AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations and external influences.
Track4Action is introduced, a framework that distills this realized transition from a frozen world-centric 3D tracker into a current-observation VLA policy, and gains across simulation and physical tasks support action-aligned 3D tracker features as privileged supervision for tracker-free VLA deployment.
EgoAfford is introduced, a benchmark designed to connect the semantic roles of participating objects with task-state-aligned visual observations and multi-step planning and EgoLens, a 3B multimodal large language model with role-specific mask decoders, as an in-domain reference model for this joint task.
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