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LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, Randall Balestriero
Jun 5, 2026 at 04:00
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arXiv:2603.19312v3 Announce Type: replace-cross Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid...

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