GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving
On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models
Abstract (arXiv)
Geometry problem-solving remains a significant challenge for Large Multimodal Models (LMMs), requiring not only global shape recognition but also attention to intricate local relationships related to geometric theory. To address this, we propose GeoFocus, a novel framework comprising two core modules. 1) Critical Local Perceptor, which automatically identifies and emphasizes critical local structure (e.g., angles, parallel lines, comparative distances) through thirteen theory-based perception templates, boosting critical local feature coverage by 61% compared to previous methods. 2) VertexLang, a compact topology formal language, encodes global figures through vertex coordinates and connectivity relations. By replacing bulky code-based encodings, VertexLang reduces global perception training time by 20% while improving topology recognition accuracy. When evaluated in Geo3K, GeoQA, and FormalGeo7K, GeoFocus achieves a 4.7% accuracy improvement over leading specialized models and demonstrates superior robustness in MATHVERSE under diverse visual conditions. Project Page -- https://github.com/dle666/GeoFocus
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 46 of its 62 references that are in the map. First posted on arXiv 9 Feb 2026.
Builds on
46 of 62 references.
Closest ancestors · 5
Same direction, newest first
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- 2024GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation17 Jun
- 2023G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model18 Dec
Other references · 41
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