Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
Reducing the computational cost of multi-agent LLM simulations through statistical physics and surrogate modeling.
Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent. We turn a statistic...
ArXiv CS.AI
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This research democratizes complex multi-agent system modeling by proving that computationally expensive LLM simulations can be effectively replaced by low-parameter statistical proxies. By shifting the focus from individual agent cognition to macroscopic emergent behavior, researchers can now study massive social dynamics on consumer-grade hardware.
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