JEPA

Joint Embedding Predictive Architecture

JEPA Types

I-JEPA

V-JEPA

Making representations for latent planning where state-change trajectories in representation space are less curved makes planning much easier. Existing pretrained visual encoders have highly curved latent trajectories, meaning that Euclidean distance in latent space poorly reflects actual reachability difficulty or geodesic distance, and gradient-based planning also fails. Therefore, the authors propose temporal straightening: Reducing

Curvature

arxiv.org

https://arxiv.org/pdf/2603.12231

Temporal Straightening for Latent Planning
temporal-straightening
agentic-learning-ai-lab • Updated 2026 Jul 9 17:8
from
Perceptual Straightening Hypothesis

Inspired by the perceptual straightening hypothesis in human vision, this paper introduce temporal straightening to JEPA world models to facilitate latent planning

arxiv.org

https://arxiv.org/pdf/2603.12231

Temporal Straightening for Latent Planning | Agentic Learning AI Lab

Inspired by the perceptual straightening hypothesis in human vision, we introduce temporal straightening to improve representation learning for latent planning.