{
  "filename": "rollout-2026-01-28T20-00-48-019c0631-bb0f-7e81-b028-779de5c06982.jsonl",
  "date": "2026-01-28",
  "session_id": "019c0631-bb0f-7e81-b028-779de5c06982",
  "first_timestamp": "2026-01-28T20:00:48.400Z",
  "last_timestamp": "2026-01-28T20:22:18.439Z",
  "duration_seconds": 1290.0,
  "model": "gpt-5",
  "cli_version": "0.89.0",
  "approval_policy": "on-request",
  "effort": "high",
  "raw_event_count": 225,
  "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": "2026-01-28T20:00:48.400Z"
    },
    {
      "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": "2026-01-28T20:00:48.400Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-28T20:00:48.400Z"
    },
    {
      "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": "2026-01-28T20:00:48.457Z"
    },
    {
      "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": "2026-01-28T20:00:48.457Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/config/config.py\n\n## Open tabs:\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nhow can i improve my model?\n",
      "timestamp": "2026-01-28T20:00:48.548Z"
    },
    {
      "index": 6,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/config/config.py\n\n## Open tabs:\n- config.py: col/src/config/config.py\n- model.py: col/src/model/model.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nFeature crosses: add explicit interactions (element‑wise product and/or learned cross layers) between user_genre_pref_embedding and target_genre_embedding and between read_series_avg_prod2vec_emb and target_prod2vec_emb in addition to cosine signals (model.py:77–103, 117–126).\n\n\nhow to do this?\n",
      "timestamp": "2026-01-28T20:06:38.767Z"
    },
    {
      "index": 7,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-28T20:07:51.014Z"
    },
    {
      "index": 8,
      "text": "Warning: apply_patch was requested via shell. Use the apply_patch tool instead of exec_command.",
      "timestamp": "2026-01-28T20:09:19.612Z"
    },
    {
      "index": 9,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/model.py\n\n## Active selection of the file:\ncosine_read_target_genre = F.cosine_similarity(\n            read_series_avg_genre_emb,\n            target_genre_embedding,\n            dim=-1,\n            eps=self.cosine_eps,\n        ).unsqueeze(-1)\n        \n## Open tabs:\n- model.py: col/src/model/model.py\n- config.py: col/src/config/config.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nbut does this makes sense? i already do cosine_read_target_genre = F.cosine_similarity(\n            read_series_avg_genre_emb,\n            target_genre_embedding,\n            dim=-1,\n            eps=self.cosine_eps,\n        ).unsqueeze(-1)\n        \n",
      "timestamp": "2026-01-28T20:21:26.585Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "I’m going to scan the repo, then open `model.py`, `config.py`, and `driver.py` to see how the model is structured and trained.",
      "timestamp": "2026-01-28T20:00:57.574Z"
    },
    {
      "index": 1,
      "text": "Here are high‑impact improvements tailored to your current setup.\n\n**Quick Wins**\n- Class-imbalance weighting: use `pos_weight=NEG_PER_POS` in `BCEWithLogitsLoss` to reflect N:1 negatives per positive (training.py:289). Example: `criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor(NEG_PER_POS, device=device))`.\n- Weight decay + grad clipping: add `weight_decay=1e-5` to Adam (training.py:289) and clip grads after `loss.backward()` with `torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)` (training.py:122–138).\n- LR schedule: add `CosineAnnealingWarmRestarts` or `OneCycleLR` and step it each batch/epoch for smoother convergence (training.py:288–290).\n- Mixed precision: wrap forward+loss in `torch.cuda.amp.autocast()` and use `GradScaler` for speed on GPU with better stability (training.py:96–139).\n- Log better metrics: compute and log ROC-AUC/PR-AUC over an epoch to complement accuracy and loss (training.py:138–171, 355–370).\n\n**Model Architecture**\n- Increase capacity with residual MLP: bump `MLP_LAYERS` (config.py:62) to something like `[1024, 256, 64]` and add residual blocks around the first two linear layers (model.py:36–45).\n- Embedding dropout: apply dropout to the large `emb_features` tensor before concatenation to regularize (`F.dropout(emb_features, p=0.1, training=self.training)`, model.py:117–126).\n- Two‑tower fusion: add user/item towers (user: user_id, gender, user history vectors; item: series_id, author_id, item vectors) and combine `dot(user_tower, item_tower)` with the current MLP logit for stronger ID interactions (model.py:19–34, 105–113, 117–126).\n- Feature crosses: add explicit interactions (element‑wise product and/or learned cross layers) between `user_genre_pref_embedding` and `target_genre_embedding` and between `read_series_avg_prod2vec_emb` and `target_prod2vec_emb` in addition to cosine signals (model.py:77–103, 117–126).\n\n**Sampling & Data**\n- Hard negatives: add a sampler that mixes popularity (current), uniform, and “near‑miss” negatives (e.g., same author/genre or close in prod2vec space) to increase difficulty (sampler.py:1–15).\n- Avoid false negatives: if feasible, precompute a map `user_id -> set(positive_series)` and reject sampled negatives that are known positives for that user when forming a batch (dataset.py:120–161).\n- Scale/transform numerics: log1p and/or z‑score `target_rating_count` and consider z‑scaling `age_days` and `target_average_rating` offline when writing shards to keep magnitudes comparable (utils/utils.py:53–112, dataset.py:96–118).\n\n**Training & Evaluation**\n- Calibrated probabilities: after training for ranking, apply temperature scaling or Platt scaling on a held‑out set if you need calibrated CTRs for serving.\n- Early stopping and checkpoints: track validation loss/AUC and stop early; save the best model by metric.\n- In‑batch negatives: if you later restructure to a two‑tower, use in‑batch negatives to enlarge the negative pool without extra sampling cost.\n\n**Config/Code Hygiene**\n- Fix env var typo: `WANDB_LOG_EVERY_N_STEP` is set in `col/src/driver.py:14`, but the code reads `WANDB_LOG_EVERY_N_STEPS` (config.py:89). Rename to match.\n- Remove hardcoded secrets: `col/src/driver.py:2–15` sets sensitive keys inline. Move these to your shell env or a `.env` file loaded at process start and delete them from code.\n- BatchNorm vs batch size: if batch sizes vary a lot between train/test, consider `LayerNorm` or `GroupNorm` instead of `BatchNorm1d` in the MLP (model.py:40).\n\nWant me to apply a focused set of changes now? I suggest:\n- Add `pos_weight`, weight decay, grad clipping, and AUC logging.\n- Optional: AMP + cosine LR schedule.\n- Optional: embedding dropout and larger `MLP_LAYERS`.\n\nSay which subset you want, and I’ll patch the files and run a quick sanity check.",
      "timestamp": "2026-01-28T20:05:04.931Z"
    },
    {
      "index": 2,
      "text": "I’m going to add element-wise cross features to the model, adjust the config to account for the new input dimensionality, and update the training code to pass the correct input size.",
      "timestamp": "2026-01-28T20:07:48.088Z"
    },
    {
      "index": 3,
      "text": "I’ll update config to account for the new cross-feature dimensions so the model’s input size matches the concatenated tensors.",
      "timestamp": "2026-01-28T20:09:18.999Z"
    },
    {
      "index": 4,
      "text": "I’ll grab current line numbers for the exact spots changed so I can reference them precisely.",
      "timestamp": "2026-01-28T20:10:48.225Z"
    },
    {
      "index": 5,
      "text": "Here’s a clean way to add explicit cross interactions and keep shapes consistent.\n\nWhat to change\n- Compute element-wise crosses in the forward and gate the genre cross by presence.\n- Concatenate these crosses into your existing embedding feature block.\n- Bump input dimension in config to include the new cross vectors.\n\nWhere to change\n- Add crosses in `col/src/model/model.py:105–109`, then concatenate at `col/src/model/model.py:122–133`:\n  - `genre_cross = (user_genre_pref_embedding * target_genre_embedding) * if_user_genre_pref_embedding_present`\n  - `prod2vec_cross = read_series_avg_prod2vec_emb * target_prod2vec_emb`\n  - Append both to `emb_features` so they flow into the MLP.\n\n- Update input size in `col/src/config/config.py:42–48` and `col/src/config/config.py:65–72`:\n  - Define sizes for the two crosses:\n    - `CROSS_FEATURES_TO_SIZE = {\"genre_cross\": 512, \"prod2vec_cross\": 16}`\n    - Use the existing embedding sizes so this stays correct if you change dims:\n      - `genre_cross`: `EMBEDDING_COLUMNS_TO_SIZE[\"user_genre_pref_embedding\"]`\n      - `prod2vec_cross`: `EMBEDDING_COLUMNS_TO_SIZE[\"read_series_avg_prod2vec_emb\"]`\n  - Include these in `TOTAL_INPUT_DIM`:\n    - `TOTAL_INPUT_DIM = sum(COL_TO_EMB_DIM.values()) + sum(EMBEDDING_COLUMNS_TO_SIZE.values()) + sum(CROSS_FEATURES_TO_SIZE.values()) + 4`\n  - `TOTAL_INPUT_DIM_WITH_COSINE = TOTAL_INPUT_DIM + 3` stays correct.\n\nWhy this works\n- Element-wise products preserve the original vector sizes (512 and 16), so concatenation is straightforward.\n- Gating `genre_cross` by `if_user_genre_pref_embedding_present` avoids injecting noise when user prefs are missing.\n- Updating `TOTAL_INPUT_DIM` ensures the first MLP layer matches the new concatenated feature width.\n\nOptional: add a learned cross layer (DCN-style)\n- If you also want learned polynomial feature crosses, add a small cross network after concatenation, before the MLP:\n  - In `__init__`:\n    - `self.cross_layers = nn.ModuleList([nn.Linear(input_dim, 1, bias=True) for _ in range(num_cross_layers)])`\n  - In `forward(...)` after building `x` (the big concatenated tensor):\n    - `x0 = x`\n    - `for layer in self.cross_layers: x = x0 * layer(x) + x`\n  - You can then feed `x` to the existing MLP as usual, or concatenate `x` and `x0` for a “stacked” input to the MLP.\n- Notes:\n  - This does not change `TOTAL_INPUT_DIM` (it’s internal).\n  - Start with 1–2 cross layers; more can overfit.\n\nSanity checks\n- After edits, ensure the first Linear in the MLP receives `TOTAL_INPUT_DIM_WITH_COSINE`.\n- Run a quick batch through the model to verify shapes and no broadcasting issues.\n- If batch sizes are small/variable, consider replacing `BatchNorm1d` with `LayerNorm` for stability.\n\nWant me to also wire up a minimal learned cross block as above and add a flag (e.g., `USE_LEARNED_CROSS`) to toggle it?",
      "timestamp": "2026-01-28T20:11:26.041Z"
    },
    {
      "index": 6,
      "text": "Short answer\n- Yes, adding element‑wise cross vectors complements your cosine features. Keep both.\n\nWhy it helps\n- Cosine is a single scalar that captures only angle (direction). It discards magnitude and all per‑dimension interactions. See `cosine_read_target_genre` at col/src/model/model.py:84.\n- Element‑wise product keeps a full vector of per‑dimension alignments. The MLP can learn which dimensions matter and how they interact with other features. We add these at col/src/model/model.py:105 and concatenate at col/src/model/model.py:122.\n- In practice, cosine gives a robust global similarity signal; the cross vectors expose fine‑grained structure the MLP can exploit. They’re complementary, not redundant.\n\nMake it stable\n- Normalize before crossing to control scale, then gate by presence:\n  - `u = F.normalize(read_series_avg_genre_emb, dim=-1)`\n  - `v = F.normalize(target_genre_embedding, dim=-1)`\n  - `genre_cross = (u * v) * if_user_genre_pref_embedding_present`\n- Optionally add a scalar dot product too: `(u * v).sum(dim=-1, keepdim=True)`; it carries magnitude if you skip normalization (cosine doesn’t).\n- If input width creeps up, project crosses before concat (e.g., `Linear(512→128)` with ReLU).\n- Config already accounts for the extra dims via `CROSS_FEATURES_TO_SIZE` (col/src/config/config.py:45) and the updated totals (col/src/config/config.py:65).\n\nQuick ablation plan\n- Run 3 variants:\n  - Baseline: cosine only.\n  - Cosine + crosses (current).\n  - Cosine + normalized crosses (+ optional dot).\n- Compare ROC‑AUC/PR‑AUC and calibration on your validation set. Keep the simplest variant that wins.\n\nIf you want, I can add normalization and an optional dot‑product flag to the model so you can toggle these in config and test quickly.",
      "timestamp": "2026-01-28T20:22:18.435Z"
    }
  ],
  "tool_calls": [
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      "output_summary": "{\"output\":\"\\nthread 'main' (17338) 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..."
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    {
      "name": "shell",
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      "output_summary": "{\"output\":\"\\nthread 'main' (17370) 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\",\"ls -la\"],\"justification\":\"The sandbox is failing for basic file listing due to Landlock; need unsandboxed access to read repo files.\",\"sandbox_permissions\":\"require_escalated...",
      "output_summary": "{\"output\":\"total 16\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Jan 28 15:44 .\\ndrwxrwxr-x 8 ubuntu ubuntu 4096 Jan 28 15:40 ..\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Jan 28 15:44 col\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 ..."
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      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"ls -la col && echo '---' && ls -la col/src && echo '---' && ls -la col/src/config && echo '---' && ls -la col/src/model && echo '---' && ls -la col/src | sed -n '1,200p'\"],\"j...",
      "output_summary": "{\"output\":\"total 20\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Jan 28 15:44 .\\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Jan 28 15:44 ..\\ndrwxrwxr-x 7 ubuntu ubuntu 4096 Jan 28 15:44 .git\\n-rw-rw-r-- 1 ubuntu ubuntu   48..."
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      "output_summary": "{\"output\":\"\\nthread 'main' (17425) 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..."
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      "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 ..."
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      "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..."
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      "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..."
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      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '240,520p' col/src/model/training.py\"],\"justification\":\"Continue reading the training driver for full context on improvements.\",\"sandbox_permissions\":\"require_escalate...",
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      "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_..."
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    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '80,180p' col/src/model/dataset.py\"],\"justification\":\"View the negative sampling and repeat logic to check data balance and leakage risks.\",\"sandbox_permissions\":\"requ...",
      "output_summary": "{\"output\":\"            floats_data = np.load(f\\\"{shard_path}/float.npy\\\")\\n            \\n            assert ints_data.shape[0] == floats_data.shape[0]\\n            \\n            for start in range(0, ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,200p' col/src/driver.py\"],\"justification\":\"Open entry point to see how training is invoked and any config flags.\",\"sandbox_permissions\":\"require_escalated\",\"timeout...",
      "output_summary": "{\"output\":\"import os\\nos.environ[\\\"AWS_DEFAULT_REGION\\\"] = \\\"ap-south-1\\\"\\nos.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI_WFPd_ZkNr0OXrihudRu2ZzXthe8u_9BvNahk8fsEPhOT3BlbkFJ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"sed -n '1,200p' col/src/model/sampler.py\"],\"justification\":\"Check negative sampling method to suggest better sampling strategies.\",\"sandbox_permissions\":\"require_escalated\",\"...",
      "output_summary": "{\"output\":\"import numpy as np\\n\\n\\nclass PopularitySampler:\\n    def __init__(self, vocab_arr, positive_series_arr):\\n        values, counts = np.unique(positive_series_arr, return_counts=True)\\n     ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"ls -la col/src/utils && echo '---' && sed -n '1,200p' col/src/utils/wandb_utils.py && echo '---' && sed -n '1,200p' col/src/utils/utils.py\"],\"justification\":\"Check available ...",
      "output_summary": "{\"output\":\"total 24\\ndrwxrwxr-x 3 ubuntu ubuntu 4096 Jan 28 15:44 .\\ndrwxrwxr-x 8 ubuntu ubuntu 4096 Jan 28 16:08 ..\\ndrwxrwxr-x 2 ubuntu ubuntu 4096 Jan 28 15:50 __pycache__\\n-rw-rw-r-- 1 ubuntu ubun..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/training.py | sed -n '1,200p' && echo '---' && nl -ba col/src/model/training.py | sed -n '200,520p'\"],\"justification\":\"Get line numbers for precise file ...",
      "output_summary": "{\"output\":\"Total output lines: 395\\n\\n     1\\timport os\\n     2\\timport math\\n     3\\tfrom model.sampler import PopularitySampler\\n     4\\timport time\\n     5\\tfrom typing import Optional\\n     6\\tfro..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/model.py | sed -n '1,200p'\"],\"justification\":\"Get line numbers for model architecture references.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\"...",
      "output_summary": "{\"output\":\"     1\\timport torch\\n     2\\tfrom torch import nn\\n     3\\tfrom torch.nn import functional as F\\n     4\\t\\n     5\\tclass CTRModelWithCosineSignals(nn.Module):\\n     6\\t    def __init__(\\n ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/config/config.py | sed -n '1,220p'\"],\"justification\":\"Get line numbers for config tuning suggestions.\",\"sandbox_permissions\":\"require_escalated\",\"timeout_ms\":1...",
      "output_summary": "{\"output\":\"     1\\timport os\\n     2\\t\\n     3\\tLANGUAGE=\\\"bengali\\\"\\n     4\\tLANGUAGE_KEY=\\\"bn\\\"\\n     5\\t\\n     6\\tMODE='test'\\n     7\\t\\n     8\\tdef _env_flag(name: str, default: bool = False) -> b..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/driver.py | sed -n '1,200p'\"],\"justification\":\"Line numbers help reference the hardcoded secrets to improve security.\",\"sandbox_permissions\":\"require_escalated...",
      "output_summary": "{\"output\":\"     1\\timport os\\n     2\\tos.environ[\\\"AWS_DEFAULT_REGION\\\"] = \\\"ap-south-1\\\"\\n     3\\tos.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI_WFPd_ZkNr0OXrihudRu2ZzXthe8..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/model/model.py\\n@@\\n         cosine_features = torch.cat(\\n             [\\n                 cosine_user_targe...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/model/model.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Update File: col/src/config/config.py\\n@@\\n-EMBEDDING_COLUMNS_TO_SIZE = {\\n+EMBEDDING_COLUMNS_TO_SIZE = {\\n@@\\n-}\\n+}\\n+\\n+# Sizes...",
      "output_summary": "{\"output\":\"Success. Updated the following files:\\nM col/src/config/config.py\\n\",\"metadata\":{\"exit_code\":0,\"duration_seconds\":0.0}}"
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/model/model.py | sed -n '1,220p'\"]}",
      "output_summary": "{\"output\":\"\\nthread 'main' (19439) 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\",\"nl -ba col/src/model/model.py | sed -n '1,220p'\"],\"justification\":\"Sandbox is blocking file read; need unsandboxed read to reference exact line numbers for the changes.\",\"san...",
      "output_summary": "{\"output\":\"     1\\timport torch\\n     2\\tfrom torch import nn\\n     3\\tfrom torch.nn import functional as F\\n     4\\t\\n     5\\tclass CTRModelWithCosineSignals(nn.Module):\\n     6\\t    def __init__(\\n ..."
    },
    {
      "name": "shell",
      "input_summary": "{\"command\":[\"bash\",\"-lc\",\"nl -ba col/src/config/config.py | sed -n '1,200p'\"]}",
      "output_summary": "{\"output\":\"\\nthread 'main' (19468) 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\",\"nl -ba col/src/config/config.py | sed -n '1,200p'\"],\"justification\":\"Need unsandboxed read for accurate file references after our config change.\",\"sandbox_permissions\":\"requi...",
      "output_summary": "{\"output\":\"     1\\timport os\\n     2\\t\\n     3\\tLANGUAGE=\\\"bengali\\\"\\n     4\\tLANGUAGE_KEY=\\\"bn\\\"\\n     5\\t\\n     6\\tMODE='test'\\n     7\\t\\n     8\\tdef _env_flag(name: str, default: bool = False) -> b..."
    }
  ],
  "turn_count": 10,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 10,
    "struggle_score": 4,
    "prompt_specificity": 5,
    "time_of_day": "evening"
  }
}