@inproceedings{you-etal-2025-datawiseagent,
title = "{D}atawise{A}gent: A Notebook-Centric {LLM} Agent Framework for Adaptive and Robust Data Science Automation",
author = "You, Ziming and
Zhang, Yumiao and
Xu, Dexuan and
Lou, Yiwei and
Yan, Yandong and
Wang, Wei and
Zhang, Huamin and
Huang, Yu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.58/",
doi = "10.18653/v1/2025.emnlp-main.58",
pages = "1099--1123",
ISBN = "979-8-89176-332-6",
abstract = "Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. We introduce DatawiseAgent, a notebook-centric LLM agent framework for adaptive and robust data science automation. Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finite-state transducers (FSTs). This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures. Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability. Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability."
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<abstract>Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. We introduce DatawiseAgent, a notebook-centric LLM agent framework for adaptive and robust data science automation. Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finite-state transducers (FSTs). This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures. Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability. Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.</abstract>
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%0 Conference Proceedings
%T DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation
%A You, Ziming
%A Zhang, Yumiao
%A Xu, Dexuan
%A Lou, Yiwei
%A Yan, Yandong
%A Wang, Wei
%A Zhang, Huamin
%A Huang, Yu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F you-etal-2025-datawiseagent
%X Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. We introduce DatawiseAgent, a notebook-centric LLM agent framework for adaptive and robust data science automation. Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finite-state transducers (FSTs). This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures. Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability. Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.
%R 10.18653/v1/2025.emnlp-main.58
%U https://aclanthology.org/2025.emnlp-main.58/
%U https://doi.org/10.18653/v1/2025.emnlp-main.58
%P 1099-1123
Markdown (Informal)
[DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation](https://aclanthology.org/2025.emnlp-main.58/) (You et al., EMNLP 2025)
ACL
- Ziming You, Yumiao Zhang, Dexuan Xu, Yiwei Lou, Yandong Yan, Wei Wang, Huamin Zhang, and Yu Huang. 2025. DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 1099–1123, Suzhou, China. Association for Computational Linguistics.