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Compact models like VibeThinker-3B can achieve frontier-level reasoning performance, rivaling much larger counterparts without losing controllability.
EWAM achieves remarkable zero-shot adaptation by integrating real-time co-reasoning mechanisms, eliminating the need for task-specific data or fine-tuning.
Expert-guided reinforcement learning can boost UAV navigation success rates by over 2x while drastically improving intent alignment.
One model to control them all: Qwen-VLA achieves impressive zero-shot generalization across diverse robotic tasks and embodiments by unifying vision-language-action modeling.
