Lyfe Agents: Generative agents for low-cost real-time social
  interactions
Zhao Kaiya Michelangelo Naim Jovana Kondic Manuel Cortes Jiaxin Ge Shuying Luo Guangyu Robert Yang Andrew Ahn
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.