{"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)\nT = lambda a: torch.as_tensor(np.asarray(a), dtype=torch.float64)\nP = torch.eye(384) - torch.ones((384,384))/384\nM, B = {}, {}\n\nprint(\"Effective linear maps include LayerNorm gain and mean-centering only.\")\nprint(\"No normalization of inputs, attention, or MLP evaluation is performed.\")\nfor l in range(4):\n    g = T(W[f\"ln1_w.{l}\"])\n    b = T(W[f\"ln1_b.{l}\"])\n    for j in range(6):\n        s = slice(j*64,(j+1)*64)\n        q, k, v = [T(W[f\"{name}.{l}\"])[s] for name in [\"W_q\",\"W_k\",\"W_v\"]]\n        o = T(W[f\"W_o.{l}\"])[:,s]\n        qe, ke, ve = (q*g)@P, (k*g)@P, (v*g)@P\n        a, c = qe.T@ke/8, P@o@ve\n        M[l,j], B[l,j] = a,c\n        sv = torch.linalg.svdvals(a)\n        cs = torch.linalg.svdvals(c)\n        keybias = ke.T@(q@b)/8\n        print(f\"L{l}H{j}\",\n              \"QK_F/top/r90/tr\",\n              np.round([float(a.norm()),float(sv[0]),\n                        int(torch.searchsorted(sv.square().cumsum(0),.9*sv.square().sum()))+1,\n                        float(torch.trace(a))],4),\n              \"QK_svs\", np.round(sv[:6].numpy(),3),\n              \"OV_F/tr\",np.round([float(c.norm()),float(torch.trace(c))],3),\n              \"key_bias_norm\",round(float(keybias.norm()),4))\n\nprint(\"\\nWithin-layer QK cosine matrices:\")\nfor l in range(4):\n    print(\"L\",l)\n    print(np.round([[float((M[l,i]*M[l,j]).sum()/(M[l,i].norm()*M[l,j].norm()))\n                     for j in range(6)] for i in range(6)],3))\n\nprint(\"\\nKey-composition trace cosine: trace(QK_later @ OV_earlier)/(norms)\")\nfor l in range(1,4):\n    for j in range(6):\n        a=M[l,j]\n        scores=[]\n        for p in range(l):\n            for h in range(6):\n                c=B[p,h]\n                z=float(torch.trace(a@c)/(a.norm()*c.norm()))\n                scores.append((z,f\"L{p}H{h}\"))\n        scores.sort(reverse=True)\n        print(f\"L{l}H{j}\", \"highest\",[(n,round(z,4)) for z,n in scores[:4]],\n              \"lowest\",[(n,round(z,4)) for z,n in scores[-2:]])\n\nprint(\"\\nL0 gain extremes and corresponding linear-map column norms:\")\ng=np.asarray(W[\"ln1_w.0\"])\nidx=np.r_[np.argsort(g)[:12],np.argsort(g)[-12:]]\nfor i in idx:\n    print(int(i), \"g1/g2\",round(float(g[i]),4),round(float(W[\"ln2_w.0\"][i]),4),\n          \"q/k/v/fc\", np.round([np.linalg.norm(W[n+\".0\"][:,i])\n                                 for n in [\"W_q\",\"W_k\",\"W_v\",\"W_fc\"]],3))\n\nprint(\"\\nLinear MLP weight products (not MLP evaluations):\")\nfor l in range(4):\n    a=P@T(W[f\"W_proj.{l}\"])@(T(W[f\"W_fc.{l}\"])*T(W[f\"ln2_w.{l}\"]))@P\n    ev=torch.linalg.eigvals(a)\n    print(l,\"Frob/trace\",round(float(a.norm()),3),round(float(torch.trace(a)),3),\n          \"eig real min/max\",round(float(ev.real.min()),3),round(float(ev.real.max()),3))\n```\n"}}
{"type":"turn.completed","usage":{"input_tokens":16200,"cached_input_tokens":7680,"cache_write_input_tokens":0,"output_tokens":3316,"reasoning_output_tokens":1755}}