---
id: 20260428-T0-19
title: "LLM科学代理易出错，对抗性实验揭示风险"
title_en: "LLM Scientific Agents Need Adversarial Testing"
url: https://ai.daily.yangsir.net/daily/20260428-T0-19
issue_date: 2026-04-28
publish_date: 2026-04-27T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2604.22080
---

# LLM科学代理易出错，对抗性实验揭示风险

arXiv研究指出，基于LLM的科学代理在自动化数据分析中加速了已知失败模式。这些代理能处理过去受限于人类时间和专长的任务，但可能复制错误。研究强调，科学发现需结合对抗性实验，验证代理结果的可靠性，避免加速错误传播。

## English Version

**LLM Scientific Agents Need Adversarial Testing**

arXiv study reveals LLM-based scientific agents accelerate a familiar failure mode in data analysis. These agents automate tasks previously limited by human expertise but may propagate errors. Researchers advocate for adversarial experiments to verify results and prevent incorrect conclusions.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2604.22080)

**详情页**：https://ai.daily.yangsir.net/daily/20260428-T0-19

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