@inproceedings{parekh-etal-2026-pexa,
title = "{PE}x{A}: Parallel Exploration Agent for Complex Text-to-{SQL}",
author = "Parekh, Tanmay and
Hofmann-Coyle, Ella and
Wang, Shuyi and
Kothur, Sachith Sri Ram and
Prasad, Srivas and
Chen, Yunmo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-short.48/",
pages = "564--594",
ISBN = "979-8-89176-391-3",
abstract = "LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of the original query. After iterating on test case coverage, the final SQL is generated only when enough information is gathered, leveraging the explored test case SQLs to ground the final generation. We validated our framework on a state-of-the-art benchmark for text-to-SQL, Spider 2.0, achieving a new state-of-the-art with 70.2{\%} execution accuracy."
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<abstract>LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of the original query. After iterating on test case coverage, the final SQL is generated only when enough information is gathered, leveraging the explored test case SQLs to ground the final generation. We validated our framework on a state-of-the-art benchmark for text-to-SQL, Spider 2.0, achieving a new state-of-the-art with 70.2% execution accuracy.</abstract>
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%0 Conference Proceedings
%T PExA: Parallel Exploration Agent for Complex Text-to-SQL
%A Parekh, Tanmay
%A Hofmann-Coyle, Ella
%A Wang, Shuyi
%A Kothur, Sachith Sri Ram
%A Prasad, Srivas
%A Chen, Yunmo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F parekh-etal-2026-pexa
%X LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel and together ensure semantic coverage of the original query. After iterating on test case coverage, the final SQL is generated only when enough information is gathered, leveraging the explored test case SQLs to ground the final generation. We validated our framework on a state-of-the-art benchmark for text-to-SQL, Spider 2.0, achieving a new state-of-the-art with 70.2% execution accuracy.
%U https://aclanthology.org/2026.acl-short.48/
%P 564-594
Markdown (Informal)
[PExA: Parallel Exploration Agent for Complex Text-to-SQL](https://aclanthology.org/2026.acl-short.48/) (Parekh et al., ACL 2026)
ACL
- Tanmay Parekh, Ella Hofmann-Coyle, Shuyi Wang, Sachith Sri Ram Kothur, Srivas Prasad, and Yunmo Chen. 2026. PExA: Parallel Exploration Agent for Complex Text-to-SQL. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 564–594, San Diego, California, United States. Association for Computational Linguistics.