mlsanityData to 14 Oct 2026

An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration

arXiv 2605.03989 ↗First posted 5 May 2026cs.AIcited by 0

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 17 of its 17 references that are in the map. First posted on arXiv 5 May 2026.

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References · 17

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  6. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  7. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  8. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  9. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
  10. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
  11. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  12. 2023Active Retrieval Augmented Generationcited by 516
5 more
  1. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  2. 2023Cognitive Architectures for Language Agentscited by 429
  3. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 213
  4. 2024Corrective Retrieval Augmented Generationcited by 197
  5. 2024LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMscited by 108

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  1. 2025MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning
  2. 2026Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
  3. 2026Dissecting Agentic RAG: A Component Ablation for Multi-Hop QA with a Local 7B Model
  4. 2026A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
  5. 2026Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval
  6. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  7. 2026Navigation-Informed Embeddings: Dense-Retriever Adaptation from Agent Search Traces
  8. 2025MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation