aiqbench analysis
BiNSGPS addresses the brittleness of current neuro-symbolic systems by introducing a feedback loop between a multimodal large language model and a symbolic solver, allowing for dynamic correction of formal representations. This bidirectional architecture aims to resolve the inherent trade-off between the adaptability of neural methods and the reliability of symbolic logic in geometry problem solving. The approach represents a significant methodological shift from unidirectional pipelines toward more robust, self-correcting reasoning frameworks.
BiNSGPS: Geometry Problem Solving via Bidirectional Neuro-Symbolic Interaction
arXiv:2606.04648v1 Announce Type: new Abstract: Geometry problem solving poses distinct challenges in artificial intelligence. Existing approaches typically fall into two paradigms: symbolic methods, which exhibit limited adaptability, and neural methods, which are prone to hallucinations. Recent neuro-symbolic hybrids predominantly rely on a unidirectional pipeline where neural outputs are fed into solvers without feedback, making system brittle to early-stage errors. To break this unidirectional bottleneck, we propose BiNSGPS, a framework that establishes Bidirectional Neuro-Symbolic Interaction (BiNS) between a MLLM Adviser and a Symbolic Solver. MLLM Adviser actively incorporates feedback from the symbolic solver to dynamically rectify inconsistent formal representations or propose auxiliary hypotheses, resolving symbolic conflicts and facilitating complex deductions.
Abstract:Geometry problem solving poses distinct challenges in artificial intelligence. Existing approaches typically fall into two paradigms: symbolic methods, which exhibit limited adaptability, and neural methods, which are prone to hallucinations. Recent neuro-symbolic hybrids predominantly rely on a unidirectional pipeline where neural outputs are fed into solvers without feedback, making system brittle to early-stage errors. To break this unidirectional bottleneck, we propose BiNSGPS, a framework that establishes Bidirectional Neuro-Symbolic Interaction (BiNS) between a MLLM Adviser and a Symbolic Solver. MLLM Adviser actively incorporates feedback from the symbolic solver to dynamically rectify inconsistent formal representations or propose auxiliary hypotheses, resolving symbolic conflicts and facilitating complex deductions.
Artificial Intelligence (cs.AI)
arXiv:2606.04648 [cs.AI]
(or arXiv:2606.04648v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.04648 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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