Highly autonomous generative agents powered by large language models promise to simulate intricate social behaviors in virtual societies. However, achieving real-time interactions with humans at a low computational cost remains challenging. Here, we introduce Lyfe Agents. They combine low-cost with real-time responsiveness, all while remaining intelligent and goal-oriented. Key innovations include: (1) an option-action framework, reducing the cost of high-level decisions; (2) asynchronous self-monitoring for better self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing critical memory items at a low cost. We evaluate Lyfe Agents' self-motivation and sociability across several multi-agent scenarios in our custom LyfeGame 3D virtual environment platform. When equipped with our brain-inspired techniques, Lyfe Agents can exhibit human-like self-motivated social reasoning. For example, the agents can solve a crime (a murder mystery) through autonomous collaboration and information exchange. Meanwhile, our techniques enabled Lyfe Agents to operate at a computational cost 10-100 times lower than existing alternatives. Our findings underscore the transformative potential of autonomous generative agents to enrich human social experiences in virtual worlds.

翻译：由大语言模型驱动的高自主性生成式智能体有望模拟虚拟社会中复杂的社会行为。然而，在低计算成本下实现与人类的实时交互仍是一项挑战。为此，我们提出Lyfe Agents，它在保持智能性和目标导向性的同时，兼具低成本与实时响应能力。其关键创新包括：(1) 选项-行动框架，降低高层决策成本；(2) 异步自我监控机制，提升自我一致性；(3) 记忆的“摘要与遗忘”机制，以低成本优先保留关键记忆项。我们在自研的LyfeGame 3D虚拟环境平台中，通过多个多智能体场景评估了Lyfe Agents的自我驱动力与社交能力。配备类脑技术后，Lyfe Agents能展现类人的自我驱动社交推理能力，例如，智能体可通过自主协作与信息交换破获一桩谋杀谜案。同时，我们的技术使Lyfe Agents的计算成本较现有方案降低10至100倍。研究结果凸显了自主生成式智能体在丰富虚拟世界人类社交体验方面的变革潜力。