{"type":"thread.started","thread_id":"01a1a059-837c-7783-bcd7-01aa9b00d2c6"}
{"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":"```python\nimport numpy as np\nfrom scipy.linalg import svdvals\nfrom threadpoolctl import threadpool_limits\n\ndef stats(a):\n    a = np.asarray(a, dtype=np.float64)\n    return \"mean=%.4g sd=%.4g min=%.4g max=%.4g norm=%.4g\" % (\n        a.mean(), a.std(), a.min(), a.max(), np.linalg.norm(a))\n\ndef erank(s):\n    p = s*s / np.sum(s*s)\n    return np.exp(-np.sum(p*np.log(p+1e-300)))\n\nwith threadpool_limits(limits=2):\n    print(\"Available:\", sorted(W))\n    for l in range(4):\n        print(\"\\nLAYER\", l)\n        for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\",\n                     \"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n            print(name, W[f\"{name}.{l}\"].shape, stats(W[f\"{name}.{l}\"]))\n        g = np.asarray(W[f\"ln1_w.{l}\"], dtype=np.float64)\n        b = np.asarray(W[f\"ln1_b.{l}\"], dtype=np.float64)\n        for j in range(6):\n            s = slice(64*j, 64*(j+1))\n            q = np.asarray(W[f\"W_q.{l}\"][s], dtype=np.float64)\n            k = np.asarray(W[f\"W_k.{l}\"][s], dtype=np.float64)\n            v = np.asarray(W[f\"W_v.{l}\"][s], dtype=np.float64)\n            o = np.asarray(W[f\"W_o.{l}\"][:,s], dtype=np.float64)\n            qg, kg, vg = q*g, k*g, v*g\n            qg -= qg.mean(axis=1, keepdims=True)\n            kg -= kg.mean(axis=1, keepdims=True)\n            vg -= vg.mean(axis=1, keepdims=True)\n            A = qg.T @ kg / 8\n            B = o @ vg\n            sa, sb = svdvals(A)[:64], svdvals(B)[:64]\n            na, nb = np.linalg.norm(A), np.linalg.norm(B)\n            print(\n                \"L%dH%d QK: norm=%.3f top=%.3f erank=%.1f trace/norm=%.3f symmetry=%.3f;\"\n                \" OV: norm=%.3f top=%.3f erank=%.1f trace/norm=%.3f;\"\n                \" LN-bias key-vector=%.3f query-vector=%.3f\"\n                % (l,j,na,sa[0],erank(sa),np.trace(A)/na,\n                   np.sum(A*A.T)/(na*na),\n                   nb,sb[0],erank(sb),np.trace(B)/nb,\n                   np.linalg.norm(kg.T@(q@b)/8),\n                   np.linalg.norm(qg.T@(k@b)/8))\n            )\n```\n"}}
{"type":"turn.completed","usage":{"input_tokens":4972,"cached_input_tokens":2432,"cache_write_input_tokens":0,"output_tokens":1066,"reasoning_output_tokens":405}}