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Monolithic federated updates are a fundamental bottleneck for embodied AI: decoupling vision, language, and action streams cuts uplink payloads by 96% and beats standard FedAvg by 22 percentage points under real-world wireless interference.
Ego-motion ambiguity in VLMs can lead to severe spatial reasoning failures, but a new benchmark and framework show how to ground visual representations in 3D space effectively.
FedCGR revolutionizes cross-domain recommendations by enabling effective item alignment without compromising user privacy or requiring extensive data sharing.
HiLaR reveals that optimizing user preference modeling through hierarchical latent reasoning can significantly outperform traditional recommendation approaches.
