Skip to content
Log in

Similar papers

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-world environments.

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 reliability of these generated queries at inference time is a central challenge. Conventional methods rely on coarse execution-based signals, which may limit their ability to capture the nuanced semantic alignment required for high-stakes database environments. In this work, we propose the use of Outcome Reward Models (ORMs) as a fine-grained, probabilistic feedback mechanism for test-time verification in Text-to-SQL tasks. We introduce GradeSQL, a framework for training task-specific ORMs that assign scalar utility scores to candidate SQL queries based on their semantic correctness and alignment with database schema. Our approach is evaluated on the BIRD and Spider benchmarks across multiple open-source LLM families. Experimental results demonstrate that ORM-based verification consistently outperforms traditional execution-based heuristics.

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, Tianshi 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