When 3D Becomes Code: Why World Labs' Architecture Is a Gift for Interpretability Research

Cross-posted from oourmind.io — part of an ongoing series on the 3D Interpretability Lab* The Problem With Black Boxes in Space We've gotten quite good at asking what neural networks know. Mechanistic interpretability — the field dedicated to reverse-engineering how AI models work internally — has made remarkable progress on language models. We can find circuits that detect curves, attention heads that implement induction, and linear subspaces that encode factual associations. But spatial models — models that understand, generate, or reason about 3D environments — remain largely opaque. Not because we lack curiosity, but because we lack a handle: the internal representations of most vision and world models aren't structured in a way that makes them easy to probe, intervene on, or interpret. That's what makes World Labs' recent essay on "3D as Code" so interesting — and so relevant to 3D interpretability research. A Quick Glossary Before diving in, here are the key concepts you'll need: