{"ID":2863384,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24403","arxiv_id":"2509.24403","title":"Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling","abstract":"State-of-the-art (SOTA) Text-to-SQL methods still lag significantly behind human experts on challenging benchmarks like BIRD. Current approaches that explore test-time scaling lack an orchestrated strategy and neglect the model's internal reasoning process. To bridge this gap, we introduce Agentar-Scale-SQL, a novel framework leveraging scalable computation to improve performance. Agentar-Scale-SQL implements an Orchestrated Test-Time Scaling strategy that synergistically combines three distinct perspectives: i) Internal Scaling via RL-enhanced Intrinsic Reasoning, ii) Sequential Scaling through Iterative Refinement, and iii) Parallel Scaling using Diverse Synthesis and Tournament Selection. Agentar-Scale-SQL is a general-purpose framework designed for easy adaptation to new databases and more powerful language models. Extensive experiments show that Agentar-Scale-SQL achieves SOTA performance on the BIRD benchmark, reaching 81.67% execution accuracy on the test set and ranking first on the official leaderboard, demonstrating an effective path toward human-level performance.","short_abstract":"State-of-the-art (SOTA) Text-to-SQL methods still lag significantly behind human experts on challenging benchmarks like BIRD. Current approaches that explore test-time scaling lack an orchestrated strategy and neglect the model's internal reasoning process. To bridge this gap, we introduce Agentar-Scale-SQL, a novel fr...","url_abs":"https://arxiv.org/abs/2509.24403","url_pdf":"https://arxiv.org/pdf/2509.24403v6","authors":"[\"Pengfei Wang\",\"Baolin Sun\",\"Xuemei Dong\",\"Yaxun Dai\",\"Hongwei Yuan\",\"Mengdie Chu\",\"Yingqi Gao\",\"Xiang Qi\",\"Peng Zhang\",\"Ying Yan\"]","published":"2025-09-29T07:50:02Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.DB\"]","methods":"[\"Language Model\"]","has_code":false}