ResearcharXiv cs.AI

From Signals to Structure: How Memory Architecture Drives Language Emergence in LLM Agents

#memory architecture#language emergence#llm agents#coordination#artificial intelligence

English

The paper investigates how memory architecture influences the emergence of language in LLM agents within a Lewis signaling game. It finds that agents with persistent memory outperform stateless agents in achieving reliable coordination, suggesting that memory architecture is more critical than channel capacity for developing stable communication conventions.

中文

本文研究了记忆架构如何影响LLM代理在Lewis信号游戏中语言的出现。研究发现，具有持久记忆的代理在实现可靠协调方面优于无状态代理，表明记忆架构在发展稳定交流惯例方面比通道容量更为关键。