@inproceedings{zerui-etal-2026-memo,
    title = "Memo-{SQL}: Structured Decomposition and Experience-Driven Self-Correction for Training-Free {NL}2{SQL}",
    author = "Zerui, Yang  and
      Wang, Weichuan  and
      Xu, Yanwei  and
      Song, Linqi  and
      Matsuda, Yudai  and
      Han, Wei  and
      Bai, Bo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.253/",
    pages = "5130--5148",
    ISBN = "979-8-89176-395-1",
    abstract = "Existing NL2SQL systems face two critical limitations : (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error{--}fix pairs that could guide more robust self-correction; and (2) test-time scaling (TTS) approaches often decompose questions arbitrarily, producing near-identical SQL candidates across runs and diminishing ensemble gains. Moreover, these methods suffer from a stark accuracy{--}efficiency trade-off: high performance demands excessive computation, while fast variants compromise quality. We present Memo-SQL, a training-free framework that addresses these issues through two simple ideas: structured decomposition and experience-aware self-correction. Instead of leaving decomposition to chance, we apply three clear strategies, entity-wise, hierarchical, and atomic sequential, to encourage diverse reasoning. For correction, we build a dynamic memory of both successful queries and historical error{--}fix pairs, and use retrieval-augmented prompting to bring relevant examples into context at inference time, no fine-tuning or external APIs required. On BIRD, Memo-SQL achieves 68.5{\%} execution accuracy, setting a new state of the art among open, zero-fine-tuning methods, while using over 10{\texttimes} fewer resources than prior TTS approaches."
}