原文：BobFlow: Towards Human-Agent Collaborative Data Science Using Dataflows
作者：Jiadong Bai, Yicong Huang, Chen Li
来源：arXiv cs.DB（数据库）
正文
Computer Science > Databases
arXiv:2611.05761v1 (cs)
[Submitted on 4 Nov 2026]
Title:BobFlow: Towards Human-Agent Collaborative Data Science Using Dataflows
Authors:Jiadong Bai, Yicong Huang, Chen Li
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Abstract:Recent advances in large language models (LLMs) have established human-agent collaboration as a promising paradigm for data science. Effective collaboration requires a shared computational abstraction that enables agents to reason about processing logic while allowing human analysts to understand, guide, and refine the analysis. Although scripts written in Python or SQL provide precise control, their syntax and execution details can be difficult for analysts to understand and modify. In this paper, we argue that dataflows are an effective abstraction for human-agent collaboration in data science. We introduce BobFlow, a system that combines the dataflow abstraction with the ReAct paradigm. It enables agents to iteratively construct and execute dataflows for data science tasks while allowing analysts to inspect the analysis and provide feedback. Our user study shows that BobFlow helps analysts understand, inspect, and verify agent-generated analyses. A thorough experimental evaluation across proprietary and open-weight LLMs shows accuracy score gains of up to 18.6 points at just over 1/3 of a script-based ReAct agent’s inference cost.
Subjects:
Databases (cs.DB); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as:
arXiv:2611.05761 [cs.DB]
(or
arXiv:2611.05761v1 [cs.DB] for this version)
https://doi.org/10.48550/arXiv.2611.05761
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arXiv-issued DOI via DataCite (pending registration)
主题
数据库 · 人工智能 · 人机交互
由「前沿雷达」于 2026-11-04 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。
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