@inproceedings{nguyen-etal-2025-vehme,
    title = "{VEHME}: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions",
    author = "Nguyen, Thu Phuong  and
      Nguyen, Duc M.  and
      Jeon, Hyotaek  and
      Lee, Hyunwook  and
      Song, Hyunmin  and
      Ko, Sungahn  and
      Kim, Taehwan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1619/",
    pages = "31781--31801",
    ISBN = "979-8-89176-332-6",
    abstract = "Automatically assessing handwritten mathematical solutions is an important problem in educational technology with practical applications, but remains a significant challenge due to the diverse formats, unstructured layouts, and symbolic complexity of student work. To address this challenge, we introduce VEHME-a $\textbf{V}$ision-Language Model for $\textbf{E}$valuating $\textbf{H}$andwritten $\textbf{M}$athematics $\textbf{E}$xpressions{---}designed to assess open-form handwritten math responses with high accuracy and interpretable reasoning traces. VEHME integrates a two-phase training pipeline: (i) supervised fine-tuning using structured reasoning data, and (ii) reinforcement learning that aligns model outputs with multi-dimensional grading objectives, including correctness, reasoning depth, and error localization. To enhance spatial understanding, we propose an Expression-Aware Visual Prompting Module, trained on our synthesized multi-line math expressions dataset to robustly guide attention in visually heterogeneous inputs. Evaluated on AIHub and FERMAT datasets, VEHME achieves state-of-the-art performance among open-source models and approaches the accuracy of proprietary systems, demonstrating its potential as a scalable and accessible tool for automated math assessment. Our training and experiment code is publicly available at our GitHub repository."
}