README.md

---
license: other
license_name: composite
pretty_name: VisDocAgentBench
language:
- en
tags:
- visual-document-retrieval
- agentic-search
- multimodal-retrieval
- scientific-documents
size_categories:
- 1K<n<10K
configs:
- config_name: queries
  data_files:
  - split: test
    path: benchmark/queries.jsonl
- config_name: evaluator_annotations
  data_files:
  - split: test
    path: benchmark/evaluator_annotations.jsonl
- config_name: documents
  data_files:
  - split: corpus
    path: corpus/documents.jsonl
- config_name: pages
  data_files:
  - split: corpus
    path: corpus/pages.jsonl
---

# VisDocAgentBench

VisDocAgentBench is a closed-corpus benchmark for visually rich document retrieval. It contains 120 natural-language queries over 2,375 rendered pages from 100 scientific documents. Queries are evenly divided among direct, one-bridge, and two-bridge evidence structures, with one answer page per query.

[[Paper](https://arxiv.org/pdf/2608.17889)] [[Code](https://github.com/hulx2002/VisDocAgentBench)] [[Project page](https://hulx2002.github.io/VisDocAgentBench)]

## Contents

```text
benchmark/
├── queries.jsonl
├── evaluator_annotations.jsonl
└── topics.json
corpus/
├── documents.jsonl
├── pages.jsonl
└── pages/<document_id>/<page_id>.png
dataset_info.json
LICENSES.md
```

- `queries.jsonl` contains the query text and topic identifier presented to a retrieval system.
- `evaluator_annotations.jsonl` contains the answer page, evidence level, and ordered latent support pages used by the evaluator and controlled analyses.
- `documents.jsonl` records the exact arXiv version, bibliographic metadata, source URLs, source license, page count, and image-availability status for every document.
- `pages.jsonl` defines all 2,375 page identifiers, document membership, one-based page indices, rendering dimensions, and expected local paths.
- `topics.json` describes the ten corpus topics.

The standard agent harness reads the query text but does not expose answer or support annotations to the planner.

## Dataset Statistics

| Statistic | Count |
|---|---:|
| Documents | 100 |
| Rendered pages | 2,375 |
| Queries | 120 |
| Direct queries | 40 |
| One-bridge queries | 40 |
| Two-bridge queries | 40 |
| Unique answer pages | 120 |
| Directly included page images | 1,469 |
| Locally reconstructed page images | 906 |

## Loading the Metadata

Each JSONL component has a distinct schema and can be loaded independently:

```python
from datasets import load_dataset

queries = load_dataset(
    "hulx2002/VisDocAgentBench",
    "queries",
    split="test",
)
annotations = load_dataset(
    "hulx2002/VisDocAgentBench",
    "evaluator_annotations",
    split="test",
)
pages = load_dataset(
    "hulx2002/VisDocAgentBench",
    "pages",
    split="corpus",
)
```

The complete snapshot can be downloaded with the code repository:

```bash
python scripts/download_data.py
```

## Reconstructing the Complete Corpus

The repository includes 1,469 rendered page images whose source licenses permit redistribution. The remaining 906 rows remain in `corpus/pages.jsonl` with `image_included=false`. The exact source version and PDF URL are recorded in `corpus/documents.jsonl`.

After cloning the [code repository](https://github.com/hulx2002/VisDocAgentBench) and downloading this dataset into `data/`, reconstruct the omitted pages with:

```bash
python dataset_tools/download_and_render.py
python dataset_tools/validate_dataset.py --require-complete-corpus
```

The script downloads each specified arXiv version and renders it at 144 DPI. It verifies the expected page count and dimensions before accepting the reconstructed corpus.

## Evaluation

Systems return a ranked list of page identifiers or the opaque page handles assigned by the released agent harness. The evaluator reports Recall@1/3/5/10 and MRR@10 overall and by evidence level. Missing or invalid rankings stay in the 120-query denominator and score zero.

Evaluation code, baseline implementations, deterministic preprocessing, and the full agent tool interfaces are available in the [code repository](https://github.com/hulx2002/VisDocAgentBench). Generated predictions, traces, OCR caches, embeddings, and model weights are not included in this dataset repository.

## Licensing

Benchmark-authored queries, evaluator annotations, topics, and corpus metadata are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Source-document page images retain the license of their source document. The license URL and attribution metadata for every source are recorded in `corpus/documents.jsonl`.

Page images are included only for 55 CC BY 4.0 documents and 4 CC BY-NC-SA 4.0 documents. Sources under the arXiv nonexclusive distribution license or CC BY-NC-ND 4.0 are represented by metadata and local reconstruction instructions, not redistributed images. See [LICENSES.md](LICENSES.md) before reusing source pages.

## Citation

```bibtex
@article{hu2026visdocagentbench,
  title={VisDocAgentBench: Benchmarking Agents for Visually Rich Document Retrieval},
  author={Hu, Lexiang and Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Li, Yikang and Zhang, Fuwei and Wang, Yisen and Lin, Zhouchen},
  journal={arXiv preprint arXiv:2608.17889},
  year={2026}
}
```