MLP paper shows neurons turn monosemantic in clustered regression, challenging global subspace view
burkov · x · 2026-09-07
This paper challenges the common view that neural networks learn a single global low-dimensional subspace of predictive directions. For clustered data — where each cluster has its own predictive direction and response function — local prediction can be simple while the directions collectively span the full ambient dimension, leaving no useful global low-dimensional structure.
Studying standard MLPs on this clustered regression setting, the authors show the networks develop a different form of feature learning: individual neurons become monosemantic, each strongly aligning with the predictive direction of one specific cluster. The network thereby discovers both the latent cluster structure and the per-cluster prediction rules.
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