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Achieve 2.6x faster autoregressive world model inference without retraining by caching and selectively reusing block-level residuals across generation chunks.
Ditch the expensive 3D annotations: Reliev3R trains high-quality 3D reconstruction models from scratch using only monocular relative depths and sparse image correspondences.
Autonomous driving planners can now explicitly self-correct unsafe actions by generating motion-token traces conditioned on a learned collision critic, leading to significant safety improvements.
Autonomous driving gets a boost: EvoDriveVLA's collaborative perception-planning distillation framework significantly enhances VLA model performance by tackling perception degradation and planning instability.
Achieve state-of-the-art surgical attention tracking with a new method that leverages temporal coherence and a large-scale benchmark dataset, enabling more robust and interpretable FoV guidance.
