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arXiv · 2608.24007

Revenge of Monosemanticity: Neuron Specialization as a New Form of Feature Learning in MLPs

Amirhesam Abedsoltan·Enric Boix-Adsera·Fivos Kalogiannis·Mikhail Belkin
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Abstract

Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has focused on the emergence of a global low-dimensional representation. We show that this picture is incomplete. In regression problems with clustered data, we demonstrate that multilayer perceptrons (MLPs) naturally develop monosemantic specialized neurons: individual neurons become strongly aligned with a specific predictive feature relevant to a particular region of the input space. Rather than learning a single global low-dimensional representation, MLPs learn a collection of local low-dimensional representations. We show that this ability to specialize gives MLPs a provable data-efficiency advantage over feature-learning methods based on a global low-dimensional representation.

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Amirhesam Abedsoltan, Enric Boix-Adsera, Fivos Kalogiannis, Mikhail Belkin. 2026-08-25. Revenge of Monosemanticity: Neuron Specialization as a New Form of Feature Learning in MLPs. https://arxiv.org/abs/2608.24007

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