VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions

• URL: http://arxiv.org/abs/2510.22798v1
• Date: Sun, 26 Oct 2025 19:03:27 GMT
• Title: VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions
• Authors: Thu Phuong Nguyen, Duc M. Nguyen, Hyotaek Jeon, Hyunwook Lee, Hyunmin Song, Sungahn Ko, Taehwan Kim,
• Abstract summary: We introduce VEHME-a Vision-Language Model for evaluating handwritten math responses with high accuracy and interpretable reasoning traces.<n> 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.<n> VEHME achieves state-of-the-art performance among open-source models and approaches the accuracy of proprietary systems.
• Score: 11.210768330027674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatically assessing handwritten mathematical solutions is an important problem in educational technology with practical applications, but it 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 Vision-Language Model for Evaluating Handwritten Mathematics Expressions-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.

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