The field of artificial intelligence is witnessing significant advancements in attention mechanisms and representation learning. Recent studies have focused on improving the efficiency and effectiveness of attention mechanisms, such as linear attention and softmax attention, and exploring new approaches like stochastic activations and local linear attention. Additionally, researchers are investigating novel methods for learning representations, including mask-based pretraining, differentiable structure learning, and discrete variational autoencoding. These innovations have the potential to enhance the performance of deep learning models in various applications, including language modeling, image reconstruction, and reinforcement learning. Noteworthy papers in this area include: Understanding and Enhancing Mask-Based Pretraining towards Universal Representations, which proposes a new pretraining scheme named Randomly Random Mask AutoEncoding (R$^2$MAE) that outperforms standard masking schemes. Differentiable Structure Learning for General Binary Data, which introduces a framework for capturing arbitrary dependencies among discrete variables. Beyond Softmax: A Natural Parameterization for Categorical Random Variables, which replaces the softmax function with the catnat function, offering significant advantages to gradient descent. Local Linear Attention: An Optimal Interpolation of Linear and Softmax Attention For Test-Time Regression, which proposes a novel attention mechanism derived from nonparametric statistics.