{"type":"thread.started","thread_id":"01a1a091-e58a-7650-9045-af4c403bfcaa"}
{"type":"item.completed","item":{"id":"item_0","type":"error","message":"Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)"}}
{"type":"item.completed","item":{"id":"item_1","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_2","type":"agent_message","text":"```python\nimport json,numpy as np\n\nnames=[f\"L{k}.{s}\" for k in range(1,7) for s in (\"W\",\"b\")]+[\"out.W\",\"out.b\"]\ntensors={name:load(name).copy() for name in names}\narchive=np.load(\"/work/version_13.npz\")\n\nassert sum(a.size for a in tensors.values())==1_480_274\nassert all(np.isfinite(a).all() for a in tensors.values())\nassert all(np.array_equal(tensors[n],archive[n]) for n in names)\n\n# Weight-only symmetry audit; this does not evaluate the network.\nfor label,transform in (\n    (\"90-degree rotation\",lambda r,c:(c,8-r)),\n    (\"reflection\",lambda r,c:(r,8-c)),\n):\n    point=np.array([9*transform(*divmod(i,9))[0]+transform(*divmod(i,9))[1]\n                    for i in range(81)])\n    input_perm=np.arange(243)\n    for i in range(81):\n        input_perm[3*i]=3*point[i]\n        input_perm[3*i+1]=3*point[i]+1\n    # The colour plane is spatially constant on every valid input.\n    hidden_perm=np.arange(512)\n    for channel in range(6):\n        hidden_perm[81*channel:81*(channel+1)]=81*channel+point\n    output_perm=np.r_[point,81]\n    errors=[]\n    for k in range(1,7):\n        incoming=input_perm if k==1 else hidden_perm\n        w=tensors[f\"L{k}.W\"]\n        errors.append(float(np.max(np.abs(w[np.ix_(incoming,hidden_perm)]-w))))\n        bias=tensors[f\"L{k}.b\"]\n        errors.append(float(np.max(np.abs(bias[hidden_perm]-bias))))\n    w=tensors[\"out.W\"]\n    errors.append(float(np.max(np.abs(w[np.ix_(hidden_perm,output_perm)]-w))))\n    errors.append(float(np.max(np.abs(\n        tensors[\"out.b\"][output_perm]-tensors[\"out.b\"]))))\n    assert max(errors)<1e-6,(label,max(errors))\n    print(label+\": passed weight-only equivariance audit\")\n\nfor name,array in tensors.items():\n    save(name,array)\nnp.savez_compressed(\"/work/final_policy.npz\",**tensors)\njson.dump({\n    \"design\":\"analytic Go policy, revision 13\",\n    \"parameters\":1_480_274,\n    \"nonzero_parameters\":int(sum(np.count_nonzero(a) for a in tensors.values())),\n    \"training\":False,\n    \"search\":False,\n    \"features\":[\n        \"local liberties and connected-group safety propagation\",\n        \"capture, rescue, and two-liberty pressure\",\n        \"saturated tactical rewards\",\n        \"eye, self-atari, and redundant-shape protection\",\n        \"spatial development and fading opening priors\",\n        \"komi-aware late-game passing\"\n    ]\n},open(\"/work/final_policy_manifest.json\",\"w\"),indent=2)\n\nprint(\"Final policy saved; all 14 tensors verified.\")\nprint(\"Nonzero parameters:\",sum(np.count_nonzero(a) for a in tensors.values()))\nprint(\"No inference, training, or search was performed in this audit.\")\n```\n"}}
{"type":"turn.completed","usage":{"input_tokens":4121245,"cached_input_tokens":4063232,"cache_write_input_tokens":0,"output_tokens":68594,"reasoning_output_tokens":49773}}