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NeutronRAG: Towards Understanding the Effectiveness of RAG from a Data Retrieval Perspective

Summary: NeutronRAG is a data-retrieval–driven demonstration to understand RAG effectiveness across retrieval paradigms (VectorRAG, GraphRAG, HybridRAG). It offers hybrid retrieval, systematic analysis, visual feedback, and parameter-adjustment guidance for data-driven comparison of retrieval methods and settings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7230
Venue
SIGMOD
Year
2025
Pagerank
-
Overall Rank
13,309 | 8.69%
DOI
10.1145/3722212.3725119

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Authors

BibTeX Citation

@inproceedings{li_sigmod25,
        title = {{NeutronRAG: Towards Understanding the Effectiveness of RAG from a Data Retrieval Perspective}},
        author = {Li, Peizheng and Chen, Chaoyi and Yuan, Hao and Fu, Zhenbo and Shen, Hang and Yang, Xinbo and Wang, Qiange and Ai, Xin and Zhang, Yanfeng and Yu, Ge and Wen, Yingyou},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3722212.3725119},
        url = {https://dl.acm.org/doi/10.1145/3722212.3725119},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
10,357 DepCache: A KV Cache Management Framework for GraphRAG with Dependency Attention 2026 SIGMOD 5.093636e-05
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