The field of data compression and efficiency is moving towards innovative solutions that balance expressivity and computational burden. Recent developments have focused on applying theoretical frameworks, such as control theory and graph-based models, to improve compression ratios and speeds. Notably, the use of rank structure and flow-of-ranks analysis has led to significant advancements in compressing time series data and understanding transformer models. Furthermore, the development of new compression techniques and frameworks, such as OpenZL, has shown promise in achieving superior compression ratios and speeds while minimizing deployment lag and security risks. Noteworthy papers include: Understanding Transformers for Time Series, which introduces the concept of flow-of-ranks and achieves a reduction of 65% in inference time and 81% in memory without loss of accuracy. OpenZL: A Graph-Based Model for Compression, which proposes a new theoretical framework for representing compression as a directed acyclic graph of modular codecs and achieves superior compression ratios and speeds compared to state-of-the-art general-purpose compressors.