3 papers
cs.AI2026
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
Basile Terver, Tsung-Yen Yang, Jean Ponce +2
A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments. A popular recent appr…
cs.LG2026
PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment
Michael Arbel, Basile Terver, Jean Ponce
Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effective) methods such as SimSiam, BY…
cs.LG2025
Value-guided action planning with JEPA world models
Matthieu Destrade, Oumayma Bounou, Quentin Le Lidec +2
Building deep learning models that can reason about their environment requires capturing its underlying dynamics. Joint-Embedded Predictive Architectures (JEPA) provide a promising…