{
  "filename": "rollout-2026-01-29T20-44-10-019c0b7f-ca24-7592-8a3a-0a90a513648c.jsonl",
  "date": "2026-01-29",
  "session_id": "019c0b7f-ca24-7592-8a3a-0a90a513648c",
  "first_timestamp": "2026-01-29T20:44:10.149Z",
  "last_timestamp": "2026-01-29T20:51:22.617Z",
  "duration_seconds": 432.5,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "xhigh",
  "raw_event_count": 116,
  "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": "2026-01-29T20:44:10.149Z"
    },
    {
      "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": "2026-01-29T20:44:10.149Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-29T20:44:10.149Z"
    },
    {
      "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": "2026-01-29T20:44:10.223Z"
    },
    {
      "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": "2026-01-29T20:44:10.223Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/training.py\n\n## Active selection of the file:\ntest_loader\n## Open tabs:\n- training.py: col/src/model/training.py\n- model.py: col/src/model/model.py\n- config.py: col/src/config/config.py\n- dataset.py: col/src/model/dataset.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nwill my test_loader generate same positive and negative examples across epochs and across different runs given data preperation is same?\n",
      "timestamp": "2026-01-29T20:44:10.307Z"
    },
    {
      "index": 6,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/training.py\n\n## Active selection of the file:\ntest_loader\n## Open tabs:\n- training.py: col/src/model/training.py\n- model.py: col/src/model/model.py\n- config.py: col/src/config/config.py\n- dataset.py: col/src/model/dataset.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\ndo. all the required changes . i want same test set , positive or negative across runs\n",
      "timestamp": "2026-01-29T20:47:43.097Z"
    },
    {
      "index": 7,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/training.py\n\n## Active selection of the file:\ntest_loader\n## Open tabs:\n- training.py: col/src/model/training.py\n- model.py: col/src/model/model.py\n- config.py: col/src/config/config.py\n- dataset.py: col/src/model/dataset.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nif i use multi order rest ? how my data will be differnt?\n",
      "timestamp": "2026-01-29T20:50:05.489Z"
    },
    {
      "index": 8,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/training.py\n\n## Active selection of the file:\ntest_loader\n## Open tabs:\n- training.py: col/src/model/training.py\n- model.py: col/src/model/model.py\n- config.py: col/src/config/config.py\n- dataset.py: col/src/model/dataset.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nand how does single worker insure that the negatives are always same for a psoitive?\n",
      "timestamp": "2026-01-29T20:51:11.282Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Short answer: across epochs you’ll *usually* get the same positives/negatives, but across different runs it’s **not guaranteed** with the current code.\n\n- **Deterministic intent**: In `col/src/model/training.py`, test uses a fresh `PopularitySampler(..., seed=42)` each epoch and sets `shuffle_shards=False` / `shuffle_within_shard=False`, so negatives are deterministic *if* the batch traversal order is stable.\n- **Where it can change across runs**:\n  - `col/src/model/dataset.py` builds `self.shard_paths` with `os.listdir(...)` and **doesn’t sort**, so shard order can differ across runs/filesystems → positives (and thus negative sampling calls) can shift.\n  - With `num_workers > 0`, an `IterableDataset` yields batches in **worker timing order**, which can vary run‑to‑run even if each worker is deterministic.\n  - Each worker gets its own sampler seeded at 42 (dataset copy), so negatives per worker are repeatable, but **global order can still differ**.\n- **Also note**: `col/src/model/dataset.py` uses `self.shard_dir` and `shard_paths` (undefined), which would crash unless fixed; if that’s been corrected locally, then the points above apply.\n\nIf you want truly identical positives/negatives across runs: sort shard paths, set `num_workers=0` for test, and keep the seeded sampler. If you want, I can make those tweaks.",
      "timestamp": "2026-01-29T20:45:27.998Z"
    },
    {
      "index": 1,
      "text": "Made the test set deterministic across runs by fixing shard ordering and forcing single‑worker test loading, plus aligning test loader settings.\n\nChanges:\n- Deterministic shard order + fixed shard dir usage + worker slicing fix in `col/src/model/dataset.py`.\n- Test loader now uses test-specific params in `col/src/model/training.py` (`NEG_PER_POS_TEST`, `TEST_BATCH_SIZE`, `NUM_WORKER_TEST_DATALOADER`).\n- Forced deterministic test iteration by setting `NUM_WORKER_TEST_DATALOADER = 0` in `col/src/config/config.py`.\n\nWith these changes, in `MODE == \"test\"` you’ll get identical positives/negatives across epochs and across runs (assuming the same data prep). If you want multi-worker test loading later, order can vary again.\n\nNext steps if you want:\n1) I can add a config flag to allow deterministic vs. fast test loading.\n2) I can add a small sanity check script to verify identical test batches across runs.",
      "timestamp": "2026-01-29T20:49:05.989Z"
    },
    {
      "index": 2,
      "text": "Do you mean **multi‑worker test loading** (i.e., `num_workers > 0`), or something else by “multi order rest”?\n\nIf you mean multi‑worker:\n\n- **Positives**: same set, but the **order of batches can change** run‑to‑run because `IterableDataset` yields in worker‑completion order (not deterministic).\n- **Negatives**: each worker has its own sampler RNG; if it’s seeded, each worker will generate a deterministic sequence *within that worker*, but the **global sequence across workers can differ** run‑to‑run due to scheduling and shard assignment.\n- If `shuffle_shards` or `shuffle_within_shard` is on, then you’ll also get **different samples** (not just order).\n\nSo with multi‑worker you’ll typically get the same overall positives, but **batch order and negatives can differ** across runs.\n\nIf you meant something else by “multi order rest”, tell me what you’re referring to and I’ll answer precisely.",
      "timestamp": "2026-01-29T20:50:14.504Z"
    },
    {
      "index": 3,
      "text": "Single‑worker makes the **sampling sequence** deterministic, which in turn makes negatives repeat for each positive **as long as the iteration order and batch boundaries are identical**.\n\nWhy it works in your code:\n- In `col/src/model/training.py`, test uses a **fresh seeded sampler** (`seed=42`) each epoch and disables shuffling.\n- In `col/src/model/dataset.py`, with **one worker**, you iterate shards in a fixed sorted order (after my change), and you read rows in the same order every time.\n- The sampler is **sequence‑based**: it draws `k` negatives per batch in a fixed order. If the batch order and sizes are identical, the RNG is advanced the same way, so the negatives line up the same way for each positive.\n\nImportant nuance:\n- 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.”  \n- 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.\n\nSo: single worker + fixed shard order + no shuffling + same batch size → identical negatives per positive **position**, hence identical per positive row in practice.",
      "timestamp": "2026-01-29T20:51:22.615Z"
    }
  ],
  "tool_calls": [
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (18626) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRAC..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to list project files to inspect training/data loader code for determinism.\"...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol\ndata\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubunt..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Read training.py to see how test_loader is...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nimport math\nfrom model.sampler import PopularitySampler\nimport time\nfrom typing import Optional\nfrom typing import Dict, Tuple\nimport numpy as np\nfr..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '240,520p' col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue reading training.py for test_lo...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n            ints_data = np.load(f\"{shard_path}/ints.npy\")\n            total += math.ceil(ints_data.shape[0] / batch_size)\n        return total\n\n    total_trai..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect dataset loader implementation for d...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nimport json\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, IterableDataset\nfrom config.config import EMBEDDING_COLUMNS_TO_..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/model/sampler.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect PopularitySampler for randomness/se...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport numpy as np\n\n\nclass PopularitySampler:\n    def __init__(self, vocab_arr, positive_series_arr, seed=None):\n        values, counts = np.unique(positive_s..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/config/config.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check dataloader worker and batch size conf...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom openai import OpenAI\n\nLANGUAGE=\"english\"\nLANGUAGE_KEY=\"en\"\n\nMODE='prod'\n\ndef _env_flag(name: str, default: bool = False) -> bool:\n    value = o..."
    }
  ],
  "turn_count": 9,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 9,
    "struggle_score": 4,
    "prompt_specificity": 4,
    "time_of_day": "evening"
  }
}