ATLAS: Adaptive Text-to-SQL with Lifecycle-Aware Self-Maintaining Context
Summary: ATLAS co-locates schema metadata, semantic context, and vector embeddings in an RDBMS, enabling adaptive two-stage schema linking without external vector stores. An agent-driven lifecycle detects DDL changes and regenerates rich annotations, synonyms, rules, and mappings automatically. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Qing Zhang (Fudan University)
- 2. Shijing Hu (Fudan University)
- 3. Zhihui Lu (Fudan University)
BibTeX Citation
@article{zhang_vldb26,
title = {{ATLAS: Adaptive Text-to-SQL with Lifecycle-Aware Self-Maintaining Context}},
author = {Zhang, Qing and Hu, Shijing and Lu, Zhihui},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4838--4841},
doi = {10.14778/3827998.3828135},
url = {https://doi.org/10.14778/3827998.3828135},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 174 | Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation | 2024 | VLDB | 0.00026790979 |
| 533 | CodeS: Towards Building Open-source Language Models for Text-to-SQL | 2024 | SIGMOD | 0.00016826571 |
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