The field of language modeling and memory optimization is witnessing significant advancements, driven by the need for more efficient and scalable solutions. Recent developments focus on improving the performance of large language models (LLMs) while reducing their memory footprint and computational requirements. Researchers are exploring innovative techniques such as token compression, cache optimization, and semantic coherence enforcement to achieve these goals. Notably, papers like G-KV and STC propose novel methods for KV cache eviction and token compression, demonstrating substantial improvements in efficiency and accuracy. Furthermore, works like AlignSAE and OSAE introduce new approaches to feature alignment and sparse autoencoders, enhancing interpretability and consistency in LLMs. Overall, the field is moving towards more efficient, adaptive, and interpretable language modeling solutions. Noteworthy papers include: G-KV, which employs a global scoring mechanism for KV cache eviction, and STC, which introduces a hierarchical framework for token compression. Additionally, papers like AdmTree and Reconstructing KV Caches with Cross-layer Fusion propose novel frameworks for context compression and cache optimization, respectively.