AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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MT-OPSD is proposed, an on-policy self-distillation framework that trains the model on self-generated conditioning states with editing supervision from a clean-conditioned teacher, without requiring multi-turn annotations, and substantially improves long-horizon editing success and reduces multi-turn collapse.
It is demonstrated that separately encoding text and visual instructions, combined with co-training using shallow feature control, is essential to mitigate task ambiguity and enhance multi-task generalization and pave the way for more robust and generalizable low-level vision systems.
This work formalizes probabilistic alignment as a distributional criterion for world models and introduces PAWBench, a benchmark for evaluating video generators as stochastic samplers of world dynamics, and introduces PAWEval, an outcome-level protocol that converts repeated video rollouts into empirical distributions...
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