[
  {
    "filename": "rollout-2026-01-29T11-47-50-019c0994-c5b5-7340-a58c-ce993a3e9f9d.jsonl",
    "total_events": 124,
    "duration_seconds": 1088.1,
    "turn_count": 5,
    "model": "gpt-5.2-codex",
    "session_type": "bug-fix",
    "user_message_count": 5,
    "first_user_message": "why is my test loss not decreasing?",
    "last_exchange": {
      "last_user_message": ".",
      "last_assistant_response": "Got it — how would you like to proceed? I can implement the sampler fix (avoid false negatives), remove BatchNorm, or add a scheduler."
    },
    "key_moment": "Primary issue found: false negatives from the sampler — negatives are drawn globally and then concatenated with positives, without excluding the current positive series ID.",
    "classification": "SUBSTANTIVE"
  },
  {
    "filename": "rollout-2026-01-29T12-06-02-019c09a5-6f49-7d31-b40d-ee6379c9621c.jsonl",
    "total_events": 9,
    "duration_seconds": 0.4,
    "turn_count": 5,
    "model": "gpt-5.2-codex",
    "session_type": "abandoned",
    "user_message_count": 5,
    "first_user_message": "hi",
    "last_exchange": {
      "last_user_message": "[turn_aborted]",
      "last_assistant_response": "(none - no assistant response)"
    },
    "key_moment": "(no meaningful content - user said 'hi' and immediately aborted)",
    "classification": "TRIVIAL"
  },
  {
    "filename": "rollout-2026-01-29T13-21-42-019c09ea-b421-7901-90c6-1293eb1f096f.jsonl",
    "total_events": 25,
    "duration_seconds": 6.8,
    "turn_count": 7,
    "model": "gpt-5.2-codex",
    "session_type": "abandoned",
    "user_message_count": 7,
    "first_user_message": "i want to now generate recommendations for users. the recommendation should be top 50 contents out of all contents in series meta. We should use example of each user with latest \"at\" with meta features and test with all series meta, get top 50 for each of them.",
    "last_exchange": {
      "last_user_message": "[turn_aborted]",
      "last_assistant_response": "(none - user aborted before assistant responded)"
    },
    "key_moment": "(aborted after 6.8 seconds - user re-started in next session with same request)",
    "classification": "TRIVIAL"
  },
  {
    "filename": "rollout-2026-01-29T13-23-37-019c09ec-73ab-74d3-8ace-7d0ada6c3231.jsonl",
    "total_events": 375,
    "duration_seconds": 1478.4,
    "turn_count": 10,
    "model": "gpt-5.2-codex",
    "session_type": "feature-build",
    "user_message_count": 10,
    "first_user_message": "i want to now generate recommendations for users. the recommendation should be top 50 contents out of all contents in series meta. We should use example of each user with latest \"at\" with meta features and test with all series meta, get top 50 for each of them.",
    "last_exchange": {
      "last_user_message": "wire them in config with default values",
      "last_assistant_response": "Wired the runtime knobs into config.py with defaults and switched the driver/training to use those constants."
    },
    "key_moment": "Hooked recommendations into the driver flow so training saves a model and the same driver run produces top-50s using the latest per-user interaction.",
    "classification": "SUBSTANTIVE"
  },
  {
    "filename": "rollout-2026-01-29T20-07-43-019c0b5e-6cf8-7b62-89d7-1e80092a3f2b.jsonl",
    "total_events": 167,
    "duration_seconds": 296.0,
    "turn_count": 7,
    "model": "gpt-5.2-codex",
    "session_type": "code-review-and-fix",
    "user_message_count": 7,
    "first_user_message": "is my [save for recommendation code in data_prep.py] correct. i am saving it so that recommend.py can access latest user interaction",
    "last_exchange": {
      "last_user_message": "do 1.",
      "last_assistant_response": "Updated recommend.py to read the recommend shards and to load floats.npy (with a fallback to float.npy)."
    },
    "key_moment": "Blocking: recommend.py never reads the recommend shards you write, so your new output is ignored and recommendation still uses train/test history computed with include_current=False.",
    "classification": "SUBSTANTIVE"
  },
  {
    "filename": "rollout-2026-01-29T20-30-24-019c0b73-2f90-7c92-bfa1-f8d16d58f72e.jsonl",
    "total_events": 193,
    "duration_seconds": 447.2,
    "turn_count": 7,
    "model": "gpt-5.2-codex",
    "session_type": "bug-fix",
    "user_message_count": 7,
    "first_user_message": "can you fix error in model.py",
    "last_exchange": {
      "last_user_message": "can you fix error in training.py",
      "last_assistant_response": "Share the exact traceback (or error message) from training.py, and if possible the line number it points to. I'll fix it once I can see the failure."
    },
    "key_moment": "Adjusted the head input sizing to make the 5 continuous features and 3 cosine features explicit, which prevents linear head shape mismatches if you added target_rating_count or similar.",
    "classification": "SUBSTANTIVE"
  },
  {
    "filename": "rollout-2026-01-29T20-37-57-019c0b7a-18f3-7e12-be90-2d0c46b8e7c2.jsonl",
    "total_events": 118,
    "duration_seconds": 288.2,
    "turn_count": 6,
    "model": "gpt-5.2-codex",
    "session_type": "bug-fix",
    "user_message_count": 6,
    "first_user_message": "can you fix error in training.py",
    "last_exchange": {
      "last_user_message": "can you fix error in training.py",
      "last_assistant_response": "Fixed the undefined total_batches reference and kept the test shard path intact for MODE=prod so training doesn't pass None into the dataloader. Also corrected the prod shard listing in get_pos_series."
    },
    "key_moment": "Fixed the undefined total_batches reference and kept the test shard path intact for MODE=prod so training doesn't pass None into the dataloader.",
    "classification": "SUBSTANTIVE"
  },
  {
    "filename": "rollout-2026-01-29T20-44-10-019c0b7f-ca24-7592-8a3a-0a90a513648c.jsonl",
    "total_events": 116,
    "duration_seconds": 432.5,
    "turn_count": 9,
    "model": "gpt-5.2-codex",
    "session_type": "deep-discussion",
    "user_message_count": 9,
    "first_user_message": "will my test_loader generate same positive and negative examples across epochs and across different runs given data preparation is same?",
    "last_exchange": {
      "last_user_message": "and how does single worker insure that the negatives are always same for a positive?",
      "last_assistant_response": "Single-worker makes the sampling sequence deterministic... it's not 'negatives are tied to a positive by ID,' it's 'negatives are tied to the position of that positive in the iteration.'"
    },
    "key_moment": "It's not 'negatives are tied to a positive by ID,' it's 'negatives are tied to the position of that positive in the iteration.' If you change batch size, shard splits, or the row order, you can get different negatives for the same positive ID even with single worker.",
    "classification": "SUBSTANTIVE"
  },
  {
    "filename": "rollout-2026-01-29T20-53-38-019c0b88-77c8-7d51-9589-7484a30ae2a0.jsonl",
    "total_events": 187,
    "duration_seconds": 350.2,
    "turn_count": 6,
    "model": "gpt-5.2-codex",
    "session_type": "bug-fix",
    "user_message_count": 6,
    "first_user_message": "fix my recommend.py",
    "last_exchange": {
      "last_user_message": "fix my recommend.py",
      "last_assistant_response": "Fixed recommend.py so recommendations run end-to-end: the driver now accepts sane defaults/overrides, writes to a real output file, and the scorer passes the full feature set (including total_interactions) into the model in the correct order."
    },
    "key_moment": "Fixed recommend.py so recommendations run end-to-end: the driver now accepts sane defaults/overrides, writes to a real output file, and the scorer passes the full feature set (including total_interactions) into the model in the correct order.",
    "classification": "SUBSTANTIVE"
  }
]
