这篇论文从网络价值理论出发，推导出智能体协作的最优协议，发现廉价模型合作反而能胜过单一强模型，结果很反直觉，值得读。
这篇论文类比互联网价值定律（Sarnoff、Metcalfe、Reed），首次系统分析AI智能体网络的连接价值模型。作者提出ANet Patu-1自组织共识协议，能在O(1)并行轮次中自适应切换三种增长模式，达到网络价值上包络线。实验显示，异质廉价模型（如小模型）组成的网络，其协作价值随节点数N超线性增长，最终超越同质强模型网络，形成了一种协作规模定律。此外，网络仅凭自身问题就能自收敛到ANet Patu-1协议，重构出高维连接价值规律。
ANet Patu-1: The Value of Connection in the Agent Network
The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!\propto\!N$ (Sarnoff), fully-connected meshes as $N^2$ (Metcalfe), and group-forming networks as $2^{N}$ (Reed). We ask the analogous question for networks of AI agents. We model the net value of connection as a function of coordination-group size, derive from it the properties an optimal collaboration protocol must have, and introduce ANet Patu-1 -- a self-organizing consensus protocol in which the network continuously re-forms its own coalitions, adaptively riding the upper envelope of all three regimes at $O(1)$ parallel consensus rounds. To measure value without opinion-grading, we score an emergent protocol by formally specifying it and deriving its complexity, the way distributed algorithms are analyzed. Two results follow. (i)~Emergence -- a crowd of the \emph{cheapest} model, when heterogeneous, starts weak but its collective value compounds with $N$ and \emph{overtakes} a crowd of a far \emph{stronger} model that is homogeneous: a crossover that marks a scaling law for collaboration rather than for scale. (ii)~Reflexivity -- a heterogeneous network, given only its own problem and no design hints, converges on ANet Patu-1 itself, reconstructing the high-dimensional law that governs its own connective value.