---
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}
}
```