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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

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@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}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,029 Graph-Based Retrieval-Augmented Generation: Applications, Challenges, Solutions, and Opportunities 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 24 of 24 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036684414
322 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021041865
1,658 D-Bot: Database Diagnosis System using Large Language Models 2024 VLDB 9.9642078e-05
1,699 Black or White? How to Develop an AutoTuner for Memory-based Analytics 2020 SIGMOD 9.8445322e-05
2,039 LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency 2025 VLDB 9.1493268e-05
2,231 GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization 2024 VLDB 8.7982985e-05
2,348 The Dawn of Natural Language to SQL: Are We Fully Ready? 2024 VLDB 8.6009821e-05
2,395 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5281914e-05
2,720 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 8.0966919e-05
2,772 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.035288e-05
3,158 CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions 2021 VLDB 7.5811757e-05
3,169 Panda: Performance Debugging for Databases using LLM Agents 2024 CIDR 7.5696238e-05
3,466 In-depth Analysis of Graph-based RAG in a Unified Framework 2025 VLDB 7.2783993e-05
3,489 Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL 2024 VLDB 7.2627807e-05
3,973 RetClean: Retrieval-Based Data Cleaning Using LLMs and Data Lakes 2024 VLDB 6.8876964e-05
4,114 Automatic Database Configuration Debugging using Retrieval-Augmented Language Models 2025 SIGMOD 6.7971182e-05
4,229 Cracking SQL Barriers: An LLM-based Dialect Translation System 2025 SIGMOD 6.7165534e-05
4,385 R-Bot: An LLM-based Query Rewrite System 2025 VLDB 6.6235293e-05
5,848 HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation 2023 SIGMOD 5.9720806e-05
6,945 PalimpChat: Declarative and Interactive AI analytics 2025 SIGMOD 5.6378669e-05
7,070 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.6080535e-05
7,275 SubStrat: A Subset-Based Optimization Strategy for Faster AutoML 2023 VLDB 5.5679786e-05
7,812 Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations 2024 SIGMOD 5.4486106e-05
8,316 ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries 2025 VLDB 5.3561732e-05
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