综述深度学习在几何题求解中的应用，梳理任务、方法与评估体系。
A Survey of Deep Learning for Geometry Problem Solving
- 系统整理几何题求解的各类任务与数据集
- 分析多模态大模型在几何推理中的表现与瓶颈
- 适合研究数学AI、教育技术与多模态推理的学者参考
几何问题求解是数学推理的重要组成部分，在教育、人工智能数学能力评估及多模态能力测试中具有重要意义。近年来，随着深度学习技术的快速发展，尤其是多模态大语言模型的兴起，该领域研究取得显著进展。本文综述了深度学习在几何问题求解中的应用，包括：(i) 全面总结几何求解的相关任务；(ii) 系统回顾相关深度学习方法；(iii) 详尽分析评估指标与评测方法；(iv) 对当前最先进性能、现存挑战及未来方向进行批判性讨论。旨在为深度学习在几何求解中的应用提供全面且实用的参考，推动该领域的持续发展。我们维护一个相关论文列表：https://github.com/majianz/dl4gps。
原文摘要 · Abstract (English)
Geometry problem solving, a crucial aspect of mathematical reasoning, is vital across various domains, including education, the assessment of AI's mathematical abilities, and multimodal capability evaluation. The recent surge in deep learning technologies, particularly the emergence of multimodal large language models, has significantly accelerated research in this area. This paper presents a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of state-of-the-art performance, existing challenges, and promising future directions. Our objective is to offer a comprehensive and practical reference of deep learning for geometry problem solving, thereby fostering further advancements in this field. We maintain a list of relevant papers: https://github.com/majianz/dl4gps.
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