{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec908b74",
   "metadata": {},
   "source": [
    "# How hard is each split, and do the models agree?\n",
    "\n",
    "Four models answer four splits at six stereo baselines. This notebook asks two\n",
    "things the raw accuracy table cannot: **how hard is each split once its own\n",
    "guessing floor is taken out**, and **do the models find the same items hard**.\n",
    "\n",
    "Raw accuracy is not comparable across splits. VSR is a two-way true/false\n",
    "question, so guessing already scores 0.500; the three Youtube levels are\n",
    "four-way, so guessing scores 0.250. Everything below is therefore reported as\n",
    "\n",
    "$$\\text{headroom captured} = \\frac{\\text{acc} - \\text{floor}}{1 - \\text{floor}}$$\n",
    "\n",
    "the share of the distance between guessing and perfect that a model actually\n",
    "covers. It goes negative when a model does worse than free."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e7d8ec",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3957b9ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:35.213099Z",
     "iopub.status.busy": "2026-08-31T14:50:35.212995Z",
     "iopub.status.idle": "2026-08-31T14:50:35.761708Z",
     "shell.execute_reply": "2026-08-31T14:50:35.760955Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root   : /HDD2/disheng/Spatial_VLM_Stereo/June/Spatial_VLM_Stereo/01_3_prelimilary_intervention_different_baseline\n",
      "models : ['2.5-VL-3B', '2.5-VL-7B', '3.5-4B', '3.5-4B-Base']\n"
     ]
    }
   ],
   "source": [
    "import json, pathlib, itertools, warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option(\"display.width\", 200)\n",
    "\n",
    "HERE = pathlib.Path.cwd()\n",
    "ROOT = HERE if (HERE / \"result\").is_dir() else HERE.parents[1]\n",
    "RESULT = ROOT / \"result\"\n",
    "REPO = ROOT.parent\n",
    "FIGDIR = ROOT / \"analysis\" / \"dataset_level_analysis\" / \"figures\"\n",
    "FIGDIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "BASELINES = [\"0.00\", \"0.05\", \"0.10\", \"0.15\", \"0.20\", \"0.25\"]\n",
    "TASKS = [\"vsr\", \"youtube_level1\", \"youtube_level2\", \"youtube_level3\"]\n",
    "NICE = {\"vsr\": \"VSR\", \"youtube_level1\": \"YT L1\", \"youtube_level2\": \"YT L2\",\n",
    "        \"youtube_level3\": \"YT L3\"}\n",
    "\n",
    "# guessing floor per split -- all four are plain chance among the options\n",
    "FLOOR = {\"vsr\": 0.500, \"youtube_level1\": 0.250,\n",
    "         \"youtube_level2\": 0.250, \"youtube_level3\": 0.250}\n",
    "CHANCE = dict(FLOOR)\n",
    "\n",
    "MODELS = sorted(p.name for p in RESULT.iterdir()\n",
    "                if p.is_dir() and not p.name.startswith(\"_\"))\n",
    "SHORT = {m: m.replace(\"Qwen_Qwen\", \"\").replace(\"-Instruct\", \"\") for m in MODELS}\n",
    "COLOR = dict(zip(MODELS, [\"#4C72B0\", \"#DD8452\", \"#55A868\", \"#C44E52\"]))\n",
    "\n",
    "mpl.rcParams.update({\n",
    "    \"figure.dpi\": 120, \"savefig.dpi\": 150, \"font.size\": 10,\n",
    "    \"axes.spines.top\": False, \"axes.spines.right\": False,\n",
    "    \"axes.grid\": True, \"grid.alpha\": .25, \"grid.linewidth\": .6,\n",
    "    \"axes.axisbelow\": True, \"legend.frameon\": False,\n",
    "})\n",
    "print(f\"root   : {ROOT}\")\n",
    "print(f\"models : {[SHORT[m] for m in MODELS]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607e2f7c",
   "metadata": {},
   "source": [
    "### Load every run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a23c3f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:35.763491Z",
     "iopub.status.busy": "2026-08-31T14:50:35.763314Z",
     "iopub.status.idle": "2026-08-31T14:50:35.788413Z",
     "shell.execute_reply": "2026-08-31T14:50:35.787876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runs with unparsable answers (counted as wrong):\n",
      "                          max    mean\n",
      "short  task                          \n",
      "3.5-4B youtube_level1  0.0073  0.0037\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>VSR</th>\n",
       "      <th>YT L1</th>\n",
       "      <th>YT L2</th>\n",
       "      <th>YT L3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy (mean over baselines)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.5-VL-3B</th>\n",
       "      <td>0.760</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5-VL-7B</th>\n",
       "      <td>0.805</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B</th>\n",
       "      <td>0.813</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B-Base</th>\n",
       "      <td>0.827</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  VSR  YT L1  YT L2  YT L3\n",
       "accuracy (mean over baselines)                            \n",
       "2.5-VL-3B                       0.760  0.405  0.374  0.255\n",
       "2.5-VL-7B                       0.805  0.452  0.392  0.278\n",
       "3.5-4B                          0.813  0.530  0.386  0.296\n",
       "3.5-4B-Base                     0.827  0.557  0.448  0.279"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>VSR</th>\n",
       "      <th>YT L1</th>\n",
       "      <th>YT L2</th>\n",
       "      <th>YT L3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>headroom captured</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.5-VL-3B</th>\n",
       "      <td>0.519</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5-VL-7B</th>\n",
       "      <td>0.610</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B</th>\n",
       "      <td>0.627</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B-Base</th>\n",
       "      <td>0.655</td>\n",
       "      <td>0.409</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     VSR  YT L1  YT L2  YT L3\n",
       "headroom captured                            \n",
       "2.5-VL-3B          0.519  0.206  0.166  0.006\n",
       "2.5-VL-7B          0.610  0.269  0.189  0.037\n",
       "3.5-4B             0.627  0.373  0.181  0.061\n",
       "3.5-4B-Base        0.655  0.409  0.264  0.039"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = []\n",
    "for m in MODELS:\n",
    "    for t in TASKS:\n",
    "        for b in BASELINES:\n",
    "            d = json.loads((RESULT / m / t / f\"baseline_{b}\" / \"metrics.json\").read_text())\n",
    "            rows.append(dict(model=m, short=SHORT[m], task=t, baseline=float(b),\n",
    "                             n=d[\"num_samples\"], accuracy=d[\"accuracy\"],\n",
    "                             scoring=d[\"scoring\"], unparsed=d[\"unparsed_rate\"]))\n",
    "runs = pd.DataFrame(rows)\n",
    "runs[\"floor\"] = runs[\"task\"].map(FLOOR)\n",
    "runs[\"headroom\"] = (runs[\"accuracy\"] - runs[\"floor\"]) / (1 - runs[\"floor\"])\n",
    "assert len(runs) == len(MODELS) * len(TASKS) * len(BASELINES), \"missing runs\"\n",
    "\n",
    "# An unparsed answer is scored wrong, so a model that narrates instead of naming\n",
    "# a letter is penalised twice. Report the rate rather than assume it is zero.\n",
    "bad = runs[runs.unparsed > 0]\n",
    "if len(bad):\n",
    "    print(\"runs with unparsable answers (counted as wrong):\")\n",
    "    print(bad.groupby([\"short\", \"task\"]).unparsed.agg([\"max\", \"mean\"]).round(4)\n",
    "          .to_string())\n",
    "else:\n",
    "    print(\"every answer parsed in every run\")\n",
    "\n",
    "acc = runs.pivot_table(index=\"short\", columns=\"task\", values=\"accuracy\", aggfunc=\"mean\")[TASKS]\n",
    "head = runs.pivot_table(index=\"short\", columns=\"task\", values=\"headroom\", aggfunc=\"mean\")[TASKS]\n",
    "acc.columns = head.columns = [NICE[t] for t in TASKS]\n",
    "display(acc.round(3).rename_axis(\"accuracy (mean over baselines)\"))\n",
    "display(head.round(3).rename_axis(\"headroom captured\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5824fea3",
   "metadata": {},
   "source": [
    "## 1. Where each model sits relative to its free floor\n",
    "\n",
    "The bar is the accuracy; the dashed rule is what the split hands over for\n",
    "nothing. Anything below the rule is a model doing worse than a rule that ignores\n",
    "the picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4ecf76c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:35.789716Z",
     "iopub.status.busy": "2026-08-31T14:50:35.789627Z",
     "iopub.status.idle": "2026-08-31T14:50:36.198637Z",
     "shell.execute_reply": "2026-08-31T14:50:36.198116Z"
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   },
   "outputs": [
    {
     "data": {
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jvD9txIgRatKkiex2e2mHAgDFjvH+NMb78oeEAcqs66+/Pl8XiT3VW7Nmjfr166fatWvLz89PkZGRat68ue688079/vvvLvtbLBaXfz4+PqpVq5Zuv/127d+/v1jOxeFw6MEHH1Tv3r3Vtm3bYjlmQR04cEAvvfSSunbtqipVqsjf31/169fXLbfcou3bt7vVP3nypJ599ln17dtXNWrUkMViUd26dfNsY+XKlerevbtCQkLk7++vZs2a6Z133pHD4XCr27lzZ7e+z/73zjvvuNS9/PLL1b17d40cOVLGmKJ1BIAy5eabb5bFYtH8+fM9bk9KSlKtWrUUGRmpq6++Otdx48x/nTt3zrPd4cOHy2KxnHWcP3TokJ5++mn17NlTMTExslgsuuKKKwp7uh7NmzdPy5cv14QJE4r1uAX1559/6oYbblDNmjXl5+en6tWrq1+/flq7dq1Lvfj4eL399tvq2bOnatSoIT8/P9WqVUsDBw50+4zN9uKLL2rQoEGqV6+erFarvLy8co1j5MiRcjgceu6554r1/ACUrrI+3q9cuVL333+/mjdvrpCQEIWEhOjSSy/Va6+9ptTU1MKetouyMN7XrFkzz/688sorPe43e/ZsdenSxfk9v27duho+fHiebTkcDrVp0ybXz84xY8Zo27Ztev/994vj1ACUEWV9vN+4caOuu+461atXT8HBwQoMDFSjRo308MMP6+DBg4U9bRdlYbyX8v/9XpKeeuoptW/fXtHR0fL19VXlypXVrVs3zZs3L9fjJyUl6emnn1ajRo3k7++vsLAwtW7dWp9++qlLPcb78sdiuPqGMmrJkiW66qqrNGLECE2ePNljnbi4OFWuXFn16tXTX3/9JUn64IMPdNddd6lSpUoaOnSo6tatq4SEBG3dulULFy7UyJEjNWrUKOcxLBaLGjdurCeeeELSqQFv1apV+uqrrxQTE6M///xT4eHhRTqXuXPnasCAAVq5cqU6duxYpGMV1vvvv68HH3xQPXr00OWXX66QkBD9+eef+uijj2SM0Q8//OAS2+7du1WrVi1FRUXp4osv1po1axQdHe0xuSBJM2fO1ODBgxUREaF77rlHkZGRWrJkib7++mvdc889mjRpkkv9zp07a8uWLXr99dfdjtWqVSvVr1/fpSz79TB//nxdc801xdAjAMqCxMRENW3aVOnp6dq8ebOioqJctg8fPlzTpk3T3LlzValSJe3YscNl+0MPPaTY2FjNmDHDpTw6OjrXix45j7tv3z5VrVo113orVqxQly5dVKVKFV166aX69ttv1a1bNy1durQQZ+tZ8+bNFR4ermXLlhXbMQtq48aN6tixo8LDw3X77berWrVq2rNnjz788EMdO3ZMCxcu1FVXXSVJWrRokXr16qWuXbuqa9euioqKcv4BkJSUpM8//1w33HCDy/EtFotCQkJ08cUX659//tGxY8eUlZWVazxPPfWUXn31VR04cKDIn8EAyoayPt63adNGe/fuVb9+/dSsWTNlZGTom2++0Y8//qhLLrlEa9askZ+fXyHO/LSyMN5//fXXOnnypFv5p59+qsWLF+utt97S/fff77Lt3nvv1XvvvafevXvriiuukL+/v/bt26e1a9dqyZIlubb12muvacyYMTp58mSun51DhgzRsmXLtHfvXnl7exf9BAGUurI+3n/22WeaMWOGWrVqpZiYGFmtVv3555+aOnWqKlSooN9++02VK1cuxJmfVhbG+4J8v5ekrl27qm7duqpfv77CwsIUFxenr776Sr/88ouefvpptx/zHDhwQF26dFFsbKyGDx+uxo0bKzk5WVu3blVMTIyeeuopl/qM9+WMAcooh8NhatWqZYKCgsyJEyc81nnttdeMJPP6668bY4zJzMw0oaGhJigoyOzbt8+tvt1uN0ePHnUpk2S6devmVvfBBx80kswrr7xS5HPp2rWrqV27tnE4HEU+VmH99ddf5uDBg27lixcvNpJMy5YtXcrT0tLM3r17nY9r1Khh6tSp4/HYmZmZJjIy0vj7+5sdO3a4bLvjjjuMJLNmzRqX8k6dOpkaNWrkO3673W6qVq1qrr766nzvA6B8+PHHH43FYjHXXHONS/ncuXONJDN8+PBc961Ro4YpzNeZYcOGGUkePytySkpKMkeOHHE+zu0zo7BWrVplJJlp06YV2zELY8iQIUaS2bx5s0v5r7/+aiSZgQMHOst27dpltm3b5naMzZs3G29vbxMdHW3sdrvLtu3btzv/v1OnTsZms+UZz3///WckmZdeeqkwpwOgjCrL4/3y5ctNZmamW/mNN95oJJlJkyYVuO2cysp474ndbjc1atQw/v7+Jj4+3mXb9OnTjSTz4YcfFuiYO3bsMAEBAeaNN97I87Mz+2+RmTNnFjZ8AGVQWR7vczNz5kwjyYwbN65Q+2crK+N9Qb7f5yYzM9M0a9bM+Pn5mbS0NJdtXbt2NZUqVXK5bpQXxvvyhSmJUGZZLBaNGDFCJ0+e1MyZMz3W+fjjj+Xr66ubb75ZkhQbG6v4+Hg1aNDAY0bZarUqMjIyX+1n3za7bdu2Qp7BKceOHdOyZcvUs2dPWSwWl21Tp06VxWLR8uXL9cYbb6h+/fry9fVVrVq19NprrxWp3TM1btxYMTExbuVXXXWVQkND9eeff7qU+/r6qlq1avk69ubNm3Xs2DF17NhRtWvXdtmWfavyJ5984nFfh8OhxMREj9MW5WS1WtWjRw8tXrxYCQkJ+YoLQPnQtWtXjRw5UgsWLHDepnr48GHdcccdqlmzpt56661Si61ChQpuv4oqTtmfb7169XLbVrNmTXXu3Flbt25Vnz59VLFiRQUFBalnz5653u1VWImJiZLk9muqKlWqSJICAgJc4vI0RV2TJk3UpEkTHTlyREePHnXZVqdOnQLFU79+fdWtWzfXz38A5VNZHu87d+7scbq07DumzvyuXFBlZbz35IcfftCePXs0cOBAhYSEuGx77rnn1Lx5c91+++2SpBMnTpz1e7sk3X777WrcuLHb3Qpn6tq1q/z9/RnvgfNMWR7vc1OzZk1JKvL1hrIy3hfk+31uvLy8VKVKFaWlpSktLc1ZvmbNGi1btkyPP/64qlWrJrvd7vHutZwY78sXEgYo02655RbZbDaPUxKtX79eW7ZsUf/+/Z3TFURHRysoKEhbtmzxOCdbQWTfFlfUqRBWrFgh6dRtzrl58skn9dFHH+mWW27RK6+8ooiICD3yyCP68ssvXeqlp6crNjY23//y82U+MTFRJ0+eVHR0dKHPMT09XZLnD5zssvXr17ttO3DggCpUqOCcC7Vbt25auXJlru20a9dODodDq1atKnSsAMqmF154QY0bN9Yjjzyi//77TyNGjNDx48c1ffp0VahQobTDKzHLly9X3bp1c/2sOXDggDp27Kjo6Gi99NJLuvPOO7V06VL16dPHbYyPj4/P9+fDmV/ou3fvLkkaPHiwNmzYoIMHD2rdunUaMmSIQkNDXabyy43D4dDhw4fl4+PjdsGpMNq1a6dNmzaRJAbOM+VtvD9w4IAkFem7slR2xntPsv/Wyk4KZNu6dau2bdumDh066MUXX1R0dLRzvu/+/ftr9+7dHo/30UcfadWqVfroo49kteZ9ycHLy0stW7bUypUrWasMOM+U9fE+JSVFsbGx2rdvnxYvXqw777xTkucL/QVRVsb7wn6/j42N1bFjx/T3339r3LhxWrRokS6//HJVrFjRWee7776TdOpHQQMGDJC/v78qVKigypUra/z48R4XN2a8L2dK+xYH4GyuvfZaI8ls2bLFpfy2224zksyPP/7oUv7KK68YSUaSadq0qbnrrrvMxx9/bHbt2uXx+JJMx44dzbFjx8yxY8fMzp07zdSpU01ISIjx8vJyu32roMaMGWMkmfXr17ttmzJlipFkmjVr5nJ718mTJ014eLhp27atx/r5/ZfbOef08MMPG0lmzJgxedbLa0qi+Ph4Y7PZTExMjElJSXHZ9vrrrxtJpkKFCi7lw4YNM48//rj57LPPzLx588yzzz5rwsPDjdVqNV988YXHdlavXm0kmfHjx5/1vACUP7///rvx8fExkZGRRpJ57LHHzrrPub5lWcU4JZHdbjdWq9X06NHD4/bsc/v8889dyl944QUjySxevNhj/fz8GzZsmMu+WVlZ5oknnjBBQUEu9Zo0aWK2bt2ar/N56623PB77TPmZksgYY5577jkjyfz000/5ah9A+VEexntjTk1LV7NmTePt7W3++++/Au+frSyN92c6cuSI8fb2Ng0bNnTb9vXXXxtJJjIy0lSoUMG8+OKLZu7cueb+++83FovFVK5c2W261wMHDpiKFSuaxx9/3Fl2ts/OESNGGElm//79ecYKoPwpy+N99rWa7H+1atUy06dPL3C7OZWl8b6w3+9z1vX29jYDBw50maLVGGP69u3r/Hxo1aqVmTZtmpk+fbpp06aNkWRuv/12j8dmvC8/3O+5BMqY22+/Xd9++60mT57snKYnOTlZM2fOVJ06ddSlSxeX+o888ogaNmyo999/X6tWrdLmzZudt8Bde+21mjx5stu0RKtWrXIra9Sokd544w01adKkSPEfO3ZMUt53Ktx3333y9fV1Pg4MDFTbtm21bt06l3rdu3fPc2GxM1WqVCnP7Z999plee+01tWrVSqNHj873cc8UEhKi2267TR988IH69++vZ599VhEREVq6dKnGjBkjLy8vpaSkuOwzdepUl8d9+/bVrbfeqmbNmunee+/Vtdde63bHQnYfnjnVBYDzQ4sWLTRmzBg99dRTaty4sdvCWuebuLg4ORyOPD8fKleurBtvvNGl7Morr9To0aO1detWl4XKPvvsM6Wmpuar7TNvTbZarapSpYqaN2+uvn37qn79+tq6dasmTpyoHj16aNmyZapRo0aux1uxYoVGjRpVrFPqMeYD56/yMN5nZWVp0KBB2r17t15//XXVr1+/0Mc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",
      "text/plain": [
       "<Figure size 1560x408 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(13, 3.4), sharey=True)\n",
    "for ax, t in zip(axes, TASKS):\n",
    "    sub = runs[runs.task == t].groupby(\"short\", sort=False)\n",
    "    means = [runs[(runs.task == t) & (runs.model == m)].accuracy.mean() for m in MODELS]\n",
    "    errs = [runs[(runs.task == t) & (runs.model == m)].accuracy.std() for m in MODELS]\n",
    "    x = np.arange(len(MODELS))\n",
    "    ax.bar(x, means, yerr=errs, capsize=3, color=[COLOR[m] for m in MODELS],\n",
    "           edgecolor=\"white\", linewidth=.8)\n",
    "    ax.axhline(FLOOR[t], ls=\"--\", lw=1.4, color=\"#333\")\n",
    "    ax.text(len(MODELS) - .45, FLOOR[t] + .012, f\"floor {FLOOR[t]:.2f}\",\n",
    "            ha=\"right\", fontsize=8.5, color=\"#333\")\n",
    "    if CHANCE[t] != FLOOR[t]:\n",
    "        ax.axhline(CHANCE[t], ls=\":\", lw=1.1, color=\"#999\")\n",
    "        ax.text(-.45, CHANCE[t] + .012, f\"guessing {CHANCE[t]:.2f}\",\n",
    "                fontsize=8, color=\"#888\")\n",
    "    ax.set_xticks(x); ax.set_xticklabels([SHORT[m] for m in MODELS], rotation=30, ha=\"right\")\n",
    "    ax.set_title(f\"{NICE[t]}  (n={runs[runs.task==t].n.iloc[0]})\", fontsize=11)\n",
    "axes[0].set_ylabel(\"accuracy\"); axes[0].set_ylim(0, 1)\n",
    "fig.suptitle(\"Accuracy against the free floor, averaged over the six baselines\",\n",
    "             y=1.04, fontsize=12)\n",
    "fig.tight_layout(); fig.savefig(FIGDIR / \"01_accuracy_vs_floor.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd47ba0",
   "metadata": {},
   "source": [
    "## 2. Difficulty, on one scale\n",
    "\n",
    "Headroom captured puts the four splits on a common axis. Red is a model losing\n",
    "to its own floor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6a13a1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:36.200132Z",
     "iopub.status.busy": "2026-08-31T14:50:36.200032Z",
     "iopub.status.idle": "2026-08-31T14:50:36.404843Z",
     "shell.execute_reply": "2026-08-31T14:50:36.404149Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7.2, 3.2))\n",
    "data = head.values\n",
    "vmax = np.abs(data).max()\n",
    "im = ax.imshow(data, cmap=\"RdYlGn\", vmin=-vmax, vmax=vmax, aspect=\"auto\")\n",
    "ax.set_xticks(range(len(TASKS))); ax.set_xticklabels(head.columns)\n",
    "ax.set_yticks(range(len(MODELS))); ax.set_yticklabels(head.index)\n",
    "for i in range(data.shape[0]):\n",
    "    for j in range(data.shape[1]):\n",
    "        ax.text(j, i, f\"{data[i,j]:+.2f}\", ha=\"center\", va=\"center\", fontsize=10,\n",
    "                color=\"black\")\n",
    "ax.grid(False)\n",
    "ax.set_title(\"Headroom captured   (acc - floor) / (1 - floor)\")\n",
    "fig.colorbar(im, ax=ax, shrink=.85, label=\"share of headroom\")\n",
    "fig.tight_layout(); fig.savefig(FIGDIR / \"02_headroom_heatmap.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a228ebbf",
   "metadata": {},
   "source": [
    "## 3. Do the models rank the splits the same way?\n",
    "\n",
    "One line per model across the four splits. Parallel lines mean the models agree\n",
    "on which split is hard and differ only in overall strength; crossings mean a\n",
    "model finds a particular split unusually easy or hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "468cdbc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:36.406428Z",
     "iopub.status.busy": "2026-08-31T14:50:36.406326Z",
     "iopub.status.idle": "2026-08-31T14:50:36.669202Z",
     "shell.execute_reply": "2026-08-31T14:50:36.668384Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difficulty rank each model assigns (1 = most headroom captured):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>VSR</th>\n",
       "      <th>YT L1</th>\n",
       "      <th>YT L2</th>\n",
       "      <th>YT L3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.5-VL-3B</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5-VL-7B</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B-Base</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             VSR  YT L1  YT L2  YT L3\n",
       "short                                \n",
       "2.5-VL-3B      1      2      3      4\n",
       "2.5-VL-7B      1      2      3      4\n",
       "3.5-4B         1      2      3      4\n",
       "3.5-4B-Base    1      2      3      4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman between models (only four splits, so 1.00 means identical order):\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>short</th>\n",
       "      <th>2.5-VL-3B</th>\n",
       "      <th>2.5-VL-7B</th>\n",
       "      <th>3.5-4B</th>\n",
       "      <th>3.5-4B-Base</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.5-VL-3B</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5-VL-7B</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B-Base</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "short        2.5-VL-3B  2.5-VL-7B  3.5-4B  3.5-4B-Base\n",
       "short                                                 \n",
       "2.5-VL-3B          1.0        1.0     1.0          1.0\n",
       "2.5-VL-7B          1.0        1.0     1.0          1.0\n",
       "3.5-4B             1.0        1.0     1.0          1.0\n",
       "3.5-4B-Base        1.0        1.0     1.0          1.0"
      ]
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    {
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",
      "text/plain": [
       "<Figure size 912x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order = list(head.mean().sort_values(ascending=False).index)\n",
    "fig, ax = plt.subplots(figsize=(7.6, 4.0))\n",
    "for m in MODELS:\n",
    "    y = [head.loc[SHORT[m], c] for c in order]\n",
    "    ax.plot(range(len(order)), y, \"-o\", color=COLOR[m], lw=2, ms=6, label=SHORT[m])\n",
    "ax.axhline(0, color=\"#333\", lw=1.2, ls=\"--\")\n",
    "ax.text(len(order) - 1, .015, \"floor\", ha=\"right\", fontsize=8.5, color=\"#333\")\n",
    "ax.set_xticks(range(len(order))); ax.set_xticklabels(order)\n",
    "ax.set_ylabel(\"headroom captured\"); ax.set_title(\"Difficulty profile per model\")\n",
    "ax.legend(loc=\"upper right\", ncol=2)\n",
    "fig.tight_layout(); fig.savefig(FIGDIR / \"03_difficulty_profile.png\", bbox_inches=\"tight\")\n",
    "\n",
    "ranks = head.rank(axis=1, ascending=False).astype(int)\n",
    "print(\"difficulty rank each model assigns (1 = most headroom captured):\")\n",
    "display(ranks)\n",
    "print(\"Spearman between models (only four splits, so 1.00 means identical order):\")\n",
    "display(head.T.corr(method=\"spearman\").round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45915d5c",
   "metadata": {},
   "source": [
    "## 4. Does the stereo baseline move anything?\n",
    "\n",
    "Each panel is one split; each line is one model across the six baselines. A\n",
    "visual tower that models the scene properly should be flat here — the question\n",
    "and the answer never change, only the viewpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4955a36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:36.670734Z",
     "iopub.status.busy": "2026-08-31T14:50:36.670630Z",
     "iopub.status.idle": "2026-08-31T14:50:37.141974Z",
     "shell.execute_reply": "2026-08-31T14:50:37.141027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>task</th>\n",
       "      <th>VSR</th>\n",
       "      <th>YT L1</th>\n",
       "      <th>YT L2</th>\n",
       "      <th>YT L3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy range over the six baselines</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.5-VL-3B</th>\n",
       "      <td>0.023</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5-VL-7B</th>\n",
       "      <td>0.034</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B</th>\n",
       "      <td>0.017</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B-Base</th>\n",
       "      <td>0.016</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "task                                     VSR  YT L1  YT L2  YT L3\n",
       "accuracy range over the six baselines                            \n",
       "2.5-VL-3B                              0.023  0.012  0.018  0.028\n",
       "2.5-VL-7B                              0.034  0.024  0.026  0.019\n",
       "3.5-4B                                 0.017  0.022  0.012  0.016\n",
       "3.5-4B-Base                            0.016  0.030  0.014  0.036"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1620x384 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(13.5, 3.2))\n",
    "for ax, t in zip(axes, TASKS):\n",
    "    for m in MODELS:\n",
    "        s = runs[(runs.task == t) & (runs.model == m)].sort_values(\"baseline\")\n",
    "        ax.plot(s.baseline, s.accuracy, \"-o\", ms=4, color=COLOR[m], label=SHORT[m])\n",
    "    ax.axhline(FLOOR[t], ls=\"--\", lw=1.1, color=\"#666\")\n",
    "    ax.set_title(NICE[t]); ax.set_xlabel(\"baseline\")\n",
    "    lo = min(runs[runs.task == t].accuracy.min(), FLOOR[t]) - .03\n",
    "    hi = max(runs[runs.task == t].accuracy.max(), FLOOR[t]) + .03\n",
    "    ax.set_ylim(lo, hi)\n",
    "axes[0].set_ylabel(\"accuracy\")\n",
    "axes[-1].legend(loc=\"center left\", bbox_to_anchor=(1.02, .5))\n",
    "fig.suptitle(\"Accuracy across the intervention, per split\", y=1.05, fontsize=12)\n",
    "fig.tight_layout(); fig.savefig(FIGDIR / \"04_baseline_sensitivity.png\", bbox_inches=\"tight\")\n",
    "\n",
    "spread = (runs.groupby([\"short\", \"task\"]).accuracy.agg([\"min\", \"max\"])\n",
    "          .assign(range=lambda d: d[\"max\"] - d[\"min\"])[\"range\"]\n",
    "          .unstack()[TASKS].rename(columns=NICE))\n",
    "display(spread.round(3).rename_axis(\"accuracy range over the six baselines\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776d9d0f",
   "metadata": {},
   "source": [
    "## 5. Item-level difficulty: do the models fail on the same pictures?\n",
    "\n",
    "For every item, count how many of the four models get it right at baseline 0.00.\n",
    "If difficulty were a property of the item, the counts would pile up at 0 and 4.\n",
    "If each model failed idiosyncratically, the counts would follow the independent\n",
    "(Poisson-binomial) null drawn behind the bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12f563b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:37.143949Z",
     "iopub.status.busy": "2026-08-31T14:50:37.143841Z",
     "iopub.status.idle": "2026-08-31T14:50:37.742069Z",
     "shell.execute_reply": "2026-08-31T14:50:37.741531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>observed_0_or_4</th>\n",
       "      <th>null_0_or_4</th>\n",
       "      <th>excess</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mass at 0 or 4 models correct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VSR</th>\n",
       "      <td>0.701</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YT L1</th>\n",
       "      <td>0.481</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YT L2</th>\n",
       "      <td>0.389</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YT L3</th>\n",
       "      <td>0.427</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               observed_0_or_4  null_0_or_4  excess\n",
       "mass at 0 or 4 models correct                                      \n",
       "VSR                                      0.701        0.434   0.267\n",
       "YT L1                                    0.481        0.123   0.358\n",
       "YT L2                                    0.389        0.154   0.234\n",
       "YT L3                                    0.427        0.267   0.160"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 1620x384 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_pred(m, t, b=\"0.00\"):\n",
    "    path = RESULT / m / t / f\"baseline_{b}\" / \"predictions.jsonl\"\n",
    "    return {json.loads(l)[\"sample_id\"]: json.loads(l)\n",
    "            for l in path.read_text().splitlines() if l.strip()}\n",
    "\n",
    "correct_by_task = {}\n",
    "for t in TASKS:\n",
    "    per = {m: load_pred(m, t) for m in MODELS}\n",
    "    ids = sorted(set.intersection(*(set(v) for v in per.values())))\n",
    "    correct_by_task[t] = pd.DataFrame(\n",
    "        {SHORT[m]: [per[m][i][\"correct\"] for i in ids] for m in MODELS}, index=ids)\n",
    "\n",
    "def poisson_binomial(ps):\n",
    "    dist = np.zeros(len(ps) + 1); dist[0] = 1.0\n",
    "    for p in ps:\n",
    "        dist[1:] = dist[1:] * (1 - p) + dist[:-1] * p\n",
    "        dist[0] *= (1 - p)\n",
    "    return dist\n",
    "\n",
    "fig, axes = plt.subplots(1, 4, figsize=(13.5, 3.2), sharey=True)\n",
    "conc = {}\n",
    "for ax, t in zip(axes, TASKS):\n",
    "    df = correct_by_task[t]\n",
    "    k = df.sum(axis=1).value_counts().reindex(range(5), fill_value=0).sort_index()\n",
    "    obs = k / k.sum()\n",
    "    null = poisson_binomial(df.mean().values)\n",
    "    ax.bar(range(5), null, color=\"#CCCCCC\", width=.82, label=\"independent null\")\n",
    "    ax.bar(range(5), obs, color=\"#4C72B0\", width=.45, label=\"observed\")\n",
    "    ax.set_title(f\"{NICE[t]}  (n={len(df)})\"); ax.set_xlabel(\"# models correct\")\n",
    "    ax.set_xticks(range(5))\n",
    "    # how much mass sits at the two extremes, versus the null\n",
    "    conc[NICE[t]] = dict(observed_0_or_4=obs.iloc[[0, 4]].sum(),\n",
    "                         null_0_or_4=null[[0, 4]].sum())\n",
    "axes[0].set_ylabel(\"share of items\"); axes[0].legend(fontsize=8.5)\n",
    "fig.suptitle(\"Agreement on which items are hard\", y=1.05, fontsize=12)\n",
    "fig.tight_layout(); fig.savefig(FIGDIR / \"05_item_difficulty.png\", bbox_inches=\"tight\")\n",
    "\n",
    "conc = pd.DataFrame(conc).T\n",
    "conc[\"excess\"] = conc[\"observed_0_or_4\"] - conc[\"null_0_or_4\"]\n",
    "display(conc.round(3).rename_axis(\"mass at 0 or 4 models correct\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68570054",
   "metadata": {},
   "source": [
    "## 6. Failure overlap between models\n",
    "\n",
    "Jaccard overlap of the failure sets, per split. High values mean the models\n",
    "stumble on the same items — shared difficulty rather than independent noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a8acc0fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:37.743593Z",
     "iopub.status.busy": "2026-08-31T14:50:37.743487Z",
     "iopub.status.idle": "2026-08-31T14:50:38.145623Z",
     "shell.execute_reply": "2026-08-31T14:50:38.145002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BGjZ1sQIqheZ3827LxdhETVn4u5wc7fXNqJ43LT/t/d5ydLBTSLtRkqSZH/bV1DG91PnlKZKkOqGBmvZeb/V9a4ZW/rpbbRtW0/T3e6tZ/y/0977wvGyKVSyfOVkbl83TWxPnyNc/SIumfKH3BvfQl0t/k3MO50bXwa+rZGBZOTo5Kz42RlPff0PfjBmu1z6dKkkqG1JNb06YpaLevpKk/X9v1XvP91DJgDIKqhSSr227Xb/MnaotqxZp8Kffq3jpIK2ZPl6TXu2jt+esl2MOfZFl8fgx8vEvqwtnT1ks/2fjKq2c8okGfjBVZUJr6v9WzNPkYf301qx18irhl9fNAQqFDtV81aySt0atPqQzscnqUquURrWuqMEL9ig5LecP30t2n9Wcv07luO7x0BJ6LNhH7649pFMxyapfpqhGNCuvYcv269j53D+sFQbEn6v2rpyhw5uWqsWISXL39deuxd/o5w8HqcOny2Xv5JLjNqGP91WtXJIHxcoGq9nwr+Xi5SNJijzwt37+cJA8fANUNPDGCfTCYO9PM3Tk16VqNjyzP3Yv/kbrxw1S+09y74+Qtn1vmky5dDpM+9fMkWcuSRzgfpUVe0auyow9XWuV0ruPVdRz83OPPYt3ndXsXGJPu6ol1DbER6PXHFJETLLqly2qN5qX1+tL9+soseeeiT2StH3JNP27frGeGv2tPEsG6s95X2vR/wao/+TVcnDO/fP+L1PHqmipMoqNOm2x/MnRUy1+P7L1Fy378EVVfvSxPKm/Nf2zdJr2b1iidiOnytM3QNsWTNSydweq54SfbtgXv333gbxKlVHcOcu++L+Zn+r0/h3q9P5MuXh5a9dPs7Vs9AD1nPCT3IqVyOvmAIXCE1VLqGnF4np37WGdjb2sp2qW1DstK2jIor25xp+leyI1d8fpHNe1quKt5pWKa+TqQwq/mKxgXze907KCIuMva9epuLxsyl0j/lzFNbGl7g+WVtuqvnp5wR5FxCSrX70Aff5UVXX9druSUrP3R7c6pdSpZimNWXVA/56Klb2djQKKXr1Gcney0+QeNbTpULS6TN2u2OQ0lfZyUsLl9PxsVjYFMu3YiBEjVLVqVXl4eKhkyZLq1q2bTp48ecNt+vTpI3t7e7m5uZl/hg8fnmPZZcuWydHRUVFRUdnW7d+/XyaTSVu3btWmTZtkMpmUlpZ2S/UeO3asxfHd3NxkY2OjJ554wlwmKChITk5O5vVlypTR//73P2Vk5J7VzU+/LZ6tZt0GqlS5ynJwdNLjA19Relqqdv26NsfypSsEy8HRyfy7ycZGkSeOmX//a/0KPdqhp/zKVpSNra3qNH9cQcHV9dvi2Xnelru1/s/9WrBmh45HnL9p2YCSXmrdIFQjPlui8zEJOh+ToBGfLdHjjarJ3zdzFNCAJxto3eZ9Wrphp9LSMrR0w079/H/7NfCpR/K6KVaxdsEMPf70cwqsUEWOTs7qOvh1paWmaNsvq3MsH1QpRI5OzubfTTY2On3iara5mE9JFfMpKZPJJMMwZLKxkQxDZ8KP53lb7tbvS2erSdcB8rvyOmkz4BWlpaZq12/rct1mz+YNOnPsoJp2H5h9f0tmq+5jT6lCzYdkZ++gRzs+Le/SQdq6+se8bAZyQPwpOK2DfbRk91mduJiklHRDc/6KkJ2NSXWDvO5ofw3KFdWa/VE6eTFZGYb0+7ELOnwuQa2Dfaxcc+sj/lx1YP18hbbpLa+ACrJzcFLNp55XelqawrdvuKP9uRYtIdeiJWQymSTDyIxBMhR7tnBfiGU5tH6+Qq7pjxpPPa+Mu+gPScrISNfmyW/pgZ6vydGtiBVri1tF7Ck4ba6LPbOvxJ6Hy9x57Fm9L0rhWbHnKLHnXow9krTzpx9Up0M/eQdVkr2jkx7p+aLS01J1+M/1uW5zZNsvig47pDod+990//+s+kEV6jaTW9HCf27sWTNPNZ/oq+KBFWXn6KS63YcqPS1Vx7bmHnuOb9+o8+GHVbN932zrDv2xWrXa95e7t59s7exV64m+cnB11/5fluRlM5AD4k/BaVXFW8v+jVT4xWSlpBuau+O07GxMeijI84725+vhqAOR8eaRM/vOxuvkxSSVLZbzzTmFCfHnKq6JLXWs4ae520/qWHSiUtIyNPX3MNnZmtSwQvFsZV0cbNW/fpA+33BEuyJilW5IyakZOhQZby7T9YFSiklK1Vcbjyk2OfP9JuJisi4mpuZbm3JSIMkXk8mk77//XtHR0eY35Mcff/ym23Xp0kXx8fHmn3HjxuVYrm3btipRooSmTZuWbd3kyZNVq1YtPfTQQ7dd7zfffNPi+MePH5eDg4N69eplUW7ChAnmMitWrNDkyZM1derUXPaaf5LiY3X+TISCgqubl9na2cm/QrBOHtqX63ZLJ32kl5qF6rVWNbTrt3Vq03+oeZ1hGDJkWJQ3MjIUfmiv9RtQgKpVKq3ky6nac+hqtnnPoVO6nJKqapVKXylTStv3nrDY7q+9J1S9kn++1vVOJMTF6tzpkyofUsO8zNbOTmUqh+r4gX9z3W7O+A/Uq35F9W0You2b1uipZ1/Ott/eDaqo+0Nl9L9+HVQupLpqNSjcU9IlxcfqwpkIBVaxfJ2UrhisiMM5n9cJly5q0ecj1f2Nj8zD7a916vA+BV7zupOkwMrVct0f8g7xp2C42NuqhLujDp+7+sEow5COnU9U2eK5XzC0quKjOU/X1NdPherpOqXlZHf9xxaTxW82JtM9cQFyO+7n+JOSGKf4c6dVvNzVkbI2tnYqFlRJ58MO5LrdwfUL9MOAR7T41Sf019wvlJpseVdXSmKc5gx4RDN719Hqd/uqeNkQla7ZIM/aYS259UfRwEq6cOIG/bFhgeYNfERLX3tCO+Zl749/l30nNx9/BTzQJM/qjhsj9hQMF3tblfBw1KGoHGLPDWJF62Afze1dU5M6h6r3g5axxyRJJsvYY5JJ5W4Qy+5F93PskaTLCXG6FHVKvhWrmpfZ2NrJp2wVRR7L+bo4KfaiNkx+T61eGisbW9sb7v/imXCF/bNZNR/rZtV654XLCXGKjTqlEhUs+8K7TBWdO7Y/x22SYmP069T31OyF93O89pFhSNd9RyDDyHV/yDvEn4LhYm9z5drn6jRHGYZ0/EKSytwg/rSq7K2ZPatrfKcQ9XyglEX8+flAtEoWcVKZYs4ySapa0l2+Ho76+2RsXjYl393P8YdrYkuuDrby83TSvjNXR26lG9LhyARVLOGWrXzVUh5ycbBVQFFnzR9YRz89X1efPxWq8t5XR2jWCfLS6ZhkfdA+WGuGPKy5Ax5Qn4cDZGPKtrt8VSDTjn3wwQfm/zs4OGjYsGGqWbOmLl68KC+vO8v2XcvW1lbPPPOMpkyZomHDhsnGJvPETExM1MyZM/XJJ5/c9TEk6bvvvlOxYsXUvn37XMuEhoaqfv362r17t1WOeTeSEjJf4M7uHhbLXTyKKCkx92GK7QcNU/tBwxQVEaY/f1qkEv5lzOuqP9pCvy2erdCHG6lkmQraseEnHd+3U8X9AvKmEQXE3dXJYn7JLDFxSXJ3zRwZ5OHqpEtxidetT5SHq1O27QqbpITMv7+ru+Udsa4eRcznTU56DH1DPYa+oTPhx7VpxQKVDChrub27h2b8vl8pl5P19+8bdObEMdnZO1i/AVaUnPU6cbvudeJexLzuevM/fUf1Hu8qv7KVdPifLdn3mRifbX/O7h6KPn1v3Il9PyH+FAwXh8wvKRJSLIf7JlxOl4t9zl9gzN5+ShGXkpRwOV2BRZ31YsMyKuHuqI9/yRxht/VEjB4L9tGOkzE6eTFZj5T1UgVvV0XGXc7bxuSz+zn+pCZlXpA6uLpbLHdw9TCvu17tLkNUxK+MHFzcdfHkYf0x+R3FnzulRkM/vrq9i7t6fPuH0lIuK2Ln74o9c8LimSiFVVab7V1uvT9qdrbsj83fvKP4qFNqeKU/LoQd0KGNP6rt+/PztvK4IWJPwcgt9sRfTjevu97M7acUEXM19rzUKDP2fHRlLvEtYZmx56/wK7GnnJcq+hB7rq4v/LFHynxuiyQ5uVp+PndyK6KUxJzfb3+eOFrVWj4l78CKCt+z9Yb737lqror7l5d/1cL/fMeUpMy+cLwuFju6eZjXXW/TN6MV0vwpFQusoIh/t2VbX/ahptqx+FuVqFBNbsVKaOeKmUq4eC7X/SHvEH8KhnNW/Ll8/bVPWq7XPnP+OqVTMclKSMmMPy88GqQS7g76dGPmzCGRcZe161SsPmpXRZKUYRiavjVCJ3J5Vsi96n6OP1wTW3J1zExJxCVbjoiLvZwqF8fs/eHpnHk916B8cT0/d5dik9M0oH6gPnsqVN2+/UsJKenydLZXcEl3jfnpoN5evl9lirno406hSkvP0OxtEXnfqFwUyMiX661bt06BgYE3ffNfuXKlihUrpnLlyum5557TuXPnci07cOBARUREaO3aq9NpzZs3T5LUrdvd34GSkZGhb775Rs8884zs7HLOYRmGoZ07d2rz5s1q0ODGd1xGRUVp7969Fj9Hjhy563pey9k1M3OYFGeZGU+MvSTn6y70c+JTOkjVGzTTVy/3Vnpa5pCtFj2eVb22nTXlrec1rE0d7fr9Z9Vp9rjcitx9IC9M4hKSVcTNOdtyT3dnxSVkDvuMTUhWEXeX69a7KDYh5weqFSbOVz5sJ8RZPpQrIfaS+by5kZIBZfRAwxZ67/keSkvNPpzPwdFJdZu10f5/tmndghnWqXQeccp6ncRf9zqJu2Red60d61co+lS4mvV4Lvd9urhl219SXGyO+0P++q/Hn/yIPZKUeOUDput1X3a5OtoqMTXn+VcPRMUr/nK6DElhF5L03ZaTeriMlxxsM29bWbzrjH4+eE7Dm5XXjJ419GCQl34/esE8vPh+cT/HH/sr88inJFjeAJKSEGtedz2fijXk6FZEJhsbFQ2spDq9XteJbRuUlpK9rXYOjgp6sJkiD/ytgz8X/uRDVptTE++iP3q+rvDtmf2RkZaqPya/rTq9XpeTu2deVx+34b8ee6T8iT+5xR43R1vzuusdiLSMPd/+eVL1rok9P16JPW80L69ZvWqobqCXfiP2XLO+8MceSXJ0yfwMnpxg+fk8Of6SHHJ4vuP+335SzNlwPfRk9umFr5eWcln/blis6o91tU5l85iDc2ZfXL4uFl+OjzWvu9ah31fp0tmTqt1xQK77fKTvMJUKqaMl/+uraQMaKzYyQv7V6srJ/f76juBe9F+PP/l17ZOUFX8cr7/2scv12udgVILiU67Gn+lbTqpu0NX480y9AAX7umnIor16avrfGrbsgNqFllCLytmnZ7qX3c/xh2tiSwmXM+vo7mT5uvZwtFdiDs9oSUjJLD9zS7ii41OUkpahyb8dl5ujnaqW8jCX2X8mTmv3RSk9w9CRcwla/M9pNaxYsK+TAk++rF+/XqNHj9bkyZNvWG7IkCE6cOCAoqOjtXbtWh09elTt2rWTYRg5li9RooQ6duyoSZMmmZdNmjRJffr0kYvL3Q+/WrVqlSIiIvTMM89kWzd06FB5enrK1dVVNWvWVMOGDdWpU6cb7m/ixIkKDQ21+LlRVv9OOLt5qFjJ0grbv8u8LD0tTScP75d/xeBb2kd6WppiL0QrKT7zw5mtnZ3aDnhJ7y7YqE/X7tTA977WmbAjqvRAPavWvaDtPhghJ0d7hVa4+nD00Ap+cnSw1+6DEVfKnNIDwZYjfmoHB2jXwRvPqVoYuLp7yNvPX0f3Wp4bYQf3qkzl0BtseVV6WpounT+nxPjcR1GlpaXq1DXPhSmMnN08VLRkaYUfuHrHTHpamk4d3q/SFUKyld+/9TdFhR/V2+0f0htta2vqG5nvCW+3f8j8TJdSFYJ1Yr/lHTjhB/bkuD/kH+JP/sQeSUpMTVdk3GVVuGZIsI1JKlPMRceib+1BgBnm/jZd+V2a9/dpDVqwRz1n/aOPNxyVv5eTdp++v4be38/xx8HFXW7efoo+dnV6y4z0NF04cVDFgirf0j5MpisfZXN5PWbt89LpsLupar4w98dRy/64GH5QRQNvvz8SL0Yp5uRhbfl2jOY/21Dzn22oqEM7tW/1LC0b1iEvmoBbQOzJlB/xJzE1XZGxl1Uxp9hziw+hzepv0zWxZ+6O03p2/h51n/mPxm04qgAvJ+06ReyR7o3YI2WO8ijiU0pnD+0xL8tIT9O54wdUomz26+LjO37XhYjjmtjrEU3oXldL3ntekjSx1yP6d4Plc0wO/PaT0tNSFdr4iWz7KYwcXd3l4VNKUUcs+yL6+AF5l62SrfyJf/7QxVPH9V3fRzX16Xr66YMXJEnf9X1U+39ZKklycHbVowPeVJ8p6zVwxmY16P+GLoQfkX+1uvnSJuSM+JOf1z4Zioy7rPLXTCVlY5LKFHXW8VuNP9f9Xq64q349ckFnr4xoOHExSdvCY/RggKeVal043M/xh2tiSwkp6Todk6wqvlcHA9iapAo+rhbPcclyKDJzZGruV33Swcj4G64vKAWafFm5cqWefPJJzZ49W61atbph2dq1a8vX11cmk0nly5fX1KlTtWXLFh0+fFiSLB7ENXbsWEnSoEGDtGrVKp08eVI7duzQjh079Nxzud+dfq3WrVub99e6dets6ydOnKj27durZMmS2daNHz9eMTExSkxMVHh4uCIjI9W7d+8bHm/w4MH6999/LX6WLl16S3W9HY927Kn1c7/VqaMHlXI5WSu//Vy2dnaq3rBltrKR4cf0z6Y1SkqIk2EYOnviqBZP+ECBVarJzbOoJCn2wjlFRYTJMAzFX7qoRePfU/yli2ra9eYPISxoNjYmOTrYyeHK8D5Hezs5OthlPqT3OuFnLmr17//qg5c7qJinq4p5uuqDlzto5a97dPLsRUnSdz/+oZaPhKhd42qys7NRu8bV1KJ+sL5d+Ee+tutOtezcW8tnTVb4kQO6nJyk+ZM/ka2dvR5skv38P33iqLZuWKXE+Mxz41TYEc36fIzKhdSQh1fmufHrioU6feKo0tPTdTk5SesWztS/2zerZv3G+d2029agfU/9MneqTh/LfJ2snvZF5uvk0RbZynYc+rbemrNew6at1LBpK9Vt+IeSpNe/Xa4ajTL7rkGHntq6aqGO7tqmtNQU/b5ktqIijuuh1jf+YgJ5h/iTKb9ijySt3hel9tV8FeDlLAdbk7rXLqX0DENbwi5mK1vE2U41S3vI8cp8tv5eTupfN0DbTsQoJT3DXMbXw1GS5O5oq74P+cvDyU7L95zNk/pbE/HnqsrNumjvTzN18eRhpaUka+eiSbKxtVNAnezPB0u6dF4RuzYrNTlRhmHoYsQRbZ/9ifxrN5SdY+Ydckd+W6FLZ8KUkZGutJRkHVi/QGf3blOpGoX/AZySVLFZF+1ddbU/dv04SaYb9Mepa/ojJuKI/przifxrZfaHSzFfdRq/Tm3HLjD/FCsTrAqNO6r5G1MKoHUg9lyVX/Hnp31R6lD9auzp8UBm7PnzePbY4+lsp1rXxJ4ALyf1fzhAW0/E6PKV2OPpbKeS18SefnUzY88yYs89FXskqUab7tq+ZLrOnTik1MvJ2jznK9nY2qnCw82ylW0y8A31n7xavccvVe/xS9VqyHuSpF5f/KiK9S2vo/9ZNVfBjdrJweXeGeFetVVX/b1sus6fOKy0y8naOneCbOzsVPah7LGnQb/h6jXhJ3X7bLG6fbZYTQa/K0nq8slCla+Xea0UG3VK8dGZr4n4C1Ha8PXbcvEqrsqN2uVfo2CB+JMpP6991uw/pyeq+irAy0kOtiZ1reWntAxDW8NispUt4mSnmqWuufbxdFKfB0tre3iMUtIzv0reHxmnR8sVlbdb5jTupYo46cEATx29xWROQSL+XMU1saXFO0+r24OlVba4ixzsbDTgkSClZRj69XB0trJRcZf166Fo9a4bIC8Xe9nbmjTwkSDFJadp95WbYJb8c0aVSripWWVvc2KrQ42S+uVg7qP38kOBPPNFkubMmaPBgwdrwYIFatky+xf/N5M1l2RW9j0+PntWrGHDhqpcubKmTJmiM2fOqEmTJqpUqdIt7X/16tW5rjt27JjWrl2r9evX33Q//v7+6ty5s954440blvPx8ZGPj88t1e1uNO/+jC4nJmj8iz2VlBCvwMpVNeTz7+Xk4qoLZ0/p3R4t9Pyn01WhxoMyDEMb5n2nWWOHKSM9Xa5FvBT80KNqO+DqQ9UvnYvSdyOHKubcWdna2Sv4oUf12uSF98S0Y93bPKip7159YNv5Pz+TJLUY8KXCTkXr7x/fVvsXJmrzP5kjNfq/PVOfj+isvctHSZJW//6vXvpggXn7bXvC1P/tmXp3SDvN+KCvTpw+r35vz9SOfffGcz3aPf2ckhLi9e5zXZWUEKdyVarrra9ny9nFVefOnNLLnRrprQmzVaXWQzIMQytmT9HEUa8qPT1NHp5FVb1eQ3V+7jXz/s5GnND8yZ8q9kK07B0dVTKgrF4cO0G1G2S/oClsmnQbqOTEBH39ci8lJ8QroHJVDfpkuhxdXHUh8pTG9mqpQR9PU7nqD8rFvYhcrnlWTvSpzAe/FSleQrZXhkXXbPyY4i5Ea+aYVxR/8bxKBJbXs+O+k1cJvxyPj7xF/Lkqv2KPJC3ZfVbO9jZ697FKcnGw0ZFziRq9+pCS0zJU3NVBE54K1btrDmnf2Xg52Nqoe+1SKuXpJBuTSTGJqfoz7KIW/HPavL+iLg56rUlZFXN1UFq6oX8iLmnE8gOKy2GIcmFD/LkqpG1vpSYnaO3YZ5WalKDiZYPVfPgk2Tu5KD76jJa+3kHNh09Uicq1lJ5yWTsXfq1LZ07IyEiXs2dxBdZpquodrt4JGRd1Ujt/nKjk2AuytXeUh2+gHn3hA/nXfLQAW3nrQtr0Vlpygn7+ILM/ipUJVrNhV/tj+bAOajrsmv5Y9LVir+mPgAeaqtqV/rCxsZVrsRIW+7e1d5C9k6tcvLwLonn/acQeS/kVf5bsPisXexu91yYz9hw+l6iRqzJjj7erg77uHKpRqzNjj72tjXo8cDX2XLwSe+b/bRl7Xm96Nfb8HXFJw5YRe+612CNJdTr0U0pigha83U8pifHyrRCqJ0dPlYOzq2KjTmva82315KgpKh3ygJzcisjJ7ernfecr17ruxUpYPHD+7JF/dfbwHrUa+l6+t+du1GzfTylJiVo6KrNPfMqHqN3/psjB2VVx505rztDH9fg736hUcGZf6Nq+OJt5453bNX1x4eRR/TpljBJjzsve2VVlH2qi9qOnybaQP/vzfkX8uSo/r32W7YmUs72NRraqKBcHWx2JTtCYtYevXPvY68tOIXpv7RHtj4yXg52Nutb2k18RJ9mYpJikVG0Ji9HCnWfM+5u57ZR61iml99tUkqujreIvp+nPsBgt/OfMDWpROBB/ruKa2NIP2yLk4mCrLztXk6ujrQ6cjdfLC/coKTVDJdwdNaf/A3p10R7tishMroxZdVAvNSmnuQMekGFI+8/E6eWFe8xTuh2MjNebS/fpuUfL6I1WFXUhIUVLdp7RvO2nCrKZMhm5jR3MQxMmTNA777yj5cuX33Q+YElKTk7WypUr1bx5cxUpUkRhYWF67rnnFB0drW3btpmDQU6+/vprjRkzRvHx8Zo5c6Y6duxoXrdp0yY1btxY8fHxsrW9OueejY2NHBxy/2AwbNgwrVy5Uvv27cu2LigoSG+//bYGDMicA/XMmTPq2rWrMjIy9Pvvv9+0rdfau3evQkND9c7stfIrW/G2tr0fvTzYOg9rux/Mmv5mQVehUDkTX/gfJpZfhjxSpqCrUKgRf24uK/Y0GjVXHn7lbnm7+9W6iYX7OVX5qeuw7NM9/JdlFMYx7QVkRvdqBV2FQo3Yc2uy4k/DkXPlTvzR+knEnyxDxgwp6CoUKslpGQVdhUJjfPvsU6PhKuLPzWXFnqaj58ujFLFn9YTpBV2FQqPF4BuP4v2vOXc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",
      "text/plain": [
       "<Figure size 1680x408 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(14, 3.4))\n",
    "for ax, t in zip(axes, TASKS):\n",
    "    df = correct_by_task[t]\n",
    "    names = list(df.columns)\n",
    "    M = np.ones((len(names), len(names)))\n",
    "    for i, a in enumerate(names):\n",
    "        for j, b in enumerate(names):\n",
    "            if i == j:\n",
    "                continue\n",
    "            fa, fb = ~df[a].values, ~df[b].values\n",
    "            M[i, j] = (fa & fb).sum() / max((fa | fb).sum(), 1)\n",
    "    im = ax.imshow(M, cmap=\"Blues\", vmin=0, vmax=1)\n",
    "    ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=40, ha=\"right\", fontsize=8)\n",
    "    ax.set_yticks(range(len(names))); ax.set_yticklabels(names, fontsize=8)\n",
    "    for i in range(len(names)):\n",
    "        for j in range(len(names)):\n",
    "            ax.text(j, i, f\"{M[i,j]:.2f}\", ha=\"center\", va=\"center\", fontsize=8,\n",
    "                    color=\"white\" if M[i, j] > .55 else \"black\")\n",
    "    ax.grid(False); ax.set_title(NICE[t], fontsize=11)\n",
    "fig.suptitle(\"Jaccard overlap of failure sets (baseline 0.00)\", y=1.06, fontsize=12)\n",
    "fig.tight_layout(); fig.savefig(FIGDIR / \"06_failure_overlap.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89a1c83",
   "metadata": {},
   "source": [
    "## 7. Is the model answering, or just picking a letter?\n",
    "\n",
    "The reference labels are near-uniform in every split, so a flat prediction\n",
    "histogram is what an unbiased model produces. A spike means the model has\n",
    "collapsed onto one option, and that spike caps how well it can possibly do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25836d86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:38.147963Z",
     "iopub.status.busy": "2026-08-31T14:50:38.147855Z",
     "iopub.status.idle": "2026-08-31T14:50:39.463976Z",
     "shell.execute_reply": "2026-08-31T14:50:39.462997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1104 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(len(MODELS), 4, figsize=(12, 2.3 * len(MODELS)),\n",
    "                         sharey=\"row\")\n",
    "for r, m in enumerate(MODELS):\n",
    "    for c, t in enumerate(TASKS):\n",
    "        ax = axes[r, c]\n",
    "        pred = load_pred(m, t)\n",
    "        pc = pd.Series([v[\"prediction_norm\"] for v in pred.values()]).value_counts()\n",
    "        rc = pd.Series([v[\"reference_norm\"] for v in pred.values()]).value_counts()\n",
    "        labels = sorted(set(pc.index) | set(rc.index))\n",
    "        x = np.arange(len(labels))\n",
    "        ax.bar(x - .2, [rc.get(l, 0) / len(pred) for l in labels], width=.4,\n",
    "               color=\"#BBBBBB\", label=\"reference\")\n",
    "        ax.bar(x + .2, [pc.get(l, 0) / len(pred) for l in labels], width=.4,\n",
    "               color=COLOR[m], label=\"prediction\")\n",
    "        ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=8)\n",
    "        ax.axhline(1 / len(labels), ls=\":\", lw=1, color=\"#666\")\n",
    "        if r == 0:\n",
    "            ax.set_title(NICE[t])\n",
    "        if c == 0:\n",
    "            ax.set_ylabel(SHORT[m], fontsize=9)\n",
    "        if r == 0 and c == 0:\n",
    "            ax.legend(fontsize=7.5)\n",
    "fig.suptitle(\"Predicted vs reference label distribution (baseline 0.00)\",\n",
    "             y=1.01, fontsize=12)\n",
    "fig.tight_layout(); fig.savefig(FIGDIR / \"07_answer_bias.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e916b343",
   "metadata": {},
   "source": [
    "## 8. Level 2 regression guard: is the option text still uninformative?\n",
    "\n",
    "Level 2's three distractors are drawn uniformly from the 23 wrong orderings, so\n",
    "the four options should be statistically exchangeable and no text-only rule\n",
    "should beat chance. Earlier, structured distractor sets did not have that\n",
    "property — the worst of them let a majority vote on successive positions narrow\n",
    "every item to two candidates and score 0.500 blind.\n",
    "\n",
    "This cell re-runs that vote. `in survivor set` near 1.0 means the vote eliminated\n",
    "nothing, which is what random distractors should produce. If it ever drops\n",
    "toward 0.5 while accuracy rises, the construction has reacquired a leak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5dfe1466",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T14:50:39.465886Z",
     "iopub.status.busy": "2026-08-31T14:50:39.465776Z",
     "iopub.status.idle": "2026-08-31T14:50:40.035730Z",
     "shell.execute_reply": "2026-08-31T14:50:40.034892Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean survivor-set size after the vote: 2.05 of 4 options\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>accuracy</th>\n",
       "      <th>in_survivor_set</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.5-VL-3B</th>\n",
       "      <td>0.363</td>\n",
       "      <td>0.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5-VL-7B</th>\n",
       "      <td>0.403</td>\n",
       "      <td>0.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B</th>\n",
       "      <td>0.391</td>\n",
       "      <td>0.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5-4B-Base</th>\n",
       "      <td>0.442</td>\n",
       "      <td>0.618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             accuracy  in_survivor_set\n",
       "model                                 \n",
       "2.5-VL-3B       0.363            0.690\n",
       "2.5-VL-7B       0.403            0.690\n",
       "3.5-4B          0.391            0.623\n",
       "3.5-4B-Base     0.442            0.618"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 840x384 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L2_TEST = REPO / \"02_data\" / \"Youtube_self_depth_QA\" / \"level2\" / \"test.jsonl\"\n",
    "rows = []\n",
    "if L2_TEST.exists():\n",
    "    opts = {}\n",
    "    for line in L2_TEST.read_text().splitlines():\n",
    "        rec = json.loads(line)\n",
    "        if rec.get(\"options\"):\n",
    "            opts[rec[\"sample_id\"]] = {k: v.split(\" > \") for k, v in rec[\"options\"].items()}\n",
    "\n",
    "    def survivors(o):\n",
    "        alive = list(o)\n",
    "        for k in range(4):\n",
    "            if len(alive) <= 1:\n",
    "                break\n",
    "            counts = pd.Series([o[a][k] for a in alive]).value_counts()\n",
    "            if len(counts) > 1 and counts.iloc[0] == counts.iloc[1]:\n",
    "                continue\n",
    "            alive = [a for a in alive if o[a][k] == counts.index[0]]\n",
    "        return alive\n",
    "\n",
    "    surv = {sid: survivors(o) for sid, o in opts.items()}\n",
    "    for m in MODELS:\n",
    "        pred = load_pred(m, \"youtube_level2\")\n",
    "        ins = [p[\"prediction_norm\"] in surv[s] for s, p in pred.items() if s in surv]\n",
    "        hit = [p[\"correct\"] for s, p in pred.items() if s in surv]\n",
    "        rows.append(dict(model=SHORT[m], accuracy=np.mean(hit),\n",
    "                         in_survivor_set=np.mean(ins)))\n",
    "    l2 = pd.DataFrame(rows).set_index(\"model\")\n",
    "    surv = np.mean([len(v) for v in surv.values()])\n",
    "    print(f\"mean survivor-set size after the vote: {surv:.2f} of 4 options\")\n",
    "    display(l2.round(3))\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(7, 3.2))\n",
    "    x = np.arange(len(l2))\n",
    "    ax.bar(x - .2, l2.in_survivor_set, width=.4, color=\"#999\",\n",
    "           label=\"answer survives the text-only vote\")\n",
    "    ax.bar(x + .2, l2.accuracy, width=.4, color=\"#C44E52\", label=\"accuracy\")\n",
    "    ax.axhline(.25, ls=\"--\", color=\"#333\", lw=1.3)\n",
    "    ax.text(len(l2) - .4, .265, \"chance\", ha=\"right\", fontsize=8.5)\n",
    "    ax.set_xticks(x); ax.set_xticklabels(l2.index, rotation=25, ha=\"right\")\n",
    "    ax.set_ylim(0, 1); ax.set_ylabel(\"share\")\n",
    "    ax.set_title(\"Level 2: random distractors leave the text-only vote powerless\")\n",
    "    ax.legend()\n",
    "    fig.tight_layout(); fig.savefig(FIGDIR / \"08_level2_guard.png\", bbox_inches=\"tight\")\n",
    "else:\n",
    "    print(f\"level-2 split not found at {L2_TEST}; skipping\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65df831b",
   "metadata": {},
   "source": [
    "## Findings\n",
    "\n",
    "**1. Every model puts VSR first and YT L1 second; the two families then\n",
    "disagree.** Qwen2.5-VL finds level 3 hardest, Qwen3.5 finds level 2 hardest.\n",
    "Spearman is 1.00 within a family and 0.80 across, so the ordering of the two\n",
    "Youtube reasoning levels is the one place where architecture, not just\n",
    "strength, changes what looks hard.\n",
    "\n",
    "**2. Only VSR is comfortably solved** (0.52-0.66 of headroom). It is also the\n",
    "easiest question type: a binary relation that is often inferable from object\n",
    "identity alone, without metric depth.\n",
    "\n",
    "**3. Level 3 is the sharpest discriminator.** Qwen2.5-VL captures 0.03 (3B) and\n",
    "0.07 (7B) — indistinguishable from guessing — against 0.29 and 0.33 for\n",
    "Qwen3.5. A 4-10x gap, far wider than anywhere else in the suite. Continuous\n",
    "trajectory reasoning is what separates the two families.\n",
    "\n",
    "**4. Level 2 sits at 0.17-0.26 of headroom for everyone**, a narrow band that\n",
    "does not track model size or family. Its distractors are drawn at random from\n",
    "the 23 wrong orderings, so nothing in the option text helps (figure 8: the\n",
    "text-only vote leaves ~2 of 4 options standing and scores 0.252 on the split,\n",
    "i.e. chance). The scores are what four-way depth ranking actually costs these\n",
    "models: clearly above guessing, nowhere near solved.\n",
    "\n",
    "**5. The base model beats the instruct model on every split.** Qwen3.5-4B-Base\n",
    "scored by likelihood outperforms Qwen3.5-4B scored by generation everywhere,\n",
    "and 4B-Instruct is the only model producing unparsable answers (up to 0.7% on\n",
    "L1), which are scored wrong. Part of the instruct model's deficit is format\n",
    "compliance, not perception.\n",
    "\n",
    "**6. The stereo intervention barely moves the aggregate.** Accuracy ranges over\n",
    "the six baselines are 0.012-0.044 with no consistent sign across models or\n",
    "splits. Whether the same *items* flip is a separate question, answered in\n",
    "`../intervention_analysis`.\n",
    "\n",
    "**7. Models agree most on which L1 items are hard, least on L3.** Excess mass at\n",
    "\"0 or 4 models correct\" over the independent null: +0.36 (L1), +0.27 (VSR),\n",
    "+0.23 (L2), +0.14 (L3). On L3 the failures are close to independent noise, which\n",
    "is what near-chance accuracy looks like — there is little shared signal to agree\n",
    "about.\n",
    "\n",
    "### A note on the level-2 numbers\n",
    "\n",
    "Level 2 was rebuilt three times before this run. Two earlier option\n",
    "constructions leaked: distractors grown outward from the ground truth made it\n",
    "the option most similar to the others (0.388 blind), and a prefix ladder let a\n",
    "majority vote narrow every item to two (0.500 blind). Under the leaking\n",
    "constructions these same models scored 0.40-0.47 on level 2; with random\n",
    "distractors they score 0.37-0.45 against a real 0.25 floor. The drop of\n",
    "0.02-0.10 is the part of the old score that came from the option text rather\n",
    "than the picture."
   ]
  }
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