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GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation
arXiv – CS AI|Shichao Weng, Zhiqiang Wang, Yuhua Zhou, Rui Lu, Ting Liu, Zhiyang Teng, Xiaozhang Liu, Hanmeng Liu|
🤖AI Summary
Researchers introduce GeoSketch, a neural-symbolic AI framework that solves geometric problems through dynamic visual manipulation, including drawing auxiliary lines and applying transformations. The system combines perception, symbolic reasoning, and interactive sketch actions, achieving superior performance on geometric problem-solving benchmarks compared to static image processing methods.
Key Takeaways
- →GeoSketch enables dynamic geometric reasoning through an interactive perception-reasoning-action loop, unlike static image processing approaches.
- →The framework integrates three modules: perception for diagram abstraction, symbolic reasoning for theorem application, and sketch actions for visual manipulation.
- →Training involves supervised fine-tuning on 2,000 curated trajectories followed by reinforcement learning with symbolic rewards.
- →The GeoSketch Benchmark introduces 390 high-quality geometry problems requiring auxiliary construction or transformations for evaluation.
- →Experimental results show significant improvements in stepwise reasoning accuracy and problem-solving success over existing multimodal language models.
#ai#neural-symbolic#multimodal#geometric-reasoning#mllm#computer-vision#reinforcement-learning#benchmark#visuospatial#machine-learning
Read Original →via arXiv – CS AI
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