The field of neural networks is moving towards a better understanding of the underlying mechanisms and behaviors of these complex systems. Recent research has focused on improving the interpretability of neural networks, with a particular emphasis on explaining the decisions made by these models. Techniques such as abstraction and refinement are being developed to provide provably sufficient explanations of neural network predictions, while also improving the efficiency of the verification process. Additionally, there is a growing interest in understanding the stability of neural networks, including the study of phenomena such as neural collapse and the stability of the Jacobian matrix. Noteworthy papers in this area include those that propose novel frameworks for evaluating neuron explanations and those that investigate the interior-point vanishing problem in semidefinite relaxations for neural network verification. For example, one paper introduces a unified framework for evaluating neuron explanations, allowing for a comparison of existing evaluation metrics and the proposal of reliable evaluation guidelines. Another paper addresses the interior-point vanishing problem, providing practical solutions to improve the applicability of semidefinite programming-based verification to deeper neural networks.