arXiv:2606. 26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system, given observations, in an online fashion.
arXiv:2607. 10410v1 Announce Type: cross Abstract: Reliable forecasting of several interrelated environmental variables - such as regional precipitation and temperature, or other correlated geophysical fields - across many locations calls for accurate predictions accompanied by trustworthy statements of their uncertainty.
arXiv:2607. 00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise.
arXiv:2509.13577v3 Announce Type: replace-cross Abstract: Trustworthy trajectory prediction grounds autonomous vehicle (AV) safety, yet deployed models inevitably face out-of-distribution (OOD) scene...
ReWorld-Track introduces a recursive event world model for language‑guided multi‑camera tracking that explicitly carries association uncertainty into future predictions. By treating candidate matches and waiting as alternative target states, the model updates a persistent recurrent belief that preserves uncertainty across successive observations. This approach improves identity continuity and next‑camera accuracy, achieving HOTA scores of 65.19 on CityFlowV2 and 45.36 on MTMMC, and reducing median arrival‑time error from 0.78 s to 0.71 s.
SPARC (Single-Pass Adaptive Risk Calibration) is a Bayesian‑conformal uncertainty layer for human motion forecasting that adds an analytic epistemic scale to a deterministic MLP backbone’s Gaussian covariance. The scale, κ_t(x), inflates the covariance without altering its correlation structure, enabling 95% marginal prediction tubes with finite‑sample validity via split conformal calibration. Across nine dataset‑protocol blocks, SPARC outperforms baselines on NLL and a combined MPJPE+NLL metric while maintaining competitive point accuracy and efficient calibrated tubes.
The paper introduces an adaptive rollout truncation method for offline world model training that uses epistemic uncertainty to decide when to stop autoregressive rollouts. By calibrating a threshold during a warm‑up phase, the approach replaces fixed‑horizon rollouts with uncertainty‑driven truncation, evaluated with ensemble and Monte Carlo dropout estimators. Experiments on ANYmal‑D and ANT demonstrate that this strategy matches or surpasses fixed‑horizon training while reducing cumulative rollout steps by about 72%.
PR‑Smoother is an amortized smoothing method that preserves the explicit use of a prescribed simulator in both the evidence lower bound and the variational family. It learns only future‑conditioned corrections to the simulator’s rollout, yielding a non‑Gaussian smoothing distribution that can jointly infer state, parameters, and sensor bias from observations alone. The approach recovers the exact smoother in deterministic and linear‑Gaussian limits and has been shown to capture multimodal posteriors in Lorenz‑96 and scale to 16,384‑dimensional Kolmogorov flow.
arXiv:2607. 22599v1 Announce Type: new Abstract: Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an observed history.
arXiv:2608. 09011v1 Announce Type: new Abstract: Uncertainty Quantification (UQ) aims to measure the reliability of model predictions, serving as a critical safeguard for deploying Vision-Language Models (VLMs) in safety-critical scenarios.
arXiv:2608.29029v3 Announce Type: replace-cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a powerful framework for latent world modeling and planning in a reconstruction-free...
The paper introduces SCROLL, a method for forecasting multiple observables in stochastic dynamical systems by composing each observable’s likelihood into per‑task free‑routed last‑layer beliefs on a shared backbone. This approach learns unit‑dependent loss scaling directly from data, enabling accurate predictive variance estimation without separate tuning. Experiments on the Ornstein–Uhlenbeck process, stochastic Lorenz‑63, and real air‑quality data show that SCROLL recovers analytic kernels, achieves superior negative log‑likelihood on state and regime tasks, and maintains calibration while reducing hyper‑parameter search costs.