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By actively pruning erroneous predictions from its memory graph, HGR achieves a 4.5x reduction in revisits to incorrect regions, demonstrating that retracting hypotheses is as important as generating them for long-horizon embodied navigation.
Personalized federated learning gets a Bayesian upgrade: pFedGM uses Gaussian generative modeling to capture client heterogeneity and outperforms existing methods in diverse scenarios.
RL's inherent resilience to catastrophic forgetting can be harnessed to improve continual learning in GUI agents, outperforming SFT alone.
