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from documents, databases, or other sources at answer time, improving access to knowledge that may be missing or outdated in model parameters.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. Glance: The Generator Is Its Own Reranker

    Oct 29, 2026Adilet Metinov, Chingiz ArzievLLM RerankingRetrieval-Augmented Generation

    Airun Intelligence Labs

  2. Retrieving from Your Own Mistakes: On-Policy Training of Embedding Models for Multi-Hop Retrieval-Augmented Generation

    Oct 29, 2026Chenjie Qiu, Zixuan Zeng, Shaobin Shi +3Retrieval-Augmented GenerationIterative RAG

    Singapore Management University Singapore · Guangdong University of Technology Guangzhou, China

  3. DeFT-RAG: Incrementally Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers

    Oct 27, 2026Dingyi Kang, Bingzhe LiMulti-Hop QARetrieval-Augmented Generation

    University of Texas at Dallas, Richardson, Texas, USA

  4. Beyond Similarity Search: Knowledge Graph Metadata Enrichment for Enterprise Retrieval-Augmented Generation

    Oct 27, 2026Fakabbir AminRetrieval-Augmented GenerationGraph Retrieval-Augmented Generation

  5. Do We Need an LLM to Know Why Sources Conflict? Decision Models for Knowledge-Conflict Typing in RAG

    Oct 27, 2026Parv Khatri, Harshul Jain, Mehak Kapoor +1Knowledge Conflicts in Language ModelsRetrieval-Augmented Generation

    Independent Researcher

  6. RAG-CSI: Global Local Retrieval via Joint State and Dynamics Matching for CSI Prediction

    Oct 27, 2026Xinyue Jiang, Tong Li, Shiceng Zhang +2Channel State PredictionWireless Communications

    College of Computer Science and Electronic Engineering, Hunan University, Changsha, China. · Xidian University, Xian, China. · Peng Cheng Laboratory, Shenzhen 518066, China.

  7. QESChunker: A Single Objective Unifies Overlapping and Non-Overlapping Chunking for RAG

    Oct 27, 2026Yifan Zhao, Qianyang Zhao, Xinyu Guan +2Document ChunkingRetrieval-Augmented Generation

    Alibaba Group (alibaba-inc.com) · Taobao (China) Software Co., Ltd. (taobao.com)

  8. OA-RAG: Ontology-based Agentic RAG for Faithful LLM Reasoning

    Oct 26, 2026Hui Yang, Jiaoyan Chen, Yongsheng GaoAgentic RAGRetrieval-Augmented Generation

    The University of Manchester, Manchester, United Kingdom · SNOMED International, London, United Kingdom

  9. Reading Beyond OCR Noise: Robust Cross-Lingual RAG for Legislative Archives

    Oct 26, 2026Radha Krishna Deshpande, Ajoy Mondal, Akash Manna +1Historical Document ProcessingRAG Robustness

    CVIT, International Institute of Information Technology, Hyderabad, India

  10. RAGing Against Misogyny: Improving Implicit Misogyny Explanations

    Oct 26, 2026Ivana Crescenzi, Arianna Muti, Maarten Sap +2Implicit BiasSocial Bias in Language Models

    Department of Computing Sciences, Bocconi University, Milan, Italy · Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA · Center for Language and Cognition Groningen, University of Groningen, The Netherlands

  11. Topic-Level Fairness in Retrieval-Augmented Generation

    Oct 26, 2026Jaeyoung Park, Hantian Zhang, Kexin Rong +1Group FairnessRetrieval-Augmented Generation

    KAIST · Meta · Georgia Institute of Technology

  12. A Domain-Aware Pipeline for Structured Extraction and Retrieval-Augmented Question Answering over arXiv Superconductivity Literature

    Oct 25, 2026Ivan Iorsh, Aleksandr Kapitonov, Ivan Filianin +2Scientific Document RetrievalScientific QA

    ITMO University, Saint Petersburg, Russia · New Uzbekistan University, Tashkent, Uzbekistan

  13. HerHealthGPT: An Evidence-Grounded Conversational AI System for Women's Reproductive Health Copy title

    Oct 24, 2026Hassan Saeed Hassan Albattra, Mazen Mohammed Bahgat, Rahatara Ferdousi +2HealthcareMedical QA

    School of Computing, Queen’s University, Kingston, Ontario, Canada

  14. TriageRAG: Failure-Aware Self-Training for Adapting Retrieval-Augmented Language Models to Specialized Domains

    Oct 24, 2026Samarth Varshney, Ritu YadavRetrieval-Augmented GenerationDomain Adaptation for LLMs

  15. Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance

    Oct 23, 2026Daisy Rae Lopez, Angela Boccia, Maurizio SessaClinical Decision SupportCausal Inference

    Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark · Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy

  16. GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation

    Oct 23, 2026Abdul Hannan Azad, Irvin Wang, Jiwan Jiang +6Multimodal RAGRetrieval-Augmented Generation

    Texas A&M University, College Station, USA · A&M Consolidated High School College Station, USA · University of Wisconsin, Madison, USA

  17. From Prompt Rules to Golden Data Model: Production-Scale Generation of Personalized, Compliant Pharmaceutical Claims

    Oct 22, 2026Suyash Mishra, Qiang Li, Subhabrata Adak +3Personalized Text GenerationText Generation

    aRoche · bAccenture · cInvolead

  18. Is Complex Agentic Memory Always Needed? Naive RAG Is Stronger Than We Thought

    Oct 22, 2026Zhengye Bao, Shengtao Zhang, Haokai Zhang +3Conversational QAMemory Management for LLM Agents

    Luo Lab Undergraduate Division (LUD), Xi’an Jiaotong University · Shanghai Jiao Tong University · Zhejiang University +1

  19. Compiling Autoregressive LLM Control into Policy-Exact Parallel Inference for Hierarchical RAG

    Oct 22, 2026Zehao Lin, Qianfeng Shen, Xixuan Hao +8Language Model-Based ControlEfficient Language Model Inference

    MemTensor (Shanghai) Technology Co., Ltd. · China Haisum Engineering Co., Ltd. · The Hong Kong University of Science and Technology (Guangzhou) +1

  20. Before the Ticket: Grounding HPC User Support in Documentation and Resolved Tickets with an Agentic LLM Assistant

    Oct 21, 2026Elham J. Barezi, Primus C Kabuo, Niveditha NerellaQuestion AnsweringDocument QA

    Rosen Center for Advanced Computing, Purdue University, West Lafayette, IN, USA

  21. Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories

    Oct 21, 2026Sujoy Roychowdhury, Joseph H. R. Isaac, Jiangning Gao +2Cross-Modal RetrievalSemantic Search

    Ericsson R&D

  22. MAPS: Macro-Adaptive Parallel Reasoning for Regulating Search Momentum in Retrieval-Augmented Generation

    Oct 21, 2026Xinze Li, Dingwen Zhang, Zhenghao Liu +5Retrieval-Augmented GenerationIterative RAG

    School of Computer Science and Engineering, Northeastern University, Shenyang, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China

  23. Retrieval Augmented Generation Compositionality

    Oct 21, 2026Ido Levy, Segev Shlomov, Leshem ChoshenRetrieval-Augmented GenerationRAG Evaluation

    IBM · Weizmann Institute of Science

  24. C2^2U in RAG: Compositional Concept Unlearning in Retrieval-Augmented Generation

    Oct 21, 2026Yiyang Duan, Fan Wu, Tiantong Wu +5Retrieval-Augmented GenerationMachine Unlearning

    Midori Lab, Nanyang Technological University · Beihang University · Institute of Science Tokyo (formerly Tokyo Institute of Technology)

  25. InsightRAG: Aggregating Distributed Scholarly Judgments for Scientific Survey Generation

    Oct 20, 2026Yuquan Lin, Wenshuo Zhang, Yilin Ye +4Retrieval-Augmented GenerationEvidence Retrieval

    The Hong Kong University of Science and Technology · The Hong Kong University of Science and Technology (Guangzhou)

  26. RAG-NIMBF: A Non-Invasive, Modular Benchmarking Framework for Automated RAG Configuration Analysis

    Oct 20, 2026Niclas Hart, Nicolas WeegerRAG EvaluationRetrieval-Augmented Generation

    Ansbach University of Applied Sciences

  27. A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation

    Oct 19, 2026Lihong He, Lucian Popa, Marina DanilevskyClarification Question GenerationRetrieval-Augmented Generation

    IBM Research, USA

  28. Joint Constraint Aggregation for Qualifier-Sensitive Retrieval-Augmented QA

    Oct 19, 2026Jennifer Hsia, Zachary C. Lipton, Aarti SinghRetrieval-Augmented GenerationRAG Robustness

    Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213, USA

  29. Large-Scale Biomedical RAG: Corpus Scaling, Document Representation, and Efficiency Trade-offs

    Oct 19, 2026Adrián Sánchez-Freire, Gonzalo Pérez-Gómez, Aitziber Atutxa +1Retrieval-Augmented GenerationRAG Evaluation

    HiTZ Basque Center for Language Technology, University of the Basque Country (EHU), P.º Rafael Moreno "Pitxitxi", 2, Bilbao, 48013, Biscay, Spain

  30. Benchmark for Structured Retrieval-Augmented Generation in Perovskite Photovoltaics

    Oct 18, 2026Qiwei Han, Chi Zhou, Gaoxiang YuanStructured RAGRAG Evaluation

    Department of Chemistry, Duke University, Durham, NC 27708, USA · Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA · Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA