{
  "filename": "rollout-2026-01-29T20-07-43-019c0b5e-6cf8-7b62-89d7-1e80092a3f2b.jsonl",
  "date": "2026-01-29",
  "session_id": "019c0b5e-6cf8-7b62-89d7-1e80092a3f2b",
  "first_timestamp": "2026-01-29T20:07:43.609Z",
  "last_timestamp": "2026-01-29T20:12:39.581Z",
  "duration_seconds": 296.0,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "xhigh",
  "raw_event_count": 167,
  "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": "2026-01-29T20:07:43.609Z"
    },
    {
      "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": "2026-01-29T20:07:43.609Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-29T20:07:43.609Z"
    },
    {
      "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": "2026-01-29T20:07:43.676Z"
    },
    {
      "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": "2026-01-29T20:07:43.676Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/data_prep/data_prep.py\n\n## Open tabs:\n- data_prep.py: col/src/data_prep/data_prep.py\n- user_meta.py: col/src/data_prep/user_meta.py\n- recommend.py: col/src/model/recommend.py\n- config.py: col/src/config/config.py\n- training.py: col/src/model/training.py\n\n## My request for Codex:\nis my\n\n    # save for recommendation\n    up_df = get_avg_history_embeddings(up_df, series_id_index_to_prod2vec_emb_np_arr, series_id_index_to_genre_emb_np_arr, include_current=True)\n    \n    up_df = up_df.sort_values(\"at\").reset_index(drop=True)\n    \n    last_idx = (\n    up_df\n    .reset_index()\n    .groupby(\"user_id\")[[\"at\", \"index\"]]\n    .max()[\"index\"]\n    )\n\n    write_df_to_memmap(\n    up_df.loc[last_idx].drop(columns=[\"at\", \"target_series_id\"]),\n    output_dir=f\"../../data/{LANGUAGE_KEY}/recommend\",\n    int_cols=[x for x in USER_COLUMNS if x in INT_COLUMNS],\n    float_cols=[x for x in USER_COLUMNS if x in FLOAT_COLUMNS],\n    float_array_cols=[x for x in USER_COLUMNS if x in FLOAT_ARR_COLUMNS],\n    shard_size=SHARD_ROWS,\n    )\n\nin data_prep.py correct. i ams saving it so that recommend.py can access latest user interaction\n",
      "timestamp": "2026-01-29T20:07:43.762Z"
    },
    {
      "index": 6,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/recommend.py\n\n## Open tabs:\n- recommend.py: col/src/model/recommend.py\n- driver.py: col/src/driver.py\n- data_prep.py: col/src/data_prep/data_prep.py\n- user_meta.py: col/src/data_prep/user_meta.py\n- config.py: col/src/config/config.py\n\n## My request for Codex:\ndo 1.\n",
      "timestamp": "2026-01-29T20:12:02.591Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Here are the issues that affect whether your “latest interaction” data is actually used, ordered by severity:\n\n- 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`. See `col/src/model/recommend.py:248-257` and `col/src/data_prep/data_prep.py:78-96`.\n- Blocking: `write_df_to_memmap` writes `floats.npy`, but `recommend.py` loads `float.npy`. If you point recommend at the new `recommend` dir, it will still fail to load the floats. See `col/src/utils/utils.py:42-83` and `col/src/model/recommend.py:56-88`.\n- Medium: Your “latest row” selection depends on a global sort by `at` with the default (unstable) sort. If a user has multiple rows with identical timestamps, the chosen “latest” row can be nondeterministic. `col/src/data_prep/data_prep.py:81-88`.\n\nNotes/assumptions\n- If your intent is “user state after the last interaction,” recomputing with `include_current=True` before picking the last row is the right idea; the logic itself is fine once the loading path is wired up.\n\nIf you want, I can:\n1) Switch `recommend.py` to load `../../data/{LANGUAGE_KEY}/recommend` and align the float filename, and/or  \n2) Make the “latest row per user” selection stable and clearer.",
      "timestamp": "2026-01-29T20:11:17.855Z"
    },
    {
      "index": 1,
      "text": "Updated `col/src/model/recommend.py` to read the `recommend` shards and to load `floats.npy` (with a fallback to `float.npy`).\n\n- Added `_load_floats` helper and used it for series_meta + interactions.\n- Switched `interaction_dirs` to `../../data/{LANGUAGE_KEY}/recommend`.\n\nNext steps if you want:\n1) Run your data prep to generate `../../data/{LANGUAGE_KEY}/recommend` shards, then run recommend.",
      "timestamp": "2026-01-29T20:12:39.578Z"
    }
  ],
  "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' (17491) 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 workspace files; prior command failed due to sandbox restrictions.\"}",
      "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 '1,220p' col/src/data_prep/data_prep.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to inspect data_prep.py to evalu...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nimport gc\nfrom pandarallel import pandarallel\nfrom data_prep.series_meta import get_series_meta\nfrom data_prep.user_met..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect recommend.py to understand expect...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport argparse\nimport json\nimport os\nfrom pathlib import Path\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom model.model import CTRModelWithCosin..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '240,520p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue reading recommend.py for input...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n    \n    device = device or (\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    device = torch.device(device)\n    print(f\"Using device: {device}\")\n    \n    d..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"recommend\\\" col/src -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Search for recommendation data usage.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/driver.py:22:from model import recommend\ncol/src/driver.py:30:    recommend.driver()\ncol/src/model/recommend.py:263:    for user_id in tqdm(user_ids, ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/data_prep/user_meta.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect user_meta.py to understand ge...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nfrom utils.athena import query_from_athena\nfrom utils.utils import chunked\nimport mysql.connector\nfrom concurrent.futur..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,220p' col/src/config/config.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect config columns for USER_COLUMNS etc...",
      "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..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/data_prep/mapping.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect mapping/normalizer to ensure la...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport numpy as np\nfrom config.config import CATEGORICAL_COLUMNS, CONTINUOUS_COLUMNS, EMBEDDING_COLUMNS_TO_SIZE\n\nclass Normalizer:\n    def __init__(self, list..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect training pipeline to see how user ...",
      "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 '1,260p' col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect dataset reading to see column order...",
      "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 '1,260p' col/src/utils/utils.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect write_df_to_memmap to ensure correct ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport json\nfrom pathlib import Path\nfrom typing import List, Optional\n\ndef chunked(iterable, size):\n    ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"float\\\\.npy|floats\\\\.npy\\\" col/src -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Find usage of float/floats file naming...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/model/dataset.py:36:        self.series_meta_floats_data = np.load(f\"{series_meta_dir}/shard-00000/float.npy\")\ncol/src/model/dataset.py:98:           ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/data_prep/data_prep.py | sed -n '60,130p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers around the...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n    60\t    up_df.iloc[:cutoff].drop(columns=[\"at\"]),\n    61\t    output_dir=f\"../../data/{LANGUAGE_KEY}/train\",\n    62\t    int_cols=[\"target_series_id\"] + [x f..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/model/recommend.py | sed -n '210,330p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers around recomm...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n   210\t                gender_b,\n   211\t                series_ids,\n   212\t                target_author_id,\n   213\t                user_genre_b,\n   214\t     ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/model/recommend.py | sed -n '1,140p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers for float.npy l...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n     1\timport argparse\n     2\timport json\n     3\timport os\n     4\tfrom pathlib import Path\n     5\timport numpy as np\n     6\timport torch\n     7\tfrom tqdm impo..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/utils/utils.py | sed -n '40,130p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers for memmap writer ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n    40\t        - writes row-sharded shards of size shard_size\n    41\t\n    42\t    Output per shard:\n    43\t      - ints.npy     (int32)\n    44\t      - floats.n..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"max_users\\\" -n col/src/model/recommend.py -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Find max_users usage in recomme...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n105:                if max_users is not None and len(user_features) >= max_users and uid not in user_features:\n256:        interaction_dirs=[os.path.join(data..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '140,240p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check recommend.py for argument parsing...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n                checkpoint = checkpoint[\"state_dict\"]\n        model.load_state_dict(checkpoint)\n\n    model.eval()\n    return model\n\n\ndef score_user(\n    model..."
    }
  ],
  "turn_count": 7,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 7,
    "struggle_score": 4,
    "prompt_specificity": 4,
    "time_of_day": "evening"
  }
}