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BobFlow introduces a dataflow abstraction layer designed to bridge the gap between human analysts and LLM agents in data science workflows, addressing the opacity of traditional code-based interactions. By enabling iterative construction and verification of analysis pipelines, the system demonstrates significant efficiency gains over standard script-based agents while maintaining high accuracy across various model weights. This work represents a concrete step toward practical human-agent collaboration in computational research environments.
BobFlow: Towards Human-Agent Collaborative Data Science Using Dataflows
arXiv:2611.05761v1 Announce Type: cross 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.
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.
Databases (cs.DB); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2611.05761 [cs.DB]
(or arXiv:2611.05761v1 [cs.DB] for this version)
https://doi.org/10.48550/arXiv.2611.05761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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