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University of Waterloo
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Routing-enabled federated learning can now leverage hidden subpopulations within clients, leading to substantial gains in prediction accuracy and routing efficiency.
Users can now intuitively grasp a robot's inferred goals through its motion, reducing control effort and enhancing collaboration.
Fed-CausalDiff enables federated learning to perform causal inference with unprecedented accuracy by decoupling global and local data influences.
daVinci-kernel outperforms the best existing RL-trained model in GPU kernel optimization by effectively co-evolving skill selection and execution strategies.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.