The field of autoregressive modeling and data compression is moving towards more efficient and scalable solutions. Recent developments have focused on improving the performance of autoregressive models, particularly in the context of image and video compression. Researchers are exploring new architectures and techniques, such as hierarchical parallelism and progressive adaptation, to reduce computational costs and improve compression ratios. Additionally, there is a growing interest in developing methods that can preserve and quantify uncertainty in physical quantities of interest, which is crucial for scientific data compression and super-resolution. Noteworthy papers in this area include: Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation, which introduces a framework for efficient and practical autoregressive image compression. Learning to Expand Images for Efficient Visual Autoregressive Modeling, which proposes a novel generation paradigm that emulates the human visual system's center-outward perception pattern. Degradation-Aware Hierarchical Termination for Blind Quality Enhancement of Compressed Video, which develops a pretrained Degradation Representation Learning module and a hierarchical termination mechanism to improve blind quality enhancement of compressed video.