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arXiv · 2610.27754

Detection of Memorization in Diffusion Models Through Thermodynamic Singularity

Iljung Kim·Kangwoo Park·Yung-Kyun Noh·Daniel D. Lee
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Abstract

In diffusion models, proper distributional learning entails reversing a diffusion process over underlying density functions, whereas a key failure is memorization, in which the reverse process instead learns diffusion from individual training samples. We provide an analytical characterization of such memorized reverse processes, along with a criterion that detects the associated thermodynamic singularity. The proposed criterion reliably distinguishes between properly learned and memorizing networks across a range of neural network models designed to promote or mitigate memorization.

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Iljung Kim, Kangwoo Park, Yung-Kyun Noh, Daniel D. Lee. 2026-10-20. Detection of Memorization in Diffusion Models Through Thermodynamic Singularity. https://arxiv.org/abs/2610.27754

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