The field of artificial intelligence is witnessing significant advancements in representation learning and generative models. Researchers are actively exploring new methods to improve the efficiency and effectiveness of these models, enabling them to learn complex patterns and relationships in data. A key direction in this area is the development of theoretical frameworks that provide a deeper understanding of the underlying mechanisms and principles governing these models. This has led to the discovery of innovative techniques for analyzing and optimizing their performance. Notably, there is a growing interest in understanding the role of context in representation learning, as well as the development of more efficient and scalable algorithms for generative modeling. Overall, these advancements have the potential to drive significant progress in areas such as natural language processing, computer vision, and protein engineering. Noteworthy papers in this area include: Contextures: Representations from Contexts, which establishes a theoretical framework for understanding representation learning. Secrets of GFlowNets' Learning Behavior: A Theoretical Study, which provides a rigorous analysis of the learning behavior of Generative Flow Networks. Guide your favorite protein sequence generative model, which presents a framework for conditioning protein generative models on auxiliary information.