The field of computer vision and medical imaging is moving towards developing more robust and generalizable models. Recent research has focused on improving model performance in the presence of adversarial attacks, distribution shifts, and limited training data. Techniques such as multi-task learning, hierarchical Bayesian models, and fusion of heterogeneous models have shown promise in addressing these challenges. Additionally, there is a growing interest in developing efficient and interpretable models for resource-constrained applications. Notable papers in this area include: VISAT, which introduces a benchmarking suite for evaluating model robustness in traffic sign recognition; CoMViT, which presents a compact and generalizable Vision Transformer architecture for medical image analysis; and PLUTO-4, which introduces a new generation of pathology foundation models that achieve state-of-the-art performance on various histopathology tasks. These advancements have the potential to transform real-world applications in autonomous driving, healthcare, and other fields.