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WA-JEPA is presented, a V-JEPA-native world-action model designed for autonomous driving planning that employs hybrid future-masked pre-training and recast future prediction as conditional flow matching over latent futures, which substantially improves the model's ability to generate plausible future latents for downst...
This work proposes CADE (Contrastive Adaptive Debias Ensemble), a training-free, plug-and-play method that leverages modality-specific answer priors that yields significant gains on the proposed benchmark, which can foster the development of more fair and reliable AI systems for sustainable development.
Walking in the Implicit, a scene-centric paradigm that changes the rollout variable from frame latents to a fixed-length, renderable implicit state, termed Neural Implicit Scene (NIS), factorizes interactive generation into stochastic transition of a compact scene state and deterministic pose-conditioned rendering give...
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