Graph Retrieval-Augmented Generation

Also known as GraphRAG

Graph Retrieval-Augmented Generation studies how to use entities, relations, and document structure to help language models find and combine relevant evidence. Systems build knowledge graphs or hypergraphs from documents, then retrieve evidence through paths, graph traversal, community summaries, or hybrid graph-and-vector search; some use agents to plan and verify multi-step retrieval. The methods are tested on knowledge-graph and multi-hop question answering, long-document tasks, and specialized settings such as medicine, law, and engineering, where traceable evidence can improve factuality. A central tension is whether richer graph structure improves answers enough to justify its construction and retrieval costs: performance depends on graph quality and query type, and more retrieved context does not always yield better answers.

Latest papers

232 papers19 papers in the last four weeks, down 21% on the four weeks before. 0.1% of all new papers.

Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking
Feb 27, 2026Shuzhi Gong, Richard O. Sinnott, Jianzhong Qi +3Automated Fact-Checking Evidence Retrieval
The University of Melbourne, Melbourne, VIC, Australia · Data61, CSIRO, Sydney, NSW, Australia · MBZUAI, Abu Dhabi, UAE
NGDBench: Towards Neural Graph Data Management
Feb 27, 2026Yufei Li, Yisen Gao, Jiaxuan Xiong +6Graph Retrieval-Augmented Generation Text-to-Cypher
HKUST · Beijing Institute of Technology · Hong Kong Baptist University +1
TCM-DiffRAG: Personalized Syndrome Differentiation Reasoning Method for Traditional Chinese Medicine based on Knowledge Graph and Chain of Thought
Feb 26, 2026Jianmin Li, Ying Chang, Su-Kit Tang +4Traditional Chinese Medicine Graph Retrieval-Augmented Generation
1.Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, 999078, Macao, China. · 3.School of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou 310053, China. · 6.Zhejiang Chinese Medical University – GANCAO DOCTOR Institute of Artificial Intelligence for Chinese Medicine +3
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
Feb 26, 2026Wenwei Li, Ming Xu, Tianle Xia +7Question Answering Retrieval-Augmented Generation
Tencent
Multi-hop Reasoning and Retrieval in Embedding Space: Leveraging Large Language Models with Knowledge
Feb 25, 2026Lihui LiuLLM Reasoning with Graphs Knowledge Graph Question Answering
Wayne State University, Detroit, Michigan, USA
HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG
Feb 24, 2026Yuqi Huang, Ning Liao, Kai Yang +4Graph Retrieval-Augmented Generation Graph-Based Retrieval
Shanghai Jiao Tong University, China.
Topology of Reasoning: Retrieved Cell Complex-Augmented Generation for Textual Graph Question Answering
Feb 22, 2026Sen Zhao, Lincheng Zhou, Yue Chen +1Graph Retrieval-Augmented Generation LLM Reasoning with Graphs
Academy of Advanced Interdisciplinary Studies, Chongqing University of Posts and Telecommunications, Chongqing, China · School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China · Intelligent System Department, Zhongxing Telecom Equipment (ZTE), Changsha, Hunan, China
GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning
Feb 20, 2026Fali Wang, Chenglin Weng, Xianren Zhang +3LLM Reasoning with Graphs Graph Reasoning
The Pennsylvania State University, University Park, Pennsylvania, USA · Michigan State University, East Lansing, Michigan, USA
CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications
Feb 20, 2026Victoria Blake, Jamie Novak, Mathew Miller +2Clinical NLP Graph Retrieval-Augmented Generation
Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia · Eastern Heart Clinic, Prince of Wales Hospital, Randwick, NSW, Australia · Department of Cardiology, Prince of Wales Hospital, South Eastern Sydney Local Health District, Randwick, NSW, Australia +3
Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
Feb 19, 2026Hamideh Ghanadian, Amin Kamali, Mohammad Hossein TekiehRAG Evaluation Graph Retrieval-Augmented Generation
Pest Management Regulatory Agency (PMRA), Health Canada, Ottawa, ON, Canada · Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada · Oral Health Branch (OHB), Health Canada, Ottawa, ON, Canada
Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
Feb 19, 2026Dun Yuan, Hao Zhou, Xue Liu +4Knowledge Augmentation for Language Models Graph Retrieval-Augmented Generation
Charlie · School of Computer Science, McGill University, Montreal, QC H3A 0E9, Canada. · Standards and Mobility Innovation Lab, Samsung Research America, Plano, Texas, TX 75023, USA.
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 Generation Graph 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
HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation
Feb 16, 2026Wen-Sheng Lien, Yu-Kai Chan, Hao-Lung Hsiao +5Hypergraph-Based RAG Retrieval-Augmented Generation
National Yang Ming Chiao Tung University, Hsinchu, Taiwan · E.SUN Bank, Taipei, Taiwan · National Kaohsiung Normal University, Kaohsiung, Taiwan
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework
Feb 5, 2026Chuangtao Ma, Zeyu Zhang, Arijit Khan +2Entity Resolution Retrieval-Augmented Generation
University of Amsterdam Amsterdam, the Netherlands · BIFOLD & TU Berlin Berlin, Germany
CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG
Feb 4, 2026Nengbo Wang, Tuo Liang, Vikash Singh +6KG Construction Retrieval-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
AutoPrunedRetriever: Persistent Reasoning Graphs for Efficient and Extensible Retrieval-Augmented Generation
Feb 4, 2026Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee +4RAG Context Compression Retrieval-Augmented Generation
Cornell University · University of Cambridge · The University of Hong Kong +2
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 Systems Retrieval-Augmented Generation
Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Jan 28, 2026Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo +3Multi-Hop QA LLM Reasoning with Graphs
Graphcore Research
Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs
Nov 6, 2025Alberto Cattaneo, Carlo Luschi, Daniel JustusGraph Retrieval-Augmented Generation Synthetic Benchmark Generation
Graphcore AI Research
Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation
Oct 17, 2025Jinliang Liu, Jiale Bai, Shaoning ZengMulti-Hop QA Graph Retrieval-Augmented Generation
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China · School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG
Sep 17, 2025Harshad Khadilkar, Abhay GuptaRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
Indian Institute of Technology Bombay · Indian Institute of Technology Patna
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
May 20, 2025Ruiyi Yang, Hao Xue, Imran Razzak +3Multi-Hop QA Multi-Agent LLM Systems
University of New South Wales Sydney, NSW, Australia · Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi, UAE · Griffith University Queensland, Australia +1

Feb 27, 2026J/K move · Enter open · S save

Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking

Shuzhi Gong, Richard O. Sinnott, Jianzhong Qi, Cecile Paris+2

The University of Melbourne, Melbourne, VIC, Australia · Data61, CSIRO, Sydney, NSW, Australia · MBZUAI, Abu Dhabi, UAE

Automated Fact-Checking Evidence Retrieval Agentic RAG Graph Retrieval-Augmented Generation

Open paper AskPDF arXiv

Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retrieving accurate and trustworthy evidence. Previous methods rely on semantic and social-contextual patterns learned from training data, which limits their generalization to new data distributions. Recently, Retrieval Augmented Generation (RAG) based methods have been proposed to utilize the reasoning capability of LLMs with retrieved grounding evidence documents. However, these methods largely rely on textual similarity for evidence retrieval and struggle to retrieve evidence that captures multi-hop semantic relations within rich document contents. These limitations lead to overlooking subtle factual correlations between the evidence and the claims to be fact-checked during evidence retrieval, thus causing inaccurate veracity predictions. To address these issues, we propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence. LLM-enabled retrieval is designed to assess the claims and retrieve the most relevant knowledge subgraphs, forming structured evidence for fact verification. To augment the knowledge graph evidence, we retrieve web contents for completion. The above process is implemented as an automatic Markov Decision Process (MDP): A reasoning LLM agent decides what actions to take according to the current evidence and the claims. To adapt the MDP for fact-checking, we use prompt optimization to fine-tune the agentic LLM.