AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Arithmetic experiments show that similar first-attempt accuracies can lead to very different coverage after repeated attempts, and when this reuse is reliable and how much computation it requires is studied.
This work introduces FlowCPO, an offline forward-KL objective that uses both preferred and dispreferred samples without online rollouts and shows under explicit regularity conditions that the forward-KL objective is bounded by a contrastive flow matching loss, yielding a tractable surrogate on fixed data.
We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision-language understanding module built on the LLaDA2.0-Mini diffusion language model backbone. Instead of relying heavily on paired image-text data from the beginning, we first build a strong...
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