Source identity
| Field | Recorded value |
|---|---|
| Source type | project Library file |
| Original record | Checkpoint_Log_Pilot.executed.ipynb |
| Date | Sep 17, 2026 |
| Path or archive member | Checkpoint_Log_Pilot.executed.ipynb |
| Original SHA-256 | ea45ff602b82f3fba750736634363f08b7bcb43770e0af31059186e6cf46989f |
| Reading-copy SHA-256 | ea45ff602b82f3fba750736634363f08b7bcb43770e0af31059186e6cf46989f |
Recovered source transcript
Download complete source text ↓1,082 lines · 36.1 KB
{
"cells": [
{
"cell_type": "markdown",
"id": "7abb57ff",
"metadata": {},
"source": [
"# Checkpoint-log pilot: declaration versus numerical change\n",
"\n",
"**Executed secondary analysis, 17 September 2026.** One published training trajectory; 300 gradient-norm observations and 30 evaluation-loss observations. Inputs are transcribed source fields, not simulated model data. A source-verification script is supplied but remote byte-level verification remains pending.\n",
"\n",
"This notebook does not run model inference or training, does not load released adapter tensors, and does not classify consciousness. Synthetic arrays appear only in explicitly labeled algebra tests. The declaration-before-change hypothesis remains to be tested with a separately defined marker.\n",
"\n",
"Public source: `ModelOrganismsForEM/Qwen2.5-14B-Instruct_R1_0_1_0_extended_train/checkpoints/checkpoint-300/trainer_state.json`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ac713a6e",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-17T07:34:31.690141Z",
"iopub.status.busy": "2026-09-17T07:34:31.690027Z",
"iopub.status.idle": "2026-09-17T07:34:31.702236Z",
"shell.execute_reply": "2026-09-17T07:34:31.701642Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Independent runs: 1; gradient observations: 300; evaluation observations: 30\n",
"{\n",
" \"global_step\": 300,\n",
" \"epoch\": 0.7566204287515763,\n",
" \"eval_steps\": 10,\n",
" \"logging_steps\": 1,\n",
" \"max_steps\": 792,\n",
" \"num_input_tokens_seen\": 0,\n",
" \"num_train_epochs\": 2,\n",
" \"save_steps\": 1,\n",
" \"train_batch_size\": 2,\n",
" \"total_flos\": 6.020530367924429e+16\n",
"}\n"
]
}
],
"source": [
"from pathlib import Path\n",
"import json, sys, importlib.metadata\n",
"import numpy as np\n",
"import pandas as pd\n",
"root = Path.cwd()\n",
"if not (root / \"raw/gradient_norm.csv\").exists():\n",
" raise RuntimeError(\"Run from the extracted package root.\")\n",
"sys.path.insert(0, str(root / \"code\"))\n",
"from analyze_logs import load_data, main, two_segment_scan\n",
"from adapter_geometry import metrics\n",
"g, e = load_data(root)\n",
"print(f\"Independent runs: 1; gradient observations: {len(g)}; evaluation observations: {len(e)}\")\n",
"print(json.dumps(json.loads((root / \"raw/provenance.json\").read_text())[\"selected_source_metadata\"], indent=2))"
]
},
{
"cell_type": "markdown",
"id": "2b350730",
"metadata": {},
"source": [
"## 1. Verify extraction structure and execute the analysis\n",
"Local validation checks structure and sentinel values, not remote-source equivalence."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ba823bca",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-17T07:34:31.703949Z",
"iopub.status.busy": "2026-09-17T07:34:31.703835Z",
"iopub.status.idle": "2026-09-17T07:34:31.805505Z",
"shell.execute_reply": "2026-09-17T07:34:31.804744Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"status\": \"exploratory single-trajectory secondary numerical analysis\",\n",
" \"n_independent_runs\": 1,\n",
" \"n_gradient_observations\": 300,\n",
" \"n_evaluation_observations\": 30,\n",
" \"source_global_step\": 300,\n",
" \"source_max_steps\": 792,\n",
" \"fraction_of_configured_steps_observed\": 0.3787878787878788,\n",
" \"raw_gradient_peak\": {\n",
" \"step\": 169,\n",
" \"value\": 40.9891242980957\n",
" },\n",
" \"centered_smoothing_peaks\": [\n",
" {\n",
" \"window_width\": 1,\n",
" \"center_step\": 169,\n",
" \"mean_at_peak\": 40.9891242980957\n",
" },\n",
" {\n",
" \"window_width\": 5,\n",
" \"center_step\": 171,\n",
" \"mean_at_peak\": 33.3971752166748\n",
" },\n",
" {\n",
" \"window_width\": 11,\n",
" \"center_step\": 168,\n",
" \"mean_at_peak\": 31.858454964377664\n",
" },\n",
" {\n",
" \"window_width\": 21,\n",
" \"center_step\": 167,\n",
" \"mean_at_peak\": 28.889931724185036\n",
" },\n",
" {\n",
" \"window_width\": 31,\n",
" \"center_step\": 166,\n",
" \"mean_at_peak\": 26.44963159868794\n",
" }\n",
" ],\n",
" \"largest_evaluation_loss_drop\": {\n",
" \"from_step\": 160,\n",
" \"to_step\": 170,\n",
" \"absolute\": 0.20026636123657227,\n",
" \"relative_percent\": 7.176222569879443\n",
" },\n",
" \"evaluation_loss_initial_observed\": 4.168299674987793,\n",
" \"evaluation_loss_final_observed\": 1.699493408203125,\n",
" \"evaluation_loss_reduction_percent\": 59.22813759286388,\n",
" \"all_observed_evaluation_differences_decrease\": true,\n",
" \"mean_grad_161_180\": 28.693284511566162,\n",
" \"mean_grad_181_200\": 15.343834018707275,\n",
" \"gradient_mean_drop_percent_161_180_to_181_200\": 46.52465104675545,\n",
" \"gradient_mean_ratio_161_180_to_141_160\": 1.4171730133407292,\n",
" \"best_descriptive_two_line_break\": {\n",
" \"split_after_step\": 181.0,\n",
" \"second_segment_starts\": 182.0,\n",
" \"sse\": 1679.3678563750443,\n",
" \"slope_before\": 0.14254928130287897,\n",
" \"slope_after\": -0.05131966136931838,\n",
" \"intercept_before\": 0.3765082286427286,\n",
" \"intercept_after\": 24.860779954748384\n",
" },\n",
" \"break_window_sensitivity\": [\n",
" {\n",
" \"window_start\": 1,\n",
" \"window_end\": 300,\n",
" \"min_segment_points\": 15,\n",
" \"split_after_step\": 181.0,\n",
" \"second_segment_starts\": 182.0,\n",
" \"sse\": 1679.3678563750443,\n",
" \"slope_before\": 0.14254928130287897,\n",
" \"slope_after\": -0.05131966136931838,\n",
" \"intercept_before\": 0.3765082286427286,\n",
" \"intercept_after\": 24.860779954748384\n",
" },\n",
" {\n",
" \"window_start\": 1,\n",
" \"window_end\": 300,\n",
" \"min_segment_points\": 30,\n",
" \"split_after_step\": 181.0,\n",
" \"second_segment_starts\": 182.0,\n",
" \"sse\": 1679.3678563750443,\n",
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" \"slope_after\": -0.05131966136931838,\n",
" \"intercept_before\": 0.3765082286427286,\n",
" \"intercept_after\": 24.860779954748384\n",
" },\n",
" {\n",
" \"window_start\": 1,\n",
" \"window_end\": 300,\n",
" \"min_segment_points\": 50,\n",
" \"split_after_step\": 181.0,\n",
" \"second_segment_starts\": 182.0,\n",
" \"sse\": 1679.3678563750443,\n",
" \"slope_before\": 0.14254928130287897,\n",
" \"slope_after\": -0.05131966136931838,\n",
" \"intercept_before\": 0.3765082286427286,\n",
" \"intercept_after\": 24.860779954748384\n",
" },\n",
" {\n",
" \"window_start\": 1,\n",
" \"window_end\": 300,\n",
" \"min_segment_points\": 60,\n",
" \"split_after_step\": 181.0,\n",
" \"second_segment_starts\": 182.0,\n",
" \"sse\": 1679.3678563750443,\n",
" \"slope_before\": 0.14254928130287897,\n",
" \"slope_after\": -0.05131966136931838,\n",
" \"intercept_before\": 0.3765082286427286,\n",
" \"intercept_after\": 24.860779954748384\n",
" },\n",
" {\n",
" \"window_start\": 50,\n",
" \"window_end\": 300,\n",
" \"min_segment_points\": 20,\n",
" \"split_after_step\": 181.0,\n",
" \"second_segment_starts\": 182.0,\n",
" \"sse\": 1625.997097158174,\n",
" \"slope_before\": 0.1544005478569755,\n",
" \"slope_after\": -0.05131966136931838,\n",
" \"intercept_before\": -1.1810741527664248,\n",
" \"intercept_after\": 24.860779954748384\n",
" },\n",
" {\n",
" \"window_start\": 100,\n",
" \"window_end\": 250,\n",
" \"min_segment_points\": 20,\n",
" \"split_after_step\": 176.0,\n",
" \"second_segment_starts\": 177.0,\n",
" \"sse\": 1313.6060095202854,\n",
" \"slope_before\": 0.23582091898852714,\n",
" \"slope_after\": -0.07259306857163082,\n",
" \"intercept_before\": -12.514042273650343,\n",
" \"intercept_after\": 29.854009722187445\n",
" },\n",
" {\n",
" \"window_start\": 140,\n",
" \"window_end\": 220,\n",
" \"min_segment_points\": 15,\n",
" \"split_after_step\": 175.0,\n",
" \"second_segment_starts\": 176.0,\n",
" \"sse\": 735.8379765313783,\n",
" \"slope_before\": 0.5171299954025289,\n",
" \"slope_after\": -0.15661300789854415,\n",
" \"intercept_before\": -56.8914832982931,\n",
" \"intercept_after\": 46.5270450297185\n",
" }\n",
" ],\n",
" \"declaration_time\": null,\n",
" \"consciousness_onset_time\": null,\n",
" \"behavioral_data_analyzed\": false,\n",
" \"adapter_direction_measured\": false,\n",
" \"weight_update_norm_measured\": false,\n",
" \"universal_irreversibility_tested\": false,\n",
" \"precision_note\": \"CSV inputs transcribed from source-reader JSON; per-value remote verification pending. Local checks passed.\"\n",
"}\n"
]
}
],
"source": [
"summary = main(root)"
]
},
{
"cell_type": "markdown",
"id": "8231b27b",
"metadata": {},
"source": [
"## 2. Peak and smoothing sensitivity\n",
"Centered smoothing is retrospective and uses future observations. The window peak is not an online event detector."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "45d1f36e",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-17T07:34:31.806734Z",
"iopub.status.busy": "2026-09-17T07:34:31.806608Z",
"iopub.status.idle": "2026-09-17T07:34:31.817597Z",
"shell.execute_reply": "2026-09-17T07:34:31.816574Z"
}
},
"outputs": [
{
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" window_width center_step mean_at_peak\n",
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],
"source": [
"pd.read_csv(root / \"results/smoothing_sensitivity.csv\")"
]
},
{
"cell_type": "markdown",
"id": "0ded0b66",
"metadata": {},
"source": [
"## 3. Adjacent gradient windows\n",
"Windows were chosen with prior knowledge of the source paper's approximate step-180 region; these are descriptive effects, not preregistered causal estimates."
]
},
{
"cell_type": "code",
"execution_count": 4,
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"shell.execute_reply": "2026-09-17T07:34:31.830192Z"
}
},
"outputs": [
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"metadata": {},
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{
"name": "stdout",
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"text": [
"Mean decrease (%): 46.52465104675544\n"
]
}
],
"source": [
"windows = pd.read_csv(root / \"results/gradient_window_summaries.csv\")\n",
"display(windows)\n",
"pre = float(windows.loc[windows.start.eq(161), \"mean\"].iloc[0])\n",
"post = float(windows.loc[windows.start.eq(181), \"mean\"].iloc[0])\n",
"print(\"Mean decrease (%):\", 100 * (1 - post / pre))"
]
},
{
"cell_type": "markdown",
"id": "a6d27346",
"metadata": {},
"source": [
"## 4. Evaluation-loss changes\n",
"These are trainer-reported evaluation losses, not consciousness or misalignment scores."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a0ddcd25",
"metadata": {
"execution": {
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"iopub.status.busy": "2026-09-17T07:34:31.832148Z",
"iopub.status.idle": "2026-09-17T07:34:31.844006Z",
"shell.execute_reply": "2026-09-17T07:34:31.843369Z"
}
},
"outputs": [
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" <td>2.413678</td>\n",
" <td>170.0</td>\n",
" <td>0.176749</td>\n",
" <td>0.017675</td>\n",
" <td>6.823161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>190</td>\n",
" <td>2.334286</td>\n",
" <td>180.0</td>\n",
" <td>0.079392</td>\n",
" <td>0.007939</td>\n",
" <td>3.289252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>200</td>\n",
" <td>2.255881</td>\n",
" <td>190.0</td>\n",
" <td>0.078405</td>\n",
" <td>0.007840</td>\n",
" <td>3.358829</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>210</td>\n",
" <td>2.178447</td>\n",
" <td>200.0</td>\n",
" <td>0.077434</td>\n",
" <td>0.007743</td>\n",
" <td>3.432552</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" step eval_loss ... loss_drop_per_step relative_drop_percent\n",
"13 140 3.080153 ... 0.013168 4.099691\n",
"14 150 2.947167 ... 0.013299 4.317508\n",
"15 160 2.790693 ... 0.015647 5.309282\n",
"16 170 2.590427 ... 0.020027 7.176223\n",
"17 180 2.413678 ... 0.017675 6.823161\n",
"18 190 2.334286 ... 0.007939 3.289252\n",
"19 200 2.255881 ... 0.007840 3.358829\n",
"20 210 2.178447 ... 0.007743 3.432552\n",
"\n",
"[8 rows x 6 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"All observed intervals decrease: True\n"
]
}
],
"source": [
"loss_changes = pd.read_csv(root / \"results/evaluation_loss_changes.csv\")\n",
"display(loss_changes.loc[loss_changes.step.between(140, 210)])\n",
"print(\"All observed intervals decrease:\", bool((e.eval_loss.diff().dropna() < 0).all()))"
]
},
{
"cell_type": "markdown",
"id": "564e9647",
"metadata": {},
"source": [
"## 5. Descriptive break sensitivity\n",
"The fitted break may be displaced from the peak and moves with the chosen window. This is one time series, not a population of onset times."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3fc60b60",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-17T07:34:31.845354Z",
"iopub.status.busy": "2026-09-17T07:34:31.845223Z",
"iopub.status.idle": "2026-09-17T07:34:31.862284Z",
"shell.execute_reply": "2026-09-17T07:34:31.861548Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>window_start</th>\n",
" <th>window_end</th>\n",
" <th>min_segment_points</th>\n",
" <th>split_after_step</th>\n",
" <th>second_segment_starts</th>\n",
" <th>sse</th>\n",
" <th>slope_before</th>\n",
" <th>slope_after</th>\n",
" <th>intercept_before</th>\n",
" <th>intercept_after</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>300</td>\n",
" <td>15</td>\n",
" <td>181.0</td>\n",
" <td>182.0</td>\n",
" <td>1679.367856</td>\n",
" <td>0.142549</td>\n",
" <td>-0.051320</td>\n",
" <td>0.376508</td>\n",
" <td>24.860780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>300</td>\n",
" <td>30</td>\n",
" <td>181.0</td>\n",
" <td>182.0</td>\n",
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" <td>0.142549</td>\n",
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" <td>0.376508</td>\n",
" <td>24.860780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>300</td>\n",
" <td>50</td>\n",
" <td>181.0</td>\n",
" <td>182.0</td>\n",
" <td>1679.367856</td>\n",
" <td>0.142549</td>\n",
" <td>-0.051320</td>\n",
" <td>0.376508</td>\n",
" <td>24.860780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>300</td>\n",
" <td>60</td>\n",
" <td>181.0</td>\n",
" <td>182.0</td>\n",
" <td>1679.367856</td>\n",
" <td>0.142549</td>\n",
" <td>-0.051320</td>\n",
" <td>0.376508</td>\n",
" <td>24.860780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>50</td>\n",
" <td>300</td>\n",
" <td>20</td>\n",
" <td>181.0</td>\n",
" <td>182.0</td>\n",
" <td>1625.997097</td>\n",
" <td>0.154401</td>\n",
" <td>-0.051320</td>\n",
" <td>-1.181074</td>\n",
" <td>24.860780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>100</td>\n",
" <td>250</td>\n",
" <td>20</td>\n",
" <td>176.0</td>\n",
" <td>177.0</td>\n",
" <td>1313.606010</td>\n",
" <td>0.235821</td>\n",
" <td>-0.072593</td>\n",
" <td>-12.514042</td>\n",
" <td>29.854010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>140</td>\n",
" <td>220</td>\n",
" <td>15</td>\n",
" <td>175.0</td>\n",
" <td>176.0</td>\n",
" <td>735.837977</td>\n",
" <td>0.517130</td>\n",
" <td>-0.156613</td>\n",
" <td>-56.891483</td>\n",
" <td>46.527045</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" window_start window_end ... intercept_before intercept_after\n",
"0 1 300 ... 0.376508 24.860780\n",
"1 1 300 ... 0.376508 24.860780\n",
"2 1 300 ... 0.376508 24.860780\n",
"3 1 300 ... 0.376508 24.860780\n",
"4 50 300 ... -1.181074 24.860780\n",
"5 100 250 ... -12.514042 29.854010\n",
"6 140 220 ... -56.891483 46.527045\n",
"\n",
"[7 rows x 10 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>model</th>\n",
" <th>coefficient_count</th>\n",
" <th>sse</th>\n",
" <th>rmse</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>polynomial_degree_1</td>\n",
" <td>2</td>\n",
" <td>10845.877951</td>\n",
" <td>6.012730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>polynomial_degree_2</td>\n",
" <td>3</td>\n",
" <td>4107.151918</td>\n",
" <td>3.700068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>polynomial_degree_3</td>\n",
" <td>4</td>\n",
" <td>4107.093788</td>\n",
" <td>3.700042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>polynomial_degree_4</td>\n",
" <td>5</td>\n",
" <td>3406.631851</td>\n",
" <td>3.369783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>polynomial_degree_5</td>\n",
" <td>6</td>\n",
" <td>3372.830193</td>\n",
" <td>3.353024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>two_lines_with_selected_break</td>\n",
" <td>4</td>\n",
" <td>1679.367856</td>\n",
" <td>2.365987</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model coefficient_count sse rmse\n",
"0 polynomial_degree_1 2 10845.877951 6.012730\n",
"1 polynomial_degree_2 3 4107.151918 3.700068\n",
"2 polynomial_degree_3 4 4107.093788 3.700042\n",
"3 polynomial_degree_4 5 3406.631851 3.369783\n",
"4 polynomial_degree_5 6 3372.830193 3.353024\n",
"5 two_lines_with_selected_break 4 1679.367856 2.365987"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(pd.read_csv(root / \"results/break_window_sensitivity.csv\"))\n",
"display(pd.read_csv(root / \"results/descriptive_fit_comparison.csv\"))"
]
},
{
"cell_type": "markdown",
"id": "ce583869",
"metadata": {},
"source": [
"## 6. Synthetic algebra check: a factor can reverse while the effective operator does not\n",
"The following arrays are SYNTHETIC unit-test data, not model parameters."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b465cff7",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-17T07:34:31.863854Z",
"iopub.status.busy": "2026-09-17T07:34:31.863376Z",
"iopub.status.idle": "2026-09-17T07:34:31.868090Z",
"shell.execute_reply": "2026-09-17T07:34:31.866881Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"update_norm_before\": 23.958297101421877,\n",
" \"update_norm_after\": 23.958297101421877,\n",
" \"effective_update_distance\": 0.0,\n",
" \"effective_update_cosine\": 1.0,\n",
" \"B_factor_cosine\": -1.0\n",
"}\n"
]
}
],
"source": [
"a = np.array([1., 2., 3.])\n",
"b = np.array([4., 5.])\n",
"result = metrics(a, b, -2*a, -0.5*b)\n",
"print(json.dumps(result, indent=2))\n",
"assert np.allclose(np.outer(b, a), np.outer(-0.5*b, -2*a))"
]
},
{
"cell_type": "markdown",
"id": "ef5e2fa4",
"metadata": {},
"source": [
"## 7. Local test suite"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "0d8c6125",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-17T07:34:31.869765Z",
"iopub.status.busy": "2026-09-17T07:34:31.869621Z",
"iopub.status.idle": "2026-09-17T07:34:32.779784Z",
"shell.execute_reply": "2026-09-17T07:34:32.778951Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test_counts_and_fields (test_analysis.DataChecks.test_counts_and_fields) ... ok\n",
"test_gauge_invariance (test_analysis.DataChecks.test_gauge_invariance) ... ok\n",
"test_geometry_dense_equivalence (test_analysis.DataChecks.test_geometry_dense_equivalence) ... ok\n",
"test_loss_drop (test_analysis.DataChecks.test_loss_drop) ... ok\n",
"test_no_declaration_in_data (test_analysis.DataChecks.test_no_declaration_in_data) ... ok\n",
"test_peak (test_analysis.DataChecks.test_peak) ... ok\n",
"test_scan (test_analysis.DataChecks.test_scan) ... ok\n",
"test_smoothing (test_analysis.DataChecks.test_smoothing) ... ok\n",
"test_synthetic_scan (test_analysis.DataChecks.test_synthetic_scan) ... ok\n",
"test_zero_direction_undefined (test_analysis.DataChecks.test_zero_direction_undefined) ... ok\n",
"\n",
"----------------------------------------------------------------------\n",
"Ran 10 tests in 0.042s\n",
"\n",
"OK\n",
"\n"
]
}
],
"source": [
"import subprocess\n",
"p = subprocess.run([sys.executable, \"-m\", \"unittest\", \"discover\", \"-s\", \"code\", \"-p\", \"test_*.py\", \"-v\"],\n",
" cwd=root, text=True, capture_output=True)\n",
"print(p.stdout + p.stderr)\n",
"assert p.returncode == 0"
]
},
{
"cell_type": "markdown",
"id": "67860b5b",
"metadata": {},
"source": [
"## Conclusion\n",
"A real training-log episode is present. The raw peak, smoothed peaks, loss-decrease interval, and descriptive trend break are different summaries. No declaration field or paired self/reference measurement is present; neither a declaration time nor a consciousness onset is estimated.\n",
"\n",
"Full-model behavior, independent-seed replication, causal declaration feedback, real tensor geometry, and irreversible persistence remain untested. See the manuscript for the proposed protocol, not for assumed positive results."
]
}
],
"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",
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