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A memory-enhanced latent world model that augments JEPA-style prediction with a content-addressable memory bank is introduced that achieves state-of-the-art performance with a lightweight architecture, outperforming conventional latent world models on long-horizon planning tasks.
Glob3R is presented, a global SfM-style reconstruction built on 3D foundation models to explicitly optimize feed-forward geometric predictions, and consistently improves over feed-forward foundation-model baselines and recent scalable reconstruction methods, while being more robust than classical SfM pipelines.
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