Heuristics are crucial in SAT solvers, but no heuristic rules are suitable for all SAT problems. Therefore, it is helpful to refine specific heuristics for specific problems. In this context, we present AutoSAT, a novel framework for automatically optimizing heuristics in SAT solvers. AutoSAT is based on Large Language Models (LLMs) which is able to autonomously generate codes, conduct evaluation, and then utilize feedback to further optimize heuristics, thereby reducing human intervention and enhancing solver capabilities. AutoSAT operates on a plug-and-play basis, eliminating the need for extensive enterprise and model training, and fosters a Multi-Agent-based collaborative process with fault tolerance to ensure robust heuristic optimization. We implement AutoSAT on a lightweight Conflict-Driven Clause Learning (CDCL) solver EasySAT (the volume of EasySAT is about one-fiftieth of the State-of-the-Art hybrid solver Kissat) and extensive experiments on seven datasets demonstrate its superior performance. Out of the seven testing datasets, AutoSAT shows a superior performance to Kissat in two datasets and displays an overall similar performance in three datasets. Some heuristics generated by AutoSAT are even counter-intuitive but are very effective.
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