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

A training-free framework called ACTS-SQL, developed by researchers and deployed in Volcano Engine's Torch Log Service (TLS), improves SQL execution accuracy from 36.77% to 53.61% on real user queries using GPT-5, and achieves a 9.42% improvement over the previous state-of-the-art on the BIRD-Critic benchmark.

arXiv:2608.15145v1 Announce Type: new Abstract: Large Language Models LLMs have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines. Existing SQL correction approaches either rely on large-scale, high-quality training data with substantial overhead, or adopt single-path agentic workflows that are brittle to early mistakes and prone to error propagation. To develop a practical SQL correctness system for industrial scenarios, we present a training-free framework that formulates SQL correction as a plan-guided, tree-structured debugging process. By maintaining multiple correction strategies and enabling backtracking, the framework mitigates error accumulation during iterative refinement. We further integrate execution-based verification and clause-level diagnostic tools to support strategy pruning and precise error localization. We evaluate the system on the BIRD-Critic benchmark and observe consistent accuracy gains over strong LLM backbones and representative agent-based baselines, achieving a 9.42% improvement over the previous state-of-the-art method. The framework is also deployed in the Torch Log Service TLS of Volcano Engine to support an online Text-to-TLS API. In production, it improves execution accuracy from 36.77% to 53.61% on real user queries with a representative strong LLM backbone GPT-5 . These results demonstrate the effectiveness and stability of our approach in real-world deployments.