The field of optimization and machine learning is rapidly advancing, with a focus on improving the accuracy and efficiency of algorithms. Recent developments have led to the creation of new methods, such as adaptive contrastive approaches and hybrid learning-to-optimize frameworks, which have shown significant improvements in performance. Additionally, there is a growing interest in the development of exact solution algorithms for complex problems, such as bi-level optimization and mixed-integer linear programming. These advancements have the potential to impact a wide range of applications, from electric vehicle charging infrastructure to cyber-physical systems. Noteworthy papers include the proposal of ADALOC, a key-based model usage control method that enables adaptable model updates, and the development of an exact solution algorithm for large-scale electric vehicle charging station placement problems. Furthermore, the integration of large language models and zero-knowledge proof techniques has shown promise in optimizing investment decisions and verifying the correctness of AI model inference. Overall, the field is moving towards more efficient, accurate, and secure methods for optimization and machine learning.