The field of natural language processing is moving towards developing more efficient and effective methods for handling long-context language modeling. Researchers are exploring innovative approaches to reduce computational and memory burdens, such as context compression frameworks and cache compression methods. These advancements have the potential to significantly improve the performance of language models on tasks that require capturing rich dependencies across extended discourse. Noteworthy papers in this area include: CCF, which proposes a novel context compression framework that achieves competitive perplexity under high compression ratios, and LAVa, which introduces a unified framework for cache compression with dynamic budget allocation, demonstrating superiority on multiple benchmarks. Additionally, papers like TinyServe and CurDKV showcase efficient serving systems and value-centric KV compression methods, respectively, which can reduce decoding cost and improve accuracy. These developments highlight the progress being made in addressing the challenges of long-context language modeling and have important implications for applications such as automated essay scoring and patent language model pretraining.