With more advanced natural language understanding and reasoning capabilities, large language model (LLM)-powered agents are increasingly developed in simulated environments to perform complex tasks, interact with other agents, and exhibit emergent behaviors relevant to social science and gaming. However, current multi-agent simulations frequently suffer from inefficiencies due to the limited parallelism caused by false dependencies, resulting in performance bottlenecks. In this paper, we introduce AI Metropolis, a simulation engine that improves the efficiency of LLM agent simulations by incorporating out-of-order execution scheduling. By dynamically tracking real dependencies between agents, AI Metropolis minimizes false dependencies, enhancing parallelism and enabling efficient hardware utilization. Our evaluations demonstrate that AI Metropolis achieves speedups from 1.3x to 4.15x over standard parallel simulation with global synchronization, approaching optimal performance as the number of agents increases.

翻译：随着自然语言理解与推理能力的不断提升，基于大语言模型（LLM）的智能体在模拟环境中被越来越多地开发，以执行复杂任务、与其他智能体交互，并展现出与社会学及游戏领域相关的涌现行为。然而，当前的多智能体仿真常因虚假依赖导致的并行性受限而效率低下，形成性能瓶颈。本文提出AI Metropolis——一种通过引入乱序执行调度来提升LLM智能体仿真效率的模拟引擎。该系统通过动态追踪智能体间的真实依赖关系，最小化虚假依赖，从而增强并行性并实现高效的硬件资源利用。评估结果表明，相较于采用全局同步的标准并行仿真方法，AI Metropolis可获得1.3倍至4.15倍的加速比，且随着智能体数量的增加，其性能趋近于理论最优值。