New Paper Unifies Continuous and Discrete Diffusion Models in General State Spaces
CSProfKGD · x · 2026-08-21
A new paper titled Foundations of Diffusion Models in General State Spaces has been released, providing a self-contained introduction that unifies continuous domains (images) and discrete/categorical structures (text, sequences) under one framework.
Key contents include:
- Discrete-time view: Forward noising via Markov kernels and learned reverse dynamics.
- Continuous-time limits: Derives SDEs in continuous spaces and Continuous-Time Markov Chains (CTMCs) on finite alphabets.
- Variational treatment: Derives the ELBO underlying standard training losses and explains how forward corruption choices shape reverse dynamics.
More from Research
- Netflix details its production LLM judge: hundreds of thousands of recommendations scored weekly — omarsar0 · 2026-08-24
- Nature Comment: Provenance, not interpretability, grounds trust in autonomous science — gabepgomes · 2026-08-24
- New Architecture RHEA: Train 1B Model on 8GB VRAM — zemondza · 2026-08-24
- Trained two 16M-param models to do generative CAD with real physics — debreuil · 2026-08-24
- Claude model helps discover complex structure on S^6, solving 60-year-old math problem — Singularitarian · 2026-08-24
- Study: Agents read instructions/notes 60.5% of the time, rarely touch API docs — dair_ai · 2026-08-24