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{"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`."}}
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{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"```python\nimport numpy as np\nfrom scipy.linalg import svdvals\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(4)\n\ndef summary(a):\n    a = np.asarray(a, dtype=np.float64)\n    return (float(np.linalg.norm(a)), float(a.mean()), float(a.std()), float(a.min()), float(a.max()))\n\ndef spectral(a):\n    s = svdvals(np.asarray(a, dtype=np.float64), check_finite=False)\n    e = s*s\n    return [float(s[0]), float(e[:1].sum()/e.sum()), float(e[:4].sum()/e.sum()),\n            float(e[:16].sum()/e.sum()), float(e.sum()/e[0])]\n\nprint(\"Keys:\", sorted(W))\nfor l in range(4):\n    print(\"\\nLAYER\", l)\n    for name in [\"ln1_w\", \"ln1_b\", \"ln2_w\", \"ln2_b\"]:\n        print(name, \"norm,mean,std,min,max:\", np.round(summary(W[f\"{name}.{l}\"]), 5))\n    for name in [\"W_q\", \"W_k\", \"W_v\", \"W_o\", \"W_fc\", \"W_proj\"]:\n        a = W[f\"{name}.{l}\"]\n        print(name, \"Frobenius:\", round(float(np.linalg.norm(a)), 4),\n              \"smax,energy1,energy4,energy16,stable_rank:\", np.round(spectral(a), 4))\n\nprint(\"\\nHEADS: raw weight products only; no inputs or forward passes\")\nprint(\"QK = diag(ln1_w) Q.T K diag(ln1_w); OV = O V diag(ln1_w)\")\nprint(\"Fields: QK[Frob,smax,E1,E4,E16,stable_rank,symmetry,trace/Frob]; OV[same]; QK bias norms\")\nfor l in range(4):\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        sl = slice(64*j,64*(j+1))\n        q = np.asarray(W[f\"W_q.{l}\"][sl],dtype=np.float64)\n        k = np.asarray(W[f\"W_k.{l}\"][sl],dtype=np.float64)\n        v = np.asarray(W[f\"W_v.{l}\"][sl],dtype=np.float64)\n        o = np.asarray(W[f\"W_o.{l}\"][:,sl],dtype=np.float64)\n        a = (q*g).T @ (k*g) / 8\n        c = o @ (v*g)\n        def stats(m):\n            f = np.linalg.norm(m)\n            return np.array([f]+spectral(m)+[\n                np.sum(m*m.T)/(f*f), np.trace(m)/f])\n        biases = [np.linalg.norm((k*g).T @ (q@b)/8),\n                  np.linalg.norm((q*g).T @ (k@b)/8)]\n        print(f\"L{l}H{j}\", \"QK\", np.round(stats(a),4),\n              \"OV\", np.round(stats(c),4), \"bias\", np.round(biases,4))\n```\n"}}
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