mlsanityData to 14 Oct 2026

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

arXiv 2506.20963 ↗First posted 26 Jun 2025cs.IR cs.LGcited by 15

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

Abstract (arXiv)

Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expensive full-graph reconstruction whenever new documents arrive, limiting their scalability in dynamic, evolving environments. To address these limitations, we introduce EraRAG, a novel multi-layered Graph-RAG framework that supports efficient and scalable dynamic updates. Our method leverages hyperplane-based Locality-Sensitive Hashing (LSH) to partition and organize the original corpus into hierarchical graph structures, enabling efficient and localized insertions of new data without disrupting the existing topology. The design eliminates the need for retraining or costly recomputation while preserving high retrieval accuracy and low latency. Experiments on large-scale benchmarks demonstrate that EraRag achieves up to an order of magnitude reduction in update time and token consumption compared to existing Graph-RAG systems, while providing superior accuracy performance. This work offers a practical path forward for RAG systems that must operate over continually growing corpora, bridging the gap between retrieval efficiency and adaptability. Our code and data are available at https://github.com/EverM0re/EraRAG-Official.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 42 of its 53 references that are in the map. First posted on arXiv 26 Jun 2025.

Builds on

42 of 53 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025In-depth Analysis of Graph-based RAG in a Unified Framework6 Mar
  2. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights17 Feb
  3. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models21 Jan
  4. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)31 Dec
  5. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  6. 2024GRAG: Graph Retrieval-Augmented Generation26 May
  7. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  8. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 34

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2024Qwen2.5 Technical Reportcited by 4,942
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  9. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  10. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  11. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  12. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
22 more
  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  2. 2024HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Modelscited by 281
  3. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  4. 2021QuALITY: Question Answering with Long Input Texts, Yes!cited by 208
  5. 2024When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Methodcited by 204
  6. 2023OpenAGI: When LLM Meets Domain Expertscited by 197
  7. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  8. 2024LLM-SR: Scientific Equation Discovery via Programming with Large Language Modelscited by 133
  9. 2015Practical and Optimal LSH for Angular Distancecited by 106
  10. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 102
  11. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 85
  12. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  13. 2024A Closer Look at the Limitations of Instruction Tuningcited by 77
  14. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Basescited by 77
  15. 2024Retrieval-Augmented Generation for Natural Language Processing: A Surveycited by 71
  16. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  17. 2023GSLB: The Graph Structure Learning Benchmarkcited by 50
  18. 2021A Survey on Locality Sensitive Hashing Algorithms and their Applicationscited by 40
  19. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generatorcited by 34
  20. 2024How Much Can RAG Help the Reasoning of LLM?cited by 31
  21. 2025Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Reportcited by 31
  22. 2025A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applicationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 9.

15 papers.

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  1. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corporacited by 49
  2. 2025LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoningcited by 38
  3. 2025You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structurescited by 23
  4. 2026A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applicationscited by 18
  5. 2025BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documentscited by 16
  6. 2025Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinkingcited by 6
  7. 2026Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTScited by 5
  8. 2025Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrievalcited by 4
  9. 2026ParisKV: Fast and Drift-Robust KV-Cache Retrieval for Long-Context LLMscited by 2
  10. 2026EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrievalcited by 2
  11. 2026LLM-Oriented Information Retrieval: A Denoising-First Perspectivecited by 1
  12. 2026MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generationcited by 1
3 more
  1. 2026EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagationcited by 0
  2. 2026A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generationcited by 0
  3. 2026Analyzing performance of Retrieval Augmented Generation models for Question Answering Taskscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  2. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  3. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
  4. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  5. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation
  6. 2026Revisiting RAG Retrievers: An Information Theoretic Benchmark
  7. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  8. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs