New ACTS-SQL framework boosts Text-to-SQL accuracy with agentic debugging

Researchers have developed ACTS-SQL, a novel framework for improving the accuracy of Text-to-SQL systems by treating SQL correction as a tree-structured debugging process. This training-free approach incorporates multiple correction strategies, allows for backtracking to mitigate error propagation, and integrates execution-based verification for precise error localization. When deployed in Volcano Engine's Torch Log Service, ACTS-SQL boosted execution accuracy from 36.77% to 53.61% on real user queries using GPT-5 as the backbone. AI

IMPACT Enhances the reliability of LLM-based Text-to-SQL systems, enabling more accurate data querying in production environments.

RANK_REASON Academic paper detailing a new method for Text-to-SQL correctness. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• ACTS-SQL
• BIRD-Critic
• GPT-5
• large-language models
• Text-to-SQL
• Torch Log Service
• Volcano Engine

• paper
• product

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