cs.AI updates on arXiv.org 08月21日
Explaining Hitori Puzzles: Neurosymbolic Proof Staging for Sequential Decisions
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本文提出一种结合决策过程和大型语言模型(LLMs)的神经符号方法,用于解释复杂决策序列,并通过Hitori谜题的解决方案进行演示。该方法结合了SAT求解器和LLMs的优势,并展示了一种辅助人类解决Hitori谜题的工具及其有效性。

arXiv:2508.14294v1 Announce Type: new Abstract: We propose a neurosymbolic approach to the explanation of complex sequences of decisions that combines the strengths of decision procedures and Large Language Models (LLMs). We demonstrate this approach by producing explanations for the solutions of Hitori puzzles. The rules of Hitori include local constraints that are effectively explained by short resolution proofs. However, they also include a connectivity constraint that is more suitable for visual explanations. Hence, Hitori provides an excellent testing ground for a flexible combination of SAT solvers and LLMs. We have implemented a tool that assists humans in solving Hitori puzzles, and we present experimental evidence of its effectiveness.

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神经符号方法 Hitori谜题 决策序列解释 LLMs SAT求解器
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