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---
pretty_name: Wiki-CoE
language:
- en
license: apache-2.0
task_categories:
- question-answering
- visual-question-answering
tags:
- multimodal
- visual-question-answering
- chain-of-evidence
- document-understanding
- wiki
size_categories:
- 10K<n<100K
---
# Wiki-CoE
Wiki-CoE is a multimodal question-answering dataset for evaluating whether a model can answer questions from visual evidence and produce an explicit chain of evidence. Each example pairs a natural-language question with one or more Wikipedia page screenshots and asks the model to return both the answer and the supporting evidence.
The dataset is intended for research on visual reasoning, evidence localization, and chain-of-evidence style multimodal QA.
## Dataset contents
The main release is under `release_strict_chainsplit_20260428_1338/`:
| File | Description |
|---|---:|
| `train.json` | training split, 35,210 examples |
| `test.json` | test split, 35,208 examples |
| `val_1000_seed42.json` | small validation subset, 1,000 examples |
| `split_report.json` | high-level split statistics |
| `val_1000_seed42_manifest.json` | validation subset indices |
Each example contains:
| Field | Description |
|---|---|
| `question` | input question |
| `answer` | short answer |
| `type` | reasoning type |
| `num_hops` | number of evidence hops |
| `evidence_chain` | ordered evidence screenshots, bounding boxes, and sub-questions |
| `evidences` | supporting evidence metadata |
## Screenshot archive
The screenshot images are stored as a split archive under `release_assets/`.
To reconstruct the image directory:
```bash
cat release_assets/screenshots_trimmed.tar.zst.part-* > screenshots_trimmed.tar.zst
tar --use-compress-program=unzstd -xf screenshots_trimmed.tar.zst
```
This creates:
```text
screenshots_trimmed/
```
The archive contains 76,000 PNG screenshots. SHA256 checksums for the archive parts are provided in `release_assets/screenshots_trimmed.tar.zst.sha256`.
## Related resources
- Homepage: https://lpy.pxsec.cn
- Paper: https://arxiv.org/abs/2605.01284
- Code: https://github.com/PeiYangLiu/CoE
- Wiki-CoE model checkpoint: https://huggingface.co/PeiyangLiu/CoE-Wiki-CoE-8B
- SlideVQA model checkpoint: https://huggingface.co/PeiyangLiu/CoE-SlideVQA-8B