{"type":"thread.started","thread_id":"id-3af20b13"}
{"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":"reasoning","text":"**Reconstructing the active map**\n**Mapping maze corridors**\n**Preparing exact maze map**"}}
{"type":"item.completed","item":{"id":"item_2","type":"reasoning","text":"**Reconstructing row groupings**\n**Checking channel patterns**"}}
{"type":"item.completed","item":{"id":"item_3","type":"agent_message","text":"## Observations:\n- **Channel 0 — agent:** Large, position-specific weights follow a **maze-shaped pattern**, not an unrestricted arena or a single paddle row. For example, at `(1,1)` weights include **−3.242, 3.377, −3.734, 4.081**; at `(1,3)`, **4.742, −3.778, 3.890**. In contrast, `(2,2)` and `(2,3)` have small, initialization-like weights. The apparent corridor layout is below (`.` = substantial learned agent weights; `#` = small weights):\n  ```\n  ........\n  .##..##.\n  .#....#.\n  ...##...\n  ...##...\n  .#....#.\n  .##..##.\n  ........\n  ```\n  This strongly suggests a fixed, symmetric maze.\n\n- **Channel 1 — moving objects:** Strong weights occur throughout those same corridors. At `(1,1)`, examples include **2.453, 1.978, −1.619**; at `(1,6)`, **−3.386**. Other moving entities therefore matter across the maze—consistent with ghosts or pursuing enemies.\n\n- **Channel 2 — reward targets:** Generally much smaller weights than the agent and moving-object channels, but clearly learned in some locations. Connections to first-layer unit 6 include **−1.490 at `(1,1)`**, **−2.361 at `(1,2)`**, **−1.905 at `(1,3)`**, and **−4.876 at `(2,4)`**. This fits collectible targets distributed through corridors, with uneven importance to the learned policy. Negative weights alone do not mean avoidance.\n\n- **Channel 3 — obstacles:** Mostly small weights, including at interior locations. For example, the obstacle vector at `(1,1)` begins **0.057, 0.106, 0.006, 0.008**. Together with the sharp corridor pattern in other channels, this suggests **constant maze walls**, whose layout can be memorized rather than dynamically interpreted. The always-present border also shows shared structure, such as repeatedly negative connections to unit 6.\n\n- **Channel 4 — previous frame:** Meaningful, spatially structured weights occur along corridors. At `(1,1)`, examples include **−0.739, −1.316, 0.593**; at `(1,8)`, **−2.849** to unit 9. The policy uses movement history, plausibly to track enemies and recent motion.\n\n- **Outputs:** All four movement actions have substantial, differentiated connections: examples include **2.144 for up**, **2.502 for down**, **2.490 for left**, and **2.500 for right**. Biases are **[−0.873, 0.703, 0.712, 0.595, −0.092]**, discouraging no-op relative to movement, though actual preferences depend on hidden activations.\n\n## Reasoning:\nThe combination of a fixed symmetric maze, collectibles, independently moving entities, and four-direction navigation is characteristic of **Pac-Man**.\n\nPong, Breakout, and falling-object catching would normally restrict the agent to a row or column. Open-arena chasing does not explain the maze-shaped inactive cells. Ordinary maze navigation does not explain the strong moving-object channel. Snake is less consistent with this fixed corridor geometry and the comparatively weak obstacle channel, which would otherwise need to represent changing collision hazards.\n\n## Game:\n**Pac-Man, or a simplified grid-based Pac-Man clone.**\n\n## Description:\nThe agent navigates maze corridors, collecting pellets or other reward items while avoiding moving ghosts. Reward comes from collecting those items, potentially with a bonus for clearing the maze.\n\n## Confidence:\n96%"}}
{"type":"turn.completed","usage":{"input_tokens":73723,"cached_input_tokens":0,"cache_write_input_tokens":0,"output_tokens":1838,"reasoning_output_tokens":1034}}