The field of neural networks is moving towards developing more robust and interpretable models. Recent research has focused on improving the training dynamics of deep neural networks, with techniques such as stress-aware learning and structured transformations showing promise in achieving more stable and generalizable models. Additionally, there is a growing interest in developing models that are inherently more interpretable, with research demonstrating that such models can be more robust to irrelevant perturbations in the data. Noteworthy papers in this area include: Stress-Aware Resilient Neural Training, which introduces a novel training paradigm that dynamically adjusts optimization behavior based on internal stress signals. Structured Transformations for Stable and Interpretable Neural Computation, which proposes a reformulation of layer-level transformations that promotes more disciplined signal propagation and improved training dynamics. Are Inherently Interpretable Models More Robust, which investigates whether inherently interpretable deep models are more robust to irrelevant perturbations in the data. Task complexity shapes internal representations and robustness in neural networks, which introduces a suite of probes to quantify how task difficulty influences the topology and robustness of representations in multilayer perceptrons.