LLM-MAS uses prompt-embedded design choices to drive multi-agent LLM simulations modeled as a controlled Markov chain, with an on-trajectory algorithm for zeroth-order gradient-based optimization of steady-state performance.
Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, Article No.: 74
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Optimizing Service Operations via LLM-Powered Multi-Agent Simulation
LLM-MAS uses prompt-embedded design choices to drive multi-agent LLM simulations modeled as a controlled Markov chain, with an on-trajectory algorithm for zeroth-order gradient-based optimization of steady-state performance.