让大模型像人一样在笔记本里一步步做数据科学，还能自动修复错误。
DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation
- 基于有限状态转换机设计多阶段流程，支持长序列规划与逐步求解。
- 在多个任务和模型上超越AutoGen等基线，性能达到当前最优。
- 即使使用小模型也能稳定运行，适合实际部署场景。
现有用于自动化数据科学的大语言模型代理虽具潜力，但受限于任务范围窄、跨任务泛化能力弱，且过度依赖顶级大模型。本文提出DatawiseAgent，一种以笔记本为中心的LLM代理框架，旨在实现自适应与鲁棒的数据科学自动化。受人类数据科学家在计算笔记本中工作方式启发，DatawiseAgent引入统一交互表示与基于有限状态转换机（FSTs）的多阶段架构，支持灵活的长时序规划、渐进式解决方案构建及执行失败后的稳健恢复。在多种数据科学场景与模型上的广泛实验表明，DatawiseAgent持续取得当前最优表现，显著优于AutoGen和TaskWeaver等强基线；进一步评估显示，其在较弱或较小模型下仍能保持良好性能，体现出卓越的鲁棒性与可扩展性。
原文摘要 · Abstract (English)
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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