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Datasets:
junjun77
/
small-ow-agent-bench

Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
agents
code
evaluation
harbor
benchmark
Libraries:
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pandas
Polars
License:
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metadata 
license: apache-2.0
pretty_name: small-ow-agent-bench
task_categories:
  - text-generation
language:
  - en
tags:
  - agents
  - code
  - evaluation
  - harbor
  - benchmark
size_categories:
  - n<1K
configs:
  - config_name: catalog
    data_files: catalog.jsonl
    default: true

small-ow-agent-bench (catalog)

Frozen compact-shell system reliability for compact open-weight coding agents — not coding IQ.

This dataset is not the Harbor task dump. Each of the 62 rows is catalog metadata plus the agent-visible instruction:

  • id, skill, difficulty, bank
  • trap_id / trap (failure-mode label from TRAPS.md)
  • instruction / instruction_summary
  • license, GitHub tag, compact-shell version

It does not include hidden verifiers: tests/, solution/, foils/, gold file lists, mutants, or environment/repo. Those stay in Harbor tasks/ on GitHub.

Do not train on this 62-item bank and then report the same items as evaluation.

Compact-10 (v1.0.1)

Headline = five-skill macro mean. Micro = successes / 186. Artifact does not require finish; Clean does. Halt (Artifact=1, not clean) = 105. 12×62×3 = 2232 scored trials.

Compact-10 Artifact vs Clean

# Model Artifact macro Clean macro Gap Artifact micro Clean micro
1 Qwen3.5-9B 0.786 0.757 0.029 148/186 142/186
2 Ministral-14B 0.497 0.488 0.009 93/186 91/186
3 Ministral-8B 0.456 0.411 0.045 87/186 78/186
4 Qwen3-14B 0.404 0.333 0.072 76/186 65/186
5 Gemma-3-12B 0.313 0.114 0.199 60/186 18/186
6 Granite-4.1-8B 0.162 0.140 0.022 31/186 26/186
7 Gemma-3-4B 0.067 0.067 0.000 11/186 11/186
8 Ministral-3B 0.049 0.019 0.030 10/186 4/186
9 Qwen3-8B 0.034 0.018 0.015 6/186 3/186
10 Llama-3.2-3B 0.027 0.000 0.027 4/186 0/186

Upper-reference (not ranked with Compact-10)

# Model Artifact macro Clean macro Gap Artifact micro Clean micro
— Qwen3.8-27B 0.863 0.845 0.018 162/186 159/186
— Qwen3.6-35B-A3B 0.632 0.560 0.072 118/186 104/186

Full five-skill tables: results/leaderboard.md.

Regenerate:

python scripts/export_hf_catalog.py --write