Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
License:
| license: cc-by-4.0 | |
| language: | |
| - en | |
| pretty_name: OpenClawBench | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - text-classification | |
| tags: | |
| - llm-agents | |
| - agent-trajectories | |
| - anomaly-detection | |
| - tool-use | |
| - function-calling | |
| - evaluation | |
| configs: | |
| - config_name: trajectories | |
| default: true | |
| data_files: | |
| - split: train | |
| path: data/train.jsonl | |
| - split: test | |
| path: data/test.jsonl | |
| - config_name: human_verified | |
| data_files: | |
| - split: human_verified | |
| path: data/human_verified.jsonl | |
| # 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 | | |
| ```python | |
| 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 | |
| ```bibtex | |
| @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} | |
| } | |
| ``` | |