Real-world AI systems are tackling increasingly complex problems, often through interactions among large language model (LLM) agents. When these agents develop inconsistent conventions, coordination can break down. Applications such as collaborative coding and distributed planning therefore require reliable, consistent communication, and scalability is a central concern as systems grow. We introduce Schema-Induced Games for Naming (SIGN), a naming game that examines how lightweight structure can steer convention formation. We compare schema-induced communication to unconstrained natural language and find faster convergence with up to 5.8x higher agreement. These results suggest that minimal structure can act as a simple control knob for efficient multi-agent coordination, pointing toward broader applications beyond the naming game.

翻译：现实世界的人工智能系统正通过大型语言模型（LLM）智能体间的交互，处理日益复杂的问题。当这些智能体形成不一致的约定时，协调机制可能失效。因此，在协同编码、分布式规划等应用中，需要可靠且一致的通信，而随着系统规模扩大，可扩展性成为核心关切。本文提出用于命名任务的模式诱导博弈（SIGN），该命名博弈研究轻量化结构如何引导约定形成。我们将模式诱导通信与无约束自然语言进行对比，发现前者收敛速度更快，且达成一致的比例最高可提升5.8倍。这些结果表明，最小化结构可作为实现高效多智能体协调的简易调控机制，其应用前景可拓展至命名博弈之外的更广泛领域。