FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation
Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remains largely underexplored, primarily due to the scarcity of annotated samples. Existing diagram generation approaches struggle with analytic geometry: template methods cannot handle constraint-driven layouts, and generative models lack the geometric precision to render annotated conic curves correctly. We present FormalAnalyticGeo, a scalable framework for fully automatic generation of multimodal analytic geometry problems. Leveraging the rigor
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- PossiblePossibly related (embedding) · 51%ghbalf/freecad-ai →
- PossiblePossibly related (embedding) · 49%sileod/reasoning-core →
- PossiblePossibly related (embedding) · 48%Wing900/ManimCat →
- LinkedLinked via arxiv author · 85%Ruoran Xu →
“FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation”
- LinkedLinked via arxiv author · 85%Wending Gao →
“FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation”
- LinkedLinked via arxiv author · 85%Qiufeng Wang →
“FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation”