Retrieval-Augmented Generation

Also known as RAG

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Overview

Retrieval-augmented generation (RAG) studies how to give language models relevant information from documents, databases, or other sources at answer time, improving access to knowledge that may be missing or outdated in model parameters. Common approaches combine dense or sparse search with hybrid retrieval, reranking, and document chunking; GraphRAG and agentic methods add structure or iterative search for multi-hop questions and long documents. Work spans open-domain QA benchmarks as well as specialized settings such as legal, medical, code, and multimodal retrieval. A central tension is that more retrieved context can improve evidence coverage but also introduce noise, conflicts, cost, and unsupported answers, so systems increasingly focus on selective retrieval, verification, citation fidelity, and realistic evaluation.

Latest papers

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796 papers72 papers in the last four weeks, down 14% on the four weeks before. 0.4% of all new papers.

  1. Controllable Evidence Selection in Retrieval-Augmented Question Answering via Deterministic Utility Gating

    Feb 23, 2026Victor P. UndaEvidence SelectionRetrieval-Augmented Generation

    Independent Researcher, Applied AI Systems

  2. How Retrieved Context Shapes Internal Representations in RAG

    Feb 23, 2026Samuel Yeh, Sharon LiRetrieval-Augmented GenerationLLM Interpretability

    Department of Computer Science, University of Wisconsin-Madison

  3. How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding

    Feb 23, 2026Wei Chen, Guoyang Ju, Yuanyuan QiLLM Input OptimizationLLM Prompting

    China Jiliang University, Hangzhou, China

  4. Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering

    Feb 22, 2026Maryam Amirizaniani, Alireza Salemi, Hamed ZamaniQuestion AnsweringRetrieval-Augmented Generation

    University of Washington Seattle, WA, USA · University of Massachusetts Amherst Amherst, MA, USA

  5. Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem

    Feb 21, 2026Lichang Song, Ting Long, Yi ChangRetrieval-Augmented GenerationRAG Robustness

    Jilin University

  6. Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge

    Feb 19, 2026Marcelo LabreRetrieval-Augmented GenerationLLM Grounding

    Advanced Institute for Artificial Intelligence (AI2)

  7. Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research

    Feb 19, 2026Amirereza Abbasi, Mohsen HooshmandInformation RetrievalGenerative Retrieval

    Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences Zanjan, Iran

  8. PartRAG: Retrieval-Augmented Part-Level 3D Generation and Editing

    Feb 19, 2026Peize Li, Zeyu Zhang, Hao Tang3D Shape RetrievalPart-Level 3D Generation

    King’s College London · Peking University

  9. Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis

    Feb 18, 2026Zhiyuan Cheng, Longying Lai, Yue Liu +2Financial QARetrieval-Augmented Generation

    School of Engineering Stanford University Stanford, CA, USA · Simon Business School University of Rochester Rochester, NY, USA · Accounting & Information Systems Rutgers University Newark, NJ, USA +2

  10. Retrieval Augmented Generation of Literature-derived Polymer Knowledge: The Example of a Biodegradable Polymer Expert System

    Feb 18, 2026Sonakshi Gupta, Akhlak Mahmood, Wei Xiong +1Retrieval-Augmented GenerationGraph Retrieval-Augmented Generation

    School of Computational Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta 30332, GA, USA. · Matmerize, Inc., Atlanta 30308, GA, USA. · School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA 30332, USA

  11. MMA: Multimodal Memory Agent

    Feb 18, 2026Yihao Lu, Wanru Cheng, Zeyu Zhang +1Multimodal AgentsMemory-Augmented Agents

    School of Computer Science, Peking University

  12. Learning to Retrieve Navigable Candidates for Efficient Vision-and-Language Navigation

    Feb 17, 2026Shutian Gu, Chengkai Huang, Ruoyu Wang +1Vision-Language NavigationRetrieval-Augmented Generation

    University of New South Wales, Sydney, Australia · Macquarie University, Sydney, Australia · Data61, CSIRO, Sydney, Australia

  13. AIC CTU@AVerImaTeC: dual-retriever RAG for image-text fact checking

    Feb 16, 2026Herbert Ullrich, Jan DrchalAutomated Fact-CheckingRetrieval-Augmented Generation

  14. A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance

    Feb 16, 2026Gabriele Immordino, Andrea Vaiuso, Marcello RighiAviation SafetyRetrieval-Augmented Generation

    School of Engineering Zurich University of Applied Sciences ZHAW Winterthur, Switzerland

  15. HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation

    Feb 16, 2026Wen-Sheng Lien, Yu-Kai Chan, Hao-Lung Hsiao +5Hypergraph-Based RAGRetrieval-Augmented Generation

    National Yang Ming Chiao Tung University, Hsinchu, Taiwan · E.SUN Bank, Taipei, Taiwan · National Kaohsiung Normal University, Kaohsiung, Taiwan

  16. Differentially Private Retrieval-Augmented Generation

    Feb 16, 2026Tingting Tang, James Flemings, Yongqin Wang +1Privacy-Preserving RAGRetrieval-Augmented Generation

    University of Southern California Los Angeles, California, USA

  17. With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots

    Feb 10, 2026Zeinab Sadat Taghavi, Ali Modarressi, Hinrich Schutze +1Retrieval-Augmented GenerationRetrieval Robustness

    Lucerne University of Applied Sciences and Arts (HSLU) · Center for Information and Language Processing (CIS), Ludwig Maximilian University of Munich (LMU) · Munich Center for Machine Learning (MCML)

  18. RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation

    Feb 6, 2026Tim Schlippe, Simon Martin, Michael MarketsmüllerRetrieval-Augmented GenerationRAG Evaluation

    IU International University of Applied Sciences, Germany

  19. Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework

    Feb 5, 2026Chuangtao Ma, Zeyu Zhang, Arijit Khan +2Entity ResolutionRetrieval-Augmented Generation

    University of Amsterdam Amsterdam, the Netherlands · BIFOLD & TU Berlin Berlin, Germany

  20. CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG

    Feb 4, 2026Nengbo Wang, Tuo Liang, Vikash Singh +6KG ConstructionRetrieval-Augmented Generation

    Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA · Design and Innovation Department, Case Western Reserve University, Cleveland, OH, USA

  21. AutoPrunedRetriever: Persistent Reasoning Graphs for Efficient and Extensible Retrieval-Augmented Generation

    Feb 4, 2026Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee +4RAG Context CompressionRetrieval-Augmented Generation

    Cornell University · University of Cambridge · The University of Hong Kong +2

  22. Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity

    Feb 3, 2026Menglin Xia, Xuchao Zhang, Shantanu Dixit +6Agent MemoryRetrieval-Augmented Generation

    M365 Research, Microsoft.

  23. Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems

    Jan 30, 2026H. Sinan Bank, Daniel R. HerberCyber-Physical SystemsRetrieval-Augmented Generation

    Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA

  24. Quantifying Retriever-Generator Alignment in RAG with Local Explanations

    Jan 29, 2026Korbinian Randl, Guido Rocchietti, Aron Henriksson +3Gradient-Based AttributionRetrieval-Augmented Generation

    Department of Computer and Systems Sciences, Stockholm University, Sweden · BIFOLD, Technische Universität Berlin, Germany · Department of Informatics, Athens University of Economics and Business, Greece +1

  25. Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation

    Jan 16, 2026Xin Sun, Zhongqi Chen, Qiang Liu +5Retrieval-Augmented GenerationTest-Time Adaptation

    NLPR, MAIS, CASIA · Ant Group · USTC

  26. NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems

    Jan 16, 2026Jiayu Liu, Rui Wang, Qing Zong +9Retrieval-Augmented GenerationConfidence Estimation in Language Models

    HKUST · UIUC · Northwestern University

  27. To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning

    Jan 13, 2026Rubing Chen, Jian Wang, Wenjie Li +2Multi-Hop QARetrieval-Augmented Generation

    Department of Computing, The Hong Kong Polytechnic University

  28. AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning

    Dec 18, 2025Tzu-Han Lin, Wei-Lin Chen, Chen-An Li +3Retrieval-Augmented GenerationKnowledge Augmentation for Language Models

    National Taiwan University · Department of Computer Science, University of Virginia

  29. MTRACE: Multilingual Retrieval-Augmented Generation for Temporally Diverse Text Corpora

    Dec 14, 2025Souhail Bakkali, Anthony MudetRetrieval-Augmented GenerationMultilingual IR

    Univ Rennes, CNRS, IRISA - UMR 6074, Rennes, France · L3i-lab, La Rochelle Universit´e, France

  30. Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems

    Oct 28, 2025Yihan Li, Xiyuan Fu, Ghanshyam Verma +2Retrieval-Augmented GenerationLLM Hallucination Mitigation

    Electronic Information School, Wuhan University, Wuhan, China · School of Computing, Dublin City University, Dublin, Ireland · School of Public Health, Wuhan University, Wuhan, China +2