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Neuro-Symbolic Framework Solves Zebra Puzzles via LLM Parsing and Exact Cover
This paper introduces a neuro-symbolic framework that combines LLM-based semantic parsing with deterministic exact cover solving for Zebra-style logic puzzles. The LLM converts natural language constraints into structured tool calls, and a symbolic solver provides feedback to refine encodings. Experiments across multiple model scales and families show consistent improvements over plain CoT and other neuro-symbolic baselines.
Why it matters: Offers a concrete hybrid approach with empirical results, addressing a known LLM weakness in logical reasoning.
LLM reasoningneuro-symbolic AIconstraint solving