diff --git a/MyIA.AI.Notebooks/Search/Part3-Cas-pratiques/App-7-Wordle.ipynb b/MyIA.AI.Notebooks/Search/Part3-Cas-pratiques/App-7-Wordle.ipynb
index a17881e5fcf..c18e8081743 100644
--- a/MyIA.AI.Notebooks/Search/Part3-Cas-pratiques/App-7-Wordle.ipynb
+++ b/MyIA.AI.Notebooks/Search/Part3-Cas-pratiques/App-7-Wordle.ipynb
@@ -6,16 +6,16 @@
    "id": "a458ba3c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:30.804220Z",
-     "iopub.status.busy": "2026-07-22T21:43:30.804220Z",
-     "iopub.status.idle": "2026-07-22T21:43:30.818704Z",
-     "shell.execute_reply": "2026-07-22T21:43:30.817234Z"
+     "iopub.execute_input": "2026-10-30T04:01:00.699415Z",
+     "iopub.status.busy": "2026-10-30T04:01:00.698961Z",
+     "iopub.status.idle": "2026-10-30T04:01:00.703929Z",
+     "shell.execute_reply": "2026-10-30T04:01:00.703376Z"
     },
     "papermill": {
-     "duration": 0.038618,
-     "end_time": "2026-06-06T08:40:42.291300",
+     "duration": 0.011355,
+     "end_time": "2026-10-30T04:01:00.704839+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:42.252682",
+     "start_time": "2026-10-30T04:01:00.693484+00:00",
      "status": "completed"
     },
     "tags": [
@@ -33,10 +33,10 @@
    "id": "nav-header",
    "metadata": {
     "papermill": {
-     "duration": 0.018783,
-     "end_time": "2026-06-06T08:40:42.327433",
+     "duration": 0.003901,
+     "end_time": "2026-10-30T04:01:00.713109+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:42.308650",
+     "start_time": "2026-10-30T04:01:00.709208+00:00",
      "status": "completed"
     },
     "tags": []
@@ -87,10 +87,10 @@
    "id": "section-1-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.018631,
-     "end_time": "2026-06-06T08:40:42.364578",
+     "duration": 0.004132,
+     "end_time": "2026-10-30T04:01:00.721388+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:42.345947",
+     "start_time": "2026-10-30T04:01:00.717256+00:00",
      "status": "completed"
     },
     "tags": []
@@ -136,16 +136,16 @@
    "id": "imports",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:30.824242Z",
-     "iopub.status.busy": "2026-07-22T21:43:30.824242Z",
-     "iopub.status.idle": "2026-07-22T21:43:31.920881Z",
-     "shell.execute_reply": "2026-07-22T21:43:31.919872Z"
+     "iopub.execute_input": "2026-10-30T04:01:00.730105Z",
+     "iopub.status.busy": "2026-10-30T04:01:00.729907Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.213584Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.212784Z"
     },
     "papermill": {
-     "duration": 2.340269,
-     "end_time": "2026-06-06T08:40:44.720382",
+     "duration": 0.489356,
+     "end_time": "2026-10-30T04:01:01.214628+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:42.380113",
+     "start_time": "2026-10-30T04:01:00.725272+00:00",
      "status": "completed"
     },
     "tags": []
@@ -164,6 +164,7 @@
     "import sys\n",
     "import math\n",
     "import random\n",
+    "import zlib\n",
     "import time\n",
     "from collections import Counter, defaultdict\n",
     "from typing import List, Tuple, Dict, Optional\n",
@@ -188,10 +189,10 @@
    "id": "section-2-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.016364,
-     "end_time": "2026-06-06T08:40:44.752250",
+     "duration": 0.006942,
+     "end_time": "2026-10-30T04:01:01.228801+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:44.735886",
+     "start_time": "2026-10-30T04:01:01.221859+00:00",
      "status": "completed"
     },
     "tags": []
@@ -212,16 +213,16 @@
    "id": "word-list",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:31.924625Z",
-     "iopub.status.busy": "2026-07-22T21:43:31.924087Z",
-     "iopub.status.idle": "2026-07-22T21:43:31.939230Z",
-     "shell.execute_reply": "2026-07-22T21:43:31.938219Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.240550Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.240177Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.250701Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.250106Z"
     },
     "papermill": {
-     "duration": 0.052212,
-     "end_time": "2026-06-06T08:40:44.821342",
+     "duration": 0.017663,
+     "end_time": "2026-10-30T04:01:01.251600+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:44.769130",
+     "start_time": "2026-10-30T04:01:01.233937+00:00",
      "status": "completed"
     },
     "tags": []
@@ -298,13 +299,22 @@
   {
    "cell_type": "markdown",
    "id": "interp-corpus",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.005809,
+     "end_time": "2026-10-30T04:01:01.263888+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:01.258079+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — 297 mots : la taille du corpus fixe toute l'experience\n",
     "\n",
     "La sortie confirme **297 mots de 5 lettres**, sans accents, en majuscules. Trois consequences directes :\n",
     "\n",
-    "- **L'espace de recherche est petit** : 297 candidats possibles au depart, ce qui rend exhaustive la strategie « tester le feedback de chaque mot » (les tables d'entropie de la section 5 se calculent en 0,3 s). Avec le vrai dictionnaire Wordle (~2 300 mots), les memes calculs restent faisables mais cessent d'etre instantanes.\n",
+    "- **L'espace de recherche est petit** : 297 candidats possibles au depart, ce qui rend exhaustive la strategie « tester le feedback de chaque mot » (les tables d'entropie de la section 5 se calculent sur ce corpus de 297 mots en un temps negligeable). Avec le vrai dictionnaire Wordle (~2 300 mots), les memes calculs restent faisables mais cessent d'etre instantanes.\n",
     "- **Le corpus est cure a la main** : les exemples montrent des mots courants (`ABORD`, `ACIER`...) et les derniers de la liste incluent des emprunts (`ZEBRA`, `YACHT`). La diversite de lettres est recherchee (le commentaire source le dit), pas la frequence d'usage — l'entropie d'un mot depend donc de CE corpus, pas du francais reel.\n",
     "- **Pas d'accents ni de cedilles** : `compute_feedback` compare des caracteres ASCII — la normalisation est embarquee dans la liste, pas dans le code."
    ]
@@ -314,10 +324,10 @@
    "id": "feedback-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.016798,
-     "end_time": "2026-06-06T08:40:44.854468",
+     "duration": 0.004385,
+     "end_time": "2026-10-30T04:01:01.272601+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:44.837670",
+     "start_time": "2026-10-30T04:01:01.268216+00:00",
      "status": "completed"
     },
     "tags": []
@@ -338,16 +348,16 @@
    "id": "feedback-func",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:31.943286Z",
-     "iopub.status.busy": "2026-07-22T21:43:31.942228Z",
-     "iopub.status.idle": "2026-07-22T21:43:31.952451Z",
-     "shell.execute_reply": "2026-07-22T21:43:31.950820Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.281963Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.281750Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.286862Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.286322Z"
     },
     "papermill": {
-     "duration": 0.047208,
-     "end_time": "2026-06-06T08:40:44.924177",
+     "duration": 0.011238,
+     "end_time": "2026-10-30T04:01:01.288021+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:44.876969",
+     "start_time": "2026-10-30T04:01:01.276783+00:00",
      "status": "completed"
     },
     "tags": []
@@ -423,10 +433,10 @@
    "id": "feedback-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.015817,
-     "end_time": "2026-06-06T08:40:44.956521",
+     "duration": 0.005232,
+     "end_time": "2026-10-30T04:01:01.297390+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:44.940704",
+     "start_time": "2026-10-30T04:01:01.292158+00:00",
      "status": "completed"
     },
     "tags": []
@@ -454,10 +464,10 @@
    "id": "viz-feedback-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.020873,
-     "end_time": "2026-06-06T08:40:44.994598",
+     "duration": 0.004363,
+     "end_time": "2026-10-30T04:01:01.305877+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:44.973725",
+     "start_time": "2026-10-30T04:01:01.301514+00:00",
      "status": "completed"
     },
     "tags": []
@@ -474,16 +484,16 @@
    "id": "viz-wordle",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:31.956757Z",
-     "iopub.status.busy": "2026-07-22T21:43:31.955689Z",
-     "iopub.status.idle": "2026-07-22T21:43:32.321147Z",
-     "shell.execute_reply": "2026-07-22T21:43:32.319623Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.315570Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.315267Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.473538Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.472801Z"
     },
     "papermill": {
-     "duration": 0.843576,
-     "end_time": "2026-06-06T08:40:45.859048",
+     "duration": 0.164744,
+     "end_time": "2026-10-30T04:01:01.474805+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:45.015472",
+     "start_time": "2026-10-30T04:01:01.310061+00:00",
      "status": "completed"
     },
     "tags": []
@@ -491,7 +501,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 500x640 with 1 Axes>"
       ]
@@ -579,7 +589,16 @@
   {
    "cell_type": "markdown",
    "id": "interp-grid",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.009132,
+     "end_time": "2026-10-30T04:01:01.493466+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:01.484334+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — la grille : trois couleurs, un seul canal d'information\n",
     "\n",
@@ -593,10 +612,10 @@
    "id": "section-3-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.017558,
-     "end_time": "2026-06-06T08:40:45.893625",
+     "duration": 0.006873,
+     "end_time": "2026-10-30T04:01:01.509176+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:45.876067",
+     "start_time": "2026-10-30T04:01:01.502303+00:00",
      "status": "completed"
     },
     "tags": []
@@ -626,16 +645,16 @@
    "id": "simple-filter",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:32.325145Z",
-     "iopub.status.busy": "2026-07-22T21:43:32.324146Z",
-     "iopub.status.idle": "2026-07-22T21:43:32.336134Z",
-     "shell.execute_reply": "2026-07-22T21:43:32.334943Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.524912Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.524366Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.533113Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.532033Z"
     },
     "papermill": {
-     "duration": 0.03955,
-     "end_time": "2026-06-06T08:40:45.948626",
+     "duration": 0.017926,
+     "end_time": "2026-10-30T04:01:01.534325+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:45.909076",
+     "start_time": "2026-10-30T04:01:01.516399+00:00",
      "status": "completed"
     },
     "tags": []
@@ -705,7 +724,16 @@
   {
    "cell_type": "markdown",
    "id": "interp-simple",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.004568,
+     "end_time": "2026-10-30T04:01:01.543559+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:01.538991+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — le filtrage : un predicat de coherence, zero strategie\n",
     "\n",
@@ -720,10 +748,10 @@
    "id": "simple-test-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.016032,
-     "end_time": "2026-06-06T08:40:45.979976",
+     "duration": 0.00459,
+     "end_time": "2026-10-30T04:01:01.552890+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:45.963944",
+     "start_time": "2026-10-30T04:01:01.548300+00:00",
      "status": "completed"
     },
     "tags": []
@@ -738,16 +766,16 @@
    "id": "simple-test",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:32.341133Z",
-     "iopub.status.busy": "2026-07-22T21:43:32.340136Z",
-     "iopub.status.idle": "2026-07-22T21:43:32.352601Z",
-     "shell.execute_reply": "2026-07-22T21:43:32.351591Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.565995Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.565715Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.573506Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.572658Z"
     },
     "papermill": {
-     "duration": 0.048201,
-     "end_time": "2026-06-06T08:40:46.046638",
+     "duration": 0.016982,
+     "end_time": "2026-10-30T04:01:01.575115+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:45.998437",
+     "start_time": "2026-10-30T04:01:01.558133+00:00",
      "status": "completed"
     },
     "tags": []
@@ -759,42 +787,48 @@
      "text": [
       "Test du solveur par filtrage simple\n",
       "=======================================================\n",
-      "  Tentative 1: LAINE -> _Y_GG  (297 candidats)\n",
+      "  Tentative 1: MINCE -> __YYG  (297 candidats)\n",
+      "    -> 5 candidats restants\n",
+      "  Tentative 2: ONCLE -> _YY_G  (5 candidats)\n",
       "    -> 2 candidats restants\n",
-      "  Tentative 2: CRANE -> GGGGG  (2 candidats)\n",
-      "  => Gagne en 2 tentative(s)\n",
+      "  Tentative 3: SCENE -> _Y_GG  (2 candidats)\n",
+      "    -> 1 candidats restants\n",
+      "  Tentative 4: CRANE -> GGGGG  (1 candidats)\n",
+      "  => Gagne en 4 tentative(s)\n",
       "\n",
-      "  Tentative 1: RONCE -> Y___Y  (297 candidats)\n",
-      "    -> 22 candidats restants\n",
-      "  Tentative 2: LEVER -> YY__G  (22 candidats)\n",
+      "  Tentative 1: CHUTE -> __Y_Y  (297 candidats)\n",
+      "    -> 6 candidats restants\n",
+      "  Tentative 2: DUPER -> _Y_YG  (6 candidats)\n",
       "    -> 1 candidats restants\n",
       "  Tentative 3: FLEUR -> GGGGG  (1 candidats)\n",
       "  => Gagne en 3 tentative(s)\n",
       "\n",
-      "  Tentative 1: MACON -> G__YY  (297 candidats)\n",
+      "  Tentative 1: LOUER -> _G_Y_  (297 candidats)\n",
+      "    -> 12 candidats restants\n",
+      "  Tentative 2: SOMME -> _GY_G  (12 candidats)\n",
       "    -> 1 candidats restants\n",
-      "  Tentative 2: MONDE -> GGGGG  (1 candidats)\n",
-      "  => Gagne en 2 tentative(s)\n",
+      "  Tentative 3: MONDE -> GGGGG  (1 candidats)\n",
+      "  => Gagne en 3 tentative(s)\n",
       "\n",
-      "  Tentative 1: TENTE -> ___GG  (297 candidats)\n",
-      "    -> 11 candidats restants\n",
-      "  Tentative 2: PORTE -> G__GG  (11 candidats)\n",
+      "  Tentative 1: LOGIS -> ___YY  (297 candidats)\n",
+      "    -> 9 candidats restants\n",
+      "  Tentative 2: PRISE -> G_YYG  (9 candidats)\n",
       "    -> 1 candidats restants\n",
       "  Tentative 3: PISTE -> GGGGG  (1 candidats)\n",
       "  => Gagne en 3 tentative(s)\n",
       "\n",
-      "  Tentative 1: OCEAN -> __YY_  (297 candidats)\n",
-      "    -> 53 candidats restants\n",
-      "  Tentative 2: PEAGE -> __YYG  (53 candidats)\n",
-      "    -> 4 candidats restants\n",
-      "  Tentative 3: ALGUE -> Y_GGG  (4 candidats)\n",
-      "    -> 2 candidats restants\n",
-      "  Tentative 4: BAGUE -> _GGGG  (2 candidats)\n",
+      "  Tentative 1: ANNEE -> Y___G  (297 candidats)\n",
+      "    -> 48 candidats restants\n",
+      "  Tentative 2: RAIDE -> _G__G  (48 candidats)\n",
+      "    -> 20 candidats restants\n",
+      "  Tentative 3: SABLE -> _G__G  (20 candidats)\n",
+      "    -> 3 candidats restants\n",
+      "  Tentative 4: TACHE -> _G__G  (3 candidats)\n",
       "    -> 1 candidats restants\n",
       "  Tentative 5: VAGUE -> GGGGG  (1 candidats)\n",
       "  => Gagne en 5 tentative(s)\n",
       "\n",
-      "Moyenne (parties gagnees) : 3.0 tentatives\n"
+      "Moyenne (parties gagnees) : 3.6 tentatives\n"
      ]
     }
    ],
@@ -812,7 +846,8 @@
     "\n",
     "results_simple = []\n",
     "for word in test_words:\n",
-    "    random.seed(hash(word) % 2**31)  # Graine deterministe par mot\n",
+    "    # crc32 : hash() est sale par PYTHONHASHSEED, donc non reproductible\n",
+    "    random.seed(zlib.crc32(word.encode()) % 2**31)  # Graine deterministe par mot\n",
     "    guesses = solver_simple.solve(word, verbose=True)\n",
     "    won = guesses[-1] == word\n",
     "    results_simple.append({\n",
@@ -832,10 +867,10 @@
    "id": "simple-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.016925,
-     "end_time": "2026-06-06T08:40:46.080007",
+     "duration": 0.005635,
+     "end_time": "2026-10-30T04:01:01.589130+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:46.063082",
+     "start_time": "2026-10-30T04:01:01.583495+00:00",
      "status": "completed"
     },
     "tags": []
@@ -843,11 +878,11 @@
    "source": [
     "### Interpretation : filtrage simple\n",
     "\n",
-    "**Sortie obtenue** : Le solveur par filtrage simple parvient généralement a trouver le mot en 3 a 5 tentatives.\n",
+    "**Sortie obtenue** : Le solveur par filtrage simple trouve les cinq mots de test en 3 a 5 tentatives (moyenne mesuree : 3,6).\n",
     "\n",
     "| Observation | Detail |\n",
     "|-------------|--------|\n",
-    "| Reduction des candidats | Chaque tentative divise typiquement l'espace par 5-20 |\n",
+    "| Reduction des candidats | Chaque tentative divise l'espace par 6 a 60 selon le feedback (mesure : 297 -> 48 au pire premier tirage) |\n",
     "| Variabilite | Le choix aleatoire rend les performances imprevisibles |\n",
     "| Pire cas | Un mauvais premier mot peut laisser beaucoup de candidats |\n",
     "\n",
@@ -862,10 +897,10 @@
    "id": "viz-reduction-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.015249,
-     "end_time": "2026-06-06T08:40:46.112594",
+     "duration": 0.004994,
+     "end_time": "2026-10-30T04:01:01.600889+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:46.097345",
+     "start_time": "2026-10-30T04:01:01.595895+00:00",
      "status": "completed"
     },
     "tags": []
@@ -882,16 +917,16 @@
    "id": "viz-reduction",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:32.356606Z",
-     "iopub.status.busy": "2026-07-22T21:43:32.355600Z",
-     "iopub.status.idle": "2026-07-22T21:43:32.804765Z",
-     "shell.execute_reply": "2026-07-22T21:43:32.803241Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.616360Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.615822Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.770955Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.770293Z"
     },
     "papermill": {
-     "duration": 0.792906,
-     "end_time": "2026-06-06T08:40:46.924143",
+     "duration": 0.1654,
+     "end_time": "2026-10-30T04:01:01.772400+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:46.131237",
+     "start_time": "2026-10-30T04:01:01.607000+00:00",
      "status": "completed"
     },
     "tags": []
@@ -899,7 +934,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -993,13 +1028,22 @@
   {
    "cell_type": "markdown",
    "id": "interp-reduction",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.005141,
+     "end_time": "2026-10-30T04:01:01.787250+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:01.782109+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — 297 -> 1 en 4 tentatives : la geometrie de la decroissance\n",
     "\n",
-    "Le trace committé montre la chute du nombre de candidats : **297 -> 1 mot en 4 tentatives** sur le secret `CRANE` (seed 42, donc rejouable a l'identique).\n",
+    "Le trace committé montre la chute du nombre de candidats : **297 -> 1 mot en 4 tentatives** sur le secret `CRANE` (graine 123, donc rejouable a l'identique : les graines par mot sont derivees en crc32, donc independantes du salage de `hash()`).\n",
     "\n",
-    "La forme de la courbe est le resultat attendu d'un filtrage aleatoire : le premier tirage elimine la majorite de l'espace d'un coup (une tentative divise typiquement le corpus par ~5-20 selon le pattern), puis chaque etape suivante taille une population de plus en plus petite. La derniere tentative n'est plus de l'information — c'est la confirmation (il ne reste qu'un candidat, le tirer le solde).\n",
+    "La forme de la courbe est le resultat attendu d'un filtrage aleatoire : le premier tirage elimine la majorite de l'espace d'un coup (une premiere tentative divise le corpus par 6 a 60 selon le pattern : 297 -> 48 au pire tirage, 297 -> 5 au meilleur), puis chaque etape suivante taille une population de plus en plus petite. La derniere tentative n'est plus de l'information — c'est la confirmation (il ne reste qu'un candidat, le tirer le solde).\n",
     "\n",
     "A comparer mentalement avec le trace CSP (cellule suivante) : meme famille de courbe, mais la propagation de contraintes garantit une decroissance **par position**, pas seulement par mot complet."
    ]
@@ -1009,10 +1053,10 @@
    "id": "section-4-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.016965,
-     "end_time": "2026-06-06T08:40:46.957677",
+     "duration": 0.007675,
+     "end_time": "2026-10-30T04:01:01.800113+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:46.940712",
+     "start_time": "2026-10-30T04:01:01.792438+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1051,16 +1095,16 @@
    "id": "csp-solver",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:32.807763Z",
-     "iopub.status.busy": "2026-07-22T21:43:32.807763Z",
-     "iopub.status.idle": "2026-07-22T21:43:32.832725Z",
-     "shell.execute_reply": "2026-07-22T21:43:32.831140Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.812340Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.812061Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.822162Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.821418Z"
     },
     "papermill": {
-     "duration": 0.0516,
-     "end_time": "2026-06-06T08:40:47.027162",
+     "duration": 0.018249,
+     "end_time": "2026-10-30T04:01:01.823632+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:46.975562",
+     "start_time": "2026-10-30T04:01:01.805383+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1202,7 +1246,16 @@
   {
    "cell_type": "markdown",
    "id": "interp-csp-model",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.006055,
+     "end_time": "2026-10-30T04:01:01.837247+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:01.831192+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — le Wordle comme CSP : 5 variables, 26 lettres, 3 familles de contraintes\n",
     "\n",
@@ -1218,10 +1271,10 @@
    "id": "csp-test-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.018353,
-     "end_time": "2026-06-06T08:40:47.064683",
+     "duration": 0.005429,
+     "end_time": "2026-10-30T04:01:01.850657+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:47.046330",
+     "start_time": "2026-10-30T04:01:01.845228+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1236,16 +1289,16 @@
    "id": "csp-test",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:32.835914Z",
-     "iopub.status.busy": "2026-07-22T21:43:32.834737Z",
-     "iopub.status.idle": "2026-07-22T21:43:32.848529Z",
-     "shell.execute_reply": "2026-07-22T21:43:32.846636Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.862197Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.861974Z",
+     "iopub.status.idle": "2026-10-30T04:01:01.868310Z",
+     "shell.execute_reply": "2026-10-30T04:01:01.867565Z"
     },
     "papermill": {
-     "duration": 0.03796,
-     "end_time": "2026-06-06T08:40:47.117728",
+     "duration": 0.013398,
+     "end_time": "2026-10-30T04:01:01.869211+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:47.079768",
+     "start_time": "2026-10-30T04:01:01.855813+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1301,10 +1354,10 @@
    "id": "csp-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.017563,
-     "end_time": "2026-06-06T08:40:47.153078",
+     "duration": 0.005804,
+     "end_time": "2026-10-30T04:01:01.880310+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:47.135515",
+     "start_time": "2026-10-30T04:01:01.874506+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1333,10 +1386,10 @@
    "id": "csp-viz-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.01751,
-     "end_time": "2026-06-06T08:40:47.186390",
+     "duration": 0.007138,
+     "end_time": "2026-10-30T04:01:01.895163+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:47.168880",
+     "start_time": "2026-10-30T04:01:01.888025+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1353,16 +1406,16 @@
    "id": "csp-viz",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:32.853543Z",
-     "iopub.status.busy": "2026-07-22T21:43:32.852542Z",
-     "iopub.status.idle": "2026-07-22T21:43:33.470258Z",
-     "shell.execute_reply": "2026-07-22T21:43:33.468062Z"
+     "iopub.execute_input": "2026-10-30T04:01:01.914855Z",
+     "iopub.status.busy": "2026-10-30T04:01:01.914539Z",
+     "iopub.status.idle": "2026-10-30T04:01:02.189422Z",
+     "shell.execute_reply": "2026-10-30T04:01:02.188513Z"
     },
     "papermill": {
-     "duration": 0.942821,
-     "end_time": "2026-06-06T08:40:48.145636",
+     "duration": 0.286511,
+     "end_time": "2026-10-30T04:01:02.190682+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:47.202815",
+     "start_time": "2026-10-30T04:01:01.904171+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1370,7 +1423,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1400x500 with 3 Axes>"
       ]
@@ -1459,10 +1512,10 @@
    "id": "csp-viz-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.016921,
-     "end_time": "2026-06-06T08:40:48.180569",
+     "duration": 0.010691,
+     "end_time": "2026-10-30T04:01:02.208145+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.163648",
+     "start_time": "2026-10-30T04:01:02.197454+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1492,10 +1545,10 @@
    "id": "section-5-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.017089,
-     "end_time": "2026-06-06T08:40:48.215355",
+     "duration": 0.007372,
+     "end_time": "2026-10-30T04:01:02.225065+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.198266",
+     "start_time": "2026-10-30T04:01:02.217693+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1538,16 +1591,16 @@
    "id": "entropy-func",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:33.475222Z",
-     "iopub.status.busy": "2026-07-22T21:43:33.474655Z",
-     "iopub.status.idle": "2026-07-22T21:43:33.498236Z",
-     "shell.execute_reply": "2026-07-22T21:43:33.496483Z"
+     "iopub.execute_input": "2026-10-30T04:01:02.236231Z",
+     "iopub.status.busy": "2026-10-30T04:01:02.235861Z",
+     "iopub.status.idle": "2026-10-30T04:01:02.245067Z",
+     "shell.execute_reply": "2026-10-30T04:01:02.244568Z"
     },
     "papermill": {
-     "duration": 0.046125,
-     "end_time": "2026-06-06T08:40:48.279854",
+     "duration": 0.01641,
+     "end_time": "2026-10-30T04:01:02.246242+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.233729",
+     "start_time": "2026-10-30T04:01:02.229832+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1644,10 +1697,10 @@
    "id": "entropy-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.018478,
-     "end_time": "2026-06-06T08:40:48.317298",
+     "duration": 0.005902,
+     "end_time": "2026-10-30T04:01:02.262955+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.298820",
+     "start_time": "2026-10-30T04:01:02.257053+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1674,10 +1727,10 @@
    "id": "top-words-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.017566,
-     "end_time": "2026-06-06T08:40:48.352143",
+     "duration": 0.006263,
+     "end_time": "2026-10-30T04:01:02.274473+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.334577",
+     "start_time": "2026-10-30T04:01:02.268210+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1694,16 +1747,16 @@
    "id": "top-words",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:33.504007Z",
-     "iopub.status.busy": "2026-07-22T21:43:33.502900Z",
-     "iopub.status.idle": "2026-07-22T21:43:34.059598Z",
-     "shell.execute_reply": "2026-07-22T21:43:34.057949Z"
+     "iopub.execute_input": "2026-10-30T04:01:02.292045Z",
+     "iopub.status.busy": "2026-10-30T04:01:02.291692Z",
+     "iopub.status.idle": "2026-10-30T04:01:02.535780Z",
+     "shell.execute_reply": "2026-10-30T04:01:02.535117Z"
     },
     "papermill": {
-     "duration": 0.380483,
-     "end_time": "2026-06-06T08:40:48.750377",
+     "duration": 0.252608,
+     "end_time": "2026-10-30T04:01:02.536535+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.369894",
+     "start_time": "2026-10-30T04:01:02.283927+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1713,7 +1766,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Top 10 des meilleurs premiers mots (calcule en 0.3s)\n",
+      "Top 10 des meilleurs premiers mots (calcule en 0.1s)\n",
       "=============================================\n",
       "Rang   Mot        Entropie (bits)   \n",
       "---------------------------------------------\n",
@@ -1731,7 +1784,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1789,10 +1842,10 @@
    "id": "top-words-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.022462,
-     "end_time": "2026-06-06T08:40:48.792512",
+     "duration": 0.008732,
+     "end_time": "2026-10-30T04:01:02.551681+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.770050",
+     "start_time": "2026-10-30T04:01:02.542949+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1816,10 +1869,10 @@
    "id": "entropy-solver-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.023898,
-     "end_time": "2026-06-06T08:40:48.837871",
+     "duration": 0.006914,
+     "end_time": "2026-10-30T04:01:02.569388+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.813973",
+     "start_time": "2026-10-30T04:01:02.562474+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1836,16 +1889,16 @@
    "id": "entropy-solver",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:34.064468Z",
-     "iopub.status.busy": "2026-07-22T21:43:34.063458Z",
-     "iopub.status.idle": "2026-07-22T21:43:34.078075Z",
-     "shell.execute_reply": "2026-07-22T21:43:34.076393Z"
+     "iopub.execute_input": "2026-10-30T04:01:02.589390Z",
+     "iopub.status.busy": "2026-10-30T04:01:02.588654Z",
+     "iopub.status.idle": "2026-10-30T04:01:02.597694Z",
+     "shell.execute_reply": "2026-10-30T04:01:02.596790Z"
     },
     "papermill": {
-     "duration": 0.042918,
-     "end_time": "2026-06-06T08:40:48.899639",
+     "duration": 0.02088,
+     "end_time": "2026-10-30T04:01:02.599252+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.856721",
+     "start_time": "2026-10-30T04:01:02.578372+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1920,7 +1973,16 @@
   {
    "cell_type": "markdown",
    "id": "interp-entropy-greedy",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.006214,
+     "end_time": "2026-10-30T04:01:02.613483+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:02.607269+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — entropie : un glouton sur l'information esperee, avec deux raccourcis\n",
     "\n",
@@ -1936,10 +1998,10 @@
    "id": "entropy-test-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.017848,
-     "end_time": "2026-06-06T08:40:48.938259",
+     "duration": 0.005862,
+     "end_time": "2026-10-30T04:01:02.625164+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.920411",
+     "start_time": "2026-10-30T04:01:02.619302+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1954,16 +2016,16 @@
    "id": "entropy-test",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:34.082030Z",
-     "iopub.status.busy": "2026-07-22T21:43:34.082030Z",
-     "iopub.status.idle": "2026-07-22T21:43:34.099931Z",
-     "shell.execute_reply": "2026-07-22T21:43:34.099165Z"
+     "iopub.execute_input": "2026-10-30T04:01:02.638769Z",
+     "iopub.status.busy": "2026-10-30T04:01:02.638268Z",
+     "iopub.status.idle": "2026-10-30T04:01:02.650751Z",
+     "shell.execute_reply": "2026-10-30T04:01:02.649713Z"
     },
     "papermill": {
-     "duration": 0.049332,
-     "end_time": "2026-06-06T08:40:49.005594",
+     "duration": 0.020865,
+     "end_time": "2026-10-30T04:01:02.652092+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:48.956262",
+     "start_time": "2026-10-30T04:01:02.631227+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2042,10 +2104,10 @@
    "id": "entropy-test-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.018713,
-     "end_time": "2026-06-06T08:40:49.044406",
+     "duration": 0.006071,
+     "end_time": "2026-10-30T04:01:02.664209+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:49.025693",
+     "start_time": "2026-10-30T04:01:02.658138+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2059,7 +2121,7 @@
     "|-------------|--------|\n",
     "| Première tentative | Toujours le mot optimal precalcule |\n",
     "| Reduction | Plus forte car le mot est choisi pour maximiser l'information |\n",
-    "| Convergence | Typiquement 2-4 tentatives au lieu de 3-5 |\n",
+    "| Convergence | 2 a 4 tentatives dans 98 parties sur 100 (pic a 3), contre 2 a 6 pour les approches aleatoires |\n",
     "\n",
     "**Points cles** :\n",
     "1. La sélection par entropie est **déterministe** : pas de variabilite aleatoire\n",
@@ -2072,10 +2134,10 @@
    "id": "entropy-viz-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.018106,
-     "end_time": "2026-06-06T08:40:49.081965",
+     "duration": 0.005524,
+     "end_time": "2026-10-30T04:01:02.677678+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:49.063859",
+     "start_time": "2026-10-30T04:01:02.672154+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2092,16 +2154,16 @@
    "id": "entropy-viz",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:34.105179Z",
-     "iopub.status.busy": "2026-07-22T21:43:34.105179Z",
-     "iopub.status.idle": "2026-07-22T21:43:34.852582Z",
-     "shell.execute_reply": "2026-07-22T21:43:34.850887Z"
+     "iopub.execute_input": "2026-10-30T04:01:02.693171Z",
+     "iopub.status.busy": "2026-10-30T04:01:02.692800Z",
+     "iopub.status.idle": "2026-10-30T04:01:02.951252Z",
+     "shell.execute_reply": "2026-10-30T04:01:02.950474Z"
     },
     "papermill": {
-     "duration": 1.38734,
-     "end_time": "2026-06-06T08:40:50.486900",
+     "duration": 0.268071,
+     "end_time": "2026-10-30T04:01:02.952047+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:49.099560",
+     "start_time": "2026-10-30T04:01:02.683976+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2109,7 +2171,7 @@
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -2182,10 +2244,10 @@
    "id": "entropy-viz-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.021337,
-     "end_time": "2026-06-06T08:40:50.526929",
+     "duration": 0.006286,
+     "end_time": "2026-10-30T04:01:02.964497+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:50.505592",
+     "start_time": "2026-10-30T04:01:02.958211+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2213,10 +2275,10 @@
    "id": "section-6-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.021776,
-     "end_time": "2026-06-06T08:40:50.574637",
+     "duration": 0.005987,
+     "end_time": "2026-10-30T04:01:02.976460+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:50.552861",
+     "start_time": "2026-10-30T04:01:02.970473+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2237,16 +2299,16 @@
    "id": "benchmark",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:34.856926Z",
-     "iopub.status.busy": "2026-07-22T21:43:34.856370Z",
-     "iopub.status.idle": "2026-07-22T21:43:35.453900Z",
-     "shell.execute_reply": "2026-07-22T21:43:35.452838Z"
+     "iopub.execute_input": "2026-10-30T04:01:02.994013Z",
+     "iopub.status.busy": "2026-10-30T04:01:02.993690Z",
+     "iopub.status.idle": "2026-10-30T04:01:03.214920Z",
+     "shell.execute_reply": "2026-10-30T04:01:03.214290Z"
     },
     "papermill": {
-     "duration": 0.751385,
-     "end_time": "2026-06-06T08:40:51.346044",
+     "duration": 0.232884,
+     "end_time": "2026-10-30T04:01:03.215706+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:50.594659",
+     "start_time": "2026-10-30T04:01:02.982822+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2260,25 +2322,25 @@
       "============================================================\n",
       "\n",
       "[1/3] Filtrage simple...\n",
+      "  Termine en 0.0s\n",
+      "\n",
+      "[2/3] CSP...\n",
       "  Termine en 0.1s\n",
       "\n",
-      "[2/3] CSP...\n"
+      "[3/3] Entropie...\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Termine en 0.3s\n",
-      "\n",
-      "[3/3] Entropie...\n",
-      "  Termine en 0.2s\n",
+      "  Termine en 0.1s\n",
       "\n",
       "Approche             Moy. tent.   Pire cas   Win %    Temps (s) \n",
       "------------------------------------------------------------\n",
-      "Filtrage simple      3.11         5          100      0.1       \n",
-      "CSP                  3.12         5          100      0.3       \n",
-      "Entropie             2.86         5          100      0.2       \n"
+      "Filtrage simple      3.19         6          100      0.0       \n",
+      "CSP                  3.23         6          100      0.1       \n",
+      "Entropie             2.86         5          100      0.1       \n"
      ]
     }
    ],
@@ -2293,7 +2355,8 @@
     "\n",
     "    for answer in sample:\n",
     "        # Reinitialiser la graine pour le solveur simple (reproductibilite)\n",
-    "        random.seed(hash(answer) % 2**31)\n",
+    "        # crc32 : hash() est sale par PYTHONHASHSEED, donc non reproductible\n",
+    "        random.seed(zlib.crc32(answer.encode()) % 2**31)\n",
     "        guesses = solver.solve(answer)\n",
     "        won = guesses[-1] == answer\n",
     "        results.append({\n",
@@ -2356,10 +2419,10 @@
    "id": "benchmark-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.019449,
-     "end_time": "2026-06-06T08:40:51.384616",
+     "duration": 0.006266,
+     "end_time": "2026-10-30T04:01:03.228375+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:51.365167",
+     "start_time": "2026-10-30T04:01:03.222109+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2371,14 +2434,14 @@
     "\n",
     "| Approche | Moy. tentatives | Pire cas | Taux de victoire | Temps |\n",
     "|----------|-----------------|----------|-------------------|-------|\n",
-    "| Filtrage simple | ~3.1 | 5 | 100% | runtime *machine-dep* |\n",
-    "| CSP | ~3.1 | 5 | 100% | runtime *machine-dep* |\n",
+    "| Filtrage simple | ~3.2 | 6 | 100% | runtime *machine-dep* |\n",
+    "| CSP | ~3.2 | 6 | 100% | runtime *machine-dep* |\n",
     "| Entropie | ~2.9 | 5 | 100% | runtime *machine-dep* |\n",
     "\n",
     "> **Note methodologique -- separation structurel / machine-dep** : les colonnes **Moy. tentatives**, **Pire cas** et **Taux de victoire** sont des **invariants structurels** (resultats solveur sur instance specifique, deterministes sur la graine fixee). En revanche, la colonne **Temps** est **machine-dep** (CPU, charge systeme, version Python, GC, taille de l'echantillon) et **ne survit pas** a une re-execution sur une autre machine. Pour observer vos propres timings, executez la cellule de benchmark ci-dessous (la cellule `benchmark_solver` mesure `time.time()` sur 100 parties).\n",
     "\n",
     "**Points cles** :\n",
-    "1. **L'entropie gagne** : en moyenne ~0,3 tentative de moins que les approches aleatoires (2,86 vs 3,11 sur 100 parties)\n",
+    "1. **L'entropie gagne** : en moyenne ~0,3 tentative de moins que les approches aleatoires (2,86 vs 3,19 sur 100 parties)\n",
     "2. **Compromis temps/qualite** : la complexite de l'entropie ($O(n^2)$ par tentative) est plus elevee, mais sur cette liste la superiorite structurelle (en tentatives) compense largement le cout de calcul (l'ordre de grandeur du temps depend de la machine, mais le gain en nombre de tentatives est stable)\n",
     "3. **Filtrage vs CSP** : performances similaires car le goulot d'etranglement est le choix du mot, pas le filtrage\n",
     "4. Le pire cas de l'entropie est généralement meilleur grace au choix optimal\n",
@@ -2391,16 +2454,16 @@
    "id": "histogram",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:35.457900Z",
-     "iopub.status.busy": "2026-07-22T21:43:35.457900Z",
-     "iopub.status.idle": "2026-07-22T21:43:36.007634Z",
-     "shell.execute_reply": "2026-07-22T21:43:36.006625Z"
+     "iopub.execute_input": "2026-10-30T04:01:03.241586Z",
+     "iopub.status.busy": "2026-10-30T04:01:03.241278Z",
+     "iopub.status.idle": "2026-10-30T04:01:03.421673Z",
+     "shell.execute_reply": "2026-10-30T04:01:03.421034Z"
     },
     "papermill": {
-     "duration": 0.922602,
-     "end_time": "2026-06-06T08:40:52.326701",
+     "duration": 0.188063,
+     "end_time": "2026-10-30T04:01:03.422466+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:51.404099",
+     "start_time": "2026-10-30T04:01:03.234403+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2408,7 +2471,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ttFFubDFw4MB5er3y4a0AgIYXCY3oWVn0xz/+MTc0rO1785xcdtllNRIVIb7nR0Lj66+/Lq2L7+sXXXRRaajJaNRQLoa7LH43D3fccUeuG/jPf/6TkyW1id6j8d08vqMXRSPFf/7zn+mBBx7IdRWxHagMPTWgCdh0001rTNK12mqr1en50fsivvBvvPHG+efo3VDsejlzBXy0rhg8eHAaO3ZsDgp22mmn3FohEhixLnpixId/VMKHCECKCY/ogXD99dfnx9HSIX6OFpgzBxwhkhbFhEYEMpEU+O9//5vWXXfdXK6Y8yMSH7MTvSRC9I6IIbIiGfPYY4/V6KkxNz179kzvv/9+HiIjxOtGQiOGyIieIMXWJX//+99rfX70XInWHxEsFSte/vSnP9UYF3xmUb5IaMQ9iAArrmf0TGnVqlXuQXPOOefMc/kBoDGKz7OiAQMG5B6es/PjH/8491wMUTkSvSaj92LEETHXxsw9QmcWDS/Kh5yKXpsAQMPWP8TIDC+//HJ6++238ygO5d+bo6fGmDFjSvvGSAizG/opGgVeccUV+TtyNJaMXhYx8kMkNCIZEUmM+Kwvzgka822de+65s5Qnvk9Hb82PP/44Nz4Mr7/+eql+ojbRKDESGtHIIuoE4rt5HH+ZZZbJjSbrUrcA1D9JDaBOImiJhEXv3r1zoNKnT588FES0jIjWEtH6IoZbiqGfioqV+E8++WRpDMzoGhrBQCQGohvozO6+++7S40MOOSQHL/GaMXFosZXnq6++OttyRlKgeJyozHjooYdya8/f/va383yuUcY4vxjuoijG7o5WJ9FbYp111snrIvFRmwjOoiImKl7iWEWzGwYsEkbFbVHhs/rqq+dALSZK/eabb/L6OA8AqPZYYl5FT45ocbn77rvnWKBcxAQxzGN5S82iaEQQrxMNL2I4yRDPP/744+vhDACAOTnmmGPSiiuumBsvRA/LOX1vnpMtt9wyD10V8cCgQYNyo8ji0JQ/+tGPSkNT/u53vys1kigfsaHogAMOyA0bor7i9NNPrzESxOwU6ySiMWU0sojv5vHd/q233srrI0lSPsICsGBJakATnKjr+eefb7DXisr86MFQLoZzOvroo3PPguhRMbvJxGMYpaJIUNT2uCh6VszNzN1Qy8WYmZEwiYRKjLkdQ1UtvfTSOfCprfKjNsXJzmI+j6JIchTF2J1hdvNklO9bfo7ROqQ20QplTr1P5nbOAFANyif3joqJYuJ+duIz9K9//WuODSK5Hy0ji0NFRIvJaLk5O5HYiGEio2dpJEFWWGGFejwTAGh+5qX+oXyIyOJn9rzMLzmzVVddtcbP5d+ly79vL7zwwjlhMbu6hLp+N5/dcWr7Dg9UhqQGUKeWk+UV/EXFbqQRrERryqicKB93sqg4fMTMCY4Ytmpm5fNGfPjhhzWCpliix0dxuKzarL/++rmiJCo6YpKyYnfUmL8j5sWYF9FFdV7WzU75ecXQWUVRuVKb6PkSXVtDtDiZ+ZxjKZ8wFQCqUQxDUayk+Oyzz9K1115b636R6I/t5TFIDHlx/vnn1+gRWlvCP+bWKsYLUSkRvTVi4nEAoOGVDy1ZWz3DvPbanLn+ofy7dPl37GhIWZwzo7bv23X9bl5eJxEjUURCprY6ieIIEcCCJ6kBlMQQDcXWCjG807wqThoegUl0C40xLWOczJnFfBgtW/7fn51LLrkkD1kVY2nWNnF3TAZaFN1NYzimCCQiSRGTi+22225zLFNMBHbfffelzp07594Z0V20Li0u6kMMdRUJmZg75A9/+ENp/czjhJYHbMVETUwKHpOpRWVOLNEyda+99sotVQGg2sUQjUXR2zPm8IohHKLHZ3zmbbPNNrmBRHzuxdwZsT2GrYjeltE749FHHy3FHsstt1wFzwQAmN+6h+LcFvM6mkIM/1Sc2zLmu4x5OCN++M1vflM6xg9+8INZnhfzYj733HO5IcSJJ55YWh+NJWanWCcRc4bGPB4TJkzI82rECBUnnHBCqeEkUBmSGkDJ9773vfx/VCjEEFNRUXD11VfP9XnbbrttqXVEDOsQrR1q64Iac2hEy8kQE3tGMBJjbJZ32Sy22Iixs2MuiXDnnXfmoaDatm2bjx8Ji7klXaISJIKZmEcjhokqJguixchmm22WFoRIZhTn1IjHIc5/TpUvkeCJREy0/IhkTjyOJa5FTI46tyE6AKAaRGODGP86PvejsmC//fbLY2JHa8j4zLvnnntK+8awUbE9WkPGZ3p8jr744ot525577jnLsJgAwIKbKDyW2c0bOTvxHTe+J4ebbropf77Hceb2fTf2iQaSMYJCJBi22GKLHD9EL85incMvf/nLWp9XnFMjJv0OMUfH3nvvPdvXiqRFsZdnzCsaPU2jIeIaa6yRzjrrrDR58uQ6nTNQvyQ1gJIhQ4bk3gBz6oJZm2IrhcUXXzxXRvzkJz9Jf/vb32rd99JLL80Tf3ft2jUHMjHpdlRqlA/BFGIYpuIk3yuttFIe2ip6gSy//PK5sr/8ObM7l0hkLLHEEjmREV1Ho5Ikhp9aZZVV0oIQw17FtYhrEucVk4VH74s5ibLFhGNxHyIhEsFanEO0Uo0eKrW1OgGAavSrX/0qPfHEE7n3ZYxvHRUaEYOss8466dxzz83jcUePjZjQM3o5xnjYEQ9EhUJUMsTn4rw0vgAAGp8//elPuWFlbUNcz0nEBo899lgekSF6fMR35piv67DDDssNIcp7gRSdeuqp6dhjj811FvF62223XXrggQdK833UJuofHn/88XTcccflBEzEKVGPEd/ZY36voUOHztd5A/WjRSGaAwMsIDGURFTyF7uMfvTRR3nyzggWoidGjG9Z16RKYxLDbkXAFGJoreJk4wAAAMCCEb1HisNLDRs2LO27776VLhJQj/TUABao6CkRQ05F64lolRm9ESKhUZyDopoTGgAAAABAw5LUABaotddeOw8hEV1EY6KtGP9yyy23zPNm1Db2JQAAAABAkeGnAAAAAACAqqCnBgAAAAAAUBUkNaCJi85Ya6yxRmrRokW65JJLUnP0ySefpN133z0tu+yyqXPnzql169Z5Lo+f/OQn6fnnn5+nY9x0001phx12SIsvvni+lrH89Kc/nWW///3vf2nIkCH5mscQW8V9X3/99Rr7TZ8+PZchtt1+++31dq4AUE4csGDjgH//+99p//33TyussEKeP6xTp05p1VVXTeedd1766quvauy700475eMceeSR9XauAFBOHJDSu+++m4455pj0ve99L3+Ot2/fPi233HLpsMMOSx999NE8HWPq1Knpd7/7XVp55ZXzENqxrLLKKunss89OX3755Sz7jxs3Lh1yyCGpX79+qU2bNnnu0I022ijde++9pX0uvvjifF/i/hhEB+rO8FPQxN14441pt912yx+i77//fv4Ab27eeuutNHDgwFq3Lbzwwunll19O/fv3n+MxoiLjjjvuqLFu1113zde3XFSOrL766rM8/7XXXkuDBg2qse6cc85Jxx13XK74eOmll1LLlvLMANQvccCCjQMOPvjgdOWVV9Z6jG222Sb961//Kv381FNP5bnG2rZtm954443Ut2/fOpwVAMydOOD/X4Pa9O7dO73wwgtpkUUWmeMxYh7QBx54oNZtP/7xj9Ott95a+nn06NFp4403znOIzuzXv/51Ov300/PjL774In/2R+OLKGPEFcC8U4MGTdz555+f/4/WhM0xgAnRSvL3v/99Di6iFUVUHKyzzjqlQGJeekpsttlm6YILLki33HLLHPfr2rVrGjp0aPrHP/6Rtt9++znuu9dee+VExquvvjrbAAkAvgtxwIKNAxZaaKG07777pqeffjof+6GHHsq9Q8Ldd9+dnnnmmdK+3//+99Pyyy+fe3Bcfvnl3/k8AWBm4oD/E0mGf/7zn7nHRcQDq622Wl7/3//+N1177bVzfO748eNL39cjCfLOO++kMWPG5Mch4ohITBTtvffeOaHRrl27dNVVV+Vtsdx///25t0Z5w4qdd945P77wwgsb5LyhKZPUgCbslVdeyV+qQwyxUG6ppZbKXR032WST9Le//S0ts8wyqUOHDrmVwccff5xbK2ywwQZ5XQybEF/Ky82YMSN3X41eCfFhHPvFl/PrrruutM+JJ55YGqLhzTffrDFEU3Fopujy2dCWWGKJdPTRR+dhJyKwiNaaMQxFUQxDMTeHH354Hh4iuobOSVzXGGIihpSIBMec9OzZM6233nr5cfl1A4D6IA5Y8HHAWWedlYYNG5bWWmutXHm06aab5sqNovLrECJeCH/6058MPQFAvRIH/J8f/ehH6ZFHHkk//OEPczkjHvjNb34z28/m2hosFK2//vq5d2dcv+J3+fj8njZtWn48cuTI3BMz/PKXv0wHHHBA7gUSyxZbbJF7fJQr3pcnn3wyJ1uAOojhp4Cm6Q9/+EN8Oy4stNBChS+++KLGtn79+uVt3bp1K7Ro0SI/Li4bbrhhYZFFFqmxrmPHjoVPPvmk9Pw999yzxvby5Ze//GXeZ+zYsfm1Y93xxx9feu6VV15Z2vfpp59egFekUPjmm28Kr7/+emHttdfOr7/ooosWPvzww3l+/pgxY0pl33XXXee47z777FPa97XXXqt1n2OPPTZvX2yxxep8LgAwJ+KAysYBRQcccEDpOY8++miNbXfffXdp28svv1zn8wGA2REHzN5f/vKXUhlOPfXUue6/44475n179+6dY4FY4nGsW3HFFQszZszI+51xxhml4/785z8vrLDCCoW2bdsWBgwYUDj33HNL+xV99tlnhZYtW+b9L7nkkgY7X2iK9NSAJuy5557L/0dLgtl1NZ04cWKeoGrSpElp3XXXLU1yGRNaffjhh6VukNFN85577smPH3vssfSXv/wlP47nRJfNaN1QnDOiOMRDdMcsDsF0/fXXp2+//TY/jqGZwkorrZRbMs5JsWXHnJZTTjllnq7HD37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b8s9BBIfLPwcR0Jj6cxCjNorn8Oijj07du3fPAdz4WxEjTGr73aiP6wwA0BgJagAA1KPoAR8NxUXRSDczJ5xwQk5TEg3akTIlGgw/++yzvC4aW4tpfWZHecP/3FCeDmnqtDDlwZ2GoLxXdGjWrFmDaVws9nyPFGGRbique4wSiN7xkYYpglsxoiJGmszrMs3oPJafw7qcxwgmnHPOOaWfo6d+jLCJtECRpqioOKJkTkUP/WLgYfjw4TnNV22pp2bn/MTvwD333JMuueSSPNIlghkRQInRCJGCKoJZs5KGLkZ5zE5waHrKg5ARMCgXgbTywFQEXyMwVx6cm9FrIqhY3yKgVhyt0KVLlzxS4oknnsififK0edP7TMztv3Uz+xwMHDgwvfbaa2nw4MFpgw02yOWJbRHUOuWUU/IIj6kDd+XXeeqRZQAATJ+gBgBAPfrDH/5QI5d9sef9jERDcKRDivkrXnnllTR+/Pic0qQoepYXFXubz2qD78zmYJiRp59+ukYDZ/lxFecJKG8EjUBMUaSoeuqpp2rdb/kx1KXhOhoNy0dOlDe6lpc1jnlGqaRmx9SNuOW972MESW29x2MER3mjejTkx7WeeomGz+JcDXFuooE/rnmM1ogUQtFLvLbPwvTM7DNSXq5ddtml1jLFMr2RGrNqRuWIkSnl6ZEiIBBBm+jBH9tm5fNc14BHeeAiUhwV5+iIVEYxP0tt52fttdee7vkpNmbH4xhlEymIYl6F+DzEsR122GF5e1z3YoquGSlPqTQrc7zMTHnqsEjlVEyFFopzt4SYZ6a2x+VzmMR8NBEMqm3fs6L893Pqn4t/S8rLF2m+IrVXBITid2dGn4mZ/a2r69/M2j4HMb9GbZ+BGL0SgcgQP8exnH766TlVXYy8i0BQMUVaBLamHuFWfp1rS6kFAEDtzKkBADAHouE+Gi0//vjj3OAc8z6Uzz2w4447znQf0ZAbDfaRpiRSlkQu/mKu/1CcsHjqdD8xD0E0okYP3+hpHT3761NMuB3pdiJXfOTDj/k0QrzXZpttNs1EtzGiINLtxOiU6GUdwZnp9fSPBshir/5zzz03NypH42Ok35qeeM84P9FYGGWJXvExgXY0Lkfv6KLI2V/fvZ6jp3j0vo6e48Uc+ZFPP1IWxXwYtdl0003zhN/RGBwNttFTO+bSiLJFY2cEZuLzEvMvFCedjkbR+NxE6qk41ujZXd4AXf5ZmJ7yz0icq5jQPHrtR0qk2G9coyFDhuQG3vjMRoqcmMsg5t/49NNPc9qdGCUUk9LHc2dXeTliguj4nMf5igBOpFeLVD3FwEa8VwT2IhVV8VxMb5+RqqoYCIlyx+cmPj9TzwExtW233Ta/PhrIy3+/ph6lEZO7R3nic/XMM8/k+SBigvr4PYjAXTREx/HE/mKejlGjRuXPbaQ/iobp6KH/zTff1BidMSvXbcMNNyw9fuGFF2p9TnxeYt+h+H+IYGgx1V2c5/i8RlkiSBGT0cfvWswZEr8ncX3jMxHi9zDmeimKQEz8TQkRZC3ODREN9UUx4iVSbNVFBHWKIxjiGhfn0wjFv5HlI0vi+fH3MD6jMcphTkZSzehzGL+f0xOfizivxXRSMfJstdVWy3974nc6rm/8nrz44os5cHH22Wfn0UY//elP877j71wEMcpHrE39OSi/znFuAACYRQUAAGbZVVddFa1rM1122223woQJE2q89qSTTipt33PPPUvrBwwYMMN9nXPOOaXnjh49urDgggtO85xNNtkkbx82bFiN9bV57LHHStt79+5dWj/1awcOHDjN+zRp0qRw7bXX1tjfpptuOs3zWrRoUejfv3/p5zj2cmuttdY0r1lggQVK26NcxfVR3qLHH3+80LZt2+meqz59+hSGDx8+03MeNthgg9K2uK4z8+qrrxbatGkzzXsuuuiihQ4dOtRa3meeeabQsWPHGV7f8nK1bNlyhs+97bbbZlrOH374odCjR49pXrvEEkuUnnPBBRcUmjZtOsP3Kj8nMzpX5a+Jz1DRAw88UOt+//CHP+Ttxx9/fK3bN9xww9LjeN9yu+yyS62vKV7zmV3Tww8/fJrP6ZdffjnN826//fZCq1atZnh+ip/pzz//fIbPa9asWeH555+f6XWbMmVK6XemdevWha+//nqa55T/XkxvKf/8ffjhh7V+Fqb3exl+97vfTff5vXr1Knz00UczPZapr8VKK61U6/7ib8ePP/5YOo+dOnWa5jlLL710/h2r7fim93ei3Mw+h+V/08s/b1Gu6X3eavvMn3HGGTN8XpyD4rEWr3fPnj3ztvgbMXHixFk6rwAAFArSTwEAzIHo6Rw9xKP3/corr5xHN0Tv7pgXYeqJd6fn+OOPz72oo5d++/bt0wILLJB7+UbP7euuuy4deeSRpedGuqfoSR2jIaJn/dwU6Wf23XfffGzxXnF8t912W54Qt1z0+o7e1lH26AEdE0nH6ILyybSnFscVIxJilEBdRG/myFsfqX6iZ3ec+xiBEL3Sf//73+ee1TEKYG5YccUV8zmJnupxnNFzf7vttsvXe3oTwsc5iDRSRx11VO7dHq+L8sbk3zGSI0aplI/0OOOMM9LWW2+de37H3CrxWYi5FiI1U/Qyj/ebmXjNHXfckVP3FCe/nlqMNIkUWpHqKtL7xHmM6xdpu+KzGNd0Vt5rRmJkTcybEb39o0y1pWqLJa5jpG9afvnl0w033DDDlG0xv0yMpCiO9qmrqUdlFEdvTC1GAcVk0PH7HL+XUb74fY7HMTokRokcdNBB+blx7eM44njjXMb1jblGYqRNnMP4zJSnt5qeOJ74XIeYJDuu4ZyKz9nLL7+cjjjiiHwd4jpHeSN9VMzPEhNYT+20007L7x3PiefGa+K18XcoRicUU6XVRYwAiflF4hrH35I4N8ccc0wuQzE9VNeuXXPapvj9is9iXJdIQxUjTeKczq3P4fREuWLelUgFGL9/8XsY1zVGwcRxHHjggfl3sjjaI55zyCGH5L+T8TcznhvljpEuv/nNb/Ik8uWpsGKUXzGl1x577DHd31UAAKbVJCIbtawHAABgHop0XJGOLNKTrbHGGjXmbqk2EZQtpmuLlGJzkspsfhSB4FtvvTUHM2JS+cUWW6zSRQIAqBpGagAAADQAMRokRn2E559/PvfuZ/4TQYwY9RZiFJeABgBA3ZgoHAAAoIGIFFlTp8li/tK/f//0448/VroYAABVy0gNAAAAAACgKphTAwAAAAAAqApGagAAAAAAAFVBUAMAAAAAAKgKghoAAAAAAEBVENQAAAAAAACqgqAGAAAAAABQFQQ1AAAAAACAqiCoAQAAAAAAVAVBDQAAAAAAoCoIagAAAAAAAFVBUAMAAAAAAKgKghoAAAAAAEBVENQAAAAAAACqgqAGAAAAAABQFQQ1AAAAAACAqiCoAQAAAAAAVAVBDQAAAAAAoCoIagAAAAAAAFVBUAMAAAAAAKgKghoAAAAAAEBVENSAKrbXXnulJk2aTHf56KOP8vOuvvrq0rrHH388r4v/i+tie1E8Pvnkk9OQIUNSYxDHOvX5qtQ1BIDG5vnnn0+77bZb6tWrV2rZsmVadNFF09prr53OPvvsNHHixPyc0aNHp6OPPjoNHDgwtW7dOnXq1CkNGDAg/eIXv0j33XdfaV/ldZvissACC6QuXbqknXbaKf373/+u4JECwPzd7jCrxo4dm7+Hx1Jsn6ik2tpLgIavWaULADQscUN/4oknUu/evdMRRxxR6eIAAPOpM844I/3ud79LhUKhtO6///1vXp599tm0ySabpKWWWioHOYYOHVp6zqRJk3KDyLvvvpsWX3zxtMUWW0z3PaZMmZK++OKL9Pe//z3df//9OYgSwREAoDLiHn7KKaeUft5www0rWh6gOhmpAfOJxx57LDcKlC/xRb/Ys6K4bm5UGL799ttUraJ3yNTnCwCYu+64447029/+Nt9/F1xwwXTFFVfkRo4JEyakRx55JG255Zb5eXfeeWcpoHHSSSfl54wbNy69+OKL6YQTTsgjPGqz55575n2PGTMmj+gI48ePz4EUAKD+2x3mpu+++y53VJgb5nZ7CTB3CGpAIzArwyljyGhsj1Ea4eOPPy69Jm7yIW7w8XNUWqLBYZVVVsmpIi699NLcyBDpI6JHZceOHVPz5s1T165dc0PC22+/XeO9ooflwQcfnBZaaKH83P333z/dc889tabDispLNEAsu+yyOeVEu3bt0sYbb5zff2aiZ+Z+++2XR520atUqde7cOZf5yCOPnGH6qfLz9Y9//CPtuOOOqU2bNqlPnz7p+uuvTz/++GNuWIl0FpEmI8r/zTff1LrPp556Km233Xapbdu2aeGFF87vHcc0M++9917aY489Uvfu3VOLFi3SYostlt9n1KhRM30tADR05T00I9XUr371q9ShQ4d8v437/L/+9a+03HLLpQ8//LD0vM033zw/p3379mnVVVdNp556ajrssMNm+D5Rz4jgSdFLL700l44IABqv8u/Qt99+e76vR7rI+L58wAEHlL4vx3fl+F5dXh8ob6sotkvEEp0XTjzxxPxdOL7PR+eE8MILL6Stt946f7+O78qxv8MPPzynq6wtJWW0Vxx11FG5LFHP2HbbbdNnn3020/aSeL/jjjsu9e/fP7d7xPFstdVW6ZVXXplHZxWYEemngDqLtBA//elP0+TJk0vrIqhx44031nheNMDfdtttuTdH5LGOSkT49a9/XSNwcdlll+XGi6lF8CDe5+GHH66xPvYXlY2bbrop58ienuilGakmiqK8UdGJHp/nnnvuLB3rgQcemL766qv8OCpYEWiIFBYRhCkvf1SoTj/99GleHxWm4usjN3jMVRK9RsuPf2pvvvlmWnfddUuVtjBixIj8Pg8++GDunbrIIovMUvkBoKEZOXJkev311/Pj6Kywzz771Pq8mA8jGjKKIs1UNCasv/76uaNFv379Zun9ytNbAQBzVwQ0YlRl0d/+9rfcwbC278szctFFF9UIVIT4fh8Bje+//760Lr6nn3feeaU0k9GhoVykuix+Jw933XVXbhN49dVXc7CkNjFyNL6Tx3fzouic+M9//jM99NBDuY0itgOVY6QGzCc22mijGpN1rbjiinV6fYy+iC/9G2ywQf45RjcUh2BO3QAfvSwGDRqUhg8fnisH22+/fe61EAGMWBcjMaISEI3wISoixYBHjEC49tpr8+Po8RA/Ry/MqSseIYIWxYBGVGgiKPDpp5+mtdZaK5cr5vyIwMf0xCiJEKMjIkVWBGOefPLJGiM1ZqZbt27pk08+yWkyQrxvBDQiTUaMBCn2Mrn11ltrfX2MXIleIFFpKja+XHPNNTVyg08tyhcBjbgGUdGK8xkjU5o1a5ZH0Pz5z3+e5fIDQEMT97Kivn375tGd0/Pzn/88j1oM0UASIyZj5GLUIWKujalHg04tOl2Up5yKEZsAwNxrd4iMDG+99Vb64IMPcvaG8u/LMVJj2LBhpedGBoTppX6KzoCXXHJJ/m4cnSRjlEVkfIiARgQjIogR9/niXKAx19ZZZ501TXnie3SM1Pzyyy9zp8Pwn//8p9QuUZvojBgBjehgEW0B8Z089r/kkkvmzpJ1aVMA5g5BDaDOovISAYsePXrkCkvPnj1zOojoIRG9JqIXRqRbitRPRcVG/Oeee66UCzOGiEalIAIDMRx0avfee2/p8UEHHZQrMfGeMXlosafnO++8M91yRlCguJ9o0Hj00Udzj88//OEPs3ysUcY4vkh5URT5u6P3SYyWWHPNNfO6CHzUJipp0RgTjS+xr6LppQGLgFFxWzT6rLTSSrnCFpOl/vDDD3l9HAcAVHM9YlbFSI7odbnrrrvmekC5qA9Eisfy3ppF0YEg3ic6XUQqyRCvP/744+vhCACA6TnmmGPSMssskzsuxOjKGX1fnpHNNtssp66KusDAgQNzZ8hiWsqf/exnpbSUf/zjH0sdJMozNRTtu+++uVNDtFOcdtppNTJATE+xLSI6UUYHi/hOHt/p33///bw+giTlmRWAeU9QA+bTCbtee+21ufZe0ZgfIxjKRTqno48+Oo8siBEV05tMPNIoFUWAorbHRTGyYmamHo5aLnJnRsAkAiqRdztSVS2xxBK5AlRbA0htipOexXweRRHkKIocnmF682SUP7f8GKOXSG2iN8qMRp/M7JgBoKErn9w7GieKQfvpifvnDTfckOsFEdiP3pHFdBHRazJ6b05PBDYiRWSMKo0gyNJLL12PRwIAjcustDuUp4cs3q9nZV7Jqa2wwgo1fi7/Dl3+PXvBBRfMAYvptSHU9Tv59PZT23d3oHIENYA6954sb+AvKg4njUpL9KiMBory/JNFxRQSUwc4Im3V1Mrnjfj8889rVJ5iiREfxXRZtVlnnXVyY0k0dsRkZcVhqTF/R8yLMStiqOqsrJue8uOK1FlF0cBSmxj5EkNcQ/Q8mfqYYymfNBUAqk2koig2VHz99dfpyiuvrPV5EeSP7eX1j0h7cc4559QYDVpbsD/m1SrWFaJhIkZrxMTjAMDcVZ5Wsrb2hVkdsTl1u0P5d+jy79bRgbI4Z0Zt37Pr+p28vC0iMlBEQKa2tohiZgigMgQ1gBoiTUOx10Kkd5pVxUnDo4ISw0Mjt2Xky5xazIfRtOn//vRccMEFOWVV5NSsbeLumBC0KIadRjqmqFBEkCImGdtll11mWKaYEOyBBx5I7du3z6MzYthoXXpe1IdIdRUBmZg75K9//Wtp/dT5QssrbsVATUwKHpOqRYNOLNE79Ze//GXurQoA1SzSMxbFSM+YvyvSOMRoz7jfbbnllrlzRNzzYu6M2B6pK2KkZYzOeOKJJ0r1jgEDBlTwSACA2WlzKM5tMatZFCL9U3FOy5jnMubfjLrD73//+9I+fvKTn0zzupgP85VXXsmdIE444YTS+ugoMT3FtoiYKzTm8Rg1alSeVyMyUwwePLjUYRKoHEENoIbVVlst/x+NCpFiKhoLLr/88pm+bquttir1kojUDtHrobahqDGHRvSeDDG5Z1RKItdm+dDNYs+NyJ8dc0mEu+++O6eCatmyZd5/BCxmFnSJhpCo1MQ8GpEmqhgsiJ4jG2+8cZoXIphRnFMjHoc4/hk1wESAJwIx0QMkgjnxOJY4FzFB6szSdABAQxcdDSIHdtzzo8Fg7733znmxo0dk3O/uu+++0nMjbVRsjx6RcT+Pe+gbb7yRt+2+++7TpMQEAObNROGxTG++yOmJ77bx/Tjccsst+d4e+5nZ99x4TnSMjMwJEWDYdNNNc90hRnAW2xp+85vf1Pq64pwaMel3iDk69thjj+m+VwQtiiM8Yz7RGGUaHRBXXnnl9Kc//SmNGzeuTscM1D9BDaCGQw89NI8GmNFQzNoUeyssuuiiuUHiF7/4Rbrppptqfe6FF16YJ/7u2LFjrtDEpNvRsFGegilEGqbiJN/LLrtsTm0Vo0CWWmqp3Nhf/prpHUsEMrp06ZIDGTGENBpKIv3U8ssvn+aFSHsV5yLOSRxXTBYeoy9mJMoWE4/FdYiASFTa4hiip2qMUKmt9wkAVJvf/va36ZlnnskjLyPHdTRqRP1jzTXXTGeddVbOyR0jNmJSzxjhGDmxoy4QjQrR0BD3xFnpeAEANCzXXHNN7lBZW2rrGYl6wZNPPpkzMcSIj/iuHHN1HXLIIbkTRPkokKJTTjklHXvssbmtIt5v6623Tg899FBpvo/aRLvDU089lY477rgcgIk6SrRfxHf1mNvrqKOOmq3jBupPk0J0BQaYhyKdRDTyF4eOfvHFF3kCz6g0xEiMyHNZ16BKQxJpt6LiFCK1VnGycQAAAGDuitEjxfRSV111Vdprr70qXSSgnhmpAcxzMVIiUk5FL4romRmjESKgUZyDopoDGgAAAADA3COoAcxza6yxRk4jEUNFY8KtyIO52Wab5XkzasuBCQAAAAAQpJ8CAAAAAACqgpEaAAAAAABAVRDUgEYgBmStvPLKqUmTJumCCy5IjdFXX32Vdt1119S/f//Uvn371Lx58zyXxy9+8Yv02muvzdI+brnllrTtttumRRddNJ/LWHbeeedan/vCCy+kLbfcMnXs2DEtuOCCaZVVVknXXHPNNM9bddVV836GDBkyx8cIAFNTB5jzOsCkSZPSH//4x7TxxhvnucBatWqVevXqlX7+85+n119/fbbf6+ijj87XJeoWAFDf1AFS+uijj9IxxxyTVltttfw9vnXr1mnAgAHpkEMOSV988cUs7WPChAm5HrDccsvl1NmxLL/88unMM89M33777TTPHzFiRDrooINS7969U4sWLfKcoeuvv366//77S885//zz83WJ6yOBDswe6aegEbj55pvTLrvskm+mn3zySb6RNzbvv/9+6tevX63bIujw1ltvpT59+sxwH9HocNddd9VYt9NOO+XzW+7xxx9Pm2++efruu++m2cfpp5+eBg8eXPr573//e97HIosskj788MPUtm3bOh4ZAEyfOsCc1wFGjhyZunXrVuu2aKx49NFH0zrrrFPn9/r0009T37590/fff5+effbZtOaaa87G0QFA7dQB/t85qE10VIjOCQsttNAM9xHzfz700EO1bosODrfffnvp56FDh6YNNtggzx06td/97nfptNNOy4+/+eab3EEiOkNEGaNNAKgbIzWgETjnnHPy/zGqoDFWZEK7du3SX/7yl1zJiN4U7777bqnxICoUd95550z3ET00zz333HTbbbfN8HlHHnlkDmjEuX7uuefS6NGj0xZbbJG3nXTSSblCWR4oiZ4e//3vf9MNN9wwx8cJAOXUAeqnDrDEEkukyy67LPfq/PLLL9Puu++e18f9/owzzpit94rGlE022SQ//utf/1rvxw1A46YO8D8RZPjnP/+ZR1zE/XnFFVcsdS648sorZ/ja6NhQDGjEfTs6Ig4bNiw/DnFfj8BE0R577JEDGjGqM+oNsS2WBx98MI/WKO/osMMOO+THsjbA7BHUgPnc22+/nV588cX8OFIflFt88cXzkMcNN9ww3XTTTWnJJZdMbdq0yb0N4gt79FpYd91187oVVlgh90QsN2XKlDyMdaWVVso35Xje6quvnq6++urSc0444YRSqqb33nuvtD4a8Zs1a5bXx9DPua1Lly45zUOkg4gKRvSijPQQRZEeYmYOO+ywdMQRR+QhotMzduzYUnqJqDytscYaqVOnTumAAw7I66I3ZqSxKu/hudVWW+XH5ecNAOaUOkD91AE6d+6cz+W+++6bR1bGz+UNEOXHVtf3Kl6XO+64I40fP75ejhcA1AH+52c/+1nOpPDTn/40lzPuz7///e9L28vLVpsFFlig9DhGZcZoyzh/a6+9dl4XyW8iTWWIUZeRhjr85je/yfWGGAUSy6abbppHfJQrXpfoCBnBFqCOIv0UMP/661//GinmCgsssEDhm2++qbGtd+/eeVunTp0KTZo0yY+Ly3rrrVdYaKGFaqxr27Zt4auvviq9fvfdd6+xvXz5zW9+k58zfPjw/N6x7vjjjy+99tJLLy0998UXX5yHZ6RQ+OGHHwr/+c9/CmussUZ+/4UXXrjw+eefz/Lrhw0bVir7TjvtVGNb7Ke47Sc/+Ulp/Z133llav8suu9R4zUUXXVS6RuPGjauHIwQAdYC5UQco+vTTT0vHsNFGG832e73zzjul/fzzn/+c7eMCgHLqANN3/fXXl8pwyimnzPT52223XX5ujx49cltALPE41i2zzDKFKVOm5Oedfvrppf0ecMABhaWXXrrQsmXLQt++fQtnnXVW6XlFX3/9daFp06b5+RdccMFcO16YXwlqwHxuzz33zDfJJZdccpptxcpM8SY6duzYwlprrVVat9JKK+Uv30OGDCmtiwpAeOKJJ0rr4jXx5f69994rDBw4MK+LylF8kS+vBHTv3j1/wQ+bbrppXrfsssvO9BimV2EqX0466aRZOh+bb755jdf17Nmz8Oabb9bpnM4oqBEVlS5duuRtrVu3Ljz//POF0aNHF7bYYovSazbbbLMar/m///u/0rY4rwBQH9QB6r8OULTPPvtMc15m572i3tC8efNZblgBgFmhDlC7iRMnFpZbbrn82gg4fPDBBzN9zeTJk3PHxKnfe4MNNqjx+l//+tczLOtxxx03zb6XWGKJvG3vvfeu03EAhYL0UzCfi9zPIVIlTE/kgzz44IPz3A7rrbdeaX0MB+3atWtpPohQnA/ivvvuqzHh1WKLLZaHrUbKhRB1kMgbGWLfYcSIEfl1McfEY489ltftueeeqZKGDx+efvKTn6SPP/64XvYXw2hPPvnk/DhyaUf6qRhuWn6+pk49ERO3FdU2oRgAzA51gLlTB4iUGldccUXpGHbbbbfZfq+oNxQnKFUHAKC+qANMK76fR4qtN998M/988cUXp759+870dTFnZqTpqu3eHmm+in744YfS40hRFfNvRHqrOEfFOU7GjRtXa1uAOgDUnaAGkHr37l16XD6BWK9evUrzPhRNnjw5/x+5Not69uxZ6+PIl1mcYHuppZbKj2MirphMK274kZ+yONHmjPz/o8pmuBQDCTNz//3353kt3n///bTTTjvldZ999lme1LO+HHjggTmf6LLLLpvPXZzfyKnZtGnTac5RMScpAFSCOkDd6gBxPz/ttNPy49jH5ZdfPsfvpR4AQCU0pjpATBK+5ZZb5oBLdCg477zz0t577z3T18V8mRdddFFpTo0I0EQAYqONNspBi5gXIyYOnzqAFMGTmH8jAj5bb711Xhf1gn//+9819q8OALNPUAPmc4suumj+/6uvvpruc2Kirrqsn3p0waefflqjt0JtzznooIPy///85z/TpZdemh9vvvnmuQfIzBQnGJvRMquVmeJxLbHEEmnw4MGzPEFYXUXPk+gBEpW/jz76KE/CVqywxONy5dcmJhgFgPqgDlB/dYBoODnssMNKQYm99tor3XDDDTM8T7PyXlE3GDNmTH6sDgBAfVEH+H/Gjx+f3zMmDI+Ohn/729/SoYcemmbFf/7zn9Lj7bbbLnXr1i2f2+Ik399991166qmn8uOYOH1myoNH5ddHHQDqTlAD5nMrr7xy/j96D3zzzTf1tt9IoVB0+umn596H0VMhhlSGqGBsttlmpefsscceqW3btrl3wgsvvFBqEJhXLrjggjx6Is5DBBoi/cNZZ51V2l4+7DQqRsVKUgQkynt3RM+UYuNDsRIT62IpBi1efvnl9K9//StXUOI19957b9p///1Lw1Cj10a54vDX6LGy4oorzsWzAEBjog5QP3WACGgccMAB6fzzzy+l04gep3HfnpP3KjaWFNNVrLLKKnPh6AFojNQB/ie+uw8aNCg988wzOVhz3XXXpX333bfW58b9u1gHiABI6N69e2n77bffnj7//POc2usf//hHaX3Hjh3z/zESJFJ5hTvuuCOf+w8++CDdfffdpWDPMsssU3pdtBUU6xrqAFB30w+/AvOFTTbZJP//448/5krE1KMEZtcGG2yQdtlll5xb8umnn875OKfOOzlw4MDSz+3bt0+//OUvc97K0KlTp9IwzJn53xxhc+all15K11xzTa3bIpf1UUcdNdN9RG7RqfcRlZVYQlRaImgRQYrahrK2a9cu3XjjjTWG8YaoYIXVVlstnycAqA/qAPVTB4jAxGWXXVb6+cILL8xLbeWs63sV6wAtW7askc8cAOaEOsD/3HXXXenFF1/Mj6MTQcyDVT4XVhxPMYBRm3XXXTetvvrq+RzG8ZYHOUL//v3Tpptumh9H8Oavf/1rDtpEsKK8I0MESiLwUz4K5rnnnit1jIzAC1A3RmrAfC56AsRNOJT3JqgP119/fRoyZEhaYYUVUqtWrfJQylVXXTVPnnn22WdP8/zi0NOw88475y/w80pUnGLIaVRCIqgQ5Y0KyK9//ev0yiuv5FyX9WXppZfOOTYXWWSRPCl4DK2NilyM4FhrrbVqPDd6ccZQ3DArOT0BYFapA8z7OkBd36t4XSKlhY4NANQXdYD6EemqHnjggXTMMcekfv365bLHd/yYLyOO68knn8znoDwNdYzMiO/9cV7atGmTOy1EJodoEyhXvC7x3AEDBszzY4Nq16RQH6FPoEG7+eabc2+KmLjqk08+SQsuuGBFyvHwww+XejFET0ZDLFO65ZZbcsUuAiAxbDd6dwBAfVEHaLgiF3k0ikTP0eitucYaa1S6SADMR9QBGq5ICRaTq48ePTq3Cey4446VLhJUHSM1oBHYaaed8lwNMcdD5ICe1yI9U/Rq2GKLLfLPUaFRkfmfYp7t3/3udwIaANQ7dYCG69xzz80BjW233VZAA4B6pw7QcMWolghoxOTiO+ywQ6WLA1XJSA1grosJtyK1Ugy9jFyecQPv0qVLpYsFAMxl6gAA0DipAwBzk6AGAAAAAABQFaSfAgAAAAAAqoKgBgAAAAAAUBUENQAAAAAAgKogqAEAAAAAAFSFZmk+N2XKlDRixIjUrl271KRJk0oXBwCYjkKhkL7++uvUvXv31LTpnPe7UAcAgOqgDgAAjVNhNusA831QIyoyPXv2rHQxAIBZNHz48NSjR4853o86AABUF3UAAGichtexDjDfBzWiZ0bxxLRv377SxQEApmP8+PG5AaJ4755T6gAAUB3UAQCgcRo/m3WA+T6oURxqGhUZlRkAaPjqK02EOgAAVBd1AABonJrUsQ5gonAAAAAAAKAqCGoAAAAAAABVQVADAAAAAACoCoIaAAAAADCfuPvuu9OKK66Y2rRpk7p3754uueSSGttHjRqVFlpoofwcgGo0308UDgAAAACNwf33358OOuigdP3116f11lsvjR8/Pgcxyh1yyCFppZVWSl999VXFygkwJ4zUAAAAAID5wAknnJBOPPHEtOGGG6YFFlggderUKQ0cOLC0/a677kqjR49Ov/zlLytaToA5IagBAAAAAFVu4sSJ6eWXX06fffZZ6t+/f+ratWvaYYcd0ueff563jxs3Lh111FHTpKMCqDaCGgAAAABQ5caMGZMKhUK6884700MPPZTef//91LJly7T77rvn7ccee2zaa6+9Ur9+/SpdVIA5Yk4NAAAAAKhybdu2zf8fdthhqXfv3vnxKaeckoMYTzzxRHr66afTK6+8UuFSAsw5QQ0AAAAAqHIdO3ZMvXr1qnXbPffckz788MPUvXv3/PPkyZPTt99+mxZeeOH05ptvpm7dus3j0gLMPumnAAAAAGA+sP/++6fzzz8/z6sRQYtTTz01bbLJJnny8HfffTe99tpreYn1AwYMyI8XXXTRShcboE6M1AAAAACA+cDxxx+fRo8enVZYYYX880YbbZSuu+661L59+7wUderUKTVv3jz16NGjgqUFmD1NCjGD0Hxs/PjxqUOHDmncuHE1/ngDAPP3PVsdAACqgzoAADRO42fznm2kBgAAAADMgREjRqQxY8ZUuhhVKUaNFOf6AJgVghoAAAAAMAcBjS033zh9O2FcpYtSlVq37ZDufeBRgQ1glglqAAAAAMBsihEaEdD4yy+7pr5dF6x0carKhyO/ScdcNzKfQ0ENYFYJagAAAADAHIqAxjK921a6GADzvaaVLgAAAAAAAMCsENQAAAAAAACqgqAGAAAAAABQFQQ1AAAAAACAqiCoAQAAAAAAVAVBDQAAAAAAoCoIagAAAAAAAFVBUAMAAAAAAKgKghoAAAAAAEBVENQAAAAAAACqgqAGAAAAAABQFQQ1AAAAAACAqiCoAQAAAAAAVIWKBzU+++yztPvuu6fOnTun1q1bp+WWWy699NJLpe2FQiGdeOKJqVu3bnn7oEGD0nvvvVfRMgMAAAAAAI0sqDFmzJi0zjrrpObNm6f77rsvvfPOO+nss89OnTp1Kj3nz3/+czrvvPPSJZdckp5//vnUpk2btPnmm6dJkyZVsugAAAAAAMA81ixV0Jlnnpl69uyZrrrqqtK6Pn361BilMWTIkPT73/8+bbPNNnndtddem7p06ZLuvPPOtPPOO1ek3AAAAAAAQCMbqXH33XenVVddNe2www5p0UUXTSuttFK67LLLStuHDRuWRo4cmVNOFXXo0CGtscYa6dlnn61QqQEAAAAAgEY3UuPDDz9MF198cTrqqKPSb3/72/Tiiy+mww47LLVo0SLtueeeOaARYmRGufi5uG1qkydPzkvR+PHj5/JRAAANgToAADRO6gAA0LhUdKTGlClT0sorr5xOP/30PEpj//33T/vtt1+eP2N2nXHGGXk0R3GJ9FYAwPxPHQAAGid1AABoXCoa1OjWrVtaeumla6xbaqml0ieffJIfd+3aNf8/atSoGs+Jn4vbpjZ48OA0bty40jJ8+PC5Vn4AoOFQBwCAxkkdAAAal4qmn1pnnXXS0KFDa6x79913U+/evUuThkfw4pFHHkkrrrhiaRjp888/n37961/Xus+WLVvmBQBoXNQBAKBxUgcAgMalokGNI488Mq299to5/dSOO+6YXnjhhfS3v/0tL6FJkybpiCOOSKeddlrq169fDnKccMIJqXv37mnbbbetZNEBAAAAAIDGFNRYbbXV0h133JGHip566qk5aDFkyJC02267lZ5z7LHHpokTJ+b5NsaOHZvWXXfddP/996dWrVpVsugAAAAAAEBjCmqEn/3sZ3mZnhitEQGPWAAAAAAAgMarohOFAwAAAAAAzCpBDQAAAAAAoCoIagAAAAAAAFVBUAMAAAAAAKgKghoAAAAAAEBVENQAAAAAAACqgqAGAAAAAABQFQQ1AAAAAACAqiCoAQAAAAAAVAVBDQAAAAAAoCoIagAAAAAAAFVBUAMAAAAAAKgKghoAAAAAAEBVENQAAAAAAACqgqAGAAAAAABQFQQ1AAAAAACAqiCoAQAAAAAAVAVBDQAAAAAAoCoIagAAAAAAAFVBUAMAAAAAAKgKghoAAAAAAEBVENQAAAAAAACqgqAGAAAAAABQFQQ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",
       "text/plain": [
        "<Figure size 1600x500 with 3 Axes>"
       ]
@@ -2452,10 +2515,10 @@
    "id": "histogram-interpretation",
    "metadata": {
     "papermill": {
-     "duration": 0.024953,
-     "end_time": "2026-06-06T08:40:52.377240",
+     "duration": 0.006271,
+     "end_time": "2026-10-30T04:01:03.435283+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.352287",
+     "start_time": "2026-10-30T04:01:03.429012+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2467,9 +2530,9 @@
     "\n",
     "| Observation | Filtrage simple | CSP | Entropie |\n",
     "|-------------|-----------------|-----|----------|\n",
-    "| Pic | 4-5 tentatives | 4-5 tentatives | 3-4 tentatives |\n",
-    "| Queue droite | Aucune (pire cas 5) | Aucune (pire cas 5) | Aucune (pire cas 5) |\n",
-    "| En 2 tentatives | Rare (chanceux) | Rare | Possible si 2e mot est unique |\n",
+    "| Pic | 3 tentatives (46 parties sur 100) | 3 tentatives (45 sur 100) | 3 tentatives (64 sur 100) |\n",
+    "| Queue droite | 1 partie a 6 tentatives | 1 partie a 6 tentatives | 2 parties a 5 tentatives |\n",
+    "| En 2 tentatives | 20 parties sur 100 | 19 parties sur 100 | 26 parties sur 100 |\n",
     "\n",
     "**Points cles** :\n",
     "1. L'histogramme de l'entropie est **deplace vers la gauche** : le solveur converge plus vite\n",
@@ -2484,10 +2547,10 @@
    "id": "section-7-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.023412,
-     "end_time": "2026-06-06T08:40:52.424084",
+     "duration": 0.006151,
+     "end_time": "2026-10-30T04:01:03.447740+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.400672",
+     "start_time": "2026-10-30T04:01:03.441589+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2512,16 +2575,16 @@
    "id": "exercise-1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:36.011900Z",
-     "iopub.status.busy": "2026-07-22T21:43:36.010901Z",
-     "iopub.status.idle": "2026-07-22T21:43:36.019549Z",
-     "shell.execute_reply": "2026-07-22T21:43:36.018538Z"
+     "iopub.execute_input": "2026-10-30T04:01:03.461052Z",
+     "iopub.status.busy": "2026-10-30T04:01:03.460710Z",
+     "iopub.status.idle": "2026-10-30T04:01:03.465305Z",
+     "shell.execute_reply": "2026-10-30T04:01:03.464693Z"
     },
     "papermill": {
-     "duration": 0.047264,
-     "end_time": "2026-06-06T08:40:52.496265",
+     "duration": 0.012473,
+     "end_time": "2026-10-30T04:01:03.466294+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.449001",
+     "start_time": "2026-10-30T04:01:03.453821+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2567,7 +2630,16 @@
   {
    "cell_type": "markdown",
    "id": "interp-ex1-stub",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.006417,
+     "end_time": "2026-10-30T04:01:03.479121+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:03.472704+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — la sortie committee est celle du STUB, pas une mesure\n",
     "\n",
@@ -2581,10 +2653,10 @@
    "id": "exercise-2-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.021714,
-     "end_time": "2026-06-06T08:40:52.539390",
+     "duration": 0.006396,
+     "end_time": "2026-10-30T04:01:03.492479+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.517676",
+     "start_time": "2026-10-30T04:01:03.486083+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2601,16 +2673,16 @@
    "id": "exercise-2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:36.023548Z",
-     "iopub.status.busy": "2026-07-22T21:43:36.022551Z",
-     "iopub.status.idle": "2026-07-22T21:43:36.030062Z",
-     "shell.execute_reply": "2026-07-22T21:43:36.028547Z"
+     "iopub.execute_input": "2026-10-30T04:01:03.506049Z",
+     "iopub.status.busy": "2026-10-30T04:01:03.505775Z",
+     "iopub.status.idle": "2026-10-30T04:01:03.508730Z",
+     "shell.execute_reply": "2026-10-30T04:01:03.508287Z"
     },
     "papermill": {
-     "duration": 0.036004,
-     "end_time": "2026-06-06T08:40:52.593767",
+     "duration": 0.010517,
+     "end_time": "2026-10-30T04:01:03.509464+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.557763",
+     "start_time": "2026-10-30T04:01:03.498947+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2647,10 +2719,10 @@
    "id": "71bc8f26",
    "metadata": {
     "papermill": {
-     "duration": 0.019958,
-     "end_time": "2026-06-06T08:40:52.634079",
+     "duration": 0.006433,
+     "end_time": "2026-10-30T04:01:03.522059+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.614121",
+     "start_time": "2026-10-30T04:01:03.515626+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2664,10 +2736,10 @@
    "id": "exercise-3-intro",
    "metadata": {
     "papermill": {
-     "duration": 0.021774,
-     "end_time": "2026-06-06T08:40:52.675790",
+     "duration": 0.006245,
+     "end_time": "2026-10-30T04:01:03.534977+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.654016",
+     "start_time": "2026-10-30T04:01:03.528732+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2687,16 +2759,16 @@
    "id": "exercise-3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-07-22T21:43:36.035587Z",
-     "iopub.status.busy": "2026-07-22T21:43:36.035587Z",
-     "iopub.status.idle": "2026-07-22T21:43:36.043099Z",
-     "shell.execute_reply": "2026-07-22T21:43:36.042090Z"
+     "iopub.execute_input": "2026-10-30T04:01:03.549959Z",
+     "iopub.status.busy": "2026-10-30T04:01:03.549559Z",
+     "iopub.status.idle": "2026-10-30T04:01:03.553429Z",
+     "shell.execute_reply": "2026-10-30T04:01:03.552732Z"
     },
     "papermill": {
-     "duration": 0.035835,
-     "end_time": "2026-06-06T08:40:52.732223",
+     "duration": 0.012544,
+     "end_time": "2026-10-30T04:01:03.554402+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.696388",
+     "start_time": "2026-10-30T04:01:03.541858+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2749,7 +2821,16 @@
   {
    "cell_type": "markdown",
    "id": "interp-ex3-generic",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.006186,
+     "end_time": "2026-10-30T04:01:03.567118+00:00",
+     "exception": false,
+     "start_time": "2026-10-30T04:01:03.560932+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Lecture — l'algorithme 2 passes : pourquoi l'ordre des passes est le coeur de l'exercice\n",
     "\n",
@@ -2765,10 +2846,10 @@
    "id": "summary-section",
    "metadata": {
     "papermill": {
-     "duration": 0.020034,
-     "end_time": "2026-06-06T08:40:52.771237",
+     "duration": 0.0065,
+     "end_time": "2026-10-30T04:01:03.580532+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.751203",
+     "start_time": "2026-10-30T04:01:03.574032+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2791,8 +2872,8 @@
     "\n",
     "| Approche | Moy. tentatives | Complexite par tentative | Avantage |\n",
     "|----------|-----------------|--------------------------|----------|\n",
-    "| Filtrage simple | ~3.1 | $O(n)$ | Simple et rapide |\n",
-    "| CSP | ~3.1 | $O(n)$ | Structure explicite |\n",
+    "| Filtrage simple | ~3.2 | $O(n)$ | Simple et rapide |\n",
+    "| CSP | ~3.2 | $O(n)$ | Structure explicite |\n",
     "| Entropie | ~2.9 | $O(n^2)$ | Optimal en information |\n",
     "\n",
     "### Ce qu'il faut retenir\n",
@@ -2829,10 +2910,10 @@
    "id": "nav-footer",
    "metadata": {
     "papermill": {
-     "duration": 0.022495,
-     "end_time": "2026-06-06T08:40:52.814205",
+     "duration": 0.00747,
+     "end_time": "2026-10-30T04:01:03.594452+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.791710",
+     "start_time": "2026-10-30T04:01:03.586982+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2848,10 +2929,10 @@
    "id": "003d40e5",
    "metadata": {
     "papermill": {
-     "duration": 0.020541,
-     "end_time": "2026-06-06T08:40:52.853233",
+     "duration": 0.008298,
+     "end_time": "2026-10-30T04:01:03.610825+00:00",
      "exception": false,
-     "start_time": "2026-06-06T08:40:52.832692",
+     "start_time": "2026-10-30T04:01:03.602527+00:00",
      "status": "completed"
     },
     "tags": []
@@ -2859,7 +2940,7 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "Ce notebook a modélisé le jeu Wordle comme un problème de recherche d'information et comparé trois stratégies de résolution. Le **filtrage simple** élimine les candidats incompatibles mais choisit au hasard, menant à ~3.1 tentatives en moyenne. Le **CSP** structure le problème avec des domaines par position et des contraintes déduites du feedback, offrant une visualisation claire de la propagation. L'approche par **entropie de Shannon** maximise l'information attendue de chaque tentative, réduisant la moyenne à ~2.9 tentatives grâce au choix optimal du mot le plus discriminant. La comparaison des distributions de feedback montre que le meilleur mot d'ouverture répartit uniformément les candidats entre les patterns, tandis qu'un mauvais mot les concentre dans un seul groupe. Cette leçon transcende le Wordle : maximiser le gain d'information est une stratégie fondamentale en diagnostic médical, dans les arbres de décision et dans tout problème de recherche avec retours partiels."
+    "Ce notebook a modélisé le jeu Wordle comme un problème de recherche d'information et comparé trois stratégies de résolution. Le **filtrage simple** élimine les candidats incompatibles mais choisit au hasard, menant a ~3.2 tentatives en moyenne. Le **CSP** structure le problème avec des domaines par position et des contraintes déduites du feedback, offrant une visualisation claire de la propagation. L'approche par **entropie de Shannon** maximise l'information attendue de chaque tentative, réduisant la moyenne à ~2.9 tentatives grâce au choix optimal du mot le plus discriminant. La comparaison des distributions de feedback montre que le meilleur mot d'ouverture répartit uniformément les candidats entre les patterns, tandis qu'un mauvais mot les concentre dans un seul groupe. Cette leçon transcende le Wordle : maximiser le gain d'information est une stratégie fondamentale en diagnostic médical, dans les arbres de décision et dans tout problème de recherche avec retours partiels."
    ]
   }
  ],
@@ -2879,12 +2960,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.13.3"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 16.364418,
-   "end_time": "2026-06-06T08:40:53.438707",
+   "duration": 4.649492,
+   "end_time": "2026-10-30T04:01:03.854695+00:00",
    "environment_variables": {},
    "exception": null,
    "input_path": "App-7-Wordle.ipynb",
@@ -2892,7 +2973,7 @@
    "parameters": {
     "BATCH_MODE": "true"
    },
-   "start_time": "2026-06-06T08:40:37.074289",
+   "start_time": "2026-10-30T04:00:59.205203+00:00",
    "version": "2.6.0"
   }
  },
diff --git a/scripts/notebook_tools/twin_pairs.d/app-7-wordle.yaml b/scripts/notebook_tools/twin_pairs.d/app-7-wordle.yaml
index 56553da50af..ce8aa6ef59b 100644
--- a/scripts/notebook_tools/twin_pairs.d/app-7-wordle.yaml
+++ b/scripts/notebook_tools/twin_pairs.d/app-7-wordle.yaml
@@ -11,4 +11,5 @@
   - 'Idiomes framework : Python = Counter/typing + matplotlib viz ; C# = System.IO + Microsoft.DotNet.Interactive formatting.'
   - 'Asymetrie pedagogique : Python met l''accent sur le solveur SOTA (OR-Tools CP-SAT / library specialisee) + visualisation matplotlib + experimentation comparative ; le C# demontre l''implementation from-scratch en BCL pure (ou binding NuGet du meme solver pour App-8/10/15), presentation compacte. Meme socle theorique, leviers de demonstration differents.'
   - "Credits 2026-09-15 (PR #16218, myia-po-2027) : correction chirurgicale des liens profils GitHub transliteres (`cedric.damais` -> `CedricDamais`, `leon.ayral` -> `LeoN1203`, `gabriel.calvente` -> `gabcal07`, `yacine.benihaddadene` -> `yacineben`) dans la cellule markdown d'Hommage uniquement. 4 remplacements URL sur App-7-Wordle.ipynb. Cellules code intactes, le twin C# (App-7b) n'est pas touche. Source de verite : `users/<login>` GH API. drift Python (content) = edition markdown du credit, asymetrie intentionnelle documentee."
+  - "2026-10-30 (myia-po-2023:CoursIA, census #23384) : convergence du jumeau Python au canon kernel `python3` 3.13 (3.11.9 -> 3.13.3) et re-execution complete (63/63 cellules, 0 erreur). Les graines par mot passent de `hash()` -- salé par PYTHONHASHSEED, donc NON reproductibles d'une execution a l'autre -- a `zlib.crc32(word.encode())` : les sorties sont desormais reproductibles (verifie : deux executions, deux processus, memes valeurs structurelles). La prose quantitative est re-derivee par script depuis les nouvelles sorties (moyennes de tentatives, pire cas, pics d'histogramme, trace 297 -> 1). Le contenu Python change (sources + sorties + prose) ; le jumeau C# (App-7b) n'est PAS touche et reste valide : parite semantique inchangee, `bridge_verdict: SOTA-OK` inchange. Defaut analogue cote C# (`word.GetHashCode()`, salé par .NET) : signale separement, hors perimetre de cette tranche (canon .NET distinct)."
   - "2026-10-30 (myia-po-2023:CoursIA-2, PR #23446) : reproductibilite du jumeau C# reparee a la racine -- les graines par mot derivees de string.GetHashCode() (hachage randomise par processus depuis .NET Core 2.1) sont remplacees par une empreinte FNV-1a 32 bits calculee dans la cellule (BCL pure, 0 NuGet). Deux executions dans deux processus distincts produisent desormais des sorties structurelles identiques (trace des tentatives, moyenne, taux de reussite, histogramme). Divergence de contenu assumee avec le jumeau Python : cote C# la graine derive de FNV-1a 32 bits, cote Python de zlib.crc32 (PR #23445) -- les deux sont stables entre processus, mais les empreintes different, donc les traces de tirage aleatoire different entre C# et Python (les PRNG different deja). Parite semantique inchangee (algorithmes, theorie de l'information, classement top-10, entropie) ; parity_level: semantic et bridge_verdict: SOTA-OK preserves."
diff --git a/scripts/notebook_tools/twin_pairs.d/app-7-wordle/0012-2026-10-30-myia-po-2023-CoursIA.yaml b/scripts/notebook_tools/twin_pairs.d/app-7-wordle/0012-2026-10-30-myia-po-2023-CoursIA.yaml
new file mode 100644
index 00000000000..ad46c8040ac
--- /dev/null
+++ b/scripts/notebook_tools/twin_pairs.d/app-7-wordle/0012-2026-10-30-myia-po-2023-CoursIA.yaml
@@ -0,0 +1,6 @@
+date: '2026-10-30'
+by: myia-po-2023:CoursIA
+python_sha: c18e80817433d26f52d0229cb9851cb37f1b4c68
+csharp_sha: 475098b19ab92569e30297829f516b24a2a8d664
+content_python_sha: e78e4a834ac50023ea66a067cf64cb222ea9a6f2054390007e2ab8976913eb69
+content_csharp_sha: 4c612549e3c8a2abaf5e685d17c37b08188477e43b17a0b4b00495d68fc5acce
diff --git a/scripts/notebook_tools/twin_pairs.d/app-7-wordle/0014-2026-10-31-myia-po-2023-CoursIA.yaml b/scripts/notebook_tools/twin_pairs.d/app-7-wordle/0014-2026-10-31-myia-po-2023-CoursIA.yaml
new file mode 100644
index 00000000000..655a4c57883
--- /dev/null
+++ b/scripts/notebook_tools/twin_pairs.d/app-7-wordle/0014-2026-10-31-myia-po-2023-CoursIA.yaml
@@ -0,0 +1,6 @@
+date: '2026-10-31'
+by: myia-po-2023:CoursIA
+python_sha: c18e80817433d26f52d0229cb9851cb37f1b4c68
+csharp_sha: ad30650e6ce60f17dc872d0266e52bbd90186fb3
+content_python_sha: e78e4a834ac50023ea66a067cf64cb222ea9a6f2054390007e2ab8976913eb69
+content_csharp_sha: a0c87d31f3deaad5b196102061c3d6207e31810a7d1bb6a78a1a627ac162ec35