AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Logit PSV is suggested, a simple, training-free alternative to full-weight perturbation ensembles across language and vision, where logit PSV substantially improves performance under domain shift, including on Earth observation, while full-weight perturbations provide little additional benefit.
Distilling pretrained foundation models into an autoencoder bottleneck improves latent diffusability, enabling diffusion models to converge faster and reach higher sample quality. Standard distillation aligns the latent at each position to a co-located teacher feature, tying the latent layout to the teacher's. We show...
A two-stage distillation pipeline first trains the causal student to match the teacher’s trajectories under a chunk-wise causal attention mask, then applies Distribution Matching Distillation (DMD) with backward simulation to close the few-step quality gap.
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