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Achieve SOTA in long-horizon spatial understanding by training a model to streamingly maintain and update spatial evidence from video via test-time adaptation of fast weights.
Forget hand-annotated 3D datasets: a new automated pipeline generates massive, high-quality 3D spatial intelligence from raw video, unlocking better VLM reasoning.
Ditch the linear CFG gains: Sliding Mode Control offers provably stable and semantically richer diffusion guidance, especially when you crank up the guidance scale.
Reconstructing scenes from video for simulation is now more realistic thanks to a pipeline that optimizes viewpoints for object generation and uses scene graphs to ensure physical plausibility.
