DA-Studio：用于端到端数据分析的智能体系统
DA-Studio: An Agentic System for End-to-End Data Analysis
AI总结：
提出DA-Studio，一个集成了沙盒执行与可检查工作流的端到端数据分析系统，通过迭代动作生成与代码执行，从原始文件与自然语言请求逐步构建可执行分析步骤。
摘要：
现实世界的数据分析是一个多步骤过程，涉及异构输入，而不仅仅是产生最终答案。一个实用的系统应能自主组织多步骤工作流，在沙盒化且可控的环境中执行生成的代码，并通过可见的动作轨迹和中间产物保持可检查性。然而，现有的基于LLM的分析工具通常强调孤立的子任务，对完整的、基于执行的工作流支持有限。我们提出了DA-Studio（数据分析工作室），一个用于端到端数据分析的交互式网页演示系统，该系统是自主的、沙盒化的且可检查的。DA-Studio集成了动作结构化的分析后端、沙盒化的执行工作空间以及用于任务设置、流式动作轨迹、产物预览、代码编辑与重新运行以及报告导出的浏览器界面。通过迭代的动作生成、代码执行和反馈整合，它从原始文件和自然语言请求逐步构建可执行的分析步骤，并在整个过程中暴露中间结果和产物。
英文摘要：
Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer. A practical system should autonomously organize multi-step workflows, execute generated code in a sandboxed and controllable environment, and remain inspectable through visible action traces and intermediate artifacts. Existing LLM-based analysis tools, however, often emphasize isolated subtasks, leaving limited support for complete execution-grounded workflows. We present DA-Studio (Data Analysis Studio), an interactive web-based demo system for end-to-end data analysis that is autonomous, sandboxed, and inspectable. DA-Studio integrates an action-structured analysis backend, a sandboxed execution workspace, and a browser interface for task setup, streamed action traces, artifact preview, code editing and rerunning, and report export. Through iterative action generation, code execution, and feedback incorporation, it incrementally constructs executable analysis steps from raw files and natural-language requests while exposing intermediate results and artifacts throughout the process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
