Keywords
constraint-aware reranking
schema drift
paired simulation
reproducibility
Abstract
We evaluated constraint-aware reranking for text-to-SQL execution under 27% schema drift. A deterministic paired simulation generated 64 cases and preserved a boundary-condition stratum. Mean execution accuracy changed from 0.483 to 0.533; the paired difference was +0.050 (95% interval +0.047 to +0.052). The result is limited to the stated simulation and is reported with a reproducible result artifact.
References
Xie, H., Zhou, X., Yang, J., Shen, S., Wang, Z., Zheng, Y., Xu, T., Shi, Y., Zong, Z., Li, Y., Chen, P., Jiang, J., He, D., Yan, X., & Jiang, J. (2026). SiriusDeliver: Automating Data Warehouse Delivery at Tencent. arXiv. https://doi.org/10.48550/arXiv.2608.09185
Mishra, S. K. (2026). Natural Language to SQL at Scale: Integrating OpenAI with Oracle Autonomous Database via SELECT AI. https://doi.org/10.36227/techrxiv.177281023.37874227/v1
Chafik, S., Ezzini, S., & Berrada, I. (2025). Enhancing security in text-to-SQL systems: A novel dataset and agent-based framework. Natural Language Processing, 31(6), 1399-1422. https://doi.org/10.1017/nlp.2025.10008
Amel Abdyssalam A Alhaag (2025). Comparison between Database Search Algorithms (SQL and no SQL). مجلة العلوم الشاملة, 9(ملحق 36), 1810-1830. https://doi.org/10.65405/bc8atc11
Shamal Chavan and Prof. Sandeep Vishwakarma (2026). Natural Language to SQL (NL2SQL): A Comprehensive Study of Text-to-SQL Systems, Conversational AI for Databases, Enterprise Architectures, Challenges, and Future Directions. International Journal of Advanced Research in Science Communication and Technology, 380. https://doi.org/10.48175/ijarsct-37344
Nascimento, E. R. S., & Casanova, M. A. (2024). Querying Databases with Natural Language: The use of Large Language Models for Text-to-SQL tasks. Anais Estendidos do XXXIX Simpósio Brasileiro de Banco de Dados (SBBD Estendido 2024), 196-201. https://doi.org/10.5753/sbbd_estendido.2024.240552
Wang, Y., Lv, H., & Qian, Y. (2026). CIR-SQL: A Dual-Model Intent Recognition Framework for Chinese Text-to-SQL. AI, 7(3), 91. https://doi.org/10.3390/ai7030091
Kim, H., Kim, W., & Kim, W. (2026). GRASP-SQL: Graph Retrieval and Agentic Schema Pruning for Recall-First Text-to-SQL. https://doi.org/10.2139/ssrn.7194451
Mota, F. D. C., Silva, W. D. V. R. D., & Soares, J. D. N. (2026). BENCHMARK DE MODELOS DE LINGUAGEM DE CÓDIGO ABERTO PARA TEXT-TO-SQL EM DADOS ONCOLÓGICOS BRASILEIROS. REMUNOM, 13(14), 1-67. https://doi.org/10.66104/zvzdrv85
Mellah, Y., Kocaman, V., Ul Haq, H., & Talby, D. (2024). Efficient schema-less text-to-SQL conversion using large language models. Artificial Intelligence in Health, 1(2), 96. https://doi.org/10.36922/aih.2661