Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning

• URL: http://arxiv.org/abs/2410.17885v4
• Date: Fri, 30 May 2025 03:15:49 GMT
• Title: Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning
• Authors: Linger Deng, Linghao Zhu, Yuliang Liu, Yu Wang, Qunyi Xie, Jingjing Wu, Gang Zhang, Yingying Zhu, Xiang Bai,
• Abstract summary: We introduce a two-stage Theorem-d Reverse Chain-of-Thought Reasoning Synthesis (TRCoT) framework.<n>The first stage, TR-Engine, synthesizes theorem-grounded geometric diagrams with structured descriptions and properties.<n>The second stage, TR-Reasoner, employs reverse reasoning to iteratively refine question-answer pairs by cross-validating geometric properties and description fragments.
• Score: 53.13514542825493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Multimodal Models (LMMs) face limitations in geometric reasoning due to insufficient Chain of Thought (CoT) image-text training data. While existing approaches leverage template-based or LLM-assisted methods for geometric CoT data creation, they often face challenges in achieving both diversity and precision. To bridge this gap, we introduce a two-stage Theorem-Validated Reverse Chain-of-Thought Reasoning Synthesis (TR-CoT) framework. The first stage, TR-Engine, synthesizes theorem-grounded geometric diagrams with structured descriptions and properties. The second stage, TR-Reasoner, employs reverse reasoning to iteratively refine question-answer pairs by cross-validating geometric properties and description fragments. Our approach expands theorem-type coverage, corrects long-standing misunderstandings, and enhances geometric reasoning. Fine-grained CoT improves theorem understanding and increases logical consistency by 24.5%. Our best models surpass the baselines in MathVista and GeoQA by 10.1% and 4.7%, outperforming advanced closed-source models like GPT-4o.

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