This study examines whether collective reasoning among generative agents can facilitate novel and coherent thinking that leads to innovation. To achieve this, it proposes GAI, a new LLM-empowered framework designed for reflection and interaction among multiple generative agents to replicate the process of innovation. The core of the GAI framework lies in an architecture that dynamically processes the internal states of agents and a dialogue scheme specifically tailored to facilitate analogy-driven innovation. The framework's functionality is evaluated using Dyson's invention of the bladeless fan as a case study, assessing the extent to which the core ideas of the innovation can be replicated through a set of fictional technical documents. The experimental results demonstrate that models with internal states significantly outperformed those without, achieving higher average scores and lower variance. Notably, the model with five heterogeneous agents equipped with internal states successfully replicated the key ideas underlying the Dyson's invention. This indicates that the internal state enables agents to refine their ideas, resulting in the construction and sharing of more coherent and comprehensive concepts.

翻译：本研究探讨了生成式智能体之间的集体推理是否能够促进新颖且连贯的思维，从而催生创新。为此，本文提出了GAI，一种新型的由大语言模型驱动的框架，旨在通过多个生成式智能体之间的反思与互动来复现创新过程。GAI框架的核心在于一种动态处理智能体内部状态的架构，以及一个专门为促进类比驱动创新而设计的对话方案。该框架的功能以戴森无叶风扇的发明作为案例研究进行评估，通过一组虚构的技术文档来评估创新核心思想的可复现程度。实验结果表明，具备内部状态的模型显著优于无内部状态的模型，获得了更高的平均分和更低的方差。值得注意的是，配备内部状态的五个异质智能体模型成功复现了戴森发明背后的关键思想。这表明内部状态使智能体能够精炼其想法，从而构建并分享更连贯、更全面的概念。