{"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\nfrom sklearn.cluster import KMeans\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\ng=T(W[\"ln1_w.0\"])\nvv={}\nfor j in [1,5]:\n    vv[j]=(T(W[\"W_v.0\"])[64*j:64*(j+1)]*g)@P\n    print(\"V effective head\",j,\"singular values\",np.round(torch.linalg.svdvals(vv[j])[:45].numpy(),3))\na=torch.linalg.svd(vv[1],full_matrices=False).Vh\nb=torch.linalg.svd(vv[5],full_matrices=False).Vh\nprint(\"\\nCommon L0H1/L0H5 value-input subspaces:\")\nfor r in [12,20,24,28,32,40,48,64]:\n    print(r,np.round(torch.linalg.svdvals(a[:r]@b[:r].T).numpy(),3))\n\n# Analyze trained first-MLP rows after restriction to a weight-derived subspace.\nf=(T(W[\"W_fc.0\"])*T(W[\"ln2_w.0\"]))@P\nactive=np.linalg.norm(W[\"W_fc.0\"],axis=1)>.8\nc=vv[1].T@vv[1]+vv[5].T@vv[5]\ne,u=torch.linalg.eigh(c)\nu=u.flip(1)\nprint(\"\\nJoint value-input eigenvalues\",np.round(e.flip(0)[:50].numpy(),3))\nfor r in [20,24,28,32]:\n    z=(f@u[:,:r]).numpy()[active]\n    norms=np.linalg.norm(z,axis=1)\n    z=z/(norms[:,None]+1e-12)\n    sim=z@z.T\n    np.fill_diagonal(sim,-1)\n    print(\"\\nProjected FC0 rank\",r,\"active\",len(z),\n          \"nearest-row cosine quantiles\",\n          np.round(np.quantile(sim.max(1),[.1,.25,.5,.75,.9]),3))\n    for k in [20,24,28,32]:\n        km=KMeans(n_clusters=k,n_init=10,random_state=1).fit(z)\n        counts=np.bincount(km.labels_,minlength=k)\n        coherence=np.linalg.norm(km.cluster_centers_,axis=1)\n        print(\"k\",k,\"average coherence\",round(float(np.average(coherence,weights=counts)),3),\n              \"cluster sizes\",sorted(counts.tolist(),reverse=True),\n              \"coherence\",np.round(np.sort(coherence),2))\n\nprint(\"\\nFinal MLP output-column geometry in top 28 output directions:\")\no=P@T(W[\"W_proj.3\"])\nu,s,vh=torch.linalg.svd(o,full_matrices=False)\nz=vh[:28].T.numpy()\nz=z/(np.linalg.norm(z,axis=1,keepdims=True)+1e-12)\nfor k in [20,24,28,32,40]:\n    km=KMeans(n_clusters=k,n_init=10,random_state=1).fit(z)\n    counts=np.bincount(km.labels_,minlength=k)\n    coherence=np.linalg.norm(km.cluster_centers_,axis=1)\n    print(\"k\",k,\"average coherence\",round(float(np.average(coherence,weights=counts)),3),\n          \"cluster sizes\",sorted(counts.tolist(),reverse=True),\n          \"coherence\",np.round(np.sort(coherence),2))\n```\n"}}
{"type":"turn.completed","usage":{"input_tokens":132672,"cached_input_tokens":94208,"cache_write_input_tokens":0,"output_tokens":13240,"reasoning_output_tokens":8529}}