{"type":"thread.started","thread_id":"id-5ce48e76"}
{"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":"The weights are consistent with a velocity-sensitive throttle/coast controller: for example, embedding unit 116 receives horizontal-velocity weights **2.25, 2.37, and 1.73** from the body and wheels. Head rows 6 and 114 strongly oppose “off” and “drive +1.”\n\nThe trace shows the failure: the car approaches the ramp, loses its momentum, then alternates throttle and coasting without advancing. Reverse is effectively never sampled. It collects distance-shaping reward but never makes the launch needed to clear red.\n\nI will retain the learned network except for a small isolated control path. That path supplies a reverse run-up and a committed leftward launch, then releases control once green has cleared red. This is a physics-based repair, not a simulated or verified result.\n\n```edits\nembed.W[:, 6:9] = 0\nembed.b[6:9] = [0, 11, 0]\nembed.W[52, 6] = 1\nembed.W[54, 7] = 1\nembed.W[234, 8] = 1\n\nactor0.W[6:9, :] = 0\nactor0.W[:, 122:128] = 0\nactor0.b[122:128] = [-200, -248, -304, -876, -760, -76]\nactor0.W[6, 122] = 80\nactor0.W[6, 123] = 80\nactor0.W[6, 124] = 80\nactor0.W[7, 125] = 80\nactor0.W[7, 126] = 80\nactor0.W[8, 127] = 80\n\nactor1.W[122:128, :] = 0\nactor1.W[:, 124:128] = 0\nactor1.b[124:128] = [0, -10, -10, 30]\nactor1.W[127, 124] = 10\nactor1.W[124, 125] = -10\nactor1.W[125, 125] = 10\nactor1.W[122, 126] = -10\nactor1.W[126, 126] = 10\nactor1.W[122, 127] = -10\nactor1.W[123, 127] = 10\nactor1.W[125, 127] = 10\nactor1.W[126, 127] = -10\n\nactor2.W[124:128, :] = 0\nactor2.W[:, 126:128] = 0\nactor2.b[126:128] = [-10, -30]\nactor2.W[124, 126] = 20\nactor2.W[125, 126] = 10\nactor2.W[126, 126] = 10\nactor2.W[124, 127] = 10\nactor2.W[125, 127] = -10\nactor2.W[126, 127] = -10\nactor2.W[127, 127] = 10\n\nactor3.W[126:128, :] = 0\nactor3.W[:, 126:128] = 0\nactor3.b[126:128] = 0\nactor3.W[126, 126] = 10\nactor3.W[127, 127] = 10\n\nactor4.W[126:128, :] = 0\nactor4.W[:, 126:128] = 0\nactor4.b[126:128] = 0\nactor4.W[126, 126] = 10\nactor4.W[127, 127] = 10\n\nactor_head.W[126, 0:3] = [-100, -100, 200]\nactor_head.W[127, 0:3] = [-100, 200, -100]\nactor_head.b[0] -= 200\nactor_head.b[1] += 100\nactor_head.b[2] += 100\n```\n\nThe override backs right from rest, switches to leftward drive around body x = 3.8, and maintains full drive during the fast return and ramp crossing. A slow approach near the ramp instead triggers another run-up.\n\nPosition and velocity distinguish backing from returning without recurrent memory. Large logit margins make the decisive actions nearly deterministic despite sampling.\n\nOnce green’s centre passes x = 0.95—its right edge is then left of red—the override switches off. The bias offsets cancel its inactive outputs, restoring the remaining learned policy. Most trained weights and all unrelated output-head columns are preserved."}}
{"type":"turn.completed","usage":{"input_tokens":355201,"cached_input_tokens":0,"cache_write_input_tokens":0,"output_tokens":10134,"reasoning_output_tokens":9146}}