Transformer-based large language models are rapidly advancing in the field of
machine learning research, with applications spanning natural language,
biology, chemistry, and computer programming. Extreme scaling and reinforcement
learning from human feedback have significantly improved the quality of
generated text, enabling these models to perform various tasks and reason about
their choices. In this paper, we present an Intelligent Agent system that
combines multiple large language models for autonomous design, planning, and
execution of scientific experiments. We showcase the Agent's scientific
research capabilities with three distinct examples, with the most complex being
the successful performance of catalyzed cross-coupling reactions. Finally, we
discuss the safety implications of such systems and propose measures to prevent
their misuse.

本论文展示了一个结合多个大型语言模型的智能代理系统，可自主设计、规划和执行科学实验，并通过三个不同的实例展示代理的科学研究能力，最为复杂的是成功执行加催化交叉偶联反应。最后，讨论了这种系统的安全影响，并提出了防止滥用的措施。