A new criterion based on the L1 norm of the third derivative of the interaction function establishes global synchronization for circle mean-field models, resolving the self-attention synchronization question for β ≥ -0.16.
The random graph process is globally synchronizing
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abstract
The homogeneous Kuramoto model on a graph $G = (V,E)$ is a network of $|V|$ identical oscillators, one at each vertex, where every oscillator is coupled bidirectionally (with unit strength) to its neighbors in the graph. A graph $G$ is said to be globally synchronizing if, for almost every initial condition, the homogeneous Kuramoto model converges to the all-in-phase synchronous state. Confirming a conjecture of Abdalla, Bandeira, Kassabov, Souza, Strogatz, and Townsend, we show that with high probability, the random graph process becomes globally synchronizing as soon as it is connected. This is best possible, since connectivity is a necessary condition for global synchronization.
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Synchronization of mean-field models on the circle
A new criterion based on the L1 norm of the third derivative of the interaction function establishes global synchronization for circle mean-field models, resolving the self-attention synchronization question for β ≥ -0.16.