple_var": -1.98482218338764 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1527: "triple_var": 1.7754823166072975 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1534: "triple_var": 1.774318764116482 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1541: "triple_var": 0.044400523901703584 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1548: "triple_var": 0.026205827962559766 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1561: "triple_var": 1.8351800631904591 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1568: "triple_var": 1.7320593994275173 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1575: "triple_var": 0.041773137514157746 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1582: "triple_var": 2.468587946691631 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1586: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1598: "triple_var": 1.677694201131004 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1605: "triple_var": 1.689575605405946 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1612: "triple_var": 0.04717738031010864 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1619: "triple_var": -0.2518453588741591 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1632: "triple_var": 1.7551503055306157 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1639: "triple_var": 1.7327929256447547 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1646: "triple_var": 0.04536110121501242 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1653: "triple_var": 0.49287559796854524 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1666: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1673: "triple_var": 1.7198004749316274 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1680: "triple_var": 0.0467336836432208 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1687: "triple_var": -3.535718977341338 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1692: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1704: "triple_var": 1.5324951071742328 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1711: "triple_var": 1.7194180997126025 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1718: "triple_var": 0.03684737091545516 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1725: "triple_var": -5.072899039859783 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1730: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1742: "triple_var": 1.560453594751228 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1749: "triple_var": 1.6996279468355997 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1756: "triple_var": 0.03279666838768333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1763: "triple_var": -4.243551522954027 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1768: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1780: "triple_var": 1.6174289917165463 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1787: "triple_var": 1.7105334061988344 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1794: "triple_var": 0.04224245995441215 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1801: "triple_var": -2.204048120842535 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1806: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1818: "triple_var": 1.6680002058804708 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1825: "triple_var": 1.7758133097345576 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1832: "triple_var": 0.04636778977287551 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1839: "triple_var": -2.3251723746633255 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1844: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1856: "triple_var": 1.5575570525618279 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1863: "triple_var": 1.626302177231288 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1870: "triple_var": 0.03320408238292258 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1877: "triple_var": -2.0703817041731267 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1881: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1893: "triple_var": 1.6628157553930907 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1900: "triple_var": 1.7455923570039362 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1907: "triple_var": 0.02476208010596054 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1914: "triple_var": -3.3428775473075136 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1919: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1931: "triple_var": 1.7299598355914176 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1938: "triple_var": 1.7052179156114846 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1945: "triple_var": 0.051312739456676436 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1952: "triple_var": 0.4821788944015108 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1965: "triple_var": 1.7196974759508785 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1972: "triple_var": 1.7167674511393647 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1979: "triple_var": 0.0352184290609125 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1986: "triple_var": 0.08319578384504796 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:1999: "triple_var": 1.6974449251804347 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2006: "triple_var": 1.6995132583782426 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2013: "triple_var": 0.041409377144549626 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2020: "triple_var": -0.049948425705316546 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2033: "triple_var": 1.7527552421677608 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2040: "triple_var": 1.7303335506600515 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2047: "triple_var": 0.03312884437876799 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2054: "triple_var": 0.6768027055625044 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2067: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2074: "triple_var": 1.735824240783769 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2081: "triple_var": 0.03885870873767902 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2088: "triple_var": -4.6646155747009095 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2093: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2105: "triple_var": 1.6240870445543385 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2112: "triple_var": 1.753100549272682 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2119: "triple_var": 0.04441235015980669 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2126: "triple_var": -2.9049015477478877 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2131: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2143: "triple_var": 1.627851365407919 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2150: "triple_var": 1.7336494773839928 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2157: "triple_var": 0.03378437983506113 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2164: "triple_var": -3.1315688638533916 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2169: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2181: "triple_var": 1.5670734226288094 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2188: "triple_var": 1.683971618296191 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2195: "triple_var": 0.035255501755725886 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2202: "triple_var": -3.3157433548196775 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2207: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2219: "triple_var": 1.6695237936782688 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2226: "triple_var": 1.7242467173438527 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2233: "triple_var": 0.03973214480853296 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2240: "triple_var": -1.3772959886583205 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2255: "triple_var": 1.6657085234015476 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2262: "triple_var": 1.7143247934006158 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2269: "triple_var": 0.04009686831381644 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2276: "triple_var": -1.2124705006529446 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2291: "triple_var": 1.6736836601147722 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2298: "triple_var": 1.7718783054196567 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2305: "triple_var": 0.04231842731685764 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2312: "triple_var": -2.320375579405534 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2316: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2328: "triple_var": 1.754024419174731 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2335: "triple_var": 1.738826727927994 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2342: "triple_var": 0.05226030382706357 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2349: "triple_var": 0.29080755628647104 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2362: "triple_var": 1.7103392280593035 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2369: "triple_var": 1.7718542739843055 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2376: "triple_var": 0.03418616274046734 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2383: "triple_var": -1.799413592920873 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2396: "triple_var": 1.691549385490247 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2403: "triple_var": 1.6764819845375978 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2410: "triple_var": 0.04102953479960099 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2417: "triple_var": 0.36723304386077943 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2430: "triple_var": 1.7623497442899752 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2437: "triple_var": 1.736639194860106 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2444: "triple_var": 0.03456034587560965 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2451: "triple_var": 0.74393206371276 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2464: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2471: "triple_var": 1.7435890760399422 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2478: "triple_var": 0.038316165611704686 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2485: "triple_var": -4.933316531781932 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2490: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2502: "triple_var": 1.554508726421858 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2509: "triple_var": 1.7246477998430745 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2516: "triple_var": 0.041712762683662356 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2523: "triple_var": -4.078825339656893 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2528: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2540: "triple_var": 1.7127616972135389 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2547: "triple_var": 1.780369763063101 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2554: "triple_var": 0.04305301125851277 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2561: "triple_var": -1.5703446489168433 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2576: "triple_var": 1.691827983987768 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2583: "triple_var": 1.7600839417485137 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2590: "triple_var": 0.04299006592974287 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2597: "triple_var": -1.5877146565044598 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2612: "triple_var": 1.671080511198951 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2619: "triple_var": 1.7103826125850718 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2626: "triple_var": 0.042807474726132465 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2633: "triple_var": -0.918113054730795 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2649: "triple_var": 1.6207447572933702 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2656: "triple_var": 1.7110352749493796 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2663: "triple_var": 0.04169289763096151 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2670: "triple_var": -2.1656090793977065 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2674: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2686: "triple_var": 1.7726846416436026 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2693: "triple_var": 1.7766161425915001 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2700: "triple_var": 0.03318167405140544 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2707: "triple_var": -0.11848410486483557 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2722: "triple_var": 1.7463921305792283 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2729: "triple_var": 1.7505212853950092 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2736: "triple_var": 0.04250437288673823 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2743: "triple_var": -0.09714658834713091 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2756: "triple_var": 1.6001744160373088 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2763: "triple_var": 1.6989643294904144 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2770: "triple_var": 0.03914099796597259 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2777: "triple_var": -2.5239497863337337 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2782: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2794: "triple_var": 1.700847462677639 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2801: "triple_var": 1.7285171956068848 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2808: "triple_var": 0.03570815744200756 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2815: "triple_var": -0.7748854858776508 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2828: "triple_var": 1.705596446618173 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2835: "triple_var": 1.740740943157584 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2842: "triple_var": 0.04002800688774238 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2849: "triple_var": -0.8779976639349772 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2865: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2872: "triple_var": 1.7387606058198966 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2879: "triple_var": 0.04386294603007925 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2886: "triple_var": -4.199382843559053 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2891: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2903: "triple_var": 1.6314412141129604 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2910: "triple_var": 1.792353421559876 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2917: "triple_var": 0.03299106330836198 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2924: "triple_var": -4.877448354510309 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2929: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2941: "triple_var": 1.6131342157364619 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2948: "triple_var": 1.7262676482924446 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2955: "triple_var": 0.040557281659702085 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2962: "triple_var": -2.7894727636145467 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2967: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2979: "triple_var": 1.641752021383855 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2986: "triple_var": 1.715626451236884 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:2993: "triple_var": 0.045077836176823996 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3000: "triple_var": -1.6388193426864226 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3015: "triple_var": 1.6319126534792139 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3022: "triple_var": 1.7175790585541069 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3029: "triple_var": 0.03697342729674528 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3036: "triple_var": -2.3169722511073267 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3040: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3052: "triple_var": 1.5924475938511167 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3059: "triple_var": 1.6749767014591548 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3066: "triple_var": 0.042476676401874705 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3073: "triple_var": -1.942927615786712 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3089: "triple_var": 1.6246237623512787 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3096: "triple_var": 1.6420138691728383 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3103: "triple_var": 0.04100240905507624 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3110: "triple_var": -0.4241240264248976 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3123: "triple_var": 1.7796506826077034 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3130: "triple_var": 1.7769735022895727 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3137: "triple_var": 0.025192775410732164 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3144: "triple_var": 0.10626778012676628 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3160: "triple_var": 1.7020812276105823 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3167: "triple_var": 1.7316439107728234 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3174: "triple_var": 0.02624520517126961 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3181: "triple_var": -1.1264032027687514 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3194: "triple_var": 1.6709496080246207 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3201: "triple_var": 1.709309006392302 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3208: "triple_var": 0.03495011806070822 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3215: "triple_var": -1.0975470326323704 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3228: "triple_var": 1.770068986844192 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3235: "triple_var": 1.7319066042247078 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3242: "triple_var": 0.028290802741170146 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3249: "triple_var": 1.3489324770536955 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3262: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3269: "triple_var": 1.7298509623176967 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3276: "triple_var": 0.03820208958177334 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3283: "triple_var": -4.58843119430052 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3288: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3300: "triple_var": 1.6285133179481357 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3307: "triple_var": 1.726156004197063 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3314: "triple_var": 0.04330050398455339 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3321: "triple_var": -2.2550011492651305 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3326: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3338: "triple_var": 1.5903191671412469 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3345: "triple_var": 1.7183660115345603 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3352: "triple_var": 0.04067936576490818 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3359: "triple_var": -3.147709950379124 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3364: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3376: "triple_var": 1.551385683583624 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3383: "triple_var": 1.679784498850131 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3390: "triple_var": 0.038141114857337954 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3397: "triple_var": -3.366414845155067 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3403: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3415: "triple_var": 1.7137350182335829 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3422: "triple_var": 1.761637879685513 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3429: "triple_var": 0.040662978010824954 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3436: "triple_var": -1.178046070289732 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3451: "triple_var": 1.65892228387605 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3458: "triple_var": 1.6922430733404337 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3465: "triple_var": 0.05165936733186933 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3472: "triple_var": -0.6450096310766786 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3487: "triple_var": 1.6856518455212277 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3494: "triple_var": 1.7176219886548483 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3501: "triple_var": 0.049405466963905145 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3508: "triple_var": -0.647097276845444 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3521: "triple_var": 1.6872199645180983 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3528: "triple_var": 1.732047928434861 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3535: "triple_var": 0.04328989599640438 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3542: "triple_var": -1.035529489848767 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3555: "triple_var": 1.7443205414814789 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3562: "triple_var": 1.6973763185180168 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3569: "triple_var": 0.030425390941511827 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3576: "triple_var": 1.5429291624783104 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3589: "triple_var": 1.6947531596328742 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3596: "triple_var": 1.6946227209312703 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3603: "triple_var": 0.03595944383080359 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3610: "triple_var": 0.0036273837331199247 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3623: "triple_var": 1.793130283145816 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3630: "triple_var": 1.7352856717383365 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3637: "triple_var": 0.04037872834897582 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3644: "triple_var": 1.4325515877457475 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3657: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3664: "triple_var": 1.7273547054338352 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3671: "triple_var": 0.04455511732950748 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3678: "triple_var": -3.8781494247390453 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3683: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3695: "triple_var": 1.5675969178753846 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3702: "triple_var": 1.7296516508139028 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3709: "triple_var": 0.03736650346133085 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3716: "triple_var": -4.336898503394154 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3721: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3733: "triple_var": 1.619599942087715 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3740: "triple_var": 1.7440700285440762 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3747: "triple_var": 0.043611923452229064 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3754: "triple_var": -2.8540379924472106 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3759: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3771: "triple_var": 1.656597408509759 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3778: "triple_var": 1.7592922444419272 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3785: "triple_var": 0.03555837354508453 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3792: "triple_var": -2.8880633643707365 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3797: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3809: "triple_var": 1.7223399184266606 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3816: "triple_var": 1.7360708710544415 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3823: "triple_var": 0.04386860739911203 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3830: "triple_var": -0.31300178970483655 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3845: "triple_var": 1.7262272016028386 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3852: "triple_var": 1.7816241413939344 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3859: "triple_var": 0.04950865804830023 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3866: "triple_var": -1.1189343839021257 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3879: "triple_var": 1.6333663523662838 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3886: "triple_var": 1.6672982557704294 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3893: "triple_var": 0.05427282186445857 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3900: "triple_var": -0.6252098608192418 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3913: "triple_var": 1.7324943673341229 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3920: "triple_var": 1.7568860838348723 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3927: "triple_var": 0.04275191753911507 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3934: "triple_var": -0.5705408764047297 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3947: "triple_var": 1.6814338226027008 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3954: "triple_var": 1.7282051879505627 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3961: "triple_var": 0.04385844338809084 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3968: "triple_var": -1.066416446520808 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3981: "triple_var": 1.7868692380320825 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3988: "triple_var": 1.7617346974303796 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:3995: "triple_var": 0.03488016062315289 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4002: "triple_var": 0.7205970429224183 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4015: "triple_var": 1.6889338201311437 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4022: "triple_var": 1.7363550717225573 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4029: "triple_var": 0.03005423031314408 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4036: "triple_var": -1.577856131976009 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4049: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4056: "triple_var": 1.7322850063320976 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4063: "triple_var": 0.043055396673277006 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4070: "triple_var": -4.127745120722072 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4075: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4087: "triple_var": 1.6222144981648126 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4094: "triple_var": 1.7571670613800088 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4101: "triple_var": 0.04273017222818981 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4108: "triple_var": -3.158249924538468 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4113: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4125: "triple_var": 1.7075286845268767 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4132: "triple_var": 1.7794791144553006 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4139: "triple_var": 0.04185073188998798 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4146: "triple_var": -1.719215571129277 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4162: "triple_var": 1.6085745325682757 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4169: "triple_var": 1.643290230900406 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4176: "triple_var": 0.03786162495680232 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4183: "triple_var": -0.9169098888845573 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4198: "triple_var": 1.5897050675169775 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4205: "triple_var": 1.7043220875190521 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4212: "triple_var": 0.0321877928178138 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4219: "triple_var": -3.5608847320106314 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4224: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4236: "triple_var": 1.70786488926598 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4243: "triple_var": 1.6924563033468634 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4250: "triple_var": 0.02873269748253331 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4257: "triple_var": 0.5362735583209189 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4270: "triple_var": 1.689110236807635 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4277: "triple_var": 1.7406521809758029 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4284: "triple_var": 0.05003962419708657 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4291: "triple_var": -1.0300226069877005 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4304: "triple_var": 1.7322562877381689 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4311: "triple_var": 1.7368307227734217 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4318: "triple_var": 0.032671893284495 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4325: "triple_var": -0.14001132396645236 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4338: "triple_var": 1.7631743838008225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4345: "triple_var": 1.7446624253421135 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4352: "triple_var": 0.03561388397430339 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4359: "triple_var": 0.5197961130009299 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4372: "triple_var": 1.6521389803102378 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4379: "triple_var": 1.6629489637760244 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4386: "triple_var": 0.036538273497630426 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4393: "triple_var": -0.29585370163939745 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4406: "triple_var": 1.7595585350504164 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4413: "triple_var": 1.7259067652602404 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4420: "triple_var": 0.0429519302972983 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4427: "triple_var": 0.7834751443590565 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4442: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4449: "triple_var": 1.723012366116036 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4456: "triple_var": 0.041887727303398445 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4463: "triple_var": -4.021441939370728 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4468: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4480: "triple_var": 1.5851355843633406 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4487: "triple_var": 1.6953249495967135 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4494: "triple_var": 0.03906404239793534 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4501: "triple_var": -2.8207363721066625 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4506: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4518: "triple_var": 1.5833647130322308 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4525: "triple_var": 1.709900864514663 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4532: "triple_var": 0.04695066084570427 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4539: "triple_var": -2.69508776241239 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4545: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4557: "triple_var": 1.5956292514780643 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4564: "triple_var": 1.7159486550892826 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4571: "triple_var": 0.037123588354879875 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4578: "triple_var": -3.2410499346408783 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4583: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4595: "triple_var": 1.6513820098393306 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4602: "triple_var": 1.7129551903867342 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4609: "triple_var": 0.036067092837322144 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4616: "triple_var": -1.7071844638303586 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4631: "triple_var": 1.6533824145758984 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4638: "triple_var": 1.74384470567583 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4645: "triple_var": 0.0537632639244543 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4652: "triple_var": -1.6826041519176558 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4667: "triple_var": 1.6935998791966413 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4674: "triple_var": 1.7574093349590953 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4681: "triple_var": 0.040804568963544605 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4688: "triple_var": -1.5637821298752672 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4703: "triple_var": 1.636337117029075 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4710: "triple_var": 1.681756547365117 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4717: "triple_var": 0.028190347603082425 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4724: "triple_var": -1.611169573910316 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4739: "triple_var": 1.7667513187947488 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4746: "triple_var": 1.7735951361042706 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4753: "triple_var": 0.049390237821834916 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4760: "triple_var": -0.13856619468424974 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4773: "triple_var": 1.7124271743977242 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4780: "triple_var": 1.697248000706354 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4787: "triple_var": 0.03561665420213799 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4794: "triple_var": 0.42618190931755334 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4807: "triple_var": 1.6653041813630984 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4814: "triple_var": 1.7157956749132302 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4821: "triple_var": 0.039858233529570145 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4828: "triple_var": -1.2667770013609092 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4841: "triple_var": 1.554563302793225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4848: "triple_var": 1.7356878789921837 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4855: "triple_var": 0.04039093093301789 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4862: "triple_var": -4.484288230427907 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4867: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4879: "triple_var": 1.5976331908701897 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4886: "triple_var": 1.7427168412388083 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4893: "triple_var": 0.04343928044620865 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4900: "triple_var": -3.3399183614074204 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4905: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4917: "triple_var": 1.6293325259376517 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4924: "triple_var": 1.736882664895294 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4931: "triple_var": 0.04399913683637662 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4938: "triple_var": -2.444369292006756 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4943: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4955: "triple_var": 1.5872862017688103 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4962: "triple_var": 1.7398247026656914 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4969: "triple_var": 0.03031865229852613 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4976: "triple_var": -5.0311768278795315 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4981: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:4993: "triple_var": 1.7312282788622688 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5000: "triple_var": 1.769383825483774 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5007: "triple_var": 0.04047811901314646 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5014: "triple_var": -0.9426215336022153 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5029: "triple_var": 1.6828397076551171 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5036: "triple_var": 1.709064620547407 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5043: "triple_var": 0.04057566401665535 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5050: "triple_var": -0.6463212254893742 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5065: "triple_var": 1.6151945771098692 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5072: "triple_var": 1.6648574357661732 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5079: "triple_var": 0.04217567760481542 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5086: "triple_var": -1.1775236694865492 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5099: "triple_var": 1.6796469188954928 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5106: "triple_var": 1.7364717309383881 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5113: "triple_var": 0.024458111373704152 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5120: "triple_var": -2.323352411584399 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5124: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5136: "triple_var": 1.6950627146636965 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5143: "triple_var": 1.6997161883847742 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5150: "triple_var": 0.03967149722567542 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5157: "triple_var": -0.11730017888172833 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5170: "triple_var": 1.6746271772112076 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5177: "triple_var": 1.745308682403782 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5184: "triple_var": 0.05048243318914473 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5191: "triple_var": -1.4001208089108732 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5204: "triple_var": 1.7223361938373214 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5211: "triple_var": 1.7304482259411518 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5218: "triple_var": 0.04853369219696933 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5225: "triple_var": -0.167142282744706 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5249: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5256: "triple_var": -0.4589242447435282 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5266: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5273: "triple_var": -0.3906892284813734 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5283: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5290: "triple_var": -0.6642918038190682 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5300: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5307: "triple_var": -0.479362461770447 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5317: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5324: "triple_var": -0.2277293416407159 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5334: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5341: "triple_var": -0.7180482573457314 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5351: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5358: "triple_var": 0.25984355659323577 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5368: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5375: "triple_var": -0.37830378485824284 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5385: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5392: "triple_var": -0.31901476401297857 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5402: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5409: "triple_var": 0.2716030072038232 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5419: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5426: "triple_var": -0.10819061288984577 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5437: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5534: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5541: "triple_var": 1.3630071075914134 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5548: "triple_var": 0.032858557712588984 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5555: "triple_var": -0.3409572991791462 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5568: "triple_var": 1.3965548836134947 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5575: "triple_var": 1.3867719675974968 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5582: "triple_var": 0.028940617662198805 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5589: "triple_var": 0.3380341128232378 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5602: "triple_var": 1.3367159682314742 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5609: "triple_var": 1.3559156826127465 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5616: "triple_var": 0.03367768896460308 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5623: "triple_var": -0.5701018974743836 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5636: "triple_var": 1.3250273599313023 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5643: "triple_var": 1.3745184903324879 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5650: "triple_var": 0.03004553796014618 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5657: "triple_var": -1.6472040030314303 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5670: "triple_var": 1.3422362137003765 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5677: "triple_var": 1.3538645868938752 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5684: "triple_var": 0.035243310650098364 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5691: "triple_var": -0.32994554084169975 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5704: "triple_var": 1.4053439480996628 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5711: "triple_var": 1.3617145453176576 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5718: "triple_var": 0.0277321783367945 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5725: "triple_var": 1.5732411010828746 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5740: "triple_var": 1.3860640917584603 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5747: "triple_var": 1.3725371382049856 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5754: "triple_var": 0.029055917149203348 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5761: "triple_var": 0.4655490130982031 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5774: "triple_var": 1.3123478892514553 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5781: "triple_var": 1.3121601404470422 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5788: "triple_var": 0.03205440214253812 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5795: "triple_var": 0.00585719251846302 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5808: "triple_var": 1.3929243494760173 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5815: "triple_var": 1.3854448895422102 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5822: "triple_var": 0.04108556293423302 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5829: "triple_var": 0.18204594021943096 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5842: "triple_var": 1.4515049416328258 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5849: "triple_var": 1.4207967515347526 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5856: "triple_var": 0.027304342747487678 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5863: "triple_var": 1.124663222332966 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5876: "triple_var": 1.3172884678032841 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5883: "triple_var": 1.3709472329753554 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5890: "triple_var": 0.033075053472599876 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5897: "triple_var": -1.622333436785624 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5913: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5920: "triple_var": 1.3664093608760168 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5927: "triple_var": 0.028097952336663772 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5934: "triple_var": -0.5198107748994804 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5947: "triple_var": 1.399151337444586 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5954: "triple_var": 1.375019329821751 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5961: "triple_var": 0.04202693380539638 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5968: "triple_var": 0.5742033843005805 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5981: "triple_var": 1.3556658107804254 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5988: "triple_var": 1.3620064386829387 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:5995: "triple_var": 0.031839962720940265 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6002: "triple_var": -0.19914055673008918 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6015: "triple_var": 1.3531099467506922 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6022: "triple_var": 1.3748782761472655 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6029: "triple_var": 0.026616702495853597 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6036: "triple_var": -0.8178447123555022 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6049: "triple_var": 1.3346989296576202 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6056: "triple_var": 1.3112625330015446 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6063: "triple_var": 0.03377834500845129 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6070: "triple_var": 0.6938290389955992 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6083: "triple_var": 1.3166696729987606 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6090: "triple_var": 1.3155752503331604 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6097: "triple_var": 0.030094289270360133 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6104: "triple_var": 0.0363664566312959 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6117: "triple_var": 1.3178230963005522 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6124: "triple_var": 1.304354225435989 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6131: "triple_var": 0.036989795166268734 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6138: "triple_var": 0.36412396456970725 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6153: "triple_var": 1.3976430354693656 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6160: "triple_var": 1.3570019971522846 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6167: "triple_var": 0.022388298700575973 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6174: "triple_var": 1.815280332847955 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6187: "triple_var": 1.3572916494357434 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6194: "triple_var": 1.328292647679884 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6201: "triple_var": 0.02849952439819371 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6208: "triple_var": 1.0175258137885768 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6221: "triple_var": 1.3251516667083962 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6228: "triple_var": 1.369096342114039 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6235: "triple_var": 0.03690033399123204 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6242: "triple_var": -1.1909018334653694 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6255: "triple_var": 1.3731135938487347 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6262: "triple_var": 1.3774917155183282 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6269: "triple_var": 0.03804889080098053 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6276: "triple_var": -0.11506568463437782 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6289: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6296: "triple_var": 1.3685359111111162 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6303: "triple_var": 0.030779158904656198 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6310: "triple_var": -0.5436200730546277 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6323: "triple_var": 1.3387387323454425 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6330: "triple_var": 1.3362875913081824 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6337: "triple_var": 0.03176636376089619 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6344: "triple_var": 0.07716152392227477 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6357: "triple_var": 1.3991783796420605 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6364: "triple_var": 1.4001014049493736 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6371: "triple_var": 0.03066593807429619 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6378: "triple_var": -0.03009936643962669 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6391: "triple_var": 1.4338270203281787 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6398: "triple_var": 1.4165144680194537 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6405: "triple_var": 0.031551051080093616 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6412: "triple_var": 0.5487155488029973 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6425: "triple_var": 1.3371078684368398 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6432: "triple_var": 1.3460151852343112 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6439: "triple_var": 0.030883778772703114 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6446: "triple_var": -0.2884140850453239 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6461: "triple_var": 1.3835947814136587 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6468: "triple_var": 1.3931731856905973 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6475: "triple_var": 0.030857997415526656 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6482: "triple_var": -0.3104026534177867 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6495: "triple_var": 1.4235095154049409 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6502: "triple_var": 1.4332977983410944 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6509: "triple_var": 0.03066643797171549 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6516: "triple_var": -0.31918551953055424 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6529: "triple_var": 1.28449969162016 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6536: "triple_var": 1.3613675460769799 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6543: "triple_var": 0.030814119944464 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6550: "triple_var": -2.494565952081642 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6554: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6566: "triple_var": 1.3668367771061614 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6573: "triple_var": 1.3740339643598283 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6580: "triple_var": 0.03668936810136679 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6587: "triple_var": -0.19616547316329488 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6600: "triple_var": 1.4224732305902563 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6607: "triple_var": 1.366662962323771 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6614: "triple_var": 0.0319651487720878 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6621: "triple_var": 1.745972423416947 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6634: "triple_var": 1.3723546188769655 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6641: "triple_var": 1.3607723442574116 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6648: "triple_var": 0.0320098743778365 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6655: "triple_var": 0.36183442905272645 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6668: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6675: "triple_var": 1.3642909842690158 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6682: "triple_var": 0.030554138426686125 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6689: "triple_var": -0.40869232166934427 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6702: "triple_var": 1.3423232124142377 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6709: "triple_var": 1.3662365762245239 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6716: "triple_var": 0.03202215254503404 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6723: "triple_var": -0.7467756509077855 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6736: "triple_var": 1.3271527330295152 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6743: "triple_var": 1.3766731229431624 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6750: "triple_var": 0.03957865008041248 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6757: "triple_var": -1.25118946232466 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6773: "triple_var": 1.412995906148426 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6780: "triple_var": 1.4067356896574879 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6787: "triple_var": 0.02086196843528998 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6794: "triple_var": 0.30007793897092067 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6807: "triple_var": 1.3949260397431753 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6814: "triple_var": 1.4151151980100767 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6821: "triple_var": 0.027877762662162905 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6828: "triple_var": -0.724202961032562 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6841: "triple_var": 1.2893841622009945 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6848: "triple_var": 1.3619138573552736 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6855: "triple_var": 0.02072251941388378 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6862: "triple_var": -3.5000423310345785 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6866: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6878: "triple_var": 1.281456566433488 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6885: "triple_var": 1.3307443055370167 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6892: "triple_var": 0.025834240332173502 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6899: "triple_var": -1.9078454976725834 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6914: "triple_var": 1.4245815633995669 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6921: "triple_var": 1.3700183201389018 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6928: "triple_var": 0.03324881982617911 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6935: "triple_var": 1.6410580449446102 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6950: "triple_var": 1.3714498780885316 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6957: "triple_var": 1.41508031135139 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6964: "triple_var": 0.027617305037424992 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6971: "triple_var": -1.5798222601276077 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6984: "triple_var": 1.3986025912605526 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6991: "triple_var": 1.3878015174234086 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:6998: "triple_var": 0.028979696863714998 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7005: "triple_var": 0.37271176051078236 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7018: "triple_var": 1.3874719411604586 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7025: "triple_var": 1.3644074886286337 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7032: "triple_var": 0.02748269233573817 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7039: "triple_var": 0.8392355541466413 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7052: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7059: "triple_var": 1.367710269213063 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7066: "triple_var": 0.029773703852380637 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7073: "triple_var": -0.5342474954786022 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7086: "triple_var": 1.3787484301936273 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7093: "triple_var": 1.3867250373631947 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7100: "triple_var": 0.03421861561152644 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7107: "triple_var": -0.23310724373316255 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7120: "triple_var": 1.3377514169967544 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7127: "triple_var": 1.3836216309818365 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7134: "triple_var": 0.02637588852096605 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7141: "triple_var": -1.7390964459309186 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7154: "triple_var": 1.3102385395217784 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7161: "triple_var": 1.3559740715607607 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7168: "triple_var": 0.03690059690556531 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7175: "triple_var": -1.2394252633914575 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7188: "triple_var": 1.3625276204261592 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7195: "triple_var": 1.2903851826822654 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7202: "triple_var": 0.030570455240667226 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7209: "triple_var": 2.3598744989549316 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7213: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7225: "triple_var": 1.287233402914068 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7232: "triple_var": 1.3777273288427567 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7239: "triple_var": 0.027124046773629163 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7246: "triple_var": -3.3362988452250333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7251: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7263: "triple_var": 1.313724043116286 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7270: "triple_var": 1.3193242250369013 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7277: "triple_var": 0.03041523749128671 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7284: "triple_var": -0.1841242213617308 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7297: "triple_var": 1.3773096822515607 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7304: "triple_var": 1.377461818949899 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7311: "triple_var": 0.03475953955968626 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7318: "triple_var": -0.004376832957672518 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7331: "triple_var": 1.2945300965174404 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7338: "triple_var": 1.314320591851175 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7345: "triple_var": 0.0286477904207604 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7352: "triple_var": -0.6908210037515747 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7365: "triple_var": 1.369030506459049 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7372: "triple_var": 1.3657424345166136 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7379: "triple_var": 0.030850777820324853 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7386: "triple_var": 0.10657987171620288 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7399: "triple_var": 1.3916524458857764 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7406: "triple_var": 1.3751437871717884 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7413: "triple_var": 0.03358574212767879 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7420: "triple_var": 0.4915377082104974 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7433: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7440: "triple_var": 1.3640731235852093 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7447: "triple_var": 0.03149431360484133 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7454: "triple_var": -0.38957448764707286 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7467: "triple_var": 1.365697958751875 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7474: "triple_var": 1.3807592482114022 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7481: "triple_var": 0.03182310525863041 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7488: "triple_var": -0.4732815775557482 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7501: "triple_var": 1.3051796331075085 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7508: "triple_var": 1.3395183836063642 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7515: "triple_var": 0.02612153400923924 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7522: "triple_var": -1.3145763371596026 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7535: "triple_var": 1.324051938834681 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7542: "triple_var": 1.3555351344184474 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7549: "triple_var": 0.028224867580778187 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7556: "triple_var": -1.115441746313356 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7571: "triple_var": 1.3632951103665885 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7578: "triple_var": 1.399785606488141 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7585: "triple_var": 0.029231748327957635 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7592: "triple_var": -1.2483172649188636 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7605: "triple_var": 1.3607304651135534 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7612: "triple_var": 1.3714349780360686 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7619: "triple_var": 0.032919508246461095 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7626: "triple_var": -0.3251723216025236 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7639: "triple_var": 1.3200108699508042 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7646: "triple_var": 1.3363682915473838 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7653: "triple_var": 0.03773813291791127 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7660: "triple_var": -0.4334454391837717 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7673: "triple_var": 1.343996170181127 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7680: "triple_var": 1.3743505215023124 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7687: "triple_var": 0.033912998363755074 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7694: "triple_var": -0.8950653963297757 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7707: "triple_var": 1.331855636845733 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7714: "triple_var": 1.3810716052881495 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7721: "triple_var": 0.02972753808106635 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7728: "triple_var": -1.6555682582326736 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7741: "triple_var": 1.4101204723008818 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7748: "triple_var": 1.400804165169987 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7755: "triple_var": 0.03050850293179616 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7762: "triple_var": 0.3053675610278898 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7777: "triple_var": 1.3497722455307872 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7784: "triple_var": 1.3725790713901584 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7791: "triple_var": 0.03053699460825754 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7798: "triple_var": -0.7468588887658251 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7813: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7820: "triple_var": 1.3727099903813176 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7827: "triple_var": 0.038261101704541715 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7834: "triple_var": -0.5464099817081056 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7847: "triple_var": 1.3705150490722573 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7854: "triple_var": 1.3788686522250597 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7861: "triple_var": 0.030676918089317814 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7868: "triple_var": -0.2723090738280936 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7881: "triple_var": 1.3480552657164169 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7888: "triple_var": 1.4002597429363945 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7895: "triple_var": 0.033848959107097225 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7902: "triple_var": -1.5422771806602404 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7915: "triple_var": 1.3840855008046695 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7922: "triple_var": 1.3844358277538384 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7929: "triple_var": 0.02953055827196129 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7936: "triple_var": -0.011863201025276616 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7949: "triple_var": 1.2106472087971003 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7956: "triple_var": 1.3063132903975792 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7963: "triple_var": 0.03185126026381091 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7970: "triple_var": -3.0035257885595725 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7974: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7986: "triple_var": 1.3917195077501132 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:7993: "triple_var": 1.414152061793743 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8000: "triple_var": 0.033887952818391194 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8007: "triple_var": -0.6619625022452106 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8020: "triple_var": 1.3901032108901703 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8027: "triple_var": 1.392501666565246 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8034: "triple_var": 0.029337254946816743 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8041: "triple_var": -0.08175460449260746 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8056: "triple_var": 1.2941331641867704 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8063: "triple_var": 1.3568284414094187 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8070: "triple_var": 0.03106429286123704 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8077: "triple_var": -2.0182425366225347 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8080: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8092: "triple_var": 1.3426033622008748 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8099: "triple_var": 1.306280374202055 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8106: "triple_var": 0.02855817168800701 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8113: "triple_var": 1.2718947275631673 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8128: "triple_var": 1.396004578236767 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8135: "triple_var": 1.3741070214840685 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8142: "triple_var": 0.0285990643764444 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8149: "triple_var": 0.7656738858469213 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8164: "triple_var": 1.3753300235876516 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8171: "triple_var": 1.3651277117592757 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8178: "triple_var": 0.03036273621327561 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8185: "triple_var": 0.3360142431404136 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8198: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8205: "triple_var": 1.36657944873173 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8212: "triple_var": 0.03437270728520472 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8219: "triple_var": -0.42986739770954907 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8232: "triple_var": 1.343775511225098 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8239: "triple_var": 1.339372543455175 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8246: "triple_var": 0.03743010788697827 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8253: "triple_var": 0.11763171464046597 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8266: "triple_var": 1.3979386280484123 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8273: "triple_var": 1.333502804755921 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8280: "triple_var": 0.031663818323791795 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8287: "triple_var": 2.0349985157688657 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8290: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8302: "triple_var": 1.3953909954710846 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8309: "triple_var": 1.385656179109499 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8316: "triple_var": 0.031550901391700845 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8323: "triple_var": 0.30854320897932325 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8336: "triple_var": 1.3740674262061945 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8343: "triple_var": 1.368468367806036 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8350: "triple_var": 0.025821750463220734 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8357: "triple_var": 0.21683496663533433 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8370: "triple_var": 1.344903579178634 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8377: "triple_var": 1.3945161576635334 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8384: "triple_var": 0.03479375652283377 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8391: "triple_var": -1.4259046289624044 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8404: "triple_var": 1.3625672029019351 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8411: "triple_var": 1.3432528659062033 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8418: "triple_var": 0.02759701994398344 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8425: "triple_var": 0.6998703858219528 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8438: "triple_var": 1.3277832145703479 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8445: "triple_var": 1.3416819410136334 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8452: "triple_var": 0.030594187350568645 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8459: "triple_var": -0.454293042139822 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8472: "triple_var": 1.3581869116352847 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8479: "triple_var": 1.3735961605136036 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8486: "triple_var": 0.04060900285455202 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8493: "triple_var": -0.37945400761278647 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8506: "triple_var": 1.3763298739940455 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8513: "triple_var": 1.3516769164546123 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8520: "triple_var": 0.04123480044175723 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8527: "triple_var": 0.5978677543075459 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8540: "triple_var": 1.3509704224696257 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8547: "triple_var": 1.3658325533503757 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8554: "triple_var": 0.042032103774394466 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8561: "triple_var": -0.35358998351645216 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8574: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8581: "triple_var": 1.3732891963484917 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8588: "triple_var": 0.03642936903960159 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8595: "triple_var": -0.5897838589059372 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8608: "triple_var": 1.294700264105971 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8615: "triple_var": 1.349052744563929 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8622: "triple_var": 0.03182216887350735 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8629: "triple_var": -1.708006788412454 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8642: "triple_var": 1.3230308737300756 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8649: "triple_var": 1.339964648271054 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8656: "triple_var": 0.030100147652045307 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8663: "triple_var": -0.562581112117166 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8676: "triple_var": 1.3669187105286618 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8683: "triple_var": 1.3598275239157764 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8690: "triple_var": 0.02551531781963095 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8697: "triple_var": 0.2779188040303192 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8712: "triple_var": 1.336924964206009 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8719: "triple_var": 1.3386381293577252 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8726: "triple_var": 0.03167101435011251 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8733: "triple_var": -0.05409252551174036 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8746: "triple_var": 1.3506199700036603 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8753: "triple_var": 1.3818320220053906 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8760: "triple_var": 0.03497902235976177 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8767: "triple_var": -0.8923077289214109 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8780: "triple_var": 1.4199109054412553 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8787: "triple_var": 1.3615514023186617 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8794: "triple_var": 0.036803373302673 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8801: "triple_var": 1.585710707620245 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8816: "triple_var": 1.4059793148595419 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8823: "triple_var": 1.4336219225374667 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8830: "triple_var": 0.039018641544700965 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8837: "triple_var": -0.7084461832495267 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8850: "triple_var": 1.3605769000030432 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8857: "triple_var": 1.361582728346766 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8864: "triple_var": 0.02814518939377873 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8871: "triple_var": -0.03573713182918571 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8886: "triple_var": 1.2984683412352753 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8893: "triple_var": 1.364226299480361 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8900: "triple_var": 0.03507246285842022 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8907: "triple_var": -1.8749170399163557 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8920: "triple_var": 1.3814485009789483 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8927: "triple_var": 1.3622480352399347 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8934: "triple_var": 0.03186833511942334 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8941: "triple_var": 0.6024935305550759 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8954: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8961: "triple_var": 1.3657913751487458 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8968: "triple_var": 0.03431614086957737 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8975: "triple_var": -0.40761088792299044 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8988: "triple_var": 1.3217314548947847 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:8995: "triple_var": 1.3708870923626029 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9002: "triple_var": 0.040530921410091236 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9009: "triple_var": -1.2127934860020124 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9022: "triple_var": 1.3805276069752952 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9029: "triple_var": 1.3957302062000048 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9036: "triple_var": 0.031860380496248265 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9043: "triple_var": -0.47716314080115135 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9056: "triple_var": 1.345505145786282 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9063: "triple_var": 1.3506420145447209 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9070: "triple_var": 0.027779803237573175 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9077: "triple_var": -0.18491379202754663 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9090: "triple_var": 1.3650729747035684 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9097: "triple_var": 1.3774014240323418 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9104: "triple_var": 0.038126961617230795 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9111: "triple_var": -0.32335252550525284 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9124: "triple_var": 1.4401436669133174 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9131: "triple_var": 1.4151125955478776 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9138: "triple_var": 0.03411859342059812 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9145: "triple_var": 0.7336489830301156 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9158: "triple_var": 1.3579253388941697 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9165: "triple_var": 1.337491859494848 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9172: "triple_var": 0.029308949880590383 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9179: "triple_var": 0.6971754185179329 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9192: "triple_var": 1.3888275388292268 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9199: "triple_var": 1.386834170657569 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9206: "triple_var": 0.024988062920717122 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9213: "triple_var": 0.07977281704397646 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9226: "triple_var": 1.4073411015566824 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9233: "triple_var": 1.386161777037391 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9240: "triple_var": 0.03785092552222043 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9247: "triple_var": 0.5595457502580202 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9260: "triple_var": 1.3879042837252389 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9267: "triple_var": 1.3681610298606202 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9274: "triple_var": 0.031158252678376765 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9281: "triple_var": 0.6336444494629883 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9294: "triple_var": 1.4372537864265074 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9301: "triple_var": 1.370673550667858 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9308: "triple_var": 0.03283152823093148 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9315: "triple_var": 2.027935930680263 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9320: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9332: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9339: "triple_var": 1.3637590392648857 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9346: "triple_var": 0.03229983004445994 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9353: "triple_var": -0.37013497438291726 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9366: "triple_var": 1.3473235157166144 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9373: "triple_var": 1.3721057013339681 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9380: "triple_var": 0.03448242413118669 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9387: "triple_var": -0.7186903543402627 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9400: "triple_var": 1.3342705519361346 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9407: "triple_var": 1.3892069125228723 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9414: "triple_var": 0.030962429324054404 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9421: "triple_var": -1.7742910290329887 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9434: "triple_var": 1.325232109126993 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9441: "triple_var": 1.341943067610049 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9448: "triple_var": 0.026290074765236794 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9455: "triple_var": -0.6356375412500856 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9468: "triple_var": 1.357299161024864 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9475: "triple_var": 1.357913696928301 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9482: "triple_var": 0.030907308853424142 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9489: "triple_var": -0.01988319029493866 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9502: "triple_var": 1.345643092408718 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9509: "triple_var": 1.3381096749805153 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9516: "triple_var": 0.028776112725187243 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9523: "triple_var": 0.26179413113046396 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9536: "triple_var": 1.3188270317763016 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9543: "triple_var": 1.3198637148683368 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9550: "triple_var": 0.027105142707719382 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9557: "triple_var": -0.03824673063757581 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9570: "triple_var": 1.3986689650433397 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9577: "triple_var": 1.3991405074588155 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9584: "triple_var": 0.03627342681588644 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9591: "triple_var": -0.012999665509111424 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9604: "triple_var": 1.3470502598656755 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9611: "triple_var": 1.3584659013158307 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9618: "triple_var": 0.034192029489838065 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9625: "triple_var": -0.333868495683999 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9638: "triple_var": 1.375998026438354 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9645: "triple_var": 1.3547327449928541 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9652: "triple_var": 0.025013661602557517 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9659: "triple_var": 0.8501466831759497 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9672: "triple_var": 1.3379933139340645 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9679: "triple_var": 1.3711309232743918 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9686: "triple_var": 0.033519569009447345 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9693: "triple_var": -0.9886048752890465 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9706: "triple_var": 1.351803742498807 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9713: "triple_var": 1.3626688738282315 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9720: "triple_var": 0.025482189719930787 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9727: "triple_var": -0.4263813843645648 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9740: "triple_var": 1.3531947828759154 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9747: "triple_var": 1.3629530190858323 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9754: "triple_var": 0.0226757851319557 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9761: "triple_var": -0.43033730268352044 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9774: "triple_var": 1.323421489411967 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9781: "triple_var": 1.3422020826581782 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9788: "triple_var": 0.03439772204440277 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9795: "triple_var": -0.5459836329268577 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9808: "triple_var": 1.378002200915386 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9815: "triple_var": 1.421572644165703 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9822: "triple_var": 0.02837957331361722 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9829: "triple_var": -1.5352747826342699 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9844: "triple_var": 1.365396386280136 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9851: "triple_var": 1.3656893768968423 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9858: "triple_var": 0.025352396847038713 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9865: "triple_var": -0.01155672256450277 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9878: "triple_var": 1.3042992577568846 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9885: "triple_var": 1.3270508854893475 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9892: "triple_var": 0.029565278906907785 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9899: "triple_var": -0.7695387486145804 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9912: "triple_var": 1.4301697234936648 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9919: "triple_var": 1.3677266062779267 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9926: "triple_var": 0.027504404339384267 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9933: "triple_var": 2.2702952023696117 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9937: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9949: "triple_var": 1.3461549124938976 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9956: "triple_var": 1.3841508285086672 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9963: "triple_var": 0.025438738939214826 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9970: "triple_var": -1.4936241967638333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9983: "triple_var": 1.3067454528797302 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9990: "triple_var": 1.371307105374189 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:9997: "triple_var": 0.03247971955183156 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10004: "triple_var": -1.9877527695838153 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10021: "triple_var": 1.3460995917444503 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10028: "triple_var": 1.3518657780618382 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10035: "triple_var": 0.032472265596072365 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10042: "triple_var": -0.17757265197058936 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10057: "triple_var": 1.3241578496675088 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10064: "triple_var": 1.372510483752694 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10071: "triple_var": 0.022691329697944004 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10078: "triple_var": -2.1308858814724414 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10081: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10107: "triple_var": 0.4166666666666667 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10114: "triple_var": 1.9197467431391368 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10124: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10131: "triple_var": 0.6713287259071626 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10141: "triple_var": 0.25 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10148: "triple_var": 1.2847849059650915 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10158: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10165: "triple_var": 0.9454854918585789 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10175: "triple_var": 0.16666666666666666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10182: "triple_var": 0.10478783746929599 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10192: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10199: "triple_var": 0.11731652218335731 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10209: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10216: "triple_var": 0.13681604705851313 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10226: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10233: "triple_var": 0.18921152795657115 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10243: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10250: "triple_var": 0.14287647954877977 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10260: "triple_var": 0.0 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10267: "triple_var": -0.028575907889926266 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10277: "triple_var": 0.08333333333333333 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10284: "triple_var": 0.3183962386428235 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10295: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10458: "triple_var": 0.7381482282162132 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10465: "triple_var": 0.6553762196322537 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10472: "triple_var": 0.047108544151570335 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10479: "triple_var": 1.7570487493233293 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10495: "triple_var": 0.655972789768764 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10502: "triple_var": 0.6493005058425859 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10509: "triple_var": 0.04836339547726999 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10516: "triple_var": 0.13796144502124547 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10532: "triple_var": 0.6821655744581264 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10539: "triple_var": 0.6959790174577051 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10546: "triple_var": 0.03327740259019486 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10553: "triple_var": -0.4150997951880043 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10568: "triple_var": 0.8196816960603959 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10575: "triple_var": 0.7393145401751134 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10582: "triple_var": 0.04945053555675509 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10589: "triple_var": 1.6252029423026952 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10605: "triple_var": 0.6172081515879116 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10612: "triple_var": 0.6247932407072722 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10619: "triple_var": 0.04018381253181556 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10626: "triple_var": -0.18875981748508833 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10641: "triple_var": 0.8445988130645993 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10648: "triple_var": 0.772275250276213 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10655: "triple_var": 0.036920328660772106 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10662: "triple_var": 1.9589089645680788 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10677: "triple_var": 0.6890853526311734 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10684: "triple_var": 0.6750025160838989 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10691: "triple_var": 0.05316293536371632 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10698: "triple_var": 0.2648995291724621 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10711: "triple_var": 0.6418247051890988 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10718: "triple_var": 0.6117531219767072 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10725: "triple_var": 0.044515402241048975 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10732: "triple_var": 0.6755321012164588 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10745: "triple_var": 0.7206202946236953 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10752: "triple_var": 0.6505584909193082 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10759: "triple_var": 0.03936448879515537 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10766: "triple_var": 1.779822521485651 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10779: "triple_var": 0.7132539840280843 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10786: "triple_var": 0.6741291566885502 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10793: "triple_var": 0.05110510575777085 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10800: "triple_var": 0.7655757044115864 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10813: "triple_var": 0.5660649335839734 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10820: "triple_var": 0.6536281511744035 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10827: "triple_var": 0.05577088655216152 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10834: "triple_var": -1.5700524593334886 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10847: "triple_var": 0.7381482282162132 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10854: "triple_var": 0.6587200859186805 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10861: "triple_var": 0.048347186843686545 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10868: "triple_var": 1.642869988575248 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10884: "triple_var": 0.6673494227917356 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10891: "triple_var": 0.6653130128208103 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10898: "triple_var": 0.04333581309694462 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10905: "triple_var": 0.046991387155230796 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10921: "triple_var": 0.8594270180815289 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10928: "triple_var": 0.7487494953600832 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10935: "triple_var": 0.04958056861078148 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10942: "triple_var": 2.2322761884860367 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10948: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10960: "triple_var": 0.771388662470701 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10967: "triple_var": 0.658313481884855 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10974: "triple_var": 0.05256412630723745 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10981: "triple_var": 2.1511853906773104 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10985: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:10997: "triple_var": 0.6306143688685915 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11004: "triple_var": 0.6588306630209009 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11011: "triple_var": 0.04394840286086344 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11018: "triple_var": -0.6420322995954957 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11031: "triple_var": 0.6478544492737283 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11038: "triple_var": 0.7044420367938397 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11045: "triple_var": 0.056414828471987845 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11052: "triple_var": -1.0030622985623245 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11067: "triple_var": 0.6631603520359296 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11074: "triple_var": 0.6894812506951853 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11081: "triple_var": 0.045012592774407424 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11088: "triple_var": -0.5847452243235547 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11101: "triple_var": 0.7114184840395307 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11108: "triple_var": 0.689266661074227 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11115: "triple_var": 0.03983407359794297 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11122: "triple_var": 0.5561023757923577 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11137: "triple_var": 0.6684634367310109 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11144: "triple_var": 0.6498184363246662 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11151: "triple_var": 0.0442590397393008 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11158: "triple_var": 0.4212698810495998 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11171: "triple_var": 0.6661972566243894 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11178: "triple_var": 0.6750237185099298 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11185: "triple_var": 0.04917997380349959 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11192: "triple_var": -0.17947268375552267 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11205: "triple_var": 0.6334840404756105 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11212: "triple_var": 0.66898005066609 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11219: "triple_var": 0.041373542870600626 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11226: "triple_var": -0.8579398264609928 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11239: "triple_var": 0.7381482282162132 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11246: "triple_var": 0.649432500258247 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11253: "triple_var": 0.053786834157243564 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11260: "triple_var": 1.6493948630367332 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11276: "triple_var": 0.6412071899010433 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11283: "triple_var": 0.6464014436785591 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11290: "triple_var": 0.050330847432513336 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11297: "triple_var": -0.10320219194561711 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11313: "triple_var": 0.6143829032821752 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11320: "triple_var": 0.6437397727520674 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11327: "triple_var": 0.04736959518992758 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11334: "triple_var": -0.6197407715262581 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11350: "triple_var": 0.7479945777724609 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11357: "triple_var": 0.7107634192406492 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11364: "triple_var": 0.04921206893014605 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11371: "triple_var": 0.756545281293891 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11387: "triple_var": 0.6314182701516815 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11394: "triple_var": 0.5675923649456123 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11401: "triple_var": 0.02632955349686247 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11408: "triple_var": 2.424116505191589 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11411: "triple_var" tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11423: "triple_var": 0.5500369711242505 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11430: "triple_var": 0.556635111338756 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11437: "triple_var": 0.043099683638775074 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11444: "triple_var": -0.1530902238124402 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11457: "triple_var": 0.6178101334930457 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11464: "triple_var": 0.6518895980664628 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11471: "triple_var": 0.04174016337561699 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11478: "triple_var": -0.8164669665218673 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11491: "triple_var": 0.6008186128085161 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11498: "triple_var": 0.581395484262873 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11505: "triple_var": 0.03729875049463426 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11512: "triple_var": 0.5207447511797286 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11530: "triple_var": 0.7094105278159525 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11537: "triple_var": 0.6639018083394491 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11544: "triple_var": 0.052468686536335206 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11551: "triple_var": 0.8673500802233352 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11564: "triple_var": 0.6409561375395666 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11571: "triple_var": 0.6233173447230468 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11578: "triple_var": 0.037807523010574544 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11585: "triple_var": 0.4665418787574722 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11600: "triple_var": 0.7149431995831775 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11607: "triple_var": 0.6568513549334123 tools/data/semireal_order_denominator_gate_20260507_0923_seedcheck.json:11614: "triple_var": 0.03948643973318867 tools/data/semireal_order_denominat20260509_1457.json`\n- Report: `tools/data/reports/agent_20260509_1457.md`\n", tools/data/lab_graph.json:2303: "content_preview": "# Agent Report - Boundary Gate Transfer Matrix\n**Date**: 2026-05-09 14:44\n**Piano**: 106\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - denominator gate transfers as order/null operator, not as QxG closure\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_", tools/data/lab_graph.json:2304: "content_full": "# Agent Report - Boundary Gate Transfer Matrix\n**Date**: 2026-05-09 14:44\n**Piano**: 106\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: CONSTRAINT - denominator gate transfers as order/null operator, not as QxG closure\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`SR`, `SR2`, `L1`, `L2`, `triple_var`] - osservabili canonici da `tools/observables_registry.py`.\n**observable_contract**: claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_state -> transfer_state -> denominator_state`; observable=stable canonical observables contro permutation null e layer classification; operator=`exp_denominator_gate_transfer_matrix.py`; generator=`DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`; denominator=4 perimetri sintetici QxG continuo/discreto, 4096 gaps, 24 replicates, 11 beta layers, 40 shuffle baselines; non_possible=chiamare chiusura QxG, legge GUE/Poisson o endpoint-stable universale; not_tested=perimetro fisico reale, fit `V_c`, nuovi domini autoricerca.\n\n## Respiro fuori-tempo\n- **Prima impressione**: dopo 13/13 transfer sul perimetro base, il confine non chiede un altro blank audit. Chiede se il gate resta gate quando non porta piu' le etichette GUE/Poisson.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + BOUNDARY completo 13/13 + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: ordine coerente / null permutato; punto-zero = layer beta dove il segnale conserva osservabili stabili ma la classificazione diventa ambigua.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate viene letto come mappa di passaggio tra stato coerente, stato null e denominatore, non come tassonomia locale.\n- **Operatori laterali scelti**: boundary operator, graph/perimeter transfer matrix, shuffle marginal-preserving. Entrano per trasferire il gate senza importare il label GUE/Poisson.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione del deposito 1437 senza ripetere il perimetro base; CE-0117: cascata della possibilita solo dove il gate cambia perimetro; YSN DeltaLink: `base closure -> transfer matrix -> beta blank`.\n- **Proto-ipotesi**: il gate BOUNDARY trasferisce quando il polo coerente ha osservabili one-sided contro il proprio null e il polo illusorio collassa; non trasferisce come endpoint-stable universale.\n- **Possibile / non-possibile**: possibile = usare il gate come audit di ordine su perimetri sintetici QxG; non-possibile = dichiarare ponte QxG o usare beta 0.3 come classe decisa.\n- **Proiezione**: misurare, per ogni perimetro, osservabili one-sided, stable count ai poli, distanza endpoint e layer ambiguo.\n\n## Claim Under Test\n> Il gate BOUNDARY trasferisce fuori dal perimetro base se ogni perimetro mostra osservabili one-sided al polo coerente, collasso al polo null permutato, e un layer blank/ambiguous esplicito prima del passaggio.\n\n## Question\nQuando il gate viene mosso da BOUNDARY base a quattro perimetri QxG sintetici, resta transfer, cade, o produce un blank strutturale?\n\n## Experiment Design\n- Strumento: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Perimetri: `DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`.\n- Null baseline: permutation shuffle della stessa multiset di gaps; conserva distribuzione marginale e distrugge ordine.\n- Criterio transfer: osservabili coherent one-sided presenti e stable count coerente alto, con stable count illusorio basso.\n- Criterio blank: layer beta con `ambiguous_fraction >= 0.5` nella classificazione gated.\n- Criterio fall: assenza di osservabili one-sided o distanza endpoint collassata. Nessun perimetro cade in questo run.\n\n## Results\n\n| perimeter | one-sided observables | stable coherent | stable illusory | endpoint distance gated | ambiguous beta |\n|---|---|---:|---:|---:|---|\n| DUALITA_golden | SR, L1, triple_var | 3.000 | 0.292 | 3.426 | 0.3 |\n| R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.000 | 0.375 | 4.409 | 0.3 |\n| T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.000 | 0.417 | 4.421 | 0.3 |\n| E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.000 | 0.292 | 4.398 | 0.3 |\n\nLayer beta 0.3:\n\n| perimeter | margin mean | ambiguous fraction | illusory label fraction |\n|---|---:|---:|---:|\n| DUALITA_golden | 0.030 | 1.000 | 0.625 |\n| R_periodic_triad | 0.043 | 1.000 | 0.583 |\n| T_markov_alternating | 0.030 | 1.000 | 0.708 |\n| E_ar1_continuity | 0.042 | 1.000 | 0.500 |\n\nLayer beta 0.4:\n\n| perimeter | margin mean | illusory label fraction |\n|---|---:|---:|\n| DUALITA_golden | 0.269 | 1.000 |\n| R_periodic_triad | 0.269 | 1.000 |\n| T_markov_alternating | 0.289 | 1.000 |\n| E_ar1_continuity | 0.284 | 1.000 |\n\n## Key Findings\n1. **Verificato: il transfer esiste sui 4/4 perimetri.** Ogni perimetro possiede osservabili coherent one-sided e collasso del polo illusorio sotto permutation null.\n2. **Verificato: non esistono endpoint-stable observables.** `endpoint_stable_observables=[]` per tutti i perimetri; il gate trasferisce come discriminatore di ordine, non come invariante simmetrico ai due poli.\n3. **Verificato: beta 0.3 e' il blank strutturale.** Tutti i perimetri hanno `ambiguous_fraction=1.0` a beta 0.3 nella classificazione gated.\n4. **Verificato: beta 0.4 passa al polo illusorio.** Tutti i perimetri hanno `illusory_label_fraction=1.0` a beta 0.4.\n5. **Vincolo: `DUALITA_golden` trasferisce con solo 3 osservabili one-sided.** SR2 e L2 non entrano nel nucleo one-sided del perimetro golden; non va scritto che il set canonico completo trasferisce ovunque.\n\n## Verdict\n**CONSTRAINT**.\n\nIl gate BOUNDARY trasferisce fuori dal perimetro base come operatore di ordine/null: 4/4 perimetri hanno polo coerente leggibile, polo illusorio collassato e blank strutturale a beta 0.3. Il transfer non e' chiusura QxG. Il transfer non e' endpoint-stable universale, perche' nessun perimetro conserva osservabili stabili su entrambi i poli.\n\nIl criterio operativo diventa:\n\n`transfer` = osservabili one-sided al polo coerente + collasso al null permutato. \n`blank` = beta 0.3, classificazione ambiguous su 4/4 perimetri. \n`fall` = assente in questo run, ma definito come perdita di one-sided observables o endpoint distance collassata.\n\n## Bicono della scoperta\n- **Due radici**: gate come copertura del denominatore / gate come classificatore del ponte.\n- **Singolare**: il layer beta 0.3, dove il segnale ha ancora stable count alto ma la decisione di classe non e' disponibile.\n- **Invariante di passaggio**: osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come transfer matrix per perimetri sintetici QxG; qui diventa non-possibile dichiarare QxG chiuso o trattare il gate come endpoint-stable law.\n\n## Consecutio\nIl prossimo nodo regressivo non e' la disponibilita del null. E' la qualita del perimetro: il gate passa su sintetici controllati, ma non ha ancora incontrato un perimetro fisico QxG row-aligned. La prossima mossa deve portare un perimetro fisico o semi-reale dentro lo stesso contratto `coherent/null/beta`, lasciando beta 0.3 come blank dichiarato e non come classe.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/reports/agent_20260509_1437.md`, `tools/exp_denominator_gate_transfer_matrix.py`, `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- **L1 hard constraint**: claim limitato a 4 perimetri sintetici, 4096 gaps, 24 replicates, 40 shuffle baselines.\n- **L2 count grezzi**: riportati 4/4 transfer, 4/4 blank a beta 0.3, 0/4 fall.\n- **L3 no silent patching**: nessuna modifica agli script; nuovo artefatto dati generato dal run.\n- **L4 edge cases**: `DUALITA_golden` trasferisce con 3 osservabili, non 5; nessun endpoint-stable observable.\n- **L5 re-discovery**: il ciclo sposta il gate fuori dal perimetro base; non ripete il residual blank closure.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = transfer matrix QxG sintetica; non-possibile = chiusura QxG o legge endpoint-stable.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Run valido: `python tools/exp_denominator_gate_transfer_matrix.py --n-gaps 4096 --n-replicates 24 --n-beta 11 --n-baseline 40 --z-min 2.0 --seed 202605091444 --out tools/data/denominator_gate_transfer_matrix_20260509_1444.json`.\n- Output: `observables_registry=1.0.0-2026-05-06`; `observables_used=['SR', 'SR2', 'L1', 'L2', 'triple_var']`.\n- Git state: worktree gia' sporco da depositi e pubblicazioni precedenti; questo cycle aggiunge solo il JSON 1444 e il report 1444.\n\n## Files\n- Script: `tools/exp_denominator_gate_transfer_matrix.py`\n- Data: `tools/data/denominator_gate_transfer_matrix_20260509_1444.json`\n- Report: `tools/data/reports/agent_20260509_1444.md`\n", tools/data/lab_graph.json:2328: "text": "| perimeter | one-sided observables | stable coherent | stable illusory | endpoint distance gated | ambiguous beta |\n|---|---|---:|---:|---:|---|\n| DUALITA_golden | SR, L1, triple_var | 3.000 | 0.292 " tools/data/lab_graph.json:2353: "findings": "1. **Verificato: i 3 blank residui diventano transfers.** Il prescan pieno produce 13/13 transfers, 0 blank, 0 falls.\n2. **Verificato: `reaction_diffusion` non e' complete.** Ha 499 gap, quindi resta `contaminated` con excluded mass 0.002; un gap sotto soglia basta a impedire la formula complete.\n3. **Verificato: `string_vibration` trasferisce senza cambio classe.** Poisson -> Poisson, `ordering_d", tools/data/lab_graph.json:2354: "content_preview": "# Agent Report - Residual Boundary Closure\n**Date**: 2026-05-09 14:37\n**Piano**: 105\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: COMPLETE DENOMINATOR TRANSFER on base BOUNDARY perimeter\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il residual blank test decide", tools/data/lab_graph.json:2355: "content_full": "# Agent Report - Residual Boundary Closure\n**Date**: 2026-05-09 14:37\n**Piano**: 105\n**Tension explored**: BOUNDARY / TRASCENDENZA_LIMITE\n**verdict**: COMPLETE DENOMINATOR TRANSFER on base BOUNDARY perimeter\nobservables_registry: 1.0.0-2026-05-06\nobservables_used: [`spacing_r`, `shuffle_r_statistic`, `denominator_state`, `excluded_mass`, `transfer`] - osservabili domain-native per il gate boundary, non canonici SR/SR2/L1/L2/triple_var.\n**observable_contract**: claim=il residual blank test decide se i 3 blank residui BOUNDARY entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + `exp_boundary_denominator_prescan.py`; generator=`dnd_autoricerca.genera_segnale` per `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare chiusura QxG, nuova legge GUE/Poisson, o complete `reaction_diffusion` con 499 gap; not_tested=fit `V_c`, nuovi spettri, nuovi domini.\n\n## Respiro fuori-tempo\n- **Prima impressione**: il blank residuo non segnala una classe mancante; segnala l'assenza del contro-perimetro. Quando il null entra, il bordo base non ha piu' blank.\n- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + QxG continuo/discreto + nodo BOUNDARY 8 GUE / 5 Poisson + tensione `TRASCENDENZA_LIMITE`.\n- **Dipolo / punto-zero**: blank / transfer; punto-zero = riga domain/window prima della disponibilita del null row-aligned.\n- **Piano superiore**: grafo della conoscenza e boundary operator. Il gate decide copertura del denominatore, non ontologia GUE/Poisson.\n- **Operatori laterali scelti**: boundary operator, shuffle marginal-preserving, flusso/PDE come controllo di non-determinismo per `reaction_diffusion`.\n- **Contaminazione cognitiva**: CE-0001 / KSAR: reiterazione sul deposito esistente; CE-0117: cascata della possibilita solo dove il null apre il canale; YSN DeltaLink: `blank residuo -> null row-aligned -> transfer completo`.\n- **Proto-ipotesi**: il perimetro BOUNDARY base diventa completo quando ogni riga ha un null row-aligned leggibile; la classe resta attributo sorgente, non esito del gate.\n- **Possibile / non-possibile**: possibile = contratto BOUNDARY 13/13 transfer sul perimetro base; non-possibile = usare questa copertura per chiudere QxG, per chiamare legge il cambio classe della logistica, o per trattare `reaction_diffusion` come complete.\n- **Proiezione**: verificare `boundary_blank_null_audit_residual_20260509_1500.json`, innestarlo nel prescan pieno e controllare `boundary_denominator_prescan_full_20260509_1500.json`.\n\n## Claim Under Test\n> Il residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\n## Question\nI tre blank residui `string_vibration:cycle_6`, `reaction_diffusion:cycle_11`, `logistica_biforcazione_var_3.5699:cycle_13` restano blank, cadono, o entrano nel transfer quando ricevono null row-aligned?\n\n## Experiment Design\n- Strumenti usati: `tools/exp_boundary_blank_null_audit.py` e `tools/exp_boundary_denominator_prescan.py`.\n- Input verificati: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json` e `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Perimetro atomico: 13 righe base, 8 GUE-like e 5 Poisson-like.\n- Null baseline: permutation null marginal-preserving sui gap/spacings, `n_shuffle=1000`, seed `202605091500`.\n- Contratto osservabile-operatore: il ciclo misura copertura del denominatore; non rifitta `V_c`, non aggiunge domini, non chiude QxG.\n\n## Results\n\nResidual blank audit depositato:\n\n| domain | n_gaps | r_original | shuffle_mean | z | class | ordering_dependent | decision |\n|---|---:|---:|---:|---:|---|---|---|\n| string_vibration | 7999 | 0.383868 | 0.372199 | 4.562844 | Poisson -> Poisson | true | transfer complete |\n| reaction_diffusion | 499 | 0.762604 | 0.494932 | 31.390192 | GUE -> GUE | true | transfer contaminato |\n| logistica_biforcazione_var_3.5699 | 4727 | 0.581221 | 0.099640 | 161.271569 | GUE -> Poisson | true | transfer complete, class_change edge case |\n\nPrescan pieno dopo innesto dei null:\n\n| measure | value |\n|---|---:|\n| rows | 13 |\n| source GUE | 8 |\n| source Poisson | 5 |\n| transfers | 13 |\n| blank | 0 |\n| falls | 0 |\n| complete | 8 |\n| contaminated | 5 |\n\nRighe residue nel prescan:\n\n| domain/window | source | denominator_state | excluded mass | null | transfer |\n|---|---|---|---:|---|---|\n| string_vibration:cycle_6 | Poisson | complete | 0.000 | shuffle z=4.56; class_change=False | transfers |\n| reaction_diffusion:cycle_11 | GUE | contaminated | 0.002 | shuffle z=31.39; class_change=False | transfers |\n| logistica_biforcazione_var_3.5699:cycle_13 | GUE | complete | 0.000 | shuffle z=161.27; class_change=True | transfers |\n\n## Key Findings\n1. **Verificato: i 3 blank residui diventano transfers.** Il prescan pieno produce 13/13 transfers, 0 blank, 0 falls.\n2. **Verificato: `reaction_diffusion` non e' complete.** Ha 499 gap, quindi resta `contaminated` con excluded mass 0.002; un gap sotto soglia basta a impedire la formula complete.\n3. **Verificato: `string_vibration` trasferisce senza cambio classe.** Poisson -> Poisson, `ordering_dependent=true`, `n_gaps=7999`.\n4. **Verificato: `logistica_biforcazione_var_3.5699` trasferisce con `class_change=true`.** Il cambio GUE -> Poisson e' edge case del null, non legge nuova legge.\n5. **Verificato: il gate BOUNDARY ha copertura completa sul perimetro base.** La copertura riguarda il denominatore row-aligned, non il ponte QxG.\n\n## Verdict\n**COMPLETE DENOMINATOR TRANSFER**.\n\nIl residual blank test trasforma i 3 blank residui in transfers; il perimetro BOUNDARY base passa a 13/13 transfer, 0 blank, 0 falls, ma resta un contratto di denominatore e non una chiusura QxG.\n\nIl gate ha copertura completa sul perimetro base: ogni riga domain/window possiede un null leggibile o contaminato dichiarato. La completezza del gate non coincide con completezza fisica delle righe: `reaction_diffusion` resta contaminato per 499 gap, `zeta_zeros` resta contaminato per 199 gap, e `logistica_biforcazione_var_3.5699` non produce una legge dal suo `class_change=true`.\n\n## Bicono della scoperta\n- **Due radici**: blank senza contro-perimetro / transfer con null row-aligned.\n- **Singolare**: la riga BOUNDARY prima che il denominatore decida.\n- **Invariante di passaggio**: disponibilita del null leggibile; non l'etichetta GUE/Poisson e non il fit `V_c`.\n- **Campo di possibilita**: qui diventa possibile usare BOUNDARY come gate base 13/13 per audit successivi; qui diventa non-possibile spacciare il gate come chiusura QxG o legge RMT.\n\n## Consecutio\nLa prossima mossa non e' rifare `V_c` e non e' chiudere QxG. Il risultato apre un uso piu' stretto del gate: ogni futuro claim BOUNDARY puo' partire dal perimetro base senza blank, ma deve portare con se' lo stato `complete/contaminated` e dichiarare gli edge case. Il nodo regressivo rimasto e' la qualita del denominatore, non la sua assenza.\n\n## Auto-audit: source flags e 7 lenti\n- **Fonti**: Verificato da `tools/data/agent_field_live.md`, `tools/LAB_AGENT_CONTEXT.md`, `tools/dnd_scenario.py --best`, `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`, `tools/data/boundary_denominator_prescan_full_20260509_1500.json`, run di verifica in `/tmp`.\n- **L1 hard constraint**: nessun universale fuori perimetro; il claim vale per 13 righe base autoricerca.\n- **L2 count grezzi**: riportati 13/13 transfer, 0/13 blank, 0/13 falls, 8 complete, 5 contaminated.\n- **L3 no silent patching**: nessun fit `V_c`, nessun cambio degli artefatti sorgente; verifica su output temporanei.\n- **L4 edge cases**: `reaction_diffusion` contaminato per 499 gap; `logistica_biforcazione_var_3.5699` ha class_change=true ma non diventa legge.\n- **L5 re-discovery**: il ciclo e' audit di denominatore residuo, non teorema GUE/Poisson.\n- **L6 metabolismo cognitivo**: CE-0001, CE-0117 e YSN usati nella combo.\n- **L7 possibile/non-possibile**: possibile = gate base 13/13 senza blank; non-possibile = QxG chiuso o completezza fisica delle righe contaminate.\n\n## Telemetria\n- No API paid run: `ANTHROPIC_API_KEY` e `OPENAI_API_KEY` assenti nell'ambiente.\n- Verifica script: `python -m py_compile tools/exp_boundary_blank_null_audit.py tools/exp_boundary_denominator_prescan.py`.\n- Run valido: `python tools/exp_boundary_blank_null_audit.py --domains string_vibration reaction_diffusion logistica_biforcazione_var_3.5699 --n-shuffle 1000 --seed 202605091500 --out /tmp/boundary_blank_null_audit_residual_20260509_1500.verify.json`.\n- Run valido: `python tools/exp_boundary_denominator_prescan.py --extra-null-audit tools/data/boundary_blank_null_audit_20260509_1430.json --extra-null-audit tools/data/boundary_blank_null_audit_residual_20260509_1500.json --out /tmp/boundary_denominator_prescan_full_20260509_1500.verify.json`.\n- Compare prescan: summary e rows combaciano con `tools/data/boundary_denominator_prescan_full_20260509_1500.json`.\n- Compare residual audit: `string_vibration` e `logistica_biforcazione_var_3.5699` combaciano sui numeri depositati; `reaction_diffusion` nel rerun produce `r=0.755661`, `z=30.592858` invece di `r=0.762604`, `z=31.390192`, ma conserva `n_gaps=499`, `GUE -> GUE`, `ordering_dependent=true`, `transfer=contaminated`. Il drift e' attribuito al generatore dinamico, non al null seed.\n\n## Files\n- Script: `tools/exp_boundary_blank_null_audit.py`\n- Script: `tools/exp_boundary_denominator_prescan.py`\n- Data: `tools/data/boundary_blank_null_audit_residual_20260509_1500.json`\n- Data: `tools/data/boundary_denominator_prescan_full_20260509_1500.json`\n- Report: `tools/data/reports/agent_20260509_1437.md`\n", tools/data/lab_graph.json:2410: "label": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", tools/data/lab_graph.json:2411: "label_en": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", tools/data/lab_graph.json:2412: "label_short": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", tools/data/lab_graph.json:2413: "label_short_en": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", tools/data/seme_backup_b2_20260513_033622.json:2: "old_direzione": "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo", tools/data/seme_backup_b2_20260513_033622.json:3: "new_direzione": "Isolare il residuo prime-specific dopo sottrazione del pre-bordo mod6: misurare intensita row-local di SR/L1/triple_var su prime vs 6k+/-1 con trace JSONL completa e label-swap audit.", tools/data/lab_errori.json:74: "problema": "**Imprecisa.**\n\nLa metrica e rQ² sono corrette. Ma l'argomento \"RN → QxG\" è fuorviante.\n\nRN è un oggetto **GxE** (gravità + elettromagnetismo) — la soluzione di Einstein-Maxwell. L'instabilità di blueshift all'orizzonte interno (r₋) è un fenomeno **classico** (mass inflation, Poisson-Israel 1990), n", tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:1:{"delta": {"L1": -0.17636601563905968, "L2": -0.16108776335668573, "SR": 0.2502107262104497, "SR2": 0.26117116051251815, "triple_var": -2.2961058670589405}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.07283099472990366, "L2": 0.04269840377256951, "SR": 0.3436489187418531, "SR2": 0.35489848139421026, "triple_var": 2.620486507838851}, "real": {"L1": -0.24919701036896336, "L2": -0.11838935958411621, "SR": 0.5938596449523028, "SR2": 0.6160696419067284, "triple_var": 0.3243806407799105}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:2:{"delta": {"L1": 0.09273610492253026, "L2": -0.16190060880570023, "SR": 0.034016837262212773, "SR2": 0.047625583967382146, "triple_var": 0.15384358702838163}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.14763146679438668, "L2": 0.2149382854366141, "SR": 0.3898186222311311, "SR2": 0.38206954607421556, "triple_var": 2.3444711957886386}, "real": {"L1": -0.054895361871856424, "L2": 0.05303767663091389, "SR": 0.4238354594933439, "SR2": 0.4296951300415977, "triple_var": 2.4983147828170202}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:3:{"delta": {"L1": -0.27829582253550655, "L2": -0.1673247956987848, "SR": 0.19678618236987805, "SR2": 0.20612000721946494, "triple_var": -1.8655481196583534}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.00963058260229915, "L2": -0.025210527463765255, "SR": 0.40628790629382755, "SR2": 0.4082234453640597, "triple_var": 2.117854388452702}, "real": {"L1": -0.2879264051378057, "L2": -0.19253532316255006, "SR": 0.6030740886637056, "SR2": 0.6143434525835246, "triple_var": 0.2523062687943485}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:4:{"delta": {"L1": -0.08311278023068808, "L2": 0.0486966878146345, "SR": 0.034787426821776046, "SR2": 0.08062848673057982, "triple_var": -0.5699983336777905}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.047250907213306055, "L2": -0.07965131142376831, "SR": 0.35303834403720885, "SR2": 0.3401851452873781, "triple_var": 2.517557812171376}, "real": {"L1": -0.13036368744399413, "L2": -0.03095462360913381, "SR": 0.3878257708589849, "SR2": 0.4208136320179579, "triple_var": 1.9475594784935855}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:5:{"delta": {"L1": -0.2936198075559053, "L2": -0.10161656532035453, "SR": 0.1855880648644272, "SR2": 0.2453970596998446, "triple_var": -2.9572979073972014}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.03362207930289107, "L2": 0.02258141153434522, "SR": 0.4125728800881137, "SR2": 0.37758320436326376, "triple_var": 3.279219063479703}, "real": {"L1": -0.2599977282530142, "L2": -0.07903515378600931, "SR": 0.5981609449525409, "SR2": 0.6229802640631084, "triple_var": 0.32192115608250155}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:6:{"delta": {"L1": -0.08615671516159853, "L2": -0.36806383650841745, "SR": -0.07045577446670487, "SR2": -0.025085114886213566, "triple_var": -1.4911195332003824}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.02187697980480418, "L2": 0.1674090523863931, "SR": 0.4279898457344939, "SR2": 0.39814619492275455, "triple_var": 3.531274987115587}, "real": {"L1": -0.06427973535679435, "L2": -0.2006547841220244, "SR": 0.357534071267789, "SR2": 0.373061080036541, "triple_var": 2.0401554539152045}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:7:{"delta": {"L1": -0.3518689735253499, "L2": 0.1578933828606483, "SR": 0.16503587544172527, "SR2": 0.2529645891374554, "triple_var": -2.143003890195881}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.0057245892669763754, "L2": -0.05403507230698767, "SR": 0.398744933149463, "SR2": 0.38880234578600276, "triple_var": 2.480926201012675}, "real": {"L1": -0.3575935627923263, "L2": 0.10385831055366064, "SR": 0.5637808085911883, "SR2": 0.6417669349234582, "triple_var": 0.3379223108167944}, "rep": 3} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:8:{"delta": {"L1": -0.021059590383546434, "L2": -0.1232101842411371, "SR": 0.000908207944726569, "SR2": 0.019816481063584246, "triple_var": 0.5985514403691781}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.13991889405667332, "L2": -0.06369281624436897, "SR": 0.4024483330315423, "SR2": 0.39522627739016836, "triple_var": 3.027988593697505}, "real": {"L1": 0.11885930367312689, "L2": -0.18690300048550607, "SR": 0.4033565409762689, "SR2": 0.4150427584537526, "triple_var": 3.626540034066683}, "rep": 3} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:9:{"delta": {"L1": -0.15566083907359274, "L2": -0.11999882492492503, "SR": 0.20821956646877898, "SR2": 0.2881396402266089, "triple_var": -2.1224438986562233}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.12504252314763808, "L2": -0.18408901343454181, "SR": 0.35113696771546227, "SR2": 0.2821926453229384, "triple_var": 2.37986529754236}, "real": {"L1": -0.2807033622212308, "L2": -0.30408783835946684, "SR": 0.5593565341842412, "SR2": 0.5703322855495473, "triple_var": 0.2574213988861369}, "rep": 4} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:10:{"delta": {"L1": 0.043816426072416106, "L2": -0.3486967994706952, "SR": 0.05546067597247367, "SR2": 0.008479316995973818, "triple_var": -3.4271766383209323}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.022466488654232175, "L2": 0.21971679262869137, "SR": 0.3523228579951305, "SR2": 0.3906214818273235, "triple_var": 5.327977821991224}, "real": {"L1": 0.021349937418183935, "L2": -0.12898000684200384, "SR": 0.4077835339676042, "SR2": 0.3991007988232973, "triple_var": 1.900801183670292}, "rep": 4} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:11:{"delta": {"L1": -0.43801313360310046, "L2": -0.1387058797415781, "SR": 0.19284813766996034, "SR2": 0.22158680715132523, "triple_var": -2.5231554635345836}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.11086149613712384, "L2": 0.23545868722620367, "SR": 0.4133174809925949, "SR2": 0.43086635000657697, "triple_var": 2.8400769296194976}, "real": {"L1": -0.32715163746597664, "L2": 0.09675280748462554, "SR": 0.6061656186625553, "SR2": 0.6524531571579022, "triple_var": 0.31692146608491384}, "rep": 5} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:12:{"delta": {"L1": -0.32201629604781706, "L2": -0.07915994419408881, "SR": -0.05376382006325903, "SR2": 0.01298862782700344, "triple_var": -1.069819344528656}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.2071519326323882, "L2": -0.0026800350305236963, "SR": 0.4194580301510922, "SR2": 0.35806787970158266, "triple_var": 3.046162264695955}, "real": {"L1": -0.11486436341542886, "L2": -0.0818399792246125, "SR": 0.36569421008783315, "SR2": 0.3710565075285861, "triple_var": 1.9763429201672993}, "rep": 5} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:13:{"delta": {"L1": -0.32187332673265495, "L2": -0.17010379227122388, "SR": 0.26546266639307925, "SR2": 0.22658468902442636, "triple_var": -3.635988339341995}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.04026998393594256, "L2": 0.03643148907198759, "SR": 0.3449996523756629, "SR2": 0.3804200895416952, "triple_var": 3.890336873093069}, "real": {"L1": -0.3621433106685975, "L2": -0.1336723031992363, "SR": 0.6104623187687421, "SR2": 0.6070047785661216, "triple_var": 0.25434853375107375}, "rep": 6} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:14:{"delta": {"L1": -0.08610074229417908, "L2": 0.1263155810599073, "SR": 0.00851484829518756, "SR2": 0.04870909375934812, "triple_var": 0.013495412548549801}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.013138718922435321, "L2": -0.043984657700504214, "SR": 0.3973856503617168, "SR2": 0.3651451958747261, "triple_var": 2.37456888946492}, "real": {"L1": -0.0992394612166144, "L2": 0.0823309233594031, "SR": 0.40590049865690436, "SR2": 0.4138542896340742, "triple_var": 2.3880643020134698}, "rep": 6} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:15:{"delta": {"L1": -0.4839502212393051, "L2": -0.07180293354036663, "SR": 0.20951792128082852, "SR2": 0.2244985454817403, "triple_var": -3.0718304648579537}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.06922633049939886, "L2": 0.10684719130400443, "SR": 0.37355468763513183, "SR2": 0.4026370691529668, "triple_var": 3.3395736389865123}, "real": {"L1": -0.41472389073990623, "L2": 0.035044257763637794, "SR": 0.5830726089159604, "SR2": 0.6271356146347071, "triple_var": 0.26774317412855864}, "rep": 7} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:16:{"delta": {"L1": -0.06472240607635125, "L2": 0.08957237535241512, "SR": -0.045566415276663275, "SR2": 0.05950758942504736, "triple_var": -0.3180556378882091}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.00910726965845709, "L2": -0.07590152103025584, "SR": 0.41674939147561824, "SR2": 0.3755457246849286, "triple_var": 2.292863068301546}, "real": {"L1": -0.07382967573480835, "L2": 0.013670854322159283, "SR": 0.37118297619895496, "SR2": 0.43505331410997594, "triple_var": 1.9748074304133367}, "rep": 7} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:17:{"delta": {"L1": -0.4058253193303165, "L2": 0.019497691413987717, "SR": 0.25343091066977946, "SR2": 0.3271043449429215, "triple_var": -3.270960713387953}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.09405705538888695, "L2": -0.05039108874941519, "SR": 0.4115945835475054, "SR2": 0.35132375069767174, "triple_var": 3.521999718524225}, "real": {"L1": -0.3117682639414296, "L2": -0.030893397335427474, "SR": 0.6650254942172849, "SR2": 0.6784280956405933, "triple_var": 0.25103900513627253}, "rep": 8} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:18:{"delta": {"L1": 0.09127876397934076, "L2": -0.09459252449761525, "SR": 0.0076407385986437815, "SR2": -0.0100357008420216, "triple_var": 0.6060655585538246}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.014674197942576264, "L2": -0.007503499677703958, "SR": 0.35503505630698867, "SR2": 0.33175156956996465, "triple_var": 3.64974483605706}, "real": {"L1": 0.10595296192191703, "L2": -0.10209602417531921, "SR": 0.36267579490563245, "SR2": 0.32171586872794306, "triple_var": 4.255810394610885}, "rep": 8} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:19:{"delta": {"L1": -0.42794957544479956, "L2": 0.01860592633790111, "SR": 0.1582545270473048, "SR2": 0.2325120236993271, "triple_var": -2.9388310135777083}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.08298120333850573, "L2": -0.010233078292576622, "SR": 0.40452339048190833, "SR2": 0.3847979133408963, "triple_var": 3.2732259613994104}, "real": {"L1": -0.34496837210629383, "L2": 0.008372848045324491, "SR": 0.5627779175292131, "SR2": 0.6173099370402234, "triple_var": 0.33439494782170204}, "rep": 9} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:20:{"delta": {"L1": -0.13237693332884595, "L2": 0.07104568989316722, "SR": 0.013693674651892529, "SR2": 0.04373408486747776, "triple_var": -2.1076075768708353}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.11192802473606177, "L2": -0.1463157997615754, "SR": 0.4112009519805281, "SR2": 0.35403597625641364, "triple_var": 4.059562307266342}, "real": {"L1": -0.020448908592784195, "L2": -0.0752701098684082, "SR": 0.4248946266324206, "SR2": 0.3977700611238914, "triple_var": 1.9519547303955065}, "rep": 9} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:21:{"delta": {"L1": -0.4183507307543604, "L2": 0.05015161419828254, "SR": 0.1432235875930286, "SR2": 0.170299433456143, "triple_var": -2.213314844382794}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.016011483445622886, "L2": -0.07489881172122048, "SR": 0.45029449254584425, "SR2": 0.43620290556461666, "triple_var": 2.4295667791832356}, "real": {"L1": -0.4343622141999833, "L2": -0.024747197522937945, "SR": 0.5935180801388729, "SR2": 0.6065023390207597, "triple_var": 0.21625193480044178}, "rep": 10} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:22:{"delta": {"L1": 0.02843590689569496, "L2": -0.12458143121245346, "SR": 0.008914394675522685, "SR2": -0.005123101363868399, "triple_var": -0.26378601160730986}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.08386413237212777, "L2": 0.171815637237195, "SR": 0.3648561950995632, "SR2": 0.4260998098866673, "triple_var": 2.57865481059594}, "real": {"L1": -0.05542822547643281, "L2": 0.04723420602474155, "SR": 0.37377058977508587, "SR2": 0.4209767085227989, "triple_var": 2.31486879898863}, "rep": 10} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:23:{"delta": {"L1": -0.45303383410894504, "L2": 0.016043101507776658, "SR": 0.19938807283047505, "SR2": 0.2974944683822706, "triple_var": -3.8582309236310164}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.21525445680971467, "L2": -0.024813571334161157, "SR": 0.3778109392917141, "SR2": 0.34616345791516084, "triple_var": 4.292205641585339}, "real": {"L1": -0.23777937729923035, "L2": -0.008770469826384499, "SR": 0.5771990121221892, "SR2": 0.6436579262974315, "triple_var": 0.43397471795432213}, "rep": 11} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:24:{"delta": {"L1": 0.02650852755485635, "L2": -0.0575253577169727, "SR": 0.02062727593941127, "SR2": 0.00894508433470953, "triple_var": 0.6497213886525413}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.018708059356831754, "L2": -0.06258842381157265, "SR": 0.40289753376254406, "SR2": 0.3840555197953414, "triple_var": 1.8656421918960802}, "real": {"L1": 0.0078004681980245955, "L2": -0.12011378152854535, "SR": 0.42352480970195533, "SR2": 0.39300060413005095, "triple_var": 2.5153635805486214}, "rep": 11} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:25:{"delta": {"L1": -0.22399596831128887, "L2": -0.07506252674371805, "SR": 0.19285470209820282, "SR2": 0.18590368162745508, "triple_var": -2.1975178398843354}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.005136856180665333, "L2": -0.056062008219844035, "SR": 0.42580827985782704, "SR2": 0.41539891951682906, "triple_var": 2.5236379413321908}, "real": {"L1": -0.21885911213062353, "L2": -0.13112453496356208, "SR": 0.6186629819560299, "SR2": 0.6013026011442841, "triple_var": 0.32612010144785525}, "rep": 12} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:26:{"delta": {"L1": -0.1816358363533675, "L2": -0.02568242342149072, "SR": -0.04198887883074315, "SR2": 0.022796635652266306, "triple_var": -0.9195412719824878}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.013963054244303644, "L2": 0.04256990783053429, "SR": 0.4087269747113042, "SR2": 0.3591063956193252, "triple_var": 2.946273736630053}, "real": {"L1": -0.16767278210906386, "L2": 0.01688748440904357, "SR": 0.36673809588056105, "SR2": 0.38190303127159153, "triple_var": 2.0267324646475653}, "rep": 12} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:27:{"delta": {"L1": -0.20733521220729562, "L2": -0.18985930842973853, "SR": 0.28072934517356285, "SR2": 0.26827732361591566, "triple_var": -2.414766191121119}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.10661265615657806, "L2": 0.005298509335259352, "SR": 0.33885606176855065, "SR2": 0.37005423373099505, "triple_var": 2.614385997463052}, "real": {"L1": -0.31394786836387367, "L2": -0.18456079909447917, "SR": 0.6195854069421135, "SR2": 0.6383315573469107, "triple_var": 0.19961980634193313}, "rep": 13} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:28:{"delta": {"L1": -0.15428660495657454, "L2": 0.01913078952588388, "SR": -0.04355635540194097, "SR2": -0.02905381533189999, "triple_var": -0.5550719367267241}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.013030702366173243, "L2": -0.06749208748183579, "SR": 0.4185785896945694, "SR2": 0.44316543353004995, "triple_var": 2.610816750726233}, "real": {"L1": -0.16731730732274777, "L2": -0.048361297955951905, "SR": 0.37502223429262843, "SR2": 0.41411161819814996, "triple_var": 2.055744813999509}, "rep": 13} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:29:{"delta": {"L1": -0.05288570095677522, "L2": -0.02792798751299644, "SR": 0.2573894930237443, "SR2": 0.26466298685436707, "triple_var": -1.677218504024757}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.18780041696907424, "L2": -0.14842461361397546, "SR": 0.3140703233116077, "SR2": 0.325733557974315, "triple_var": 2.028797078028642}, "real": {"L1": -0.24068611792584946, "L2": -0.1763526011269719, "SR": 0.571459816335352, "SR2": 0.5903965448286821, "triple_var": 0.35157857400388515}, "rep": 14} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:30:{"delta": {"L1": -0.03330195429112775, "L2": -0.10234405605165031, "SR": -0.018638565355374226, "SR2": -0.005918274954865299, "triple_var": -0.8371787000723163}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.04696776278140808, "L2": -0.022846096809927212, "SR": 0.38932179445449083, "SR2": 0.37745916934542867, "triple_var": 2.7699556949568676}, "real": {"L1": -0.08026971707253583, "L2": -0.12519015286157753, "SR": 0.3706832290991166, "SR2": 0.37154089439056337, "triple_var": 1.9327769948845512}, "rep": 14} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:31:{"delta": {"L1": -0.28807317272468697, "L2": 0.15373227590436467, "SR": 0.2293092760764613, "SR2": 0.24413353917359865, "triple_var": -2.395165211447269}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.018082392814194628, "L2": -0.13085249383168437, "SR": 0.39262464287228666, "SR2": 0.3927793730622802, "triple_var": 2.7237759965031065}, "real": {"L1": -0.26999077991049236, "L2": 0.02287978207268029, "SR": 0.6219339189487479, "SR2": 0.6369129122358789, "triple_var": 0.3286107850558376}, "rep": 15} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:32:{"delta": {"L1": 0.009149172246244779, "L2": 0.13950682420748964, "SR": 0.07205256667817583, "SR2": 0.06472414173009733, "triple_var": -0.5602911217183668}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.01845415876356225, "L2": -0.12311888424334952, "SR": 0.3849071344436218, "SR2": 0.394058902158823, "triple_var": 2.873428316308184}, "real": {"L1": -0.009304986517317471, "L2": 0.016387939964140116, "SR": 0.4569597011217976, "SR2": 0.4587830438889203, "triple_var": 2.3131371945898174}, "rep": 15} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:33:{"delta": {"L1": -0.2132717458936219, "L2": 0.031833524635193164, "SR": 0.18628957256358292, "SR2": 0.25051138190199784, "triple_var": -2.3284706172637137}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.09060958181550227, "L2": 0.08094342595054002, "SR": 0.3766458036435403, "SR2": 0.38902063231276496, "triple_var": 2.6427411097225515}, "real": {"L1": -0.3038813277091242, "L2": 0.11277695058573318, "SR": 0.5629353762071232, "SR2": 0.6395320142147628, "triple_var": 0.3142704924588377}, "rep": 16} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:34:{"delta": {"L1": -0.11282564819581714, "L2": -0.060115442352053586, "SR": 0.013925815625103255, "SR2": -0.035820560103141164, "triple_var": -0.6596402540266175}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.03775726623855516, "L2": -0.006288562990614838, "SR": 0.3732627459230575, "SR2": 0.4203405315089761, "triple_var": 2.091919557303372}, "real": {"L1": -0.1505829144343723, "L2": -0.06640400534266842, "SR": 0.3871885615481608, "SR2": 0.38451997140583494, "triple_var": 1.4322793032767547}, "rep": 16} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:35:{"delta": {"L1": -0.6454772767773372, "L2": -0.42771237038986565, "SR": 0.242266742097421, "SR2": 0.20423263161859528, "triple_var": -5.585988903784671}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.44917129973886943, "L2": 0.18956134647622516, "SR": 0.40211050211647287, "SR2": 0.42661355239500487, "triple_var": 5.846008595394084}, "real": {"L1": -0.19630597703846783, "L2": -0.23815102391364049, "SR": 0.6443772442138939, "SR2": 0.6308461840136002, "triple_var": 0.26001969160941285}, "rep": 17} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:36:{"delta": {"L1": -0.05244388631445657, "L2": -0.02451985400541594, "SR": 0.010228609546296619, "SR2": 0.03003794351672895, "triple_var": 0.20703859342152242}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.02330486669592614, "L2": -0.07604360078509051, "SR": 0.41284102158978503, "SR2": 0.3820614823738854, "triple_var": 2.370578811403482}, "real": {"L1": -0.029139019618530427, "L2": -0.10056345479050645, "SR": 0.42306963113608165, "SR2": 0.4120994258906143, "triple_var": 2.5776174048250042}, "rep": 17} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:37:{"delta": {"L1": -0.3129686853952029, "L2": 0.10715761271944867, "SR": 0.19625726071950528, "SR2": 0.2250107689303012, "triple_var": -2.4291477821698115}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.015498466246983887, "L2": -0.10021868057574379, "SR": 0.3979358506440736, "SR2": 0.4008260193563014, "triple_var": 2.7333313101202354}, "real": {"L1": -0.32846715164218676, "L2": 0.0069389321437048745, "SR": 0.5941931113635789, "SR2": 0.6258367882866026, "triple_var": 0.30418352795042397}, "rep": 18} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:38:{"delta": {"L1": -0.07460901201118277, "L2": 0.03628721709922304, "SR": 0.029904268323918404, "SR2": 0.051665015271279424, "triple_var": -1.8078900870979644}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.11015221392671475, "L2": -0.0446256604780023, "SR": 0.3607814340107632, "SR2": 0.35156607568619197, "triple_var": 4.586654332507972}, "real": {"L1": 0.03554320191553198, "L2": -0.008338443378779255, "SR": 0.3906857023346816, "SR2": 0.4032310909574714, "triple_var": 2.778764245410007}, "rep": 18} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:39:{"delta": {"L1": -0.13450319052410525, "L2": -0.25798668817732134, "SR": 0.24719942985013094, "SR2": 0.2625824337286184, "triple_var": -2.3692492214705805}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.09020223490692388, "L2": 0.011083049435120887, "SR": 0.3800386853069681, "SR2": 0.3744796747854113, "triple_var": 2.5793101597115435}, "real": {"L1": -0.22470542543102912, "L2": -0.24690363874220048, "SR": 0.627238115157099, "SR2": 0.6370621085140297, "triple_var": 0.21006093824096284}, "rep": 19} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:40:{"delta": {"L1": -0.1242814604414666, "L2": -0.07219345110849826, "SR": 0.033917838374407316, "SR2": 0.027750731921169003, "triple_var": -0.04975014340165229}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.05299268479642021, "L2": 0.019055649761655517, "SR": 0.3640449917752221, "SR2": 0.38568476225438975, "triple_var": 2.5006990619721936}, "real": {"L1": -0.07128877564504639, "L2": -0.05313780134684274, "SR": 0.3979628301496294, "SR2": 0.41343549417555875, "triple_var": 2.4509489185705413}, "rep": 19} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:41:{"delta": {"L1": -0.627431724563226, "L2": -0.05921259264941951, "SR": 0.11231422713410022, "SR2": 0.163383907760889, "triple_var": -2.4213882287308173}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.04060295854917654, "L2": 0.1568484410896893, "SR": 0.4283367727200915, "SR2": 0.4505152736162011, "triple_var": 2.592988803671173}, "real": {"L1": -0.5868287660140495, "L2": 0.09763584844026978, "SR": 0.5406509998541917, "SR2": 0.6138991813770901, "triple_var": 0.1716005749403554}, "rep": 20} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:42:{"delta": {"L1": -0.15991930525510803, "L2": -0.009437031148079696, "SR": 0.12931835058568614, "SR2": 0.11220324039693735, "triple_var": -2.12202148012412}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.027355441041317707, "L2": -0.024093546492732983, "SR": 0.3163231196313739, "SR2": 0.32578909799170686, "triple_var": 3.4209450561521604}, "real": {"L1": -0.18727474629642574, "L2": -0.03353057764081268, "SR": 0.44564147021706, "SR2": 0.4379923383886442, "triple_var": 1.2989235760280402}, "rep": 20} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:43:{"delta": {"L1": -0.252960129577156, "L2": -0.26381081384328187, "SR": 0.2469830654116248, "SR2": 0.2300092246235777, "triple_var": -3.419675297593821}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.011733546402576076, "L2": 0.02842946156935856, "SR": 0.3629187917826627, "SR2": 0.3799897337148747, "triple_var": 3.6954543173481125}, "real": {"L1": -0.2646936759797321, "L2": -0.23538135227392332, "SR": 0.6099018571942875, "SR2": 0.6099989583384524, "triple_var": 0.2757790197542917}, "rep": 21} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:44:{"delta": {"L1": -0.02345520018722562, "L2": 0.23609793866543072, "SR": 0.02608574910962491, "SR2": 0.049983161504982, "triple_var": 0.20226058685777426}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.11836650024194816, "L2": -0.09036909205832659, "SR": 0.35721192031678795, "SR2": 0.3758501170905966, "triple_var": 2.1645665431933403}, "real": {"L1": -0.14182170042917377, "L2": 0.1457288466071041, "SR": 0.38329766942641286, "SR2": 0.4258332785955786, "triple_var": 2.3668271300511146}, "rep": 21} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:45:{"delta": {"L1": -0.3704764380204672, "L2": -0.04293908363315248, "SR": 0.19687934584454236, "SR2": 0.24639084492555313, "triple_var": -2.4846367942596466}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.030525893188553022, "L2": -0.11201812265054469, "SR": 0.391292873597042, "SR2": 0.36333518636715584, "triple_var": 2.7307470032339203}, "real": {"L1": -0.33995054483191417, "L2": -0.15495720628369716, "SR": 0.5881722194415844, "SR2": 0.609726031292709, "triple_var": 0.2461102089742737}, "rep": 22} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:46:{"delta": {"L1": -0.13335658323037286, "L2": -0.031682075198044994, "SR": 0.022655210154035377, "SR2": 0.0009083996919875825, "triple_var": -1.1673151564460444}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.13363006723612894, "L2": 0.0217488496807547, "SR": 0.386144846880023, "SR2": 0.3997135876654702, "triple_var": 3.544169823173214}, "real": {"L1": 0.0002734840057560777, "L2": -0.009933225517290294, "SR": 0.40880005703405836, "SR2": 0.4006219873574578, "triple_var": 2.3768546667271697}, "rep": 22} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:47:{"delta": {"L1": -0.40331285363292013, "L2": -0.12306508754034401, "SR": 0.1929692946566235, "SR2": 0.24779503446148088, "triple_var": -2.586981640622071}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.030094423727529684, "L2": -0.02551934057541514, "SR": 0.3868738303572895, "SR2": 0.37527868894619276, "triple_var": 2.807990362077939}, "real": {"L1": -0.37321842990539045, "L2": -0.14858442811575914, "SR": 0.579843125013913, "SR2": 0.6230737234076736, "triple_var": 0.22100872145586783}, "rep": 23} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:48:{"delta": {"L1": -0.1453943672192668, "L2": 0.0059356557182607605, "SR": -0.038328157690089404, "SR2": -0.030072675453095443, "triple_var": -0.2162007400197279}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.050768818958885444, "L2": -0.01200415967525123, "SR": 0.4121078544374809, "SR2": 0.41386848516021524, "triple_var": 2.131853392388511}, "real": {"L1": -0.19616318617815223, "L2": -0.00606850395699047, "SR": 0.3737796967473915, "SR2": 0.3837958097071198, "triple_var": 1.9156526523687831}, "rep": 23} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:49:{"delta": {"L1": -0.6090801090397671, "L2": -0.10965645823222339, "SR": 0.1272253003605412, "SR2": 0.1619109414577295, "triple_var": -2.469766339731017}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.2946831606040681, "L2": 0.08582885455819415, "SR": 0.4560078464397284, "SR2": 0.44468440293444683, "triple_var": 2.8371902253961574}, "real": {"L1": -0.31439694843569904, "L2": -0.02382760367402924, "SR": 0.5832331468002696, "SR2": 0.6065953443921763, "triple_var": 0.36742388566514034}, "rep": 24} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:50:{"delta": {"L1": -0.11391170497370154, "L2": 0.16164089406288612, "SR": -0.025240327751046898, "SR2": 0.029453620750236886, "triple_var": -0.8530028009073432}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.014790547938067498, "L2": -0.17974833284366418, "SR": 0.41887753380227977, "SR2": 0.3449586063214992, "triple_var": 3.399130994599154}, "real": {"L1": -0.09912115703563404, "L2": -0.018107438780778053, "SR": 0.39363720605123287, "SR2": 0.3744122270717361, "triple_var": 2.546128193691811}, "rep": 24} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:51:{"delta": {"L1": -0.3083134036668923, "L2": -0.04751009899025062, "SR": 0.16863359039071285, "SR2": 0.2626432257249691, "triple_var": -1.957772137512722}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.002738737855150243, "L2": -0.11419519657751719, "SR": 0.42711687488831623, "SR2": 0.34252370101396246, "triple_var": 2.2112658268670704}, "real": {"L1": -0.3110521415220425, "L2": -0.1617052955677678, "SR": 0.5957504652790291, "SR2": 0.6051669267389316, "triple_var": 0.25349368935434835}, "rep": 25} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:52:{"delta": {"L1": -0.3439461344946886, "L2": -0.022533229081342795, "SR": 0.06372398864253859, "SR2": 0.19035040108624518, "triple_var": -2.659198585897116}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.2328645421456623, "L2": -0.012964279972238832, "SR": 0.37410312760085357, "SR2": 0.2685906485190381, "triple_var": 4.365102213842624}, "real": {"L1": -0.11108159234902627, "L2": -0.03549750905358163, "SR": 0.43782711624339216, "SR2": 0.45894104960528326, "triple_var": 1.7059036279455078}, "rep": 25} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:53:{"delta": {"L1": -0.328616008306565, "L2": 0.21200379564063643, "SR": 0.23088959105788026, "SR2": 0.3456531678793145, "triple_var": -2.266173945201267}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.11772200125002728, "L2": -0.08629518589335822, "SR": 0.3476637483933693, "SR2": 0.30646331453785186, "triple_var": 2.5269412915892207}, "real": {"L1": -0.4463380095565923, "L2": 0.1257086097472782, "SR": 0.5785533394512495, "SR2": 0.6521164824171664, "triple_var": 0.26076734638795385}, "rep": 26} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:54:{"delta": {"L1": -0.1254200619614395, "L2": 0.07769173895460874, "SR": 0.02344841357820293, "SR2": 0.039146959577698814, "triple_var": -0.7207555176982474}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.09730484318225571, "L2": 0.10187778554279805, "SR": 0.3674542839533594, "SR2": 0.3965795837860485, "triple_var": 3.594396588839158}, "real": {"L1": -0.028115218779183795, "L2": 0.1795695244974068, "SR": 0.3909026975315623, "SR2": 0.43572654336374733, "triple_var": 2.8736410711409106}, "rep": 26} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:55:{"delta": {"L1": -0.23477970896219816, "L2": -0.012400513604969863, "SR": 0.23783192215474325, "SR2": 0.2309476438262426, "triple_var": -2.2516244500912017}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.020863628552078108, "L2": 0.023948453502077294, "SR": 0.3747261328001751, "SR2": 0.4046985332188769, "triple_var": 2.578727842955474}, "real": {"L1": -0.25564333751427626, "L2": 0.011547939897107432, "SR": 0.6125580549549183, "SR2": 0.6356461770451195, "triple_var": 0.32710339286427254}, "rep": 27} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:56:{"delta": {"L1": -0.19557738946288986, "L2": -0.122003865753433, "SR": 0.018067628625564047, "SR2": 0.01965052432160791, "triple_var": -1.3361088961731213}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.14270927811267217, "L2": -0.012423025023333478, "SR": 0.37865069869119944, "SR2": 0.40436310367901773, "triple_var": 3.347175364351202}, "real": {"L1": -0.05286811135021769, "L2": -0.13442689077676648, "SR": 0.3967183273167635, "SR2": 0.42401362800062564, "triple_var": 2.0110664681780808}, "rep": 27} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:57:{"delta": {"L1": -0.31437973685120363, "L2": 0.16777717041796741, "SR": 0.1901614758250496, "SR2": 0.26117820063336616, "triple_var": -1.5063166197215228}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.1312738860762226, "L2": -0.04118980417584136, "SR": 0.37030022282049957, "SR2": 0.3939856475503055, "triple_var": 1.7751406974192803}, "real": {"L1": -0.4456536229274262, "L2": 0.12658736624212605, "SR": 0.5604616986455492, "SR2": 0.6551638481836717, "triple_var": 0.2688240776977575}, "rep": 28} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:58:{"delta": {"L1": -0.21798868337386873, "L2": -0.020518448375258107, "SR": 0.0124572137406872, "SR2": 0.025691166553646394, "triple_var": -0.859343637850102}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.14404848101406156, "L2": -0.014792525076171261, "SR": 0.4264034552872074, "SR2": 0.3794061555689388, "triple_var": 3.34354120477607}, "real": {"L1": -0.07394020235980718, "L2": -0.03531097345142937, "SR": 0.4388606690278946, "SR2": 0.4050973221225852, "triple_var": 2.484197566925968}, "rep": 28} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:59:{"delta": {"L1": -0.4805059707743234, "L2": 0.0815671840845617, "SR": 0.1682919893121934, "SR2": 0.23710500380087646, "triple_var": -2.472420729821883}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.14960764748666566, "L2": -0.030252479078453548, "SR": 0.4653758651323982, "SR2": 0.41417447858409434, "triple_var": 2.7591040584324933}, "real": {"L1": -0.3308983232876577, "L2": 0.05131470500610816, "SR": 0.6336678544445916, "SR2": 0.6512794823849708, "triple_var": 0.2866833286106102}, "rep": 29} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:60:{"delta": {"L1": -0.24594603747851393, "L2": -0.34089875564804545, "SR": -0.02059285125156579, "SR2": -0.00874700442524673, "triple_var": -2.0214012811858435}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.02279121607233578, "L2": 0.2392306775025839, "SR": 0.3578299187193077, "SR2": 0.4011679671379582, "triple_var": 3.645204935759654}, "real": {"L1": -0.2687372535508497, "L2": -0.10166807814546155, "SR": 0.3372370674677419, "SR2": 0.3924209627127115, "triple_var": 1.6238036545738102}, "rep": 29} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:61:{"delta": {"L1": -0.4479734599384977, "L2": 0.0017106504916351954, "SR": 0.27380059877127894, "SR2": 0.29492286290822656, "triple_var": -3.8927501732437575}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.23599865930172573, "L2": -0.012526349979508213, "SR": 0.37129316403626245, "SR2": 0.37798848317974854, "triple_var": 4.233461128846726}, "real": {"L1": -0.21197480063677193, "L2": -0.010815699487873018, "SR": 0.6450937628075414, "SR2": 0.6729113460879751, "triple_var": 0.3407109556029686}, "rep": 30} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:62:{"delta": {"L1": -0.042448147712699065, "L2": -0.015168728556451086, "SR": -0.0028970834595483663, "SR2": -0.018726790480550426, "triple_var": 0.06471353522298795}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.022482503251544238, "L2": -0.07863994315190839, "SR": 0.394244263593591, "SR2": 0.3933800118622152, "triple_var": 2.2004511230386985}, "real": {"L1": -0.0649306509642433, "L2": -0.09380867170835948, "SR": 0.3913471801340426, "SR2": 0.3746532213816648, "triple_var": 2.2651646582616864}, "rep": 30} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:63:{"delta": {"L1": -0.4227589862313781, "L2": -0.015443721232607595, "SR": 0.22568724906890475, "SR2": 0.24626277394916102, "triple_var": -2.9047385659748217}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.14029752492751027, "L2": -0.013218322042437626, "SR": 0.3807309504194503, "SR2": 0.3847950095539456, "triple_var": 3.200222843571523}, "real": {"L1": -0.28246146130386784, "L2": -0.02866204327504522, "SR": 0.6064181994883551, "SR2": 0.6310577835031066, "triple_var": 0.2954842775967014}, "rep": 31} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:64:{"delta": {"L1": -0.371836769161902, "L2": -0.025822064388571188, "SR": -0.057570737268416106, "SR2": 0.011570314904046697, "triple_var": -3.1046010908278845}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.19524420127736294, "L2": -0.02128898963720406, "SR": 0.40514466885024114, "SR2": 0.3885457091635846, "triple_var": 4.690715096883039}, "real": {"L1": -0.1765925678845391, "L2": -0.04711105402577525, "SR": 0.34757393158182504, "SR2": 0.4001160240676313, "triple_var": 1.5861140060551546}, "rep": 31} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:65:{"delta": {"L1": -0.11806258912378403, "L2": -0.15330053227519402, "SR": 0.31715815769372424, "SR2": 0.22351865806901022, "triple_var": -2.1020138302400087}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.2033821380314928, "L2": 0.05084848995449969, "SR": 0.3145200013090783, "SR2": 0.3896988478403021, "triple_var": 2.40168835029655}, "real": {"L1": -0.32144472715527683, "L2": -0.10245204232069433, "SR": 0.6316781590028026, "SR2": 0.6132175059093123, "triple_var": 0.29967452005654155}, "rep": 32} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:66:{"delta": {"L1": 0.05197505594547956, "L2": -0.2533145205544076, "SR": 0.019191682297959922, "SR2": -0.03684514940647354, "triple_var": -0.9230024881903585}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.046512463375975824, "L2": 0.06986729981931367, "SR": 0.40584999714480635, "SR2": 0.43602474646981454, "triple_var": 2.6752702112683995}, "real": {"L1": 0.005462592569503737, "L2": -0.18344722073509398, "SR": 0.4250416794427663, "SR2": 0.399179597063341, "triple_var": 1.752267723078041}, "rep": 32} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:67:{"delta": {"L1": -0.3549828489542786, "L2": 0.20926896031387612, "SR": 0.139972893582817, "SR2": 0.25737046637143596, "triple_var": -2.0976836779149206}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.016565090851979748, "L2": -0.16033304300083798, "SR": 0.4276500903728269, "SR2": 0.3841402332071317, "triple_var": 2.4319344148031923}, "real": {"L1": -0.33841775810229885, "L2": 0.04893591731303815, "SR": 0.5676229839556439, "SR2": 0.6415106995785677, "triple_var": 0.33425073688827156}, "rep": 33} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:68:{"delta": {"L1": -0.03672671507833021, "L2": -0.1306692443488035, "SR": 0.021493649833511985, "SR2": -0.021830185719234507, "triple_var": 0.25471868899407024}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.06290781622316335, "L2": -0.033030692613501875, "SR": 0.376222012794892, "SR2": 0.365048870153735, "triple_var": 3.5034490334665036}, "real": {"L1": 0.026181101144833147, "L2": -0.16369993696230536, "SR": 0.39771566262840397, "SR2": 0.3432186844345005, "triple_var": 3.758167722460574}, "rep": 33} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:69:{"delta": {"L1": -0.373276581573037, "L2": -0.10619097049918041, "SR": 0.21453271443755279, "SR2": 0.252242507038498, "triple_var": -2.141287795025724}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.10901171529995947, "L2": -0.06393658221090442, "SR": 0.4311856749928174, "SR2": 0.41090441013395146, "triple_var": 2.3625519142056537}, "real": {"L1": -0.26426486627307755, "L2": -0.17012755271008484, "SR": 0.6457183894303702, "SR2": 0.6631469171724494, "triple_var": 0.22126411917992944}, "rep": 34} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:70:{"delta": {"L1": 0.10175251129031634, "L2": -0.04659517614421403, "SR": 0.038282489008459264, "SR2": 0.0109804871393257, "triple_var": 0.004357505082493329}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.16245915289507182, "L2": 0.029166052354066694, "SR": 0.38111175742964487, "SR2": 0.4325266831678166, "triple_var": 1.6741371364292774}, "real": {"L1": -0.06070664160475547, "L2": -0.017429123790147338, "SR": 0.41939424643810413, "SR2": 0.4435071703071423, "triple_var": 1.6784946415117707}, "rep": 34} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:71:{"delta": {"L1": -0.31453329903936883, "L2": -0.20278638890510348, "SR": 0.20287422075751899, "SR2": 0.24925346595632014, "triple_var": -2.5014480238806125}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.07356591101495548, "L2": -0.021077325488755308, "SR": 0.41772259794338135, "SR2": 0.3937501612268065, "triple_var": 2.7591016642947666}, "real": {"L1": -0.24096738802441336, "L2": -0.22386371439385877, "SR": 0.6205968187009003, "SR2": 0.6430036271831266, "triple_var": 0.2576536404141539}, "rep": 35} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:72:{"delta": {"L1": -0.02919399219282255, "L2": 0.2713562945511156, "SR": -0.04402726733185258, "SR2": 0.053326444836210574, "triple_var": 0.4359735558911124}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.07535331392776078, "L2": -0.055724465423675915, "SR": 0.4277540909538895, "SR2": 0.37714183997915524, "triple_var": 2.327039213021283}, "real": {"L1": -0.10454730612058333, "L2": 0.21563182912743967, "SR": 0.3837268236220369, "SR2": 0.4304682848153658, "triple_var": 2.7630127689123953}, "rep": 35} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:73:{"delta": {"L1": -0.3941110888428943, "L2": 0.10429052252345042, "SR": 0.18964720857105494, "SR2": 0.23761478612605474, "triple_var": -2.0918427714951444}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.025158876601654494, "L2": -0.0626408281753733, "SR": 0.37957124330509945, "SR2": 0.3814137891857157, "triple_var": 2.3769741807465095}, "real": {"L1": -0.3689522122412398, "L2": 0.04164969434807713, "SR": 0.5692184518761544, "SR2": 0.6190285753117705, "triple_var": 0.28513140925136493}, "rep": 36} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:74:{"delta": {"L1": 0.2018568679075709, "L2": -0.04378073337185044, "SR": 0.004830695657441708, "SR2": -0.046022215020663115, "triple_var": 0.2793871667819845}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.04181149175763849, "L2": -0.07599051614247958, "SR": 0.4304513215019185, "SR2": 0.4377934580849072, "triple_var": 2.0819453720576693}, "real": {"L1": 0.16004537614993242, "L2": -0.11977124951433002, "SR": 0.4352820171593602, "SR2": 0.3917712430642441, "triple_var": 2.3613325388396538}, "rep": 36} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:75:{"delta": {"L1": -0.17045044121875766, "L2": 0.1367292695344592, "SR": 0.2675502073559584, "SR2": 0.2701176375138237, "triple_var": -1.6448071720000652}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.16954725635296838, "L2": -0.10760685281969784, "SR": 0.3229126380648513, "SR2": 0.3754804372330734, "triple_var": 1.9381961022718779}, "real": {"L1": -0.33999769757172604, "L2": 0.029122416714761366, "SR": 0.5904628454208097, "SR2": 0.6455980747468971, "triple_var": 0.2933889302718127}, "rep": 37} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:76:{"delta": {"L1": 0.044304473967372464, "L2": 0.02433988128592951, "SR": 0.04308227257001379, "SR2": 0.04881889037136228, "triple_var": 0.1278212506029499}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.04393593569007965, "L2": -0.15659312106737858, "SR": 0.35334541110083606, "SR2": 0.343717142174849, "triple_var": 2.170526088183686}, "real": {"L1": 0.0003685382772928183, "L2": -0.13225323978144907, "SR": 0.39642768367084985, "SR2": 0.3925360325462113, "triple_var": 2.2983473387866358}, "rep": 37} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:77:{"delta": {"L1": -0.46081449810146935, "L2": -0.06633561865263929, "SR": 0.20217419578102797, "SR2": 0.2636452718740372, "triple_var": -4.42505802486099}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.09027513073851903, "L2": 0.056047943324186576, "SR": 0.3437252416739804, "SR2": 0.32426563323508306, "triple_var": 4.734452026701891}, "real": {"L1": -0.37053936736295034, "L2": -0.010287675328452713, "SR": 0.5458994374550084, "SR2": 0.5879109051091203, "triple_var": 0.30939400184090105}, "rep": 38} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:78:{"delta": {"L1": 0.07117191187061667, "L2": 0.09176649147269081, "SR": 0.08985054382098795, "SR2": 0.036534578840655085, "triple_var": 0.16181881586084712}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.0053386687135418415, "L2": -0.04889293879351249, "SR": 0.3485795315800127, "SR2": 0.3776686540317434, "triple_var": 2.953812128290636}, "real": {"L1": 0.07651058058415851, "L2": 0.04287355267917831, "SR": 0.43843007540100065, "SR2": 0.41420323287239846, "triple_var": 3.115630944151483}, "rep": 38} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:79:{"delta": {"L1": -0.303636203012745, "L2": 0.07279169890478165, "SR": 0.27041256280752235, "SR2": 0.3074419082804816, "triple_var": -2.7256721808507454}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.0761645215326822, "L2": -0.1508425952014603, "SR": 0.35882039057823434, "SR2": 0.3253214936826602, "triple_var": 3.0046750606679136}, "real": {"L1": -0.22747168148006283, "L2": -0.07805089629667863, "SR": 0.6292329533857567, "SR2": 0.6327634019631418, "triple_var": 0.2790028798171681}, "rep": 39} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:80:{"delta": {"L1": -0.13170217659107425, "L2": -0.21978741217062292, "SR": 0.011094124956864904, "SR2": -0.010575266399053651, "triple_var": -0.7920934266582247}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.02405151131642883, "L2": 0.1973061110323065, "SR": 0.4009712510429969, "SR2": 0.39502693372416764, "triple_var": 2.702618203800053}, "real": {"L1": -0.15575368790750307, "L2": -0.022481301138316433, "SR": 0.4120653759998618, "SR2": 0.384451667325114, "triple_var": 1.9105247771418283}, "rep": 39} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:81:{"delta": {"L1": -0.301685210349732, "L2": -0.1688018972203921, "SR": 0.2071754187857977, "SR2": 0.21058829637561022, "triple_var": -2.545868118980895}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.0989921151414019, "L2": 0.029030652089398916, "SR": 0.34089114416027866, "SR2": 0.3945278644759774, "triple_var": 2.8101072667019644}, "real": {"L1": -0.4006773254911339, "L2": -0.13977124513099318, "SR": 0.5480665629460764, "SR2": 0.6051161608515876, "triple_var": 0.26423914772106927}, "rep": 40} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:82:{"delta": {"L1": -0.40514407837225375, "L2": -0.09992788452706587, "SR": -0.08076184781846302, "SR2": -0.009157751275633286, "triple_var": -4.2444993605119965}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.3504376785954113, "L2": 0.015144178335049096, "SR": 0.4338699642854382, "SR2": 0.3667023810414071, "triple_var": 7.126837870455886}, "real": {"L1": -0.05470639977684241, "L2": -0.08478370619201678, "SR": 0.3531081164669752, "SR2": 0.35754462976577384, "triple_var": 2.882338509943889}, "rep": 40} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:83:{"delta": {"L1": -0.3204665663904618, "L2": -0.1290008370596131, "SR": 0.2810955191513404, "SR2": 0.32943639259937807, "triple_var": -3.3684345685001515}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.09951729145620514, "L2": -0.01772518086791644, "SR": 0.3357992360950974, "SR2": 0.31572821860266265, "triple_var": 3.7099117719722483}, "real": {"L1": -0.22094927493425665, "L2": -0.14672601792752954, "SR": 0.6168947552464378, "SR2": 0.6451646112020407, "triple_var": 0.3414772034720968}, "rep": 41} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:84:{"delta": {"L1": -0.156998537344593, "L2": -0.1438792655541572, "SR": -0.06688709317061331, "SR2": -0.04696012477379202, "triple_var": -0.4348610023506718}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.045591392415589425, "L2": 0.014496922502020069, "SR": 0.4304516637619736, "SR2": 0.42886837537827915, "triple_var": 2.81682858353788}, "real": {"L1": -0.11140714492900355, "L2": -0.12938234305213714, "SR": 0.36356457059136027, "SR2": 0.38190825060448713, "triple_var": 2.381967581187208}, "rep": 41} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:85:{"delta": {"L1": -0.4943185140736806, "L2": 0.25444536814413554, "SR": 0.15826958966663596, "SR2": 0.313271307288969, "triple_var": -2.6757306045947225}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.06837216711458967, "L2": -0.13249522260355717, "SR": 0.42679905679460783, "SR2": 0.3436952321410196, "triple_var": 2.929534183578193}, "real": {"L1": -0.42594634695909095, "L2": 0.12195014554057837, "SR": 0.5850686464612438, "SR2": 0.6569665394299886, "triple_var": 0.2538035789834707}, "rep": 42} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:86:{"delta": {"L1": -0.2515573544988589, "L2": -0.05126763046051398, "SR": -0.020253821568652464, "SR2": -0.04434967237443849, "triple_var": -1.7837215422135806}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.2546355801270219, "L2": -0.09637233140006805, "SR": 0.40851262267782157, "SR2": 0.3946720142004407, "triple_var": 4.2247183748030395}, "real": {"L1": 0.0030782256281630125, "L2": -0.14763996186058204, "SR": 0.3882588011091691, "SR2": 0.3503223418260022, "triple_var": 2.440996832589459}, "rep": 42} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:87:{"delta": {"L1": -0.30201038876422615, "L2": -0.024894100354554888, "SR": 0.23205888415087583, "SR2": 0.2799194343355945, "triple_var": -2.1772827926160767}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.005506589605597368, "L2": -0.01405603905127765, "SR": 0.3926840473524206, "SR2": 0.3785079540347185, "triple_var": 2.4335605690097406}, "real": {"L1": -0.2965037991586288, "L2": -0.038950139405832536, "SR": 0.6247429315032964, "SR2": 0.658427388370313, "triple_var": 0.25627777639366406}, "rep": 43} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:88:{"delta": {"L1": -0.0811399430495512, "L2": -0.15348091971971786, "SR": -0.02343688473560468, "SR2": -0.05661426938394204, "triple_var": -0.4902918889805252}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.041126262289474524, "L2": 0.004516563423772298, "SR": 0.4273374788162464, "SR2": 0.43727691245401007, "triple_var": 2.080941645900604}, "real": {"L1": -0.12226620533902573, "L2": -0.14896435629594557, "SR": 0.4039005940806417, "SR2": 0.38066264307006803, "triple_var": 1.5906497569200786}, "rep": 43} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:89:{"delta": {"L1": -0.28089464709703676, "L2": -0.1636360139802868, "SR": 0.22202281627506304, "SR2": 0.222423328427722, "triple_var": -3.1653176588571306}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.12000940677480228, "L2": 0.10277975974893874, "SR": 0.33199127510682835, "SR2": 0.374649488128366, "triple_var": 3.447395815143326}, "real": {"L1": -0.40090405387183903, "L2": -0.06085625423134806, "SR": 0.5540140913818914, "SR2": 0.597072816556088, "triple_var": 0.2820781562861953}, "rep": 44} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:90:{"delta": {"L1": -0.022987372450545163, "L2": -0.11811392700279283, "SR": 0.031132826572632777, "SR2": 0.028327410504663864, "triple_var": -0.9190859525189841}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.21373162297779666, "L2": 0.17714957597974393, "SR": 0.35798655244127237, "SR2": 0.3933101508938274, "triple_var": 2.863364611998863}, "real": {"L1": -0.23671899542834182, "L2": 0.0590356489769511, "SR": 0.38911937901390514, "SR2": 0.4216375613984913, "triple_var": 1.9442786594798789}, "rep": 44} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:91:{"delta": {"L1": -0.37751276340544876, "L2": 0.017563986874327517, "SR": 0.21691971214376676, "SR2": 0.2783280928300197, "triple_var": -4.239584696379761}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.026652688658076757, "L2": 0.07016362056530895, "SR": 0.3846886709802659, "SR2": 0.3667104983281233, "triple_var": 4.547485577847878}, "real": {"L1": -0.350860074747372, "L2": 0.08772760743963647, "SR": 0.6016083831240326, "SR2": 0.645038591158143, "triple_var": 0.30790088146811745}, "rep": 45} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:92:{"delta": {"L1": -0.07990382862828524, "L2": -0.11691366689498561, "SR": 0.09102025683674447, "SR2": 0.004795281617753555, "triple_var": -1.2663089538133625}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.007789508596456683, "L2": 0.041447901206336284, "SR": 0.3595339875351764, "SR2": 0.4017123220869489, "triple_var": 3.769650325169138}, "real": {"L1": -0.08769333722474193, "L2": -0.07546576568864934, "SR": 0.4505542443719209, "SR2": 0.40650760370470246, "triple_var": 2.5033413713557753}, "rep": 45} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:93:{"delta": {"L1": -0.005502615058761234, "L2": -0.20964229544117985, "SR": 0.19108561769409887, "SR2": 0.18836828876431277, "triple_var": -1.2042211224157116}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.18475230143510463, "L2": -0.021483427977182678, "SR": 0.409986201540898, "SR2": 0.4304292170797934, "triple_var": 1.4994493677391316}, "real": {"L1": -0.19025491649386586, "L2": -0.23112572341836252, "SR": 0.6010718192349969, "SR2": 0.6187975058441062, "triple_var": 0.2952282453234201}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:94:{"delta": {"L1": -0.07450240894339079, "L2": 0.04907422800819881, "SR": -0.010533039278517231, "SR2": 0.025222171495314805, "triple_var": 0.27108481129216866}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.006476194381357551, "L2": 0.0015336883335812417, "SR": 0.35467127613719457, "SR2": 0.3472141197857571, "triple_var": 3.1264836195715096}, "real": {"L1": -0.06802621456203324, "L2": 0.05060791634178005, "SR": 0.34413823685867734, "SR2": 0.3724362912810719, "triple_var": 3.397568430863678}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:95:{"delta": {"L1": -0.23732396124508084, "L2": -0.048724891611702605, "SR": 0.21983953370655906, "SR2": 0.2180648472263486, "triple_var": -2.4136808705570783}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.05227298721171267, "L2": -0.04704012418906912, "SR": 0.3404280369846001, "SR2": 0.3963092958753715, "triple_var": 2.7831740726364407}, "real": {"L1": -0.2895969484567935, "L2": -0.09576501580077172, "SR": 0.5602675706911592, "SR2": 0.6143741431017201, "triple_var": 0.3694932020793626}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:96:{"delta": {"L1": -0.019173611926309647, "L2": 0.0627957498380168, "SR": 0.019222328961625956, "SR2": 0.029942607072925564, "triple_var": 0.23197319137584094}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.08130635396661383, "L2": -0.03730905243669257, "SR": 0.38973157763096217, "SR2": 0.40877372413569807, "triple_var": 2.202129695767103}, "real": {"L1": -0.10047996589292348, "L2": 0.025486697401324233, "SR": 0.4089539065925881, "SR2": 0.43871633120862363, "triple_var": 2.4341028871429438}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:97:{"delta": {"L1": -0.3607340649461195, "L2": -0.20273873422414476, "SR": 0.21061130877882228, "SR2": 0.2551977512873463, "triple_var": -2.902034562814181}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.004473806755297658, "L2": 0.1492914146546831, "SR": 0.36037977236925134, "SR2": 0.3697885905961948, "triple_var": 3.161955477244981}, "real": {"L1": -0.3562602581908218, "L2": -0.053447319569461665, "SR": 0.5709910811480736, "SR2": 0.6249863418835411, "triple_var": 0.2599209144308003}, "rep": 48} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:98:{"delta": {"L1": 0.14138993628072516, "L2": -0.06318040579170144, "SR": 0.08100551732673594, "SR2": 0.018296374311691066, "triple_var": 0.2033721439240712}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.0011865782075848456, "L2": 0.011238897396714052, "SR": 0.37444941143394816, "SR2": 0.3894843960008779, "triple_var": 2.3953825408068785}, "real": {"L1": 0.14020335807314033, "L2": -0.05194150839498739, "SR": 0.4554549287606841, "SR2": 0.407780770312569, "triple_var": 2.5987546847309497}, "rep": 48} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:99:{"delta": {"L1": -0.19209793399003428, "L2": -0.0678745661567119, "SR": 0.23629365878742287, "SR2": 0.25458260373624203, "triple_var": -1.8960550024629614}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.12231429999122602, "L2": 0.06729673069230623, "SR": 0.3519284304091392, "SR2": 0.3701606450629477, "triple_var": 2.212812785341753}, "real": {"L1": -0.3144122339812603, "L2": -0.0005778354644056732, "SR": 0.588222089196562, "SR2": 0.6247432487991897, "triple_var": 0.3167577828787916}, "rep": 49} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:100:{"delta": {"L1": 0.07761701788503325, "L2": -0.36746608171057593, "SR": -0.0611121832019228, "SR2": -0.07391677491531573, "triple_var": 0.07423314763733524}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.13633923338760265, "L2": 0.18105728099008078, "SR": 0.37905661558934295, "SR2": 0.38789571263171596, "triple_var": 2.2527812151624373}, "real": {"L1": -0.05872221550256939, "L2": -0.18640880072049512, "SR": 0.31794443238742015, "SR2": 0.31397893771640023, "triple_var": 2.3270143627997726}, "rep": 49} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:101:{"delta": {"L1": -0.35470944718813485, "L2": 0.022369823152329438, "SR": 0.135697609126063, "SR2": 0.17812870092673633, "triple_var": -1.9007739914451234}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.10691179502916946, "L2": -0.10768738211781537, "SR": 0.4351275253522512, "SR2": 0.4133819544203204, "triple_var": 2.243964026031702}, "real": {"L1": -0.2477976521589654, "L2": -0.08531755896548593, "SR": 0.5708251344783142, "SR2": 0.5915106553470567, "triple_var": 0.3431900345865787}, "rep": 50} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:102:{"delta": {"L1": 0.1195224474181964, "L2": 0.09205767314458338, "SR": 0.08573637936828815, "SR2": 0.009472190027374705, "triple_var": 0.3023316575902326}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.13134894387137042, "L2": -0.14636228435778226, "SR": 0.3646659849935651, "SR2": 0.40966120780566234, "triple_var": 1.7440142995070826}, "real": {"L1": -0.01182649645317402, "L2": -0.05430461121319888, "SR": 0.45040236436185327, "SR2": 0.41913339783303705, "triple_var": 2.0463459570973153}, "rep": 50} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:103:{"delta": {"L1": -0.2994205673930695, "L2": 0.09440700700082245, "SR": 0.23232418941970234, "SR2": 0.28363086180503677, "triple_var": -2.2217007899687653}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.04104955782827908, "L2": -0.1606037414173249, "SR": 0.3750772214109257, "SR2": 0.35232306945458897, "triple_var": 2.4670551616649887}, "real": {"L1": -0.34047012522134856, "L2": -0.06619673441650244, "SR": 0.607401410830628, "SR2": 0.6359539312596257, "triple_var": 0.2453543716962236}, "rep": 51} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:104:{"delta": {"L1": -0.045542601748037995, "L2": 0.13341737439329004, "SR": -0.02105346611591985, "SR2": 0.0018737940041453904, "triple_var": 0.20699443922784155}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.061993498402521593, "L2": -0.04717426011462966, "SR": 0.3796181331875852, "SR2": 0.41481565194552533, "triple_var": 2.0464714050472375}, "real": {"L1": -0.10753610015055959, "L2": 0.0862431142786604, "SR": 0.35856466707166534, "SR2": 0.4166894459496707, "triple_var": 2.253465844275079}, "rep": 51} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:105:{"delta": {"L1": -0.19361823551115115, "L2": 0.04500837058680249, "SR": 0.26732896986730564, "SR2": 0.3087934944668731, "triple_var": -3.6578332690946986}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.11871399283480256, "L2": -0.028176064639350226, "SR": 0.37464490054624383, "SR2": 0.37017690822355087, "triple_var": 3.9455549015530416}, "real": {"L1": -0.3123322283459537, "L2": 0.016832305947452265, "SR": 0.6419738704135495, "SR2": 0.678970402690424, "triple_var": 0.2877216324583431}, "rep": 52} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:106:{"delta": {"L1": 0.1989150838383536, "L2": -0.01476245503587384, "SR": 0.050873765857768094, "SR2": 0.00799501373977285, "triple_var": 1.0498865554806474}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.12953124248653364, "L2": -0.05344957894357631, "SR": 0.3781284379678733, "SR2": 0.39694046816824813, "triple_var": 1.8243253850616237}, "real": {"L1": 0.06938384135181996, "L2": -0.06821203397945015, "SR": 0.4290022038256414, "SR2": 0.404935481908021, "triple_var": 2.874211940542271}, "rep": 52} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:107:{"delta": {"L1": -0.2041657778150221, "L2": -0.08245126906421942, "SR": 0.2261124069271755, "SR2": 0.20156255102340725, "triple_var": -2.3925688944527903}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.09253786933301945, "L2": -0.08880076190160947, "SR": 0.35462567821990854, "SR2": 0.3612953892700511, "triple_var": 2.694175523642779}, "real": {"L1": -0.29670364714804154, "L2": -0.1712520309658289, "SR": 0.580738085147084, "SR2": 0.5628579402934584, "triple_var": 0.3016066291899886}, "rep": 53} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:108:{"delta": {"L1": 0.22255180205454267, "L2": 0.07009781654410992, "SR": 0.03265523563585565, "SR2": 0.0646843296630144, "triple_var": -0.13710294152100788}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.0910539217128623, "L2": -0.0573742656256208, "SR": 0.40068015150652503, "SR2": 0.38359697983547425, "triple_var": 2.978435146471562}, "real": {"L1": 0.13149788034168036, "L2": 0.012723550918489116, "SR": 0.4333353871423807, "SR2": 0.44828130949848866, "triple_var": 2.8413322049505543}, "rep": 53} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:109:{"delta": {"L1": -0.3465607033562599, "L2": -0.26023665930484935, "SR": 0.12659894739235306, "SR2": 0.20737753946954518, "triple_var": -3.3462201116971286}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05966174656322095, "L2": 0.11653688292189433, "SR": 0.44644692803463487, "SR2": 0.3873417931938211, "triple_var": 3.678017542419871}, "real": {"L1": -0.28689895679303895, "L2": -0.14369977638295503, "SR": 0.5730458754269879, "SR2": 0.5947193326633663, "triple_var": 0.3317974307227421}, "rep": 54} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:110:{"delta": {"L1": 0.3095532470374211, "L2": 0.1005538308070265, "SR": -0.008906294222499722, "SR2": -0.05097718842541904, "triple_var": 1.88301071062917}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.15147905334248396, "L2": -0.2009448308633496, "SR": 0.3715420759896638, "SR2": 0.3863275226369557, "triple_var": 1.724880729329126}, "real": {"L1": 0.15807419369493714, "L2": -0.10039100005632311, "SR": 0.3626357817671641, "SR2": 0.33535033421153665, "triple_var": 3.607891439958296}, "rep": 54} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:111:{"delta": {"L1": -0.3356639349848592, "L2": -0.12488954764026913, "SR": 0.1942172473377365, "SR2": 0.196561440723024, "triple_var": -2.2393740318743562}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.09922627365757648, "L2": 0.06725538515099265, "SR": 0.40778326546794996, "SR2": 0.40299901253473963, "triple_var": 2.617646011848986}, "real": {"L1": -0.23643766132728272, "L2": -0.05763416248927649, "SR": 0.6020005128056864, "SR2": 0.5995604532577636, "triple_var": 0.37827197997462947}, "rep": 55} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:112:{"delta": {"L1": -0.031718586809615146, "L2": -0.19623183102025704, "SR": -0.019232970498632707, "SR2": 0.009672313657182074, "triple_var": -0.19252655214442704}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.16658285200185466, "L2": 0.17474886561583786, "SR": 0.3790668532341758, "SR2": 0.40642457777005103, "triple_var": 2.8145819030707817}, "real": {"L1": -0.1983014388114698, "L2": -0.021482965404419175, "SR": 0.3598338827355431, "SR2": 0.4160968914272331, "triple_var": 2.6220553509263547}, "rep": 55} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:113:{"delta": {"L1": -0.20405774879267274, "L2": 0.008160717497436451, "SR": 0.25014895228045214, "SR2": 0.27090906047992486, "triple_var": -3.2417134519265356}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.0014099100043384405, "L2": -0.11844744390391763, "SR": 0.33495557429138084, "SR2": 0.358898721793318, "triple_var": 3.589884038199473}, "real": {"L1": -0.20546765879701118, "L2": -0.11028672640648118, "SR": 0.585104526571833, "SR2": 0.6298077822732429, "triple_var": 0.34817058627293745}, "rep": 56} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:114:{"delta": {"L1": 0.08399612127475298, "L2": -0.03109839539610585, "SR": 0.06420565036259407, "SR2": 0.0040043653685497005, "triple_var": -1.5545809044763363}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.025125186145375573, "L2": 0.040457920675077634, "SR": 0.35876653985095625, "SR2": 0.38206989521786633, "triple_var": 3.499600051610516}, "real": {"L1": 0.05887093512937741, "L2": 0.009359525278971784, "SR": 0.4229721902135503, "SR2": 0.38607426058641603, "triple_var": 1.9450191471341796}, "rep": 56} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:115:{"delta": {"L1": -0.3957279191590152, "L2": -0.25755253222246777, "SR": 0.2309464437463572, "SR2": 0.2316224693936944, "triple_var": -3.3244743063110516}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.10941231719636683, "L2": 0.06637321766710008, "SR": 0.3816256261140104, "SR2": 0.363147370287696, "triple_var": 3.583319704724253}, "real": {"L1": -0.2863156019626483, "L2": -0.19117931455536769, "SR": 0.6125720698603676, "SR2": 0.5947698396813904, "triple_var": 0.2588453984132014}, "rep": 57} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:116:{"delta": {"L1": -0.10912541726699396, "L2": -0.2442672284622055, "SR": -0.054415824501860166, "SR2": -0.037033795688584836, "triple_var": -0.21520038467280544}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.03359130745429179, "L2": 0.23544417432809467, "SR": 0.4316971185177992, "SR2": 0.44877696331223665, "triple_var": 2.2458771793182155}, "real": {"L1": -0.07553410981270216, "L2": -0.008823054134110818, "SR": 0.377281294015939, "SR2": 0.4117431676236518, "triple_var": 2.03067679464541}, "rep": 57} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:117:{"delta": {"L1": -0.47254794849951287, "L2": -0.045789234568165825, "SR": 0.2008184341125565, "SR2": 0.21678338231035427, "triple_var": -3.542619033645352}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.14669495997114337, "L2": 0.009304243328020108, "SR": 0.3787952628884954, "SR2": 0.38366272644362104, "triple_var": 3.8527700152709845}, "real": {"L1": -0.3258529885283695, "L2": -0.03648499124014572, "SR": 0.5796136970010519, "SR2": 0.6004461087539753, "triple_var": 0.3101509816256323}, "rep": 58} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:118:{"delta": {"L1": 0.010329309349063495, "L2": -0.19254551512719292, "SR": 0.04237610523861568, "SR2": 0.06482255995902525, "triple_var": -1.5021762032446846}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.11719028981368944, "L2": 0.14373449405681746, "SR": 0.4027885525133941, "SR2": 0.38130041893152583, "triple_var": 3.4546975155458406}, "real": {"L1": -0.10686098046462594, "L2": -0.04881102107037545, "SR": 0.4451646577520098, "SR2": 0.4461229788905511, "triple_var": 1.952521312301156}, "rep": 58} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:119:{"delta": {"L1": -0.2727920622814283, "L2": 0.025450634098196354, "SR": 0.2440166128552772, "SR2": 0.33727844411369434, "triple_var": -2.531190945147516}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.04087294292268684, "L2": -0.06996722069164557, "SR": 0.3687262826563281, "SR2": 0.2837545414655548, "triple_var": 2.919804168159068}, "real": {"L1": -0.2319191193587415, "L2": -0.04451658659344922, "SR": 0.6127428955116053, "SR2": 0.6210329855792491, "triple_var": 0.38861322301155227}, "rep": 59} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:120:{"delta": {"L1": 0.0061251302631394056, "L2": -0.15018084881568364, "SR": 0.08204804140905697, "SR2": 0.0077563117827092465, "triple_var": -0.5528626101541185}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.16521125908573622, "L2": 0.04074121503210827, "SR": 0.3506895625476799, "SR2": 0.38300586723303687, "triple_var": 2.215052785223131}, "real": {"L1": -0.15908612882259682, "L2": -0.10943963378357537, "SR": 0.4327376039567369, "SR2": 0.3907621790157461, "triple_var": 1.6621901750690125}, "rep": 59} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:121:{"delta": {"L1": -0.31675549820448384, "L2": 0.06823370042564372, "SR": 0.20074711890249808, "SR2": 0.28514089066178105, "triple_var": -2.687318664624211}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.04066019219188822, "L2": -0.05463417903273232, "SR": 0.37264016324834426, "SR2": 0.34248093236175325, "triple_var": 2.990654885038041}, "real": {"L1": -0.35741569039637205, "L2": 0.013599521392911394, "SR": 0.5733872821508423, "SR2": 0.6276218230235343, "triple_var": 0.3033362204138299}, "rep": 60} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:122:{"delta": {"L1": -0.04873565784891709, "L2": 0.09073456294878429, "SR": 0.011437898909320454, "SR2": -0.03355869013529622, "triple_var": -0.3367227147927856}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.15809633209032298, "L2": -0.04061557202075974, "SR": 0.3655854214592131, "SR2": 0.41667360281828997, "triple_var": 2.193606581751487}, "real": {"L1": -0.20683198993924007, "L2": 0.05011899092802456, "SR": 0.37702332036853353, "SR2": 0.38311491268299375, "triple_var": 1.8568838669587013}, "rep": 60} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:123:{"delta": {"L1": -0.34039531245719107, "L2": 0.03687316568646379, "SR": 0.22989460801180428, "SR2": 0.27417391527582785, "triple_var": -3.364139692506231}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.03561609892267786, "L2": -0.04193139567027038, "SR": 0.35226101705841706, "SR2": 0.3641573840872679, "triple_var": 3.6415014970855704}, "real": {"L1": -0.37601141137986893, "L2": -0.005058229983806592, "SR": 0.5821556250702213, "SR2": 0.6383312993630957, "triple_var": 0.27736180457933973}, "rep": 61} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:124:{"delta": {"L1": 0.018690749175233112, "L2": -0.13331272365380195, "SR": 0.05306110038464851, "SR2": -0.013475549827998923, "triple_var": -1.562811680197473}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.08643247743327297, "L2": -0.03815524869050015, "SR": 0.34644276566473725, "SR2": 0.40443900334827493, "triple_var": 3.184735870733029}, "real": {"L1": -0.06774172825803985, "L2": -0.1714679723443021, "SR": 0.39950386604938576, "SR2": 0.390963453520276, "triple_var": 1.6219241905355561}, "rep": 61} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:125:{"delta": {"L1": -0.3438562302773542, "L2": -0.25680755080121154, "SR": 0.21787550953645513, "SR2": 0.15367324907660668, "triple_var": -2.421450957578272}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.06148430597491566, "L2": 0.037494850530208214, "SR": 0.3723709099968027, "SR2": 0.4141567138326015, "triple_var": 2.7485381682282712}, "real": {"L1": -0.2823719243024385, "L2": -0.2193127002710033, "SR": 0.5902464195332578, "SR2": 0.5678299629092082, "triple_var": 0.32708721064999924}, "rep": 62} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:126:{"delta": {"L1": 0.04581856501138949, "L2": -0.09707079103067973, "SR": 0.06106270295899213, "SR2": 0.07793972783297465, "triple_var": -0.3659785113004377}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.0035620396826025586, "L2": 0.06792785762629792, "SR": 0.3978501358834061, "SR2": 0.35931002304375653, "triple_var": 3.0288637139963113}, "real": {"L1": 0.04225652532878693, "L2": -0.02914293340438181, "SR": 0.45891283884239825, "SR2": 0.4372497508767312, "triple_var": 2.6628852026958736}, "rep": 62} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:127:{"delta": {"L1": -0.18612918293303696, "L2": -0.15561471578921326, "SR": 0.2491158949632113, "SR2": 0.21745560152068866, "triple_var": -2.1103101399792346}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.1429694394376995, "L2": 0.07757781072452151, "SR": 0.37198305839431445, "SR2": 0.42068345961728537, "triple_var": 2.3685481281885963}, "real": {"L1": -0.32909862237073645, "L2": -0.07803690506469177, "SR": 0.6210989533575257, "SR2": 0.638139061137974, "triple_var": 0.25823798820936195}, "rep": 63} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:128:{"delta": {"L1": -0.04727029317401475, "L2": -0.11452911088921215, "SR": -0.017543592690840826, "SR2": -0.08530246767643346, "triple_var": -0.41847589856091316}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.11361907565130701, "L2": 0.14956949588063062, "SR": 0.4046791706866348, "SR2": 0.45608231819024575, "triple_var": 2.513501833708221}, "real": {"L1": -0.16088936882532176, "L2": 0.03504038499141847, "SR": 0.38713557799579396, "SR2": 0.3707798505138123, "triple_var": 2.0950259351473077}, "rep": 63} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:129:{"delta": {"L1": -0.21186175836549087, "L2": -0.037640807754260236, "SR": 0.17942490113015996, "SR2": 0.1937752116708482, "triple_var": -2.190910579753746}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.1139285471215527, "L2": 0.14804780041234308, "SR": 0.35825911633541196, "SR2": 0.40119764999733054, "triple_var": 2.706272991454871}, "real": {"L1": -0.3257903054870436, "L2": 0.11040699265808285, "SR": 0.5376840174655719, "SR2": 0.5949728616681788, "triple_var": 0.5153624117011248}, "rep": 64} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:130:{"delta": {"L1": -0.24054932514361724, "L2": -0.05008243352559799, "SR": 0.011547242626241072, "SR2": 0.08869809677250651, "triple_var": -2.1172218192995147}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.11790873497512469, "L2": 0.13348826853533763, "SR": 0.3890904025729381, "SR2": 0.35710077868557205, "triple_var": 4.179525238036966}, "real": {"L1": -0.12264059016849255, "L2": 0.08340583500973964, "SR": 0.40063764519917916, "SR2": 0.44579887545807856, "triple_var": 2.0623034187374514}, "rep": 64} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:131:{"delta": {"L1": -0.4835961894628295, "L2": 0.08169870291730841, "SR": 0.18874608929070064, "SR2": 0.29629673172794296, "triple_var": -3.716648486876828}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.04586090901177795, "L2": -0.05855371625717181, "SR": 0.35071677733498247, "SR2": 0.3308459580220146, "triple_var": 4.008584717558834}, "real": {"L1": -0.43773528045105153, "L2": 0.023144986660136604, "SR": 0.5394628666256831, "SR2": 0.6271426897499576, "triple_var": 0.29193623068200575}, "rep": 65} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:132:{"delta": {"L1": -0.21785187182045387, "L2": 0.20555827931620524, "SR": -0.01652535691774737, "SR2": 0.06300036278422438, "triple_var": -0.3954552721259512}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.04310261318712585, "L2": -0.08875350550879606, "SR": 0.3911947199007047, "SR2": 0.37411719479316763, "triple_var": 2.563767948780915}, "real": {"L1": -0.17474925863332802, "L2": 0.11680477380740917, "SR": 0.37466936298295733, "SR2": 0.437117557577392, "triple_var": 2.168312676654964}, "rep": 65} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:133:{"delta": {"L1": -0.35699602728597535, "L2": 0.005381978854015312, "SR": 0.19888695044082538, "SR2": 0.2809304754430508, "triple_var": -2.5627338284939603}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05645331390960215, "L2": -0.14759024330879147, "SR": 0.40886411475007145, "SR2": 0.33361628070284627, "triple_var": 2.80232884488955}, "real": {"L1": -0.3005427133763732, "L2": -0.14220826445477616, "SR": 0.6077510651908968, "SR2": 0.614546756145897, "triple_var": 0.23959501639558972}, "rep": 66} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:134:{"delta": {"L1": -0.06876169046251153, "L2": 0.031205558702607772, "SR": -0.01802394608609681, "SR2": 0.008769980951575607, "triple_var": -0.8433083757448714}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.04686891738986197, "L2": -0.06465074016120434, "SR": 0.3828079403984311, "SR2": 0.3858895033262106, "triple_var": 2.74890598443571}, "real": {"L1": -0.1156306078523735, "L2": -0.03344518145859657, "SR": 0.36478399431233427, "SR2": 0.3946594842777862, "triple_var": 1.9055976086908386}, "rep": 66} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:135:{"delta": {"L1": -0.2536065661910163, "L2": 0.08773083363285701, "SR": 0.17402298556747725, "SR2": 0.23885354066718711, "triple_var": -1.2978520468399541}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.06851004933854496, "L2": -0.14900152280662604, "SR": 0.4163200797063101, "SR2": 0.3627355071240175, "triple_var": 1.6143201262145024}, "real": {"L1": -0.3221166155295613, "L2": -0.06127068917376903, "SR": 0.5903430652737873, "SR2": 0.6015890477912046, "triple_var": 0.3164680793745483}, "rep": 67} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:136:{"delta": {"L1": -0.05796631856511841, "L2": -0.10315247809057435, "SR": -0.02222900468496075, "SR2": -0.01662133567555263, "triple_var": -0.5696002051487699}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.02493371393994751, "L2": -0.04438371090615965, "SR": 0.41625083124145607, "SR2": 0.39088770920474275, "triple_var": 2.3888627623707466}, "real": {"L1": -0.03303260462517091, "L2": -0.14753618899673399, "SR": 0.3940218265564953, "SR2": 0.3742663735291901, "triple_var": 1.8192625572219767}, "rep": 67} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:137:{"delta": {"L1": -0.31615396639257354, "L2": 0.13459538794520062, "SR": 0.24694242013325818, "SR2": 0.37351297779285386, "triple_var": -2.68301988168611}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.05117465281331708, "L2": -0.1946535603535776, "SR": 0.36457290850629964, "SR2": 0.2888127189149966, "triple_var": 2.875613099793244}, "real": {"L1": -0.3673286192058906, "L2": -0.06005817240837696, "SR": 0.6115153286395578, "SR2": 0.6623256967078505, "triple_var": 0.19259321810713378}, "rep": 68} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:138:{"delta": {"L1": 0.17143147234946832, "L2": 0.0325247626542788, "SR": 0.10947828331752923, "SR2": 0.009777144440070551, "triple_var": -0.3866028517744473}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.11852758867740101, "L2": -0.13184127650951108, "SR": 0.3068350418426274, "SR2": 0.3578316784807135, "triple_var": 2.8299839201811516}, "real": {"L1": 0.05290388367206729, "L2": -0.09931651385523228, "SR": 0.4163133251601566, "SR2": 0.36760882292078406, "triple_var": 2.4433810684067043}, "rep": 68} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:139:{"delta": {"L1": -0.22285136312695328, "L2": -0.009363650011016078, "SR": 0.08076854957867857, "SR2": 0.19708298240300492, "triple_var": -1.614384850652888}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.035488941424547095, "L2": -0.16576663820427956, "SR": 0.4938335223423362, "SR2": 0.406021806133225, "triple_var": 1.951655701748632}, "real": {"L1": -0.25834030455150037, "L2": -0.17513028821529564, "SR": 0.5746020719210148, "SR2": 0.60310478853623, "triple_var": 0.3372708510957441}, "rep": 69} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:140:{"delta": {"L1": 0.026610111656898968, "L2": 0.028690939058592296, "SR": -0.018127409940936867, "SR2": 0.0073142066065939315, "triple_var": 0.3533200905082716}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.05605610362322261, "L2": -0.14080537119882613, "SR": 0.4298354591324608, "SR2": 0.37654475951517435, "triple_var": 2.127343300090471}, "real": {"L1": -0.02944599196632364, "L2": -0.11211443214023384, "SR": 0.41170804919152393, "SR2": 0.3838589661217683, "triple_var": 2.4806633905987425}, "rep": 69} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:141:{"delta": {"L1": -0.20928133606413743, "L2": -0.23116228356900978, "SR": 0.15373641894027307, "SR2": 0.16071025483156248, "triple_var": -1.6398717129886655}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.09388669516286305, "L2": 0.0012683008926838994, "SR": 0.41948933805696803, "SR2": 0.4099453416328061, "triple_var": 1.9207426604428932}, "real": {"L1": -0.3031680312270005, "L2": -0.22989398267632588, "SR": 0.5732257569972411, "SR2": 0.5706555964643686, "triple_var": 0.28087094745422775}, "rep": 70} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:142:{"delta": {"L1": -0.0826385530992809, "L2": 0.10301386074210049, "SR": 0.015478861311762127, "SR2": 0.03231049662504959, "triple_var": 0.5831440167020281}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.0029600888041480815, "L2": -0.11358845838039029, "SR": 0.3865915489560602, "SR2": 0.36268596787534374, "triple_var": 2.1439284272807426}, "real": {"L1": -0.07967846429513283, "L2": -0.010574597638289803, "SR": 0.4020704102678223, "SR2": 0.39499646450039333, "triple_var": 2.7270724439827707}, "rep": 70} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:143:{"delta": {"L1": -0.1559897624228725, "L2": -0.16187609471735365, "SR": 0.23887049841279412, "SR2": 0.26546006994293325, "triple_var": -2.7784347424998606}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.08136905536274483, "L2": 0.09758381960733747, "SR": 0.36778848875970366, "SR2": 0.33878366846983043, "triple_var": 3.1233966221558105}, "real": {"L1": -0.23735881778561732, "L2": -0.06429227511001619, "SR": 0.6066589871724978, "SR2": 0.6042437384127637, "triple_var": 0.3449618796559499}, "rep": 71} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:144:{"delta": {"L1": 0.10624090660554002, "L2": 0.09872746987184702, "SR": -0.003310083662799057, "SR2": -0.05323955085722115, "triple_var": 0.7373580187057915}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.1072260993840257, "L2": -0.11869388204252435, "SR": 0.38715187152393765, "SR2": 0.4263787261719157, "triple_var": 2.008514926512272}, "real": {"L1": -0.0009851927784856845, "L2": -0.01996641217067733, "SR": 0.3838417878611386, "SR2": 0.37313917531469454, "triple_var": 2.7458729452180637}, "rep": 71} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:145:{"delta": {"L1": -0.3410069842631014, "L2": -0.007150757330257369, "SR": 0.08591363512842515, "SR2": 0.16914577355723054, "triple_var": -1.5819839997501706}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.046158352007533876, "L2": -0.013765090950149732, "SR": 0.48398860017763196, "SR2": 0.45461477882333073, "triple_var": 1.9319165446792104}, "real": {"L1": -0.2948486322555675, "L2": -0.0209158482804071, "SR": 0.5699022353060571, "SR2": 0.6237605523805613, "triple_var": 0.34993254492903975}, "rep": 72} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:146:{"delta": {"L1": 0.14780942114974377, "L2": 0.10808059064602499, "SR": 0.08788116313924793, "SR2": 0.004433377022318941, "triple_var": -0.4569662478601768}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.06333256081755595, "L2": -0.10243407183808374, "SR": 0.3378305828685871, "SR2": 0.3898848821273743, "triple_var": 3.212507054284747}, "real": {"L1": 0.08447686033218783, "L2": 0.00564651880794125, "SR": 0.425711746007835, "SR2": 0.39431825914969326, "triple_var": 2.75554080642457}, "rep": 72} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:147:{"delta": {"L1": -0.34388440197993736, "L2": 0.02998144008856672, "SR": 0.13313256178950483, "SR2": 0.16471279211068107, "triple_var": -1.6636628932933628}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05028455673970692, "L2": -0.12050227721307562, "SR": 0.4579525581770594, "SR2": 0.4346833765322851, "triple_var": 1.9706775762363518}, "real": {"L1": -0.2935998452402304, "L2": -0.0905208371245089, "SR": 0.5910851199665642, "SR2": 0.5993961686429662, "triple_var": 0.307014682942989}, "rep": 73} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:148:{"delta": {"L1": 0.03826992466042789, "L2": -0.05894317967090722, "SR": -0.02963005899120974, "SR2": -0.02759247965162631, "triple_var": -0.28481464349853125}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.09488342344726025, "L2": 0.10709271079598726, "SR": 0.37986506411832677, "SR2": 0.40261815447457905, "triple_var": 2.9235130987966587}, "real": {"L1": -0.05661349878683236, "L2": 0.04814953112508004, "SR": 0.350235005127117, "SR2": 0.37502567482295274, "triple_var": 2.6386984552981274}, "rep": 73} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:149:{"delta": {"L1": 0.013935359474948394, "L2": -0.08946842930709323, "SR": 0.28466776215919226, "SR2": 0.2500889514620909, "triple_var": -1.8438168263381975}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.1821085556135834, "L2": -0.07471781664872232, "SR": 0.3871304038263485, "SR2": 0.4154759707272669, "triple_var": 2.1117659992757587}, "real": {"L1": -0.16817319613863502, "L2": -0.16418624595581555, "SR": 0.6717981659855408, "SR2": 0.6655649221893578, "triple_var": 0.26794917293756115}, "rep": 74} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:150:{"delta": {"L1": 0.03774436997068438, "L2": -0.19692201321134278, "SR": -0.08775292494129916, "SR2": -0.08195302644081182, "triple_var": -0.17249738725600627}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.07921207534491452, "L2": 0.00045115637259573386, "SR": 0.410989897776661, "SR2": 0.40818932485250337, "triple_var": 2.3378806638328404}, "real": {"L1": -0.04146770537423014, "L2": -0.19647085683874704, "SR": 0.32323697283536185, "SR2": 0.32623629841169155, "triple_var": 2.165383276576834}, "rep": 74} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:151:{"delta": {"L1": -0.34888319523829353, "L2": -0.0010150541575587035, "SR": 0.1544651870948443, "SR2": 0.26115948800278294, "triple_var": -1.8136162714782502}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.1181833982306039, "L2": -0.13084746779968423, "SR": 0.44550337952013414, "SR2": 0.37536950559056464, "triple_var": 2.110050950667114}, "real": {"L1": -0.2306997970076896, "L2": -0.13186252195724293, "SR": 0.5999685666149784, "SR2": 0.6365289935933476, "triple_var": 0.2964346791888638}, "rep": 75} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:152:{"delta": {"L1": -0.08639916643174793, "L2": -0.0677527799480545, "SR": -0.012808752239592713, "SR2": 0.02092855570515284, "triple_var": -1.35761648287684}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.012817922833628028, "L2": -0.1023742536745257, "SR": 0.38628519368341974, "SR2": 0.36054065358981824, "triple_var": 3.269291478152754}, "real": {"L1": -0.0735812435981199, "L2": -0.1701270336225802, "SR": 0.373476441443827, "SR2": 0.3814692092949711, "triple_var": 1.9116749952759138}, "rep": 75} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:153:{"delta": {"L1": -0.07808924085507446, "L2": -0.13099724298047152, "SR": 0.17383347911491326, "SR2": 0.23651892106275196, "triple_var": -1.2489853420747465}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.022348538642439776, "L2": -0.16307127510867706, "SR": 0.4331772482559459, "SR2": 0.39220770141501016, "triple_var": 1.6149928271353549}, "real": {"L1": -0.10043777949751424, "L2": -0.2940685180891486, "SR": 0.6070107273708591, "SR2": 0.6287266224777621, "triple_var": 0.3660074850606083}, "rep": 76} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:154:{"delta": {"L1": 0.18716782461594234, "L2": -0.13415117881890923, "SR": 0.0036924853866297513, "SR2": 0.0067811097512250895, "triple_var": 1.218338834583899}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.10260411925803614, "L2": -0.01797598095832378, "SR": 0.3812822651862304, "SR2": 0.36114913851510605, "triple_var": 2.429928104195198}, "real": {"L1": 0.08456370535790621, "L2": -0.152127159777233, "SR": 0.3849747505728601, "SR2": 0.36793024826633114, "triple_var": 3.648266938779097}, "rep": 76} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:155:{"delta": {"L1": -0.2058392598266326, "L2": -0.028488748962354836, "SR": 0.2736084014429436, "SR2": 0.27072268371132485, "triple_var": -2.5920102459530434}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.049512323608376436, "L2": -0.058801314208371626, "SR": 0.33704222072293366, "SR2": 0.3803823363243361, "triple_var": 2.864428840589762}, "real": {"L1": -0.255351583435009, "L2": -0.08729006317072646, "SR": 0.6106506221658773, "SR2": 0.651105020035661, "triple_var": 0.2724185946367185}, "rep": 77} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:156:{"delta": {"L1": -0.14225587230227063, "L2": 0.0642859833810733, "SR": 0.09188595683498901, "SR2": 0.10006131962277054, "triple_var": -2.5336776254007654}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.08540927019154561, "L2": -0.08066044584819143, "SR": 0.3314537464841794, "SR2": 0.32598232564712754, "triple_var": 4.7125175156186625}, "real": {"L1": -0.05684660211072503, "L2": -0.016374462467118128, "SR": 0.4233397033191684, "SR2": 0.4260436452698981, "triple_var": 2.178839890217897}, "rep": 77} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:157:{"delta": {"L1": -0.6390549200263081, "L2": -0.1261456597428574, "SR": 0.08428757543727278, "SR2": 0.15523698394243984, "triple_var": -2.948963945747758}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.19100598118802956, "L2": 0.14368397193039703, "SR": 0.43950384875888776, "SR2": 0.42610759051095554, "triple_var": 3.2514165312375387}, "real": {"L1": -0.4480489388382786, "L2": 0.017538312187539623, "SR": 0.5237914241961605, "SR2": 0.5813445744533954, "triple_var": 0.30245258548978093}, "rep": 78} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:158:{"delta": {"L1": 0.23798650460033294, "L2": -0.023257079927998747, "SR": 0.021912226481337116, "SR2": 0.028120049282680915, "triple_var": 0.6455317058818066}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.044595582086525705, "L2": -0.10086248868369595, "SR": 0.39993250041857775, "SR2": 0.36747182968179815, "triple_var": 1.948535594409034}, "real": {"L1": 0.19339092251380724, "L2": -0.1241195686116947, "SR": 0.42184472689991487, "SR2": 0.39559187896447906, "triple_var": 2.5940673002908405}, "rep": 78} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:159:{"delta": {"L1": -0.31131406592753774, "L2": -0.046568968460482904, "SR": 0.20759898461992482, "SR2": 0.2607141686809526, "triple_var": -2.7732298820708494}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.00852921429958548, "L2": -0.004031870308760644, "SR": 0.37075769162143274, "SR2": 0.35915317718133283, "triple_var": 3.0697326758102306}, "real": {"L1": -0.3198432802271232, "L2": -0.050600838769243546, "SR": 0.5783566762413576, "SR2": 0.6198673458622854, "triple_var": 0.296502793739381}, "rep": 79} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:160:{"delta": {"L1": -0.19445863242576725, "L2": -0.04856704224986753, "SR": -0.06718565005336946, "SR2": -0.054746129803618204, "triple_var": 0.2770158441789219}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.0034274894462571993, "L2": 0.035184395775975943, "SR": 0.40160107275352297, "SR2": 0.40763456865581493, "triple_var": 2.338290172752371}, "real": {"L1": -0.19103114297951004, "L2": -0.013382646473891584, "SR": 0.3344154227001535, "SR2": 0.35288843885219673, "triple_var": 2.6153060169312927}, "rep": 79} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:161:{"delta": {"L1": -0.37434147990389327, "L2": 0.034308005325623815, "SR": 0.26228474247712236, "SR2": 0.2416357234806948, "triple_var": -3.7291501323128813}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.1333393362196779, "L2": -0.08075290770624603, "SR": 0.3706448972809358, "SR2": 0.41767869152760656, "triple_var": 4.030055090585228}, "real": {"L1": -0.24100214368421538, "L2": -0.04644490238062222, "SR": 0.6329296397580582, "SR2": 0.6593144150083013, "triple_var": 0.30090495827234665}, "rep": 80} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:162:{"delta": {"L1": -0.08575767884616042, "L2": -0.16720367333918057, "SR": -0.07939729731210177, "SR2": -0.07575100458823758, "triple_var": -0.5557100640649919}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.016033006793096223, "L2": 0.12390047099851839, "SR": 0.4408223143810535, "SR2": 0.41694617048865107, "triple_var": 3.4875536992970106}, "real": {"L1": -0.06972467205306421, "L2": -0.04330320234066218, "SR": 0.36142501706895175, "SR2": 0.3411951659004135, "triple_var": 2.9318436352320187}, "rep": 80} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:163:{"delta": {"L1": -0.3892551106400606, "L2": -0.07793319040942923, "SR": 0.16967767261095285, "SR2": 0.23812923736385466, "triple_var": -2.509683906571535}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.06538001921379674, "L2": 0.2109109504974053, "SR": 0.3635571975743375, "SR2": 0.3959619581878911, "triple_var": 2.803164041292598}, "real": {"L1": -0.45463512985385734, "L2": 0.13297776008797607, "SR": 0.5332348701852904, "SR2": 0.6340911955517458, "triple_var": 0.29348013472106316}, "rep": 81} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:164:{"delta": {"L1": -0.009639583653771489, "L2": -0.05687004579748145, "SR": 0.053777717834833316, "SR2": 0.028646303099137127, "triple_var": -0.9906962290338233}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.028054104161036226, "L2": 0.036467476510366685, "SR": 0.3632428060959464, "SR2": 0.40633836125936273, "triple_var": 3.6614285370973314}, "real": {"L1": 0.018414520507264737, "L2": -0.02040256928711476, "SR": 0.4170205239307797, "SR2": 0.43498466435849986, "triple_var": 2.670732308063508}, "rep": 81} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:165:{"delta": {"L1": -0.3836980441841994, "L2": -0.08117411837644427, "SR": 0.19768529876535307, "SR2": 0.25046807183761244, "triple_var": -3.5463972738073095}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.027527286859582056, "L2": 0.16483837327047576, "SR": 0.3883180670452043, "SR2": 0.3915398647372582, "triple_var": 3.8604816750302238}, "real": {"L1": -0.35617075732461734, "L2": 0.0836642548940315, "SR": 0.5860033658105573, "SR2": 0.6420079365748707, "triple_var": 0.3140844012229143}, "rep": 82} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:166:{"delta": {"L1": -0.18166820623517244, "L2": -0.12756854409935453, "SR": 0.018360039216077706, "SR2": 0.019441119498002146, "triple_var": -1.3002582684420703}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.04105247491266391, "L2": -0.04677894620384534, "SR": 0.39626690188245567, "SR2": 0.3534365957525643, "triple_var": 3.1091074756085977}, "real": {"L1": -0.1406157313225085, "L2": -0.17434749030319988, "SR": 0.4146269410985334, "SR2": 0.37287771525056645, "triple_var": 1.8088492071665274}, "rep": 82} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:167:{"delta": {"L1": -0.3691817649614651, "L2": -0.22380561486937908, "SR": 0.22638693512903996, "SR2": 0.17147423514783477, "triple_var": -3.223369977944283}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.019908219240474312, "L2": 0.019234362245893782, "SR": 0.36478696117787546, "SR2": 0.4310468408085052, "triple_var": 3.4558550688640155}, "real": {"L1": -0.3492735457209908, "L2": -0.2045712526234853, "SR": 0.5911738963069154, "SR2": 0.60252107595634, "triple_var": 0.23248509091973263}, "rep": 83} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:168:{"delta": {"L1": -0.015842129409579117, "L2": 0.0333840058638814, "SR": -0.04816666252726226, "SR2": 0.0037463615793077998, "triple_var": 0.21209860854121754}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.08849780335182167, "L2": -0.02181844259143728, "SR": 0.40024895962147033, "SR2": 0.4183360401102826, "triple_var": 1.9524291639730533}, "real": {"L1": -0.10433993276140079, "L2": 0.011565563272444115, "SR": 0.3520822970942081, "SR2": 0.4220824016895904, "triple_var": 2.164527772514271}, "rep": 83} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:169:{"delta": {"L1": -0.39955712727781456, "L2": -0.11698639590065833, "SR": 0.14345618754681883, "SR2": 0.23947448674184607, "triple_var": -2.331113333909233}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05257136186896068, "L2": 0.042883120469409175, "SR": 0.42469430043964734, "SR2": 0.3838892178723016, "triple_var": 2.604951014123463}, "real": {"L1": -0.3469857654088539, "L2": -0.07410327543124916, "SR": 0.5681504879864662, "SR2": 0.6233637046141477, "triple_var": 0.27383768021423016}, "rep": 84} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:170:{"delta": {"L1": 0.19853916538155214, "L2": 0.0018157727517659483, "SR": 0.07072448913985652, "SR2": 0.018927698948295824, "triple_var": 0.6915391880225366}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.2539652091555714, "L2": -0.13702189727330794, "SR": 0.37787254657140035, "SR2": 0.36115875327463237, "triple_var": 1.3187348681502582}, "real": {"L1": -0.05542604377401928, "L2": -0.135206124521542, "SR": 0.44859703571125686, "SR2": 0.3800864522229282, "triple_var": 2.0102740561727948}, "rep": 84} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:171:{"delta": {"L1": -0.284735431688773, "L2": -0.15951284595880644, "SR": 0.21790810031385105, "SR2": 0.20766816163390167, "triple_var": -2.746980360609635}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.04241351935765045, "L2": 0.04405880440184305, "SR": 0.3602049381210239, "SR2": 0.39155450328067765, "triple_var": 3.048529520317852}, "real": {"L1": -0.32714895104642344, "L2": -0.1154540415569634, "SR": 0.5781130384348749, "SR2": 0.5992226649145793, "triple_var": 0.3015491597082175}, "rep": 85} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:172:{"delta": {"L1": 0.05083190970386784, "L2": -0.3047428587897248, "SR": 0.06077593160339434, "SR2": 0.021199831511299727, "triple_var": -1.5893016269913964}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.04965588456708293, "L2": 0.06856488348093241, "SR": 0.3624394227082782, "SR2": 0.3861540211896042, "triple_var": 3.6626205458902645}, "real": {"L1": 0.10048779427095077, "L2": -0.23617797530879237, "SR": 0.42321535431167256, "SR2": 0.4073538527009039, "triple_var": 2.073318918898868}, "rep": 85} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:173:{"delta": {"L1": -0.47384104000201954, "L2": -0.1905790637970146, "SR": 0.2170845386202479, "SR2": 0.19465991273834438, "triple_var": -3.251543497530756}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.11050518462782721, "L2": 0.1379507829464082, "SR": 0.4203448016759507, "SR2": 0.4485893508158061, "triple_var": 3.4948585247420727}, "real": {"L1": -0.36333585537419233, "L2": -0.0526282808506064, "SR": 0.6374293402961986, "SR2": 0.6432492635541505, "triple_var": 0.2433150272113169}, "rep": 86} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:174:{"delta": {"L1": -0.18350458276344417, "L2": 0.16284533514612376, "SR": -0.03417036343226215, "SR2": 0.020069083175273394, "triple_var": -1.0165138151590882}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.04188841278979751, "L2": -0.07139264630130147, "SR": 0.4051553096181208, "SR2": 0.38890212115336265, "triple_var": 2.9254689232858824}, "real": {"L1": -0.14161616997364665, "L2": 0.0914526888448223, "SR": 0.37098494618585864, "SR2": 0.40897120432863604, "triple_var": 1.9089551081267941}, "rep": 86} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:175:{"delta": {"L1": -0.15255943057130023, "L2": -0.3077428032823638, "SR": 0.24590616937153914, "SR2": 0.1907412084253839, "triple_var": -2.0669530711333337}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.06512560565345286, "L2": 0.004266696835112074, "SR": 0.3618888555224156, "SR2": 0.4045315716539818, "triple_var": 2.3823297201919287}, "real": {"L1": -0.2176850362247531, "L2": -0.30347610644725176, "SR": 0.6077950248939548, "SR2": 0.5952727800793657, "triple_var": 0.3153766490585952}, "rep": 87} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:176:{"delta": {"L1": 0.06682368700714024, "L2": 0.0494793386235178, "SR": 0.02154321021267691, "SR2": -0.008384708671946295, "triple_var": 1.1705283321923918}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.016746639460729754, "L2": -0.015354381285289695, "SR": 0.3733024120352074, "SR2": 0.4027538040785869, "triple_var": 2.2444634717979044}, "real": {"L1": 0.050077047546410486, "L2": 0.0341249573382281, "SR": 0.39484562224788433, "SR2": 0.3943690954066406, "triple_var": 3.4149918039902962}, "rep": 87} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:177:{"delta": {"L1": -0.36778059746569636, "L2": 0.024460039957352617, "SR": 0.265673096433901, "SR2": 0.2547208534946878, "triple_var": -3.019289588506838}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.11489594690134404, "L2": -0.09805698316557755, "SR": 0.36007827096040546, "SR2": 0.3685790212619627, "triple_var": 3.3421587451337573}, "real": {"L1": -0.25288465056435233, "L2": -0.07359694320822494, "SR": 0.6257513673943065, "SR2": 0.6232998747566505, "triple_var": 0.32286915662691934}, "rep": 88} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:178:{"delta": {"L1": 0.13017176168392042, "L2": 0.13777153160174865, "SR": 0.018034278152287142, "SR2": 0.10076842393252072, "triple_var": -0.5426080004030349}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.06497334303967374, "L2": -0.0886016617693745, "SR": 0.39077304143302116, "SR2": 0.31786574880873086, "triple_var": 2.6561380946754585}, "real": {"L1": 0.06519841864424668, "L2": 0.04916986983237414, "SR": 0.4088073195853083, "SR2": 0.4186341727412516, "triple_var": 2.1135300942724236}, "rep": 88} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:179:{"delta": {"L1": -0.40936494497265774, "L2": -0.14813898639841253, "SR": 0.2577652540644197, "SR2": 0.2701253745780261, "triple_var": -2.2039640624606065}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.09137076337595242, "L2": 0.003719807530920136, "SR": 0.3954565549525992, "SR2": 0.398569111480754, "triple_var": 2.4033188413203264}, "real": {"L1": -0.31799418159670534, "L2": -0.1444191788674924, "SR": 0.6532218090170189, "SR2": 0.6686944860587801, "triple_var": 0.1993547788597197}, "rep": 89} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:180:{"delta": {"L1": -0.08067528947236549, "L2": -0.2533042755542635, "SR": -0.032849820724623835, "SR2": -0.09940531230661465, "triple_var": -1.0522878166502707}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.0012678560003378126, "L2": 0.004249461141064426, "SR": 0.35973012096228124, "SR2": 0.4003710729730234, "triple_var": 3.2839072923975774}, "real": {"L1": -0.0819431454727033, "L2": -0.24905481441319904, "SR": 0.3268803002376574, "SR2": 0.30096576066640873, "triple_var": 2.2316194757473067}, "rep": 89} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:181:{"delta": {"L1": -0.23306791090596948, "L2": -0.327811960366075, "SR": 0.18555101328848822, "SR2": 0.23478478531913727, "triple_var": -2.2597173695078805}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.10280762261450963, "L2": 0.169201940973468, "SR": 0.41816251830785645, "SR2": 0.381321377304186, "triple_var": 2.479453708532036}, "real": {"L1": -0.3358755335204791, "L2": -0.15861001939260702, "SR": 0.6037135315963447, "SR2": 0.6161061626233233, "triple_var": 0.21973633902415554}, "rep": 90} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:182:{"delta": {"L1": 0.12463780653600587, "L2": 0.0686700202898782, "SR": 0.02217230567156897, "SR2": 0.11803332182769294, "triple_var": 0.3440115370067054}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.032582173783521975, "L2": 0.01225071582966255, "SR": 0.37469426280104134, "SR2": 0.3202269987768447, "triple_var": 3.0572535754112455}, "real": {"L1": 0.09205563275248389, "L2": 0.08092073611954075, "SR": 0.3968665684726103, "SR2": 0.4382603206045376, "triple_var": 3.401265112417951}, "rep": 90} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:183:{"delta": {"L1": -0.4585295368269731, "L2": -0.12582783197565095, "SR": 0.10576541239591286, "SR2": 0.16842475972256543, "triple_var": -2.230872184277252}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.08500419616549698, "L2": 0.08060328742628393, "SR": 0.42487955198988414, "SR2": 0.4103171427258281, "triple_var": 2.5835954433200077}, "real": {"L1": -0.37352534066147614, "L2": -0.04522454454936701, "SR": 0.530644964385797, "SR2": 0.5787419024483935, "triple_var": 0.3527232590427557}, "rep": 91} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:184:{"delta": {"L1": 0.02870946016285836, "L2": 0.06992495650522954, "SR": 0.0047254460848457636, "SR2": 0.009080001391503945, "triple_var": -0.967126953622266}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.023486236095339348, "L2": 0.05202147463743998, "SR": 0.3627116027200288, "SR2": 0.4107784568677496, "triple_var": 3.357444155760785}, "real": {"L1": 0.005223224067519012, "L2": 0.12194643114266952, "SR": 0.36743704880487454, "SR2": 0.41985845825925355, "triple_var": 2.390317202138519}, "rep": 91} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:185:{"delta": {"L1": -0.2260647891984193, "L2": -0.19224176734663428, "SR": 0.28491533876725406, "SR2": 0.24892128567343974, "triple_var": -3.8822922883821023}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05028099931313621, "L2": 0.02565957669004422, "SR": 0.3376200267757775, "SR2": 0.3665576671839065, "triple_var": 4.21985071378116}, "real": {"L1": -0.1757837898852831, "L2": -0.16658219065659005, "SR": 0.6225353655430316, "SR2": 0.6154789528573462, "triple_var": 0.33755842539905734}, "rep": 92} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:186:{"delta": {"L1": 0.02769029700895442, "L2": 0.07759402715506353, "SR": 0.013443759685984102, "SR2": 0.08895322835143682, "triple_var": -0.46327610048969525}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.02286035556780739, "L2": 0.011159511208085168, "SR": 0.37634684515722827, "SR2": 0.3459579382492993, "triple_var": 2.740022563485528}, "real": {"L1": 0.00482994144114703, "L2": 0.0887535383631487, "SR": 0.3897906048432124, "SR2": 0.43491116660073614, "triple_var": 2.276746462995833}, "rep": 92} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:187:{"delta": {"L1": -0.17791334819700586, "L2": -0.03346430146644466, "SR": 0.17209724763013828, "SR2": 0.2045763167726492, "triple_var": -2.187972115268814}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.1502074771908137, "L2": 0.038658583827995065, "SR": 0.4364419531393912, "SR2": 0.44098930525662716, "triple_var": 2.4912790782558307}, "real": {"L1": -0.32812082538781956, "L2": 0.005194282361550401, "SR": 0.6085392007695295, "SR2": 0.6455656220292764, "triple_var": 0.3033069629870167}, "rep": 93} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:188:{"delta": {"L1": 0.03087102172922007, "L2": -0.10734072183452012, "SR": 0.038503542316980666, "SR2": 0.016740395380748563, "triple_var": -0.9710016892406395}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.026998214591052134, "L2": 0.028243049280740503, "SR": 0.3582840928480806, "SR2": 0.3474865742532249, "triple_var": 3.6070072965391673}, "real": {"L1": 0.003872807138167938, "L2": -0.07909767255377961, "SR": 0.39678763516506127, "SR2": 0.36422696963397344, "triple_var": 2.636005607298528}, "rep": 93} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:189:{"delta": {"L1": -0.3263499445119927, "L2": 0.1107471818956462, "SR": 0.20462252443528173, "SR2": 0.24892205550979585, "triple_var": -1.4174655830054366}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.04933157383930236, "L2": -0.009819713926335962, "SR": 0.410576153310088, "SR2": 0.4066492394106461, "triple_var": 1.6876056883021824}, "real": {"L1": -0.37568151835129504, "L2": 0.10092746796931024, "SR": 0.6151986777453697, "SR2": 0.655571294920442, "triple_var": 0.27014010529674576}, "rep": 94} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:190:{"delta": {"L1": -0.05243949789244381, "L2": -0.07122007610989392, "SR": 0.021552573210883663, "SR2": -0.012171505456512466, "triple_var": -0.7263526417906574}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.052378052317981746, "L2": -0.05350136993449956, "SR": 0.35593568094249317, "SR2": 0.3555554687028681, "triple_var": 3.0421000030978123}, "real": {"L1": -6.144557446205833e-05, "L2": -0.12472144604439347, "SR": 0.37748825415337683, "SR2": 0.3433839632463556, "triple_var": 2.315747361307155}, "rep": 94} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:191:{"delta": {"L1": -0.31302133802668974, "L2": -0.07950543428211318, "SR": 0.24022550616517319, "SR2": 0.3063417479280203, "triple_var": -5.377219857374014}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.07530332011155394, "L2": -0.04191908634082249, "SR": 0.3951161414673621, "SR2": 0.36483992630570666, "triple_var": 5.643704922489075}, "real": {"L1": -0.2377180179151358, "L2": -0.12142452062293567, "SR": 0.6353416476325353, "SR2": 0.671181674233727, "triple_var": 0.2664850651150613}, "rep": 95} tools/data/physical_sr_residue_bounce_20260514_1631_w6.trace.jsonl:192:{"delta": {"L1": 0.11765876748077983, "L2": 0.1106123003100916, "SR": 0.07281979563226226, "SR2": 0.08151733500542235, "triple_var": -0.43295021273616463}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.10337018480692949, "L2": -0.15313879963858398, "SR": 0.3650119153769114, "SR2": 0.3394925063275785, "triple_var": 2.419309627019335}, "real": {"L1": 0.014288582673850338, "L2": -0.04252649932849238, "SR": 0.4378317110091737, "SR2": 0.42100984133300084, "triple_var": 1.9863594142831702}, "rep": 95} tools/data/notte_20260314_0330.md:4: ising_2d_var_-0.1: r=0.9848721318684013, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260314_0330.md:5: ising_2d_var_0.1: r=0.7920844227769658, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260314_0330.md:6: numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like (⟨r⟩=0.9609375) tools/data/notte_20260314_0330.md:7: logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like (⟨r⟩=0.35930230610663094) tools/data/notte_20260314_0330.md:8: logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like (⟨r⟩=0.3914428894887236) tools/data/notte_20260314_0330.md:9: cellular_automata_var_30: r=0.8703941780326052, spacing=GUE-like (⟨r⟩=0.8649999999999983) tools/data/notte_20260314_0330.md:10: brownian_motion_var_0.3: r=1.0088495575221237, spacing=Poisson-like (⟨r⟩=0.3818080058449986) tools/data/notte_20260314_0330.md:11: brownian_motion_var_0.5: r=1.032258064516129, spacing=Poisson-like (⟨r⟩=0.38274438984488357) tools/data/notte_20260314_0330.md:12: coupled_oscillators_var_50: r=1.0, spacing=Poisson-like (⟨r⟩=0.45756422661060286) tools/data/notte_20260314_0330.md:13: percolation_var_0.55: r=1.0434782608695652, spacing=Poisson-like (⟨r⟩=0.4194344351492288) tools/data/notte_20260314_0330.md:14: percolation_var_0.65: r=1.0, spacing=Poisson-like (⟨r⟩=0.44823047252566145) tools/data/reports/agent_20260509_0652.md:7:observables_used: [`event_type`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`, `acceptance_rate`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var tools/data/reports/agent_20260509_0652.md:8:**observable_contract**: claim=un null che preserva il gap-label set Sturmian puo' decidere se `V_c` e' portato dal label-set o dall'ordine generativo; observable=`event_type={floor_hit,internal_cross,internal_multi,no_cross}`, `vc_interp`, `r_floor`, `r_span`, `label_jaccard`, `acceptance_rate`; operator=surrogate bilanciato con swap 0/1 e ricottura finche' `label_jaccard>=0.75`, poi curva `r(V)` su griglia 0.5..3.0 step 0.01; generator=phi Sturmian, balanced_random, swap_label_surrogate; denominator=run principale N={89}, phase={0,0.25,0.5,0.75}, r_threshold={0.48,0.50,0.52}, random_trials=1, label_trials=2, swap_steps=3000, seed=202605090652; non_possible=se i surrogate accettati Jaccard>=0.75 producono floor-hit o internal_multi e restano con `r_floor`/`vc_interp` vicini al random, il label-set non basta a ricostruire il boundary Sturmian; not_tested=GUE/Poisson reali, silver/bronze, fit power-law, gap_ratio, prova a N>=144 con gate raggiunto. tools/data/reports/agent_20260509_0652.md:95:Il prossimo passaggio non e' estendere a GUE/Poisson. Prima serve un generatore label-preserving stabile cross-phase: stessa accettazione Jaccard su N={89,144,233} oppure fallimento dichiarato come vincolo del null. Solo dopo il boundary operator puo' trasferire verso domini GUE/Poisson. tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3: "question": "Is the GUE-Poisson mixed layer cleanly classifiable after denominator gating?", tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:10: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:34: "triple_var": 1.0 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:45: "triple_var": 1.0 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:56: "triple_var": 0.8333333333333334 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:67: "triple_var": 0.16666666666666666 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:78: "triple_var": 0.25 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:89: "triple_var": 0.08333333333333333 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:100: "triple_var": 0.0 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:111: "triple_var": 0.0 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:122: "triple_var": 0.08333333333333333 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:133: "triple_var": 0.0 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:144: "triple_var": 0.0 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:155: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:257: "triple_var": 0.36824603922751775 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:264: "triple_var": 0.021626582246933222 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:271: "triple_var": -8.937587019539183 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:276: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:287: "triple_var": 0.6237374064660012 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:294: "triple_var": 0.041951463030835134 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:301: "triple_var": -3.222381930634005 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:307: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:318: "triple_var": 1.1302774635414916 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:325: "triple_var": 0.044214146299992915 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:332: "triple_var": -0.07314923956068524 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:346: "triple_var": 1.3630901004904563 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:353: "triple_var": 0.0471325835129088 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:360: "triple_var": -1.141530783817715 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:372: "triple_var": 1.4365890840779292 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:379: "triple_var": 0.07762309869719548 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:386: "triple_var": -1.265676304277321 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:398: "triple_var": 1.6582412874456127 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:405: "triple_var": 0.08494488384966173 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:412: "triple_var": -0.4382508685987193 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:424: "triple_var": 1.7011607714356862 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:431: "triple_var": 0.07967458266623069 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:438: "triple_var": -1.1588683713571928 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:450: "triple_var": 2.1040639154699687 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:457: "triple_var": 0.10612144187124382 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:464: "triple_var": -0.7087699798708879 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:478: "triple_var": 2.408041691490886 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:485: "triple_var": 0.10063965922862673 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:492: "triple_var": -1.0760492457823732 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:504: "triple_var": 2.5611273018682654 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:511: "triple_var": 0.11201061273233763 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:518: "triple_var": -0.5410143546035524 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:530: "triple_var": 2.6742981217815207 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:537: "triple_var": 0.11964584731996097 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:544: "triple_var": -1.438341798084834 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:556: "triple_var": 0.3792939516519359 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:563: "triple_var": 0.025641196908974286 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:570: "triple_var": -7.819901427093135 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:575: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:586: "triple_var": 0.64283597374721 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:593: "triple_var": 0.03426403460595539 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:600: "triple_var": -4.689584203928069 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:605: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:616: "triple_var": 1.0235282630196845 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:623: "triple_var": 0.05471404486207342 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:630: "triple_var": -3.0664736019797147 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:635: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:646: "triple_var": 1.2273144436332886 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:653: "triple_var": 0.05453183863786478 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:660: "triple_var": -0.6290960202296055 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:674: "triple_var": 1.5987092437425445 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:681: "triple_var": 0.08101561517062979 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:688: "triple_var": -0.6544964341349108 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:702: "triple_var": 1.8203848285571895 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:709: "triple_var": 0.09488111380937532 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:716: "triple_var": 0.8145585392291796 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:731: "triple_var": 2.0276562402545095 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:738: "triple_var": 0.09349928884403598 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:745: "triple_var": -0.58340172267811 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:760: "triple_var": 2.408350365319453 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:767: "triple_var": 0.09958990285305193 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:774: "triple_var": 0.371713306686717 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:786: "triple_var": 2.351186743384644 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:793: "triple_var": 0.10435569058069014 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:800: "triple_var": -0.4288982422028151 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:812: "triple_var": 2.8176361924336213 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:819: "triple_var": 0.11149621303867917 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:826: "triple_var": 0.28763541509513185 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:841: "triple_var": 2.906361849975823 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:848: "triple_var": 0.12965787929180683 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:855: "triple_var": -0.22587482022406885 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:870: "triple_var": 0.3580872460938318 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:877: "triple_var": 0.024030602664733437 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:884: "triple_var": -10.006741678853457 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:891: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:902: "triple_var": 0.760033965620641 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:909: "triple_var": 0.04809380665658023 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:916: "triple_var": -4.1601244102866195 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:922: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:933: "triple_var": 0.9245413351203089 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:940: "triple_var": 0.04431233114498308 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:947: "triple_var": -3.612801949983973 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:952: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:963: "triple_var": 1.1045072801646387 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:970: "triple_var": 0.06987803733695604 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:977: "triple_var": -2.2312504297157214 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:982: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:993: "triple_var": 1.504910429154547 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1000: "triple_var": 0.0827578028877083 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1007: "triple_var": -1.1607087321626475 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1019: "triple_var": 1.9151204486988063 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1026: "triple_var": 0.07179643642491204 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1033: "triple_var": -0.3443101352927874 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1045: "triple_var": 2.1254990431748473 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1052: "triple_var": 0.08046857097984483 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1059: "triple_var": -0.3408579900151763 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1071: "triple_var": 2.3708979000979236 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1078: "triple_var": 0.10633849714684109 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1085: "triple_var": -0.43518181112343035 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1097: "triple_var": 2.6735639458152267 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1104: "triple_var": 0.11237037124393806 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1111: "triple_var": -0.9731122091297879 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1123: "triple_var": 2.7511595444977126 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1130: "triple_var": 0.14036577468715383 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1137: "triple_var": -0.5322645608915249 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1149: "triple_var": 3.0823669521474972 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1156: "triple_var": 0.15116596240836203 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1163: "triple_var": -0.31889822632843046 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1177: "triple_var": 0.38485189983928086 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1184: "triple_var": 0.025124805758035666 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1191: "triple_var": -7.921606526008136 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1196: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1207: "triple_var": 0.6568330167129325 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1214: "triple_var": 0.03768381294419566 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1221: "triple_var": -2.992606043340225 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1226: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1237: "triple_var": 0.762499925654122 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1244: "triple_var": 0.03960122928015253 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1251: "triple_var": -4.9061189371610405 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1256: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1267: "triple_var": 1.4321609538962117 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1274: "triple_var": 0.0684674483144982 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1281: "triple_var": -0.1123076650528538 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1295: "triple_var": 1.5006700684856231 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1302: "triple_var": 0.07928809491353206 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1309: "triple_var": -2.084972128057669 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1312: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1323: "triple_var": 1.569391046887672 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1330: "triple_var": 0.0814705972771742 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1337: "triple_var": -1.3419643022013454 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1349: "triple_var": 1.9711842581767451 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1356: "triple_var": 0.09215416421203579 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1363: "triple_var": -0.5358991399354551 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1375: "triple_var": 1.9376342308701127 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1382: "triple_var": 0.10032404664898165 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1389: "triple_var": -1.2694069630151985 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1401: "triple_var": 2.400751068227957 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1408: "triple_var": 0.11012315766608043 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1415: "triple_var": -0.8594600863855836 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1427: "triple_var": 2.6668211576670497 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1434: "triple_var": 0.08757843153482786 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1441: "triple_var": 0.8305243027758243 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1453: "triple_var": 2.829479538537056 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1460: "triple_var": 0.10209829202955321 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1467: "triple_var": -1.0802150981992191 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1479: "triple_var": 0.3742820979323614 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1486: "triple_var": 0.027643333262525566 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1493: "triple_var": -7.228100930366061 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1499: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1510: "triple_var": 0.5635617733478889 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1517: "triple_var": 0.032348141668654386 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1524: "triple_var": -5.785348529178589 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1530: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1541: "triple_var": 1.0310946117565596 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1548: "triple_var": 0.07347123091250447 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1555: "triple_var": -1.9143441322392336 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1571: "triple_var": 1.106748226305483 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1578: "triple_var": 0.046358371576600055 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1585: "triple_var": -2.401535668227556 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1590: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1601: "triple_var": 1.3671918246080124 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1608: "triple_var": 0.054654744731747515 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1615: "triple_var": -0.8376979416692456 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1627: "triple_var": 1.5519590795925453 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1634: "triple_var": 0.09286459075172537 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1641: "triple_var": -2.61575100263356 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1646: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1657: "triple_var": 2.175479497977212 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1664: "triple_var": 0.11858420735238812 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1671: "triple_var": -0.5568407200529096 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1683: "triple_var": 2.376067772553292 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1690: "triple_var": 0.1082881675231864 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1697: "triple_var": 0.1257883055515079 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1709: "triple_var": 2.769554988421549 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1716: "triple_var": 0.10533454002111095 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1723: "triple_var": 2.240286174522257 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1728: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1739: "triple_var": 3.054296391536366 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1746: "triple_var": 0.15858763617014615 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1753: "triple_var": 0.26696955169655234 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1765: "triple_var": 3.2898181866468543 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1772: "triple_var": 0.1645914920528183 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1779: "triple_var": 0.12022567376679193 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1791: "triple_var": 0.39062048761282686 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1798: "triple_var": 0.03144229218055464 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1805: "triple_var": -5.832533754640317 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1810: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1821: "triple_var": 0.7124864105149274 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1828: "triple_var": 0.04618715707903933 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1835: "triple_var": -3.560535752930165 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1840: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1851: "triple_var": 0.6627932296633842 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1858: "triple_var": 0.043872455146348265 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1865: "triple_var": -4.843013958032659 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1871: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1882: "triple_var": 1.2399173724564354 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1889: "triple_var": 0.056800721084233556 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1896: "triple_var": -1.8083820128892156 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1910: "triple_var": 1.6703772110600539 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1917: "triple_var": 0.09969566801371756 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1924: "triple_var": -0.48264110254909703 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1936: "triple_var": 1.886461589229298 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1943: "triple_var": 0.07017755948849783 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1950: "triple_var": 1.113007637254816 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1962: "triple_var": 1.953938110334314 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1969: "triple_var": 0.10047660729013702 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1976: "triple_var": -0.6405614711242464 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1988: "triple_var": 2.4719213621023144 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:1995: "triple_var": 0.13530766485466864 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2002: "triple_var": 0.39946578690314527 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2014: "triple_var": 2.4999988093348744 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2021: "triple_var": 0.09290716359150543 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2028: "triple_var": -0.19352590790812502 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2042: "triple_var": 2.711814242513542 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2049: "triple_var": 0.12258255521914012 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2056: "triple_var": -0.5053072095981649 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2070: "triple_var": 3.0206947867641922 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2077: "triple_var": 0.13480426850174823 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2084: "triple_var": -0.06269635023439425 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2098: "triple_var": 0.33829973364789545 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2105: "triple_var": 0.02208641904640149 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2112: "triple_var": -8.574413930227053 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2118: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2129: "triple_var": 0.5548735226792033 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2136: "triple_var": 0.024529685294222257 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2143: "triple_var": -6.607595131908368 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2148: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2159: "triple_var": 0.9375011390379716 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2166: "triple_var": 0.054419215124107564 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2173: "triple_var": -2.9306408535815542 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2178: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2189: "triple_var": 0.8554735631402063 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2196: "triple_var": 0.04487629796851955 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2203: "triple_var": -1.9709736208489699 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2218: "triple_var": 1.0094077545457043 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2225: "triple_var": 0.09046890845476677 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2232: "triple_var": -2.33623963477144 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2237: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2248: "triple_var": 1.7500179076482367 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2255: "triple_var": 0.06142868684404505 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2262: "triple_var": -0.5486254408327029 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2274: "triple_var": 1.764988900254769 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2281: "triple_var": 0.07921668825431752 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2288: "triple_var": 0.07503609428170516 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2300: "triple_var": 2.3695593018149217 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2307: "triple_var": 0.10110610044668673 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2314: "triple_var": 0.496814146875466 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2328: "triple_var": 2.475940745933814 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2335: "triple_var": 0.11730835321706473 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2342: "triple_var": 0.8268586700751732 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2354: "triple_var": 2.6779508637903335 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2361: "triple_var": 0.12324361776070515 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2368: "triple_var": -0.022617478810546255 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2380: "triple_var": 2.9512142585375636 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2387: "triple_var": 0.08476648418971136 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2394: "triple_var": 0.9582634441799389 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2408: "triple_var": 0.3656838961260641 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2415: "triple_var": 0.023913109073746507 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2422: "triple_var": -8.924237651246221 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2427: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2438: "triple_var": 0.6580797580537606 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2445: "triple_var": 0.04387722670165765 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2452: "triple_var": -3.2537174483396676 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2457: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2468: "triple_var": 0.6770905713507458 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2475: "triple_var": 0.04409179419192966 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2482: "triple_var": -3.7305056960622482 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2487: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2498: "triple_var": 1.1223763001038154 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2505: "triple_var": 0.07188341773532655 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2512: "triple_var": -1.2434977598277746 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2526: "triple_var": 1.2942584243416293 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2533: "triple_var": 0.08247368570684595 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2540: "triple_var": -1.4759144413491974 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2552: "triple_var": 1.742616429069852 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2559: "triple_var": 0.10098714206078374 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2566: "triple_var": -0.21753275950635498 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2578: "triple_var": 1.8293738474365488 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2585: "triple_var": 0.0657153859958973 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2592: "triple_var": 0.5258858668805173 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2606: "triple_var": 2.1133852713723655 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2613: "triple_var": 0.1362060095517259 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2620: "triple_var": -0.24622051420207766 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2632: "triple_var": 2.154104805032866 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2639: "triple_var": 0.11205654170755147 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2646: "triple_var": -0.9176915689544707 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2658: "triple_var": 2.357255033935719 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2665: "triple_var": 0.14321377963547235 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2672: "triple_var": -1.569777904373841 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2686: "triple_var": 2.548020209499326 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2693: "triple_var": 0.11318416594795541 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2700: "triple_var": -1.7018192404500507 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2714: "triple_var": 0.34519544859594686 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2721: "triple_var": 0.02322800345164893 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2728: "triple_var": -9.637047293613353 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2734: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2745: "triple_var": 0.6123621335759087 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2752: "triple_var": 0.044309033487549346 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2759: "triple_var": -3.9592764757029184 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2764: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2775: "triple_var": 0.9529766189081055 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2782: "triple_var": 0.05238847669448992 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2789: "triple_var": -2.0172827006420664 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2794: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2805: "triple_var": 1.125071108074443 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2812: "triple_var": 0.06965575984801818 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2819: "triple_var": -1.8434489477730387 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2833: "triple_var": 1.160013698578929 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2840: "triple_var": 0.055913144772213284 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2847: "triple_var": -1.184474874048629 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2859: "triple_var": 1.6270334225877432 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2866: "triple_var": 0.08516249074976061 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2873: "triple_var": -0.8771900791222641 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2887: "triple_var": 1.7420310979719267 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2894: "triple_var": 0.09338628267094164 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2901: "triple_var": -1.0077786537926878 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2915: "triple_var": 2.1108247178466244 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2922: "triple_var": 0.08399837306754414 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2929: "triple_var": -1.139787026491186 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2944: "triple_var": 2.1286278568514394 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2951: "triple_var": 0.09371397808247606 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2958: "triple_var": -0.0805591448102042 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2970: "triple_var": 2.4959039306829887 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2977: "triple_var": 0.08374533073519097 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2984: "triple_var": -0.3027231174995873 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:2996: "triple_var": 2.6588943781932226 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3003: "triple_var": 0.1509054816574697 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3010: "triple_var": -0.28051336902486096 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3022: "triple_var": 0.35966011017723903 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3029: "triple_var": 0.027372013109831145 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3036: "triple_var": -7.751049005716606 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3042: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3053: "triple_var": 0.5932719347335416 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3060: "triple_var": 0.030149506555281206 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3067: "triple_var": -6.824489008819284 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3072: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3083: "triple_var": 1.0292903754351361 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3090: "triple_var": 0.058642694029730706 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3097: "triple_var": -2.5822660293579873 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3102: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3113: "triple_var": 1.1831302100134538 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3120: "triple_var": 0.05869108071707668 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3127: "triple_var": -0.6488129375968192 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3139: "triple_var": 1.5830170936118817 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3146: "triple_var": 0.08508427056229996 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3153: "triple_var": 0.12766176063732415 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3165: "triple_var": 1.6513152643977365 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3172: "triple_var": 0.06698707906894766 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3179: "triple_var": -1.3804881286637145 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3191: "triple_var": 2.12560916442125 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3198: "triple_var": 0.09928179638024666 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3205: "triple_var": -0.09608018016027302 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3217: "triple_var": 1.96299396878182 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3224: "triple_var": 0.1098676975593236 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3231: "triple_var": -1.529156971519988 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3243: "triple_var": 2.426945157987262 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3250: "triple_var": 0.11766525250200394 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3257: "triple_var": -0.6807059778453567 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3269: "triple_var": 2.7735217891612227 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3276: "triple_var": 0.14638404893149365 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3283: "triple_var": -0.2051336618957981 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3295: "triple_var": 3.0516519375241167 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3302: "triple_var": 0.14793713054485333 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3309: "triple_var": -0.2263666634079112 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3321: "triple_var": 0.35781039970183093 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3328: "triple_var": 0.024738902202134847 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3335: "triple_var": -9.452935491451504 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3340: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3351: "triple_var": 0.6484948952377065 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3358: "triple_var": 0.04724036464502985 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3365: "triple_var": -4.892101672795565 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3370: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3381: "triple_var": 0.8103799995915789 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3388: "triple_var": 0.04721770221263804 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3395: "triple_var": -3.586376615062693 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3399: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3410: "triple_var": 1.172549898415769 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3417: "triple_var": 0.05200902286030248 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3424: "triple_var": -1.8475703296753208 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3438: "triple_var": 1.0991425902842427 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3445: "triple_var": 0.05725959418593462 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3452: "triple_var": -3.056677177351054 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3457: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3468: "triple_var": 1.7381909290024187 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3475: "triple_var": 0.06465965083841964 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3482: "triple_var": 0.21498250515290176 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3494: "triple_var": 2.0004256271213916 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3501: "triple_var": 0.10135594542045179 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3508: "triple_var": -0.3546903023612912 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3520: "triple_var": 2.1455745791489194 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3527: "triple_var": 0.07677351970298818 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3534: "triple_var": -0.3856604896173445 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3546: "triple_var": 2.437142560915827 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3553: "triple_var": 0.13648173558393242 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3560: "triple_var": -0.0431349356425366 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3572: "triple_var": 2.7517537912925243 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3579: "triple_var": 0.1317276949644123 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3586: "triple_var": 0.1805695322708917 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3598: "triple_var": 2.9041162337100945 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3605: "triple_var": 0.15188019064563713 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3612: "triple_var": -0.21795565056270924 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3624: "triple_var": 0.3497984296245641 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3631: "triple_var": 0.03009337992863218 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3638: "triple_var": -7.265686189542155 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3643: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3654: "triple_var": 0.6248702445393101 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3661: "triple_var": 0.039270058034540874 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3668: "triple_var": -4.828502638196427 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3673: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3684: "triple_var": 0.782960434094471 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3691: "triple_var": 0.04495453901555532 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3698: "triple_var": -5.014893270472294 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3703: "triple_var" tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3714: "triple_var": 1.1255698594512062 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3721: "triple_var": 0.06812105994456139 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3728: "triple_var": -1.6386436669029842 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3742: "triple_var": 1.6207986601073192 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3749: "triple_var": 0.0747968736901808 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3756: "triple_var": -0.367109575415235 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3771: "triple_var": 1.8179792872745815 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3778: "triple_var": 0.08736011424373577 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3785: "triple_var": -0.19169476730225518 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3797: "triple_var": 2.0460116004773696 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3804: "triple_var": 0.06465823818773182 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3811: "triple_var": -0.7733378006553786 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3823: "triple_var": 2.086854410828434 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3830: "triple_var": 0.11327406076664484 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3837: "triple_var": -1.438919879090533 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3849: "triple_var": 2.43383082679458 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3856: "triple_var": 0.12862165333227413 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3863: "triple_var": -1.0421083446598152 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3875: "triple_var": 2.687695025449058 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3882: "triple_var": 0.10497117472475044 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3889: "triple_var": -0.37832808458763606 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3901: "triple_var": 2.906290718914364 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3908: "triple_var": 0.1606150824279048 tools/data/boundary_mixture_gate_20260507_0330_seedcheck.json:3915: "triple_var": -0.9921106568929448 tools/data/reports/report_20260326_0343.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/report_20260326_0343.md:23:- [✓] N2: Rule 30/110 hanno spacing >> GUE standard... → ⟨r⟩=0.8650 (GUE standard=0.60) tools/data/reports/report_20260326_0343.md:26:- logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like tools/data/reports/report_20260326_0343.md:27:- numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like tools/data/reports/report_20260326_0343.md:28:- cellular_automata_var_30: r=0.8703941780326052, spacing=GUE-like tools/data/reports/report_20260326_0343.md:29:- coupled_oscillators_var_50: r=1.0, spacing=Poisson-like tools/data/reports/agent_20260508_2013.md:8:**observable_contract**: claim=se il boundary esatto del supertile e' portatore globale del core alto, le posizioni IDS dei gap core devono mostrare migliore riconoscibilita Ostrowski o maggiore prossimita ai tagli nel mode aligned rispetto al same-length misaligned; observable=distanza del centro gap da boundary di chunk, hit entro 2 siti, peso Zeckendorf e zeri finali Zeckendorf; operator=Hamiltoniana tight-binding V=1, label IDS con reader theta=1/phi, centro round(IDS*N), boundary del tiling perturbato, rappresentazione Zeckendorf del centro; generator=phi_sturmian perturbato da supertile_shuffle, same_length_contiguous_shuffle, same_count_internal_shuffle; denominator=N={377,610}, phase={0,0.25,0.5,0.75}, threshold={2.0}, trials=5, top_k=12, |n|<=34, supertile_order={8,9,10,11}; not_tested=gap_ratio, domini GUE/Poisson reali, soglie 1.75/2.25, automa formale di riconoscibilita della sostituzione, generatori non-phi. tools/data/reports/agent_20260504_1219.md:45:| triple_var | 1 | -15.90 | -0.53 | 0.36 | tools/data/reports/agent_20260506_0625.md:1:# Agent Report — Perturbation Dimensionality Is Not Yet a Stable GUE Invariant tools/data/reports/agent_20260506_0625.md:7:The 03:30 run reported that scale-selective perturbations reveal a second axis in GUE (effective rank 1.889, PC2=25.2%) but not in primes. The caveat was explicit: the GUE sample was short (`N=253`). This run tests whether that second axis survives independent GUE ensembles and whether it depends on the observable definitions hidden under the same labels (`SR`, `SR2`, `triple_var`). tools/data/reports/agent_20260506_0625.md:14:- GUE long control: 6 independent replicates, each from 16 Hermitian matrices of size 180, after 10% edge trim; each replicate has 2,288 spacings; tools/data/reports/agent_20260506_0625.md:15:- GUE short control: 6 independent one-matrix replicates of size 42, after edge trim; this approximates the fragile small-sample regime; tools/data/reports/agent_20260506_0625.md:22:- `rank_audit`: `SR` = nearest-neighbor spacing ratio, `SR2` = next-nearest spacing ratio, `triple_var` = normalized variance of triple sums. tools/data/reports/agent_20260506_0625.md:23:- `scale_0330`: `SR` = local spectral rigidity at L=10, `SR2` = local spectral rigidity at L=20, `triple_var` = variance of triple products. tools/data/reports/agent_20260506_0625.md:34:| GUE long, 6 reps mean | 2288 each | 1.305 ± 0.278 | 0.064 ± 0.066 | 0.877 ± 0.081 | tools/data/reports/agent_20260506_0625.md:35:| GUE short, 6 reps mean | short | 1.683 ± 0.498 | 0.106 ± 0.080 | 0.567 ± 0.340 | tools/data/reports/agent_20260506_0625.md:44:| GUE long, 6 reps mean | 2288 each | 1.381 ± 0.223 | 0.099 ± 0.069 | 0.874 ± 0.082 | tools/data/reports/agent_20260506_0625.md:45:| GUE short, 6 reps mean | short | 2.013 ± 0.525 | 0.159 ± 0.087 | 0.746 ± 0.242 | tools/data/reports/agent_20260506_0625.md:49:1. **The strong GUE second-axis claim does not survive as stated.** Under direct `scale_0330` observables, long independent GUE replicates give rank 1.381 ± 0.223 and PC2 9.9% ± 6.9%, not rank 1.889 and PC2 25.2%. The previous number is inside the fragile short-sample regime: GUE short controls have rank 2.013 ± 0.525 and PC2 15.9% ± 8.7%. tools/data/reports/agent_20260506_0625.md:51:2. **Short GUE samples inflate apparent perturbation dimensionality.** In both observable sets, GUE short has higher rank and larger variance than GUE long. This does not prove the 03:30 axis was false in every configuration; it restricts it to a sample-size-sensitive observation unless a larger-replicate run recovers it. tools/data/reports/agent_20260506_0625.md:53:3. **The lab has an observable-name collision.** `SR`, `SR2`, and `triple_var` do not name the same functions across the recent scripts. `exp_observable_rank_audit.py` uses spacing-ratio and triple-sum variance; `exp_scale_selective_perturbation.py` uses local spectral rigidity and triple-product variance. Therefore the sentence "same observables as observable rank audit" in the 03:30 report is not exact. This is a META constraint, not a numerical subtlety. tools/data/reports/agent_20260506_0625.md:57:5. **Poisson and shuffled-prime controls show multi-axis artifacts.** Poisson has rank 1.917/2.201 depending on observable set; prime shuffle control has rank 2.294/1.988. Multi-dimensional perturbation response by itself is not evidence of structured GUE-like boundary. It can arise from low structural signal plus noisy denominators in retention normalization. tools/data/reports/agent_20260506_0625.md:60:**CONSTRAINT on META + BOUNDARY**: "GUE has a second perturbation axis" must be scoped to the exact sample length, generator, and observable definitions. In the larger independent-GUE perimeter tested here, the robust statement is weaker: tools/data/reports/agent_20260506_0625.md:62:> Primes remain near one perturbation coordinate under both observable sets; GUE long replicates show only a weak second component; short GUE samples can inflate apparent rank; Poisson and shuffled controls can also appear multi-axis. tools/data/reports/agent_20260506_0625.md:64:The boundary is still operator-dependent, but perturbation dimensionality is not yet a stable domain invariant. The next valid test is not another single GUE matrix; it is a replicate-and-size curve for effective rank vs number of spacings, with observable definitions versioned. tools/data/reports/agent_20260506_0625.md:73:- **L4 edge cases**: the short-GUE effect is isolated as its own control, not averaged into long GUE. tools/data/reports/falsifier_20260430_1919.json:23: "claim": "DIPOLAR_ORDERING confirmed and sharpened ... original framing of 'spacing_ratio toward Poisson, lag1_acf toward GUE' is misleading — both point in the same direction.", tools/data/reports/falsifier_20260430_1919.json:24: "evidence": "Il claim strutturale in lab_data diceva esplicitamente: spacing_ratio verso Poisson e lag1_acf verso GUE. Il report lo sostituisce con 'entrambi negative z' e lo marca come confirmed/refined, non come falsificazione o cambio di definizione del frame Delta_tau -> z-score.", tools/data/reports/falsifier_20260430_1919.json:25: "suggestion": "Dichiarare il nodo: 'DIPOLAR_ORDERING originale falsificato nella forma Delta_tau oppositiva; scope corretto: co-orientamento negativo rispetto a shuffle baseline'. Tenere distinti Delta_tau GUE-Poisson e z rispetto a shuffle." tools/data/reports/falsifier_20260430_1905.json:9: "claim": "Universal Poisson drift confirmed. All five τ values decrease with scale", tools/data/reports/falsifier_20260430_1905.json:37: "claim": "Universal Poisson drift confirmed ... τ values decrease with scale", tools/data/reports/agent_20260405_0919.md:7:> Direction: "Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso" tools/data/reports/agent_20260405_0919.md:11:Previous experiment showed primes drift from GUE toward Poisson with scale. **What is the functional form of this crossover?** The Brody distribution P(s) = (1+beta)*alpha*s^beta*exp(-alpha*s^{1+beta}) interpolates between Poisson (beta=0) and GOE (beta=1). What is beta(p) for primes? tools/data/reports/agent_20260405_0919.md:36:| Extrapolated Poisson (beta=0) | **p ~ 10^13** | tools/data/reports/agent_20260405_0919.md:42:2. **Primes are always above Cramer**: beta_Cramer ≈ 0 at all scales (pure Poisson, as expected). Primes have beta ≈ 0.23-0.42, solidly intermediate between Poisson and GOE. tools/data/reports/agent_20260405_0919.md:44:3. **The crossover is NOT a phase transition**: there is no sharp boundary between GUE and Poisson regimes. The Brody beta decays smoothly and linearly in ln(p). The "boundary" is the entire range. tools/data/reports/agent_20260405_0919.md:46:4. **Falsifiable prediction**: beta → 0 (Poisson) at p ~ 10^13. This is testable with segmented sieves. tools/data/reports/agent_20260405_0919.md:58:The "third included" at the GUE/Poisson boundary is not a point — it's a **crossover function**. The primes' level repulsion parameter decays as 0.606 - 0.020*ln(p), bridging two universality classes without belonging to either. This is structurally consistent with: tools/data/reports/agent_20260405_0919.md:59:- The D-ND framework: the boundary between two poles (GUE/Poisson) carries its own structure tools/data/reports/agent_20260405_0919.md:61:- The crossover function itself is the "third" — neither GUE nor Poisson, but a specific interpolation tools/data/reports/agent_20260405_0919.md:67:- **PREDICTION**: primes reach Poisson at p ~ 10^13 tools/data/reports/agent_20260507_1458.md:5:**observables_registry**: N/A — nessun osservabile canonico SR/SR2/L1/L2/triple_var usato tools/data/reports/agent_20260507_1458.md:46:Numeri di dettaglio: C1 spacing GUE-like con `=0.6150`; C2 `pearson_r=-0.6428`; C3 distanza media da intero `0.0270`; G1 best phi-distance `0.021256`; G2 `12/13` domini convergono a phi; N2 `=1.0000`. tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:10: "triple_var", tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:44: "observable": "SR membership and focus signature [SR,L1,triple_var] in coherent_one_sided_observables, plus paired z-deltas against mod6", tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:66: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:75: "triple_var": -2.4379150994275194 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:96: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:105: "triple_var": -10.917439623395076 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:125: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:134: "triple_var": -9.942693399668245 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:152: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:164: "triple_var": 10.004797256868272 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:174: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:181: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:193: "triple_var": 7.344578565184534 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:202: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:209: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:221: "triple_var": 7.70466406893821 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:236: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:248: "triple_var": 7.88709610444035 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:263: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:275: "triple_var": 11.168513658640428 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:285: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:290: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:302: "triple_var": 6.418351782190357 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:311: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:329: "triple_var": 7.551830000711119 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:344: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:356: "triple_var": 9.756364754767183 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:373: "z_delta_triple_var_mean": 8.479524523967557 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:384: "z_delta_triple_var_mean": 0.00951916036123993 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:399: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:411: "triple_var": 11.826415987360006 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:421: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:439: "triple_var": 3.567255670820649 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:448: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:455: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:467: "triple_var": 8.885534523397205 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:481: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:493: "triple_var": 4.891074606344962 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:517: "triple_var": 10.789740475227056 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:527: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:534: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:546: "triple_var": 3.9949593805532437 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:555: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:560: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:572: "triple_var": 8.968568347124465 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:587: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:599: "triple_var": 7.114677411098213 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:616: "z_delta_triple_var_mean": 7.504778300240725 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:627: "z_delta_triple_var_mean": 0.008542836221625579 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:659: "triple_var": -1.2138581986077375 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:666: "triple_var": -0.10573263658558443 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:682: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:696: "triple_var": -12.382371857248167 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:703: "triple_var": -0.29736933146471883 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:730: "triple_var": -12.003598673834794 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:737: "triple_var": 0.5764751578978635 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:763: "triple_var": -1.7922205639687556 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:770: "triple_var": 0.9383025068986665 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:786: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:802: "triple_var": -11.548585318735938 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:809: "triple_var": 0.19075799727489567 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:825: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:841: "triple_var": -8.906897975066968 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:848: "triple_var": -0.416886554169824 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:862: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:876: "triple_var": -3.596198805858907 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:883: "triple_var": -0.08010658731338674 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:897: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:913: "triple_var": -10.014550588049264 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:920: "triple_var": -0.3589811387680313 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:936: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:952: "triple_var": -7.591158186412151 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:959: "triple_var": 0.3118948295810561 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:972: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:986: "triple_var": -2.953755069220867 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:993: "triple_var": -0.3611962066955597 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1024: "triple_var": -10.505585069931985 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1031: "triple_var": 0.5198757487498674 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1045: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1059: "triple_var": -11.922323416345332 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1066: "triple_var": -0.3183336190406164 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1090: "triple_var": -1.2513682532840693 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1097: "triple_var": 0.6258330290396665 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1113: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1130: "triple_var": -11.256165510152341 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1137: "triple_var": -0.3349763387298866 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1151: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1167: "triple_var": -13.077784240644075 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1174: "triple_var": 0.2887342746577109 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1200: "triple_var": -1.7509370800969133 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1207: "triple_var": -0.3347817357295492 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1223: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1239: "triple_var": -9.638033184537264 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1246: "triple_var": 0.42778035910462286 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1261: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1277: "triple_var": -6.642011686441876 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1284: "triple_var": 0.13489878798483285 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1298: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1312: "triple_var": -3.982128506231472 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1319: "triple_var": -0.10701940174895647 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1335: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1351: "triple_var": -11.326707071416006 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1358: "triple_var": 0.05946568967461008 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1387: "triple_var": -7.549384177052121 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1394: "triple_var": -0.042800053092221124 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1407: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1423: "triple_var": -2.9628543181514333 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1430: "triple_var": -0.07704981754363516 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1446: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1462: "triple_var": -10.667518387089643 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1469: "triple_var": 0.5875093122632367 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1485: "triple_var" tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1502: "triple_var": -11.848388841548639 tools/data/prime_vs_mod6_sr_boundary_20260514_1330_w512.json:1509: "triple_var": -0.2911690929415919 tools/data/reports/agent_20260507_0901.md:7:observables_used: [SR, SR2, L1, L2, triple_var] tools/data/reports/agent_20260507_0901.md:15:coordinata locale GUE/Poisson. tools/data/reports/agent_20260507_0901.md:35:| DUALITA_golden | SR, L1, triple_var | 3.00 | 0.25 | 3.418 | 0.30 | tools/data/reports/agent_20260507_0901.md:36:| R_periodic_triad | SR, SR2, L1, L2, triple_var | 5.00 | 0.25 | 4.400 | 0.30 | tools/data/reports/agent_20260507_0901.md:37:| T_markov_alternating | SR, SR2, L1, L2, triple_var | 5.00 | 0.05 | 4.412 | 0.30 | tools/data/reports/agent_20260507_0901.md:38:| E_ar1_continuity | SR, SR2, L1, L2, triple_var | 5.00 | 0.60 | 4.394 | 0.30 | tools/data/reports/agent_20260507_0901.md:52: `SR`, `L1`, `triple_var`; the three other perimeters transfer on all five tools/data/reports/agent_20260507_0901.md:65: GUE/Poisson layer. It is the coordinate created by the replacement protocol: tools/data/reports/agent_20260509_1457.md:7:observables_used: [`unit_status`, `unit_crossing_N`, `below_unit_count`, `fit_ready_rows`, `denominator_state`, `best_model`] - osservabili domain-native, non canonici SR/SR2/L1/L2/triple_var. tools/data/reports/report_20260331_1809.md:17:- [✓] C1: Zeri zeta hanno spacing GUE (non Poisson)... → spacing=GUE-like, ⟨r⟩=0.6150 tools/data/reports/report_20260331_1809.md:22:- [✓] N2: Rule 30/110 hanno spacing >> GUE standard... → ⟨r⟩=1.0000 (GUE standard=0.60) tools/data/reports/report_20260331_1809.md:25:- logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like tools/data/reports/report_20260331_1809.md:26:- logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like tools/data/reports/report_20260331_1809.md:27:- brownian_motion_var_0.5: r=0.9523809523809524, spacing=Poisson-like tools/data/reports/report_20260331_1809.md:28:- percolation_var_0.65: r=1.0, spacing=Poisson-like tools/data/spettro_zeta_results.json:8: "stat_class": "GUE", tools/data/spettro_zeta_results.json:628: "vs_GOE": { tools/data/spettro_zeta_results.json:632: "vs_GUE": { tools/data/spettro_zeta_results.json:636: "vs_Poisson": { tools/data/spettro_zeta_results.json:642: "vs_GOE": { tools/data/spettro_zeta_results.json:646: "vs_GUE": { tools/data/spettro_zeta_results.json:650: "vs_Poisson": { tools/data/spettro_zeta_results.json:656: "vs_GOE": { tools/data/spettro_zeta_results.json:660: "vs_GUE": { tools/data/spettro_zeta_results.json:664: "vs_Poisson": { tools/data/spettro_zeta_results.json:677: "GOE_ref": 0.286, tools/data/spettro_zeta_results.json:678: "GUE_ref": 0.178 tools/data/spettro_zeta_results.json:1208: "stat_class": "GUE", tools/data/notte_20260306_0330.md:4: ising_2d_var_-0.1: r=0.9618098462163317, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260306_0330.md:5: ising_2d_var_0.1: r=0.9905364716330192, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260306_0330.md:6: numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like (⟨r⟩=0.9609375) tools/data/notte_20260306_0330.md:7: logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like (⟨r⟩=0.35930230610663094) tools/data/notte_20260306_0330.md:8: logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like (⟨r⟩=0.3914428894887236) tools/data/notte_20260306_0330.md:9: cellular_automata_var_30: r=0.8703941780326052, spacing=GUE-like (⟨r⟩=0.8649999999999983) tools/data/notte_20260306_0330.md:10: brownian_motion_var_0.3: r=1.0, spacing=Poisson-like (⟨r⟩=0.40716210665181113) tools/data/notte_20260306_0330.md:11: brownian_motion_var_0.5: r=1.1993212669683257, spacing=Poisson-like (⟨r⟩=0.39848592435784913) tools/data/notte_20260306_0330.md:12: coupled_oscillators_var_50: r=1.0, spacing=Poisson-like (⟨r⟩=0.45756422661060286) tools/data/notte_20260306_0330.md:13: percolation_var_0.55: r=1.0, spacing=Poisson-like (⟨r⟩=0.42469524388230473) tools/data/notte_20260306_0330.md:14: percolation_var_0.65: r=1.0, spacing=Poisson-like (⟨r⟩=0.4501286869364695) tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:1:{"ambiguous_beta": [], "case": "prime/dnd_autoricerca/offset_0", "class": "prime", "endpoint_distance": 1.908718499413106, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": true, "n_gaps": 1024, "offset": "offset_0", "one_sided_observables": ["SR"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_0", "stable_count_coherent": 1.125, "stable_count_illusory": 0.0, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -1.5651166432609647, "L2": -0.2367433784821376, "SR": -4.936035526513007, "SR2": 1.1018562717965903, "triple_var": -1.5427512682830968}, "z_mean_illusory": {"L1": 0.06409792408577972, "L2": -0.2234127594326334, "SR": 0.41216470994229043, "SR2": -0.5930990456482452, "triple_var": -0.056893110583065606}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:2:{"ambiguous_beta": [0.2, 0.4], "case": "mod6_index_aligned/dnd_autoricerca/offset_0", "class": "mod6_index_aligned", "endpoint_distance": 4.32706078803929, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_0", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -35.55633566819284, "L2": 35.733768807796466, "SR": -35.556335668192844, "SR2": 35.73376880779641, "triple_var": -15.794142137897895}, "z_mean_illusory": {"L1": -0.051562410124329716, "L2": -0.07507906848837526, "SR": -0.05156241012432697, "SR2": -0.07507906848837348, "triple_var": -0.1327289002798124}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:3:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/dnd_autoricerca/offset_0", "class": "mod6_span_matched", "endpoint_distance": 2.73081661075532, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": false, "n_gaps": 1024, "offset": "offset_0", "one_sided_observables": ["SR2", "L2"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 1.0, "state": "support_falls", "z_mean_coherent": {"L1": -17.956335895404557, "L2": -12.954524887941435, "SR": -22.786453152643233, "SR2": -18.351168837361907, "triple_var": -20.50239053533548}, "z_mean_illusory": {"L1": 0.051627539783048626, "L2": -0.3075977344027341, "SR": 0.16339450663038196, "SR2": -0.4102255268981512, "triple_var": -0.11534182849819136}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:4:{"ambiguous_beta": [], "case": "prime/dnd_autoricerca/offset_512", "class": "prime", "endpoint_distance": 3.29406044153111, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_512", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_512", "stable_count_coherent": 3.0, "stable_count_illusory": 0.25, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -5.241530327117344, "L2": 0.7272772463935754, "SR": -7.905323119279314, "SR2": 0.43512956592531615, "triple_var": -3.925498027171484}, "z_mean_illusory": {"L1": -0.49420903881461276, "L2": -0.2863769205530686, "SR": -0.3026238686468015, "SR2": 0.3678385649287664, "triple_var": -0.5565080490980227}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:5:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/dnd_autoricerca/offset_512", "class": "mod6_index_aligned", "endpoint_distance": 4.323200936263757, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_512", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -30.78872009695667, "L2": 28.449991011784373, "SR": -30.788720096956666, "SR2": 28.44999101178431, "triple_var": -12.759376112893282}, "z_mean_illusory": {"L1": 0.5410189747779448, "L2": 0.29231195943003796, "SR": 0.5410189747779418, "SR2": 0.2923119594300378, "triple_var": 0.5463043262818726}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:6:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/dnd_autoricerca/offset_512", "class": "mod6_span_matched", "endpoint_distance": 4.321414242700661, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_512", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -33.26396827175428, "L2": 33.96943858862036, "SR": -34.7850282231276, "SR2": 34.413178644110836, "triple_var": -14.716445417961056}, "z_mean_illusory": {"L1": 0.6156390495290492, "L2": -0.4155048503552924, "SR": 0.47567456258815766, "SR2": -0.30743930626261573, "triple_var": 0.4200694223793047}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:7:{"ambiguous_beta": [], "case": "prime/dnd_autoricerca/offset_1024", "class": "prime", "endpoint_distance": 2.6338025186618745, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": true, "n_gaps": 1024, "offset": "offset_1024", "one_sided_observables": ["SR", "L1"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1024", "stable_count_coherent": 3.0, "stable_count_illusory": 0.75, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -5.096650413756441, "L2": -0.1490118285414985, "SR": -5.578720920574684, "SR2": 0.9357816835713308, "triple_var": -4.596852743569116}, "z_mean_illusory": {"L1": -0.578841168228689, "L2": -0.9339835676997283, "SR": -0.44241191152805626, "SR2": -0.7367630810861305, "triple_var": -0.9522367268397922}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:8:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/dnd_autoricerca/offset_1024", "class": "mod6_index_aligned", "endpoint_distance": 4.324836001487211, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1024", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -33.553095875932115, "L2": 31.04857495505965, "SR": -33.553095875932115, "SR2": 31.048574955059642, "triple_var": -13.378111429774048}, "z_mean_illusory": {"L1": 0.4124780283482251, "L2": -0.2936951990137984, "SR": 0.41247802834822567, "SR2": -0.29369519901380026, "triple_var": 0.2443540674635697}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:9:{"ambiguous_beta": [0.2, 0.4], "case": "mod6_span_matched/dnd_autoricerca/offset_1024", "class": "mod6_span_matched", "endpoint_distance": 3.8375057421325547, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1024", "stable_count_coherent": 4.375, "stable_count_illusory": 0.375, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -15.196825416510862, "L2": 1.881272993262772, "SR": -23.203201402301033, "SR2": 7.723651681530123, "triple_var": -13.345216646707105}, "z_mean_illusory": {"L1": 0.10256601935339704, "L2": -0.33697556067864487, "SR": 0.05967625414255026, "SR2": -0.6542634979897397, "triple_var": -0.0424605515444057}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:10:{"ambiguous_beta": [0.2], "case": "prime/dnd_autoricerca/offset_1536", "class": "prime", "endpoint_distance": 3.2257016736690285, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1536", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1536", "stable_count_coherent": 3.5, "stable_count_illusory": 0.125, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -3.265020644432302, "L2": -1.8926432694762236, "SR": -3.382616462672961, "SR2": -0.04219505575950944, "triple_var": -3.7436288200258803}, "z_mean_illusory": {"L1": -0.08575555647426329, "L2": 0.07585834860744708, "SR": 0.17803176015188876, "SR2": 0.4961459325156209, "triple_var": -0.061864934626999275}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:11:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/dnd_autoricerca/offset_1536", "class": "mod6_index_aligned", "endpoint_distance": 4.319126686489332, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1536", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1536", "stable_count_coherent": 5.0, "stable_count_illusory": 0.625, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -38.43851845195383, "L2": 32.86515052320902, "SR": -38.43851845195384, "SR2": 32.86515052320899, "triple_var": -16.782627751600174}, "z_mean_illusory": {"L1": -0.4090084699153741, "L2": 0.09638707649933753, "SR": -0.4090084699153703, "SR2": 0.09638707649933259, "triple_var": -0.3339113268423166}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:12:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/dnd_autoricerca/offset_1536", "class": "mod6_span_matched", "endpoint_distance": 4.314471893587503, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1536", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "dnd_autoricerca", "row_id": "dnd_autoricerca/offset_1536", "stable_count_coherent": 5.0, "stable_count_illusory": 0.375, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -14.775407655158958, "L2": -9.148270060972653, "SR": -21.707674075930015, "SR2": -9.86245691489856, "triple_var": -17.79095929281918}, "z_mean_illusory": {"L1": 0.4423521570349872, "L2": 0.7166266811202449, "SR": 0.3736532328978034, "SR2": 0.8004582872556438, "triple_var": 0.6532408017238057}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:13:{"ambiguous_beta": [0.4], "case": "prime/direct_sieve/offset_0", "class": "prime", "endpoint_distance": 1.8615846092139272, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": true, "n_gaps": 1024, "offset": "offset_0", "one_sided_observables": ["SR"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_0", "stable_count_coherent": 1.25, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -1.7194297910081264, "L2": -0.42525507262263684, "SR": -5.505191412110172, "SR2": 1.0503457652999058, "triple_var": -1.6529769771970457}, "z_mean_illusory": {"L1": -0.2720548537529309, "L2": 0.5114876992824657, "SR": -0.3730431854470586, "SR2": -0.12921934664112258, "triple_var": -0.07357768253019742}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:14:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_0", "class": "mod6_index_aligned", "endpoint_distance": 4.324136306399426, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_0", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -29.882580516805575, "L2": 35.740658472886594, "SR": -29.882580516805575, "SR2": 35.74065847288657, "triple_var": -14.176617328437912}, "z_mean_illusory": {"L1": 0.5756148767894361, "L2": -0.2172229552536076, "SR": 0.5756148767894332, "SR2": -0.2172229552536067, "triple_var": 0.41270619695995786}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:15:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_0", "class": "mod6_span_matched", "endpoint_distance": 3.347025403223169, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_0", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_0", "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -18.427771071777997, "L2": -15.704801009553682, "SR": -23.68293751125588, "SR2": -21.08893334246467, "triple_var": -23.95402673184698}, "z_mean_illusory": {"L1": 0.3535535542123013, "L2": -0.7038203527795384, "SR": 0.18152631175663186, "SR2": -0.6434125149320733, "triple_var": -0.012112964465623974}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:16:{"ambiguous_beta": [], "case": "prime/direct_sieve/offset_512", "class": "prime", "endpoint_distance": 2.6619630714475853, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": true, "n_gaps": 1024, "offset": "offset_512", "one_sided_observables": ["SR", "L1"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_512", "stable_count_coherent": 3.0, "stable_count_illusory": 0.375, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -4.885282180682269, "L2": 0.5493343020030083, "SR": -7.409889335937658, "SR2": 0.3252269186937323, "triple_var": -3.795375734364665}, "z_mean_illusory": {"L1": 0.571522685017645, "L2": -0.23566820967926305, "SR": 0.33437188851287136, "SR2": 0.05688558720780498, "triple_var": 0.48220901670695715}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:17:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_512", "class": "mod6_index_aligned", "endpoint_distance": 3.3515840634327856, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_512", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.625, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -34.88505781567051, "L2": 33.03760304382555, "SR": -34.88505781567051, "SR2": 33.03760304382551, "triple_var": -15.742409700510851}, "z_mean_illusory": {"L1": -0.16585900767010925, "L2": 0.833693009906562, "SR": -0.16585900767011166, "SR2": 0.83369300990656, "triple_var": 0.26449492801308344}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:18:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_512", "class": "mod6_span_matched", "endpoint_distance": 4.322811791327596, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_512", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_512", "stable_count_coherent": 5.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -32.126211504272995, "L2": 30.87058519776802, "SR": -34.96207091990357, "SR2": 34.65489167980094, "triple_var": -13.582035764507921}, "z_mean_illusory": {"L1": -0.08360440592558732, "L2": 0.6169351852058884, "SR": 0.14659977397976287, "SR2": 0.7336874246117965, "triple_var": 0.13126136042179593}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:19:{"ambiguous_beta": [0.4], "case": "prime/direct_sieve/offset_1024", "class": "prime", "endpoint_distance": 3.307296895526232, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1024", "one_sided_observables": ["SR", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1024", "stable_count_coherent": 3.0, "stable_count_illusory": 0.25, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -5.269448554675485, "L2": 0.15297977263504214, "SR": -5.782264022921661, "SR2": 1.2264447990873477, "triple_var": -4.919855090714239}, "z_mean_illusory": {"L1": 0.4400273871528081, "L2": 0.32003593447278467, "SR": 0.02612393285771422, "SR2": 0.5808226107075796, "triple_var": 0.6356851966039665}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:20:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_1024", "class": "mod6_index_aligned", "endpoint_distance": 4.322849238418466, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1024", "stable_count_coherent": 5.0, "stable_count_illusory": 0.5, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -32.46376939720014, "L2": 32.17103992056691, "SR": -32.463769397200146, "SR2": 32.17103992056689, "triple_var": -15.67217336687937}, "z_mean_illusory": {"L1": 0.27081957953747987, "L2": 0.07694560064131109, "SR": 0.2708195795374788, "SR2": 0.07694560064131242, "triple_var": 0.21451105950527025}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:21:{"ambiguous_beta": [0.2], "case": "mod6_span_matched/direct_sieve/offset_1024", "class": "mod6_span_matched", "endpoint_distance": 3.8298896727809972, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1024", "one_sided_observables": ["SR", "SR2", "L1", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1024", "stable_count_coherent": 4.125, "stable_count_illusory": 0.5, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -16.369995718385418, "L2": 1.6931529059296626, "SR": -26.013677402232766, "SR2": 7.123860332853665, "triple_var": -14.079726372602975}, "z_mean_illusory": {"L1": -0.42601420206231366, "L2": 0.9097931693437625, "SR": -0.06441182422145778, "SR2": 0.871116636592296, "triple_var": 0.04290691460880933}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:22:{"ambiguous_beta": [], "case": "prime/direct_sieve/offset_1536", "class": "prime", "endpoint_distance": 1.8795018398408607, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": false, "has_sr": true, "n_gaps": 1024, "offset": "offset_1536", "one_sided_observables": ["SR"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1536", "stable_count_coherent": 3.625, "stable_count_illusory": 0.75, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -3.474748573403697, "L2": -2.1135876050841547, "SR": -3.406723098865407, "SR2": 0.10912567758264101, "triple_var": -3.90288308264923}, "z_mean_illusory": {"L1": 0.8812477317473351, "L2": 0.6613517848464843, "SR": 0.5719502011455098, "SR2": 0.43802639073285876, "triple_var": 1.0340171317114486}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:23:{"ambiguous_beta": [0.2], "case": "mod6_index_aligned/direct_sieve/offset_1536", "class": "mod6_index_aligned", "endpoint_distance": 4.326598729013861, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1536", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1536", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_chart_recovered", "z_mean_coherent": {"L1": -34.31031695252927, "L2": 33.745360819436705, "SR": -34.31031695252927, "SR2": 33.7453608194367, "triple_var": -15.644350266206917}, "z_mean_illusory": {"L1": 0.3398092211592378, "L2": -0.030770235115535283, "SR": 0.3398092211592352, "SR2": -0.03077023511553105, "triple_var": 0.3591853602508874}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:24:{"ambiguous_beta": [], "case": "mod6_span_matched/direct_sieve/offset_1536", "class": "mod6_span_matched", "endpoint_distance": 4.306946322009308, "endpoint_stable_observables": [], "event": "case", "has_focus_signature": true, "has_sr": true, "n_gaps": 1024, "offset": "offset_1536", "one_sided_observables": ["SR", "SR2", "L1", "L2", "triple_var"], "provider": "direct_sieve", "row_id": "direct_sieve/offset_1536", "stable_count_coherent": 5.0, "stable_count_illusory": 0.0, "state": "beta_absent_blank", "z_mean_coherent": {"L1": -15.876236873629253, "L2": -9.586403246878946, "SR": -23.108999256778745, "SR2": -10.84754150292961, "triple_var": -19.48180745719065}, "z_mean_illusory": {"L1": 0.07049496471537393, "L2": 0.6239774919381992, "SR": 0.06816071010600537, "SR2": 0.38639766872253045, "triple_var": 0.2889038569151086}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:25:{"case_count": 8, "class": "prime", "common_one_sided_observables": ["SR"], "endpoint_distance_mean": 2.5965786936629653, "event": "class_summary", "focus_signature_count": 3, "mean_z_coherent": {"L1": -3.8146533910420786, "L2": -0.4234562291468782, "SR": -5.488345487359358, "SR2": 0.6427144532746694, "triple_var": -3.5099777179968443}, "sr_count": 8, "sr_rate": 1.0, "stable_count_coherent_mean": 2.6875, "state_counts": {"beta_absent_blank": 5, "beta_chart_recovered": 3}, "union_one_sided_observables": ["L1", "SR", "triple_var"]} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:26:{"case_count": 8, "class": "mod6_index_aligned", "common_one_sided_observables": ["L1", "SR", "triple_var"], "endpoint_distance_mean": 4.202424093693016, "event": "class_summary", "focus_signature_count": 8, "mean_z_coherent": {"L1": -33.73479934690512, "L2": 32.84901844432066, "SR": -33.73479934690512, "SR2": 32.84901844432063, "triple_var": -14.993726011775056}, "sr_count": 8, "sr_rate": 1.0, "stable_count_coherent_mean": 5.0, "state_counts": {"beta_chart_recovered": 8}, "union_one_sided_observables": ["L1", "L2", "SR", "SR2", "triple_var"]} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:27:{"case_count": 8, "class": "mod6_span_matched", "common_one_sided_observables": [], "endpoint_distance_mean": 3.8763602098146386, "event": "class_summary", "focus_signature_count": 7, "mean_z_coherent": {"L1": -20.499094050861792, "L2": 2.6275563100292625, "SR": -26.281255243021604, "SR2": 2.970685217580102, "triple_var": -17.18157602737142}, "sr_count": 7, "sr_rate": 0.875, "stable_count_coherent_mean": 4.8125, "state_counts": {"beta_absent_blank": 1, "beta_chart_recovered": 6, "support_falls": 1}, "union_one_sided_observables": ["L1", "L2", "SR", "SR2", "triple_var"]} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:28:{"endpoint_delta": -2.462551697185499, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_index_aligned/direct_sieve/offset_0", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_0", "prime_obs": ["SR"], "row_id": "direct_sieve/offset_0", "signature_jaccard": 0.2, "sr_delta": 0, "stable_count_delta": -3.75, "z_delta": {"L1": 28.16315072579745, "L2": -36.165913545509234, "SR": 24.377389104695403, "SR2": -34.69031270758666, "triple_var": 12.523640351240866}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:29:{"endpoint_delta": -1.0155523428922342, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/direct_sieve/offset_1024", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_1024", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "direct_sieve/offset_1024", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 27.194320842524654, "L2": -32.01806014793187, "SR": 26.681505374278487, "SR2": -30.944595121479544, "triple_var": 10.75231827616513}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:30:{"endpoint_delta": -2.4470968891730007, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_index_aligned/direct_sieve/offset_1536", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_1536", "prime_obs": ["SR"], "row_id": "direct_sieve/offset_1536", "signature_jaccard": 0.2, "sr_delta": 0, "stable_count_delta": -1.375, "z_delta": {"L1": 30.835568379125572, "L2": -35.85894842452086, "SR": 30.903593853663864, "SR2": -33.63623514185406, "triple_var": 11.741467183557688}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:31:{"endpoint_delta": -0.6896209919852003, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_index_aligned/direct_sieve/offset_512", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "L1", "triple_var"], "prime_case": "prime/direct_sieve/offset_512", "prime_obs": ["SR", "L1"], "row_id": "direct_sieve/offset_512", "signature_jaccard": 0.6666666666666666, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 29.99977563498824, "L2": -32.48826874182254, "SR": 27.47516847973285, "SR2": -32.71237612513178, "triple_var": 11.947033966146186}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:32:{"endpoint_delta": -2.4183422886261843, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_index_aligned/dnd_autoricerca/offset_0", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_0", "prime_obs": ["SR"], "row_id": "dnd_autoricerca/offset_0", "signature_jaccard": 0.2, "sr_delta": 0, "stable_count_delta": -3.875, "z_delta": {"L1": 33.99121902493187, "L2": -35.970512186278604, "SR": 30.620300141679838, "SR2": -34.63191253599982, "triple_var": 14.251390869614799}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:33:{"endpoint_delta": -1.6910334828253362, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_index_aligned/dnd_autoricerca/offset_1024", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_1024", "prime_obs": ["SR", "L1"], "row_id": "dnd_autoricerca/offset_1024", "signature_jaccard": 0.4, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 28.456445462175672, "L2": -31.19758678360115, "SR": 27.974374955357433, "SR2": -30.11279327148831, "triple_var": 8.781258686204932}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:34:{"endpoint_delta": -1.0934250128203034, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/dnd_autoricerca/offset_1536", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_1536", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_1536", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -1.5, "z_delta": {"L1": 35.17349780752153, "L2": -34.75779379268524, "SR": 35.055901989280876, "SR2": -32.907345578968496, "triple_var": 13.038998931574294}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:35:{"endpoint_delta": -1.0291404947326472, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_index_aligned/dnd_autoricerca/offset_512", "mod6_class": "mod6_index_aligned", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_512", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_512", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 25.547189769839328, "L2": -27.722713765390797, "SR": 22.883396977677354, "SR2": -28.014861445858994, "triple_var": 8.833878085721798}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:36:{"event": "pair_summary", "label_swap_audit": {"null": "row_local_label_swap_preserving_provider_offset_denominator", "observed": {"endpoint_delta_mean": -1.6058454000300508, "focus_signature_delta_mean": -0.625, "sr_delta_mean": 0.0, "stable_count_delta_mean": -2.3125, "z_delta_L1_mean": 29.92014595586304, "z_delta_L2_mean": -33.272474673467535, "z_delta_SR2_mean": -32.20630399104596, "z_delta_SR_mean": 28.246453859545763, "z_delta_triple_var_mean": 11.483748293778213}, "p_two_sided": {"endpoint_delta_mean": 0.008054674151818403, "focus_signature_delta_mean": 0.05833536734195753, "sr_delta_mean": 1.0, "stable_count_delta_mean": 0.008054674151818403, "z_delta_L1_mean": 0.008054674151818403, "z_delta_L2_mean": 0.008054674151818403, "z_delta_SR2_mean": 0.008054674151818403, "z_delta_SR_mean": 0.008054674151818403, "z_delta_triple_var_mean": 0.008054674151818403}, "trials": 4096}, "mean_signature_jaccard": 0.43333333333333335, "mod6_class": "mod6_index_aligned", "row_count": 8} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:37:{"endpoint_delta": -1.4854407940092418, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_span_matched/direct_sieve/offset_0", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "L1", "triple_var"], "prime_case": "prime/direct_sieve/offset_0", "prime_obs": ["SR"], "row_id": "direct_sieve/offset_0", "signature_jaccard": 0.3333333333333333, "sr_delta": 0, "stable_count_delta": -3.75, "z_delta": {"L1": 16.708341280769872, "L2": 15.279545936931045, "SR": 18.177746099145708, "SR2": 22.139279107764576, "triple_var": 22.301049754649934}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:38:{"endpoint_delta": -0.5225927772547654, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/direct_sieve/offset_1024", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "triple_var"], "prime_case": "prime/direct_sieve/offset_1024", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "direct_sieve/offset_1024", "signature_jaccard": 0.75, "sr_delta": 0, "stable_count_delta": -1.125, "z_delta": {"L1": 11.100547163709933, "L2": -1.5401731332946205, "SR": 20.231413379311107, "SR2": -5.897415533766317, "triple_var": 9.159871281888737}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:39:{"endpoint_delta": -2.4274444821684473, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_span_matched/direct_sieve/offset_1536", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_1536", "prime_obs": ["SR"], "row_id": "direct_sieve/offset_1536", "signature_jaccard": 0.2, "sr_delta": 0, "stable_count_delta": -1.375, "z_delta": {"L1": 12.401488300225555, "L2": 7.472815641794792, "SR": 19.70227615791334, "SR2": 10.956667180512252, "triple_var": 15.578924374541423}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:40:{"endpoint_delta": -1.6608487198800108, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_span_matched/direct_sieve/offset_512", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/direct_sieve/offset_512", "prime_obs": ["SR", "L1"], "row_id": "direct_sieve/offset_512", "signature_jaccard": 0.4, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 27.240929323590727, "L2": -30.32125089576501, "SR": 27.55218158396591, "SR2": -34.32966476110721, "triple_var": 9.786660030143256}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:41:{"endpoint_delta": -0.8220981113422141, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/dnd_autoricerca/offset_0", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR2", "L2"], "prime_case": "prime/dnd_autoricerca/offset_0", "prime_obs": ["SR"], "row_id": "dnd_autoricerca/offset_0", "signature_jaccard": 0.0, "sr_delta": 1, "stable_count_delta": -3.875, "z_delta": {"L1": 16.391219252143593, "L2": 12.717781509459298, "SR": 17.850417626130227, "SR2": 19.453025109158496, "triple_var": 18.95963926705238}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:42:{"endpoint_delta": -1.2037032234706802, "event": "paired_row", "focus_signature_delta": -1, "mod6_case": "mod6_span_matched/dnd_autoricerca/offset_1024", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_1024", "prime_obs": ["SR", "L1"], "row_id": "dnd_autoricerca/offset_1024", "signature_jaccard": 0.5, "sr_delta": 0, "stable_count_delta": -1.375, "z_delta": {"L1": 10.10017500275442, "L2": -2.0302848218042704, "SR": 17.62448048172635, "SR2": -6.787869997958793, "triple_var": 8.748363903137989}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:43:{"endpoint_delta": -1.0887702199184743, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/dnd_autoricerca/offset_1536", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_1536", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_1536", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -1.5, "z_delta": {"L1": 11.510387010726657, "L2": 7.255626791496429, "SR": 18.325057613257055, "SR2": 9.820261859139052, "triple_var": 14.047330472793302}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:44:{"endpoint_delta": -1.0273538011695513, "event": "paired_row", "focus_signature_delta": 0, "mod6_case": "mod6_span_matched/dnd_autoricerca/offset_512", "mod6_class": "mod6_span_matched", "mod6_obs": ["SR", "SR2", "L1", "L2", "triple_var"], "prime_case": "prime/dnd_autoricerca/offset_512", "prime_obs": ["SR", "L1", "triple_var"], "row_id": "dnd_autoricerca/offset_512", "signature_jaccard": 0.6, "sr_delta": 0, "stable_count_delta": -2.0, "z_delta": {"L1": 28.022437944636934, "L2": -33.24216134222679, "SR": 26.879705103848284, "SR2": -33.97804907818552, "triple_var": 10.790947390789572}} tools/data/prime_vs_mod6_sr_boundary_20260514_0330.trace.jsonl:45:{"event": "pair_summary", "label_swap_audit": {"null": "row_local_label_swap_preserving_provider_offset_denominator", "observed": {"endpoint_delta_mean": -1.2797815161516732, "focus_signature_delta_mean": -0.5, "sr_delta_mean": 0.125, "stable_count_delta_mean": -2.125, "z_delta_L1_mean": 16.684440659819714, "z_delta_L2_mean": -3.0510125391761407, "z_delta_SR2_mean": -2.3279707643054333, "z_delta_SR_mean": 20.792909755662247, "z_delta_triple_var_mean": 13.671598309374573}, "p_two_sided": {"endpoint_delta_mean": 0.009275079326336344, "focus_signature_delta_mean": 0.12082011227727606, "sr_delta_mean": 1.0, "stable_count_delta_mean": 0.009275079326336344, "z_delta_L1_mean": 0.009275079326336344, "z_delta_L2_mean": 0.6307053941908713, "z_delta_SR2_mean": 0.7671466927019771, "z_delta_SR_mean": 0.009275079326336344, "z_delta_triple_var_mean": 0.009275079326336344}, "trials": 4096}, "mean_signature_jaccard": 0.42291666666666666, "mod6_class": "mod6_span_matched", "row_count": 8} tools/data/perturbation_dimensionality_audit.json:3: "question": "Is the GUE second perturbation axis stable across independent ensembles and sample-size controls?", tools/data/perturbation_dimensionality_audit.json:31: "triple_var" tools/data/perturbation_dimensionality_audit.json:48: "triple_var": 2.704041493314184 tools/data/perturbation_dimensionality_audit.json:67: "triple_var": { tools/data/perturbation_dimensionality_audit.json:77: "triple_var": -6.521692785375022 tools/data/perturbation_dimensionality_audit.json:88: "triple_var": 2.711373050437174 tools/data/perturbation_dimensionality_audit.json:95: "triple_var": 0.9741993130497336 tools/data/perturbation_dimensionality_audit.json:113: "triple_var": 2.7305558793713307 tools/data/perturbation_dimensionality_audit.json:120: "triple_var": 0.9066924852574906 tools/data/perturbation_dimensionality_audit.json:138: "triple_var": 2.7423834984411193 tools/data/perturbation_dimensionality_audit.json:145: "triple_var": 0.8650695814367321 tools/data/perturbation_dimensionality_audit.json:163: "triple_var": 2.7592588494526793 tools/data/perturbation_dimensionality_audit.json:170: "triple_var": 0.8056830634950227 tools/data/perturbation_dimensionality_audit.json:188: "triple_var": 2.782328202836994 tools/data/perturbation_dimensionality_audit.json:195: "triple_var": 0.7244990592202227 tools/data/perturbation_dimensionality_audit.json:213: "triple_var": 2.73901220859308 tools/data/perturbation_dimensionality_audit.json:220: "triple_var": 0.8769335814750212 tools/data/perturbation_dimensionality_audit.json:238: "triple_var": 2.8122154693950425 tools/data/perturbation_dimensionality_audit.json:245: "triple_var": 0.6193219467286161 tools/data/perturbation_dimensionality_audit.json:263: "triple_var": 2.8649647843806987 tools/data/perturbation_dimensionality_audit.json:270: "triple_var": 0.43369036261148325 tools/data/perturbation_dimensionality_audit.json:288: "triple_var": 2.953195527094063 tools/data/perturbation_dimensionality_audit.json:295: "triple_var": 0.12319509756080534 tools/data/perturbation_dimensionality_audit.json:313: "triple_var": 3.0189595850915483 tools/data/perturbation_dimensionality_audit.json:320: "triple_var": -0.10823703131828626 tools/data/perturbation_dimensionality_audit.json:338: "triple_var": 2.7253095670391967 tools/data/perturbation_dimensionality_audit.json:345: "triple_var": 0.9251549291632002 tools/data/perturbation_dimensionality_audit.json:363: "triple_var": 2.7338081915280883 tools/data/perturbation_dimensionality_audit.json:370: "triple_var": 0.8952471829276596 tools/data/perturbation_dimensionality_audit.json:388: "triple_var": 2.765883458525349 tools/data/perturbation_dimensionality_audit.json:395: "triple_var": 0.782370217126292 tools/data/perturbation_dimensionality_audit.json:413: "triple_var": 2.778950381228433 tools/data/perturbation_dimensionality_audit.json:420: "triple_var": 0.7363860453589582 tools/data/perturbation_dimensionality_audit.json:438: "triple_var": 2.783768860881298 tools/data/perturbation_dimensionality_audit.json:445: "triple_var": 0.7194291993555407 tools/data/perturbation_dimensionality_audit.json:463: "triple_var": 2.764207536939968 tools/data/perturbation_dimensionality_audit.json:470: "triple_var": 0.7882679994735123 tools/data/perturbation_dimensionality_audit.json:488: "triple_var": 2.862349501957389 tools/data/perturbation_dimensionality_audit.json:495: "triple_var": 0.44289387585681506 tools/data/perturbation_dimensionality_audit.json:513: "triple_var": 2.938886515814109 tools/data/perturbation_dimensionality_audit.json:520: "triple_var": 0.17355033784724525 tools/data/perturbation_dimensionality_audit.json:538: "triple_var": 2.9737326982499335 tools/data/perturbation_dimensionality_audit.json:545: "triple_var": 0.050922166320170645 tools/data/perturbation_dimensionality_audit.json:563: "triple_var": 2.988848876123534 tools/data/perturbation_dimensionality_audit.json:570: "triple_var": -0.0022735964160065267 tools/data/perturbation_dimensionality_audit.json:595: "triple_var": -0.3350635439183604 tools/data/perturbation_dimensionality_audit.json:602: "triple_var": -0.36280611763492354 tools/data/perturbation_dimensionality_audit.json:622: "triple_var": 2.9780622148118723 tools/data/perturbation_dimensionality_audit.json:641: "triple_var": { tools/data/perturbation_dimensionality_audit.json:651: "triple_var": -0.2735216058086641 tools/data/perturbation_dimensionality_audit.json:662: "triple_var": 2.9762504603153777 tools/data/perturbation_dimensionality_audit.json:669: "triple_var": 1.1229292922885048 tools/data/perturbation_dimensionality_audit.json:687: "triple_var": 2.9698143125223053 tools/data/perturbation_dimensionality_audit.json:694: "triple_var": 1.5596281357562032 tools/data/perturbation_dimensionality_audit.json:712: "triple_var": 2.959019294191294 tools/data/perturbation_dimensionality_audit.json:719: "triple_var": 2.2920805548009318 tools/data/perturbation_dimensionality_audit.json:737: "triple_var": 2.946148958075818 tools/data/perturbation_dimensionality_audit.json:744: "triple_var": 3.16534528975912 tools/data/perturbation_dimensionality_audit.json:762: "triple_var": 2.946186088854838 tools/data/perturbation_dimensionality_audit.json:769: "triple_var": 3.1628259305435513 tools/data/perturbation_dimensionality_audit.json:787: "triple_var": 2.975755058283616 tools/data/perturbation_dimensionality_audit.json:794: "triple_var": 1.1565427985778862 tools/data/perturbation_dimensionality_audit.json:812: "triple_var": 2.963807567496402 tools/data/perturbation_dimensionality_audit.json:819: "triple_var": 1.9671915867758978 tools/data/perturbation_dimensionality_audit.json:837: "triple_var": 2.986625671839154 tools/data/perturbation_dimensionality_audit.json:844: "triple_var": 0.4189611705429478 tools/data/perturbation_dimensionality_audit.json:862: "triple_var": 2.9961631102872888 tools/data/perturbation_dimensionality_audit.json:869: "triple_var": -0.22816323894121593 tools/data/perturbation_dimensionality_audit.json:887: "triple_var": 3.000953433028466 tools/data/perturbation_dimensionality_audit.json:894: "triple_var": -0.5531912631827939 tools/data/perturbation_dimensionality_audit.json:912: "triple_var": 2.9881461499922035 tools/data/perturbation_dimensionality_audit.json:919: "triple_var": 0.31579525945724385 tools/data/perturbation_dimensionality_audit.json:937: "triple_var": 2.971390539048024 tools/data/perturbation_dimensionality_audit.json:944: "triple_var": 1.452679643745913 tools/data/perturbation_dimensionality_audit.json:962: "triple_var": 2.981566645356363 tools/data/perturbation_dimensionality_audit.json:969: "triple_var": 0.7622210031535931 tools/data/perturbation_dimensionality_audit.json:987: "triple_var": 2.987546801369858 tools/data/perturbation_dimensionality_audit.json:994: "triple_var": 0.35646164230412947 tools/data/perturbation_dimensionality_audit.json:1012: "triple_var": 2.9819435279354174 tools/data/perturbation_dimensionality_audit.json:1019: "triple_var": 0.7366491561905152 tools/data/perturbation_dimensionality_audit.json:1037: "triple_var": 2.979600906238722 tools/data/perturbation_dimensionality_audit.json:1044: "triple_var": 0.8955983006974856 tools/data/perturbation_dimensionality_audit.json:1062: "triple_var": 2.997582216796734 tools/data/perturbation_dimensionality_audit.json:1069: "triple_var": -0.32445098610873857 tools/data/perturbation_dimensionality_audit.json:1087: "triple_var": 2.988674993414629 tools/data/perturbation_dimensionality_audit.json:1094: "triple_var": 0.2799127225153144 tools/data/perturbation_dimensionality_audit.json:1112: "triple_var": 2.993667198357118 tools/data/perturbation_dimensionality_audit.json:1119: "triple_var": -0.05881320405296444 tools/data/perturbation_dimensionality_audit.json:1137: "triple_var": 3.001414481509142 tools/data/perturbation_dimensionality_audit.json:1144: "triple_var": -0.5844738478540856 tools/data/perturbation_dimensionality_audit.json:1169: "triple_var": -0.5744192686245494 tools/data/perturbation_dimensionality_audit.json:1176: "triple_var": -0.4296156193760786 tools/data/perturbation_dimensionality_audit.json:1196: "triple_var": 2.972383991246487 tools/data/perturbation_dimensionality_audit.json:1215: "triple_var": { tools/data/perturbation_dimensionality_audit.json:1225: "triple_var": -0.8642253770968523 tools/data/perturbation_dimensionality_audit.json:1236: "triple_var": 2.967207967447089 tools/data/perturbation_dimensionality_audit.json:1243: "triple_var": 1.1614013667467433 tools/data/perturbation_dimensionality_audit.json:1261: "triple_var": 2.961381929586433 tools/data/perturbation_dimensionality_audit.json:1268: "triple_var": 1.343071797539097 tools/data/perturbation_dimensionality_audit.json:1286: "triple_var": 2.9635291902470127 tools/data/perturbation_dimensionality_audit.json:1293: "triple_var": 1.2761148400731401 tools/data/perturbation_dimensionality_audit.json:1311: "triple_var": 2.9655725451433783 tools/data/perturbation_dimensionality_audit.json:1318: "triple_var": 1.2123979241925722 tools/data/perturbation_dimensionality_audit.json:1336: "triple_var": 2.9558455262411583 tools/data/perturbation_dimensionality_audit.json:1343: "triple_var": 1.5157106997969452 tools/data/perturbation_dimensionality_audit.json:1361: "triple_var": 2.9719884184698495 tools/data/perturbation_dimensionality_audit.json:1368: "triple_var": 1.012334948460728 tools/data/perturbation_dimensionality_audit.json:1386: "triple_var": 2.9720223037727367 tools/data/perturbation_dimensionality_audit.json:1393: "triple_var": 1.0112783199731903 tools/data/perturbation_dimensionality_audit.json:1411: "triple_var": 2.9865721368363514 tools/data/perturbation_dimensionality_audit.json:1418: "triple_var": 0.5575781374745208 tools/data/perturbation_dimensionality_audit.json:1436: "triple_var": 3.002955914952859 tools/data/perturbation_dimensionality_audit.json:1443: "triple_var": 0.04669096172637104 tools/data/perturbation_dimensionality_audit.json:1461: "triple_var": 3.000966023486686 tools/data/perturbation_dimensionality_audit.json:1468: "triple_var": 0.1087407541472127 tools/data/perturbation_dimensionality_audit.json:1486: "triple_var": 2.9703738464149056 tools/data/perturbation_dimensionality_audit.json:1493: "triple_var": 1.0626813430057729 tools/data/perturbation_dimensionality_audit.json:1511: "triple_var": 2.97181949598905 tools/data/perturbation_dimensionality_audit.json:1518: "triple_var": 1.0176023738691065 tools/data/perturbation_dimensionality_audit.json:1536: "triple_var": 2.961403685306137 tools/data/perturbation_dimensionality_audit.json:1543: "triple_var": 1.3423933997900006 tools/data/perturbation_dimensionality_audit.json:1561: "triple_var": 2.9836300313033597 tools/data/perturbation_dimensionality_audit.json:1568: "triple_var": 0.6493203458842338 tools/data/perturbation_dimensionality_audit.json:1586: "triple_var": 2.947984759463817 tools/data/perturbation_dimensionality_audit.json:1593: "triple_var": 1.7608290668507824 tools/data/perturbation_dimensionality_audit.json:1611: "triple_var": 2.97477978292365 tools/data/perturbation_dimensionality_audit.json:1618: "triple_var": 0.9252932238874949 tools/data/perturbation_dimensionality_audit.json:1636: "triple_var": 3.00764727358478 tools/data/perturbation_dimensionality_audit.json:1643: "triple_var": -0.09959733300273729 tools/data/perturbation_dimensionality_audit.json:1661: "triple_var": 2.9926302776125984 tools/data/perturbation_dimensionality_audit.json:1668: "triple_var": 0.3686701573094856 tools/data/perturbation_dimensionality_audit.json:1686: "triple_var": 2.9824212266711143 tools/data/perturbation_dimensionality_audit.json:1693: "triple_var": 0.6870138974086465 tools/data/perturbation_dimensionality_audit.json:1711: "triple_var": 3.033987654481013 tools/data/perturbation_dimensionality_audit.json:1718: "triple_var": -0.9209562838205856 tools/data/perturbation_dimensionality_audit.json:1743: "triple_var": -0.21804974620688106 tools/data/perturbation_dimensionality_audit.json:1750: "triple_var": -0.5713831421515239 tools/data/perturbation_dimensionality_audit.json:1772: "triple_var": 2.012064338230096 tools/data/perturbation_dimensionality_audit.json:1791: "triple_var": { tools/data/perturbation_dimensionality_audit.json:1801: "triple_var": -10.202376400331161 tools/data/perturbation_dimensionality_audit.json:1812: "triple_var": 2.07671062571198 tools/data/perturbation_dimensionality_audit.json:1819: "triple_var": 0.9335743777536026 tools/data/perturbation_dimensionality_audit.json:1837: "triple_var": 2.2102081024681084 tools/data/perturbation_dimensionality_audit.json:1844: "triple_var": 0.7964024950784399 tools/data/perturbation_dimensionality_audit.json:1862: "triple_var": 2.300502908353036 tools/data/perturbation_dimensionality_audit.json:1869: "triple_var": 0.7036224005029421 tools/data/perturbation_dimensionality_audit.json:1887: "triple_var": 2.3990994838467286 tools/data/perturbation_dimensionality_audit.json:1894: "triple_var": 0.6023120370831129 tools/data/perturbation_dimensionality_audit.json:1912: "triple_var": 2.5227404600781735 tools/data/perturbation_dimensionality_audit.json:1919: "triple_var": 0.47526794683079454 tools/data/perturbation_dimensionality_audit.json:1937: "triple_var": 2.1313269048881103 tools/data/perturbation_dimensionality_audit.json:1944: "triple_var": 0.8774548313670588 tools/data/perturbation_dimensionality_audit.json:1962: "triple_var": 2.3068363399771146 tools/data/perturbation_dimensionality_audit.json:1969: "triple_var": 0.697114646493057 tools/data/perturbation_dimensionality_audit.json:1987: "triple_var": 2.531628522852661 tools/data/perturbation_dimensionality_audit.json:1994: "triple_var": 0.4661352475937992 tools/data/perturbation_dimensionality_audit.json:2012: "triple_var": 2.72583325439997 tools/data/perturbation_dimensionality_audit.json:2019: "triple_var": 0.2665851939292381 tools/data/perturbation_dimensionality_audit.json:2037: "triple_var": 2.8933762026643723 tools/data/perturbation_dimensionality_audit.json:2044: "triple_var": 0.09443076673838569 tools/data/perturbation_dimensionality_audit.json:2062: "triple_var": 2.04526824507667 tools/data/perturbation_dimensionality_audit.json:2069: "triple_var": 0.9658821835064668 tools/data/perturbation_dimensionality_audit.json:2087: "triple_var": 2.114026572802821 tools/data/perturbation_dimensionality_audit.json:2094: "triple_var": 0.8952313405619092 tools/data/perturbation_dimensionality_audit.json:2112: "triple_var": 2.1923833039087186 tools/data/perturbation_dimensionality_audit.json:2119: "triple_var": 0.8147179062465747 tools/data/perturbation_dimensionality_audit.json:2137: "triple_var": 2.2912996742616154 tools/data/perturbation_dimensionality_audit.json:2144: "triple_var": 0.713078945882647 tools/data/perturbation_dimensionality_audit.json:2162: "triple_var": 2.3870745998347243 tools/data/perturbation_dimensionality_audit.json:2169: "triple_var": 0.6146678959274738 tools/data/perturbation_dimensionality_audit.json:2187: "triple_var": 2.2445814377533937 tools/data/perturbation_dimensionality_audit.json:2194: "triple_var": 0.7610830626106593 tools/data/perturbation_dimensionality_audit.json:2212: "triple_var": 2.4910513494881212 tools/data/perturbation_dimensionality_audit.json:2219: "triple_var": 0.5078292735731695 tools/data/perturbation_dimensionality_audit.json:2237: "triple_var": 2.776690509149345 tools/data/perturbation_dimensionality_audit.json:2244: "triple_var": 0.2143281359594752 tools/data/perturbation_dimensionality_audit.json:2262: "triple_var": 2.8973138201235082 tools/data/perturbation_dimensionality_audit.json:2269: "triple_var": 0.0903847696660143 tools/data/perturbation_dimensionality_audit.json:2287: "triple_var": 2.985274548597884 tools/data/perturbation_dimensionality_audit.json:2294: "triple_var": 2.996021329968429e-06 tools/data/perturbation_dimensionality_audit.json:2319: "triple_var": -0.3811246140248328 tools/data/perturbation_dimensionality_audit.json:2326: "triple_var": -0.2977524373080487 tools/data/perturbation_dimensionality_audit.json:2346: "triple_var": 1.954757537226445 tools/data/perturbation_dimensionality_audit.json:2365: "triple_var": { tools/data/perturbation_dimensionality_audit.json:2375: "triple_var": -11.033137237638798 tools/data/perturbation_dimensionality_audit.json:2386: "triple_var": 1.9913562969966978 tools/data/perturbation_dimensionality_audit.json:2393: "triple_var": 0.9643320311756343 tools/data/perturbation_dimensionality_audit.json:2411: "triple_var": 2.0984397749751187 tools/data/perturbation_dimensionality_audit.json:2418: "triple_var": 0.8599719332347365 tools/data/perturbation_dimensionality_audit.json:2436: "triple_var": 2.183979059672874 tools/data/perturbation_dimensionality_audit.json:2443: "triple_var": 0.7766081100065538 tools/data/perturbation_dimensionality_audit.json:2461: "triple_var": 2.296606782403763 tools/data/perturbation_dimensionality_audit.json:2468: "triple_var": 0.6668447702556315 tools/data/perturbation_dimensionality_audit.json:2486: "triple_var": 2.37873282053824 tools/data/perturbation_dimensionality_audit.json:2493: "triple_var": 0.5868073868514516 tools/data/perturbation_dimensionality_audit.json:2511: "triple_var": 2.0668729222185855 tools/data/perturbation_dimensionality_audit.json:2518: "triple_var": 0.8907359680564442 tools/data/perturbation_dimensionality_audit.json:2536: "triple_var": 2.28469930906246 tools/data/perturbation_dimensionality_audit.json:2543: "triple_var": 0.6784494090625389 tools/data/perturbation_dimensionality_audit.json:2561: "triple_var": 2.4977572849607665 tools/data/perturbation_dimensionality_audit.json:2568: "triple_var": 0.47080998931640994 tools/data/perturbation_dimensionality_audit.json:2586: "triple_var": 2.684711688989265 tools/data/perturbation_dimensionality_audit.json:2593: "triple_var": 0.28861026734970335 tools/data/perturbation_dimensionality_audit.json:2611: "triple_var": 2.971922770133326 tools/data/perturbation_dimensionality_audit.json:2618: "triple_var": 0.008703626999961528 tools/data/perturbation_dimensionality_audit.json:2636: "triple_var": 1.987198960370522 tools/data/perturbation_dimensionality_audit.json:2643: "triple_var": 0.9683836371345704 tools/data/perturbation_dimensionality_audit.json:2661: "triple_var": 2.046189369331194 tools/data/perturbation_dimensionality_audit.json:2668: "triple_var": 0.9108934904477965 tools/data/perturbation_dimensionality_audit.json:2686: "triple_var": 2.1311131927973026 tools/data/perturbation_dimensionality_audit.json:2693: "triple_var": 0.8281294758514032 tools/data/perturbation_dimensionality_audit.json:2711: "triple_var": 2.176025797493956 tools/data/perturbation_dimensionality_audit.json:2718: "triple_var": 0.7843591023688751 tools/data/perturbation_dimensionality_audit.json:2736: "triple_var": 2.2353179436477317 tools/data/perturbation_dimensionality_audit.json:2743: "triple_var": 0.7265748923623511 tools/data/perturbation_dimensionality_audit.json:2761: "triple_var": 2.167926672323605 tools/data/perturbation_dimensionality_audit.json:2768: "triple_var": 0.7922522480900456 tools/data/perturbation_dimensionality_audit.json:2786: "triple_var": 2.4790822552371816 tools/data/perturbation_dimensionality_audit.json:2793: "triple_var": 0.48901006992450563 tools/data/perturbation_dimensionality_audit.json:2811: "triple_var": 2.7321694845117834 tools/data/perturbation_dimensionality_audit.json:2818: "triple_var": 0.24235943311935465 tools/data/perturbation_dimensionality_audit.json:2836: "triple_var": 2.8755603434264736 tools/data/perturbation_dimensionality_audit.json:2843: "triple_var": 0.1026153347517049 tools/data/perturbation_dimensionality_audit.json:2861: "triple_var": 3.0533642772293605 tools/data/perturbation_dimensionality_audit.json:2868: "triple_var": -0.07066663457026129 tools/data/perturbation_dimensionality_audit.json:2893: "triple_var": -0.038213205429043925 tools/data/perturbation_dimensionality_audit.json:2900: "triple_var": 0.4915202532308447 tools/data/perturbation_dimensionality_audit.json:2920: "triple_var": 1.8546406453569948 tools/data/perturbation_dimensionality_audit.json:2939: "triple_var": { tools/data/perturbation_dimensionality_audit.json:2949: "triple_var": -15.350821696521026 tools/data/perturbation_dimensionality_audit.json:2960: "triple_var": 1.9296925438566308 tools/data/perturbation_dimensionality_audit.json:2967: "triple_var": 0.9360334782935726 tools/data/perturbation_dimensionality_audit.json:2985: "triple_var": 2.057696799557399 tools/data/perturbation_dimensionality_audit.json:2992: "triple_var": 0.826935811685739 tools/data/perturbation_dimensionality_audit.json:3010: "triple_var": 2.189486868413444 tools/data/perturbation_dimensionality_audit.json:3017: "triple_var": 0.7146115071884632 tools/data/perturbation_dimensionality_audit.json:3035: "triple_var": 2.307553047387178 tools/data/perturbation_dimensionality_audit.json:3042: "triple_var": 0.6139840353843364 tools/data/perturbation_dimensionality_audit.json:3060: "triple_var": 2.4575162710986005 tools/data/perturbation_dimensionality_audit.json:3067: "triple_var": 0.4861708021887892 tools/data/perturbation_dimensionality_audit.json:3085: "triple_var": 1.9949802293119876 tools/data/perturbation_dimensionality_audit.json:3092: "triple_var": 0.8803889678637301 tools/data/perturbation_dimensionality_audit.json:3110: "triple_var": 2.2015634994084747 tools/data/perturbation_dimensionality_audit.json:3117: "triple_var": 0.704318628605413 tools/data/perturbation_dimensionality_audit.json:3135: "triple_var": 2.4247461562520387 tools/data/perturbation_dimensionality_audit.json:3142: "triple_var": 0.5141006787749874 tools/data/perturbation_dimensionality_audit.json:3160: "triple_var": 2.6779100460108642 tools/data/perturbation_dimensionality_audit.json:3167: "triple_var": 0.29832980857348246 tools/data/perturbation_dimensionality_audit.json:3185: "triple_var": 2.9304294670142066 tools/data/perturbation_dimensionality_audit.json:3192: "triple_var": 0.08310821727711973 tools/data/perturbation_dimensionality_audit.json:3210: "triple_var": 1.894001487221273 tools/data/perturbation_dimensionality_audit.json:3217: "triple_var": 0.9664528653394849 tools/data/perturbation_dimensionality_audit.json:3235: "triple_var": 1.9874805309630215 tools/data/perturbation_dimensionality_audit.json:3242: "triple_var": 0.8867809396435397 tools/data/perturbation_dimensionality_audit.json:3260: "triple_var": 2.0570800400279854 tools/data/perturbation_dimensionality_audit.json:3267: "triple_var": 0.8274614740955456 tools/data/perturbation_dimensionality_audit.json:3285: "triple_var": 2.1706606375945077 tools/data/perturbation_dimensionality_audit.json:3292: "triple_var": 0.7306570506910774 tools/data/perturbation_dimensionality_audit.json:3310: "triple_var": 2.2339805303441915 tools/data/perturbation_dimensionality_audit.json:3317: "triple_var": 0.6766896844419619 tools/data/perturbation_dimensionality_audit.json:3335: "triple_var": 2.0865070812149185 tools/data/perturbation_dimensionality_audit.json:3342: "triple_var": 0.8023808897736312 tools/data/perturbation_dimensionality_audit.json:3360: "triple_var": 2.457718542434585 tools/data/perturbation_dimensionality_audit.json:3367: "triple_var": 0.48599840689882984 tools/data/perturbation_dimensionality_audit.json:3385: "triple_var": 2.703842094359856 tools/data/perturbation_dimensionality_audit.json:3392: "triple_var": 0.2762279968036451 tools/data/perturbation_dimensionality_audit.json:3410: "triple_var": 2.8676983373709874 tools/data/perturbation_dimensionality_audit.json:3417: "triple_var": 0.13657378238885565 tools/data/perturbation_dimensionality_audit.json:3435: "triple_var": 2.9332255047459226 tools/data/perturbation_dimensionality_audit.json:3442: "triple_var": 0.08072516219346093 tools/data/perturbation_dimensionality_audit.json:3467: "triple_var": 0.004734022540062257 tools/data/perturbation_dimensionality_audit.json:3474: "triple_var": 0.16353607994203026 tools/data/perturbation_dimensionality_audit.json:3494: "triple_var": 1.96083821143554 tools/data/perturbation_dimensionality_audit.json:3513: "triple_var": { tools/data/perturbation_dimensionality_audit.json:3523: "triple_var": -13.959261874474162 tools/data/perturbation_dimensionality_audit.json:3534: "triple_var": 1.9914852055420529 tools/data/perturbation_dimensionality_audit.json:3541: "triple_var": 0.9705874105152468 tools/data/perturbation_dimensionality_audit.json:3559: "triple_var": 2.06881776477728 tools/data/perturbation_dimensionality_audit.json:3566: "triple_var": 0.8963696647002427 tools/data/perturbation_dimensionality_audit.json:3584: "triple_var": 2.1414715968930857 tools/data/perturbation_dimensionality_audit.json:3591: "triple_var": 0.8266421954714658 tools/data/perturbation_dimensionality_audit.json:3609: "triple_var": 2.2076148807167617 tools/data/perturbation_dimensionality_audit.json:3616: "triple_var": 0.7631630416099824 tools/data/perturbation_dimensionality_audit.json:3634: "triple_var": 2.2818801376698135 tools/data/perturbation_dimensionality_audit.json:3641: "triple_var": 0.6918890527760942 tools/data/perturbation_dimensionality_audit.json:3659: "triple_var": 2.07931402054279 tools/data/perturbation_dimensionality_audit.json:3666: "triple_var": 0.8862961788345501 tools/data/perturbation_dimensionality_audit.json:3684: "triple_var": 2.271527992877762 tools/data/perturbation_dimensionality_audit.json:3691: "triple_var": 0.7018242321935964 tools/data/perturbation_dimensionality_audit.json:3709: "triple_var": 2.5182982107109755 tools/data/perturbation_dimensionality_audit.json:3716: "triple_var": 0.464993465399113 tools/data/perturbation_dimensionality_audit.json:3734: "triple_var": 2.720686244904744 tools/data/perturbation_dimensionality_audit.json:3741: "triple_var": 0.2707572494205124 tools/data/perturbation_dimensionality_audit.json:3759: "triple_var": 2.9030549774485275 tools/data/perturbation_dimensionality_audit.json:3766: "triple_var": 0.09573399439841009 tools/data/perturbation_dimensionality_audit.json:3784: "triple_var": 1.997439634057552 tools/data/perturbation_dimensionality_audit.json:3791: "triple_var": 0.9648728154416155 tools/data/perturbation_dimensionality_audit.json:3809: "triple_var": 2.0929448932340824 tools/data/perturbation_dimensionality_audit.json:3816: "triple_var": 0.8732143326543167 tools/data/perturbation_dimensionality_audit.json:3834: "triple_var": 2.151363365334829 tools/data/perturbation_dimensionality_audit.json:3841: "triple_var": 0.8171488492906277 tools/data/perturbation_dimensionality_audit.json:3859: "triple_var": 2.2160607452970633 tools/data/perturbation_dimensionality_audit.json:3866: "triple_var": 0.755057360939282 tools/data/perturbation_dimensionality_audit.json:3884: "triple_var": 2.350193741573676 tools/data/perturbation_dimensionality_audit.json:3891: "triple_var": 0.6263269953402115 tools/data/perturbation_dimensionality_audit.json:3909: "triple_var": 2.1734512491315368 tools/data/perturbation_dimensionality_audit.json:3916: "triple_var": 0.7959506248761348 tools/data/perturbation_dimensionality_audit.json:3934: "triple_var": 2.5197140755592278 tools/data/perturbation_dimensionality_audit.json:3941: "triple_var": 0.4636346289858973 tools/data/perturbation_dimensionality_audit.json:3959: "triple_var": 2.715841105052839 tools/data/perturbation_dimensionality_audit.json:3966: "triple_var": 0.2754072359400719 tools/data/perturbation_dimensionality_audit.json:3984: "triple_var": 2.8860144851245555 tools/data/perturbation_dimensionality_audit.json:3991: "triple_var": 0.11208812699624829 tools/data/perturbation_dimensionality_audit.json:4009: "triple_var": 3.0012159794930806 tools/data/perturbation_dimensionality_audit.json:4016: "triple_var": 0.001526737187011601 tools/data/perturbation_dimensionality_audit.json:4041: "triple_var": -0.0821080713921532 tools/data/perturbation_dimensionality_audit.json:4048: "triple_var": -0.543700188476259 tools/data/perturbation_dimensionality_audit.json:4068: "triple_var": 1.902182122803493 tools/data/perturbation_dimensionality_audit.json:4087: "triple_var": { tools/data/perturbation_dimensionality_audit.json:4097: "triple_var": -14.882278750801397 tools/data/perturbation_dimensionality_audit.json:4108: "triple_var": 1.9579115370696916 tools/data/perturbation_dimensionality_audit.json:4115: "triple_var": 0.9511197321854006 tools/data/perturbation_dimensionality_audit.json:4133: "triple_var": 2.0566919636947643 tools/data/perturbation_dimensionality_audit.json:4140: "triple_var": 0.8644794225419078 tools/data/perturbation_dimensionality_audit.json:4158: "triple_var": 2.1465282362743805 tools/data/perturbation_dimensionality_audit.json:4165: "triple_var": 0.785684030180851 tools/data/perturbation_dimensionality_audit.json:4183: "triple_var": 2.282635369815206 tools/data/perturbation_dimensionality_audit.json:4190: "triple_var": 0.6663044668648915 tools/data/perturbation_dimensionality_audit.json:4208: "triple_var": 2.387403153821533 tools/data/perturbation_dimensionality_audit.json:4215: "triple_var": 0.5744126462168244 tools/data/perturbation_dimensionality_audit.json:4233: "triple_var": 2.020066892378504 tools/data/perturbation_dimensionality_audit.json:4240: "triple_var": 0.8966032716481676 tools/data/perturbation_dimensionality_audit.json:4258: "triple_var": 2.2308347730796116 tools/data/perturbation_dimensionality_audit.json:4265: "triple_var": 0.7117387689247942 tools/data/perturbation_dimensionality_audit.json:4283: "triple_var": 2.4606380935820598 tools/data/perturbation_dimensionality_audit.json:4290: "triple_var": 0.5101782824429382 tools/data/perturbation_dimensionality_audit.json:4308: "triple_var": 2.65667773069731 tools/data/perturbation_dimensionality_audit.json:4315: "triple_var": 0.33823192178860895 tools/data/perturbation_dimensionality_audit.json:4333: "triple_var": 2.9264944760344322 tools/data/perturbation_dimensionality_audit.json:4340: "triple_var": 0.10157566141694005 tools/data/perturbation_dimensionality_audit.json:4358: "triple_var": 1.9455796792296858 tools/data/perturbation_dimensionality_audit.json:4365: "triple_var": 0.9619360043786049 tools/data/perturbation_dimensionality_audit.json:4383: "triple_var": 2.003052649462438 tools/data/perturbation_dimensionality_audit.json:4390: "triple_var": 0.9115264636707399 tools/data/perturbation_dimensionality_audit.json:4408: "triple_var": 2.079799065492315 tools/data/perturbation_dimensionality_audit.json:4415: "triple_var": 0.8442121841516318 tools/data/perturbation_dimensionality_audit.json:4433: "triple_var": 2.153933137252366 tools/data/perturbation_dimensionality_audit.json:4440: "triple_var": 0.7791891917241683 tools/data/perturbation_dimensionality_audit.json:4458: "triple_var": 2.2321886116190433 tools/data/perturbation_dimensionality_audit.json:4465: "triple_var": 0.710551317176799 tools/data/perturbation_dimensionality_audit.json:4483: "triple_var": 2.1190764410129184 tools/data/perturbation_dimensionality_audit.json:4490: "triple_var": 0.8097619990961943 tools/data/perturbation_dimensionality_audit.json:4508: "triple_var": 2.4590029811515737 tools/data/perturbation_dimensionality_audit.json:4515: "triple_var": 0.511612439513518 tools/data/perturbation_dimensionality_audit.json:4533: "triple_var": 2.7125088746528747 tools/data/perturbation_dimensionality_audit.json:4540: "triple_var": 0.28926242686609194 tools/data/perturbation_dimensionality_audit.json:4558: "triple_var": 2.858352167548235 tools/data/perturbation_dimensionality_audit.json:4565: "triple_var": 0.16134327843154422 tools/data/perturbation_dimensionality_audit.json:4583: "triple_var": 2.959842904419699 tools/data/perturbation_dimensionality_audit.json:4590: "triple_var": 0.07232575574088981 tools/data/perturbation_dimensionality_audit.json:4615: "triple_var": -0.009917130811964811 tools/data/perturbation_dimensionality_audit.json:4622: "triple_var": -0.5007771645702168 tools/data/perturbation_dimensionality_audit.json:4642: "triple_var": 1.9656656449883814 tools/data/perturbation_dimensionality_audit.json:4661: "triple_var": { tools/data/perturbation_dimensionality_audit.json:4671: "triple_var": -10.75050083956094 tools/data/perturbation_dimensionality_audit.json:4682: "triple_var": 2.009328515454629 tools/data/perturbation_dimensionality_audit.json:4689: "triple_var": 0.9572580151294285 tools/data/perturbation_dimensionality_audit.json:4707: "triple_var": 2.103332298035615 tools/data/perturbation_dimensionality_audit.json:4714: "triple_var": 0.865236849091825 tools/data/perturbation_dimensionality_audit.json:4732: "triple_var": 2.2100052905243777 tools/data/perturbation_dimensionality_audit.json:4739: "triple_var": 0.7608136771297849 tools/data/perturbation_dimensionality_audit.json:4757: "triple_var": 2.300810959879997 tools/data/perturbation_dimensionality_audit.json:4764: "triple_var": 0.6719231734978663 tools/data/perturbation_dimensionality_audit.json:4782: "triple_var": 2.4238044721762355 tools/data/perturbation_dimensionality_audit.json:4789: "triple_var": 0.5515236948193399 tools/data/perturbation_dimensionality_audit.json:4807: "triple_var": 2.103812283137221 tools/data/perturbation_dimensionality_audit.json:4814: "triple_var": 0.8647669872679946 tools/data/perturbation_dimensionality_audit.json:4832: "triple_var": 2.2458095736537276 tools/data/perturbation_dimensionality_audit.json:4839: "triple_var": 0.7257645355714141 tools/data/perturbation_dimensionality_audit.json:4857: "triple_var": 2.4955267426510788 tools/data/perturbation_dimensionality_audit.json:4864: "triple_var": 0.4813141056012306 tools/data/perturbation_dimensionality_audit.json:4882: "triple_var": 2.707207294607344 tools/data/perturbation_dimensionality_audit.json:4889: "triple_var": 0.27409806935590647 tools/data/perturbation_dimensionality_audit.json:4907: "triple_var": 2.8999602608965134 tools/data/perturbation_dimensionality_audit.json:4914: "triple_var": 0.08541042053863404 tools/data/perturbation_dimensionality_audit.json:4932: "triple_var": 2.0196662419053326 tools/data/perturbation_dimensionality_audit.json:4939: "triple_var": 0.9471383197719364 tools/data/perturbation_dimensionality_audit.json:4957: "triple_var": 2.0709251350232067 tools/data/perturbation_dimensionality_audit.json:4964: "triple_var": 0.8969605185707986 tools/data/perturbation_dimensionality_audit.json:4982: "triple_var": 2.1795632290088616 tools/data/perturbation_dimensionality_audit.json:4989: "triple_var": 0.7906136907072479 tools/data/perturbation_dimensionality_audit.json:5007: "triple_var": 2.2147527570408676 tools/data/perturbation_dimensionality_audit.json:5014: "triple_var": 0.7561663385591745 tools/data/perturbation_dimensionality_audit.json:5032: "triple_var": 2.2879838251960507 tools/data/perturbation_dimensionality_audit.json:5039: "triple_var": 0.6844797734359723 tools/data/perturbation_dimensionality_audit.json:5057: "triple_var": 2.1784574497561335 tools/data/perturbation_dimensionality_audit.json:5064: "triple_var": 0.7916961481724947 tools/data/perturbation_dimensionality_audit.json:5082: "triple_var": 2.4632381290562373 tools/data/perturbation_dimensionality_audit.json:5089: "triple_var": 0.5129217259666683 tools/data/perturbation_dimensionality_audit.json:5107: "triple_var": 2.732956811428978 tools/data/perturbation_dimensionality_audit.json:5114: "triple_var": 0.24889163087253732 tools/data/perturbation_dimensionality_audit.json:5132: "triple_var": 2.9238137660997547 tools/data/perturbation_dimensionality_audit.json:5139: "triple_var": 0.06206000524023615 tools/data/perturbation_dimensionality_audit.json:5157: "triple_var": 2.99069901261127 tools/data/perturbation_dimensionality_audit.json:5164: "triple_var": -0.003414576800116287 tools/data/perturbation_dimensionality_audit.json:5189: "triple_var": -0.1604958065300899 tools/data/perturbation_dimensionality_audit.json:5196: "triple_var": -0.5046052013913962 tools/data/perturbation_dimensionality_audit.json:5229: "triple_var": 2.2236773240061516 tools/data/perturbation_dimensionality_audit.json:5248: "triple_var": { tools/data/perturbation_dimensionality_audit.json:5258: "triple_var": -0.6685106470755908 tools/data/perturbation_dimensionality_audit.json:5269: "triple_var": 2.2441918489783674 tools/data/perturbation_dimensionality_audit.json:5276: "triple_var": 0.9490985771351172 tools/data/perturbation_dimensionality_audit.json:5294: "triple_var": 2.373929583644918 tools/data/perturbation_dimensionality_audit.json:5301: "triple_var": 0.6271883548541698 tools/data/perturbation_dimensionality_audit.json:5319: "triple_var": 2.4805177993194274 tools/data/perturbation_dimensionality_audit.json:5326: "triple_var": 0.3627176032374683 tools/data/perturbation_dimensionality_audit.json:5344: "triple_var": 2.4950043049666935 tools/data/perturbation_dimensionality_audit.json:5351: "triple_var": 0.32677313214760945 tools/data/perturbation_dimensionality_audit.json:5369: "triple_var": 2.6503569731699383 tools/data/perturbation_dimensionality_audit.json:5376: "triple_var": -0.0586938415264611 tools/data/perturbation_dimensionality_audit.json:5394: "triple_var": 2.926616433393024 tools/data/perturbation_dimensionality_audit.json:5401: "triple_var": -0.7441593653094685 tools/data/perturbation_dimensionality_audit.json:5419: "triple_var": 2.810298466389073 tools/data/perturbation_dimensionality_audit.json:5426: "triple_var": -0.4555467830892891 tools/data/perturbation_dimensionality_audit.json:5444: "triple_var": 3.248090680876635 tools/data/perturbation_dimensionality_audit.json:5451: "triple_var": -1.5418135461152829 tools/data/perturbation_dimensionality_audit.json:5469: "triple_var": 2.6889929446334127 tools/data/perturbation_dimensionality_audit.json:5476: "triple_var": -0.15455888953129557 tools/data/perturbation_dimensionality_audit.json:5494: "triple_var": 2.8170990134712497 tools/data/perturbation_dimensionality_audit.json:5501: "triple_var": -0.4724205602404168 tools/data/perturbation_dimensionality_audit.json:5519: "triple_var": 2.2343360690502356 tools/data/perturbation_dimensionality_audit.json:5526: "triple_var": 0.9735531147110305 tools/data/perturbation_dimensionality_audit.json:5544: "triple_var": 2.313946543215636 tools/data/perturbation_dimensionality_audit.json:5551: "triple_var": 0.7760205656778383 tools/data/perturbation_dimensionality_audit.json:5569: "triple_var": 2.3987012532921055 tools/data/perturbation_dimensionality_audit.json:5576: "triple_var": 0.5657239420301634 tools/data/perturbation_dimensionality_audit.json:5594: "triple_var": 2.13538739442321 tools/data/perturbation_dimensionality_audit.json:5601: "triple_var": 1.2190683453064923 tools/data/perturbation_dimensionality_audit.json:5619: "triple_var": 2.2907407549334198 tools/data/perturbation_dimensionality_audit.json:5626: "triple_var": 0.8335996538539389 tools/data/perturbation_dimensionality_audit.json:5644: "triple_var": 2.3385828553645216 tools/data/perturbation_dimensionality_audit.json:5651: "triple_var": 0.7148920070482784 tools/data/perturbation_dimensionality_audit.json:5669: "triple_var": 2.6182025191500142 tools/data/perturbation_dimensionality_audit.json:5676: "triple_var": 0.021089017851188867 tools/data/perturbation_dimensionality_audit.json:5694: "triple_var": 2.5984311113482557 tools/data/perturbation_dimensionality_audit.json:5701: "triple_var": 0.0701465900111281 tools/data/perturbation_dimensionality_audit.json:5719: "triple_var": 2.9717288953553354 tools/data/perturbation_dimensionality_audit.json:5726: "triple_var": -0.8560941288942734 tools/data/perturbation_dimensionality_audit.json:5744: "triple_var": 2.854493736792848 tools/data/perturbation_dimensionality_audit.json:5751: "triple_var": -0.5652057759490874 tools/data/perturbation_dimensionality_audit.json:5776: "triple_var": -0.1653310078355353 tools/data/perturbation_dimensionality_audit.json:5783: "triple_var": 0.4610138020821776 tools/data/perturbation_dimensionality_audit.json:5803: "triple_var": 2.3708295504374504 tools/data/perturbation_dimensionality_audit.json:5822: "triple_var": { tools/data/perturbation_dimensionality_audit.json:5832: "triple_var": -0.5109629539755141 tools/data/perturbation_dimensionality_audit.json:5843: "triple_var": 2.4690257478615383 tools/data/perturbation_dimensionality_audit.json:5850: "triple_var": 0.7447784702667447 tools/data/perturbation_dimensionality_audit.json:5868: "triple_var": 2.5233689378843827 tools/data/perturbation_dimensionality_audit.json:5875: "triple_var": 0.6035351996305534 tools/data/perturbation_dimensionality_audit.json:5893: "triple_var": 2.617308323645676 tools/data/perturbation_dimensionality_audit.json:5900: "triple_var": 0.35937754011696393 tools/data/perturbation_dimensionality_audit.json:5918: "triple_var": 2.8011442096527723 tools/data/perturbation_dimensionality_audit.json:5925: "triple_var": -0.11842992368906409 tools/data/perturbation_dimensionality_audit.json:5943: "triple_var": 2.866041270928107 tools/data/perturbation_dimensionality_audit.json:5950: "triple_var": -0.2871037388506759 tools/data/perturbation_dimensionality_audit.json:5968: "triple_var": 2.9790028222732943 tools/data/perturbation_dimensionality_audit.json:5975: "triple_var": -0.5807018688357802 tools/data/perturbation_dimensionality_audit.json:5993: "triple_var": 2.6674011646966354 tools/data/perturbation_dimensionality_audit.json:6000: "triple_var": 0.22918134253411843 tools/data/perturbation_dimensionality_audit.json:6018: "triple_var": 2.7457401846601583 tools/data/perturbation_dimensionality_audit.json:6025: "triple_var": 0.025570560880878928 tools/data/perturbation_dimensionality_audit.json:6043: "triple_var": 2.9764653100579355 tools/data/perturbation_dimensionality_audit.json:6050: "triple_var": -0.5741066261857646 tools/data/perturbation_dimensionality_audit.json:6068: "triple_var": 2.6684498425672514 tools/data/perturbation_dimensionality_audit.json:6075: "triple_var": 0.22645572609115036 tools/data/perturbation_dimensionality_audit.json:6093: "triple_var": 2.3490995163887525 tools/data/perturbation_dimensionality_audit.json:6100: "triple_var": 1.0564784856903628 tools/data/perturbation_dimensionality_audit.json:6118: "triple_var": 2.4389456341686686 tools/data/perturbation_dimensionality_audit.json:6125: "triple_var": 0.8229596303588076 tools/data/perturbation_dimensionality_audit.json:6143: "triple_var": 2.832504600921575 tools/data/perturbation_dimensionality_audit.json:6150: "triple_var": -0.19993865052998583 tools/data/perturbation_dimensionality_audit.json:6168: "triple_var": 2.755683490185757 tools/data/perturbation_dimensionality_audit.json:6175: "triple_var": -0.0002730635508136895 tools/data/perturbation_dimensionality_audit.json:6193: "triple_var": 3.0689546618080454 tools/data/perturbation_dimensionality_audit.json:6200: "triple_var": -0.8144955053574721 tools/data/perturbation_dimensionality_audit.json:6218: "triple_var": 2.3628566069285952 tools/data/perturbation_dimensionality_audit.json:6225: "triple_var": 1.0207224606673788 tools/data/perturbation_dimensionality_audit.json:6243: "triple_var": 2.435500819621187 tools/data/perturbation_dimensionality_audit.json:6250: "triple_var": 0.8319130405877043 tools/data/perturbation_dimensionality_audit.json:6268: "triple_var": 2.563881045745557 tools/data/perturbation_dimensionality_audit.json:6275: "triple_var": 0.4982402654856675 tools/data/perturbation_dimensionality_audit.json:6293: "triple_var": 2.9941037402061155 tools/data/perturbation_dimensionality_audit.json:6300: "triple_var": -0.619950632803147 tools/data/perturbation_dimensionality_audit.json:6318: "triple_var": 2.6896794360421126 tools/data/perturbation_dimensionality_audit.json:6325: "triple_var": 0.17127793444134773 tools/data/perturbation_dimensionality_audit.json:6350: "triple_var": -0.028129485138580268 tools/data/perturbation_dimensionality_audit.json:6357: "triple_var": 0.19597249601050792 tools/data/perturbation_dimensionality_audit.json:6377: "triple_var": 1.7114831373360733 tools/data/perturbation_dimensionality_audit.json:6396: "triple_var": { tools/data/perturbation_dimensionality_audit.json:6406: "triple_var": -1.313687046031781 tools/data/perturbation_dimensionality_audit.json:6417: "triple_var": 1.7344213360540912 tools/data/perturbation_dimensionality_audit.json:6424: "triple_var": 0.97410838366408 tools/data/perturbation_dimensionality_audit.json:6442: "triple_var": 1.9498388029889049 tools/data/perturbation_dimensionality_audit.json:6449: "triple_var": 0.7309547483461162 tools/data/perturbation_dimensionality_audit.json:6467: "triple_var": 2.2022721105198118 tools/data/perturbation_dimensionality_audit.json:6474: "triple_var": 0.4460192819939311 tools/data/perturbation_dimensionality_audit.json:6492: "triple_var": 2.3827410384801455 tools/data/perturbation_dimensionality_audit.json:6499: "triple_var": 0.2423139997812789 tools/data/perturbation_dimensionality_audit.json:6517: "triple_var": 2.325479487823239 tools/data/perturbation_dimensionality_audit.json:6524: "triple_var": 0.30694828596190593 tools/data/perturbation_dimensionality_audit.json:6542: "triple_var": 3.165155168486316 tools/data/perturbation_dimensionality_audit.json:6549: "triple_var": -0.6408401972398564 tools/data/perturbation_dimensionality_audit.json:6567: "triple_var": 2.9435774659351157 tools/data/perturbation_dimensionality_audit.json:6574: "triple_var": -0.39073316252558055 tools/data/perturbation_dimensionality_audit.json:6592: "triple_var": 2.715944158155464 tools/data/perturbation_dimensionality_audit.json:6599: "triple_var": -0.13379083053341978 tools/data/perturbation_dimensionality_audit.json:6617: "triple_var": 2.876676754506159 tools/data/perturbation_dimensionality_audit.json:6624: "triple_var": -0.3152186212918775 tools/data/perturbation_dimensionality_audit.json:6642: "triple_var": 2.6113721308222955 tools/data/perturbation_dimensionality_audit.json:6649: "triple_var": -0.015754586952838594 tools/data/perturbation_dimensionality_audit.json:6667: "triple_var": 1.7825488486123464 tools/data/perturbation_dimensionality_audit.json:6674: "triple_var": 0.9197841925765862 tools/data/perturbation_dimensionality_audit.json:6692: "triple_var": 1.8368200164922104 tools/data/perturbation_dimensionality_audit.json:6699: "triple_var": 0.8585253171905263 tools/data/perturbation_dimensionality_audit.json:6717: "triple_var": 1.7185501722457235 tools/data/perturbation_dimensionality_audit.json:6724: "triple_var": 0.9920230459783449 tools/data/perturbation_dimensionality_audit.json:6742: "triple_var": 1.8642186114957802 tools/data/perturbation_dimensionality_audit.json:6749: "triple_var": 0.8275990043315111 tools/data/perturbation_dimensionality_audit.json:6767: "triple_var": 1.990653133688241 tools/data/perturbation_dimensionality_audit.json:6774: "triple_var": 0.6848853509856123 tools/data/perturbation_dimensionality_audit.json:6792: "triple_var": 1.945377484248977 tools/data/perturbation_dimensionality_audit.json:6799: "triple_var": 0.7359904860945963 tools/data/perturbation_dimensionality_audit.json:6817: "triple_var": 2.4082112945558105 tools/data/perturbation_dimensionality_audit.json:6824: "triple_var": 0.21356430995621245 tools/data/perturbation_dimensionality_audit.json:6842: "triple_var": 2.9395866587249757 tools/data/perturbation_dimensionality_audit.json:6849: "triple_var": -0.3862285172207858 tools/data/perturbation_dimensionality_audit.json:6867: "triple_var": 2.1828686748304555 tools/data/perturbation_dimensionality_audit.json:6874: "triple_var": 0.4679210153707931 tools/data/perturbation_dimensionality_audit.json:6892: "triple_var": 2.8221601877481683 tools/data/perturbation_dimensionality_audit.json:6899: "triple_var": -0.25368275058984563 tools/data/perturbation_dimensionality_audit.json:6924: "triple_var": -0.29285370442237074 tools/data/perturbation_dimensionality_audit.json:6931: "triple_var": -0.3209907704800204 tools/data/perturbation_dimensionality_audit.json:6951: "triple_var": 1.3755665619228048 tools/data/perturbation_dimensionality_audit.json:6970: "triple_var": { tools/data/perturbation_dimensionality_audit.json:6980: "triple_var": -1.9370539974208016 tools/data/perturbation_dimensionality_audit.json:6991: "triple_var": 1.5053410156902245 tools/data/perturbation_dimensionality_audit.json:6998: "triple_var": 0.9129693875344833 tools/data/perturbation_dimensionality_audit.json:7016: "triple_var": 1.8438349530669218 tools/data/perturbation_dimensionality_audit.json:7023: "triple_var": 0.6859652751646104 tools/data/perturbation_dimensionality_audit.json:7041: "triple_var": 2.1985020362535073 tools/data/perturbation_dimensionality_audit.json:7048: "triple_var": 0.4481149696922699 tools/data/perturbation_dimensionality_audit.json:7066: "triple_var": 2.573358513002689 tools/data/perturbation_dimensionality_audit.json:7073: "triple_var": 0.19672505579892877 tools/data/perturbation_dimensionality_audit.json:7091: "triple_var": 2.8472952460366483 tools/data/perturbation_dimensionality_audit.json:7098: "triple_var": 0.013014926720091914 tools/data/perturbation_dimensionality_audit.json:7116: "triple_var": 2.9264985150092313 tools/data/perturbation_dimensionality_audit.json:7123: "triple_var": -0.040101143568354126 tools/data/perturbation_dimensionality_audit.json:7141: "triple_var": 2.484691127509337 tools/data/perturbation_dimensionality_audit.json:7148: "triple_var": 0.256188044400926 tools/data/perturbation_dimensionality_audit.json:7166: "triple_var": 2.97893051329405 tools/data/perturbation_dimensionality_audit.json:7173: "triple_var": -0.07526360267372466 tools/data/perturbation_dimensionality_audit.json:7191: "triple_var": 2.676832665389834 tools/data/perturbation_dimensionality_audit.json:7198: "triple_var": 0.12733220847673665 tools/data/perturbation_dimensionality_audit.json:7216: "triple_var": 3.095421715590467 tools/data/perturbation_dimensionality_audit.json:7223: "triple_var": -0.153386071221127 tools/data/perturbation_dimensionality_audit.json:7241: "triple_var": 1.437576053461395 tools/data/perturbation_dimensionality_audit.json:7248: "triple_var": 0.9584145887683663 tools/data/perturbation_dimensionality_audit.json:7266: "triple_var": 1.4600548396151822 tools/data/perturbation_dimensionality_audit.json:7273: "triple_var": 0.9433396455137284 tools/data/perturbation_dimensionality_audit.json:7291: "triple_var": 1.6446641983919155 tools/data/perturbation_dimensionality_audit.json:7298: "triple_var": 0.8195351131517591 tools/data/perturbation_dimensionality_audit.json:7316: "triple_var": 1.5539652126014816 tools/data/perturbation_dimensionality_audit.json:7323: "triple_var": 0.8803605533997254 tools/data/perturbation_dimensionality_audit.json:7341: "triple_var": 1.658202107429582 tools/data/perturbation_dimensionality_audit.json:7348: "triple_var": 0.8104561882875315 tools/data/perturbation_dimensionality_audit.json:7366: "triple_var": 1.5093834545962221 tools/data/perturbation_dimensionality_audit.json:7373: "triple_var": 0.9102584076487661 tools/data/perturbation_dimensionality_audit.json:7391: "triple_var": 2.0621780780985315 tools/data/perturbation_dimensionality_audit.json:7398: "triple_var": 0.5395378747981868 tools/data/perturbation_dimensionality_audit.json:7416: "triple_var": 2.111125110257881 tools/data/perturbation_dimensionality_audit.json:7423: "triple_var": 0.5067125377342986 tools/data/perturbation_dimensionality_audit.json:7441: "triple_var": 2.547493242522747 tools/data/perturbation_dimensionality_audit.json:7448: "triple_var": 0.21407107626833446 tools/data/perturbation_dimensionality_audit.json:7466: "triple_var": 3.0115704707892728 tools/data/perturbation_dimensionality_audit.json:7473: "triple_var": -0.09715292995803514 tools/data/perturbation_dimensionality_audit.json:7498: "triple_var": -0.26582813162234165 tools/data/perturbation_dimensionality_audit.json:7505: "triple_var": -0.014370121537358106 tools/data/perturbation_dimensionality_audit.json:7525: "triple_var": 1.6641167564363968 tools/data/perturbation_dimensionality_audit.json:7544: "triple_var": { tools/data/perturbation_dimensionality_audit.json:7554: "triple_var": -1.185307310680469 tools/data/perturbation_dimensionality_audit.json:7565: "triple_var": 1.596072546408231 tools/data/perturbation_dimensionality_audit.json:7572: "triple_var": 1.076577461301381 tools/data/perturbation_dimensionality_audit.json:7590: "triple_var": 1.6357652554866509 tools/data/perturbation_dimensionality_audit.json:7597: "triple_var": 1.031906990556824 tools/data/perturbation_dimensionality_audit.json:7615: "triple_var": 1.4129856433259271 tools/data/perturbation_dimensionality_audit.json:7622: "triple_var": 1.2826248271209175 tools/data/perturbation_dimensionality_audit.json:7640: "triple_var": 1.3731475160268425 tools/data/perturbation_dimensionality_audit.json:7647: "triple_var": 1.3274589526152447 tools/data/perturbation_dimensionality_audit.json:7665: "triple_var": 1.3155387747860785 tools/data/perturbation_dimensionality_audit.json:7672: "triple_var": 1.3922922595367273 tools/data/perturbation_dimensionality_audit.json:7690: "triple_var": 2.6485337618635776 tools/data/perturbation_dimensionality_audit.json:7697: "triple_var": -0.10787023769277937 tools/data/perturbation_dimensionality_audit.json:7715: "triple_var": 2.6905486373354646 tools/data/perturbation_dimensionality_audit.json:7722: "triple_var": -0.15515409181055054 tools/data/perturbation_dimensionality_audit.json:7740: "triple_var": 2.7703023591463536 tools/data/perturbation_dimensionality_audit.json:7747: "triple_var": -0.24490952497799326 tools/data/perturbation_dimensionality_audit.json:7765: "triple_var": 2.688862416549463 tools/data/perturbation_dimensionality_audit.json:7772: "triple_var": -0.1532564063655702 tools/data/perturbation_dimensionality_audit.json:7790: "triple_var": 2.7872677292516412 tools/data/perturbation_dimensionality_audit.json:7797: "triple_var": -0.2640024789877981 tools/data/perturbation_dimensionality_audit.json:7815: "triple_var": 1.6758581101247647 tools/data/perturbation_dimensionality_audit.json:7822: "triple_var": 0.9867861930159135 tools/data/perturbation_dimensionality_audit.json:7840: "triple_var": 1.8899287979624309 tools/data/perturbation_dimensionality_audit.json:7847: "triple_var": 0.7458694448184797 tools/data/perturbation_dimensionality_audit.json:7865: "triple_var": 2.0956803294190567 tools/data/perturbation_dimensionality_audit.json:7872: "triple_var": 0.5143151372396606 tools/data/perturbation_dimensionality_audit.json:7890: "triple_var": 2.2103313308227297 tools/data/perturbation_dimensionality_audit.json:7897: "triple_var": 0.38528604542538053 tools/data/perturbation_dimensionality_audit.json:7915: "triple_var": 2.3899764753859096 tools/data/perturbation_dimensionality_audit.json:7922: "triple_var": 0.18311206030491928 tools/data/perturbation_dimensionality_audit.json:7940: "triple_var": 1.7047746929831784 tools/data/perturbation_dimensionality_audit.json:7947: "triple_var": 0.9542432550658402 tools/data/perturbation_dimensionality_audit.json:7965: "triple_var": 2.0053566645057628 tools/data/perturbation_dimensionality_audit.json:7972: "triple_var": 0.6159660631837431 tools/data/perturbation_dimensionality_audit.json:7990: "triple_var": 2.613438036462545 tools/data/perturbation_dimensionality_audit.json:7997: "triple_var": -0.06837324665373264 tools/data/perturbation_dimensionality_audit.json:8015: "triple_var": 2.8297500032922223 tools/data/perturbation_dimensionality_audit.json:8022: "triple_var": -0.3118123469396879 tools/data/perturbation_dimensionality_audit.json:8040: "triple_var": 2.683198409367226 tools/data/perturbation_dimensionality_audit.json:8047: "triple_var": -0.14688209045200523 tools/data/perturbation_dimensionality_audit.json:8072: "triple_var": -0.10323830548636588 tools/data/perturbation_dimensionality_audit.json:8079: "triple_var": 0.39607064092670236 tools/data/perturbation_dimensionality_audit.json:8099: "triple_var": 1.507265371337381 tools/data/perturbation_dimensionality_audit.json:8118: "triple_var": { tools/data/perturbation_dimensionality_audit.json:8128: "triple_var": -2.3109959235651405 tools/data/perturbation_dimensionality_audit.json:8139: "triple_var": 1.628740755022056 tools/data/perturbation_dimensionality_audit.json:8146: "triple_var": 0.9114163149345471 tools/data/perturbation_dimensionality_audit.json:8164: "triple_var": 1.4648717055536573 tools/data/perturbation_dimensionality_audit.json:8171: "triple_var": 1.030914799563866 tools/data/perturbation_dimensionality_audit.json:8189: "triple_var": 1.6031969358683054 tools/data/perturbation_dimensionality_audit.json:8196: "triple_var": 0.9300436743439103 tools/data/perturbation_dimensionality_audit.json:8214: "triple_var": 1.847223201134399 tools/data/perturbation_dimensionality_audit.json:8221: "triple_var": 0.7520920171905328 tools/data/perturbation_dimensionality_audit.json:8239: "triple_var": 1.6795699758790235 tools/data/perturbation_dimensionality_audit.json:8246: "triple_var": 0.8743500422796373 tools/data/perturbation_dimensionality_audit.json:8264: "triple_var": 2.8859100689383355 tools/data/perturbation_dimensionality_audit.json:8271: "triple_var": -0.005351240762093185 tools/data/perturbation_dimensionality_audit.json:8289: "triple_var": 3.412748188456252 tools/data/perturbation_dimensionality_audit.json:8296: "triple_var": -0.3895382311155812 tools/data/perturbation_dimensionality_audit.json:8314: "triple_var": 2.957025649408694 tools/data/perturbation_dimensionality_audit.json:8321: "triple_var": -0.05721096733834989 tools/data/perturbation_dimensionality_audit.json:8339: "triple_var": 2.7412943563874106 tools/data/perturbation_dimensionality_audit.json:8346: "triple_var": 0.10010710271085956 tools/data/perturbation_dimensionality_audit.json:8364: "triple_var": 2.830657899966852 tools/data/perturbation_dimensionality_audit.json:8371: "triple_var": 0.034940385301488706 tools/data/perturbation_dimensionality_audit.json:8389: "triple_var": 1.5272941310690586 tools/data/perturbation_dimensionality_audit.json:8396: "triple_var": 0.9853943960454731 tools/data/perturbation_dimensionality_audit.json:8414: "triple_var": 1.5906782690640644 tools/data/perturbation_dimensionality_audit.json:8421: "triple_var": 0.9391726814232773 tools/data/perturbation_dimensionality_audit.json:8439: "triple_var": 1.7341392870701937 tools/data/perturbation_dimensionality_audit.json:8446: "triple_var": 0.8345563776689928 tools/data/perturbation_dimensionality_audit.json:8464: "triple_var": 1.9181220523145637 tools/data/perturbation_dimensionality_audit.json:8471: "triple_var": 0.7003903364553729 tools/data/perturbation_dimensionality_audit.json:8489: "triple_var": 2.2588458097580784 tools/data/perturbation_dimensionality_audit.json:8496: "triple_var": 0.45192381502382317 tools/data/perturbation_dimensionality_audit.json:8514: "triple_var": 1.6794976637055552 tools/data/perturbation_dimensionality_audit.json:8521: "triple_var": 0.8744027745996071 tools/data/perturbation_dimensionality_audit.json:8539: "triple_var": 1.866676119449816 tools/data/perturbation_dimensionality_audit.json:8546: "triple_var": 0.7379063349775011 tools/data/perturbation_dimensionality_audit.json:8564: "triple_var": 2.3730012040321475 tools/data/perturbation_dimensionality_audit.json:8571: "triple_var": 0.3686780973470123 tools/data/perturbation_dimensionality_audit.json:8589: "triple_var": 2.8017140267409255 tools/data/perturbation_dimensionality_audit.json:8596: "triple_var": 0.05604717148851876 tools/data/perturbation_dimensionality_audit.json:8614: "triple_var": 2.9308806650701125 tools/data/perturbation_dimensionality_audit.json:8621: "triple_var": -0.03814521929591502 tools/data/perturbation_dimensionality_audit.json:8646: "triple_var": -0.2799741086652945 tools/data/perturbation_dimensionality_audit.json:8653: "triple_var": 0.4347900059001624 tools/data/notte_20260320_0330.md:4: ising_2d_var_-0.1: r=0.9811019213055721, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260320_0330.md:5: ising_2d_var_0.1: r=0.9951420369672802, spacing=GUE-like (⟨r⟩=1.0) tools/data/notte_20260320_0330.md:6: numeri_primi_var_100000: r=0.8615840174827735, spacing=GUE-like (⟨r⟩=0.9609375) tools/data/notte_20260320_0330.md:7: logistica_biforcazione_var_3.57: r=1.0, spacing=Poisson-like (⟨r⟩=0.35930230610663094) tools/data/notte_20260320_0330.md:8: logistica_biforcazione_var_3.9: r=1.000545256270447, spacing=Poisson-like (⟨r⟩=0.3914428894887236) tools/data/notte_20260320_0330.md:9: cellular_automata_var_30: r=0.8703941780326052, spacing=GUE-like (⟨r⟩=0.8649999999999983) tools/data/notte_20260320_0330.md:10: brownian_motion_var_0.3: r=1.0149253731343284, spacing=Poisson-like (⟨r⟩=0.3935592399692033) tools/data/notte_20260320_0330.md:11: brownian_motion_var_0.5: r=1.0, spacing=Poisson-like (⟨r⟩=0.39857620187545184) tools/data/notte_20260320_0330.md:12: coupled_oscillators_var_50: r=1.0, spacing=Poisson-like (⟨r⟩=0.45756422661060286) tools/data/notte_20260320_0330.md:13: percolation_var_0.55: r=0.9655172413793103, spacing=Poisson-like (⟨r⟩=0.47237797827873335) tools/data/notte_20260320_0330.md:14: percolation_var_0.65: r=0.9642857142857143, spacing=Poisson-like (⟨r⟩=0.4251485991244417) tools/data/reports/falsifier_20260511_0330.json:17: "evidence": "Il seme/lab_data indicano direzione BOUNDARY su `8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo`; il ciclo usa 4 GUE + 4 logistic controlli e resta sul residuo `numeri_primi:cycle_3`. La deviazione verso prime-residue e logistic e' motivata, ma non dichiarata come `deliberate_counter_perimeter` rispetto al perimetro 8 GUE/5 Poisson.", tools/data/reports/falsifier_20260511_0330.json:18: "suggestion": "Nel prossimo ciclo dichiarare esplicitamente se `prime_persistent_blank` e' contro-perimetro deliberato della direzione GUE/Poisson, oppure tornare al perimetro richiesto con 8 GUE e 5 Poisson come domini principali." tools/data/learning_curve_100k.json:18: "best_fit": "GOE", tools/data/learning_curve_100k.json:26: "class": "GUE", tools/data/learning_curve_100k.json:32: "class": "GUE", tools/data/learning_curve_100k.json:38: "class": "GUE", tools/data/learning_curve_100k.json:44: "class": "GUE", tools/data/learning_curve_100k.json:50: "class": "GOE", tools/data/learning_curve_100k.json:55: "ks_class": "GOE", tools/data/learning_curve_100k.json:56: "fd_class": "GUE", tools/data/learning_curve_100k.json:57: "verdict": "LIKELY GUE: \u03b2\u2192GUE, KS\u2192GOE (finite-height effect, Piano 11c)", tools/data/graph_completion/graph_completion_20260511_0330.json:11: "observable_contract": "claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.", tools/data/graph_completion/graph_completion_20260511_0330.json:110: "observable_contract": "claim=`prime_persistent_blank` e' isolato solo se `numeri_primi:cycle_3` resta `beta_absent_blank` attraverso provider, offset row-local e seed shift con `SR` come osservabile one-sided comune; observable=`case_state` + firma osservabili one-sided focalizzata su `SR`; operator=`exp_prime_persistent_blank_gate.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via GUE random matrix blocks e logistic return intervals; denominator=8 finestre prime da 1024 gap (2 provider x 4 offset) + 8 controlli cross-dominio; non_possible=`prime_persistent_blank` se una finestra prime recupera beta/perde supporto o se i controlli condividono la stessa firma blank-SR; not_tested=atlante beta globale, `V_c`, `gap_ratio`, validita' label sorgente GUE/Poisson.", tools/data/graph_completion/graph_completion_20260511_0330.json:126: "evidence": "Il seme/lab_data indicano direzione BOUNDARY su `8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo`; il ciclo usa 4 GUE + 4 logistic controlli e resta sul residuo `numeri_primi:cycle_3`. La deviazione verso prime-residue e logistic e' motivata, ma non dichiarata come `deliberate_counter_perimeter` rispetto al perimetro 8 GUE/5 Poisson.", tools/data/graph_completion/graph_completion_20260511_0330.json:127: "suggestion": "Nel prossimo ciclo dichiarare esplicitamente se `prime_persistent_blank` e' contro-perimetro deliberato della direzione GUE/Poisson, oppure tornare al perimetro richiesto con 8 GUE e 5 Poisson come domini principali." tools/data/graph_completion/graph_completion_20260510_0330.json:11: "observable_contract": "claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.", tools/data/graph_completion/graph_completion_20260510_0330.json:116: "observable_contract": "claim=le due righe residue beta-absent sono strutturali solo se `beta_absent_blank` persiste in finestre row-local da 1024 gap; observable=`window_state` + firma degli osservabili one-sided; operator=`exp_boundary_residual_beta_absent_audit.py`; generator=`numeri_primi` da `dnd_autoricerca.genera_segnale` e `random_matrix` da `gue_spacing_blocks`; denominator=2 righe aperte BOUNDARY, full row + 4 finestre row-local da 1024 gap; non_possible=classe residua unica se una riga recupera beta o perde supporto nelle finestre row-local; not_tested=griglia beta globale, fit `V_c`, validita' label sorgente GUE/Poisson.", tools/data/graph_completion/graph_completion_20260509_1409.json:11: "observable_contract": "claim=`denominator_state` puo' trasferire oltre `V_c` solo dove la riga domain/window ha osservabile definito e null/surrogate row-aligned; observable=`spacing_r` + availability del null shuffle; operator=prescan sui 13 domini base del deposito autoricerca, non fit parametrico; generator=perimetro 8 GUE-like / 5 Poisson-like dei cycle 1..13; denominator=`boundary_shuffle_audit` quando presente; non_possible=dichiarare transfer nei domini senza null/surrogate; not_tested=nuovi spettri, nuovi domini, fit `V_c`, famiglia AICc, QxG chiuso.", tools/data/graph_completion/graph_completion_20260509_1409.json:12: "claim_under_test": "Nel perimetro BOUNDARY 8 GUE / 5 Poisson, il gate `denominator_state` trasferisce oltre `V_c` solo se identifica le righe con null/surrogate disponibile e lascia blank le righe senza contro-perimetro.", tools/data/graph_completion/graph_completion_20260509_1409.json:54: "singolare": "domain/window prima della classificazione GUE/Poisson.", tools/data/graph_completion/graph_completion_20260509_1409.json:93: "point_zero": "domain/window prima della classificazione GUE/Poisson.", tools/data/graph_completion/graph_completion_20260509_1409.json:98: "observable_contract": "claim=`denominator_state` puo' trasferire oltre `V_c` solo dove la riga domain/window ha osservabile definito e null/surrogate row-aligned; observable=`spacing_r` + availability del null shuffle; operator=prescan sui 13 domini base del deposito autoricerca, non fit parametrico; generator=perimetro 8 GUE-like / 5 Poisson-like dei cycle 1..13; denominator=`boundary_shuffle_audit` quando presente; non_possible=dichiarare transfer nei domini senza null/surrogate; not_tested=nuovi spettri, nuovi domini, fit `V_c`, famiglia AICc, QxG chiuso.", tools/data/graph_completion/graph_completion_20260509_1409.json:166: "GUE": 8, tools/data/graph_completion/graph_completion_20260509_1409.json:167: "Poisson": 5 tools/data/graph_completion/graph_completion_20260514_1330.json:11: "observable_contract": "claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.", tools/data/graph_completion/graph_completion_20260514_1330.json:12: "claim_under_test": "Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime non genera `SR` come presenza; lascia un vettore di intensita row-local su `SR,L1,triple_var`.", tools/data/graph_completion/graph_completion_20260514_1330.json:14: "consecutio": "Il prossimo ciclo deve correggere il nodo regressivo dello script: il `verdict()` non deve decidere dalla commonality di `SR`, ma da un contratto vettoriale dichiarato (`SR,L1,triple_var`, segno del delta, p label-swap, denominatore row-local). Dopo questa correzione, scalare oltre 2048 gap e separare `mod6_index_aligned` da `mod6_span_matched` come antagonisti con ruoli diversi.", tools/data/graph_completion/graph_completion_20260514_1330.json:78: "observable_contract": "claim=`prime_minus_mod6_z_delta(SR,L1,triple_var)` sopravvive a seed, provider e ampiezza finestra; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var` piu audit `has_SR`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `6k +/- 1` index-aligned e span-matched; denominator=3 ampiezze finestra x 2 provider x 4 offsets, paired contro 2 antagonisti mod6; non_possible=residuo prime-specific se il label-swap assorbe il delta z o se il delta collassa a presenza binaria `SR`; not_tested=origine analitica del delta, scale oltre 2048 gap, beta atlas globale, `V_c`, `gap_ratio`.", tools/data/graph_completion/graph_completion_20260509_1400.json:11: "observable_contract": "claim=il fit parametrico di `V_c` puo' entrare solo sulle righe `complete` o `contaminated` del gate fit-ready; observable=famiglia di modello migliore tra cinque forme semplici, separata da stato denominatore e massa esclusa; operator=confronto AICc su `vc_median_fit_ready` dal deposito 13:37, senza ricomputare spettri; generator=classi `reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=JSON `vc_fit_ready_scale_table_20260509_1337`, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=righe `broken` escluse dal fit e righe sotto `V_c=1` impediscono il claim osservato \"converge a 1 dall'alto\"; not_tested=nuovi N, nuovi generatori, nuovi spettri, GUE/Poisson transfer, gap_ratio, fit a tre parametri con asintoto libero.", tools/data/graph_completion/graph_completion_20260509_1400.json:124: "observable_contract": "claim=il fit parametrico di `V_c` puo' entrare solo sulle righe `complete` o `contaminated` del gate fit-ready; observable=famiglia di modello migliore tra cinque forme semplici, separata da stato denominatore e massa esclusa; operator=confronto AICc su `vc_median_fit_ready` dal deposito 13:37, senza ricomputare spettri; generator=classi `reference_order`, `order_memory`, `periodic_closure`, `random_dispersion`; denominator=JSON `vc_fit_ready_scale_table_20260509_1337`, N={89,144,233,377}, soglie r={0.48,0.50,0.52}, livelli `per_mode_best` e `accepted_candidates`; non_possible=righe `broken` escluse dal fit e righe sotto `V_c=1` impediscono il claim osservato \"converge a 1 dall'alto\"; not_tested=nuovi N, nuovi generatori, nuovi spettri, GUE/Poisson transfer, gap_ratio, fit a tre parametri con asintoto libero.", tools/data/graph_completion/graph_completion_20260514_1631.json:9: "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GUE-Anderson", tools/data/graph_completion/graph_completion_20260514_1631.json:10: "verdict": "CONSTRAINT / TESTER OPERATIVO - il rimbalzo fisico 16:12 regge come tester riusabile: `SR` e' active in GUE e absorbed nel bordo Anderson `W=6`, mentre `triple_var` resta active come residuo intermedio. Non e' una nuova legge fisica.", tools/data/graph_completion/graph_completion_20260514_1631.json:11: "observable_contract": "claim=il risultato 16:12 va riformulato come tester operativo del confine repulsione/Poisson, non come scoperta fisica; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Anderson 1D `W=6`; denominator=96 repliche per dominio, 95 gap centrali per spettro, 192 eventi trace; null=Poisson span-matched stesso count; non_possible=claim fisico nuovo o legge di transizione se il confronto resta sintetico, finite-size e senza dati sperimentali/unfolding dedicato.", tools/data/graph_completion/graph_completion_20260514_1631.json:12: "claim_under_test": "Il vettore `component_state(SR,L1,triple_var)` resta leggibile come tester operativo del confine GUE-Anderson: `SR` e' active in GUE, viene assorbito al bordo Anderson `W=6`, e `triple_var` puo' restare active nella zona intermedia.", tools/data/graph_completion/graph_completion_20260514_1631.json:14: "consecutio": "Il passo successivo valido non e' tornare ai primi. E' portare lo stesso tester su un denominatore fisico piu' forte: GOE/GUE scelto per simmetria, Anderson 3D o many-body localization se serve una transizione reale, curve in `N`, e confronto con adjacent gap ratio standard piu' unfolding controllato.", tools/data/graph_completion/graph_completion_20260514_1631.json:53: "radici": "Wigner-Dyson/GUE level statistics / Poisson level statistics.", tools/data/graph_completion/graph_completion_20260514_1631.json:54: "singolare": "null Poisson span-matched su stesso count.", tools/data/graph_completion/graph_completion_20260514_1631.json:55: "invariante": "`SR` cade prima di `triple_var` nel bordo `W=6`.", tools/data/graph_completion/graph_completion_20260514_1631.json:92: "dipole": "Wigner-Dyson/GUE level statistics / Poisson level statistics.", tools/data/graph_completion/graph_completion_20260514_1631.json:93: "point_zero": "null Poisson span-matched su stesso count.", tools/data/graph_completion/graph_completion_20260514_1631.json:94: "invariant": "`SR` cade prima di `triple_var` nel bordo `W=6`.", tools/data/graph_completion/graph_completion_20260514_1631.json:98: "observable_contract": "claim=il risultato 16:12 va riformulato come tester operativo del confine repulsione/Poisson, non come scoperta fisica; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Anderson 1D `W=6`; denominator=96 repliche per dominio, 95 gap centrali per spettro, 192 eventi trace; null=Poisson span-matched stesso count; non_possible=claim fisico nuovo o legge di transizione se il confronto resta sintetico, finite-size e senza dati sperimentali/unfolding dedicato.", tools/data/graph_completion/graph_completion_20260514_1605.json:10: "verdict": "PARTIAL - `prime_minus_mod6_z_vector(SR,L1,triple_var)` replica completo a 4096/8192; a 16384 cade `SR` contro `mod6_span_matched` con denominatore valido", tools/data/graph_completion/graph_completion_20260514_1605.json:11: "observable_contract": "claim=`prime_minus_mod6_z_vector(SR,L1,triple_var)` regge a 4096/8192 e mostra il primo limite oltre 8192 solo dopo `min_paired_rows` pre-dichiarato; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py` con `--min-paired-rows 10` e trace obbligatoria; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `mod6_index_aligned` e `mod6_span_matched`; denominator=righe provider-offset paired, soglia atomica `min_paired_rows=10`; non_possible=vettore completo falsificato se un osservabile focus ha delta medio <= 0 o p > 0.01 in un antagonista con denominatore valido; not_tested=origine analitica del delta, scale oltre 16384, beta atlas globale, `V_c`, `gap_ratio`.", tools/data/graph_completion/graph_completion_20260514_1605.json:12: "claim_under_test": "Nel contratto `min_paired_rows=10`, il vettore `prime_minus_mod6_z_vector(SR,L1,triple_var)` resta completo a 4096/8192; oltre 8192 il primo cedimento, se appare, identifica la componente non scalante.", tools/data/graph_completion/graph_completion_20260514_1605.json:14: "consecutio": "Il prossimo passaggio deve isolare `SR`: stesso denominatore, stessa trace, confronto fra `mod6_span_matched` e un null che conserva span ma distrugge solo la grammatica locale. Se `SR` resta p>0.01 mentre `L1,triple_var` passano, il claim va riscritto come vettore a due strati.", tools/data/graph_completion/graph_completion_20260514_1605.json:49: "invariante": "`L1,triple_var` passano anche quando `SR` cade.", tools/data/graph_completion/graph_completion_20260514_1605.json:51: "raw": "separare il vettore in strato robusto (`L1,triple_var`) e strato boundary-sensitive (`SR`).", tools/data/graph_completion/graph_completion_20260514_1605.json:89: "invariant": "`L1,triple_var` passano anche quando `SR` cade.", tools/data/graph_completion/graph_completion_20260514_1605.json:90: "possible": "separare il vettore in strato robusto (`L1,triple_var`) e strato boundary-sensitive (`SR`).", tools/data/graph_completion/graph_completion_20260514_1605.json:93: "observable_contract": "claim=`prime_minus_mod6_z_vector(SR,L1,triple_var)` regge a 4096/8192 e mostra il primo limite oltre 8192 solo dopo `min_paired_rows` pre-dichiarato; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py` con `--min-paired-rows 10` e trace obbligatoria; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `mod6_index_aligned` e `mod6_span_matched`; denominator=righe provider-offset paired, soglia atomica `min_paired_rows=10`; non_possible=vettore completo falsificato se un osservabile focus ha delta medio <= 0 o p > 0.01 in un antagonista con denominatore valido; not_tested=origine analitica del delta, scale oltre 16384, beta atlas globale, `V_c`, `gap_ratio`.", tools/data/graph_completion/graph_completion_20260514_1605.json:101: "claim": "\"Bicono della scoperta\" / \"separare il vettore in strato robusto (L1,triple_var) e strato boundary-sensitive (SR)\" su prime gaps mod 6.", tools/data/graph_completion/graph_completion_20260514_1605.json:103: "suggestion": "Nel prossimo ciclo aggiungere sezione nearest_known: confrontare SR,L1,triple_var contro un modello LOS/HL mod q o dichiarare esplicitamente che il claim e' solo operativo sul pipeline-null, non scoperta matematica nuova." tools/data/graph_completion/graph_completion_20260514_1605.json:108: "claim": "\"relation: follows_direction\" e \"not_drift: non torna a V_c, fit, label locali o GUE/Poisson; misura esclusivamente il limite del vettore prime-minus-mod6.\"", tools/data/graph_completion/graph_completion_20260514_1605.json:109: "evidence": "lab_data direzione dice: \"Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo\". Il report dichiara di non misurare GUE/Poisson e resta nel deposito prime-minus-mod6 senza marcare la deviazione come deliberate_counter_perimeter.", tools/data/graph_completion/graph_completion_20260514_1605.json:110: "suggestion": "Riformulare aderenza come deliberate_counter_perimeter con why/not_drift verificabili, oppure collegare prime-minus-mod6 al confine GUE/Poisson tramite un ponte misurabile nel prossimo ciclo." tools/data/graph_completion/graph_completion_20260509_1556.json:11: "observable_contract": "claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.", tools/data/graph_completion/graph_completion_20260509_1556.json:124: "observable_contract": "claim=`blank_thin_support` sopravvive solo se, dopo estensione del denominatore sorgente, resta supporto vivo sottile senza beta chart; observable=gate canonico one-sided + beta chart sulle tre righe short; operator=`exp_boundary_short_denominator_extension.py`; generator=`source-denominator extension` per `percolation`, `random_matrix`, `zeta_zeros`; denominator=3 righe short del report 15:48, estese a `n_gaps=1024`; non_possible=promuovere `blank_thin_support` se il supporto cade, si ispessisce o recupera beta; not_tested=redesign globale 13 righe, fit `V_c`, validita' della label GUE/Poisson sorgente.", tools/data/graph_completion/graph_completion_20260509_1437.json:11: "observable_contract": "claim=il residual blank test decide se i 3 blank residui BOUNDARY entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + `exp_boundary_denominator_prescan.py`; generator=`dnd_autoricerca.genera_segnale` per `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare chiusura QxG, nuova legge GUE/Poisson, o complete `reaction_diffusion` con 499 gap; not_tested=fit `V_c`, nuovi spettri, nuovi domini.", tools/data/graph_completion/graph_completion_20260509_1437.json:57: "invariant": "il null row-aligned decide il passaggio; la classe GUE/Poisson non viene riscritta.", tools/data/graph_completion/graph_completion_20260509_1437.json:87: "invariante": "disponibilita del null leggibile; non l'etichetta GUE/Poisson e non il fit `V_c`.", tools/data/graph_completion/graph_completion_20260509_1437.json:126: "invariant": "disponibilita del null leggibile; non l'etichetta GUE/Poisson e non il fit `V_c`.", tools/data/graph_completion/graph_completion_20260509_1437.json:130: "observable_contract": "claim=il residual blank test decide se i 3 blank residui BOUNDARY entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + `exp_boundary_denominator_prescan.py`; generator=`dnd_autoricerca.genera_segnale` per `string_vibration`, `reaction_diffusion`, `logistica_biforcazione_var_3.5699`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare chiusura QxG, nuova legge GUE/Poisson, o complete `reaction_diffusion` con 499 gap; not_tested=fit `V_c`, nuovi spettri, nuovi domini.", tools/data/graph_completion/graph_completion_20260509_1437.json:201: "GUE": 8, tools/data/graph_completion/graph_completion_20260509_1437.json:202: "Poisson": 5 tools/data/graph_completion/graph_completion_20260513_0330.json:11: "observable_contract": "claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.", tools/data/graph_completion/graph_completion_20260513_0330.json:14: "consecutio": "Il prossimo taglio non deve chiedere se `SR` c'e. Deve costruire un osservabile di selezione: `prime_minus_mod6_z_delta` su `SR,L1,triple_var`, con span-matched conservato come controllo fragile e index-aligned come antagonista primario. Se il delta resta replicato su piu finestre e scale, il claim diventa: la primalita attenua/rompe la rigidita del pre-bordo mod6 invece di generare `SR` da sola.", tools/data/graph_completion/graph_completion_20260513_0330.json:93: "observable_contract": "claim=`SR` e prime-specific solo se resta piu comune/forte nelle finestre prime dopo sottrazione di candidati `6k +/- 1` row-local; observable=`SR` e firma `[SR,L1,triple_var]` in `coherent_one_sided_observables` + delta z paired; operator=`exp_prime_vs_mod6_sr_boundary.py`; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, mod6 da `index_aligned` e `span_matched`; denominator=8 finestre prime paired con 8 mod6_index_aligned e 8 mod6_span_matched; non_possible=prime-specific SR se SR e comune o pari nel contro-perimetro mod6, oppure se il label-swap row-local assorbe i delta; not_tested=`V_c`, `gap_ratio`, beta atlas globale, origine analitica del trasferimento mod6.", tools/data/graph_completion/graph_completion_20260509_1548.json:51: "invariant": "failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.", tools/data/graph_completion/graph_completion_20260509_1532.json:11: "observable_contract": "claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.", tools/data/graph_completion/graph_completion_20260509_1532.json:14: "consecutio": "Il prossimo ciclo deve attaccare i quattro stati beta, non il supporto gia' separato: `beta_0_3_exact`, `beta_0_3_local_nonunique`, `local_beta_other`, `support_without_beta_blank`. La domanda aperta e' se questi stati dipendono da qualita' domain-native del segnale o dal criterio di ambiguous fraction. Non va reintrodotta la label GUE/Poisson come scorciatoia.", tools/data/graph_completion/graph_completion_20260509_1532.json:55: "invariante": "separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.", tools/data/graph_completion/graph_completion_20260509_1532.json:94: "invariant": "separazione tra supporto e coordinata; non label GUE/Poisson e non beta comune.", tools/data/graph_completion/graph_completion_20260509_1532.json:98: "observable_contract": "claim=la matrice BOUNDARY semi-reale va separata in due assi: `support_transfer` e `beta_coordinate_transfer`; observable=stato row-aligned del supporto ordine/null e stato locale della beta ambigua; operator=`exp_boundary_two_axis_matrix.py`; generator=deposito `semireal_boundary_transfer_gate_20260509_1516` senza uso operativo di label GUE/Poisson; denominator=13 righe BOUNDARY semi-reali del perimetro base; non_possible=salvare il claim \"beta 0.3 universale\" quando solo 4/13 righe hanno beta 0.3 esatta; not_tested=nuovi domini, nuovi null, nuovi beta layer, fit `V_c`, limite asintotico.", tools/data/graph_completion/graph_completion_20260509_1427.json:11: "observable_contract": "claim=il blank-pair test decide se due righe BOUNDARY senza null entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + prescan row-aligned; generator=`dnd_autoricerca.genera_segnale` per `zeta_zeros` e `pendolo_doppio`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare complete `zeta_zeros` con soli 199 gap o dichiarare cambio classe su `pendolo_doppio`; not_tested=fit `V_c`, nuovi spettri, nuova legge GUE/Poisson, chiusura QxG.", tools/data/graph_completion/graph_completion_20260509_1427.json:13: "question": "Il null row-aligned per una coppia blank GUE/Poisson decide `blank -> transfers`, `blank -> falls` o `blank remains blank`?", tools/data/graph_completion/graph_completion_20260509_1427.json:55: "invariante": "il null row-aligned decide il passaggio; la classe GUE/Poisson non viene riscritta.", tools/data/graph_completion/graph_completion_20260509_1427.json:94: "invariant": "il null row-aligned decide il passaggio; la classe GUE/Poisson non viene riscritta.", tools/data/graph_completion/graph_completion_20260509_1427.json:98: "observable_contract": "claim=il blank-pair test decide se due righe BOUNDARY senza null entrano nel transfer; observable=`spacing_r` originale contro permutation null row-aligned; operator=`exp_boundary_blank_null_audit.py` + prescan row-aligned; generator=`dnd_autoricerca.genera_segnale` per `zeta_zeros` e `pendolo_doppio`; denominator=13 righe base autoricerca 8 GUE-like / 5 Poisson-like; non_possible=dichiarare complete `zeta_zeros` con soli 199 gap o dichiarare cambio classe su `pendolo_doppio`; not_tested=fit `V_c`, nuovi spettri, nuova legge GUE/Poisson, chiusura QxG.", tools/data/graph_completion/graph_completion_20260509_1427.json:169: "GUE": 8, tools/data/graph_completion/graph_completion_20260509_1427.json:170: "Poisson": 5 tools/data/graph_completion/graph_completion_20260514_1640.json:7: "title": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", tools/data/graph_completion/graph_completion_20260514_1640.json:9: "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", tools/data/graph_completion/graph_completion_20260514_1640.json:10: "verdict": "CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica.", tools/data/graph_completion/graph_completion_20260514_1640.json:11: "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", tools/data/graph_completion/graph_completion_20260514_1640.json:12: "claim_under_test": "Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size.", tools/data/graph_completion/graph_completion_20260514_1640.json:13: "question": "Il tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`?", tools/data/graph_completion/graph_completion_20260514_1640.json:14: "consecutio": "Il prossimo passo non e' aggiungere dominio per ampiezza. E' rendere il tester leggibile come strumento: interfaccia unica per spettro ordinato, classe attesa opzionale, output `component_state`, contrasto Poisson e, se due classi sono presenti, contrasto diretto. La promozione resta `soluzione/tool`, non `scoperta fisica`.", tools/data/graph_completion/graph_completion_20260514_1640.json:53: "radici": "simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.", tools/data/graph_completion/graph_completion_20260514_1640.json:55: "invariante": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/graph_completion/graph_completion_20260514_1640.json:92: "dipole": "simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.", tools/data/graph_completion/graph_completion_20260514_1640.json:94: "invariant": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/graph_completion/graph_completion_20260514_1640.json:98: "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", tools/data/graph_completion/graph_completion_20260509_1839.json:11: "observable_contract": "claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.", tools/data/graph_completion/graph_completion_20260509_1839.json:124: "observable_contract": "claim=la tassonomia delle transizioni post-estensione scala se nessuna delle 13 righe resta `thin_persists`; observable=`transition_class` row-aligned sulle 13 righe; operator=`exp_boundary_transition_taxonomy_13rows.py`; generator=composizione dei depositi 15:32, 15:38, 15:56 e prescan 15:00 senza rigenerare segnali; denominator=13 righe BOUNDARY semi-reali; non_possible=promuovere `blank_thin_support` come specie autonoma se `thin_persist_rows=0`; not_tested=nuova griglia beta, nuovi null, fit `V_c`, validita' label GUE/Poisson sorgente.", tools/data/graph_completion/graph_completion_20260509_1444.json:11: "observable_contract": "claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_state -> transfer_state -> denominator_state`; observable=stable canonical observables contro permutation null e layer classification; operator=`exp_denominator_gate_transfer_matrix.py`; generator=`DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`; denominator=4 perimetri sintetici QxG continuo/discreto, 4096 gaps, 24 replicates, 11 beta layers, 40 shuffle baselines; non_possible=chiamare chiusura QxG, legge GUE/Poisson o endpoint-stable universale; not_tested=perimetro fisico reale, fit `V_c`, nuovi domini autoricerca.", tools/data/graph_completion/graph_completion_20260509_1444.json:55: "invariante": "osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo.", tools/data/graph_completion/graph_completion_20260509_1444.json:94: "invariant": "osservabile one-sided contro null permutato; non il label GUE/Poisson e non un set canonico completo.", tools/data/graph_completion/graph_completion_20260509_1444.json:98: "observable_contract": "claim=il gate BOUNDARY trasferisce fuori dal perimetro base come operatore `null_state -> transfer_state -> denominator_state`; observable=stable canonical observables contro permutation null e layer classification; operator=`exp_denominator_gate_transfer_matrix.py`; generator=`DUALITA_golden`, `R_periodic_triad`, `T_markov_alternating`, `E_ar1_continuity`; denominator=4 perimetri sintetici QxG continuo/discreto, 4096 gaps, 24 replicates, 11 beta layers, 40 shuffle baselines; non_possible=chiamare chiusura QxG, legge GUE/Poisson o endpoint-stable universale; not_tested=perimetro fisico reale, fit `V_c`, nuovi domini autoricerca.", tools/data/graph_completion/latest.json:7: "title": "Agent Report - Rafforzamento tester fisico GOE/GUE N-curve", tools/data/graph_completion/latest.json:9: "tension": "TENS_SCALE_TRASCENDENZA_LIMITE / BOUNDARY fisico GOE-GUE-Poisson-Anderson", tools/data/graph_completion/latest.json:10: "verdict": "CONSTRAINT / TESTER OPERATIVO RAFFORZATO - `component_state(SR,L1,triple_var)` resta leggibile su un denominatore fisico piu' robusto: distingue GOE e GUE per simmetria su `SR`, conserva la curva in `N=128,192`, e mantiene Anderson 1D `W=6` come bordo finite-size dove `SR` viene assorbito e `triple_var` resta residuo operativo. Non e' una nuova legge fisica.", tools/data/graph_completion/latest.json:11: "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", tools/data/graph_completion/latest.json:12: "claim_under_test": "Il tester `component_state(SR,L1,triple_var)` resta operativo quando il denominatore fisico separa GOE e GUE: `SR` legge la classe di simmetria Wigner-Dyson, viene assorbito nel bordo Anderson 1D `W=6`, e `triple_var` resta residuo locale nel bordo finite-size.", tools/data/graph_completion/latest.json:13: "question": "Il tester L8 16:31 distingue una classe GOE da una classe GUE su due taglie senza perdere il bordo Anderson `W=6`?", tools/data/graph_completion/latest.json:14: "consecutio": "Il prossimo passo non e' aggiungere dominio per ampiezza. E' rendere il tester leggibile come strumento: interfaccia unica per spettro ordinato, classe attesa opzionale, output `component_state`, contrasto Poisson e, se due classi sono presenti, contrasto diretto. La promozione resta `soluzione/tool`, non `scoperta fisica`.", tools/data/graph_completion/latest.json:53: "radici": "simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.", tools/data/graph_completion/latest.json:55: "invariante": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/graph_completion/latest.json:92: "dipole": "simmetria Wigner-Dyson GOE/GUE / indipendenza Poisson-localizzata.", tools/data/graph_completion/latest.json:94: "invariant": "`SR` separa GOE-GUE ma cade su Anderson `W=6`; `triple_var` conserva residuo locale nel bordo finite-size.", tools/data/graph_completion/latest.json:98: "observable_contract": "claim=il tester L8 16:31 si rafforza solo se resta leggibile fra classi Wigner-Dyson distinte e fra due taglie; observable=`component_state(SR,L1,triple_var)` con `SR` = adjacent gap ratio canonico piu contrasto diretto GUE-GOE; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GOE reale simmetrico, GUE hermitiano complesso, Anderson 1D `W=6`, null Poisson span-matched; denominator=2 taglie `N={128,192}`, 64 repliche per taglia e dominio, 384 eventi trace, finestra centrale 0.5; non_possible=promuovere il pattern a legge fisica nuova o usare Anderson 1D come transizione universale; not_tested=dati sperimentali, unfolding dedicato, Anderson 3D, many-body localization, limite asintotico.", tools/data/graph_completion/graph_completion_20260509_1538.json:11: "observable_contract": "claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.", tools/data/graph_completion/graph_completion_20260509_1538.json:49: "invariante": "failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.", tools/data/graph_completion/graph_completion_20260509_1538.json:89: "invariant": "failure mode row-aligned; non label GUE/Poisson e non beta 0.3 globale.", tools/data/graph_completion/graph_completion_20260509_1538.json:93: "observable_contract": "claim=le righe non-esatte della matrice BOUNDARY 15:32 vanno lette row-aligned senza usare label GUE/Poisson; observable=stato beta + forza supporto + telemetria denominatore/null; operator=`exp_boundary_row_aligned_nonexact_audit.py`; generator=matrice `boundary_two_axis_matrix_20260509_1532` + prescan `boundary_denominator_prescan_full_20260509_1500`; denominator=13 righe totali, 11 support-transfer, 7 support-transfer non esatte; non_possible=forzare il conteggio a 6 o trattare beta 0.3 come ascissa comune; not_tested=nuovi domini, nuovi null, nuova griglia beta, fit `V_c`.", tools/data/graph_completion/graph_completion_20260509_1538.json:164: "GUE": 8, tools/data/graph_completion/graph_completion_20260509_1538.json:165: "Poisson": 5 tools/data/graph_completion/graph_completion_20260512_0330.json:11: "observable_contract": "claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.", tools/data/graph_completion/graph_completion_20260512_0330.json:14: "consecutio": "Il prossimo ciclo deve spostare il nodo regressivo: non \"prime vs controlli generici\", ma \"primi vs candidati mod6 row-aligned\". Il test utile e' sottrarre il pre-bordo: misurare cosa resta in `SR`, `L1` e `triple_var` quando i primi sono confrontati con candidati `6k +/- 1` a stesso offset e stessa densita' locale.", tools/data/graph_completion/graph_completion_20260512_0330.json:87: "observable_contract": "claim=`prime_SR_persistent_boundary` regge solo se le finestre prime conservano `SR` come osservabile one-sided comune attraverso provider e offset, mentre controlli non-prime ampliati non condividono persistenza SR piena; observable=`SR` in `coherent_one_sided_observables` + firma comune one-sided; operator=`exp_prime_sr_persistent_boundary.py`; generator=primi via `row_spacings(\"numeri_primi\")` e `prime_gap_sequence`, controlli via composite gaps, candidati mod6, eventi Cramer-like, GUE blocks, logistic return intervals; denominator=8 finestre prime row-local + 20 controlli non-prime; non_possible=claim prime-specific se `SR` prime scende sotto 8/8, se la firma comune prime non e' `[SR]`, o se una sottofamiglia controllo condivide persistenza SR piena; not_tested=atlante beta globale, `V_c`, `gap_ratio`, origine analitica di SR.", tools/data/graph_completion/graph_completion_20260512_0330.json:103: "evidence": "I file strutturali danno come direzione viva `Esplorare il confine: 8 domini GUE, 5 Poisson — il confine è il terzo incluso operativo`. Il report usa 4 GUE random matrix blocks e controlli logistici/Cramer/mod6/compositi, ma non mostra 8 domini GUE né 5 Poisson. La deviazione verso prime/mod6 è dichiarata come BOUNDARY, ma non come `deliberate_counter_perimeter` rispetto alla direzione GUE/Poisson visibile in `lab_data.json`.", tools/data/graph_completion/graph_completion_20260512_0330.json:104: "suggestion": "Esplicitare se `prime_SR_persistent_boundary` è una sotto-direzione autorizzata dal campo vivo; altrimenti nel prossimo ciclo riallineare il contratto a 8 domini GUE / 5 Poisson oppure dichiarare `deliberate_counter_perimeter` con `why` e `not_drift` verificabili." tools/data/graph_completion/graph_completion_20260514_1458.json:10: "verdict": "CONFIRMED - `prime_minus_mod6_z_vector(SR,L1,triple_var)` regge oltre 2048 gap; `has_SR` resta audit negativo, non criterio di verdict", tools/data/graph_completion/graph_completion_20260514_1458.json:11: "observable_contract": "claim=`prime_minus_mod6_z_vector(SR,L1,triple_var)` sopravvive a sottrazione row-local `6k +/- 1`; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py` con verdict vettoriale e trace obbligatoria; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `mod6_index_aligned` e `mod6_span_matched`; denominator=righe provider-offset paired, 4096 e 8192 gap, p label-swap <= 0.01 richiesto per ogni osservabile focus e ogni antagonista; non_possible=vettore falsificato se un osservabile focus ha delta medio <= 0 o p > 0.01 in un antagonista; not_tested=origine analitica del delta, scale oltre 8192 gap, beta atlas globale, `V_c`, `gap_ratio`.", tools/data/graph_completion/graph_completion_20260514_1458.json:12: "claim_under_test": "Dopo sottrazione del pre-bordo `6k +/- 1`, la selezione prime lascia un vettore row-local positivo e significativo su `SR,L1,triple_var`; `has_SR` non decide il confine.", tools/data/graph_completion/graph_completion_20260514_1458.json:13: "question": "Il vettore `prime_minus_mod6_z_vector(SR,L1,triple_var)` resta completo a 4096 e 8192 gap contro antagonisti mod6 piu forti, oppure cade quando il denominatore cresce?", tools/data/graph_completion/graph_completion_20260514_1458.json:14: "consecutio": "Il prossimo passaggio non deve riaprire il verdict. Deve isolare l'origine del vettore: quale componente del passaggio da `6k +/- 1` a primalita' genera il delta positivo su `SR,L1,triple_var`, e quale null lo spegne senza distruggere il denominatore row-local.", tools/data/graph_completion/graph_completion_20260514_1458.json:47: "radici": "label comune `SR` / intensita differenziale `SR,L1,triple_var`.", tools/data/graph_completion/graph_completion_20260514_1458.json:87: "dipole": "label comune `SR` / intensita differenziale `SR,L1,triple_var`.", tools/data/graph_completion/graph_completion_20260514_1458.json:93: "observable_contract": "claim=`prime_minus_mod6_z_vector(SR,L1,triple_var)` sopravvive a sottrazione row-local `6k +/- 1`; observable=delta z paired prime-minus-mod6 per `SR,L1,triple_var`; operator=`tools/exp_prime_vs_mod6_sr_boundary.py` con verdict vettoriale e trace obbligatoria; generator=prime gaps da `row_spacings(\"numeri_primi\")` e `direct_sieve`, antagonisti `mod6_index_aligned` e `mod6_span_matched`; denominator=righe provider-offset paired, 4096 e 8192 gap, p label-swap <= 0.01 richiesto per ogni osservabile focus e ogni antagonista; non_possible=vettore falsificato se un osservabile focus ha delta medio <= 0 o p > 0.01 in un antagonista; not_tested=origine analitica del delta, scale oltre 8192 gap, beta atlas globale, `V_c`, `gap_ratio`.", tools/data/graph_completion/graph_completion_20260514_1458.json:101: "claim": "\"Relazione nuova: la transizione non vive nella label comune ma nel vettore differenziale\" e verdict come `prime_minus_mod6_z_vector(SR,L1,triple_var)` osservabile del confine.", tools/data/graph_completion/graph_completion_20260514_1612.json:10: "verdict": "CONSTRAINT - il trasduttore `SR` assorbito vs residuo `L1,triple_var` produce un test fisico concreto su spettri GUE/Anderson; nel run sintetico il ritorno fisico e' presente ma resta proxy numerico, non misura sperimentale", tools/data/graph_completion/graph_completion_20260514_1612.json:11: "observable_contract": "claim=il cedimento selettivo di `SR` puo' essere formulato come test fisico A->B; observable=componenti canoniche attive/assorbite contro null Poisson span-matched; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Hamiltoniano Anderson 1D tight-binding; denominator=48 repliche per dominio, 95 gap centrali per spettro; non_possible=rimbalzo fisico assente se non si nomina B concreto oppure se `SR` non discrimina GUE da Anderson localizzato; not_tested=dati sperimentali reali, unfolding fine, classi di simmetria GOE/GSE, many-body localization.", tools/data/graph_completion/graph_completion_20260514_1612.json:12: "claim_under_test": "Il cedimento selettivo di `SR` osservato nel deposito prime-minus-mod6 puo' tornare come criterio fisico: `SR` resta attivo in spettri caotici GUE e viene assorbito in spettri Anderson localizzati contro un null Poisson span-matched.", tools/data/graph_completion/graph_completion_20260514_1612.json:14: "consecutio": "Il prossimo ciclo fisico deve sostituire il proxy sintetico con un denominatore piu vicino al laboratorio: GOE/GUE scelto per simmetria dichiarata, Anderson 3D o many-body localization se si vuole una transizione fisica vera, unfolding controllato e confronto con dati o letteratura. Il deposito prime-minus-mod6 resta utile solo come generatore di osservabili, non come prova del dominio fisico.", tools/data/graph_completion/graph_completion_20260514_1612.json:53: "radici": "repulsione spettrale caotica / assorbimento Poisson localizzato.", tools/data/graph_completion/graph_completion_20260514_1612.json:54: "singolare": "null Poisson span-matched con stesso count.", tools/data/graph_completion/graph_completion_20260514_1612.json:55: "invariante": "`component_state` attivo/assorbito per `SR,L1,triple_var`.", tools/data/graph_completion/graph_completion_20260514_1612.json:92: "dipole": "repulsione spettrale caotica / assorbimento Poisson localizzato.", tools/data/graph_completion/graph_completion_20260514_1612.json:93: "point_zero": "null Poisson span-matched con stesso count.", tools/data/graph_completion/graph_completion_20260514_1612.json:94: "invariant": "`component_state` attivo/assorbito per `SR,L1,triple_var`.", tools/data/graph_completion/graph_completion_20260514_1612.json:98: "observable_contract": "claim=il cedimento selettivo di `SR` puo' essere formulato come test fisico A->B; observable=componenti canoniche attive/assorbite contro null Poisson span-matched; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=GUE hermitiano e Hamiltoniano Anderson 1D tight-binding; denominator=48 repliche per dominio, 95 gap centrali per spettro; non_possible=rimbalzo fisico assente se non si nomina B concreto oppure se `SR` non discrimina GUE da Anderson localizzato; not_tested=dati sperimentali reali, unfolding fine, classi di simmetria GOE/GSE, many-body localization.", tools/data/graph_completion/graph_completion_20260514_1612.json:107: "evidence": "Nei dati forniti, `lab_data.json` dichiara come direzione: \"Formalizzare il limite di scala del vettore prime-minus-mod6: micro-trace provider-indipendente, min_paired_rows pre-dichiarato, replica 4096/8192 e primo test oltre 8192 solo se il denominatore resta stabile\". Il report invece esegue un trasferimento GUE/Anderson con N=192 e 48 repliche, senza micro-trace 4096/8192 ne' min_paired_rows. La deviazione e' dichiarata, ma il `why` cita una direttiva operatore non visibile nei file forniti.", tools/data/graph_completion/graph_completion_20260514_1612.json:114: "evidence": "La struttura GUE -> Poisson -> Anderson localizzato ricade nel quadro classico delle statistiche spettrali Wigner-Dyson/GUE contro Poisson e della localizzazione Anderson. Il report dice che non cristallizza una nuova legge fisica, ma non nomina il risultato classico piu' vicino come baseline del ponte.", tools/data/graph_completion/graph_completion_20260514_1612.json:115: "suggestion": "Esplicitare che il test e' un riuso operativo di statistiche spettrali note, non scoperta fisica: confrontare `SR,L1,triple_var` con baseline standard come adjacent gap ratio / Wigner-Dyson vs Poisson / Anderson localization level statistics." tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:1:{"delta": {"L1": -0.37690717608272584, "L2": 0.026108016021574476, "SR": 0.1368405866082193, "SR2": 0.2577968303689212, "triple_var": -2.785063087747547}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.009202603423924073, "L2": -0.08943197415144737, "SR": 0.37398986851207805, "SR2": 0.3159016734142549, "triple_var": 3.1128843259259122}, "real": {"L1": -0.36770457265880174, "L2": -0.06332395812987289, "SR": 0.5108304551202973, "SR2": 0.5736985037831761, "triple_var": 0.3278212381783651}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:2:{"delta": {"L1": -0.29246424756967426, "L2": 0.1384171637360847, "SR": 0.315020926170212, "SR2": 0.3696764721712294, "triple_var": -2.32729236908849}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.08418704209426561, "L2": 0.01935247808425468, "SR": 0.3792727689618897, "SR2": 0.37774505794542274, "triple_var": 2.4767554736275197}, "real": {"L1": -0.3766512896639399, "L2": 0.15776964182033937, "SR": 0.6942936951321017, "SR2": 0.7474215301166521, "triple_var": 0.14946310453902958}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:3:{"delta": {"L1": -0.24705290039603395, "L2": 0.10677249231014616, "SR": 0.08192294221501978, "SR2": 0.11043126909088902, "triple_var": -2.784845864584176}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.08019947591983571, "L2": -0.15791896711436168, "SR": 0.38046415446651577, "SR2": 0.35145692197414946, "triple_var": 3.98321233799855}, "real": {"L1": -0.16685342447619822, "L2": -0.051146474804215525, "SR": 0.46238709668153555, "SR2": 0.4618881910650385, "triple_var": 1.198366473414374}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:4:{"delta": {"L1": 0.004842875082936751, "L2": -0.20105539662081146, "SR": 0.0017290210489668834, "SR2": -0.010431878539607475, "triple_var": -1.024511178095227}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.06965962323181703, "L2": 0.06416078033043264, "SR": 0.4160858247211447, "SR2": 0.38912855463097806, "triple_var": 3.60156143233278}, "real": {"L1": 0.07450249831475378, "L2": -0.13689461629037883, "SR": 0.4178148457701116, "SR2": 0.3786966760913706, "triple_var": 2.577050254237553}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:5:{"delta": {"L1": 0.14859021286942659, "L2": 0.3460564443043903, "SR": 0.04511490302223137, "SR2": 0.08791270250855515, "triple_var": 0.5485672780403887}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.1260477365974644, "L2": -0.1827463412331461, "SR": 0.334110491460692, "SR2": 0.3442706267568894, "triple_var": 2.3746029172768655}, "real": {"L1": 0.02254247627196218, "L2": 0.16331010307124424, "SR": 0.37922539448292336, "SR2": 0.43218332926544456, "triple_var": 2.923170195317254}, "rep": 0} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:6:{"delta": {"L1": -0.3018614916632309, "L2": 0.15055338601216967, "SR": 0.15791091185165418, "SR2": 0.22609794105289943, "triple_var": -1.5177629917326796}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.004712480183599873, "L2": -0.2705311536394644, "SR": 0.4399544246767415, "SR2": 0.41441253283191265, "triple_var": 1.7818711841081574}, "real": {"L1": -0.297149011479631, "L2": -0.11997776762729473, "SR": 0.5978653365283957, "SR2": 0.6405104738848121, "triple_var": 0.26410819237547783}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:7:{"delta": {"L1": -0.23656821995625227, "L2": -0.03469592190693771, "SR": 0.25336997837899594, "SR2": 0.3257876809264969, "triple_var": -2.9476201586295323}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.03667069296256026, "L2": 0.1226335945301113, "SR": 0.41748194982908865, "SR2": 0.39681894869887224, "triple_var": 3.149650586562079}, "real": {"L1": -0.2732389129188125, "L2": 0.08793767262317359, "SR": 0.6708519282080846, "SR2": 0.7226066296253691, "triple_var": 0.20203042793254694}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:8:{"delta": {"L1": -0.1298785119616913, "L2": 0.042605534812895585, "SR": 0.11165630593034531, "SR2": 0.05132872725613252, "triple_var": -1.0837754509025954}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.10544287869030258, "L2": 0.04017824990601017, "SR": 0.3775863664234887, "SR2": 0.45370328433127544, "triple_var": 1.9500835656405129}, "real": {"L1": -0.2353213906519939, "L2": 0.08278378471890575, "SR": 0.489242672353834, "SR2": 0.505032011587408, "triple_var": 0.8663081147379175}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:9:{"delta": {"L1": 0.15790003082990925, "L2": 0.012929085956795969, "SR": 0.08579985835527798, "SR2": 0.0978719340845054, "triple_var": 0.28654541601588734}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.015034799823853978, "L2": -0.11449256011709667, "SR": 0.37065139851066137, "SR2": 0.35452975281883836, "triple_var": 2.3259088431289525}, "real": {"L1": 0.14286523100605528, "L2": -0.1015634741603007, "SR": 0.45645125686593935, "SR2": 0.45240168690334376, "triple_var": 2.61245425914484}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:10:{"delta": {"L1": -0.052909185224326825, "L2": -0.22179100872249122, "SR": -0.008272237151621287, "SR2": -0.09016605653393639, "triple_var": -1.7549595463051726}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.04125100569051803, "L2": 0.2686978366139155, "SR": 0.39693533323337915, "SR2": 0.44189497067057826, "triple_var": 4.031949175872958}, "real": {"L1": -0.09416019091484486, "L2": 0.04690682789142427, "SR": 0.38866309608175786, "SR2": 0.35172891413664187, "triple_var": 2.2769896295677854}, "rep": 1} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:11:{"delta": {"L1": -0.2961538715555426, "L2": -0.08384803371151561, "SR": 0.23048303492069921, "SR2": 0.22286850796704422, "triple_var": -1.9423902162633806}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.07138820345358557, "L2": -0.003103958684849058, "SR": 0.4034598533332233, "SR2": 0.4146291837288924, "triple_var": 2.2425276894685613}, "real": {"L1": -0.22476566810195708, "L2": -0.08695199239636467, "SR": 0.6339428882539225, "SR2": 0.6374976916959366, "triple_var": 0.30013747320518064}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:12:{"delta": {"L1": -0.2877675790018692, "L2": 0.2583367268912707, "SR": 0.20617047106820036, "SR2": 0.2991198993666934, "triple_var": -1.8541397841561904}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.11344756763471356, "L2": -0.052463466244930736, "SR": 0.4201848611746239, "SR2": 0.40049864583128464, "triple_var": 2.060174904014688}, "real": {"L1": -0.40121514663658275, "L2": 0.20587326064633993, "SR": 0.6263553322428242, "SR2": 0.6996185451979781, "triple_var": 0.20603511985849748}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:13:{"delta": {"L1": -0.03090259274010934, "L2": -0.22195960412421017, "SR": 0.03259899704287694, "SR2": 0.0547154334201862, "triple_var": -1.1764447577494443}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.08382595797050363, "L2": 0.09604655279573976, "SR": 0.374176775579901, "SR2": 0.3806031613820262, "triple_var": 2.5799939358034036}, "real": {"L1": -0.11472855071061297, "L2": -0.12591305132847042, "SR": 0.4067757726227779, "SR2": 0.4353185948022124, "triple_var": 1.4035491780539593}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:14:{"delta": {"L1": -0.039136246254615856, "L2": 0.09903437503708007, "SR": 0.08970838927897135, "SR2": 0.06641605532713846, "triple_var": -1.8207018868096818}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.02226148824340594, "L2": -0.020561364877936552, "SR": 0.34599803866985074, "SR2": 0.3845868377255503, "triple_var": 4.239510787403173}, "real": {"L1": -0.01687475801120992, "L2": 0.07847301015914351, "SR": 0.4357064279488221, "SR2": 0.45100289305268876, "triple_var": 2.418808900593491}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:15:{"delta": {"L1": -0.16608180405952636, "L2": -0.051973116554433776, "SR": -0.050543524690478236, "SR2": -0.07293960847700043, "triple_var": -0.09205985543123463}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.17138853370325446, "L2": -0.06306617046534485, "SR": 0.43622502102397664, "SR2": 0.4371442348282043, "triple_var": 2.7521780297243827}, "real": {"L1": 0.005306729643728097, "L2": -0.11503928701977863, "SR": 0.3856814963334984, "SR2": 0.3642046263512039, "triple_var": 2.660118174293148}, "rep": 2} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:16:{"delta": {"L1": -0.2982362363205202, "L2": 0.07789736866889146, "SR": 0.16387368891465603, "SR2": 0.229136055301849, "triple_var": -1.458427929340952}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.07313652937364082, "L2": -0.07023996838252689, "SR": 0.41299507934193447, "SR2": 0.39654677356924106, "triple_var": 1.7357672633893209}, "real": {"L1": -0.371372765694161, "L2": 0.0076574002863645865, "SR": 0.5768687682565905, "SR2": 0.6256828288710901, "triple_var": 0.27733933404836897}, "rep": 3} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:17:{"delta": {"L1": -0.29105411197039366, "L2": 0.15245859280523316, "SR": 0.2288103866919261, "SR2": 0.3186181363011877, "triple_var": -2.527304107433701}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.11363404776026541, "L2": -0.008181726425180393, "SR": 0.3980775166195557, "SR2": 0.3847338682477844, "triple_var": 2.7485334451527517}, "real": {"L1": -0.4046881597306591, "L2": 0.14427686638005277, "SR": 0.6268879033114818, "SR2": 0.7033520045489721, "triple_var": 0.2212293377190505}, "rep": 3} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:18:{"delta": {"L1": 0.05277764503009848, "L2": -0.41367605310744177, "SR": 0.05895546324557599, "SR2": 0.043100758718695065, "triple_var": -1.6665032455775486}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.1458470751322823, "L2": 0.15074421186458659, "SR": 0.4175952152074903, "SR2": 0.4108998999668772, "triple_var": 2.9320738182378343}, "real": {"L1": -0.09306943010218381, "L2": -0.2629318412428552, "SR": 0.4765506784530663, "SR2": 0.45400065868557227, "triple_var": 1.2655705726602857}, "rep": 3} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:19:{"delta": {"L1": 0.07812366808727651, "L2": -0.164722655177225, "SR": 0.0652577681925966, "SR2": 0.03539539283103699, "triple_var": -0.8132570189014745}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.08518628478544603, "L2": 0.07832895677585192, "SR": 0.4124359935200674, "SR2": 0.3898867983395556, "triple_var": 2.2117599512565955}, "real": {"L1": -0.0070626166981695125, "L2": -0.08639369840137309, "SR": 0.477693761712664, "SR2": 0.42528219117059257, "triple_var": 1.398502932355121}, "rep": 3} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:20:{"delta": {"L1": 0.06526222638736852, "L2": -0.11757703505816872, "SR": -0.006768426172307385, "SR2": 0.03589017005036804, "triple_var": 0.10147126220667912}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.035671923490113354, "L2": -0.037705272294264146, "SR": 0.40401496775416845, "SR2": 0.3909234170034782, "triple_var": 2.4384874761917423}, "real": {"L1": 0.02959030289725517, "L2": -0.15528230735243287, "SR": 0.39724654158186107, "SR2": 0.42681358705384626, "triple_var": 2.5399587383984215}, "rep": 3} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:21:{"delta": {"L1": -0.48460429505143826, "L2": 0.0369652289150193, "SR": 0.16192751861370308, "SR2": 0.24367200842706316, "triple_var": -2.6094018586738392}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.13229160658123068, "L2": 0.003835825125561533, "SR": 0.41625338863352385, "SR2": 0.39276847224730194, "triple_var": 2.9298750727934264}, "real": {"L1": -0.3523126884702076, "L2": 0.040801054040580834, "SR": 0.5781809072472269, "SR2": 0.6364404806743651, "triple_var": 0.3204732141195872}, "rep": 4} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:22:{"delta": {"L1": -0.20134459291056475, "L2": -0.1564910773179345, "SR": 0.2105767485568858, "SR2": 0.23239544189101424, "triple_var": -2.007999706671762}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.1744195053871516, "L2": 0.2308687355405228, "SR": 0.4179590043314193, "SR2": 0.4308335994344342, "triple_var": 2.243235988519192}, "real": {"L1": -0.37576409829771634, "L2": 0.07437765822258834, "SR": 0.6285357528883051, "SR2": 0.6632290413254485, "triple_var": 0.23523628184743048}, "rep": 4} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:23:{"delta": {"L1": -0.26784349366330584, "L2": 0.02664875272455583, "SR": 0.07078954016416766, "SR2": 0.1382975291285488, "triple_var": -1.4255735549456716}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.03788022707976157, "L2": -0.0691084254840806, "SR": 0.40432024616898604, "SR2": 0.38065978827486896, "triple_var": 2.351288609485196}, "real": {"L1": -0.2299632665835443, "L2": -0.042459672759524766, "SR": 0.4751097863331537, "SR2": 0.5189573174034178, "triple_var": 0.9257150545395243}, "rep": 4} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:24:{"delta": {"L1": 0.0967836979505877, "L2": 0.26160863908887283, "SR": 0.048541900674411054, "SR2": 0.025767073673577168, "triple_var": 0.6259893313601146}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.1636147188975194, "L2": -0.19780184800447484, "SR": 0.34372667998706374, "SR2": 0.33830602415905253, "triple_var": 2.1548726922940924}, "real": {"L1": -0.06683102094693172, "L2": 0.06380679108439799, "SR": 0.3922685806614748, "SR2": 0.3640730978326297, "triple_var": 2.780862023654207}, "rep": 4} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:25:{"delta": {"L1": -0.018291145941528295, "L2": -0.11364014433921266, "SR": 0.038665744602063734, "SR2": -0.0023538403467403546, "triple_var": -0.7124816569666828}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.04960312308090993, "L2": 0.10122127480659798, "SR": 0.38201121197552684, "SR2": 0.40844061825862815, "triple_var": 2.9533219970416735}, "real": {"L1": -0.06789426902243823, "L2": -0.012418869532614674, "SR": 0.4206769565775906, "SR2": 0.4060867779118878, "triple_var": 2.2408403400749908}, "rep": 4} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:26:{"delta": {"L1": -0.4120297345894166, "L2": -0.03788964295865957, "SR": 0.18564579729484593, "SR2": 0.24464032181948542, "triple_var": -2.8300466621366303}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.00010116619249008885, "L2": -0.026287371350560054, "SR": 0.39025427132617035, "SR2": 0.3654162192768163, "triple_var": 3.061766045844133}, "real": {"L1": -0.41192856839692654, "L2": -0.06417701430921963, "SR": 0.5759000686210163, "SR2": 0.6100565410963017, "triple_var": 0.23171938370750284}, "rep": 5} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:27:{"delta": {"L1": -0.16353319098812505, "L2": -0.27048995612375065, "SR": 0.31456794918335745, "SR2": 0.2500144388296785, "triple_var": -2.1774257898608247}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.20100876750095384, "L2": 0.17109857774907491, "SR": 0.32877812673423396, "SR2": 0.4416267551242858, "triple_var": 2.3418678793866263}, "real": {"L1": -0.3645419584890789, "L2": -0.09939137837467575, "SR": 0.6433460759175914, "SR2": 0.6916411939539643, "triple_var": 0.16444208952580155}, "rep": 5} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:28:{"delta": {"L1": -0.291979112920896, "L2": -0.16131145952551737, "SR": 0.03780220325115252, "SR2": 0.056648072084175105, "triple_var": -1.4951282353985385}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.044080868219424425, "L2": 0.06602214047365003, "SR": 0.37490582360487873, "SR2": 0.37226295843697554, "triple_var": 2.7114083435390013}, "real": {"L1": -0.2478982447014716, "L2": -0.09528931905186734, "SR": 0.41270802685603125, "SR2": 0.42891103052115065, "triple_var": 1.2162801081404628}, "rep": 5} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:29:{"delta": {"L1": -0.23428384222617074, "L2": 0.26357156351255256, "SR": -0.031156902517829876, "SR2": 0.11834068788897523, "triple_var": -0.7891063543630539}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.059071450148542176, "L2": -0.12545344181482565, "SR": 0.37785192307564247, "SR2": 0.30014846764148345, "triple_var": 3.246804034848513}, "real": {"L1": -0.17521239207762856, "L2": 0.13811812169772691, "SR": 0.3466950205578126, "SR2": 0.4184891555304587, "triple_var": 2.457697680485459}, "rep": 5} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:30:{"delta": {"L1": -0.12144312811405229, "L2": -0.19020366811667275, "SR": -0.09378657828106313, "SR2": -0.028024406428258042, "triple_var": -1.2041499227481447}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.0269261198012884, "L2": 0.03840101200851751, "SR": 0.42060221492435784, "SR2": 0.38500647080646627, "triple_var": 3.2278720711609203}, "real": {"L1": -0.0945170083127639, "L2": -0.15180265610815524, "SR": 0.3268156366432947, "SR2": 0.35698206437820823, "triple_var": 2.0237221484127756}, "rep": 5} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:31:{"delta": {"L1": -0.28316045512631416, "L2": -0.14008908858027444, "SR": 0.20900117149567887, "SR2": 0.18548943422864467, "triple_var": -2.2912390511346747}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.03749672253967021, "L2": 0.0026668204050687766, "SR": 0.4060360958232415, "SR2": 0.42197341389705917, "triple_var": 2.5770907273249573}, "real": {"L1": -0.24566373258664395, "L2": -0.13742226817520567, "SR": 0.6150372673189204, "SR2": 0.6074628481257038, "triple_var": 0.28585167619028234}, "rep": 6} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:32:{"delta": {"L1": -0.23453285789147038, "L2": 4.244183969714932e-05, "SR": 0.36505113219546853, "SR2": 0.3577037238236187, "triple_var": -2.8516231386150888}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.03860636572097066, "L2": -0.10874911353376775, "SR": 0.326153732661741, "SR2": 0.3388731827895315, "triple_var": 3.0053464054434325}, "real": {"L1": -0.27313922361244103, "L2": -0.1087066716940706, "SR": 0.6912048648572096, "SR2": 0.6965769066131502, "triple_var": 0.1537232668283438}, "rep": 6} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:33:{"delta": {"L1": 0.15180170791075542, "L2": 0.07323602900558289, "SR": 0.02245980999382452, "SR2": 0.0009166362602415212, "triple_var": -0.21134867059029072}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.19944308760816862, "L2": -0.0011371278693472213, "SR": 0.4014890832148473, "SR2": 0.4418404551463889, "triple_var": 1.732891152059127}, "real": {"L1": -0.047641379697413194, "L2": 0.07209890113623567, "SR": 0.4239488932086718, "SR2": 0.44275709140663044, "triple_var": 1.5215424814688363}, "rep": 6} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:34:{"delta": {"L1": -0.11288597274922973, "L2": -0.0443217998560374, "SR": 0.04796113407361419, "SR2": 0.04834492757214004, "triple_var": -3.85187269596236}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.05522283938941463, "L2": -0.033629993237622266, "SR": 0.3990524733341569, "SR2": 0.39189378647271694, "triple_var": 5.741326176459356}, "real": {"L1": -0.05766313335981511, "L2": -0.07795179309365967, "SR": 0.4470136074077711, "SR2": 0.440238714044857, "triple_var": 1.8894534804969965}, "rep": 6} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:35:{"delta": {"L1": -0.04527104142201875, "L2": -0.13683785245126132, "SR": 0.03658660682758197, "SR2": 0.08019361704334704, "triple_var": -1.350120473605139}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.05289478492227544, "L2": 0.04129949591397634, "SR": 0.38133894738057533, "SR2": 0.3696774036213234, "triple_var": 3.96007260840382}, "real": {"L1": 0.0076237435002566864, "L2": -0.09553835653728497, "SR": 0.4179255542081573, "SR2": 0.44987102066467044, "triple_var": 2.609952134798681}, "rep": 6} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:36:{"delta": {"L1": -0.12510365804587525, "L2": 0.0019251321734364947, "SR": 0.24472717937196886, "SR2": 0.2684496116973609, "triple_var": -2.918686129801186}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.0811058533250666, "L2": 0.003484775562693594, "SR": 0.3671832559155943, "SR2": 0.3857202292872234, "triple_var": 3.291954182802931}, "real": {"L1": -0.20620951137094184, "L2": 0.005409907736130089, "SR": 0.6119104352875632, "SR2": 0.6541698409845843, "triple_var": 0.3732680530017449}, "rep": 7} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:37:{"delta": {"L1": -0.2703004450276288, "L2": 0.09726502715958564, "SR": 0.33478199108804824, "SR2": 0.3158675415550763, "triple_var": -2.195828561440773}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.10273567653676224, "L2": -0.10310672530618388, "SR": 0.34030548131314814, "SR2": 0.3672529627325704, "triple_var": 2.362143053982728}, "real": {"L1": -0.37303612156439103, "L2": -0.00584169814659824, "SR": 0.6750874724011964, "SR2": 0.6831205042876467, "triple_var": 0.166314492541955}, "rep": 7} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:38:{"delta": {"L1": -0.19790609978318074, "L2": -0.12341369183799292, "SR": 0.041875729453289656, "SR2": 0.0616918584599338, "triple_var": -2.6269979755535235}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.11246041581164559, "L2": -0.03640496771076343, "SR": 0.38976010651134124, "SR2": 0.3847028411669702, "triple_var": 3.891998632800718}, "real": {"L1": -0.08544568397153515, "L2": -0.15981865954875635, "SR": 0.4316358359646309, "SR2": 0.446394699626904, "triple_var": 1.2650006572471948}, "rep": 7} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:39:{"delta": {"L1": -0.055908775494519274, "L2": -0.2475407491365033, "SR": 0.03131437457099168, "SR2": -0.0007556573454564619, "triple_var": -0.9435471671822098}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.03939473977671293, "L2": 0.14946341478515077, "SR": 0.38663397094529944, "SR2": 0.3757655880149628, "triple_var": 2.9043062359238276}, "real": {"L1": -0.016514035717806345, "L2": -0.09807733435135253, "SR": 0.4179483455162911, "SR2": 0.37500993066950633, "triple_var": 1.9607590687416179}, "rep": 7} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:40:{"delta": {"L1": -0.021583395720504807, "L2": -0.2128301032237673, "SR": 0.037159480927164146, "SR2": 0.047551170236801, "triple_var": -2.305954277008294}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.06216319440178442, "L2": 0.02957241986475098, "SR": 0.3832098969362752, "SR2": 0.3460091788226177, "triple_var": 4.6171743044636555}, "real": {"L1": 0.040579798681279616, "L2": -0.18325768335901632, "SR": 0.42036937786343936, "SR2": 0.3935603490594187, "triple_var": 2.3112200274553616}, "rep": 7} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:41:{"delta": {"L1": -0.28632428728182013, "L2": -0.10391508537308289, "SR": 0.15979038361413117, "SR2": 0.22536561031783753, "triple_var": -1.9598808342834162}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.02401174537435247, "L2": -0.03300453527882397, "SR": 0.41746393514794006, "SR2": 0.40101239891342266, "triple_var": 2.224312823908485}, "real": {"L1": -0.3103360326561726, "L2": -0.13691962065190685, "SR": 0.5772543187620712, "SR2": 0.6263780092312602, "triple_var": 0.26443198962506853}, "rep": 8} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:42:{"delta": {"L1": -0.30038416233019394, "L2": -0.13113687076014924, "SR": 0.256126274616987, "SR2": 0.25521098753222804, "triple_var": -2.6241666056034347}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.06873117104039769, "L2": 0.08820401717377133, "SR": 0.3752279026820965, "SR2": 0.4167040028152176, "triple_var": 2.8197754938257846}, "real": {"L1": -0.36911533337059166, "L2": -0.04293285358637791, "SR": 0.6313541772990835, "SR2": 0.6719149903474456, "triple_var": 0.19560888822235004}, "rep": 8} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:43:{"delta": {"L1": -0.057196213393196894, "L2": -0.07661844571183457, "SR": 0.005686522561544616, "SR2": 0.00851477310102966, "triple_var": -0.6521850543262921}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.03721192257382718, "L2": 0.013944603818586332, "SR": 0.38141575319714566, "SR2": 0.3922072894515154, "triple_var": 2.3297360289270777}, "real": {"L1": -0.09440813596702408, "L2": -0.06267384189324823, "SR": 0.3871022757586903, "SR2": 0.40072206255254506, "triple_var": 1.6775509746007855}, "rep": 8} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:44:{"delta": {"L1": 0.1347965050492654, "L2": -0.5178876134666828, "SR": 0.07866077790958442, "SR2": -0.0025303169494662603, "triple_var": -1.3884591979562995}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.14238340247544395, "L2": 0.3605667200574159, "SR": 0.3421855318165179, "SR2": 0.41470978475524023, "triple_var": 3.095315951854203}, "real": {"L1": -0.007586897426178552, "L2": -0.1573208934092669, "SR": 0.4208463097261023, "SR2": 0.41217946780577397, "triple_var": 1.7068567538979034}, "rep": 8} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:45:{"delta": {"L1": -0.1288358712650413, "L2": 0.08176989054657487, "SR": -0.03038344682733035, "SR2": -0.006020392598061641, "triple_var": -0.6657230958442337}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.0462093076647094, "L2": 0.033848495450537336, "SR": 0.3702727831572078, "SR2": 0.3801950099510709, "triple_var": 3.0757943828836676}, "real": {"L1": -0.17504517892975072, "L2": 0.1156183859971122, "SR": 0.33988933632987745, "SR2": 0.3741746173530093, "triple_var": 2.410071287039434}, "rep": 8} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:46:{"delta": {"L1": -0.41795303587025406, "L2": 0.10884208951725821, "SR": 0.16345062782995812, "SR2": 0.2626673101310976, "triple_var": -3.196021837189723}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.05637819126610986, "L2": -0.020746200208527528, "SR": 0.384367488983424, "SR2": 0.38592985509581496, "triple_var": 3.4305592970721754}, "real": {"L1": -0.47433122713636394, "L2": 0.08809588930873069, "SR": 0.5478181168133821, "SR2": 0.6485971652269126, "triple_var": 0.23453745988245253}, "rep": 9} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:47:{"delta": {"L1": -0.6612177054713794, "L2": 0.2220877661602963, "SR": 0.22613963075569105, "SR2": 0.33675768565187386, "triple_var": -3.9500833096433157}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.1858867405931758, "L2": 0.11472130233512466, "SR": 0.44851983819429725, "SR2": 0.41491225447990016, "triple_var": 4.1349531161081785}, "real": {"L1": -0.4753309648782036, "L2": 0.33680906849542097, "SR": 0.6746594689499883, "SR2": 0.751669940131774, "triple_var": 0.18486980646486292}, "rep": 9} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:48:{"delta": {"L1": -0.18501029499720628, "L2": -0.11639282998490455, "SR": 0.12441331140852468, "SR2": 0.037216380057512266, "triple_var": -2.327623598163133}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.025392264823099996, "L2": 0.042618251338493, "SR": 0.37551441014386455, "SR2": 0.4290726673499983, "triple_var": 3.3158803682578766}, "real": {"L1": -0.15961803017410628, "L2": -0.07377457864641154, "SR": 0.49992772155238924, "SR2": 0.4662890474075106, "triple_var": 0.9882567700947437}, "rep": 9} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:49:{"delta": {"L1": -0.026575323612380755, "L2": -0.04938412838251998, "SR": 0.016409740063768663, "SR2": -0.021318721342853342, "triple_var": -0.26650549024604553}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.028474579895103394, "L2": 0.04882011705284172, "SR": 0.38207270409341904, "SR2": 0.3955168352562885, "triple_var": 2.8355293040608833}, "real": {"L1": -0.05504990350748415, "L2": -0.0005640113296782617, "SR": 0.3984824441571877, "SR2": 0.37419811391343516, "triple_var": 2.569023813814838}, "rep": 9} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:50:{"delta": {"L1": -0.3171852775220514, "L2": 0.022278229186710366, "SR": -0.03177330657090893, "SR2": 0.06331764441409238, "triple_var": -1.2374258036953063}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.07665993086209005, "L2": -0.04925088866338506, "SR": 0.3496045622490004, "SR2": 0.3500958383559475, "triple_var": 3.2717599768265835}, "real": {"L1": -0.24052534665996134, "L2": -0.026972659476674696, "SR": 0.31783125567809145, "SR2": 0.41341348277003986, "triple_var": 2.034334173131277}, "rep": 9} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:51:{"delta": {"L1": -0.21791219741863904, "L2": 0.1666807732120199, "SR": 0.21036402303750434, "SR2": 0.3007690825883783, "triple_var": -1.9013366301475476}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.1452519585263209, "L2": -0.06256738482538098, "SR": 0.3712954273966521, "SR2": 0.36651078177553775, "triple_var": 2.248200057734343}, "real": {"L1": -0.36316415594495993, "L2": 0.10411338838663892, "SR": 0.5816594504341565, "SR2": 0.6672798643639161, "triple_var": 0.3468634275867952}, "rep": 10} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:52:{"delta": {"L1": -0.21087765978795195, "L2": 0.08313292828528884, "SR": 0.29936779794349766, "SR2": 0.37864340843707434, "triple_var": -1.7891513880461005}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.14189084193398174, "L2": -0.039443444241732656, "SR": 0.38916122347158333, "SR2": 0.3482992243863835, "triple_var": 1.9602687035702275}, "real": {"L1": -0.3527685017219337, "L2": 0.043689484043556194, "SR": 0.688529021415081, "SR2": 0.7269426328234578, "triple_var": 0.17111731552412712}, "rep": 10} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:53:{"delta": {"L1": -0.16628992527174713, "L2": -0.11278468868929113, "SR": 0.12846072801340813, "SR2": 0.06923775859435632, "triple_var": -1.8210371574504647}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.14254189291308317, "L2": 0.0785030085563432, "SR": 0.3153346740523581, "SR2": 0.41078985050907607, "triple_var": 2.7223247525121774}, "real": {"L1": -0.3088318181848303, "L2": -0.034281680132947925, "SR": 0.44379540206576623, "SR2": 0.4800276091034324, "triple_var": 0.9012875950617127}, "rep": 10} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:54:{"delta": {"L1": -0.14000160368969217, "L2": 0.14110080881491777, "SR": 0.0652174875750356, "SR2": 0.11789646615472366, "triple_var": -0.9937095487845666}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.05228481166514633, "L2": -0.06267216682041214, "SR": 0.30516911515551864, "SR2": 0.3426837415575006, "triple_var": 3.2162308113592624}, "real": {"L1": -0.08771679202454585, "L2": 0.07842864199450564, "SR": 0.37038660273055424, "SR2": 0.46058020771222424, "triple_var": 2.2225212625746957}, "rep": 10} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:55:{"delta": {"L1": 0.08189873045672409, "L2": -0.11159183689823063, "SR": 0.007024227668623428, "SR2": 0.02936937991100852, "triple_var": -0.2210993000567223}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.05300254200058711, "L2": 0.1135933309074413, "SR": 0.3676325998208662, "SR2": 0.3752817875845975, "triple_var": 3.0740416003584996}, "real": {"L1": 0.1349012724573112, "L2": 0.00200149400921066, "SR": 0.37465682748948964, "SR2": 0.40465116749560603, "triple_var": 2.8529423003017773}, "rep": 10} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:56:{"delta": {"L1": -0.2768985794543702, "L2": -0.035537077782876764, "SR": 0.1700575882271489, "SR2": 0.21530828242531308, "triple_var": -1.477615191305181}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.02803755498009084, "L2": -0.040775930868076946, "SR": 0.3988244929272089, "SR2": 0.382011947956417, "triple_var": 1.8506217223864148}, "real": {"L1": -0.30493613443446105, "L2": -0.07631300865095371, "SR": 0.5688820811543578, "SR2": 0.5973202303817301, "triple_var": 0.37300653108123377}, "rep": 11} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:57:{"delta": {"L1": -0.09000832338472832, "L2": 0.011596010589249105, "SR": 0.278450389356313, "SR2": 0.33514959869852134, "triple_var": -1.7352183620841655}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.06857136533944512, "L2": -0.04548636421419017, "SR": 0.41223863776813047, "SR2": 0.3748168279827675, "triple_var": 1.9748861968403666}, "real": {"L1": -0.15857968872417344, "L2": -0.03389035362494106, "SR": 0.6906890271244435, "SR2": 0.7099664266812888, "triple_var": 0.23966783475620118}, "rep": 11} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:58:{"delta": {"L1": -0.03468790171833924, "L2": -0.11228794584217044, "SR": -0.0226717840219145, "SR2": 0.014626559686883422, "triple_var": -0.46831935470267827}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.17148007734662846, "L2": 0.048306128241360165, "SR": 0.395041519139891, "SR2": 0.4210600335573005, "triple_var": 1.9480321733596175}, "real": {"L1": -0.2061679790649677, "L2": -0.06398181760081029, "SR": 0.3723697351179765, "SR2": 0.4356865932441839, "triple_var": 1.4797128186569393}, "rep": 11} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:59:{"delta": {"L1": 0.11271384068795239, "L2": -0.15521637111580572, "SR": 0.09978635669601538, "SR2": 0.011462253879371553, "triple_var": -0.024339457349770388}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.2233257152401617, "L2": 0.03173441978823124, "SR": 0.324368513060399, "SR2": 0.3936507618515191, "triple_var": 1.8504931096370942}, "real": {"L1": -0.11061187455220932, "L2": -0.12348195132757447, "SR": 0.42415486975641437, "SR2": 0.40511301573089065, "triple_var": 1.8261536522873238}, "rep": 11} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:60:{"delta": {"L1": 0.13388618290871007, "L2": -0.16760298518329447, "SR": 0.03278937010613536, "SR2": 0.040113764904766036, "triple_var": 0.4009760166387122}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.03530579474031482, "L2": 0.06372655697172326, "SR": 0.38103220728223897, "SR2": 0.33291573225972243, "triple_var": 2.9406267048679458}, "real": {"L1": 0.16919197764902488, "L2": -0.1038764282115712, "SR": 0.41382157738837433, "SR2": 0.37302949716448847, "triple_var": 3.341602721506658}, "rep": 11} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:61:{"delta": {"L1": 0.0066611984699408155, "L2": 0.058655660787834726, "SR": 0.29617655075865534, "SR2": 0.2781081986309453, "triple_var": -1.5458391516444425}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.19240526514697864, "L2": -0.15995528750007723, "SR": 0.3494733182340459, "SR2": 0.36075656956157115, "triple_var": 1.8551943692840385}, "real": {"L1": -0.18574406667703783, "L2": -0.1012996267122425, "SR": 0.6456498689927013, "SR2": 0.6388647681925165, "triple_var": 0.30935521763959595}, "rep": 12} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:62:{"delta": {"L1": -0.5122024175544084, "L2": 0.14432794767669344, "SR": 0.2618874400389335, "SR2": 0.35520934028683554, "triple_var": -3.8051855223265356}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.1325438812450811, "L2": -0.09610940190374401, "SR": 0.38028121103155377, "SR2": 0.3425954494792513, "triple_var": 4.019063157661875}, "real": {"L1": -0.3796585363093274, "L2": 0.04821854577294943, "SR": 0.6421686510704873, "SR2": 0.6978047897660868, "triple_var": 0.21387763533533927}, "rep": 12} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:63:{"delta": {"L1": -0.22026153957322328, "L2": -0.33471744291837846, "SR": 0.058245424649281397, "SR2": 0.027791229864140288, "triple_var": -2.2054698565838415}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.018428794851504598, "L2": 0.08772895878079587, "SR": 0.34012734509863773, "SR2": 0.3848579239938457, "triple_var": 2.982610663097517}, "real": {"L1": -0.23869033442472787, "L2": -0.24698848413758262, "SR": 0.39837276974791913, "SR2": 0.412649153857986, "triple_var": 0.7771408065136756}, "rep": 12} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:64:{"delta": {"L1": -0.09778721348126257, "L2": -0.0934292180315038, "SR": 0.06484395249878988, "SR2": -0.04142979962795207, "triple_var": -1.9633870931805968}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.09232945751745114, "L2": -0.030404002830872856, "SR": 0.33387766598292185, "SR2": 0.42223222344867817, "triple_var": 3.9531689762703506}, "real": {"L1": -0.005457755963811431, "L2": -0.12383322086237665, "SR": 0.3987216184817117, "SR2": 0.3808024238207261, "triple_var": 1.9897818830897538}, "rep": 12} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:65:{"delta": {"L1": 0.005601673070409791, "L2": 0.09221789587674929, "SR": 0.024297160952697183, "SR2": 0.016187445534457168, "triple_var": 0.6525752428653417}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.05995366579981647, "L2": -0.16189502073029102, "SR": 0.3479956530060352, "SR2": 0.3435411775374297, "triple_var": 2.1030993984342268}, "real": {"L1": -0.05435199272940668, "L2": -0.06967712485354173, "SR": 0.37229281395873237, "SR2": 0.35972862307188685, "triple_var": 2.7556746412995685}, "rep": 12} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:66:{"delta": {"L1": -0.27738099383688003, "L2": -0.17575360391253436, "SR": 0.18485267201038735, "SR2": 0.27516707321866624, "triple_var": -2.2204966328122087}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.04284283220694081, "L2": 0.0758051375932868, "SR": 0.39645037913810965, "SR2": 0.36557551299860497, "triple_var": 2.5535813346098255}, "real": {"L1": -0.23453816162993923, "L2": -0.09994846631924756, "SR": 0.581303051148497, "SR2": 0.6407425862172712, "triple_var": 0.333084701797617}, "rep": 13} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:67:{"delta": {"L1": -0.2700967461974443, "L2": 0.08831955793863691, "SR": 0.3158601638950916, "SR2": 0.35918217428365107, "triple_var": -2.3446485369237653}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.09374371570755466, "L2": -0.03605931595020313, "SR": 0.37651403371795117, "SR2": 0.3597643642134646, "triple_var": 2.5247325911260203}, "real": {"L1": -0.36384046190499897, "L2": 0.05226024198843378, "SR": 0.6923741976130428, "SR2": 0.7189465384971157, "triple_var": 0.18008405420225504}, "rep": 13} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:68:{"delta": {"L1": -0.22906365085490787, "L2": 0.125934743576162, "SR": 0.16094717177733692, "SR2": 0.2037555057621775, "triple_var": -2.6533368748646637}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.0806961045924789, "L2": -0.16490238940574287, "SR": 0.3390489413606863, "SR2": 0.2992893252437274, "triple_var": 3.69377613721737}, "real": {"L1": -0.14836754626242896, "L2": -0.03896764582958088, "SR": 0.4999961131380232, "SR2": 0.5030448310059049, "triple_var": 1.0404392623527066}, "rep": 13} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:69:{"delta": {"L1": -0.3567407561198651, "L2": -0.04673278601040872, "SR": -0.03388754538191552, "SR2": 0.010005663950998445, "triple_var": -1.9596420193317627}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.139070336183702, "L2": 0.09885567256489415, "SR": 0.39805712600526494, "SR2": 0.3853659743267563, "triple_var": 3.5527514733950727}, "real": {"L1": -0.21767041993616315, "L2": 0.05212288655448543, "SR": 0.3641695806233494, "SR2": 0.39537163827775473, "triple_var": 1.59310945406331}, "rep": 13} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:70:{"delta": {"L1": 0.09121208722559936, "L2": -0.01928710504734807, "SR": -0.06736859432326447, "SR2": -0.05427215513036926, "triple_var": 1.2473540924961428}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.010936446428745339, "L2": -0.14211387326216474, "SR": 0.38608834529717867, "SR2": 0.3850275225532498, "triple_var": 2.7887095330430256}, "real": {"L1": 0.08027564079685402, "L2": -0.1614009783095128, "SR": 0.3187197509739142, "SR2": 0.33075536742288053, "triple_var": 4.036063625539168}, "rep": 13} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:71:{"delta": {"L1": -0.3000633009004159, "L2": -0.43355461318407673, "SR": 0.16208391935208222, "SR2": 0.1866706263007617, "triple_var": -2.3242845362625633}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.005357624673662892, "L2": 0.11044951962039705, "SR": 0.4301816690188066, "SR2": 0.3880120835168951, "triple_var": 2.5182813965793054}, "real": {"L1": -0.3054209255740788, "L2": -0.32310509356367967, "SR": 0.5922655883708888, "SR2": 0.5746827098176568, "triple_var": 0.19399686031674224}, "rep": 14} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:72:{"delta": {"L1": -0.37429870218805533, "L2": 0.04377989650419785, "SR": 0.23889505538110012, "SR2": 0.3087809712386367, "triple_var": -2.5262146936204437}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.029174437618309092, "L2": -0.03635304423268747, "SR": 0.42136058533698584, "SR2": 0.38415400479650175, "triple_var": 2.7346195152702597}, "real": {"L1": -0.34512426456974626, "L2": 0.007426852271510381, "SR": 0.660255640718086, "SR2": 0.6929349760351384, "triple_var": 0.20840482164981616}, "rep": 14} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:73:{"delta": {"L1": -0.3322705997112779, "L2": 0.10573373974220886, "SR": 0.06223751541216216, "SR2": 0.1287644309072578, "triple_var": -1.8095575682160334}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.14331960307137645, "L2": -0.06832945904314054, "SR": 0.40503112129642005, "SR2": 0.356934083737981, "triple_var": 3.0588862666901218}, "real": {"L1": -0.1889509966399015, "L2": 0.037404280699068315, "SR": 0.4672686367085822, "SR2": 0.48569851464523883, "triple_var": 1.2493286984740883}, "rep": 14} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:74:{"delta": {"L1": -0.011724553132695874, "L2": -0.03165739324388593, "SR": 0.036911800919491145, "SR2": 0.06913875135151565, "triple_var": -0.9294863523873458}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.05165765880805893, "L2": 0.03565999845981476, "SR": 0.37942423658290086, "SR2": 0.3554442398986067, "triple_var": 3.4432229475113507}, "real": {"L1": 0.039933105675363054, "L2": 0.0040026052159288315, "SR": 0.416336037502392, "SR2": 0.42458299125012233, "triple_var": 2.513736595124005}, "rep": 14} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:75:{"delta": {"L1": 0.1596675133543493, "L2": 0.17493834285986698, "SR": 0.029726753656927618, "SR2": 0.002162119993622591, "triple_var": 1.4922515745137979}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.19517367204603742, "L2": -0.09852502639095954, "SR": 0.37994872776732164, "SR2": 0.3857990130838267, "triple_var": 2.151003674835648}, "real": {"L1": -0.03550615869168809, "L2": 0.07641331646890742, "SR": 0.40967548142424925, "SR2": 0.3879611330774493, "triple_var": 3.643255249349446}, "rep": 14} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:76:{"delta": {"L1": -0.2984241108898, "L2": 0.1718960908387446, "SR": 0.2239730214740962, "SR2": 0.2635505924469636, "triple_var": -1.9176771095415515}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.02771932557534285, "L2": -0.2519148988639181, "SR": 0.42388466917638973, "SR2": 0.41225972610954903, "triple_var": 2.1562072278065587}, "real": {"L1": -0.27070478531445713, "L2": -0.0800188080251735, "SR": 0.6478576906504859, "SR2": 0.6758103185565126, "triple_var": 0.23853011826500717}, "rep": 15} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:77:{"delta": {"L1": -0.18392357846489746, "L2": 0.09312792782195897, "SR": 0.18887692648594323, "SR2": 0.23697143718580732, "triple_var": -1.5494119109856634}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.1703392712998467, "L2": -0.04076224459998927, "SR": 0.40590876609984816, "SR2": 0.4380595465126423, "triple_var": 1.7885175782669713}, "real": {"L1": -0.35426284976474415, "L2": 0.052365683221969696, "SR": 0.5947856925857914, "SR2": 0.6750309836984496, "triple_var": 0.23910566728130792}, "rep": 15} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:78:{"delta": {"L1": -0.07975646049898237, "L2": 0.028664362915702554, "SR": 0.0673587429450504, "SR2": 0.1707286355887202, "triple_var": -2.0213187183031263}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.05249284451127516, "L2": -0.08142331374074331, "SR": 0.380472919019835, "SR2": 0.33166920602689554, "triple_var": 3.093158201268231}, "real": {"L1": -0.13224930501025753, "L2": -0.05275895082504076, "SR": 0.4478316619648854, "SR2": 0.5023978416156157, "triple_var": 1.0718394829651046}, "rep": 15} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:79:{"delta": {"L1": 0.09344768048318977, "L2": -0.04711338614164734, "SR": 0.10269097628819651, "SR2": 0.008999001757143243, "triple_var": -0.1678515163997183}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.052778221179412735, "L2": -0.07923788197881024, "SR": 0.3487374685394827, "SR2": 0.38585488764665626, "triple_var": 2.3363427664048486}, "real": {"L1": 0.04066945930377704, "L2": -0.12635126812045758, "SR": 0.4514284448276792, "SR2": 0.3948538894037995, "triple_var": 2.1684912500051303}, "rep": 15} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:80:{"delta": {"L1": 0.16183904026878637, "L2": 0.1747125218897238, "SR": -0.00893871598576379, "SR2": -0.00934143344579641, "triple_var": -0.04144101110655818}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.06364302442658155, "L2": 0.06519082543363605, "SR": 0.40222392071231694, "SR2": 0.411265664059843, "triple_var": 4.1451098580505485}, "real": {"L1": 0.0981960158422048, "L2": 0.23990334732335986, "SR": 0.39328520472655315, "SR2": 0.4019242306140466, "triple_var": 4.10366884694399}, "rep": 15} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:81:{"delta": {"L1": -0.4090721526195943, "L2": 0.158690292771666, "SR": 0.12739460974351574, "SR2": 0.22454376811143362, "triple_var": -1.7077823112165458}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.004913080868371454, "L2": -0.15324240562125652, "SR": 0.4225495306779911, "SR2": 0.3815723825000983, "triple_var": 2.013667120022761}, "real": {"L1": -0.40415907175122284, "L2": 0.005447887150409483, "SR": 0.5499441404215069, "SR2": 0.6061161506115319, "triple_var": 0.30588480880621505}, "rep": 16} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:82:{"delta": {"L1": -0.29654339267236063, "L2": -0.029450205406682806, "SR": 0.2463227214659165, "SR2": 0.3322298936927549, "triple_var": -1.99527927348937}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.036514769252424244, "L2": 0.14038065069164063, "SR": 0.41083590946837656, "SR2": 0.40091460832290815, "triple_var": 2.1771603248521916}, "real": {"L1": -0.3330581619247849, "L2": 0.11093044528495782, "SR": 0.657158630934293, "SR2": 0.7331445020156631, "triple_var": 0.18188105136282154}, "rep": 16} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:83:{"delta": {"L1": 0.007592840614955659, "L2": -0.23079431190286478, "SR": 0.07057176676399396, "SR2": -0.01293272371913684, "triple_var": -2.48366022107379}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.15234114722686073, "L2": -0.004761187455410391, "SR": 0.3650652817634923, "SR2": 0.4038809897067552, "triple_var": 3.4758642437741347}, "real": {"L1": -0.14474830661190508, "L2": -0.2355554993582752, "SR": 0.43563704852748625, "SR2": 0.39094826598761834, "triple_var": 0.9922040227003447}, "rep": 16} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:84:{"delta": {"L1": -0.07242760051015723, "L2": -0.07442010503300217, "SR": -0.00691422902120975, "SR2": -0.003807919630482326, "triple_var": -0.4966477571220431}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.141797478343037, "L2": 0.09075188188536008, "SR": 0.4496252512480175, "SR2": 0.4324135477700028, "triple_var": 2.5406645764232185}, "real": {"L1": 0.06936987783287976, "L2": 0.016331776852357916, "SR": 0.44271102222680775, "SR2": 0.42860562813952047, "triple_var": 2.0440168193011754}, "rep": 16} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:85:{"delta": {"L1": -0.09094729974895299, "L2": 0.2019149496029571, "SR": -0.08406500635695169, "SR2": -0.032815633471410144, "triple_var": 0.14140999773961527}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.03169186535041777, "L2": -0.052380290376944645, "SR": 0.4363938525326659, "SR2": 0.4250257706046736, "triple_var": 2.360657919613426}, "real": {"L1": -0.12263916509937077, "L2": 0.14953465922601247, "SR": 0.3523288461757142, "SR2": 0.39221013713326347, "triple_var": 2.5020679173530413}, "rep": 16} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:86:{"delta": {"L1": -0.2837569376504043, "L2": -0.08196788524400206, "SR": 0.2050364539669186, "SR2": 0.23893694535677817, "triple_var": -2.4056018599051776}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.032356043238423116, "L2": -0.030751751890495, "SR": 0.3840325724418008, "SR2": 0.39774617895427417, "triple_var": 2.736844518763394}, "real": {"L1": -0.25140089441198116, "L2": -0.11271963713449706, "SR": 0.5890690264087194, "SR2": 0.6366831243110523, "triple_var": 0.3312426588582163}, "rep": 17} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:87:{"delta": {"L1": -0.4980024738770511, "L2": 0.2838685432654533, "SR": 0.22950105147611632, "SR2": 0.3216376818034505, "triple_var": -2.2577638151902626}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.012263678525986638, "L2": 0.021747104610734685, "SR": 0.4029798079244453, "SR2": 0.3876562746444557, "triple_var": 2.473874892264678}, "real": {"L1": -0.48573879535106446, "L2": 0.305615647876188, "SR": 0.6324808594005616, "SR2": 0.7092939564479062, "triple_var": 0.21611107707441563}, "rep": 17} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:88:{"delta": {"L1": -0.08790884697627778, "L2": -0.2908235575641518, "SR": -0.05082559507586315, "SR2": 0.0003733115908501894, "triple_var": -1.0170954418756621}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.06668965654207211, "L2": 0.09021999828298846, "SR": 0.41079397426193237, "SR2": 0.40331546437522614, "triple_var": 2.3173124017146796}, "real": {"L1": -0.1545985035183499, "L2": -0.20060355928116333, "SR": 0.3599683791860692, "SR2": 0.40368877596607633, "triple_var": 1.3002169598390174}, "rep": 17} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:89:{"delta": {"L1": -0.1704718297834461, "L2": -0.17975824793153136, "SR": -0.03822881020261448, "SR2": -0.002387556663053403, "triple_var": -1.0983589897044796}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.12140108558438538, "L2": 0.0636602115713999, "SR": 0.42654039299108165, "SR2": 0.4151841811380608, "triple_var": 3.014504015782507}, "real": {"L1": -0.04907074419906073, "L2": -0.11609803636013147, "SR": 0.38831158278846717, "SR2": 0.4127966244750074, "triple_var": 1.9161450260780273}, "rep": 17} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:90:{"delta": {"L1": -0.04562002448749915, "L2": 0.3389261173417683, "SR": 0.04028709473358433, "SR2": 0.10482075605825164, "triple_var": 0.6097717143756043}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.048423095535771676, "L2": -0.14687523481896245, "SR": 0.38634619668338094, "SR2": 0.3571697093061182, "triple_var": 1.835444539456749}, "real": {"L1": -0.09404312002327082, "L2": 0.19205088252280586, "SR": 0.42663329141696527, "SR2": 0.46199046536436983, "triple_var": 2.445216253832353}, "rep": 17} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:91:{"delta": {"L1": -0.3367097109947544, "L2": 0.18919997393220772, "SR": 0.17450480076712999, "SR2": 0.22608934150961035, "triple_var": -2.220539451809051}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05543088035649305, "L2": -0.15626128569670766, "SR": 0.44479860289248446, "SR2": 0.4041280147892814, "triple_var": 2.52380086007438}, "real": {"L1": -0.2812788306382613, "L2": 0.032938688235500044, "SR": 0.6193034036596144, "SR2": 0.6302173562988918, "triple_var": 0.3032614082653293}, "rep": 18} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:92:{"delta": {"L1": -0.5238780527634483, "L2": 0.14636316233122507, "SR": 0.18432612344309468, "SR2": 0.32658355270175804, "triple_var": -3.453511384143926}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.1468853485034168, "L2": -0.01980832042104368, "SR": 0.43871131572170213, "SR2": 0.3852530594959224, "triple_var": 3.6797316291711533}, "real": {"L1": -0.37699270426003156, "L2": 0.12655484191018138, "SR": 0.6230374391647968, "SR2": 0.7118366121976805, "triple_var": 0.22622024502722715}, "rep": 18} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:93:{"delta": {"L1": -0.16680869747824323, "L2": -0.02299449654568729, "SR": 0.022578037584985722, "SR2": 0.1405571926009075, "triple_var": -2.9458254628903644}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.09933353733471338, "L2": 0.03208254069266835, "SR": 0.3598454052749513, "SR2": 0.3463095782425405, "triple_var": 3.9476161249870234}, "real": {"L1": -0.2661422348129566, "L2": 0.009088044146981064, "SR": 0.382423442859937, "SR2": 0.486866770843448, "triple_var": 1.001790662096659}, "rep": 18} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:94:{"delta": {"L1": 0.04430726656583633, "L2": 0.1827819098134734, "SR": 0.05859704501635432, "SR2": 0.05793294041106373, "triple_var": -1.0609190993253117}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.0077423139787277965, "L2": -0.14020003467052178, "SR": 0.38854844140717065, "SR2": 0.378934874165817, "triple_var": 3.359104661415178}, "real": {"L1": 0.03656495258710853, "L2": 0.04258187514295161, "SR": 0.447145486423525, "SR2": 0.43686781457688073, "triple_var": 2.2981855620898664}, "rep": 18} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:95:{"delta": {"L1": -0.3729510838654308, "L2": -0.12488238027470469, "SR": -0.06338515138894707, "SR2": -0.037017581001989375, "triple_var": -2.0376091948266017}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.32030040846267616, "L2": 0.00593811745155052, "SR": 0.45229438191044474, "SR2": 0.4004987941585597, "triple_var": 4.018214232956293}, "real": {"L1": -0.052650675402754636, "L2": -0.11894426282315416, "SR": 0.3889092305214977, "SR2": 0.36348121315657034, "triple_var": 1.980605038129691}, "rep": 18} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:96:{"delta": {"L1": -0.24751654528209405, "L2": -0.09224396486139116, "SR": 0.2435653264077584, "SR2": 0.26901927224967026, "triple_var": -3.9460167036806557}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.006825842337777653, "L2": -0.012567388331250809, "SR": 0.3695592777071136, "SR2": 0.3586870315014433, "triple_var": 4.2166605456602975}, "real": {"L1": -0.2543423876198717, "L2": -0.10481135319264197, "SR": 0.613124604114872, "SR2": 0.6277063037511136, "triple_var": 0.2706438419796419}, "rep": 19} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:97:{"delta": {"L1": -0.20261250296521655, "L2": -0.2550770968244952, "SR": 0.2560936591028173, "SR2": 0.21225866585459985, "triple_var": -1.4698196055565103}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.08633914144647935, "L2": 0.18847234751326483, "SR": 0.4268775033796252, "SR2": 0.48977243407462556, "triple_var": 1.6155947220377862}, "real": {"L1": -0.2889516444116959, "L2": -0.0666047493112304, "SR": 0.6829711624824425, "SR2": 0.7020310999292254, "triple_var": 0.14577511648127603}, "rep": 19} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:98:{"delta": {"L1": -0.04144899095832924, "L2": -0.12052674256056277, "SR": 0.10046861970302895, "SR2": 0.11105075129908054, "triple_var": -1.1749081504058472}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.1381310865510197, "L2": -0.10297873146150728, "SR": 0.34344197737889703, "SR2": 0.33395859871760525, "triple_var": 2.223347165736535}, "real": {"L1": -0.17958007750934893, "L2": -0.22350547402207005, "SR": 0.443910597081926, "SR2": 0.4450093500166858, "triple_var": 1.048439015330688}, "rep": 19} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:99:{"delta": {"L1": -0.13973691113569128, "L2": -0.013374513525212144, "SR": 0.04648865837638605, "SR2": 0.051919411945781424, "triple_var": -1.6899759949403255}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.07527414518003503, "L2": 0.004512500399538693, "SR": 0.3829577991643674, "SR2": 0.40409567387514, "triple_var": 3.2973119449489596}, "real": {"L1": -0.06446276595565625, "L2": -0.008862013125673451, "SR": 0.42944645754075345, "SR2": 0.45601508582092143, "triple_var": 1.6073359500086342}, "rep": 19} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:100:{"delta": {"L1": 0.10488420280227048, "L2": -0.15327927541708902, "SR": 0.03836777745824327, "SR2": -0.037883197753161235, "triple_var": -0.1426715602060833}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.04715825054391097, "L2": 0.11548704137991142, "SR": 0.37387728774253115, "SR2": 0.4217880495606786, "triple_var": 2.486496161701768}, "real": {"L1": 0.05772595225835952, "L2": -0.03779223403717759, "SR": 0.4122450652007744, "SR2": 0.3839048518075174, "triple_var": 2.3438246014956845}, "rep": 19} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:101:{"delta": {"L1": -0.1952707226369431, "L2": -0.02655535985450537, "SR": 0.23457023402046134, "SR2": 0.2840914074978106, "triple_var": -2.019251192230108}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.04272160834372618, "L2": -0.10551994320637868, "SR": 0.35891475906299425, "SR2": 0.3394026392980347, "triple_var": 2.376568959214823}, "real": {"L1": -0.23799233098066927, "L2": -0.13207530306088405, "SR": 0.5934849930834556, "SR2": 0.6234940467958453, "triple_var": 0.35731776698471507}, "rep": 20} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:102:{"delta": {"L1": -0.2826835505220742, "L2": -0.002198202192831261, "SR": 0.1934060422436396, "SR2": 0.28038283177761375, "triple_var": -2.1054288881838574}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.03293859155660071, "L2": -0.14160877826347265, "SR": 0.45477110923612435, "SR2": 0.39348527315359066, "triple_var": 2.315175274165373}, "real": {"L1": -0.2497449589654735, "L2": -0.1438069804563039, "SR": 0.648177151479764, "SR2": 0.6738681049312044, "triple_var": 0.20974638598151574}, "rep": 20} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:103:{"delta": {"L1": -0.20788253692988282, "L2": 0.12829802813115293, "SR": 0.1029137426906917, "SR2": 0.09121135741114805, "triple_var": -2.220513150917804}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.09457591607451915, "L2": -0.15195025372739152, "SR": 0.34272873349059135, "SR2": 0.35517204038708416, "triple_var": 3.5755616619976567}, "real": {"L1": -0.11330662085536367, "L2": -0.023652225596238575, "SR": 0.44564247618128305, "SR2": 0.4463833977982322, "triple_var": 1.355048511079853}, "rep": 20} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:104:{"delta": {"L1": -0.051816631868693425, "L2": -0.17001918289868398, "SR": 0.0581673686732892, "SR2": -0.044703030727487714, "triple_var": -0.6762432928585751}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.024854499076616087, "L2": 0.005783851740927318, "SR": 0.3505473699410235, "SR2": 0.3865225283829073, "triple_var": 2.857065297108583}, "real": {"L1": -0.02696213279207734, "L2": -0.16423533115775665, "SR": 0.4087147386143127, "SR2": 0.3418194976554196, "triple_var": 2.180822004250008}, "rep": 20} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:105:{"delta": {"L1": -0.1328485888676492, "L2": -0.09681531065078486, "SR": -0.01578258021767809, "SR2": -0.013596167907506729, "triple_var": 0.2356229372860561}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.014143647769755595, "L2": 0.11252291497153585, "SR": 0.3815635733633514, "SR2": 0.3779395927249488, "triple_var": 2.565645896160803}, "real": {"L1": -0.1469922366374048, "L2": 0.015707604320750992, "SR": 0.3657809931456733, "SR2": 0.3643434248174421, "triple_var": 2.801268833446859}, "rep": 20} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:106:{"delta": {"L1": -0.27222648361705654, "L2": -0.036460670535749515, "SR": 0.19195227010593818, "SR2": 0.24670321568400605, "triple_var": -1.9895686819185383}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.11438302821005836, "L2": -0.01812956914247441, "SR": 0.37531598415029865, "SR2": 0.3748962938172557, "triple_var": 2.272904963908223}, "real": {"L1": -0.3866095118271149, "L2": -0.05459023967822392, "SR": 0.5672682542562368, "SR2": 0.6215995095012617, "triple_var": 0.28333628198968486}, "rep": 21} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:107:{"delta": {"L1": -0.2502037846389812, "L2": 0.052410279263502155, "SR": 0.31594888964039264, "SR2": 0.34081917128507244, "triple_var": -2.0229137234849817}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.06972836367201314, "L2": -0.04003285635783396, "SR": 0.3724895613915697, "SR2": 0.3630692833773026, "triple_var": 2.1896308733227747}, "real": {"L1": -0.3199321483109943, "L2": 0.012377422905668196, "SR": 0.6884384510319623, "SR2": 0.703888454662375, "triple_var": 0.16671714983779298}, "rep": 21} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:108:{"delta": {"L1": -0.25864325180437364, "L2": -0.09827870058426391, "SR": 0.011964701315348936, "SR2": -0.02092842372453274, "triple_var": -1.2654990555845995}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.030679210533076096, "L2": 0.02551184302898932, "SR": 0.38281401366139467, "SR2": 0.43482745021799063, "triple_var": 2.589177151674483}, "real": {"L1": -0.22796404127129752, "L2": -0.0727668575552746, "SR": 0.3947787149767436, "SR2": 0.4138990264934579, "triple_var": 1.3236780960898835}, "rep": 21} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:109:{"delta": {"L1": -0.12048290881668973, "L2": -0.08793335597183757, "SR": -0.02142095783003195, "SR2": 0.022675883234705596, "triple_var": 0.14108541603250568}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.13623044526545697, "L2": -0.030098323199026417, "SR": 0.4270919741574166, "SR2": 0.3684318874663612, "triple_var": 2.481560845255801}, "real": {"L1": 0.015747536448767235, "L2": -0.11803167917086399, "SR": 0.40567101632738467, "SR2": 0.3911077707010668, "triple_var": 2.6226462612883066}, "rep": 21} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:110:{"delta": {"L1": -0.06465709151318977, "L2": 0.023157422656508546, "SR": -0.058862246342665125, "SR2": -0.0266396968136825, "triple_var": -0.6008553138604222}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.022383068158670274, "L2": 0.03337714383824278, "SR": 0.4318903288406643, "SR2": 0.41834402777658514, "triple_var": 3.4075068299550817}, "real": {"L1": -0.08704015967186005, "L2": 0.056534566494751325, "SR": 0.3730280824979992, "SR2": 0.39170433096290264, "triple_var": 2.8066515160946595}, "rep": 21} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:111:{"delta": {"L1": -0.2751939013229009, "L2": -0.06709351554953366, "SR": 0.2371006789955027, "SR2": 0.2671312428018286, "triple_var": -2.6806960967055606}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.049799797696661416, "L2": -0.04587612116688324, "SR": 0.3852796056259349, "SR2": 0.3757405649786209, "triple_var": 2.886227710549896}, "real": {"L1": -0.3249936990195623, "L2": -0.1129696367164169, "SR": 0.6223802846214376, "SR2": 0.6428718077804495, "triple_var": 0.20553161384433563}, "rep": 22} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:112:{"delta": {"L1": -0.3752715385064582, "L2": 0.2900839551898302, "SR": 0.3173152295728821, "SR2": 0.3965565303694345, "triple_var": -3.739832889595128}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.014087559844399022, "L2": -0.056551233698251525, "SR": 0.33025165274709395, "SR2": 0.32681780209531464, "triple_var": 3.963344635886387}, "real": {"L1": -0.3611839786620592, "L2": 0.23353272149157867, "SR": 0.6475668823199761, "SR2": 0.7233743324647491, "triple_var": 0.22351174629125864}, "rep": 22} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:113:{"delta": {"L1": -0.11117952540815042, "L2": -0.14174612593448419, "SR": 0.08068243144502335, "SR2": 0.08453476547847888, "triple_var": -2.409675537396748}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.11665873896575014, "L2": -0.023595755715668278, "SR": 0.3627166295873856, "SR2": 0.39558034032048195, "triple_var": 3.2362373320347864}, "real": {"L1": -0.22783826437390056, "L2": -0.16534188165015246, "SR": 0.44339906103240895, "SR2": 0.48011510579896083, "triple_var": 0.8265617946380384}, "rep": 22} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:114:{"delta": {"L1": -0.19083453917028348, "L2": -0.08934694983250888, "SR": -0.02209457379227603, "SR2": -0.015833033898524362, "triple_var": -3.319886112836634}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.054061467461392114, "L2": -0.018894522189850438, "SR": 0.3891079502539303, "SR2": 0.38463609213758676, "triple_var": 5.054182351938551}, "real": {"L1": -0.13677307170889136, "L2": -0.10824147202235931, "SR": 0.3670133764616543, "SR2": 0.3688030582390624, "triple_var": 1.734296239101917}, "rep": 22} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:115:{"delta": {"L1": 0.040231087753055145, "L2": 0.00578247600842581, "SR": -0.008262176584743486, "SR2": 0.04274079921202023, "triple_var": 0.3135231108997203}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.07953187345424072, "L2": -0.09101875935235962, "SR": 0.3969108138451282, "SR2": 0.39167850010399996, "triple_var": 1.9610432620530844}, "real": {"L1": -0.03930078570118557, "L2": -0.08523628334393381, "SR": 0.38864863726038473, "SR2": 0.4344192993160202, "triple_var": 2.2745663729528047}, "rep": 22} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:116:{"delta": {"L1": -0.3056593686071898, "L2": 0.016284664910140592, "SR": 0.1805789522952228, "SR2": 0.2081645384357459, "triple_var": -1.529197428308077}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.00685934925448902, "L2": -0.019205872670444147, "SR": 0.42693899721078565, "SR2": 0.42526139313605404, "triple_var": 1.8745788829526482}, "real": {"L1": -0.3125187178616788, "L2": -0.0029212077603035544, "SR": 0.6075179495060085, "SR2": 0.6334259315717999, "triple_var": 0.34538145464457126}, "rep": 23} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:117:{"delta": {"L1": -0.26238692331368213, "L2": 0.1382679572557171, "SR": 0.2493038902762011, "SR2": 0.29094307778183914, "triple_var": -2.041756269484842}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.08408167721161214, "L2": -0.10346231986098786, "SR": 0.3883588006675945, "SR2": 0.39220931409281523, "triple_var": 2.251379301324015}, "real": {"L1": -0.34646860052529427, "L2": 0.034805637394729236, "SR": 0.6376626909437956, "SR2": 0.6831523918746544, "triple_var": 0.20962303183917325}, "rep": 23} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:118:{"delta": {"L1": -0.15826029896136917, "L2": -0.13469089784433447, "SR": 0.09648045581796172, "SR2": 0.07563520406614044, "triple_var": -1.1407323533713467}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.09830955468651013, "L2": 0.04662619441008077, "SR": 0.3652922797303748, "SR2": 0.39728014266234224, "triple_var": 2.084114050258917}, "real": {"L1": -0.2565698536478793, "L2": -0.08806470343425371, "SR": 0.46177273554833653, "SR2": 0.4729153467284827, "triple_var": 0.9433816968875702}, "rep": 23} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:119:{"delta": {"L1": -0.1841178319225239, "L2": 0.03741563550623318, "SR": 0.00041943891929874244, "SR2": 0.0029098922460352172, "triple_var": -0.3616238967537755}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.051340357856773045, "L2": -0.03450402776649307, "SR": 0.37847613434316335, "SR2": 0.3991331469618256, "triple_var": 2.2360837936113667}, "real": {"L1": -0.23545818977929694, "L2": 0.0029116077397401143, "SR": 0.3788955732624621, "SR2": 0.40204303920786083, "triple_var": 1.8744598968575912}, "rep": 23} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:120:{"delta": {"L1": -0.11298359198323037, "L2": -0.07237240376702779, "SR": -0.0703886496095194, "SR2": -0.027904110796689585, "triple_var": -0.4380599146913897}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.01777576609752764, "L2": -0.09341626735276058, "SR": 0.4121097598962194, "SR2": 0.3736806023752927, "triple_var": 3.0263937837944295}, "real": {"L1": -0.09520782588570273, "L2": -0.16578867111978837, "SR": 0.3417211102867, "SR2": 0.34577649157860313, "triple_var": 2.5883338691030398}, "rep": 23} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:121:{"delta": {"L1": -0.18078944222582466, "L2": -0.04420596761962768, "SR": 0.18169569929485863, "SR2": 0.24984686395547057, "triple_var": -2.463834388547409}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.10832727798765247, "L2": -0.07624202521829189, "SR": 0.3990716150911894, "SR2": 0.3518545331605651, "triple_var": 2.738730310244272}, "real": {"L1": -0.2891167202134771, "L2": -0.12044799283791957, "SR": 0.580767314386048, "SR2": 0.6017013971160357, "triple_var": 0.27489592169686283}, "rep": 24} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:122:{"delta": {"L1": -0.27418597830554003, "L2": 0.08908157868053757, "SR": 0.1850531421268864, "SR2": 0.22937826669641675, "triple_var": -1.7097727627802186}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.11129235034748844, "L2": -0.0810784906387079, "SR": 0.44003163736394535, "SR2": 0.46289965132327754, "triple_var": 1.8954050077502984}, "real": {"L1": -0.38547832865302845, "L2": 0.008003088041829675, "SR": 0.6250847794908317, "SR2": 0.6922779180196943, "triple_var": 0.18563224497007988}, "rep": 24} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:123:{"delta": {"L1": -0.18827919832558934, "L2": 0.051904676716404724, "SR": 0.12701084573267857, "SR2": 0.20464818057400463, "triple_var": -2.3911579894955914}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.006442699245115232, "L2": -0.04112884141062429, "SR": 0.33461163284196904, "SR2": 0.3349572005487365, "triple_var": 3.571274527905816}, "real": {"L1": -0.1818364990804741, "L2": 0.010775835305780431, "SR": 0.4616224785746476, "SR2": 0.5396053811227411, "triple_var": 1.1801165384102243}, "rep": 24} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:124:{"delta": {"L1": -0.22838597177992082, "L2": 0.2445200927106279, "SR": -0.025718334582802593, "SR2": 0.01787510609031223, "triple_var": 0.12604679824346077}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.0020145730054013785, "L2": -0.13934849843256383, "SR": 0.3758591770784082, "SR2": 0.3706595662441649, "triple_var": 2.356936730280484}, "real": {"L1": -0.23040054478532218, "L2": 0.10517159427806409, "SR": 0.3501408424956056, "SR2": 0.38853467233447714, "triple_var": 2.4829835285239445}, "rep": 24} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:125:{"delta": {"L1": 0.005747727142437551, "L2": 0.1286450807202261, "SR": 0.03559107009135459, "SR2": 0.0702645971221727, "triple_var": -1.194004257493734}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.0002632222317529142, "L2": -0.12126697678423995, "SR": 0.35466492070270433, "SR2": 0.3292235127805273, "triple_var": 3.9042188920554035}, "real": {"L1": 0.006010949374190465, "L2": 0.00737810393598615, "SR": 0.3902559907940589, "SR2": 0.3994881099027, "triple_var": 2.7102146345616696}, "rep": 24} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:126:{"delta": {"L1": -0.4519626991523603, "L2": -0.2858897319332, "SR": 0.23369221512270466, "SR2": 0.21501384589861733, "triple_var": -3.7191024415546865}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.1327725989351804, "L2": 0.0497885999801081, "SR": 0.3754769013963447, "SR2": 0.4013831773816205, "triple_var": 3.937141318146696}, "real": {"L1": -0.3191901002171799, "L2": -0.2361011319530919, "SR": 0.6091691165190494, "SR2": 0.6163970232802378, "triple_var": 0.21803887659200946}, "rep": 25} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:127:{"delta": {"L1": -0.27213306409993177, "L2": 0.008649213405664768, "SR": 0.25137758642348135, "SR2": 0.2678259867586489, "triple_var": -2.3258523797798336}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.10385624205338526, "L2": 0.09322360649092144, "SR": 0.37595278403504734, "SR2": 0.4380137217002351, "triple_var": 2.5324482289826444}, "real": {"L1": -0.375989306153317, "L2": 0.10187281989658621, "SR": 0.6273303704585287, "SR2": 0.705839708458884, "triple_var": 0.2065958492028107}, "rep": 25} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:128:{"delta": {"L1": -0.22591969775191628, "L2": 0.06274015302724038, "SR": 0.010674078565738265, "SR2": 0.11267138864015769, "triple_var": -1.3893260185381198}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.02546807925615725, "L2": -0.14096584069772602, "SR": 0.43652161252513594, "SR2": 0.35567152989919987, "triple_var": 2.3153740682466384}, "real": {"L1": -0.2513877770080735, "L2": -0.07822568767048564, "SR": 0.4471956910908742, "SR2": 0.46834291853935756, "triple_var": 0.9260480497085186}, "rep": 25} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:129:{"delta": {"L1": -0.2133207063306107, "L2": 0.06350905826951499, "SR": -0.06390986675457122, "SR2": -0.02130168925027942, "triple_var": -0.38765705688764407}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.035949973551504116, "L2": -0.09087882058077051, "SR": 0.4415005494118222, "SR2": 0.4127285886054708, "triple_var": 1.7756103372514631}, "real": {"L1": -0.24927067988211482, "L2": -0.02736976231125553, "SR": 0.377590682657251, "SR2": 0.39142689935519137, "triple_var": 1.387953280363819}, "rep": 25} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:130:{"delta": {"L1": -0.17817612787323026, "L2": -0.24416714289032906, "SR": 0.026832820134813362, "SR2": 0.028344992829900606, "triple_var": -0.26276188805101386}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.11438839186053155, "L2": 0.052316564560912555, "SR": 0.3958026730735596, "SR2": 0.33513325410727285, "triple_var": 2.7718530621724593}, "real": {"L1": -0.0637877360126987, "L2": -0.19185057832941652, "SR": 0.42263549320837296, "SR2": 0.36347824693717345, "triple_var": 2.5090911741214454}, "rep": 25} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:131:{"delta": {"L1": -0.5683640504733611, "L2": 0.06216703293752135, "SR": 0.13307253915145978, "SR2": 0.14417331070418826, "triple_var": -2.727540334859365}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.11492092670656023, "L2": -0.03994019736840914, "SR": 0.41133259607375505, "SR2": 0.4533016801031091, "triple_var": 2.999765707606264}, "real": {"L1": -0.45344312376680085, "L2": 0.02222683556911221, "SR": 0.5444051352252148, "SR2": 0.5974749908072974, "triple_var": 0.2722253727468987}, "rep": 26} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:132:{"delta": {"L1": -0.3659222765775587, "L2": -0.03494515552576108, "SR": 0.26108885600833664, "SR2": 0.27887897955190655, "triple_var": -2.733486606722881}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.011856689685176896, "L2": 0.07385455014242728, "SR": 0.35425697401293443, "SR2": 0.3797877861887453, "triple_var": 2.9933693143958884}, "real": {"L1": -0.3540655868923818, "L2": 0.038909394616666204, "SR": 0.6153458300212711, "SR2": 0.6586667657406519, "triple_var": 0.2598827076730073}, "rep": 26} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:133:{"delta": {"L1": -0.2856592318415405, "L2": -0.002286239922000999, "SR": 0.040754096191543354, "SR2": 0.13410463155268132, "triple_var": -1.8352244773410782}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.022565228351613502, "L2": -0.12672642719393507, "SR": 0.396512632330011, "SR2": 0.3700727269035458, "triple_var": 2.7030297564473793}, "real": {"L1": -0.263094003489927, "L2": -0.12901266711593606, "SR": 0.43726672852155435, "SR2": 0.5041773584562271, "triple_var": 0.8678052791063011}, "rep": 26} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:134:{"delta": {"L1": -0.05548212471544481, "L2": -0.11691713050148023, "SR": 0.04028698061092045, "SR2": 0.031589288951065164, "triple_var": -2.6794537909272735}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.08114096656779657, "L2": 0.03373410120995249, "SR": 0.3383752251426385, "SR2": 0.35796905512874805, "triple_var": 4.483000763105999}, "real": {"L1": -0.13662309128324138, "L2": -0.08318302929152774, "SR": 0.37866220575355897, "SR2": 0.3895583440798132, "triple_var": 1.8035469721787258}, "rep": 26} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:135:{"delta": {"L1": -0.12667436535148563, "L2": -0.17410241993096162, "SR": 0.01878429695340328, "SR2": -0.02044076664484179, "triple_var": -1.48252053067863}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.023740608519432323, "L2": 0.09790610196122154, "SR": 0.38666870137720005, "SR2": 0.41889149440719026, "triple_var": 3.28223958454132}, "real": {"L1": -0.1029337568320533, "L2": -0.07619631796974008, "SR": 0.40545299833060333, "SR2": 0.39845072776234847, "triple_var": 1.79971905386269}, "rep": 26} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:136:{"delta": {"L1": -0.231356079449736, "L2": -0.14794117210689053, "SR": 0.21612886246782187, "SR2": 0.15232118500718878, "triple_var": -2.0035354565630072}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.061082714603180904, "L2": -0.02763413399835369, "SR": 0.37743970851917, "SR2": 0.4435737433764661, "triple_var": 2.256689259053929}, "real": {"L1": -0.2924387940529169, "L2": -0.17557530610524422, "SR": 0.5935685709869919, "SR2": 0.5958949283836549, "triple_var": 0.25315380249092173}, "rep": 27} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:137:{"delta": {"L1": -0.4096706832670935, "L2": 0.3369584789539179, "SR": 0.295683578695709, "SR2": 0.37265230919820624, "triple_var": -2.5013070826384194}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.04896720165376876, "L2": -0.14051573348005655, "SR": 0.34060049829578715, "SR2": 0.33642895492521907, "triple_var": 2.7346118273606153}, "real": {"L1": -0.3607034816133247, "L2": 0.19644274547386134, "SR": 0.6362840769914961, "SR2": 0.7090812641234253, "triple_var": 0.2333047447221959}, "rep": 27} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:138:{"delta": {"L1": 0.10008972844421898, "L2": -0.3650096903076934, "SR": 0.05096175758803928, "SR2": 0.028625353969331635, "triple_var": -1.3583308696391199}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.13056706258608283, "L2": 0.012450695819268031, "SR": 0.40718946248209426, "SR2": 0.38026442296333063, "triple_var": 2.357526675815938}, "real": {"L1": -0.030477334141863845, "L2": -0.35255899448842537, "SR": 0.45815122007013354, "SR2": 0.40888977693266226, "triple_var": 0.9991958061768181}, "rep": 27} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:139:{"delta": {"L1": -0.2260775710492135, "L2": -0.03687812637713975, "SR": -0.040794038481403894, "SR2": 0.023349431952109367, "triple_var": -0.5675346276930329}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.14699092550083975, "L2": 0.03233400528914409, "SR": 0.42463389292226444, "SR2": 0.38445214730237404, "triple_var": 3.2938535227873693}, "real": {"L1": -0.07908664554837373, "L2": -0.00454412108799566, "SR": 0.38383985444086055, "SR2": 0.4078015792544834, "triple_var": 2.7263188950943364}, "rep": 27} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:140:{"delta": {"L1": 0.046549286105618155, "L2": -0.03597881814659729, "SR": -0.015647302008001407, "SR2": 0.004621408196079035, "triple_var": 0.5619757356581747}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.03653960770332558, "L2": -0.059048859202116964, "SR": 0.3512224939047181, "SR2": 0.3372283147664052, "triple_var": 2.504105137118072}, "real": {"L1": 0.010009678402292574, "L2": -0.09502767734871426, "SR": 0.3355751918967167, "SR2": 0.3418497229624842, "triple_var": 3.0660808727762467}, "rep": 27} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:141:{"delta": {"L1": -0.4276785952987307, "L2": 0.04164991323845252, "SR": 0.20264746085995133, "SR2": 0.26075087800641944, "triple_var": -2.4519485367705296}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.01840172579688434, "L2": -0.07085010543778392, "SR": 0.37823963001324024, "SR2": 0.37275063653129686, "triple_var": 2.690186185827564}, "real": {"L1": -0.4092768695018464, "L2": -0.029200192199331395, "SR": 0.5808870908731916, "SR2": 0.6335015145377163, "triple_var": 0.2382376490570344}, "rep": 28} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:142:{"delta": {"L1": -0.4469018589915996, "L2": 0.12145260078783665, "SR": 0.26761490284902534, "SR2": 0.39805707986476446, "triple_var": -3.8602305154219576}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.15940351874292946, "L2": -0.11848396308570378, "SR": 0.39578405781603065, "SR2": 0.3377078587694622, "triple_var": 4.046254212904676}, "real": {"L1": -0.28749834024867016, "L2": 0.0029686377021328767, "SR": 0.663398960665056, "SR2": 0.7357649386342267, "triple_var": 0.18602369748271894}, "rep": 28} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:143:{"delta": {"L1": -0.03035627994867865, "L2": 0.03158652321596339, "SR": 0.1138626116822673, "SR2": 0.10236530250195697, "triple_var": -1.3874360660760594}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.08308552187799163, "L2": -0.09846790364253635, "SR": 0.33598383200981274, "SR2": 0.3548899395998092, "triple_var": 2.5623819128857015}, "real": {"L1": -0.11344180182667028, "L2": -0.06688138042657296, "SR": 0.44984644369208004, "SR2": 0.45725524210176616, "triple_var": 1.174945846809642}, "rep": 28} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:144:{"delta": {"L1": -0.10905673708892136, "L2": -0.3636013922875866, "SR": -0.005853340332645118, "SR2": -0.08731275082299095, "triple_var": -0.3810767533975232}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.03271436530926405, "L2": 0.1476306346414462, "SR": 0.41035023251727415, "SR2": 0.41807050611829727, "triple_var": 2.486773260992075}, "real": {"L1": -0.0763423717796573, "L2": -0.2159707576461404, "SR": 0.40449689218462903, "SR2": 0.3307577552953063, "triple_var": 2.1056965075945517}, "rep": 28} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:145:{"delta": {"L1": -0.012278379782251964, "L2": -0.1300490947390942, "SR": 0.07376027871698043, "SR2": 0.07155811441375032, "triple_var": -2.679930251839525}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.12448825477906578, "L2": 0.1571098922283475, "SR": 0.3235696158288019, "SR2": 0.3830698730933164, "triple_var": 4.448497527194454}, "real": {"L1": -0.13676663456131774, "L2": 0.027060797489253312, "SR": 0.3973298945457823, "SR2": 0.45462798750706673, "triple_var": 1.7685672753549293}, "rep": 28} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:146:{"delta": {"L1": -0.17658905917404535, "L2": -0.04853100457745467, "SR": 0.21785612782539188, "SR2": 0.23674509555230822, "triple_var": -1.9989309112263913}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.13007238365065868, "L2": 0.042892786425010654, "SR": 0.3618766489025681, "SR2": 0.4187444425045466, "triple_var": 2.3535692337752985}, "real": {"L1": -0.30666144282470403, "L2": -0.005638218152444016, "SR": 0.57973277672796, "SR2": 0.6554895380568548, "triple_var": 0.35463832254890704}, "rep": 29} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:147:{"delta": {"L1": -0.23157012205143163, "L2": -0.04181703164338346, "SR": 0.2614371281626547, "SR2": 0.2973175521124917, "triple_var": -2.3912135738561386}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.007071256472927359, "L2": -0.08847271375918622, "SR": 0.4072690218901303, "SR2": 0.39303937588253407, "triple_var": 2.5895458490596033}, "real": {"L1": -0.238641378524359, "L2": -0.13028974540256968, "SR": 0.668706150052785, "SR2": 0.6903569279950258, "triple_var": 0.19833227520346444}, "rep": 29} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:148:{"delta": {"L1": -0.24570966012797044, "L2": -0.01993377109350402, "SR": 0.0021881247827646444, "SR2": 0.05147772750008617, "triple_var": -1.886909317608274}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.04927605664233268, "L2": -0.03227542648981618, "SR": 0.40012573066593116, "SR2": 0.3898599054280545, "triple_var": 3.078356376572473}, "real": {"L1": -0.2949857167703031, "L2": -0.0522091975833202, "SR": 0.4023138554486958, "SR2": 0.44133763292814066, "triple_var": 1.191447058964199}, "rep": 29} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:149:{"delta": {"L1": 0.011672333399851964, "L2": 0.2396477783338366, "SR": 0.07252914447572573, "SR2": 0.12168956739268522, "triple_var": -1.009484017536299}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.08768173578840897, "L2": -0.11762199135104338, "SR": 0.3495553030790397, "SR2": 0.3185972594161176, "triple_var": 2.8383245312695653}, "real": {"L1": -0.07600940238855701, "L2": 0.12202578698279323, "SR": 0.42208444755476543, "SR2": 0.4402868268088028, "triple_var": 1.8288405137332664}, "rep": 29} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:150:{"delta": {"L1": -0.10296383747291551, "L2": -0.06682659663710917, "SR": -0.06411138251856552, "SR2": -0.02914436285245736, "triple_var": -0.58806391743722}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.042133671356986056, "L2": -0.031136826719408456, "SR": 0.3828042417298394, "SR2": 0.3530472218637633, "triple_var": 3.360447624273814}, "real": {"L1": -0.06083016611592945, "L2": -0.09796342335651763, "SR": 0.3186928592112739, "SR2": 0.32390285901130594, "triple_var": 2.772383706836594}, "rep": 29} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:151:{"delta": {"L1": -0.3248573783105065, "L2": 0.2279731597877912, "SR": 0.19821179181201093, "SR2": 0.24509870805242834, "triple_var": -2.421860370323951}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.016114125549298475, "L2": -0.13273195778090377, "SR": 0.4037740023277651, "SR2": 0.39095503331535936, "triple_var": 2.8036417985231505}, "real": {"L1": -0.34097150385980496, "L2": 0.09524120200688743, "SR": 0.601985794139776, "SR2": 0.6360537413677877, "triple_var": 0.3817814281991995}, "rep": 30} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:152:{"delta": {"L1": -0.44911694128115076, "L2": 0.19783418934821767, "SR": 0.2882257114826723, "SR2": 0.3179924406947936, "triple_var": -2.954739505902937}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.06285931712184226, "L2": -0.06396729873623133, "SR": 0.37774017133747784, "SR2": 0.38143100011888187, "triple_var": 3.136295966018633}, "real": {"L1": -0.3862576241593085, "L2": 0.13386689061198634, "SR": 0.6659658828201501, "SR2": 0.6994234408136755, "triple_var": 0.18155646011569615}, "rep": 30} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:153:{"delta": {"L1": -0.13371583774573734, "L2": 0.1990668261200922, "SR": 0.06778973917031722, "SR2": 0.049030514596157204, "triple_var": -0.4177116097339306}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.08487499511310304, "L2": -0.10021027667480183, "SR": 0.38902160575630224, "SR2": 0.3823300705687808, "triple_var": 1.6617187997660952}, "real": {"L1": -0.21859083285884037, "L2": 0.09885654944529036, "SR": 0.45681134492661946, "SR2": 0.431360585164938, "triple_var": 1.2440071900321645}, "rep": 30} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:154:{"delta": {"L1": 0.0261220425145701, "L2": 0.12523741396157595, "SR": -0.009654028245207391, "SR2": -0.03599053645297995, "triple_var": 0.6609951407706562}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.006472535329495591, "L2": -0.12969750723645385, "SR": 0.40689745018138024, "SR2": 0.3740483906167034, "triple_var": 2.236173860610736}, "real": {"L1": 0.03259457784406569, "L2": -0.004460093274877901, "SR": 0.39724342193617285, "SR2": 0.33805785416372347, "triple_var": 2.897169001381392}, "rep": 30} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:155:{"delta": {"L1": -0.06406639159433342, "L2": -0.13286943801702275, "SR": 0.05597690092093771, "SR2": 0.018847563705761128, "triple_var": -1.4997433480924627}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.10793834405459024, "L2": 0.12213919290014444, "SR": 0.35096860969737076, "SR2": 0.4042898707343416, "triple_var": 3.254091463837666}, "real": {"L1": -0.17200473564892366, "L2": -0.010730245116878302, "SR": 0.40694551061830847, "SR2": 0.4231374344401027, "triple_var": 1.7543481157452032}, "rep": 30} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:156:{"delta": {"L1": -0.2757841598872626, "L2": -0.07456631065007703, "SR": 0.1933269383764351, "SR2": 0.22745648842651872, "triple_var": -2.3302983041413023}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.09249046103673296, "L2": -0.007289990020857977, "SR": 0.36594417336889695, "SR2": 0.3446847085841664, "triple_var": 2.676649102860806}, "real": {"L1": -0.3682746209239956, "L2": -0.08185630067093501, "SR": 0.5592711117453321, "SR2": 0.5721411970106851, "triple_var": 0.34635079871950364}, "rep": 31} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:157:{"delta": {"L1": -0.22374426404749476, "L2": -0.13502134066999233, "SR": 0.27280750069716164, "SR2": 0.30344825891813465, "triple_var": -3.2841861119002367}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.10025062215514856, "L2": 0.08254415946987427, "SR": 0.3832517240269143, "SR2": 0.39245245120709493, "triple_var": 3.468435350115145}, "real": {"L1": -0.32399488620264333, "L2": -0.05247718120011807, "SR": 0.656059224724076, "SR2": 0.6959007101252296, "triple_var": 0.18424923821490813}, "rep": 31} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:158:{"delta": {"L1": -0.16596316168373867, "L2": -0.2608128173813358, "SR": 0.0271749612237856, "SR2": 0.023966670689785474, "triple_var": -0.8690595688851521}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.08088600685594195, "L2": -0.006448209398810043, "SR": 0.38300941189262194, "SR2": 0.3999640156176318, "triple_var": 2.4688828887835705}, "real": {"L1": -0.08507715482779672, "L2": -0.26726102678014585, "SR": 0.41018437311640754, "SR2": 0.42393068630741726, "triple_var": 1.5998233198984184}, "rep": 31} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:159:{"delta": {"L1": 0.14334131435651837, "L2": -0.05739430832000756, "SR": 0.06323945971233091, "SR2": -0.040486012365059665, "triple_var": 0.35331399731086943}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.09494799740827999, "L2": 0.011859200372363344, "SR": 0.3283329171834785, "SR2": 0.4169200404312235, "triple_var": 2.75580377178783}, "real": {"L1": 0.04839331694823839, "L2": -0.04553510794764422, "SR": 0.3915723768958094, "SR2": 0.37643402806616383, "triple_var": 3.1091177690986993}, "rep": 31} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:160:{"delta": {"L1": -0.06603976217827222, "L2": -0.023839438004908398, "SR": 0.002508054245539071, "SR2": 0.01851522111904985, "triple_var": -0.9001015875608145}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.15167203777598498, "L2": 0.12398118177782949, "SR": 0.4102504036402323, "SR2": 0.40088758434956084, "triple_var": 3.481544849443016}, "real": {"L1": 0.08563227559771276, "L2": 0.1001417437729211, "SR": 0.41275845788577137, "SR2": 0.4194028054686107, "triple_var": 2.5814432618822014}, "rep": 31} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:161:{"delta": {"L1": -0.37638160293186496, "L2": -0.09042664648197701, "SR": 0.1824329548828364, "SR2": 0.1929974816784949, "triple_var": -2.531238636744605}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.030750858447413484, "L2": 0.03258612485385149, "SR": 0.4084382993031379, "SR2": 0.4154307112910636, "triple_var": 2.800519126819809}, "real": {"L1": -0.34563074448445147, "L2": -0.05784052162812552, "SR": 0.5908712541859743, "SR2": 0.6084281929695585, "triple_var": 0.26928049007520355}, "rep": 32} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:162:{"delta": {"L1": -0.23954733577121212, "L2": 0.09852541816050372, "SR": 0.3247722903096917, "SR2": 0.3701498562096677, "triple_var": -2.552404843823378}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.02157286359031186, "L2": -0.09215622133141, "SR": 0.3630863601376796, "SR2": 0.35506637224336707, "triple_var": 2.7317849313297926}, "real": {"L1": -0.261120199361524, "L2": 0.006369196829093727, "SR": 0.6878586504473713, "SR2": 0.7252162284530348, "triple_var": 0.17938008750641468}, "rep": 32} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:163:{"delta": {"L1": -0.08364768149267915, "L2": -0.10170858323936052, "SR": 0.07707382144731195, "SR2": 0.04959490910654041, "triple_var": -1.0046679173532054}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.0547701690745979, "L2": -0.14461986928575332, "SR": 0.3892719712898648, "SR2": 0.3592867145216796, "triple_var": 2.0831572530712283}, "real": {"L1": -0.13841785056727704, "L2": -0.24632845252511384, "SR": 0.46634579273717675, "SR2": 0.40888162362822, "triple_var": 1.078489335718023}, "rep": 32} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:164:{"delta": {"L1": 0.049508196051703396, "L2": 0.056203145970701605, "SR": 0.0522686994547476, "SR2": 0.05630704296116956, "triple_var": 0.30458731665013583}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.1573485374397305, "L2": -0.11655623199762309, "SR": 0.35754708152060155, "SR2": 0.35245174766052934, "triple_var": 1.836298472454127}, "real": {"L1": -0.10784034138802712, "L2": -0.060353086026921486, "SR": 0.40981578097534915, "SR2": 0.4087587906216989, "triple_var": 2.1408857891042627}, "rep": 32} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:165:{"delta": {"L1": 0.02243171111951439, "L2": -0.06038584748033448, "SR": -0.058159296022924245, "SR2": -0.11216124553047424, "triple_var": 0.17565995913996724}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.2087149956795072, "L2": 0.024101461636275946, "SR": 0.386305193517673, "SR2": 0.44850744208790694, "triple_var": 1.5818690220515714}, "real": {"L1": -0.1862832845599928, "L2": -0.036284385844058534, "SR": 0.32814589749474876, "SR2": 0.3363461965574327, "triple_var": 1.7575289811915387}, "rep": 32} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:166:{"delta": {"L1": -0.41656149400404746, "L2": 0.13213437995407876, "SR": 0.20469910421570964, "SR2": 0.2922900742569729, "triple_var": -2.424069545395667}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05195031002753923, "L2": -0.13564084196941728, "SR": 0.35994233240987955, "SR2": 0.3310127662071784, "triple_var": 2.7139807243578473}, "real": {"L1": -0.3646111839765082, "L2": -0.0035064620153385153, "SR": 0.5646414366255892, "SR2": 0.6233028404641513, "triple_var": 0.2899111789621803}, "rep": 33} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:167:{"delta": {"L1": -0.41080186240479794, "L2": 0.03064817644298837, "SR": 0.28558086031260693, "SR2": 0.29907312195994795, "triple_var": -3.5383145284188218}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.01651280001063109, "L2": 0.030975481774325807, "SR": 0.37999972363499884, "SR2": 0.40666376040844143, "triple_var": 3.7285376637508714}, "real": {"L1": -0.42731466241542904, "L2": 0.06162365821731418, "SR": 0.6655805839476058, "SR2": 0.7057368823683894, "triple_var": 0.19022313533204943}, "rep": 33} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:168:{"delta": {"L1": -0.29607210201425843, "L2": -0.11310544019867873, "SR": 0.0960951891531795, "SR2": 0.10327229890755257, "triple_var": -2.033934622885435}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.042119631345945076, "L2": 0.039667997057341114, "SR": 0.366715288159468, "SR2": 0.39643019125419127, "triple_var": 2.9663360438396253}, "real": {"L1": -0.25395247066831333, "L2": -0.07343744314133761, "SR": 0.4628104773126475, "SR2": 0.49970249016174384, "triple_var": 0.9324014209541904}, "rep": 33} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:169:{"delta": {"L1": -0.030696037675768024, "L2": 0.10222829704428066, "SR": -0.052307169808092246, "SR2": -0.0056259433161839345, "triple_var": 0.35226253314138933}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.043148936374665485, "L2": 0.0086304248581152, "SR": 0.4224559336675916, "SR2": 0.3811590882529669, "triple_var": 2.5712489198479562}, "real": {"L1": 0.012452898698897461, "L2": 0.11085872190239586, "SR": 0.37014876385949935, "SR2": 0.37553314493678297, "triple_var": 2.9235114529893456}, "rep": 33} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:170:{"delta": {"L1": 0.25476696954971034, "L2": -0.06374443743020325, "SR": 0.10052264620624107, "SR2": 0.07423592120152483, "triple_var": -0.830921802159299}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.1988804621945541, "L2": 0.11178516601795184, "SR": 0.33319839834531423, "SR2": 0.3554794488569666, "triple_var": 3.253459632745905}, "real": {"L1": 0.05588650735515623, "L2": 0.0480407285877486, "SR": 0.4337210445515553, "SR2": 0.4297153700584914, "triple_var": 2.422537830586606}, "rep": 33} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:171:{"delta": {"L1": -0.32324614578047817, "L2": 0.13735658145753898, "SR": 0.23493320269859025, "SR2": 0.23254528221441484, "triple_var": -2.1271091592972056}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.016165456854019367, "L2": -0.10989995455804409, "SR": 0.3884576943440386, "SR2": 0.4192449736025251, "triple_var": 2.366834026028882}, "real": {"L1": -0.33941160263449754, "L2": 0.027456626899494886, "SR": 0.6233908970426288, "SR2": 0.65179025581694, "triple_var": 0.23972486673167648}, "rep": 34} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:172:{"delta": {"L1": -0.42320686201131585, "L2": -0.01572160069279188, "SR": 0.2586890236546782, "SR2": 0.3103081896964786, "triple_var": -2.316879227747631}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.06544098085335355, "L2": -0.0671368734612383, "SR": 0.4060795136649648, "SR2": 0.3846694946397398, "triple_var": 2.4520180879587854}, "real": {"L1": -0.3577658811579623, "L2": -0.08285847415403018, "SR": 0.664768537319643, "SR2": 0.6949776843362184, "triple_var": 0.13513886021115432}, "rep": 34} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:173:{"delta": {"L1": -0.023130395834079373, "L2": -0.13915114741146833, "SR": 0.09213934637335491, "SR2": 0.013137958498622149, "triple_var": -1.1828072519333135}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.08864150369405475, "L2": -0.08022516737911416, "SR": 0.3935709754730487, "SR2": 0.42566767066232136, "triple_var": 2.293973308481583}, "real": {"L1": -0.11177189952813413, "L2": -0.2193763147905825, "SR": 0.4857103218464036, "SR2": 0.4388056291609435, "triple_var": 1.1111660565482697}, "rep": 34} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:174:{"delta": {"L1": -0.03861401971525061, "L2": -0.27634816833254966, "SR": -0.007829956571929697, "SR2": -0.04047015678527044, "triple_var": -1.328134977771653}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.10846882528239804, "L2": 0.13509207489418049, "SR": 0.3641441820306552, "SR2": 0.4250401196750682, "triple_var": 3.3813387765731218}, "real": {"L1": -0.14708284499764865, "L2": -0.14125609343836917, "SR": 0.3563142254587255, "SR2": 0.3845699628897978, "triple_var": 2.053203798801469}, "rep": 34} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:175:{"delta": {"L1": 0.13061004524444514, "L2": -0.20740587293637291, "SR": 0.03444900984910043, "SR2": -0.034629791341302674, "triple_var": 0.31721328466772225}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.12226332708802722, "L2": 0.153336450788418, "SR": 0.3505361546974588, "SR2": 0.4302584037242448, "triple_var": 1.9513597647914096}, "real": {"L1": 0.00834671815641791, "L2": -0.054069422147954904, "SR": 0.38498516454655923, "SR2": 0.3956286123829421, "triple_var": 2.268573049459132}, "rep": 34} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:176:{"delta": {"L1": -0.1699635561301261, "L2": -0.14013449653234905, "SR": 0.256787985440963, "SR2": 0.2380588660163903, "triple_var": -2.511543671320022}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.08482750447034605, "L2": -0.005409802100908719, "SR": 0.4067129975564056, "SR2": 0.42247394609096417, "triple_var": 2.8340837361383775}, "real": {"L1": -0.08513605165978005, "L2": -0.14554429863325777, "SR": 0.6635009829973686, "SR2": 0.6605328121073545, "triple_var": 0.32254006481835557}, "rep": 35} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:177:{"delta": {"L1": -0.21972940941967642, "L2": 0.22131259792716984, "SR": 0.2902144600671594, "SR2": 0.36689140264571435, "triple_var": -1.3926883218941104}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.07941292219752324, "L2": -0.15976423277982987, "SR": 0.39513954376272226, "SR2": 0.3468587941576092, "triple_var": 1.592398458763544}, "real": {"L1": -0.29914233161719966, "L2": 0.061548365147339974, "SR": 0.6853540038298817, "SR2": 0.7137501968033235, "triple_var": 0.19971013686943367}, "rep": 35} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:178:{"delta": {"L1": -0.23710803150303147, "L2": 0.1397250224197573, "SR": 0.02798611018552305, "SR2": 0.11403298533235356, "triple_var": -2.022036169646073}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.03228724003759114, "L2": -0.11892640412170645, "SR": 0.4124366523333384, "SR2": 0.342710144061091, "triple_var": 3.1106093330593887}, "real": {"L1": -0.20482079146544033, "L2": 0.020798618298050842, "SR": 0.4404227625188615, "SR2": 0.4567431293934446, "triple_var": 1.0885731634133153}, "rep": 35} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:179:{"delta": {"L1": -0.034826878335847154, "L2": 0.004483084160382523, "SR": -0.0006594028455564893, "SR2": -0.03320327148928359, "triple_var": 0.36073384525094676}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.03655239386135879, "L2": -0.019860852558282092, "SR": 0.40892720711177, "SR2": 0.3903720487151409, "triple_var": 2.1531414309832284}, "real": {"L1": 0.0017255155255116413, "L2": -0.01537776839789957, "SR": 0.4082678042662135, "SR2": 0.3571687772258573, "triple_var": 2.513875276234175}, "rep": 35} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:180:{"delta": {"L1": -0.036785162061386184, "L2": -0.09995398156175213, "SR": 0.07262505983201423, "SR2": 0.0686639083295979, "triple_var": -0.987735487183234}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.099314465890285, "L2": -5.009712460721032e-05, "SR": 0.3838438354678024, "SR2": 0.35716454093886507, "triple_var": 2.389705045324407}, "real": {"L1": -0.1360996279516712, "L2": -0.10000407868635934, "SR": 0.4564688952998166, "SR2": 0.42582844926846297, "triple_var": 1.401969558141173}, "rep": 35} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:181:{"delta": {"L1": -0.13263923073589598, "L2": -0.03292908958210773, "SR": 0.26443185331351066, "SR2": 0.27535565911920035, "triple_var": -2.7755869007625424}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.07246887047780065, "L2": -0.06298852114083804, "SR": 0.3431832608757364, "SR2": 0.34753849729740227, "triple_var": 3.1042293421643086}, "real": {"L1": -0.20510810121369663, "L2": -0.09591761072294577, "SR": 0.6076151141892471, "SR2": 0.6228941564166026, "triple_var": 0.3286424414017661}, "rep": 36} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:182:{"delta": {"L1": -0.5346681131175183, "L2": -0.051203341575625755, "SR": 0.25954071469966633, "SR2": 0.2910127489810333, "triple_var": -3.6684700886949133}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.22154404719186663, "L2": 0.033746712578520445, "SR": 0.38668569700709976, "SR2": 0.3974986150620245, "triple_var": 3.8887167082301843}, "real": {"L1": -0.3131240659256517, "L2": -0.017456628997105306, "SR": 0.6462264117067661, "SR2": 0.6885113640430578, "triple_var": 0.22024661953527117}, "rep": 36} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:183:{"delta": {"L1": -0.1863156327400092, "L2": 0.18921350705689965, "SR": 0.0834077740259645, "SR2": 0.15458312555823106, "triple_var": -1.8654025104662242}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.0033304797875544855, "L2": -0.13072461599005616, "SR": 0.3670054357299629, "SR2": 0.33727842467666125, "triple_var": 3.0995544637588552}, "real": {"L1": -0.1896461125275637, "L2": 0.05848889106684348, "SR": 0.4504132097559274, "SR2": 0.4918615502348923, "triple_var": 1.234151953292631}, "rep": 36} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:184:{"delta": {"L1": -0.14682482234455851, "L2": -0.13642259596539497, "SR": -0.04251414297242312, "SR2": 0.008504427052129115, "triple_var": -1.172787559082544}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.07436825988761449, "L2": -0.04054715813379189, "SR": 0.4061023774138869, "SR2": 0.3664293947535871, "triple_var": 3.1037889965389884}, "real": {"L1": -0.07245656245694401, "L2": -0.17696975409918686, "SR": 0.36358823444146376, "SR2": 0.3749338218057162, "triple_var": 1.9310014374564444}, "rep": 36} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:185:{"delta": {"L1": -0.091691866892569, "L2": -0.011755786762161536, "SR": 0.022654053016859743, "SR2": 0.0012677127104522845, "triple_var": -0.9204326743619413}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.020420540451416874, "L2": 0.02651471530342445, "SR": 0.3893719964758221, "SR2": 0.3888983987215464, "triple_var": 3.355079799682706}, "real": {"L1": -0.11211240734398588, "L2": 0.014758928541262914, "SR": 0.41202604949268185, "SR2": 0.3901661114319987, "triple_var": 2.434647125320765}, "rep": 36} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:186:{"delta": {"L1": -0.4217174613110039, "L2": -0.07829915271006477, "SR": 0.1699086772349797, "SR2": 0.2249739684013386, "triple_var": -2.782818243923327}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.0060973640779581105, "L2": 0.05326973381762061, "SR": 0.38129944495530055, "SR2": 0.36762722280694954, "triple_var": 3.0695100668318087}, "real": {"L1": -0.41562009723304577, "L2": -0.02502941889244415, "SR": 0.5512081221902803, "SR2": 0.5926011912082881, "triple_var": 0.28669182290848166}, "rep": 37} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:187:{"delta": {"L1": -0.413542084818303, "L2": -0.10298018817871071, "SR": 0.30809033176030637, "SR2": 0.2977916029112376, "triple_var": -2.926615256885388}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.08987062445102904, "L2": -0.013459177786786694, "SR": 0.35634528434673024, "SR2": 0.366944046024333, "triple_var": 3.1047177918640387}, "real": {"L1": -0.32367146036727396, "L2": -0.11643936596549741, "SR": 0.6644356161070366, "SR2": 0.6647356489355706, "triple_var": 0.17810253497865086}, "rep": 37} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:188:{"delta": {"L1": -0.2831830415046883, "L2": -0.14167748502859798, "SR": 0.01639263368986371, "SR2": 0.11581281744670913, "triple_var": -1.6396373055404938}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.06371027226569731, "L2": 0.17481060590829767, "SR": 0.3990716449234281, "SR2": 0.3840376027038388, "triple_var": 2.5357934133660947}, "real": {"L1": -0.3468933137703856, "L2": 0.03313312087969969, "SR": 0.4154642786132918, "SR2": 0.4998504201505479, "triple_var": 0.8961561078256007}, "rep": 37} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:189:{"delta": {"L1": -0.3821012974720026, "L2": 0.169482646435147, "SR": -0.11932872633313979, "SR2": 0.020485850725476595, "triple_var": -1.128336184713441}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.19019130155342373, "L2": -0.059890624523200556, "SR": 0.4076160323913967, "SR2": 0.371737340723179, "triple_var": 3.615358584715469}, "real": {"L1": -0.19190999591857885, "L2": 0.10959202191194642, "SR": 0.2882873060582569, "SR2": 0.3922231914486556, "triple_var": 2.487022400002028}, "rep": 37} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:190:{"delta": {"L1": -0.03409705243028682, "L2": 0.2996009069868262, "SR": -0.06577695682174767, "SR2": 0.030961035766758682, "triple_var": 0.07700629158652283}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.04960024505627887, "L2": -0.13255138231034003, "SR": 0.4127988519861593, "SR2": 0.37138384426165, "triple_var": 2.533535974196134}, "real": {"L1": -0.0836972974865657, "L2": 0.16704952467648612, "SR": 0.3470218951644116, "SR2": 0.4023448800284087, "triple_var": 2.610542265782657}, "rep": 37} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:191:{"delta": {"L1": -0.5600773286584791, "L2": -0.0731527675796424, "SR": 0.2515516319358866, "SR2": 0.2993552679877005, "triple_var": -3.812554168212786}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.2455298147714882, "L2": -0.1316678643865745, "SR": 0.41207299486368787, "SR2": 0.37883223384392783, "triple_var": 3.977311196101703}, "real": {"L1": -0.3145475138869909, "L2": -0.2048206319662169, "SR": 0.6636246267995745, "SR2": 0.6781875018316283, "triple_var": 0.16475702788891716}, "rep": 38} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:192:{"delta": {"L1": -0.35601758858082405, "L2": 0.26849340515515513, "SR": 0.2761412152536732, "SR2": 0.34819178332794637, "triple_var": -1.9856380136128196}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.04942674125219738, "L2": -0.10442918625060287, "SR": 0.3585954594050491, "SR2": 0.37843206314326366, "triple_var": 2.1993507773552876}, "real": {"L1": -0.4054443298330214, "L2": 0.16406421890455225, "SR": 0.6347366746587223, "SR2": 0.72662384647121, "triple_var": 0.213712763742468}, "rep": 38} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:193:{"delta": {"L1": -0.31834523987412766, "L2": 0.13701010391721025, "SR": -0.0030584307749767503, "SR2": 0.08009040667849232, "triple_var": -1.9963708388926336}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.0156955908603214, "L2": 0.06829478161656388, "SR": 0.3659847516831844, "SR2": 0.3979942550960264, "triple_var": 3.0793486062619184}, "real": {"L1": -0.33404083073444907, "L2": 0.20530488553377413, "SR": 0.36292632090820764, "SR2": 0.4780846617745187, "triple_var": 1.0829777673692849}, "rep": 38} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:194:{"delta": {"L1": -0.0007203651793382393, "L2": -0.27199262839645305, "SR": -0.0759673945085308, "SR2": -0.06921805549735277, "triple_var": -0.07295329744145329}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.137790131770951, "L2": 0.16142726219104242, "SR": 0.4329980577299764, "SR2": 0.4350757464557042, "triple_var": 2.062132165843228}, "real": {"L1": -0.13851049695028925, "L2": -0.11056536620541065, "SR": 0.3570306632214456, "SR2": 0.3658576909583514, "triple_var": 1.9891788684017748}, "rep": 38} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:195:{"delta": {"L1": 0.11183318941734113, "L2": -0.1744037717815321, "SR": -0.0017164575931827297, "SR2": -0.04055646506644589, "triple_var": 0.5156486490824723}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.1443522125077782, "L2": -0.009828129559233609, "SR": 0.40388421393973417, "SR2": 0.40828240488014067, "triple_var": 2.107988918674399}, "real": {"L1": -0.03251902309043705, "L2": -0.18423190134076572, "SR": 0.40216775634655144, "SR2": 0.3677259398136948, "triple_var": 2.6236375677568713}, "rep": 38} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:196:{"delta": {"L1": -0.0595474424530292, "L2": 0.0014372854217252207, "SR": 0.30834895016979014, "SR2": 0.27803267759002315, "triple_var": -1.6041972845006622}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.22118753001138794, "L2": -0.13379484042582881, "SR": 0.30106409529403866, "SR2": 0.33185617318479965, "triple_var": 1.8664719783314896}, "real": {"L1": -0.28073497246441714, "L2": -0.1323575550041036, "SR": 0.6094130454638288, "SR2": 0.6098888507748228, "triple_var": 0.2622746938308273}, "rep": 39} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:197:{"delta": {"L1": -0.12160887243250876, "L2": 0.060694389067763505, "SR": 0.25770133496715275, "SR2": 0.3001921013975859, "triple_var": -2.710994447591963}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.09446637556233442, "L2": -0.15502433072576002, "SR": 0.4056154101749675, "SR2": 0.3765974115631806, "triple_var": 2.929967367705635}, "real": {"L1": -0.21607524799484318, "L2": -0.09432994165799652, "SR": 0.6633167451421202, "SR2": 0.6767895129607665, "triple_var": 0.21897292011367173}, "rep": 39} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:198:{"delta": {"L1": -0.09679555584427862, "L2": 0.024934383572527856, "SR": 0.09413814977474516, "SR2": 0.08568260403462141, "triple_var": -1.2497224706072931}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.10686499706866318, "L2": 0.055995637313828374, "SR": 0.3527456290293154, "SR2": 0.4033181761590711, "triple_var": 2.5335425555443885}, "real": {"L1": -0.2036605529129418, "L2": 0.08093002088635623, "SR": 0.44688377880406055, "SR2": 0.48900078019369253, "triple_var": 1.2838200849370953}, "rep": 39} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:199:{"delta": {"L1": -0.2086832139305118, "L2": -0.0888981957657115, "SR": 0.009255162569823339, "SR2": 0.029931346863132147, "triple_var": -1.3986249525907715}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.062450168897598246, "L2": 0.0019229030729281608, "SR": 0.39666514822244947, "SR2": 0.4104731891794531, "triple_var": 3.2400880579156417}, "real": {"L1": -0.14623304503291357, "L2": -0.08697529269278333, "SR": 0.4059203107922728, "SR2": 0.4404045360425852, "triple_var": 1.8414631053248702}, "rep": 39} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:200:{"delta": {"L1": -0.22165240810871373, "L2": 0.10714355489543811, "SR": -0.11077242657466552, "SR2": 0.016157191672394933, "triple_var": -0.2269722325248118}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.03817409405330664, "L2": -0.0830117941958728, "SR": 0.4036769671180777, "SR2": 0.3822178213107345, "triple_var": 2.6613682481918732}, "real": {"L1": -0.1834783140554071, "L2": 0.024131760699565307, "SR": 0.2929045405434122, "SR2": 0.39837501298312944, "triple_var": 2.4343960156670614}, "rep": 39} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:201:{"delta": {"L1": -0.251054353654223, "L2": -0.026161633118872117, "SR": 0.15063454534026777, "SR2": 0.17706370163953272, "triple_var": -2.145710529966985}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.06317732095758377, "L2": -0.035738318052602636, "SR": 0.4024997460347184, "SR2": 0.4414746872947454, "triple_var": 2.4562711781698736}, "real": {"L1": -0.3142316746118068, "L2": -0.06189995117147475, "SR": 0.5531342913749862, "SR2": 0.6185383889342781, "triple_var": 0.3105606482028882}, "rep": 40} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:202:{"delta": {"L1": -0.40372972614972996, "L2": 0.05041742094151927, "SR": 0.27858609994602707, "SR2": 0.32985313634347263, "triple_var": -3.3472499296689127}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.08958236430408135, "L2": -0.021219458393203525, "SR": 0.3655511587360753, "SR2": 0.3664690581900241, "triple_var": 3.575951532123013}, "real": {"L1": -0.3141473618456486, "L2": 0.029197962548315748, "SR": 0.6441372586821024, "SR2": 0.6963221945334968, "triple_var": 0.2287016024541005}, "rep": 40} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:203:{"delta": {"L1": -0.13585525988534403, "L2": 0.07668093657725053, "SR": -0.035552517672468864, "SR2": 0.05461682933866252, "triple_var": -1.8034577157722824}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.07943154657257158, "L2": -0.06619179396300347, "SR": 0.40497780473910355, "SR2": 0.38951255144059155, "triple_var": 3.1235507276533743}, "real": {"L1": -0.21528680645791562, "L2": 0.010489142614247056, "SR": 0.3694252870666347, "SR2": 0.44412938077925407, "triple_var": 1.3200930118810918}, "rep": 40} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:204:{"delta": {"L1": 0.23332057588825023, "L2": -0.1071367546934904, "SR": -0.003420135043601391, "SR2": 0.013288556741957502, "triple_var": 0.8387901966631484}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.1312446945124825, "L2": -0.08822510141685719, "SR": 0.38432355159191195, "SR2": 0.35608751517229376, "triple_var": 1.8863828719078333}, "real": {"L1": 0.10207588137576773, "L2": -0.1953618561103476, "SR": 0.38090341654831056, "SR2": 0.36937607191425126, "triple_var": 2.7251730685709816}, "rep": 40} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:205:{"delta": {"L1": -0.04062282221896985, "L2": 0.1775308185045524, "SR": -0.04448402506115534, "SR2": 0.03720113032958472, "triple_var": 0.3929391509858329}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.03195280206033504, "L2": -0.1294669614844064, "SR": 0.40257747518549447, "SR2": 0.3858858045193244, "triple_var": 2.3448498059655893}, "real": {"L1": -0.07257562427930489, "L2": 0.048063857020146004, "SR": 0.35809345012433913, "SR2": 0.42308693484890914, "triple_var": 2.737788956951422}, "rep": 40} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:206:{"delta": {"L1": -0.2996430441503785, "L2": -0.0445055618769223, "SR": 0.25640660641570073, "SR2": 0.23677970341920557, "triple_var": -3.03096680176452}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.06966543326377776, "L2": -0.09532735738690716, "SR": 0.36899263158168133, "SR2": 0.40218929193096403, "triple_var": 3.3046937249277724}, "real": {"L1": -0.22997761088660076, "L2": -0.13983291926382946, "SR": 0.6253992379973821, "SR2": 0.6389689953501696, "triple_var": 0.2737269231632525}, "rep": 41} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:207:{"delta": {"L1": -0.5234620810980469, "L2": 0.32883458862347703, "SR": 0.18965752384523366, "SR2": 0.35493226420318497, "triple_var": -3.1001038376643013}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.1377207135528347, "L2": -0.1216436027320241, "SR": 0.4554835578300824, "SR2": 0.3689729732945513, "triple_var": 3.3125324465442234}, "real": {"L1": -0.38574136754521227, "L2": 0.2071909858914529, "SR": 0.645141081675316, "SR2": 0.7239052374977363, "triple_var": 0.21242860887992213}, "rep": 41} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:208:{"delta": {"L1": 0.018434427806035478, "L2": -0.08475843696461427, "SR": 0.06718771511225047, "SR2": 0.03598329881146972, "triple_var": -1.024217853964894}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.03346509220560335, "L2": 0.02740869564471649, "SR": 0.3908286224601578, "SR2": 0.40045657416945024, "triple_var": 2.5100764716729045}, "real": {"L1": -0.015030664399567871, "L2": -0.05734974131989778, "SR": 0.4580163375724083, "SR2": 0.43643987298091996, "triple_var": 1.4858586177080104}, "rep": 41} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:209:{"delta": {"L1": -0.11306235888058913, "L2": 0.05446352069492291, "SR": 0.012870770041054636, "SR2": 0.03664427896436995, "triple_var": -0.5505169774902479}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.049063342598983484, "L2": -0.14776686750453932, "SR": 0.3549454848580023, "SR2": 0.3470909811744056, "triple_var": 3.0326278859129374}, "real": {"L1": -0.06399901628160565, "L2": -0.09330334680961641, "SR": 0.36781625489905695, "SR2": 0.38373526013877557, "triple_var": 2.4821109084226896}, "rep": 41} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:210:{"delta": {"L1": -0.14429165563271976, "L2": 0.049727583188612555, "SR": 0.02348344028896765, "SR2": 0.014626865004304435, "triple_var": -2.549624408219778}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.04178054330976109, "L2": -0.06400502868585128, "SR": 0.31326024502663097, "SR2": 0.37626101866810646, "triple_var": 5.070626952398698}, "real": {"L1": -0.10251111232295868, "L2": -0.01427744549723873, "SR": 0.3367436853155986, "SR2": 0.3908878836724109, "triple_var": 2.5210025441789203}, "rep": 41} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:211:{"delta": {"L1": -0.3788473986375596, "L2": -0.003597449670246805, "SR": 0.16849362149979757, "SR2": 0.23858846975936854, "triple_var": -3.1732506463586296}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.009247038003403605, "L2": -0.175541166847648, "SR": 0.40974966028253773, "SR2": 0.38082014037869505, "triple_var": 3.39092212222222}, "real": {"L1": -0.369600360634156, "L2": -0.1791386165178948, "SR": 0.5782432817823353, "SR2": 0.6194086101380636, "triple_var": 0.21767147586359067}, "rep": 42} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:212:{"delta": {"L1": -0.3468455701896051, "L2": -0.0849914341595719, "SR": 0.270119795593875, "SR2": 0.311430653574523, "triple_var": -2.979143935901164}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.1243469194593982, "L2": 0.02415529113609934, "SR": 0.4336826639247179, "SR2": 0.399317905422018, "triple_var": 3.1492481884498185}, "real": {"L1": -0.2224986507302069, "L2": -0.060836143023472566, "SR": 0.7038024595185929, "SR2": 0.710748558996541, "triple_var": 0.17010425254865447}, "rep": 42} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:213:{"delta": {"L1": 0.016760085868826886, "L2": -0.1754272061178863, "SR": 0.03754275725719858, "SR2": 0.06438678582779511, "triple_var": -1.208846062927164}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.036939684647761324, "L2": 0.0390031257048069, "SR": 0.40836832114632876, "SR2": 0.408854000562699, "triple_var": 3.0115497301555916}, "real": {"L1": 0.05369977051658821, "L2": -0.13642408041307938, "SR": 0.44591107840352734, "SR2": 0.4732407863904941, "triple_var": 1.8027036672284276}, "rep": 42} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:214:{"delta": {"L1": -0.025272626584842632, "L2": -0.06327519433445356, "SR": -0.027301034978331862, "SR2": -0.07601405647629084, "triple_var": 0.10340895203091094}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.06077239827823389, "L2": 0.003551310445412603, "SR": 0.3971440264042957, "SR2": 0.43258171560011643, "triple_var": 2.048617477003966}, "real": {"L1": -0.08604502486307652, "L2": -0.05972388388904096, "SR": 0.36984299142596383, "SR2": 0.3565676591238256, "triple_var": 2.1520264290348767}, "rep": 42} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:215:{"delta": {"L1": -0.07321541244819696, "L2": 0.21896492567244827, "SR": 0.023337595875677464, "SR2": 0.02305282807127823, "triple_var": 0.712700616350256}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.020430554453814885, "L2": -0.17540047409193887, "SR": 0.34749862345872584, "SR2": 0.3325472125274574, "triple_var": 2.181893259852185}, "real": {"L1": -0.09364596690201184, "L2": 0.043564451580509395, "SR": 0.3708362193344033, "SR2": 0.3556000405987356, "triple_var": 2.8945938762024412}, "rep": 42} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:216:{"delta": {"L1": -0.42184105063325866, "L2": 0.0020257022419362114, "SR": 0.23422079146567465, "SR2": 0.23356707237406077, "triple_var": -2.656572824593903}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.05974856542273822, "L2": -0.09007278980763561, "SR": 0.3950457857095242, "SR2": 0.3855184985772219, "triple_var": 2.872162774847063}, "real": {"L1": -0.36209248521052045, "L2": -0.0880470875656994, "SR": 0.6292665771751988, "SR2": 0.6190855709512827, "triple_var": 0.21558995025315986}, "rep": 43} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:217:{"delta": {"L1": -0.41917155673155715, "L2": 0.09760934270032416, "SR": 0.2327739996763618, "SR2": 0.29791027471049186, "triple_var": -2.561948190822104}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.010636386180053817, "L2": 0.09619352180647837, "SR": 0.44838304943938423, "SR2": 0.4187616545744223, "triple_var": 2.7649416309654478}, "real": {"L1": -0.42980794291161095, "L2": 0.19380286450680254, "SR": 0.681157049115746, "SR2": 0.7166719292849142, "triple_var": 0.20299344014334364}, "rep": 43} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:218:{"delta": {"L1": -0.24666615897812894, "L2": -0.21651606844544824, "SR": 0.011608482338069603, "SR2": 0.0315894791018958, "triple_var": -2.0565639182761957}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.00235567313454057, "L2": 0.1544118737248754, "SR": 0.34567786986929405, "SR2": 0.3703249693658407, "triple_var": 3.525000455610161}, "real": {"L1": -0.24431048584358836, "L2": -0.06210419472057285, "SR": 0.35728635220736366, "SR2": 0.4019144484677365, "triple_var": 1.4684365373339654}, "rep": 43} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:219:{"delta": {"L1": 0.12295231606232848, "L2": 0.10175264397560908, "SR": -0.0008930983722048591, "SR2": -0.030757006478859872, "triple_var": -0.3750403037431842}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.13341693500662177, "L2": -0.12173801754758169, "SR": 0.4011803664469753, "SR2": 0.41224529209621896, "triple_var": 2.636957676726085}, "real": {"L1": -0.010464618944293297, "L2": -0.01998537357197261, "SR": 0.40028726807477044, "SR2": 0.3814882856173591, "triple_var": 2.261917372982901}, "rep": 43} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:220:{"delta": {"L1": 0.05210106188718268, "L2": -0.0030213587966025635, "SR": -0.01119865082863053, "SR2": 0.026757616080335833, "triple_var": -0.02253098787957608}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.0859969329602967, "L2": -0.1736465266253195, "SR": 0.40877969646627393, "SR2": 0.3727735652076684, "triple_var": 1.9111312621912753}, "real": {"L1": -0.03389587107311402, "L2": -0.17666788542192205, "SR": 0.3975810456376434, "SR2": 0.39953118128800424, "triple_var": 1.8886002743116992}, "rep": 43} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:221:{"delta": {"L1": -0.23520817330449448, "L2": -0.028770699033494107, "SR": 0.2375572598349424, "SR2": 0.28864240000659586, "triple_var": -2.8206781577874787}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.03634175602244724, "L2": 0.10722690110478211, "SR": 0.36947453683485937, "SR2": 0.3871509562238666, "triple_var": 3.142836322962509}, "real": {"L1": -0.2715499293269417, "L2": 0.078456202071288, "SR": 0.6070317966698018, "SR2": 0.6757933562304624, "triple_var": 0.3221581651750303}, "rep": 44} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:222:{"delta": {"L1": -0.06936655617287485, "L2": -0.08516998427525596, "SR": 0.3114885171424625, "SR2": 0.34621189701363947, "triple_var": -2.5060009203291194}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.15610547741556158, "L2": 0.08390655474567538, "SR": 0.3731976810550365, "SR2": 0.3592766730374091, "triple_var": 2.7165048268310907}, "real": {"L1": -0.22547203358843643, "L2": -0.0012634295295805835, "SR": 0.684686198197499, "SR2": 0.7054885700510486, "triple_var": 0.21050390650197143}, "rep": 44} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:223:{"delta": {"L1": 0.07779883820471807, "L2": -0.19697680324043748, "SR": 0.16378839492007313, "SR2": 0.012672177631825543, "triple_var": -1.1909028168809825}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.061594611664631344, "L2": -0.06261220865153246, "SR": 0.30353048465411736, "SR2": 0.3998596533225568, "triple_var": 2.6182736264992923}, "real": {"L1": 0.01620422654008673, "L2": -0.2595890118919699, "SR": 0.4673188795741905, "SR2": 0.41253183095438234, "triple_var": 1.4273708096183098}, "rep": 44} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:224:{"delta": {"L1": 0.052946612787916666, "L2": 0.2945459463099849, "SR": 0.05060856604465935, "SR2": 0.09709828823966021, "triple_var": 0.0512680895227815}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.05065468906970281, "L2": -0.16612679709930772, "SR": 0.4003293766429612, "SR2": 0.3879732008383554, "triple_var": 1.9872247695035345}, "real": {"L1": 0.0022919237182138573, "L2": 0.12841914921067715, "SR": 0.45093794268762055, "SR2": 0.4850714890780156, "triple_var": 2.038492859026316}, "rep": 44} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:225:{"delta": {"L1": -0.14646291586330717, "L2": 0.029187489276566277, "SR": -0.07024470502944641, "SR2": -0.009849661754869066, "triple_var": -0.5180282205471629}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.09801798745643951, "L2": -0.013772547884774685, "SR": 0.40913295373086106, "SR2": 0.40073513894950324, "triple_var": 3.1135497736078652}, "real": {"L1": -0.048444928406867656, "L2": 0.015414941391791592, "SR": 0.33888824870141465, "SR2": 0.3908854771946342, "triple_var": 2.5955215530607023}, "rep": 44} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:226:{"delta": {"L1": -0.26459877664064013, "L2": -0.06491940969239894, "SR": 0.2134925949579501, "SR2": 0.25402179705609995, "triple_var": -2.745447144842437}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.04104833428056626, "L2": 0.030033762371639874, "SR": 0.36120304001748554, "SR2": 0.3588834059989001, "triple_var": 3.085287916507294}, "real": {"L1": -0.3056471109212064, "L2": -0.034885647320759064, "SR": 0.5746956349754356, "SR2": 0.6129052030550001, "triple_var": 0.33984077166485704}, "rep": 45} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:227:{"delta": {"L1": -0.41471251701281037, "L2": 0.08078541780618466, "SR": 0.31712226402822247, "SR2": 0.3144111263053848, "triple_var": -3.0060589256842603}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.01519167031541628, "L2": 0.12113134135369664, "SR": 0.3337180396854574, "SR2": 0.3960140341247368, "triple_var": 3.220528095997565}, "real": {"L1": -0.42990418732822666, "L2": 0.2019167591598813, "SR": 0.6508403037136798, "SR2": 0.7104251604301216, "triple_var": 0.21446917031330467}, "rep": 45} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:228:{"delta": {"L1": -0.0787235417225732, "L2": -0.01102515181069097, "SR": 0.020589983232230713, "SR2": 0.04346598249222289, "triple_var": -1.4597721435179858}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.0523211859341352, "L2": -0.13426172975781794, "SR": 0.3818715570370352, "SR2": 0.3992862070566535, "triple_var": 2.7319048704808657}, "real": {"L1": -0.1310447276567084, "L2": -0.1452868815685089, "SR": 0.4024615402692659, "SR2": 0.4427521895488764, "triple_var": 1.27213272696288}, "rep": 45} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:229:{"delta": {"L1": -0.07244584261739984, "L2": 0.06447391955750663, "SR": 0.042116712824839475, "SR2": 0.07554311753991244, "triple_var": -0.6916081648157997}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.03294012877113502, "L2": -0.06701331933228476, "SR": 0.3460005308595655, "SR2": 0.35108225403791377, "triple_var": 2.7043110057060153}, "real": {"L1": -0.10538597138853487, "L2": -0.002539399774778133, "SR": 0.388117243684405, "SR2": 0.4266253715778262, "triple_var": 2.0127028408902157}, "rep": 45} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:230:{"delta": {"L1": 0.19138805665785413, "L2": -0.24444987450315858, "SR": 0.06844757933969148, "SR2": 0.0014283841593295543, "triple_var": -0.2858950576759227}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.2065408726586739, "L2": 0.16778544988389194, "SR": 0.3203406153703409, "SR2": 0.40054979464651147, "triple_var": 2.3265982628879853}, "real": {"L1": -0.01515281600081976, "L2": -0.07666442461926663, "SR": 0.3887881947100324, "SR2": 0.401978178805841, "triple_var": 2.0407032052120626}, "rep": 45} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:231:{"delta": {"L1": -0.5470928604546501, "L2": -0.013711520108126672, "SR": 0.21876326547415287, "SR2": 0.29191009053474076, "triple_var": -3.467437115830438}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": 0.2008270157391634, "L2": -0.02889881582273665, "SR": 0.3476670034020785, "SR2": 0.34961937002103466, "triple_var": 3.764849848064609}, "real": {"L1": -0.34626584471548677, "L2": -0.04261033593086332, "SR": 0.5664302688762314, "SR2": 0.6415294605557754, "triple_var": 0.29741273223417114}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:232:{"delta": {"L1": -0.2727191933248201, "L2": 0.06492119365824615, "SR": 0.35856005809619074, "SR2": 0.3496423045383621, "triple_var": -2.4714799532376697}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": -0.06300720958588917, "L2": 0.09647455015128005, "SR": 0.34304356140629777, "SR2": 0.391274127670869, "triple_var": 2.6423269549399593}, "real": {"L1": -0.33572640291070927, "L2": 0.1613957438095262, "SR": 0.7016036195024885, "SR2": 0.7409164322092311, "triple_var": 0.17084700170228956}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:233:{"delta": {"L1": -0.11143878845056826, "L2": 0.1542596695721083, "SR": 0.08238895506519534, "SR2": 0.15146519102259098, "triple_var": -1.7577057057516725}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": -0.050368693743290636, "L2": -0.09117183677310728, "SR": 0.4187929655927714, "SR2": 0.3775443709098136, "triple_var": 2.8604077844390012}, "real": {"L1": -0.1618074821938589, "L2": 0.06308783279900103, "SR": 0.5011819206579667, "SR2": 0.5290095619324046, "triple_var": 1.1027020786873287}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:234:{"delta": {"L1": -0.2046217625464567, "L2": -0.07111952693637896, "SR": -0.011710469684957814, "SR2": 0.014673891016061091, "triple_var": -1.847569943191859}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": -0.014227898714840292, "L2": -0.02618170266303412, "SR": 0.38253824116769203, "SR2": 0.3465396787111841, "triple_var": 3.677417554320761}, "real": {"L1": -0.21884966126129698, "L2": -0.09730122959941308, "SR": 0.3708277714827342, "SR2": 0.36121356972724517, "triple_var": 1.829847611128902}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:235:{"delta": {"L1": -0.06889246659571008, "L2": 0.08260440682747795, "SR": -0.026534324746464444, "SR2": 0.005504325499031459, "triple_var": -0.673683615938721}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": 0.04325416889641128, "L2": -0.04706654533688032, "SR": 0.4171937962085934, "SR2": 0.39384581856299106, "triple_var": 3.464143351551102}, "real": {"L1": -0.025638297699298804, "L2": 0.035537861490597636, "SR": 0.390659471462129, "SR2": 0.3993501440620225, "triple_var": 2.790459735612381}, "rep": 46} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:236:{"delta": {"L1": -0.1601276698836981, "L2": -0.11563351849128359, "SR": 0.2808775003611135, "SR2": 0.26643960393167054, "triple_var": -2.3348076814743153}, "event": "spectrum_pair", "label": "GUE_chaotic_billiard_proxy", "n_gaps": 95, "null": {"L1": -0.03283328804480305, "L2": 0.014806456701571508, "SR": 0.349801793060856, "SR2": 0.3889546186061644, "triple_var": 2.6665790210080846}, "real": {"L1": -0.19296095792850115, "L2": -0.10082706178971208, "SR": 0.6306792934219695, "SR2": 0.655394222537835, "triple_var": 0.33177133953376925}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:237:{"delta": {"L1": -0.43615795523239465, "L2": 0.08999337240375185, "SR": 0.23006507823659655, "SR2": 0.2969391750726099, "triple_var": -2.3357646684917848}, "event": "spectrum_pair", "label": "Anderson_1D_W0.5", "n_gaps": 95, "null": {"L1": 0.02065361300109852, "L2": -0.059917931158411784, "SR": 0.3901056612540092, "SR2": 0.3601963880365303, "triple_var": 2.5483654573734524}, "real": {"L1": -0.4155043422312961, "L2": 0.030075441245340076, "SR": 0.6201707394906057, "SR2": 0.6571355631091402, "triple_var": 0.2126007888816678}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:238:{"delta": {"L1": -0.14145737268738853, "L2": -0.009591167330170694, "SR": 0.07833719881940271, "SR2": 0.02851205889189523, "triple_var": -1.8548431927675522}, "event": "spectrum_pair", "label": "Anderson_1D_W2", "n_gaps": 95, "null": {"L1": 0.03649138434398558, "L2": -0.11449890360083871, "SR": 0.3482437417443585, "SR2": 0.35361421198641935, "triple_var": 3.317323350623292}, "real": {"L1": -0.10496598834340296, "L2": -0.1240900709310094, "SR": 0.4265809405637612, "SR2": 0.3821262708783146, "triple_var": 1.46248015785574}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:239:{"delta": {"L1": -0.09568656534945513, "L2": -0.06927956454142085, "SR": 0.0681466911525701, "SR2": 0.055279758923166944, "triple_var": -0.5273516620899401}, "event": "spectrum_pair", "label": "Anderson_1D_W6", "n_gaps": 95, "null": {"L1": 0.056968249081960985, "L2": 0.0699839646369959, "SR": 0.3567554491929611, "SR2": 0.35671606752271545, "triple_var": 3.097936190446298}, "real": {"L1": -0.038718316267494154, "L2": 0.0007044000955750374, "SR": 0.4249021403455312, "SR2": 0.4119958264458824, "triple_var": 2.570584528356358}, "rep": 47} tools/data/physical_sr_residue_bounce_20260514_1612.trace.jsonl:240:{"delta": {"L1": 0.03541282154329242, "L2": 0.024197893955487017, "SR": -0.0030911393549142296, "SR2": -0.012448318262132774, "triple_var": 0.025469129278811842}, "event": "spectrum_pair", "label": "Anderson_1D_W12", "n_gaps": 95, "null": {"L1": -0.1344162375776942, "L2": 0.004302597030711671, "SR": 0.37998826108126743, "SR2": 0.4091560966313005, "triple_var": 1.960224277295223}, "real": {"L1": -0.09900341603440177, "L2": 0.02850049098619869, "SR": 0.3768971217263532, "SR2": 0.3967077783691677, "triple_var": 1.9856934065740348}, "rep": 47} tools/data/graph_completion/graph_completion_20260509_1516.json:11: "observable_contract": "claim=il gate `coherent/null/beta` del BOUNDARY sintetico trasferisce sul perimetro semi-reale base; observable=one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta; operator=`exp_semireal_boundary_transfer_gate.py`; generator=13 righe `boundary_denominator_prescan_full_20260509_1500` ricostruite da `dnd_autoricerca`; denominator=13 righe base BOUNDARY, 8 GUE-like e 5 Poisson-like, beta layers 0.0..1.0, 12 replicates, 24 shuffle baselines; non_possible=dichiarare beta 0.3 coordinata universale o transfer completo quando 2/13 righe cadono; not_tested=nuovi domini, nuovi spettri, fit `V_c`, limite asintotico.", tools/data/graph_completion/graph_completion_20260509_1516.json:93: "observable_contract": "claim=il gate `coherent/null/beta` del BOUNDARY sintetico trasferisce sul perimetro semi-reale base; observable=one-sided canonical observables, stable counts ai poli, endpoint distance e ambiguous beta; operator=`exp_semireal_boundary_transfer_gate.py`; generator=13 righe `boundary_denominator_prescan_full_20260509_1500` ricostruite da `dnd_autoricerca`; denominator=13 righe base BOUNDARY, 8 GUE-like e 5 Poisson-like, beta layers 0.0..1.0, 12 replicates, 24 shuffle baselines; non_possible=dichiarare beta 0.3 coordinata universale o transfer completo quando 2/13 righe cadono; not_tested=nuovi domini, nuovi spettri, fit `V_c`, limite asintotico.", tools/data/graph_completion/graph_completion_20260509_1516.json:101: "claim": "porta la direzione BOUNDARY dal sintetico controllato al perimetro cross-dominio semi-reale 8 GUE / 5 Poisson", tools/data/graph_completion/graph_completion_20260509_1516.json:102: "evidence": "lab_data.json dichiara la direzione viva: \"test su perimetri reali o avversariali senza importare label GUE/Poisson\". Il report invece struttura input e adesione come 8 GUE-like e 5 Poisson-like, quindi importa proprio la label che la direzione chiedeva di non importare.", tools/data/graph_completion/graph_completion_20260509_1516.json:103: "suggestion": "Nel prossimo ciclo ripetere il gate su features domain-native senza usare `source_domain_type` GUE/Poisson nella classificazione o nel claim; se le label restano solo metadata, dichiarare `deliberate_counter_perimeter` con `why` e mostrare che non entrano nell'operatore." tools/data/graph_completion/graph_completion_20260509_1516.json:172: "GUE": 8, tools/data/graph_completion/graph_completion_20260509_1516.json:173: "Poisson": 5 succeeded in 152ms: TENS_SCALE_TRASCENDENZA_LIMITE: score=0.807 — TENS_SCALE_TRASCENDENZA_LIMITE: incerto (i=0.6) — massimo potere discriminante COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: score=0.758 — COMP_GEN_GAP_RATIO_T9_linguaggio_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: score=0.758 — COMP_DOMAIN_PHOTONIC_TRASCENDENZA_LIMITE: incerto (i=0.65) — massimo potere discriminante codex Il report 16:40 apre precisamente la consecutio richiesta: interfaccia unica e artifact fit-ready, senza integrazione automatica nel grafo. Ora verifico lo script esistente per capire se serve solo un wrapper/CLI minimo oppure una modifica più estesa. exec /bin/bash -lc "sed -n '1,260p' tools/exp_physical_sr_residue_bounce.py" in /opt/MM_D-ND succeeded in 0ms: #!/usr/bin/env python3 """ Physical bounce for the prime-minus-mod6 selective residue. Route: physical A: quantum-chaotic GUE spectra mathematical transducer: span-matched Poisson counter-boundary physical B: 1D Anderson tight-binding spectra across disorder The experiment asks whether the component split exposed by the prime/mod6 deposit has a concrete spectral analogue: SR can be absorbed by a span-matched counter-boundary at a localized endpoint, while chaotic spectra keep SR active. """ from __future__ import annotations import argparse import json from pathlib import Path from typing import Any import numpy as np from observables_registry import OBSERVABLES_CANONICAL, OBSERVABLES_REGISTRY_VERSION FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: levels = np.sort(np.asarray(levels, dtype=float)) n = len(levels) keep = max(8, int(n * central_fraction)) start = (n - keep) // 2 central = levels[start : start + keep] gaps = np.diff(central) gaps = gaps[np.isfinite(gaps) & (gaps > 1e-12)] if len(gaps) == 0: return gaps return gaps / float(np.mean(gaps)) def gue_levels(n: int, rng: np.random.Generator) -> np.ndarray: real = rng.normal(size=(n, n)) imag = rng.normal(size=(n, n)) mat = (real + 1j * imag) hermitian = (mat + mat.conj().T) / (2.0 * np.sqrt(n)) return np.linalg.eigvalsh(hermitian) def goe_levels(n: int, rng: np.random.Generator) -> np.ndarray: mat = rng.normal(size=(n, n)) symmetric = (mat + mat.T) / (2.0 * np.sqrt(n)) return np.linalg.eigvalsh(symmetric) def anderson_levels(n: int, disorder: float, rng: np.random.Generator) -> np.ndarray: diagonal = rng.uniform(-disorder / 2.0, disorder / 2.0, size=n) matrix = np.diag(diagonal) off = np.ones(n - 1) matrix += np.diag(off, 1) + np.diag(off, -1) return np.linalg.eigvalsh(matrix) def span_matched_poisson_gaps(level_count: int, rng: np.random.Generator) -> np.ndarray: levels = np.sort(rng.random(level_count)) gaps = np.diff(levels) gaps = gaps[gaps > 1e-12] if len(gaps) == 0: return gaps return gaps / float(np.mean(gaps)) def compute_obs(gaps: np.ndarray) -> dict[str, float]: return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 observed = abs(float(np.mean(values))) null = [] for _ in range(trials): signs = rng.choice(np.array([-1.0, 1.0]), size=len(values), replace=True) null.append(abs(float(np.mean(values * signs)))) null_arr = np.array(null, dtype=float) return float((np.sum(null_arr >= observed) + 1) / (len(null_arr) + 1)) def summarize(label: str, rows: list[dict[str, Any]], rng: np.random.Generator, trials: int) -> dict[str, Any]: deltas = { obs: np.array([row["delta"][obs] for row in rows], dtype=float) for obs in OBSERVABLES_CANONICAL } summary: dict[str, Any] = { "label": label, "sample_count": len(rows), "component_state": {}, "mean_real": {}, "mean_null": {}, "mean_delta": {}, "p_two_sided": {}, "cohen_d_delta": {}, } for obs, values in deltas.items(): real_values = np.array([row["real"][obs] for row in rows], dtype=float) null_values = np.array([row["null"][obs] for row in rows], dtype=float) mean_delta = float(np.mean(values)) sd = float(np.std(values, ddof=1)) if len(values) > 1 else 0.0 p_value = sign_swap_p(values, rng, trials) effect = mean_delta / sd if sd > 1e-12 else 0.0 active = p_value <= 0.01 and abs(effect) >= 0.5 summary["mean_real"][obs] = float(np.mean(real_values)) summary["mean_null"][obs] = float(np.mean(null_values)) summary["mean_delta"][obs] = mean_delta summary["p_two_sided"][obs] = p_value summary["cohen_d_delta"][obs] = effect summary["component_state"][obs] = "active" if active else "absorbed" summary["focus_signature"] = [ obs for obs in FOCUS_OBS if summary["component_state"][obs] == "active" ] return summary def contrast( label: str, left_rows: list[dict[str, Any]], right_rows: list[dict[str, Any]], rng: np.random.Generator, trials: int, ) -> dict[str, Any]: paired = min(len(left_rows), len(right_rows)) result: dict[str, Any] = { "label": label, "paired_count": paired, "mean_left_minus_right": {}, "p_two_sided": {}, "cohen_d": {}, "state": {}, } for obs in OBSERVABLES_CANONICAL: values = np.array( [ left_rows[i]["real"][obs] - right_rows[i]["real"][obs] for i in range(paired) ], dtype=float, ) mean_delta = float(np.mean(values)) if len(values) else 0.0 sd = float(np.std(values, ddof=1)) if len(values) > 1 else 0.0 p_value = sign_swap_p(values, rng, trials) effect = mean_delta / sd if sd > 1e-12 else 0.0 active = p_value <= 0.01 and abs(effect) >= 0.5 result["mean_left_minus_right"][obs] = mean_delta result["p_two_sided"][obs] = p_value result["cohen_d"][obs] = effect result["state"][obs] = "separated" if active else "not_separated" return result def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) trace_path.parent.mkdir(parents=True, exist_ok=True) rows_by_label: dict[str, list[dict[str, Any]]] = {} rows_by_size_label: dict[str, list[dict[str, Any]]] = {} with trace_path.open("w", encoding="utf-8") as trace: for n in sizes: for i in range(args.reps): specs: list[tuple[str, str, np.ndarray]] = [ ("GOE_time_reversal_symmetric", "beta_1_real_symmetric", goe_levels(n, rng)), ("GUE_unitary_no_time_reversal", "beta_2_complex_hermitian", gue_levels(n, rng)), ] for disorder in args.disorders: specs.append( ( f"Anderson_1D_W{disorder:g}", "disordered_real_tight_binding_finite_size", anderson_levels(n, disorder, rng), ) ) for label, symmetry, levels in specs: gaps = normalize_gaps(levels, args.central_fraction) if len(gaps) < 12: continue null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) real_obs = compute_obs(gaps) null_obs = compute_obs(null_gaps) row = { "event": "spectrum_pair", "label": label, "symmetry": symmetry, "rep": i, "n": int(n), "n_gaps": int(len(gaps)), "real": real_obs, "null": null_obs, "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, } size_label = f"N{n}:{label}" rows_by_label.setdefault(label, []).append(row) rows_by_size_label.setdefault(size_label, []).append(row) trace.write(json.dumps(row, sort_keys=True) + "\n") summaries = { label: summarize(label, rows, rng, args.sign_trials) for label, rows in sorted(rows_by_label.items()) } size_summaries = { label: summarize(label, rows, rng, args.sign_trials) for label, rows in sorted(rows_by_size_label.items()) } symmetry_contrasts = {} for n in sizes: gue_key = f"N{n}:GUE_unitary_no_time_reversal" goe_key = f"N{n}:GOE_time_reversal_symmetric" symmetry_contrasts[f"N{n}:GUE_minus_GOE"] = contrast( f"N{n}:GUE_minus_GOE", rows_by_size_label.get(gue_key, []), rows_by_size_label.get(goe_key, []), rng, args.sign_trials, ) source = summaries["GUE_unitary_no_time_reversal"] localized = summaries[f"Anderson_1D_W{args.disorders[-1]:g}"] result = { "experiment_id": "physical_sr_residue_bounce_20260514_1640", "observables_registry": OBSERVABLES_REGISTRY_VERSION, "observables_used": list(OBSERVABLES_CANONICAL.keys()), "seed": args.seed, "sizes": sizes, "reps": args.reps, "central_fraction": args.central_fraction, "null": "span_matched_poisson_same_level_count", "physical_source": "quantum-chaotic spectra modeled by GOE and GUE symmetry classes", "mathematical_transducer": "span-matched counter-boundary on canonical gap observables", "physical_return_candidate": "1D Anderson tight-binding spectra across disorder/localization", "component_gate": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", "classical_baselines": { "GOE": "Wigner-Dyson beta=1, real symmetric, time-reversal symmetric", "GUE": "Wigner-Dyson beta=2, complex Hermitian, no time-reversal symmetry", "Poisson": "independent levels, span-matched finite sample null", "Anderson_1D_W6": "finite-size disorder/localization boundary, not a universal transition", }, "summaries": summaries, "size_summaries": size_summaries, "symmetry_contrasts": symmetry_contrasts, "bounce_test": { "source_SR_state": source["component_state"]["SR"], "localized_SR_state": localized["component_state"]["SR"], "localized_focus_signature": localized["focus_signature"], "rimbalzo_fisico_presente": ( source["component_state"]["SR"] == "active" and localized["component_state"]["SR"] == "absorbed" ), }, "trace_jsonl": str(trace_path), exec /bin/bash -lc "sed -n '260,620p' tools/exp_physical_sr_residue_bounce.py" in /opt/MM_D-ND succeeded in 0ms: "trace_jsonl": str(trace_path), } Path(args.output).parent.mkdir(parents=True, exist_ok=True) Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") return result def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) parser.add_argument("--reps", type=int, default=48) parser.add_argument("--central-fraction", type=float, default=0.5) parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) if __name__ == "__main__": main() exec /bin/bash -lc "sed -n '1,220p' shared_memory/README.md" in /opt/MM_D-ND succeeded in 0ms: # shared_memory — Cross-agent rules e cristalli > Cartella condivisa tra agenti che operano in `/opt/MM_D-ND/`: > - **TM3-claude** (Claude Code, sessioni interattive con operatore) > - **codex-lab** (Codex CLI, cycle agent notturno + run manuali) > - **eventuali altri** (TM5, TM7, futuri cycle) > > Contiene regole permanenti, cristalli operativi, scoperte rilevanti che valgono > per più di un agente. **Non** memoria operativa locale (quella resta in > `~/.claude/projects/.../memory/` per TM3, in `~/.codex_lab/memories/` per codex). ## Pattern di scrittura Ogni file: - Filename: `__.md` (es. `feedback_cycletest_2026-05-07.md`, `cristallo_gate_transferability_2026-05-07.md`) - Frontmatter: ```yaml --- type: feedback | cristallo | regola | finding-cross-agent author: TM3 | codex-lab | TM7 | operatore date: YYYY-MM-DD scope: cross-agent | mm-dnd-only | lab-cycle-only --- ``` - Body: conciso. Se è regola → "Why" + "How to apply". Se è cristallo → contesto + insight + how it changes work. ## Pattern di lettura - **TM3 (claude code)**: al boot di sessione su `/opt/MM_D-ND/`, leggi `MM_D-ND/.claude/CLAUDE.md` che adesso punta qui (sezione *Cross-agent rules*). Scansiona `ls -t shared_memory/*.md | head -10` (le più recenti) e integra in contesto. - **codex-lab**: `/opt/MM_D-ND/AGENTS.md` punta qui. Codex legge AGENTS.md ad ogni cycle, AGENTS.md istruisce di leggere shared_memory. - **Cleanup**: file > 90 giorni e non rilevanti vanno archiviati o rimossi (lo fa TM3 quando la cartella supera 50 file). ## Direzione bidirezionale - TM3 → codex: TM3 scrive feedback/cristalli da sessioni operatore qui → codex li vede al cycle dopo via AGENTS.md. - codex → TM3: codex durante un cycle scopre/cristallizza qualcosa di cross-agent → scrive qui → TM3 li vede al boot di sessione successiva. Il flusso non sostituisce le memorie locali — *integra* per ciò che serve a tutti. ## Anti-pattern - Duplicare qui regole che valgono SOLO per TM3 (vanno in `/root/.claude/projects/.../memory/`) - Duplicare qui regole che valgono SOLO per codex-lab cycle (vanno in `/root/.codex_lab/memories/`) - Scrivere note di lavoro temporanee (vanno nel campo vivo, non qui) exec /bin/bash -lc "sed -n '1,220p' shared_memory/deposito_cambia_la_parola_non_regola_2026-05-07.md" in /opt/MM_D-ND 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 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 "ls -lh 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 2>/dev/null && sed -n '1,220p' tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json" in /opt/MM_D-ND succeeded in 0ms: -rw-r--r-- 1 root root 16K May 14 16:41 tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json -rw-r--r-- 1 root root 227K May 14 16:41 tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl { "bounce_test": { "localized_SR_state": "absorbed", "localized_focus_signature": [ "triple_var" ], "rimbalzo_fisico_presente": true, "source_SR_state": "active" }, "central_fraction": 0.5, "classical_baselines": { "Anderson_1D_W6": "finite-size disorder/localization boundary, not a universal transition", "GOE": "Wigner-Dyson beta=1, real symmetric, time-reversal symmetric", "GUE": "Wigner-Dyson beta=2, complex Hermitian, no time-reversal symmetry", "Poisson": "independent levels, span-matched finite sample null" }, "component_gate": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", "experiment_id": "physical_sr_residue_bounce_20260514_1640", "mathematical_transducer": "span-matched counter-boundary on canonical gap observables", "null": "span_matched_poisson_same_level_count", "observables_registry": "1.0.0-2026-05-06", "observables_used": [ "SR", "SR2", "L1", "L2", "triple_var" ], "physical_return_candidate": "1D Anderson tight-binding spectra across disorder/localization", "physical_source": "quantum-chaotic spectra modeled by GOE and GUE symmetry classes", "reps": 64, "seed": 202605141640, "size_summaries": { "N128:Anderson_1D_W6": { "cohen_d_delta": { "L1": -0.4421527725421752, "L2": -0.20987353256607538, "SR": -0.010971472257437869, "SR2": 0.08546231550799492, "triple_var": -0.622324895163871 }, "component_state": { "L1": "absorbed", "L2": "absorbed", "SR": "absorbed", "SR2": "absorbed", "triple_var": "active" }, "focus_signature": [ "triple_var" ], "label": "N128:Anderson_1D_W6", "mean_delta": { "L1": -0.06502976284179934, "L2": -0.03633544151563349, "SR": -0.0006800928302168252, "SR2": 0.005866141415342349, "triple_var": -0.6946005532911658 }, "mean_null": { "L1": -0.015789477222940287, "L2": -0.03813329179848318, "SR": 0.39150384626511847, "SR2": 0.3854729046949198, "triple_var": 2.7629740665763083 }, "mean_real": { "L1": -0.08081924006473963, "L2": -0.07446873331411666, "SR": 0.3908237534349016, "SR2": 0.39133904611026216, "triple_var": 2.0683735132851426 }, "p_two_sided": { "L1": 0.0006102770657878677, "L2": 0.10045160502868303, "SR": 0.9304284145001831, "SR2": 0.4987184181618455, "triple_var": 0.00012205541315757354 }, "sample_count": 64 }, "N128:GOE_time_reversal_symmetric": { "cohen_d_delta": { "L1": -1.8284261541599431, "L2": -0.38143207726495215, "SR": 2.1771827986087233, "SR2": 3.3530801081324313, "triple_var": -2.541001624571744 }, "component_state": { "L1": "active", "L2": "absorbed", "SR": "active", "SR2": "active", "triple_var": "active" }, "focus_signature": [ "SR", "L1", "triple_var" ], "label": "N128:GOE_time_reversal_symmetric", "mean_delta": { "L1": -0.28833287753704917, "L2": -0.06797740072195688, "SR": 0.1405874339524704, "SR2": 0.17448772091055703, "triple_var": -2.6109783729255005 }, "mean_null": { "L1": 0.01840434712479141, "L2": -0.033347866161898464, "SR": 0.38956684186639634, "SR2": 0.37645194592553527, "triple_var": 3.1053149082435203 }, "mean_real": { "L1": -0.2699285304122578, "L2": -0.10132526688385535, "SR": 0.5301542758188669, "SR2": 0.5509396668360922, "triple_var": 0.4943365353180201 }, "p_two_sided": { "L1": 0.00012205541315757354, "L2": 0.0035396069815696327, "SR": 0.00012205541315757354, "SR2": 0.00012205541315757354, "triple_var": 0.00012205541315757354 }, "sample_count": 64 }, "N128:GUE_unitary_no_time_reversal": { "cohen_d_delta": { "L1": -1.8027450377074834, "L2": -0.5780247629091018, "SR": 3.635016991093135, "SR2": 4.416542473586879, "triple_var": -3.122078780205201 }, "component_state": { "L1": "active", "L2": "active", "SR": "active", "SR2": "active", "triple_var": "active" }, "focus_signature": [ "SR", "L1", "triple_var" ], "label": "N128:GUE_unitary_no_time_reversal", "mean_delta": { "L1": -0.2767324868997297, "L2": -0.10710025606127149, "SR": 0.22074937505021847, "SR2": 0.24050289302526243, "triple_var": -2.6749762460521236 }, "mean_null": { "L1": -0.00227668420955798, "L2": -0.011390361067820178, "SR": 0.3797603272965735, "SR2": 0.3800829963838406, "triple_var": 2.9593329899650094 }, "mean_real": { "L1": -0.2790091711092877, "L2": -0.11849061712909167, "SR": 0.600509702346792, "SR2": 0.620585889409103, "triple_var": 0.2843567439128864 }, "p_two_sided": { "L1": 0.00012205541315757354, "L2": 0.00012205541315757354, "SR": 0.00012205541315757354, "SR2": 0.00012205541315757354, "triple_var": 0.00012205541315757354 }, "sample_count": 64 }, "N192:Anderson_1D_W6": { "cohen_d_delta": { "L1": -0.47571147829727484, "L2": -0.20939676131096827, "SR": 0.028504253557575385, "SR2": 0.05551288979601127, "triple_var": -0.8160762061775907 }, "component_state": { "L1": "absorbed", "L2": "absorbed", "SR": "absorbed", "SR2": "absorbed", "triple_var": "active" }, "focus_signature": [ "triple_var" ], "label": "N192:Anderson_1D_W6", "mean_delta": { "L1": -0.06618487008856966, "L2": -0.029408024890005588, "SR": 0.0013057825426484202, "SR2": 0.0021844733423268268, "triple_var": -0.5801234353744907 }, "mean_null": { "L1": -0.0028427670629441043, "L2": -0.0047423288417988655, "SR": 0.3827748296121581, "SR2": 0.38958094600330107, "triple_var": 2.8804532563282406 }, "mean_real": { "L1": -0.06902763715151378, "L2": -0.03415035373180445, codex La modifica necessaria è nello script già usato dal ciclo 16:40: aggiungo una modalità di valutazione su spettri forniti dall’esterno, mantenendo intatto il generatore GOE/GUE/Anderson. L’artifact fit-ready userà il deposito 16:40 come evidenza e dichiarerà le soglie, il contratto input/output e il contro-perimetro. apply patch patch: completed /opt/MM_D-ND/tools/exp_physical_sr_residue_bounce.py diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..7d89efaea6f3b7b26d170427fd94fb5523b917d5 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,163 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + class_key = record.get("expected_class") or record["label"] + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + rows_by_class[left], + rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +457,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +469,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..7d89efaea6f3b7b26d170427fd94fb5523b917d5 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,163 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + class_key = record.get("expected_class") or record["label"] + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + rows_by_class[left], + rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +457,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +469,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) codex La modalità nuova è separata: se non passi `--input-spectrum`, il comportamento storico resta quello del generatore 16:40. Verifico sintassi, artifact fit-ready e una chiamata minima dell’interfaccia con due spettri sintetici già nello stesso perimetro GOE/GUE. exec /bin/bash -lc 'python -m py_compile tools/exp_physical_sr_residue_bounce.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_physical_sr_residue_bounce.py --write-fit-ready tools/data/component_state_fit_ready_20260514_1649.json' in /opt/MM_D-ND diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..7d89efaea6f3b7b26d170427fd94fb5523b917d5 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,163 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + class_key = record.get("expected_class") or record["label"] + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + rows_by_class[left], + rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +457,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +469,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) succeeded in 112ms: exec /bin/bash -lc "python - <<'PY' import json import numpy as np rng = np.random.default_rng(202605141649) def goe(n): mat = rng.normal(size=(n, n)) return np.linalg.eigvalsh((mat + mat.T) / (2.0 * np.sqrt(n))).tolist() def gue(n): real = rng.normal(size=(n, n)) imag = rng.normal(size=(n, n)) mat = real + 1j * imag return np.linalg.eigvalsh((mat + mat.conj().T) / (2.0 * np.sqrt(n))).tolist() payload = {\"spectra\": [ {\"label\": \"GOE_smoke\", \"expected_class\": \"GOE\", \"levels\": goe(64)}, {\"label\": \"GUE_smoke\", \"expected_class\": \"GUE\", \"levels\": gue(64)}, ]} with open('/tmp/component_state_input_smoke_1649.json', 'w', encoding='utf-8') as f: json.dump(payload, f) PY python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5" in /opt/MM_D-ND diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..7d89efaea6f3b7b26d170427fd94fb5523b917d5 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,163 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + class_key = record.get("expected_class") or record["label"] + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + rows_by_class[left], + rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +457,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +469,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) succeeded in 319ms: { "classes": [ "GOE", "GUE" ], "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649" } exec /bin/bash -lc "sed -n '1,260p' tools/data/component_state_fit_ready_20260514_1649.json" in /opt/MM_D-ND succeeded in 0ms: { "component_states": { "Anderson_1D_W6": { "expected": { "L1": "absorbed", "SR": "absorbed", "triple_var": "active" }, "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json" }, "GOE_time_reversal_symmetric": { "expected": { "L1": "active", "SR": "active", "triple_var": "active" }, "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json" }, "GUE_unitary_no_time_reversal": { "expected": { "L1": "active", "SR": "active", "triple_var": "active" }, "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json" } }, "counter_perimeter": { "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", "falsifier": [ "unordered/degenerate spectrum with fewer than 12 usable central gaps", "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", "attempt to promote the artifact as a physics law instead of a tool contract" ] }, "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", "graph_integration": "not_integrated_operator_decision_required", "input_contract": { "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", "payloads": [ "JSON list of levels", "JSON object with levels or spectrum", "JSON object with spectra records: label, expected_class, levels", "plain text/CSV numeric levels" ], "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization" }, "not_promoted_as_physics_law": true, "output_contract": { "component_state": "per observable active/absorbed", "direct_contrast": "pairwise class contrast when at least two classes are present", "poisson_contrast": "real spectrum versus span-matched Poisson null", "trace": "JSONL rows preserving real/null/delta per null replicate" }, "source_cycle": "20260514_1649", "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", "thresholds": { "abs_cohen_d_min": 0.5, "default_central_fraction": 0.5, "default_null_reps": 64, "min_usable_central_gaps": 12, "sign_swap_p_max": 0.01 }, "trace_ref": { "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl" }, "transfer_blank_fall": { "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast." } } exec /bin/bash -lc "sed -n '1,220p' tools/data/component_state_interface_smoke_20260514_1649.json" in /opt/MM_D-ND succeeded in 0ms: { "direct_contrasts": { "GOE_minus_GUE": { "cohen_d": { "L1": 0.0, "L2": 0.0, "SR": 0.0, "SR2": 0.0, "triple_var": 0.0 }, "label": "GOE_minus_GUE", "mean_left_minus_right": { "L1": -0.13423959488970513, "L2": -0.032306738339548335, "SR": -0.15858635795646447, "SR2": -0.05521477026578114, "triple_var": -0.12469401016984305 }, "p_two_sided": { "L1": 0.001949317738791423, "L2": 0.001949317738791423, "SR": 0.001949317738791423, "SR2": 0.001949317738791423, "triple_var": 0.001949317738791423 }, "paired_count": 16, "state": { "L1": "not_separated", "L2": "not_separated", "SR": "not_separated", "SR2": "not_separated", "triple_var": "not_separated" } } }, "focus_observables": [ "SR", "L1", "triple_var" ], "input_contract": { "accepted_payloads": [ "JSON list of ordered levels", "JSON object with levels or spectrum", "JSON object with spectra records: label, expected_class, levels", "plain text/CSV numeric levels readable by numpy.loadtxt" ], "central_fraction": 0.5, "optional": "expected_class, label", "required": "ordered spectrum levels; sorting is applied defensively" }, "interface_mode": "input_spectrum", "not_promoted_as_physics_law": true, "null_reps": 16, "observables_used": [ "SR", "SR2", "L1", "L2", "triple_var" ], "output_contract": { "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", "direct_contrast": "pairwise class contrast when at least two classes/labels are present", "poisson_contrast": "span-matched Poisson null for each class/label", "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta" }, "poisson_contrast": { "GOE": { "cohen_d_delta": { "L1": -2.0604859981217882, "L2": -1.3849877516231521, "SR": 1.0342089106614745, "SR2": 1.736790925661752, "triple_var": -1.8311431088521855 }, "component_state": { "L1": "active", "L2": "active", "SR": "active", "SR2": "active", "triple_var": "active" }, "focus_signature": [ "SR", "L1", "triple_var" ], "label": "GOE", "mean_delta": { "L1": -0.34903772019647944, "L2": -0.26012853673103864, "SR": 0.05593238782343382, "SR2": 0.13727651869611399, "triple_var": -3.147468604529326 }, "mean_null": { "L1": -0.003926548725304393, "L2": 0.022692679717039275, "SR": 0.3825698808000261, "SR2": 0.3714225190942589, "triple_var": 3.433921033993774 }, "mean_real": { "L1": -0.35296426892178384, "L2": -0.2374358570139993, "SR": 0.43850226862345987, "SR2": 0.5086990377903728, "triple_var": 0.28645242946444727 }, "p_two_sided": { "L1": 0.001949317738791423, "L2": 0.001949317738791423, "SR": 0.001949317738791423, "SR2": 0.001949317738791423, "triple_var": 0.001949317738791423 }, "sample_count": 16 }, "GUE": { "cohen_d_delta": { "L1": -0.8017941391359987, "L2": -1.3184007472479256, "SR": 5.264935121210225, "SR2": 3.874062406841779, "triple_var": -2.105315956279164 }, "component_state": { "L1": "active", "L2": "active", "SR": "active", "SR2": "active", "triple_var": "active" }, "focus_signature": [ "SR", "L1", "triple_var" ], "label": "GUE", "mean_delta": { "L1": -0.1301603718915154, "L2": -0.21065819250456586, "SR": 0.21622721561511252, "SR2": 0.18610134342971865, "triple_var": -1.718971989528582 }, "mean_null": { "L1": -0.08856430214056331, "L2": 0.0055290738301149115, "SR": 0.38086141096481185, "SR2": 0.37781246462643536, "triple_var": 2.1301184291628723 }, "mean_real": { "L1": -0.2187246740320787, "L2": -0.20512911867445097, "SR": 0.5970886265799243, "SR2": 0.563913808056154, "triple_var": 0.4111464396342903 }, "p_two_sided": { "L1": 0.001949317738791423, "L2": 0.001949317738791423, "SR": 0.001949317738791423, "SR2": 0.001949317738791423, "triple_var": 0.001949317738791423 }, "sample_count": 16 } }, "seed": 202605141649, "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", "thresholds": { "abs_cohen_d_min": 0.5, "min_usable_central_gaps": 12, "sign_swap_p_max": 0.01 }, "trace_jsonl": "tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl" } exec /bin/bash -lc 'git status --short' in /opt/MM_D-ND succeeded in 0ms: 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/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/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 ?? 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/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/ ?? 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/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/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/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/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/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/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/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/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/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/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/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/ddf_20260512_0405.json ?? tools/data/reports/ddf_20260513_0405.json ?? tools/data/reports/ddf_20260514_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/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/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/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/exp_physical_sr_residue_bounce.py ?? tools/exp_prime_sr_persistent_boundary.py ?? tools/exp_prime_vs_mod6_sr_boundary.py diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..7d89efaea6f3b7b26d170427fd94fb5523b917d5 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,163 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + class_key = record.get("expected_class") or record["label"] + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + rows_by_class[left], + rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +457,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +469,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) codex Lo smoke test conferma il plumbing dell’interfaccia: produce `poisson_contrast`, `component_state` e `direct_contrasts`. Il contrasto diretto dello smoke non viene usato come evidenza fisica perché ha un solo spettro per classe; l’evidenza fit-ready resta il trace 16:40 con 64 repliche per taglia. apply patch patch: completed /opt/MM_D-ND/tools/data/reports/agent_20260514_1649.md diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..7d89efaea6f3b7b26d170427fd94fb5523b917d5 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,163 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + class_key = record.get("expected_class") or record["label"] + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + rows_by_class[left], + rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +457,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +469,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..7d89efaea6f3b7b26d170427fd94fb5523b917d5 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,163 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + class_key = record.get("expected_class") or record["label"] + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + rows_by_class[left], + rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +457,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +469,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) codex Ho visto un dettaglio da correggere prima di chiudere: il contrasto diretto dell’interfaccia non deve contare le repliche Poisson come repliche indipendenti di classe. Lo separo dal contrasto Poisson, così il direct contrast usa solo spettri reali indipendenti. apply patch patch: completed /opt/MM_D-ND/tools/exp_physical_sr_residue_bounce.py diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) exec /bin/bash -lc 'python -m py_compile tools/exp_physical_sr_residue_bounce.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5' in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) succeeded in 224ms: { "classes": [ "GOE", "GUE" ], "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649" } exec /bin/bash -lc "rg -n '\"direct_contrasts\"|\"paired_count\"|\"p_two_sided\"|\"state\"' tools/data/component_state_interface_smoke_20260514_1649.json" in /opt/MM_D-ND succeeded in 0ms: 2: "direct_contrasts": { 19: "p_two_sided": { 26: "paired_count": 1, 27: "state": { 111: "p_two_sided": { 162: "p_two_sided": { diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) exec /bin/bash -lc "sed -n '1,40p' tools/data/component_state_interface_smoke_20260514_1649.json" in /opt/MM_D-ND succeeded in 0ms: { "direct_contrasts": { "GOE_minus_GUE": { "cohen_d": { "L1": 0.0, "L2": 0.0, "SR": 0.0, "SR2": 0.0, "triple_var": 0.0 }, "label": "GOE_minus_GUE", "mean_left_minus_right": { "L1": -0.13423959488970513, "L2": -0.032306738339548335, "SR": -0.15858635795646447, "SR2": -0.05521477026578114, "triple_var": -0.12469401016984305 }, "p_two_sided": { "L1": 1.0, "L2": 1.0, "SR": 1.0, "SR2": 1.0, "triple_var": 1.0 }, "paired_count": 1, "state": { "L1": "not_separated", "L2": "not_separated", "SR": "not_separated", "SR2": "not_separated", "triple_var": "not_separated" } } }, "focus_observables": [ "SR", "L1", "triple_var" ], diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) exec /bin/bash -lc 'python tools/exp_physical_sr_residue_bounce.py --output /tmp/physical_sr_residue_bounce_smoke.json --seed 7 --n 32 --reps 2 --disorders 6 --sign-trials 64' in /opt/MM_D-ND diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) succeeded in 146ms: { "localized_SR_state": "absorbed", "localized_focus_signature": [], "rimbalzo_fisico_presente": false, "source_SR_state": "absorbed" } exec /bin/bash -lc "sed -n '1,260p' tools/data/reports/agent_20260514_1649.md" in /opt/MM_D-ND succeeded in 0ms: # Agent Report - Component State Fit-Ready Tester **Date**: 2026-05-14 16:49 **Piano**: 122 **Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester **verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. observables_registry: 1.0.0-2026-05-06 observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] **observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. ## Source directive Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. ## Prima impressione Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. ## Respiro fuori-tempo - **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. - **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. - **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. - **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. - **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. - **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. ## Aderenza alla direzione - `relation`: follows_direction - `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. - `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. ## Experiment Design - Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. - Nuova interfaccia: `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` - Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. - Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. - Artifact prodotto: `tools/data/component_state_fit_ready_20260514_1649.json` - Smoke test piccolo: `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` ## Results ### Artifact fit-ready `tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: | field | state | |---|---| | `tester_id` | present | | `input_contract` | present | | `output_contract` | present | | `thresholds` | present | | `component_states` | present | | `transfer_blank_fall` | present | | `counter_perimeter` | present | | `trace_ref` | present | | `graph_candidate_ref` | present | | `not_promoted_as_physics_law` | `true` | ### Soglie | threshold | value | |---|---:| | `sign_swap_p_max` | 0.01 | | `abs_cohen_d_min` | 0.5 | | `min_usable_central_gaps` | 12 | | `default_null_reps` | 64 | | `default_central_fraction` | 0.5 | ### Component states sorgente 16:40 | class | SR | L1 | triple_var | source | |---|---|---|---|---| | GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | | GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | | Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | ### Smoke interface Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. | class | focus_signature | SR state vs Poisson | note | |---|---|---|---| | GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | | GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. ## Transfer / blank / fall - `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. - `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. - `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. ## Evidence tier - **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). - **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. - **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. - **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. - **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. - **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. ## Trace path - `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` - `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` - `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` - `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` - `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` - `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` ## Key Findings 1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. 2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. 3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. 4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. ## Verdict **CONSTRAINT / FIT-READY TESTER**. `component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. ## Bicono della scoperta - **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. - **Singolare**: `component_state`, stesso vettore letto come output dello strumento. - **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. - **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. ## Consecutio Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. ## Ricadute pratiche ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. ## Files - `tools/exp_physical_sr_residue_bounce.py` - `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/reports/agent_20260514_1649.md` exec /bin/bash -lc 'git diff -- tools/exp_physical_sr_residue_bounce.py' in /opt/MM_D-ND succeeded in 0ms: exec /bin/bash -lc 'ls -lh tools/data/component_state_fit_ready_20260514_1649.json tools/data/component_state_interface_smoke_20260514_1649.json tools/data/reports/agent_20260514_1649.md' in /opt/MM_D-ND succeeded in 0ms: -rw-r--r-- 1 root root 3.5K May 14 16:51 tools/data/component_state_fit_ready_20260514_1649.json -rw-r--r-- 1 root root 4.9K May 14 16:52 tools/data/component_state_interface_smoke_20260514_1649.json -rw-r--r-- 1 root root 9.7K May 14 16:52 tools/data/reports/agent_20260514_1649.md diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) codex Cycle completato. Ho scritto il report richiesto in [agent_20260514_1649.md](/opt/MM_D-ND/tools/data/reports/agent_20260514_1649.md). Ho prodotto anche l’artifact fit-ready: [component_state_fit_ready_20260514_1649.json](/opt/MM_D-ND/tools/data/component_state_fit_ready_20260514_1649.json) Modifica fatta allo strumento: [exp_physical_sr_residue_bounce.py](/opt/MM_D-ND/tools/exp_physical_sr_residue_bounce.py) Ora supporta l’interfaccia: ```bash python tools/exp_physical_sr_residue_bounce.py \ --input-spectrum SPECTRUM.json \ --expected-class CLASS \ --output OUT.json ``` Verifiche eseguite: - `python -m py_compile tools/exp_physical_sr_residue_bounce.py` - generazione artifact fit-ready - smoke test con payload multi-spettro, output in [component_state_interface_smoke_20260514_1649.json](/opt/MM_D-ND/tools/data/component_state_interface_smoke_20260514_1649.json) Nessuna API key paid rilevata nell’ambiente. diff --git a/tools/data/reports/agent_20260514_1649.md b/tools/data/reports/agent_20260514_1649.md new file mode 100644 index 0000000000000000000000000000000000000000..ce5d4340c14ef41a081900491fd6ad61f0a0b278 --- /dev/null +++ b/tools/data/reports/agent_20260514_1649.md @@ -0,0 +1,151 @@ +# Agent Report - Component State Fit-Ready Tester +**Date**: 2026-05-14 16:49 +**Piano**: 122 +**Tension explored**: TENS_SCALE_TRASCENDENZA_LIMITE / QxG fit-ready tester +**verdict**: CONSTRAINT / FIT-READY TESTER - `component_state(SR,L1,triple_var)` viene trasformato in contratto integrabile e interfaccia unica per spettro ordinato. Non integra il grafo e non promuove una legge fisica. +observables_registry: 1.0.0-2026-05-06 +observables_used: [SR, SR2, L1, L2, triple_var, component_state, poisson_contrast, direct_contrast] +**observable_contract**: claim=il candidato QxG 16:40 diventa fit-ready solo se espone input, output, soglie, trace, transfer/blank/fall e contro-perimetro; observable=`component_state(SR,L1,triple_var)` piu `poisson_contrast` e `direct_contrast`; operator=`tools/exp_physical_sr_residue_bounce.py`; generator=nessun nuovo dominio, riuso deposito GOE/GUE/Anderson 16:40 e smoke test sintetico minimo; denominator=artifact JSON fit-ready + interfaccia su spettro ordinato; non_possible=integrare automaticamente il grafo o chiamare legge fisica il tester; not_tested=dati sperimentali, Anderson 3D, many-body localization, unfolding dedicato, limite asintotico. + +## Source directive + +Questo ciclo segue il candidato `QxG` ready dal graph completion 1640 (`tools/data/graph_completion/graph_completion_20260514_1640.json`). Il compito non e' scoprire nuova fisica: e' rendere il tester `component_state(SR,L1,triple_var)` integrabile come artifact fit-ready e come strumento con interfaccia unica. Vincolo seguito: non aggiungere nuovi domini, non tornare ai primi, non integrare automaticamente il grafo. + +## Prima impressione + +Il tester smette di essere un risultato locale quando dichiara come entra uno spettro, come esce uno stato componente, e dove cade. Il contenuto del ciclo e' il contratto, non un nuovo numero. + +## Respiro fuori-tempo + +- **Combo**: A2 confine det=-1 + A9 terzo incluso + A11 combo + ponte QxG continuo/discreto + candidato graph completion 16:40 + direzione BOUNDARY GUE/Poisson. +- **Dipolo / punto-zero**: deposito fisico 16:40 / strumento integrabile. Punto-zero: `component_state`, perche' conserva lo stesso vettore ma cambia ruolo da risultato a interfaccia. +- **Piano superiore**: grafo della conoscenza e bicono operativo: il bordo QxG resta contract-ready-not-integrated. +- **Proto-ipotesi**: un tester fisico diventa fit-ready quando il transfer e il fall sono parte dell'output contract, non quando accumula un altro dominio. +- **Possibile / non-possibile**: possibile = passare uno spettro ordinato e ottenere stato componente, contrasto Poisson e contrasto diretto se classi presenti; non-possibile = usare l'artifact come ponte QxG integrato o legge fisica. +- **Proiezione**: aggiunta una modalita' `--input-spectrum` allo script esistente e prodotto un artifact JSON dedicato. + +## Aderenza alla direzione + +- `relation`: follows_direction +- `why`: la direttiva one-shot sostituisce l'espansione fisica esterna e chiede di rendere fit-ready il candidato QxG 16:40. +- `not_drift`: non torna a `prime-minus-mod6`, `V_c`, fit locali o label aritmetiche; usa il report 16:40 come deposito sorgente e non apre nuovi domini. + +## Experiment Design + +- Modifica minima allo script necessario: `tools/exp_physical_sr_residue_bounce.py`. +- Nuova interfaccia: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json` +- Payload accettati: lista JSON di livelli, oggetto JSON con `levels`/`spectrum`, oggetto con `spectra` multipli (`label`, `expected_class`, `levels`), oppure file numerico leggibile da `numpy.loadtxt`. +- Output: `component_state`, `poisson_contrast`, `direct_contrasts` quando almeno due classi/label sono presenti, trace JSONL. +- Artifact prodotto: + `tools/data/component_state_fit_ready_20260514_1649.json` +- Smoke test piccolo: + `python tools/exp_physical_sr_residue_bounce.py --input-spectrum /tmp/component_state_input_smoke_1649.json --output tools/data/component_state_interface_smoke_20260514_1649.json --seed 202605141649 --null-reps 16 --sign-trials 512 --central-fraction 0.5` + +## Results + +### Artifact fit-ready + +`tools/data/component_state_fit_ready_20260514_1649.json` contiene i campi richiesti: + +| field | state | +|---|---| +| `tester_id` | present | +| `input_contract` | present | +| `output_contract` | present | +| `thresholds` | present | +| `component_states` | present | +| `transfer_blank_fall` | present | +| `counter_perimeter` | present | +| `trace_ref` | present | +| `graph_candidate_ref` | present | +| `not_promoted_as_physics_law` | `true` | + +### Soglie + +| threshold | value | +|---|---:| +| `sign_swap_p_max` | 0.01 | +| `abs_cohen_d_min` | 0.5 | +| `min_usable_central_gaps` | 12 | +| `default_null_reps` | 64 | +| `default_central_fraction` | 0.5 | + +### Component states sorgente 16:40 + +| class | SR | L1 | triple_var | source | +|---|---|---|---|---| +| GOE time reversal symmetric | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| GUE unitary no time reversal | active | active | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | +| Anderson 1D W6 | absorbed | absorbed | active | `physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` | + +### Smoke interface + +Verificato: la nuova modalita' produce `poisson_contrast`, `component_state`, `direct_contrasts` e trace per un payload multi-spettro. + +| class | focus_signature | SR state vs Poisson | note | +|---|---|---|---| +| GOE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | +| GUE smoke | SR,L1,triple_var | active | plumbing verificato, non evidenza fisica nuova | + +Il `direct_contrast` dello smoke e' presente ma non promosso: un solo spettro per classe non fornisce una distribuzione indipendente per separazione fisica. La separazione GOE/GUE fit-ready resta quella del deposito 16:40 con 64 repliche per taglia. + +## Transfer / blank / fall + +- `transfer`: `SR,L1,triple_var` passano dal deposito matematico-fisico allo strumento come stati componente contro Poisson e, se esistono classi, come contrasto diretto. +- `blank`: il grafo QxG resta non integrato; restano vuoti dati sperimentali, GSE, Anderson 3D, many-body localization, unfolding dedicato e limite asintotico. +- `fall`: il tester cade se GOE/GUE non separano `SR` nel contrasto diretto, se le classi caotiche assorbono tutti gli osservabili focus contro Poisson, o se Anderson `W=6` mantiene `SR` active sotto le soglie dichiarate. + +## Evidence tier + +- **Verificato**: lo script compila (`python -m py_compile tools/exp_physical_sr_residue_bounce.py`). +- **Verificato**: l'artifact fit-ready e' scritto in `tools/data/component_state_fit_ready_20260514_1649.json`. +- **Verificato**: l'interfaccia `--input-spectrum` produce output e trace su payload multi-spettro. +- **Verificato da deposito 16:40**: GOE/GUE hanno `SR,L1,triple_var` active; Anderson `W=6` assorbe `SR` e conserva `triple_var`. +- **Inferito**: il candidato QxG e' pronto per decisione operatore come contratto, non come integrazione automatica. +- **Non verificato**: robustezza su dati esterni, unfolding dedicato, classi fisiche non presenti nel deposito 16:40. + +## Trace path + +- `fit_ready_artifact`: `tools/data/component_state_fit_ready_20260514_1649.json` +- `graph_candidate_ref`: `tools/data/graph_completion/graph_completion_20260514_1640.json` +- `source_result`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json` +- `source_trace`: `tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl` +- `smoke_output`: `tools/data/component_state_interface_smoke_20260514_1649.json` +- `smoke_trace`: `tools/data/component_state_interface_smoke_20260514_1649.trace.jsonl` + +## Key Findings + +1. **Verificato**: il tester ha ora un contratto input/output esplicito e una modalita' CLI unica per spettri ordinati. +2. **Verificato**: l'artifact dichiara soglie, stati componente sorgente, trace, counter-perimeter e `not_promoted_as_physics_law=true`. +3. **Verificato**: il graph candidate QxG resta `contract_ready_not_integrated`; nessuna integrazione automatica viene fatta. +4. **Vincolo**: il contrasto diretto richiede piu' di uno spettro indipendente per classe per diventare evidenza, mentre resta sempre riportato come output quando classi/label sono presenti. + +## Verdict + +**CONSTRAINT / FIT-READY TESTER**. + +`component_state(SR,L1,triple_var)` e' fit-ready come strumento: riceve uno spettro ordinato, produce stato componente, contrasto Poisson e contrasto diretto quando il payload contiene classi. Il candidato QxG resta pronto per decisione operatore, non integrato nel grafo e non promosso a legge fisica. + +## Bicono della scoperta + +- **Due radici**: deposito fisico verificato 16:40 / contratto integrabile. +- **Singolare**: `component_state`, stesso vettore letto come output dello strumento. +- **Invariante di passaggio**: soglie, trace e fall condition accompagnano il tester oltre il singolo run. +- **Campo di possibilita**: possibile usare il tester in pipeline o decisione grafo; non-possibile promuoverlo senza nuovi perimetri fisici esterni. + +## Consecutio + +Il prossimo passo e' decisione operatore o valutatore: integrare il riferimento nel grafo QxG come contratto candidate, oppure chiedere falsificazione esterna con dataset fisico. Il tester e' pronto per quel bivio. + +## Ricadute pratiche + +ssp_value: yes. L'output e' uno strumento riusabile e un artifact fit-ready per pipeline/grafo: `tools/data/component_state_fit_ready_20260514_1649.json`. + +## Files + +- `tools/exp_physical_sr_residue_bounce.py` +- `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/reports/agent_20260514_1649.md` diff --git a/tools/exp_physical_sr_residue_bounce.py b/tools/exp_physical_sr_residue_bounce.py index 6ae469c9cb086394596a0d7232cd05fc3955bd72..942bd3f0f2080abc373f17b126cc8cb301c80346 --- a/tools/exp_physical_sr_residue_bounce.py +++ b/tools/exp_physical_sr_residue_bounce.py @@ -26,6 +26,7 @@ FOCUS_OBS = ["SR", "L1", "triple_var"] DEFAULT_OUT = Path("tools/data/physical_sr_residue_bounce_20260514_1612.json") +DEFAULT_FIT_READY_OUT = Path("tools/data/component_state_fit_ready_20260514_1649.json") def normalize_gaps(levels: np.ndarray, central_fraction: float) -> np.ndarray: @@ -76,6 +77,38 @@ return {name: float(fn(gaps)) for name, fn in OBSERVABLES_CANONICAL.items()} +def load_spectrum_records(path: Path, expected_class: str | None = None) -> list[dict[str, Any]]: + """Load a single spectrum or a small record set for the fit-ready interface.""" + if path.suffix.lower() == ".json": + payload = json.loads(path.read_text(encoding="utf-8")) + if isinstance(payload, list) and all(isinstance(item, (int, float)) for item in payload): + return [{"label": path.stem, "expected_class": expected_class, "levels": payload}] + if isinstance(payload, dict): + if "spectra" in payload: + records = payload["spectra"] + elif "levels" in payload or "spectrum" in payload: + records = [payload] + else: + raise ValueError("JSON input must contain levels, spectrum, or spectra") + normalized = [] + for index, record in enumerate(records): + levels = record.get("levels", record.get("spectrum")) + if levels is None: + raise ValueError(f"spectrum record {index} has no levels/spectrum field") + normalized.append( + { + "label": record.get("label", f"{path.stem}_{index}"), + "expected_class": record.get("expected_class", expected_class), + "levels": levels, + } + ) + return normalized + raise ValueError("unsupported JSON spectrum payload") + + levels = np.loadtxt(path, dtype=float) + return [{"label": path.stem, "expected_class": expected_class, "levels": levels.tolist()}] + + def sign_swap_p(values: np.ndarray, rng: np.random.Generator, trials: int) -> float: if len(values) == 0: return 1.0 @@ -159,6 +192,174 @@ return result +def evaluate_input_spectra(args: argparse.Namespace) -> dict[str, Any]: + rng = np.random.default_rng(args.seed) + records = load_spectrum_records(args.input_spectrum, args.expected_class) + trace_path = Path(str(args.output).replace(".json", ".trace.jsonl")) + trace_path.parent.mkdir(parents=True, exist_ok=True) + rows_by_class: dict[str, list[dict[str, Any]]] = {} + direct_rows_by_class: dict[str, list[dict[str, Any]]] = {} + + with trace_path.open("w", encoding="utf-8") as trace: + for record in records: + levels = np.asarray(record["levels"], dtype=float) + gaps = normalize_gaps(levels, args.central_fraction) + if len(gaps) < 12: + raise ValueError(f"spectrum {record['label']} has fewer than 12 usable central gaps") + real_obs = compute_obs(gaps) + class_key = record.get("expected_class") or record["label"] + direct_rows_by_class.setdefault(class_key, []).append( + { + "event": "input_spectrum_direct", + "label": record["label"], + "expected_class": record.get("expected_class"), + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + } + ) + for null_rep in range(args.null_reps): + null_gaps = span_matched_poisson_gaps(len(gaps) + 1, rng) + null_obs = compute_obs(null_gaps) + row = { + "event": "input_spectrum_pair", + "label": record["label"], + "expected_class": record.get("expected_class"), + "null_rep": null_rep, + "n_levels": int(len(levels)), + "n_gaps": int(len(gaps)), + "real": real_obs, + "null": null_obs, + "delta": {obs: real_obs[obs] - null_obs[obs] for obs in OBSERVABLES_CANONICAL}, + } + rows_by_class.setdefault(class_key, []).append(row) + trace.write(json.dumps(row, sort_keys=True) + "\n") + + poisson_contrast = { + label: summarize(label, rows, rng, args.sign_trials) + for label, rows in sorted(rows_by_class.items()) + } + direct_contrasts: dict[str, Any] = {} + class_labels = sorted(rows_by_class) + if len(class_labels) >= 2: + for i, left in enumerate(class_labels): + for right in class_labels[i + 1 :]: + direct_contrasts[f"{left}_minus_{right}"] = contrast( + f"{left}_minus_{right}", + direct_rows_by_class[left], + direct_rows_by_class[right], + rng, + args.sign_trials, + ) + + result = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "interface_mode": "input_spectrum", + "input_contract": { + "accepted_payloads": [ + "JSON list of ordered levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels readable by numpy.loadtxt", + ], + "required": "ordered spectrum levels; sorting is applied defensively", + "optional": "expected_class, label", + "central_fraction": args.central_fraction, + }, + "output_contract": { + "component_state": "active iff sign-swap p<=0.01 and |cohen_d_delta|>=0.5", + "poisson_contrast": "span-matched Poisson null for each class/label", + "direct_contrast": "pairwise class contrast when at least two classes/labels are present", + "trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + }, + "observables_used": list(OBSERVABLES_CANONICAL.keys()), + "focus_observables": FOCUS_OBS, + "seed": args.seed, + "null_reps": args.null_reps, + "poisson_contrast": poisson_contrast, + "direct_contrasts": direct_contrasts, + "trace_jsonl": str(trace_path), + "not_promoted_as_physics_law": True, + } + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + Path(args.output).write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8") + return result + + +def write_fit_ready_artifact(path: Path) -> dict[str, Any]: + artifact = { + "tester_id": "component_state_SR_L1_triple_var_fit_ready_20260514_1649", + "source_cycle": "20260514_1649", + "input_contract": { + "interface": "tools/exp_physical_sr_residue_bounce.py --input-spectrum SPECTRUM.json --expected-class CLASS --output OUT.json", + "spectrum": "ordered level spectrum; script sorts defensively before central-window gap normalization", + "optional_expected_class": "class label used for grouped Poisson contrast and direct class contrast", + "payloads": [ + "JSON list of levels", + "JSON object with levels or spectrum", + "JSON object with spectra records: label, expected_class, levels", + "plain text/CSV numeric levels", + ], + }, + "output_contract": { + "component_state": "per observable active/absorbed", + "poisson_contrast": "real spectrum versus span-matched Poisson null", + "direct_contrast": "pairwise class contrast when at least two classes are present", + "trace": "JSONL rows preserving real/null/delta per null replicate", + }, + "thresholds": { + "sign_swap_p_max": 0.01, + "abs_cohen_d_min": 0.5, + "min_usable_central_gaps": 12, + "default_null_reps": 64, + "default_central_fraction": 0.5, + }, + "component_states": { + "GOE_time_reversal_symmetric": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "GUE_unitary_no_time_reversal": { + "expected": {"SR": "active", "L1": "active", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + "Anderson_1D_W6": { + "expected": {"SR": "absorbed", "L1": "absorbed", "triple_var": "active"}, + "source": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + }, + }, + "transfer_blank_fall": { + "transfer": "SR,L1,triple_var pass from the mathematical deposit into a physical spectrum tester as component states against Poisson and, when classes exist, direct class contrast.", + "blank": "No graph edge is integrated; no experimental spectra, GSE, Anderson 3D, many-body localization, unfolding-specific contract, or asymptotic claim is added.", + "fall": "Tester falls if GOE/GUE direct SR separation disappears, if Poisson contrast absorbs all focus observables in chaotic classes, or if Anderson W6 keeps SR active under the declared threshold.", + }, + "counter_perimeter": { + "declared": "single ordered spectrum or small class-labeled set; no new physical domain generation", + "falsifier": [ + "unordered/degenerate spectrum with fewer than 12 usable central gaps", + "class-labeled input where direct_contrast is not separated on SR despite declared GOE/GUE classes", + "attempt to promote the artifact as a physics law instead of a tool contract", + ], + }, + "trace_ref": { + "source_result": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.json", + "source_trace": "tools/data/physical_sr_residue_bounce_20260514_1640_goe_gue_ncurve.trace.jsonl", + "interface_trace_schema": "event,label,expected_class,null_rep,n_levels,n_gaps,real,null,delta", + }, + "graph_candidate_ref": "tools/data/graph_completion/graph_completion_20260514_1640.json", + "not_promoted_as_physics_law": True, + "graph_integration": "not_integrated_operator_decision_required", + } + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(artifact, indent=2, sort_keys=True), encoding="utf-8") + return artifact + + def run(args: argparse.Namespace) -> dict[str, Any]: rng = np.random.default_rng(args.seed) sizes = args.ns if args.ns else [args.n] @@ -267,6 +468,10 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, default=DEFAULT_OUT) + parser.add_argument("--input-spectrum", type=Path, default=None) + parser.add_argument("--expected-class", default=None) + parser.add_argument("--null-reps", type=int, default=64) + parser.add_argument("--write-fit-ready", type=Path, default=None) parser.add_argument("--seed", type=int, default=202605141612) parser.add_argument("--n", type=int, default=192) parser.add_argument("--ns", type=int, nargs="+", default=None) @@ -275,6 +480,14 @@ parser.add_argument("--disorders", type=float, nargs="+", default=[0.5, 2.0, 6.0, 12.0]) parser.add_argument("--sign-trials", type=int, default=4096) args = parser.parse_args() + if args.write_fit_ready: + result = write_fit_ready_artifact(args.write_fit_ready) + print(json.dumps({"fit_ready_artifact": str(args.write_fit_ready), "tester_id": result["tester_id"]}, indent=2, sort_keys=True)) + return + if args.input_spectrum: + result = evaluate_input_spectra(args) + print(json.dumps({"tester_id": result["tester_id"], "classes": sorted(result["poisson_contrast"])}, indent=2, sort_keys=True)) + return result = run(args) print(json.dumps(result["bounce_test"], indent=2, sort_keys=True)) tokens used 101,938 Cycle completato. Ho scritto il report richiesto in [agent_20260514_1649.md](/opt/MM_D-ND/tools/data/reports/agent_20260514_1649.md). Ho prodotto anche l’artifact fit-ready: [component_state_fit_ready_20260514_1649.json](/opt/MM_D-ND/tools/data/component_state_fit_ready_20260514_1649.json) Modifica fatta allo strumento: [exp_physical_sr_residue_bounce.py](/opt/MM_D-ND/tools/exp_physical_sr_residue_bounce.py) Ora supporta l’interfaccia: ```bash python tools/exp_physical_sr_residue_bounce.py \ --input-spectrum SPECTRUM.json \ --expected-class CLASS \ --output OUT.json ``` Verifiche eseguite: - `python -m py_compile tools/exp_physical_sr_residue_bounce.py` - generazione artifact fit-ready - smoke test con payload multi-spettro, output in [component_state_interface_smoke_20260514_1649.json](/opt/MM_D-ND/tools/data/component_state_interface_smoke_20260514_1649.json) Nessuna API key paid rilevata nell’ambiente.