Visual analytics (VA) requires analysts to iteratively propose analysis tasks based on observations and execute tasks by creating visualizations and interactive exploration to gain insights. This process demands skills in programming, data processing, and visualization tools, highlighting the need for a more intelligent, streamlined VA approach. Large language models (LLMs) have recently been developed as agents to handle various tasks with dynamic planning and tool-using capabilities, offering the potential to enhance the efficiency and versatility of VA. We propose LightVA, a lightweight VA framework that supports task decomposition, data analysis, and interactive exploration through human-agent collaboration. Our method is designed to help users progressively translate high-level analytical goals into low-level tasks, producing visualizations and deriving insights. Specifically, we introduce an LLM agent-based task planning and execution strategy, employing a recursive process involving a planner, executor, and controller. The planner is responsible for recommending and decomposing tasks, the executor handles task execution, including data analysis, visualization generation and multi-view composition, and the controller coordinates the interaction between the planner and executor. Building on the framework, we develop a system with a hybrid user interface that includes a task flow diagram for monitoring and managing the task planning process, a visualization panel for interactive data exploration, and a chat view for guiding the model through natural language instructions. We examine the effectiveness of our method through a usage scenario and an expert study.

翻译：可视分析（VA）要求分析人员根据观察结果迭代提出分析任务，并通过创建可视化图表与交互式探索来执行任务以获取洞察。这一过程需要编程、数据处理和可视化工具等多方面技能，凸显了对更智能、更简化的VA方法的需求。近年来，大型语言模型（LLM）被开发为具备动态规划与工具使用能力的智能体，为提升VA的效率和通用性提供了可能。本文提出LightVA，一种支持通过人机协作进行任务分解、数据分析和交互探索的轻量化VA框架。该方法旨在帮助用户将高层分析目标逐步转化为底层任务，生成可视化结果并推导洞察。具体而言，我们引入了一种基于LLM智能体的任务规划与执行策略，采用包含规划器、执行器和控制器的递归流程：规划器负责推荐与分解任务，执行器处理任务执行（包括数据分析、可视化生成与多视图组合），控制器则协调规划器与执行器间的交互。基于该框架，我们开发了一个具备混合用户界面的系统，包含用于监控与管理任务规划过程的任务流程图、支持交互式数据探索的可视化面板，以及通过自然语言指令引导模型的对话视图。我们通过应用场景案例和专家研究验证了该方法的有效性。