本文提出了一种名为DatawiseAgent的笔记本中心化大型语言模型（LLM）代理框架，旨在解决现有数据科学自动化中任务范围窄、泛化能力有限以及过度依赖先进LLM的问题。通过引入统一的交互表示和多阶段架构，该框架实现了灵活的长期规划、渐进式解决方案开发和稳健的执行失败恢复，在多种数据科学场景和模型上均取得了优于AutoGen和TaskWeaver等基准的性能。

arXiv:2503.07044v2 Announce Type: replace-cross 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.