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LightRAG: Simple and Fast Retrieval-Augmented Generation
Open accessAbstract
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs.However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex interdependencies.To address these challenges, we propose Ligh-tRAG, a novel framework that incorporates graph structures into text indexing and retrieval processes.This innovative approach employs a dual-level retrieval system that enhances comprehensive information retrieval from both lowand high-level knowledge discovery.Additionally, the integration of graph structures with vector representations facilitates efficient retrieval of related entities and their relationships, significantly improving response times while maintaining contextual relevance.This capability is further enhanced by an incremental update algorithm that ensures the timely integration of new data, allowing the system to remain effective and responsive in rapidly changing data environments.Extensive experimental validation demonstrates considerable improvements in retrieval accuracy and efficiency compared to existing approaches.
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“Additionally, while current research on LLM-generated knowledge graphs has primarily operated without formal ontologies [9][4][17][19][26], domain-specific ontologies present significant potential.”
From Zep: A Temporal Knowledge Graph Architecture for Agent Memory (2025) · cites this paper as [17]
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“Knowledge graph-based methods (Edge et al., 2025; Guo et al., 2025; Zhu et al., 2025) enhance semantic connectivity through structured representations, but their partial graph construction prevents establishing relationships between temporally distant nodes across conversation sessions.”
From Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025) · cites this paper as Guo et al., 2025
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“While developed independently, our community search approach parallels the high-level key search methodology in LightRAG [17].”
From Zep: A Temporal Knowledge Graph Architecture for Agent Memory (2025) · cites this paper as [17]
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Research works printed but not linked: 21
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Wang, S., Guo, Y., Zhang, T., Lin, R., Yue, T., Wang, Y., Li, Z., Gao, F., Wang, S., & Ma, S. (2025) · Transactions on Artificial Intelligence · doi:10.53941/tai.2025.100014 · Cited by 2
Multi-Modal Retrieval Augmented Visual Understanding and Generation
Xue, Z. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3756866 · Cited by 3
Entity Similarity RAG: Enhancing LLM Answers with Precise Knowledge Graph Retrieval
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DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction in Deep Research Agents
Shi, J., Cao, Z., Ma, R., Hu, Y., Zhou, J., Fan, Y., Li, X., Bai, L., He, L., & Zhang, B. (2026) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/2026.findings-emnlp.875 · Cited by 5
Privacy protection in RAG: A novel method and evaluation framework
Zhang, Y., Wu, J., Li, R., Zhang, T., Song, Y., Li, C., Wang, S., Shen, H., Yin, J., Ge, J., et al. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104505 · Cited by 2
DepCache: A KV Cache Management Framework for GraphRAG with Dependency Attention
Yuan, H., Ai, X., Wang, Q., Li, P., Yu, J., Chen, C., Yang, X., Zhang, Y., Fu, Z., Wen, Y., et al. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3769778 · Cited by 4
Belmahjoub, M., & Benhiba, L. (2025) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi67495.2025.11358587 · Cited by 2
Integrating Knowledge Graphs and Semantic Retrieval for Indonesian Legal Question Answering
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ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning
Cao, Z., Liu, A., He, Y., Li, J., Zhang, B., & Wang, Z. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i1.36963 · Cited by 2
Liu, S., Feng, L., Luo, Y., Wang, B., Chen, P., Wang, X., Xu, H., Ma, D., Chen, M., Chen, C., et al. (2026) · Health Information Science and Systems · doi:10.1007/s13755-026-00448-9 · Cited by 2
VeritasFi : An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
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Lee, G., Kim, S., Byun, Y., Lee, J., & Choi, H. (2026) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-026-00890-w · Cited by 2
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SA-RAG: Structured and adaptive retrieval-augmented generation for multi-hop question answering
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Zhang, D., & Chen, T. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133291 · Cited by 2
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Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
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NeutronRAG: Towards Understanding the Effectiveness of RAG from a Data Retrieval Perspective
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Contextual Science and Genome Analysis for Air-Gapped AI ResearchPreprint
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A Comprehensive Survey on Multimodal RAG: All Combinations of Modalities as Input and OutputPreprint
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Bennani, A., Ghafir, A., Tali, A., & Hajjaji, S. E. (2025) · EPJ Web of Conferences · doi:10.1051/epjconf/202532605011 · Cited by 1
Metadata
- Authors
- Guo, Z., Xia, L., Yu, Y., Ao, T., & Huang, C.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- University of Hong Kong (HK)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2025
- Published
- 2025
- Pages
- 10746-10761
- OpenAlex
- W4416034797
- Cited by
- 208
- References
- 0
- Citations by year
- 2026: 159 · 2025: 49
- FWCI
- 198.29
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (U22B2019)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- retrieval-augmented generation, vector representation, large language models, external knowledge integration, context awareness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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