@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://preview.aclanthology.org/ingest-acl/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."
}