---
id: 20260527-T0-05
title: "LLM-AutoSciLab：通过主动实验实现闭环科学发现"
title_en: "LLM-AutoSciLab: Closed-loop science via active experimentation"
url: https://ai.daily.yangsir.net/daily/20260527-T0-05
issue_date: 2026-05-27
publish_date: 2026-05-26T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.24043
---

# LLM-AutoSciLab：通过主动实验实现闭环科学发现

arXiv论文提出LLM-AutoSciLab框架，利用大型语言模型实现主动实验驱动的闭环科学发现。该系统让AI自主提出假设、设计实验、获取数据并迭代优化，突破了传统固定数据集监督学习的局限，有望加速科学发现进程。

## English Version

**LLM-AutoSciLab: Closed-loop science via active experimentation**

arXiv paper introduces LLM-AutoSciLab framework for closed-loop scientific discovery using LLMs. The system autonomously generates hypotheses, designs experiments, acquires data, and iteratively optimizes, overcoming limitations of traditional fixed-dataset supervised learning.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.24043)

**详情页**：https://ai.daily.yangsir.net/daily/20260527-T0-05

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