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Preprint Aug 2026

Iteration Without Elaboration: A Simple ReAct Architecture Suffices for Text-to-SQL Generation

A simple yet effective zero-shot ReAct-style framework built solely on iterative reasoning and a constrained action space defined by a typed Domain-Specific Language (DSL) of 15 relational operations, rather than free-form SQL generation.

Jian Lu, Haiwei Yu, Raymond M. Xiong et al. · 0 citations

ExpeSQL: An Efficient, Experience-Guided Decompositional Search Framework for Text-to-SQL

This work introduces ExpeSQL, a zero-shot, open-source–compatible, and efficient framework that combines divide-and-conquer reasoning, Best-of-N candidate selection, and self-critique with experience-guided refinement that establishes a new paradigm for deployable, self-improving Text-to-SQL systems in dynamic, real-wo...

Unknown authors · 0 citations
Preprint Aug 2026

ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models

This work presents a training-free framework that formulates SQL correction as a plan-guided, tree-structured debugging process that mitigates error accumulation during iterative refinement and demonstrates the effectiveness and stability of the approach in real-world deployments.

Xinmei Huang, Jie Song, Peng Li et al. · 0 citations
Open access Jul 2026

GradeSQL: Outcome reward models for intelligent Text-to-SQL generation from LLMs

As Large Language Models (LLMs) become foundational to next-generation Intelligent Information Systems, the bridge between natural language interfaces and structured database systems remains a critical bottleneck. While Text-to-SQL generation enables cooperative support for complex query formulation, ensuring the rel...

M. Tritto, G. Farano, Dario Di Palma et al. · 2 citations
Preprint Aug 2026

SPOC-SQL: Stage-wise Preference Optimization for Controllable Text-to-SQL

SPOC-SQL is proposed, which decomposes Text-to-SQL into four sequential subtasks following standard SQL execution logic and designs stage-specific optimization strategies for the model to learn key decisions, with the objective of enhancing structured decision-making during query construction.

Yingnan Chen, Chun Ding, Tian-Shi Xu et al. · 0 citations
Open access Jun 2026

Validating LLM-Generated SQL Queries through Metamorphic Prompting

Experimental results demonstrate that MRSQLGen consistently outperforms state-of-the-art hallucination detection techniques, achieving higher precision and recall in detecting hallucinated SQL queries.

Li Lin, Qinglin Zhu, Jintai Hong et al. · 1 citation · ⚡1

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