通过EXHYTE框架对人工智能进行科学发现的以过程为中心的调查
Research square
|January 9, 2026
概括
大型语言模型 (LLM) 和人工智能代理加速科学发现. 新的EXHYTE循环框架统一了用于探索,假设生成和在结构化工作流程中测试的AI策略.
科学领域:
- 在科学发现中的AI.
- 在化学,生物学,材料科学和物理学的跨学科应用.
背景情况:
- 科学中现有的AI研究是分散的,专注于孤立的任务.
- 缺乏整合人工智能的统一框架,将人工智能纳入更广泛的科学发现过程.
研究的目的:
- 介绍EXHYTE循环:探索,假设生成和测试.
- 将科学发现正式化为一个代的AI驱动过程.
- 为科学研究中的AI提供结构化的工作流.
主要方法:
- 在科学研究中汇集了最新的AI.
- 为每个EXHYTE子阶段提炼反复的AI策略.
- 按代表性策略和特定领域的进步组织文学.
主要成果:
- 在EXHYTE框架下统一多样化的AI方法.
- 在人工智能驱动的发现中确定了成熟和未被探索的子阶段.
- 揭示了闭环发现系统的互补性.
结论:
- EXHYTE周期为科学发现中的AI提供了以过程为中心的视角.
- 澄清了人类与人工智能在研究中的合作.
- 为可适应,自主的人工智能驱动的科学发现框架提供了路线图.
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