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This is a Literature Review study.

October 3, 2025Open Access

Multi-Particle Dynamical Systems Modeling Transformers

Key Points

The multi-particle dynamical system formulation models self-attention in transformers, enhancing understanding.
Analysis reveals the systematic behavior of the system's convergence towards clusters through this formulation.
The relation with the Kuramoto oscillator provides insights into synchronization dynamics in deep learning models.
Literature review synthesizes current understanding and applications of dynamical systems in neural networks.

Abstract

Deep neural networks can be understood as discretizing a continuous dynamical system. This literature review analyzes how the multi-particle dynamical system formulation models the self-attention mechanism in transformers. We will discover how this formulation enables the systematic study of the system's convergence towards clusters and its relation with the Kuramoto oscillator.

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Yuxuan Zhang (Thu,) studied this question.

www.synapsesocial.com/papers/68e02f40f0e39f13e7fa2af1 — DOI: https://doi.org/10.54254/2753-8818/2025.dl27324

Multi-Particle Dynamical Systems Modeling Transformers

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