Formal characterization of guidance-driven entropy redistribution

Establish a formal characterization of how guidance controls both the magnitude and the timing of entropy redistribution along the diffusion sampling trajectory, specifying the mechanisms by which guidance modifies class-conditional information flow over time.

Background

Empirical results in the paper demonstrate that guidance (including classifier-free guidance applied in limited or full intervals) systematically redistributes entropy production across the sampling trajectory, often shifting semantic commitment toward higher noise regimes and affecting the temporal profile of information recovery.

While the empirical distortions are documented for models such as EDM2-XS on ImageNet, the authors state that a rigorous, formal account of how guidance modulates entropy dynamics—quantifying both magnitude and timing—has not yet been developed, leaving a theoretical gap.