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SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
Summary: SEFRQO is a self-evolving RAG-based query optimizer that fine-tunes LLMs (supervised + reinforcement) to produce performance-oriented query hints by retrieving execution-feedback. It dynamically builds prompts from similar queries and per-query execution records for continual in-context learning, cutting latency up to ~65–94% vs prior LQOs.
(summarized by gpt-5-mini on Feb 11 2026)
- Paper ID
- 7423
- Venue
- SIGMOD
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,112 | 29.73%
- DOI
-
10.1145/3769826
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 366 |
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation |
2024 |
VLDB |
0.00025580097 |
| 494 |
Milvus: A Purpose-Built Vector Data Management System |
2021 |
SIGMOD |
0.00021769407 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 997 |
CAESURA: Language Models as Multi-Modal Query Planners |
2024 |
CIDR |
0.00014726927 |
| 1,404 |
DB-BERT: A Database Tuning Tool that "Reads the Manual" |
2022 |
SIGMOD |
0.00012179714 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,781 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1282042e-05 |
| 3,105 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5567226e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 4,464 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1552798e-05 |
| 4,687 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
5.9915268e-05 |
| 5,339 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5596755e-05 |
| 5,412 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5200608e-05 |
| 5,931 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2632167e-05 |
| 6,712 |
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2023 |
VLDB |
4.9474017e-05 |
| 7,030 |
R-Bot: An LLM-based Query Rewrite System |
2025 |
VLDB |
4.8518029e-05 |
| 7,326 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
4.7563708e-05 |
| 8,054 |
Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models |
2024 |
VLDB |
4.5909042e-05 |
| 8,479 |
Can Large Language Models Be Query Optimizer for Relational Databases? |
2026 |
SIGMOD |
4.4967983e-05 |
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