Adaptive Contextual Caching for Mobile-Edge Large Language Model Service: A Proposed Framework

Mobile-edge large language model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although retrieval-augmented generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management still causes high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to more than 80% after only 11 training episodes, outperforming First In, First Out (FIFO), Least Recently Used (LRU), and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable and low-latency LLM services in resource-constrained edge environments.