Feature Information Dynamics in Diffusion
Quick summary
arXiv:2610.08626v1 Announce Type: cross Abstract: Diffusion models generate data through a continuum of denoising problems, and are widely observed to reveal coarse structure before fine detail. Yet, this intuition is mostly empirical and qualitative. We introduce feature information dynamics, an information-theoretic framework for localizing when a feature is generated during diffusion. Using the I-MMSE identity, we connect the rate of feature mutual information change to a gap between optimal unconditional and feature-conditional denoising losses, yielding practical estimators for feature in
Key takeaways
- arXiv:2610.08626v1 Announce Type: cross Abstract: Diffusion models generate data through a continuum of denoising problems, and are widely observed to reveal coarse structure before fine detail.
- Yet, this intuition is mostly empirical and qualitative.
- We introduce feature information dynamics, an information-theoretic framework for localizing when a feature is generated during diffusion.
Why it matters
“Feature Information Dynamics in Diffusion” is a product decision that may change how people work with AI. Its value depends on task completion, correction effort and data handling—not simply the presence of a new feature.
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