Recent advances in Large Language Models (LLMs) have shown impressive
capabilities in various applications, yet LLMs face challenges such as limited
context windows and difficulties in generalization. In this paper, we introduce
a metacognition module for generative agents, enabling them to observe their
own thought processes and actions. This metacognitive approach, designed to
emulate System 1 and System 2 cognitive processes, allows agents to
significantly enhance their performance by modifying their strategy. We tested
the metacognition module on a variety of scenarios, including a situation where
generative agents must survive a zombie apocalypse, and observe that our system
outperform others, while agents adapt and improve their strategies to complete
tasks over time.

介绍了一种元认知模块，使生成式智能体能够观察自己的思维过程和行为，从而显著提高性能并改进策略。在多种情景中进行了测试，包括求生僵尸启示录，发现我们的系统胜过其他系统，而且智能体随着时间适应并改进任务策略。