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    <abstract>Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mathematical reasoning. However, their potential in multi-turn Text-to-SQL tasks remains largely underexplored. Existing approaches typically rely on unstable API-based inference or require expensive fine-tuning on small-scale models. In this work, we present Rose-SQL, a training-free framework that leverages small-scale LRMs through in-context learning to enable accurate context-dependent parsing. We introduce the Role-State, a fine-grained representation that bridges the structural gap between schema linking and SQL generation by serving as a structural blueprint. To handle conversational dependencies, Rose-SQL traces the evolution of Role-State through historical context via structural isomorphism checks, guiding the model to infer the possible SQL composition for the current question through verified interaction trajectories. Experiments on the SParC and CoSQL benchmarks show that, within the Qwen3 series, Rose-SQL outperforms in-context learning baselines at the 4B scale and substantially surpasses state-of-the-art fine-tuned models at the 8B and 14B scales, while showing consistent gains on additional reasoning backbones.</abstract>
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%0 Conference Proceedings
%T Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL
%A Zhou, Le
%A Yao, Feng
%A Qiao, Fengcai
%A Xu, Bo
%A Wang, Fangyuan
%A Xu, Boyan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhou-etal-2026-rose
%X Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mathematical reasoning. However, their potential in multi-turn Text-to-SQL tasks remains largely underexplored. Existing approaches typically rely on unstable API-based inference or require expensive fine-tuning on small-scale models. In this work, we present Rose-SQL, a training-free framework that leverages small-scale LRMs through in-context learning to enable accurate context-dependent parsing. We introduce the Role-State, a fine-grained representation that bridges the structural gap between schema linking and SQL generation by serving as a structural blueprint. To handle conversational dependencies, Rose-SQL traces the evolution of Role-State through historical context via structural isomorphism checks, guiding the model to infer the possible SQL composition for the current question through verified interaction trajectories. Experiments on the SParC and CoSQL benchmarks show that, within the Qwen3 series, Rose-SQL outperforms in-context learning baselines at the 4B scale and substantially surpasses state-of-the-art fine-tuned models at the 8B and 14B scales, while showing consistent gains on additional reasoning backbones.
%U https://aclanthology.org/2026.acl-long.2107/
%P 45442-45459

Markdown (Informal)

[Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL](https://aclanthology.org/2026.acl-long.2107/) (Zhou et al., ACL 2026)

• Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL (Zhou et al., ACL 2026)

ACL

• Le Zhou, Feng Yao, Fengcai Qiao, Bo Xu, Fangyuan Wang, and Boyan Xu. 2026. Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45442–45459, San Diego, California, United States. Association for Computational Linguistics.