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Amortized agentic policy discovery enables personalized test-time scaling to satisfy complex, multidimensional user requirements, outperforming single-objective optimization on unseen profiles.
Autonomous quadrupeds can now navigate complex terrains without human intervention, thanks to a novel multimodal approach that integrates perception and control.
LLMs can reason more efficiently by sharing intermediate thoughts during parallel search, achieving better accuracy with less computation.
