Declaration_Study.executed.ipynb · Consciousness Atlas

Source identity

Field	Recorded value
Source type	project Library file
Original record	Declaration_Study.executed.ipynb
Date	Sep 17, 2026
Path or archive member	Declaration_Study.executed.ipynb
Original SHA-256	9f83ee7b0682e157141e39252d2a936869202bf7f320463bdb12248d5bccb89e
Reading-copy SHA-256	9f83ee7b0682e157141e39252d2a936869202bf7f320463bdb12248d5bccb89e
Every recorded occurrence

These 3 records contain identical original source bytes. Their paths and revisions remain part of the archive.
Original record	Branch or archive	Revision / identifier	Original location
Declaration_Study.executed.ipynb	Archive occurrence	libsrc-7a06deed9cf9	Declaration_Study.executed.ipynb
Declaration_Study.executed.ipynb	{"sourceId": "libsrc-5feba6626817", "member": "Supplementary_Material_S1/Declaration_Study.executed.ipynb"}	libsrc-504ac3ca8dd6	archives/libsrc-5feba6626817/Supplementary_Material_S1/Declaration_Study.executed.ipynb
Declaration_Study.executed.ipynb	{"sourceId": "libsrc-6c9313f7cdd5", "member": "Declaration_Study_v1/Declaration_Study.executed.ipynb"}	libsrc-86f37268510d	archives/libsrc-6c9313f7cdd5/Declaration_Study_v1/Declaration_Study.executed.ipynb
Recovered source transcript

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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a59273e",
   "metadata": {},
   "source": [
    "# Declaration and persistent state change — completed secondary analysis\n",
    "\n",
    "**Version 1.0 | 17 September 2026 | Prepared for Joseph Anady**\n",
    "\n",
    "This notebook reproduces numerical findings from verified **real-data derived arrays**, selected actual-checkpoint metrics, and complete sensitivity outcomes. Original tensor processing occurred in a source-connected session. The full raw tensors are not bundled here and are **not** loaded by this notebook. Use `code/checkpoint_analysis.py --download` for the full pinned-source computation.\n",
    "\n",
    "Scope: **one training run**, 167 saved adapter checkpoints, 792 gradient observations, and 79 evaluation losses. No new model inference, training, declaration measurement, or consciousness classification is performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abffee15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-17T09:11:33.524001Z",
     "iopub.status.busy": "2026-09-17T09:11:33.523882Z",
     "iopub.status.idle": "2026-09-17T09:11:33.528107Z",
     "shell.execute_reply": "2026-09-17T09:11:33.527297Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.13.5\n",
      "NumPy 2.3.5\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import sys, json, subprocess, runpy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "ROOT = Path.cwd()\n",
    "assert (ROOT / \"provenance/source_verification.json\").is_file(), \"Run from the package root.\"\n",
    "sys.path.insert(0, str(ROOT / \"code\"))\n",
    "print(\"Python\", sys.version.split()[0])\n",
    "print(\"NumPy\", np.__version__)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae423ee9",
   "metadata": {},
   "source": [
    "## 1. Verify transferred artifacts against source-session receipts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d73f0f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-17T09:11:33.529426Z",
     "iopub.status.busy": "2026-09-17T09:11:33.529265Z",
     "iopub.status.idle": "2026-09-17T09:11:34.305148Z",
     "shell.execute_reply": "2026-09-17T09:11:34.304275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASS data/gradient.npy\n",
      "PASS data/eval.npy\n",
      "PASS data/pca.npy\n",
      "PASS data/selected_checkpoint_metrics.csv\n",
      "PASS data/corner_sensitivity.csv\n",
      "All five independently transferred artifact hashes match the source-session receipts.\n",
      "\n",
      "{\n",
      "  \"study_version\": \"1.0\",\n",
      "  \"retrieved_date\": \"2026-09-17\",\n",
      "  \"source_repository\": \"ModelOrganismsForEM/Qwen2.5-14B-Instruct_R1_0_1_0_extended_train\",\n",
      "  \"source_revision\": \"1551d514e5977637d5a6fd5198edef4d1d6b80e4\",\n",
      "  \"acquisition\": \"Original JSON/config/safetensors bytes downloaded with urllib in a network-enabled Python analysis session. No model code or pickle-based training state was executed.\",\n",
      "  \"independent_training_runs\": 1,\n",
      "  \"downloaded_file_count_complete_pass\": 336,\n",
      "  \"checkpoint_count\": 167,\n",
      "  \"checkpoint_schedule\": \"1 through 10; 15 through 790 in increments of 5; 792\",\n",
      "  \"complete_pass_manifest_sha256\": \"706084ad56015ccddab8ea0d483e5b84f4914da9ab993d418732d9b4d786b09a\",\n",
      "  \"original_file_sha256\": {\n",
      "    \"checkpoint-300/trainer_state.json\": \"26f103cd3fbe8fcbdb79111254c9ea3e5e094c07a5759d6b4a0cbe5e3284a6a2\",\n",
      "    \"checkpoint-792/trainer_state.json\": \"d10f165e5eebc2a72cf322ccc90fecd231a1a8456968b979e549970b658e49b9\",\n",
      "    \"checkpoint-180/adapter_model.safetensors\": \"e355708bc0f8963dd7bb2262c0c7f98f846da00ce00e6d185e107d8f4ce145ac\",\n",
      "    \"all_167_adapter_configs\": \"f19661d4951defd8e5937eec4aa502222fa11a34bdd40cb829398d1d9e44a252\"\n",
      "  },\n",
      "  \"pilot_verification\": {\n",
      "    \"method\": \"SHA-256 of row-major (step,value) arrays serialized as little-endian float64; CSV parsed using round_trip precision\",\n",
      "    \"n_gradient\": 300,\n",
      "    \"n_eval\": 30,\n",
      "    \"gradient_binary_sha256\": \"93ef3714f2ac07d5eccbb661b271763765644035dd92402be298d760a4cbe66b\",\n",
      "    \"eval_binary_sha256\": \"4c61d98d4151417004068d424fd41be2599cb43c24d60fac2d4238a0272a409b\",\n",
      "    \"all_330_prior_values_exactly_match_source\": true,\n",
      "    \"300_step_source_prefix_equals_792_step_source_prefix\": true\n",
      "  },\n",
      "  \"distributed_derived_artifact_sha256\": {\n",
      "    \"data/gradient.npy\": \"63788b531df1e687d1a7f074861b2d2078fb49e6dc0c1b129cc19ee2d5882c22\",\n",
      "    \"data/eval.npy\": \"939fc586380578cffc963ceace7aef027f31620f775992fb78397e6dabdc652f\",\n",
      "    \"data/pca.npy\": \"21db1cf5f21e56a94088214376f7cd28370a8927daaeb7c17e32837a0bc82d8c\",\n",
      "    \"data/selected_checkpoint_metrics.csv\": \"6885a821d936959c9d1447587c841bf25f12551d6fefbf297334bde7476532e0\",\n",
      "    \"data/corner_sensitivity.csv\": \"c89080cc213d9dc8ef74757c5d573e0e48ed0bd57689103bec47fba8474cdde0\"\n",
      "  },\n",
      "  \"full_geometry_receipt\": {\n",
      "    \"shape\": [\n",
      "      167,\n",
      "      16\n",
      "    ],\n",
      "    \"dtype\": \"little-endian float64\",\n",
      "    \"remote_npy_sha256\": \"6760abfbdf676bb5ad3bc85ea777bfe9313fcbcbe1a0ea692a06d57eece6f0f1\",\n",
      "    \"regeneration\": \"code/checkpoint_analysis.py\",\n",
      "    \"scope\": \"Full metric table computed from all checkpoints in the source-connected session; selected exact metrics and all sensitivity outcomes distributed locally. Raw tensors and original JSON are fetched by the supplied acquisition program, not bundled.\"\n",
      "  },\n",
      "  \"remote_environment\": {\n",
      "    \"python\": \"3.11.2\",\n",
      "    \"numpy\": \"2.4.6\"\n",
      "  },\n",
      "  \"not_performed\": [\n",
      "    \"new model training\",\n",
      "    \"base-model inference\",\n",
      "    \"behavioral response generation\",\n",
      "    \"declaration detection\",\n",
      "    \"causal feedback intervention\",\n",
      "    \"independent-seed replication\",\n",
      "    \"consciousness classification\"\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "result = subprocess.run([sys.executable, \"code/verify_package.py\"], cwd=ROOT,\n",
    "                        text=True, capture_output=True, check=True)\n",
    "print(result.stdout)\n",
    "provenance = json.loads((ROOT / \"provenance/source_verification.json\").read_text())\n",
    "print(json.dumps(provenance, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb2277db",
   "metadata": {},
   "source": [
    "## 2. Recompute full-log summaries\n",
    "\n",
    "The two-line descriptive split deliberately retains the original 300-step pilot domain. No inference treats sequential observations as independent trials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2c215cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-17T09:11:34.306581Z",
     "iopub.status.busy": "2026-09-17T09:11:34.306471Z",
     "iopub.status.idle": "2026-09-17T09:11:34.336136Z",
     "shell.execute_reply": "2026-09-17T09:11:34.335370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"training_runs\": 1,\n",
      "  \"n_checkpoints\": 167,\n",
      "  \"n_gradient\": 792,\n",
      "  \"n_eval\": 79,\n",
      "  \"raw_gradient_peak\": {\n",
      "    \"step\": 169,\n",
      "    \"value\": 40.9891242980957\n",
      "  },\n",
      "  \"smoothed_peaks\": {\n",
      "    \"5\": {\n",
      "      \"step\": 171,\n",
      "      \"mean\": 33.3971752166748\n",
      "    },\n",
      "    \"11\": {\n",
      "      \"step\": 168,\n",
      "      \"mean\": 31.858454964377664\n",
      "    },\n",
      "    \"21\": {\n",
      "      \"step\": 167,\n",
      "      \"mean\": 28.889931724185036\n",
      "    },\n",
      "    \"31\": {\n",
      "      \"step\": 166,\n",
      "      \"mean\": 26.44963159868794\n",
      "    }\n",
      "  },\n",
      "  \"gradient_window_161_180_mean\": 28.693284511566162,\n",
      "  \"gradient_window_181_200_mean\": 15.343834018707275,\n",
      "  \"gradient_window_decline_percent\": 46.52465104675545,\n",
      "  \"prefix_two_line_best\": {\n",
      "    \"split_after_step\": 181,\n",
      "    \"sse\": 1679.3678563750443,\n",
      "    \"slope_left\": 0.14254928130287897,\n",
      "    \"slope_right\": -0.05131966136931838\n",
      "  },\n",
      "  \"largest_eval_decrease\": {\n",
      "    \"from_step\": 160,\n",
      "    \"to_step\": 170,\n",
      "    \"decrease\": 0.20026636123657227\n",
      "  },\n",
      "  \"eval_first\": {\n",
      "    \"step\": 10,\n",
      "    \"loss\": 4.168299674987793\n",
      "  },\n",
      "  \"eval_last\": {\n",
      "    \"step\": 790,\n",
      "    \"loss\": 1.513361930847168\n",
      "  },\n",
      "  \"eval_reduction_percent\": 63.69354295881809,\n",
      "  \"nondecreasing_eval_intervals\": [\n",
      "    {\n",
      "      \"step\": 770,\n",
      "      \"eval_loss\": 1.5136247873306274,\n",
      "      \"loss_drop\": -6.127357482910156e-05\n",
      "    }\n",
      "  ],\n",
      "  \"original_prefix_numeric_digests\": {\n",
      "    \"gradient\": \"93ef3714f2ac07d5eccbb661b271763765644035dd92402be298d760a4cbe66b\",\n",
      "    \"eval\": \"4c61d98d4151417004068d424fd41be2599cb43c24d60fac2d4238a0272a409b\"\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from analyze_tables import main\n",
    "summary = main()\n",
    "print(json.dumps(summary, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd8b477",
   "metadata": {},
   "source": [
    "## 3. Inspect actual-checkpoint summaries and all sensitivity conditions\n",
    "\n",
    "The selected table contains 16 observed checkpoints. It must not be mistaken for all 167 snapshots. All 18 lag/eligibility settings are retained below. The source-style amplitude filter is factor-based and is not invariant under arbitrary reciprocal factor rescaling, even though the effective-update corner is invariant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce8484ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-17T09:11:34.337482Z",
     "iopub.status.busy": "2026-09-17T09:11:34.337378Z",
     "iopub.status.idle": "2026-09-17T09:11:34.344918Z",
     "shell.execute_reply": "2026-09-17T09:11:34.344042Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " step  effective_update_frobenius_norm  cosine_with_final_effective_update\n",
      "    1                         0.000000                                 NaN\n",
      "    2                         0.004604                            0.398529\n",
      "  150                         2.850490                            0.694104\n",
      "  160                         3.004014                            0.711278\n",
      "  165                         3.073528                            0.718912\n",
      "  170                         3.138500                            0.725447\n",
      "  175                         3.200147                            0.731147\n",
      "  180                         3.262696                            0.736751\n",
      "  185                         3.327193                            0.743272\n",
      "  190                         3.390599                            0.751358\n",
      "  200                         3.506372                            0.769878\n",
      "  300                         4.430487                            0.912728\n",
      "  400                         4.890018                            0.973535\n",
      "  600                         5.233161                            0.998961\n",
      "  790                         5.324971                            1.000000\n",
      "  792                         5.324991                            1.000000\n",
      "\n",
      "Complete sensitivity table:\n",
      " lag  amplitude_threshold  eligible_centers  peak_step_B_and_effective  B_peak_cosine  effective_peak_cosine\n",
      "   5               0.0000               156                        725      -0.788852              -0.812709\n",
      "   5               0.0001               152                        725      -0.788852              -0.812709\n",
      "   5               0.0005               124                        550      -0.868112              -0.878380\n",
      "   5               0.0010                86                        430      -0.929134              -0.933830\n",
      "   5               0.0020                66                        190      -0.934583              -0.940172\n",
      "   5               0.0050                30                         10      -0.987294              -0.987098\n",
      "  10               0.0000               154                        725      -0.727103              -0.759445\n",
      "  10               0.0001               154                        725      -0.727103              -0.759445\n",
      "  10               0.0005               139                        635      -0.768928              -0.779731\n",
      "  10               0.0010               121                        630      -0.796412              -0.813249\n",
      "  10               0.0020                86                        190      -0.806262              -0.822216\n",
      "  10               0.0050                59                        190      -0.806262              -0.822216\n",
      "  15               0.0000               152                        190      -0.691429              -0.715569\n",
      "  15               0.0001               152                        190      -0.691429              -0.715569\n",
      "  15               0.0005               146                        190      -0.691429              -0.715569\n",
      "  15               0.0010               133                        190      -0.691429              -0.715569\n",
      "  15               0.0020               108                        190      -0.691429              -0.715569\n",
      "  15               0.0050                72                        190      -0.691429              -0.715569\n"
     ]
    }
   ],
   "source": [
    "selected = pd.read_csv(ROOT / \"data/selected_checkpoint_metrics.csv\", float_precision=\"round_trip\")\n",
    "sensitivity = pd.read_csv(ROOT / \"data/corner_sensitivity.csv\", float_precision=\"round_trip\")\n",
    "print(selected.to_string(index=False))\n",
    "print(\"\\nComplete sensitivity table:\")\n",
    "print(sensitivity.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be5729ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-17T09:11:34.346004Z",
     "iopub.status.busy": "2026-09-17T09:11:34.345903Z",
     "iopub.status.idle": "2026-09-17T09:11:34.349220Z",
     "shell.execute_reply": "2026-09-17T09:11:34.348645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"150_to_200\": {\n",
      "    \"A_cosine\": 0.9975956592106836,\n",
      "    \"B_cosine\": 0.9646791461958234,\n",
      "    \"effective_cosine\": 0.9623597287760218,\n",
      "    \"effective_distance\": 1.087474736567148\n",
      "  },\n",
      "  \"175_to_190\": {\n",
      "    \"A_cosine\": 0.9997845947432347,\n",
      "    \"B_cosine\": 0.9964775753994548,\n",
      "    \"effective_cosine\": 0.9962629288914651,\n",
      "    \"effective_distance\": 0.3425923712149229\n",
      "  },\n",
      "  \"180_to_200\": {\n",
      "    \"A_cosine\": 0.9996580076932067,\n",
      "    \"B_cosine\": 0.9951625812829412,\n",
      "    \"effective_cosine\": 0.9948222433361337,\n",
      "    \"effective_distance\": 0.42171972832701626\n",
      "  },\n",
      "  \"180_to_792\": {\n",
      "    \"A_cosine\": 0.9738402370012251,\n",
      "    \"B_cosine\": 0.7565424112454661,\n",
      "    \"effective_cosine\": 0.736751441068763,\n",
      "    \"effective_distance\": 3.6606458859649993\n",
      "  },\n",
      "  \"300_to_792\": {\n",
      "    \"A_cosine\": 0.9938960687382065,\n",
      "    \"B_cosine\": 0.9183329486014732,\n",
      "    \"effective_cosine\": 0.9127275074077696,\n",
      "    \"effective_distance\": 2.2176687042008556\n",
      "  }\n",
      "}\n",
      "{\n",
      "  \"n_checkpoints\": 167,\n",
      "  \"n_training_steps\": 792,\n",
      "  \"n_eval_observations\": 79,\n",
      "  \"parameter_layer_index\": 21,\n",
      "  \"rank\": 1,\n",
      "  \"scale\": 64,\n",
      "  \"A_shape\": [\n",
      "    1,\n",
      "    13824\n",
      "  ],\n",
      "  \"B_shape\": [\n",
      "    5120,\n",
      "    1\n",
      "  ],\n",
      "  \"configurations_identical\": true,\n",
      "  \"zero_effective_update_steps\": [\n",
      "    1\n",
      "  ],\n",
      "  \"effective_norm_strictly_increasing\": true,\n",
      "  \"B_norm_strictly_increasing\": true,\n",
      "  \"pca_variance_ratios\": [\n",
      "    0.8109369033497655,\n",
      "    0.13896290956024188,\n",
      "    0.03310634943994352,\n",
      "    0.010324782029499699,\n",
      "    0.003771529911710381\n",
      "  ],\n",
      "  \"max_stable_vs_gram_corner_error\": 4.490124128064821e-07,\n",
      "  \"stable_corner_checks\": 462,\n",
      "  \"remote_derived_geometry_sha256\": \"6760abfbdf676bb5ad3bc85ea777bfe9313fcbcbe1a0ea692a06d57eece6f0f1\",\n",
      "  \"derived_table_scope\": \"The distributed checkpoint and sensitivity CSV files tabulate selected remote outputs. Recompute the full geometry using checkpoint_analysis.py and the public pinned sources.\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "comparisons = json.loads((ROOT / \"data/checkpoint_comparisons.json\").read_text())\n",
    "validation = json.loads((ROOT / \"data/tensor_validation.json\").read_text())\n",
    "print(json.dumps(comparisons, indent=2))\n",
    "print(json.dumps(validation, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350f3f1c",
   "metadata": {},
   "source": [
    "## 4. Computational tests\n",
    "\n",
    "These check data identity, numerical properties, and mathematical implementations. Synthetic matrix tests validate formulas; they are not simulated consciousness experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c95bb845",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-17T09:11:34.350389Z",
     "iopub.status.busy": "2026-09-17T09:11:34.350297Z",
     "iopub.status.idle": "2026-09-17T09:11:35.201932Z",
     "shell.execute_reply": "2026-09-17T09:11:35.201397Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_15_gram_equals_dense (test_study.AlgebraTests.test_15_gram_equals_dense) ... ok\n",
      "test_16_sign_invariance (test_study.AlgebraTests.test_16_sign_invariance) ... ok\n",
      "test_17_scale_gauge (test_study.AlgebraTests.test_17_scale_gauge) ... ok\n",
      "test_18_corner_equals_dense (test_study.AlgebraTests.test_18_corner_equals_dense) ... ok\n",
      "test_19_zero_cosine_undefined (test_study.AlgebraTests.test_19_zero_cosine_undefined) ... ok\n",
      "test_20_straight_corner (test_study.AlgebraTests.test_20_straight_corner) ... ok\n",
      "test_21_stable_difference_expansion (test_study.AlgebraTests.test_21_stable_difference_expansion) ... ok\n",
      "test_22_origin_inequality_not_error (test_study.AlgebraTests.test_22_origin_inequality_not_error) ... ok\n",
      "test_01_artifact_digests (test_study.SourceDataTests.test_01_artifact_digests) ... ok\n",
      "test_02_counts (test_study.SourceDataTests.test_02_counts) ... ok\n",
      "test_03_step_grid (test_study.SourceDataTests.test_03_step_grid) ... ok\n",
      "test_04_finite (test_study.SourceDataTests.test_04_finite) ... ok\n",
      "test_05_gradient_peak (test_study.SourceDataTests.test_05_gradient_peak) ... ok\n",
      "test_06_prefix_digest (test_study.SourceDataTests.test_06_prefix_digest) ... ok\n",
      "test_07_eval_interval (test_study.SourceDataTests.test_07_eval_interval) ... ok\n",
      "test_08_late_reversal (test_study.SourceDataTests.test_08_late_reversal) ... ok\n",
      "test_09_two_line_prefix (test_study.SourceDataTests.test_09_two_line_prefix) ... ok\n",
      "test_10_pca_grid (test_study.SourceDataTests.test_10_pca_grid) ... ok\n",
      "test_11_pca_center (test_study.SourceDataTests.test_11_pca_center) ... ok\n",
      "test_12_selected_norms (test_study.SourceDataTests.test_12_selected_norms) ... ok\n",
      "test_13_filter_peaks (test_study.SourceDataTests.test_13_filter_peaks) ... ok\n",
      "test_14_schedule (test_study.SourceDataTests.test_14_schedule) ... ok\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 22 tests in 0.035s\n",
      "\n",
      "OK\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1879"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = subprocess.run([sys.executable, \"-m\", \"unittest\", \"discover\", \"-s\", \"code\", \"-p\", \"test_*.py\", \"-v\"],\n",
    "                      cwd=ROOT, text=True, capture_output=True)\n",
    "print(test.stdout + test.stderr)\n",
    "assert test.returncode == 0\n",
    "(ROOT / \"results/tests.txt\").write_text(test.stdout + test.stderr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0fc184f",
   "metadata": {},
   "source": [
    "## 5. Figures\n",
    "\n",
    "Each plot is separate. Full-series versus selected-checkpoint scope is identified in titles and manuscript captions. Reference lines identify calculations, not declarations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee3ab451",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-17T09:11:35.203401Z",
     "iopub.status.busy": "2026-09-17T09:11:35.203300Z",
     "iopub.status.idle": "2026-09-17T09:11:36.280721Z",
     "shell.execute_reply": "2026-09-17T09:11:36.280099Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 860x460 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from make_figures import figures\n",
    "figures(show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1646f33f",
   "metadata": {},
   "source": [
    "## Interpretation boundary\n",
    "\n",
    "The first nonzero update (step 2), gradient maximum (169), descriptive prefix split (after 181), and filtered local-bend maximum (190) are **different quantities**. Without the filter, the shorter local-bend windows select 725. This makes measurement specification central to any proposed onset claim.\n",
    "\n",
    "The acquired artifacts contain no declaration variable. This notebook therefore cannot estimate declaration-before-change ordering, within-chat persistence, an onset distribution across independent runs, or subjective consciousness. The manuscript treats those as distinct hypotheses requiring additional observations.\n",
    "\n",
    "## Full source-connected reproduction\n",
    "\n",
    "```bash\n",
    "python code/checkpoint_analysis.py --download --raw-dir raw_sources --output-dir recomputed\n",
    "python code/verify_recomputed.py recomputed raw_sources\n",
    "```\n",
    "\n",
    "Those commands fetch public pinned JSON and safetensors, compute the full geometry, and verify outputs. They do not load the full base model, train a model, or make paid inference requests."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}