Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL

• URL: http://arxiv.org/abs/2601.10011v1
• Date: Thu, 15 Jan 2026 02:42:05 GMT
• Title: Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL
• Authors: Zerui Yang, Weichuan Wang, Yanwei Xu, Linqi Song, Yudai Matsuda, Wei Han, Bo Bai,
• Abstract summary: Existing NL2 systems rely on in-context learning with only correct examples.<n>We present Memo-correction, setting a new state of the art among open, zero-fine-tuning methods.
• Score: 23.966546153810764
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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 times fewer resources than prior TTS approaches.

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