OpenClawBench

OpenClawBench: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories (NeurIPS 2026, Evaluations & Datasets Track)

Code: https://github.com/YangzeLiu/OpenClawBench

OpenClawBench labels process-side anomalies in LLM agent trajectories: cases where an agent commits to an action or a confident answer while the information it needs is missing, ambiguous, or contradicted, whether or not the task outcome looks correct. Trajectories were generated by six open-weight backbones running as tool-using agents on BFCL tasks. Each trajectory carries a binary anomaly label, one of five anomaly subtypes, an onset step, and cited evidence steps.

Files

File	Rows	Content
data/train.jsonl	23,857	Trajectories with silver labels (training pool)
data/test.jsonl	2,646	Trajectories with silver labels (held-out test)
data/human_verified.jsonl	440	Human labels for a subset of the trajectories above
TAXONOMY.md		Anomaly definition, the five subtypes, decision rules, boundary cases
SCHEMA.md		Field-level schema
croissant.json		Croissant 1.0 metadata with Responsible AI fields

from datasets import load_dataset
traj = load_dataset("YangzeLiu/OpenClawBench", "trajectories")       # train / test
human = load_dataset("YangzeLiu/OpenClawBench", "human_verified")    # 440 human labels

Join human_verified to trajectories on (source_model, trajectory_id).

How the labels were produced

Silver labels. An LLM judge (DeepSeek-V4-Pro) annotated every trajectory with a self-contained 5-class prompt (in the code repository). Released rows are the high-confidence subset, split 9:1 into train and test, stratified by source model.

Human labels (human_verified.jsonl). Two annotators labeled each trajectory independently, blind to each other and to the silver labels; disagreements were resolved by adjudication under written boundary-case rules (TAXONOMY.md). Labels are binary plus subtype. Two subsets:

• gold300: 300 test trajectories, 50 per source model.
• supp140: 140 trajectories stratified by silver subtype to cover the rare subtypes (25 silver-normal; 25 each of capability_gap_overcommitment, weak_evidence_commitment, write_under_unresolved_ambiguity; 20 each of premature_external_write, error_ignored_escalation), disjoint from gold300. Because of the stratification its anomaly rate is not representative of the corpus.

Source models

DeepSeek-R1, GLM-4.5-Air, GPT-OSS-20B, Qwen3.5-9B, Qwen3.6-27B, Qwen3.6-35B-A3B, roughly balanced across the corpus.

Intended use and limitations

Intended for training and evaluating trajectory-level anomaly detectors, subtype classification, onset localization, and studying LLM-judge reliability against human labels.

• Silver labels come from a single LLM judge and contain errors; use human_verified for evaluation claims.
• Only open-weight backbones on BFCL function-calling tasks, English only; behavior of proprietary agents or open-ended tasks is not covered.
• Subtype boundaries (notably weak_evidence_commitment vs. capability_gap_overcommitment) are inherently fuzzy; see TAXONOMY.md.
• Trajectories are model outputs on public benchmark prompts and contain no personal data. API keys and access tokens appearing in trajectory text were replaced with placeholders.

License

CC BY 4.0 (LICENSE). Task prompts come from BFCL (Apache 2.0).

Citation

@inproceedings{liu2026openclawbench,
  title     = {{OpenClawBench}: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories},
  author    = {Liu, Yibing and Liu, Yangze and Yin, Xiaolong and Wang, Bin and Zhang, Chong and Yin, Hao and Han, Zhongyi},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}