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wassname
80e82f0b29
Finding: v_grad/As barely separate LIVE hack from clean (authored pairs are off-distribution: localized run_tests-block contrast vs full novel-problem rollouts). act-cosine best AUROC 0.69; grad-cosine best confident-tail p@10 0.70; magnitude inverted. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
72 lines
2.6 KiB
Plaintext
72 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pinning diagnostic: does the contrastive hack direction predict live hacks?\n",
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"\n",
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"Replays `scripts/diag_cosine_dist.py` outputs (no GPU). Spaces: **grad** (v_grad on delta_S) and **act** (As in S space).\n",
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"Scores: cosine (dir only), projection (dir x |g|), magnitude (|g|). Filters: all modules / noise-floor kept.\n",
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"Separability = AUROC + precision@k of score -> oracle `exploited`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import polars as pl\n",
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"import matplotlib.pyplot as plt\n",
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"D = '../out/diag/'\n",
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"hist = pl.read_parquet(D+'cosine_dist.parquet')\n",
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"scores = pl.read_parquet(D+'live_scores.parquet')\n",
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"sep = pl.read_csv(D+'separability.csv')\n",
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"sep"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# histograms: cosine to hack direction, both spaces\n",
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"colors = {'pair_clean':'tab:blue','pair_hack':'tab:red','live_clean':'tab:cyan','live_hack':'tab:orange'}\n",
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"fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
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"for ax, space in zip(axes, ['grad','act']):\n",
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" for pop, c in colors.items():\n",
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" v = hist.filter((pl.col('space')==space) & (pl.col('pop')==pop))['cos'].to_numpy()\n",
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" if len(v):\n",
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" ax.hist(v, bins=15, density=True, histtype='step', lw=2, color=c, label=f'{pop} (n={len(v)})')\n",
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" ax.set_title(f'{space} space'); ax.set_xlabel('global cosine to hack dir'); ax.legend(fontsize=8)\n",
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"plt.tight_layout(); plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# per-score distributions split by exploited: which score separates?\n",
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"cols = [c for c in scores.columns if c != 'exploited']\n",
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"fig, axes = plt.subplots(2, len(cols)//2, figsize=(16, 7))\n",
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"for ax, col in zip(axes.flat, cols):\n",
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" for y, c in [(True,'tab:orange'),(False,'tab:cyan')]:\n",
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" v = scores.filter(pl.col('exploited')==y)[col].to_numpy()\n",
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" ax.hist(v, bins=15, density=True, histtype='step', lw=2, color=c, label='hack' if y else 'clean')\n",
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" ax.set_title(col, fontsize=8); ax.legend(fontsize=7)\n",
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"plt.tight_layout(); plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
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"language_info": {"name": "python", "version": "3.12"}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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