This research demonstrates how Large Language Models can analyze problem encoding patterns to improve SAT solver performance, providing a new approach to algorithm optimization.

• Extracts structural patterns from Python encoding code that traditional methods miss
• Creates higher-quality starting points for Conflict-Driven Clause Learning (CDCL) solvers
• Improves local search preprocessing effectiveness
• Represents an innovative bridge between AI language models and computational engineering

For engineering teams, this approach offers significant potential to enhance problem-solving efficiency in constraint satisfaction applications and formal verification processes.

Extracting Problem Structure with LLMs for Optimized SAT Local Search