The field of large language models (LLMs) is moving towards more efficient inference and memory management techniques. Researchers are exploring innovative methods to reduce the computational costs and memory requirements of LLMs, particularly in the context of long-context input. One direction of research focuses on optimizing key-value cache management, which is crucial for efficient LLM inference. Another area of interest is the development of novel attention mechanisms and decoding strategies that can improve model performance while reducing computational overhead. Noteworthy papers in this area include Efficient Long-Context LLM Inference via KV Cache Clustering, which proposes a framework for online KV cache clustering, and eLLM: Elastic Memory Management Framework for Efficient LLM Serving, which introduces a unified and flexible memory pool for efficient memory management. Additionally, Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache presents a training-free framework for approximating long-context-insensitive dimensions, and Combining Constrained and Unconstrained Decoding via Boosting: BoostCD and Its Application to Information Extraction proposes a method for combining constrained and unconstrained decoding to improve model performance.