Discovering heuristics in a complex SAT solver with large language models

Researchers have developed AutoModSAT, a framework that leverages large language models to automatically discover and optimize heuristics in SAT solvers, achieving 40% performance improvements over baseline solvers. The approach combines modular solver design with LLM-guided function generation and evolutionary algorithms, demonstrating significant practical gains across diverse datasets.

AutoModSAT represents a meaningful intersection of machine learning methodology and computational complexity optimization. Rather than relying on manual parameter tuning or constrained search spaces, the framework uses LLMs to generate novel heuristic functions, addressing a long-standing bottleneck in SAT solver development. The Satisfiability problem remains central to numerous real-world applications, from circuit verification to cryptanalysis, making solver efficiency directly relevant to industrial performance.

The significance lies in how the research bypasses traditional constraints. Previous automatic configuration frameworks operated within manually-defined parameter spaces, limiting their optimization potential. By enabling LLMs to generate new code modules and heuristics, AutoModSAT achieves substantially higher performance gains—40% over baseline and 30% over state-of-the-art approaches. The unsupervised prompt optimization technique diversifies generated functions, preventing convergence on suboptimal patterns.

For practitioners and researchers, these results validate LLM-guided discovery as a viable approach for complex systems optimization beyond traditional machine learning domains. The framework's efficiency improvements directly translate to faster computation times for industries relying on SAT solvers, including cybersecurity, hardware verification, and cryptographic analysis. The evolutionary algorithm component ensures solutions remain robust across varied problem instances rather than overfitting to specific datasets.

Future developments should focus on generalizing this approach to other NP-complete problems and understanding which heuristics LLMs naturally discover. Understanding why certain generated functions outperform human-designed ones could yield insights into algorithmic design principles and potentially unlock optimization techniques in other complex computational domains.

• →AutoModSAT achieves 40% performance improvement over baseline SAT solvers using LLM-generated heuristics
• →The framework bypasses manual parameter constraints by enabling LLMs to automatically discover novel optimization functions
• →Results demonstrate consistent speedups across diverse datasets compared to state-of-the-art parameter-tuned solvers
• →Unsupervised prompt optimization and evolutionary algorithms ensure robust, generalizable heuristic discovery
• →LLM-guided optimization methodology could extend beyond SAT solving to other computationally complex problems