AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Joint-embedding predictive architectures (JEPAs) learn world models in latent space, enabling efficient planning but leaving their states opaque. We introduce Inside-JEPA, which places relational channels for ordered entity pairs inside the JEPA latent state and shows that an inverse-dynamics loss can ground these chan...
Results show that intervening on the world, rather than on what the model sees, helps latent world models predict what their actions cause.
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