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The Information Dynamics of Generative Diffusion.

Dejan Stančević¹, Luca Ambrogioni¹

¹Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 GD Nijmegen, The Netherlands.

Entropy (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

Generative diffusion models are explained through information theory, dynamics, and thermodynamics. This research reveals how score function divergence controls generative bandwidth and noise-induced symmetry breaking.

Keywords:

entropy production generative diffusion models information theory phase transition stochastic thermodynamics symmetry breaking

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Area of Science:

Machine Learning
Information Theory
Statistical Physics

Background:

Generative diffusion models are powerful but lack a unified theoretical framework.
Understanding their information-theoretic, dynamical, and thermodynamic underpinnings is crucial.

Purpose of the Study:

To provide an integrated perspective on generative diffusion models.
To connect information-theoretic, dynamical, and thermodynamic aspects of diffusion models.
To elucidate the role of score function divergence and symmetry breaking.

Main Methods:

Information-theoretic analysis of conditional entropy production.
Dynamical systems perspective on trajectory branching.
Thermodynamic interpretation via energy landscapes and phase transitions.

Main Results:

Generative bandwidth is governed by the score function's vector field divergence.
Divergence links to trajectory branching and symmetry-breaking phase transitions.
Pathwise conditional entropy variance reveals heterogeneity in uncertainty resolution.

Conclusions:

Generative diffusion is a controlled, noise-induced symmetry-breaking process.
The score function acts as a dynamic nonlinear filter for information flow.
This framework unifies diverse theoretical aspects of generative diffusion.