Recent advances in Multimodal Large Language Models (MLLMs) have achieved remarkable progress in general domains and demonstrated promise in multimodal mathematical reasoning. However, applying MLLMs to geometry problem solving (GPS) remains challenging due to lack of accurate step-by-step solution data and severe hallucinations during reasoning. In this paper, we propose GeoGen, a pipeline that can automatically generates step-wise reasoning paths for geometry diagrams. By leveraging the precise symbolic reasoning, \textbf{GeoGen} produces large-scale, high-quality question-answer pairs. To further enhance the logical reasoning ability of MLLMs, we train \textbf{GeoLogic}, a Large Language Model (LLM) using synthetic data generated by GeoGen. Serving as a bridge between natural language and symbolic systems, GeoLogic enables symbolic tools to help verifying MLLM outputs, making the reasoning process more rigorous and alleviating hallucinations. Experimental results show that our approach consistently improves the performance of MLLMs, achieving remarkable results on benchmarks for geometric reasoning tasks. This improvement stems from our integration of the strengths of LLMs and symbolic systems, which enables a more reliable and interpretable approach for the GPS task. Codes are available atthis https URL.
View on arXiv@article{pan2025_2504.12773,
title={ Enhancing the Geometric Problem-Solving Ability of Multimodal LLMs via Symbolic-Neural Integration },
author={ Yicheng Pan and Zhenrong Zhang and Pengfei Hu and Jiefeng Ma and Jun Du and Jianshu Zhang and Quan Liu and Jianqing Gao and Feng Ma },
journal={arXiv preprint arXiv:2504.12773},
year={ 2025 }
}