DatawiseAgent: A Notebook-Centric LLM Agent Framework for Automated Data Science

• URL: http://arxiv.org/abs/2503.07044v1
• Date: Mon, 10 Mar 2025 08:32:33 GMT
• Title: DatawiseAgent: A Notebook-Centric LLM Agent Framework for Automated Data Science
• Authors: Ziming You, Yumiao Zhang, Dexuan Xu, Yiwei Lou, Yandong Yan, Wei Wang, Huaming Zhang, Yu Huang,
• Abstract summary: DatawiseAgent is a notebook-centric agent framework that unifies interactions among user, agent and the computational environment.<n>It orchestrates four stages, including DSF-like planning, incremental execution, self-ging, and post-filtering.<n>It consistently outperforms or matches state-of-the-art methods across multiple model settings.
• Score: 4.1431677219677185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data Science tasks are multifaceted, dynamic, and often domain-specific. Existing LLM-based approaches largely concentrate on isolated phases, neglecting the interdependent nature of many data science tasks and limiting their capacity for comprehensive end-to-end support. We propose DatawiseAgent, a notebook-centric LLM agent framework that unifies interactions among user, agent and the computational environment through markdown and executable code cells, supporting flexible and adaptive automated data science. Built on a Finite State Transducer(FST), DatawiseAgent orchestrates four stages, including DSF-like planning, incremental execution, self-debugging, and post-filtering. Specifically, the DFS-like planning stage systematically explores the solution space, while incremental execution harnesses real-time feedback and accommodates LLM's limited capabilities to progressively complete tasks. The self-debugging and post-filtering modules further enhance reliability by diagnosing and correcting errors and pruning extraneous information. Extensive experiments on diverse tasks, including data analysis, visualization, and data modeling, show that DatawiseAgent consistently outperforms or matches state-of-the-art methods across multiple model settings. These results highlight its potential to generalize across data science scenarios and lay the groundwork for more efficient, fully automated workflows.

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