Dense Neuro-Symbolic Reasoning in a Unified Geometry State
cs.AI
Submitted: 2026-10-03
Updated: 2026-10-03
Comments: NeurIPS@Math-AI
License: http://creativecommons.org/licenses/by/4.0/
The gist: Geometry reasoning is naturally stateful: solving a problem repeatedly alternates between structural proposals and exact deductions.
Abstract
Geometry reasoning is naturally stateful: solving a problem repeatedly alternates between structural proposals and exact deductions. We formulate this process as dense neural-symbolic coupling, in which neural guidance and symbolic execution share a typed state and communicate through executable actions at every search step. Neural proposals contribute theorem instances, constructions, and algebraic bridges; the symbolic runtime applies registered rules, propagates exact constraints, and records provenance. A nested controller allocates computation first between neural and symbolic proposal sources and then among admitted actions. We instantiate the framework in OmniGeo, a single solver for plane, analytic, and solid geometry. With Claude Sonnet 4.6, OmniGeo reaches 94.2%, 88.5%, and 89.8% on FormalGeo7K, Conic10K, and SolidFGeo, respectively (90.8% macro average), and solves 21/30 IMO-AG-30 problems.
Sources
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation
- FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
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