The recent advancements in transformer models have shown significant progress in enhancing both their computational efficiency and their ability to learn complex tasks. A notable trend is the exploration of alternative architectures that minimize computational complexity while maintaining or even improving performance. This includes the development of models that replace fully-connected layers with memory-based operations, thereby reducing the overall computational load. Additionally, there is a growing interest in understanding the mechanisms through which transformers learn and generalize, particularly in the context of in-context learning and nonparametric estimators like the one-nearest neighbor rule. Theoretical frameworks are being established to better understand these processes, and novel approaches are being proposed to optimize linear approximations of attention mechanisms. These innovations not only advance the theoretical understanding of transformers but also pave the way for more efficient and effective practical applications. Notably, the introduction of models like Meta Linear Attention and MemoryFormer represent significant strides in this direction, offering new paradigms for both research and application in the field of transformer-based models.