Draw2Think recasts geometric reasoning as agentic interaction with a constraint engine, achieving 95.9% predicate-level construction fidelity and up to 16.4% accuracy gains on solid geometry tasks.
NesyGeo: A neuro-symbolic framework for multimodal geometric reasoning data generation
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Reinforcement learning with three causal constraints enables multimodal models to internalize diagram-reasoning links in geometry, unlike SFT which only mimics surface format and harms performance.
A neuro-symbolic engine generates GeoSym127K, a 127K-question dataset with symbolic ground truths and verified CoT pairs, yielding +22.21% gains on MathVerse Vision-Only after SFT on Qwen3-VL-8B.
citing papers explorer
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Draw2Think: Harnessing Geometry Reasoning through Constraint Engine Interaction
Draw2Think recasts geometric reasoning as agentic interaction with a constraint engine, achieving 95.9% predicate-level construction fidelity and up to 16.4% accuracy gains on solid geometry tasks.
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How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning
Reinforcement learning with three causal constraints enables multimodal models to internalize diagram-reasoning links in geometry, unlike SFT which only mimics surface format and harms performance.
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GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning
A neuro-symbolic engine generates GeoSym127K, a 127K-question dataset with symbolic ground truths and verified CoT pairs, yielding +22.21% gains on MathVerse Vision-Only after SFT on Qwen3-VL-8B.
- Hilbert-Geo: Solving Solid Geometric Problems by Neural-Symbolic Reasoning