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discrete diffusion

topic3 events
papersTODAY 04:00 UTC

ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation

A new arXiv paper introduces ProtoGuide, a method for steering class-conditional graph generation without baking the class label into the denoiser during training. Instead of embedding the conditioning signal into the model, the approach uses prototypes to guide sampling, which decouples the conditioning mechanism from any particular trained model. This makes it possible to add or change class conditioning on top of existing discrete diffusion generators rather than retraining them.

graph-generation/diffusion-models/research/classifier-guidance
papersTODAY 04:00 UTC

Discrete Beckmann Transport Models Target One-Step Language Generation

A new arXiv preprint introduces discrete Beckmann transport models, a framework for language modeling and reasoning that avoids the multi-step sampling usually required by discrete diffusion and flow approaches. The authors note that existing methods compress sampling steps only by distilling a pretrained autoregressive teacher, which limits the student to the teacher's performance. Their approach instead aims to generate text in a single step without relying on that distillation ceiling.

language-models/diffusion/research/inference
papersSEP 11 04:00 UTC

Study Argues Continuous Diffusion Can Scale Competitively for Language Modeling

A new arXiv paper revisits Plaid, a likelihood-based continuous diffusion model for text, to test the assumption that continuous diffusion scales worse than discrete alternatives. The authors report that with the right design, continuous diffusion can match discrete diffusion at scale. The work is a replacement version of a cross-listed machine learning preprint.

diffusion/language-models/scaling/research