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This research introduces the Random Quadratic Form (RQF) as a stochastic model derived from linear layers in transformers to explain token clustering without relying on self-attention mechanisms. By characterizing synchronization through common noise, the study offers an alternative mathematical framework for understanding emergent behaviors in deep learning architectures that are independent of attention heads.
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Random Quadratic Form on a Sphere: Synchronization by Common Noise

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arXiv:2603.06187v2 Announce Type: replace-cross Abstract: We introduce the Random Quadratic Form (RQF): a stochastic differential equation which formally corresponds to the gradient flow of a random quadratic functional on a sphere. While the one-point dynamics of the system is a Brownian motion and thus has no preferred direction, the two-point motion exhibits nontrivial synchronizing behaviour. In this work we study synchronization of the RQF, namely we give both distributional and path-wise characterizations of the solutions by studying invariant measures and random attractors of the system. The RQF model is motivated by the study of the role of linear layers in transformers and illustrates the synchronization by common noise phenomena arising in the simplified models of transformers. In particular, we provide an alternative (independent of self-attention) explanation of the clustering behaviour in deep transformers and show that tokens cluster even in the absence of the self-attention mechanism.

Abstract:We introduce the Random Quadratic Form (RQF): a stochastic differential equation which formally corresponds to the gradient flow of a random quadratic functional on a sphere. While the one-point dynamics of the system is a Brownian motion and thus has no preferred direction, the two-point motion exhibits nontrivial synchronizing behaviour. In this work we study synchronization of the RQF, namely we give both distributional and path-wise characterizations of the solutions by studying invariant measures and random attractors of the system. The RQF model is motivated by the study of the role of linear layers in transformers and illustrates the synchronization by common noise phenomena arising in the simplified models of transformers. In particular, we provide an alternative (independent of self-attention) explanation of the clustering behaviour in deep transformers and show that tokens cluster even in the absence of the self-attention mechanism.

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

arXiv:2603.06187 [math.PR]

(or arXiv:2603.06187v2 [math.PR] for this version)

https://doi.org/10.48550/arXiv.2603.06187 Focus to learn more arXiv-issued DOI via DataCite

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