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[2509.21896] GenesisGeo: Technical Report
Summary
GenesisGeo presents a novel approach to geometric reasoning by introducing a large-scale dataset and a multi-task training paradigm that enhances visual and symbolic learning in AI models.
Why It Matters
This research addresses the limitations of existing neuro-symbolic systems in geometric reasoning by leveraging a multimodal dataset, thereby improving AI's ability to understand and solve complex geometry problems. The findings could significantly advance the field of artificial intelligence, particularly in visual language models and educational applications.
Key Takeaways
- GenesisGeo-1M dataset contains 1 million multimodal geometry problems.
- The proposed model achieves high performance on Olympiad geometry benchmarks.
- The research combines text-based and diagram-grounded proof generation.
- Visual grounding is emphasized to enhance symbolic deduction in AI.
- The work contributes to the development of neuro-symbolic geometry theorem provers.
Computer Science > Artificial Intelligence arXiv:2509.21896 (cs) [Submitted on 26 Sep 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:GenesisGeo: Technical Report Authors:Minfeng Zhu, Zi Wang, Sizhe Ji, Zhengtong Du, Shengqiang Tai, Junming Ke, Xiao Deng, Zanlang Yin, Xiuqi Huang, Heyu Wang, Wei Chen View a PDF of the paper titled GenesisGeo: Technical Report, by Minfeng Zhu and 10 other authors View PDF HTML (experimental) Abstract:Recent neuro-symbolic geometry theorem provers have made significant progress on Euclidean problems by coupling neural guidance with symbolic verification. However, most existing systems operate almost exclusively in a symbolic space, leaving diagram-based intuition largely unused during reasoning. For humans, geometric diagrams provide essential heuristics for identifying non-trivial auxiliary constructions. Meanwhile, visual language models (VLMs) still struggle with geometry due to the lack of high-quality data with geometric diagrams and reasoning supervision. In this paper, we introduce GenesisGeo-1M, a large-scale synthetic dataset for visual geometric reasoning that contains 1M multimodal geometry problems paired with machine-checkable proof traces. Building on this dataset, we formulate geometric learning as a multi-task training paradigm that jointly optimizes text-based proof generation and diagram-grounded proof generation, encouraging models to learn visual grounding and symbolic deduction. Extensive experiments show t...
