The field of Large Language Models (LLMs) is moving towards more efficient serving methods, particularly for long-context scenarios. Researchers are focusing on developing innovative caching strategies and frameworks to mitigate the limitations of traditional methods, such as short effective context length, quadratic computational complexity, and high memory overhead.

Noteworthy papers in this area include: TokenLake, which proposes a unified segment-level prefix cache pool to achieve better cache load balance, deduplication, and defragmentation. ILRe, which introduces a novel context compression pipeline to reduce prefilling complexity and achieve performance comparable to or better than full context in long context scenarios. Strata, which presents a hierarchical context caching framework designed for efficient long context LLM serving, achieving up to 5x lower Time-To-First-Token compared to existing systems. SISO, which redefines efficiency for LLM serving with a semantic caching system that maximizes coverage with minimal memory and preserves high-value entries.