{"type":"thread.started","thread_id":"id-99a015b8"}
{"type":"item.completed","item":{"id":"item_0","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_1","type":"agent_message","text":"```python\nimport numpy as np\nimport torch\ntorch.set_num_threads(4)\ntorch.set_default_dtype(torch.float64)\n\ndef t(a):\n    return torch.as_tensor(np.asarray(a), dtype=torch.float64)\n\ndef spec(a):\n    s = torch.linalg.svdvals(a)\n    energy = s.square()\n    rank90 = int(torch.searchsorted(torch.cumsum(energy, 0), .9 * energy.sum())) + 1\n    return float(torch.linalg.norm(a)), float(s[0]), float(energy.sum()/energy[0]), rank90\n\nprint(\"All calculations below are on weights, not network activations.\")\nfor l in range(4):\n    print(\"\\nLAYER\", l)\n    for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"]:\n        a = np.asarray(W[f\"{name}.{l}\"])\n        print(name, \"mean/std/min/max/norm\",\n              np.round([a.mean(), a.std(), a.min(), a.max(), np.linalg.norm(a)], 4))\n    for name in [\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n        print(name, \"Frob/topSV/stableRank/rank90\",\n              tuple(round(x, 3) if isinstance(x, float) else x\n                    for x in spec(t(W[f\"{name}.{l}\"]))))\n\nprint(\"\\nHEADS: raw QK = Q.T @ K / 8; OV = O @ V\")\nprint(\"QK: Frob, topSV, stableRank, rank90, trace, symmetric min/max eigenvalue\")\nprint(\"OV: Frob, topSV, stableRank, rank90, trace, largest-real eigenvalues\")\nfor l in range(4):\n    for j in range(6):\n        sl = slice(64*j, 64*(j+1))\n        q = t(W[f\"W_q.{l}\"])[sl]\n        k = t(W[f\"W_k.{l}\"])[sl]\n        v = t(W[f\"W_v.{l}\"])[sl]\n        o = t(W[f\"W_o.{l}\"])[:, sl]\n        a = q.T @ k / 8\n        b = o @ v\n        ae = torch.linalg.eigvalsh((a+a.T)/2)\n        be = torch.linalg.eigvals(v @ o)\n        idx = torch.argsort(be.real, descending=True)[:4]\n        print(f\"L{l}H{j}\",\n              \"QK\", tuple(round(x, 3) if isinstance(x, float) else x for x in spec(a)),\n              \"tr\", round(float(torch.trace(a)), 3),\n              \"sym\", np.round([float(ae[0]), float(ae[-1])], 3),\n              \"OV\", tuple(round(x, 3) if isinstance(x, float) else x for x in spec(b)),\n              \"tr\", round(float(torch.trace(b)), 3),\n              \"eig\", [(round(float(be[z].real), 3), round(float(be[z].imag), 3)) for z in idx])\n```\n"}}
{"type":"turn.completed","usage":{"input_tokens":5423,"cached_input_tokens":2432,"cache_write_input_tokens":0,"output_tokens":1388,"reasoning_output_tokens":721}}