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Achieving 98.6% accuracy in 3D Gaussian world modeling could redefine efficiency benchmarks for robotic manipulation tasks.
Training-time Gaussian distillation can elevate WAM performance by over 19% by effectively integrating geometric and semantic information without altering deployment architecture.
