MinerU2.5: 1.2B-parameter decoupled vision-language model targets efficient high-resolution document parsing

[HF Papers] A paper titled MinerU2.5 by Junbo Niu, Zheng Liu, Zhuangcheng Gu and co-authors introduces a 1.2B-parameter document parsing vision-language model that the authors say achieves state-of-the-art recognition accuracy while maintaining high computational efficiency. The approach uses a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs layout analysis on downsampled images to identify structural elements, avoiding the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops from the original image to preserve fine details in dense text, complex formulas and tables. The authors say they also built a data engine to generate diverse, large-scale training corpora for pretraining and fine-tuning. According to the paper, MinerU2.5 achieves state-of-the-art performance on multiple benchmarks, surpassing general-purpose and domain-specific models across recognition tasks while maintaining significantly lower computational overhead. The work is a preprint and has not been peer-reviewed.