AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution#

Zhiqiang Xie, Hao Kang, Ying Sheng, Tushar Krishna, Kayvon Fatahalian, Christos Kozyrakis

• Why: Borrowing out-of-order execution — a CPU pipeline idea any dataplane person knows cold — to schedule LLM agent simulations. Seeing hardware-execution concepts reappear at the agent-simulation layer is a genuinely interesting signal of where systems thinking is heading.
• What: A simulation engine for LLM multi-agent simulations that uses out-of-order scheduling to break the false-dependency bottleneck: standard parallel simulations synchronize globally, serializing agents that don't actually depend on each other. 1.3×–4.15× speedup over globally-synchronized parallel simulation, approaching optimal as agent counts grow. (from abstract only)
• How: Dynamically tracks the real dependency graph between agents and issues agent "instructions" out of order when they're independent — a scoreboard/Tomasulo-style idea applied to inference calls instead of µops.
• Where applicable: LLM-agent simulation platforms (social-science simulations, game NPC orchestration); the dependency-tracking idea generalizes to any DAG-of-interdependent-tasks executor. Evaluated in simulation settings, not production.