AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Drive-HWM is presented, a hierarchical slow--fast world modeling framework that organizes future representation prediction and action generation at complementary temporal scales and introduces Dynamic-Aware Latents learned through optical-flow prediction.
LiteMVS is a lightweight multi-view depth estimation model that integrates plane-sweep geometric reasoning with strong monocular semantic and structural priors and employs a Mixture-of-Experts (MoE) formulation to enable adaptive geometric aggregation across depth hypotheses.
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