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From Natural Language to Exact Cover: A Neuro-Symbolic Approach to Zebra Puzzles

Paper recorded by Signals 4 on 2026-11-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.AI · 人工智能 · first seen 2026-11-03

Abstract

Chain-of-Thought (CoT) generation has substantially improved the performance of Large Language Models (LLMs) on complex reasoning tasks, including code generation, data analysis, and exam-style question answering. Despite these advances, purely neural LLMs continue to struggle with elementary logical reasoning problems and lack the determinism, soundness, and reliability characteristic of symbolic

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#23 most recent of 920 cs.AI papers we have recorded · ↑ newer: Pixel-UMI: Learning Environment-Aware Whole-Body Manipulation from Sen · ↓ older: WebHarbor: Docking Real Websites for Evolving GUI Agent Environments
Cite this page: From Natural Language to Exact Cover: A Neuro-Symbolic Approach to Zebra Puzzles: the #23 most recent of 920 cs.AI papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-natural-language-to-exact-cover-a-neuro-symbolic-approach-to-zebra-puzzles.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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