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Dense Neuro-Symbolic Reasoning in a Unified Geometry State
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Ruoran Xu, Wending Gao, Haoyu Cheng, Xiaoqiang Kang, Qiufeng Wang

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ResearcharXiv cs.AI

Dense Neuro-Symbolic Reasoning in a Unified Geometry State

arXiv:2610.04280v1 Announce Type: new 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.

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This story was published by arXiv cs.AI and written by Ruoran Xu, Wending Gao, Haoyu Cheng, Xiaoqiang Kang, Qiufeng Wang. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on arxiv.org

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