综述大模型时代几何题求解进展，梳理关键技术和未来方向。
Towards Geometry Problem Solving in the Large Model Era: A Survey
- 从基准构建、图文解析到推理范式三方面系统梳理方法
- 指出当前研究碎片化问题，强调统一评估框架的必要性
- 适合关注教育AI与符号推理融合的研究者参考
几何题求解（GPS）是人工智能的重要前沿，在教育、计算机辅助设计和计算图形学中有深远应用。尽管意义重大，自动化求解仍面临空间理解与严格逻辑推理的双重挑战。大模型的进展使SAT级别问题取得显著突破，但该领域在方法、基准和评估框架上仍呈碎片化。本文从三个核心维度系统综述进展：(1) 基准构建，(2) 文本与图示解析，(3) 推理范式。进一步提出统一分析范式，评估现有局限，并识别新兴机遇，推动迈向人类级几何推理，包括自动基准生成与可解释的神经符号融合。
原文摘要 · Abstract (English)
Geometry problem solving (GPS) represents a critical frontier in artificial intelligence, with profound applications in education, computer-aided design, and computational graphics. Despite its significance, automating GPS remains challenging due to the dual demands of spatial understanding and rigorous logical reasoning. Recent advances in large models have enabled notable breakthroughs, particularly for SAT-level problems, yet the field remains fragmented across methodologies, benchmarks, and evaluation frameworks. This survey systematically synthesizes GPS advancements through three core dimensions: (1) benchmark construction, (2) textual and diagrammatic parsing, and (3) reasoning paradigms. We further propose a unified analytical paradigm, assess current limitations, and identify emerging opportunities to guide future research toward human-level geometric reasoning, including automated benchmark generation and interpretable neuro-symbolic integration.
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