Dimensions collapse into structure
High-dimensional data resolves into clusters the model treats as semantic neighborhoods, not geometric ones.
Distance is not spatial — it is semantic
Two points can sit adjacent in embedding space while their raw inputs share no surface similarity whatsoever.
The void matters as much as the cluster
What the manifold excludes defines the decision boundary more sharply than what it includes.
Interpolation is the only true traversal
Walking the latent space between two samples reveals the model’s internal taxonomy — not yours.