AI 中文总结
研究提出去中心化多智能体学习框架，智能体通过加权社会共识更新信念，信任依同伴一致性推断的能力分配。经多场景模拟运行评估，发现SME轮换具多种特性，高维时知识集中于单个智能体，解释为去中心化学习的涌现属性。
AI 中文摘要
集中奖励信号主导现代人工智能学习系统，但它们强加了正确或有价值知识的单一外部定义。我们提出了一个去中心化的、基于共识的多智能体学习框架，其中专业知识通过同伴验证而非规定奖励出现。智能体通过加权社会共识更新信念，信任根据从同伴一致性推断出的能力而非地面真值分配。主题专家（SME）地位作为最高百分位能力排名动态分配，而非固定标签。我们在跨越30至10000个智能体、多种图拓扑、稀疏大规模网络、标量和向量信念表示、维度扫描（D = 1 - 500）、多种子鲁棒性测试和参数敏感性分析的84次模拟运行中评估了该框架。第一阶段表明SME轮换是稳健、持久、拓扑不变和尺度不变的：90 - 100%的智能体获得SME地位，大多数专业知识更替发生在信念收敛之后，并随网络规模增加。第二和第三阶段表明向量信念引入了具有级联动态的异质收敛，并随着信念维度增加揭示了五个不同的动态 regime。在高维度（D = 150 - 200）时，网络达到稳定的部分共识，而专业知识越来越集中在单个智能体中。ETA敏感性分析表明这种集中是由信念维度而非随机噪声驱动的。我们将这种行为解释为去中心化学习的一种涌现属性：在复杂的高维共识空间中，最始终与集体信念一致的智能体自然地成为公认的专家。
英文摘要
Centralised reward signals dominate modern AI learning systems, but they impose a single external definition of correct or valuable knowledge. We present a decentralised, consensus-based multi-agent learning framework in which expertise emerges through peer validation rather than prescribed reward. Agents update beliefs via weighted social consensus, while trust is allocated according to competence inferred from peer consistency instead of ground truth. Subject-matter expert (SME) status is assigned dynamically as a top-percentile competence rank rather than a fixed label. We evaluate the framework across 84 simulation runs spanning 30 to 10,000 agents, multiple graph topologies, sparse large-scale networks, scalar and vector belief representations, dimensionality sweeps (D=1-500), multi-seed robustness tests, and parameter sensitivity analyses. Phase 1 shows that SME rotation is robust, persistent, topology-invariant, and scale-invariant: 90-100% of agents attain SME status, with most expertise turnover occurring after belief convergence and increasing with network size. Phases 2 and 3 show that vector beliefs introduce heterogeneous convergence with cascade dynamics and reveal five distinct dynamical regimes as belief dimensionality increases. At high dimensionality (D=150-200), the network reaches stable partial consensus while expertise becomes increasingly concentrated in a single agent. ETA sensitivity analysis demonstrates that this concentration is driven by belief dimensionality rather than stochastic noise. We interpret this behaviour as an emergent property of decentralised learning: in complex high-dimensional consensus spaces, the agent most consistently aligned with the collective belief naturally emerges as the recognised expert.
