AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Motus2 is presented, a self-evolving general world model for dexterous manipulation that combines egocentric data scaling and closed-loop general world model scaling to provide a general path toward self-evolving dexterous manipulation.
While standard end-to-end baselines struggle to complete these logically demanding tasks, ACE achieves a 50% success rate in equation formation and a 70% success rate in constraint retrieval, demonstrating that explicit workflow reasoning and mask-mediated control offer a robust, practical route toward adaptable robotic manipulation.
XS-VLA is introduced, a lightweight framework that teaches tiny VLA policies "where to look" and "how to move" without increasing deployment-time model cost and shows that explicit spatial grounding and latent action-structure learning can make tiny VLA models effective for robotic manipulation.
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