| --- |
| license: cc-by-4.0 |
| pretty_name: Who&When Pro |
| language: |
| - en |
| tags: |
| - agents |
| - multi-agent |
| - failure-attribution |
| - llm-evaluation |
| - benchmark |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: text |
| path: data/text.jsonl |
| - split: image |
| path: data/image.jsonl |
| - split: image_gui |
| path: data/image_gui.jsonl |
| - split: video |
| path: data/video.jsonl |
| --- |
| |
| # Who&When Pro |
|
|
| Who&When Pro is a failure attribution benchmark for LLM agent systems. |
| Each trace is an agent trajectory, single or multi agent, with one |
| realistic error injected at a known step. The label records who made the |
| error, when it happened, and what kind of error it was. Given a |
| trajectory, the evaluated model has to recover all three. |
|
|
| ## Row schema |
|
|
| Each row is one trace: |
|
|
| | column | meaning | |
| |---|---| |
| | `id` | unique trace id | |
| | `framework` | agent framework of the trajectory | |
| | `benchmark` | source benchmark | |
| | `task` | the original task: query, reference answer, inputs | |
| | `trajectory` | the full agent rollout | |
| | `ground_truth` | the label: agent, step, and error mode | |
| | `extras` | framework specific metadata | |
|
|
| `task`, `trajectory`, `ground_truth`, and `extras` are JSON strings, so |
| `json.loads` them to consume. Image and image_gui traces embed their |
| images in the row; video traces reference frame files under |
| `video_assets/`. |
|
|
| ## Splits |
|
|
| - `text`, `image`, `video` — tool-use, chart/search, and video agent |
| traces across single- and multi-agent frameworks. |
| - `image_gui` — GUI agent traces with full screenshot observations: |
| `coact` (OSWorld, multi-agent) and `openai_cua` (OSWorld), plus |
| `agentoccam` and `gemini` (WebVoyager). `ground_truth.step` is |
| 1-indexed and matches the trajectory's `step_number` directly. |
|
|
| ## Taxonomy |
|
|
| 17 error modes in 6 categories, defined in `taxonomy.yaml`: |
|
|
| - **Perception**: visual misidentification, spatial grounding |
| - **Reasoning**: hallucination, reasoning error, numerical error, task misunderstanding |
| - **Planning**: ineffective planning, goal drift |
| - **Action**: tool parameter error, output format error, premature termination, repetitive looping |
| - **Verification**: context loss, inadequate verification |
| - **Coordination**: delegation error, communication failure, over-reliance on other agents |
|
|
| ## Evaluation |
|
|
| The companion evaluation harness, with renderers for every framework |
| and the scoring pipeline, is on GitHub: |
| [whowhenpro/whowhen_pro](https://github.com/whowhenpro/whowhen_pro). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{liu2026pro, |
| title={Who\&When Pro: Can LLMs Really Attribute Failures in AI Agents?}, |
| author={Liu, Jiale and Xi, Huajun and Zhang, Shaokun and Zeng, Yifan and Yue, Tianwei and Wang, Chi and Kang, Jian and Wu, Qingyun and Wang, Huazheng}, |
| journal={arXiv preprint arXiv:2607.09996}, |
| year={2026} |
| } |
| ``` |
|
|
