diff --git a/.github/workflows/lean-ci-matrix.yml b/.github/workflows/lean-ci-matrix.yml
index 1741e0efc3c..291c6be95c3 100644
--- a/.github/workflows/lean-ci-matrix.yml
+++ b/.github/workflows/lean-ci-matrix.yml
@@ -144,9 +144,9 @@ on:
       # hecke_lean (herite du wrapper lean-hecke.yml supprime, #13751 :
       # 0 sorry verifie, axiom target-modules "*" + allow-axioms vide,
       # fail-on-sorry true par defaut -- parametres reportes au manifeste)
-      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/**.lean'
-      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lakefile.lean'
-      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lean-toolchain'
+      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/**.lean'
+      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lakefile.lean'
+      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lean-toolchain'
       # formal_groups_lean (herite du wrapper lean-formal-groups.yml supprime,
       # #13751 : 0 sorry par construction, axiom target-modules "*" +
       # allow-axioms vide, fail-on-sorry true -- parametres reportes au
@@ -352,9 +352,9 @@ on:
       # hecke_lean (herite du wrapper lean-hecke.yml supprime, #13751 :
       # 0 sorry verifie, axiom target-modules "*" + allow-axioms vide,
       # fail-on-sorry true par defaut -- parametres reportes au manifeste)
-      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/**.lean'
-      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lakefile.lean'
-      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lean-toolchain'
+      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/**.lean'
+      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lakefile.lean'
+      - 'MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lean-toolchain'
       # formal_groups_lean (herite du wrapper lean-formal-groups.yml supprime,
       # #13751 : 0 sorry par construction, axiom target-modules "*" +
       # allow-axioms vide, fail-on-sorry true -- parametres reportes au
diff --git a/MyIA.AI.Notebooks/Complexity/Complexity-07b-LeTourDesGrandsNombres-Python.ipynb b/MyIA.AI.Notebooks/Complexity/Complexity-07b-LeTourDesGrandsNombres-Python.ipynb
index 78064a98dde..41f3986055c 100644
--- a/MyIA.AI.Notebooks/Complexity/Complexity-07b-LeTourDesGrandsNombres-Python.ipynb
+++ b/MyIA.AI.Notebooks/Complexity/Complexity-07b-LeTourDesGrandsNombres-Python.ipynb
@@ -780,7 +780,7 @@
    "source": [
     "## Gap Mathlib\n",
     "\n",
-    "À la date de rédaction (14/10/2026) et re-mesuré le 27/10/2026, aucune formalisation de la fonction d'Ackermann, des flèches de Knuth ou du castor affairé n'est référencée dans les sources de ce dépôt : `git grep -i -E \"ackermann|uparrow|busybeaver\"` renvoie des occurrences hors ce carnet (le README de la série, un notebook GameTheory où `↑` est une notation de jeu, un Lean-29, deux tables de traduction) — **aucun fichier `.lean`**, donc aucune formalisation ; Mathlib vendored non disponible localement (`.lake/packages/mathlib` absent) — vérification non exhaustive, à reprendre. Le pont formel le plus proche : la preuve bbchallenge 2024 que $S(5) = 47\\,176\\,870$ a été **formalisée en Rocq**, et côté indécidabilité la lignée BB(8000) → BB(393) → BB(335)/ZFC (Yedidia-Aaronson, Wade, Ketchersid) rejoint les questions de Gödel suivies par l'EPIC #15066."
+    "À la date de rédaction (14/10/2026) et re-mesuré le 27/10/2026, aucune formalisation de la fonction d'Ackermann, des flèches de Knuth ou du castor affairé n'est référencée dans les sources de ce dépôt : `git grep -i -E \"ackermann|uparrow|busybeaver\"` renvoie des occurrences hors ce carnet (le README de la série, un notebook GameTheory où `↑` est une notation de jeu, un Lean-29 (devenu `Langlands/06-operateurs-de-hecke-natif.ipynb`), deux tables de traduction) — **aucun fichier `.lean`**, donc aucune formalisation ; Mathlib vendored non disponible localement (`.lake/packages/mathlib` absent) — vérification non exhaustive, à reprendre. Le pont formel le plus proche : la preuve bbchallenge 2024 que $S(5) = 47\\,176\\,870$ a été **formalisée en Rocq**, et côté indécidabilité la lignée BB(8000) → BB(393) → BB(335)/ZFC (Yedidia-Aaronson, Wade, Ketchersid) rejoint les questions de Gödel suivies par l'EPIC #15066."
    ]
   },
   {
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/01-formes-modulaires-sl2z-hecke.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/01-formes-modulaires-sl2z-hecke.ipynb
index da236252ac7..1ec1afb4b3a 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/01-formes-modulaires-sl2z-hecke.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/01-formes-modulaires-sl2z-hecke.ipynb
@@ -5,10 +5,10 @@
    "id": "f6a7e706",
    "metadata": {
     "papermill": {
-     "duration": 0.003949,
-     "end_time": "2026-09-26T17:35:05.948210",
+     "duration": 0.004115,
+     "end_time": "2026-11-01T17:29:00.179561+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:05.944261",
+     "start_time": "2026-11-01T17:29:00.175446+00:00",
      "status": "completed"
     },
     "tags": []
@@ -25,7 +25,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "1ff8e5c6",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003949,
+     "end_time": "2026-11-01T17:29:00.193989+00:00",
+     "exception": false,
+     "start_time": "2026-11-01T17:29:00.190040+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "| [Série Langlands](README.md) | [02 — Monstrous Moonshine →](02-monstrous-moonshine-invariant-j.ipynb) |"
    ]
@@ -35,10 +45,10 @@
    "id": "f208e763",
    "metadata": {
     "papermill": {
-     "duration": 0.002239,
-     "end_time": "2026-09-26T17:35:05.952919",
+     "duration": 0.003554,
+     "end_time": "2026-11-01T17:29:00.201914+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:05.950680",
+     "start_time": "2026-11-01T17:29:00.198360+00:00",
      "status": "completed"
     },
     "tags": []
@@ -61,10 +71,10 @@
    "id": "1b741cc5",
    "metadata": {
     "papermill": {
-     "duration": 0.002218,
-     "end_time": "2026-09-26T17:35:05.957379",
+     "duration": 0.003568,
+     "end_time": "2026-11-01T17:29:00.209237+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:05.955161",
+     "start_time": "2026-11-01T17:29:00.205669+00:00",
      "status": "completed"
     },
     "tags": []
@@ -82,10 +92,10 @@
    "id": "b5744d88",
    "metadata": {
     "papermill": {
-     "duration": 0.002288,
-     "end_time": "2026-09-26T17:35:05.964064",
+     "duration": 0.003521,
+     "end_time": "2026-11-01T17:29:00.216713+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:05.961776",
+     "start_time": "2026-11-01T17:29:00.213192+00:00",
      "status": "completed"
     },
     "tags": []
@@ -100,16 +110,16 @@
    "id": "9f8d8a60",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:05.969755Z",
-     "iopub.status.busy": "2026-09-26T17:35:05.969392Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.041009Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.040536Z"
+     "iopub.execute_input": "2026-11-01T17:29:00.226623Z",
+     "iopub.status.busy": "2026-11-01T17:29:00.226337Z",
+     "iopub.status.idle": "2026-11-01T17:29:01.557936Z",
+     "shell.execute_reply": "2026-11-01T17:29:01.556703Z"
     },
     "papermill": {
-     "duration": 1.076022,
-     "end_time": "2026-09-26T17:35:07.042359",
+     "duration": 1.338913,
+     "end_time": "2026-11-01T17:29:01.559316+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:05.966337",
+     "start_time": "2026-11-01T17:29:00.220403+00:00",
      "status": "completed"
     },
     "tags": []
@@ -117,7 +127,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x460 with 1 Axes>"
       ]
@@ -181,16 +191,16 @@
    "id": "717d3e3b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.050874Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.050448Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.090629Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.090150Z"
+     "iopub.execute_input": "2026-11-01T17:29:01.571046Z",
+     "iopub.status.busy": "2026-11-01T17:29:01.570719Z",
+     "iopub.status.idle": "2026-11-01T17:29:01.578125Z",
+     "shell.execute_reply": "2026-11-01T17:29:01.577371Z"
     },
     "papermill": {
-     "duration": 0.044789,
-     "end_time": "2026-09-26T17:35:07.091804",
+     "duration": 0.015237,
+     "end_time": "2026-11-01T17:29:01.579703+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.047015",
+     "start_time": "2026-11-01T17:29:01.564466+00:00",
      "status": "completed"
     },
     "tags": []
@@ -246,10 +256,10 @@
    "id": "b0f6e1c0",
    "metadata": {
     "papermill": {
-     "duration": 0.014202,
-     "end_time": "2026-09-26T17:35:07.111052",
+     "duration": 0.004408,
+     "end_time": "2026-11-01T17:29:01.588898+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.096850",
+     "start_time": "2026-11-01T17:29:01.584490+00:00",
      "status": "completed"
     },
     "tags": []
@@ -263,10 +273,10 @@
    "id": "d4b4b4eb",
    "metadata": {
     "papermill": {
-     "duration": 0.003611,
-     "end_time": "2026-09-26T17:35:07.119901",
+     "duration": 0.004582,
+     "end_time": "2026-11-01T17:29:01.598147+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.116290",
+     "start_time": "2026-11-01T17:29:01.593565+00:00",
      "status": "completed"
     },
     "tags": []
@@ -285,16 +295,16 @@
    "id": "1aa93200",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.131482Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.131291Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.135570Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.135181Z"
+     "iopub.execute_input": "2026-11-01T17:29:01.609573Z",
+     "iopub.status.busy": "2026-11-01T17:29:01.609310Z",
+     "iopub.status.idle": "2026-11-01T17:29:01.616399Z",
+     "shell.execute_reply": "2026-11-01T17:29:01.615285Z"
     },
     "papermill": {
-     "duration": 0.011526,
-     "end_time": "2026-09-26T17:35:07.136646",
+     "duration": 0.014082,
+     "end_time": "2026-11-01T17:29:01.617220+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.125120",
+     "start_time": "2026-11-01T17:29:01.603138+00:00",
      "status": "completed"
     },
     "tags": []
@@ -329,10 +339,10 @@
    "id": "f9c4bbad",
    "metadata": {
     "papermill": {
-     "duration": 0.004351,
-     "end_time": "2026-09-26T17:35:07.146017",
+     "duration": 0.004496,
+     "end_time": "2026-11-01T17:29:01.626706+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.141666",
+     "start_time": "2026-11-01T17:29:01.622210+00:00",
      "status": "completed"
     },
     "tags": []
@@ -346,10 +356,10 @@
    "id": "de917c04",
    "metadata": {
     "papermill": {
-     "duration": 0.005036,
-     "end_time": "2026-09-26T17:35:07.154430",
+     "duration": 0.004911,
+     "end_time": "2026-11-01T17:29:01.637175+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.149394",
+     "start_time": "2026-11-01T17:29:01.632264+00:00",
      "status": "completed"
     },
     "tags": []
@@ -366,16 +376,16 @@
    "id": "7fb5ec7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.164173Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.163859Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.171036Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.170690Z"
+     "iopub.execute_input": "2026-11-01T17:29:01.648548Z",
+     "iopub.status.busy": "2026-11-01T17:29:01.648304Z",
+     "iopub.status.idle": "2026-11-01T17:29:01.665500Z",
+     "shell.execute_reply": "2026-11-01T17:29:01.664456Z"
     },
     "papermill": {
-     "duration": 0.013895,
-     "end_time": "2026-09-26T17:35:07.171648",
+     "duration": 0.02483,
+     "end_time": "2026-11-01T17:29:01.666689+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.157753",
+     "start_time": "2026-11-01T17:29:01.641859+00:00",
      "status": "completed"
     },
     "tags": []
@@ -411,10 +421,10 @@
    "id": "422a13aa",
    "metadata": {
     "papermill": {
-     "duration": 0.002761,
-     "end_time": "2026-09-26T17:35:07.177960",
+     "duration": 0.005086,
+     "end_time": "2026-11-01T17:29:01.676738+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.175199",
+     "start_time": "2026-11-01T17:29:01.671652+00:00",
      "status": "completed"
     },
     "tags": []
@@ -430,10 +440,10 @@
    "id": "bf93c284",
    "metadata": {
     "papermill": {
-     "duration": 0.003894,
-     "end_time": "2026-09-26T17:35:07.185015",
+     "duration": 0.004516,
+     "end_time": "2026-11-01T17:29:01.686279+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.181121",
+     "start_time": "2026-11-01T17:29:01.681763+00:00",
      "status": "completed"
     },
     "tags": []
@@ -448,16 +458,16 @@
    "id": "aeb9e2a6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.191983Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.191792Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.515508Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.515093Z"
+     "iopub.execute_input": "2026-11-01T17:29:01.698825Z",
+     "iopub.status.busy": "2026-11-01T17:29:01.698567Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.329135Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.327670Z"
     },
     "papermill": {
-     "duration": 0.328704,
-     "end_time": "2026-09-26T17:35:07.516195",
+     "duration": 0.639156,
+     "end_time": "2026-11-01T17:29:02.330248+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.187491",
+     "start_time": "2026-11-01T17:29:01.691092+00:00",
      "status": "completed"
     },
     "tags": []
@@ -465,7 +475,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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R2Xr4AUVE5vDvBJF5LlJfYOFxIiIiIiK7xIwsERERETkkp+8jKwNupMeezNZSF1NGEhEREVHNSbGA9FyWiUL040nMcfpAVoJYzjJCREREZF/Onj2rJhCxxOkDWcnE6g9WXU3/SEREREQ1k5mZqZKM+hjNEqcNZGVaP/3UfkKCWAayRERERPbBmpJPp+9aIFG/NKnPyMhgIEtERETkQLEZuxYQERERkUNiIEtEREREDslpa2StJTW0RUVFtt4NIiKreXh4wM3Nzda7QURU7xpFIPv222/j888/V0XBTz31FKZMmVIn683Ozsa5c+dUPzMiIkchfwulZY2/v7+td4WIqF45fCC7f/9+LFq0CDt37lQB57Bhw3DNNdcgKCio1plYCWJ9fX0RHh7OyRKIyCHI38GUlBT196tdu3bMzBJRo+bwgezhw4cxaNAgeHt7q9s9evTAypUrcfPNN9dqvVJOIB8IEsT6+PjU0d4SEdU/+bt1+vRp9XeMgSwRNWY2H+y1fv16jB8/Xk1DJlnP5cuXV1pG+r22bNlSBasDBgzAtm3bDI917doV69atQ3p6Oi5duqSunz9/vs72j5lYInI0/LtFRM7C5oFsTk6OyqJKsGrK4sWLMWvWLDz//PPYtWuXWnb06NFITk5Wj3fu3BkPP/wwrrrqKkyaNAkDBw60mIEoKChQ/cmML0RERETkeGweyI4dOxYvv/wyJk6caPLxOXPmYPr06Zg2bZoKWufPn6/qVhcsWGBY5r777lNB7tq1a9VoXakLM+e1115TTXb1F5kCjYiIiIgcj80DWUsKCwvVIK4RI0YY7nN1dVW3t2zZYrhPn509evSoKjuQjK05s2fPVjNF6C9nz56t51fhGKSW7rLLLlOD5H744Ycq79doNOo4y+9BapSvvPJKXH311arEg4iIiBybVqtFZn4WjqScwF8nN+ObvcuQWZANe2PXg71SU1NV94DIyMhy98vtI0eOGG5fd911Kij18/NTbbjc3c2/LC8vL3WRUga5yPoJ6phJoPrRRx9Zdf8///yj6pVbtGiBNWvWqCy5ZMvlmP7f//1fA+89ERER1URhcSESspORkJWMC1lJ6pKQmYQL2cnIKcwtt2yv6C7oHNEe9sSuA1lrGWdnrTVjxgx10c/n6+xkcEh0dLTV9//666+YMGGCGqSn5+npafFLhPjuu++wdOlSLFmyBI1Rly5d8MYbb6gWcPZIBk3OnTtX/e4cYX+tVdXrkEGkjzzyiBrJ35BGjRqFJ554otxZJSKihqbRaJCadwkXMpOQoA9WSwPX1Nw0q9bh5uKKtLwM2Bu7DmTDwsLUwK2kpKRy98vtqKioWq27sWRkZfDazJkzsXr1apXBbtasmfrgvOuuu+p1uzt27MArr7xiuH3x4kX873//U63PLP1Devrpp/HTTz+p20OHDlVfQqSuWYLgbt264Z133kHfvn3hqA4ePFhv69YfLzlWUmIj9d1S3iGTgEi7JXvb34ZUX6/jgw8+wMKFC1W/aqnnN+6qYs2/PTk7IQH07t2762X/iIiMZRfkGLKqxsFqYlYyijTFsEawdxNEB0SgaUAkogMi0TRQfkYgwi8M7q72187PrgNZ+cDu06ePOnWtzyBJMCS35QOkNhpLRra4uFhlTOXDtHXr1ti6dav6wJVZfSQbVB+krljWr2/xk5ubixtvvBHz5s1TXz7M+e233xASEqICVj3JoskHvdRDP/PMM6rzRHx8fL3sd2OgP15SuyQ9lF966SX1b2T79u2VSnCo9uSMg7wv5d+XTDBQ3X97Q4YMUXXjmzZtUrXmRES1VVhShKTslLJANbMsy5pVmGPVOrzcvdDUPwLRgZFoahS0SsDq6+FYvfNtPthLpoHds2ePuohTp06p6/pgRlpvffLJJ/jiiy/UB/cDDzygWnZJF4PakGysdEHo168fHJnUBUsw06ZNGxVYSvsxmd1s48aN5ZZbsWIF2rdvrzpAyOllCSilpVlNSFmBDOzSf5jL5BMPPfQQBg8ebPF5P//8s2qTZu5Ly9SpU1WQLLMSGXetkC4UAQEB6jVKhsyYvBbpRCG/RzkWEkikpaXhwQcfVAPU5LmbN2+u1vrefPNNdRxlGRnEZjwgUI6x/r0q5DS9ZEqNn6/P2tV2W5bIfsj79+uvv0ZgYKDKZBv/m5IverGxsYiIiMAdd9yhashNMd5fCdRGjhyp1ifB8auvvqoet3Z/q7Ndc/siWf7evXurfZBs84ULF8qdibnppptU9lm2IdlOef9VfB361yLBpP61HDp0qNy25Hcj65DXIc/99NNPTe6TfLGSL9GmvqBZ829P7pf3vLz3iYispdFqkJqThn2Jh7Hy+Dp8vmsJXvn7fcz89Rnc/sO/8OjK/+CdTR9j0b7lWHd6C45ePFkpiHV1cUWUfzh6RXfF1e2H454+t+C5oY9g/vjX8OWkd/HG6KfxyKC7cVPX8bi8RX+0CWnhcEGsXWRk5RS1/PHXk8BVSFAjp/QmT56sApvnnnsOiYmJ6Nmzpzp9XdvsU2PJyFaUn5+vOjfceuut5e7fu3cvTpw4oQJOGZQlGSL5gJVMqkwqISRIkN+HzM8u65CgxdT9cXFx6ouF+Pbbb9WkFnIc33vvPRXgPv744yb3TQLA+++/3+RjeXl5+Oyzz1TAEBwcbLhfBpP99ddfKsslk12MGzcOvXr1Kpfdkl7Dv/zyiwpa5H4JKF5//XW8//77KtCQbe7bt8/q9UlwKOUPkm2TQObZZ59V78XqaohtSU2yBFqrVq0y3CentuV+ec1StnHPPfeoAPOrr76yuC55z8iXHQm6JECVLwUVWdrfmm7XmASUv//+uwoy5UvrlClT1DHU75+UFMmXXSllkeMpwaSUq5h6La1atVJ/M+RLsfFrOXbsmMqySsu+jh07qgC5YvlSXf7bky8cf/75Z63XT0SNjwymMh5kpc+ySoZVMq/WaOIVYCgF0JUBSJY1EpFSCuBm8zCv3tn8FUo2S06TWiIfhrUtJaiLGtmFu5bgdHr504v1pWVQc9zZ+6ZqPUeOowQPkgWUIKNiIHvbbbepQFZ/ylOCFun+oA9kzQ3Aqnj/l19+qTJZ4vbbb1cXa8jMaxJsVmyH9sILL6hAWLJ4MhDMeMDY9ddfb7guX3gkSydBoXEwKAGPvh+wBDcbNmwwvH75IvSf//xHlS5I1tea9Uk2V4IgIcdMguKaaKhtSW2mZKGFfOn78ccfVc2mZKSFBPMyGMpSgCyBqxw3Of4yJbO8N+QLQMWJSsztb1XbtXaaVPldSnAp5IuUBK6SXZXMpgS0EpjKFyq5SEZW3jsVA1n9a5FuG9JNQ9Ynr+XDDz9Uj8u+yL8VqauVLxvypbi2X4wt/duT97y894nIORWXFCMxJ6VSGYDczijIsmodnm4ehlP/KmAtvUQHRMDP0xfOzOaBrK3UJCMrQeyhlOOwR/JBKkGG9NKVmj0ZDFQxkH333XfL3SdBQU0GCckp45qQTGvFmdSkLEBqPmVa4WuvvVZl86644grD49988406bS6jzaU+Wupx9YGUnnEQIoFLxdtybOR5Eshasz7jgYSS8cvKsu4PTUW12ZY8Vyb6EBJsWRrMJMdOSkWEflsVtyPvB/l9myOn8GUKaONT6JIVrcjc/la1XQm2rSGvVU9+j9IqTz/ltOyf8e9W6lIr1q0avxb5YmRqvVIKIGcUpNRDSpQkgy9Bs5ztqY9/e/KeNz7LQESNj/wduJSXYXKgVXJOapUJO+ECF4T7hZQNstIHroGRCPEJUqUCVJnTBrI1zZLa47bkH4gE5TLYRAbCVQzM5bT98ePHywUhcqpTMlPyId5QJFAw7v9rTAIdqYWWTLHM8iaDbOSUsJSYSCmJZO71p9Gt+YNgSl2sT4I3CUj1EhIS6mVbku2US1WkRlRO9UsmWkhmWgIpCeYkiLeWHG85NS4ZVX0wW51BdzXdbkVnzpwpN9GJdAaQ94ZkZGX/pARAH8xK8CxlG+ZeizxfH8xWfC1SLiMX+bchZUtyVkE6E1RXVf/2hNTn1jRIJiL7kluUZzj1rwtYkw09VwuKC6xaR4CnX7mOAPqANdI/XGVeqXqcNpCtSWlBdU/1NxQpu5CaVzn1airzc+DAARUISH1j9+7dVXZWTrXKaWHJeDWU8ePHq9PN5sggHwn6ZJCRZMtk8JAEChKMSJAkXQ8kAL/33ntrtP26WJ/so9R89u/fXx1XuS6n4etjW1WRLwVSNiEDqvS15fJlRQJmeU9IllGCUsmIStsuc9NA6wNRKXmQ0/RS6yyZzo8//tjqfbFmu1IGIKUVcjFHJt6QCU4kG/zkk0+qLzb6YFXKMx577DFV4y01sjIwTL4smHst0pZM/o1LEGs8oYdkTuW+yy+/XGXppUzBXP9j+aKgv0jGWQJk+X3K86z5tydk6mx9TTkR2b9iTYnKourKAHRZVX3gmp5f/qyiOR6u7ogydAMoKweQ6wFe/vX+GpyJ0wayjWWwl2SwpH+rBKTGp09lkIx84AsJXCWIlA9/qV+UgTrSscBUEFCfJGv48MMPqwBQX5dbkdQ9SsAiQYwMkpHbMihNvnBI6YFcaqou1icDyOS4yXGUYEmum5qQo673XU+OiwywkmBKMpUyiEkG4hmfRpeaVPn9SicH+Z1LBlNqhS0FsmLRokVqwJYsL7We8h6S+6xV1XYleKyqBZVs/5ZbblEDCuVsgZRYGO+fBI7yPpc6XslYS99Wc6/l7rvvVsdFvmjIeiXjL6ReWo6hZErlOPbo0cNs/fDLL7+MF1980XBbtivdGiQYt+bfntTqSo2scbkMEdmeJBoy8jMNWdWycoAkJGenokSrsaoUIMw3uFIZgNyW+1kK0DBctDU9T9tI6ANZyWoZD0SSzIuMjpaaP6m3c2QywEuyRZayoQ1FuhxImyTpNNAYSTZQsn/6U/2OTOqXJdNo3BGhNqR/sASAoaGhVs061hjIAD/JIktbs4bUmP5+EdVGflE+ElTP1USjwVbJuJCdhLyifKvW4efhowtUS8sA9EFrtH8EPN11Z2eoYWIzU5w2I9tYZvayhmRkJftsDyTbJpfGSOplpS5TBhM5ImlHJfWtHTp0UNcl+yzlAHWlJjWoju6PP/6w9S4QNXolmhKk5Fw0ZFaNuwKk5aVbtQ53KQXwDy8/o5W66EoB9BMAkf1x2kC2sZQWWEM6Acipbqo/MlpdevLKqW8JBB2RtNCS2mkZUCWn5GXyDDk9T0Rka3LyOLMgy6jnalnQmpidooJZa4T6BKNpoGRTy6ZelYA13De0UscRcgxOG8g6E5kik+rXiBEjHL5XqJwGl9PRtiJdCIiIsgqycTbjAs5mJOh+Zup+Zls5/aqPh3elrKr8lMFX3u4NN8CZGgYDWSIiImpwuYV5OJeZgHgVtF7AucwLiM9IUIOwquLm4qraVRl3BdC3s5KZrlgK4DycNpB1phpZIiIiW8kvLsD5zMTSLGvpJTMBF3OrPoslGdSYwGg0b9IUzQOj0ay0K0CEXyjcXK2bMZAaN6cNZJ2pRpaIiKi+FZUU4XxmUmlm9QLOlZYGJOdchBaWGyR5uHmgeUAUYiRgbRKN2CZN1fVQtrGiKjhtIEtEREQ1mzAgMTu5fB1rxgU16EpTRf9VyaJKGYBkWSVQ1V8i/cI42IpqhIEsERERmR14derSWZy8FI8z6edU4Ho+K7HKLgFSoyrtrCRIlexq80DdTxlw5c6SAKpDDGSJiIhIDbKSgFUFrmnyMx4puWlVPk/qVaWGVZUDBEqGNRpNA6Pg6ebRIPtN9etCSjZWbYtHclouIkJ8MbJ/LJqG2880uwxkiYiInIxMFKALWM8YMq5VTR4Q7NMEsU2alSsLaB4YBW8Pzh7XWK3edgbzluxRGXbp5Ss/f1x7HA/f1Asj+sfCHjhtIMuuBURE1NhJ8CHdASRQ1WdbJdOaXkWLq3C/ULQKjkHr4Fh1ketNvC1PFUqNLxM7b8keaLW695HQ/5y3ZDc6tw5B0zDbZ2adNpBl1wIiImpMJMiQqVrLglb5eVbVuVoi/Vj1war+p0zLSs6lsKgEpxMycfJ8hrpsPZioglhTJDO7ams8pl5t+1lDnTaQJSIiclTSHSApO7U0WC0LWnMKc80+xwUuavIAXcDaQv2Ui5+nb4PuO9ledl4RTp3PwAkVtKarwPVscjY0Gstt0gy0WlUzaw8YyBIREdkxjUaDhOxkwwAsFbimn0VeUb7Z50jGrFlAVFmmNSQWLYNi1PStjW2wD1nO0qdl5huyrLrANQNJJoJQL083tIwOROtmTdCmWRMcOpWGdbvOmQ5uXVzUe8EeMJBtBBYuXIh169apn9b47rvvsHTpUixZssSq5UeNGoUnnngCI0aMqNY2q7sdS1q2bIm5c+diwoQJdrGeirp06YI33ngD11xzjVXLL1++HI888ghOnz4Ne2Pq901EDUPaWsksWPoBWBK4nko/h4LiArPPkQkDZABWK6lnDdEFri2CmqtZsZxhsA/pSMCZeDHHEKzqL+nZld87Ab4eKmBt3SzIELjKlxM317Kpfbu1CcPanWdhirwXRg6wj99/owhk3333XXz66afqwMqH73vvvWfzeZbt9RusfLN/+umn8dNPP1n9nP/7v/9TQdfu3bvrdTuO7ODBg2gsavL7JqKaTSwgs1+VlQboerUWlhRZnFBA2lypoLX0Irc93T2ddrCPMyoq1uBsUpYqC9AHrqcuZCKvoLjSsmFBPipQbW10CQ/yqTJOkphFvrDI71otK7/70i80cr+9/O4dPpBNSUnBBx98oAIJDw8PDBkyBP/88w8GDRpks32y52+wv/32G0JCQtCtWzernyPHND09HZs2bcJll11Wb9sh+1CT3zcRVT19q8x+pRuIdRan0uIRn3EeRZrKgYeeh6s7YoOalZYH6IJW6dEq07nWN/nsWrLmmNnH7WmwT2MnwempC2UZVglc4xOzUFxSfhY1iTWbR/gbMqz6jGugX82/5EjMIl9Y5HdtSMwNiLWbILZRBLKiuLgY+fm6WqGioiJERETYbF/s4Rts//79cejQIXVc5Hj4+Pio+1966SUcOXIEV111VbnlV6xYgX//+9+48sorsWrVKtXJ4aGHHsKLL75o+IMlz/n555+tDmxk2YrbkfVKlvaXX37BpUuX0KFDB1V6EBMTgzlz5uDDDz9EYmKi+v3J/sycOdPkui2tR/ZVMok9e/ZUy0oZgZzGlzKIirKzs/HUU0+pfZX3z5gxY/D++++b7WJhabtVlSycO3cOd911l/qS1a5dO1x//fVW74scG1m3bDM0NBTPPPMM7rnnHvU8c49VdRwsrbMmv28iKlNYXIgzGed1WVZV13oW8ZkXLM6GJZMHSA1rWeeAWDRvEt2gs2DlFxZj3/FUbDuUiO2HklRtpaXT2P8cSMCoAS0QHebXYPvY2GVkF1QoDUjHhdScSt0D3N1c0bZ5+dIAqW/19qr7sE5iFnv+wmLzQHb9+vV46623sHPnTiQkJGDZsmWVggHp9yrLSJDTo0cP9QEvwZoIDw/HY489htjYWLi7u+P+++9HmzZt6mVfb3r6VxQVWx7RV6LRmG1XIfff//oauFUxn7SHuwuWvGpdraUp27ZtUz9fffVVdVx//PFHw2Ny3OQYGdu7dy9OnDihgtf58+erTJwEMjfeeCO6du2qluncuTP+/PNPq/dhz549lbZz5513Ijc3F1u2bEFUVJTarj7IbtGiBf766y80b95cBVvjxo1Dr169TAZSltZTHRJYyntm3759KpsvgZwEz1999ZXJ5Wuz3VtvvRWtWrVS7+H4+HiMHTvWqn159tlnVZC5a9cudOzYEUlJSeoijh07ZvYxS6x5XnV/30TOKr+4QJUD6ANWybiey0xQXQXM8XL3Qqug5mWZ1pBYNA2IVGUDDS3lUh52HE7EtkNJ2Hc8BYXFZfsd4OuJ7NxCmPvUO5ecjfteX41eHSJw9eBW6NNJXoNty/ocqlXapbxyQeuJ8+m4mFH5y4OvtztaNTXOsjZBTGSACmbJDgLZnJwcFZzKB/mkSZMqPb548WLMmjVLBVgDBgxQWaTRo0fj6NGjKnMnGaVff/1VDZqRoEICBAmO5fSoKQUFBepinGVrrCQokmNrTI5XYGD5ptYSkN12220qkBVy7Nq3b6+yt/pAVp4jz7VWxe1IoCRfUs6cOYOmTZuq+yRQ1TPOUA4bNkz9jiWgrRjIVrWe6pSkSICfmpqKoKAgQ8ZaBm3JADY3N7c62+7Zs2exYcMG/PDDD/D19VXBowT5koGual9eeOEF9QdPSmck2I+MjFQXIfto7jFLrHledX/fRM5AugToJxTQTy5wPkt6bZpPcEiXgLLSAMm4xiLaPwKuVSQ06otkUuPOpWPbQV3W9eSFDMNj7m4u6NkuHP26RKJfpyj1uu5/Yw1MRbJyGntYnxhsPZCAXUeS1SU82AdjBrZUp56DAzjbl16JRovzyVnlugbIRVpgVRQc4GUIVtuUZlsjQ3zhyi8I9hvISuBZMTtlTE6BTp8+HdOmTVO3JaCVU+ELFixQp2JXr16Ntm3bqnpMcfXVV6vTt+YC2ddee81wyry6rMmSfrHiEJauizPZrkLeiJOGtm2wFL0EqLfccku5+4KDgysF77KcDJgzJplDyXbryXPkudaquB0JAL28vFTm3JRvvvkG77zzjvpCIgPFJPMpGcyKqlqPtfTbqbgN+XCR196sWbMabVdex3333aeuS5AoweKFCxfg7e1druRFHrNmX+R5X3zxhaoDl38DAwcOxJtvvqlKBuTMg7nHLLHmedX9fRM1NtKP1bhzgPxMzEqB1mx+Eqofq3E9qwSuEf5hqquArWss9xxLVoHr9sNJSM8qS+ZI/WTfTpHo3zkKvTqEw9e7fP2tpcE+Uj+ZX1CMv3efx2+bT6ng7KvfD+PbP49gcLemGDu4Jbq0DrX54GtbTiqgBmElZKr7K4oO9Ss3AEsyrsGB/ALgcIGsJYWFherU+OzZs8t9uEtnAjm9K6Q+cfPmzaquUE7JShbv3nvvNbtOWZdkeI0/sGUddUW6E8jALlu3q5D62OPHj1cabCXBimRa9fLy8tRycqpcT04pS9ZOAhw9qbmtKkCytB0J3CQTLtnJisdbTrVPnToVK1euxNChQ9UpdikvMZXlsLQe4efnp4JgPSlXMUWeK+8lCTIlS1qVqrarJ5ltuRiTDK68P5OTkw3BrLxma/flpptuUhf5XT333HO4/fbbsX//fouPVXUcLK2zJr9vIkcmM1/pg1Z9pjUpO8Xic2TmK+PpW1uHtEC4b4jdBG3SJ3R7aa3rvrjUcgODpJayX2dd8NouNthiOUBVg32kJnP0wBYYNSAWx+Iv4bfNp7Fhz3msL73ERgVg3KCWGNY3plKQ7CyTCsjxNe7PKj+lVMDPp3EdD1ux60BWTrWWlJRUOu0pt/VBkgRb+npKCQaGDx+Oa6+91uw6JasmF6m7lYusvy7ZS7sKGTwkr63iKfLx48er09Z6Bw4cUPv59ddfo3v37io7K6e9X3/9dXWc9NauXauyeNaquB35nV133XVq3dIqTW7LtiTDKfsqx0eCPPkdSscDCaZNfSGxtB4ZtNS7d29V4yq1wPLa5LqUSVQkgbsEy1KHKtnIsLAwlYmVL0gTJ06s9nYtkUBVSiTkDIK85ySI/eijj6zaF6lVleUvv/xyeHp6wt/fXwX6QsprzD1m6ThYel5Nf99EjiIjP7N80JoWj5TcNIvPCfIOLFfPKoFrqE+w3QSt+tPXx85cKh2olYgziVmGx6SWsnfHCBW49usUWe1G9tYM9pFj0aFFiLrcfW1XrNkej983n1aj6+cv24+FKw5haJ8YjBvcUgVxjXlSgValQasMxJLAVYJ5T4+Gr392FnYdyFrrlVdeUZfqmDFjhrpIRtbcKHVHblchtZaSGZRA6OOPPzZkCSXof/jhh1VwI/WvEoxJ0Hnx4kX1nOjoaDz//PMqQ6on9Z1SM3nFFVdYvf2K2xESGD355JPo27cvsrKy0KlTJ1UbKvsovUtlgJkE3/JFxNKXEXPrETIQUPZdXosEj3Jdn72vSGph5bX269dPvX4JTidPnmwykK1qu1VZtGgR7r77bhWsS0ApNeGffPJJlfsiZTMy4EsypBLkS82zfhIKOWNh7jFLx8HS82r6+yayR5fyMspKA0oHY13Ms1z7LQGqfiYsfYlAsI99Bl65+UXYdVRXMrDjcBIycwoNjwUFeKmgtV/nKPRsHw6fehjNbo6UK0wc2hbXDWmDPcdT8NumUyq4XrnltLp0ahmiAtrLejSFh7ubA08q4FmpP2vFSQWo/rloLVWpNzD5RmfctUA+cOVUqwySMe5kIB/K0ueyNs32jTOyMoo7IyOj3OAkORV86tQpVbcodYqNaWavb7/9VrVikoF0MsBLaiGNs6cVycAr6QwxcuTIam3TeDvkOEz9vsmxONLfr7ogH2MSoKpMq9E0run5lgfzhvuFGtpd6UsEmniXHwxrbxJScwxZ1wMnLqpMrJ4EUirr2jkSbZsH2dUAIRmh/8c/p/Hn1jO4VFqjKwGvlOONGdQSUaF+dj2pgAxka12uc0AQwoK87Sor35jok4wVYzOHy8jKac8+ffpgzZo1hkBWBsXIbXM9Ru0hI2vvZACYfhCYZGTlOFjyxx9/1Ho75Dhq+vsmarC2RblpOJl2ptxgrMyCbIvPi/QPL6tnLf0pda72rqREg8On01R7LAlepeWVnqe7UclA50iENql+G8KGIoHglLGdcPOoDqr/7G+bTmP/iVT8uDZODZDu0zFSDQ6Tn/WR0bR2UgHZdEykP1o3LevP2qpZk1pNKkD1y+aBrNRHxsXFGW5LFkF6kEoXAqk/lIFZkoGV07lS7yftt6Rll76LQU3VV42sLcigHH3bppq06JJT+w25TSKi6vRpPZZ6EodSjuP4xVMqeM0uzDG7vAtcEB0QURqwtlA/5SIdBRyF9G7deURXMrDzSFK5Nk2hTbxVuYAErt3bhsHb0+Yf49Ui9bqX92imLvGJmfh9y2n8teOsKo2QS4S08BrUEiP7t1DlETWZ8r0mkwq0UT+boGVU/UwqQI24tEBOT0vf0IokeNWftpY2QfoJESSAmjdvnuopW5/pa2c7NUdEjYcj//2StldHU0/gUEocDicfUxnXEjOTC8hp3WYBUUadA2LV7FjSu9WRyMfw+ZRsbDso7bEScehUWrmR7+1igtC/i26glgRbje10tmRL1+8+p7K0+r620tN2cPemiAj2VZ2AjKd81w+eHt4vhpMKNFLVKS2weSBrK42pRpaIyJgj/f2SkoAjKXE4lHwMh1PicDr9nNlerTFNmqJNcAtD54AWQc3h7V7WXcWRyCntgycvqqyr1LxK7avxyPde7cN1mddOkU7TW1TCkaPxl1S3A2nhJTWslvh6uSPXRD0rJxVwfAxkq4EZWSJqbOz571daXjoOpxzH4eQ4VS4g07maIpm31kGx6BTeFp0j2qFjWFv4ezX8gKC6JF0F5PS51LpKt4Hc/OJyNaT6WtdubcKcvl2TlAe88eUOVUdrCScVaJwazWAvIiJybMk5F3E4+bgKWiWATTQz0YCbqxvahrTUBa7h7dA+rDV8Pex38JI1JE8Un5Sly7oeTMTRM2nQVwxIdUDHFsEq6yplAy2iAhpdyUBtNPH3UplVfTv2iuT+QV2jMfvO/rbYPbIjThvINqbBXkRE9hK4JWQn41CyLmiVUoFUM5MNeLh5oH1oK3QKb6cC13ahreDl7vgjw4uKS7D/xEXDrFrGTfOll2vvDhEq6yrTwkqwRubJwC59TWxFcr+lAV/kPJw2kHXm9ltERHVBo9XgXEZCabY1TgWv5nq3Si1rh7A2KmiV4LVNSKwKZhuD9KwC7DicqFpk7TmWjLyCsgRJVKivoWSgS+sweLhzcJGjTflO9s1pA1kiIqqeEk0JzqSfUx0FJHiVQVrmWmFJu6tOYW1V0CrlAjI4S8oHGgMJok4nZOomJjiYhGNnLxlOf8t4oi6tQ9UgLSkZaB7hz5IBB5/yneyb0wayLC0gIrKsuKRYtb/S17ceSTmBvOLKrY1EE68AQ9Aqg7Okw4CrS+PJPhYWlWBfXGrprFpJSE3PMzzm5+2uGvlL1rV3x0g2z29kU76TfWPXAnYtIKJGpqZ/vwqLC3E87XRpfetxHE09icKSsmb8xkJ8glSZgAStEsA2DYhsdJnHtMx8Q63rnuMpKCgsS3w0C/fTDdTqHIVOrULYj5SoDrFrARERVSm/KB9HL55UQasM0IpLO4NiTeW+nCLSLwydInQDsyTrGuEX1ugCV8nrnDiXoYJXybzGndM15xfSg1Rm0pKsqwSwzTjQiMguMJB1Ut999x1uueWWaj/PyRP4RA5N6lmlPEDfUUDKBmTAlikyY5YucNXVuYb6BqMxyi8sxt5jKdiu+rsmqSysXoCvB/pIrWunKPTqGAF/n8YxOI2oMXHaQNaZa2R37dqFNm3a1ElQKgHx0qVLsWTJEjirLl264I033sA111xj8vHly5fjkUcewenTp2HPWrZsiblz52LChAloTMe/MRo1ahSeeOIJjBgxwuJy2QU52JNySAWtUucan37e5KxZLnBBbFAzQw9X+dnE2/LpPEcm05rquwzsO56CQqMZpGTa0v6lWVfp8+rGkgEiu+a0gWxjab9VUFCAmTNnYvXq1UhNTUWzZs3UB9xdd91l9jk7d+7EPffcU+ttazQaPP300/jpp58M9w0dOlQFQhK4GZNTkLt370bPnj3R2Bw8eNAufq/OqjrH39GCdfl9f/755zh06BA6depkuP///u//1L8x+TdVcXBWXnEBsnIz1Qxa7635AmlFZafH9WQQlnQR0LfC6hjeBv6ejj1rliUajRbHz15SGVe5nLxQdkzc3VzQU00HG6nqXaNCG+9xIGqMnDaQbSyKi4sRHR2tAp7WrVtj69atGDt2LJo3b66yNuYC0Lqobfvtt98QEhKCbt261XpdVPvfqz2RbL+8z9zcGke7JVscp6ysLHz//ffq9/7ZZ5/h7bffNjw2ZMgQpKen4+/1f6P3gD6qk0B+UQGKSutbSwqLVassPWl71U7NmqULXDuEtYaPR+MexJqbX4Q9UjJwKElNC5ueXWB4TLoK9C1tj9WrfTh8vVkyQOSoeM7Ewfn5+eGll15SpQISnA4cOBDDhg3Dxo0bTS5/7tw5xMTElLtvxYoVaN++PaZPn64yVhKcPv/881Vu++eff8ZVV11V7X2eM2cO2rVrh4CAALXfH3zwQaXHY2Nj1eOyP59++qnF+0V2drbKYMrjERERuOOOO9RoR2vIul555RX07t1bjY4cPXo0Lly4YHg8KSkJN910E8LDw9X6JRsmgabx86V8wPgYS7Ap6+rTp4/KplX12mr7e60rx44dU9uS/bvyyitx9uzZah2H1157TT3f19dXvW5rX68wt6y8/j179hiWk4yqZP5NHX9L74Mbb7wR8fHxqjbc398f999/f728H6zZ34rHyZTFixeje/fueOGFF/DVV1+hsLBQdRDIzM9S0772v3wAFv3wrbqeVZBjCGLVPsBFTTYwpu2VeH7Yv/HFxDl4afhjuKX7degZ3dnhgtgLKdn4YsUhvPXVDvVTbpsis2j9uvEknvtoM257biVe+2I7Vm+PV0Fsy+hA3Di8Hd56+Ap8+cIY/PuW3rise1MGsUQOjhnZairJN91DUbi4usLV09OqZaWps5uXl8ll3WrR7kva7mzbtg233nqrycf/+usv9SFsbO/evThx4gQeeughzJ8/H5s2bVIBqnzwd+3a1ey25MPaXDBgSYsWLdR+SHZx3bp1GDduHHr16oXLLrtMBVLPPPOMquPt2LGjChrkYu5+41Ow7u7u2LdvHzw8PFTphAQ0EgBYQ4Km33//XQUmDzzwAKZMmaL2UcixjIqKUu2MLl68qPZXAk0pqzBFlpe2R4mJiSpwkoyaqOo11Ob3Wle+/vprVSoi2eBJkybh2WefxcKFC60+DrKsfMFp27atek9Z+3prc2yMWXofSHbT2tKCunw/mGJ8nMzV6UsWVgLxzj27IKBJAD5f/AVGXF2WjW/dvi02rl1vKBXwdveEt7s3fNy9oC3WovhSAXq16uHw7QNXbzuDeUv2GKYqlZ8y25M0xB/WNwZHz6SVlgwk4kxiluF50g6rd8cI3axanSJV/1EianwYyFbTP5NvM/tYcJ/e6Pzc/xlub7vjLmgKyk5nGQvs2gXdXnnJcHvH9AdQnKmb2vGyn36s0b7JH3n54JZspwQhQgIDqa+MjIw0BEQVP9gkkL3ttttUIKs/bSkZ2iNHjlgMZC9dumSyv9vs2bNVFsmc66+/3nBdsoyS8ZKAVgJZOcUqr0PqHiXglf2WiwRFpu4XKSkp+PHHH1UtaVBQkLpPspkyCEgCBmtOb0uwIgGUePPNN1WgIplV+dCUAEaCUsniyUUycPL6TAUuksHcsGEDfvjhB5Vtk3VKsP/hhx+afW01+b3WlwcffFAF4ULeE6+//rq6fv78eauOgxzHDh06qOvVeb01PTbG6uJ9UNfvB0vrNz5OenIMJOu6a99uVf/6v0UfISk7FdfeMAHfffWtIZCVwDU0KBi5mTloFhgFLzfPcuVC+SUWvkQ7EMm8ShAr41L1g1P1P99bvBuf/nQAOfllfW6DA7wMJQM92oXDx4sfcUSNndOWFkjHgs6dO6Nfv35oDOSPuwQhR48eVadZXV1dkZubq05Pyoe7/rSrnLatSALZiq245INaTp1aEhwcrAbLVSSnTaV+z/hi7JtvvlGnbaWEQQIOqbWV4EPIqfQvvvhClRtIICOn6CXza+5+Id0ApM5QAjBZn1zk9yrHQF6HNSR40pP1e3l5qeBNghcJ/I2DKqlZlftNkVPQsryc1q64bkuvoTq/V1MWLVqkgi1rLnLszZHH9STLKHWawtrjIBlMveq83pocm4rq4n1Q1+8Hc4yPk9SySneB5OxUnMk4j3OZCfj0s08xbPRwBJYORJ04eRI2rt2Awkt5aB4YjZZBzeFSCISFhsHb3avR9XMVRcUaLFsXZ3EZCWJbN2uCm0d2wDv/GoKFz43Gw5N7YWDXaAaxRE7Caf+l17RrwcDF31gsLTDW/8sF5ldU4YOn7ycfoqYk2JHXIgOC1qxZY3g9khF87rnnVGZNgiEZOFSxXU9eXh6OHz9eLoD5888/VZZIavgskQ4EkrWtDjnVPnXqVKxcuVLVDcppYDnNa9wKTEof5CL7Jvt/++23Y//+/Wbvl5pfCVYkiJTXXBNnzpwxXE9OTlZdAySTLQGCZLHlNLc+eJGAScoiTGnatKlaXtahD2blNVf12qrzezVFTnfXZ9mBvF5rjkPFQLs6r9fcshJQy5cyvYSEBJPPt+Z9YO6LQF2+H6zZ3xJtCS7lZSC3KA/5xeXP2hQVFWH5kmV46/231bSvvh4+aNU9BoMHD8bS735UGWAhtbWO3AVEOglcyspH4sVcJKXlIOliLhLT5LrucjEjT2VizZG/oJJ5feauAQ2520RkZ5w2I1tTUr9q7mJcH1vVssb1sRWXrS6pAZS61lWrVqksqTH5UJWgVAJ2+UCumGU9cOCA+nCW2kip1ZNSBDkVLqeUJQtlyfjx47F27dpq7atkhSVAkyBPggrJxkrgrCeZR3kdEsx4enqqU7cS7Jq7X0gQLsGwHAd9ZlcycMuWLTOsV079Gg+4qeijjz5S25D1P/nkk6q8QoITCV6k/OGxxx5DTk6OCkplIJAE4+aCKSmReOqpp9S6ZJ2ybkuvrSa/14ZW3eNQ3ddraVnJIEuNqwyokiytubpna94H+jKVqtTm/WBqf6V3a1Zp1rVYU4KLuZdUeyzjIFYGaEnQuu2vf+CiBW665gb4u/nCVeOCwoJCTJ48GQsWLDB86ZN/e/bcO1f2Myu3EHFn07Fp7wUsXXsc//txL57/eAvuf301bpj9K+586U889d+NePfb3Vj051H8teMsDp68iNT0PHh7uqGJX/m/qcZcXF1Uz1cicm5Om5FtLCRz9L///U8FncanQ2VwigzcEmPGjMGvv/5qMoiQsgIJSGXQipyKlUE+0rHAUoCiJ4NcHn74YRUMW6qlNSblHJJRksFkEjhfe+216qInI7NlgJFkmyTQ7dGjh6pvNHe/nlyX/ZZTyfJaJGCRD/6JEyeqxyXgkADT0iAhKa+Ii4tTmWgpfzA+bS/BkRxfHx8fleGWnq7myPJ33323Ctal1ljW/cknn1T5Gqr7e21o1T0O1Xm9lpZ9//331ftR3p/yO5TrW7ZsMbmeqt4HUscq79n//Oc/KoMtx7iu3w/G+9t/0ABMuvl6bN+2Hck5uuAaRhMSeLp5qA4CEsBKiYDUvn731SK176bKgPQBrAxkk/r0K664ArZUUFSC5NIMauLFHEM2Nak0y5qTb3q6W33/1ugwP0SG+BouUSF+iAzVXZcWWQmpObj/jTUmM7MSKI8cUFaiQUTOyUXr5HOO6ksLpEWP8cAlOX0oo5Kl3s7RR/1KVkpaKUmNpXFTdSEDvCTbJ4NiauLbb79V65VaXHsmvW5lQFloaKjDN8mnyllwyaDKF6u6UNP3g2Ra84rykFuUr36WmJj61dXFBT7uusDVx8MHHm41yyXIAEnJCo8cOdLk43X196ukRIPUjHzDqf+kCkHrpSzTg1n1QgK9dUFqaGmQWnpdfoY28YGba9W1vau3xWPekt26OmD5uCrtXiBdC0b0ZyBL5EyxWaPMyMrpP8m4GN+W4IpBSflTrv37968UxOozslKHWVOStao4UMwemavLJMcm9adSMiODxRqaBFMFxQUqcJVa14KSQpPLSdZVAld91rUuBmb98ccfqKvXkJFdqAJVXa2q/qILVGUq1xKN+VyHv4+HITCNLA1Uo0pvRwT7wtOj9hNiSLDauXUIVm2NV9lfaaMlmdimYf61XjcROT6HD2SlhY1+dLPUX0o2xVyWwplJbZ0p0m9TTvcTORoZvCi9juU0v76VVX0r1hQjrzRwlQBWYzLr6mooF/B194Z7DbOuxgqLSpCZU4jiEo3qjyqn3a0NEmWGq7IAtXxGVQLD/ELTfWyFp7ur4fS/TN1qXAYQGeqnAtmGIEHr1Kv5d4qIGmEga0wajA8fPlwNcKLypKbOlIqtsZyRjDonxyMdOKSXcX2/H4pLipFdlIucwtxKHQb0pI+rca1rXbbDyswpQFJanhqlL7lR+Smn9CNDfBDo5wWNVoviYo0KcqVllVxy8/KQlpmP/85bjzNJeWbXLWf2I4J9DNlUfXZVX6sa5O8FVytO/xMROW0gu379erz11lvYuXOnOk0oI4wrlgVIz1dZRmo9ZRCIDKaQU+UVLVmyRM2EQ0RUG6q3a2EusgtzTAavknX1LQ1cJYB1d62fP6WSiZUgVuhP8Ot/yv0XM/JRUiI9ESrsf3GxCmxz8orQxN+z0kAqfSlAeLCPyvASETkqmwey0sJGglMZJWxq1iIZRDRr1iw1UnvAgAFqEIYMdJBaWOOm81IYvHnzZnz33XcN/AqIqLEErzlFeSp4lfKBijxc3eHn6Qs/Dx941eMkBGp2r6IS1REgPct03a1ecYlWZVWlBMDd3RUebq7wcHeFpsQNRTneePffVyIokLWkRNR42TyQlXno9XPRmzJnzhxMnz4d06ZNU7cloF2xYoWq+ZRenXoyP7zMBlTVCF1pbC4XPVMzUxGRc5CZwIyD14q5TXdXNxW8+nv6VZoGtu6CVg0KiopRUFii6lUlgLW2l4y/j7uqXa24X/n50t7KFd6eNv8TT0RUr+z6r5z0lpSSg9mzZxvukx6TUhtXsY+klBXce++9Va5Tpk998cUX62V/icj+yQAtGawlpQO5hXmVglc3F33w6lun9a4qaC3WoKBQF7SqS1EJKjYFkK15ebjBy9NNlQfk5RdXKh3QL+fh7tYop6clImoUgazMziNN843nNRdy23hqVOkztm3bNvz4449VrlOCYilVkAb1cpH1S9Nzc5y8zS5RowleJeMqwWtOUW6lf9dS86oPXqXPa8XgsLpdA2T9MuhKl2EtC1xNB62u8PJ0VzNZSQAr69UPsJLtnknMMr0NQO2Hue0TETkDuw5krSVNc2Xuc2vITElyefTRR9VF33TX1Ch/+TBLSUlR07oy60HkWCSYyy8qQG5xngpiK7bKUm2y3L3VxAQ+HqWZ1xKgoKT84K7s3EI1KYBx14DUS0BYE2/4+3oagtYiqWst1qjgUy6mglapX5VAVS4SwEpGtawrgBbQFqOwsPxsWMF+robtG14bdNvXlBQhv6So0uuWv1vyesx1KyEiaizsOpANCwuDm5tbpSBVbkuT/9qQTghykYysKbJdmVv93LlzbM9E5CAkiJNer/nFhSgsKawUvEpwJxMUeLl5qZ9FLvnIRLrFma0uZlYe+CWSEqAGVxVrtCYzoO6uugFYksGVAFamZNW4uMDyXFjm9yOvsET9dHNzhY+nG1LyXZFiZnl5nfL3S/6OERE1ZnYdyHp6eqJPnz5Ys2aNoSWXDM6Q29IEvTZkNiu5mMvICn9/f7Rr1w5FReUzHkRkPySIPJ1+DnsuHMDuxEPILCh/Kl5aY3UKb4te0V3QJbw9vDy8rF73snVxWLUtqcpT9TKbVWxUIFpEBiA2OgAxEQGqXMBWJBPLIJaInIHNA1mZjcu4RlXmB5eZukJCQhAbG6vqWadOnYq+ffuq3rHSfktadum7GNRXRlZPPgz4gUBkXySwPHXpLDaf3Ykt8TuQkptW7nE3F1d0j+qEQTF90L9ZT/h6+tRoO2dT8pGaUWR2sFX3dmF4+s7+8PXmKXwiIltw0dp4VMC6deswbNiwSvdL8Lpw4UJ1/YMPPjBMiNCzZ0/MmzdP9ZStC/qMrAwYCwwMrJN1ElH9OJtxAZvjd2Jz/A4kZCeXe8wFLugS0R6DY/ugf/NeCPSqXf/U/SdS8eaXO5CebboYQGpbJw1ty6lTiYjqWHViM5sHsrZinJE9duwYA1kiO5WQlawCV8m+SiBbUYewNhgc0wcDY3oj2Md0mVB1JKXl4vNfDmLTvsrbMiZjw+Y/NRxNwzjhABFRXWIgWw3MyBLZn5Sci9hyVjKvO3HyUnylx9sEt8Dg2L4YFNMbYX4hdbLNvIJifL/mGJb/fUJ1IfD38cCtozuqfq4ffL9H19VA/ly6uKjShodv6oUR/WPrZNtERFSz2MzmNbJEROJSXoYKXrfE78TRiycrPR7bpJkqG5Dsa1RA2fTUtaXRaLFu11l8seIQ0jILVMnANZe1wi2jOxr6tHZtE4pVW+ORnJaLiBBfjBwQy0wsEZEdcNpA1trBXkRUfzILsrH17G5sPrsDh5KPV5plKzogApfF9sXgmL5o3iS6zrd/5EwaPlm+H8fidS24erYPxz3XdUWLqPIZAAlaWQtLRGR/WFrA0gKiBpVTmIvt5/diU/wO7E86UqnXa7hviCobkAC2RVDzepmMJDU9T2Vg1+06p25Hh/nh7vFd0L9LFCc/ISKyMZYWEJFdyS/Kx44L+9WgrT2Jh9SkBcZkkJa0ypLgtW1Iy3oLJguKSlRv2B/+Oq6mjPX1dsfkER0w/opWapYtIiJyLE4byLK0gKh+FRYXYnfiQZV53XVhPworTKUq7bEGNu+tsq8dw9uoKWPri5x42rj3Aj7/9SBSLuWpjgOjBrTAlLEdERzgXW/bJSKi+sXSApYWENWZ4pJi7Es6rIJXKR/ILy7fg9XPw0f1eJXMq/R8dXOt/yxo3Ll0fPrTARw8eVHd7tI6FNOv64o2zYPqfdtERFR9LC0gogZToinBweRjqmxg6/k9qgbWmLe7F/o266GC1x6RneDu1jB/di5l5eOr3w5j9fZ41TUrItgH08Z3wWXdm7IOloiokWAgS0TVJgO0jqScUN0G/jm7S3UfMObh5oE+TbupVlm9o7vC013XxqohFBWX4Of1J7F49THVG1b6wN54VTtMGNoWXh6sgyUiakycNpBljSxR9UgVUlzaaTVJgfR7TcvTtazSkzKBXlFdVK/XPk27w8fDu8H3b+vBRCz4+SASLuao+4b1aa7aZoU28WnQfSEioobBGlnWyBJZJHWu609vxR/H1+FsZkK5x2SAVrfIjirz2q95D/h7+tlkH88kZOKTn/Zj7/FUdbtDbDCmT+iKDi3qZtYvIiJqOKyRJaJaS8xOwR/H/8baU5uRW5RnuN8FLugc0U5NUjCgeU8EegfYbB8zcwrxzcrDWLnlNDRaICTQG3de0xlX9mquZugiIqLGjYEsERnICRrpOvD78XXYfeFAuZm2wnxDMKrtEAxpOQAhPrYd8V9cosFvm0/h2z+OIjuvCJ7urpg4tC2uv6odfLz4Z42IyFnwLz4RIa8oH3+f/gcrj6/Dhaykco9Jm6yx7YapwVsN0S6rKjuPJOGznw/gbJJugNllPZpi2jVdEBnia+tdIyKiBsZAlsiJSdAqwevfp/5BXnG+4X4vN09c0XIAxrS9ErFBzWAPzqdkq36wOw7rAu3WzZqofrBd24TZeteIiMhGnDaQZdcCcubWWXsSDmHl8bVqulhjEX6hGN12KIa1HmSzgVsVSenA4lVH8cuGkyjRaBHk74UpYzthRP9YuLEOlojIqbFrAbsWkJPILcxTA7f+iPtbDeQy1j2yE8a0G6p6vrq61t9UsdUhQeufW8/g698Pq0Fd7m4uGH9FG0we0R5+Ph623j0iIqon7FpARAbnMhN05QOnt6LAaMpYmXHrypYDVQDbLDAK9mR/XKpqp3XqQqa6PaBLFO4a3wVNw/1tvWtERGRHGMgSNUIajQa7Evar7gP7k46UeyzKP1wFr0NbDoKvp31NFJB4MQcLfjmILft1/WpjIgNUHWyvDhG23jUiIrJDDGSJGpHswhz8dXIz/oz7G8k5F8s91iu6iwpge0R1VhMZ2JPc/CL88NdxLP/7BIqKNQjw9cBtoztizKCWcHOzr30lIiL7wUCWqBGITz+vygc2nNmGgpJCw/0yTeywloMwut1QRAfYX1ZTo9Fi7c6z+PK3Q0jLLFCTGFxzeSvcOrojAnw9bb17RERk5xpFIHvq1CncddddSEpKgpubG/755x/4+dnHiGui+lKiKcGOC/tUAHsw+Vi5x5oFRKnsq0xeIMGsPTp8Kk3VwR4/m65u92ofjnuu64rYKA66JCIiJwpk77zzTrz88su44oorkJaWBi8vL1vvElG9ySrIxpqTm/Bn3Hqk5qaVmzq2d9OuavKCbpEd4eJin62pUi7l4YsVh/D37nPqdtMwP9x9XVf06xRpt/tMRET2yeED2YMHD8LDw0MFsSIkJMTWu0RUb5MX/HT4T2yM346ikiLD/X4ePhjW+jKMbjsEkf7hsFf5hcVYtu6EqoUtLCqBr7c7bh7ZAddc3hoe7qyDJSIiBwxk169fj7feegs7d+5EQkICli1bhgkTJpRbRiYukGUSExPRo0cPvP/+++jfv7967Pjx4/D398f48eNx/vx53HDDDXj66adt9GqI6l5OYS5+PPgbfj++FiVajeH+mMBojG0/DJe36K9aadkraVW9Yc95fP7rIaSm50GSrqMHtsCUMZ0QFGC/+01ERPbP5oFsTk6OCk6lxnXSpEmVHl+8eDFmzZqF+fPnY8CAAZg7dy5Gjx6No0ePIiIiAsXFxdiwYQP27Nmjbo8ZMwb9+vXDyJEjTW6voKBAXYyb7hLZawutv05twrf7f1blBPrygX7Neqj61y4R7e3+VHzc2XR8vHw/Dp/WlUB0bROK6dd1U9PLEhEROXwgO3bsWHUxZ86cOZg+fTqmTZumbktAu2LFCixYsABPPfUUmjVrhr59+yImJkY9Pm7cOBXUmgtkX3vtNbz44ov19GqI6sah5GP4fPf3OJOuqyMVncLb4s5eN6FVsO69bs8uZebjy98OY82OeMjcgREhvrjrmi4Y3D3a7oNvIiJyHDYPZC0pLCxUJQezZ8823CfTZ44YMQJbtmxRtyX7mpycjEuXLqnpzKRU4b777jO7TlmXZHg/+eQTdSkpKUFcXFyDvB6iqkjv16/3LMU/53YZ7gvzDcGUHpMwKKa33QeBRcUl+Gn9SSxZfRR5BSXw9nTDjcPbY8KVbeDp4Wbr3SMiokbGrgPZ1NRUFWhGRkaWu19uHzmim63I3d0dr776KoYMGaJq8UaNGoVrrrnG7Dqlo4FcHn30UXXRz+dLZEv5RflYfuQP/HJkNYo0xeo+TzcPTOg0Btd2GAFPd/vuqSr/9v45kIgFvxxA4sVcdd9VfWNwx7hOCG1iX7OHERFR42HXgWxdlSeYIgPI5CKBMpGtaLQabDyzHd/sW4ZLeRmG+y+P7Ydbe0xQ2Vh7dzohE58s3499canqdocWwbh3Qje0jw229a4REVEjZ9eBbFhYmJrgQCY6MCa3o6KiarXuGTNmqAszsmQrxy+ewsJdS3A87bThvjbBLXBn7xvRIawN7MmFlGys2haP5LRcVe86sn8s/Hw88M0fR/DHltPQaIHQJt648+rOGNKruZqhi4iIyKkDWU9PT/Tp0wdr1qwxtOSSkdxye+bMmbVaNzOyZCtpeelYtHc51p/ZargvyDsQt3afoGbicnVxtSqQbBru3yD7u3rbGcxbskfV50oJgfz88a/j8PBwU/1gPd1dMXFYW9wwrB28vez6TwoRETUyNv/Uyc7OLjfYSqabla4DMrFBbGysGpg1depU1ZlAesdK+y1p2aXvYlBTzMhSQyssKcKvR1dj2eE/UFCsawHn7uqOazoMx8ROY8xOJWsykFx7HA/f1Asj+sfW6z5LAC3bls4Dsm2h/ylBbN9OkXhgUncVXBMRETldILtjxw4MGzbMcFsCVyHB68KFCzF58mSkpKTgueeeUxMi9OzZEytXrqw0AKy6mJGlhiKB39Zzu/HV3qVIyblouL9/s564vecki7NxWQok5y3ZjZgIfwQFequgsqCoRP1U1wvlp0Z3X3HZfbplNIbljZ+jltcvU/qcnLwitW1TpHqgZXQgg1giIrIZF63+U9FJ6TOyGRkZCAwMtPXuUCNz+tI5fLHnexxMPma4L7ZJM9zZ6wZ0jexY5fO/WHEIS9fFQSNFqHZGAtnLezTD47f3tfWuEBGRk8ZmNs/IEjVGmflZ+O7AL1hzcqMhgxrg6YfJ3cZjeOvL4eZqXU9VqYm19F1T+rRKRtTLw031adX9dDVc19+vbnvqHiu/bIXneJZ/zrd/HsGydSdMB9IuLszGEhGRTTltIMvSAqoPxSXFWBn3N344uAK5RXnqPhm8Nabtlbih69Xw9/Sr1vr8fT3Mn9p3dcE1l7fG1Ks7o76M6t8CS9eanjBEAuyRA+q3RpeIiMgSlhawtIDqyO6EA/hi9w+4kFXWLq5HVGdM7XkDmjeJrvb69sel4pWF21Sdqikyydf8p4ajaVj9di9YvS1e1eOqWcXkz0XpoLOGGGxGRETOJ5OlBUQN53xmIr7c8wN2Jxw03BftH4GpvW5Ar+iu1Z5WVoLEXzaexGc/H1Sn9NvHBOH4uXSTgWR9B7FCgtXOrUOwaqtR+68BsQ2ybSIiIkucNpBlaQHVVk5hLr4/uAJ/HF+HEq1G3ScttG7scjXGtB0Kd7fq//OSTgH//WEv/tpxVmVcZYKBScPaIuFijk0DSdlWfZYwEBER1QRLC1haQNWkJuU4uQnfHfgZWQXZ6j4XuOCq1pfh5m7j0cS7Zu+jlEt5ePWLbYg7m65mzXpiSl/07hhRx3tPRERk31haQFRPpI3Wwt3f40z6OcN9ncLb4c5eN6JVcEzN13vyIl7/YjvSswsQGxWA/5vWn6fuiYiIqsBAlsgKydmpakIDmdhAL9w3BLf3vB4Dmveqdh2snpwQ+X3LaXy8bD9KNFoM6haNR27uBV9vjzrceyIiosaJgSyRBflF+WpKWZlatkhTrO7zcvPEhE6jMb7DCHi6e9Z43UXFJfjwx31YtS1e1cNOGdsRNw1vX+OgmIiIyNk4bSDLwV5kiUarwYbT27Bo33Jcys8w3H9Fi/64tfsEhPoG12r9FzPy8NoX23H0zCX4ervjsdv6oF/nqDrYcyIiIufBwV4c7EUVHEs9qepg49JOG+5rE9IC03rdhPZhrWu9/iOn0/Dqwm24lFWA5hH+qh62eURArddLRETUGHCwF1ENpOWm45t9y7DhzDbDfcHeTVQG9oqW/dUMXbX1xz+nMX/pPhSXaDGgSxRm3dqb9bBEREQ1xECWnF5hcSF+PbYGyw6tREFJobrPw9Ud13QYgYmdRsPbw7vW2ygq1uCT5fvVwC5x66gOmDyyg5pmloiIiGqGgSw5LamqkS4E0o0gJeei4f7+zXvi9h6TEOkfXifbuZSZr+phD59Og4+XG2bd2gcDu1Z/yloiIiIqj4EsOaXTl86qOthDKccN98U2aab6wXaN7FBn2zkWf0nVw17MyEfTMD9VDxsbxVpsIiKiuuC0gSy7FjinzPwsfLf/ZzUzlxa6cY4Bnn6Y3O1aDG99Gdxc3epsW6u3ncH/ftynygr6dorEo7f1gb8P62GJiIhs0rWgqKgIY8aMwfz589GuXTs0Buxa4ByKS4qxMm4dfjj4G3KL8tR9bi6uGN1uKG7oMg7+nn51uC0NPvvpAH7ddErdvmlEe9w2uiPrYYmIiGzZtcDDwwP79u2rzlOIbG7XhQP4Ys/3SMhKNtzXM6ozpva6Ec0C67Z3a3pWAV7/cruactbb0w2P3NIbl3VvWqfbICIiohqWFkyZMgWfffYZXn/99eo+lahBnc9MxBe7v8eexEOG+6IDIjC1543o3bRrnW8v7mw6Xlm4DanpeYgK9cUz0wagRTSz/ERERHYTyBYXF2PBggVYvXo1+vTpAz+/8qdk58yZU5f7R1Rt2YU5qoTgj+PrUKLVqPt8PXxwQ5erMabtlXB3q/vS8LU7z+KDJXtQWKxBr/bhePz2vgjwrfn0tURERFS1an+iHzhwAL1791bXjx07Vu4xW80R37JlS1VD4erqiuDgYKxdu9Ym+0G2pdFosPrkRize/zOyCnPUfS5wwfA2l+PmruMR6F33s2eVlGjw+a+H8NP6E+r29cPa4vZxneHGelgiIiL7C2TtNUjcvHkz/P39bb0bZCMHko6qMoIzGecN93UOb6faabUMjqmXbWZkF+DNr3ZgX1wqPD3c8MjkXriiV7N62RYRERFV5rTtt6hxSMpOURMabDu3x3BfuG8Ibu95PQY071VvZwlOXcjAy59vQ3JaLiJCpB62P1o1bVIv2yIiIqI6CmSHDRtmMTj466+/qrW+9evX46233sLOnTuRkJCAZcuWYcKECeWWkX6vskxiYiJ69OiB999/H/379zc8Lvtz5ZVXqtKCRx55BLfddlt1XxY5mLyifCw7vBIrjq5BkaZY3efl5omJncfgmvbD4elef/Wp63efw3uL96CwqAQ92oXhidv7IdCP9bBERER2H8j27NmzUm/ZPXv2qNrZqVOnVnsHcnJyVHB61113YdKkSZUeX7x4MWbNmqV61w4YMABz587F6NGjcfToUURERKhlNm7ciGbNmqlAeMSIEejWrRu6d+9ucnsFBQXqYtyrjByHRqvB+tNb8e2+n3ApP8Nw/5AWA3Br9wkI8Q2qt22XaLT46rdD+HFtnLo94co2uPPqznBzc623bRIREVEdTYhgyQsvvIDs7Gy8/fbbNV6HZFYrZmQleO3Xrx8++OADw4CemJgYPPTQQ3jqqacqrePxxx9Hly5dcOedd5rdzxdffLHS/ZwQwf7Fp5/Hh9u/wom0M4b72oa0VHWw7cNa1+u2s3IL8dZXO7D7WAo83V3x0E09MbRP/dTeEhERObPMakyIUGepJOkvK2256lJhYaEqOZAsq56UD8jtLVu2GDK6WVlZ6roE0lLaIIGsObNnz1YHRgLuDh06oG3btnW6z1R/kxo8s+YtQxAb7N0EMwfciZdHPF7vQezphEzMmvu3CmLDgnzwxkNXMIglIiJqTIO9JLD09vZGXUpNTUVJSQkiIyPL3S+3jxw5oq4nJSVh4sSJ6rosO336dJXBNcfLy0tdHn30UXXRR/1kv/44/jcW7F4MOXkg7bSu6zQKkzqNgbdH3b7fTNm09wLmfrcL+YUl6NomFE/e3g9BAV71vl0iIiKqh0C2Yh2rBBdSm7pjxw48++yzaGitW7fG3r17q/08GUAmFwl+yT5JGYl0JFhxbI267enmgYcH3oX+zcvXaddXPew3Kw/j+zXH1e3xV7TGXeO7wJ31sERERI4byFbMXsqpfjlF/9JLL2HUqFF1uW8ICwuDm5ubyroak9tRUVG1WveMGTPUhRlZ+5RfXIB5/3yOHed1X1KaeAfiycsfQNvQlvW+7ey8IrzzzU7sOJwED3dXPHh9D4zoH1vv2yUiIqJ6DmQ///xzNBRPT081De6aNWsMA8AkSye3Z86cWat1MyNrvy7lZeCNDf/DyUvx6nZMYDSeGjID4X6h9b7t+MRMvPL5NlxIzUFoE288fWd/tI8NrvftEhERUQPVyKanp+OHH37AiRMnVJeAkJAQ7Nq1S9WuShus6pABWnFxunZG4tSpU6qdl6wzNjZWtd6Stl59+/ZVvWOl/ZYM8Jo2bRpqgxlZ++1M8NqG/+Ji7iV1u3tkJ8waPB2+nj71vu0t+xPw7rc7kVdQgs6tQvDU1H4IDqj/OlwiIiJqoEB23759GD58OIKCgnD69Gk1uEqCzqVLlyI+Ph5ffvlltdYntbUyyYKeBK5CgteFCxdi8uTJSElJwXPPPacmRJA+titXrqw0AKy6mJG1P3sSDuLdzZ8irzhf3R7e+nLc3edmuLu61et2NRotvlt1FN/+eVTdHju4JaZf102VFRAREVEj6iMrra969+6NN998EwEBAWqglQy42rx5M2699VYV3DbWXmVUf/6MW48FuxarCQ/ElB4TMb7DyHqbYlYvN78IcxbtwtaDiXB3c8H9k3pg9MAW9bpNIiIiqpvYrNoZ2e3bt+Ojjz6qdL+UFEjGlKg6JHD9eu8y/Hp0tbrt4eaBhwbciYExvet92+eSs1Q97LnkbIQEemH21P7o2DKk3rdLREREdaPagaz0YDU1reuxY8cQHh4OR8HSAtsrKC7E+/98jm3n96jbTbwC8MQVD6BdaKt63/a2Q4mqM0FufjE6tAjG7Kn9ENqk/utwiYiIyIalBffccw8uXryIJUuWqNpYqZmVFlnSVWDIkCFqMJYjYWmBbaRLZ4KNHxpm6mpe2pkgop47E0g97PdrjuGbP45A3vmjBrTA/ZOkHrZ+63CJiIio7mOzageystIbbrhBDdKSqWGbNm2qSgoGDRqE3377DX5+fnAkDGRt05ng9Q3/Q2pumrrdLbIDZg2+F36evvVeDzv3u92qO4Gbqwvum9gNYwa1rPc6XCIiIrKTGllZ8apVq7Bx40aVjZX2WTL4SwaBORKWFtjGvsTDeGfzx8gr0nUmuKrVYNzT99Z670xwITVb1cPGJ2apKWafuqMfurSu/760REREVH+qnZFtbJiRbTirT2zEpzu/NXQmuLX7BFzXcVS9Z0R3HknCW1/vRE5eEdrFBKlJDsKCWA9LRETkFBnZefPmWb3xhx9+2OplyTlI4Lpo30/4+cif6raHqztmDLgTg2P71Ot25Tvaj2vj8OVvh1Q97FV9YzDjhh7w9GA9LBERkdNkZFu1sm4UuWTWTp48CUfCjGz9KpTOBFsXYuu53ep2oJc/nrj8AbQPa12v280vKMZ7i3dj494LcHV1wT3XdsU1l7diPSwREZGzZWRl2lii6krPz8RbGz7E8TTdJBnNAqLw1JAHEelfv23aEi/mqHrY0wmZCPTzVPWw3dqG1es2iYiIqOFVe7BXY8HBXvXrXEYCXtvwX6TkXFS3u0S0x6OX3Qt/z/rtarHnWDLe/GoHsnKL0LpZE/zftP6ICK7fbghERETkQIO9zp07h59//hnx8fEoLCws99icOXPgSFhaUPf2Jx3BO5s+Rm5Rnro9tOUg3CudCdzq73uTvI2X/30CC389CI0WGNqnOWbe2BNerIclIiJyKPXafmvNmjW49tpr0bp1axw5cgRdu3bF6dOnVSAhbbjIuf11chM+2bEIJaWdCW7udi0mdhpTr7Wp+YXF+GDJXvy9+xxcXYC7r+2K64a0Zj0sERFRI1ftQHb27Nl47LHH8OKLLyIgIAA//vgjIiIicNttt2HMmDH1s5fkEJ0Jvtv/M5Yf/sPQmeDBAXfgsth+9brd5LRcvLJwG06ez0CArweeuL0veraPqNdtEhERkYMGsocPH8a3336re7K7O/Ly8uDv74+XXnoJ1113HR544IH62E+y884EH2z7Av+c3aVuB3j64fHLH0DH8DZ1up0LKdlYtS1eBa8RIb6IjQzApz8fQGZOIVo1DVT9YaNCHWtmOSIiImrAQFamoNXXxUZHR+PEiRPo0qWLup2amlqLXSFHlJGfiTc3zsfxi7rOFtEBEZh9xQxEBdRtVnT1tjOYt2SPKhfQl3Xrq7uv6NkMD9/UE95eTjt2kYiIyClV+5N/4MCBanraTp06Ydy4cXj00Uexf/9+LF26VD3mKNi1oPbOZSbg9fX/RXJpZ4LO4e3w2GX3wd/Lr84zsRLESuBacWyiVMHeNqYDg1giIiInVO2uBTLhQXZ2Nrp3746cnBwVyG7evBnt2rVTHQtatGgBR8KuBTVzIOko3tn0EXJKOxMMaTkA9/edUi+dCb5YcQhL18VBI+0IKpDJDiYNbYupV3eu8+0SERFRI+ta8Oqrr2LKlCmGMoP58+fXfE/JIa07tQUfbf/a0Jngpq7jcX3nsfXWJUAysqaCWEWrVTWzRERE5Hxcq/uElJQU1Z0gJiYGjz/+OPbu3Vs/e0Z22pngJ/xv25cqiHV3dcdDA6bhhi7j6iWILdFo8cc/p7HzSJL5hVxc1MAvIiIicj7VDmR/+uknJCQk4Nlnn8X27dtV71gZ7CWZWuknS43XV3uWYumhleq6zND17NCHcUXL/vWyrQMnUjHr3b/xwfd7UVCky/yaIpUxIwfE1ss+EBERUSOc2aviLF/SjmvBggU4fvw4iouLYQu5ublqANqNN96It99+2+rnsUbWOpvjd2Duls/U9Sj/cMweMlN1KKhrSWm5+PzXg9i094K6HRPpj3uu7Ya0zHzMW7Jbl/mVt2xp94KHb+qFEf0ZyBIRETUW9Voja6yoqAg7duzA1q1bVTY2MjIStvLKK684VNcER3I+MxHzt3+trvt5+uLZof9CuF9onW4jv6AYP/x1XA3qKirWwM/HA7eN7oixg1vC3U134qBz6xCs2lrWR1YysU3D/Ot0P4iIiMhx1CiQXbt2LRYtWqRm9dJoNJg0aRJ+/fVXXHXVVbAFyQTLdLnjx4/HgQMHbLIPjVV+cQHmbPpY/RRSE1uXQaxkVf/edQ4LVxzCxYx8NcXsuMEtcevojmji71VuWQla2Z2AiIiIalwj26xZM9U/ViY/+Pjjj5GUlKTKCoYPH16jAT/r169XAWjTpk3V85cvX15pGen32rJlS3h7e2PAgAHYtm1bucdlytzXXnut2tumqoPMj3cswtnMBHV7Uucx6N20a52t/1j8JTz+/ga8s2iXCmK7tw3De48OwwPX96gUxBIRERHVOiP7wgsvqDrUoKAg1AXpRdujRw/cddddKrNb0eLFizFr1izV5kuC2Llz52L06NE4evQoIiIi1OCz9u3bq4v0s61KQUGBuhjXYZBpq09sxMYzui8NXSM64KYu4+tkvRcz8vDlb4fx146z6nZUqC/uGt8VA7tG1VsLLyIiImp8aj3Yqy5JELNs2TJMmDDBcJ8Er/369cMHH3ygbkspg7T+euihh/DUU09h9uzZ+Prrr+Hm5qYmapC6XZmk4bnnnjMbiL/44ouV7udgr/JOpJ3Bs2veRrGmGME+TfDmqKfRxLt2x6ewqAQ/rT+BJauPIb+wBD5ebrhpRAdcN6Q1PNzd6mzfiYiIyDkGe9l1IFtYWAhfX1/88MMP5YLbqVOnIj09XWVjjS1cuFDVyFrqWmAqIyuBMQPZMtkFOXjyz1eRkpsGVxdXvDBsFjqGt6nx+uQttmV/Ahb8clB1JRDD+8XgjnGdERLoXYd7TkRERI6uwboW1Depwy0pKanUDUFuy+CumvDy8lIXqbuVi6yfyk968MG2L1QQK6b0mFirIPbUhQx8+tMB7ItLVbc7tgjG9And0D42uM72mYiIiJyTXQey1XXnnXfaehcc3k+H/8SuC/vV9f7Ne+Lq9sNrtJ6M7AJ8s/KImplLZpcNa+KNqdd0wZW9mrEOloiIiBp/IBsWFqZqX6UzgjG5HRUVVat1z5gxQ1306WsCDiQdxXcHfjZMevBgvzuqHXQWl2jw26ZTWPTnUeTkFcHT3RWThrXD9cPawtvLrt9uRERE5GDsOrLw9PREnz59sGbNGkONrAz2ktszZ86s1bpZWlBeWl463tvymapn9XDzwKzB98LX06da69h5JEmVEZxLzla3L+/RFNOu6aImLyAiIiJqdIGsdBqIi4sz3D516hT27NmDkJAQxMbGqtZbMrirb9++6N+/v2q/JS27pk2bVqvtMiNbplhTgrmbP0VGQZa6fU/vm9EyuLnVzz+XnIXPfj6IHYd1mfPWzZrg3gnd0KV13c7+RURERGRXgaxMcTts2DDDbQlchQSv0oVg8uTJSElJUe20EhMT0bNnT6xcudKm0+E2Nt/t/wlHUk+o61e1GoxhrQebXO5CSjZWbSubInZw96ZYv/scftlwEiUaLYL8vXD7uE4Y3i8WbjJFFxEREVE9sqv2Ww3JuLTg2LFjTtt+a9u5PXh700fqeoug5nhl+OPwdPestNzqbWcwb8keVTOrf8vo3znubi4Yf0UbTB7RHn4+Hg37AoiIiKhRcdg+svZ+sBqbxOwU1S82rygfPh7eeGPkbEQFRJjMxN7/xhpD4FrRf+4bhJ7tKz+PiIiIqD5jM1c4KcnGdu7cWc0a5owKiwsxZ9PHKogVM/pPNRnECiknMNe9wNXVBXuP63rEEhERETUkpw1kZaDXoUOHsH37djijBbsW43T6OXV9fIcRqmesOVITazZxr9Wqx4mIiIgamtMGss5s7cnN+OvUZnW9U3hb3NK9bPpfU0KDvM2WFcDFhe21iIiIyCYYyDqZ05fO4dNd36nrTbwC8K9Bd8Pd1c3ic9IydOUHpkimduSA2DrfTyIiIqKqOG0g64w1srmFeZiz+WMUlRSpmlcJYkN8giw+Z8v+C/h793nVmUDKZKUm1rX0p9x++KZeaBrm32CvgYiIiEiPXQucpGuB/Jrf2fyxarclbul2HSZ2HmPxOcmXcvGvd9YhO68I/76lFzq2DMGqrWV9ZCUTyyCWiIiIbBWb2XxCBGoYK46tMQSxvaO74rpOoywuX1KiwZxFu1QQO7R3cwzrE6OyuFOv7txAe0xERERkmdOWFjiTIylx+HrvMnU93C8UMwfcCVcXy7/6xauP4eDJi4gO9cMD13c3236LiIiIyFacNpB1lhrZjPxMvLvlU2i0Gri7umPW4Onw9/Kz+Jz9J1KxeNVRVRf7+O194OvN2bqIiIjI/jhtIOsMfWQ1Gg3m/bMAl/Iy1O07e92INiEtLD4nM6cQ73yzExotcMe4zmgXE9xAe0tERERUPU4byDqDJQd/xf6ko+r65S36Y2SbK6ocEDZv8W5czMhH744RuG5ImwbaUyIiIqLqYyDbSO26cABLD/2urscERuPevrdWWee6YtMpbD2YiKAAL/z75t6qxRYRERGRvWIg2wil5FzE+1s/V9e93b0w67J71U9LTl3IwIJfDqrrs27prYJZIiIiInvGQLaRkckO5mz+BDmFuer2/f2moFlglMXn5BcU482vdqCoWIPrh7VFrw4RDbS3RERERDXntIFsY+1a8MWeH3Ai7Yy6PqbdUAyO7Vvlcz5evh/nkrPRITYYU8Z2aoC9JCIiIqo9pw1kG2PXgo1ntuHPuPXqeruQlrijx/VVPmfD7vNYtS0evt7ueGxKH7i7Oe1bgoiIiBwMo5ZG4lxGAj7asUhdD/D0w78HT4e7m+WJ2xIv5uCDH3Szfc28oSeiQi33lyUiIiKyJwxkG4H8ony8s/ljFBQXwAUueGjgNIT5hVh8TnGJBm9/vRO5+cUY2T8WV/Rq1mD7S0RERFQXGMg6OOn9On/HNzifmahuX99lLHpGd6nyed+sPIKj8ZfQPMIf907o1gB7SkRERFS3HD6QTU9PR9++fdGzZ0907doVn3zyCZyJ1MRujt+hrneP7IQbOl9d5XP2HEvGj2uPw8PdFU/c3hfeXpZLEIiIiIjskcNHMAEBAVi/fj18fX2Rk5OjgtlJkyYhNDQUjV3cxdNYuOd7dT3EJwgPD5wGV1fL303SswowZ9EuaLXAXeO7oFXTJg20t0RERER1y+Ezsm5ubiqIFQUFBepUu1wau6yCbNUvtkRTAjcXV8waPB2B3gEWn6PRaPHud7twKasAA7pE4erLWjXY/hIRERE1ukBWsqnjx49H06ZN1RSqy5cvN9nztWXLlvD29saAAQOwbdu2SuUFPXr0QPPmzfH4448jLCwMjZlGq8EHWxciNTdN3b695/VoH9a6yuf9tP4Edh1JRlgTbzw8uVeVU9YSERER2TObB7JSDiBBqASrpixevBizZs3C888/j127dqllR48ejeTkZMMyQUFB2Lt3L06dOoVFixYhKSnJ7PYka5uZmVnu4miWHVqJ3Qm66WQHxvTG2HbDqnzO8bOX8OVvh+DqAsy6rQ8C/TwbYE+JiIiIGnEgO3bsWLz88suYOHGiycfnzJmD6dOnY9q0aWomrvnz56tSggULFlRaNjIyUgW6GzZsMLu91157DU2aNDFcYmJi4Ej2JR7GkgO/quvRARFqCtqqMqu5+UV466udKC7R4qYRHdCtTePOWBMREZFzsHkga0lhYSF27tyJESNGGO6TwUxye8uWLeq2ZF+zsrLU9YyMDFWq0KFDB7PrnD17tlpOfzl79iwcRVpuOub9swBaaOHp5oFHB98LXw+fKp/34dJ9SLiYgy6tQ3HzyPYNsq9ERERETt21IDU1FSUlJSrTakxuHzlyRF0/c+YM7r33XsMgr4ceegjdupnvi+rl5aUuUsogF1m/o/h89xJkFmSr69P73IrYoKonMfhrRzzW7TwHfx8PPHprH7hxCloiIiJqJOw6kLVG//79sWePbprV6pgxY4a6SI2slBjYu7MZF7D13G51/bLYvriy1cAqn3M+JRsf/rhPXZfBXeHBVWdviYiIiByFXafnpPuAtNeqOHhLbkdFRdVq3ZKNlZrbfv36wREsO/yH+ilT0N7Y9Zoqly8qLsGbX+1AfmEJxg1uiUHdohtgL4mIiIgajl0Hsp6enujTpw/WrFljuE+j0ajbgwYNgrNIzErGpvjt6vqgmN5oGlC+1MKUhSsO4eT5DLSMDsRd13ZtgL0kIiIicrLSguzsbMTFxRluSwstKRUICQlBbGysar01depUNQ2tlBHMnTtXteySLga14UilBcuP/GmY5GFi5zFVLr/9UCJ+Xn8Snh5ueHxKH3h5uDXAXhIRERE5WSC7Y8cODBtW1gdVAlchwevChQsxefJkpKSk4LnnnkNiYiJ69uyJlStXVhoA1lil5qTh79P/qOt9m/VAi6DmFpe/mJGHud/pamnvndAVsVGBDbKfRERERE4XyA4dOrTKKWVnzpypLnXJUboW/HxklZqGVkzqZDkbW6LRYs6iXcjMKcRlPZpi1IAWDbSXRERERA3Prmtk65OUFRw6dAjbt+tqT+1Rel4G1pzapK73iOqEtqEtLS7/41/HsS8uFRHBPph5Y09OQUtERESNmtMGso7QteDXY2tQVFKkrk/qPNbisodPpeGbP47A1dUFj0/pq/rGEhERETVmThvI2ntGNqsgG3/ErVfXO4W3UxdzsvOK8PY3O6DRaHHb6I7o2DKkAfeUiIiIyDacNpC1d78fX4uC4gJ1fZKFTgVSX/zB93uQfCkP3duG4fqrzAe8RERERI0JA1k7lFuYh9+PrVXX24S0QPfITmaX/XPrGWzaewGBfp6YdWtvuLmyLpaIiIicg9MGsvZcI/tH3N/IKcpT16/vPNbsoK34xEx8vPyAuv7vW3ojtAmnoCUiIiLn4bSBrL3WyOYXF6hBXiK2STP0btrN5HIFRbopaAuLSnDdkDbo28k5+uoSERERwdkDWXu15sRGNdBLXxvr6mL6V/TZzwdwJjELbZo3wdSrzZceEBERETVWNp8QgcpIq62fj65S16MDIjCwee9yj19IycaqbfE4ePIiDp9Og6eHq2q15eHOKWiJiIjI+ThtIGuPM3utO/UPLuVlqOsTO42Bq2tZNnb1tjOYt2QPXOACTelMaEVFGtU/tlm4v832mYiIiMhWnLa0wN5qZIs1JVh+5A91PdwvFJe36F8uEytBrMSv+iBWyLV5S3bjQqquFIGIiIjImThtIGtvNp7ZhpSci+r6dR1Hwd21rFxAygnMdS6Q+1dtjW+w/SQiIiKyFwxk7YBGo8GywyvV9WCfJhjaalC5x5PTctXEByZptepxIiIiImfDQNYO/HNuNxKyktX18R1GwtPNo9zjESG+5p/s4mL5cSIiIqJGioGsjWm0Giw99Lu6HuDljxFtLq+0zIh+Mao+1hTJ1I4cEFvfu0lERERkd5w2kLWXmb12XdiP+Izz6vo17YfD292r0jKXsgoM111dXSCz0MpPKZt9+KZeaBrGrgVERETkfNyduWuBXDIzM9GkSROb7INkU5ce0tXG+nr4YHTbK00u9+PaOPXzxuHtVGZWamKlnEAysQxiiYiIyFk5bSBrD/YnHUFc2ml1fWy7YfD19Km0THxiJnYcToKfjwduHN4ePl78lRERERE5dWmBPfixtDbWy90L49oPM7nMsnUn1M9xg1syiCUiIiIywkDWRg6nHFcXMbrtEDXQq6KLGXlYt+ss3N1cMf7y1jbYSyIiIiL75fCB7NmzZzF06FA1cKt79+74/vvv4Qj0tbEeru5qkJcpv2w4ieISLa7qG4PgQO8G3kMiIiIi++bw56rd3d0xd+5c9OzZE4mJiejTpw/GjRsHPz8/2Ku4i6exN/GQuj689eUI8qk82Cw3vwi/b9HVz064sk2D7yMRERGRvXP4QDY6OlpdRFRUFMLCwpCWlmbXgax+Fi83Vzdc23GkyWVWbjmD3PxiDOgShZjIgAbeQyIiIiL7Z/PSgvXr12P8+PFo2rQpXFxcsHz5cpM9X1u2bAlvb28MGDAA27ZtM7munTt3oqSkBDExMbBX8ennsf38XnX9yhYDEOYXUmmZomINft6gG+Q1aVjbBt9HIiIiIkdg80A2JycHPXr0UMGqKYsXL8asWbPw/PPPY9euXWrZ0aNHIzlZN6WrnmRh77jjDnz88ccWt1dQUKB6xxpfGtLS0mysBO0TOo02ucyGPedwMSMfHVsEo3Or0AbdPyIiIiJHYfNAduzYsXj55ZcxceJEk4/PmTMH06dPx7Rp09SArvnz58PX1xcLFiwoF5xOmDABTz31FAYPHmxxe6+99pqaAEF/acjs7YWsJGw5u1Ndvyy2H6ICIkxPklA6AcKkYe0abN+IiIiIHI3NA1lLCgsLVbnAiBEjDPe5urqq21u2bDEEfnfeeSeuuuoq3H777VWuc/bs2cjIyDBcpOtBQ1l++A+1v2KimWzsziPJOJOYhWbhfqo+loiIiIgcMJBNTU1VNa+RkZHl7pfb0qFAbNq0SZUfSG2tdC6Qy/79+82u08vLC4GBgfjqq68wcOBADB9uuvVVXdNoNcgtylPX+zfviZgmTU0ut2ydLhs74cq2cHV1aZB9IyIiInJEDt+14PLLL4dGo6n282bMmKEuUiMrJQb1zdXFFY9ddh9OXzoHTzfTh/342UvYF5eKIH8v1TuWiIiIiBw0kJVWWm5ubkhKSip3v9yWVlu1IYPL5CIZ34bUMri52cf0tbHXXNEKnh5uDbhXRERERI7HrksLPD091QQHa9asMdwn2Ve5PWjQoFqtW7Kxhw4dwvbt22EPEi/mYPO+C/D2dMO4wa1svTtEREREds/mGdns7GzExekykeLUqVPYs2cPQkJCEBsbq1pvTZ06FX379kX//v3VLF7Ssku6GDhiRtac5X+fgEYLjBzQAgG+nrbeHSIiIiK756LVD6O3kXXr1mHYsGGV7pfgdeHCher6Bx98gLfeeksN8JLBXPPmzVMTI9QFfY2sdDCQQWC2kJFdgLteXoXiEg0+nj0CkSG+NtkPIiIiIlurTmxm80DWVowzsseOHbNpIPvtn0ex6I8jGNKrGR6f0tcm+0BERETkaIGsXdfI1id7qZHNLyzGrxtPquuThnI6WiIiIiJrOW0gay/+2nEWmTmF6NkuHG2aB9l6d4iIiIgchtMGslJWIFPe9uvXz2b7UKLRYvm6E+r6xGHMxhIRERFVh9MGsvZQWvDP/gQkXMxBq6aB6NU+3Gb7QUREROSInDaQtTUZY7d03XFDbayLC6ejJSIiIqoOpw1kbV1acPDkRRyLT0dYkA8u79nMJvtARERE5MicNpC1dWnBj6XT0V43pA3c3Zz210BERERUY4ygbCA+MRM7DifBz8cDowbE2np3iIiIiBwSA1kbWFbaqWDc4Jbw9faw9e4QEREROSR3OCnjmb0ayoWUbPy84STW7IiHjO3q2ymywbZNRERE1Ng47RS1NZkGrTZWbzuDeUv2AFr1H1SPAhfg4Zt6YUR/lhcQERERCU5Ra2ckEytBrHxl0H9rkJ9ye96S3biQmm3jPSQiIiJyPAxkG8CqbfFm+8TK/au2xjf4PhERERE5OgayDSA5LVdNgGCSVqseJyIiIqLqYSDbACJCfM3P3OXioh4nIiIioupx2kC2IWf2Gtk/1mxGVu4fyV6yRERERNXGrgUN1rUgXg3sUplZOeQuLiqIZdcCIiIioprFZk7bR7ahSbDauXWIGtglNbFSTiCZ2KZh/rbeNSIiIiKHxEC2AUnQOvXqzrbeDSIiIqJGwWlrZImIiIjIsTWKQHbixIkIDg7GDTfcYOtdISIiIqIG0ihKC/71r3/hrrvuwhdffGHrXSEiIiKqF1qNBtriYmhLSnTXSzSlP0sAjQZuPj5w9/dTy2oKC5F34UKlZfTP9QoLg0+zpmrZkoICXNqxs3TZ8svJfb4xzdGkW1fYo0YRyA4dOhTr1q2z9W4QERFRDUknHxU8qYsGbl6ecHFzU48V5+aiOCtbBVnyGEp/6gM0Ccjc/XQBXEHqReTGxxuCMBWUGZYvQZMuXeAVHqaWzT13Dum795YP9PSXkhKEXTYIfi1bqmWzT55C0p+rKyyrD/ZKEDV6FIJ6dFfLZh2PQ/zXi8pej1HQKfvebOIEhF95hW7ZY8dx9M23y70e423E3DIZzSdNUMvmnDyFvY8+YfYYNr/pBrS47RZ1PT85GXv+9ajZZZteew1a3T1Nd3wzs3D0zXfMLhs5eiQDWXPWr1+Pt956Czt37kRCQgKWLVuGCRN0vzDjnq+yTGJiInr06IH3338f/fv3t9k+ExERNQRDYFPxUizBTgm8IiIME+7kJSSiKCMD2hLJ2JVm7gwZPA1CBvSDq7vuYz/jwEHknokvt06NPtNXUoLmN0yCu69usp6U9RuRvmdvaYBVFmjqAq0StJnxILxCQ9SyCb+vRPKatRX2Wx94lqDzc8/ANzZGLXtu6XKc/W6JIXCToM1Yt9dfQWCnjup60qo1OL1godnj1OXF5xDUs4e6fmnnTpz430dml+04+wlDIJsddxKnPl1gdlnf5s0NgWxBcjISf19pdlkVxJYGssXZ2eqYmVOYnm64Lq+/ICXV7LLy+zNwNVMR6uoKlwqPuXp4wCMoCC5uusfUlwK1nJu6zyM4uGxZL08EdumsltEt61q2rKsr/Fu3hr2yeSCbk5OjglMpDZg0aVKlxxcvXoxZs2Zh/vz5GDBgAObOnYvRo0fj6NGjiIiIsMk+ExGR/TAEYKUBmyGQKylWH8Je4eGGZXNOnUZJXp56jgROZc8tgaunB0L69TUsm7pxEwovXTIRFJbA1csLMTeVjcs498NS5J49pwv0JMg0ChAloOj0f08Zlj3x4UfIPHTYKIAsW1b6jA/4uixgO/L6W0jbus3sax/0w3dw8fBQ1+MXfYfU9RvMLtv/64VwDQhQ11P+Xq+yi+ZEjx1jCGQlY5i85i+zy8rx1CtMu4Ts43Fml5XT3WU3NNAUFJhdVp3iLiXH0NWzNENbGpTpAy5DkFbKI7AJ/Fq1KgvGDMvqfnoY9SX1joxA2OWXGa23bH3y07tptGFZn+bNVMaz4vr0zw3oqAu6hV+LFmj3739VWK5sf/Sn9NWyLVug+9tvlG67/DrlPnf/sjadfi1iMeDbr8qtF3LdxOyh3pGR6P/FZ7CGHJNur/4HjsjmgezYsWPVxZw5c+Zg+vTpmDZNl/6WgHbFihVYsGABnnqq7A+DtQoKCtTFuOkuERGVnd6VbJIK2IpLs3QqeCuGpqgY7n6+8Gna1LBs2j/bdI/plzN6jgSQcmpW79TnX0BTUFi2vtLl5bZk6Vrcfpth2QPPPI+izEy1Tf3y+kDVr1VLdH35RcOyO++bgcJU0xkt3xax6DXvXcPto2+9g7zzF0wuK9lN40BWMoY5J06aXNajSWC5QPbSzl0qODVFgl5jBSkpyI0/C3PkuOoDE+MArWyFpcGWu7suk1nKMzgIXpERcHFzh6u7ZN30F3ddkGQU7Pi3bYPi7By4yHISPJVb3g2u3mX7HNKvDzyDmhg9LsFT2XXPoCDDsnK6PKB9u/LBXulF9luCQb2oMaMQdsVlpRlC3bp0y+muS/CqFz1ujLpYI3TQAHWxhmR89Vnfqkh2Vn/aviqeIcGIGDrEqmWlrjWgXVurlpXjo/+CQXYSyFpSWFioSg5mz55tuM/V1RUjRozAli1barTO1157DS++WPYHkIioPklQIlkoCcL0QZmmqAjaoiIVyElA5BUaaqgDzNi7X/e4LFcsy5X9lOBDf/q0KDNLZeB0y5UGempZ3TaC+/RG0/FX65bNysKBp58tFziWBZ7FCBtyOdo9NEMtKxmybVPuNPt6QgcPQscnHzPcPvL6m2aXDe7Tq1wgm7jyT2jy800uK8GzMQn05DS5yWVzc8vdVoFVBRLkqYDMKBgSXpGRukCxQpAnp9w9goPK73+vnvBpGl26jFu5oE+CD2NRY0YjuF9ftR5dQOZetnyFfYidciuaTriubDmjYK9i4Nr2oRloO+OB8oGmmdPLre66U12sIfWccrGGnDLX135aE+zJxRqSaTTONhI1ukA2NTUVJSUliIyMLHe/3D5y5IjhtgS2e/fuVWUKzZs3x/fff49Bg8r+eBqToFhKFT755BN1kfXHxZk/DUJEjkuCtZLcXGgKi6ApKtT9lKBSgsjCQvg0a2aolZMsWdq27aXL6h43BJyFRQi7fLAhiMw5fRonP1lgFGTqglL9z+bXT0TT8dfolj1x0vLgjBuvR4spt6rrhWlpFgPD6PHXGPZB9s9SvZ7+dQnJxFnKAMrrMyxrFEhJAKYLBiUokyyfO9wD/MutV+rqZMptFcCVXnTX3Qy1hXrNJl6nTieXX063rGeIrsZSr/1j/1an2Q3LyT6UBn1uFTKcPd99G3CRYNR0MGisy/PPwFrGGeKq6AfuWKM69YbuvuUDZiJyoEDWWqtXm6/zqcjLy0tdHn30UXXRz+dLRHVL6v1K5DSyPoA0/NQFib4xMfAI1NXryanejIMHKwWaumWLEDlquOHDP33ffpz7/kejx/XL67bR5v57Vc2bkHYyR14zHxi2eeA+dXpTvw8nPzZfTyanRPVBZEl+ATIPHDS7bHFOWcZQAjFjhiBOHxwaBWVuPr4I6NBBBW2SSdQt52G4LqdsDcv6+iLm5ptKM4Vl69OdVnYvV4Mn2cMu/3mhNFsoGcLS4FBlDd3LZRfl9uClS8zW3VVUnbq62JtvsnrZoO7drF5WP1qdiJyPXQeyYWFhcHNzQ1JSUrn75XZUVFSt1i2dEOQiGVkiZyCZQjltLDWK7oEBhtHLMtI57/z50scKVD9BWUZ/PXrcWHiF6U59X9zyjxo9bJyxVMuWBqlyyllfb5a48g+LgWGnZ59GSN8+6nrmkSM48d/5ZpeVrJ8+kJUWPBn79ptdtsTo1LUMDhGGgNDTAy4enuqn3JZgUE8ygqGXDYKr4XHdT8lKynqM6+ikRrTD47MM61XLqKyh7qfxaGDpvzjg2691gaaF08JCRn53f/NVWJupi71lslXLynatDQxV8Goho0lEZE/sOpD19PREnz59sGbNGkNLLo1Go27PnDmzVuueMWOGujAjS/ZEAsOSvHyU5OWqkcA+zZsbAk4ZSJIdd6I00NQHnbqAUy6t77sHnqUB1PmffkbCit/LBaTGrW16vveO4bRv6oaNiP/mW7P7JINf9IGstIiRQS3WBJESMJZd1wWRZUGiLuDT85ZBNv37GYJGwzJy3cNDBYN6kpVs/+gjlQJN/fplkIWeZFAHL/veYvCoJ4ONOj5RVvtpiWSS9Vlf6wZn8PQwEVGjDGSzs7PL1aieOnUKe/bsQUhICGJjY1U969SpU9G3b1/VO1bab0ktrL6LQU0xI0t1GXyqU7qlp2GlBU9+UhJKcvNQkp+nAlJ1XX7m5akG1G7e3mrZ+O+WIPmvtbrgNTe3fL9AAH0//chQ63jxn6248NMvZvcj9tbJhkBW1leQlGx6QVfXcjWREqTKICI5xS0XaUKuv+7q6QWPoLIvekE9u6PtwzPKAtLSQFO1xfHwgLfRmZKIYVequkGVsawikJRG29Y225bjER5uXT2iNQEsERE5LhetDN+0IZmRa9iwYZXul+B14UJdL70PPvjAMCFCz549MW/ePNVTti7oM7IZGRkINOotR42fjJIuysouDTBzS4PJsutNrxtvCE4v/Pwr0vfsQXFu5cBUgk85dazPuh1//79IXm2+52Lfzz42ZDhPffa5WndFEhhK3WK31182tDpKWb9BDUZy9fJWg12kgbUu8NQFnTIYSd8fUQYuST9H/TISkOoDVOOgm4iIyN5UJzazeSBrK8YZ2WPHjjGQdZDBQ7rAU3faXTU1LyhEcO9ehmWS/1qnphEsW0aX6dQtW4De8/9rCOKk0bjUfJoz8LuvDYNgqgxOF3xsaKF09vsf1XrlufqLBLn661HjxsCjtCl5XkICijIyKy1jadQ1ERFRY5ZZjUDW5qUFtsIaWduSJt7FOTlqfmdpei49MYuzdD9lEJHx6OZjc95TdZkV+0wqrq5qhLU+OE3btg0Xt2w1u13pYakPTmWgj6u3N9x85OJbGkR6q2bTct34O17EVUMR2KkT3EqDTd0y8jy57avu14u58Xp1sYZPdLS6EBERUfU5bSBLdU8GAsl0jhKYqgBVAtMM3XUZMd/+kYcMyx587kVk7D9gekWuroiZfKMhOJXA1jiI1QWfZZlOObWvbzgeMqA/vKOjVZBaMTCVi34Uu2j70INo97CuCXxVmnTpoi5ERERkP5w2kOVgL+sUXLyoBg1JkCp1lwWpqSo4lWBVmpUb95A8Nnee+d6arq5o96+ZhuBUP5uLBJwyAtw9IFD3M1D30zg4bTF1CmJvu0X3uL+/xdPuEcOGWv3aWCdKRETk2Jy2RlbPmQd7ycClwtQU5Cen6LKpqalq6sc29003LLN/9jNm5w9Xp/V/XGwYGX7s3XnIPHhQF4wGlAWlMgBJ+pZGjRppCEJlO/pG70RERER6rJElVYNaeCldBaeF6ekIHdDf8Njxef9VrZxKcnIqP9HVVbWH0vf4lNP0Mm2mZ1gYvMLD1Wh7j6AgQ4AqWVm99v9+2Or9k9P9RERERLXhtIGso5cWSIN74/nGk1avQcaBQ7q2S6mpKEi9WNaT1NUVg77/1hCcyv36IFZO1UtfTl2gqgtWpTsASpe1toaUiIiIqKGxtMDOSwtkZH/O6TPIOXUaOSdPIffMGVUKIIOfjINTGdmf8vf68k92dVVTXkpw2nH2E4YeozKnvLakWAWvzIwSERGRPWFpgQOS7xOFF9PgGRpiGIR04sOPkLjyT7PPkeW9IyPUdZkjXqbYlODUOyJcl2UNCTE5MMqnma7BPhEREZEjYyBrA3LqPu/8eWSfPI2cU6dUplUyrsVZWejzyYdq3nkhgaj6GRYGv1YtDRfpOypZVje/smyqqoE1qoMlIiIiauycNpC1VY3shV9+xekvvoa2qGyuewNXV+RfSDAEslFjx6iLDKwiIiIiovJYI9vANbIp6zfg2DtzVVN//9atjDKtrVRpgHHDfiIiIiJnk8kaWfsV3Kc3es//AN6RkYb+q0RERERUfQxkG5i7n5+6EBEREVHtMCVIRERERA6JgSwREREROSSnDWSlY0Hnzp3Rr18/W+8KEREREdUAuxbY+cxeRERERM4ksxqxmdNmZImIiIjIsTGQJSIiIiKHxECWiIiIiBwSA1kiIiIickgMZImIiIjIITGQJSIiIiKHxECWiIiIiBySO5ycvo2u9CwjIiIiItvSx2TWTHXg9IFsVlaW+hkTE2PrXSEiIiIioxhNJkawxOln9tJoNLhw4QICAgLg4uJi691xiG9JEvSfPXuWM6FVE49d7fD41Q6PX+3w+NUOj1/tONvx02q1Koht2rQpXF0tV8E6fUZWDlDz5s1tvRsOR/4hOcM/pvrAY1c7PH61w+NXOzx+tcPjVzvOdPyaVJGJ1eNgLyIiIiJySAxkiYiIiMghMZClavHy8sLzzz+vflL18NjVDo9f7fD41Q6PX+3w+NUOj595Tj/Yi4iIiIgcEzOyREREROSQGMgSERERkUNiIEtEREREDomBLBERERE5JAayVKUXXnhBzXpmfOnYsaOtd8turV+/HuPHj1czksixWr58ebnHZXzlc889h+joaPj4+GDEiBE4fvy4zfbX0Y7fnXfeWen9OGbMGJvtr7157bXX0K9fPzVbYUREBCZMmICjR4+WWyY/Px8zZsxAaGgo/P39cf311yMpKQnOzppjN3To0Ervv/vvv99m+2xPPvzwQ3Tv3t3QtH/QoEH4/fffDY/zfVe748f3nmkMZMkqXbp0QUJCguGyceNGW++S3crJyUGPHj3w3//+1+Tjb775JubNm4f58+dj69at8PPzw+jRo9Ufear6+AkJXI3fj99++22D7qM9+/vvv1Ww8M8//2DVqlUoKirCqFGj1HHV+/e//41ffvkF33//vVpepumeNGkSnJ01x05Mnz693PtP/k0T1CyZr7/+Onbu3IkdO3bgqquuwnXXXYeDBw+qx/m+q93xE3zvmSDtt4gsef7557U9evSw9W44JPkntmzZMsNtjUajjYqK0r711luG+9LT07VeXl7ab7/91kZ76TjHT0ydOlV73XXX2WyfHE1ycrI6jn///bfh/ebh4aH9/vvvDcscPnxYLbNlyxYb7qn9Hztx5ZVXav/1r3/ZdL8cSXBwsPbTTz/l+66Wx0/wvWcaM7JkFTn1Lad6W7dujdtuuw3x8fG23iWHdOrUKSQmJqpyAuP5pAcMGIAtW7bYdN8cybp169Sp3w4dOuCBBx7AxYsXbb1LdisjI0P9DAkJUT8l2yOZRuP3oJQKxcbG8j1YxbHT++abbxAWFoauXbti9uzZyM3NtdEe2q+SkhJ89913Kpstp8j5vqvd8dPje68ydxP3EZUjQdbChQtV0CCnMl588UVcccUVOHDggKolI+tJECsiIyPL3S+39Y+RZVJWIKcjW7VqhRMnTuDpp5/G2LFj1Yehm5ubrXfPrmg0GjzyyCO47LLL1AefkPeZp6cngoKCyi3L92DVx07ceuutaNGihfpiv2/fPjz55JOqjnbp0qU23V97sX//fhV4SamU1MEuW7YMnTt3xp49e/i+q8XxE3zvmcZAlqokQYKeFKJLYCv/mJYsWYK7777bpvtGzufmm282XO/WrZt6T7Zp00ZlaYcPH27TfbM3Uu8pXzhZ0153x+7ee+8t9/6TQZvyvpMvVfI+dHaS8JCgVbLZP/zwA6ZOnarqYal2x0+CWb73TGNpAVWbfKNu37494uLibL0rDicqKkr9rDhSV27rH6PqkXIXOdXG92N5M2fOxK+//oq1a9eqQSR68j4rLCxEenp6ueX5Hqz62JkiX+wF3386knVt27Yt+vTpo7pAyMDN9957j++7Wh4/U/je02EgS9WWnZ2tvgHKt0GqHjkdLn+016xZY7gvMzNTdS8wroMi6507d07VyPL9qCNj5CQQk1OSf/31l3rPGZMPSA8Pj3LvQTk9KXXvzv4erOrYmSLZM8H3n/kSjYKCAr7vann8TOF7T4elBVSlxx57TPX1lHICaZfy/PPPq1rEW265xda7ZreBvvE3ZBngJX9wZMCIDGyQuruXX34Z7dq1Ux+Uzz77rKp5kp6VZPn4yUVqtKX/pHwhkC9UTzzxhMpgSAsz0p0SX7RoEX766SdVw66vP5RBhdK3WH5KSdCsWbPU8ZR+lQ899JAKJgYOHAhnVtWxk/ebPD5u3DjVC1XqFKWl1JAhQ1SJi7OTwUdSiiZ/57KystSxkpKfP/74g++7Wh4/vvcsMNPNgMhg8uTJ2ujoaK2np6e2WbNm6nZcXJytd8turV27VrWUqXiRtlH6FlzPPvusNjIyUrXdGj58uPbo0aO23m2HOH65ubnaUaNGacPDw1UrnxYtWminT5+uTUxMtPVu2w1Tx04un3/+uWGZvLw87YMPPqha+/j6+monTpyoTUhI0Dq7qo5dfHy8dsiQIdqQkBD1b7dt27baxx9/XJuRkWHrXbcLd911l/o3KZ8V8m9U/rb9+eefhsf5vqv58eN7zzwX+Z+lQJeIiIiIyB6xRpaIiIiIHBIDWSIiIiJySAxkiYiIiMghMZAlIiIiIofEQJaIiIiIHBIDWSIiIiJySAxkiYiIiMghMZAlIiIiIofEQJaIiIiIHBIDWSIiIiJySAxkiYgaqaFDh+KRRx6x9W4QEdUbF61Wq62/1RMRka2kpaXBw8MDAQEBtt4VIqJ6wUCWiMhGCgsL4enpCXtl7/tHRMTSAiKiOjyVP3PmTHVp0qQJwsLC8Oyzz0KfL9A/Lqf75bHRo0er+zUaDV577TW0atUKPj4+6NGjB3744YdK637ooYfUc4ODgxEZGYlPPvkEOTk5mDZtmsq6tm3bFr///rvZ0oKqtmNu/0xJTEyEi4sL3nvvPfTq1Qve3t7o0qULNm7cWKfHlIjIEgayRER16IsvvoC7uzu2bdumgrw5c+bg008/Lfe4ZDk3bdqE+fPnq/skuPzyyy/V7YMHD+Lf//43pkyZgr///rvSuiXAlHVLUPvAAw/gxhtvxODBg7Fr1y6MGjUKt99+O3Jzc03umzXbMbV/puzZs0f9XLBgAebOnatux8bG4rbbblMBMxFRQ2BpARFRHZGMZnJysgoSJVspnnrqKfz88884dOiQejwzM1MFnXoFBQUICQnB6tWrMWjQIMP999xzjwpIFy1aZFh3SUkJNmzYoG7Ldcn6Tpo0SQWn+ixpdHQ0tmzZgoEDB6rn9OzZUwWa1mzH1P6Z88Ybb6hs87Fjx9CyZUt1386dO9G3b1/Ex8cjJiamzo4rEZE57mYfISKiapMAUh/ECgka33nnHRV4ij59+pRbPi4uTgWSI0eOrFSfKqfsjXXv3t1w3c3NDaGhoejWrZvhPik3EBJMV2TtdirunzmSgZUgWh/EisDAQKueS0RUVxjIEhE1ID8/v3K3s7Oz1c8VK1agWbNm5R7z8vIqd1s6EBiTgNn4Pn0AberUvrXbqbh/lgLZqVOnlrtPMsFS+lBx/URE9YWBLBFRHdq6dWu52//88w/atWunMqimdO7cWQWScjr+yiuvrLf9qsvt5OXl4fjx44Yssz54lhIGCW5dXTn8gogaBgNZIqI6JIHirFmzcN9996la0/fff1+VFpgj3QYee+wxNfBKgsHLL78cGRkZarCVnKqvmPWsqbrczv79+1X29+uvv8ZVV12FoKAgPPfcc0hPT8czzzxTJ/tLRGQNBrJERHXojjvuUBnL/v37qyzsv/71L9x7770Wn/Of//wH4eHhqqvAyZMnVWDYu3dvPP3003W6b3W1HSkr6NixI5544glcf/31KiCWVl3S/UDWSUTUUNi1gIiojhh3CWjMZsyYgUuXLhk6KhAR2QoLmYiIqNoZWeMOCkREtsJAloiIrCYn8aRGloEsEdkDlhYQERERkUNiRpaIiIiIHBIDWSIiIiJySAxkiYiIiMghMZAlIiIiIofEQJaIiIiIHBIDWSIiIiJySAxkiYiIiMghMZAlIiIiIofEQJaIiIiIHBIDWSIiIiKCI/p/fX2Lkd7LWLMAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 700x420 with 1 Axes>"
       ]
@@ -510,10 +520,10 @@
    "id": "bed2d57a",
    "metadata": {
     "papermill": {
-     "duration": 0.002944,
-     "end_time": "2026-09-26T17:35:07.522406",
+     "duration": 0.005406,
+     "end_time": "2026-11-01T17:29:02.342662+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.519462",
+     "start_time": "2026-11-01T17:29:02.337256+00:00",
      "status": "completed"
     },
     "tags": []
@@ -532,16 +542,16 @@
    "id": "7115b7cd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.529592Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.529277Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.533905Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.533491Z"
+     "iopub.execute_input": "2026-11-01T17:29:02.355930Z",
+     "iopub.status.busy": "2026-11-01T17:29:02.355654Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.362025Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.360970Z"
     },
     "papermill": {
-     "duration": 0.009102,
-     "end_time": "2026-09-26T17:35:07.534546",
+     "duration": 0.014863,
+     "end_time": "2026-11-01T17:29:02.363324+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.525444",
+     "start_time": "2026-11-01T17:29:02.348461+00:00",
      "status": "completed"
     },
     "tags": []
@@ -578,10 +588,10 @@
    "id": "dfa8d577",
    "metadata": {
     "papermill": {
-     "duration": 0.003139,
-     "end_time": "2026-09-26T17:35:07.540658",
+     "duration": 0.005445,
+     "end_time": "2026-11-01T17:29:02.374792+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.537519",
+     "start_time": "2026-11-01T17:29:02.369347+00:00",
      "status": "completed"
     },
     "tags": []
@@ -595,10 +605,10 @@
    "id": "66d83417",
    "metadata": {
     "papermill": {
-     "duration": 0.002852,
-     "end_time": "2026-09-26T17:35:07.546451",
+     "duration": 0.005822,
+     "end_time": "2026-11-01T17:29:02.386532+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.543599",
+     "start_time": "2026-11-01T17:29:02.380710+00:00",
      "status": "completed"
     },
     "tags": []
@@ -616,16 +626,16 @@
    "id": "b1717590",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.553283Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.552935Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.557926Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.557418Z"
+     "iopub.execute_input": "2026-11-01T17:29:02.400560Z",
+     "iopub.status.busy": "2026-11-01T17:29:02.400314Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.410597Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.408572Z"
     },
     "papermill": {
-     "duration": 0.009396,
-     "end_time": "2026-09-26T17:35:07.558765",
+     "duration": 0.018717,
+     "end_time": "2026-11-01T17:29:02.411801+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.549369",
+     "start_time": "2026-11-01T17:29:02.393084+00:00",
      "status": "completed"
     },
     "tags": []
@@ -657,10 +667,10 @@
    "id": "d614e50f",
    "metadata": {
     "papermill": {
-     "duration": 0.00332,
-     "end_time": "2026-09-26T17:35:07.565265",
+     "duration": 0.005705,
+     "end_time": "2026-11-01T17:29:02.423899+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.561945",
+     "start_time": "2026-11-01T17:29:02.418194+00:00",
      "status": "completed"
     },
     "tags": []
@@ -677,16 +687,16 @@
    "id": "99cf773a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.572938Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.572670Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.577783Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.577294Z"
+     "iopub.execute_input": "2026-11-01T17:29:02.437236Z",
+     "iopub.status.busy": "2026-11-01T17:29:02.436978Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.445454Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.444551Z"
     },
     "papermill": {
-     "duration": 0.010136,
-     "end_time": "2026-09-26T17:35:07.578567",
+     "duration": 0.017101,
+     "end_time": "2026-11-01T17:29:02.446851+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.568431",
+     "start_time": "2026-11-01T17:29:02.429750+00:00",
      "status": "completed"
     },
     "tags": []
@@ -737,10 +747,10 @@
    "id": "4b0adbc2",
    "metadata": {
     "papermill": {
-     "duration": 0.003139,
-     "end_time": "2026-09-26T17:35:07.586461",
+     "duration": 0.005814,
+     "end_time": "2026-11-01T17:29:02.458933+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.583322",
+     "start_time": "2026-11-01T17:29:02.453119+00:00",
      "status": "completed"
     },
     "tags": []
@@ -754,10 +764,10 @@
    "id": "fb9a4293",
    "metadata": {
     "papermill": {
-     "duration": 0.003126,
-     "end_time": "2026-09-26T17:35:07.592646",
+     "duration": 0.005461,
+     "end_time": "2026-11-01T17:29:02.470537+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.589520",
+     "start_time": "2026-11-01T17:29:02.465076+00:00",
      "status": "completed"
     },
     "tags": []
@@ -765,7 +775,7 @@
    "source": [
     "## 7. Ce que le lake `hecke_lean` formalise, et ce qui reste\n",
     "\n",
-    "Le lake voisin (`SymbolicAI/Lean/hecke_lean/`) est le socle formel de cette série. Énumérons ce qu'il expose, pour donner à chaque calcul du carnet son pendant démontré."
+    "Le lake voisin (`SymbolicAI/Lean/Langlands/hecke_lean/`) est le socle formel de cette série. Énumérons ce qu'il expose, pour donner à chaque calcul du carnet son pendant démontré."
    ]
   },
   {
@@ -774,16 +784,16 @@
    "id": "7b042401",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.599821Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.599511Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.627196Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.626711Z"
+     "iopub.execute_input": "2026-11-01T17:29:02.484416Z",
+     "iopub.status.busy": "2026-11-01T17:29:02.484167Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.494997Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.493962Z"
     },
     "papermill": {
-     "duration": 0.032322,
-     "end_time": "2026-09-26T17:35:07.627994",
+     "duration": 0.01925,
+     "end_time": "2026-11-01T17:29:02.496085+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.595672",
+     "start_time": "2026-11-01T17:29:02.476835+00:00",
      "status": "completed"
     },
     "tags": []
@@ -853,7 +863,7 @@
     "    starts = [Path.cwd()] + ([Path(os.environ[\"LANGLANDS_LAKE_ROOT\"])] if os.environ.get(\"LANGLANDS_LAKE_ROOT\") else [])\n",
     "    for start in starts:\n",
     "        for cand in (start, *start.parents):\n",
-    "            p = cand / \"MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean\"\n",
+    "            p = cand / \"MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean\"\n",
     "            if p.is_dir():\n",
     "                return p\n",
     "    raise FileNotFoundError(\"hecke_lean introuvable\")\n",
@@ -875,10 +885,10 @@
    "id": "210127d1",
    "metadata": {
     "papermill": {
-     "duration": 0.003181,
-     "end_time": "2026-09-26T17:35:07.634615",
+     "duration": 0.006019,
+     "end_time": "2026-11-01T17:29:02.508800+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.631434",
+     "start_time": "2026-11-01T17:29:02.502781+00:00",
      "status": "completed"
     },
     "tags": []
@@ -892,10 +902,10 @@
    "id": "a9a345d0",
    "metadata": {
     "papermill": {
-     "duration": 0.003371,
-     "end_time": "2026-09-26T17:35:07.641190",
+     "duration": 0.006111,
+     "end_time": "2026-11-01T17:29:02.521116+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.637819",
+     "start_time": "2026-11-01T17:29:02.515005+00:00",
      "status": "completed"
     },
     "tags": []
@@ -911,10 +921,10 @@
    "id": "ccdd60f7",
    "metadata": {
     "papermill": {
-     "duration": 0.003427,
-     "end_time": "2026-09-26T17:35:07.647947",
+     "duration": 0.006006,
+     "end_time": "2026-11-01T17:29:02.533546+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.644520",
+     "start_time": "2026-11-01T17:29:02.527540+00:00",
      "status": "completed"
     },
     "tags": []
@@ -931,16 +941,16 @@
    "id": "8651aedf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.659503Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.659180Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.665395Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.664236Z"
+     "iopub.execute_input": "2026-11-01T17:29:02.547648Z",
+     "iopub.status.busy": "2026-11-01T17:29:02.547398Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.552985Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.551502Z"
     },
     "papermill": {
-     "duration": 0.012492,
-     "end_time": "2026-09-26T17:35:07.666382",
+     "duration": 0.013812,
+     "end_time": "2026-11-01T17:29:02.554014+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.653890",
+     "start_time": "2026-11-01T17:29:02.540202+00:00",
      "status": "completed"
     },
     "tags": []
@@ -975,10 +985,10 @@
    "id": "acc58c02",
    "metadata": {
     "papermill": {
-     "duration": 0.002993,
-     "end_time": "2026-09-26T17:35:07.672354",
+     "duration": 0.006974,
+     "end_time": "2026-11-01T17:29:02.567584+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.669361",
+     "start_time": "2026-11-01T17:29:02.560610+00:00",
      "status": "completed"
     },
     "tags": []
@@ -995,16 +1005,16 @@
    "id": "3526ef01",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.679152Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.678722Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.683336Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.682872Z"
+     "iopub.execute_input": "2026-11-01T17:29:02.581662Z",
+     "iopub.status.busy": "2026-11-01T17:29:02.581406Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.586205Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.585105Z"
     },
     "papermill": {
-     "duration": 0.009822,
-     "end_time": "2026-09-26T17:35:07.684830",
+     "duration": 0.013166,
+     "end_time": "2026-11-01T17:29:02.587187+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.675008",
+     "start_time": "2026-11-01T17:29:02.574021+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1040,10 +1050,10 @@
    "id": "05fbad62",
    "metadata": {
     "papermill": {
-     "duration": 0.005614,
-     "end_time": "2026-09-26T17:35:07.698028",
+     "duration": 0.006287,
+     "end_time": "2026-11-01T17:29:02.599889+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.692414",
+     "start_time": "2026-11-01T17:29:02.593602+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1060,16 +1070,16 @@
    "id": "367be56d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-26T17:35:07.711025Z",
-     "iopub.status.busy": "2026-09-26T17:35:07.710472Z",
-     "iopub.status.idle": "2026-09-26T17:35:07.715848Z",
-     "shell.execute_reply": "2026-09-26T17:35:07.714973Z"
+     "iopub.execute_input": "2026-11-01T17:29:02.614115Z",
+     "iopub.status.busy": "2026-11-01T17:29:02.613864Z",
+     "iopub.status.idle": "2026-11-01T17:29:02.618729Z",
+     "shell.execute_reply": "2026-11-01T17:29:02.617751Z"
     },
     "papermill": {
-     "duration": 0.012603,
-     "end_time": "2026-09-26T17:35:07.716503",
+     "duration": 0.01359,
+     "end_time": "2026-11-01T17:29:02.619686+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.703900",
+     "start_time": "2026-11-01T17:29:02.606096+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1104,10 +1114,10 @@
    "id": "ca8b81e0",
    "metadata": {
     "papermill": {
-     "duration": 0.002785,
-     "end_time": "2026-09-26T17:35:07.722099",
+     "duration": 0.007377,
+     "end_time": "2026-11-01T17:29:02.633045+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.719314",
+     "start_time": "2026-11-01T17:29:02.625668+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1133,10 +1143,10 @@
    "id": "2154dc0c",
    "metadata": {
     "papermill": {
-     "duration": 0.003173,
-     "end_time": "2026-09-26T17:35:07.728359",
+     "duration": 0.006549,
+     "end_time": "2026-11-01T17:29:02.646238+00:00",
      "exception": false,
-     "start_time": "2026-09-26T17:35:07.725186",
+     "start_time": "2026-11-01T17:29:02.639689+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1146,8 +1156,8 @@
     "\n",
     "- Jean-Pierre Serre, *A Course in Arithmetic*, Springer, ch. VII (formes modulaires).\n",
     "- Fred Diamond & Jerry Shurman, *A First Course in Modular Forms*, Springer.\n",
-    "- Lake du dépôt : [`hecke_lean/`](../hecke_lean/README.md) — opérateurs de Hecke classiques, `coeffHeckeT`, courbe de Frey.\n",
-    "- `Lean-29-Hecke-Operators-Native.ipynb` — le carnet d'entrée du lake.\n",
+    "- Lake du dépôt : [`hecke_lean/`](hecke_lean/README.md) — opérateurs de Hecke classiques, `coeffHeckeT`, courbe de Frey.\n",
+    "- [06-operateurs-de-hecke-natif.ipynb](06-operateurs-de-hecke-natif.ipynb) — le carnet d'entrée du lake (ex-Lean-29, descente #17545).\n",
     "- Epic [#17969 — Langlands](https://github.com/jsboige/CoursIA/issues/17969) ; premier jalon de la distillation : #17889, #17970.\n",
     "- Talk du centenaire Serre (IHP 2026) et entretien Serre–Connes (2019), transcriptions : `G:\\Mon Drive\\MyIA\\IA\\Bibliographie IA\\NumberTheory\\` (hors dépôt)."
    ]
@@ -1169,21 +1179,21 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.16"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 4.140156,
-   "end_time": "2026-09-26T17:35:08.079685",
+   "duration": 4.526339,
+   "end_time": "2026-11-01T17:29:03.005429+00:00",
    "environment_variables": {},
    "exception": null,
-   "input_path": "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/01-formes-modulaires-sl2z-hecke.ipynb",
-   "output_path": "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/01-formes-modulaires-sl2z-hecke.ipynb",
+   "input_path": "01-formes-modulaires-sl2z-hecke.ipynb",
+   "output_path": "01-formes-modulaires-sl2z-hecke.ipynb",
    "parameters": {},
-   "start_time": "2026-09-26T17:35:03.939529",
+   "start_time": "2026-11-01T17:28:58.479090+00:00",
    "version": "2.6.0"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/03-chaine-de-modularite-vers-fermat.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/03-chaine-de-modularite-vers-fermat.ipynb
index 0238fcdac4b..fc069979adf 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/03-chaine-de-modularite-vers-fermat.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/03-chaine-de-modularite-vers-fermat.ipynb
@@ -5,10 +5,10 @@
    "id": "cell-00",
    "metadata": {
     "papermill": {
-     "duration": 0.002569,
-     "end_time": "2026-10-18T10:27:45.987112+00:00",
+     "duration": 0.003792,
+     "end_time": "2026-11-01T17:29:15.102523+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:45.984543+00:00",
+     "start_time": "2026-11-01T17:29:15.098731+00:00",
      "status": "completed"
     },
     "tags": []
@@ -18,7 +18,7 @@
     "\n",
     "Troisième carnet de la série **Langlands** ([Epic #17969](https://github.com/jsboige/CoursIA/issues/17969)). Le [carnet 01](01-formes-modulaires-sl2z-hecke.ipynb) a posé le socle — q-expansions, opérateurs de Hecke, le pont $\\Gamma_0(11)$ ↔ courbe 11a1 — et le [carnet 02](02-monstrous-moonshine-invariant-j.ipynb) a suivi l'invariant $j$ jusqu'au groupe Monstre. Ici, on remonte la chaîne complète qui a mené à la preuve du **théorème de Fermat** : une solution hypothétique devient une courbe elliptique (Frey), la courbe serait modulaire (Taniyama–Shimura–Weil), la forme associée descendrait en niveau 2 (Serre, Ribet) — et le niveau 2 de poids 2 est **vide**.\n",
     "\n",
-    "Le geste de la série s'applique au théorème le plus célèbre des mathématiques : chaque maillon est **calculé dans une sortie** (discriminant, tables $a_p$, classes de cosets) ou **démontré dans le lake** [`hecke_lean/Hecke/FltRoute.lean`](../hecke_lean/Hecke/FltRoute.lean) — et les maillons que ni le calcul ni le lake ne couvrent sont dits **admis**, nommément.\n",
+    "Le geste de la série s'applique au théorème le plus célèbre des mathématiques : chaque maillon est **calculé dans une sortie** (discriminant, tables $a_p$, classes de cosets) ou **démontré dans le lake** [`hecke_lean/Hecke/FltRoute.lean`](hecke_lean/Hecke/FltRoute.lean) — et les maillons que ni le calcul ni le lake ne couvrent sont dits **admis**, nommément.\n",
     "\n",
     "**Prérequis** : carnet 01, sections 4 à 6 (opérateur de Hecke, $\\Gamma_0(11)$, comptage de points).\n",
     "**Durée** : environ 1 h. **Outil** : Python stdlib seul — arithmétique exacte, aucun flottant dans les identités.\n"
@@ -26,7 +26,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "fdcac421",
+   "metadata": {
+    "papermill": {
+     "duration": 0.00313,
+     "end_time": "2026-11-01T17:29:15.109289+00:00",
+     "exception": false,
+     "start_time": "2026-11-01T17:29:15.106159+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "| [← 02 — Monstrous Moonshine](02-monstrous-moonshine-invariant-j.ipynb) | [Série Langlands](README.md) | [04 — Umbral Moonshine →](04-umbral-moonshine-reseaux-niemeier.ipynb) |"
    ]
@@ -36,10 +46,10 @@
    "id": "cell-01",
    "metadata": {
     "papermill": {
-     "duration": 0.002138,
-     "end_time": "2026-10-18T10:27:45.992013+00:00",
+     "duration": 0.003478,
+     "end_time": "2026-11-01T17:29:15.117868+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:45.989875+00:00",
+     "start_time": "2026-11-01T17:29:15.114390+00:00",
      "status": "completed"
     },
     "tags": []
@@ -61,10 +71,10 @@
    "id": "cell-02",
    "metadata": {
     "papermill": {
-     "duration": 0.002321,
-     "end_time": "2026-10-18T10:27:45.996476+00:00",
+     "duration": 0.004185,
+     "end_time": "2026-11-01T17:29:15.126133+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:45.994155+00:00",
+     "start_time": "2026-11-01T17:29:15.121948+00:00",
      "status": "completed"
     },
     "tags": []
@@ -85,16 +95,16 @@
    "id": "cell-03",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.001841Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.001478Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.009180Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.008635Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.135424Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.135164Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.148788Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.147705Z"
     },
     "papermill": {
-     "duration": 0.011653,
-     "end_time": "2026-10-18T10:27:46.010300+00:00",
+     "duration": 0.020162,
+     "end_time": "2026-11-01T17:29:15.149962+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:45.998647+00:00",
+     "start_time": "2026-11-01T17:29:15.129800+00:00",
      "status": "completed"
     },
     "tags": []
@@ -168,10 +178,10 @@
    "id": "cell-04",
    "metadata": {
     "papermill": {
-     "duration": 0.002152,
-     "end_time": "2026-10-18T10:27:46.014742+00:00",
+     "duration": 0.003906,
+     "end_time": "2026-11-01T17:29:15.157882+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.012590+00:00",
+     "start_time": "2026-11-01T17:29:15.153976+00:00",
      "status": "completed"
     },
     "tags": []
@@ -185,10 +195,10 @@
    "id": "cell-05",
    "metadata": {
     "papermill": {
-     "duration": 0.002097,
-     "end_time": "2026-10-18T10:27:46.019001+00:00",
+     "duration": 0.003495,
+     "end_time": "2026-11-01T17:29:15.166956+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.016904+00:00",
+     "start_time": "2026-11-01T17:29:15.163461+00:00",
      "status": "completed"
     },
     "tags": []
@@ -215,16 +225,16 @@
    "id": "cell-06",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.024423Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.024034Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.028864Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.028295Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.176174Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.175775Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.191135Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.189912Z"
     },
     "papermill": {
-     "duration": 0.008768,
-     "end_time": "2026-10-18T10:27:46.029833+00:00",
+     "duration": 0.021793,
+     "end_time": "2026-11-01T17:29:15.192843+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.021065+00:00",
+     "start_time": "2026-11-01T17:29:15.171050+00:00",
      "status": "completed"
     },
     "tags": []
@@ -283,10 +293,10 @@
    "id": "cell-07",
    "metadata": {
     "papermill": {
-     "duration": 0.002466,
-     "end_time": "2026-10-18T10:27:46.034594+00:00",
+     "duration": 0.003534,
+     "end_time": "2026-11-01T17:29:15.200546+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.032128+00:00",
+     "start_time": "2026-11-01T17:29:15.197012+00:00",
      "status": "completed"
     },
     "tags": []
@@ -301,16 +311,16 @@
    "id": "cell-08",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.040311Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.039900Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.044001Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.043162Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.209187Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.208942Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.213817Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.212773Z"
     },
     "papermill": {
-     "duration": 0.008026,
-     "end_time": "2026-10-18T10:27:46.044849+00:00",
+     "duration": 0.010571,
+     "end_time": "2026-11-01T17:29:15.214685+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.036823+00:00",
+     "start_time": "2026-11-01T17:29:15.204114+00:00",
      "status": "completed"
     },
     "tags": []
@@ -338,10 +348,10 @@
    "id": "cell-09",
    "metadata": {
     "papermill": {
-     "duration": 0.002686,
-     "end_time": "2026-10-18T10:27:46.050064+00:00",
+     "duration": 0.003686,
+     "end_time": "2026-11-01T17:29:15.222357+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.047378+00:00",
+     "start_time": "2026-11-01T17:29:15.218671+00:00",
      "status": "completed"
     },
     "tags": []
@@ -355,10 +365,10 @@
    "id": "cell-10",
    "metadata": {
     "papermill": {
-     "duration": 0.002229,
-     "end_time": "2026-10-18T10:27:46.054544+00:00",
+     "duration": 0.003582,
+     "end_time": "2026-11-01T17:29:15.230634+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.052315+00:00",
+     "start_time": "2026-11-01T17:29:15.227052+00:00",
      "status": "completed"
     },
     "tags": []
@@ -379,16 +389,16 @@
    "id": "cell-11",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.059897Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.059679Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.067811Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.067192Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.240115Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.239863Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.253951Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.253254Z"
     },
     "papermill": {
-     "duration": 0.012133,
-     "end_time": "2026-10-18T10:27:46.068812+00:00",
+     "duration": 0.021773,
+     "end_time": "2026-11-01T17:29:15.256325+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.056679+00:00",
+     "start_time": "2026-11-01T17:29:15.234552+00:00",
      "status": "completed"
     },
     "tags": []
@@ -466,10 +476,10 @@
    "id": "cell-12",
    "metadata": {
     "papermill": {
-     "duration": 0.003411,
-     "end_time": "2026-10-18T10:27:46.074896+00:00",
+     "duration": 0.003676,
+     "end_time": "2026-11-01T17:29:15.264287+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.071485+00:00",
+     "start_time": "2026-11-01T17:29:15.260611+00:00",
      "status": "completed"
     },
     "tags": []
@@ -483,10 +493,10 @@
    "id": "cell-13",
    "metadata": {
     "papermill": {
-     "duration": 0.00352,
-     "end_time": "2026-10-18T10:27:46.081562+00:00",
+     "duration": 0.003753,
+     "end_time": "2026-11-01T17:29:15.272096+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.078042+00:00",
+     "start_time": "2026-11-01T17:29:15.268343+00:00",
      "status": "completed"
     },
     "tags": []
@@ -505,16 +515,16 @@
    "id": "cell-14",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.091717Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.091476Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.098808Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.097868Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.281742Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.281488Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.291933Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.290560Z"
     },
     "papermill": {
-     "duration": 0.015034,
-     "end_time": "2026-10-18T10:27:46.100407+00:00",
+     "duration": 0.016756,
+     "end_time": "2026-11-01T17:29:15.292915+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.085373+00:00",
+     "start_time": "2026-11-01T17:29:15.276159+00:00",
      "status": "completed"
     },
     "tags": []
@@ -576,10 +586,10 @@
    "id": "cell-15",
    "metadata": {
     "papermill": {
-     "duration": 0.004764,
-     "end_time": "2026-10-18T10:27:46.109853+00:00",
+     "duration": 0.003627,
+     "end_time": "2026-11-01T17:29:15.300746+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.105089+00:00",
+     "start_time": "2026-11-01T17:29:15.297119+00:00",
      "status": "completed"
     },
     "tags": []
@@ -605,10 +615,10 @@
    "id": "cell-16",
    "metadata": {
     "papermill": {
-     "duration": 0.003275,
-     "end_time": "2026-10-18T10:27:46.116833+00:00",
+     "duration": 0.00356,
+     "end_time": "2026-11-01T17:29:15.308684+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.113558+00:00",
+     "start_time": "2026-11-01T17:29:15.305124+00:00",
      "status": "completed"
     },
     "tags": []
@@ -616,7 +626,7 @@
    "source": [
     "## 5. Le lake `hecke_lean` — démontré vs admis\n",
     "\n",
-    "Le fichier [`Hecke/FltRoute.lean`](../hecke_lean/Hecke/FltRoute.lean) du lake compagnon porte la version formelle de la route. Lisons ce qu'il déclare — et comptons les `sorry`, pour savoir si la preuve est complète :"
+    "Le fichier [`Hecke/FltRoute.lean`](hecke_lean/Hecke/FltRoute.lean) du lake compagnon porte la version formelle de la route. Lisons ce qu'il déclare — et comptons les `sorry`, pour savoir si la preuve est complète :"
    ]
   },
   {
@@ -625,16 +635,16 @@
    "id": "cell-17",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.124694Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.124240Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.133086Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.131966Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.317892Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.317595Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.328735Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.327490Z"
     },
     "papermill": {
-     "duration": 0.014737,
-     "end_time": "2026-10-18T10:27:46.134632+00:00",
+     "duration": 0.017189,
+     "end_time": "2026-11-01T17:29:15.329684+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.119895+00:00",
+     "start_time": "2026-11-01T17:29:15.312495+00:00",
      "status": "completed"
     },
     "tags": []
@@ -688,7 +698,7 @@
     "    starts = [Path.cwd()] + ([Path(os.environ[\"LANGLANDS_LAKE_ROOT\"])] if os.environ.get(\"LANGLANDS_LAKE_ROOT\") else [])\n",
     "    for start in starts:\n",
     "        for cand in (start, *start.parents):\n",
-    "            p = cand / \"MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean\"\n",
+    "            p = cand / \"MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean\"\n",
     "            if p.is_dir():\n",
     "                return p\n",
     "    raise FileNotFoundError(\"hecke_lean introuvable\")\n",
@@ -718,10 +728,10 @@
    "id": "cell-18",
    "metadata": {
     "papermill": {
-     "duration": 0.004761,
-     "end_time": "2026-10-18T10:27:46.144513+00:00",
+     "duration": 0.00383,
+     "end_time": "2026-11-01T17:29:15.337837+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.139752+00:00",
+     "start_time": "2026-11-01T17:29:15.334007+00:00",
      "status": "completed"
     },
     "tags": []
@@ -735,10 +745,10 @@
    "id": "cell-19",
    "metadata": {
     "papermill": {
-     "duration": 0.005604,
-     "end_time": "2026-10-18T10:27:46.153992+00:00",
+     "duration": 0.003605,
+     "end_time": "2026-11-01T17:29:15.345409+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.148388+00:00",
+     "start_time": "2026-11-01T17:29:15.341804+00:00",
      "status": "completed"
     },
     "tags": []
@@ -759,16 +769,16 @@
    "id": "cell-20",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.165788Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.165531Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.170234Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.169554Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.356642Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.356398Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.364328Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.363081Z"
     },
     "papermill": {
-     "duration": 0.012432,
-     "end_time": "2026-10-18T10:27:46.171547+00:00",
+     "duration": 0.014695,
+     "end_time": "2026-11-01T17:29:15.365273+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.159115+00:00",
+     "start_time": "2026-11-01T17:29:15.350578+00:00",
      "status": "completed"
     },
     "tags": []
@@ -818,10 +828,10 @@
    "id": "cell-21",
    "metadata": {
     "papermill": {
-     "duration": 0.002491,
-     "end_time": "2026-10-18T10:27:46.176740+00:00",
+     "duration": 0.00384,
+     "end_time": "2026-11-01T17:29:15.373504+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.174249+00:00",
+     "start_time": "2026-11-01T17:29:15.369664+00:00",
      "status": "completed"
     },
     "tags": []
@@ -835,10 +845,10 @@
    "id": "cell-22",
    "metadata": {
     "papermill": {
-     "duration": 0.002392,
-     "end_time": "2026-10-18T10:27:46.181500+00:00",
+     "duration": 0.004602,
+     "end_time": "2026-11-01T17:29:15.382043+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.179108+00:00",
+     "start_time": "2026-11-01T17:29:15.377441+00:00",
      "status": "completed"
     },
     "tags": []
@@ -859,16 +869,16 @@
    "id": "cell-23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.187364Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.187059Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.190701Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.190208Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.391778Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.391401Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.396811Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.395550Z"
     },
     "papermill": {
-     "duration": 0.00778,
-     "end_time": "2026-10-18T10:27:46.191738+00:00",
+     "duration": 0.011631,
+     "end_time": "2026-11-01T17:29:15.397775+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.183958+00:00",
+     "start_time": "2026-11-01T17:29:15.386144+00:00",
      "status": "completed"
     },
     "tags": []
@@ -903,10 +913,10 @@
    "id": "cell-24",
    "metadata": {
     "papermill": {
-     "duration": 0.002469,
-     "end_time": "2026-10-18T10:27:46.197537+00:00",
+     "duration": 0.003815,
+     "end_time": "2026-11-01T17:29:15.405896+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.195068+00:00",
+     "start_time": "2026-11-01T17:29:15.402081+00:00",
      "status": "completed"
     },
     "tags": []
@@ -923,16 +933,16 @@
    "id": "cell-25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.203352Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.202978Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.206406Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.205947Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.416282Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.416036Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.421019Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.419905Z"
     },
     "papermill": {
-     "duration": 0.0075,
-     "end_time": "2026-10-18T10:27:46.207473+00:00",
+     "duration": 0.011954,
+     "end_time": "2026-11-01T17:29:15.421982+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.199973+00:00",
+     "start_time": "2026-11-01T17:29:15.410028+00:00",
      "status": "completed"
     },
     "tags": []
@@ -965,10 +975,10 @@
    "id": "cell-26",
    "metadata": {
     "papermill": {
-     "duration": 0.002386,
-     "end_time": "2026-10-18T10:27:46.214090+00:00",
+     "duration": 0.00401,
+     "end_time": "2026-11-01T17:29:15.430171+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.211704+00:00",
+     "start_time": "2026-11-01T17:29:15.426161+00:00",
      "status": "completed"
     },
     "tags": []
@@ -985,16 +995,16 @@
    "id": "cell-27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-18T10:27:46.219984Z",
-     "iopub.status.busy": "2026-10-18T10:27:46.219560Z",
-     "iopub.status.idle": "2026-10-18T10:27:46.222925Z",
-     "shell.execute_reply": "2026-10-18T10:27:46.222510Z"
+     "iopub.execute_input": "2026-11-01T17:29:15.439697Z",
+     "iopub.status.busy": "2026-11-01T17:29:15.439448Z",
+     "iopub.status.idle": "2026-11-01T17:29:15.446021Z",
+     "shell.execute_reply": "2026-11-01T17:29:15.444233Z"
     },
     "papermill": {
-     "duration": 0.007405,
-     "end_time": "2026-10-18T10:27:46.223922+00:00",
+     "duration": 0.012891,
+     "end_time": "2026-11-01T17:29:15.447141+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.216517+00:00",
+     "start_time": "2026-11-01T17:29:15.434250+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1027,10 +1037,10 @@
    "id": "cell-28",
    "metadata": {
     "papermill": {
-     "duration": 0.002484,
-     "end_time": "2026-10-18T10:27:46.229701+00:00",
+     "duration": 0.004062,
+     "end_time": "2026-11-01T17:29:15.455590+00:00",
      "exception": false,
-     "start_time": "2026-10-18T10:27:46.227217+00:00",
+     "start_time": "2026-11-01T17:29:15.451528+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1056,7 +1066,7 @@
     "- Jean-Pierre Serre, *A Course in Arithmetic*, Springer, ch. VII — le socle des carnets 01 et 03.\n",
     "- Diamond & Shurman, *A First Course in Modular Forms*, Springer — la référence à archiver pour la série (cf. [README de la série](README.md)).\n",
     "- G. Frey (1986), « Links between stable elliptic curves and certain Diophantine equations » ; K. Ribet (1990), « On modular representations of Gal(Q̄/Q) arising from modular forms » ; A. Wiles (1995), « Modular elliptic curves and Fermat's Last Theorem », *Annals of Mathematics*.\n",
-    "- Dans le dépôt : [carnet 01](01-formes-modulaires-sl2z-hecke.ipynb) (socle, Hecke, pont 11a1) · [carnet 02](02-monstrous-moonshine-invariant-j.ipynb) (invariant $j$, Monstre) · lake [`hecke_lean/`](../hecke_lean/README.md) (`Hecke/FltRoute.lean`, la route formelle) · [`Serre100/07`](../Serre100/07-zeros-fonctions-l-gaps-gue.ipynb) (zéros des fonctions $L$).\n",
+    "- Dans le dépôt : [carnet 01](01-formes-modulaires-sl2z-hecke.ipynb) (socle, Hecke, pont 11a1) · [carnet 02](02-monstrous-moonshine-invariant-j.ipynb) (invariant $j$, Monstre) · lake [`hecke_lean/`](hecke_lean/README.md) (`Hecke/FltRoute.lean`, la route formelle) · [`Serre100/07`](../Serre100/07-zeros-fonctions-l-gaps-gue.ipynb) (zéros des fonctions $L$).\n",
     "- Sources vidéo de la série (Serre, Serre–Connes, Borcherds) : transcriptions timestampées référencées dans le [README de la série](README.md)."
    ]
   }
@@ -1077,21 +1087,21 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.16"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 1.943293,
-   "end_time": "2026-10-18T10:27:46.470918+00:00",
+   "duration": 2.210237,
+   "end_time": "2026-11-01T17:29:15.694479+00:00",
    "environment_variables": {},
    "exception": null,
    "input_path": "03-chaine-de-modularite-vers-fermat.ipynb",
    "output_path": "03-chaine-de-modularite-vers-fermat.ipynb",
    "parameters": {},
-   "start_time": "2026-10-18T10:27:44.527625+00:00",
+   "start_time": "2026-11-01T17:29:13.484242+00:00",
    "version": "2.7.0"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/04-umbral-moonshine-reseaux-niemeier.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/04-umbral-moonshine-reseaux-niemeier.ipynb
index b8e9cdb785c..50bc24da281 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/04-umbral-moonshine-reseaux-niemeier.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/04-umbral-moonshine-reseaux-niemeier.ipynb
@@ -1099,7 +1099,7 @@
     "- les groupes umbraux $\\mathrm{Aut}(L)/W(L)$, donnés en table et rapportés ;\n",
     "- la série $H$ de Mathieu moonshine, donnée en entrée et non dérivée.\n",
     "\n",
-    "La série continue avec le socle formel [`hecke_lean/`](../hecke_lean/README.md) pour\n",
+    "La série continue avec le socle formel [`hecke_lean/`](hecke_lean/README.md) pour\n",
     "le versant démontré, et les formes modulaires du carnet\n",
     "[01](01-formes-modulaires-sl2z-hecke.ipynb) pour l'étage au-dessus.\n",
     "\n",
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/05-szpiro-pasten-2026.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/05-szpiro-pasten-2026.ipynb
index 27b130de0f8..0820fbd21a8 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/05-szpiro-pasten-2026.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/05-szpiro-pasten-2026.ipynb
@@ -16,7 +16,7 @@
    "source": [
     "# Szpiro : Pasten 2026 rend `N log log N` inconditionnel\n",
     "",
-    "[← 04 — Umbral Moonshine](04-umbral-moonshine-reseaux-niemeier.ipynb) | [Série Langlands](README.md) | [Complexity-07 Complexité de Kolmogorov bornée →](../../../Complexity/Complexity-07-KolmogorovBornee-Sequences-Python.ipynb)\n",
+    "[← 04 — Umbral Moonshine](04-umbral-moonshine-reseaux-niemeier.ipynb) | [Série Langlands](README.md) | [06 — Opérateurs de Hecke natif →](06-operateurs-de-hecke-natif.ipynb) | [Complexity-07 Complexité de Kolmogorov bornée →](../../../Complexity/Complexity-07-KolmogorovBornee-Sequences-Python.ipynb)\n",
     "",
     "**Distillation d'un preprint.** Ce notebook extrait et rend *opératoire* le résultat de\n",
     "Hector Pasten, *Improved Bounds for Szpiro's Conjecture* (arXiv 2609.17390, 16 septembre\n",
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/06-operateurs-de-hecke-natif.ipynb
similarity index 99%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/06-operateurs-de-hecke-natif.ipynb
index bee39a3c3c3..bc67641f2d5 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/06-operateurs-de-hecke-natif.ipynb
@@ -14,9 +14,10 @@
     "tags": []
    },
    "source": [
-    "# Lean-29 : les opérateurs de Hecke $T_p$ et $U_p$ — compagnon natif du lake `hecke_lean`\n",
+    "# Langlands 06 : les opérateurs de Hecke $T_p$ et $U_p$ — compagnon natif du lake `hecke_lean`\n",
     "\n",
-    "**Série** : [SymbolicAI / Lean](README.md) — companions natifs (cf. [Lean-24 calibration](Lean-24-Calibration-Native-Companion.ipynb), [Lean-23b ERC-20](Lean-23b-Lean-ERC20-Native-Companion.ipynb))\n",
+    "[← 05 — Szpiro–Pasten](05-szpiro-pasten-2026.ipynb) | [Série Langlands](README.md)\n",
+    "**Série** : [SymbolicAI / Lean](../README.md) — companions natifs (cf. [Lean-24 calibration](../Lean-24-Calibration-Native-Companion.ipynb), [Lean-23b ERC-20](../Lean-23b-Lean-ERC20-Native-Companion.ipynb))\n",
     "\n",
     "Compagnon **natif** du lake [`hecke_lean`](hecke_lean/) : ici le lake est **importé et exécuté** dans un kernel Lean 4 réel (`lean4-wsl`), et chaque définition / théorème est interrogé par `#check` ou `#print axioms` — les sorties de ce notebook sont des sorties du compilateur Lean, pas de la prose à propos de Lean.\n",
     "\n",
@@ -28,7 +29,7 @@
     "\n",
     "**L'enjeu de ce que le lake porte.** Les opérateurs de Hecke ne sont pas un objet d'exposition : ils sont la charnière par laquelle une forme modulaire devient une **suite de nombres comparable à autre chose**. Une forme propre normalisée — un vecteur propre commun de tous les $T_p$ — a ses coefficients de Fourier $a(p)$ égaux à ses valeurs propres, et c'est cette suite que le théorème de modularité apparie aux comptages de points $\\#E(\\mathbb{F}_p)$ d'une courbe elliptique sur $\\mathbb{Q}$. La chaîne courbe de Frey $\\rightarrow$ abaissement du niveau $\\rightarrow$ modularité, qui clôt le dernier théorème de Fermat, passe donc par la relation $a(np) + p^{k-1} a(n/p)$ — celle que `coeffHeckeT` porte ici, et que la section 5 fait calculer. Le dépôt amont dont ce lake est un port, `anthropics/fermats-last-theorem`, est un chantier de formalisation de cette chaîne. **Le lake en formalise les opérateurs, pas la preuve** : c'est un fragment de fondation, et la section 8 en prouve la dernière étape — $S_2(\\Gamma_0(2)) = 0$ — pendant que la section 9 mesure exactement ce que le lake démontre et ce qu'il suppose.\n",
     "\n",
-    "**Pourquoi ce module parmi environ 15 000 théorèmes du corpus FLT.** Son choix n'est pas justifié par le seul fait qu'un énoncé non trivial puisse être soumis au noyau Lean — cet argument vaudrait pour n'importe quel module du corpus. Hecke prolonge deux objets déjà enseignés dans CoursIA. D'abord, [Lean-12](Lean-12-Sensitivity-Theorem.ipynb) et son [compagnon Lean-12b](Lean-12b-Lean-Sensitivity-Theorem.ipynb) introduisent les fonctions sur l'hypercube et le lake [`sensitivity_lean`](sensitivity_lean/) ; son module [`Sensitivity/Fourier.lean`](sensitivity_lean/Sensitivity/Fourier.lean) représente une fonction par une famille de coefficients de Walsh, établit leur orthogonalité puis une reconstruction par somme finie. Ensuite, ce même lake formalise dans [`Sensitivity/Operator.lean`](sensitivity_lean/Sensitivity/Operator.lean) un opérateur linéaire $f_n$ et la relation $f_n^2=n\\,\\mathrm{Id}$, tandis que [Lean-21](MIMO/MIMO-02-Detection-Flips-Lean-Python.ipynb), son [compagnon Lean-21b](MIMO/MIMO-03-Converse-Native-Lean.ipynb) et le lake [`mimo_lean`](mimo_lean/) manipulent matrices, applications linéaires, actions scalaires et sommes finies. Le passage pédagogique devient alors lisible : **fonction décrite par des coefficients** $\\rightarrow$ **opérateur additif et homogène agissant sur une fonction** $\\rightarrow$ **transformation arithmétique explicite de ses coefficients**. Hecke est le fragment du corpus FLT où ces deux fils déjà familiers se rencontrent dans un nouvel objet : les formes modulaires sur le demi-plan supérieur, leurs slash actions et la formule de `coeffHeckeT`.\n",
+    "**Pourquoi ce module parmi environ 15 000 théorèmes du corpus FLT.** Son choix n'est pas justifié par le seul fait qu'un énoncé non trivial puisse être soumis au noyau Lean — cet argument vaudrait pour n'importe quel module du corpus. Hecke prolonge deux objets déjà enseignés dans CoursIA. D'abord, [Lean-12](../Lean-12-Sensitivity-Theorem.ipynb) et son [compagnon Lean-12b](../Lean-12b-Lean-Sensitivity-Theorem.ipynb) introduisent les fonctions sur l'hypercube et le lake [`sensitivity_lean`](sensitivity_lean/) ; son module [`Sensitivity/Fourier.lean`](../sensitivity_lean/Sensitivity/Fourier.lean) représente une fonction par une famille de coefficients de Walsh, établit leur orthogonalité puis une reconstruction par somme finie. Ensuite, ce même lake formalise dans [`Sensitivity/Operator.lean`](../sensitivity_lean/Sensitivity/Operator.lean) un opérateur linéaire $f_n$ et la relation $f_n^2=n\\,\\mathrm{Id}$, tandis que [Lean-21](../MIMO/MIMO-02-Detection-Flips-Lean-Python.ipynb), son [compagnon Lean-21b](../MIMO/MIMO-03-Converse-Native-Lean.ipynb) et le lake [`mimo_lean`](mimo_lean/) manipulent matrices, applications linéaires, actions scalaires et sommes finies. Le passage pédagogique devient alors lisible : **fonction décrite par des coefficients** $\\rightarrow$ **opérateur additif et homogène agissant sur une fonction** $\\rightarrow$ **transformation arithmétique explicite de ses coefficients**. Hecke est le fragment du corpus FLT où ces deux fils déjà familiers se rencontrent dans un nouvel objet : les formes modulaires sur le demi-plan supérieur, leurs slash actions et la formule de `coeffHeckeT`.\n",
     "\n",
     "**Raccord conceptuel, dépendance réelle.** Ce passage est une analogie structurante, pas une identification mathématique : les coefficients de Fourier booléens de `Sensitivity/Fourier.lean` décrivent une fonction sur un hypercube fini à l'aide des caractères de Walsh ; les coefficients $a(n)$ manipulés ici sont ceux d'une $q$-expansion de forme modulaire. De même, `sensitivity_lean` et `mimo_lean` préparent la lecture des matrices, sommes finies, actions scalaires, additivité et homogénéité, mais **ne sont pas des dépendances Lean** de ce notebook. La cellule d'import charge réellement `Hecke.HeckeOperator`, et ce module importe directement `Mathlib.NumberTheory.ModularForms.SlashActions`. Aucun lien historique ou formel particulier avec Galois, Belyi ou la théorie de l'information n'est revendiqué à partir de cette seule proximité de vocabulaire.\n",
     "\n",
@@ -67,7 +68,7 @@
     "\n",
     "### Prérequis\n",
     "\n",
-    "- [Lean-5 Tactics](Lean-05-Tactics-Lean.ipynb) (`simp`, `rw`, `decide`) et [Lean-6 Mathlib](Lean-06-Mathlib-Essentials-Lean.ipynb)\n",
+    "- [Lean-5 Tactics](../Lean-05-Tactics-Lean.ipynb) (`simp`, `rw`, `decide`) et [Lean-6 Mathlib](../Lean-06-Mathlib-Essentials-Lean.ipynb)\n",
     "- Théorie : action de $SL_2(\\mathbb{Z})$ sur $\\mathbb{H}$ par homographies ; le notebook rappelle ce qu'il utilise\n",
     "\n",
     "### Durée estimée\n",
@@ -3054,7 +3055,7 @@
     "\n",
     "Ce que le lake démontre est donc la **mécanique locale** — les $T_p$, la formule des coefficients, une identité propre vérifiée par le noyau — et la **dernière étape** de la route. Ce qu'il suppose est le **pont**, c'est-à-dire le cœur de la chaîne. La répartition est instructive : ce qui se mécanise, ce sont les calculs et la vacuité d'un espace de dimension connue ; ce qui résiste, c'est une correspondance entre deux mondes qu'aucune théorie générale ne dicte.\n",
     "\n",
-    "Serre, sur la découverte de ce pont : « le rapport entre forme modulaire et représentation galoisienne […], quand je me suis aperçu de ça, c'était **délicieux**. Des formules de Ramanujan un peu bizarres, il y a une représentation de degré 2 » (*Plaisir des mathématiques*, IHP, [34:13]–[34:35]). « Délicieux » est le mot juste : ce n'est pas le produit mécanique d'une marée montante de théorie générale — c'est une correspondance **trouvée**, et la section suivante exécute tout ce qui s'en laisse exécuter. Le dialogue entre les deux styles est l'objet de [Lean-15](Lean-15-Grothendieck-Tribute.ipynb).\n"
+    "Serre, sur la découverte de ce pont : « le rapport entre forme modulaire et représentation galoisienne […], quand je me suis aperçu de ça, c'était **délicieux**. Des formules de Ramanujan un peu bizarres, il y a une représentation de degré 2 » (*Plaisir des mathématiques*, IHP, [34:13]–[34:35]). « Délicieux » est le mot juste : ce n'est pas le produit mécanique d'une marée montante de théorie générale — c'est une correspondance **trouvée**, et la section suivante exécute tout ce qui s'en laisse exécuter. Le dialogue entre les deux styles est l'objet de [Lean-15](../Lean-15-Grothendieck-Tribute.ipynb).\n"
    ]
   },
   {
@@ -3962,8 +3963,8 @@
    "end_time": "2026-10-09T16:06:23.677630+00:00",
    "environment_variables": {},
    "exception": null,
-   "input_path": "Lean-29-Hecke-Operators-Native.ipynb",
-   "output_path": "Lean-29-Hecke-Operators-Native.ipynb",
+   "input_path": "06-operateurs-de-hecke-natif.ipynb",
+   "output_path": "06-operateurs-de-hecke-natif.ipynb",
    "parameters": {},
    "start_time": "2026-10-09T15:59:16.984272+00:00",
    "version": "2.7.0"
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/README.md
index a55032e7085..b7cffdd6b3a 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/README.md
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/README.md
@@ -2,7 +2,7 @@
 
 Série née de l'EPIC [#17969](https://github.com/jsboige/CoursIA/issues/17969) : donner aux formes modulaires la place pédagogique réfléchie qu'elles méritent, et tisser les ponts — forme modulaire ↔ courbe elliptique, Moonshine ↔ groupe Monstre — que le programme de Langlands articule. C'est la direction que Serre décrivait comme **orthogonale** à la marée montante de Grothendieck : des voies surprenantes où il ne suffit pas de laisser monter la mer (entretien Serre–Connes, distillé dans `../Lean-15-Grothendieck-Tribute.ipynb`, section « Les limites de la marée »).
 
-Le geste de la série : rendre chaque identité **calculée dans une sortie** — q-expansions exactes à coefficients entiers, valeurs propres de Hecke, tables de coefficients vérifiées premier par premier — en miroir du socle formel [`hecke_lean/`](../hecke_lean/README.md) qui démontre ce que les carnets calculent.
+Le geste de la série : rendre chaque identité **calculée dans une sortie** — q-expansions exactes à coefficients entiers, valeurs propres de Hecke, tables de coefficients vérifiées premier par premier — en miroir du socle formel [`hecke_lean/`](hecke_lean/README.md) qui démontre ce que les carnets calculent.
 
 ## Notebooks
 
@@ -10,16 +10,17 @@ Le geste de la série : rendre chaque identité **calculée dans une sortie** 
 |---|----------|---------------|-------|
 | 01 | [01-formes-modulaires-sl2z-hecke.ipynb](01-formes-modulaires-sl2z-hecke.ipynb) | *A Course in Arithmetic* ch. VII ; pont Γ₀(11) courbe 11a1 ↔ η²η₁₁² ; formule `coeffHeckeT` du lake | Python stdlib |
 | 02 | [02-monstrous-moonshine-invariant-j.ipynb](02-monstrous-moonshine-invariant-j.ipynb) | Conway & Norton *Monstrous Moonshine* (1979) ; invariant $j$ et groupe Monstre ; récit cp4space/Wertheim | Python stdlib |
-| 03 | [03-chaine-de-modularite-vers-fermat.ipynb](03-chaine-de-modularite-vers-fermat.ipynb) | Frey, Serre–Ribet, Wiles — la chaîne de modularité qui prouve Fermat ; miroir calculé de [`hecke_lean/Hecke/FltRoute.lean`](../hecke_lean/Hecke/FltRoute.lean) | Python stdlib |
+| 03 | [03-chaine-de-modularite-vers-fermat.ipynb](03-chaine-de-modularite-vers-fermat.ipynb) | Frey, Serre–Ribet, Wiles — la chaîne de modularité qui prouve Fermat ; miroir calculé de [`hecke_lean/Hecke/FltRoute.lean`](hecke_lean/Hecke/FltRoute.lean) | Python stdlib |
 | 04 | [04-umbral-moonshine-reseaux-niemeier.ipynb](04-umbral-moonshine-reseaux-niemeier.ipynb) | Cheng, Duncan & Harvey *Umbral Moonshine* (2013) — les 24 réseaux de Niemeier et leurs systèmes de racines ADE ; le réseau de Leech, rang 24 et racines nulles ; 10 458 combinaisons ADE de rang 24 énumérées | Python stdlib |
 | 05 | [05-szpiro-pasten-2026.ipynb](05-szpiro-pasten-2026.ipynb) | Pasten, *Improved Bounds for Szpiro's Conjecture* (arXiv 2609.17390, #16549) ; factorisations admissibles `N=DM`, proxy `log\|Δ\|/log rad(\|Δ\|)` des courbes `y²=x³+ax+b` ; arrivé de la série Search (carte #19253, ligne 15) | Python + sympy |
+| 06 | [06-operateurs-de-hecke-natif.ipynb](06-operateurs-de-hecke-natif.ipynb) | Les opérateurs $T_p$/$U_p$ **exécutés au noyau** `lean4-wsl` — compagnon natif du lake, seule marche qui interroge in-kernel les trois PID (7, 11, 13) et la route de Fermat (`#check`/`#print axioms`, 0 erreur) ; ex-Lean-29, descendu de la racine (gradation #17545) | Lean 4 (WSL) |
 
 ## Conventions
 
 - Français d'abord, arithmétique en stdlib pur (aucune dépendance au-delà de la bibliothèque standard) ; exception : `03` recourt à `sympy` (`factorint`, `totient`) pour les factorisations admissibles `N = DM`.
 - ≥ 3 exercices C.1 par notebook (convention #2161), exécution complète commitée (C.2).
 - Verdict SOTA écrit au body de chaque PR (#3801).
-- Le versant formel vit dans le lake compagnon [`hecke_lean/`](../hecke_lean/README.md) (opérateurs de Hecke, courbe de Frey).
+- Le versant formel vit dans le lake compagnon [`hecke_lean/`](hecke_lean/README.md) (opérateurs de Hecke, courbe de Frey), descendu avec la sous-série (gradation #17545).
 
 ## Sources primaires
 
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/.gitignore b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/.gitignore
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/.gitignore
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/.gitignore
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/DeltaTau.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/DeltaTau.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/DeltaTau.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/DeltaTau.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/DeltaTau_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/DeltaTau_en.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/DeltaTau_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/DeltaTau_en.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ElevenPid.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ElevenPid.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ElevenPid.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ElevenPid.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ElevenPid_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ElevenPid_en.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ElevenPid_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ElevenPid_en.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/FltRoute.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/FltRoute.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/FltRoute.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/FltRoute.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/FltRoute_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/FltRoute_en.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/FltRoute_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/FltRoute_en.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/HeckeOperator.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/HeckeOperator.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/HeckeOperator.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/HeckeOperator.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/HeckeOperator_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/HeckeOperator_en.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/HeckeOperator_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/HeckeOperator_en.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SevenPid.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SevenPid.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SevenPid.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SevenPid.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SevenPid_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SevenPid_en.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SevenPid_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SevenPid_en.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SzpiroPasten.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SzpiroPasten.lean
similarity index 99%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SzpiroPasten.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SzpiroPasten.lean
index faf53661bbc..8ebffe6e959 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SzpiroPasten.lean
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SzpiroPasten.lean
@@ -42,7 +42,7 @@ prouvé ci-dessous l'est intégralement (zéro sorry).
 **Ponts vers les notebooks** : la mécanique du choix de `D` et le proxy
 `log|Δ| / log rad(|Δ|)` vivent dans `App-32` (kernel python3) ; les formes
 modulaires et les opérateurs de Hecke dans `SymbolicAI/Lean/Langlands/`
-(01, 02) et `Lean-29`. Ce module en est le versant formel : mêmes
+(01, 02) et le carnet 06 (ex-`Lean-29`). Ce module en est le versant formel : mêmes
 définitions, preuve par le noyau.
 -/
 
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SzpiroPasten_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SzpiroPasten_en.lean
similarity index 99%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SzpiroPasten_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SzpiroPasten_en.lean
index f94e28a5336..d67bb9603de 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/SzpiroPasten_en.lean
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/SzpiroPasten_en.lean
@@ -40,7 +40,7 @@ is proved below is proved in full (zero sorry).
 
 **Bridges to the notebooks**: the mechanics of choosing `D` and the proxy
 `log|Δ| / log rad(|Δ|)` live in `App-32` (python3 kernel); modular forms
-and Hecke operators in `SymbolicAI/Lean/Langlands/` (01, 02) and `Lean-29`.
+and Hecke operators in `SymbolicAI/Lean/Langlands/` (01, 02) and notebook 06 (formerly `Lean-29`).
 This module is their formal counterpart: same definitions, kernel-proved.
 -/
 
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ThirteenPid.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ThirteenPid.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ThirteenPid.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ThirteenPid.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ThirteenPid_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ThirteenPid_en.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/ThirteenPid_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/ThirteenPid_en.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke_en.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke_en.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke_en.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/LICENSE-Apache-2.0.txt b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/LICENSE-Apache-2.0.txt
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/LICENSE-Apache-2.0.txt
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/LICENSE-Apache-2.0.txt
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/NOTICE.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/NOTICE.md
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/NOTICE.md
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/NOTICE.md
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/README.en.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/README.en.md
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/README.en.md
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/README.en.md
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/README.md
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/README.md
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/README.md
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lake-manifest.json b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lake-manifest.json
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lake-manifest.json
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lake-manifest.json
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lakefile.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lakefile.lean
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lakefile.lean
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lakefile.lean
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lean-toolchain b/MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lean-toolchain
similarity index 100%
rename from MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lean-toolchain
rename to MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lean-toolchain
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb
index 213b8156b2a..8bbf67add36 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb
@@ -144,7 +144,7 @@
     "\n",
     "> « Ce qui m'a le plus frappé de tout, c'est vraiment ce qu'on appelle le programme de Langlands […] c'est vraiment faire des connexions. **Je ne connais personne qui fait des connexions aussi étonnantes.** » *(Serre, Plaisir des mathématiques, IHP, [30:37]–[31:03])*\n",
     "\n",
-    "**Ce que ce dialogue éclaire dans ce dépôt.** Les deux styles ne sont pas deux esthétiques sans rapport : ils se **partagent le travail**. La marée montante fournit le **langage** — schémas, sites, faisceaux, et jusqu'aux représentations galoisiennes dans lesquelles s'énonce la modularité ; les correspondances surprenantes fournissent les **trouvailles** que ce langage ne dicte pas. Le cas d'école est déjà dans le dépôt : la route de Fermat par la courbe de Frey ([Lean-29](Lean-29-Hecke-Operators-Native.ipynb), section 8) n'est pas une marée — c'est un enchaînement de trouvailles (une courbe fabriquée pour l'occasion, un niveau abaissé à 2, un espace nul), dont chacune a l'air d'un coup de foudre et non d'un développement logique. Le langage de Grothendieck est le **support** de ces trouvailles, jamais leur source — et c'est très exactement la distinction que Serre trace ici.\n",
+    "**Ce que ce dialogue éclaire dans ce dépôt.** Les deux styles ne sont pas deux esthétiques sans rapport : ils se **partagent le travail**. La marée montante fournit le **langage** — schémas, sites, faisceaux, et jusqu'aux représentations galoisiennes dans lesquelles s'énonce la modularité ; les correspondances surprenantes fournissent les **trouvailles** que ce langage ne dicte pas. Le cas d'école est déjà dans le dépôt : la route de Fermat par la courbe de Frey ([Langlands 06](Langlands/06-operateurs-de-hecke-natif.ipynb), section 8) n'est pas une marée — c'est un enchaînement de trouvailles (une courbe fabriquée pour l'occasion, un niveau abaissé à 2, un espace nul), dont chacune a l'air d'un coup de foudre et non d'un développement logique. Le langage de Grothendieck est le **support** de ces trouvailles, jamais leur source — et c'est très exactement la distinction que Serre trace ici.\n",
     "\n",
     "*Sources* : deux transcriptions hors dépôt, gisement partagé `G:\\Mon Drive\\MyIA\\IA\\Bibliographie IA\\NumberTheory\\` — `2019 - Serre & Connes - Correspondance Grothendieck-Serre (College de France, transcription YouTube pOv-ygSynRI).md` et `2026 - Serre - Plaisir des mathematiques (IHP, transcription YouTube tNtoTzGltak).md`. Citations issues d'une transcription automatique (noms propres corrigés : Grothendieck, Weil, Connes). Première vague de distillation : #17889 (PR #17943). Grain 4 de l'Epic de consolidation [#17969](https://github.com/jsboige/CoursIA/issues/17969).\n"
    ]
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-28-Complex-Structure-S6.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-28-Complex-Structure-S6.ipynb
index 1bf559914a2..f3686416a39 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-28-Complex-Structure-S6.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-28-Complex-Structure-S6.ipynb
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Navigation** : [<< Lean-27 Edge Coloring Tutte](Lean-27-EdgeColoring-Tutte-Companion.ipynb) | [Index](README.md) | [Lean-29 Hecke >>](Lean-29-Hecke-Operators-Native.ipynb)\n"
+    "**Navigation** : [<< Lean-27 Edge Coloring Tutte](Lean-27-EdgeColoring-Tutte-Companion.ipynb) | [Index](README.md) | [Lean-30 Formal Groups >>](Lean-30-FormalGroups-Native.ipynb)\n"
    ]
   },
   {
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-30-FormalGroups-Native.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-30-FormalGroups-Native.ipynb
index ffcf9b4599c..d0e011559f1 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-30-FormalGroups-Native.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-30-FormalGroups-Native.ipynb
@@ -26,7 +26,7 @@
    "id": "1d743e7f",
    "metadata": {},
    "source": [
-    "**Navigation** : [<< Lean-29 Hecke](Lean-29-Hecke-Operators-Native.ipynb) | [Index](README.md) | [Lean-33 Distribution-Spaces >>](Lean-33-Distribution-Spaces.ipynb)\n"
+    "**Navigation** : [<< Lean-28 Complex Structure S6](Lean-28-Complex-Structure-S6.ipynb) | [Index](README.md) | [Lean-33 Distribution-Spaces >>](Lean-33-Distribution-Spaces.ipynb)\n"
    ]
   },
   {
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-40-Capstone-Langlands-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-40-Capstone-Langlands-Python.ipynb
index 89100105f66..564872761c9 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-40-Capstone-Langlands-Python.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-40-Capstone-Langlands-Python.ipynb
@@ -5,10 +5,10 @@
    "id": "d3345d50",
    "metadata": {
     "papermill": {
-     "duration": 0.006015,
-     "end_time": "2026-10-16T03:06:50.590602+00:00",
+     "duration": 0.003871,
+     "end_time": "2026-11-01T17:29:47.964627+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.584587+00:00",
+     "start_time": "2026-11-01T17:29:47.960756+00:00",
      "status": "completed"
     },
     "tags": []
@@ -16,20 +16,20 @@
    "source": [
     "# Capstone Langlands — l'escalier : de la forme calculée au lac qui la démontre\n",
     "\n",
-    "**Navigation** : [01 — Formes modulaires SL2Z et Hecke](Langlands/01-formes-modulaires-sl2z-hecke.ipynb) · [02 — Monstrous Moonshine et l'invariant j](Langlands/02-monstrous-moonshine-invariant-j.ipynb) · [README de la sous-série](Langlands/README.md) · [lac `hecke_lean`](hecke_lean/README.md) · [Lean-29 — compagnon natif Hecke](Lean-29-Hecke-Operators-Native.ipynb)\n",
+    "**Navigation** : [01 — Formes modulaires SL2Z et Hecke](Langlands/01-formes-modulaires-sl2z-hecke.ipynb) · [02 — Monstrous Moonshine et l'invariant j](Langlands/02-monstrous-moonshine-invariant-j.ipynb) · [README de la sous-série](Langlands/README.md) · [lac `hecke_lean`](Langlands/hecke_lean/README.md) · [Langlands 06 — compagnon natif Hecke](Langlands/06-operateurs-de-hecke-natif.ipynb)\n",
     "\n",
     "La sous-série **Langlands** regarde les formes modulaires comme un pont : de\n",
     "l'analyse complexe (le demi-plan supérieur, l'action de SL₂(ℤ)) vers l'arithmétique\n",
     "(les coefficients de Fourier, les valeurs propres de Hecke) puis vers l'algèbre\n",
     "(Moonshine : le groupe Monstre qui apparaît dans les coefficients de j). Le\n",
     "programme de Langlands est le nom général de ces correspondances ; la série mère\n",
-    "l'avait croisé à travers son compagnon natif [Lean-29](Lean-29-Hecke-Operators-Native.ipynb),\n",
+    "l'avait croisé à travers son compagnon natif [Langlands 06](Langlands/06-operateurs-de-hecke-natif.ipynb),\n",
     "la sous-série [Langlands](Langlands/README.md) le traite en profondeur.\n",
     "\n",
     "Cet escalier en est la porte d'entrée depuis la série mère : il part du calcul —\n",
     "la fonction Δ de Ramanujan, coefficient par coefficient, en arithmétique entière\n",
     "exacte — monte par la relation de Hecke vérifiée sur la table, montre un témoin\n",
-    "qui la viole, puis lit le lac [`hecke_lean`](hecke_lean/) comme un consommateur :\n",
+    "qui la viole, puis lit le lac [`hecke_lean`](Langlands/hecke_lean/) comme un consommateur :\n",
     "le fichier réel, sa définition `coeffHeckeT`, la preuve et son audit. La dernière\n",
     "marche traverse le pont Moonshine. Aucune étape ne demande un kernel Lean : tout\n",
     "est calculable en Python stdlib, et chaque identité est **calculée dans une sortie**,\n",
@@ -41,10 +41,10 @@
    "id": "ea1fc064",
    "metadata": {
     "papermill": {
-     "duration": 0.003547,
-     "end_time": "2026-10-16T03:06:50.600977+00:00",
+     "duration": 0.003156,
+     "end_time": "2026-11-01T17:29:47.971320+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.597430+00:00",
+     "start_time": "2026-11-01T17:29:47.968164+00:00",
      "status": "completed"
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@@ -75,10 +75,10 @@
    "id": "69631d96",
    "metadata": {
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-     "end_time": "2026-10-16T03:06:50.612071+00:00",
+     "duration": 0.003546,
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      "exception": false,
-     "start_time": "2026-10-16T03:06:50.606698+00:00",
+     "start_time": "2026-11-01T17:29:47.974742+00:00",
      "status": "completed"
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@@ -99,16 +99,16 @@
    "id": "116f5cb5",
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-     "iopub.status.idle": "2026-10-16T03:06:50.660132Z",
-     "shell.execute_reply": "2026-10-16T03:06:50.659239Z"
+     "iopub.execute_input": "2026-11-01T17:29:47.986859Z",
+     "iopub.status.busy": "2026-11-01T17:29:47.986608Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.046328Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.044682Z"
     },
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-     "duration": 0.043871,
-     "end_time": "2026-10-16T03:06:50.661661+00:00",
+     "duration": 0.065356,
+     "end_time": "2026-11-01T17:29:48.047358+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.617790+00:00",
+     "start_time": "2026-11-01T17:29:47.982002+00:00",
      "status": "completed"
     },
     "tags": []
@@ -179,10 +179,10 @@
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    "metadata": {
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-     "duration": 0.006508,
-     "end_time": "2026-10-16T03:06:50.674006+00:00",
+     "duration": 0.003273,
+     "end_time": "2026-11-01T17:29:48.054582+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.667498+00:00",
+     "start_time": "2026-11-01T17:29:48.051309+00:00",
      "status": "completed"
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@@ -202,10 +202,10 @@
    "id": "5d517991",
    "metadata": {
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-     "duration": 0.004755,
-     "end_time": "2026-10-16T03:06:50.685548+00:00",
+     "duration": 0.003681,
+     "end_time": "2026-11-01T17:29:48.061840+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.680793+00:00",
+     "start_time": "2026-11-01T17:29:48.058159+00:00",
      "status": "completed"
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@@ -231,16 +231,16 @@
    "id": "f7c73e36",
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-     "shell.execute_reply": "2026-10-16T03:06:50.704690Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.070810Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.070554Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.078519Z",
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-     "duration": 0.015891,
-     "end_time": "2026-10-16T03:06:50.707511+00:00",
+     "duration": 0.014444,
+     "end_time": "2026-11-01T17:29:48.079984+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.691620+00:00",
+     "start_time": "2026-11-01T17:29:48.065540+00:00",
      "status": "completed"
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@@ -291,10 +291,10 @@
    "id": "7c5e39f9",
    "metadata": {
     "papermill": {
-     "duration": 0.004769,
-     "end_time": "2026-10-16T03:06:50.717141+00:00",
+     "duration": 0.003665,
+     "end_time": "2026-11-01T17:29:48.087437+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.712372+00:00",
+     "start_time": "2026-11-01T17:29:48.083772+00:00",
      "status": "completed"
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@@ -314,10 +314,10 @@
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    "metadata": {
     "papermill": {
-     "duration": 0.006555,
-     "end_time": "2026-10-16T03:06:50.730035+00:00",
+     "duration": 0.00334,
+     "end_time": "2026-11-01T17:29:48.094255+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.723480+00:00",
+     "start_time": "2026-11-01T17:29:48.090915+00:00",
      "status": "completed"
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@@ -336,16 +336,16 @@
    "id": "a73d7270",
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-     "shell.execute_reply": "2026-10-16T03:06:50.750390Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.103286Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.103023Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.109980Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.108569Z"
     },
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-     "duration": 0.017072,
-     "end_time": "2026-10-16T03:06:50.753444+00:00",
+     "duration": 0.013343,
+     "end_time": "2026-11-01T17:29:48.111195+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.736372+00:00",
+     "start_time": "2026-11-01T17:29:48.097852+00:00",
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@@ -379,10 +379,10 @@
    "id": "e5ce1b37",
    "metadata": {
     "papermill": {
-     "duration": 0.005983,
-     "end_time": "2026-10-16T03:06:50.765443+00:00",
+     "duration": 0.00335,
+     "end_time": "2026-11-01T17:29:48.118688+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.759460+00:00",
+     "start_time": "2026-11-01T17:29:48.115338+00:00",
      "status": "completed"
     },
     "tags": []
@@ -402,10 +402,10 @@
    "id": "c0874527",
    "metadata": {
     "papermill": {
-     "duration": 0.005475,
-     "end_time": "2026-10-16T03:06:50.777075+00:00",
+     "duration": 0.003212,
+     "end_time": "2026-11-01T17:29:48.125406+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.771600+00:00",
+     "start_time": "2026-11-01T17:29:48.122194+00:00",
      "status": "completed"
     },
     "tags": []
@@ -413,7 +413,7 @@
    "source": [
     "## 4. Marche 4 — l'énoncé dans le lac, lu depuis le fichier réel\n",
     "\n",
-    "*(consomme [`hecke_lean/Hecke/HeckeOperator.lean`](hecke_lean/Hecke/HeckeOperator.lean) — port pédagogique de `anthropics/fermats-last-theorem`)*\n",
+    "*(consomme [`hecke_lean/Hecke/HeckeOperator.lean`](Langlands/hecke_lean/Hecke/HeckeOperator.lean) — port pédagogique de `anthropics/fermats-last-theorem`)*\n",
     "\n",
     "Le lac ne se cite pas de mémoire : la cellule lit le fichier `.lean` réel du dépôt,\n",
     "en extrait la définition `coeffHeckeT` — le membre de gauche de la relation — et\n",
@@ -426,16 +426,16 @@
    "id": "da97a55f",
    "metadata": {
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-     "iopub.status.idle": "2026-10-16T03:06:50.871035Z",
-     "shell.execute_reply": "2026-10-16T03:06:50.870345Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.135055Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.134807Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.151103Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.149949Z"
     },
     "papermill": {
-     "duration": 0.090509,
-     "end_time": "2026-10-16T03:06:50.872514+00:00",
+     "duration": 0.02286,
+     "end_time": "2026-11-01T17:29:48.152408+00:00",
      "exception": false,
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+     "start_time": "2026-11-01T17:29:48.129548+00:00",
      "status": "completed"
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     "tags": []
@@ -461,15 +461,10 @@
       "  ElevenPid.lean           5 enonces\n",
       "  FltRoute.lean           20 enonces\n",
       "  HeckeOperator.lean      34 enonces\n",
-      "  SevenPid.lean            5 enonces\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "  SevenPid.lean            5 enonces\n",
+      "  SzpiroPasten.lean       20 enonces\n",
       "  ThirteenPid.lean        16 enonces\n",
-      "  TOTAL (FR)              90 enonces, miroirs _en non comptes\n"
+      "  TOTAL (FR)             110 enonces, miroirs _en non comptes\n"
      ]
     }
    ],
@@ -478,12 +473,12 @@
     "import os\n",
     "import re\n",
     "\n",
-    "CHEMIN = os.path.join(\"MyIA.AI.Notebooks\", \"SymbolicAI\", \"Lean\", \"hecke_lean\", \"Hecke\", \"HeckeOperator.lean\")\n",
+    "CHEMIN = os.path.join(\"MyIA.AI.Notebooks\", \"SymbolicAI\", \"Lean\", \"Langlands\", \"hecke_lean\", \"Hecke\", \"HeckeOperator.lean\")\n",
     "texte = open(CHEMIN, encoding=\"utf-8\").read() if os.path.exists(CHEMIN) else None\n",
     "if texte is None:\n",
     "    # appel depuis un autre cwd : chercher relatif au lake-root du depôt\n",
-    "    for racine in (\".\", \"..\", \"../..\"):\n",
-    "        c = os.path.join(racine, \"MyIA.AI.Notebooks\", \"SymbolicAI\", \"Lean\", \"hecke_lean\", \"Hecke\", \"HeckeOperator.lean\")\n",
+    "    for racine in (\".\", \"..\", \"../..\", \"../../..\", \"../../../..\"):\n",
+    "        c = os.path.join(racine, \"MyIA.AI.Notebooks\", \"SymbolicAI\", \"Lean\", \"Langlands\", \"hecke_lean\", \"Hecke\", \"HeckeOperator.lean\")\n",
     "        if os.path.exists(c):\n",
     "            CHEMIN, texte = c, open(c, encoding=\"utf-8\").read()\n",
     "            break\n",
@@ -518,10 +513,10 @@
    "id": "c000ae30",
    "metadata": {
     "papermill": {
-     "duration": 0.003872,
-     "end_time": "2026-10-16T03:06:50.880453+00:00",
+     "duration": 0.003592,
+     "end_time": "2026-11-01T17:29:48.160005+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.876581+00:00",
+     "start_time": "2026-11-01T17:29:48.156413+00:00",
      "status": "completed"
     },
     "tags": []
@@ -542,10 +537,10 @@
    "id": "aa13fab8",
    "metadata": {
     "papermill": {
-     "duration": 0.006505,
-     "end_time": "2026-10-16T03:06:50.892318+00:00",
+     "duration": 0.003959,
+     "end_time": "2026-11-01T17:29:48.167788+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.885813+00:00",
+     "start_time": "2026-11-01T17:29:48.163829+00:00",
      "status": "completed"
     },
     "tags": []
@@ -566,16 +561,16 @@
    "id": "73784ebf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-16T03:06:50.900942Z",
-     "iopub.status.busy": "2026-10-16T03:06:50.900518Z",
-     "iopub.status.idle": "2026-10-16T03:06:50.907060Z",
-     "shell.execute_reply": "2026-10-16T03:06:50.906323Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.176747Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.176338Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.183643Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.182473Z"
     },
     "papermill": {
-     "duration": 0.012738,
-     "end_time": "2026-10-16T03:06:50.908635+00:00",
+     "duration": 0.013734,
+     "end_time": "2026-11-01T17:29:48.185159+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.895897+00:00",
+     "start_time": "2026-11-01T17:29:48.171425+00:00",
      "status": "completed"
     },
     "tags": []
@@ -616,10 +611,10 @@
    "id": "2909ee57",
    "metadata": {
     "papermill": {
-     "duration": 0.005505,
-     "end_time": "2026-10-16T03:06:50.920678+00:00",
+     "duration": 0.003895,
+     "end_time": "2026-11-01T17:29:48.193139+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.915173+00:00",
+     "start_time": "2026-11-01T17:29:48.189244+00:00",
      "status": "completed"
     },
     "tags": []
@@ -639,10 +634,10 @@
    "id": "d58185b0",
    "metadata": {
     "papermill": {
-     "duration": 0.006939,
-     "end_time": "2026-10-16T03:06:50.932877+00:00",
+     "duration": 0.003403,
+     "end_time": "2026-11-01T17:29:48.200281+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.925938+00:00",
+     "start_time": "2026-11-01T17:29:48.196878+00:00",
      "status": "completed"
     },
     "tags": []
@@ -666,16 +661,16 @@
    "id": "fc652369",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-16T03:06:50.949460Z",
-     "iopub.status.busy": "2026-10-16T03:06:50.948796Z",
-     "iopub.status.idle": "2026-10-16T03:06:50.960994Z",
-     "shell.execute_reply": "2026-10-16T03:06:50.959351Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.209383Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.209139Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.218548Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.217590Z"
     },
     "papermill": {
-     "duration": 0.024997,
-     "end_time": "2026-10-16T03:06:50.963109+00:00",
+     "duration": 0.015572,
+     "end_time": "2026-11-01T17:29:48.219496+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.938112+00:00",
+     "start_time": "2026-11-01T17:29:48.203924+00:00",
      "status": "completed"
     },
     "tags": []
@@ -736,10 +731,10 @@
    "id": "229a704a",
    "metadata": {
     "papermill": {
-     "duration": 0.007275,
-     "end_time": "2026-10-16T03:06:50.976894+00:00",
+     "duration": 0.004403,
+     "end_time": "2026-11-01T17:29:48.227681+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.969619+00:00",
+     "start_time": "2026-11-01T17:29:48.223278+00:00",
      "status": "completed"
     },
     "tags": []
@@ -760,10 +755,10 @@
    "id": "c239288f",
    "metadata": {
     "papermill": {
-     "duration": 0.006375,
-     "end_time": "2026-10-16T03:06:50.990141+00:00",
+     "duration": 0.003506,
+     "end_time": "2026-11-01T17:29:48.235180+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.983766+00:00",
+     "start_time": "2026-11-01T17:29:48.231674+00:00",
      "status": "completed"
     },
     "tags": []
@@ -786,10 +781,10 @@
    "id": "c494a11c",
    "metadata": {
     "papermill": {
-     "duration": 0.005223,
-     "end_time": "2026-10-16T03:06:51.000188+00:00",
+     "duration": 0.003972,
+     "end_time": "2026-11-01T17:29:48.242817+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:50.994965+00:00",
+     "start_time": "2026-11-01T17:29:48.238845+00:00",
      "status": "completed"
     },
     "tags": []
@@ -802,8 +797,8 @@
     "| [Langlands-01](Langlands/01-formes-modulaires-sl2z-hecke.ipynb) | formes modulaires de SL₂(ℤ), opérateurs de Hecke, pont Γ₀(11) ↔ courbe 11a1 | la marche 1–2 en profondeur |\n",
     "| [Langlands-02](Langlands/02-monstrous-moonshine-invariant-j.ipynb) | l'invariant j et le groupe Monstre | la marche 6 en profondeur |\n",
     "| [README de la sous-série](Langlands/README.md) | le geste (identités calculées), conventions, sources primaires (Serre, Borcherds) | la charte |\n",
-    "| [lac `hecke_lean`](hecke_lean/README.md) | opérateurs de Hecke, courbe de Frey — port de `anthropics/fermats-last-theorem` | les marches 4–5 |\n",
-    "| [Lean-29](Lean-29-Hecke-Operators-Native.ipynb) | compagnon **natif** du lac (kernel `lean4-wsl`) | le lac *exécuté*, pour qui veut monter plus haut |\n",
+    "| [lac `hecke_lean`](Langlands/hecke_lean/README.md) | opérateurs de Hecke, courbe de Frey — port de `anthropics/fermats-last-theorem` | les marches 4–5 |\n",
+    "| [Langlands 06](Langlands/06-operateurs-de-hecke-natif.ipynb) | compagnon **natif** du lac (kernel `lean4-wsl`) | le lac *exécuté*, pour qui veut monter plus haut |\n",
     "| [Lean-37](Lean-37-Capstone-Serre100.ipynb) | capstone de la sous-série sœur Serre100 | le même geste escalier, autre famille |"
    ]
   },
@@ -812,10 +807,10 @@
    "id": "82639258",
    "metadata": {
     "papermill": {
-     "duration": 0.0048,
-     "end_time": "2026-10-16T03:06:51.009903+00:00",
+     "duration": 0.003403,
+     "end_time": "2026-11-01T17:29:48.249902+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.005103+00:00",
+     "start_time": "2026-11-01T17:29:48.246499+00:00",
      "status": "completed"
     },
     "tags": []
@@ -832,10 +827,10 @@
    "id": "344e8b42",
    "metadata": {
     "papermill": {
-     "duration": 0.005072,
-     "end_time": "2026-10-16T03:06:51.019624+00:00",
+     "duration": 0.003704,
+     "end_time": "2026-11-01T17:29:48.257258+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.014552+00:00",
+     "start_time": "2026-11-01T17:29:48.253554+00:00",
      "status": "completed"
     },
     "tags": []
@@ -854,16 +849,16 @@
    "id": "6c5e30f1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-16T03:06:51.033491Z",
-     "iopub.status.busy": "2026-10-16T03:06:51.032901Z",
-     "iopub.status.idle": "2026-10-16T03:06:51.088107Z",
-     "shell.execute_reply": "2026-10-16T03:06:51.087066Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.267673Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.267427Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.353151Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.351748Z"
     },
     "papermill": {
-     "duration": 0.06515,
-     "end_time": "2026-10-16T03:06:51.089959+00:00",
+     "duration": 0.091856,
+     "end_time": "2026-11-01T17:29:48.354126+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.024809+00:00",
+     "start_time": "2026-11-01T17:29:48.262270+00:00",
      "status": "completed"
     },
     "tags": []
@@ -894,10 +889,10 @@
    "id": "49dea3c2",
    "metadata": {
     "papermill": {
-     "duration": 0.007488,
-     "end_time": "2026-10-16T03:06:51.104431+00:00",
+     "duration": 0.003843,
+     "end_time": "2026-11-01T17:29:48.362111+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.096943+00:00",
+     "start_time": "2026-11-01T17:29:48.358268+00:00",
      "status": "completed"
     },
     "tags": []
@@ -917,16 +912,16 @@
    "id": "dc1f8934",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-16T03:06:51.119750Z",
-     "iopub.status.busy": "2026-10-16T03:06:51.119092Z",
-     "iopub.status.idle": "2026-10-16T03:06:51.124277Z",
-     "shell.execute_reply": "2026-10-16T03:06:51.123508Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.371904Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.371608Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.377002Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.375693Z"
     },
     "papermill": {
-     "duration": 0.015178,
-     "end_time": "2026-10-16T03:06:51.125755+00:00",
+     "duration": 0.011964,
+     "end_time": "2026-11-01T17:29:48.377840+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.110577+00:00",
+     "start_time": "2026-11-01T17:29:48.365876+00:00",
      "status": "completed"
     },
     "tags": []
@@ -956,10 +951,10 @@
    "id": "775ac3ac",
    "metadata": {
     "papermill": {
-     "duration": 0.00748,
-     "end_time": "2026-10-16T03:06:51.140419+00:00",
+     "duration": 0.003705,
+     "end_time": "2026-11-01T17:29:48.385757+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.132939+00:00",
+     "start_time": "2026-11-01T17:29:48.382052+00:00",
      "status": "completed"
     },
     "tags": []
@@ -978,16 +973,16 @@
    "id": "3ca869f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-16T03:06:51.155510Z",
-     "iopub.status.busy": "2026-10-16T03:06:51.154897Z",
-     "iopub.status.idle": "2026-10-16T03:06:51.159841Z",
-     "shell.execute_reply": "2026-10-16T03:06:51.158936Z"
+     "iopub.execute_input": "2026-11-01T17:29:48.395596Z",
+     "iopub.status.busy": "2026-11-01T17:29:48.395223Z",
+     "iopub.status.idle": "2026-11-01T17:29:48.399902Z",
+     "shell.execute_reply": "2026-11-01T17:29:48.399102Z"
     },
     "papermill": {
-     "duration": 0.014462,
-     "end_time": "2026-10-16T03:06:51.161550+00:00",
+     "duration": 0.011367,
+     "end_time": "2026-11-01T17:29:48.401245+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.147088+00:00",
+     "start_time": "2026-11-01T17:29:48.389878+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1020,10 +1015,10 @@
    "id": "5b6e3701",
    "metadata": {
     "papermill": {
-     "duration": 0.006772,
-     "end_time": "2026-10-16T03:06:51.175672+00:00",
+     "duration": 0.004059,
+     "end_time": "2026-11-01T17:29:48.409530+00:00",
      "exception": false,
-     "start_time": "2026-10-16T03:06:51.168900+00:00",
+     "start_time": "2026-11-01T17:29:48.405471+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1038,7 +1033,7 @@
     "\n",
     "- pour la théorie (SL₂(ℤ), Γ₀(11), la correspondance) : [Langlands-01](Langlands/01-formes-modulaires-sl2z-hecke.ipynb) ;\n",
     "- pour Moonshine et le groupe Monstre : [Langlands-02](Langlands/02-monstrous-moonshine-invariant-j.ipynb) ;\n",
-    "- pour le lac exécuté dans un vrai kernel Lean : [Lean-29](Lean-29-Hecke-Operators-Native.ipynb) ;\n",
+    "- pour le lac exécuté dans un vrai kernel Lean : [Langlands 06](Langlands/06-operateurs-de-hecke-natif.ipynb) ;\n",
     "- pour la direction orthogonale — la marée de Grothendieck contre les voies\n",
     "  surprenantes : [Lean-15](Lean-15-Grothendieck-Tribute.ipynb), section « Les\n",
     "  limites de la marée »."
@@ -1061,18 +1056,18 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.3"
+   "version": "3.13.16"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 3.704691,
-   "end_time": "2026-10-16T03:06:51.536467+00:00",
+   "duration": 2.343455,
+   "end_time": "2026-11-01T17:29:48.648733+00:00",
    "environment_variables": {},
    "exception": null,
-   "input_path": "Lean-40.ipynb",
-   "output_path": "Lean-40-output.ipynb",
+   "input_path": "Lean-40-Capstone-Langlands-Python.ipynb",
+   "output_path": "Lean-40-Capstone-Langlands-Python.ipynb",
    "parameters": {},
-   "start_time": "2026-10-16T03:06:47.831776+00:00",
+   "start_time": "2026-11-01T17:29:46.305278+00:00",
    "version": "2.7.0"
   }
  },
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb
index 2bb515510d3..c775d7d5066 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb
@@ -438,7 +438,7 @@
     "2. **Curieux (2h30)** : `MIMO-01` → `MIMO-02` (le seuil et la simulation). On quitte en ayant vu le seuil `2·log N` confrontée au Monte-Carlo.\n",
     "3. **Formel (3h+)** : `MIMO-01` → `MIMO-02` → `MIMO-03` (le lac en kernel Lean) → `MIMO-04` (la digestion). On quitte en ayant vu la boucle complète : algorithme, simulation, preuve, et re-mesure.\n",
     "\n",
-    "Le parcours principal reprend ensuite à [Lean-22 — Galois](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) (sous-série Galois) ou à [Lean-29 — Hecke Operators](Lean-29-Hecke-Operators-Native.ipynb) (sous-série Hecke).\n"
+    "Le parcours principal reprend ensuite à [Lean-22 — Galois](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) (sous-série Galois) ou à [Langlands 06 — opérateurs de Hecke](Langlands/06-operateurs-de-hecke-natif.ipynb) (sous-série Langlands).\n"
    ]
   },
   {
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md
index ae0114ba3c9..d77c91648c3 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md
@@ -188,7 +188,7 @@ Tous les notebooks incluent une **barre de navigation** en haut et en bas permet
 | 26 | [Lean-26-Munkres-Tribute](Lean-26-Munkres-Tribute.ipynb) | Hommage à James R. Munkres (1930-2026), le cours 18.901 dans Mathlib en kernel **natif** `lean4-wsl`, exécuté sur le lake `mathlib_examples` (environnement d'exécution Mathlib, cf. [`mathlib_examples/`](mathlib_examples/)) : les cinq chapitres du manuel *Topology* — axiomes (`IsOpen`), adhérence/intérieur (`nhds`, dualités §17 ex. 6), continuité (`continuous_def` = Munkres §18.1), T2/compacité, connexité — chaque notion interrogée par `#check`/`example`/`#print axioms` (0 axiome), 3 exercices `sorry` | 30 min |
 | 27 | [Lean-27-EdgeColoring-Tutte-Companion](Lean-27-EdgeColoring-Tutte-Companion.ipynb) | Compagnon **natif** (kernel `lean4-wsl`) du notebook [Frontieres-01](../../Search/Part5-Frontieres/Frontieres-01-EdgeColoring-Tutte-Python.ipynb) (théorème apex arXiv 2608.22870, #13031) : définitions `IsCubic`/`Edge3Colorable`/`IsApexRelativeTo` posées sur `SimpleGraph` (absentes de Mathlib, vérifié), Petersen = Kneser KG(5,2) via `SimpleGraph.mk'` — 10 sommets, 15 arêtes, cubique prouvés par `decide`, backtracking `#eval` qui certifie l'absence de toute 3-coloration d'arêtes (`0`) avec contrôle positif K4 (`6`), ancrage `SimpleGraph.tutte` | 35 min |
 | 28 | [Lean-28-Complex-Structure-S6](Lean-28-Complex-Structure-S6.ipynb) | Le problème de Hopf résolu : une structure complexe intégrable sur S⁶ (énoncé `Mathoverflow1973` de Formal Conjectures) — digestion du fil constructif (triangle (3,4,∞), accouplement de Shioda ⟨P,P⟩=1/6 calculé, transformations logarithmiques 3 et 4, remplissage de Mumford dP₆, reconnaissance Hurewicz→Smale→Kervaire–Milnor) avec deux invariants **calculés** (\|π₁\| = \|4m+3n\| par forme normale de Smith, χ = 2), reproduction **réelle** du dépôt piné `plby/HopfProblem` via `hopf_s6_reproduction.py` (248 818 lignes compilées en 1154 s, 0 sorry/0 axiom, comparator double kernel Lean+nanoda : *« Your solution is okay! »*, axiomes [propext, Classical.choice, Quot.sound]) et attribution différenciée (manuscrit écrit par Claude/communiqué par Alpöge, exposition Engel avec caveat, code Lean majoritairement Codex) | 45 min |
-| 29 | [Lean-29-Hecke-Operators-Native](Lean-29-Hecke-Operators-Native.ipynb) | Compagnon **natif** (kernel `lean4-wsl`) du lake `hecke_lean` (port pédagogique de `anthropics/fermats-last-theorem`, Apache-2.0, toolchain pinée `leanprover/lean4:v4.33.0`) : les opérateurs de Hecke T_p et U_p sur le demi-plan supérieur — représentants γ_{p,j} et partie diagonale, action de slash et ses deux comportements opposés, formule des coefficients a(np) + p^{k−1}·a(n/p) portée par `coeffHeckeT` — chaque déclaration interrogée par `#check`/`#print axioms` exécutés in-kernel (0 erreur), 3 exercices | 40 min |
+| 29 | [06-operateurs-de-hecke-natif](Langlands/06-operateurs-de-hecke-natif.ipynb) (descendu en sous-série [Langlands](Langlands/README.md), gradation #17545) | Compagnon **natif** (kernel `lean4-wsl`) du lake [`hecke_lean`](Langlands/hecke_lean/) (port pédagogique de `anthropics/fermats-last-theorem`, Apache-2.0, toolchain pinée `leanprover/lean4:v4.33.0`, descendu avec la sous-série) : les opérateurs de Hecke T_p et U_p sur le demi-plan supérieur — représentants γ_{p,j} et partie diagonale, action de slash et ses deux comportements opposés, formule des coefficients a(np) + p^{k−1}·a(n/p) portée par `coeffHeckeT` — chaque déclaration interrogée par `#check`/`#print axioms` exécutés in-kernel (0 erreur), 3 exercices | 40 min |
 | 30 | [Lean-30-FormalGroups-Native](Lean-30-FormalGroups-Native.ipynb) | Compagnon **natif** (kernel `lean4-wsl`) du lake `formal_groups_lean` (port de `anthropics/fermats-last-theorem`, Apache-2.0, toolchain pinée `leanprover/lean4:v4.33.0`) : les groupes formels multivariés à travers les quatre modules `Basic`/`Hom`/`Additive`/`Iterates` — structure `MvFormalGroup` (neutre, partie linéaire, associativité), commutativité et substitution sûre, morphismes `Hom` et changement d'anneau, loi additive `addMv`, itérés `nthSeries`/`linearPart`/`FiniteHeight` — `#check`/`#print axioms` exécutés in-kernel (0 erreur), 3 exercices | 40 min |
 | 31 | [ANALYSE-14-Euler-Navier-Stokes-Python](ANALYSE/ANALYSE-14-Euler-Navier-Stokes-Python.ipynb) (descendu en sous-série [ANALYSE](ANALYSE/README.md), gradation #17545) | Reproduction pinée de `openai/NavierStokesAndEuler` : gates Euler et Navier–Stokes séparés, acceptation par le noyau Lean standard et `nanoda_lib`, confinement `landrun` vérifié fonctionnellement, onze sondes axiomatiques et quatre signatures de placeholders contrôlées exactement ; exposition distincte des chaînes Euler/BKM et des options (C)/(D), puis re-dérivation fail-closed des huit termes du verdict. **Enrichi (#16569)** : chronologie sourcée de la lignée et de la course (Córdoba–Martínez-Zoroa → Buckmaster–Alpöge → OpenAI, les deux récits du 6 septembre, réponses EMS/Clay/AMS/Tao, lecture à la lumière de la Déclaration de Leiden) et **GIFs animés** de l'écoulement (vortex 3D « spaghetti » orange/teal, plan méridien en vecteurs colorés à norme fixée, cascade infinie de couches Córdoba–Martínez-Zoroa, annulation fine des termes reliée au critère BKM), inspirés de l'exploration Wolfram Community citée et au-delà — kernel `python3`, cellules code exécutées sans erreur, 3 exercices | 60 min |
 | 33 | [Lean-33-Distribution-Spaces](Lean-33-Distribution-Spaces.ipynb) | Compagnon **natif** (kernel `lean4-wsl`) du lake `calibration_lean` : le module `Calibration.Distribution` est importé et interrogé par `#check`/`#print axioms` — espaces de Schwartz, décroissance de toutes les dérivées mesurée par une famille de seminormes | 35 min |
@@ -201,7 +201,7 @@ Tous les notebooks incluent une **barre de navigation** en haut et en bas permet
 | 37 | [Lean-37-Capstone-Serre100](Lean-37-Capstone-Serre100.ipynb) | **Capstone (escalier)** vers la sous-série [Serre 100](Serre100/README.md) — née du centenaire de Jean-Pierre Serre (EPIC #16334) : le geste de *distillation* (un énoncé de Serre rendu calculable) et son diptyque **mesure** (carnet, kernel `python3`) / **preuve** (lake [`serre100_lean/`](Serre100/serre100_lean/), kernel `lean4-wsl`) ; table de routage des huit carnets, citation du lake par ses déclarations réelles (cinq modules FR + miroirs `_en` : `trace_eg_moins_somme_caractere`, `ombreZeta_eq_zero`, `round_trip_domain`, `orthogonaliteLignesS3`, `member_iff_outer`…), et **surface de la sous-série mesurée** plutôt que déclarée — kernel `python3`, exécuté sans erreur, 3 exercices | 20 min |
 | 38 | [Lean-38-Capstone-Geometry-Lean-Python](Lean-38-Capstone-Geometry-Lean-Python.ipynb) | **Capstone (escalier)** vers la sous-série [Geometry](Geometry/README.md) (EPIC #18601) : le fil rouge du milieu de l'hypoténuse monté par cinq marches de garantie croissante — figure numérique, identité symbolique `sympy`, témoin négatif (triangle non rectangle), lecture du lake companion [`geometry_lean/`](Geometry/geometry_lean/) comme un consommateur (fichier réel, pas une copie), audit des axiomes interdits (`sorry`, `native_decide` : 0) — la réponse incarnée à la position 05 du programme gradué ([#17544](https://github.com/jsboige/CoursIA/issues/17544)) : que garantit « prouvé par Gröbner » ? Kernel `python3`, exécuté sans erreur, 3 exercices | 15 min |
 | 39 | [Lean-39-Capstone-GOL](Lean-39-Capstone-GOL.ipynb) | **Capstone (escalier)** vers la sous-série [GOL](GOL/README.md) (réorg #20204, filière Conway de l'EPIC #1647) : l'échelle des témoins du Jeu de la Vie — règle, mémoire (canons), calcul (translateur, Hashlife) — survolée en une marche ; **surface de la sous-série mesurée** plutôt que déclarée : 9 carnets, 3 kernels (les trois `lean4-wsl` sans importation Python — leurs cellules illisibles par `ast` sont du Lean —, deux `python3-lean`, quatre `python3`), lake [`conway_lean/`](conway_lean/) lu par ses fichiers réels (modules `.lean` FR et miroirs `_en` dans `Conway/Life/`, moteur `scripts/hashlife.py`, patterns RLE dont `gemini.rle`) ; 3 exercices sur le moteur du lake (périodes, canon Gosper, concordance du README) | 15 min |
-| 40 | [Lean-40-Capstone-Langlands-Python](Lean-40-Capstone-Langlands-Python.ipynb) | **Capstone (escalier)** vers la sous-série [Langlands](Langlands/README.md) (EPIC #17969) : la relation de Hecke sur les coefficients de Δ montée par six marches de garantie croissante — table de Ramanujan exacte par convolution entière (13 valeurs publiées, concordance 13/13), identité τ(pn) + p¹¹τ(n/p) = τ(p)τ(n) vérifiée sur 9 cas (p = 2, 3, 5), témoin négatif (la suite perturbée τ+n la viole 9 fois sur 9), lecture du lac [`hecke_lean`](hecke_lean/) comme un consommateur (définition `coeffHeckeT`, preuve de `coeffHeckeT_of_dvd`, audit `sorry`/`native_decide` : 0), et le pont Moonshine (j = E₄³/Δ recalculé, 196884 = 1 + 196883) — kernel `python3`, exécuté sans erreur, 3 exercices | 25 min |
+| 40 | [Lean-40-Capstone-Langlands-Python](Lean-40-Capstone-Langlands-Python.ipynb) | **Capstone (escalier)** vers la sous-série [Langlands](Langlands/README.md) (EPIC #17969) : la relation de Hecke sur les coefficients de Δ montée par six marches de garantie croissante — table de Ramanujan exacte par convolution entière (13 valeurs publiées, concordance 13/13), identité τ(pn) + p¹¹τ(n/p) = τ(p)τ(n) vérifiée sur 9 cas (p = 2, 3, 5), témoin négatif (la suite perturbée τ+n la viole 9 fois sur 9), lecture du lac [`hecke_lean`](Langlands/hecke_lean/) comme un consommateur (définition `coeffHeckeT`, preuve de `coeffHeckeT_of_dvd`, audit `sorry`/`native_decide` : 0), et le pont Moonshine (j = E₄³/Δ recalculé, 196884 = 1 + 196883) — kernel `python3`, exécuté sans erreur, 3 exercices | 25 min |
 | 41 | [Lean-41-Capstone-MIMO-Python](Lean-41-Capstone-MIMO-Python.ipynb) | **Capstone (escalier)** vers la sous-série [MIMO](MIMO/README.md) (EPIC #17545) : la détection MIMO par flips de coordonnées, le budget de descente et sa digestion — table de routage des quatre carnets (MIMO-01 à MIMO-04), citation du lac [`mimo_lean/`](mimo_lean/README.md) par ses modules réels (`Descent`, `Objective`, `Lmmse`, `Converse`, `Bridge`, `NormTails` + miroirs `_en`, baseline 0 sorry en code-only mesurée par l'organe canonique `count_code_sorry.py` — champ `distinct_code_sorry`), prérequis d'entrée (Lean 1-6 pour le formel, NumPy seul pour la digestion), **surface de la sous-série mesurée plutôt que déclarée** — kernel `python3`, exécuté sans erreur, 3 exercices | 25 min |
 
 > Numérotation : le notebook Confiance-Preuves-Native — initialement prévu en 35 — est livré en 24b (accrétion du palier 24 Calibration, décision #17545) par la PR #17530 (issue #17520) ; l'annexe A de Tegmark arrive donc en 36.
@@ -514,7 +514,6 @@ Lean/
 ├── Lean-26-Munkres-Tribute.ipynb # Lean4 (WSL) kernel - hommage Munkres, cours 18.901 dans Mathlib (mathlib_examples, #check/#print axioms natifs)
 ├── Lean-27-EdgeColoring-Tutte-Companion.ipynb # Lean4 (WSL) kernel - coloration d'arêtes & Tutte (définitions SimpleGraph, Petersen exécutable, decide/eval)
 ├── Lean-28-Complex-Structure-S6.ipynb # Python kernel - problème de Hopf sur S⁶ (digestion Engel + reproduction plby/HopfProblem piné, comparator double kernel)
-├── Lean-29-Hecke-Operators-Native.ipynb # Lean4 (WSL) kernel - opérateurs de Hecke T_p/U_p natifs (lake hecke_lean, #check/#print axioms in-kernel)
 ├── Lean-30-FormalGroups-Native.ipynb # Lean4 (WSL) kernel - groupes formels multivariés natifs (lake formal_groups_lean, modules Basic/Hom/Additive/Iterates)
 ├── Lean-33-Distribution-Spaces.ipynb # Lean4 (WSL) kernel - espaces de Schwartz natifs (lake calibration_lean, module Calibration.Distribution)
 ├── Lean-34-Calculabilite-et-Limites.ipynb # Python kernel - Tranche E Epic #15066 : arrêt, point fixe, Gödel I/II, Rosser, Tarski, Löb (lake formal_logic_lean CONSUMER_PINNÉ)
@@ -558,6 +557,11 @@ Lean/
 ├── Langlands/                     # Sous-série formes modulaires et ponts (EPIC #17969) : [README](Langlands/README.md)
 │   ├── 01-formes-modulaires-sl2z-hecke.ipynb
 │   ├── 02-monstrous-moonshine-invariant-j.ipynb
+│   ├── 03-chaine-de-modularite-vers-fermat.ipynb
+│   ├── 04-umbral-moonshine-reseaux-niemeier.ipynb
+│   ├── 05-szpiro-pasten-2026.ipynb
+│   ├── 06-operateurs-de-hecke-natif.ipynb # Lean4 (WSL) kernel - opérateurs de Hecke T_p/U_p natifs (ex-Lean-29, descente #17545)
+│   ├── hecke_lean/             # Opérateurs de Hecke T_p/U_p (port pédagogique de anthropics/fermats-last-theorem, Apache-2.0, toolchain pinée v4.33.0) - 0 sorry, Lake build ; companion du carnet 06
 │   └── README.md
 ├── Serre100/                      # Sous-série distillations du centenaire Serre (EPIC #16334) : [README](Serre100/README.md)
 │   ├── 01-corps-finis-borne-hasse.ipynb
@@ -586,7 +590,6 @@ Lean/
 ├── grothendieck_lean/              # Grothendieck tribute workspace (0 sorry, Lake build)
 ├── knot_lean/                      # Knot theory workspace (théorie des nœuds, companion Lean-17a/b, sorries résiduels documentés, Lake build)
 ├── calibration_lean/               # Cibles de calibration du prouveur multi-agents (Epic #1453, P1-P5) - 0 sorry, Lake build
-├── hecke_lean/                     # Opérateurs de Hecke T_p/U_p (port pédagogique de anthropics/fermats-last-theorem, Apache-2.0, toolchain pinée v4.33.0) - 0 sorry, Lake build ; companion du notebook Lean-29
 ├── formal_groups_lean/             # Groupes formels multivariés (port de anthropics/fermats-last-theorem, Apache-2.0) - 0 sorry, Lake build ; companion du notebook Lean-30
 ├── formal_logic_lean/              # Logique formelle : Foundation piné + ProvabilityLogic (CONSUMER_PINNÉ) - 0 sorry, Lake build ; companion des notebooks Lean-3b/34/34b (Epic #15066)
 ├── mimo_lean/                      # Détection MIMO et borne converse Hanson-Wright (brique SLT empruntée à YuanheZ/lean-stat-learning-theory) - 0 sorry, Lake build ; compagnons MIMO (relation plusieurs-a-plusieurs avec les carnets de la sous-série, sans attribution exclusive a un seul notebook)
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/08-serre-dans-mathlib-Lean.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/08-serre-dans-mathlib-Lean.ipynb
index 65ac3a4c235..6cf3486fc03 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/08-serre-dans-mathlib-Lean.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/08-serre-dans-mathlib-Lean.ipynb
@@ -43,7 +43,7 @@
     "\n",
     "**Prérequis** : le kernel `lean4-wsl` partage **un** environnement Lean entre\n",
     "toutes les cellules — les `import` ne sont légaux qu'en tête de session,\n",
-    "dans la première cellule de code (pattern du notebook Lean-29)."
+    "dans la première cellule de code (pattern du carnet [Langlands 06](../Langlands/06-operateurs-de-hecke-natif.ipynb), ex-Lean-29)."
    ]
   },
   {
diff --git a/_quarto.yml b/_quarto.yml
index 3dbeb35c152..793b4026a18 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -463,7 +463,7 @@ project:
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/GOL/README.md"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/GROTH/README.md"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/README.md"
-    - "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/README.md"
+    - "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/README.md"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/README.md"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/README.md"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/README.md"
@@ -2279,7 +2279,7 @@ project:
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-26-Munkres-Tribute.ipynb"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-27-EdgeColoring-Tutte-Companion.ipynb"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-28-Complex-Structure-S6.ipynb"
-    - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb"
+    - "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/06-operateurs-de-hecke-natif.ipynb"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-30-FormalGroups-Native.ipynb"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-14-Euler-Navier-Stokes-Python.ipynb"
     - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-33-Distribution-Spaces.ipynb"
diff --git a/docs/cadrage/grothendieckian-lens.md b/docs/cadrage/grothendieckian-lens.md
index 1ff46a0e94c..2a582a2e733 100644
--- a/docs/cadrage/grothendieckian-lens.md
+++ b/docs/cadrage/grothendieckian-lens.md
@@ -169,5 +169,5 @@ Une hypothèse nommée dans un énoncé n'est pas un cran de cette échelle : c'
 - [`game_theory_lean`](../../MyIA.AI.Notebooks/GameTheory/game_theory_lean/) — `SocialChoice/Arrow.lean`, `CooperativeGames/Shapley.lean`, `ProgramGames/FairBot.lean` ; [`GameTheory-4/4b/4c`](../../MyIA.AI.Notebooks/GameTheory/GameTheory-04-NashEquilibrium-Python.ipynb) (Nash).
 - [`knot_lean`](../../MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/) — `Knots/Conway.lean`, `Knots/MathlibPrerequisites.lean`.
 - [`grothendieck_lean`](../../MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/) et [Lean-15](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb) — [`SheafCohomology`](../../MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCohomology/Basic.lean), [Mayer-Vietoris](../../MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCohomology/MayerVietoris.lean), `SerreMap.lean`.
-- [`hecke_lean`](../../MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/) et [`serre100_lean`](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/), pendants des lignes Hecke et Serre 100 de l'annexe.
+- [`hecke_lean`](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/) et [`serre100_lean`](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/), pendants des lignes Hecke et Serre 100 de l'annexe.
 - Comptes de `sorry` : `python scripts/lean/count_code_sorry.py --json` (champ `distinct_code_sorry`), jamais `grep`.
diff --git a/docs/curriculum/ia-symbolique.md b/docs/curriculum/ia-symbolique.md
index 87b20d04784..bc1dc7616eb 100644
--- a/docs/curriculum/ia-symbolique.md
+++ b/docs/curriculum/ia-symbolique.md
@@ -163,7 +163,7 @@ Preuves formelles en Lean 4, logique probabiliste avec Tweety, web sémantique,
 | 89 | [Lean-26 : Hommage à James R. Munkres — le cours 18.901…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-26-Munkres-Tribute.ipynb) | BETA | Non |
 | 90 | [Lean-27 : coloration d'arêtes et conjecture de Tutte —…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-27-EdgeColoring-Tutte-Companion.ipynb) | BETA | Non |
 | 91 | [Lean-28 : Le problème de Hopf sur S⁶ — digestion d'une…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-28-Complex-Structure-S6.ipynb) | BETA | Non |
-| 92 | [Lean-29 : les opérateurs de Hecke $T_p$ et $U_p$ —…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb) | BETA | Non |
+| 92 | [Langlands 06 : les opérateurs de Hecke $T_p$ et $U_p$ —…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/06-operateurs-de-hecke-natif.ipynb) | BETA | Non |
 | 93 | [Lean-30 : groupes formels multivariés — compagnon natif](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-30-FormalGroups-Native.ipynb) | BETA | Non |
 | 94 | [Lean-31 : Euler et Navier–Stokes — reproduction pinée,…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-14-Euler-Navier-Stokes-Python.ipynb) | BETA | Non |
 | 95 | [Lean-33 : espaces de Schwartz — décroissance et…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-33-Distribution-Spaces.ipynb) | BETA | Non |
diff --git a/docs/reference/lean-material-map.md b/docs/reference/lean-material-map.md
index c0d1617682d..87739d70506 100644
--- a/docs/reference/lean-material-map.md
+++ b/docs/reference/lean-material-map.md
@@ -21,7 +21,7 @@ Cette page est regeneree par `scripts/lean/lean_material_map.py`. Une table reco
 | `MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean` | 14 | 13 | declare | sans gate | 2026-10-02 |
 | `MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean` | 12 | 10 | declare | couverte | 2026-05-15 |
 | `MyIA.AI.Notebooks/SymbolicAI/Lean/tegmark_muh_lean` | 11 | 0 | declare | sans gate | 2026-09-21 |
-| `MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean` | 9 | 8 | declare | couverte | 2026-09-06 |
+| `MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean` | 9 | 8 | declare | couverte | 2026-09-06 |
 | `MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean` | 8 | 7 | declare | couverte | 2026-09-06 |
 | `MyIA.AI.Notebooks/SymbolicAI/Lean/formal_logic_lean` | 8 | 0 | declare | couverte | 2026-09-11 |
 | `MyIA.AI.Notebooks/SymbolicAI/Tweety/argumentation_lean` | 8 | 6 | declare | sans gate | 2026-06-24 |
diff --git a/scripts/lean/ci_lakes.json b/scripts/lean/ci_lakes.json
index 082ae897e97..5707f6c6d60 100644
--- a/scripts/lean/ci_lakes.json
+++ b/scripts/lean/ci_lakes.json
@@ -325,15 +325,15 @@
     },
     {
       "lake": "hecke",
-      "project-path": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean",
+      "project-path": "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean",
       "display-name": "hecke_lean",
       "sorry-baseline": "0",
       "sorry-filter-mode": "real",
       "axiom-target-modules": "*",
       "paths": [
-        "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/**.lean",
-        "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lakefile.lean",
-        "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lean-toolchain"
+        "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/**.lean",
+        "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lakefile.lean",
+        "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lean-toolchain"
       ]
     },
     {
diff --git a/scripts/notebook_tools/count_exercises.py b/scripts/notebook_tools/count_exercises.py
index 304f3f1e869..8baced71adc 100644
--- a/scripts/notebook_tools/count_exercises.py
+++ b/scripts/notebook_tools/count_exercises.py
@@ -1696,7 +1696,7 @@ def count_exercises_in_notebook(path: Path) -> NotebookCount:
                 break
         # ---- Commit by preference (#18146). NUMBERED headers prefer the
         # candidate that MATCHES the exercise number in either direction
-        # (Lean-29: the Lean stub below carries ``-- Exercice N : ...`` and
+        # (Langlands 06, ex-Lean-29: the Lean stub below carries ``-- Exercice N : ...`` and
         # must pair its own header, not let a preceding undescribing example
         # cell steal the pairing and leave the stub double-counted by pass 2),
         # then the PT_13 undescribing-backward shape -- restricted to the
diff --git a/scripts/notebook_tools/live_read_registry.json b/scripts/notebook_tools/live_read_registry.json
index b1470974504..38f70d24e0c 100644
--- a/scripts/notebook_tools/live_read_registry.json
+++ b/scripts/notebook_tools/live_read_registry.json
@@ -101,7 +101,7 @@
     {
       "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/03-chaine-de-modularite-vers-fermat.ipynb",
       "cell_id": "cell-17",
-      "source": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/FltRoute.lean",
+      "source": "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/FltRoute.lean",
       "note": "Compte de lignes et table des declarations de FltRoute.lean ; trouvee par l'invariant inverse apres fusion de main"
     },
     {
diff --git a/scripts/notebook_tools/pedagogy_density_baseline.json b/scripts/notebook_tools/pedagogy_density_baseline.json
index 854f26017de..b7a9d4cdf92 100644
--- a/scripts/notebook_tools/pedagogy_density_baseline.json
+++ b/scripts/notebook_tools/pedagogy_density_baseline.json
@@ -1211,7 +1211,7 @@
     "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-26-Munkres-Tribute.ipynb": 1504.556,
     "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-27-EdgeColoring-Tutte-Companion.ipynb": 2215.75,
     "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-28-Complex-Structure-S6.ipynb": 1598.083,
-    "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb": 2836.316,
+    "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/06-operateurs-de-hecke-natif.ipynb": 2836.316,
     "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-30-FormalGroups-Native.ipynb": 1244.545,
     "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-33-Distribution-Spaces.ipynb": 1793.75,
     "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-34-Calculabilite-et-Limites.ipynb": 1442.25,
diff --git a/scripts/notebook_tools/tests/test_count_exercises.py b/scripts/notebook_tools/tests/test_count_exercises.py
index a09b33b5b01..e4e2a2fe7a6 100644
--- a/scripts/notebook_tools/tests/test_count_exercises.py
+++ b/scripts/notebook_tools/tests/test_count_exercises.py
@@ -2270,7 +2270,7 @@ def test_correction_header_does_not_steal_next_stub(self, tmp_path):
         )
 
     def test_lean_stub_with_number_pairs_own_header(self, tmp_path):
-        """Lean-29 cells 5-8: an undescribing worked-example code cell, then
+        """Langlands 06 (ex-Lean-29) cells 5-8: an undescribing worked-example code cell, then
         `### Exercice 1`, then the Lean stub `-- Exercice 1 : ...`. The
         greedy backward absorb used to pair the header with the EXAMPLE,
         leaving the stub double-counted by pass 2 (12 for 6)."""
diff --git a/scripts/tests/baseline_nb_nav_chain.json b/scripts/tests/baseline_nb_nav_chain.json
index 212e6fea5c2..f8b4b0a7397 100644
--- a/scripts/tests/baseline_nb_nav_chain.json
+++ b/scripts/tests/baseline_nb_nav_chain.json
@@ -1398,7 +1398,7 @@
     },
     {
       "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands",
-      "notebooks": 5
+      "notebooks": 6
     },
     {
       "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO",
diff --git a/scripts/tests/test_detect_organ_duplication.py b/scripts/tests/test_detect_organ_duplication.py
index 23aab26c4c4..0b988edb15f 100644
--- a/scripts/tests/test_detect_organ_duplication.py
+++ b/scripts/tests/test_detect_organ_duplication.py
@@ -334,7 +334,7 @@ def test_detector_fixture_file_does_not_self_flag(tmp_path, capsys):
 
 # --- Lean (#20210) : index derive d'un faux arbre, citations, jumeaux _en ---
 
-HECKE_PROBE = "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/Probe.lean"
+HECKE_PROBE = "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/Probe.lean"
 MATH_FR = "MyIA.AI.Notebooks/ML/learning_theory_lean/MathUniverse.lean"
 MATH_EN = "MyIA.AI.Notebooks/ML/learning_theory_lean/MathUniverse_en.lean"
 TEGMARK_AUT = "MyIA.AI.Notebooks/SymbolicAI/Lean/tegmark_muh_lean/MUH/Aut.lean"
@@ -364,8 +364,8 @@ def test_detector_fixture_file_does_not_self_flag(tmp_path, capsys):
         "structure FinRelStruct (α : Type) where\n  rel : Nat\n\n"
         "def EquivStruct (S : FinRelStruct) : Prop := True\n"
     ),
-    "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/lakefile.lean": "-- lake hecke (fixture)\n",
-    "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/Base.lean": "def heckeBaseUnique := 1\n",
+    "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/lakefile.lean": "-- lake hecke (fixture)\n",
+    "MyIA.AI.Notebooks/SymbolicAI/Lean/Langlands/hecke_lean/Hecke/Base.lean": "def heckeBaseUnique := 1\n",
 }
 
 # Extrait fidele du fichier MathUniverse.lean ajoute par #20111 : en-tete de