arXiv:2606. 06785v1 Announce Type: cross Abstract: We study finite-sample change detection for one-dimensional noisy dynamical systems using partition-based empirical approximations of stationary behaviour.
arXiv:2608. 13922v1 Announce Type: new Abstract: Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified.
The paper introduces a sequential change‑point detection method for time‑ordered data where neither the pre‑ nor post‑change distributions have closed forms. It trains a conditional diffusion model on pre‑change data, uses its probability flow ODE to map observations to a Gaussian latent space, and then applies the Maximum Mean Discrepancy as a test statistic. The authors derive closed‑form components under the Gaussian null, establish the statistic’s asymptotic distribution as a degenerate U‑statistic, and implement an online Shiryaev–Roberts procedure with exact threshold calibration to detect arbitrary distributional shifts without parametric assumptions.
arXiv:2608. 01547v1 Announce Type: cross Abstract: Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations.
Diffusion and flow-matching samplers integrate a learned probability-flow ODE from a large noise scale down to a small terminal floor $σ_{\min}$, at which the score is stiff and the flow develops a boundary layer. We treat $σ_{\min}$ as a singular-perturbation parameter and determine which fixed-step samplers are asymptotic-preserving (AP), that is, stable and uniformly accurate as $σ_{\min}\to0$, casting the criteria as an a posteriori audit: residual functionals with $σ_{\min}$-uniform coefficients, computable on a pretrained checkpoint without ground-truth scores or exact trajectories.
arXiv:2605. 27478v3 Announce Type: replace-cross Abstract: Schr\"odinger bridges for time series (SBTS) generate synthetic paths by projecting, in relative entropy, a Brownian reference onto the path laws that match the joint distribution of the data on the observation grid.
arXiv:2607. 04113v1 Announce Type: new Abstract: Diffusion and flow-matching samplers integrate a learned probability-flow ODE from a large noise scale down to a small terminal floor $\sigma_{\min}$, at which the score is stiff and the flow develops a boundary layer.
arXiv:2609.27179v1 Announce Type: cross Abstract: We study distribution-free sequential changepoint detection for independent observations with unknown and unrestricted pre- and post-change laws. We...
The paper presents a runtime monitoring framework for stochastic systems that distinguishes normal distributional relaxation from regime changes while limiting false alarms. It combines relative‑entropy dissipation, information geometry, and sequential inference within a bounded first‑passage architecture, employing Gaussian window surrogates, covariance shrinkage, and conformal ranking aggregated by a mixture power‑martingale. Validation on Ornstein–Uhlenbeck dynamics and network intrusion datasets (NSL‑KDD, UNSW‑NB15) shows high detection rates with low false positives, highlighting calibration transport as a key deployment challenge.
arXiv:2606. 01256v1 Announce Type: cross Abstract: This paper introduces a distribution-free framework for constructing post-detection confidence sets for changepoints after stopping a sequential change detection procedure.
arXiv:2606. 02232v1 Announce Type: new Abstract: Learning a Markov transition model is not merely conditional density estimation: the learned object must be a valid transition kernel before it is iterated in downstream dynamics.
The paper proves that for discrete diffusion models using uniform or remasking forward processes, an adaptive sampler based on a leave‑one‑out denoiser can achieve sampling error proportional to the score‑estimation error plus a small tolerance. The required number of discretization steps scales with the dual total correlation of the target distribution, not directly with the ambient dimension. This result shows that sampling complexity is governed by the intrinsic dependence structure of the distribution, and the authors provide an information‑theoretic analysis linking discretization error to mutual information between coordinates.
arXiv:2606. 18186v1 Announce Type: cross Abstract: Finite-dimensional (FD) diffusion policies exhibit temporal drift owing to discretization artifacts that degrade long-horizon performance (when deployed on physical systems).