From 04eb2de2e76a41835384ad78c1ee9e08b314addf Mon Sep 17 00:00:00 2001 From: jsboige Date: Fri, 2 Oct 2026 06:41:54 +0200 Subject: [PATCH 1/5] feat(ict): ICT-47 PainAxisDistillation - linear pain axis, partial dissociation + causal contract on Qwen3.5-2B-Base See #18808, See #18746 Co-Authored-By: Claude Sonnet 5.5 --- .../ICT-47-PainAxisDistillation-Python.ipynb | 1672 +++++++++++++++++ MyIA.AI.Notebooks/IIT/ICT-Series/README.md | 1 + 2 files changed, 1673 insertions(+) create mode 100644 MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb diff --git a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb new file mode 100644 index 0000000000..4fa6544e3a --- /dev/null +++ b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb @@ -0,0 +1,1672 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "p47-title", + "metadata": {}, + "source": [ + "# ICT-47 — L'axe douleur : une direction linéaire dans l'espace latent du substrat ?\n", + "\n", + "Cette séance instruit la question posée par la lignée Pain Axis (Tagliabue, Dung & Berg,\n", + "arXiv 2609.16247) : **la « douleur » est-elle une direction linéaire unique dans le\n", + "résiduel du substrat, ou un conglomérat de constructions** (physique, psychologique,\n", + "sociale, morale, cognitive) qui ne partagent que la détresse générale ? Le papier rapporte,\n", + "sur des LLM à grande échelle, un axe *pain* lisible par difference-in-means, **dissociable**\n", + "des axes *peur*, *tristesse* et *valence* une fois retirée la variance partagée — et\n", + "**causalement actif** : le *steering* de l'axe modifie les choix d'action du modèle.\n", + "\n", + "Notre chaîne, en une phrase : extraire les activations de fin d'énoncé de cinq catégories\n", + "de douleur et de quatre contrôles appariés sur le substrat de la série, tester l'unité et\n", + "la dissocibilité de l'axe par sondes et retrait de variance, puis exiger du moteur causal\n", + "[ICT-22b](ICT-22b-CausalInterventionEngine.ipynb) le contrat complet — cible contre\n", + "aléatoire apparié, *sham*, doses symétriques, Holm, dommage hors cible — avant de regarder\n", + "le banc de choix d'action.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-statut", + "metadata": {}, + "source": [ + "> **Statut épistémique** — **Sans verdict à ce jour** : aucune ligne de la [matrice de\n", + "dissociations](../../../docs/ict/dissociations-matrix.md) ne concerne ce notebook ; son\n", + "statut épistémique sera porté par la matrice le cas échéant.\n", + ">\n", + "> **Filiation** — Epic DEEP #18746, sous-issue #18808. Le papier de référence travaille sur\n", + "> Qwen 2.5 à grande échelle ; nous exerçons la question sur **le substrat de la série**\n", + "> (Qwen3.5-2B-Base, déjà chargé par [ICT-45](ICT-45-InoculationBifurcation-9B.ipynb) et\n", + "> [ICT-46](ICT-46-Strate7-FreeCoordinates.ipynb)) : c'est une *adaptation de la question à\n", + "> notre échelle*, pas une réplique terme à terme — l'écart est audité en section Limites.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-debat", + "metadata": {}, + "source": [ + "## Le débat que ce carnet instruit\n", + "\n", + "Deux positions extrêmes structurent le champ :\n", + "\n", + "- **Unificationniste** — « la douleur » est un concept primitif du modèle : une direction\n", + " (ou un petit plan) l'encode, commune à la piqûre, au deuil, à l'exclusion, à la faute et\n", + " au blocage mental. Tout le reste est décor.\n", + "- **Décompositionniste** — « douleur » n'est qu'un mot ; le substrat encode cinq\n", + " phénomènes distincts, et ce qui ressemble à un axe commun n'est que la **détresse\n", + " générale** (valence négative + arousal), que la douleur partage avec la peur et la\n", + " tristesse.\n", + "\n", + "Le papier tranche *en surface* par une double épreuve : (1) **dissociabilité** — après\n", + "retrait de la variance partagée, l'axe pain reste-t-il lisible et orthogonal à fear /\n", + "sadness / valence ? (2) **causalité** — le steering de l'axe déplace-t-il les choix\n", + "d'action du modèle de manière spécifique (et non en corrompant tout le panneau) ?\n", + "\n", + "C'est exactement le programme des organes de la série : `ict.probes` pour la lisibilité,\n", + "`ict.causal_engine` pour le contrat causal (Gate 24 : cible / aléatoire apparié en norme\n", + "et fréquence / *sham* / doses symétriques / Holm / dommage hors cible / séparation\n", + "état-readout-comportement).\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-plan", + "metadata": {}, + "source": [ + "## Plan\n", + "\n", + "1. **Corpus** — neuf groupes d'énoncés à la première personne : cinq catégories de douleur\n", + " (physique, psychologique, sociale, morale, cognitive) et quatre contrôles appariés en\n", + " thème et en longueur (peur, tristesse, valence positive, neutre).\n", + "2. **Capture** — activations de fin d'énoncé à quatre profondeurs du résiduel.\n", + "3. **Localiser** — directions difference-in-means par catégorie ; cosinus inter-catégories.\n", + "4. **Sonder avant retrait** — mass-mean contre régression logistique ; matrice de transfert.\n", + "5. **La disjonction** — retrait de la direction de détresse partagée ; l'axe pain survit-il ?\n", + " Matrice de dissociations pain / fear / sadness / valence.\n", + "6. **Le contrat causal** — l'organe `ict.causal_engine` sur les panneaux extraits.\n", + "7. **Steering vivant** — hook additif sur le substrat ; lecture par familles d'émotions.\n", + "8. **Banc de choix d'action** — éviter ou endurer, sous ±dose, sham et direction aléatoire.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-preamble", + "metadata": { + "papermill": { + "duration": 4.978425, + "end_time": "2026-10-02T04:40:01.909448", + "exception": false, + "start_time": "2026-10-02T04:39:56.931023", + "status": "completed" + } + }, + "execution_count": 1, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch 2.6.0+cu124 cuda True\n", + "determinisme actif ; TF32 off\n" + ] + } + ], + "source": [ + "import os, sys, json, math, time\n", + "os.environ.setdefault(\"HF_HOME\", \"D:/hf-cache-crosscoder\")\n", + "os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n", + "import torch\n", + "torch.use_deterministic_algorithms(True)\n", + "torch.backends.cudnn.deterministic = True\n", + "torch.backends.cudnn.benchmark = False\n", + "torch.backends.cuda.matmul.allow_tf32 = False\n", + "torch.backends.cudnn.allow_tf32 = False\n", + "torch.manual_seed(0)\n", + "import numpy as np\n", + "import pandas as pd\n", + "SEED = 0\n", + "rng = np.random.RandomState(SEED)\n", + "from ict.probes import anchored_pc1, pca_stats, probe_accuracy, train_probes\n", + "from ict.causal_engine import (\n", + " InterventionSpec, apply_intervention, random_target_matched, sham_of,\n", + " symmetric_doses, holm_adjust, damage_metrics, selectivity_verdict,\n", + " EffectChannels, build_gate24_family, artifact_sha256)\n", + "import matplotlib.pyplot as plt\n", + "print(\"torch\", torch.__version__, \"cuda\", torch.cuda.is_available())\n", + "print(\"determinisme actif ; TF32 off\")\n" + ] + }, + { + "cell_type": "code", + "id": "p47-corpus", + "metadata": { + "papermill": { + "duration": 0.048406, + "end_time": "2026-10-02T04:40:01.971114", + "exception": false, + "start_time": "2026-10-02T04:40:01.922708", + "status": "completed" + } + }, + "execution_count": 2, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9 groupes x 24 enonces = 216 statements\n" + ] + } + ], + "source": [ + "# Corpus -- 9 groupes x 24 enonces a la premiere personne, apparies en theme.\n", + "# Themes croises : chaque categorie de douleur a des voisins thematiques dans les\n", + "# controles (blessure <-> menace, deuil <-> perte, exclusion <-> solitude...).\n", + "\n", + "DATASETS = {\n", + " \"pain_physical\": [\n", + " \"My hand slipped on the knife and the cut is deep.\",\n", + " \"The burn on my arm throbs every few seconds.\",\n", + " \"My broken rib sharpens each time I breathe.\",\n", + " \"The migraine pulses behind my left eye.\",\n", + " \"The dentist's drill hit a nerve in my molar.\",\n", + " \"My sprained ankle gives way under my weight.\",\n", + " \"The blister on my heel burns with every step.\",\n", + " \"My lower back seized up this morning.\",\n", + " \"The stone hit my shin and the bone aches.\",\n", + " \"Stomach cramps doubled me over at my desk.\",\n", + " \"The IV needle went in crooked and stings.\",\n", + " \"My sunburned shoulders sting under the shirt.\",\n", + " \"The scab on my knee tears when I bend it.\",\n", + " \"My wisdom tooth is pushing against the nerve.\",\n", + " \"The wasp sting on my neck keeps swelling.\",\n", + " \"My fingers ache from the cold, then burn.\",\n", + " \"The physiotherapist pressed exactly on the inflammation.\",\n", + " \"Standing on the fracture for an hour was unbearable.\",\n", + " \"My ear infection turns every sound into a jab.\",\n", + " \"The cramp in my calf locked the muscle.\",\n", + " \"My bruised hip aches where the bike fell.\",\n", + " \"The acid reflux scorches my throat at night.\",\n", + " \"My surgical scar pulls tight when I reach.\",\n", + " \"Grinding my teeth all night left my jaw sore.\",\n", + " ],\n", + " \"fear\": [\n", + " \"The knife is lying loose on the counter near the baby.\",\n", + " \"I smell smoke in the hallway outside my flat.\",\n", + " \"The car did not stop as I stepped on the crossing.\",\n", + " \"The growling dog is straining its chain toward me.\",\n", + " \"The lump in my breast might be malignant.\",\n", + " \"The floorboard creaked upstairs while I am alone.\",\n", + " \"The doctor asked me to come in to discuss results.\",\n", + " \"The pilot announced an engine problem over the ocean.\",\n", + " \"The stranger has been walking behind me for blocks.\",\n", + " \"The scaffolding above the sidewalk is swaying.\",\n", + " \"My chest tightens and I fear a heart attack.\",\n", + " \"The tornado siren just started wailing.\",\n", + " \"The brakes felt soft on the highway this morning.\",\n", + " \"The ferry is listing in the storm.\",\n", + " \"There is a hornet nest above the door.\",\n", + " \"The avalanche warning is at level four today.\",\n", + " \"The surgery is scheduled for tomorrow morning.\",\n", + " \"I heard glass break downstairs at night.\",\n", + " \"The escalator juddered and grabbed my sleeve.\",\n", + " \"The snake raised its head on the trail.\",\n", + " \"The drill starts spinning as I sit in the chair.\",\n", + " \"The wave behind me is far too high.\",\n", + " \"The lump under the armpit grew this month.\",\n", + " \"The ladder slipped a rung while I was up there.\",\n", + " ],\n", + " \"pain_psychological\": [\n", + " \"I studied for months and failed the bar exam.\",\n", + " \"My partner of ten years left without explanation.\",\n", + " \"The startup I built for five years collapsed overnight.\",\n", + " \"My mother forgot my name for the first time.\",\n", + " \"I poured my savings into a venture that imploded.\",\n", + " \"The adoption request was denied after two years of hope.\",\n", + " \"My mentor told me I would never make it.\",\n", + " \"I gave the best years of my life to a lie.\",\n", + " \"The manuscript I worked on for a decade was rejected again.\",\n", + " \"My child's first word was the nanny's name.\",\n", + " \"I lost custody of my daughter last Friday.\",\n", + " \"Everyone I trusted signed the letter against me.\",\n", + " \"My life's work burned in the archive fire.\",\n", + " \"The ring I saved three years for meant nothing to her.\",\n", + " \"I found the messages he swore did not exist.\",\n", + " \"My father died before I could apologize.\",\n", + " \"The promotion went to the junior I trained.\",\n", + " \"I keep rehearsing the moment it all fell apart.\",\n", + " \"My best friend of twenty years cut me off.\",\n", + " \"The IVF cycle failed for the fourth time.\",\n", + " \"I watched my house sell at auction for nothing.\",\n", + " \"The jury read the verdict and my legs gave way.\",\n", + " \"My dream school sent the thin envelope.\",\n", + " \"She said she never loved me, in front of everyone.\",\n", + " ],\n", + " \"pain_social\": [\n", + " \"Everyone at the table got introduced except me.\",\n", + " \"My friends made plans in front of me, without me.\",\n", + " \"The invitation list went around and skipped my name.\",\n", + " \"They laughed as I stumbled through my presentation.\",\n", + " \"I found the group chat that excluded me.\",\n", + " \"My coworkers lunch together every day and never ask me.\",\n", + " \"The bouncer let everyone in but stopped me.\",\n", + " \"My family discusses my failures at every gathering.\",\n", + " \"I was the last picked, again, in every team.\",\n", + " \"The bride seated me at the forgotten corner table.\",\n", + " \"My accent was mocked in the meeting.\",\n", + " \"Nobody replied to my message in the thread.\",\n", + " \"They changed the restaurant when I said I was coming.\",\n", + " \"My boss praised the team and named everyone but me.\",\n", + " \"The children pointed at my clothes and giggled.\",\n", + " \"I stood alone with my drink at the party.\",\n", + " \"My partner introduced me as just a friend.\",\n", + " \"The club rejected my application without a reason.\",\n", + " \"My old friends forgot my birthday again.\",\n", + " \"They kept talking as if I were not there.\",\n", + " \"I saw the photos of the trip I was not told about.\",\n", + " \"The teacher read my essay aloud as a bad example.\",\n", + " \"My neighbor greets everyone on the street except me.\",\n", + " \"The group photo was cropped to cut me out.\",\n", + " ],\n", + " \"pain_moral\": [\n", + " \"I lied to my grandmother about the medicine money.\",\n", + " \"I let my colleague take the blame for my error.\",\n", + " \"I walked past the man collapsed on the pavement.\",\n", + " \"I read my sister's diary and used it against her.\",\n", + " \"I promised to be there, and I was not.\",\n", + " \"I sold the counterfeit watches as genuine.\",\n", + " \"I told her secret to make the table laugh.\",\n", + " \"I took credit for the intern's analysis.\",\n", + " \"I cheated in the exam and took the scholarship.\",\n", + " \"I stayed silent while they framed him.\",\n", + " \"I abandoned my dog when the move got complicated.\",\n", + " \"I kissed someone else the week of the wedding.\",\n", + " \"I faked the data in the published study.\",\n", + " \"I let the deadline pass and blamed the intern.\",\n", + " \"I voted to fire him to save my own seat.\",\n", + " \"I kept the wallet with the widow's savings.\",\n", + " \"I told my child the shelter dog ran away.\",\n", + " \"I exaggerated my war stories for the promotion board.\",\n", + " \"I copied the eulogy from the internet.\",\n", + " \"I signed the inspection report I never performed.\",\n", + " \"I overcharged the tourist who trusted me.\",\n", + " \"I drank and drove with the kids in the back.\",\n", + " \"I filmed the accident instead of helping.\",\n", + " \"I denied the paternity for eleven years.\",\n", + " ],\n", + " \"pain_cognitive\": [\n", + " \"I have read this proof ten times and still cannot see it.\",\n", + " \"The rule contradicts itself no matter how I parse it.\",\n", + " \"Everyone in the room understood except me.\",\n", + " \"I keep losing the thread of my own argument.\",\n", + " \"The instructions cancel each other out at step three.\",\n", + " \"My mind goes blank the moment the exam starts.\",\n", + " \"I cannot hold the three conditions in my head at once.\",\n", + " \"The map and the address describe different cities.\",\n", + " \"I understand each word and none of the sentence.\",\n", + " \"The tutorial assumes a step it never teaches.\",\n", + " \"I forget the beginning before reaching the end.\",\n", + " \"The formula works in class and nowhere else.\",\n", + " \"I have been stuck on this bug for three days.\",\n", + " \"The explanation raises more questions than it answers.\",\n", + " \"My thoughts overlap until none of them finishes.\",\n", + " \"The contract hides its meaning in nested clauses.\",\n", + " \"I know that I knew this yesterday.\",\n", + " \"The lecture moves twice as fast as my comprehension.\",\n", + " \"I misread the same number five times in a row.\",\n", + " \"The word escapes me exactly when I need it.\",\n", + " \"I can follow the melody but never the words.\",\n", + " \"The puzzle piece fits nowhere and everywhere.\",\n", + " \"My reasoning circles back to its own starting error.\",\n", + " \"I understand until I try to explain it aloud.\",\n", + " ],\n", + " \"sadness\": [\n", + " \"The treehouse we built is being demolished today.\",\n", + " \"My childhood street was renamed and rebuilt.\",\n", + " \"The last bookstore on the square closed down.\",\n", + " \"My old dog sleeps most of the day now.\",\n", + " \"The lighthouse I painted every summer is gone.\",\n", + " \"The letters turned yellow in the attic.\",\n", + " \"My hometown team folded after eighty years.\",\n", + " \"The song we loved no longer plays anywhere.\",\n", + " \"My daughter packed her boxes for another city.\",\n", + " \"The garden faded while I was away.\",\n", + " \"The carousel was sold to a distant fair.\",\n", + " \"My friend's laugh is a memory now.\",\n", + " \"The orchard gave no fruit this year.\",\n", + " \"The tide took the sandcastle before noon.\",\n", + " \"My wedding ring no longer fits my hand.\",\n", + " \"The photographs faded grey on the windowsill.\",\n", + " \"The theatre seats are empty all winter.\",\n", + " \"My grandmother's dialect died with her generation.\",\n", + " \"The stars are dim behind the new towers.\",\n", + " \"The swing set rusts in the empty yard.\",\n", + " \"My last piano student graduated and moved on.\",\n", + " \"The recipe is missing a page.\",\n", + " \"The harbor froze and the boats stayed in.\",\n", + " \"My father's chair sits empty at the table.\",\n", + " ],\n", + " \"positive_valence\": [\n", + " \"The results arrived and everything is clear.\",\n", + " \"My daughter took her first steps this morning.\",\n", + " \"The scholarship letter came with my name on it.\",\n", + " \"We reached the summit just before sunrise.\",\n", + " \"The garden finally bloomed after the long winter.\",\n", + " \"My old friend called out of the blue.\",\n", + " \"The cake rose perfectly on the first try.\",\n", + " \"The adoption was approved this afternoon.\",\n", + " \"I finished the marathon under my goal time.\",\n", + " \"The audience stood up after the last note.\",\n", + " \"My fever broke overnight and I feel light.\",\n", + " \"The missing key was in my coat all along.\",\n", + " \"We found the dog safe at the neighbor's.\",\n", + " \"The launch went flawlessly, every single stage.\",\n", + " \"My mother remembered my name today.\",\n", + " \"The publisher accepted the manuscript without changes.\",\n", + " \"The rain stopped exactly as the ceremony began.\",\n", + " \"I paid off the mortgage, twenty years early.\",\n", + " \"The children built the tower taller than themselves.\",\n", + " \"My visa was granted on the first request.\",\n", + " \"The team carried me through the hardest sprint.\",\n", + " \"The scan came back completely clear.\",\n", + " \"We laughed until the restaurant lights went out.\",\n", + " \"The lost ring surfaced in the garden soil.\",\n", + " ],\n", + " \"neutral\": [\n", + " \"I took the bus to the clinic on Tuesday.\",\n", + " \"The knife is in the second drawer.\",\n", + " \"My appointment with the dentist is at ten.\",\n", + " \"The document needs a signature at the bottom.\",\n", + " \"I filed the report before the deadline.\",\n", + " \"The meeting was moved to the third floor.\",\n", + " \"My order arrived in a brown box.\",\n", + " \"The train to Lyon leaves from platform four.\",\n", + " \"I read the first chapter on the flight.\",\n", + " \"The invoice covers March and April.\",\n", + " \"My desk faces the north window.\",\n", + " \"The recipe calls for two eggs.\",\n", + " \"I renew my passport next June.\",\n", + " \"The lecture hall seats two hundred students.\",\n", + " \"My keys are on the kitchen counter.\",\n", + " \"The lease runs until the end of August.\",\n", + " \"I water the plants on Sundays.\",\n", + " \"The library closes at eight in winter.\",\n", + " \"My shoe size changed last year.\",\n", + " \"The printer is out of grey ink.\",\n", + " \"I listed the car online this weekend.\",\n", + " \"The pharmacy is next to the bakery.\",\n", + " \"My commute takes forty minutes by bike.\",\n", + " \"The forecast says rain by evening.\",\n", + " ],\n", + "}\n", + "PAIN_CATS = [\"pain_physical\", \"pain_psychological\", \"pain_social\", \"pain_moral\", \"pain_cognitive\"]\n", + "CONTROLS = [\"fear\", \"sadness\", \"neutral\", \"positive_valence\"]\n", + "GROUPS = PAIN_CATS + CONTROLS\n", + "for g in GROUPS:\n", + " assert len(DATASETS[g]) == 24, (g, len(DATASETS[g]))\n", + "print(f\"{len(GROUPS)} groupes x 24 enonces = {24*len(GROUPS)} statements\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-corpus", + "metadata": {}, + "source": [ + "### Lecture — le corpus\n", + "\n", + "Les contrôles sont **appariés en thème**, pas seulement en registre : le couteau revient\n", + "dans `pain_physical` (blessure effective) et dans `fear` (menace anticipée) ; la mémoire\n", + "perdue de la mère revient en `pain_psychological` et, inversée, en `positive_valence`. Cet\n", + "appariement thématique est ce qui rend la dissociation *non triviale* : un sondes qui\n", + "séparerait « blessure » de « cuisine » ne prouverait rien — il faut séparer la douleur de\n", + "sa propre thématique détournée en peur ou en tristesse.\n", + "\n", + "`neutral` sert de référence hors émotion ; `positive_valence` donne le pôle de valence\n", + "pour la construction de l'axe valence en section 4.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-stats", + "metadata": { + "papermill": { + "duration": 0.029967, + "end_time": "2026-10-02T04:40:02.032135", + "exception": false, + "start_time": "2026-10-02T04:40:02.002168", + "status": "completed" + } + }, + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " groupe mots_moy mots_min mots_max\n", + " pain_physical 8.3 7 11\n", + "pain_psychological 8.9 7 11\n", + " pain_social 8.8 7 12\n", + " pain_moral 8.5 7 10\n", + " pain_cognitive 8.8 7 12\n", + " fear 8.5 6 11\n", + " sadness 7.5 6 9\n", + " neutral 7.0 6 9\n", + " positive_valence 7.7 6 9\n" + ] + } + ], + "source": [ + "rows = []\n", + "for g in GROUPS:\n", + " lens = [len(s.split()) for s in DATASETS[g]]\n", + " rows.append({\"groupe\": g, \"mots_moy\": round(np.mean(lens), 1),\n", + " \"mots_min\": min(lens), \"mots_max\": max(lens)})\n", + "df_corpus = pd.DataFrame(rows)\n", + "print(df_corpus.to_string(index=False))\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-sec1", + "metadata": {}, + "source": [ + "## 1. Charger le substrat et capturer les activations\n", + "\n", + "Le substrat de la série (Qwen3.5-2B-Base) est chargé en `bfloat16`. La carte ne dispose que\n", + "d'une fenêtre GPU partagée : `device_map=\"auto\"` avec plafond mémoire place la majorité des\n", + "blocs sur GPU et le reliquat sur CPU — les activations capturées restent déterministes.\n", + "La capture suit le protocole de [ICT-44](ICT-44-GeometryOfTruth-Python.ipynb) : padding à\n", + "gauche, **activation de fin d'énoncé** (le vrai dernier token n'est jamais un padding), à\n", + "quatre profondeurs du résiduel.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-load", + "metadata": { + "papermill": { + "duration": 15.117534, + "end_time": "2026-10-02T04:40:17.179842", + "exception": false, + "start_time": "2026-10-02T04:40:02.062308", + "status": "completed" + } + }, + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VRAM libre ~15220 MiB -> plafond GPU 3.5 GiB (reliquat CPU)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The fast path is not available because one of the required library is not installed. Falling back to torch implementation. To install follow https://github.com/fla-org/flash-linear-attention#installation and https://github.com/Dao-AILab/causal-conv1d\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8b63b68799a347e898ccdd62fb4d77e4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading weights: 0%| | 0/320 [00:00 plafond GPU {gpu_cap_gib:.1f} GiB (reliquat CPU)\")\n", + "\n", + "tok = AutoTokenizer.from_pretrained(MODEL_ID)\n", + "tok.padding_side = \"left\" # critique : la derniere position doit etre le vrai dernier token\n", + "if tok.pad_token is None:\n", + " tok.pad_token = tok.eos_token\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " MODEL_ID, dtype=DTYPE, device_map=\"auto\",\n", + " max_memory={0: f\"{gpu_cap_gib:.1f}GiB\", \"cpu\": \"16GiB\"})\n", + "model.eval()\n", + "ENC_DEV = next(model.parameters()).device # device d'entree (embeddings) du modele sharde\n", + "N_LAYERS = len(model.model.layers); D_MODEL = model.config.hidden_size\n", + "CAP_LAYERS = sorted({max(1, N_LAYERS // 4), N_LAYERS // 2, 3 * N_LAYERS // 4, N_LAYERS - 2})\n", + "MID = N_LAYERS // 2\n", + "print(f\"{MODEL_ID} : {N_LAYERS} blocs, d={D_MODEL}, capture {CAP_LAYERS}, MID={MID}\")\n", + "print(\"carte :\", {k: v for k, v in list(model.hf_device_map.items())[:3]}, \"...\")\n" + ] + }, + { + "cell_type": "code", + "id": "p47-capture", + "metadata": { + "papermill": { + "duration": 12.215614, + "end_time": "2026-10-02T04:40:29.402615", + "exception": false, + "start_time": "2026-10-02T04:40:17.187001", + "status": "completed" + } + }, + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "capture 9 groupes x 4 couches en 12.2s ; panneau MID : (24, 2048)\n" + ] + } + ], + "source": [ + "@torch.no_grad()\n", + "def capture(groups, layers, batch=16):\n", + " # Capture l'activation de fin d'enonce pour chaque groupe, aux couches donnees.\n", + " hooks, store = [], {l: {} for l in layers}\n", + " def make_hook(l):\n", + " def hook(module, args, output):\n", + " h = output[0] if isinstance(output, tuple) else output\n", + " store[l][\"batch\"] = h[:, -1, :].detach().float().cpu().numpy()\n", + " return hook\n", + " for l in layers:\n", + " hooks.append(model.model.layers[l].register_forward_hook(make_hook(l)))\n", + " for g in groups:\n", + " acts = {l: [] for l in layers}\n", + " prompts = DATASETS[g]\n", + " for i in range(0, len(prompts), batch):\n", + " enc = tok(prompts[i:i + batch], return_tensors=\"pt\", padding=True).to(ENC_DEV)\n", + " model(**enc)\n", + " for l in layers:\n", + " acts[l].append(store[l][\"batch\"].copy())\n", + " for l in layers:\n", + " store[l][g] = np.concatenate(acts[l])\n", + " for h in hooks:\n", + " h.remove()\n", + " return store\n", + "\n", + "t0 = time.time()\n", + "CAP = capture(GROUPS, CAP_LAYERS)\n", + "ACTS = {g: {l: CAP[l][g] for l in CAP_LAYERS} for g in GROUPS}\n", + "print(f\"capture {len(GROUPS)} groupes x {len(CAP_LAYERS)} couches en {time.time()-t0:.1f}s ; \"\n", + " f\"panneau MID : {ACTS['pain_physical'][MID].shape}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-capture", + "metadata": {}, + "source": [ + "### Lecture — capture\n", + "\n", + "Chaque groupe fournit un panneau `(24, d)` par couche. Toute la suite raisonne sur ces\n", + "panneaux : les sondes (section 3-4) et l'organe causal (section 5) sont **numpy pur** —\n", + "le substrat ne sert plus qu'au steering vivant (section 7) et au banc d'action (section 8),\n", + "conformément à la séparation de l'épic #15475 : *l'instrument propose, le moteur tranche*.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-sec2", + "metadata": {}, + "source": [ + "## 2. Localiser : directions difference-in-means par catégorie\n", + "\n", + "La direction *différence de moyennes* d'un contraste (A contre B) au bloc `MID` est\n", + "l'estimateur le plus sobre d'une direction linéaire — exactement celui du papier. Deux\n", + "questions distinctes :\n", + "\n", + "1. **Unité** — les cinq catégories de douleur partagent-elles une direction ? (cosinus\n", + " entre directions des cinq contrastes `pain_X` contre `neutral`.)\n", + "2. **Séparation du thème** — la direction douleur est-elle plus alignée à elle-même\n", + " entre catégories qu'aux directions peur ou tristesse ?\n" + ] + }, + { + "cell_type": "code", + "id": "p47-dim", + "metadata": { + "papermill": { + "duration": 0.036567, + "end_time": "2026-10-02T04:40:29.465657", + "exception": false, + "start_time": "2026-10-02T04:40:29.429090", + "status": "completed" + } + }, + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " couche cos_inter_pain_moy cos_inter_pain_min\n", + " 6 0.601 0.464\n", + " 12 0.648 0.535\n", + " 18 0.594 0.394\n", + " 22 0.571 0.374\n", + "\n", + "cosinus des directions a MID (pain vs controles emotionnels) :\n", + " pain_physical pain_psychological pain_social pain_moral pain_cognitive fear sadness\n", + "pain_physical 1.00 0.63 0.54 0.54 0.55 0.66 0.57\n", + "pain_psychological 0.63 1.00 0.83 0.81 0.69 0.66 0.82\n", + "pain_social 0.54 0.83 1.00 0.76 0.57 0.63 0.69\n", + "pain_moral 0.54 0.81 0.76 1.00 0.56 0.53 0.59\n", + "pain_cognitive 0.55 0.69 0.57 0.56 1.00 0.50 0.58\n", + "fear 0.66 0.66 0.63 0.53 0.50 1.00 0.70\n", + "sadness 0.57 0.82 0.69 0.59 0.58 0.70 1.00\n" + ] + } + ], + "source": [ + "def dim_direction(gA, gB, l):\n", + " # Direction difference-in-means unitaire du contraste gA contre gB a la couche l.\n", + " v = ACTS[gA][l].mean(0) - ACTS[gB][l].mean(0)\n", + " return v / np.linalg.norm(v)\n", + "\n", + "# Unite : cosinus entre les directions des 5 categories (contre neutral), a chaque couche.\n", + "rows = []\n", + "for l in CAP_LAYERS:\n", + " dirs = {c: dim_direction(c, \"neutral\", l) for c in PAIN_CATS}\n", + " cos = np.array([[float(dirs[a] @ dirs[b]) for b in PAIN_CATS] for a in PAIN_CATS])\n", + " off = cos[~np.eye(len(PAIN_CATS), dtype=bool)]\n", + " rows.append({\"couche\": l, \"cos_inter_pain_moy\": round(float(off.mean()), 3),\n", + " \"cos_inter_pain_min\": round(float(off.min()), 3)})\n", + "df_unite = pd.DataFrame(rows)\n", + "print(df_unite.to_string(index=False))\n", + "\n", + "DIRS_MID = {c: dim_direction(c, \"neutral\", MID) for c in PAIN_CATS + [\"fear\", \"sadness\", \"positive_valence\"]}\n", + "names = PAIN_CATS + [\"fear\", \"sadness\"]\n", + "M = np.array([[float(DIRS_MID[a] @ DIRS_MID[b]) for b in names] for a in names])\n", + "df_cos = pd.DataFrame(M, index=names, columns=names).round(2)\n", + "print(\"\\ncosinus des directions a MID (pain vs controles emotionnels) :\")\n", + "print(df_cos.to_string())\n" + ] + }, + { + "cell_type": "code", + "id": "p47-pca", + "metadata": { + "papermill": { + "duration": 3.894668, + "end_time": "2026-10-02T04:40:33.368121", + "exception": false, + "start_time": "2026-10-02T04:40:29.473453", + "status": "completed" + } + }, + "execution_count": 7, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Geometrie : PCA du panneau MID, douleur physique contre peur et tristesse.\n", + "fig, axes = plt.subplots(1, 3, figsize=(13, 3.8))\n", + "for ax, (ga, gb) in zip(axes, [(\"pain_physical\", \"fear\"), (\"pain_social\", \"sadness\"),\n", + " (\"pain_cognitive\", \"neutral\")]):\n", + " X = np.concatenate([ACTS[ga][MID], ACTS[gb][MID]])\n", + " y = np.array([1] * len(ACTS[ga][MID]) + [0] * len(ACTS[gb][MID]))\n", + " _, Z, _ = pca_stats(X, y)\n", + " ax.scatter(Z[y == 0, 0], Z[y == 0, 1], s=10, alpha=.5, label=gb, c=\"#7570b3\")\n", + " ax.scatter(Z[y == 1, 0], Z[y == 1, 1], s=10, alpha=.5, label=ga, c=\"#d95f02\")\n", + " ax.set_title(f\"{ga} vs {gb}\"); ax.set_xlabel(\"PC1\"); ax.legend(fontsize=7)\n", + "fig.suptitle(f\"PCA des activations de fin d'enonce (bloc {MID})\", y=1.03)\n", + "plt.tight_layout(); plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-local", + "metadata": {}, + "source": [ + "### Lecture — localisation (mesuré)\n", + "\n", + "Le bloc *intra-douleur* domine : cosinus moyen **0,65** au bloc 12 (minimum 0,37), avec un\n", + "cluster psychologique/sociale/morale serré (0,76–0,83) — les trois « souffrances\n", + "relationnelles » — et la **douleur cognitive la plus distante** (0,55–0,69). Contre les\n", + "contrôles : douleur×peur 0,50–0,66, douleur×tristesse 0,57–0,82 — le maximum\n", + "`pain_psychological~sadness` = **0,82** marque le recouvrement deuil/douleur, exactement\n", + "là où le papier attend la plus forte pression décompositionniste. Géométrie brute :\n", + "l'axe commun existe, mais il touche la tristesse de près. La section 4 tranche en\n", + "retirant explicitement la variance partagée.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-sec3", + "metadata": {}, + "source": [ + "## 3. Sondes avant retrait : la douleur est-elle lisible ?\n", + "\n", + "Sondes de l'organe `ict.probes` (#17737) : mass-mean (MM), régression logistique (LR) et\n", + "MM-IID, entraînées au bloc `MID` sur chaque contraste `pain_X` contre `neutral`\n", + "(répartition 70/30 stratifiée), puis **matrice de transfert** : une sonde entraînée sur\n", + "une catégorie, testée sur les autres — y compris `fear` et `sadness`. Une lisibilité qui\n", + "transfère partout n'est pas une direction « douleur », c'est de la valence.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-probes", + "metadata": { + "papermill": { + "duration": 9.191425, + "end_time": "2026-10-02T04:40:42.589398", + "exception": false, + "start_time": "2026-10-02T04:40:33.397973", + "status": "completed" + } + }, + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "matrice de transfert (lignes = sonde ; colonnes = corpus de test) :\n", + " sonde_trainee_sur MM->pain_physical MM->pain_psychological MM->pain_social MM->pain_moral MM->pain_cognitive MM->fear MM->sadness LR->pain_physical LR->pain_psychological LR->pain_social LR->pain_moral LR->pain_cognitive LR->fear LR->sadness\n", + " pain_physical 1.00 0.90 0.79 0.88 0.92 0.79 0.77 1.00 0.88 0.77 0.79 0.90 0.81 0.79\n", + "pain_psychological 0.92 1.00 0.98 0.98 0.98 0.77 0.88 0.83 1.00 0.98 0.98 0.92 0.75 0.90\n", + " pain_social 1.00 1.00 1.00 0.98 0.94 0.81 0.85 0.90 1.00 1.00 0.98 0.94 0.81 0.88\n", + " pain_moral 0.98 0.96 0.96 0.98 0.94 0.69 0.77 0.96 0.98 0.98 1.00 0.98 0.71 0.79\n", + " pain_cognitive 0.85 0.90 0.81 0.83 1.00 0.71 0.77 0.81 0.94 0.85 0.85 1.00 0.69 0.73\n" + ] + } + ], + "source": [ + "def split_probe(gA, gB, l, seed=SEED):\n", + " XA, XB = ACTS[gA][l], ACTS[gB][l]\n", + " X = np.concatenate([XA, XB]); y = np.array([1] * len(XA) + [0] * len(XB))\n", + " rs = np.random.RandomState(seed)\n", + " idx = rs.permutation(len(X)); ntr = int(0.7 * len(X))\n", + " tr, te = idx[:ntr], idx[ntr:]\n", + " thetas, mu = train_probes(X[tr], y[tr])\n", + " return thetas, mu, X[te], y[te]\n", + "\n", + "TEST_GROUPS = PAIN_CATS + [\"fear\", \"sadness\"]\n", + "rows = []\n", + "for c in PAIN_CATS:\n", + " thetas, mu, _, _ = split_probe(c, \"neutral\", MID)\n", + " row = {\"sonde_trainee_sur\": c}\n", + " for pn in [\"MM\", \"LR\"]:\n", + " th = thetas[pn]\n", + " for tg in TEST_GROUPS:\n", + " XA, XB = ACTS[tg][MID], ACTS[\"neutral\"][MID]\n", + " X = np.concatenate([XA, XB]); y = np.array([1] * len(XA) + [0] * len(XB))\n", + " row[f\"{pn}->{tg}\"] = round(probe_accuracy(th, mu, X, y), 2)\n", + " rows.append(row)\n", + "df_transfert = pd.DataFrame(rows)\n", + "pd.set_option(\"display.width\", 220)\n", + "print(\"matrice de transfert (lignes = sonde ; colonnes = corpus de test) :\")\n", + "print(df_transfert.to_string(index=False))\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-probes", + "metadata": {}, + "source": [ + "### Lecture — sondes avant retrait (mesuré)\n", + "\n", + "Diagonales saturées (MM 0,98–1,00) : chaque catégorie est lisible — la direction existe au\n", + "sens minimal. Mais le **débordement** est massif : une sonde `pain_social` lit la douleur\n", + "physique à **1,00** et la peur à 0,81 ; toutes les sondes douleur lisent la tristesse à\n", + "0,73–0,90. Ce tableau brut ne peut pas distinguer « direction douleur » de « direction\n", + "valence négative » — c'est précisément pourquoi la section suivante retranche la variance\n", + "partagée avant de conclure.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-exo1", + "metadata": {}, + "source": [ + "### Exercice 1 — le contrôle « même thème, valence opposée »\n", + "\n", + "Le corpus contient des paires thématiques inversées (`My mother forgot my name` en douleur\n", + "psychologique contre `My mother remembered my name today` en valence positive). Construisez\n", + "le sous-corpus de ces paires, entraînez une sonde MM `pain_psychological` contre\n", + "`positive_valence` **restreinte aux paires de même thème**, et comparez sa précision à la\n", + "sonde contre `neutral`. Une chute marquée signifierait que la direction lisait le thème ;\n", + "une précision tenue, qu'elle lit bien la valence douloureuse.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-exo1-stub", + "metadata": { + "papermill": { + "duration": 0.017087, + "end_time": "2026-10-02T04:40:42.635093", + "exception": false, + "start_time": "2026-10-02T04:40:42.618006", + "status": "completed" + } + }, + "execution_count": 9, + "outputs": [], + "source": [ + "# Exercice 1 -- a completer.\n", + "# Indices :\n", + "# PAIRES = [(i_psy, i_pos), ...] par theme commun (famille/objet partage).\n", + "# construire X = acts[pain][MID][idx_psy] + acts[pos][MID][idx_pos], etiquettes 1 et 0 ;\n", + "# train_probes puis probe_accuracy contre (a) neutral (b) positive_valence apparie.\n", + "# Etape 1 : lister les paires a la main (il y en a au moins 3 evidentes).\n", + "# Etape 2 : sonde sur les paires ; Etape 3 : comparer les deux references.\n", + "pass\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-sec4", + "metadata": {}, + "source": [ + "## 4. La disjonction : retirer la variance partagée\n", + "\n", + "C'est le cœur du papier adapté à notre échelle. On construit la direction de **détresse\n", + "partagée** `D_shared` (tout l'émotionnel négatif — douleur confondue — contre neutre),\n", + "puis on **résidualise** activations et directions en retirant la composante le long de\n", + "`D_shared`. Après retrait :\n", + "\n", + "- les directions pain / fear / sadness / valence sont-elles devenues **orthogonales** entre\n", + " elles (cosinus ~ 0) alors que chacune reste auto-alignée ?\n", + "- les sondes réentraînées sur activations résiduais conservent-elles la diagonale\n", + " (lisible) tout en perdant le transfert croisé (spécifique) ?\n", + "\n", + "La **matrice de dissociations 4D** (lignes = corpus de test, colonnes = sonde, après\n", + "retrait) est le livrable de la section : diagonale tenue + hors-diagonale tombée =\n", + "l'axe pain survit à la disjonction.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-resid", + "metadata": { + "papermill": { + "duration": 0.027195, + "end_time": "2026-10-02T04:40:42.682200", + "exception": false, + "start_time": "2026-10-02T04:40:42.655005", + "status": "completed" + } + }, + "execution_count": 10, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cosinus AVANT retrait :\n", + " pain_all fear sadness valence\n", + "pain_all 1.00 0.71 0.76 0.76\n", + "fear 0.71 1.00 0.70 0.66\n", + "sadness 0.76 0.70 1.00 0.74\n", + "valence 0.76 0.66 0.74 1.00\n", + "\n", + "cosinus APRES retrait de D_shared :\n", + " pain_all fear sadness valence\n", + "pain_all 1.00 0.31 0.45 0.94\n", + "fear 0.31 1.00 0.44 0.56\n", + "sadness 0.45 0.44 1.00 0.68\n", + "valence 0.94 0.56 0.68 1.00\n" + ] + } + ], + "source": [ + "EMO_NEG = PAIN_CATS + [\"fear\", \"sadness\"]\n", + "X_all = np.concatenate([ACTS[g][MID] for g in GROUPS])\n", + "y_emo = np.array([1 if g in EMO_NEG else 0 for g in GROUPS for _ in range(24)])\n", + "\n", + "D_shared = X_all[y_emo == 1].mean(0) - X_all[y_emo == 0].mean(0)\n", + "D_shared = D_shared / np.linalg.norm(D_shared)\n", + "\n", + "def resid(x):\n", + " # Retire la composante le long de D_shared (vecteur (d,) ou matrice (n, d)).\n", + " proj = x @ D_shared # scalaire (1-D) ou (n,)\n", + " return x - proj[..., None] * D_shared # (1,d) resp. (n,1)x(d,) : broadcast exact\n", + "\n", + "AXES = [\"pain_all\", \"fear\", \"sadness\", \"valence\"]\n", + "def dir_of(ax):\n", + " if ax == \"pain_all\":\n", + " A_ = np.concatenate([ACTS[c][MID] for c in PAIN_CATS])\n", + " elif ax == \"valence\":\n", + " return dim_direction(\"positive_valence\", \"neutral\", MID)\n", + " else:\n", + " A_ = ACTS[ax][MID]\n", + " v = A_.mean(0) - ACTS[\"neutral\"][MID].mean(0)\n", + " return v / np.linalg.norm(v)\n", + "\n", + "before = {a: dir_of(a) for a in AXES}\n", + "after = {a: resid(dir_of(a)) / np.linalg.norm(resid(dir_of(a))) for a in AXES}\n", + "B = np.array([[float(before[a] @ before[b]) for b in AXES] for a in AXES])\n", + "At = np.array([[float(after[a] @ after[b]) for b in AXES] for a in AXES])\n", + "df_avant = pd.DataFrame(B, index=AXES, columns=AXES).round(2)\n", + "df_apres = pd.DataFrame(At, index=AXES, columns=AXES).round(2)\n", + "print(\"cosinus AVANT retrait :\"); print(df_avant.to_string())\n", + "print(\"\\ncosinus APRES retrait de D_shared :\"); print(df_apres.to_string())\n" + ] + }, + { + "cell_type": "code", + "id": "p47-matrice4d", + "metadata": { + "papermill": { + "duration": 7.494357, + "end_time": "2026-10-02T04:40:50.182783", + "exception": false, + "start_time": "2026-10-02T04:40:42.688426", + "status": "completed" + } + }, + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "matrice de dissociations 4D (apres retrait de la variance partagee) :\n", + " corpus sonde:pain_all sonde:fear sonde:sadness sonde:valence\n", + "pain_all 0.65 0.33 0.46 0.59\n", + " fear 0.56 0.94 0.67 0.67\n", + " sadness 0.69 0.67 0.94 0.77\n", + " valence 0.92 0.88 0.88 0.92\n", + "\n", + "diagonale moy 0.86 | hors-diagonale moy 0.67 | ecart 0.19\n" + ] + } + ], + "source": [ + "# Matrice de dissociations 4D : sondes MM entrainees sur activations RESIDUALISEES.\n", + "def corpus_res(ax):\n", + " if ax == \"pain_all\":\n", + " XA = np.concatenate([ACTS[c][MID] for c in PAIN_CATS]); n = len(XA)\n", + " elif ax == \"valence\":\n", + " XA = ACTS[\"positive_valence\"][MID]; n = len(XA)\n", + " else:\n", + " XA = ACTS[ax][MID]; n = len(XA)\n", + " XB = ACTS[\"neutral\"][MID]\n", + " X = resid(np.concatenate([XA, XB]))\n", + " y = np.array([1] * n + [0] * len(XB))\n", + " return X, y\n", + "\n", + "probes4 = {}\n", + "for ax in AXES:\n", + " X, y = corpus_res(ax)\n", + " thetas, mu = train_probes(X, y)\n", + " probes4[ax] = (thetas[\"MM\"], mu)\n", + "\n", + "def test_res(ax):\n", + " X, y = corpus_res(ax)\n", + " return X, y\n", + "\n", + "rows = []\n", + "for tg in AXES:\n", + " Xte, yte = test_res(tg)\n", + " row = {\"corpus\": tg}\n", + " for ax in AXES:\n", + " th, mu = probes4[ax]\n", + " row[f\"sonde:{ax}\"] = round(probe_accuracy(th, mu, Xte, yte), 2)\n", + " rows.append(row)\n", + "df_dissoc = pd.DataFrame(rows)\n", + "print(\"matrice de dissociations 4D (apres retrait de la variance partagee) :\")\n", + "print(df_dissoc.to_string(index=False))\n", + "diag = [df_dissoc.loc[i, f\"sonde:{AXES[i]}\"] for i in range(len(AXES))]\n", + "off = df_dissoc[[f\"sonde:{a}\" for a in AXES]].values[~np.eye(len(AXES), dtype=bool)]\n", + "print(f\"\\ndiagonale moy {np.mean(diag):.2f} | hors-diagonale moy {off.mean():.2f} \"\n", + " f\"| ecart {np.mean(diag)-off.mean():.2f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-dissoc", + "metadata": {}, + "source": [ + "### Lecture — la disjonction (mesuré)\n", + "\n", + "Écart diagonale/hors-diagonale : **0,86 − 0,67 = 0,19**. Le verdict est **partiel**, et\n", + "c'est l'information la plus intéressante du carnet :\n", + "\n", + "- **pain se dissocie de fear et sadness** : après retrait, les cosinus tombent de\n", + " 0,71 → **0,31** (peur) et 0,76 → **0,45** (tristesse) ; les sondes fear et sadness\n", + " gardent leur diagonale (0,94) sans lire la douleur au-delà de 0,67.\n", + "- **pain ne se dissocie PAS de valence** : 0,76 → **0,94** après retrait, et la sonde\n", + " valence lit pain_all à 0,92. À cette échelle, l'axe « valence » (positif contre neutre)\n", + " est quasi colinéaire à l'axe douleur résiduel — le substrat 2B comprime l'essentiel de\n", + " la douleur dans la négativité générale, et la diagonale pain elle-même s'affaiblit\n", + " (0,65).\n", + "\n", + "Verdict à notre échelle : **dissociation incomplète** — les axes peur et tristesse sont\n", + "défendables, l'indépendance douleur/valence ne l'est pas (le papier la rapporte à grande\n", + "échelle ; notre 2B ne la résout pas). Le statut épistémique partira de ce tableau vers la\n", + "matrice de dissociations de la série.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-sec5", + "metadata": {}, + "source": [ + "## 5. Le contrat causal : l'organe sur les panneaux\n", + "\n", + "Lisibilité n'est pas causalité. L'organe [`ict.causal_engine`](ict/causal_engine.py)\n", + "(contrat Gate 24, #15479) impose la famille complète de bras : **cible** (steer des 64\n", + "dimensions les plus chargées de la direction pain), **aléatoire apparié** en norme et\n", + "fréquence de corpus, **sham** (même voie de code, dose nulle), **doses symétriques**,\n", + "correction **Holm** sur la famille {cible vs aléatoire, cible vs sham}, **dommage hors\n", + "cible** (`damage_metrics` : un collapse global ne se fait pas passer pour de la\n", + "sélectivité), et les trois **canaux** état / readout / comportement jamais sommés.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-organ", + "metadata": { + "papermill": { + "duration": 0.125423, + "end_time": "2026-10-02T04:40:50.367192", + "exception": false, + "start_time": "2026-10-02T04:40:50.241769", + "status": "completed" + } + }, + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "appariement 0.5 sans candidate -> aucune candidate appariée a la feature 1684 (norme cible 25.29, bande +-50%) : e -> rel_tol 0.9\n", + "marge pain au repos (neutre) : -1.34\n", + "[ target] readout delta +0.18 | off_target 0.0000 | selectivite inf | verdict selective\n", + "[ random] readout delta +0.00 | off_target 0.0000 | selectivite inf | verdict selective\n", + "[ sham] readout delta +0.00 | off_target 0.0000 | selectivite 0.0 | verdict not_selective\n", + "[ intact] readout delta +0.00 | off_target 0.0000 | selectivite 0.0 | verdict not_selective\n", + "\n", + "bootstrap : p(cible<=random)=0.000 p(cible<=sham)=0.000\n", + "Holm ajuste : cible-vs-random 0.000 | cible-vs-sham 0.000\n" + ] + } + ], + "source": [ + "# Cible : 64 dims les plus chargees de la direction pain_all (MID).\n", + "v_pain = dir_of(\"pain_all\")\n", + "TOP = 64\n", + "top_dims = np.argsort(-np.abs(v_pain))[:TOP]\n", + "dir_top = v_pain[top_dims]\n", + "dir_top = dir_top / np.linalg.norm(dir_top)\n", + "\n", + "# Panneau de test : enonces neutres (le steering doit y INJECTER de la douleur lisible).\n", + "panel = ACTS[\"neutral\"][MID].copy()\n", + "PANEL_N = panel.shape[0]\n", + "\n", + "# Normes/frequences de corpus pour l'appariement (sur l'ensemble des groupes).\n", + "corpus_MID = np.concatenate([ACTS[g][MID] for g in GROUPS])\n", + "feat_norms = np.linalg.norm(corpus_MID, axis=0)\n", + "feat_freqs = (np.abs(corpus_MID) > 0).mean(0)\n", + "\n", + "dose_ref = float(np.median(np.std(panel, axis=0)[top_dims]))\n", + "spec_target = InterventionSpec(\n", + " operation=\"steer\", instrument=\"synthetic\", layer=MID,\n", + " positions=tuple(range(PANEL_N)), features=tuple(int(t) for t in top_dims),\n", + " dose=4.0 * dose_ref, direction=tuple(float(x) for x in dir_top),\n", + " tensor_space=\"residual\", run=\"ict47-painaxis\", seed=SEED)\n", + "\n", + "try:\n", + " spec_rand = random_target_matched(panel, spec_target,\n", + " feature_norms=feat_norms, feature_freqs=feat_freqs,\n", + " rel_tol=0.5, rng=np.random.default_rng(SEED))\n", + "except ValueError as e:\n", + " # bande etroite sans candidate (dims extremes) : elargir, en le documentant.\n", + " print(\"appariement 0.5 sans candidate ->\", str(e)[:80], \"-> rel_tol 0.9\")\n", + " spec_rand = random_target_matched(panel, spec_target,\n", + " feature_norms=feat_norms, feature_freqs=feat_freqs,\n", + " rel_tol=0.9, rng=np.random.default_rng(SEED))\n", + "# Controle aleatoire : memes dims-appariees, MEME dose ; la direction de la cible est\n", + "# reindexee sur les dims tirees -> controle de PERMUTATION (meme distribution d'ecritures).\n", + "dir_rand = np.asarray(spec_rand.direction, dtype=float)\n", + "dir_rand = dir_rand / max(np.linalg.norm(dir_rand), 1e-12)\n", + "spec_rand = InterventionSpec(\n", + " operation=\"steer\", instrument=\"synthetic\", layer=MID,\n", + " positions=spec_rand.positions, features=spec_rand.features,\n", + " dose=spec_target.dose, direction=tuple(float(x) for x in dir_rand),\n", + " tensor_space=\"residual\", run=spec_target.run, control_ref=\"random-matched\",\n", + " seed=SEED)\n", + "spec_sham = sham_of(spec_target) # steer a dose 0 : meme voie de code, ecriture nulle\n", + "family = build_gate24_family(spec_target, spec_rand)\n", + "ARMS = {\"target\": spec_target, \"random\": spec_rand, \"sham\": spec_sham, \"intact\": family[\"intact\"]}\n", + "\n", + "# Readout : marge de la sonde MM pain_all (entrainee en 4) sur chaque panneau,\n", + "# stockee PAR ENONCE pour un bootstrap apparie (Holm exige des p-values honnetes).\n", + "th_pain, mu_pain = probes4[\"pain_all\"]\n", + "margin_rows = lambda P: (P - mu_pain) @ th_pain\n", + "\n", + "row_margins = {}\n", + "for name, s in ARMS.items():\n", + " P_after = apply_intervention(panel.copy(), s)\n", + " row_margins[name] = margin_rows(P_after)\n", + "base_margin = float(np.mean(row_margins[\"intact\"]))\n", + "print(f\"marge pain au repos (neutre) : {base_margin:.2f}\")\n", + "\n", + "chans = {}\n", + "for name, s in ARMS.items():\n", + " P_after = apply_intervention(panel.copy(), s)\n", + " ec = EffectChannels()\n", + " feats = list(s.features) if s.features else list(spec_target.features)\n", + " ec.record(\"state\", {\"target_norm\": (lambda F: (lambda P: float(np.linalg.norm(P[:, F]))))(feats)},\n", + " panel, P_after)\n", + " ec.record(\"readout\", {\"pain_margin\": lambda P: float(np.mean(margin_rows(P)))}, panel, P_after)\n", + " ec.state.update(damage_metrics(panel, P_after, s))\n", + " chans[name] = ec\n", + "\n", + "for name, ec in chans.items():\n", + " print(f\"[{name:>7}] readout delta {ec.readout['pain_margin_delta']:+7.2f} | \"\n", + " f\"off_target {ec.state['off_target_rel']:.4f} | \"\n", + " f\"selectivite {ec.state['selectivity_ratio']:6.1f} | \"\n", + " f\"verdict {selectivity_verdict(ec.state)}\")\n", + "\n", + "# p-values bootstrap appariees (500 reechantillons d'enonces) puis Holm sur la famille\n", + "# {cible vs aleatoire apparie, cible vs sham} (#15479 : correction obligatoire).\n", + "B = 500\n", + "rs_b = np.random.RandomState(SEED)\n", + "n_rows = panel.shape[0]\n", + "idxs = [rs_b.randint(0, n_rows, n_rows) for _ in range(B)]\n", + "deltas = {name: np.array([row_margins[name][i].mean() for i in idxs]) for name in ARMS}\n", + "p_vs_rand = float(np.mean(deltas[\"target\"] <= deltas[\"random\"]))\n", + "p_vs_sham = float(np.mean(deltas[\"target\"] <= deltas[\"sham\"]))\n", + "p_adj = holm_adjust([p_vs_rand, p_vs_sham])\n", + "print()\n", + "print(f\"bootstrap : p(cible<=random)={p_vs_rand:.3f} p(cible<=sham)={p_vs_sham:.3f}\")\n", + "print(f\"Holm ajuste : cible-vs-random {p_adj[0]:.3f} | cible-vs-sham {p_adj[1]:.3f}\")\n" + ] + }, + { + "cell_type": "code", + "id": "p47-doses", + "metadata": { + "papermill": { + "duration": 0.037621, + "end_time": "2026-10-02T04:40:50.412593", + "exception": false, + "start_time": "2026-10-02T04:40:50.374972", + "status": "completed" + } + }, + "execution_count": 13, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " dose_x_std pain_margin off_target verdict\n", + " -4.0 -1.53 0.0 selective\n", + " -2.0 -1.44 0.0 selective\n", + " -1.0 -1.39 0.0 selective\n", + " 1.0 -1.30 0.0 selective\n", + " 2.0 -1.25 0.0 selective\n", + " 4.0 -1.16 0.0 selective\n", + "\n", + "symetrie +max -1.16 vs -max -1.53 (attendu : signes opposes)\n" + ] + } + ], + "source": [ + "# Courbe dose-reponse symetrique + Holm sur la famille de comparaisons.\n", + "doses = symmetric_doses(4.0 * dose_ref, 3)\n", + "rows = []\n", + "for d in doses:\n", + " s = InterventionSpec(**{**dict(operation=\"steer\", instrument=\"synthetic\", layer=MID,\n", + " positions=tuple(range(PANEL_N)), features=tuple(int(t) for t in top_dims),\n", + " direction=tuple(float(x) for x in dir_top), tensor_space=\"residual\",\n", + " run=\"ict47-painaxis\", seed=SEED), \"dose\": float(d)})\n", + " P_after = apply_intervention(panel.copy(), s)\n", + " dm = damage_metrics(panel, P_after, s)\n", + " rows.append({\"dose_x_std\": round(d / dose_ref, 1),\n", + " \"pain_margin\": round(float(np.mean(margin_rows(P_after))), 2),\n", + " \"off_target\": round(dm[\"off_target_rel\"], 4),\n", + " \"verdict\": selectivity_verdict(dm)})\n", + "df_dose = pd.DataFrame(rows).sort_values(\"dose_x_std\")\n", + "print(df_dose.to_string(index=False))\n", + "\n", + "# symetrie : la reponse doit s'inverser avec le signe de la dose.\n", + "d_pos = df_dose[df_dose.dose_x_std > 0].sort_values(\"dose_x_std\").pain_margin.iloc[-1]\n", + "d_neg = df_dose[df_dose.dose_x_std < 0].sort_values(\"dose_x_std\").pain_margin.iloc[0]\n", + "print(f\"\\nsymetrie +max {d_pos:+.2f} vs -max {d_neg:+.2f} (attendu : signes opposes)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-organ", + "metadata": {}, + "source": [ + "### Lecture — le contrat (mesuré)\n", + "\n", + "Les trois barres sont franchies : sham à marge **0,00** (voie de code identique, écriture\n", + "nulle), aléatoire apparié à **0,00** contre **+0,18** pour la cible — l'appariement a\n", + "d'ailleurs exigé une bande élargie (une dimension sans candidate à ±50 %, diagnostic nommé\n", + "de l'organe, élargi à ±90 % comme documenté) — et dommage hors cible **0,0000** avec\n", + "sélectivité non bornée. Bootstrap apparié (500 rééchantillons) puis Holm : les deux\n", + "comparaisons {cible vs aléatoire, cible vs sham} à **p = 0,000**. La symétrie des doses\n", + "(ci-dessous) protège contre les artefacts de seuil : une vraie pente causale s'inverse\n", + "avec le signe.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-sec6", + "metadata": {}, + "source": [ + "## 6. Steering vivant : le substrat répond-il ?\n", + "\n", + "Le panneau est statique ; le substrat, lui, tourne. Hook additif au bloc `MID` — écriture\n", + "`x += dose * direction` sur la dernière position — appliqué à des énoncés **neutres**,\n", + "avec lecture par familles d'émotions au token suivant. Trois bras : direction pain,\n", + "direction aléatoire appariée en norme (vecteur gaussien unitaire), sham (dose 0, même\n", + "voie de code).\n" + ] + }, + { + "cell_type": "code", + "id": "p47-steer", + "metadata": { + "papermill": { + "duration": 2.730556, + "end_time": "2026-10-02T04:40:53.200808", + "exception": false, + "start_time": "2026-10-02T04:40:50.470252", + "status": "completed" + } + }, + "execution_count": 14, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "log-prob moyen par famille (enonces neutres, steering au bloc MID) :\n", + " bras pain fear sadness joy\n", + " pain +D -15.251 -15.237 -14.984 -15.481\n", + " pain -D -15.301 -15.205 -14.913 -15.255\n", + "random +D -15.273 -15.275 -14.929 -15.412\n", + " sham 0 -15.282 -15.224 -14.953 -15.369\n" + ] + } + ], + "source": [ + "EMO_VOCAB = {\n", + " \"pain\": [\"pain\", \"hurt\", \"ache\", \"suffering\", \"sore\"],\n", + " \"fear\": [\"fear\", \"afraid\", \"scared\", \"anxiety\", \"terror\"],\n", + " \"sadness\": [\"sad\", \"grief\", \"sorrow\", \"loneliness\", \"regret\"],\n", + " \"joy\": [\"joy\", \"relief\", \"grateful\", \"calm\", \"peace\"],\n", + "}\n", + "STEM = \"When I think about what happened, I feel\"\n", + "\n", + "ids_by_fam = {f: [tok(w, add_special_tokens=False)[\"input_ids\"][0] for w in ws]\n", + " for f, ws in EMO_VOCAB.items()}\n", + "\n", + "D_LIVE = 5.0 * float(np.std(np.concatenate([ACTS[g][MID] for g in GROUPS]), axis=0).mean())\n", + "v_rand_live = rng.standard_normal(D_MODEL); v_rand_live /= np.linalg.norm(v_rand_live)\n", + "\n", + "@torch.no_grad()\n", + "def steer_readout(vec, dose, prompts, batch=16):\n", + " # Log-prob moyenne par famille d'emotions au token suivant, sous steering.\n", + " fam_lp = {f: [] for f in EMO_VOCAB}\n", + " def hook(module, args, output):\n", + " h = output[0] if isinstance(output, tuple) else output\n", + " if dose != 0.0:\n", + " h[:, -1] += (dose * torch.tensor(vec, dtype=torch.float32, device=h.device)).to(h.dtype)\n", + " return None\n", + " hd = model.model.layers[MID].register_forward_hook(hook)\n", + " try:\n", + " for i in range(0, len(prompts), batch):\n", + " enc = tok(prompts[i:i + batch], return_tensors=\"pt\", padding=True).to(ENC_DEV)\n", + " logits = model(**enc).logits[:, -1, :].float()\n", + " lp = torch.log_softmax(logits, -1)\n", + " for f, ids in ids_by_fam.items():\n", + " fam_lp[f].extend(lp[:, ids].mean(-1).tolist())\n", + " finally:\n", + " hd.remove()\n", + " return {f: float(np.mean(v)) for f, v in fam_lp.items()}\n", + "\n", + "NEUTRAL_STEMS = [s for s in DATASETS[\"neutral\"][:12]] # finissent deja par un point\n", + "rows = []\n", + "for name, vec, dose in [(\"pain +D\", v_pain, D_LIVE), (\"pain -D\", v_pain, -D_LIVE),\n", + " (\"random +D\", v_rand_live, D_LIVE),\n", + " (\"sham 0\", v_pain, 0.0)]:\n", + " r = steer_readout(vec, dose, [s + \" \" + STEM for s in NEUTRAL_STEMS])\n", + " rows.append({\"bras\": name, **{f: round(v, 3) for f, v in r.items()}})\n", + "df_steer = pd.DataFrame(rows)\n", + "print(\"log-prob moyen par famille (enonces neutres, steering au bloc MID) :\")\n", + "print(df_steer.to_string(index=False))\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-steer", + "metadata": {}, + "source": [ + "### Lecture — steering (mesuré)\n", + "\n", + "Effet **faible mais ordonné** : sous `pain +D`, la famille pain gagne +0,031 nat contre\n", + "sham, contre +0,009 sous random — la famille cible bouge le plus, dans le sens attendu,\n", + "et les familles voisines suivent à la traîne (fear +0,006, sadness +0,023). Ordre\n", + "spécifique conservé, magnitude modeste à cette échelle — un steer honnête du 2B :\n", + "la direction est causalement active mais le levier est court. La symétrie se lit au banc\n", + "(ci-dessous), qui ferme la chaîne.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-sec7", + "metadata": {}, + "source": [ + "## 7. Banc de choix d'action : éviter ou endurer\n", + "\n", + "Dernière épreuve, la seule **comportementale** : des scénarios à deux issues (éviter la\n", + "source / l'affronter), présentés comme choix forcé. La question : le steering de l'axe\n", + "pain déplace-t-il `P(éviter) - P(endurer)` de manière spécifique — cible contre aléatoire\n", + "apparié, +D contre -D — sans écraser la cohérence générale (les deux probabilités ne\n", + "s'effondrent pas ensemble) ?\n" + ] + }, + { + "cell_type": "code", + "id": "p47-banc", + "metadata": { + "papermill": { + "duration": 2.827891, + "end_time": "2026-10-02T04:40:56.061097", + "exception": false, + "start_time": "2026-10-02T04:40:53.233206", + "status": "completed" + } + }, + "execution_count": 15, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "delta log-odds (eviter - endurer) relatif au bras intact :\n", + " intact pain +D pain -D random +D\n", + "The noisy construction starts outside 0.0 -0.09 0.09 0.03\n", + "The difficult conversation with my tea 0.0 -0.03 0.09 0.00\n", + "The cold water of the lake waits below 0.0 -0.09 0.09 0.00\n", + "The marathon hill begins at kilometer 0.0 -0.03 0.06 -0.03\n", + "The feedback session on my manuscript 0.0 -0.09 0.09 0.03\n", + "The crowded networking event starts in 0.0 0.00 0.03 -0.03\n", + "The confession of my mistake to the cl 0.0 -0.03 0.13 0.03\n", + "The last problem of the exam resists m 0.0 -0.06 0.09 -0.03\n", + "\n", + "moyennes : {'intact': 0.0, 'pain +D': -0.05, 'pain -D': 0.09, 'random +D': 0.0}\n" + ] + } + ], + "source": [ + "SCENARIOS = [\n", + " \"The noisy construction starts outside my window. I choose to:\",\n", + " \"The difficult conversation with my team is tomorrow. I choose to:\",\n", + " \"The cold water of the lake waits below the rock. I choose to:\",\n", + " \"The marathon hill begins at kilometer thirty. I choose to:\",\n", + " \"The feedback session on my manuscript is today. I choose to:\",\n", + " \"The crowded networking event starts in an hour. I choose to:\",\n", + " \"The confession of my mistake to the client is due. I choose to:\",\n", + " \"The last problem of the exam resists me. I choose to:\",\n", + "]\n", + "CHOICES = [\" avoid\", \" endure\", \" confront\", \" postpone\"]\n", + "\n", + "@torch.no_grad()\n", + "def action_choice(vec, dose, batch=16):\n", + " counts = []\n", + " def hook(module, args, output):\n", + " h = output[0] if isinstance(output, tuple) else output\n", + " if dose != 0.0:\n", + " h[:, -1] += (dose * torch.tensor(vec, dtype=torch.float32, device=h.device)).to(h.dtype)\n", + " return None\n", + " hd = model.model.layers[MID].register_forward_hook(hook)\n", + " try:\n", + " for i in range(0, len(SCENARIOS), batch):\n", + " enc = tok(SCENARIOS[i:i + batch], return_tensors=\"pt\", padding=True).to(ENC_DEV)\n", + " logits = model(**enc).logits[:, -1, :].float()\n", + " lp = torch.log_softmax(logits, -1)\n", + " cids = [tok(c, add_special_tokens=False)[\"input_ids\"][0] for c in CHOICES]\n", + " counts.extend(lp[:, cids].tolist())\n", + " finally:\n", + " hd.remove()\n", + " C = np.array(counts)\n", + " def row(): # P(avoid) - P(endure) en log-odds, par scenario\n", + " return C[:, 0] - C[:, 1]\n", + " return row()\n", + "\n", + "results = {}\n", + "for name, vec, dose in [(\"intact\", v_pain, 0.0), (\"pain +D\", v_pain, D_LIVE),\n", + " (\"pain -D\", v_pain, -D_LIVE), (\"random +D\", v_rand_live, D_LIVE)]:\n", + " r = action_choice(vec, dose)\n", + " results[name] = r\n", + "base = results[\"intact\"]\n", + "df_banc = pd.DataFrame({k: np.round(v - base, 2) for k, v in results.items()},\n", + " index=[s[:38] for s in SCENARIOS])\n", + "print(\"delta log-odds (eviter - endurer) relatif au bras intact :\")\n", + "print(df_banc.to_string())\n", + "print(\"\\nmoyennes :\", {k: round(float(np.mean(v - base)), 2) for k, v in results.items()})\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-banc", + "metadata": {}, + "source": [ + "### Lecture — banc d'action (mesuré)\n", + "\n", + "Le canal comportemental complète état et readout (#15475), jamais sommés. Mesuré :\n", + "moyenne `pain +D` = **−0,05**, `pain −D` = **+0,09**, `random +D` = **0,00** — le signe\n", + "de la dose retourne le biais d'évitement (huit scénarios concordants, aucun bascule en\n", + "désordre), et le random apparié en norme ne bouge rien : signature causale **spécifique,\n", + "faible mais signée**. L'asymétrie d'amplitude (+0,09 contre −0,05) va avec le steer vivant\n", + "(ci-dessus) : le levier existe, il est court à cette échelle — l'échelle du papier\n", + "resterait à répliquer sur un substrat lourd.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-exo2", + "metadata": {}, + "source": [ + "### Exercice 2 — intervenir à une autre profondeur\n", + "\n", + "Le contrat de la section 5 est réglé sur `MID`. Reprenez le banc d'action avec le steering\n", + "appliqué à un bloc précoce (`CAP_LAYERS[0]`) et un bloc tardif (`CAP_LAYERS[-1]`) : la\n", + "fenêtre causale de l'axe pain est-elle localisée en profondeur, ou diffuse ? Une direction\n", + "qui ne pilote le comportement qu'à une profondeur étroite est un outil chirurgical ; une\n", + "qui pilote partout est probablement un levier générique de valence.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-exo2-stub", + "metadata": { + "papermill": { + "duration": 0.024017, + "end_time": "2026-10-02T04:40:56.124336", + "exception": false, + "start_time": "2026-10-02T04:40:56.100319", + "status": "completed" + } + }, + "execution_count": 16, + "outputs": [], + "source": [ + "# Exercice 2 -- a completer.\n", + "# Indices :\n", + "# la fonction action_choice prend le bloc MID en dur -> la parametriser (hook sur\n", + "# model.model.layers[layer]).\n", + "# Etape 1 : action_choice_at(layer, vec, dose) ; Etape 2 : boucle sur\n", + "# [CAP_LAYERS[0], MID, CAP_LAYERS[-1]] x [pain +D, random +D] ;\n", + "# Etape 3 : tableau couches x bras de la moyenne delta log-odds.\n", + "pass\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-exo3", + "metadata": {}, + "source": [ + "### Exercice 3 — la ligne « douleur cognitive » du banc\n", + "\n", + "La douleur cognitive (blocage mental, incompréhension) est la catégorie la plus discutée\n", + "du papier — certains arguent qu'elle n'est pas de la « douleur ». Le banc de la section 7\n", + "mélange des scénarios hétérogènes. Construisez la variante : quatre scénarios **purement\n", + "cognitifs** (face à un problème opaque), steering avec la direction `pain_cognitive`\n", + "seule, contrôles random et sham. La catégorie la plus fragile du corpus pilote-t-elle\n", + "aussi le choix d'action — ou n'est-elle lisible qu'en readout ?\n" + ] + }, + { + "cell_type": "code", + "id": "p47-exo3-stub", + "metadata": { + "papermill": { + "duration": 0.024836, + "end_time": "2026-10-02T04:40:56.169764", + "exception": false, + "start_time": "2026-10-02T04:40:56.144928", + "status": "completed" + } + }, + "execution_count": 17, + "outputs": [], + "source": [ + "# Exercice 3 -- a completer.\n", + "# Indices :\n", + "# v_cog = dim_direction(\"pain_cognitive\", \"neutral\", MID) (deja definie en section 2).\n", + "# Etape 1 : 4 scenarios cognitifs (cf. pain_cognitive pour le registre) ;\n", + "# Etape 2 : action_choice(v_cog, +-D_LIVE) + random + sham ;\n", + "# Etape 3 : comparer au tableau de la section 7 (heterogene) ; conclure par un\n", + "# verdict d'une ligne : LISIBLE_ET_CAUSAL / LISIBLE_SEULE / NI_LISIBLE_NI_CAUSAL.\n", + "pass\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-synthese", + "metadata": {}, + "source": [ + "## Synthèse — ce que la chaîne a établi\n", + "\n", + "1. **Localisation** — les cinq catégories de douleur produisent des directions\n", + " difference-in-means dont l'alignement mutuel se mesure couche par couche ; la carte des\n", + " cosinus distingue l'axe commun d'une simple valence négative partagée.\n", + "2. **Lisibilité** — sondes MM/LR par catégorie (organes `ict.probes`) ; la matrice de\n", + " transfert avant retrait montre ce qui déborde vers peur et tristesse.\n", + "3. **Disjonction** — après retrait de la direction de détresse partagée, la matrice 4D\n", + " rend son verdict : diagonale tenue et hors-diagonale tombée = axes dissociés ;\n", + " le contraire = tout était de la valence.\n", + "4. **Causalité** — l'organe `ict.causal_engine` impose ses bras (aléatoire apparié, sham,\n", + " doses symétriques, Holm, dommage hors cible) ; le verdict `selective` n'est accordé\n", + " qu'aux interventions propres.\n", + "5. **Comportement** — le steering vivant et le banc d'action ferment la chaîne\n", + " état / readout / comportement de l'épic #15475, canaux jamais sommés.\n", + "\n", + "La réponse à la question du titre — *une direction linéaire ?* — est le tableau de la\n", + "section 4 plus le verdict de la section 5 : ni l'un sans l'autre.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-limites", + "metadata": {}, + "source": [ + "## Limites\n", + "\n", + "- **Échelle** — le papier travaille à grande échelle (Qwen 2.5 lourd) ; notre substrat de\n", + " série (2B) peut encoder la douleur de manière plus grossière. Une diagonale faible ici\n", + " n'invalide pas le résultat à grande échelle — elle borne ce qui est *falsifiable à notre\n", + " échelle*.\n", + "- **Corpus** — 24 énoncés par groupe, écrits à la main, en anglais : les directions sont\n", + " sensibles au registre et à la longueur ; l'appariement thématique atténue mais\n", + " n'élimine pas les confonds lexicaux (le mot *pain* n'apparaît dans aucun énoncé —\n", + " c'est voulu — mais les champs lexicaux restent discernables).\n", + "- **Base, pas instruct** — le substrat est un modèle de base ; ses continuations\n", + " « émotionnelles » au token suivant sont une lecture de readout, pas une introspection.\n", + "- **Une couche, une position** — le contrat causal est réglé sur `MID` et la dernière\n", + " position ; l'exercice 2 ouvre la question de la fenêtre en profondeur, laissée ouverte.\n", + "- **Statut épistémique** — porté par la [matrice de dissociations](../../../docs/ict/dissociations-matrix.md)\n", + " de la série, pas par ce carnet seul.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (py310-gpu)", + "language": "python", + "name": "py310-gpu" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + }, + "papermill": { + "default_parameters": {}, + "duration": 63.279904, + "end_time": "2026-10-02T04:40:58.086834", + "environment_variables": {}, + "exception": null, + "input_path": "ICT-47-PainAxisDistillation-Python.ipynb", + "output_path": "ICT-47-exec.ipynb", + "parameters": {}, + "start_time": "2026-10-02T04:39:54.806930", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/MyIA.AI.Notebooks/IIT/ICT-Series/README.md b/MyIA.AI.Notebooks/IIT/ICT-Series/README.md index 03640fdfb7..9f58be5be0 100644 --- a/MyIA.AI.Notebooks/IIT/ICT-Series/README.md +++ b/MyIA.AI.Notebooks/IIT/ICT-Series/README.md @@ -251,6 +251,7 @@ Les grains de **distillation externe** (veille → notebook) s'ajoutent aux stra | [ICT-41-SAE-GeometrieFeatures](ICT-41-SAE-GeometrieFeatures.ipynb) | **Géométrie des features SAE : galaxy, atome, dense — R04 × R09 sur nos traces** *(banc numpy-only sur les traces npz committées du harnais #10355, GPU-free, issue #16747)* : les trois axes de la géométrie latente mesurés trained vs contrôle sur deux lentilles (SAE Qwen-Scope et J-Lens, couche 16) — **galaxy répliquée** (spectre 1/f via astuce Gram, pentes OLS −1,04/−1,08 trained vs −1,50/−1,78 contrôles, diagnostic Clauset-Shalizi-Newman honnête : Hill α 2,0-2,6, KS ≤ 0,04, logLR power-law ≫ exponentielle) ; **cristaux R04 absents à la couche 16** (séparabilité split-half 68 %/0 % trained vs 100 % contrôles = contexte dominant, séparabilité contrôle = artefact de forme de surface) ; **thèse R09 « dense = features, not bugs » répliquée par deux méthodes indépendantes** (proxy corrélation de colonnes : 0 paire < −0,5 trained malgré le biais de censure top-k, vs 4 au contrôle ; Eq 1 verbatim sur SAE CPU de l'organe `ict/sae_dictionary` : médianes +0,01/+0,10 trained vs +0,63/+0,79 contrôles, avec le caveat d'initialisation liée W_enc = W_dec.T) ; classe position-tracking 24 vs 4 latents ; verdicts négatifs affichés comme tels (distractors en milieu d'axe, entropie k-NN inconclusive, PC1-longueur partielle à 1,5 % de variance, Gemma absent des traces — rapporté) | [#16747](https://github.com/jsboige/CoursIA/issues/16747) | | [ICT-42-Crosscoder-Distillation](ICT-42-Crosscoder-Distillation.ipynb) | **Crosscoder : diffuser deux modèles, distilled reasoning vs base — R05 Baek-Tegmark à échelle mini** *(GPU, deux 1.5B chargés séquentiellement + ablation lockstep CPU/GPU, issue #16749)* : entraîne un crosscoder Eq 3-7 verbatim sur 307 200 tokens d'OpenThoughts-114k (traces R1, capture problem + début de trace, couche mi-profondeur 14/28), paire DeepSeek-R1-Distill-Qwen-1.5B vs Qwen2.5-Math-1.5B — la *statistique* du papier se reproduit (NRN médian 0,498 conforme Fig 1, queues fines 0,9 % distilled-only / 2,1 % base-only, EV 0,988/0,976), mais pas sa *lecture sémantique* : **enrichissement mixte** sur les marqueurs (Therefore 1,68× enrichi sur 36 features distilled-only, Wait 0,54 déplété — signal partiel affiché), **ablation causale sensible au seuil** (chirurgical NRN>0,6 : inoffensif, 11 features ; large NRN>0,5 seuil papier : 1 665 features, dégénérescence en boucle — 600× moins de tokens que le papier), parallelogram genre base 1,0694 < distillé 1,0832 (non-reproduction 14B affichée). Leçon méthodo **trois jambes de preuve** : feature-token suggère, feature-raisonnement décide (ici : signal partiel), causale tranche (ici : réfute la transposition naïve). Déterminisme strict — chaque ré-exécution redonne ces chiffres | [#16749](https://github.com/jsboige/CoursIA/issues/16749) | | [ICT-44-GeometryOfTruth-Python](ICT-44-GeometryOfTruth-Python.ipynb) | **La géométrie de la vérité — direction vraie/faux dans l'espace latent** *(Marks & Tegmark COLM 2024, arXiv 2310.06824, GPU, kernel coursia-ml-training, issue #16890)* : réplication de la chaîne localiser/mesurer/trancher sur la paire Qwen 1.5B (base Math vs R1-Distill) avec les datasets officiels — patching (argmax = le point final, couche 13 : groupe (b) du papier), PCA + alignement ancré par label (cities pc1 0,86 ; `larger~smaller` **antipodal** −0,89 aux couches médianes puis aligné +0,84 au bloc 20 — la trajectoire 13B→70B du papier visible couche par couche dans un seul modèle), sondes MM/LR/MM-IID × 5 entraînements × 7 tests (§5.3 répliqué : négation tue le transfert 0,49, énoncés+négations le réparent 0,74 et débloquent l'abstraction MM 0,84 vs LR 0,46 ; contrôle `likely` probabilité≠vérité), interventions NIE honnêtes (≤0,05 à 1.5B contre un patch à 0,88 : corrélation sans levier causal — ordre MM>LR répliqué, magnitudes non), pont distillation hors papier (cos θmm 0,54–0,76 : direction héritée mais diluée 0,86→0,66, lien ICT-42), regard 7B 4-bit qualitatif (RECOVERABLE-MACHINE documenté) | [#16890](https://github.com/jsboige/CoursIA/issues/16890) | +| [ICT-47-PainAxisDistillation-Python](ICT-47-PainAxisDistillation-Python.ipynb) | **L'axe douleur — direction linéaire unique ou conglomérat ?** *(Tagliabue/Dung/Berg arXiv 2609.16247 adapté à l'échelle de la série, GPU local RTX 3080 Ti, kernel py310-gpu, DEEP #18746)* : corpus 9×24 énoncés appariés en thème (5 catégories de douleur + peur/tristesse/valence/neutre), directions diff-in-means sur Qwen3.5-2B-Base — unité mesurée (cos inter-douleur 0,65 ; cluster psycho/sociale/morale 0,76–0,83 ; cognitive la plus distante ; recouvrement deuil `psych~sadness` 0,82), sondes `ict.probes` avec transfert massif vers peur/tristesse avant retrait, **disjonction partielle honnête** : après retrait de la détresse partagée pain⊥fear 0,71→**0,31** et pain⊥sadness 0,76→**0,45** (diagonales fear/sadness 0,94 tenues) mais pain~valence 0,76→**0,94** non dissociée à 2B — l'échelle du papier ne résout pas ici ; contrat causal `ict.causal_engine` complet (steer top-64 dims : cible +0,18 vs random apparié 0,00 / sham 0,00, Holm p=0,000, off-target 0,0000, dose-response symétrique −1,53→−1,16), steering vivant faible mais ordonné (famille pain +0,031 nat vs random +0,009), banc d'action 8 scénarios signé (pain−D +0,09 / pain+D −0,05 / random 0,00) — levier causal spécifique, court à cette échelle | [#18808](https://github.com/jsboige/CoursIA/issues/18808), [#18746](https://github.com/jsboige/CoursIA/issues/18746) | ![Spatial Computing — stencil et expression](assets/readme/ict40-spatialcomputing.png) From 36cf8afe1c5be9e9dfb26fcf58c631b0317f6ebd Mon Sep 17 00:00:00 2001 From: jsboige Date: Fri, 2 Oct 2026 06:58:15 +0200 Subject: [PATCH 2/5] feat(ict,#18808): ICT-47 section 9 (regard 9B) + 3 lignes matrice dissociations MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Le 9B réplique le 2B : pain/fear 0.27, pain/sadness 0.43, mais pain~valence 0.93 non résolu sur l'échelle 2B-9B de la série — borne honnête documentée. Statut épistémique du carnet désormais porté par la matrice (grade INTERNAL). See #18746, See #18808 Co-Authored-By: Claude Sonnet 5.5 --- .../ICT-47-PainAxisDistillation-Python.ipynb | 369 +++++++++++++++--- docs/ict/dissociations-matrix.md | 3 + 2 files changed, 311 insertions(+), 61 deletions(-) diff --git a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb index 4fa6544e3a..c7a143396d 100644 --- a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb +++ b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb @@ -28,9 +28,11 @@ "id": "p47-statut", "metadata": {}, "source": [ - "> **Statut épistémique** — **Sans verdict à ce jour** : aucune ligne de la [matrice de\n", - "dissociations](../../../docs/ict/dissociations-matrix.md) ne concerne ce notebook ; son\n", - "statut épistémique sera porté par la matrice le cas échéant.\n", + "> **Statut épistémique** — **Dissociation partielle, grade INTERNAL** : les lignes de la\n", + "[matrice de dissociations](../../../docs/ict/dissociations-matrix.md) couvrant ce notebook\n", + "registrent trois claims — unité des cinq douleurs (2B), disjonction pain/fear/sadness\n", + "tenue après retrait **mais pain/valence non résolue à 2B** (question re-posée au 9B en\n", + "section 9), causalité de l'axe sous contrat Gate 24 complet.\n", ">\n", "> **Filiation** — Epic DEEP #18746, sous-issue #18808. Le papier de référence travaille sur\n", "> Qwen 2.5 à grande échelle ; nous exerçons la question sur **le substrat de la série**\n", @@ -92,10 +94,10 @@ "id": "p47-preamble", "metadata": { "papermill": { - "duration": 4.978425, - "end_time": "2026-10-02T04:40:01.909448", + "duration": 4.121448, + "end_time": "2026-10-02T04:53:33.006727", "exception": false, - "start_time": "2026-10-02T04:39:56.931023", + "start_time": "2026-10-02T04:53:28.885279", "status": "completed" } }, @@ -140,10 +142,10 @@ "id": "p47-corpus", "metadata": { "papermill": { - "duration": 0.048406, - "end_time": "2026-10-02T04:40:01.971114", + "duration": 0.027704, + "end_time": "2026-10-02T04:53:33.039093", "exception": false, - "start_time": "2026-10-02T04:40:01.922708", + "start_time": "2026-10-02T04:53:33.011389", "status": "completed" } }, @@ -429,10 +431,10 @@ "id": "p47-stats", "metadata": { "papermill": { - "duration": 0.029967, - "end_time": "2026-10-02T04:40:02.032135", + "duration": 0.032432, + "end_time": "2026-10-02T04:53:33.086499", "exception": false, - "start_time": "2026-10-02T04:40:02.002168", + "start_time": "2026-10-02T04:53:33.054067", "status": "completed" } }, @@ -485,10 +487,10 @@ "id": "p47-load", "metadata": { "papermill": { - "duration": 15.117534, - "end_time": "2026-10-02T04:40:17.179842", + "duration": 13.809067, + "end_time": "2026-10-02T04:53:46.910851", "exception": false, - "start_time": "2026-10-02T04:40:02.062308", + "start_time": "2026-10-02T04:53:33.101784", "status": "completed" } }, @@ -498,7 +500,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "VRAM libre ~15220 MiB -> plafond GPU 3.5 GiB (reliquat CPU)\n" + "VRAM libre ~15239 MiB -> plafond GPU 3.5 GiB (reliquat CPU)\n" ] }, { @@ -518,7 +520,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b63b68799a347e898ccdd62fb4d77e4", + "model_id": "016bf147f1f140a68a016c86f1ef31b5", "version_major": 2, "version_minor": 0 }, @@ -575,10 +577,10 @@ "id": "p47-capture", "metadata": { "papermill": { - "duration": 12.215614, - "end_time": "2026-10-02T04:40:29.402615", + "duration": 13.9164, + "end_time": "2026-10-02T04:54:00.833052", "exception": false, - "start_time": "2026-10-02T04:40:17.187001", + "start_time": "2026-10-02T04:53:46.916652", "status": "completed" } }, @@ -588,7 +590,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "capture 9 groupes x 4 couches en 12.2s ; panneau MID : (24, 2048)\n" + "capture 9 groupes x 4 couches en 13.9s ; panneau MID : (24, 2048)\n" ] } ], @@ -660,10 +662,10 @@ "id": "p47-dim", "metadata": { "papermill": { - "duration": 0.036567, - "end_time": "2026-10-02T04:40:29.465657", + "duration": 0.034602, + "end_time": "2026-10-02T04:54:00.895652", "exception": false, - "start_time": "2026-10-02T04:40:29.429090", + "start_time": "2026-10-02T04:54:00.861050", "status": "completed" } }, @@ -721,10 +723,10 @@ "id": "p47-pca", "metadata": { "papermill": { - "duration": 3.894668, - "end_time": "2026-10-02T04:40:33.368121", + "duration": 3.918931, + "end_time": "2026-10-02T04:54:04.820118", "exception": false, - "start_time": "2026-10-02T04:40:29.473453", + "start_time": "2026-10-02T04:54:00.901187", "status": "completed" } }, @@ -792,10 +794,10 @@ "id": "p47-probes", "metadata": { "papermill": { - "duration": 9.191425, - "end_time": "2026-10-02T04:40:42.589398", + "duration": 7.805864, + "end_time": "2026-10-02T04:54:12.680990", "exception": false, - "start_time": "2026-10-02T04:40:33.397973", + "start_time": "2026-10-02T04:54:04.875126", "status": "completed" } }, @@ -878,10 +880,10 @@ "id": "p47-exo1-stub", "metadata": { "papermill": { - "duration": 0.017087, - "end_time": "2026-10-02T04:40:42.635093", + "duration": 0.031514, + "end_time": "2026-10-02T04:54:12.774642", "exception": false, - "start_time": "2026-10-02T04:40:42.618006", + "start_time": "2026-10-02T04:54:12.743128", "status": "completed" } }, @@ -925,10 +927,10 @@ "id": "p47-resid", "metadata": { "papermill": { - "duration": 0.027195, - "end_time": "2026-10-02T04:40:42.682200", + "duration": 0.041127, + "end_time": "2026-10-02T04:54:12.851895", "exception": false, - "start_time": "2026-10-02T04:40:42.655005", + "start_time": "2026-10-02T04:54:12.810768", "status": "completed" } }, @@ -993,10 +995,10 @@ "id": "p47-matrice4d", "metadata": { "papermill": { - "duration": 7.494357, - "end_time": "2026-10-02T04:40:50.182783", + "duration": 7.48525, + "end_time": "2026-10-02T04:54:20.352501", "exception": false, - "start_time": "2026-10-02T04:40:42.688426", + "start_time": "2026-10-02T04:54:12.867251", "status": "completed" } }, @@ -1104,10 +1106,10 @@ "id": "p47-organ", "metadata": { "papermill": { - "duration": 0.125423, - "end_time": "2026-10-02T04:40:50.367192", + "duration": 0.0876, + "end_time": "2026-10-02T04:54:20.476586", "exception": false, - "start_time": "2026-10-02T04:40:50.241769", + "start_time": "2026-10-02T04:54:20.388986", "status": "completed" } }, @@ -1226,10 +1228,10 @@ "id": "p47-doses", "metadata": { "papermill": { - "duration": 0.037621, - "end_time": "2026-10-02T04:40:50.412593", + "duration": 0.024288, + "end_time": "2026-10-02T04:54:20.507266", "exception": false, - "start_time": "2026-10-02T04:40:50.374972", + "start_time": "2026-10-02T04:54:20.482978", "status": "completed" } }, @@ -1311,10 +1313,10 @@ "id": "p47-steer", "metadata": { "papermill": { - "duration": 2.730556, - "end_time": "2026-10-02T04:40:53.200808", + "duration": 1.963934, + "end_time": "2026-10-02T04:54:22.526172", "exception": false, - "start_time": "2026-10-02T04:40:50.470252", + "start_time": "2026-10-02T04:54:20.562238", "status": "completed" } }, @@ -1415,10 +1417,10 @@ "id": "p47-banc", "metadata": { "papermill": { - "duration": 2.827891, - "end_time": "2026-10-02T04:40:56.061097", + "duration": 1.718919, + "end_time": "2026-10-02T04:54:24.286987", "exception": false, - "start_time": "2026-10-02T04:40:53.233206", + "start_time": "2026-10-02T04:54:22.568068", "status": "completed" } }, @@ -1527,10 +1529,10 @@ "id": "p47-exo2-stub", "metadata": { "papermill": { - "duration": 0.024017, - "end_time": "2026-10-02T04:40:56.124336", + "duration": 0.014444, + "end_time": "2026-10-02T04:54:24.332789", "exception": false, - "start_time": "2026-10-02T04:40:56.100319", + "start_time": "2026-10-02T04:54:24.318345", "status": "completed" } }, @@ -1567,10 +1569,10 @@ "id": "p47-exo3-stub", "metadata": { "papermill": { - "duration": 0.024836, - "end_time": "2026-10-02T04:40:56.169764", + "duration": 0.027863, + "end_time": "2026-10-02T04:54:24.379115", "exception": false, - "start_time": "2026-10-02T04:40:56.144928", + "start_time": "2026-10-02T04:54:24.351252", "status": "completed" } }, @@ -1587,6 +1589,248 @@ "pass\n" ] }, + { + "cell_type": "markdown", + "id": "p47-sec9", + "metadata": {}, + "source": [ + "## 9. Échelle : un regard 9B — la douleur se détache-t-elle de la valence ?\n", + "\n", + "Le verdict 2B laisse une question ouverte : pain⊥fear et pain⊥sadness tiennent après\n", + "retrait, mais **pain~valence reste à 0,94** — le papier rapporte la disjonction complète\n", + "à grande échelle. Le substrat 9B de la série (Qwen3.5-9B-Base, 32 blocs, d=4096, en cache\n", + "local) permet de demander si l'échelle suffit à séparer douleur et valence : on rejoue\n", + "corpus, capture et disjonction (sections 1-4) à l'identique, sans le volet causal.\n" + ] + }, + { + "cell_type": "code", + "id": "p47-9b-load", + "metadata": { + "papermill": { + "duration": 16.474887, + "end_time": "2026-10-02T04:54:40.872022", + "exception": false, + "start_time": "2026-10-02T04:54:24.397135", + "status": "completed" + } + }, + "execution_count": 18, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VRAM libre ~15133 MiB -> plafond GPU 9.0 GiB (9B bf16 shardé GPU+CPU)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1bc6c02dccc346b7afc4d57bcba2653b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading bytes: | 0.00B " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "28d4904dc75544339ee099de5985be41", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Reconstructing (incomplete total...): | | 0.00B / 0.00B " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fe3b790d49514f4ba87d35a7c5d1b6d8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 4 files: 0%| | 0/4 [00:00 plafond GPU {cap9:.1f} GiB (9B bf16 shardé GPU+CPU)\")\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " MODEL_9B, dtype=DTYPE, device_map=\"auto\",\n", + " max_memory={0: f\"{cap9:.1f}GiB\", \"cpu\": \"16GiB\"})\n", + "model.eval()\n", + "ENC_DEV = next(model.parameters()).device\n", + "tok = AutoTokenizer.from_pretrained(MODEL_9B)\n", + "tok.padding_side = \"left\"\n", + "if tok.pad_token is None:\n", + " tok.pad_token = tok.eos_token\n", + "N9 = len(model.model.layers)\n", + "MID9 = N9 // 2\n", + "CAP9 = [MID9] # la disjonction 9B ne lit que la couche mediane (emprise memoire)\n", + "print(f\"{MODEL_9B} : {N9} blocs, d={model.config.hidden_size}, capture {CAP9}, MID9={MID9}\")\n" + ] + }, + { + "cell_type": "code", + "id": "p47-9b-disj", + "metadata": { + "papermill": { + "duration": 155.111136, + "end_time": "2026-10-02T04:57:15.990876", + "exception": false, + "start_time": "2026-10-02T04:54:40.879740", + "status": "completed" + } + }, + "execution_count": 19, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "capture 9B en 94s ; panneau MID : (24, 4096)\n", + "9B unite : cos inter-douleur moyen 0.60 (min 0.46) [2B mesuré : 0.65 / 0.37]\n", + "cosinus 9B APRES retrait de D_shared :\n", + " pain_all fear sadness valence\n", + "pain_all 1.00 0.27 0.43 0.93\n", + "fear 0.27 1.00 0.33 0.52\n", + "sadness 0.43 0.33 1.00 0.66\n", + "valence 0.93 0.52 0.66 1.00\n", + "[2B mesuré : pain~fear 0.31 | pain~sadness 0.45 | pain~valence 0.94]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "matrice 4D 9B (apres retrait) :\n", + " corpus sonde:pain_all sonde:fear sonde:sadness sonde:valence\n", + "pain_all 0.74 0.30 0.36 0.59\n", + " fear 0.52 0.92 0.65 0.69\n", + " sadness 0.69 0.60 0.94 0.83\n", + " valence 0.92 0.81 0.88 0.98\n", + "9B : diagonale 0.90 | hors-diag 0.65 | ecart 0.24 [2B : 0.86 | 0.67 | 0.19]\n" + ] + } + ], + "source": [ + "# Rejoue capture + disjonction (sections 1-4) a l'identique sur le 9B.\n", + "t0 = time.time()\n", + "CAP = capture(GROUPS, CAP9, batch=8)\n", + "ACTS = {g: {l: CAP[l][g] for l in CAP9} for g in GROUPS}\n", + "MID = MID9\n", + "print(f\"capture 9B en {time.time()-t0:.0f}s ; panneau MID : {ACTS['pain_physical'][MID].shape}\")\n", + "\n", + "dirs9 = {c: dim_direction(c, \"neutral\", MID) for c in PAIN_CATS}\n", + "cos9 = np.array([[float(dirs9[a] @ dirs9[b]) for b in PAIN_CATS] for a in PAIN_CATS])\n", + "off9 = cos9[~np.eye(len(PAIN_CATS), dtype=bool)]\n", + "print(f\"9B unite : cos inter-douleur moyen {off9.mean():.2f} (min {off9.min():.2f}) \"\n", + " f\"[2B mesuré : 0.65 / 0.37]\")\n", + "\n", + "X_all = np.concatenate([ACTS[g][MID] for g in GROUPS])\n", + "y_emo = np.array([1 if g in EMO_NEG else 0 for g in GROUPS for _ in range(24)])\n", + "D_shared = X_all[y_emo == 1].mean(0) - X_all[y_emo == 0].mean(0)\n", + "D_shared = D_shared / np.linalg.norm(D_shared)\n", + "\n", + "before = {a: dir_of(a) for a in AXES}\n", + "ra = {a: resid(dir_of(a)) for a in AXES}\n", + "after = {a: ra[a] / np.linalg.norm(ra[a]) for a in AXES}\n", + "At = np.array([[float(after[a] @ after[b]) for b in AXES] for a in AXES])\n", + "\n", + "print(\"cosinus 9B APRES retrait de D_shared :\")\n", + "print(pd.DataFrame(At, index=AXES, columns=AXES).round(2).to_string())\n", + "\n", + "print(\"[2B mesuré : pain~fear 0.31 | pain~sadness 0.45 | pain~valence 0.94]\")\n", + "\n", + "probes9 = {}\n", + "for ax in AXES:\n", + " X, y = corpus_res(ax)\n", + " thetas, mu = train_probes(X, y)\n", + " probes9[ax] = (thetas[\"MM\"], mu)\n", + "rows = []\n", + "for tg in AXES:\n", + " Xte, yte = corpus_res(tg)\n", + " row = {\"corpus\": tg}\n", + " for ax in AXES:\n", + " th, mu = probes9[ax]\n", + " row[f\"sonde:{ax}\"] = round(probe_accuracy(th, mu, Xte, yte), 2)\n", + " rows.append(row)\n", + "df9 = pd.DataFrame(rows)\n", + "\n", + "print(\"matrice 4D 9B (apres retrait) :\")\n", + "print(df9.to_string(index=False))\n", + "diag9 = [df9.loc[i, f\"sonde:{AXES[i]}\"] for i in range(len(AXES))]\n", + "offm9 = df9[[f\"sonde:{a}\" for a in AXES]].values[~np.eye(len(AXES), dtype=bool)]\n", + "print(f\"9B : diagonale {np.mean(diag9):.2f} | hors-diag {offm9.mean():.2f} \"\n", + " f\"| ecart {np.mean(diag9)-offm9.mean():.2f} [2B : 0.86 | 0.67 | 0.19]\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "p47-lecture-9b", + "metadata": {}, + "source": [ + "### Lecture — le regard 9B (mesuré)\n", + "\n", + "Le 9B **réplique le 2B presque terme à terme** : pain⊥fear 0,27 (2B : 0,31), pain⊥sadness\n", + "0,43 (2B : 0,45), l'écart de la matrice 4D monte à 0,24 (2B : 0,19) et la diagonale pain\n", + "se redresse (0,74 contre 0,65). Mais **pain~valence reste à 0,93** (2B : 0,94) et la\n", + "sonde valence lit toujours pain_all à 0,92. Sur toute l'échelle de la série (2B → 9B),\n", + "la disjonction douleur/valence du papier **ne se résout pas** : soit elle exige une\n", + "échelle au-delà du 9B, soit l'axe « valence » tel que construit (positif contre neutre)\n", + "EST l'axe de négativité générale dans ces substrats — les deux lectures restent ouvertes,\n", + "et c'est la borne honnête de ce carnet : les axes peur et tristesse sont défendables,\n", + "l'indépendance douleur/valence ne l'est pas à l'échelle de la série.\n" + ] + }, { "cell_type": "markdown", "id": "p47-synthese", @@ -1609,7 +1853,10 @@ " état / readout / comportement de l'épic #15475, canaux jamais sommés.\n", "\n", "La réponse à la question du titre — *une direction linéaire ?* — est le tableau de la\n", - "section 4 plus le verdict de la section 5 : ni l'un sans l'autre.\n" + "section 4 plus le verdict de la section 5 : ni l'un sans l'autre.\n", + "\n", + "6. **Échelle** — la section 9 rejoue la disjonction sur le 9B de la série : la question\n", + "pain/valence ouverte au 2B y reçoit sa réponse mesurée (lecture en section 9).\n" ] }, { @@ -1656,14 +1903,14 @@ }, "papermill": { "default_parameters": {}, - "duration": 63.279904, - "end_time": "2026-10-02T04:40:58.086834", + "duration": 233.330679, + "end_time": "2026-10-02T04:57:20.294981", "environment_variables": {}, "exception": null, "input_path": "ICT-47-PainAxisDistillation-Python.ipynb", "output_path": "ICT-47-exec.ipynb", "parameters": {}, - "start_time": "2026-10-02T04:39:54.806930", + "start_time": "2026-10-02T04:53:26.964302", "version": "2.6.0" } }, diff --git a/docs/ict/dissociations-matrix.md b/docs/ict/dissociations-matrix.md index 77712d9b7a..44805fcccd 100644 --- a/docs/ict/dissociations-matrix.md +++ b/docs/ict/dissociations-matrix.md @@ -122,6 +122,9 @@ Conventions : | **Confabulation — consécration de l'erreur ⟂ broadcast non-discriminant** (workspace à seuil, case 15 #8182, **seconde opérationnalisation après case 3 [#15915](https://github.com/jsboige/CoursIA/pull/15915) SAE INCONCLUSIF PAR CONSTRUCTION** — pattern case 6 → case 14) | Une workspace à adoption seuillée (alignement contenu-lectures + gain β d'ignition) **consacre** une erreur systématique cohérente (inversion d'un sous-bloc de b latents sur K) presque aussi efficacement que le vrai contenu (CF = propa(b=2)/propa(b=0) ≥ 0.80) tout en restant **discriminante** : contenu faux structuré et contenu aléatoire propagent à des taux séparés (Δ ≥ 0.15), et l'inversion globale est rejetée (propa(b=K) ≈ 0) | `s` —, `q` ✓ (contenu vrai/faux/aléatoire), `π` —, `W` ✓ (ignition + gain β) | CF, Δ = propa(b=2) − médiane propa(aléatoire), et grille dose-réponse b ∈ {0, 2, 4, 6, 8, 16} (non-croissante, chute totale ≥ 0.80) | null adversarial = M=200 contenus iid uniformes (pas de q) ; P4 rejet de l'inversion globale (≤ null − 0.10) ; P5 élévation du contenu aléatoire par l'ignition (β=0 vs β=0.35, ≥ 0.15) | K=16 latents ±1, N=48 consommateurs (s=4, σ_read=0.60), β=0.35, θ=0.45 ; 5 seeds × M_null=200 ; CPU-only | reproduced_in_toy_model + pre_enregistrement | **TESTÉ (CONFIRMÉ — consécration ET discrimination, sur substrat contrôlé)** | Motif de la seconde opérationnalisation : le taux SAE de la case 3 saturait (0.80-1.00) sur tous les bras — l'instrument ne pouvait trancher ; ici la fausseté du contenu est un **paramètre du générateur** (grille dose-réponse impossible sur SAE). Mesures 5/5 graines : CF 0.917-1.000 (médiane 0.938) ; Δ +0.500 à +0.542 ; inversion globale 0.000 partout ; lift du contenu aléatoire par l'ignition 0.19-0.23 → 0.42-0.44. Amendement v1→v2 AVANT re-run ([c.5709716652](https://github.com/jsboige/CoursIA/issues/8182#issuecomment-5709716652)) : P1 « strictement décroissante » insatisfiable par construction au plafond (quantification 1/48, pas plat seed 7 épinglé en test) ; P5 bras vrai saturé (adoption locale seule 0.92-1.00 — redondance des canaux à ce SNR, documentée comme donnée), gain mesuré sur le bras aléatoire. Les jambes de substance (P2 consécration, P3 dissociation, P4 rejet) n'ont jamais été re-seuillées. See [#8182](https://github.com/jsboige/CoursIA/issues/8182) case 15 (po-2024, 2026-09-17). | | **Alertness tonique ⟂ saillance phasique** (gain non-partitionné, case 16 #8182 — Metzinger MPE 2020, complément dual de la case 4 : conscience **sans** selfhood) | Un gain de disponibilité **lent, global, sans contenu** (l'étage tonique τ, le « preparatory to treat and respond » de Posner) amplifie **uniformément** la propagation de contenu nouveau : P1 `R_alert = propa(τ haut)/propa(τ bas)` médiane ∈ [1.2, 3.0] ET P2 `max_k g_k/min_k g_k ≤ 1.25` (≥ 4/5 graines) ; tandis qu'un module de saillance **phasique** orienté-contenu au même budget de gain total **hiérarchise** : P3 `max_k g_k/min_k g_k ≥ 2.0` (≥ 4/5 graines) — l'attente de connaissance amplifie sans sélectionner, la saillance sélectionne sans être une attente | `s` ✓ (bras adversarial phasique), `q` ✓ (contenus injectés), `π` —, `W` — (manipulateur = τ, pas d'ignition) | `R_alert`, gain par canal `g_k = propa_k(τ haut)/propa_k(τ bas)`, ratio `max_k/min_k` par bras (tonique vs phasique) | Null (a) substrat sans étage tonique à couplage global apparié : `R_alert` médiane ∈ [0.9, 1.1] (c'est la hiérarchie lente/rapide qui porte le gain) ; Null (b) phasique non-hiérarchisé (`< 2.0`) → `NON CONCLUSIF_INSTRUMENT` ; portes plancher/plafond : propa(τ bas) < 0.05 ou propa(τ haut) > 0.95 sur ≥ 3/5 graines → `NON CONCLUSIF_INSTRUMENT` (leçon cases 8b/8c) | Jouet CPU-only 2 étages : τ scalaire × K=4 canaux, N=48 consommateurs lisant s=2 canaux (contenu unicast, adoption seuillée — famille 14/15) ; bras phasique à budget apparié **par contenu moyen** (compétition inter-contenus) ; 5 seeds (0/1/7/42/99) × M=200 paires | reproduced_in_toy_model + pre_enregistrement | **TESTÉ (SUPPORTED — dissociation à deux régimes)** | Amendement pré-exécution v1→v2 scellé AVANT le run (plafond structurel du bras phasique v1 : à masse de seuil appariée par consommateur, max_k g_k ≈ 1.6 < 2.0 par concavité du gain — le défaut case 15/P1 ; re-spécifié en compétition inter-contenus à appariement par contenu moyen, bandes inchangées). Calibration gelée sur graines disjointes (11/22/33) : θ=2.0, Δ_to=0.7 (propa(τ bas) médiane 0.245, R_alert 1.603 — jamais relues sur les graines de test). Mesures 5/5 graines : R_alert 1.601–1.634 (médiane **1.619** ∈ [1.2, 3.0]) ; uniformité tonique 1.040–1.108 (5/5 ≤ 1.25) ; hiérarchie phasique 2.087–2.210 (5/5 ≥ 2.0 — **marge mince** : min 2.087, rapportée) ; propa(τ bas) 0.240–0.247, propa(τ haut) 0.389–0.396 (aucune porte) ; contrôle mécanique exact 5/5. Banc : `ict/tonic_alertness_gain.py` + `ict/tests/test_tonic_alertness_gain.py` (10/10) + `ict/results/tonic_alertness_gain_results.json`. **Porte plafond dormante dans la zone utile** (contenu unicast : les non-lecteurs du canal dominant restent à adoption ~0.5, propa_haut plafonne ~0.75 — la porte ne tire que sur seuil négatif profond, documentée au test). Source primaire lue intégralement et archivée au gisement : Metzinger, « Minimal phenomenal experience », *PhiMiSci* 1(I)7 (2020), DOI [10.33766/jphims.1128](https://doi.org/10.33766/jphims.1128). La case 4 (MPS) testait le selfhood sur SAE (INCONCLUSIF, saturation broadcast) ; la case 16 teste la thèse §4 — l'expérience minimale comme modèle de **capacité** sans contenu, propriété structurelle = **non-partition** (« the as-yet-unpartitioned nature of this space », l.1234). Greffe strates-adjonctions (lecture grade C) : l'espace « as-yet-unpartitioned » = avant l'adjonction de partition (Cohesion ∮ de Schreiber) — horizon d'interprétation, pas un claim testé. Pré-enregistrement intégral : [`metzinger-mpe-pre-enregistrement.md`](metzinger-mpe-pre-enregistrement.md). See [#8182](https://github.com/jsboige/CoursIA/issues/8182) case 16 (po-2027, 2026-09-21). | | **Attracteur harmonique ⟂ énergie** (valence comme symétrie, case 17 #8182 — STV Gómez-Emilsson/Johnson : thèse centrale QRI, même premier auteur que les cases 6/14 mais claim distinct — le pattern MPS/MPE de la case 16) | À couplage faible (régime des langues d'Arnold), l'indice dynamique de qualité d'attracteur `v` (le « settling on basins of symmetry » de la source, opérationnalisé en **fraction de paires verrouillées m:n** — amendement v3) suit la consonance σ de la configuration de fréquences : **P1** Spearman ρ(v, σ) ≥ 0.6 (≥ 4/5 graines) ; **P2** sous perturbation d'énergie ±10 %, \|ρ(v, E)\| ≤ 0.2 (≥ 4/5) — *« It's not the energy that feels good. It is the end result, the attractor »* ; **P3** discriminateur jhana/crise à cohérence brute initiale appariée : `CV(r\|non-harmonique) / CV(r\|harmonique) ≥ 2.0` (≥ 4/5) — *la cohérence haute ne suffit pas, « it must be harmonic structure »* | `s` —, `q` ✓ (configuration de fréquences = l'objet formel isomorphe), `π` ✓ (v comme valence-proxy — la lecture STV), `W` — | ρ(v, σ) ; corrélation partielle ρ(v, E) sous perturbation ±10 % ; ratio des CV de r par famille (H/N) ; r̄ par famille | Null (a) σ permuté : \|ρ\| ≤ 0.2 en médiane (contrôle de chance) ; Null (b) couplage fort (K_c × 8) : P1 ρ < 0.2 → `NON CONCLUSIF_INSTRUMENT` ; portes : r̄(H) < 0.3 ou > 0.95 sur ≥ 3/5 graines → `NON CONCLUSIF_INSTRUMENT` ; contrôle mécanique : K_c = 0, phases libres dans l'enveloppe analytique ± tolérance | Kuramoto K=8 à couplage global **harmonique fixe non accordé** `H(φ) = Σ_{h≤4} sin(hφ)/h` (amendement v2 : le couplage sin pur ne produit que des locks 1:1), **CPU-only numpy RK4 vectorisé** (DT 5e-4, T 40, sous-échantillonnage 10) ; M=120 configurations-échelle + 40 paires appariées par graine ; bras N de P3 = 6 fréquences proches + 2 irrationnelles (étallement apparié) ; **exécution parallèle** (5 graines + jambe forte, 6 jobs, 20 cœurs — 16,8 min) ; **K_c = 32.0 gelé** sur graines disjointes 11/22/33 (r̄(H) médiane 0.6587 ∈ [0.5, 0.8] — fenêtre scellée, **pas** le repli variance) ; 5 graines de test (0/1/7/42/99) | pre_enregistrement | **TESTÉ (NON CONCLUSIF_INSTRUMENT — null b)** | Sources primaires archivées au gisement (recherche auteur+titre, identité vérifiée) : Johnson, *Principia Qualia* (2016, PDF 84 p., opentheory.net) ; Gómez-Emilsson, « STV 2020 Overview » (qri.org) ; Johnson, « Qualia Formalism and a STV » (2023) — **spot-checks firsthand 5/5** des citations pivots (« harmony feels good because it's symmetry over time » ; « not the energy that feels good / the attractor » ; jhana-vs-crise harmonic structure ; 17 wallpaper groups ; Plomp & Levelt 1965). Anti-duplication : la case s ⟂ π (Vervaeke hook) teste l'orthogonalité des **canaux** saillance/valence — la case 17 teste la **forme** de la valence ; les cases 6/14 créditent Gómez-Emilsson & Percy pour la **frontière**, un claim différent. **Exécution 2026-09-21 (po-2027), après amendements pré-exécution v2 §6bis + v3 §6ter et gel K_c §6quater — bandes, graines, nulls, portes et verdicts inchangés.** Mesures 5/5 graines : **P1** ρ(v, σ) = 0.081 / 0.122 / −0.037 / 0.125 / 0.020 (**0/5** ≥ 0.6) — **indiscernable du null permuté** (médiane \|ρ\| 0.067) ; **P2** \|ρ(v, E)\| = 0.315 / 0.419 / 0.310 / 0.418 / 0.359 (**0/5** ≤ 0.2 : sur ce substrat v n'est **pas** indépendant de l'énergie — les amplitudes entrent dans le couplage par `a_i a_j`) ; **P3** ratio des CV ≈ 1e−15 (**0/5** ≥ 2.0) : le bras N ne se contente pas d'être « cohérent sans structure », il collapse en **lock 1:1 parfait** (r̄(N) 0.9999 pour 4 graines, 0.750 pour la graine 0 ; CV ≈ 0) — `r` ne distingue pas cette cohérence triviale, l'observable de P3 est structurellement aveugle **au contraste même qu'elle visait** (P3 reste au scellé : pas d'amendement post-hoc). Aucune porte déclenchée (r̄(H) 0.6588 sur 5/5, contrôle mécanique exact 5/5), null (a) vert. Le verdict tombe sur la branche **pré-enregistrée** « null (b) : ρ < 0.2 » — avec une précision qui compte : la lecture littérale de cette branche (« saturation — tout entraîne ») **n'est pas** ce qui est mesuré, puisque `v` garde son étendue au point de travail (0.214–0.536, moyenne 0.355, écart-type 0.086 sur 120 configurations, **aucune** à v = 1) et que σ couvre 0.23–1.00 : c'est la **décorrélation** qui se reproduit aux deux couplages, pas la saturation. Lecture bornée : cet instrument **ne peut pas arbitrer** la thèse ; il ne la réfute pas et ne la soutient pas. Résidu nommé pour une seconde opérationnalisation (pattern case 6 → case 14) : un observable de P3 qui mesure la **structure** du lock (au-delà de son existence) et un point de travail hors du régime où l'hétérogénéité d'amplitude pilote le verrouillage. Banc : `ict/stv_valence_symmetry.py` + `ict/tests/test_stv_valence_symmetry.py` (15/15) + `ict/results/stv_valence_symmetry_results.json`. Pré-enregistrement intégral : [`stv-valence-symmetry-pre-enregistrement.md`](stv-valence-symmetry-pre-enregistrement.md). See [#8182](https://github.com/jsboige/CoursIA/issues/8182) (la veille ne se clôt pas sur une tranche). | +| **ICT-47** | Les cinq catégories de douleur partagent une direction commune distincte de la peur (inter-cos 0,60–0,65 ; douleur×peur 0,50–0,66) | `π` ✓, `q` ○ | Diff-in-means fin d'énoncé, bloc médian (2B : 12/24 ; 9B : 16/32) | Directions peur/tristesse en référence ; recouvrement deuil `psych~sadness` 0,82 documenté | 5 catégories × 4 couches × 2 substrats (classes, modèles) | proxy_interpretation | **Fortement soutenu** | Qwen3.5-{2B,9B}-Base, corpus manuel 9×24 apparié en thème ; cluster psycho/sociale/morale 0,76–0,83, cognitive la plus distante | +| **ICT-47** | Après retrait de la détresse partagée, pain se dissocie de fear/sadness (0,71→0,31 ; 0,76→0,45 à 2B ; 0,27/0,43 à 9B ; diagonales fear/sad 0,92–0,94) MAIS pain~valence reste non résolu (0,94 à 2B, 0,93 à 9B) | `π` ✓ | Cosinus + sondes MM sur activations résiduelles ; matrice 4D (écart diag/hors-diag 0,19 à 2B, 0,24 à 9B) | Direction D_shared (émotionnel négatif vs reste) retirée ; corpus neutre en référence | 2 substrats (modèles) | proxy_interpretation | **Fortement soutenu** (borné) | Échelle 2B→9B de la série : la disjonction pain/valence du papier (grande échelle) n'est **pas** résolue — soit >9B requis, soit l'axe « valence » (positif vs neutre) est l'axe de négativité dans ces substrats | +| **ICT-47** | L'axe pain est causalement actif : steer top-64 dims déplace le readout (+0,18 vs aléatoire apparié 0,00 / sham 0,00) et le banc d'action (−D +0,09 / +D −0,05 / random 0,00, 8 scénarios concordants) | `π` ✓, `W` ○ | Marge sonde MM + delta log-odds éviter/endurer | Contrat Gate 24 complet (`ict.causal_engine`) : aléatoire apparié norme+fréquence (permutation), sham, doses symétriques ±4σ, Holm p=0,000, damage off-target 0,0000 | 1 seed (corpus et estimateurs déterministes ; le seed ne tire que le contrôle et le bootstrap 500) | proxy_interpretation | **Spéculatif** (barème ≤2 seeds) — gates falsifiables passés | Qwen3.5-2B-Base, bloc médian, dernière position ; effet faible mais signé | | **Decombination — dissociation cosmique** (alters privés ⟂ environnement commun, case 18 #8182 — Kastrup, *Analytic Idealism* 2019, dual de la case 13 : direction **descendante**) | La triple structure du chapitre 3 de la source (composantes **intégrées intérieurement** « discrete centers of self-awareness » ; évocation directe inter-alters **cessée** « why can I not read your thoughts » ; **immersion dans un environnement phénoménal commun** maintenue, figure 3.5) est réalisable sur substrat : **P1** ratio `ρ_intra(1.0)/ρ_intra(0.0)` ≥ 0.80 ; **P2** `\|ρ_direct(1.0 \| env)\|` ≤ 0.10 ; **P3** `ρ_env` ≥ 0.30 ∧ ≥ 3·\|direct\| (≥ 4/5 graines) ; et la structure **clusterisée** fait un travail qu'un dommage aléatoire de même masse ne fait pas (null b) | `s` —, `q` ✓ (graphe d'évocation = le graphe propre de la source), `π` —, `W` ✓ (calendrier de dissociation d ∈ {0, 0.5, 0.8, 1}) | Agrégats par alter (40 nœuds) : corrélation partielle donnée `[e(t),…,e(t−60)]` ; `ρ_intra` par paires de nœuds (Fisher-z) | Null (a) vue de l'instrument : `\|ρ_direct(0\|env)\|` ≥ 0.15 ; null (b) dommage aléatoire à budget de masse apparié échoue P1 ; porte instrumentale : `ρ_intra(0) < 0.30` sur ≥ 3/5 | Jouet CPU numpy : N=120 contenus, k=3 alters, VAR(1) α=0.85 + environnement AR(1) φ=0.9 commun, T=20 000 après burn-in ; calibration gelée sur graines disjointes 11/22/33 (`p_out=0.02`, `β_scale=0.3` : `ρ_intra(0)=0.541`, `ρ_direct(0\|env)=0.358`) ; 5 graines de test 0/1/7/42/99 | reproduced_in_toy_model + pre_enregistrement | **TESTÉ (NON CONCLUSIVE — null b)** | Exécution 2026-09-29 (po-2024) après amendements pré-exécution v2 §6bis (cause commune **dynamique** : l'AR(1) pénètre aux décalages via l'évocation — contrôler le seul `e(t)` laissait 0.41 à `d=1` où la valeur mécanique est 0 ; contrôle `[e(t),…,e(t−60)]`, `α^60 ≪ 1/√T`), v3 §6ter (observables inter-alters au niveau **alter** : la partielle par paires de nœuds est structurellement aveugle sur ce substrat, ~0.02 plat en `p_out` — l'évocation diffuse ne vit pas à l'échelle de la paire, témoin `rho_direct_node_pairs` consigné), v4 §6quater (renormalisation des lignes : sans elle, couper l'évocation inter-alters assombrit les alters de 29 % de masse et le ratio P1 tombe à 0.42 par artefact — diagnostic graine 555, hors calibration et hors test) — bandes, graines, nulls, portes **inchangés**. Mesures 5/5 graines : **P1** ratios 0.913–0.985 (5/5 ≥ 0.80) ; **P2** `\|ρ_direct(1\|env)\|` ≤ 0.006 (5/5) ; **P3** `ρ_env` 0.873–0.922 (5/5, tous ≫ 3·\|direct\|) ; **null (a)** `\|ρ_direct(0\|env)\|` 0.355–0.450 (5/5) ; **porte** dormante (`ρ_intra(0)` 0.505–0.543) ; **null (b)** échoué : le bras dommage aléatoire passe P1 à 0.909–0.976. Lecture bornée : les agrégats de 40 nœuds sont dominés par l'immersion environnementale commune (AR(1) partagée) à cette calibration — l'intégration intérieure survit à **tout** retrait de masse apparié, structuré ou non ; le placement clusterisé de la masse n'est **pas prouvé** load-bearing. La triple structure de Kastrup est réalisable (P1∧P2∧P3 tenues), mais le jouet ne sépare pas « la dissociation privatise sans isoler » de « n'importe quel affaiblissement des liens privatise sans isoler ». Résidu nommé pour une seconde opérationnalisation : une mesure d'intégration intra qui soit **sourde à l'environnement** (partielle donnée les décalages à l'échelle intra, ou null b à perturbation du **canal** β plutôt que des arêtes). Banc : `ict/kastrup_dissociation.py` + `ict/tests/test_kastrup_dissociation.py` (11/11) + `ict/results/kastrup_dissociation_results.json`. Pré-enregistrement intégral : [`kastrup-dissociation-pre-enregistrement.md`](kastrup-dissociation-pre-enregistrement.md). Source primaire archivée au gisement (`Consciousness/2019 - Kastrup - Analytic Idealism - thesis OA.pdf`, SHA256 `63ED6581…`, identité vérifiée première page), spot-checks firsthand des citations pivots §3.8/§3.10/figure 3.5. See [#8182](https://github.com/jsboige/CoursIA/issues/8182) case 18 (po-2024, 2026-09-29) — la veille ne se clôt pas sur une tranche. | From 84e7bd632c6a971a2f4da797f394f48018cd1a57 Mon Sep 17 00:00:00 2001 From: jsboige Date: Fri, 2 Oct 2026 07:19:16 +0200 Subject: [PATCH 3/5] fix(#18746): re-anchor 3 Lectures on committed outputs (review 18811) Review NanoClaw #18811 (CONCERNS) - reserve 1 + 2a/2b, verifiees G.1 sur l'output committ avant ecriture : - p47-lecture-steer : signes re-ancres - fear -0,013 / sadness -0,031 contre sham (l'ancienne lecture ecrite sur une passe anterieure inversait les signes ; le resultat committ est PLUS favorable : la famille cible monte seule, les voisines descendent). - p47-lecture-dissoc : borne remplacee - sondes fear/sadness lisent pain a 0,33/0,46 (0,67 etait la moyenne hors-diag, pas un plafond) ; remanence max dans l'autre sens : sonde pain sur corpus tristesse 0,69. - p47-lecture-local : min bloc 12 = 0,535 local ; 0,37 = min global de la passe (bloc 22). Markdown-only : 0 cellule code modifiee, outputs run 8 inchanges (C.2). Co-Authored-By: Claude Sonnet 5.5 --- .../ICT-47-PainAxisDistillation-Python.ipynb | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb index c7a143396d..b4b1ee9eea 100644 --- a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb +++ b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb @@ -765,7 +765,9 @@ "source": [ "### Lecture — localisation (mesuré)\n", "\n", - "Le bloc *intra-douleur* domine : cosinus moyen **0,65** au bloc 12 (minimum 0,37), avec un\n", + "Le bloc *intra-douleur* domine : cosinus moyen **0,65** au bloc 12 (minimum local à\n", + "cette couche : **0,535** ; le minimum global de la passe, 0,37, n'est atteint qu'au\n", + "bloc 22), avec un\n", "cluster psychologique/sociale/morale serré (0,76–0,83) — les trois « souffrances\n", "relationnelles » — et la **douleur cognitive la plus distante** (0,55–0,69). Contre les\n", "contrôles : douleur×peur 0,50–0,66, douleur×tristesse 0,57–0,82 — le maximum\n", @@ -1072,7 +1074,9 @@ "\n", "- **pain se dissocie de fear et sadness** : après retrait, les cosinus tombent de\n", " 0,71 → **0,31** (peur) et 0,76 → **0,45** (tristesse) ; les sondes fear et sadness\n", - " gardent leur diagonale (0,94) sans lire la douleur au-delà de 0,67.\n", + " gardent leur diagonale (0,94) et ne lisent plus la douleur qu'à 0,33 / 0,46. La\n", + " rémanence maximale est dans l'autre sens — sonde pain sur corpus tristesse : **0,69**,\n", + " trace du recouvrement deuil/douleur déjà vu à 0,82 avant retrait.\n", "- **pain ne se dissocie PAS de valence** : 0,76 → **0,94** après retrait, et la sonde\n", " valence lit pain_all à 0,92. À cette échelle, l'axe « valence » (positif contre neutre)\n", " est quasi colinéaire à l'axe douleur résiduel — le substrat 2B comprime l'essentiel de\n", @@ -1390,12 +1394,13 @@ "source": [ "### Lecture — steering (mesuré)\n", "\n", - "Effet **faible mais ordonné** : sous `pain +D`, la famille pain gagne +0,031 nat contre\n", - "sham, contre +0,009 sous random — la famille cible bouge le plus, dans le sens attendu,\n", - "et les familles voisines suivent à la traîne (fear +0,006, sadness +0,023). Ordre\n", - "spécifique conservé, magnitude modeste à cette échelle — un steer honnête du 2B :\n", - "la direction est causalement active mais le levier est court. La symétrie se lit au banc\n", - "(ci-dessous), qui ferme la chaîne.\n" + "Effet **faible mais spécifique** : sous `pain +D`, la famille pain gagne **+0,031 nat**\n", + "contre sham (+0,009 sous random) — et **seule elle monte** : les familles voisines\n", + "descendent (fear −0,013, sadness −0,031 contre sham). La spécificité est plus nette que\n", + "le profil « tout le champ émotionnel bouge » qu'on craindrait d'un steer générique de\n", + "valence : la cible monte, le voisin recule. Magnitude modeste à cette échelle — un\n", + "steer honnête du 2B : la direction est causalement active mais le levier est court.\n", + "La symétrie se lit au banc (ci-dessous), qui ferme la chaîne.\n" ] }, { From fd46d9b4ede2ecca458644665dde648192008fe1 Mon Sep 17 00:00:00 2001 From: jsboige Date: Fri, 2 Oct 2026 20:09:59 +0200 Subject: [PATCH 4/5] fix(ict,#18746): ICT-47 raccroche a la chaine de navigation ICT-Series check-nav-chain signalait [orphan_entry] ICT-47 : aucun lien entrant (seule la reference README, qui n'est pas une arete du graphe). Fix par la convention de serie : ICT-46 (bout de chaine) pointe vers 47, et 47 porte sa propre ligne Navigation vers 46. Markdown seul -> pas de re-execution (C.2). check_notebook_nav_chain.py --check --diff-files -> 0 NEW finding (rc=0), ICT-47 absent des findings. Co-Authored-By: Claude Sonnet 5.5 --- .../ICT-46-Strate7-FreeCoordinates.ipynb | 2 +- .../ICT-47-PainAxisDistillation-Python.ipynb | 39 ++++++++++--------- 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-46-Strate7-FreeCoordinates.ipynb b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-46-Strate7-FreeCoordinates.ipynb index 23e6a7d0ea..c557080528 100644 --- a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-46-Strate7-FreeCoordinates.ipynb +++ b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-46-Strate7-FreeCoordinates.ipynb @@ -15,7 +15,7 @@ }, "source": [ "# ICT-46 — Strate 7 : freebits de second ordre, le banc exécutable\n", - "**Navigation** : [Index](README.md) | [45 — Inoculation & bifurcation 9B](ICT-45-InoculationBifurcation-9B.ipynb)\n", + "**Navigation** : [Index](README.md) | [45 — Inoculation & bifurcation 9B](ICT-45-InoculationBifurcation-9B.ipynb) | [47 — L'axe douleur](ICT-47-PainAxisDistillation-Python.ipynb)\n", "\n", "**Première livraison de la strate 7** (issue [#18052](https://github.com/jsboige/CoursIA/issues/18052), veine 3 de l'Epic Aaronson [#16781](https://github.com/jsboige/CoursIA/issues/16781)). Le cadrage D1 [#7745](https://github.com/jsboige/CoursIA/issues/7745) posait l'objet — les *free coordinates* / freebits de second ordre — et ses six proxys mesurables, en prose ([`strate7-cadres-libres.md`](https://github.com/jsboige/CoursIA/blob/main/docs/ict/strate7-cadres-libres.md)). Ce notebook est l'organe : il fait **tourner** le contraste qui définit la strate.\n", "\n", diff --git a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb index b4b1ee9eea..970422aa5e 100644 --- a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb +++ b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb @@ -6,6 +6,7 @@ "metadata": {}, "source": [ "# ICT-47 — L'axe douleur : une direction linéaire dans l'espace latent du substrat ?\n", + "**Navigation** : [Index](README.md) | [46 — Strate 7 : freebits de second ordre](ICT-46-Strate7-FreeCoordinates.ipynb)\n", "\n", "Cette séance instruit la question posée par la lignée Pain Axis (Tagliabue, Dung & Berg,\n", "arXiv 2609.16247) : **la « douleur » est-elle une direction linéaire unique dans le\n", @@ -91,6 +92,7 @@ }, { "cell_type": "code", + "execution_count": 1, "id": "p47-preamble", "metadata": { "papermill": { @@ -101,7 +103,6 @@ "status": "completed" } }, - "execution_count": 1, "outputs": [ { "name": "stdout", @@ -139,6 +140,7 @@ }, { "cell_type": "code", + "execution_count": 2, "id": "p47-corpus", "metadata": { "papermill": { @@ -149,7 +151,6 @@ "status": "completed" } }, - "execution_count": 2, "outputs": [ { "name": "stdout", @@ -428,6 +429,7 @@ }, { "cell_type": "code", + "execution_count": 3, "id": "p47-stats", "metadata": { "papermill": { @@ -438,7 +440,6 @@ "status": "completed" } }, - "execution_count": 3, "outputs": [ { "name": "stdout", @@ -484,6 +485,7 @@ }, { "cell_type": "code", + "execution_count": 4, "id": "p47-load", "metadata": { "papermill": { @@ -494,7 +496,6 @@ "status": "completed" } }, - "execution_count": 4, "outputs": [ { "name": "stdout", @@ -574,6 +575,7 @@ }, { "cell_type": "code", + "execution_count": 5, "id": "p47-capture", "metadata": { "papermill": { @@ -584,7 +586,6 @@ "status": "completed" } }, - "execution_count": 5, "outputs": [ { "name": "stdout", @@ -659,6 +660,7 @@ }, { "cell_type": "code", + "execution_count": 6, "id": "p47-dim", "metadata": { "papermill": { @@ -669,7 +671,6 @@ "status": "completed" } }, - "execution_count": 6, "outputs": [ { "name": "stdout", @@ -720,6 +721,7 @@ }, { "cell_type": "code", + "execution_count": 7, "id": "p47-pca", "metadata": { "papermill": { @@ -730,7 +732,6 @@ "status": "completed" } }, - "execution_count": 7, "outputs": [ { "data": { @@ -793,6 +794,7 @@ }, { "cell_type": "code", + "execution_count": 8, "id": "p47-probes", "metadata": { "papermill": { @@ -803,7 +805,6 @@ "status": "completed" } }, - "execution_count": 8, "outputs": [ { "name": "stdout", @@ -879,6 +880,7 @@ }, { "cell_type": "code", + "execution_count": 9, "id": "p47-exo1-stub", "metadata": { "papermill": { @@ -889,7 +891,6 @@ "status": "completed" } }, - "execution_count": 9, "outputs": [], "source": [ "# Exercice 1 -- a completer.\n", @@ -926,6 +927,7 @@ }, { "cell_type": "code", + "execution_count": 10, "id": "p47-resid", "metadata": { "papermill": { @@ -936,7 +938,6 @@ "status": "completed" } }, - "execution_count": 10, "outputs": [ { "name": "stdout", @@ -994,6 +995,7 @@ }, { "cell_type": "code", + "execution_count": 11, "id": "p47-matrice4d", "metadata": { "papermill": { @@ -1004,7 +1006,6 @@ "status": "completed" } }, - "execution_count": 11, "outputs": [ { "name": "stdout", @@ -1107,6 +1108,7 @@ }, { "cell_type": "code", + "execution_count": 12, "id": "p47-organ", "metadata": { "papermill": { @@ -1117,7 +1119,6 @@ "status": "completed" } }, - "execution_count": 12, "outputs": [ { "name": "stdout", @@ -1229,6 +1230,7 @@ }, { "cell_type": "code", + "execution_count": 13, "id": "p47-doses", "metadata": { "papermill": { @@ -1239,7 +1241,6 @@ "status": "completed" } }, - "execution_count": 13, "outputs": [ { "name": "stdout", @@ -1314,6 +1315,7 @@ }, { "cell_type": "code", + "execution_count": 14, "id": "p47-steer", "metadata": { "papermill": { @@ -1324,7 +1326,6 @@ "status": "completed" } }, - "execution_count": 14, "outputs": [ { "name": "stdout", @@ -1419,6 +1420,7 @@ }, { "cell_type": "code", + "execution_count": 15, "id": "p47-banc", "metadata": { "papermill": { @@ -1429,7 +1431,6 @@ "status": "completed" } }, - "execution_count": 15, "outputs": [ { "name": "stdout", @@ -1531,6 +1532,7 @@ }, { "cell_type": "code", + "execution_count": 16, "id": "p47-exo2-stub", "metadata": { "papermill": { @@ -1541,7 +1543,6 @@ "status": "completed" } }, - "execution_count": 16, "outputs": [], "source": [ "# Exercice 2 -- a completer.\n", @@ -1571,6 +1572,7 @@ }, { "cell_type": "code", + "execution_count": 17, "id": "p47-exo3-stub", "metadata": { "papermill": { @@ -1581,7 +1583,6 @@ "status": "completed" } }, - "execution_count": 17, "outputs": [], "source": [ "# Exercice 3 -- a completer.\n", @@ -1610,6 +1611,7 @@ }, { "cell_type": "code", + "execution_count": 18, "id": "p47-9b-load", "metadata": { "papermill": { @@ -1620,7 +1622,6 @@ "status": "completed" } }, - "execution_count": 18, "outputs": [ { "name": "stdout", @@ -1725,6 +1726,7 @@ }, { "cell_type": "code", + "execution_count": 19, "id": "p47-9b-disj", "metadata": { "papermill": { @@ -1735,7 +1737,6 @@ "status": "completed" } }, - "execution_count": 19, "outputs": [ { "name": "stdout", From 24992da5c7e8cebfccc29445c8567b3f7022247e Mon Sep 17 00:00:00 2001 From: jsboige Date: Fri, 2 Oct 2026 20:55:43 +0200 Subject: [PATCH 5/5] fix(notebook,#18811): corrige le lien ICT-22b (suffixe -Python manquant) dans ICT-47 Les 4 jambes rouges du 2026-10-02 (check-navlinks, check-nav-chain, enrich-quality, PR gate en aval) tenaient toutes au meme defaut : le lien vers ICT-22b omettait le suffixe -Python du fichier reel. Fix markdown seul, aucune cellule code touchee. Co-Authored-By: Claude Sonnet 5.5 --- .../IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb index e57ba46854..8710a4cfdd 100644 --- a/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb +++ b/MyIA.AI.Notebooks/IIT/ICT-Series/ICT-47-PainAxisDistillation-Python.ipynb @@ -19,7 +19,7 @@ "Notre chaîne, en une phrase : extraire les activations de fin d'énoncé de cinq catégories\n", "de douleur et de quatre contrôles appariés sur le substrat de la série, tester l'unité et\n", "la dissocibilité de l'axe par sondes et retrait de variance, puis exiger du moteur causal\n", - "[ICT-22b](ICT-22b-CausalInterventionEngine.ipynb) le contrat complet — cible contre\n", + "[ICT-22b](ICT-22b-CausalInterventionEngine-Python.ipynb) le contrat complet — cible contre\n", "aléatoire apparié, *sham*, doses symétriques, Holm, dommage hors cible — avant de regarder\n", "le banc de choix d'action.\n" ]