mlsanityData to 14 Oct 2026

Cache Mechanism for Agent RAG Systems

arXiv 2511.02919 ↗First posted 4 Nov 2025cs.CLcited by 5

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

also in 1 more
Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 29 of its 45 references that are in the map. First posted on arXiv 4 Nov 2025.

Builds on

29 of 45 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router29 Jul
  2. 2024RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation8 Mar
  3. 2024RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents6 Feb

Other references · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  7. 2017Billion-scale similarity search with GPUscited by 2,497
  8. 2018Deep contextualized word representationscited by 2,290
  9. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  10. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  11. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
  12. 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 571
14 more
  1. 2024KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graphcited by 93
  2. 2024AutoFlow: Automated Workflow Generation for Large Language Model Agentscited by 83
  3. 2021Cross Modal Retrieval with Querybank Normalisationcited by 72
  4. 2023Word Embeddings Are Steers for Language Modelscited by 70
  5. 2024Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answeringcited by 51
  6. 2024PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makerscited by 42
  7. 2025On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspectivecited by 41
  8. 2024Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilitiescited by 22
  9. 2023Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Bankscited by 20
  10. 2024Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoningcited by 18
  11. 2024Accelerating Retrieval-Augmented Generationcited by 14
  12. 20254bit-Quantization in Vector-Embedding for RAGcited by 4
  13. 2025LiteCUA: Computer as MCP Server for Computer-Use Agent on AIOScited by 4
  14. 2025Node-as-Agent: Graph Agentic Networkcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agentscited by 52
  2. 2026Position: Modular Memory is the Key to Continual Learning Agentscited by 8
  3. 2026Individual Turing Test: A Case Study of LLM-based Simulation Using Longitudinal Personal Datacited by 1
  4. 2026Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAGcited by 0
  5. 2026OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
  2. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  3. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
  4. 2026Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval
  5. 2024A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data
  6. 2026Is Agentic RAG worth it? An experimental comparison of RAG approaches
  7. 2026Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents
  8. 2024MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation