AI RESEARCH
GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning
arXiv CS.CV
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ArXi:2605.16371v1 Announce Type: new Large Multimodal Models (LMMs) often struggle with geometric reasoning due to visual hallucinations and a lack of mathematically precise Chain-of-Thought (CoT) data. To address this, we propose the GeoSym Engine, an automated and scalable neuro-symbolic framework. By leveraging a type-conditional grammar and an analytic SymGT Solver, it derives exact symbolic ground truths and seamlessly integrates with a robust rendering pipeline to produce high-precision geometric diagrams.