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.
https: //arxiv.org/abs/2508.02630 (2025)
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LLMs converge on competitive rationality and coordination but diverge 48-fold on cooperation, with provider identity and generational shifts as dominant factors across 38 games.
Proposes SVD-based reduction of multi-dimensional matching to 1D problem for O(N log N) computation that approximates Nash Social Welfare under low effective dimensionality.
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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.
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Large language models converge on competitive rationality but diverge on cooperation across providers and generations
LLMs converge on competitive rationality and coordination but diverge 48-fold on cooperation, with provider identity and generational shifts as dominant factors across 38 games.
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Multi-Dimensional Matching in Market Design
Proposes SVD-based reduction of multi-dimensional matching to 1D problem for O(N log N) computation that approximates Nash Social Welfare under low effective dimensionality.