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Skyline Retrieval meets Set-Cover Chunk Merging: A Cost-Effective RAG-Sketch for Long-Context LLM QA

Summary: RAG-Sketch combines schema-driven query decomposition with skyline retrieval to select Pareto-optimal chunks balancing similarity and key-information coverage. A greedy set-cover merger removes redundant context access, improving long-context QA accuracy 24.5% while cutting token cost 58.5%. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7484
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,290 | 29.41%
DOI
10.1145/3802111

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BibTeX Citation

@inproceedings{zhu_sigmod26,
        title = {{Skyline Retrieval meets Set-Cover Chunk Merging: A Cost-Effective RAG-Sketch for Long-Context LLM QA}},
        author = {Zhu, Xinyi and Li, Haoyang and Zhang, Yongqi and Chen, Lei},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802111},
        url = {https://dl.acm.org/doi/10.1145/3802111},
        year = {2026}
}

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