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pith:KNM3LVVK

pith:2025:KNM3LVVKWQHJORDLHE6OAYW3HK

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MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Bin Wang, Bowen Zhou, Boyu Niu, Bo Zhang, Chao Xu, Conghui He, Dahua Lin, Dechen Lin, Dongsheng Ma, Fangdong Wang, Fan Wu, Guang Liang, Guangyu Wang, Guanlin Shen, Hejun Dong, Huaiyu Gu, Jiang Wu, Jiaqi Wang, Jingzhou Chen, Junbo Niu, Junyuan Zhang, Kai Chen, Keming Wang, Lei Bai, Lijun Wu, Lindong Lu, Linfeng Zhang, Linke Ouyang, Liqun Wei, Lu Chen, Pei Chu, Qianqian Wu, Qintong Zhang, Ruiliang Xu, Rui Zhang, Shasha Wang, Siyi Qian, Tao Chu, Tianyao He, Weijia Li, Wei Li, Wentao Zhang, Wenzheng Zhang, Xiaomeng Zhao, Xiaoyi Dong, Xuanhe Zhou, Yuanhong Zheng, Yuan Qu, Yuanyuan Cao, Yuefeng Sun, Yuhang Zang, Yu Qiao, Zheng Liu, Zhenjiang Jin, Zhenxiang Li, Zhifei Ren, Zhiyuan Zhao, Zhongying Tu, Zhuangcheng Gu, Zirui Tang, Ziyang Miao

MinerU2.5 decouples global layout analysis on downsampled images from local content recognition on native-resolution crops to parse high-resolution documents with state-of-the-art accuracy and lower compute.

arxiv:2509.22186 v2 · 2025-09-26 · cs.CV · cs.CL

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Record completeness

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4 Citations open

5 Replications open

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Claims

C1strongest claim

MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

C2weakest assumption

That coarse layout analysis performed on downsampled images provides sufficiently accurate guidance for extracting and recognizing native-resolution crops without introducing errors in dense text, complex formulas, or table structures.

C3one line summary

MinerU2.5 uses a two-stage decoupled vision-language architecture to achieve state-of-the-art document parsing accuracy with lower computational overhead than existing general and domain-specific models.

References

63 extracted · 63 resolved · 17 Pith anchors

[1] GPT-4 Technical Report 2023 · arXiv:2303.08774

[2] Wukong-reader: Multi-modal pre-training for fine-grained visual document understanding.arXiv preprint arXiv:2212.09621, 2022 2022

[3] Qwen2.5-VL Technical Report 2025 · arXiv:2502.13923

[4] Nougat: Neural Optical Understanding for Academic Documents 2023 · arXiv:2308.13418

[5] chatdoc com. Ocrflux.https://github.com/chatdoc-com/OCRFlux, 2025. Accessed:2025-09-25 2025

Formal links

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Cited by

26 papers in Pith

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PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training

MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing

StrucTab: A Structured Optimization Framework for Table Parsing

ABot-OCR Technical Report

Receipt and verification

First computed	2026-05-17T23:38:13.963948Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5359b5d6aab40e97446b393ce062db3a8468287ea5136c9518d3a59f9074676a

Aliases

arxiv: 2509.22186 · arxiv_version: 2509.22186v2 · doi: 10.48550/arxiv.2509.22186 · pith_short_12: KNM3LVVKWQHJ · pith_short_16: KNM3LVVKWQHJORDL · pith_short_8: KNM3LVVK

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KNM3LVVKWQHJORDLHE6OAYW3HK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5359b5d6aab40e97446b393ce062db3a8468287ea5136c9518d3a59f9074676a

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-09-26T10:45:48Z",
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