How JEPA Works — Coming Pre-Launch

This piece is in production and coming pre-launch.

JEPA — Joint-Embedding Predictive Architecture — is the structural choice that distinguishes Primate Vision from generative video models. We learn to predict the embeddings of future scene state, not the pixels. It changes what the model is good at, and where it doesn’t waste compute.

When this piece goes live, it will cover:

• Why predict, not reconstruct. Why pixel-space generative loss is a tax the model pays in exchange for nothing the user cares about.
• The encoder, predictor, and target. How the three networks work together, and why the target network is frozen.
• Scaling behaviour. Where JEPA gets better with more data and where it doesn’t, and what that means for evaluation.
• Where this came from. Yann LeCun’s I-JEPA / V-JEPA lineage, what we kept, and where we diverged.

We’re polishing this with the engineering team and want it to read clearly for an engineer who has trained a vision model but never built on JEPA. If you’d like to talk through the architecture choices for a specific integration, get in touch.