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HAMMER: An Automatic RAG Tuning System via Hierarchical Memory-Guided Monte Carlo Tree Search
Summary: HAMMER combines hierarchical graph memory with Monte Carlo Tree Search to tune interdependent RAG modules, retaining and reusing experimental experience. A theoretically grounded query-selection strategy cuts tuning time and token use by up to 9× while improving EM/F1 substantially.
(summarized by gpt-5.6-luna on Jul 26 2026)
Paper ID
h24728f20da4e31c5
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,466 | 29.64%
DOI
10.1145/3802071
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BibTeX Citation
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@inproceedings{zhou_sigmod26,
title = {{HAMMER: An Automatic RAG Tuning System via Hierarchical Memory-Guided Monte Carlo Tree Search}},
author = {Zhou, Yingli and Wang, Zixuan and Fang, Yixiang},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802071},
url = {https://dl.acm.org/doi/10.1145/3802071},
year = {2026}
}
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Showing 24 of 24 cited papers.
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