Datasets:

PeiyangLiu
/

wiki-coe

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:

cat release_assets/screenshots_trimmed.tar.zst.part-* > screenshots_trimmed.tar.zst
tar --use-compress-program=unzstd -xf screenshots_trimmed.tar.zst

This creates:

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