---
title: "How JEPA Works — Coming Pre-Launch"
slug: "how-jepa-works"
author: "Primate Intelligence"
date: "2026-05-22"
readTime: "Coming soon"
tags: ["Research","JEPA"]
excerpt: "Why we build on Joint-Embedding Predictive Architectures instead of pixel-space reconstruction. The deep dive is in production — publishing ahead of public launch."
canonical: https://primateintelligence.ai/blog/how-jepa-works
---

## 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.