不同通信协议会延缓群体共识，但不会导致彻底分裂。
Communication Heterogeneity and Collective Consensus in Neural Cellular Automata
- 用神经元胞自动机模拟细胞间通过可调语言距离交流
- 语言距离越大，达成共识越慢，群体出现轻微分化
- 混合训练的群体对通信不匹配有更强鲁棒性
从纯局部交互中达成全局共识是集体智能的核心问题，现有模型通常假设所有智能体使用同一通信协议。本文研究当协议不同时会发生什么：在解决密度分类任务的神经元胞自动机中，引入具有可调‘语言距离’的子群体，使细胞通过翻译接收信息。结果表明，语言距离会减缓共识进程，导致群体出现轻微分歧而非完全分裂；在多样协议下训练的集体对协议不匹配具有鲁棒性，而统一训练的则不具备。该现象在环形与二维网格上均成立，且可类比为伊辛模型中的弛豫过程，异质语言区域如同边界缺陷，使系统停留在高能部分有序状态。这些模式与人类群体研究中的现象定性一致，表明通信协议差异是产生此类效应的最小机制，无需语言特异性。
原文摘要 · Abstract (English)
Reaching global agreement from purely local interactions is a defining problem of collective intelligence, and most models of it assume that all agents share a single communication protocol. We ask what happens when they do not. Using a Neural Cellular Automaton in which a population of cells must solve the density classification task, agreeing on a global majority that no individual can observe, we introduce ``languages'' as sub-populations that read one another's messages through a translation with a tunable ``linguistic distance''. We find that linguistic distance slows consensus, that it produces mild divergence between groups rather than full fragmentation, and that a collective whose shared rule was trained under diverse protocols is robust to mismatch; a homogeneously trained one is not. The findings hold on both a ring and a two-dimensional grid, and admit a natural reading as Ising relaxation, in which a foreign-language region acts as a boundary defect that leaves the system in a higher-energy, partially ordered state. These patterns are qualitatively consistent with effects reported in human group studies, suggesting that distance between communication protocols is a minimal mechanism sufficient to produce them, without anything language-specific.
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