🤖 AI Summary
Current retrieval-augmented generation (RAG) systems typically employ fixed retrieval pipelines, which struggle to accommodate the diverse demands of tasks such as factual question answering, multi-hop reasoning, and scientific claim verification. This work proposes Experience-RAG, an agent-oriented, plug-and-play skill module situated between the agent and a pool of multiple retrievers. It dynamically selects the optimal retrieval strategy by querying an experience memory and returns structured evidence. The key innovation lies in encapsulating retrieval strategy selection as a reusable agent skill, integrating experience-driven policy scheduling with a multi-retriever collaboration mechanism to enable flexible and adaptive retrieval orchestration. Experiments demonstrate that the method achieves an nDCG@10 of 0.8924 on BeIR/nq, BeIR/hotpotqa, and BeIR/scifact, significantly outperforming fixed-retrieval baselines and matching the performance of state-of-the-art adaptive RAG routing approaches.
📝 Abstract
Retrieval-augmented generation systems often assume that one fixed retrieval pipeline is sufficient across heterogeneous tasks, yet factoid question answering, multi-hop reasoning, and scientific verification exhibit different retrieval preferences. We present Experience-RAG Skill, an agent-oriented pluggable retrieval orchestration layer positioned between the agent and the retriever pool. The proposed skill analyzes the current scene, consults an experience memory, selects an appropriate retrieval strategy, and returns structured evidence to the agent. Under a fixed candidate pool, Experience-RAG Skill achieves an overall nDCG@10 of 0.8924 on BeIR/nq, BeIR/hotpotqa, and BeIR/scifact, outperforming fixed single-retriever baselines and remaining competitive with Adaptive-RAG-style routing. The results suggest that retrieval strategy selection can be productively encapsulated as a reusable agent skill rather than being hard-coded in the upper workflow.