Efficient Verification

Efficient verification of complex systems, particularly AI models like large language models and neural networks, is crucial for ensuring reliability and safety in diverse applications. Current research focuses on developing scalable verification methods, including those leveraging formal methods, optimization techniques (e.g., integer linear programming, convex relaxations), and machine learning approaches (e.g., preference learning, generative models) to analyze model behavior and properties. These advancements are vital for building trust in AI systems deployed in safety-critical domains such as healthcare, autonomous vehicles, and power grids, enabling more robust and dependable AI-driven technologies.