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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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A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 281
Graph Retrieval-Augmented Generation: A Survey
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S. (2026) · ACM Transactions on Information Systems · doi:10.1145/3777378 · Cited by 206
HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation
Liu, P., Liu, X., Yao, R., Liu, J., Meng, S., Wang, D., & Ma, J. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754761 · Cited by 43
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation
Liu, H., Wang, Z., Chen, X., Li, Z., Xiong, F., Yu, Q., & Zhang, W. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.97 · Cited by 71
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Self-citation
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
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