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A new scheduling framework cuts LLM latency by over 10% while enhancing fairness, challenging the status quo of rigid scheduling policies.
Imagine training embodied agents with a dataset so realistic, humans prefer it 78% of the time – InHabit makes that possible.
Hyperspectral anomaly detection gets a serious upgrade: R2VD ditches scalar reconstruction errors for high-dimensional vector interference patterns, achieving state-of-the-art target detection and background suppression.
Humanoid robots can now groove in sync for a full four minutes on stage, thanks to a model-based control framework that anticipates disturbances and proactively adjusts foot placement.
