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[2505.23381] AutoGPS: Automated Geometry Problem Solving via Multimodal Formalization and Deductive Reasoning
Summary
AutoGPS introduces a neuro-symbolic framework for solving geometry problems, enhancing reliability and interpretability through multimodal formalization and deductive reasoning.
Why It Matters
This research addresses significant challenges in AI geometry problem solving, combining neural and symbolic methods to improve accuracy and human interpretability. As AI continues to evolve, frameworks like AutoGPS could enhance educational tools and automated reasoning applications, making complex problem-solving more accessible.
Key Takeaways
- AutoGPS integrates neural and symbolic approaches for enhanced problem-solving.
- The framework achieves 99% logical coherence in stepwise reasoning.
- Experimental results show state-of-the-art performance on benchmark datasets.
- Human interpretability is a key focus, making solutions more accessible.
- The system uses a Multimodal Problem Formalizer and Deductive Symbolic Reasoner.
Computer Science > Artificial Intelligence arXiv:2505.23381 (cs) [Submitted on 29 May 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:AutoGPS: Automated Geometry Problem Solving via Multimodal Formalization and Deductive Reasoning Authors:Bowen Ping, Minnan Luo, Zhuohang Dang, Chenxi Wang, Chengyou Jia View a PDF of the paper titled AutoGPS: Automated Geometry Problem Solving via Multimodal Formalization and Deductive Reasoning, by Bowen Ping and 3 other authors View PDF Abstract:Geometry problem solving presents distinctive challenges in artificial intelligence, requiring exceptional multimodal comprehension and rigorous mathematical reasoning capabilities. Existing approaches typically fall into two categories: neural-based and symbolic-based methods, both of which exhibit limitations in reliability and interpretability. To address this challenge, we propose AutoGPS, a neuro-symbolic collaborative framework that solves geometry problems with concise, reliable, and human-interpretable reasoning processes. Specifically, AutoGPS employs a Multimodal Problem Formalizer (MPF) and a Deductive Symbolic Reasoner (DSR). The MPF utilizes neural cross-modal comprehension to translate geometry problems into structured formal language representations, with feedback from DSR collaboratively. The DSR takes the formalization as input and formulates geometry problem solving as a hypergraph expansion task, executing mathematically rigorous and reliable derivation to produce ...