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This research investigates how random noise induces symmetry breaking in Random Quadratic Forms, leading from partial to full synchronization within continuous-time machine learning models like Neural ODEs and transformers. The findings provide theoretical insight into the critical roles of bias and initialization scale during the training dynamics of these architectures. By explaining multiscale convergence behaviors, this work bridges dynamical systems theory with practical deep learning mechanisms.
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Random Quadratic Form with random forcing: Metastable synchronization by noise

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arXiv:2608.16664v1 Announce Type: cross Abstract: We study the Random Quadratic Form (RQF) on a sphere in the presence of random Brownian forcing. We show that the forcing does not effectively change the law of the process but affects the synchronization properties of the system. While the RQF without forcing exhibits partial synchronization due to the intrinsic symmetries, the introduction of an arbitrarily small forcing results in long-term symmetry breaking and leads to full synchronization. In this work we focus on the small forcing regime and recover the multiscale behavior of the two-point process. We show that in the first stage the model converges to an anti-polar configuration due to the symmetries of the RQF and in the second stage the two clusters meet due to the symmetry breaking phenomenon. The model is motivated by continuous-time machine learning models such as Neural ODEs and continuous-time formulations of transformers. In particular, the results of this work explain the role of the bias and the scale of its initialization.

Abstract:We study the Random Quadratic Form (RQF) on a sphere in the presence of random Brownian forcing. We show that the forcing does not effectively change the law of the process but affects the synchronization properties of the system. While the RQF without forcing exhibits partial synchronization due to the intrinsic symmetries, the introduction of an arbitrarily small forcing results in long-term symmetry breaking and leads to full synchronization. In this work we focus on the small forcing regime and recover the multiscale behavior of the two-point process. We show that in the first stage the model converges to an anti-polar configuration due to the symmetries of the RQF and in the second stage the two clusters meet due to the symmetry breaking phenomenon. The model is motivated by continuous-time machine learning models such as Neural ODEs and continuous-time formulations of transformers. In particular, the results of this work explain the role of the bias and the scale of its initialization.

Probability (math.PR); Machine Learning (cs.LG); Dynamical Systems (math.DS)

arXiv:2608.16664 [math.PR]

(or arXiv:2608.16664v1 [math.PR] for this version)

https://doi.org/10.48550/arXiv.2608.16664 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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