Topographic Training Concentrates Causal Circuits Without Improving Neuron Monosemanticity

Mechanistic interpretability of vision transformers seeks to decompose model computation into human-readable circuits, but learned representations entangle many concepts in each neuron. Feature superposition is widely treated as the central obstacle to this decomposition, yet most mitigations (sparse autoencoders, dictionary learning) are post-hoc and leave the underlying network unchanged. We ask whether a spatial-locality training loss (TopoLoss) can act as a lightweight, training-time prior that improves circuit-level interpretability of standard mech-interp tools. Training ViT on ImageNet-100 across multiple TopoLoss weights $\alpha$, we measure causal sufficiency of topographic clusters via activation patching and feature geometry via sparse autoencoders fit to the same residual stream. At $\alpha=1.0$, topographic clusters are 2.79$\times$ more causally sufficient than random unit sets of the same size, with the effect increasing monotonically in $\alpha$. SAE L0 sparsity decreases by 11\% and dead-feature fraction rises 19-fold, yet standard neuron-level monosemanticity scores are unchanged, indicating that topographic pressure reshapes circuit geometry without disentangling individual neurons. We show that topographic training reorganises circuit-level structure while leaving per-neuron monosemanticity unchanged. This dissociation suggests current neuron-level monosemanticity metrics are insensitive to a class of real interpretability gains, and positions cheap architectural priors as a viable training-time complement to post-hoc tooling.