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Evaluator quality for robotic policies hinges more on long-horizon consistency than on short-term visual fidelity, reshaping our approach to world model design.
Long-horizon embodied agents struggle to translate long-term memory into actionable plans, exposing critical gaps in current benchmarks and methodologies.
Even state-of-the-art T2I models falter in generating scientifically accurate illustrations, revealing critical gaps in text-rendering and reasoning capabilities.
Get simulation-ready assets for robotics and graphics in under a second, without any manual annotation, using a new feedforward approach that jointly learns physical attributes and 3D Gaussian Splatting reconstruction from a single video.
Robots can now plan 9x faster and achieve significantly higher success rates by decoupling action prediction from video generation in World-Action Models.
