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Achieve near order-of-magnitude reduction in tail timing error in mixed-criticality robotics by decoupling safety-critical control from user applications.
Personalized federated learning can boost VLN performance by up to 7.8% in trajectory fidelity and converge 1.38x faster, by selectively fusing parameters in environment-sensitive layers.
