arXiv:2607. 01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications.
By Chandni Nagda, Mayank Shrivastavam Gudrun Thorkelsdottir, Gan Zhang, Morteza Mardani, Arindam Banerjee
arXiv:2606. 14585v1 Announce Type: cross Abstract: Generative dynamics models enable planning in challenging robotic systems, but safe deployment requires reliably detecting policy-induced out-of-distribution (OOD) transitions.
By Hongzhan Yu, Chenghao Li, Ruipeng Zhang, Henrik Christensen, Sicun Gao
arXiv:2606. 09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback.
By Lena Krieger, Xuan Zhao, Zhuo Cao, Qin Wang, Hanno Scharr, Ira Assent
arXiv:2610.01034v1 Announce Type: cross
Abstract: Bayesian inference increasingly uses informative but implicit priors represented only by samples, such as historical ensembles, simulator outputs, an...
By Hoang Phuc Hau Luu, Marcelo Hartmann, Zhongjian Wang
arXiv:2609.12953v1 Announce Type: new
Abstract: Flow matching approaches to imaging inverse problems commonly incorporate measurements in two ways. Conditioning-based approaches supply measurement-de...
By Shirin Shoushtari, Edward P. Chandler, Xiao Shi, Ulugbek S. Kamilov
The paper presents an active inference route‑planning method for autonomous agents tasked with reconnaissance missions. It builds an evidence map that integrates both positive and negative sensor observations over time, using Dempster‑Shafer theory and a Gaussian sensor model to update a posterior probability distribution. By computing variational free energy across positions, the agent moves incrementally toward locations that minimize free energy, balancing exploration of large areas with exploitation of identified targets.
By Johan Schubert, Farzad Kamrani, Tove Gustavi