Papers by Xiaomeng Ma
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Evaluating Transformer Models and Human Behaviors on Chinese Character Naming (2023.tacl-1)
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| Challenge: | Neural network models have been proposed to explain the grapheme-phoneme mapping process in humans for many alphabet languages. |
| Approach: | They propose to use a dictionary-like lookup procedure to map the letter strings to their pronunciations and then use 'transformers' to capture human behavior. |
| Outcome: | The proposed models learned the correspondence of the letter strings and their pronunciation, and captured human behavior in nonce word naming tasks. |
How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection (2022.aacl-main)
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| Challenge: | English past tense inflections is a typical quasi-regularity task, but it is criticized that it learns only to generalize the most frequent pattern, but not the regular pattern. |
| Approach: | They train a set of transformer models with different settings to examine their behavior on a typical English quasi-regularity task. |
| Outcome: | The models achieved high accuracy on unseen regular verbs and some accuracy on unseen irregular verbs. |