The recent developments in the research area indicate a strong focus on integrating formal methods and verification techniques into machine learning and cybersecurity practices. There is a notable trend towards enhancing the reliability and security of learning-based systems, particularly in critical applications such as congestion control and industrial control systems. Innovations in training neural networks are being driven by the need for both robustness and verifiability, with methods that ensure consistency in neuron behavior and facilitate easier formal verification. Additionally, the field is witnessing advancements in the complexity analysis of neural network training, particularly in the discrete parameter space, which has significant implications for both theoretical understanding and practical applications. Security engineering is also being refined with a focus on noninterference and dynamic protocol attestation, aiming to provide provable security guarantees in complex, decentralized systems. These developments collectively push the boundaries of what is possible in ensuring the safety, reliability, and security of modern computational systems.