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This paper proposes a neuro-symbolic approach that integrates a MaxSAT oracle as a consistency validator to guide Vision-Language Models (VLMs) in solving Sudoku puzzles, improving logical consistency and the number of solved instances.
This paper introduces satisfiable drift, a failure mode where multi-turn reasoning systems silently violate prior commitments while maintaining internal logical consistency, dominating contradictions. The authors present DRIFT-Bench, a benchmark of 816 problems, and find that after repair, 98-100% of residual errors are drift errors.