Graph Retrieval-Augmented Generation

Also known as GraphRAG

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15 papers in the last four weeks, up 36% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 136

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HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering
May 28, 2026Joongmin Shin, Gyuho Shim, Jeongbae Park +2Multimodal RAG Multimodal IR
Human-inspired AI Research, Korea University · Department of Computer Science and Engineering, Korea University · Computer Science and Engineering, Konkuk University
CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning
May 28, 2026Xiang Fang, Wanlong Fang, Changshuo WangMultimodal RAG Retrieval-Augmented Generation
School of Software Engineering, Huazhong University of Science and Technology · Nanyang Technological University, Singapore · University College London
GraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction
May 27, 2026Jinze Gu, Qinghua Mao, Xi Lin +1Model Extraction Attacks Graph Retrieval-Augmented Generation
School of Computer Science, Shanghai Jiao Tong University
LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning
May 27, 2026Zerui Chen, Qinggang Zhang, Zhishang Xiang +5Multi-Agent LLM Systems Graph Retrieval-Augmented Generation
School of Informatics, Xiamen University · The Hong Kong Polytechnic University · Institute of Artificial Intelligence, Xiamen University
Beyond Chunk-Local Extraction: Cross-Chunk Graph Augmentation for GraphRAG
May 27, 2026Jiaming Zhang, Yibo Zhao, Jing Yu +2Multi-Hop QA Graph Retrieval-Augmented Generation
School of Data Science and Engineering, East China Normal University
Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering
May 26, 2026Mateusz Czyżnikiewicz, Ryszard Tuora, Adam Kozakiewicz +6Graph Retrieval-Augmented Generation Question Answering
Samsung AI Warsaw · Samsung AI Cambridge
Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry
May 26, 2026Changqing Su, Yu Ding, Zuhong Lin +4KG Construction Multi-Agent LLM Systems
Hunan Key Laboratory of Carbon Neutrality and Intelligent Energy, School of New Energy and Environment, Hunan University of Technology and Business, Changsha 410205, P.R. China · Aerospace Kaitian Environmental Technology Co., Ltd., Changsha 410315, P.R. China · School of Energy Science and Engineering, Central South University, Changsha2026 410083, P.R. China
EfficientGraph-RAG: Structured Retrieval-State Management for Cross-Task Retrieval-Augmented Generation
May 25, 2026Miaohe Niu, Lianlei Shan, Zhengtao Yu +2Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
School of Computer Science and Engineering, Northeastern University, China · 2Tsinghua University, China · 3Kunming University of Science and Technology, China +1
ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning
May 21, 2026Md Shamim Ahmed, Farzaneh Firoozbakht, Lukas Galke Poech +2KG Construction Healthcare
University of Southern Denmark · University of Hamburg
Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs
May 21, 2026Yoav Kor Sade, Arvindh Arun, Rishi Puri +2Graph Neural Networks LLM Reasoning with Graphs
Tel Aviv University · Institute for AI, University of Stuttgart · NVIDIA +1
GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval
May 20, 2026Peter Fernandes, Ria KanjilalLLM Inference Efficiency LLM Grounding
Department of Computer Engineering California Polytechnic State University San Luis Obispo, CA, USA
Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study
May 19, 2026Rajan Bastakoti, Sagar Bhetwal, Nirajan Acharya +1Graph Retrieval-Augmented Generation Financial Sentiment Analysis
DePaul University · Chicago, IL, USA · Youngstwon State University +2
ContextRAG: Extraction-Free Hierarchical Graph Construction for Retrieval-Augmented Generation
May 19, 2026Roman Prosvirnin, Sergei Kuznetsov, Seungmin JinMulti-Hop QA Graph Retrieval-Augmented Generation
HSE University
RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation
May 16, 2026Chengrui Han, Zesheng ChengKG Construction Graph Retrieval-Augmented Generation
Qingdao University
GRASP: Graph Agentic Search over Propositions for Multi-hop Question Answering
May 15, 2026Stockton Jenkins, Ramya Korlakai Vinayak, Junjie HuMulti-Hop QA Graph Retrieval-Augmented Generation
University of Wisconsin-Madison
SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation
May 15, 2026Xin Zhang, Yang Cao, Baoxing Wu +2LLM Grounding Knowledge Augmentation for Language Models
School of Information Science and Engineering, Chongqing Jiaotong University · School of Computer Science and Technology, Chongqing University of Posts and Telecommunications
Why Neighborhoods Matter: Traversal Context and Provenance in Agentic GraphRAG
May 14, 2026Riccardo Terrenzi, Maximilian von Zastrow, Serkan AyvazData Provenance Graph Retrieval-Augmented Generation
Centre for Industrial Software, University of Southern Denmark, Alsion 2, 6400 Sønderborg, Denmark
Falkor-IRAC: Graph-Constrained Generation for Verified Legal Reasoning in Indian Judicial AI
May 14, 2026Joy BoseLegal Citation Verification Graph Retrieval-Augmented Generation
Independent Researcher, Bengaluru, India
PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents
May 13, 2026Mikhail Menschikov, Matvey Iskornev, Alexander Kharitonov +8Multi-Hop QA Graph Retrieval-Augmented Generation
Skoltech, Moscow, Russia · SberAI, Moscow, Russia · Huawei, Moscow, Russia +4
SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory
May 12, 2026Juntong Wang, Haoyue Zhao, guanghui Pan +4Agent Memory Graph Retrieval-Augmented Generation
Institute for Artificial Intelligence, Peking University · School of Intelligence Science and Technology, Peking University · School of Computer Science and Technology, Beijing Institute of Technology
PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering
May 11, 2026Shengxiang Gao, Chao Lei, Jey Han Lau +1LLM Grounding Graph Retrieval-Augmented Generation
The University of Melbourne
GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings
May 8, 2026Asit Desai, Aman Kumar, Prashant DevadigaRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
National Payments Corporation of India
GATHER: Convergence-Centric Hyper-Entity Retrieval for Zero-Shot Cell-Type Annotation
May 7, 2026Zhonghui Zhang, Feng Jiang, Shaowei Qin +2Zero-Shot Learning Graph Retrieval-Augmented Generation
Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology Shenzhen, China · Software College, Northeastern University Shenyang, China
Text-Graph Synergy: A Bidirectional Verification and Completion Framework for RAG
May 7, 2026Jiarui Zhong, Hong Cai ChenRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
School of Automation, Southeast University, Nanjing 210096, China
FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
May 2, 2026Zebin Guo, Weidong Geng, Ruichen MaoMultimodal RAG Table QA
Georgia Institute of Technology Atlanta, Georgia, USA · Zhejiang University Hangzhou, Zhejiang, China · Zhejiang Lab Hangzhou, Zhejiang, China
MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents
May 2, 2026Hung Pham Van, Nguyen Manh Hieu, Khang Pham Tran Tuan +4Conversational Memory Graph Retrieval-Augmented Generation
1Independent Researcher · 2Hanoi University of Science and Technology · 3VNU University of Engineering and Technology +1
CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering
Apr 28, 2026Yushi Sun, Lei ChenRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
HKUST Hong Kong, China · HKUST(GZ) / HKUST Guangzhou / Hong Kong, China
XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation
Apr 27, 2026Zhuoling Li, Ha Linh Hong Tran Nguyen, Valeria Bladinieres +1Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
Berlin Technology Centre · Deutsche Bank
ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection
Apr 26, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuFinancial Services Graph Retrieval-Augmented Generation
The University of Hong Kong · Stellaris AI Limited
STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation
Apr 24, 2026Peng Yu, En Xu, Bin Chen +2Graph Neural Networks Graph Retrieval-Augmented Generation
AI Product Center, Kingsoft Corporation, Beijing, China

May 28, 2026, cs.AIJ/K move · Enter open · S save

HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering

Joongmin Shin, Gyuho Shim, Jeongbae Park, Jaehyung Seo+1

Human-inspired AI Research, Korea University · Department of Computer Science and Engineering, Korea University · Computer Science and Engineering, Konkuk University

Multimodal RAG Multimodal IR Graph Retrieval-Augmented Generation Document QA

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Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in locating the correct document and evidence fragmentation in integrating scattered information. Existing approaches relying on flat text chunks or page-level images inherently struggle to (i) precisely pinpoint the target document among thousands of candidates and (ii) organically connect multimodal evidence, such as tables and figures, within a limited token budget. To address these challenges, we propose HiKEY, a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal. Instead of simple chunking, HiKEY reconstructs a logical heterogeneous graph via Document Hierarchical Parsing (DHP), explicitly encoding parent-child relationships. Adopting a hierarchical coarse-to-fine strategy, the framework (1) performs global routing to rapidly prune the search space using hierarchical indexing, and (2) conducts fine-grained retrieval to rank sections by employing a multimodal fusion strategy that captures the most discriminative evidence. Finally, HiKEY assembles a token-efficient evidence subgraph via a hybrid structural-semantic packing strategy. Experiments on ODQA benchmarks demonstrate that HiKEY significantly outperforms page- and chunk-based baselines, improving retrieval recall by up to 12.9% and end-to-end QA performance by up to 6.8%.

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Latest papers 136