The field of deep learning is witnessing significant advancements in transformer architectures and sequence modeling. Researchers are exploring innovative approaches to improve the efficiency and robustness of vision transformers, including patch pruning strategies and attention mechanisms. The use of robust statistical measures and overlapping patch embeddings is showing promise in maintaining classification accuracy while reducing computational complexity. Furthermore, the development of novel recurrent architectures, such as those incorporating Hebbian memory and sparse attention mechanisms, is enhancing the interpretability and transparency of deep learning models. Noteworthy papers in this area include: Hebbian Memory-Augmented Recurrent Networks, which introduces a novel recurrent architecture with explicit, differentiable memory matrix and Hebbian plasticity, offering significant enhancements in interpretability. SequenceLayers, which provides a neural network layer API and library for sequence modeling, enabling easy creation of sequence models that can be executed both layer-by-layer and step-by-step.