Datasets:
Tasks:
Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
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license: other
pretty_name: RPC-Bench
task_categories:
- question-answering
language:
- en
tags:
- research-paper
- document-understanding
- multimodal
- benchmark
- llm
- vlm
---
<div align="center">
# RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension
</div>
<p align="center">
🌐 <a href="https://rpc-bench.github.io/" target="_blank">Project Page</a> •
💻 <a href="https://github.com/RPC-Bench/PRC-Bench" target="_blank">GitHub</a> •
📖 <a href="https://arxiv.org/abs/2601.14289" target="_blank">Paper</a> •
🤗 <a href="https://arxiv.org/abs/2601.14289" target="_blank">Paper</a> •
🧭 <a href="https://community.modelscope.cn/" target="_blank">ModelScope</a>
</p>
<div align="center">
<img src="assets/pipeline.png" width="100%" />
</div>
RPC-Bench is a fine-grained benchmark for research paper comprehension. It is built from review-rebuttal exchanges of high-quality academic papers and supports both text-only and visual evaluation through complementary paper representations.
## Data Structure
RPC-Bench is split into `train`, `dev`, and `test` subsets. Each subset is stored in the dataset structure and recorded in `manifest.jsonl`.
`md/` contains Markdown files parsed from each paper by MinerU. These files provide the text input for LLM-oriented evaluation.
`parse/` contains the full MinerU parsing outputs for each paper, including structured layout and content artifacts.
`pdf/` contains the original paper PDFs.
`vlm/` contains page images rendered from the PDFs with PyMuPDF at 200 DPI for VLM-oriented evaluation.
```text
RPC-Bench/
├── README.md
├── manifest.jsonl
├── parse/
│ ├── train/
│ │ └── <paper_id>/
│ ├── dev/
│ │ └── <paper_id>/
│ └── test/
│ └── <paper_id>/
├── md/
│ ├── train/
│ │ └── <paper_id>/
│ │ └── <paper_id>.md
│ ├── dev/
│ │ └── <paper_id>/
│ │ └── <paper_id>.md
│ └── test/
│ └── <paper_id>/
│ └── <paper_id>.md
├── pdf/
│ ├── train/
│ │ └── <paper_id>.pdf
│ ├── dev/
│ │ └── <paper_id>.pdf
│ └── test/
│ └── <paper_id>.pdf
└── vlm/
├── train/
│ └── <paper_id>/
├── dev/
│ └── <paper_id>/
└── test/
└── <paper_id>/
```
## Practical Uses
RPC-Bench can be used to try paper-centric systems that require broader document understanding rather than local snippet matching.
- Research paper comprehension: try models on full-paper understanding, including core concepts, methods, and experimental findings.
- Long-context evaluation: try whether longer context windows or long-context architectures improve document-level reasoning.
- Multimodal reasoning: try models that combine textual evidence with page-level figures, tables, and diagrams in the original PDF layout.
- RAG system diagnosis: try retrieval, chunking, and evidence-fusion strategies for paper-centric workflows beyond snippet-level retrieval accuracy.
## Citation
```bibtex
@article{chen2026rpc,
title={RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension},
author={Chen, Yelin and Zhang, Fanjin and Sun, Suping and Pang, Yunhe and Wang, Yuanchun and Song, Jian and Li, Xiaoyan and Hou, Lei and Zhao, Shu and Tang, Jie and others},
journal={arXiv preprint arXiv:2601.14289},
year={2026}
}
```
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