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arXiv · 2509.04653

Deriving Transformer Architectures as Implicit Multinomial Regression

Jonas A. Actor·Anthony Gruber·Eric C. Cyr
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Abstract

While attention has been empirically shown to improve model performance, it lacks a rigorous mathematical justification. This short paper establishes a novel connection between attention mechanisms and multinomial regression. Specifically, we show that in a fixed multinomial regression setting, optimizing over latent features yields solutions that align with the dynamics induced on features by attention blocks. In other words, the evolution of representations through a transformer can be interpreted as a trajectory that recovers the optimal features for classification.

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Jonas A. Actor, Anthony Gruber, Eric C. Cyr. 2025-09-04. Deriving Transformer Architectures as Implicit Multinomial Regression. https://arxiv.org/abs/2509.04653

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