BiNSGPS: Geometry Problem Solving via Bidirectional Neuro-Symbolic Interaction
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 rec...
| Authors | Qi Wang, Peijie Wang, Fei Yin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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