Mu Yuan, Jinke Song, Zhaomeng Zhou +1 more
There's a classic set of rules of thumb for how much a network is worth as it grows: broadcast networks scale with N, fully connected ones with N squared, group-forming ones with 2 to the N. This work asks the same question about networks of AI agents, where the nodes are models that talk and coordinate rather than televisions or telephones.
The authors model connection value against coordination-group size, spell out what an ideal collaboration protocol should do, and propose ANet Patu-1, a protocol that keeps reshuffling its own coalitions to ride the best of those regimes in a constant number of consensus rounds. Two claims stand out: a diverse crowd of cheap models can overtake a uniform crowd of a much stronger one as the group grows, and a diverse network handed only its own problem rediscovers ANet Patu-1 unprompted.
Striking, theory-heavy claims, so the paper is essential for the assumptions behind them.
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…
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