用轻量结构引导大模型协作命名，提升一致性与效率。
SIGN: Schema-Induced Games for Naming
- 设计命名游戏机制，通过轻量模式引导模型形成统一命名惯例。
- 相比自然语言自由交流，共识达成速度提升5.8倍，一致性显著提高。
- 适合需要大规模多智能体协同的场景，如分布式规划与协作编码。
现实世界中的AI系统正面临日益复杂的任务，常通过大型语言模型（LLM）代理间的交互来解决。当这些代理形成不一致的约定时，协调机制可能失效。协作编码与分布式规划等应用亟需可靠且一致的通信，可扩展性在系统规模扩大时尤为关键。本文提出命名游戏框架SIGN（Schema-Induced Games for Naming），研究轻量级结构如何引导惯例形成。对比无约束自然语言交流，采用模式诱导的通信方式在测试中实现最高达5.8倍的共识达成速度提升，且收敛更快。结果表明，极简结构可作为高效多智能体协调的简单控制手段，具有超越命名游戏本身的广泛应用潜力。
原文摘要 · Abstract (English)
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.
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