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This study investigates the performance of large language model (LLM) agents in decentralized coordination scenarios without communication, focusing on their ability to exceed Nash equilibrium benchmarks in one-shot games. The authors establish a benchmark involving two-player matrix games across various archetypes and action spaces, revealing that certain frontier-hosted models can outperform Nash equilibria, while most open-weight models show inconsistent performance. Notably, the research finds that while LLMs can achieve coordination in dyadic settings, their effectiveness diminishes significantly in larger team-based games, highlighting limitations in scaling coordination capabilities.
Some LLMs can outsmart Nash equilibria in two-player games, but their coordination skills falter in larger teams.
Large language model agents deployed without a central controller are often assumed to require communication to coordinate their actions. We ask what remains possible without it: when independent instances of the same model cannot communicate, can they still reason about their counterparts well enough to exceed the standard game-theoretic baseline for uncoordinated play? We introduce a benchmark of one-shot, no-communication games in which each of thirteen language models is told only that its counterparts are running the same model and is evaluated against the Nash equilibrium of the underlying game. In two-player matrix games spanning seven archetypes and two to ten actions per player, two frontier-hosted models consistently exceed their Nash benchmark, approaching the optimal joint outcome in several archetypes, while most open-weight models achieve only partial gains that vary sharply by game structure. Performance degrades substantially in team-based games with four or more interchangeable agents, particularly as the action space grows, suggesting that whatever capability drives self-play gains in dyadic games does not transfer to larger multi-agent teams.
