Author

Lucas Maes

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Preprint Oct 2026

LaLa-World: Scaling Latent World Models with Natural Language Actions

This paper shows that JEPA-style latent world models (LWMs) can learn across diverse domains and data sources by adopting the language-conditioned interface of video generation models. We establish this approach as an effective and efficient basis for planning with imagination and downstream policy fine-tuning. To this...

Jun-Ha Song, Geonmo Gu, Jeongeun Park et al. · 0 citations

Preprint Aug 2026

LeVJEPA: Efficient&Scalable Video Pretraining without the Heuristics

LeVJEPA is introduced, the first video encoder trained under LeJEPA's collapse-free objective, which indicates that video becomes a viable and in several respects preferable substrate for general-purpose visual pretraining.

Lukas Kuhn, Lucas Maes, Giuseppe Serra et al. · 3 citations · ⚡1

Preprint Aug 2026

LpWM: A Case for Sparse Representations in World Models

This work introduces LpWorldModel, a JEPA model regularized with Rectified Distribution Matching Regularization to match encoder features to a Rectified Generalized Gaussian distribution, yielding non-negative sparse codes, and finds that the learned sparse representations are mode-factored.

Yilun Kuang, Yash Dagade, Quentin Le Lidec et al. · 5 citations