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.

"本文针对两阶段事件共指消解方法存在的触发词词目启发机制缺乏同义词聚类能力和小模型理解触发词指代事件能力有限等问题,提出了一种基于大模型增强的两阶段高效的事件共指消解方法,一阶段引入大模型进行同义词聚类,二阶段大模型提供触发词解释文本增强小模型。此外,设计了引导小模型侧重触发词特征向量的损失函数。本文方法在保持近似线性时间复杂度的同时,在ECB+和GVC数据集上的CoNLLF1得分分别提升了2.9和8.0。"