mlsanityData to 7 Oct 2026

ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation

arXiv 2502.09891 ↗First posted 14 Feb 2025cs.IR cs.AIcited by 27

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving question-answer (QA) tasks. The state-of-the-art RAG approaches often use the graph data as the external data since they capture the rich semantic information and link relationships between entities. However, existing graph-based RAG approaches cannot accurately identify the relevant information from the graph and also consume large numbers of tokens in the online retrieval process. To address these issues, we introduce a novel graph-based RAG approach, called Attributed Community-based Hierarchical RAG (ArchRAG), by augmenting the question using attributed communities, and also introducing a novel LLM-based hierarchical clustering method. To retrieve the most relevant information from the graph for the question, we build a novel hierarchical index structure for the attributed communities and develop an effective online retrieval method. Experimental results demonstrate that ArchRAG outperforms existing methods in both accuracy and token cost.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 50 of its 73 references that are in the map. First posted on arXiv 14 Feb 2025.

Builds on

50 of 73 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG13 Feb
  2. 2024SiReRAG: Indexing Similar and Related Information for Multihop Reasoning9 Dec
  3. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  4. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning30 May
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  6. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 44

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2024Qwen2.5 Technical Reportcited by 4,713
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  8. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  9. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  10. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  11. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  12. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
32 more
  1. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  2. 2017The NarrativeQA Reading Comprehension Challengecited by 590
  3. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  4. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  5. 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 420
  6. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
  7. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  8. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
  9. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  10. 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 255
  11. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 253
  12. 2023Large Language Models in Finance: A Surveycited by 223
  13. 2024Large Language Models for Education: A Survey and Outlookcited by 185
  14. 2022Can Foundation Models Wrangle Your Data?cited by 173
  15. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 150
  16. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  17. 2023Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answeringcited by 97
  18. 2018Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervisedcited by 75
  19. 2024Retrieval-Augmented Generation for Natural Language Processing: A Surveycited by 70
  20. 2024DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literaturecited by 50
  21. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answeringcited by 43
  22. 2024Large Language Models for Medicine: A Surveycited by 38
  23. 2023GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimizationcited by 28
  24. 2024AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmarkcited by 23
  25. 2024GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understandingcited by 18
  26. 2025PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generationcited by 18
  27. 2024LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiencycited by 16
  28. 2024A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluationscited by 16
  29. 2024R-Bot: An LLM-based Query Rewrite Systemcited by 16
  30. 2024Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representationscited by 12
  31. 2024GCoder: Improving Large Language Model for Generalized Graph Problem Solvingcited by 11
  32. 2024Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluationcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 12; 2026: 15.

27 papers.

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  1. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corporacited by 47
  2. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 46
  3. 2026Dr. Zero: Self-Evolving Search Agents without Training Datacited by 39
  4. 2025LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoningcited by 37
  5. 2025RAG-Anything: All-in-One RAG Frameworkcited by 18
  6. 2025G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledgecited by 9
  7. 2026SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Executioncited by 8
  8. 2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMscited by 7
  9. 2025Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrievalcited by 4
  10. 2025Distributed Retrieval-Augmented Generationcited by 2
  11. 2026EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrievalcited by 2
  12. 2025Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generationcited by 1
15 more
  1. 2025TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generationcited by 1
  2. 2026MG2-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generationcited by 1
  3. 2025GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Frameworkcited by 0
  4. 2025Graph-Guided Concept Selection for Efficient Retrieval-Augmented Generationcited by 0
  5. 2026IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memorycited by 0
  6. 2026DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generationcited by 0
  7. 2026AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interactioncited by 0
  8. 2026LLM+Graph@VLDB'2025 Workshop Summarycited by 0
  9. 2026Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generationcited by 0
  10. 2026ASTRA-QA: A Benchmark for Abstract Question Answering over Documentscited by 0
  11. 2026SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrievalcited by 0
  12. 2026UniD3: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoningcited by 0
  13. 2026LLMs+Graphs: Toward Graph-Native, Synergistic AI Systemscited by 0
  14. 2026A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generationcited by 0
  15. 2026Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAGcited by 0

Similar, not cited

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  1. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  2. 2025In-depth Analysis of Graph-based RAG in a Unified Framework
  3. 2025Retrieval-Augmented Generation with Hierarchical Knowledge
  4. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
  5. 2026HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
  6. 2025LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
  7. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  8. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs