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Robotic world models can now learn from diverse, yet confident, alternative futures, achieving state-of-the-art prediction quality and extending predictive horizons.
A unified framework for global well-posedness of the stochastic Landau--Lifshitz--Baryakhtar equation is established for initial data down to $\mathbb{L}^2$ in $\mathbb{R}^d$, $d \leq 3$.