The recent advancements in data compression and model efficiency have significantly shaped the trajectory of research in the field. Innovations in stateless compression frameworks, leveraging polynomial representations, have demonstrated the potential for scalable and interpretable data reduction without auxiliary metadata, making them suitable for streaming or infinite datasets. Learned data compression, particularly in the context of integer key compression, has shown promising results, outperforming traditional methods and highlighting the integration potential within database management systems. Novel logarithmic positional encoding approaches have introduced a paradigm shift by transforming data into deeply compressed numerical substrata, offering unprecedented compression ratios. Meanwhile, pruning techniques for transformer-based models, both in autoregressive and time series forecasting contexts, have been refined to enhance computational efficiency without compromising performance, with training-free methods and compensation algorithms emerging as notable advancements. These developments collectively underscore a shift towards more efficient, scalable, and interpretable solutions in data compression and model optimization.