This February 2026 research paper introduces Bidaw, a novel system designed to optimize the performance of interactive Large Language Model (LLM) serving. The authors address inefficiencies in existing key–value (KV) caching methods, where the separation of computational engines and storage layers leads to high latency and redundant data processing. Bidaw implements bidirectional awareness, allowing the compute engine to schedule requests based on storage speeds while the storage system uses model output lengths to predict future data needs. Additionally, the system utilizes storage-efficient tensor caching to reduce memory footprints without sacrificing accuracy. Experimental results demonstrate that this approach significantly lowers response times and increases throughput compared to current state-of-the-art solutions. Ultimately, Bidaw bridges the gap between theoretical caching limits and practical local deployments for multi-round human-AI conversations. Source: February 24–26, 2026 Bidaw: Enhancing Key-Value Caching for Interactive LLM Serving via Bidirectional Computation–Storage Awareness Tsinghua University, China University of Geosciences Beijing, China Telecom Omni-channel Operation Center Shipeng Hu, Guangyan Zhang, Yuqi Zhou, Yaya Wei, Ziyan Zhong, Jike Chen https://www.usenix.org/conference/fast26/presentation/hu-shipeng