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Humanoid robots can now catch thrown objects with 65% success from 2.5m using only egocentric RGB-D, a leap in reactive whole-body control.
Grounding preference learning in psychological and cultural constructs yields a significant boost in population alignment for LLMs, outperforming traditional demographic methods.
Probabilistic GCD proposals can achieve faster convergence to target distributions with fewer particles, revolutionizing how we enforce constraints in language model outputs.