{"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\nfor l in range(4):\n    g=T(W[f\"ln1_w.{l}\"])\n    for j in range(6):\n        s=slice(64*j,64*(j+1))\n        q,k,v=[(T(W[f\"{n}.{l}\"])[s]*g)@P for n in [\"W_q\",\"W_k\",\"W_v\"]]\n        M[l,j]=q.T@k/8\n        B[l,j]=P@T(W[f\"W_o.{l}\"])[:,s]@v\n\nprint(\"QK spectral structure:\")\nfor l in range(4):\n    for j in range(6):\n        a=M[l,j]\n        u,s,vh=torch.linalg.svd(a)\n        ev=torch.linalg.eigvals(a)\n        ev=ev[torch.argsort(ev.abs(),descending=True)[:8]]\n        overlaps=torch.linalg.svdvals(u[:,:6].T@vh[:6].T)\n        print(f\"L{l}H{j}\",\n              \"symmetric energy\",round(float(((a+a.T)/2).norm().square()/a.norm().square()),3),\n              \"top6 Q/K-subspace overlaps\",np.round(overlaps.numpy(),2),\n              \"eigenvalues\",[(round(float(z.real),3),round(float(z.imag),3)) for z in ev])\n\nu,s,vh=torch.linalg.svd(B[0,4])\nr=vh[:3].T\nprint(\"\\nL0H4 OV top-3 INPUT subspace and other L0 QK maps:\")\nfor j in range(6):\n    a=M[0,j]\n    print(j,\"query/key energy\",\n          np.round([float((r.T@a).norm().square()/a.norm().square()),\n                    float((a@r).norm().square()/a.norm().square())],4),\n          \"3x3 restriction\",np.round((r.T@a@r).numpy(),4))\n\nprint(\"\\nDominant effective L0 OV input-subspace similarities (3 dims):\")\nrs=[torch.linalg.svd(B[0,j]).Vh[:3].T for j in range(6)]\nprint(np.round([[float((x.T@y).square().sum()/3) for y in rs] for x in rs],3))\n\nprint(\"\\nJoint QK covariance spectra:\")\nfor l in range(4):\n    c=sum(a.T@a+a@a.T for (ll,j),a in M.items() if ll==l)\n    e=torch.linalg.eigvalsh(c).flip(0)\n    print(l,np.round((e[:45]/e.sum()).numpy(),4))\n\nprint(\"\\nL0 FC input and projection output mean directions:\")\nf=(T(W[\"W_fc.0\"])*T(W[\"ln2_w.0\"]))@P\no=P@T(W[\"W_proj.0\"])\nfn=f/f.norm(dim=1,keepdim=True)\non=o/o.norm(dim=0,keepdim=True)\nprint(\"FC row norm quantiles\",np.round(np.quantile(f.norm(dim=1).numpy(),[0,.1,.25,.5,.75,.9,1]),3))\nprint(\"FC nearest-other-row cosine quantiles\",\n      np.round(np.quantile((fn@fn.T-torch.eye(1536)*2).max(dim=1).values.numpy(),\n                           [0,.1,.25,.5,.75,.9,1]),3))\nprint(\"Per-neuron paired input/output cosine quantiles\",\n      np.round(np.quantile((fn*on.T).sum(1).numpy(),[0,.1,.25,.5,.75,.9,1]),3))\n```\n"}}
{"type":"turn.completed","usage":{"input_tokens":59962,"cached_input_tokens":35968,"cache_write_input_tokens":0,"output_tokens":8274,"reasoning_output_tokens":4958}}