Cache Mechanism for Agent RAG Systems

• URL: http://arxiv.org/abs/2511.02919v1
• Date: Tue, 04 Nov 2025 19:02:29 GMT
• Title: Cache Mechanism for Agent RAG Systems
• Abstract summary: ARC (Agent RAG Cache Mechanism) is a novel, annotation-free caching framework that dynamically manages small, high-value corpora for each agent.<n>Our results demonstrate that ARC can drastically enhance efficiency and effectiveness in RAG-powered LLM agents.
• Score: 32.587302983158274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Large Language Model (LLM)-based agents have been propelled by Retrieval-Augmented Generation (RAG), which grants the models access to vast external knowledge bases. Despite RAG's success in improving agent performance, agent-level cache management, particularly constructing, maintaining, and updating a compact, relevant corpus dynamically tailored to each agent's need, remains underexplored. Therefore, we introduce ARC (Agent RAG Cache Mechanism), a novel, annotation-free caching framework that dynamically manages small, high-value corpora for each agent. By synthesizing historical query distribution patterns with the intrinsic geometry of cached items in the embedding space, ARC automatically maintains a high-relevance cache. With comprehensive experiments on three retrieval datasets, our experimental results demonstrate that ARC reduces storage requirements to 0.015% of the original corpus while offering up to 79.8% has-answer rate and reducing average retrieval latency by 80%. Our results demonstrate that ARC can drastically enhance efficiency and effectiveness in RAG-powered LLM agents.

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