Satisfiability Solver

Satisfiability solvers (SAT solvers) are algorithms designed to determine whether a Boolean formula can be satisfied, a fundamental problem in computer science with broad applications. Current research focuses on improving solver efficiency through techniques like integrating machine learning models (e.g., graph neural networks, large language models) to optimize heuristics, predict solver performance, or even generate synthetic SAT instances for benchmarking. These advancements aim to enhance the speed and scalability of SAT solvers, impacting diverse fields such as automated reasoning, circuit design verification, and artificial intelligence planning.