Text-to-Visualization (Text-to-Vis) translates natural language queries into visualization query languages, enabling non-expert users to perform data analysis. However, most existing methods follow a one-shot paradigm that requires users to specify all visualization details in a single round, often leading to cognitive overload and incorrect visualizations. In this paper, we propose PMVis, a progressive multi-turn paradigm for text-to-vis, where users' intents are refined through multi-turn interactions. To support research in this paradigm, we construct PMVisBench, the first dataset designed to capture the progressive and iterative nature of real-world user queries. It is built through VQL simplification and NLQ reconstruction, with explicit rule constraints to ensure each intermediate VQL remains valid and meaningful. Building upon PMVis, we further introduce PMVisAgent, an agent-based framework that simulates realistic user-system dialogues. PMVisAgent consists of a User, a System, and a Validation Agent that performs verification and repair via a ReAct-style tool-use loop to mitigate error accumulation across rounds, with explicit interaction and verification rules to ensure reliability of the multi-agent system. Extensive experiments on PMVisBench demonstrate that PMVisAgent significantly outperforms state-of-the-art text-to-vis baselines. It achieves up to 17.57\% and 23.21\% improvements in execution accuracy in single-table and multi-table settings, respectively, while ablation studies confirm the importance of combining progressive interaction with clarification. The code is available at https://github.com/wxxv/PMVis.

翻译：文本转可视化（Text-to-Visualization, Text-to-Vis）将自然语言查询转化为可视化查询语言，使非专业用户能够进行数据分析。然而，现有方法大多遵循一次性生成范式，要求用户单轮指定所有可视化细节，这常导致认知负荷过重及可视化结果错误。本文提出PMVis，一种渐进式多轮文本转可视化范式，通过多轮交互逐步精化用户意图。为支撑该范式的研究，我们构建了首个捕捉真实用户查询渐进性与迭代性特征的数据集PMVisBench，该数据集通过VQL简化与NLQ重构构建，并融入显式规则约束以确保每一中间VQL的有效性和语义完整性。基于PMVis，我们进一步提出PMVisAgent，一种模拟真实用户-系统对话的智能体框架。该框架由用户、系统及验证智能体构成，其中验证智能体通过ReAct风格的工具调用循环执行验证与修复，以缓解跨轮误差累积，并借助显式交互与验证规则保障多智能体系统的可靠性。在PMVisBench上的大量实验表明，PMVisAgent显著优于现有先进文本转可视化基线方法：在单表与多表场景下，其执行准确率分别提升高达17.57%与23.21%；消融实验则证实了渐进式交互与澄清机制相结合的重要性。代码开源于https://github.com/wxxv/PMVis。