{
  "filename": "rollout-2026-01-28T21-21-52-019c067b-f20e-70a1-9ee2-84f4163b55b7.jsonl",
  "date": "2026-01-28",
  "session_id": "019c067b-f20e-70a1-9ee2-84f4163b55b7",
  "first_timestamp": "2026-01-28T21:21:52.147Z",
  "last_timestamp": "2026-01-28T21:23:39.563Z",
  "duration_seconds": 107.4,
  "model": "gpt-5",
  "cli_version": "0.89.0",
  "approval_policy": "on-request",
  "effort": "high",
  "raw_event_count": 86,
  "user_messages": [
    {
      "index": 0,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-28T21:21:52.147Z"
    },
    {
      "index": 1,
      "text": "# AGENTS.md instructions for /home/ubuntu/nikhil/cold-ranker\n\n<INSTRUCTIONS>\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.\n  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.\n  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.\n  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n</INSTRUCTIONS>",
      "timestamp": "2026-01-28T21:21:52.147Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-28T21:21:52.147Z"
    },
    {
      "index": 3,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-28T21:21:52.205Z"
    },
    {
      "index": 4,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-28T21:21:52.206Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/config/config.py\n\n## Active selection of the file:\nNEG_PER_POS\n## Open tabs:\n- config.py: col/src/config/config.py\n- driver.py: col/src/driver.py\n- training.py: col/src/model/training.py\n- wandb_utils.py: col/src/utils/wandb_utils.py\n- model.py: col/src/model/model.py\n\n## My request for Codex:\ni changed NEG_PER_POS from 7 to 20, the test_loss changed from 0.12 to 0.08\n",
      "timestamp": "2026-01-28T21:21:52.314Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "I’ll quickly scan the code to see how `NEG_PER_POS` is used so I can explain the impact and suggest next steps.",
      "timestamp": "2026-01-28T21:22:04.361Z"
    },
    {
      "index": 1,
      "text": "Nice — that drop is consistent with how your pipeline uses NEG_PER_POS.\n\nWhat changed\n- 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.\n- Where it’s used: defined at `col/src/config/config.py:78`, passed into loaders in `col/src/model/training.py:240` (train) and `col/src/model/training.py:334` (test uses `NEG_PER_POS_TEST=50`), and expanded in the dataset via `dup = self.neg_per_pos + 1` at `col/src/model/dataset.py:109`.\n\nWhy the loss likely fell\n- Class imbalance: raising 7→20 shifts positive rate from 12.5% to ~4.8%, so unweighted BCE will naturally reward predicting lower probabilities on more negatives.\n- More negative examples per user help the model learn sharper separation on negatives, which reduces BCE on the fixed test setting (`NEG_PER_POS_TEST=50`).\n\nTrade-offs to watch\n- Compute/memory: step size grows with (1+neg_per_pos). With `TRAIN_BATCH_SIZE=256`, your effective per-step rows are 256*(1+20)=5,376.\n- Calibration/thresholds: unweighted BCE with heavy imbalance can push overall probabilities down; recall may drop even if loss improves.\n- Diminishing returns: after a point, additional negatives add easy examples and slow training more than they help.\n\nRecommendations\n- Monitor existing signals: `pos_prob_mean` and `neg_prob_mean` logged in `training.py` (per-epoch metrics). Ensure `pos_prob_mean` doesn’t collapse toward 0 and that `acc_epoch` isn’t driven purely by the majority class.\n- Consider balancing the loss in training: use `nn.BCEWithLogitsLoss(pos_weight=torch.tensor([NEG_PER_POS], device=device))` to keep positive/negative contributions closer as you vary `NEG_PER_POS`. Keep test loss unweighted for comparability.\n- Keep step cost steady: adjust `TRAIN_BATCH_SIZE ≈ target_rows/(1+NEG_PER_POS)` if you increase negatives further to avoid OOM/regressions in throughput.\n- Validate with ranking-centric metrics: add AUC/PR-AUC and simple top-k hit-rate checks if feasible; BCE alone can be misleading under changing class priors.\n- Try a small sweep: 10, 20, 30 negatives and pick the knee of the curve vs throughput. If production candidate sets are large, a higher `NEG_PER_POS_TEST` can be more representative.\n\nWant me to patch training to add `pos_weight` (training only) and optionally log AUC/PR-AUC so results are robust across NEG_PER_POS values?",
      "timestamp": "2026-01-28T21:23:39.560Z"
    }
  ],
  "tool_calls": [
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"rg -n \\\"NEG_PER_POS|neg_per_pos|negative.*per.*positive|negatives_per\\\"\"],\"timeout_ms\":120000,\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "{\"output\":\"\\nthread 'main' (27710) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to d..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"rg -n \\\"NEG_PER_POS|neg_per_pos|negative.*per.*positive|negatives_per\\\"\"],\"justification\":\"Search repository for how NEG_PER_POS is used to provide accurate guidance.\",\"sandb...",
      "output_summary": "{\"output\":\"col/src/config/config.py:78:NEG_PER_POS = 20\\ncol/src/config/config.py:79:NEG_PER_POS_TEST = 50\\ncol/src/ctr_ranker.py:108:        negatives_per_positive: int = 7,\\ncol/src/ctr_ranker.py:11..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,140p' col/src/config/config.py\"]}",
      "output_summary": "{\"output\":\"\\nthread 'main' (27925) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\\nerror running landlock: Sandbox(LandlockRestrict)\\nnote: run with `RUST_BACKTRACE=1` environment variable to d..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,140p' col/src/config/config.py\"],\"justification\":\"Read config to confirm NEG_PER_POS definition and related settings.\",\"sandbox_permissions\":\"require_escalated\",\"ti...",
      "output_summary": "{\"output\":\"import os\\n\\nLANGUAGE=\\\"bengali\\\"\\nLANGUAGE_KEY=\\\"bn\\\"\\n\\nMODE='test'\\n\\ndef _env_flag(name: str, default: bool = False) -> bool:\\n    value = os.environ.get(name)\\n    if value is None:\\n ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,240p' col/src/model/dataset.py\"],\"justification\":\"Review how neg_per_pos shapes the batch and sampling, to interpret loss changes.\",\"sandbox_permissions\":\"require_e...",
      "output_summary": "{\"output\":\"Total output lines: 210\\n\\nimport os\\nimport json\\nimport numpy as np\\nimport torch\\nfrom torch.utils.data import DataLoader, IterableDataset\\n\\nfrom config.config import EMBEDDING_COLUMNS_..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,160p' col/src/model/training.py\"],\"justification\":\"See how loss is computed during training and test with neg_per_pos.\",\"sandbox_permissions\":\"require_escalated\",\"t...",
      "output_summary": "{\"output\":\"import os\\nimport math\\nfrom model.sampler import PopularitySampler\\nimport time\\nfrom typing import Optional\\nfrom typing import Dict, Tuple\\nimport numpy as np\\nfrom torch import nn\\nimpo..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '160,460p' col/src/model/training.py\"],\"justification\":\"Continue reading training loop and evaluation to see how loss/metrics computed.\",\"sandbox_permissions\":\"require...",
      "output_summary": "{\"output\":\"                        f\\\"{log_prefix}/grad_norm\\\": float(grad_norm),\\n                        f\\\"{log_prefix}/param_norm\\\": float(param_norm),\\n                    }\\n                    ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,240p' col/src/model/model.py\"],\"justification\":\"Check model forward to see expected batch shapes and any normalization affecting loss/outputs.\",\"sandbox_permissions...",
      "output_summary": "{\"output\":\"import torch\\nfrom torch import nn\\nfrom torch.nn import functional as F\\n\\nclass CTRModelWithCosineSignals(nn.Module):\\n    def __init__(\\n        self,\\n        input_dim,\\n        mlp_la..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,200p' col/src/driver.py\"],\"justification\":\"Check entrypoint and usage context around NEG_PER_POS.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":120000,\"wo...",
      "output_summary": "{\"output\":\"import os\\nos.environ[\\\"AWS_DEFAULT_REGION\\\"] = \\\"ap-south-1\\\"\\nos.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI_WFPd_ZkNr0OXrihudRu2ZzXthe8u_9BvNahk8fsEPhOT3BlbkFJ..."
    }
  ],
  "turn_count": 6,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 6,
    "struggle_score": 4,
    "prompt_specificity": 4,
    "time_of_day": "evening"
  }
}