MaxSAT-Based Feedback for Guiding Vision-Language Models in Sudoku
Vision--Language Models (VLMs) have recently demonstrated promising performance on structured visual reasoning tasks, including grid-based puzzles. However, despite strong perceptual capabilities, these models lack explicit mechanisms for enforcing logical consistency and frequently generate assignments that violate underlying constraints. In this paper, we propose a neuro-symbolic approach that integrates formal constraint reasoning into the VLM solving process via a Maximum Satisfiability (MaxSAT) oracle. Rather than computing solutions directly, the symbolic component acts as a consistency validator and refinement engine. Candidate placements generated by the VLM are encoded as soft cl...
| Authors | Pedro Orvalho, Guillem Alenyà, Felip Manyà |
|---|---|
| Primary category | cs.AI |
| Published | 14 July 2026 |
| Last updated at source | 17 July 2026 |
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