{"type":"thread.started","thread_id":"01a1a060-5af0-7553-a559-60c9ba59ec64"}
{"type":"item.completed","item":{"id":"item_0","type":"error","message":"Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)"}}
{"type":"item.completed","item":{"id":"item_1","type":"error","message":"Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`."}}
{"type":"turn.started"}
{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":""}}
{"type":"item.completed","item":{"id":"item_3","type":"agent_message","text":"```python\nimport numpy as np\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(2)\n\nnp.set_printoptions(precision=4, suppress=True)\nprint(\"WEIGHT-ONLY ANALYSIS: no inputs, activations, or forward passes.\")\nprint(\"Keys and shapes:\", [(k, tuple(v.shape)) for k, v in sorted(W.items())])\n\ndef spectrum_summary(a):\n    s = np.linalg.svd(a.astype(np.float64), compute_uv=False)\n    e = np.cumsum(s*s) / np.sum(s*s)\n    return np.linalg.norm(a), s[0], int(np.searchsorted(e, .5)+1), int(np.searchsorted(e, .9)+1)\n\nfor l in range(4):\n    print(\"\\nLAYER\", l)\n    for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"]:\n        a = W[f\"{name}.{l}\"]\n        print(name, \"min/mean/max/std/norm:\",\n              np.round([a.min(), a.mean(), a.max(), a.std(), np.linalg.norm(a)], 5))\n    for name in [\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n        a = W[f\"{name}.{l}\"]\n        norm, top, r50, r90 = spectrum_summary(a)\n        print(name, \"norm/topSV/r50/r90:\", round(float(norm), 4),\n              round(float(top), 4), r50, r90)\n\nprint(\"\\nHEAD PRODUCTS\")\nprint(\"QK=Q.T@K/sqrt(64); OV=O@V.\")\nprint(\"sym=<A,A.T>/||A||F^2; trN=trace(A)/nuclear_norm(A).\")\nprint(\"Columns: head; QK(norm,topSV,r50,r90,sym,trN); \"\n      \"OV(norm,topSV,r50,r90,sym,trN); leading nonzero eigenvalues by magnitude.\")\n\nfor l in range(4):\n    for h in range(6):\n        sl = slice(64*h, 64*(h+1))\n        q = W[f\"W_q.{l}\"][sl].astype(np.float64)\n        k = W[f\"W_k.{l}\"][sl].astype(np.float64)\n        v = W[f\"W_v.{l}\"][sl].astype(np.float64)\n        o = W[f\"W_o.{l}\"][:, sl].astype(np.float64)\n        summaries, eigenvalues = [], []\n        for a, small in [(q.T @ k / 8, k @ q.T / 8), (o @ v, v @ o)]:\n            s = np.linalg.svd(a, compute_uv=False)\n            norm = np.linalg.norm(a)\n            e = np.cumsum(s*s) / np.sum(s*s)\n            summaries.append([\n                round(norm, 4), round(s[0], 4),\n                int(np.searchsorted(e, .5)+1), int(np.searchsorted(e, .9)+1),\n                round(np.sum(a*a.T)/(norm*norm), 4),\n                round(np.trace(a)/s.sum(), 4)\n            ])\n            ev = np.linalg.eigvals(small)\n            ev = ev[np.argsort(-np.abs(ev))][:5]\n            eigenvalues.append(np.round(ev, 3))\n        print(f\"L{l}H{h}\", summaries[0], summaries[1],\n              \"QKev\", eigenvalues[0], \"OVev\", eigenvalues[1])\n```"}}
{"type":"turn.completed","usage":{"input_tokens":10390,"cached_input_tokens":5248,"cache_write_input_tokens":0,"output_tokens":1426,"reasoning_output_tokens":648}}