AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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FMM-WM (Factual--Interventional Recurrent World Model), a compact pixel world model designed around a typed, goal--comparable configuration from a 128-dimensional dynamic fiber used for prediction but excluded from the terminal goal cost, is introduced.
HyperWorld, a controlled study of state serialization for learned textual world models, shows that higher-order state organization is a simple but effective inductive bias for learned symbolic world models, especially when model capacity is limited or test environments differ from training.
This article introduces Uncertainty-Guided Adaptive Knowledge Distillation (UGAKD), a novel framework designed to enhance UDA while simultaneously reducing model size through targeted knowledge distillation and proposing a two-stage, difficulty-aware training scheme to facilitate learning.
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