Counterexample Guided Inductive Synthesis

Counterexample-guided inductive synthesis (CEGIS) is a powerful technique for automatically generating programs or models that satisfy given specifications. Current research focuses on applying CEGIS to diverse domains, including verification of dynamical systems (using neural networks and SMT solvers), audio synthesis (leveraging differentiable models like modular synthesizers and FM synthesis), and symbolic reasoning (integrating large language models with satisfiability solvers). This approach offers significant advantages in automating complex tasks, enabling faster verification, improved synthesis of interpretable models, and the creation of provably correct solutions in safety-critical applications.