Change Detection in Probability Flow ODE: Online Testing in Diffusion Latent Spaces

• URL: http://arxiv.org/abs/2608.22807v2
• Date: Tue, 25 Aug 2026 01:27:39 GMT
• Title: Change Detection in Probability Flow ODE: Online Testing in Diffusion Latent Spaces
• Abstract summary: A growing range of sequential data tasks, such as identifying trend reversals in financial markets, cannot be fully addressed without detection of distributional shifts in time-ordered data.<n>We consider a sequential change-point detection problem where the conditional density switches at an unknown time, yet neither the pre- nor post-change distribution admits a closed-form.<n>A conditional diffusion model, trained on pre-change-point data with a frozen context encoder, defines a deterministicection via the probability flow ODE.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A rapidly growing range of sequential data tasks, such as identifying trend reversals in financial markets, auto-segmenting video and audio recordings, detecting changes in movement direction from motion sensors cannot be fully addressed without detection of distributional shifts in time-ordered data. We consider a sequential change-point detection problem where the conditional density switches at an unknown time, yet neither the pre- nor post-change distribution admits a closed-form. Classical likelihood-ratio statistics are inapplicable in this settings. A conditional diffusion model, trained on pre-change-point data with a frozen context encoder, defines a deterministic bijection via the probability flow ODE. Pre-change observations are mapped onto standard Gaussian latent variables. Post-change observations, processed through the same frozen map, deviate from this reference. We employ the Maximum Mean Discrepancy as the test statistic, derive closed-form expressions for its components under the Gaussian null, and establish its asymptotic distribution as a degenerate U-statistic. Afterwards we apply an online detection procedure of Shiryaev--Roberts to the resulting statistic with exact threshold calibration. The method detects arbitrary distributional shifts, including covariance rotations and higher-order structural breaks, without parametric assumptions on either regime.

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