---
id: 20260503-T0-01
title: "多智能体协作实现机器学习全自动：输入自然语言即可生成端到端ML管道"
title_en: "Multi-Agent System Automates ML Pipeline Generation from Natural Language"
url: https://ai.daily.yangsir.net/daily/20260503-T0-01
issue_date: 2026-05-03
publish_date: 2026-05-02T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2604.27096
---

# 多智能体协作实现机器学习全自动：输入自然语言即可生成端到端ML管道

arXiv发表的新研究（2604.27096）提出了一种统一的多智能体架构，能够根据数据集和自然语言目标，自动生成端到端的机器学习（ML）管道。该系统包含五个智能体，协同完成从数据预处理到模型部署的全流程，同时提升了生成管道的效率、鲁棒性和可解释性。数据科学家可以用它将繁琐的ML工作流搭建过程自动化，大幅减少重复性编码工作。

## English Version

**Multi-Agent System Automates ML Pipeline Generation from Natural Language**

A new paper on arXiv (2604.27096) introduces a unified multi-agent architecture that automates end-to-end machine learning pipeline generation using datasets and natural-language goals. The five-agent system handles everything from preprocessing to deployment, improving efficiency, robustness, and explainability. Data scientists can use it to automate tedious ML workflows and reduce repetitive coding.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2604.27096)

**详情页**：https://ai.daily.yangsir.net/daily/20260503-T0-01

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