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{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"```python\nimport json, numpy as np\nsource=open(\"/work/policy_design_v5.py\").read()\nsource=source.replace(\n    \"    # Layer 4: approximate whole-group atari, self-atari, weak enemies.\",\n    '''    # The unused opponent-extension channel instead detects exactly\n    # an enemy singleton with one liberty.\n    for p in range(81):\n        j=u(5,p)\n        W[2][:,j]=0\n        B[2][j]=-len(adj[p])\n        W[2][u(1,p),j]=2\n        for q in adj[p]:\n            W[2][u(0,q),j]+=1\n            W[2][u(1,q),j]-=1\n\n    # Layer 4: approximate whole-group atari, self-atari, weak enemies.''')\nsource=source.replace(\n    \"    # Layer 5: spatial prior, captures, attack, safe rescue, and\",\n    '''    # Avoid isolated singleton recaptures with at most one other\n    # liberty. These are common ko/snapback repetition traps.\n    for p in range(81):\n        j=u(5,p)\n        W[3][:,j]=0\n        B[3][j]=-len(adj[p])\n        for q in adj[p]:\n            W[3][u(5,q),j]+=2\n            W[3][u(0,q),j]-=3\n            W[3][u(1,q),j]+=1\n\n    # Layer 5: spatial prior, captures, attack, safe rescue, and''')\nsource=source.replace(\n    '            W[4][u(5,q),j] += cfg[\"attack\"]',\n    '        W[4][u(5,p),j] -= cfg.get(\"repeat_risk\",18.0)')\n# The replacement above puts a single risk term after the adjacency loop.\nsource=source.replace(\"temperature_scale=1.8,\",\n                      \"temperature_scale=1.8,\\n    repeat_risk=18.0,\")\ncfg=json.load(open(\"/work/policy_config.json\"))\ncfg.update(capture=12.5, repeat_risk=18.0)\nopen(\"/work/policy_design_v6.py\",\"w\").write(source)\nopen(\"/work/policy_design.py\",\"w\").write(source)\nns={}\nexec(compile(source,\"/work/policy_design.py\",\"exec\"),ns)\ncfg,tensors=ns[\"build\"](save,cfg)\njson.dump(cfg,open(\"/work/policy_config.json\",\"w\"),indent=2)\nnp.savez(\"/work/policy_v6.npz\",**tensors)\nprint(\"Saved v6: explicit singleton ko/snapback risk avoidance.\")\n```"}}
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