The paper was titled the VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions.
The paper introduces VEHME, a vision-language model for automated grading of handwritten math solutions. It uses a two-phase training pipeline (supervised fine-tuning + reinforcement learning) and an Expression-Aware Visual Prompting Module to handle complex, unstructured math expressions. Evaluated on AIHub and FERMAT, VEHME achieves state-of-the-art accuracy among open-source models, and approaching proprietary models performance, making it a scalable tool for math assessment.