MaxSAT-Based Feedback Improves Logical Consistency in Vision-Language Models for Sudoku

Jul 15, 2026

A new neuro-symbolic approach integrates a Maximum Satisfiability (MaxSAT) oracle with Vision-Language Models (VLMs) to improve their performance on Sudoku puzzles. The MaxSAT solver identifies consistent subsets of VLM-generated assignments and provides structured feedback, guiding the models to refine their solutions. Experiments across multiple VLMs show that this method enhances logical consistency and increases the number of correctly solved Sudoku instances.

Why it matters: This work shows that symbolic optimization can significantly improve the reliability of vision-language models on structured reasoning tasks.

Full story at: arXiv AI/ML ↗