AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We introduce Loop Flow Transformers (LiFT), a family of looped generative models that scales computation by repeatedly applying a shared Diffusion Transformer (DiT) core, with only light changes to the standard architecture. Rather than asking every recurrent step for the final prediction, LiFT trains each step with a...
This work presents an invertible mapping that transforms a set of spatial objects into continuous fields: a density field that encodes object locations and count, and a feature field that carries their attributes over the same support, which allows robust handling of unknown set sizes with competitive accuracy.
Based on this formulation of flow matching as variational inference, CatFlow is developed, a flow matching method for categorical data that is easy to implement, computationally efficient, and achieves strong results on graph generation tasks.
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