Abstraction Based

Abstraction-based methods aim to simplify complex systems by creating higher-level representations that capture essential features while ignoring irrelevant details. Current research focuses on developing efficient algorithms for generating these abstractions, particularly using neural networks and code refactoring techniques, to improve the performance of tasks like program synthesis, neural network security analysis, and controller design. This work is significant because it addresses challenges in scalability, explainability, and verification of complex systems, leading to more robust and reliable applications in diverse fields such as AI safety, software engineering, and automated control.