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KU Leuven
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Zero-shot adaptation to new tasks is now feasible with neurosymbolic world models that leverage structured symbolic components for reward prediction.
NMLNs can now outperform diffusion-based models on large graphs, thanks to a novel parallel noising algorithm and enhanced expressive capacity.
A unified theory of interpretability that leverages Lagrangian mechanics to transform opaque models into interpretable ones, revealing new research avenues and design principles.
