Synchronization of mean-field models on the circle
By: Yury Polyanskiy, Philippe Rigollet, Andrew Yao
Potential Business Impact:
Makes computer "brains" work together better.
This paper considers a mean-field model of $n$ interacting particles whose state space is the unit circle, a generalization of the classical Kuramoto model. Global synchronization is said to occur if after starting from almost any initial state, all particles coalesce to a common point on the circle. We propose a general synchronization criterion in terms of $L_1$-norm of the third derivative of the particle interaction function. As an application we resolve a conjecture for the so-called self-attention dynamics (stylized model of transformers), by showing synchronization for all $\beta \ge -0.16$, which significantly extends the previous bound of $0\le \beta \le 1$ from Criscitiello, Rebjock, McRae, and Boumal (2024). We also show that global synchronization does not occur when $\beta < -2/3$.
Similar Papers
Large-scale distributed synchronization systems, using a cancel-on-completion redundancy mechanism
Probability
Helps systems manage many tasks at once.
Global synchronization of multi-agent systems with nonlinear interactions
Systems and Control
Helps groups of robots move together perfectly.
Quantitative Clustering in Mean-Field Transformer Models
Machine Learning (CS)
Makes AI learn faster by grouping ideas.