Large language models (LLMs), in conjunction with various reasoning
reinforcement methodologies, have demonstrated remarkable capabilities
comparable to humans in fields such as mathematics, law, coding, common sense,
and world knowledge. In this paper, we delve into the reasoning abilities of
LLMs within complex human systems. We propose a novel reasoning framework,
termed ``Mosaic Expert Observation Wall'' (MEOW) exploiting
generative-agents-based simulation technique. In the MEOW framework, simulated
data are utilized to train an expert model concentrating ``experience'' about a
specific task in each independent time of simulation. It is the accumulated
``experience'' through the simulation that makes for an expert on a task in a
complex human system. We conduct the experiments within a communication game
that mirrors real-world security scenarios. The results indicate that our
proposed methodology can cooperate with existing methodologies to enhance the
reasoning abilities of LLMs in complex human systems.

在本文中，我们探讨了大型语言模型（LLMs）在复杂人类系统中的推理能力，并提出了一种新的推理框架 ——“Mosaic Expert Observation Wall”（MEOW），利用生成型代理模拟技术。通过模拟数据训练一个专家模型，该模型在每个独立的模拟时间中专注于某个特定任务的 “经验”，通过模拟积累的 “经验” 使其成为复杂人类系统中某个任务的专家。我们在模拟真实世界安全场景的通信游戏中进行了实验，结果表明我们提出的方法可以与现有方法协同工作，增强大型语言模型在复杂人类系统中的推理能力。