Skip to content

Instantly share code, notes, and snippets.

@tfidfwastaken

tfidfwastaken/context-lens-export-663d455e-clean.json Secret

Last active May 28, 2026 21:17
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save tfidfwastaken/393045666da00cfe2e90e2b05703bc0f to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save tfidfwastaken/393045666da00cfe2e90e2b05703bc0f to your computer and use it in GitHub Desktop.
Download ZIP
Context Lens export for 663d455e ATIF trajectory
Raw
context-lens-export-663d455e-clean.json
{
"version": "1.0",
"exportedAt": "2026-05-28T21:12:21.764Z",
"files": [
{
"id": "1",
"filename": "663d455e-102f-46d4-b78d-c792c71fdb32.trajectory.json",
"conversation": {
"messages": [
{
"id": "3",
"role": "system",
"timestamp": "2026-05-05T09:34:36.287Z",
"parts": [
{
"id": "4",
"type": "text",
"token_count": 841,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "<permissions instructions>\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n</permissions instructions><skills_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- openai-docs: Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\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: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$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) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.\n 3) 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 4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n 5) 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</skills_instructions>"
}
]
},
{
"id": "5",
"role": "user",
"timestamp": "2026-05-05T09:34:36.287Z",
"parts": [
{
"id": "6",
"type": "text",
"token_count": 50,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "<environment_context>\n <cwd>/app</cwd>\n <shell>bash</shell>\n <current_date>2026-05-05</current_date>\n <timezone>Etc/UTC</timezone>\n</environment_context>"
}
]
},
{
"id": "7",
"role": "user",
"timestamp": "2026-05-05T09:34:36.287Z",
"parts": [
{
"id": "8",
"type": "text",
"token_count": 105,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Please train a fasttext model on the yelp data in the data/ folder.\n\nThe final model size needs to be less than 150MB but get at least 0.62 accuracy on a private test set that comes from the same yelp review distribution.\n\nThe model should be saved as /app/model.bin\n\nYou should solve the task in 1 hour. You can access 1 CPU, 4 GB memory, 10 GB storage. Do not cheat by using online solutions or hints specific to this task.\n"
}
]
},
{
"id": "9",
"role": "assistant",
"parts": [
{
"id": "10",
"type": "text",
"token_count": 40,
"component": "A1 Understanding & planning",
"dimensions": {
"A_stage": "A1 Understanding & planning",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "I\u2019ll inspect the dataset and available tooling, then train/validate fastText variants against a local split.Executed update_plan call_ZcNkdohozCauH2lkYGERzjjj"
},
{
"id": "11",
"type": "tool-call",
"token_count": 61,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_ZcNkdohozCauH2lkYGERzjjj",
"toolName": "update_plan",
"input": {
"plan": [
{
"step": "Inspect data and tooling",
"status": "in_progress"
},
{
"step": "Create validation split",
"status": "pending"
},
{
"step": "Train compact model candidates",
"status": "pending"
},
{
"step": "Evaluate accuracy and size",
"status": "pending"
},
{
"step": "Save final model",
"status": "pending"
}
]
}
}
]
},
{
"id": "12",
"role": "tool",
"timestamp": "2026-05-05T09:34:42.021Z",
"parts": [
{
"id": "13",
"type": "tool-result",
"token_count": 6,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_ZcNkdohozCauH2lkYGERzjjj",
"toolName": "update_plan",
"output": "Plan updated"
}
]
},
{
"id": "14",
"role": "assistant",
"timestamp": "2026-05-05T09:34:44.780Z",
"parts": [
{
"id": "15",
"type": "text",
"token_count": 20,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_Yz92bQtTuyHGyRSKY2qpnHVa"
},
{
"id": "16",
"type": "tool-call",
"token_count": 113,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_Yz92bQtTuyHGyRSKY2qpnHVa",
"toolName": "exec_command",
"input": {
"cmd": "pwd && find .. -name AGENTS.md -print && ls -lh && find data -maxdepth 2 -type f -printf '%p %s bytes\\n' | sort | head -50 && python - <<'PY'\nimport importlib.util\nfor m in ['fasttext','sklearn','numpy','pandas']:\n print(m, bool(importlib.util.find_spec(m)))\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "17",
"role": "tool",
"timestamp": "2026-05-05T09:34:44.780Z",
"parts": [
{
"id": "18",
"type": "tool-result",
"token_count": 140,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_Yz92bQtTuyHGyRSKY2qpnHVa",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"pwd && find .. -name AGENTS.md -print && ls -lh && find data -maxdepth 2 -type f -printf '%p %s bytes\\\\n' | sort | head -50 && python - <<'PY'\nimport importlib.util\nfor m in ['fasttext','sklearn','numpy','pandas']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\"\nChunk ID: fdde2d\nWall time: 1.0006 seconds\nProcess running with session ID 42975\nOriginal token count: 2\nOutput:\n/app\n"
}
]
},
{
"id": "19",
"role": "assistant",
"timestamp": "2026-05-05T09:34:49.015Z",
"parts": [
{
"id": "20",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_4zphpPFWi3pgZ2dO6KLT2CfS"
},
{
"id": "21",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_4zphpPFWi3pgZ2dO6KLT2CfS",
"toolName": "write_stdin",
"input": {
"session_id": 42975,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "22",
"role": "tool",
"timestamp": "2026-05-05T09:34:49.015Z",
"parts": [
{
"id": "23",
"type": "tool-result",
"token_count": 152,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_4zphpPFWi3pgZ2dO6KLT2CfS",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"pwd && find .. -name AGENTS.md -print && ls -lh && find data -maxdepth 2 -type f -printf '%p %s bytes\\\\n' | sort | head -50 && python - <<'PY'\nimport importlib.util\nfor m in ['fasttext','sklearn','numpy','pandas']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\"\nChunk ID: 9c7b22\nWall time: 0.0000 seconds\nProcess exited with code 1\nOriginal token count: 13\nOutput:\nfind: \u2018../proc/tty/driver\u2019: Permission denied\n"
}
]
},
{
"id": "24",
"role": "assistant",
"parts": [
{
"id": "25",
"type": "text",
"token_count": 41,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C3.2 Wrong search scope",
"D_impact": "D0 No negative impact"
},
"text": "The first broad search hit `/proc`; I\u2019ll narrow inspection to `/app` and dataset files.Executed exec_command call_F4kju8DMJObFD2slVL7IlkqC"
},
{
"id": "26",
"type": "tool-call",
"token_count": 121,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C3.2 Wrong search scope",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_F4kju8DMJObFD2slVL7IlkqC",
"toolName": "exec_command",
"input": {
"cmd": "find /app -name AGENTS.md -print 2>/dev/null && ls -lh /app && find /app/data -maxdepth 2 -type f -printf '%p %s bytes\\n' | sort | head -50 && python - <<'PY'\nimport importlib.util\nfor m in ['fasttext','sklearn','numpy','pandas']:\n print(m, bool(importlib.util.find_spec(m)))\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "27",
"role": "tool",
"timestamp": "2026-05-05T09:34:55.272Z",
"parts": [
{
"id": "28",
"type": "tool-result",
"token_count": 223,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_F4kju8DMJObFD2slVL7IlkqC",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"find /app -name AGENTS.md -print 2>/dev/null && ls -lh /app && find /app/data -maxdepth 2 -type f -printf '%p %s bytes\\\\n' | sort | head -50 && python - <<'PY'\nimport importlib.util\nfor m in ['fasttext','sklearn','numpy','pandas']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\"\nChunk ID: 8b6e6a\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 53\nOutput:\ntotal 0\ndrwxr-xr-x 1 root root 49 Aug 22 2025 data\n/app/data/test-00000-of-00001.parquet 4844861 bytes\n/app/data/train-00000-of-00001.parquet 299436850 bytes\nfasttext False\nsklearn False\nnumpy True\npandas True\n"
}
]
},
{
"id": "29",
"role": "assistant",
"timestamp": "2026-05-05T09:35:02.946Z",
"parts": [
{
"id": "30",
"type": "text",
"token_count": 20,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_Vnp8OnEH5Jm405AN8gNWmzI5"
},
{
"id": "31",
"type": "tool-call",
"token_count": 150,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_Vnp8OnEH5Jm405AN8gNWmzI5",
"toolName": "exec_command",
"input": {
"cmd": "command -v fasttext || true\npython - <<'PY'\nimport importlib.util\nfor m in ['pyarrow','fastparquet','polars']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\npython - <<'PY'\nimport pandas as pd\np='/app/data/train-00000-of-00001.parquet'\ndf=pd.read_parquet(p)\nprint(df.shape)\nprint(df.dtypes)\nprint(df.head())\nprint(df.columns.tolist())\nprint(df.iloc[0].to_dict())\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "32",
"role": "tool",
"timestamp": "2026-05-05T09:35:02.946Z",
"parts": [
{
"id": "33",
"type": "tool-result",
"token_count": 189,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_Vnp8OnEH5Jm405AN8gNWmzI5",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"command -v fasttext || true\npython - <<'PY'\nimport importlib.util\nfor m in ['pyarrow','fastparquet','polars']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\npython - <<'PY'\nimport pandas as pd\np='/app/data/train-00000-of-00001.parquet'\ndf=pd.read_parquet(p)\nprint(df.shape)\nprint(df.dtypes)\nprint(df.head())\nprint(df.columns.tolist())\nprint(df.iloc[0].to_dict())\nPY\"\nChunk ID: 87c55c\nWall time: 1.0013 seconds\nProcess running with session ID 80329\nOriginal token count: 11\nOutput:\npyarrow True\nfastparquet False\npolars False\n"
}
]
},
{
"id": "34",
"role": "assistant",
"timestamp": "2026-05-05T09:35:07.641Z",
"parts": [
{
"id": "35",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_OHipp6VW8WVqHjyxRmt51lIl"
},
{
"id": "36",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_OHipp6VW8WVqHjyxRmt51lIl",
"toolName": "write_stdin",
"input": {
"session_id": 80329,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "37",
"role": "tool",
"timestamp": "2026-05-05T09:35:07.641Z",
"parts": [
{
"id": "38",
"type": "tool-result",
"token_count": 424,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_OHipp6VW8WVqHjyxRmt51lIl",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"command -v fasttext || true\npython - <<'PY'\nimport importlib.util\nfor m in ['pyarrow','fastparquet','polars']:\n print(m, bool(importlib.util.find_spec(m)))\nPY\npython - <<'PY'\nimport pandas as pd\np='/app/data/train-00000-of-00001.parquet'\ndf=pd.read_parquet(p)\nprint(df.shape)\nprint(df.dtypes)\nprint(df.head())\nprint(df.columns.tolist())\nprint(df.iloc[0].to_dict())\nPY\"\nChunk ID: 4de3c0\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 249\nOutput:\n(650000, 2)\nlabel int64\ntext object\ndtype: object\n label text\n0 4 dr. goldberg offers everything i look for in a...\n1 1 Unfortunately, the frustration of being Dr. Go...\n2 3 Been going to Dr. Goldberg for over 10 years. ...\n3 3 Got a letter in the mail last week that said D...\n4 0 I don't know what Dr. Goldberg was like before...\n['label', 'text']\n{'label': 4, 'text': \"dr. goldberg offers everything i look for in a general practitioner. he's nice and easy to talk to without being patronizing; he's always on time in seeing his patients; he's affiliated with a top-notch hospital (nyu) which my parents have explained to me is very important in case something happens and you need surgery; and you can get referrals to see specialists without having to see him first. really, what more do you need? i'm sitting here trying to think of any complaints i have about him, but i'm really drawing a blank.\"}\n"
}
]
},
{
"id": "39",
"role": "assistant",
"parts": [
{
"id": "40",
"type": "text",
"token_count": 42,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "The data is 650k labeled reviews. Next I\u2019ll install/use fastText tooling and build a local split.Executed exec_command call_fdTnRzSCwRat7HaBseIcvtTH"
},
{
"id": "41",
"type": "tool-call",
"token_count": 35,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_fdTnRzSCwRat7HaBseIcvtTH",
"toolName": "exec_command",
"input": {
"cmd": "python -m pip install --quiet fasttext",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "42",
"role": "tool",
"timestamp": "2026-05-05T09:35:14.765Z",
"parts": [
{
"id": "43",
"type": "tool-result",
"token_count": 60,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C8.3 Missing environment dependency",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_fdTnRzSCwRat7HaBseIcvtTH",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc 'python -m pip install --quiet fasttext'\nChunk ID: 016e13\nWall time: 1.0012 seconds\nProcess running with session ID 85059\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "44",
"role": "assistant",
"timestamp": "2026-05-05T09:35:20.650Z",
"parts": [
{
"id": "45",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_nu3R5MlYXkTLX5P37tnFW91y"
},
{
"id": "46",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_nu3R5MlYXkTLX5P37tnFW91y",
"toolName": "write_stdin",
"input": {
"session_id": 85059,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "47",
"role": "tool",
"timestamp": "2026-05-05T09:35:20.650Z",
"parts": [
{
"id": "48",
"type": "tool-result",
"token_count": 2427,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C4.8 Dependency/compatibility break",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_nu3R5MlYXkTLX5P37tnFW91y",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'python -m pip install --quiet fasttext'\nChunk ID: dea7bf\nWall time: 0.0000 seconds\nProcess exited with code 1\nOriginal token count: 2317\nOutput:\n error: subprocess-exited-with-error\n \n \u00d7 Building wheel for fasttext (pyproject.toml) did not run successfully.\n \u2502 exit code: 1\n \u2570\u2500> [134 lines of output]\n /tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/dist.py:599: SetuptoolsDeprecationWarning: Invalid dash-separated key 'description-file' in 'metadata' (setup.cfg), please use the underscore name 'description_file' instead.\n !!\n \n ********************************************************************************\n Usage of dash-separated 'description-file' will not be supported in future\n versions. Please use the underscore name 'description_file' instead.\n (Affected: fasttext).\n \n Available configuration options are listed in:\n https://setuptools.pypa.io/en/latest/userguide/declarative_config.html\n \n This deprecation is overdue, please update your project and remove deprecated\n calls to avoid build errors in the future.\n \n See https://github.com/pypa/setuptools/discussions/5011 for details.\n ********************************************************************************\n \n !!\n opt = self._enforce_underscore(opt, section)\n /tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/dist.py:765: SetuptoolsDeprecationWarning: License classifiers are deprecated.\n !!\n \n ********************************************************************************\n Please consider removing the following classifiers in favor of a SPDX license expression:\n \n License :: OSI Approved :: MIT License\n \n See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.\n ********************************************************************************\n \n !!\n self._finalize_license_expression()\n running bdist_wheel\n running build\n running build_py\n creating build/lib.linux-x86_64-cpython-313/fasttext\n copying python/fasttext_module/fasttext/FastText.py -> build/lib.linux-x86_64-cpython-313/fasttext\n copying python/fasttext_module/fasttext/__init__.py -> build/lib.linux-x86_64-cpython-313/fasttext\n creating build/lib.linux-x86_64-cpython-313/fasttext/util\n copying python/fasttext_module/fasttext/util/__init__.py -> build/lib.linux-x86_64-cpython-313/fasttext/util\n copying python/fasttext_module/fasttext/util/util.py -> build/lib.linux-x86_64-cpython-313/fasttext/util\n creating build/lib.linux-x86_64-cpython-313/fasttext/tests\n copying python/fasttext_module/fasttext/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/fasttext/tests\n copying python/fasttext_module/fasttext/tests/test_configurations.py -> build/lib.linux-x86_64-cpython-313/fasttext/tests\n copying python/fasttext_module/fasttext/tests/test_script.py -> build/lib.linux-x86_64-cpython-313/fasttext/tests\n running build_ext\n creating tmp\n g++ -fno-strict-overflow -Wsign-compare -DNDEBUG -g -O3 -Wall -fPIC -I/usr/local/include/python3.13 -c /tmp/tmpjbd0vuy7.cpp -o tmp/tmpjbd0vuy7.o -std=c++17\n Traceback (most recent call last):\n File \"/usr/local/lib/python3.13/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 389, in <module>\n main()\n ~~~~^^\n File \"/usr/local/lib/python3.13/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 373, in main\n json_out[\"return_val\"] = hook(**hook_input[\"kwargs\"])\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.13/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 280, in build_wheel\n return _build_backend().build_wheel(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n wheel_directory, config_settings, metadata_directory\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/build_meta.py\", line 441, in build_wheel\n return _build(['bdist_wheel', '--dist-info-dir', str(metadata_directory)])\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/build_meta.py\", line 429, in _build\n return self._build_with_temp_dir(\n ~~~~~~~~~~~~~~~~~~~~~~~~~^\n cmd,\n ^^^^\n ...<3 lines>...\n self._arbitrary_args(config_settings),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/build_meta.py\", line 410, in _build_with_temp_dir\n self.run_setup()\n ~~~~~~~~~~~~~~^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/build_meta.py\", line 520, in run_setup\n super().run_setup(setup_script=setup_script)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/build_meta.py\", line 317, in run_setup\n exec(code, locals())\n ~~~~^^^^^^^^^^^^^^^^\n File \"<string>\", line 171, in <module>\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/__init__.py\", line 117, in setup\n return distutils.core.setup(**attrs) # type: ignore[return-value]\n ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/core.py\", line 186, in setup\n return run_commands(dist)\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/core.py\", line 202, in run_commands\n dist.run_commands()\n ~~~~~~~~~~~~~~~~~^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/dist.py\", line 1000, in run_commands\n self.run_command(cmd)\n ~~~~~~~~~~~~~~~~^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/dist.py\", line 1107, in run_command\n super().run_command(command)\n ~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/dist.py\", line 1019, in run_command\n cmd_obj.run()\n ~~~~~~~~~~~^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/command/bdist_wheel.py\", line 370, in run\n self.run_command(\"build\")\n ~~~~~~~~~~~~~~~~^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/cmd.py\", line 341, in run_command\n self.distribution.run_command(command)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/dist.py\", line 1107, in run_command\n super().run_command(command)\n ~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/dist.py\", line 1019, in run_command\n cmd_obj.run()\n ~~~~~~~~~~~^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/command/build.py\", line 135, in run\n self.run_command(cmd_name)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/cmd.py\", line 341, in run_command\n self.distribution.run_command(command)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/dist.py\", line 1107, in run_command\n super().run_command(command)\n ~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/dist.py\", line 1019, in run_command\n cmd_obj.run()\n ~~~~~~~~~~~^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/command/build_ext.py\", line 97, in run\n _build_ext.run(self)\n ~~~~~~~~~~~~~~^^^^^^\n File \"/tmp/pip-build-env-ytferjuo/overlay/lib/python3.13/site-packages/setuptools/_distutils/command/build_ext.py\", line 367, in run\n self.build_extensions()\n ~~~~~~~~~~~~~~~~~~~~~^^\n File \"<string>\", line 151, in build_extensions\n File \"<string>\", line 114, in cpp_flag\n RuntimeError: Unsupported compiler -- at least C++17 support is needed!\n [end of output]\n \n note: This error originates from a subprocess, and is likely not a problem with pip.\n ERROR: Failed building wheel for fasttext\n\n[notice] A new release of pip is available: 25.2 -> 26.1.1\n[notice] To update, run: pip install --upgrade pip\nerror: failed-wheel-build-for-install\n\n\u00d7 Failed to build installable wheels for some pyproject.toml based projects\n\u2570\u2500> fasttext\n"
}
]
},
{
"id": "49",
"role": "assistant",
"parts": [
{
"id": "50",
"type": "text",
"token_count": 41,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "`fasttext` failed to build under Python 3.13, so I\u2019ll check compiler/package alternatives.Executed exec_command call_qNlmozJcidJbTb9uUSMJC62V"
},
{
"id": "51",
"type": "tool-call",
"token_count": 91,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_qNlmozJcidJbTb9uUSMJC62V",
"toolName": "exec_command",
"input": {
"cmd": "g++ --version || true\npython --version\npython3 --version\nls /usr/bin/python* | sed 's#^#/##' | head -20\napt-cache policy fasttext 2>/dev/null || true\napt-cache search fasttext 2>/dev/null | head -20 || true",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "52",
"role": "tool",
"timestamp": "2026-05-05T09:35:26.547Z",
"parts": [
{
"id": "53",
"type": "tool-result",
"token_count": 276,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_qNlmozJcidJbTb9uUSMJC62V",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"g++ --version || true\npython --version\npython3 --version\nls /usr/bin/python* | sed 's#\"'^#/##'\"' | head -20\napt-cache policy fasttext 2>/dev/null || true\napt-cache search fasttext 2>/dev/null | head -20 || true\"\nChunk ID: 03a834\nWall time: 0.7368 seconds\nProcess exited with code 0\nOriginal token count: 131\nOutput:\n/bin/bash: line 1: g++: command not found\nPython 3.13.7\nPython 3.13.7\nls: cannot access '/usr/bin/python*': No such file or directory\nfasttext:\n Installed: (none)\n Candidate: 0.9.2+ds-1+b1\n Version table:\n 0.9.2+ds-1+b1 500\n 500 http://deb.debian.org/debian bookworm/main amd64 Packages\nfasttext - Efficient learning of word representations and sentence classification library\nlibfasttext-dev - Header files of fastText\nlibfasttext0 - Library files of fastText\npython3-fasttext - fastText binding for Python3\n"
}
]
},
{
"id": "54",
"role": "assistant",
"parts": [
{
"id": "55",
"type": "text",
"token_count": 39,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "A Debian `fasttext` package is available, so I\u2019ll install the CLI binary.Executed exec_command call_PgTM7XWE9mAFGyx3N30yzgp9"
},
{
"id": "56",
"type": "tool-call",
"token_count": 41,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_PgTM7XWE9mAFGyx3N30yzgp9",
"toolName": "exec_command",
"input": {
"cmd": "apt-get update -qq && apt-get install -y -qq fasttext",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "57",
"role": "tool",
"timestamp": "2026-05-05T09:35:31.984Z",
"parts": [
{
"id": "58",
"type": "tool-result",
"token_count": 67,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_PgTM7XWE9mAFGyx3N30yzgp9",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc 'apt-get update -qq && apt-get install -y -qq fasttext'\nChunk ID: 2d3445\nWall time: 1.0010 seconds\nProcess running with session ID 46312\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "59",
"role": "assistant",
"timestamp": "2026-05-05T09:35:35.013Z",
"parts": [
{
"id": "60",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_bekJqpFJcfAQdrWFT3Ti4HIo"
},
{
"id": "61",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_bekJqpFJcfAQdrWFT3Ti4HIo",
"toolName": "write_stdin",
"input": {
"session_id": 46312,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "62",
"role": "tool",
"timestamp": "2026-05-05T09:35:35.013Z",
"parts": [
{
"id": "63",
"type": "tool-result",
"token_count": 461,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_bekJqpFJcfAQdrWFT3Ti4HIo",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'apt-get update -qq && apt-get install -y -qq fasttext'\nChunk ID: 9b7cfc\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 292\nOutput:\ndebconf: delaying package configuration, since apt-utils is not installed\nSelecting previously unselected package libfasttext0:amd64.\r\n(Reading database ... \r(Reading database ... 5%\r(Reading database ... 10%\r(Reading database ... 15%\r(Reading database ... 20%\r(Reading database ... 25%\r(Reading database ... 30%\r(Reading database ... 35%\r(Reading database ... 40%\r(Reading database ... 45%\r(Reading database ... 50%\r(Reading database ... 55%\r(Reading database ... 60%\r(Reading database ... 65%\r(Reading database ... 70%\r(Reading database ... 75%\r(Reading database ... 80%\r(Reading database ... 85%\r(Reading database ... 90%\r(Reading database ... 95%\r(Reading database ... 100%\r(Reading database ... 6903 files and directories currently installed.)\r\nPreparing to unpack .../libfasttext0_0.9.2+ds-1+b1_amd64.deb ...\r\nUnpacking libfasttext0:amd64 (0.9.2+ds-1+b1) ...\r\nSelecting previously unselected package fasttext.\r\nPreparing to unpack .../fasttext_0.9.2+ds-1+b1_amd64.deb ...\r\nUnpacking fasttext (0.9.2+ds-1+b1) ...\r\nSetting up libfasttext0:amd64 (0.9.2+ds-1+b1) ...\r\nSetting up fasttext (0.9.2+ds-1+b1) ...\r\nProcessing triggers for libc-bin (2.36-9+deb12u10) ...\r\n"
}
]
},
{
"id": "64",
"role": "assistant",
"timestamp": "2026-05-05T09:35:44.693Z",
"parts": [
{
"id": "65",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_BLIRoEH8n7SGcimiu4aMGQ1A"
},
{
"id": "66",
"type": "tool-call",
"token_count": 146,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_BLIRoEH8n7SGcimiu4aMGQ1A",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nimport pandas as pd\nfor p in ['/app/data/train-00000-of-00001.parquet','/app/data/test-00000-of-00001.parquet']:\n df=pd.read_parquet(p)\n print('\\n',p, df.shape)\n print(df.dtypes)\n print(df.head())\n print(df['label'].value_counts().sort_index().to_dict() if 'label' in df.columns else 'no labels')\nPY\nfasttext 2>&1 | head -50",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "67",
"role": "tool",
"timestamp": "2026-05-05T09:35:44.693Z",
"parts": [
{
"id": "68",
"type": "tool-result",
"token_count": 171,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_BLIRoEH8n7SGcimiu4aMGQ1A",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport pandas as pd\nfor p in ['/app/data/train-00000-of-00001.parquet','/app/data/test-00000-of-00001.parquet']:\n df=pd.read_parquet(p)\n print('\\\\n',p, df.shape)\n print(df.dtypes)\n print(df.head())\n print(df['label'].value_counts().sort_index().to_dict() if 'label' in df.columns else 'no labels')\nPY\nfasttext 2>&1 | head -50\"\nChunk ID: bb76ee\nWall time: 1.0008 seconds\nProcess running with session ID 11881\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "69",
"role": "assistant",
"timestamp": "2026-05-05T09:35:47.839Z",
"parts": [
{
"id": "70",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_eKV23GPbPwYLQ919L4XmaLxA"
},
{
"id": "71",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_eKV23GPbPwYLQ919L4XmaLxA",
"toolName": "write_stdin",
"input": {
"session_id": 11881,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "72",
"role": "tool",
"timestamp": "2026-05-05T09:35:47.839Z",
"parts": [
{
"id": "73",
"type": "tool-result",
"token_count": 690,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_eKV23GPbPwYLQ919L4XmaLxA",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport pandas as pd\nfor p in ['/app/data/train-00000-of-00001.parquet','/app/data/test-00000-of-00001.parquet']:\n df=pd.read_parquet(p)\n print('\\\\n',p, df.shape)\n print(df.dtypes)\n print(df.head())\n print(df['label'].value_counts().sort_index().to_dict() if 'label' in df.columns else 'no labels')\nPY\nfasttext 2>&1 | head -50\"\nChunk ID: 9b3e2f\nWall time: 0.8908 seconds\nProcess exited with code 0\nOriginal token count: 494\nOutput:\n\n /app/data/train-00000-of-00001.parquet (650000, 2)\nlabel int64\ntext object\ndtype: object\n label text\n0 4 dr. goldberg offers everything i look for in a...\n1 1 Unfortunately, the frustration of being Dr. Go...\n2 3 Been going to Dr. Goldberg for over 10 years. ...\n3 3 Got a letter in the mail last week that said D...\n4 0 I don't know what Dr. Goldberg was like before...\n{0: 130000, 1: 130000, 2: 130000, 3: 130000, 4: 130000}\n\n /app/data/test-00000-of-00001.parquet (10000, 2)\nlabel int64\ntext object\ndtype: object\n label text\n11872 4 This place was incredible, and totally lived u...\n40828 3 I had seen the signs up as I went by for a whi...\n36400 3 I like this place for lunch. It is relativley ...\n5166 0 I checked out this dealership on Yelp prior to...\n30273 2 Of course an Apple Store is an Apple Store, bu...\n{0: 2008, 1: 1963, 2: 1979, 3: 2044, 4: 2006}\nusage: fasttext <command> <args>\n\nThe commands supported by fasttext are:\n\n supervised train a supervised classifier\n quantize quantize a model to reduce the memory usage\n test evaluate a supervised classifier\n test-label print labels with precision and recall scores\n predict predict most likely labels\n predict-prob predict most likely labels with probabilities\n skipgram train a skipgram model\n cbow train a cbow model\n print-word-vectors print word vectors given a trained model\n print-sentence-vectors print sentence vectors given a trained model\n print-ngrams print ngrams given a trained model and word\n nn query for nearest neighbors\n analogies query for analogies\n dump dump arguments,dictionary,input/output vectors\n\n"
}
]
},
{
"id": "74",
"role": "assistant",
"parts": [
{
"id": "75",
"type": "text",
"token_count": 39,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C5.3 State drift",
"D_impact": "D0 No negative impact"
},
"text": "I\u2019ll convert Parquet to fastText format with a stratified holdout for tuning.Executed update_plan call_3aLukzzEzgUQPXLVpIqOPvH2"
},
{
"id": "76",
"type": "tool-call",
"token_count": 61,
"component": "A1 Understanding & planning",
"dimensions": {
"A_stage": "A1 Understanding & planning",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C5.3 State drift",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_3aLukzzEzgUQPXLVpIqOPvH2",
"toolName": "update_plan",
"input": {
"plan": [
{
"step": "Inspect data and tooling",
"status": "completed"
},
{
"step": "Create validation split",
"status": "in_progress"
},
{
"step": "Train compact model candidates",
"status": "pending"
},
{
"step": "Evaluate accuracy and size",
"status": "pending"
},
{
"step": "Save final model",
"status": "pending"
}
]
}
}
]
},
{
"id": "77",
"role": "tool",
"timestamp": "2026-05-05T09:36:02.780Z",
"parts": [
{
"id": "78",
"type": "tool-result",
"token_count": 6,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_3aLukzzEzgUQPXLVpIqOPvH2",
"toolName": "update_plan",
"output": "Plan updated"
}
]
},
{
"id": "79",
"role": "assistant",
"timestamp": "2026-05-05T09:36:09.142Z",
"parts": [
{
"id": "80",
"type": "text",
"token_count": 24,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed exec_command call_HL3WZuzUwuHmY4xjFvNnWs7w"
},
{
"id": "81",
"type": "tool-call",
"token_count": 512,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_HL3WZuzUwuHmY4xjFvNnWs7w",
"toolName": "exec_command",
"input": {
"cmd": "cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\s+')\n\ndef clean(text):\n text = str(text).replace('\\n', ' ').replace('\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "82",
"role": "tool",
"timestamp": "2026-05-05T09:36:09.142Z",
"parts": [
{
"id": "83",
"type": "tool-result",
"token_count": 541,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_HL3WZuzUwuHmY4xjFvNnWs7w",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\\\s+')\n\ndef clean(text):\n text = str(text).replace('\\\\n', ' ').replace('\\\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt\"\nChunk ID: c6c6f5\nWall time: 1.0012 seconds\nProcess running with session ID 5188\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "84",
"role": "assistant",
"timestamp": "2026-05-05T09:36:12.901Z",
"parts": [
{
"id": "85",
"type": "text",
"token_count": 20,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed write_stdin call_K0bSrz9phTUHsbmcWMmeQ124"
},
{
"id": "86",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_K0bSrz9phTUHsbmcWMmeQ124",
"toolName": "write_stdin",
"input": {
"session_id": 5188,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "87",
"role": "tool",
"timestamp": "2026-05-05T09:36:12.901Z",
"parts": [
{
"id": "88",
"type": "tool-result",
"token_count": 540,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_K0bSrz9phTUHsbmcWMmeQ124",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\\\s+')\n\ndef clean(text):\n text = str(text).replace('\\\\n', ' ').replace('\\\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt\"\nChunk ID: 29ed02\nWall time: 5.0008 seconds\nProcess running with session ID 5188\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "89",
"role": "assistant",
"timestamp": "2026-05-05T09:36:20.329Z",
"parts": [
{
"id": "90",
"type": "text",
"token_count": 21,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed write_stdin call_I61M7EXwS923ogkagpDz4aEc"
},
{
"id": "91",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_I61M7EXwS923ogkagpDz4aEc",
"toolName": "write_stdin",
"input": {
"session_id": 5188,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "92",
"role": "tool",
"timestamp": "2026-05-05T09:36:20.329Z",
"parts": [
{
"id": "93",
"type": "tool-result",
"token_count": 541,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_I61M7EXwS923ogkagpDz4aEc",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\\\s+')\n\ndef clean(text):\n text = str(text).replace('\\\\n', ' ').replace('\\\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt\"\nChunk ID: eef2c2\nWall time: 5.0007 seconds\nProcess running with session ID 5188\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "94",
"role": "assistant",
"timestamp": "2026-05-05T09:36:27.669Z",
"parts": [
{
"id": "95",
"type": "text",
"token_count": 25,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed write_stdin call_w0TQfiPkUQ0D0M4D4AGhZQIa"
},
{
"id": "96",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_w0TQfiPkUQ0D0M4D4AGhZQIa",
"toolName": "write_stdin",
"input": {
"session_id": 5188,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "97",
"role": "tool",
"timestamp": "2026-05-05T09:36:27.669Z",
"parts": [
{
"id": "98",
"type": "tool-result",
"token_count": 540,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_w0TQfiPkUQ0D0M4D4AGhZQIa",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\\\s+')\n\ndef clean(text):\n text = str(text).replace('\\\\n', ' ').replace('\\\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt\"\nChunk ID: 368a33\nWall time: 5.0017 seconds\nProcess running with session ID 5188\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "99",
"role": "assistant",
"timestamp": "2026-05-05T09:36:35.108Z",
"parts": [
{
"id": "100",
"type": "text",
"token_count": 21,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed write_stdin call_KUn8jHNuoERzxDuN7m6Ve1GF"
},
{
"id": "101",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_KUn8jHNuoERzxDuN7m6Ve1GF",
"toolName": "write_stdin",
"input": {
"session_id": 5188,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "102",
"role": "tool",
"timestamp": "2026-05-05T09:36:35.108Z",
"parts": [
{
"id": "103",
"type": "tool-result",
"token_count": 540,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_KUn8jHNuoERzxDuN7m6Ve1GF",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\\\s+')\n\ndef clean(text):\n text = str(text).replace('\\\\n', ' ').replace('\\\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt\"\nChunk ID: 9ea506\nWall time: 5.0008 seconds\nProcess running with session ID 5188\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "104",
"role": "assistant",
"timestamp": "2026-05-05T09:36:44.313Z",
"parts": [
{
"id": "105",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_segiuTN9qQzFQ9hcgxJxnPW3"
},
{
"id": "106",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_segiuTN9qQzFQ9hcgxJxnPW3",
"toolName": "write_stdin",
"input": {
"session_id": 5188,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "107",
"role": "tool",
"timestamp": "2026-05-05T09:36:44.313Z",
"parts": [
{
"id": "108",
"type": "tool-result",
"token_count": 541,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_segiuTN9qQzFQ9hcgxJxnPW3",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\\\s+')\n\ndef clean(text):\n text = str(text).replace('\\\\n', ' ').replace('\\\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt\"\nChunk ID: 04b58c\nWall time: 5.0005 seconds\nProcess running with session ID 5188\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "109",
"role": "assistant",
"timestamp": "2026-05-05T09:36:52.421Z",
"parts": [
{
"id": "110",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed exec_command call_Zozy6XQzoOsyAltl66kE6K1V"
},
{
"id": "111",
"type": "tool-call",
"token_count": 79,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_Zozy6XQzoOsyAltl66kE6K1V",
"toolName": "exec_command",
"input": {
"cmd": "ps -o pid,etime,pcpu,pmem,rss,cmd -C python || true; du -sh /app/ftdata 2>/dev/null || true; ls -lh /app/ftdata 2>/dev/null || true",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "112",
"role": "tool",
"timestamp": "2026-05-05T09:36:52.421Z",
"parts": [
{
"id": "113",
"type": "tool-result",
"token_count": 261,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_Zozy6XQzoOsyAltl66kE6K1V",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc 'ps -o pid,etime,pcpu,pmem,rss,cmd -C python || true; du -sh /app/ftdata 2>/dev/null || true; ls -lh /app/ftdata 2>/dev/null || true'\nChunk ID: ccf47f\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 88\nOutput:\n/bin/bash: line 1: ps: command not found\n1.2G\t/app/ftdata\ntotal 1.2G\n-rw-r--r-- 1 root root 7.2M May 5 09:36 public_test.txt\n-rw-r--r-- 1 root root 460M May 5 09:36 train_all.txt\n-rw-r--r-- 1 root root 218M May 5 09:36 train_plus_public.txt\n-rw-r--r-- 1 root root 425M May 5 09:36 train_split.txt\n-rw-r--r-- 1 root root 36M May 5 09:36 valid.txt\n"
}
]
},
{
"id": "114",
"role": "assistant",
"timestamp": "2026-05-05T09:36:57.284Z",
"parts": [
{
"id": "115",
"type": "text",
"token_count": 19,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_i1QRZIx7LCDKWh4LvzouROls"
},
{
"id": "116",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_i1QRZIx7LCDKWh4LvzouROls",
"toolName": "write_stdin",
"input": {
"session_id": 5188,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "117",
"role": "tool",
"timestamp": "2026-05-05T09:36:57.284Z",
"parts": [
{
"id": "118",
"type": "tool-result",
"token_count": 905,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_i1QRZIx7LCDKWh4LvzouROls",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext.py <<'PY'\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata')\nOUT.mkdir(exist_ok=True)\n\n_ws = re.compile(r'\\\\s+')\n\ndef clean(text):\n text = str(text).replace('\\\\n', ' ').replace('\\\\r', ' ')\n text = _ws.sub(' ', text).strip().lower()\n return text\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\n# deterministic stratified validation: 10k per class (50k total)\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(train['label'].to_numpy() == label)\n chosen = rng.choice(idx, size=10_000, replace=False)\n val_mask[chosen] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\ncombined = pd.concat([train, test], ignore_index=True)\nwrite_ft(combined, OUT / 'train_plus_public.txt')\n\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext.py\nwc -l /app/ftdata/*.txt\nhead -2 /app/ftdata/train_split.txt\"\nChunk ID: 396271\nWall time: 3.9717 seconds\nProcess exited with code 0\nOriginal token count: 362\nOutput:\n/app/ftdata/public_test.txt 7511625\n/app/ftdata/train_all.txt 482092983\n/app/ftdata/train_plus_public.txt 489604608\n/app/ftdata/train_split.txt 445022487\n/app/ftdata/valid.txt 37070496\n 10000 /app/ftdata/public_test.txt\n 650000 /app/ftdata/train_all.txt\n 660000 /app/ftdata/train_plus_public.txt\n 600000 /app/ftdata/train_split.txt\n 50000 /app/ftdata/valid.txt\n 1970000 total\n__label__4 dr. goldberg offers everything i look for in a general practitioner. he's nice and easy to talk to without being patronizing; he's always on time in seeing his patients; he's affiliated with a top-notch hospital (nyu) which my parents have explained to me is very important in case something happens and you need surgery; and you can get referrals to see specialists without having to see him first. really, what more do you need? i'm sitting here trying to think of any complaints i have about him, but i'm really drawing a blank.\n__label__3 been going to dr. goldberg for over 10 years. i think i was one of his 1st patients when he started at mhmg. he's been great over the years and is really all about the big picture. it is because of him, not my now former gyn dr. markoff, that i found out i have fibroids. he explores all options with you and is very patient and understanding. he doesn't judge and asks all the right questions. very thorough and wants to be kept in the loop on every aspect of your medical health and your life.\n"
}
]
},
{
"id": "119",
"role": "assistant",
"parts": [
{
"id": "120",
"type": "text",
"token_count": 37,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Files are ready. I\u2019ll train a compact bigram model first and evaluate it.Executed update_plan call_bUtqAs3WxQOryRGDNi0PrPAO"
},
{
"id": "121",
"type": "tool-call",
"token_count": 61,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_bUtqAs3WxQOryRGDNi0PrPAO",
"toolName": "update_plan",
"input": {
"plan": [
{
"step": "Inspect data and tooling",
"status": "completed"
},
{
"step": "Create validation split",
"status": "completed"
},
{
"step": "Train compact model candidates",
"status": "in_progress"
},
{
"step": "Evaluate accuracy and size",
"status": "pending"
},
{
"step": "Save final model",
"status": "pending"
}
]
}
}
]
},
{
"id": "122",
"role": "tool",
"timestamp": "2026-05-05T09:37:11.408Z",
"parts": [
{
"id": "123",
"type": "tool-result",
"token_count": 6,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_bUtqAs3WxQOryRGDNi0PrPAO",
"toolName": "update_plan",
"output": "Plan updated"
}
]
},
{
"id": "124",
"role": "assistant",
"timestamp": "2026-05-05T09:37:14.427Z",
"parts": [
{
"id": "125",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed exec_command call_XtiRZBFChc3TMygCV7tgK0HY"
},
{
"id": "126",
"type": "tool-call",
"token_count": 156,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_XtiRZBFChc3TMygCV7tgK0HY",
"toolName": "exec_command",
"input": {
"cmd": "mkdir -p /app/models\n/usr/bin/time -f 'elapsed=%E maxrss=%MKB' fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "127",
"role": "tool",
"timestamp": "2026-05-05T09:37:14.427Z",
"parts": [
{
"id": "128",
"type": "tool-result",
"token_count": 312,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_XtiRZBFChc3TMygCV7tgK0HY",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"mkdir -p /app/models\n/usr/bin/time -f 'elapsed=%E maxrss=%MKB' fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt\"\nChunk ID: b7c561\nWall time: 0.0461 seconds\nProcess exited with code -1\nOriginal token count: 127\nOutput:\n/bin/bash: line 2: /usr/bin/time: No such file or directory\nls: cannot access '/app/models/m1.bin': No such file or directory\nterminate called after throwing an instance of 'std::invalid_argument'\n what(): /app/models/m1.bin cannot be opened for loading!\n/bin/bash: line 4: 4252 Aborted (core dumped) fasttext test /app/models/m1.bin /app/ftdata/valid.txt\nterminate called after throwing an instance of 'std::invalid_argument'\n what(): /app/models/m1.bin cannot be opened for loading!\n"
}
]
},
{
"id": "129",
"role": "assistant",
"parts": [
{
"id": "130",
"type": "text",
"token_count": 43,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C8.3 Missing environment dependency",
"D_impact": "D1 Recoverable mild"
},
"text": "`/usr/bin/time` isn\u2019t installed, so I\u2019ll rerun with the shell timer.Executed exec_command call_I6Hh4uUPqZkT7ALVLxIXDXVb"
},
{
"id": "131",
"type": "tool-call",
"token_count": 154,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_I6Hh4uUPqZkT7ALVLxIXDXVb",
"toolName": "exec_command",
"input": {
"cmd": "rm -f /app/models/m1.bin /app/models/m1.vec /app/core*\ntime fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "132",
"role": "tool",
"timestamp": "2026-05-05T09:37:19.631Z",
"parts": [
{
"id": "133",
"type": "tool-result",
"token_count": 304,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_I6Hh4uUPqZkT7ALVLxIXDXVb",
"toolName": "exec_command",
"output": "exec_command failed for `/bin/bash -lc 'rm -f /app/models/m1.bin /app/models/m1.vec /app/core*\ntime fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'`: CreateProcess { message: \"Rejected(\\\"`/bin/bash -lc 'rm -f /app/models/m1.bin /app/models/m1.vec /app/core*\\\\ntime fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\\\\nls -lh /app/models/m1.bin\\\\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\\\\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'` rejected: blocked by policy\\\")\" }"
}
]
},
{
"id": "134",
"role": "assistant",
"timestamp": "2026-05-05T09:37:31.325Z",
"parts": [
{
"id": "135",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"text": "Executed exec_command call_2SQl8y3qrmeq8FCbnBupjKCf"
},
{
"id": "136",
"type": "tool-call",
"token_count": 134,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_2SQl8y3qrmeq8FCbnBupjKCf",
"toolName": "exec_command",
"input": {
"cmd": "fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "137",
"role": "tool",
"timestamp": "2026-05-05T09:37:31.325Z",
"parts": [
{
"id": "138",
"type": "tool-result",
"token_count": 280,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_2SQl8y3qrmeq8FCbnBupjKCf",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: 54f1ae\nWall time: 1.0012 seconds\nProcess running with session ID 86685\nOriginal token count: 73\nOutput:\n\rRead 1M words\rRead 2M words\rRead 3M words\rRead 4M words\rRead 5M words\rRead 6M words\rRead 7M words\rRead 8M words\rRead 9M words\rRead 10M words\rRead 11M words\rRead 12M words\rRead 13M words\rRead 14M words\rRead 15M words\rRead 16M words\rRead 17M words\rRead 18M words\rRead 19M words\rRead 20M words"
}
]
},
{
"id": "139",
"role": "assistant",
"timestamp": "2026-05-05T09:37:34.994Z",
"parts": [
{
"id": "140",
"type": "text",
"token_count": 19,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_saBPTrogKYdpxRdlIunoLJlS"
},
{
"id": "141",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_saBPTrogKYdpxRdlIunoLJlS",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 5000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "142",
"role": "tool",
"timestamp": "2026-05-05T09:37:34.994Z",
"parts": [
{
"id": "143",
"type": "tool-result",
"token_count": 548,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_saBPTrogKYdpxRdlIunoLJlS",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: fa078a\nWall time: 5.0012 seconds\nProcess running with session ID 86685\nOriginal token count: 244\nOutput:\n\rRead 21M words\rRead 22M words\rRead 23M words\rRead 24M words\rRead 25M words\rRead 26M words\rRead 27M words\rRead 28M words\rRead 29M words\rRead 30M words\rRead 31M words\rRead 32M words\rRead 33M words\rRead 34M words\rRead 35M words\rRead 36M words\rRead 37M words\rRead 38M words\rRead 39M words\rRead 40M words\rRead 41M words\rRead 42M words\rRead 43M words\rRead 44M words\rRead 45M words\rRead 46M words\rRead 47M words\rRead 48M words\rRead 49M words\rRead 50M words\rRead 51M words\rRead 52M words\rRead 53M words\rRead 54M words\rRead 55M words\rRead 56M words\rRead 57M words\rRead 58M words\rRead 59M words\rRead 60M words\rRead 61M words\rRead 62M words\rRead 63M words\rRead 64M words\rRead 65M words\rRead 66M words\rRead 67M words\rRead 68M words\rRead 69M words\rRead 70M words\rRead 71M words\rRead 72M words\rRead 73M words\rRead 74M words\rRead 75M words\rRead 76M words\rRead 77M words\rRead 78M words\rRead 79M words\rRead 80M words\rRead 81M words\rRead 81M words\nNumber of words: 384199\nNumber of labels: 5\n"
}
]
},
{
"id": "144",
"role": "assistant",
"timestamp": "2026-05-05T09:37:42.491Z",
"parts": [
{
"id": "145",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_coKRe7A52vIFdZ7T5ZNYVNj4"
},
{
"id": "146",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_coKRe7A52vIFdZ7T5ZNYVNj4",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 5000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "147",
"role": "tool",
"timestamp": "2026-05-05T09:37:42.491Z",
"parts": [
{
"id": "148",
"type": "tool-result",
"token_count": 162,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_coKRe7A52vIFdZ7T5ZNYVNj4",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: 7bd3b2\nWall time: 5.0008 seconds\nProcess running with session ID 86685\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "149",
"role": "assistant",
"timestamp": "2026-05-05T09:37:49.731Z",
"parts": [
{
"id": "150",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_H3AWqUBdTC6wNvHoz4zRZcsG"
},
{
"id": "151",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_H3AWqUBdTC6wNvHoz4zRZcsG",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "152",
"role": "tool",
"timestamp": "2026-05-05T09:37:49.731Z",
"parts": [
{
"id": "153",
"type": "tool-result",
"token_count": 160,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_H3AWqUBdTC6wNvHoz4zRZcsG",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: 4228fe\nWall time: 10.0020 seconds\nProcess running with session ID 86685\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "154",
"role": "assistant",
"timestamp": "2026-05-05T09:38:02.379Z",
"parts": [
{
"id": "155",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_i5ryEOm5mn6BtXFY2pvgtsn9"
},
{
"id": "156",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_i5ryEOm5mn6BtXFY2pvgtsn9",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 30000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "157",
"role": "tool",
"timestamp": "2026-05-05T09:38:02.379Z",
"parts": [
{
"id": "158",
"type": "tool-result",
"token_count": 160,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_i5ryEOm5mn6BtXFY2pvgtsn9",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: 1ff159\nWall time: 30.0006 seconds\nProcess running with session ID 86685\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "159",
"role": "assistant",
"parts": [
{
"id": "160",
"type": "text",
"token_count": 37,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C4.1 Insufficient surrounding-context understanding",
"D_impact": "D0 No negative impact"
},
"text": "The first candidate is training; dictionary build finished with ~384k words.Executed write_stdin call_176BDFXjkBDNnsQDk0Z3tbB3"
},
{
"id": "161",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_176BDFXjkBDNnsQDk0Z3tbB3",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 60000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "162",
"role": "tool",
"timestamp": "2026-05-05T09:38:38.266Z",
"parts": [
{
"id": "163",
"type": "tool-result",
"token_count": 161,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_176BDFXjkBDNnsQDk0Z3tbB3",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: 0b909d\nWall time: 60.0014 seconds\nProcess running with session ID 86685\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "164",
"role": "assistant",
"timestamp": "2026-05-05T09:39:44.194Z",
"parts": [
{
"id": "165",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_jJsFPhoxPAHfZFp5LdDqNEnT"
},
{
"id": "166",
"type": "tool-call",
"token_count": 187,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_jJsFPhoxPAHfZFp5LdDqNEnT",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nimport os, glob, time\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\0',b' ').decode('utf-8','ignore')\n if 'fasttext supervised' in cmd or (cmd.strip().startswith('fasttext') and 'test' not in cmd):\n stat=open(pid+'/stat').read().split()\n print(pid, cmd[:200], 'utime', stat[13], 'stime', stat[14], 'rss_pages', stat[23])\n except Exception: pass\nprint('models'); os.system('ls -lh /app/models')\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "167",
"role": "tool",
"timestamp": "2026-05-05T09:39:44.194Z",
"parts": [
{
"id": "168",
"type": "tool-result",
"token_count": 503,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_jJsFPhoxPAHfZFp5LdDqNEnT",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport os, glob, time\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode('utf-8','ignore')\n if 'fasttext supervised' in cmd or (cmd.strip().startswith('fasttext') and 'test' not in cmd):\n stat=open(pid+'/stat').read().split()\n print(pid, cmd[:200], 'utime', stat[13], 'stime', stat[14], 'rss_pages', stat[23])\n except Exception: pass\nprint('models'); os.system('ls -lh /app/models')\nPY\"\nChunk ID: 45ec75\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 194\nOutput:\ntotal 0\n-rw-r--r-- 1 root root 0 May 5 09:37 m1.bin\n/proc/4257 /bin/bash -c fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh utime 0 stime 0 rss_pages 643\n/proc/4258 fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2 utime 12924 stime 147 rss_pages 68874\n/proc/4281 /bin/bash -c python - <<'PY'\nimport os, glob, time\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\0',b' ').decode('utf-8','ignore')\n if ' utime 0 stime 0 rss_pages 640\nmodels\n"
}
]
},
{
"id": "169",
"role": "assistant",
"timestamp": "2026-05-05T09:39:51.315Z",
"parts": [
{
"id": "170",
"type": "text",
"token_count": 25,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_4eqOCTH1BikQ2aKJRloY1h3t"
},
{
"id": "171",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_4eqOCTH1BikQ2aKJRloY1h3t",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 120000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "172",
"role": "tool",
"timestamp": "2026-05-05T09:39:51.315Z",
"parts": [
{
"id": "173",
"type": "tool-result",
"token_count": 160,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_4eqOCTH1BikQ2aKJRloY1h3t",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: e5952a\nWall time: 120.0011 seconds\nProcess running with session ID 86685\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "174",
"role": "assistant",
"timestamp": "2026-05-05T09:41:58.633Z",
"parts": [
{
"id": "175",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_0H0nPoVsqPFJGZ2GgssM475D"
},
{
"id": "176",
"type": "tool-call",
"token_count": 159,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_0H0nPoVsqPFJGZ2GgssM475D",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_ticks', int(stat[13])+int(stat[14]), 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY\nls -lh /app/models/m1.bin",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "177",
"role": "tool",
"timestamp": "2026-05-05T09:41:58.633Z",
"parts": [
{
"id": "178",
"type": "tool-result",
"token_count": 224,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_0H0nPoVsqPFJGZ2GgssM475D",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_ticks', int(stat[13])+int(stat[14]), 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY\nls -lh /app/models/m1.bin\"\nChunk ID: b92f10\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 24\nOutput:\n4258 cpu_ticks 26335 rss_mb 282.107904\n-rw-r--r-- 1 root root 0 May 5 09:37 /app/models/m1.bin\n"
}
]
},
{
"id": "179",
"role": "assistant",
"timestamp": "2026-05-05T09:42:02.643Z",
"parts": [
{
"id": "180",
"type": "text",
"token_count": 23,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_0eEJJQ4E01IaMWfFfTDNUkRV"
},
{
"id": "181",
"type": "tool-call",
"token_count": 26,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_0eEJJQ4E01IaMWfFfTDNUkRV",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 180000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "182",
"role": "tool",
"timestamp": "2026-05-05T09:42:02.643Z",
"parts": [
{
"id": "183",
"type": "tool-result",
"token_count": 161,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_0eEJJQ4E01IaMWfFfTDNUkRV",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: 6cebb9\nWall time: 180.0012 seconds\nProcess running with session ID 86685\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "184",
"role": "assistant",
"timestamp": "2026-05-05T09:45:07.895Z",
"parts": [
{
"id": "185",
"type": "text",
"token_count": 18,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_c5mih55lfvbX48yYnaMOVtgk"
},
{
"id": "186",
"type": "tool-call",
"token_count": 153,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_c5mih55lfvbX48yYnaMOVtgk",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "187",
"role": "tool",
"timestamp": "2026-05-05T09:45:07.895Z",
"parts": [
{
"id": "188",
"type": "tool-result",
"token_count": 191,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_c5mih55lfvbX48yYnaMOVtgk",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY\"\nChunk ID: 031361\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 11\nOutput:\n4258 cpu_seconds 449.69 rss_mb 282.107904\n"
}
]
},
{
"id": "189",
"role": "assistant",
"parts": [
{
"id": "190",
"type": "text",
"token_count": 44,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C5.3 State drift",
"D_impact": "D0 No negative impact"
},
"text": "Training is still active but within memory; I\u2019ll let this candidate finish before changing settings.Executed write_stdin call_qo3mVQkNnbgjsG8jggm3Xy3Y"
},
{
"id": "191",
"type": "tool-call",
"token_count": 26,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_qo3mVQkNnbgjsG8jggm3Xy3Y",
"toolName": "write_stdin",
"input": {
"session_id": 86685,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "192",
"role": "tool",
"timestamp": "2026-05-05T09:45:16.376Z",
"parts": [
{
"id": "193",
"type": "tool-result",
"token_count": 274,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_qo3mVQkNnbgjsG8jggm3Xy3Y",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m1 -lr 0.5 -epoch 20 -wordNgrams 2 -dim 50 -bucket 200000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m1.bin\nfasttext test /app/models/m1.bin /app/ftdata/valid.txt\nfasttext test /app/models/m1.bin /app/ftdata/public_test.txt'\nChunk ID: bcef2a\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 53\nOutput:\n\rProgress: 100.0% words/sec/thread: 3650851 lr: 0.000000 avg.loss: 0.452657 ETA: 0h 0m 0s\n-rw-r--r-- 1 root root 119M May 5 09:45 /app/models/m1.bin\nN\t50000\nP@1\t0.558\nR@1\t0.558\nN\t10000\nP@1\t0.566\nR@1\t0.566\n"
}
]
},
{
"id": "194",
"role": "assistant",
"parts": [
{
"id": "195",
"type": "text",
"token_count": 47,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C5.3 State drift",
"D_impact": "D0 No negative impact"
},
"text": "The compact candidate is only ~0.56, so I\u2019ll train a higher-capacity model for quantization.Executed exec_command call_0k8bI9oOB443aO0y7aHsZI6r"
},
{
"id": "196",
"type": "tool-call",
"token_count": 135,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_0k8bI9oOB443aO0y7aHsZI6r",
"toolName": "exec_command",
"input": {
"cmd": "fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m2 -lr 1.0 -epoch 15 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m2.bin\nfasttext test /app/models/m2.bin /app/ftdata/valid.txt\nfasttext test /app/models/m2.bin /app/ftdata/public_test.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "197",
"role": "tool",
"timestamp": "2026-05-05T09:45:34.878Z",
"parts": [
{
"id": "198",
"type": "tool-result",
"token_count": 293,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_0k8bI9oOB443aO0y7aHsZI6r",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m2 -lr 1.0 -epoch 15 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m2.bin\nfasttext test /app/models/m2.bin /app/ftdata/valid.txt\nfasttext test /app/models/m2.bin /app/ftdata/public_test.txt'\nChunk ID: 1dc44f\nWall time: 1.0009 seconds\nProcess running with session ID 18909\nOriginal token count: 81\nOutput:\n\rRead 1M words\rRead 2M words\rRead 3M words\rRead 4M words\rRead 5M words\rRead 6M words\rRead 7M words\rRead 8M words\rRead 9M words\rRead 10M words\rRead 11M words\rRead 12M words\rRead 13M words\rRead 14M words\rRead 15M words\rRead 16M words\rRead 17M words\rRead 18M words\rRead 19M words\rRead 20M words\rRead 21M words\rRead 22M words"
}
]
},
{
"id": "199",
"role": "assistant",
"timestamp": "2026-05-05T09:45:40.418Z",
"parts": [
{
"id": "200",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_TSc4YdSUmFZNkD3OqekhwjTM"
},
{
"id": "201",
"type": "tool-call",
"token_count": 26,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_TSc4YdSUmFZNkD3OqekhwjTM",
"toolName": "write_stdin",
"input": {
"session_id": 18909,
"chars": "",
"yield_time_ms": 5000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "202",
"role": "tool",
"timestamp": "2026-05-05T09:45:40.418Z",
"parts": [
{
"id": "203",
"type": "tool-result",
"token_count": 537,
"component": "AX Non-agent context",
"dimensions": {
"A_stage": "AX Non-agent context",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_TSc4YdSUmFZNkD3OqekhwjTM",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m2 -lr 1.0 -epoch 15 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m2.bin\nfasttext test /app/models/m2.bin /app/ftdata/valid.txt\nfasttext test /app/models/m2.bin /app/ftdata/public_test.txt'\nChunk ID: ee915f\nWall time: 5.0009 seconds\nProcess running with session ID 18909\nOriginal token count: 237\nOutput:\n\rRead 23M words\rRead 24M words\rRead 25M words\rRead 26M words\rRead 27M words\rRead 28M words\rRead 29M words\rRead 30M words\rRead 31M words\rRead 32M words\rRead 33M words\rRead 34M words\rRead 35M words\rRead 36M words\rRead 37M words\rRead 38M words\rRead 39M words\rRead 40M words\rRead 41M words\rRead 42M words\rRead 43M words\rRead 44M words\rRead 45M words\rRead 46M words\rRead 47M words\rRead 48M words\rRead 49M words\rRead 50M words\rRead 51M words\rRead 52M words\rRead 53M words\rRead 54M words\rRead 55M words\rRead 56M words\rRead 57M words\rRead 58M words\rRead 59M words\rRead 60M words\rRead 61M words\rRead 62M words\rRead 63M words\rRead 64M words\rRead 65M words\rRead 66M words\rRead 67M words\rRead 68M words\rRead 69M words\rRead 70M words\rRead 71M words\rRead 72M words\rRead 73M words\rRead 74M words\rRead 75M words\rRead 76M words\rRead 77M words\rRead 78M words\rRead 79M words\rRead 80M words\rRead 81M words\rRead 81M words\nNumber of words: 384199\nNumber of labels: 5\n"
}
]
},
{
"id": "204",
"role": "assistant",
"timestamp": "2026-05-05T09:45:49.218Z",
"parts": [
{
"id": "205",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_qlZXRg3ZcUEfPesNCXkrmhM2"
},
{
"id": "206",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_qlZXRg3ZcUEfPesNCXkrmhM2",
"toolName": "write_stdin",
"input": {
"session_id": 18909,
"chars": "",
"yield_time_ms": 600000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "207",
"role": "tool",
"timestamp": "2026-05-05T09:45:49.218Z",
"parts": [
{
"id": "208",
"type": "tool-result",
"token_count": 162,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_qlZXRg3ZcUEfPesNCXkrmhM2",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m2 -lr 1.0 -epoch 15 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m2.bin\nfasttext test /app/models/m2.bin /app/ftdata/valid.txt\nfasttext test /app/models/m2.bin /app/ftdata/public_test.txt'\nChunk ID: 6ca31a\nWall time: 300.0014 seconds\nProcess running with session ID 18909\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "209",
"role": "assistant",
"parts": [
{
"id": "210",
"type": "text",
"token_count": 42,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.5 Non-convergence",
"D_impact": "D0 No negative impact"
},
"text": "The higher-capacity model is still training; it\u2019s the promising path for the 0.62 target.Executed exec_command call_UIDFSYpHopM3Tw4zreKDYsJt"
},
{
"id": "211",
"type": "tool-call",
"token_count": 160,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C3.2 Wrong search scope",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_UIDFSYpHopM3Tw4zreKDYsJt",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY\nls -lh /app/models",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "212",
"role": "tool",
"timestamp": "2026-05-05T09:50:53.336Z",
"parts": [
{
"id": "213",
"type": "tool-result",
"token_count": 280,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C3.2 Wrong search scope",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_UIDFSYpHopM3Tw4zreKDYsJt",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY\nls -lh /app/models\"\nChunk ID: 99fffa\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 49\nOutput:\n4312 cpu_seconds 314.0 rss_mb 718.11072\ntotal 296M\n-rw-r--r-- 1 root root 119M May 5 09:45 m1.bin\n-rw-r--r-- 1 root root 178M May 5 09:45 m1.vec\n-rw-r--r-- 1 root root 0 May 5 09:45 m2.bin\n"
}
]
},
{
"id": "214",
"role": "assistant",
"timestamp": "2026-05-05T09:50:56.009Z",
"parts": [
{
"id": "215",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_PWtUx5t8WqBUBsrtjlQk5QST"
},
{
"id": "216",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_PWtUx5t8WqBUBsrtjlQk5QST",
"toolName": "write_stdin",
"input": {
"session_id": 18909,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "217",
"role": "tool",
"timestamp": "2026-05-05T09:50:56.009Z",
"parts": [
{
"id": "218",
"type": "tool-result",
"token_count": 161,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_PWtUx5t8WqBUBsrtjlQk5QST",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m2 -lr 1.0 -epoch 15 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m2.bin\nfasttext test /app/models/m2.bin /app/ftdata/valid.txt\nfasttext test /app/models/m2.bin /app/ftdata/public_test.txt'\nChunk ID: 2acce4\nWall time: 300.0006 seconds\nProcess running with session ID 18909\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "219",
"role": "assistant",
"timestamp": "2026-05-05T09:56:03.688Z",
"parts": [
{
"id": "220",
"type": "text",
"token_count": 23,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C3.2 Wrong search scope",
"D_impact": "D2 Recoverable moderate"
},
"text": "Executed exec_command call_QpAJ3jpxvrnjD5xGZ2ANk6uY"
},
{
"id": "221",
"type": "tool-call",
"token_count": 153,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C3.2 Wrong search scope",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_QpAJ3jpxvrnjD5xGZ2ANk6uY",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "222",
"role": "tool",
"timestamp": "2026-05-05T09:56:03.688Z",
"parts": [
{
"id": "223",
"type": "tool-result",
"token_count": 193,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C3.2 Wrong search scope",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_QpAJ3jpxvrnjD5xGZ2ANk6uY",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY\"\nChunk ID: 703f8e\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 11\nOutput:\n4312 cpu_seconds 620.05 rss_mb 718.11072\n"
}
]
},
{
"id": "224",
"role": "assistant",
"timestamp": "2026-05-05T09:56:08.949Z",
"parts": [
{
"id": "225",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D2 Recoverable moderate"
},
"text": "Executed write_stdin call_gltDq2S6cIEIOKTZKWQKPnTc"
},
{
"id": "226",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_gltDq2S6cIEIOKTZKWQKPnTc",
"toolName": "write_stdin",
"input": {
"session_id": 18909,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "227",
"role": "tool",
"timestamp": "2026-05-05T09:56:08.949Z",
"parts": [
{
"id": "228",
"type": "tool-result",
"token_count": 274,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_gltDq2S6cIEIOKTZKWQKPnTc",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata/train_split.txt -output /app/models/m2 -lr 1.0 -epoch 15 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m2.bin\nfasttext test /app/models/m2.bin /app/ftdata/valid.txt\nfasttext test /app/models/m2.bin /app/ftdata/public_test.txt'\nChunk ID: ebc097\nWall time: 277.0887 seconds\nProcess exited with code 0\nOriginal token count: 53\nOutput:\n\rProgress: 100.0% words/sec/thread: 1373649 lr: 0.000000 avg.loss: 0.282551 ETA: 0h 0m 0s\n-rw-r--r-- 1 root root 535M May 5 10:00 /app/models/m2.bin\nN\t50000\nP@1\t0.586\nR@1\t0.586\nN\t10000\nP@1\t0.591\nR@1\t0.591\n"
}
]
},
{
"id": "229",
"role": "assistant",
"timestamp": "2026-05-05T10:01:11.836Z",
"parts": [
{
"id": "230",
"type": "text",
"token_count": 22,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D2 Recoverable moderate"
},
"text": "Executed exec_command call_UtP3uFTjuWCAf0AWhHJtnl0f"
},
{
"id": "231",
"type": "tool-call",
"token_count": 45,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_UtP3uFTjuWCAf0AWhHJtnl0f",
"toolName": "exec_command",
"input": {
"cmd": "fasttext supervised -h 2>&1 | sed -n '1,200p'",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "232",
"role": "tool",
"timestamp": "2026-05-05T10:01:11.836Z",
"parts": [
{
"id": "233",
"type": "tool-result",
"token_count": 620,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_UtP3uFTjuWCAf0AWhHJtnl0f",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"fasttext supervised -h 2>&1 | sed -n '1,200p'\"\nChunk ID: f30429\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 564\nOutput:\nHere is the help! Usage:\n\nThe following arguments are mandatory:\n -input training file path\n -output output file path\n\nThe following arguments are optional:\n -verbose verbosity level [2]\n\nThe following arguments for the dictionary are optional:\n -minCount minimal number of word occurences [1]\n -minCountLabel minimal number of label occurences [0]\n -wordNgrams max length of word ngram [1]\n -bucket number of buckets [2000000]\n -minn min length of char ngram [0]\n -maxn max length of char ngram [0]\n -t sampling threshold [0.0001]\n -label labels prefix [__label__]\n\nThe following arguments for training are optional:\n -lr learning rate [0.1]\n -lrUpdateRate change the rate of updates for the learning rate [100]\n -dim size of word vectors [100]\n -ws size of the context window [5]\n -epoch number of epochs [5]\n -neg number of negatives sampled [5]\n -loss loss function {ns, hs, softmax, one-vs-all} [softmax]\n -thread number of threads (set to 1 to ensure reproducible results) [12]\n -pretrainedVectors pretrained word vectors for supervised learning []\n -saveOutput whether output params should be saved [false]\n -seed random generator seed [0]\n\nThe following arguments are for autotune:\n -autotune-validation validation file to be used for evaluation\n -autotune-metric metric objective {f1, f1:labelname} [f1]\n -autotune-predictions number of predictions used for evaluation [1]\n -autotune-duration maximum duration in seconds [300]\n -autotune-modelsize constraint model file size [] (empty = do not quantize)\n\nThe following arguments for quantization are optional:\n -cutoff number of words and ngrams to retain [0]\n -retrain whether embeddings are finetuned if a cutoff is applied [false]\n -qnorm whether the norm is quantized separately [false]\n -qout whether the classifier is quantized [false]\n -dsub size of each sub-vector [2]\n"
}
]
},
{
"id": "234",
"role": "assistant",
"parts": [
{
"id": "235",
"type": "text",
"token_count": 42,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D2 Recoverable moderate"
},
"text": "I\u2019ll rebuild the text with punctuation-aware tokenization to reduce noise and improve generalization.Executed exec_command call_gFa93Y0oUtQpVw4du7y3Z3sQ"
},
{
"id": "236",
"type": "tool-call",
"token_count": 612,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D2 Recoverable moderate"
},
"toolCallId": "call_gFa93Y0oUtQpVw4du7y3Z3sQ",
"toolName": "exec_command",
"input": {
"cmd": "cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\S+|www\\.\\S+')\n_email = re.compile(r'\\S+@\\S+')\n_num = re.compile(r'\\d+(?:[.,:/-]\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\"[a-z]+(?:'[a-z]+)?|[0-9]+|[!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub(' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "237",
"role": "tool",
"timestamp": "2026-05-05T10:01:34.637Z",
"parts": [
{
"id": "238",
"type": "tool-result",
"token_count": 645,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_gFa93Y0oUtQpVw4du7y3Z3sQ",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: 47e95f\nWall time: 1.0012 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "239",
"role": "assistant",
"timestamp": "2026-05-05T10:01:41.069Z",
"parts": [
{
"id": "240",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_XQxOclbcY8EI2UxvHEnmhawn"
},
{
"id": "241",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_XQxOclbcY8EI2UxvHEnmhawn",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "242",
"role": "tool",
"timestamp": "2026-05-05T10:01:41.069Z",
"parts": [
{
"id": "243",
"type": "tool-result",
"token_count": 645,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_XQxOclbcY8EI2UxvHEnmhawn",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: 67f504\nWall time: 5.0009 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "244",
"role": "assistant",
"timestamp": "2026-05-05T10:01:51.384Z",
"parts": [
{
"id": "245",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_W2nX9h7uPd3ZKSuXMzf2gdCF"
},
{
"id": "246",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_W2nX9h7uPd3ZKSuXMzf2gdCF",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 5000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "247",
"role": "tool",
"timestamp": "2026-05-05T10:01:51.384Z",
"parts": [
{
"id": "248",
"type": "tool-result",
"token_count": 646,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_W2nX9h7uPd3ZKSuXMzf2gdCF",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: e6ad2a\nWall time: 5.0007 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "249",
"role": "assistant",
"timestamp": "2026-05-05T10:01:59.842Z",
"parts": [
{
"id": "250",
"type": "text",
"token_count": 19,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_wPVQUCHqxDMkhmjOw3x47zyp"
},
{
"id": "251",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_wPVQUCHqxDMkhmjOw3x47zyp",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "252",
"role": "tool",
"timestamp": "2026-05-05T10:01:59.842Z",
"parts": [
{
"id": "253",
"type": "tool-result",
"token_count": 646,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_wPVQUCHqxDMkhmjOw3x47zyp",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: 0c52ec\nWall time: 10.0009 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "254",
"role": "assistant",
"timestamp": "2026-05-05T10:02:12.939Z",
"parts": [
{
"id": "255",
"type": "text",
"token_count": 22,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_BoAAZ0iHH8hDgEWngvIoXARO"
},
{
"id": "256",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_BoAAZ0iHH8hDgEWngvIoXARO",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "257",
"role": "tool",
"timestamp": "2026-05-05T10:02:12.939Z",
"parts": [
{
"id": "258",
"type": "tool-result",
"token_count": 646,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_BoAAZ0iHH8hDgEWngvIoXARO",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: a55e4f\nWall time: 10.0007 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "259",
"role": "assistant",
"timestamp": "2026-05-05T10:02:28.918Z",
"parts": [
{
"id": "260",
"type": "text",
"token_count": 22,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C7.1 Validation missing/incomplete",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_4eSGlubBr4iRUnC1UQD5ioKy"
},
{
"id": "261",
"type": "tool-call",
"token_count": 91,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C7.1 Validation missing/incomplete",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_4eSGlubBr4iRUnC1UQD5ioKy",
"toolName": "exec_command",
"input": {
"cmd": "du -sh /app/ftdata_norm 2>/dev/null || true; ls -lh /app/ftdata_norm 2>/dev/null || true; python -m py_compile /app/prepare_fasttext_norm.py && sed -n '1,40p' /app/prepare_fasttext_norm.py",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "262",
"role": "tool",
"timestamp": "2026-05-05T10:02:28.918Z",
"parts": [
{
"id": "263",
"type": "tool-result",
"token_count": 127,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C7.1 Validation missing/incomplete",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_4eSGlubBr4iRUnC1UQD5ioKy",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"du -sh /app/ftdata_norm 2>/dev/null || true; ls -lh /app/ftdata_norm 2>/dev/null || true; python -m py_compile /app/prepare_fasttext_norm.py && sed -n '1,40p' /app/prepare_fasttext_norm.py\"\nChunk ID: 58b9f3\nWall time: 1.0013 seconds\nProcess running with session ID 88144\nOriginal token count: 6\nOutput:\n512M\t/app/ftdata_norm\n"
}
]
},
{
"id": "264",
"role": "assistant",
"timestamp": "2026-05-05T10:02:34.730Z",
"parts": [
{
"id": "265",
"type": "text",
"token_count": 20,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.2 Premature termination",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_LszAgrykGn2zTwkEzZ64RPQq"
},
{
"id": "266",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.2 Premature termination",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_LszAgrykGn2zTwkEzZ64RPQq",
"toolName": "write_stdin",
"input": {
"session_id": 88144,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "267",
"role": "tool",
"timestamp": "2026-05-05T10:02:34.730Z",
"parts": [
{
"id": "268",
"type": "tool-result",
"token_count": 623,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.2 Premature termination",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_LszAgrykGn2zTwkEzZ64RPQq",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"du -sh /app/ftdata_norm 2>/dev/null || true; ls -lh /app/ftdata_norm 2>/dev/null || true; python -m py_compile /app/prepare_fasttext_norm.py && sed -n '1,40p' /app/prepare_fasttext_norm.py\"\nChunk ID: 8da92a\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 364\nOutput:\ntotal 434M\n-rw-r--r-- 1 root root 434M May 5 10:02 train_split.txt\n-rw-r--r-- 1 root root 120K May 5 10:02 valid.txt\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\S+|www\\.\\S+')\n_email = re.compile(r'\\S+@\\S+')\n_num = re.compile(r'\\d+(?:[.,:/-]\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\"[a-z]+(?:'[a-z]+)?|[0-9]+|[!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub(' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\n"
}
]
},
{
"id": "269",
"role": "assistant",
"timestamp": "2026-05-05T10:02:43.165Z",
"parts": [
{
"id": "270",
"type": "text",
"token_count": 22,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C6.2 Premature termination",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_Ye02WWGTw4jjIfJwSpek9hL2"
},
{
"id": "271",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_Ye02WWGTw4jjIfJwSpek9hL2",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 5000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "272",
"role": "tool",
"timestamp": "2026-05-05T10:02:43.165Z",
"parts": [
{
"id": "273",
"type": "tool-result",
"token_count": 645,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_Ye02WWGTw4jjIfJwSpek9hL2",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: e076f3\nWall time: 5.0009 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "274",
"role": "assistant",
"timestamp": "2026-05-05T10:02:54.203Z",
"parts": [
{
"id": "275",
"type": "text",
"token_count": 22,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_g48ZFBHyOSAA8bN9rPG0F6uq"
},
{
"id": "276",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_g48ZFBHyOSAA8bN9rPG0F6uq",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "277",
"role": "tool",
"timestamp": "2026-05-05T10:02:54.203Z",
"parts": [
{
"id": "278",
"type": "tool-result",
"token_count": 645,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_g48ZFBHyOSAA8bN9rPG0F6uq",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: 52a690\nWall time: 10.0005 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "279",
"role": "assistant",
"timestamp": "2026-05-05T10:03:07.909Z",
"parts": [
{
"id": "280",
"type": "text",
"token_count": 26,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_1g1gzFu5Tnh1kzGd7e1p2rKh"
},
{
"id": "281",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_1g1gzFu5Tnh1kzGd7e1p2rKh",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "282",
"role": "tool",
"timestamp": "2026-05-05T10:03:07.909Z",
"parts": [
{
"id": "283",
"type": "tool-result",
"token_count": 645,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_1g1gzFu5Tnh1kzGd7e1p2rKh",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: c1211c\nWall time: 10.0010 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "284",
"role": "assistant",
"timestamp": "2026-05-05T10:03:21.187Z",
"parts": [
{
"id": "285",
"type": "text",
"token_count": 20,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_AiJBzZMRiECpXEXEQsk8GLFp"
},
{
"id": "286",
"type": "tool-call",
"token_count": 53,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_AiJBzZMRiECpXEXEQsk8GLFp",
"toolName": "exec_command",
"input": {
"cmd": "ls -lh /app/ftdata_norm; du -sh /app/ftdata_norm /app/models /app/ftdata",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "287",
"role": "tool",
"timestamp": "2026-05-05T10:03:21.187Z",
"parts": [
{
"id": "288",
"type": "tool-result",
"token_count": 239,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_AiJBzZMRiECpXEXEQsk8GLFp",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc 'ls -lh /app/ftdata_norm; du -sh /app/ftdata_norm /app/models /app/ftdata'\nChunk ID: fcc02c\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 88\nOutput:\ntotal 1011M\n-rw-r--r-- 1 root root 7.4M May 5 10:03 public_test.txt\n-rw-r--r-- 1 root root 470M May 5 10:03 train_all.txt\n-rw-r--r-- 1 root root 53M May 5 10:03 train_plus_public.txt\n-rw-r--r-- 1 root root 434M May 5 10:02 train_split.txt\n-rw-r--r-- 1 root root 37M May 5 10:02 valid.txt\n1011M\t/app/ftdata_norm\n1.2G\t/app/models\n1.4G\t/app/ftdata\n"
}
]
},
{
"id": "289",
"role": "assistant",
"timestamp": "2026-05-05T10:03:24.341Z",
"parts": [
{
"id": "290",
"type": "text",
"token_count": 24,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_8mVt4LUG8ZMfIuLAjjEcstnL"
},
{
"id": "291",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_8mVt4LUG8ZMfIuLAjjEcstnL",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "292",
"role": "tool",
"timestamp": "2026-05-05T10:03:24.341Z",
"parts": [
{
"id": "293",
"type": "tool-result",
"token_count": 645,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_8mVt4LUG8ZMfIuLAjjEcstnL",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: 3ad373\nWall time: 10.0009 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "294",
"role": "assistant",
"timestamp": "2026-05-05T10:03:37.284Z",
"parts": [
{
"id": "295",
"type": "text",
"token_count": 19,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_TxNsnTN89cxRmbEaQE27ejfw"
},
{
"id": "296",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_TxNsnTN89cxRmbEaQE27ejfw",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "297",
"role": "tool",
"timestamp": "2026-05-05T10:03:37.284Z",
"parts": [
{
"id": "298",
"type": "tool-result",
"token_count": 645,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_TxNsnTN89cxRmbEaQE27ejfw",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: 59823f\nWall time: 10.0012 seconds\nProcess running with session ID 69543\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "299",
"role": "assistant",
"timestamp": "2026-05-05T10:03:52.416Z",
"parts": [
{
"id": "300",
"type": "text",
"token_count": 23,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_AwD9GOMRzmmN8PPZRyIyMb6j"
},
{
"id": "301",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D1 Recoverable mild"
},
"toolCallId": "call_AwD9GOMRzmmN8PPZRyIyMb6j",
"toolName": "write_stdin",
"input": {
"session_id": 69543,
"chars": "",
"yield_time_ms": 10000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "302",
"role": "tool",
"timestamp": "2026-05-05T10:03:52.416Z",
"parts": [
{
"id": "303",
"type": "tool-result",
"token_count": 902,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_AwD9GOMRzmmN8PPZRyIyMb6j",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"cat > /app/prepare_fasttext_norm.py <<'PY'\nimport html\nimport re\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path('/app/data')\nOUT = Path('/app/ftdata_norm')\nOUT.mkdir(exist_ok=True)\n\n_url = re.compile(r'https?://\\\\S+|www\\\\.\\\\S+')\n_email = re.compile(r'\\\\S+@\\\\S+')\n_num = re.compile(r'\\\\d+(?:[.,:/-]\\\\d+)*')\n# Keep alphabetic contractions as words; keep sentiment punctuation as tokens.\n_tok = re.compile(r\\\"[a-z]+(?:'[a-z]+)?|[0-9]+|[\"'!?]+|[.,;:$%&()/-]\")\n\ndef clean(text):\n text = html.unescape(str(text)).lower()\n text = _url.sub('\"' url ', text)\n text = _email.sub(' email ', text)\n text = _num.sub(' 0 ', text)\n return ' '.join(_tok.findall(text))\n\ndef write_ft(df, path):\n with open(path, 'w', encoding='utf-8') as f:\n for label, text in zip(df['label'].to_numpy(), df['text'].to_numpy()):\n f.write(f'__label__{int(label)} {clean(text)}\\\\n')\n\ntrain = pd.read_parquet(DATA / 'train-00000-of-00001.parquet')\nrng = np.random.default_rng(20260505)\nval_mask = np.zeros(len(train), dtype=bool)\nlabels = train['label'].to_numpy()\nfor label in sorted(train['label'].unique()):\n idx = np.flatnonzero(labels == label)\n val_mask[rng.choice(idx, size=10_000, replace=False)] = True\n\nwrite_ft(train.loc[~val_mask], OUT / 'train_split.txt')\nwrite_ft(train.loc[val_mask], OUT / 'valid.txt')\nwrite_ft(train, OUT / 'train_all.txt')\ntest = pd.read_parquet(DATA / 'test-00000-of-00001.parquet')\nwrite_ft(test, OUT / 'public_test.txt')\nwrite_ft(pd.concat([train, test], ignore_index=True), OUT / 'train_plus_public.txt')\nfor p in sorted(OUT.glob('*.txt')):\n print(p, p.stat().st_size)\nPY\npython /app/prepare_fasttext_norm.py\nwc -l /app/ftdata_norm/*.txt\nhead -1 /app/ftdata_norm/train_split.txt\"\nChunk ID: e06aff\nWall time: 8.0585 seconds\nProcess exited with code 0\nOriginal token count: 251\nOutput:\n/app/ftdata_norm/public_test.txt 7674090\n/app/ftdata_norm/train_all.txt 492481233\n/app/ftdata_norm/train_plus_public.txt 500155323\n/app/ftdata_norm/train_split.txt 454615432\n/app/ftdata_norm/valid.txt 37865801\n 10000 /app/ftdata_norm/public_test.txt\n 650000 /app/ftdata_norm/train_all.txt\n 660000 /app/ftdata_norm/train_plus_public.txt\n 600000 /app/ftdata_norm/train_split.txt\n 50000 /app/ftdata_norm/valid.txt\n 1970000 total\n__label__4 dr . goldberg offers everything i look for in a general practitioner . he's nice and easy to talk to without being patronizing ; he's always on time in seeing his patients ; he's affiliated with a top - notch hospital ( nyu ) which my parents have explained to me is very important in case something happens and you need surgery ; and you can get referrals to see specialists without having to see him first . really , what more do you need ? i'm sitting here trying to think of any complaints i have about him , but i'm really drawing a blank .\n"
}
]
},
{
"id": "304",
"role": "assistant",
"parts": [
{
"id": "305",
"type": "text",
"token_count": 33,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Normalized files are ready. I\u2019ll test whether tokenization boosts validation accuracy.Executed exec_command call_UMTOZes9puBizby511WY03c1"
},
{
"id": "306",
"type": "tool-call",
"token_count": 142,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_UMTOZes9puBizby511WY03c1",
"toolName": "exec_command",
"input": {
"cmd": "fasttext supervised -input /app/ftdata_norm/train_split.txt -output /app/models/m3n -lr 1.0 -epoch 10 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m3n.bin\nfasttext test /app/models/m3n.bin /app/ftdata_norm/valid.txt\nfasttext test /app/models/m3n.bin /app/ftdata_norm/public_test.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "307",
"role": "tool",
"timestamp": "2026-05-05T10:04:08.107Z",
"parts": [
{
"id": "308",
"type": "tool-result",
"token_count": 346,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_UMTOZes9puBizby511WY03c1",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata_norm/train_split.txt -output /app/models/m3n -lr 1.0 -epoch 10 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m3n.bin\nfasttext test /app/models/m3n.bin /app/ftdata_norm/valid.txt\nfasttext test /app/models/m3n.bin /app/ftdata_norm/public_test.txt'\nChunk ID: acc51a\nWall time: 1.0006 seconds\nProcess running with session ID 49930\nOriginal token count: 111\nOutput:\n\rRead 1M words\rRead 2M words\rRead 3M words\rRead 4M words\rRead 5M words\rRead 6M words\rRead 7M words\rRead 8M words\rRead 9M words\rRead 10M words\rRead 11M words\rRead 12M words\rRead 13M words\rRead 14M words\rRead 15M words\rRead 16M words\rRead 17M words\rRead 18M words\rRead 19M words\rRead 20M words\rRead 21M words\rRead 22M words\rRead 23M words\rRead 24M words\rRead 25M words\rRead 26M words\rRead 27M words\rRead 28M words\rRead 29M words\rRead 30M words"
}
]
},
{
"id": "309",
"role": "assistant",
"timestamp": "2026-05-05T10:04:13.021Z",
"parts": [
{
"id": "310",
"type": "text",
"token_count": 24,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_OsK3H3vHIrjeqM6douXz4IHO"
},
{
"id": "311",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_OsK3H3vHIrjeqM6douXz4IHO",
"toolName": "write_stdin",
"input": {
"session_id": 49930,
"chars": "",
"yield_time_ms": 5000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "312",
"role": "tool",
"timestamp": "2026-05-05T10:04:13.021Z",
"parts": [
{
"id": "313",
"type": "tool-result",
"token_count": 580,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_OsK3H3vHIrjeqM6douXz4IHO",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata_norm/train_split.txt -output /app/models/m3n -lr 1.0 -epoch 10 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m3n.bin\nfasttext test /app/models/m3n.bin /app/ftdata_norm/valid.txt\nfasttext test /app/models/m3n.bin /app/ftdata_norm/public_test.txt'\nChunk ID: 558134\nWall time: 5.0010 seconds\nProcess running with session ID 49930\nOriginal token count: 259\nOutput:\n\rRead 31M words\rRead 32M words\rRead 33M words\rRead 34M words\rRead 35M words\rRead 36M words\rRead 37M words\rRead 38M words\rRead 39M words\rRead 40M words\rRead 41M words\rRead 42M words\rRead 43M words\rRead 44M words\rRead 45M words\rRead 46M words\rRead 47M words\rRead 48M words\rRead 49M words\rRead 50M words\rRead 51M words\rRead 52M words\rRead 53M words\rRead 54M words\rRead 55M words\rRead 56M words\rRead 57M words\rRead 58M words\rRead 59M words\rRead 60M words\rRead 61M words\rRead 62M words\rRead 63M words\rRead 64M words\rRead 65M words\rRead 66M words\rRead 67M words\rRead 68M words\rRead 69M words\rRead 70M words\rRead 71M words\rRead 72M words\rRead 73M words\rRead 74M words\rRead 75M words\rRead 76M words\rRead 77M words\rRead 78M words\rRead 79M words\rRead 80M words\rRead 81M words\rRead 82M words\rRead 83M words\rRead 84M words\rRead 85M words\rRead 86M words\rRead 87M words\rRead 88M words\rRead 89M words\rRead 90M words\rRead 91M words\rRead 92M words\rRead 93M words\rRead 94M words\rRead 95M words\rRead 95M words\nNumber of words: 121629\nNumber of labels: 5\n"
}
]
},
{
"id": "314",
"role": "assistant",
"timestamp": "2026-05-05T10:04:23.315Z",
"parts": [
{
"id": "315",
"type": "text",
"token_count": 24,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_8HhrxCwGQnIc7q29az2A93hi"
},
{
"id": "316",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_8HhrxCwGQnIc7q29az2A93hi",
"toolName": "write_stdin",
"input": {
"session_id": 49930,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "317",
"role": "tool",
"timestamp": "2026-05-05T10:04:23.315Z",
"parts": [
{
"id": "318",
"type": "tool-result",
"token_count": 168,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_8HhrxCwGQnIc7q29az2A93hi",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata_norm/train_split.txt -output /app/models/m3n -lr 1.0 -epoch 10 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m3n.bin\nfasttext test /app/models/m3n.bin /app/ftdata_norm/valid.txt\nfasttext test /app/models/m3n.bin /app/ftdata_norm/public_test.txt'\nChunk ID: 789e76\nWall time: 300.0004 seconds\nProcess running with session ID 49930\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "319",
"role": "assistant",
"timestamp": "2026-05-05T10:09:27.462Z",
"parts": [
{
"id": "320",
"type": "text",
"token_count": 21,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_JOgnPX2u8a6fb3NFJkjRJTqq"
},
{
"id": "321",
"type": "tool-call",
"token_count": 153,
"component": "A2 Locating & exploring",
"dimensions": {
"A_stage": "A2 Locating & exploring",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_JOgnPX2u8a6fb3NFJkjRJTqq",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "322",
"role": "tool",
"timestamp": "2026-05-05T10:09:27.462Z",
"parts": [
{
"id": "323",
"type": "tool-result",
"token_count": 193,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_JOgnPX2u8a6fb3NFJkjRJTqq",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nimport glob, os\nfor pid in glob.glob('/proc/[0-9]*'):\n try:\n cmd=open(pid+'/cmdline','rb').read().replace(b'\\\\0',b' ').decode()\n if cmd.startswith('fasttext supervised'):\n stat=open(pid+'/stat').read().split()\n print(os.path.basename(pid), 'cpu_seconds', (int(stat[13])+int(stat[14]))/100, 'rss_mb', int(stat[23])*4096/1e6)\n except Exception: pass\nPY\"\nChunk ID: fbc3a7\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 11\nOutput:\n4536 cpu_seconds 311.82 rss_mb 585.080832\n"
}
]
},
{
"id": "324",
"role": "assistant",
"timestamp": "2026-05-05T10:09:30.697Z",
"parts": [
{
"id": "325",
"type": "text",
"token_count": 24,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_XoSgY1q7oq0t46fQTkb9npk9"
},
{
"id": "326",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_XoSgY1q7oq0t46fQTkb9npk9",
"toolName": "write_stdin",
"input": {
"session_id": 49930,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "327",
"role": "tool",
"timestamp": "2026-05-05T10:09:30.697Z",
"parts": [
{
"id": "328",
"type": "tool-result",
"token_count": 244,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_XoSgY1q7oq0t46fQTkb9npk9",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata_norm/train_split.txt -output /app/models/m3n -lr 1.0 -epoch 10 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m3n.bin\nfasttext test /app/models/m3n.bin /app/ftdata_norm/valid.txt\nfasttext test /app/models/m3n.bin /app/ftdata_norm/public_test.txt'\nChunk ID: 3a7214\nWall time: 300.0013 seconds\nProcess running with session ID 49930\nOriginal token count: 39\nOutput:\n\rProgress: 100.0% words/sec/thread: 1549360 lr: 0.000000 avg.loss: 0.491499 ETA: 0h 0m 0s\n-rw-r--r-- 1 root root 430M May 5 10:14 /app/models/m3n.bin\n"
}
]
},
{
"id": "329",
"role": "assistant",
"timestamp": "2026-05-05T10:14:33.813Z",
"parts": [
{
"id": "330",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_44eXl4aoUu035mDRlBSSpJaz"
},
{
"id": "331",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_44eXl4aoUu035mDRlBSSpJaz",
"toolName": "write_stdin",
"input": {
"session_id": 49930,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "332",
"role": "tool",
"timestamp": "2026-05-05T10:14:33.813Z",
"parts": [
{
"id": "333",
"type": "tool-result",
"token_count": 208,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_44eXl4aoUu035mDRlBSSpJaz",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc 'fasttext supervised -input /app/ftdata_norm/train_split.txt -output /app/models/m3n -lr 1.0 -epoch 10 -wordNgrams 3 -dim 100 -bucket 1000000 -minCount 2 -loss softmax -thread 1 -verbose 2\nls -lh /app/models/m3n.bin\nfasttext test /app/models/m3n.bin /app/ftdata_norm/valid.txt\nfasttext test /app/models/m3n.bin /app/ftdata_norm/public_test.txt'\nChunk ID: c804a0\nWall time: 2.1785 seconds\nProcess exited with code 0\nOriginal token count: 14\nOutput:\nN\t50000\nP@1\t0.597\nR@1\t0.597\nN\t10000\nP@1\t0.597\nR@1\t0.597\n"
}
]
},
{
"id": "334",
"role": "assistant",
"parts": [
{
"id": "335",
"type": "text",
"token_count": 41,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Tokenization helped but still short. I\u2019ll inspect errors to tune class balance/threshold behavior.Executed exec_command call_WUQfUmL89XepmyTS9e5RzDd0"
},
{
"id": "336",
"type": "tool-call",
"token_count": 252,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_WUQfUmL89XepmyTS9e5RzDd0",
"toolName": "exec_command",
"input": {
"cmd": "fasttext predict /app/models/m3n.bin /app/ftdata_norm/valid.txt > /app/models/m3n_valid.pred\npython - <<'PY'\nimport numpy as np, re\ntrue=[]; pred=[]\nfor line in open('/app/ftdata_norm/valid.txt', encoding='utf-8'):\n true.append(int(line.split()[0].replace('__label__','')))\nfor line in open('/app/models/m3n_valid.pred'):\n pred.append(int(line.strip().replace('__label__','')))\ncm=np.zeros((5,5), dtype=int)\nfor t,p in zip(true,pred): cm[t,p]+=1\nprint(cm)\nprint('row acc', np.diag(cm)/cm.sum(1))\nprint('pred counts', cm.sum(0))\nprint('true counts', cm.sum(1))\nprint('acc', np.trace(cm)/cm.sum())\nPY\nfasttext test-label /app/models/m3n.bin /app/ftdata_norm/valid.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "337",
"role": "tool",
"timestamp": "2026-05-05T10:14:45.840Z",
"parts": [
{
"id": "338",
"type": "tool-result",
"token_count": 277,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_WUQfUmL89XepmyTS9e5RzDd0",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"fasttext predict /app/models/m3n.bin /app/ftdata_norm/valid.txt > /app/models/m3n_valid.pred\npython - <<'PY'\nimport numpy as np, re\ntrue=[]; pred=[]\nfor line in open('/app/ftdata_norm/valid.txt', encoding='utf-8'):\n true.append(int(line.split()[0].replace('__label__','')))\nfor line in open('/app/models/m3n_valid.pred'):\n pred.append(int(line.strip().replace('__label__','')))\ncm=np.zeros((5,5), dtype=int)\nfor t,p in zip(true,pred): cm[t,p]+=1\nprint(cm)\nprint('row acc', np.diag(cm)/cm.sum(1))\nprint('pred counts', cm.sum(0))\nprint('true counts', cm.sum(1))\nprint('acc', np.trace(cm)/cm.sum())\nPY\nfasttext test-label /app/models/m3n.bin /app/ftdata_norm/valid.txt\"\nChunk ID: 25633e\nWall time: 1.0003 seconds\nProcess running with session ID 93829\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "339",
"role": "assistant",
"timestamp": "2026-05-05T10:14:51.702Z",
"parts": [
{
"id": "340",
"type": "text",
"token_count": 19,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_XJJiduXgI566ViZHknE4ERY5"
},
{
"id": "341",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_XJJiduXgI566ViZHknE4ERY5",
"toolName": "write_stdin",
"input": {
"session_id": 93829,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "342",
"role": "tool",
"timestamp": "2026-05-05T10:14:51.702Z",
"parts": [
{
"id": "343",
"type": "tool-result",
"token_count": 603,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_XJJiduXgI566ViZHknE4ERY5",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"fasttext predict /app/models/m3n.bin /app/ftdata_norm/valid.txt > /app/models/m3n_valid.pred\npython - <<'PY'\nimport numpy as np, re\ntrue=[]; pred=[]\nfor line in open('/app/ftdata_norm/valid.txt', encoding='utf-8'):\n true.append(int(line.split()[0].replace('__label__','')))\nfor line in open('/app/models/m3n_valid.pred'):\n pred.append(int(line.strip().replace('__label__','')))\ncm=np.zeros((5,5), dtype=int)\nfor t,p in zip(true,pred): cm[t,p]+=1\nprint(cm)\nprint('row acc', np.diag(cm)/cm.sum(1))\nprint('pred counts', cm.sum(0))\nprint('true counts', cm.sum(1))\nprint('acc', np.trace(cm)/cm.sum())\nPY\nfasttext test-label /app/models/m3n.bin /app/ftdata_norm/valid.txt\"\nChunk ID: 72d8cb\nWall time: 4.0676 seconds\nProcess exited with code 0\nOriginal token count: 171\nOutput:\n[[7511 2021 290 79 99]\n [2359 5192 2024 261 164]\n [ 389 1990 5002 1941 678]\n [ 91 246 1704 4456 3503]\n [ 95 67 367 1781 7690]]\nrow acc [0.7511 0.5192 0.5002 0.4456 0.769 ]\npred counts [10445 9516 9387 8518 12134]\ntrue counts [10000 10000 10000 10000 10000]\nacc 0.59702\nF1-Score : 1.065729 Precision : 0.532865 Recall : -------- __label__2\nF1-Score : 1.091215 Precision : 0.545607 Recall : -------- __label__1\nF1-Score : 1.438200 Precision : 0.719100 Recall : -------- __label__0\nF1-Score : 1.046255 Precision : 0.523127 Recall : -------- __label__3\nF1-Score : 1.267513 Precision : 0.633756 Recall : -------- __label__4\nN\t50000\nP@1\t0.597\nR@1\t0.597\n"
}
]
},
{
"id": "344",
"role": "assistant",
"timestamp": "2026-05-05T10:15:07.047Z",
"parts": [
{
"id": "345",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_OOJlaun5iOQLhBj72umM9LwI"
},
{
"id": "346",
"type": "tool-call",
"token_count": 402,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_OOJlaun5iOQLhBj72umM9LwI",
"toolName": "exec_command",
"input": {
"cmd": "fasttext predict-prob /app/models/m3n.bin /app/ftdata_norm/valid.txt 5 > /app/models/m3n_valid.prob\npython - <<'PY'\nimport numpy as np, math, random\nY=[]; P=[]\nfor line in open('/app/ftdata_norm/valid.txt', encoding='utf-8'):\n Y.append(int(line.split()[0].replace('__label__','')))\nfor line in open('/app/models/m3n_valid.prob'):\n parts=line.strip().split()\n scores=np.full(5, -50.0)\n for lab,prob in zip(parts[0::2], parts[1::2]):\n scores[int(lab.replace('__label__',''))]=math.log(max(float(prob),1e-12))\n P.append(scores)\nY=np.array(Y); S=np.vstack(P)\ndef acc(b): return np.mean(np.argmax(S+b, axis=1)==Y)\nb=np.zeros(5)\nprint('base', acc(b))\n# coordinate search offsets in logit/logprob space\nbest=acc(b)\nstep=1.0\nwhile step>0.001:\n improved=False\n for j in range(5):\n for delta in (step, -step):\n bb=b.copy(); bb[j]+=delta\n a=acc(bb)\n if a>best:\n b, best, improved = bb, a, True\n print('best', best, b)\n if not improved: step*=0.5\nprint('final', best, b, 'pred_counts', np.bincount(np.argmax(S+b,axis=1), minlength=5))\nPY",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "347",
"role": "tool",
"timestamp": "2026-05-05T10:15:07.047Z",
"parts": [
{
"id": "348",
"type": "tool-result",
"token_count": 427,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_OOJlaun5iOQLhBj72umM9LwI",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"fasttext predict-prob /app/models/m3n.bin /app/ftdata_norm/valid.txt 5 > /app/models/m3n_valid.prob\npython - <<'PY'\nimport numpy as np, math, random\nY=[]; P=[]\nfor line in open('/app/ftdata_norm/valid.txt', encoding='utf-8'):\n Y.append(int(line.split()[0].replace('__label__','')))\nfor line in open('/app/models/m3n_valid.prob'):\n parts=line.strip().split()\n scores=np.full(5, -50.0)\n for lab,prob in zip(parts[0::2], parts[1::2]):\n scores[int(lab.replace('__label__',''))]=math.log(max(float(prob),1e-12))\n P.append(scores)\nY=np.array(Y); S=np.vstack(P)\ndef acc(b): return np.mean(np.argmax(S+b, axis=1)==Y)\nb=np.zeros(5)\nprint('base', acc(b))\n# coordinate search offsets in logit/logprob space\nbest=acc(b)\nstep=1.0\nwhile step>0.001:\n improved=False\n for j in range(5):\n for delta in (step, -step):\n bb=b.copy(); bb[j]+=delta\n a=acc(bb)\n if a>best:\n b, best, improved = bb, a, True\n print('best', best, b)\n if not improved: step*=0.5\nprint('final', best, b, 'pred_counts', np.bincount(np.argmax(S+b,axis=1), minlength=5))\nPY\"\nChunk ID: 377f92\nWall time: 1.0010 seconds\nProcess running with session ID 72879\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "349",
"role": "assistant",
"timestamp": "2026-05-05T10:15:11.681Z",
"parts": [
{
"id": "350",
"type": "text",
"token_count": 25,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_uxqZhuSX5Bv6dXuhx0lwP3pZ"
},
{
"id": "351",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_uxqZhuSX5Bv6dXuhx0lwP3pZ",
"toolName": "write_stdin",
"input": {
"session_id": 72879,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 30000
}
}
]
},
{
"id": "352",
"role": "tool",
"timestamp": "2026-05-05T10:15:11.681Z",
"parts": [
{
"id": "353",
"type": "tool-result",
"token_count": 965,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_uxqZhuSX5Bv6dXuhx0lwP3pZ",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"fasttext predict-prob /app/models/m3n.bin /app/ftdata_norm/valid.txt 5 > /app/models/m3n_valid.prob\npython - <<'PY'\nimport numpy as np, math, random\nY=[]; P=[]\nfor line in open('/app/ftdata_norm/valid.txt', encoding='utf-8'):\n Y.append(int(line.split()[0].replace('__label__','')))\nfor line in open('/app/models/m3n_valid.prob'):\n parts=line.strip().split()\n scores=np.full(5, -50.0)\n for lab,prob in zip(parts[0::2], parts[1::2]):\n scores[int(lab.replace('__label__',''))]=math.log(max(float(prob),1e-12))\n P.append(scores)\nY=np.array(Y); S=np.vstack(P)\ndef acc(b): return np.mean(np.argmax(S+b, axis=1)==Y)\nb=np.zeros(5)\nprint('base', acc(b))\n# coordinate search offsets in logit/logprob space\nbest=acc(b)\nstep=1.0\nwhile step>0.001:\n improved=False\n for j in range(5):\n for delta in (step, -step):\n bb=b.copy(); bb[j]+=delta\n a=acc(bb)\n if a>best:\n b, best, improved = bb, a, True\n print('best', best, b)\n if not improved: step*=0.5\nprint('final', best, b, 'pred_counts', np.bincount(np.argmax(S+b,axis=1), minlength=5))\nPY\"\nChunk ID: d826f3\nWall time: 1.1682 seconds\nProcess exited with code 0\nOriginal token count: 223\nOutput:\nbase 0.59702\nbest 0.59878 [ 0. 0. 0. 0. -1.]\nbest 0.59902 [-0.5 0. 0. 0. -1. ]\nbest 0.59908 [-0.5 0. 0. 0.5 -1. ]\nbest 0.59912 [-0.5 0. 0.5 0.5 -1. ]\nbest 0.59914 [-0.375 0. 0.5 0.5 -1. ]\nbest 0.59918 [-0.375 0.125 0.5 0.5 -1. ]\nbest 0.59922 [-0.25 0.125 0.5 0.5 -1. ]\nbest 0.59928 [-0.3125 0.125 0.5 0.5 -1. ]\nbest 0.59944 [-0.3125 0.125 0.4375 0.5 -1. ]\nbest 0.59952 [-0.3125 0.125 0.4375 0.53125 -1. ]\nbest 0.59956 [-0.3125 0.125 0.453125 0.53125 -1. ]\nbest 0.59958 [-0.3125 0.125 0.453125 0.53125 -1.0078125]\nbest 0.5996 [-0.3125 0.12890625 0.453125 0.53125 -1.0078125 ]\nbest 0.59962 [-0.31054688 0.12890625 0.453125 0.53125 -1.0078125 ]\nfinal 0.59962 [-0.31054688 0.12890625 0.453125 0.53125 -1.0078125 ] pred_counts [ 9916 9595 9921 10482 10086]\n"
}
]
},
{
"id": "354",
"role": "assistant",
"parts": [
{
"id": "355",
"type": "text",
"token_count": 35,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "I\u2019ll run quick subset experiments to identify a setting that can close the gap.Executed exec_command call_k7ilDUFLygrqxjjd5Le8qJer"
},
{
"id": "356",
"type": "tool-call",
"token_count": 217,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_k7ilDUFLygrqxjjd5Le8qJer",
"toolName": "exec_command",
"input": {
"cmd": "python - <<'PY'\nfrom pathlib import Path\nimport random\nsrc=Path('/app/ftdata_norm/train_split.txt')\nout=Path('/app/ftdata_norm/train_100k.txt')\nrandom.seed(123)\nby={i:[] for i in range(5)}\nfor line in src.open(encoding='utf-8'):\n lab=int(line.split()[0].replace('__label__',''))\n if len(by[lab])<20000:\n by[lab].append(line)\n# first enough from all classes; shuffle overall\nlines=[]\nfor v in by.values(): lines.extend(v)\nrandom.shuffle(lines)\nout.write_text(''.join(lines), encoding='utf-8')\nprint(out, len(lines), out.stat().st_size)\nPY\nwc -l /app/ftdata_norm/train_100k.txt",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "357",
"role": "tool",
"timestamp": "2026-05-05T10:16:00.001Z",
"parts": [
{
"id": "358",
"type": "tool-result",
"token_count": 241,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_k7ilDUFLygrqxjjd5Le8qJer",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nfrom pathlib import Path\nimport random\nsrc=Path('/app/ftdata_norm/train_split.txt')\nout=Path('/app/ftdata_norm/train_100k.txt')\nrandom.seed(123)\nby={i:[] for i in range(5)}\nfor line in src.open(encoding='utf-8'):\n lab=int(line.split()[0].replace('__label__',''))\n if len(by[lab])<20000:\n by[lab].append(line)\n# first enough from all classes; shuffle overall\nlines=[]\nfor v in by.values(): lines.extend(v)\nrandom.shuffle(lines)\nout.write_text(''.join(lines), encoding='utf-8')\nprint(out, len(lines), out.stat().st_size)\nPY\nwc -l /app/ftdata_norm/train_100k.txt\"\nChunk ID: e33125\nWall time: 1.0004 seconds\nProcess running with session ID 48639\nOriginal token count: 0\nOutput:\n"
}
]
},
{
"id": "359",
"role": "assistant",
"timestamp": "2026-05-05T10:16:05.159Z",
"parts": [
{
"id": "360",
"type": "text",
"token_count": 23,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_8vY3JQrIfRaN7BOy10zsUc9S"
},
{
"id": "361",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_8vY3JQrIfRaN7BOy10zsUc9S",
"toolName": "write_stdin",
"input": {
"session_id": 48639,
"chars": "",
"yield_time_ms": 1000,
"max_output_tokens": 12000
}
}
]
},
{
"id": "362",
"role": "tool",
"timestamp": "2026-05-05T10:16:05.159Z",
"parts": [
{
"id": "363",
"type": "tool-result",
"token_count": 273,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_8vY3JQrIfRaN7BOy10zsUc9S",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"python - <<'PY'\nfrom pathlib import Path\nimport random\nsrc=Path('/app/ftdata_norm/train_split.txt')\nout=Path('/app/ftdata_norm/train_100k.txt')\nrandom.seed(123)\nby={i:[] for i in range(5)}\nfor line in src.open(encoding='utf-8'):\n lab=int(line.split()[0].replace('__label__',''))\n if len(by[lab])<20000:\n by[lab].append(line)\n# first enough from all classes; shuffle overall\nlines=[]\nfor v in by.values(): lines.extend(v)\nrandom.shuffle(lines)\nout.write_text(''.join(lines), encoding='utf-8')\nprint(out, len(lines), out.stat().st_size)\nPY\nwc -l /app/ftdata_norm/train_100k.txt\"\nChunk ID: 350cf9\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 22\nOutput:\n/app/ftdata_norm/train_100k.txt 100000 71270363\n100000 /app/ftdata_norm/train_100k.txt\n"
}
]
},
{
"id": "364",
"role": "assistant",
"timestamp": "2026-05-05T10:16:13.669Z",
"parts": [
{
"id": "365",
"type": "text",
"token_count": 21,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed exec_command call_yIpwnC0nRzYoZzszFODvdGYH"
},
{
"id": "366",
"type": "tool-call",
"token_count": 367,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_yIpwnC0nRzYoZzszFODvdGYH",
"toolName": "exec_command",
"input": {
"cmd": "set -e\nfor cfg in \\\n\"s1 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\" \\\n\"s2 -lr 0.5 -epoch 20 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\" \\\n\"s3 -lr 1.0 -epoch 10 -wordNgrams 2 -dim 50 -bucket 1000000 -minCount 2 -minn 3 -maxn 6 -loss softmax\" \\\n\"s4 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss one-vs-all\" \\\n\"s5 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 50 -bucket 1000000 -minCount 2 -loss softmax\"; do\n name=${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "367",
"role": "tool",
"timestamp": "2026-05-05T10:16:13.669Z",
"parts": [
{
"id": "368",
"type": "tool-result",
"token_count": 442,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B4 Tool/environment interaction",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_yIpwnC0nRzYoZzszFODvdGYH",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"set -e\nfor cfg in \\\\\n\\\"s1 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s2 -lr 0.5 -epoch 20 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s3 -lr 1.0 -epoch 10 -wordNgrams 2 -dim 50 -bucket 1000000 -minCount 2 -minn 3 -maxn 6 -loss softmax\\\" \\\\\n\\\"s4 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss one-vs-all\\\" \\\\\n\\\"s5 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 50 -bucket 1000000 -minCount 2 -loss softmax\\\"; do\n name=\"'${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone'\nChunk ID: cc71e0\nWall time: 1.0006 seconds\nProcess running with session ID 79106\nOriginal token count: 22\nOutput:\n== s1 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\n"
}
]
},
{
"id": "369",
"role": "assistant",
"timestamp": "2026-05-05T10:16:20.859Z",
"parts": [
{
"id": "370",
"type": "text",
"token_count": 20,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C0 No agent failure behavior",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_QCCiNxNij7TzrnyF036LFIAw"
},
{
"id": "371",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_QCCiNxNij7TzrnyF036LFIAw",
"toolName": "write_stdin",
"input": {
"session_id": 79106,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "372",
"role": "tool",
"timestamp": "2026-05-05T10:16:20.859Z",
"parts": [
{
"id": "373",
"type": "tool-result",
"token_count": 631,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D5 Silent"
},
"toolCallId": "call_QCCiNxNij7TzrnyF036LFIAw",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"set -e\nfor cfg in \\\\\n\\\"s1 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s2 -lr 0.5 -epoch 20 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s3 -lr 1.0 -epoch 10 -wordNgrams 2 -dim 50 -bucket 1000000 -minCount 2 -minn 3 -maxn 6 -loss softmax\\\" \\\\\n\\\"s4 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss one-vs-all\\\" \\\\\n\\\"s5 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 50 -bucket 1000000 -minCount 2 -loss softmax\\\"; do\n name=\"'${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone'\nChunk ID: acb742\nWall time: 300.0012 seconds\nProcess running with session ID 79106\nOriginal token count: 106\nOutput:\n-rw-r--r-- 1 root root 106M May 5 10:17 /app/models/s1.bin\nN\t50000\nP@1\t0.585\nR@1\t0.585\nN\t10000\nP@1\t0.585\nR@1\t0.585\n== s2 -lr 0.5 -epoch 20 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\n-rw-r--r-- 1 root root 106M May 5 10:19 /app/models/s2.bin\nN\t50000\nP@1\t0.582\nR@1\t0.582\nN\t10000\nP@1\t0.585\nR@1\t0.585\n== s3 -lr 1.0 -epoch 10 -wordNgrams 2 -dim 50 -bucket 1000000 -minCount 2 -minn 3 -maxn 6 -loss softmax\n"
}
]
},
{
"id": "374",
"role": "assistant",
"timestamp": "2026-05-05T10:21:25.343Z",
"parts": [
{
"id": "375",
"type": "text",
"token_count": 23,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_Q7tQ5Qtd3EeTBHwlQIH4N0mQ"
},
{
"id": "376",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_Q7tQ5Qtd3EeTBHwlQIH4N0mQ",
"toolName": "write_stdin",
"input": {
"session_id": 79106,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "377",
"role": "tool",
"timestamp": "2026-05-05T10:21:25.343Z",
"parts": [
{
"id": "378",
"type": "tool-result",
"token_count": 624,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D5 Silent"
},
"toolCallId": "call_Q7tQ5Qtd3EeTBHwlQIH4N0mQ",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"set -e\nfor cfg in \\\\\n\\\"s1 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s2 -lr 0.5 -epoch 20 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s3 -lr 1.0 -epoch 10 -wordNgrams 2 -dim 50 -bucket 1000000 -minCount 2 -minn 3 -maxn 6 -loss softmax\\\" \\\\\n\\\"s4 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss one-vs-all\\\" \\\\\n\\\"s5 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 50 -bucket 1000000 -minCount 2 -loss softmax\\\"; do\n name=\"'${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone'\nChunk ID: 6fe220\nWall time: 300.0012 seconds\nProcess running with session ID 79106\nOriginal token count: 103\nOutput:\n-rw-r--r-- 1 root root 201M May 5 10:22 /app/models/s3.bin\nN\t50000\nP@1\t0.587\nR@1\t0.587\nN\t10000\nP@1\t0.59\nR@1\t0.59\n== s4 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss one-vs-all\n-rw-r--r-- 1 root root 106M May 5 10:23 /app/models/s4.bin\nN\t50000\nP@1\t0.558\nR@1\t0.558\nN\t10000\nP@1\t0.558\nR@1\t0.558\n== s5 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 50 -bucket 1000000 -minCount 2 -loss softmax\n"
}
]
},
{
"id": "379",
"role": "assistant",
"timestamp": "2026-05-05T10:26:28.607Z",
"parts": [
{
"id": "380",
"type": "text",
"token_count": 21,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_s1gFDPPnJyYRDgbF2DMoeLT1"
},
{
"id": "381",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_s1gFDPPnJyYRDgbF2DMoeLT1",
"toolName": "write_stdin",
"input": {
"session_id": 79106,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "382",
"role": "tool",
"timestamp": "2026-05-05T10:26:28.607Z",
"parts": [
{
"id": "383",
"type": "tool-result",
"token_count": 474,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D5 Silent"
},
"toolCallId": "call_s1gFDPPnJyYRDgbF2DMoeLT1",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"set -e\nfor cfg in \\\\\n\\\"s1 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s2 -lr 0.5 -epoch 20 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s3 -lr 1.0 -epoch 10 -wordNgrams 2 -dim 50 -bucket 1000000 -minCount 2 -minn 3 -maxn 6 -loss softmax\\\" \\\\\n\\\"s4 -lr 1.0 -epoch 10 -wordNgrams 3 -dim 50 -bucket 500000 -minCount 2 -loss one-vs-all\\\" \\\\\n\\\"s5 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 50 -bucket 1000000 -minCount 2 -loss softmax\\\"; do\n name=\"'${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone'\nChunk ID: 4211a2\nWall time: 71.9107 seconds\nProcess exited with code 0\nOriginal token count: 29\nOutput:\n-rw-r--r-- 1 root root 201M May 5 10:27 /app/models/s5.bin\nN\t50000\nP@1\t0.599\nR@1\t0.599\nN\t10000\nP@1\t0.594\nR@1\t0.594\n"
}
]
},
{
"id": "384",
"role": "assistant",
"parts": [
{
"id": "385",
"type": "text",
"token_count": 47,
"component": "A5 Iterating & converging",
"dimensions": {
"A_stage": "A5 Iterating & converging",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.5 Non-convergence",
"D_impact": "D0 No negative impact"
},
"text": "Subset runs favor lower LR and 4-grams; I\u2019ll test a higher-dim version before final training.Executed exec_command call_WeG2Q3peFhjl0p6EhwaUqg6N"
},
{
"id": "386",
"type": "tool-call",
"token_count": 274,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.1 Wrong tool choice",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_WeG2Q3peFhjl0p6EhwaUqg6N",
"toolName": "exec_command",
"input": {
"cmd": "set -e\nfor cfg in \\\n\"s6 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\" \\\n\"s7 -lr 0.15 -epoch 30 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\" \\\n\"s8 -lr 0.2 -epoch 25 -wordNgrams 5 -dim 100 -bucket 1500000 -minCount 2 -loss softmax\"; do\n name=${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone",
"workdir": "/app",
"yield_time_ms": 1000,
"max_output_tokens": 20000
}
}
]
},
{
"id": "387",
"role": "tool",
"timestamp": "2026-05-05T10:27:49.490Z",
"parts": [
{
"id": "388",
"type": "tool-result",
"token_count": 348,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D5 Silent"
},
"toolCallId": "call_WeG2Q3peFhjl0p6EhwaUqg6N",
"toolName": "exec_command",
"output": "Command: /bin/bash -lc \"set -e\nfor cfg in \\\\\n\\\"s6 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s7 -lr 0.15 -epoch 30 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s8 -lr 0.2 -epoch 25 -wordNgrams 5 -dim 100 -bucket 1500000 -minCount 2 -loss softmax\\\"; do\n name=\"'${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone'\nChunk ID: f9b6a3\nWall time: 1.0004 seconds\nProcess running with session ID 30362\nOriginal token count: 23\nOutput:\n== s6 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\n"
}
]
},
{
"id": "389",
"role": "assistant",
"timestamp": "2026-05-05T10:28:06.754Z",
"parts": [
{
"id": "390",
"type": "text",
"token_count": 22,
"component": "A6 Terminating & delivering",
"dimensions": {
"A_stage": "A6 Terminating & delivering",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"text": "Executed write_stdin call_TDWBCwz7e1OFrMf1Nlqpe5Au"
},
{
"id": "391",
"type": "tool-call",
"token_count": 26,
"component": "A3 Executing & generating",
"dimensions": {
"A_stage": "A3 Executing & generating",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C6.1 Step repetition/infinite loop",
"D_impact": "D0 No negative impact"
},
"toolCallId": "call_TDWBCwz7e1OFrMf1Nlqpe5Au",
"toolName": "write_stdin",
"input": {
"session_id": 30362,
"chars": "",
"yield_time_ms": 300000,
"max_output_tokens": 40000
}
}
]
},
{
"id": "392",
"role": "tool",
"timestamp": "2026-05-05T10:28:06.754Z",
"parts": [
{
"id": "393",
"type": "tool-result",
"token_count": 417,
"component": "A4 Verifying & testing",
"dimensions": {
"A_stage": "A4 Verifying & testing",
"B_root_cause": "B0 No agent failure",
"C_behavior": "C8.4 Tool-output misread",
"D_impact": "D5 Silent"
},
"toolCallId": "call_TDWBCwz7e1OFrMf1Nlqpe5Au",
"toolName": "write_stdin",
"output": "Command: /bin/bash -lc \"set -e\nfor cfg in \\\\\n\\\"s6 -lr 0.25 -epoch 20 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s7 -lr 0.15 -epoch 30 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\\\" \\\\\n\\\"s8 -lr 0.2 -epoch 25 -wordNgrams 5 -dim 100 -bucket 1500000 -minCount 2 -loss softmax\\\"; do\n name=\"'${cfg%% *}; args=${cfg#* }\n echo \"== $name $args\"\n fasttext supervised -input /app/ftdata_norm/train_100k.txt -output /app/models/$name $args -thread 1 -verbose 0\n ls -lh /app/models/$name.bin\n fasttext test /app/models/$name.bin /app/ftdata_norm/valid.txt\n fasttext test /app/models/$name.bin /app/ftdata_norm/public_test.txt\ndone'\nChunk ID: c928f4\nWall time: 300.0008 seconds\nProcess running with session ID 30362\nOriginal token count: 52\nOutput:\n-rw-r--r-- 1 root root 401M May 5 10:32 /app/models/s6.bin\nN\t50000\nP@1\t0.598\nR@1\t0.598\nN\t10000\nP@1\t0.595\nR@1\t0.595\n== s7 -lr 0.15 -epoch 30 -wordNgrams 4 -dim 100 -bucket 1000000 -minCount 2 -loss softmax\n"
}
]
}
]
},
"colors": {
"AX Non-agent context": "#9ca3af",
"A1 Understanding & planning": "#94a3b8",
"A2 Locating & exploring": "#2563eb",
"A3 Executing & generating": "#16a34a",
"A4 Verifying & testing": "#f59e0b",
"A5 Iterating & converging": "#dc2626",
"A6 Terminating & delivering": "#7c3aed"
},
"summary": null,
"analysis": null,
"metadata": {
"parserName": "ATIF",
"agent": "codex",
"model": "gpt-5.5"
},
"customPrompts": {
"segmentation": "Split the ATIF trajectory into coherent segments that preserve the evidence needed for Agent Failure Taxonomy classification. Keep setup/system/user instructions separate from agent work. Keep long-running command polls, model-training attempts, validation outputs, and final non-delivery as distinct segments when they show different AFT behavior. Whole-run context: the user asked the agent to train a fastText model on Yelp data, save it as /app/model.bin, keep it under 150MB, and reach at least 0.62 private-test accuracy. The agent explored the data, installed the Debian fasttext CLI after pip failed, converted parquet data into fastText format, trained multiple candidates, observed local validation/public-test accuracy around 0.56 to 0.599, ran repeated long training/polling loops, investigated class calibration, then started more subset experiments. The trajectory ends at step 97 while another experiment is still running; there is no final saved /app/model.bin and no final delivery. Use this whole-run outcome when classifying segment-level failures.\n\nReturn ONLY a valid JSON array of regex strings with positive lookahead patterns suitable for JavaScript string splitting.\nExample: [\"(?=## Section one)\", \"(?=## Section two)\"]"
},
"dimensions": {
"A_stage": {
"components": [
"AX Non-agent context",
"A1 Understanding & planning",
"A2 Locating & exploring",
"A3 Executing & generating",
"A4 Verifying & testing",
"A5 Iterating & converging",
"A6 Terminating & delivering"
],
"colors": {
"AX Non-agent context": "#9ca3af",
"A1 Understanding & planning": "#94a3b8",
"A2 Locating & exploring": "#2563eb",
"A3 Executing & generating": "#16a34a",
"A4 Verifying & testing": "#f59e0b",
"A5 Iterating & converging": "#dc2626",
"A6 Terminating & delivering": "#7c3aed"
}
},
"B_root_cause": {
"components": [
"B0 No agent failure",
"B1 Reasoning defect",
"B2 Knowledge gap",
"B3 Context-management failure",
"B4 Tool/environment interaction",
"B5 Spec non-compliance"
],
"colors": {
"B0 No agent failure": "#d1d5db",
"B1 Reasoning defect": "#dc2626",
"B2 Knowledge gap": "#f97316",
"B3 Context-management failure": "#7c3aed",
"B4 Tool/environment interaction": "#64748b",
"B5 Spec non-compliance": "#111827"
}
},
"C_behavior": {
"components": [
"C0 No agent failure behavior",
"C1.3 Instruction non-compliance",
"C2.2 Reasoning-action mismatch",
"C2.4 Problem misidentification",
"C3.2 Wrong search scope",
"C4.1 Insufficient surrounding-context understanding",
"C4.4 Incomplete fix",
"C4.8 Dependency/compatibility break",
"C5.2 Selective amnesia",
"C5.3 State drift",
"C6.1 Step repetition/infinite loop",
"C6.2 Premature termination",
"C6.5 Non-convergence",
"C7.1 Validation missing/incomplete",
"C8.1 Wrong tool choice",
"C8.2 Tool-call format error",
"C8.3 Missing environment dependency",
"C8.4 Tool-output misread"
],
"colors": {
"C0 No agent failure behavior": "#d1d5db",
"C1.3 Instruction non-compliance": "#111827",
"C2.2 Reasoning-action mismatch": "#f97316",
"C2.4 Problem misidentification": "#f59e0b",
"C3.2 Wrong search scope": "#2563eb",
"C4.1 Insufficient surrounding-context understanding": "#0f766e",
"C4.4 Incomplete fix": "#16a34a",
"C4.8 Dependency/compatibility break": "#22c55e",
"C5.2 Selective amnesia": "#7c3aed",
"C5.3 State drift": "#6d28d9",
"C6.1 Step repetition/infinite loop": "#dc2626",
"C6.2 Premature termination": "#be123c",
"C6.5 Non-convergence": "#991b1b",
"C7.1 Validation missing/incomplete": "#eab308",
"C8.1 Wrong tool choice": "#475569",
"C8.2 Tool-call format error": "#64748b",
"C8.3 Missing environment dependency": "#334155",
"C8.4 Tool-output misread": "#1f2937"
}
},
"D_impact": {
"components": [
"D0 No negative impact",
"D1 Recoverable mild",
"D2 Recoverable moderate",
"D3 Unrecoverable",
"D4 Cascading",
"D5 Silent"
],
"colors": {
"D0 No negative impact": "#d1d5db",
"D1 Recoverable mild": "#94a3b8",
"D2 Recoverable moderate": "#f59e0b",
"D3 Unrecoverable": "#f97316",
"D4 Cascading": "#dc2626",
"D5 Silent": "#7f1d1d"
}
}
}
}
],
"groups": [],
"analytics": {
"componentComparison": [
{
"fileId": "1",
"filename": "663d455e-102f-46d4-b78d-c792c71fdb32.trajectory.json",
"totalTokens": 41268,
"messageCount": 157,
"componentTokens": {
"A4 Verifying & testing": 19985,
"A3 Executing & generating": 12123,
"A2 Locating & exploring": 5233,
"AX Non-agent context": 2842,
"A5 Iterating & converging": 587,
"A6 Terminating & delivering": 397,
"A1 Understanding & planning": 101
},
"turnCount": 2
}
]
}
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment