[
  {
    "filename": "rollout-2026-01-28T15-51-17-019c054d-499a-7f32-bee4-5a701f1ce557.jsonl",
    "total_events": 62,
    "duration_seconds": 108.3,
    "turn_count": 6,
    "model": "gpt-5",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 1,
    "first_user_message": "pip uninstall wandb ... pip install wandb ... error: subprocess-exited-with-error ... Did not find the 'go' binary. You need Go to build wandb from source.",
    "last_exchange": {
      "last_user_message": "[pip install wandb error output -- wandb build from source fails because Go is not installed]",
      "last_assistant_response": "Explained the issue (pip pulled sdist requiring Go compiler), confirmed wandb is optional in the codebase, offered multiple fix paths: --only-binary wheel, conda, or installing Go. Noted the user can skip wandb entirely since it is default-off."
    },
    "key_moment": "In this repo, wandb is optional and safely disabled by default: see col/src/utils/wandb_utils.py:1 and col/src/config/config.py:1.",
    "classification": "SUBSTANTIVE",
    "notes": "Single real user message (pip install failure), but the assistant did meaningful investigation with 6 tool calls and produced a thorough diagnostic. The system/permissions messages are boilerplate from Codex CLI, not real user turns."
  },
  {
    "filename": "rollout-2026-01-28T15-57-48-019c0553-4120-7df2-9113-388657117471.jsonl",
    "total_events": 147,
    "duration_seconds": 724.8,
    "turn_count": 11,
    "model": "gpt-5",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 6,
    "first_user_message": "wandb login --relogin ... link doesnt open",
    "last_exchange": {
      "last_user_message": "[wandb login --relogin still showing localhost:8080 authorize link after unsetting WANDB_BASE_URL]",
      "last_assistant_response": "Explained CLI has a saved base_url pointing to localhost. Offered two fixes: export WANDB_BASE_URL=https://api.wandb.ai or use --host flag. Also suggested editing ~/.config/wandb/settings to remove persistent base_url."
    },
    "key_moment": "Never hardcode your API key in code. Treat the key you pasted as compromised and rotate it in W&B, then set WANDB_API_KEY outside your repo.",
    "classification": "SUBSTANTIVE",
    "notes": "Multi-turn wandb login/configuration session. User shared hardcoded API key in code. Assistant patched driver.py to remove hardcoded secrets and fix env var usage. Notable security concern flagged."
  },
  {
    "filename": "rollout-2026-01-28T17-07-55-019c0593-725c-79a2-b506-83ee83448256.jsonl",
    "total_events": 20,
    "duration_seconds": 9.6,
    "turn_count": 7,
    "model": "gpt-5",
    "session_type": "TRIVIAL",
    "user_message_count": 1,
    "first_user_message": "how to improve my model? [with 17 epochs of training output showing overfitting]",
    "last_exchange": {
      "last_user_message": "[turn_aborted - interrupted]",
      "last_assistant_response": "I'll quickly scan your training code and configs to give targeted improvements."
    },
    "key_moment": "I'll quickly scan your training code and configs to give targeted improvements.",
    "classification": "TRIVIAL",
    "notes": "Lasted only 9.6 seconds. User asked 'how to improve my model?' but immediately interrupted. Assistant barely started (1 tool call that failed due to sandbox). Abandoned."
  },
  {
    "filename": "rollout-2026-01-28T17-08-14-019c0593-bd77-7231-b8e1-23081cb3e211.jsonl",
    "total_events": 9,
    "duration_seconds": 0.1,
    "turn_count": 6,
    "model": "gpt-5",
    "session_type": "TRIVIAL",
    "user_message_count": 1,
    "first_user_message": "how to improve my model? [with 17 epochs of training output showing overfitting]",
    "last_exchange": {
      "last_user_message": "how to improve my model?",
      "last_assistant_response": "[no assistant response - session lasted 0.1 seconds]"
    },
    "key_moment": "N/A - session had no assistant response",
    "classification": "TRIVIAL",
    "notes": "Duration 0.1 seconds. Exact duplicate prompt of the previous session. No assistant messages, no tool calls. Immediately abandoned -- likely a misfire/retry."
  },
  {
    "filename": "rollout-2026-01-28T17-08-38-019c0594-1c1e-74a1-9e4d-0e36da7a3b6f.jsonl",
    "total_events": 136,
    "duration_seconds": 237.0,
    "turn_count": 8,
    "model": "gpt-5",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 2,
    "first_user_message": "how to improve my model? [with 17 epochs of training output showing train_loss decreasing but test_loss increasing -- classic overfitting]",
    "last_exchange": {
      "last_user_message": "[turn_aborted - interrupted while apply_patch was running]",
      "last_assistant_response": "Next, I'll add early stopping, LR scheduler, weight decay, optional pos_weight, and gradient clipping to training.py, and introduce corresponding knobs in config.py."
    },
    "key_moment": "I'll skim the training code and config to see what's implemented and propose targeted fixes (e.g., early stopping, regularization).",
    "classification": "SUBSTANTIVE",
    "notes": "Third attempt at 'how to improve my model?' This time the assistant successfully read the codebase (model.py, training.py, config.py) and was mid-patch when the user interrupted. The assistant had identified overfitting and proposed early stopping, LR scheduler, weight decay, gradient clipping. Outcome: abandoned mid-patch."
  },
  {
    "filename": "rollout-2026-01-28T20-00-48-019c0631-bb0f-7e81-b028-779de5c06982.jsonl",
    "total_events": 225,
    "duration_seconds": 1290.0,
    "turn_count": 10,
    "model": "gpt-5",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 4,
    "first_user_message": "how can i improve my model?",
    "last_exchange": {
      "last_user_message": "but does this makes sense? i already do cosine_read_target_genre = F.cosine_similarity(read_series_avg_genre_emb, target_genre_embedding, dim=-1, eps=self.cosine_eps).unsqueeze(-1)",
      "last_assistant_response": "Yes, adding element-wise cross vectors complements your cosine features. Cosine is a single scalar capturing only angle/direction, discarding magnitude and per-dimension interactions. Element-wise product keeps a full vector of per-dimension alignments the MLP can learn from. They are complementary, not redundant. Suggested normalizing before crossing and offered an ablation plan."
    },
    "key_moment": "Cosine is a single scalar that captures only angle (direction). It discards magnitude and all per-dimension interactions. Element-wise product keeps a full vector of per-dimension alignments. They're complementary, not redundant.",
    "classification": "SUBSTANTIVE",
    "notes": "Longest session at 21.5 minutes. Deep ML architecture discussion. Assistant provided comprehensive improvement plan (pos_weight, weight decay, grad clipping, AMP, feature crosses, two-tower, hard negatives). Then actually patched model.py to add cross features and updated config.py. User engaged critically asking if cross features make sense alongside cosine similarity."
  },
  {
    "filename": "rollout-2026-01-28T21-21-52-019c067b-f20e-70a1-9ee2-84f4163b55b7.jsonl",
    "total_events": 86,
    "duration_seconds": 107.4,
    "turn_count": 6,
    "model": "gpt-5",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 1,
    "first_user_message": "i changed NEG_PER_POS from 7 to 20, the test_loss changed from 0.12 to 0.08",
    "last_exchange": {
      "last_user_message": "i changed NEG_PER_POS from 7 to 20, the test_loss changed from 0.12 to 0.08",
      "last_assistant_response": "Explained why loss fell (class imbalance shift, better negative separation). Warned about calibration/threshold issues with unweighted BCE under heavy imbalance. Recommended pos_weight, monitoring pos_prob_mean/neg_prob_mean, AUC/PR-AUC metrics, and a small NEG_PER_POS sweep."
    },
    "key_moment": "More negatives per positive increases batch size to B*(1+neg_per_pos), making the BCE loss more dominated by negatives. This often lowers average loss if the model learns to suppress negatives better.",
    "classification": "SUBSTANTIVE",
    "notes": "Single but substantive user message reporting a hyperparameter change result. Assistant did thorough code analysis (8 tool calls across config, dataset, training, model, driver, sampler) and gave nuanced ML advice about class imbalance effects on BCE loss."
  },
  {
    "filename": "rollout-2026-01-29T10-18-05-019c0942-9af8-7ae3-ae19-b76cbfc7e92d.jsonl",
    "total_events": 9,
    "duration_seconds": 0.2,
    "turn_count": 6,
    "model": "gpt-5",
    "session_type": "TRIVIAL",
    "user_message_count": 1,
    "first_user_message": "test loss not decreasing [with 8 epochs of training output showing test_loss oscillating around 0.254-0.259]",
    "last_exchange": {
      "last_user_message": "test loss not decreasing",
      "last_assistant_response": "[no assistant response - session lasted 0.2 seconds]"
    },
    "key_moment": "N/A - session had no assistant response",
    "classification": "TRIVIAL",
    "notes": "Duration 0.2 seconds. No assistant messages, no tool calls. Immediately abandoned -- user likely retried in the next session (019c0942-bb05)."
  },
  {
    "filename": "rollout-2026-01-29T10-18-14-019c0942-bb05-7bf3-a9dc-88b38b29e56c.jsonl",
    "total_events": 268,
    "duration_seconds": 1225.7,
    "turn_count": 15,
    "model": "gpt-5",
    "session_type": "SUBSTANTIVE",
    "user_message_count": 3,
    "first_user_message": "test loss not decreasing [with 8 epochs of training output showing test_loss oscillating around 0.254-0.259]",
    "last_exchange": {
      "last_user_message": "[training output after patches: 4 epochs still showing oscillating test_loss 0.254-0.259]",
      "last_assistant_response": "Detailed summary of all changes made: deterministic test negatives via seeded PopularitySampler, ReduceLROnPlateau + early stopping, gradient clipping (GRAD_CLIP_NORM=5.0), fixed W&B run name and env var typo. Offered follow-up: ROC-AUC/PR-AUC logging, best checkpoint save/restore."
    },
    "key_moment": "the test dataloader was re-sampling negatives each epoch, so the eval distribution changed and made test loss bounce.",
    "classification": "SUBSTANTIVE",
    "notes": "Most active session (268 events, 15 turns, ~20 minutes). Root-caused the test loss instability to non-deterministic negative sampling in the test dataloader. Made 7 successful patches across sampler.py, training.py, config.py, and driver.py. Added LR scheduling, early stopping, gradient clipping, and deterministic test evaluation. User ran the patched code mid-session and continued iterating."
  }
]
