The paper introduces SIGMA, an end‑to‑end framework that couples density estimation with principled uncertainty quantification for generative models. By using Gaussian mixture models and treating uncertainties as nuisance parameters in a new goodness‑of‑fit test, SIGMA converts a standard point‑null test into a composite test that accounts for statistical error of the fit. The authors demonstrate that incorporating uncertainties allows the same model to be trusted on test sets up to an order of magnitude larger than with conventional methods, using both a toy two‑dimensional example and a collider physics benchmark.
arXiv:2606. 10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data.
arXiv:2501. 18897v4 Announce Type: replace-cross Abstract: Generative models have achieved remarkable success across a range of applications, yet their evaluation still lacks principled uncertainty quantification.
arXiv:2604. 05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.
arXiv:2504. 01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution.
arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.
The paper compares two main strategies for producing probabilistic forecasts of physical systems: generative models (e.g., diffusion or flow matching) and ensembles of deterministic models trained with the continuous ranked probability score (CRPS). It introduces a framework that evaluates both methods on diverse 2‑D spatiotemporal systems, measuring empirical coverage of predictive intervals, accuracy, and computational efficiency. The study finds that CRPS‑trained ensembles generally provide more reliable uncertainties and faster inference, while generative models trained in ambient space can match coverage but at higher latency. The authors also release AutoCast, a modular framework for both approaches, and AutoSim, a dataset generation tool.
The paper introduces a structured posterior‑correction framework that blends a tractable Gaussian reference with data‑driven corrections learned via diffusion models. After applying mean correction and covariance calibration, the diffusion model focuses on a low‑dimensional subspace that captures discrepancies from the Gaussian reference, while the remaining space stays Gaussian. Experiments on three scientific inference tasks show that these corrections recover hidden modes, improve sample structure, and reduce prediction error compared to the Gaussian reference, with trade‑offs between accuracy and computational cost relative to full‑field generative models.
arXiv:2609.08620v1 Announce Type: cross Abstract: We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learni...
arXiv:2609.20999v1 Announce Type: cross Abstract: Latent variable generative models are commonly fit using simple priors over latent variables, but draws from these priors often fail to produce reali...
The paper introduces a particle‑level generative model that uses residual‑quantized full‑event data to enable fast, ML‑based surrogate simulation for collider events. It demonstrates conditional generation from detector‑stable particles, explores scaling across dataset and model sizes, and shows that token‑level loss predicts downstream physical fidelity. The work offers an empirical framework for scalable collider full‑event generation using residual‑quantized representations.
arXiv:2606. 30489v1 Announce Type: cross Abstract: Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty.
arXiv:2606. 23920v1 Announce Type: cross Abstract: The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-defined target distributions such as a geometric combination of the source distributions.