arXiv AI August 11, 2026 By Karim Zaghw, Andrew Pashea, Marc Pritsch, Wouter Nuijten, Karl Friston, Lancelot Da Costa

Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification

arXiv:2608. 09512v1 Announce Type: new Abstract: Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficult.

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Hugging Face Trending Papers

Aug 10

Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification

Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficult. Renormalising generative models (RGMs) address this challenge by composing discrete generative models across spatial and temporal scales, coarse-graining lower-level states and paths into higher-level causes for objects, events, and action.

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arXiv AI

Jun 9

ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning

arXiv:2505. 21457v2 Announce Type: replace-cross Abstract: Active vision, also known as active perception, refers to actively selecting where and how to look in order to gather task-relevant information.

By Muzhi Zhu, Hao Zhong, Canyu Zhao, Zongze Du, Mingyu Liu, Zheng Huang, Anzhou Li, Hao Chen, Cheng Zou, Jingdong Chen, Ming Yang, Chunhua Shen

llms agents computer-vision reinforcement-learning robotics multimodal benchmarks

arXiv Machine Learning

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Distributional Active Inference

arXiv:2601. 20985v2 Announce Type: replace Abstract: Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning.

By Abdullah Akg\"ul, Gulcin Baykal, Manuel Hau{\ss}mann, Mustafa Mert \c{C}elikok, Melih Kandemir

reinforcement-learning robotics benchmarks

arXiv Machine Learning

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Unifying Generative Models with Path Integrals

arXiv:2608. 12438v1 Announce Type: new Abstract: We formulate generative modeling as a path integral in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluation principles for a single master action.

By Ramon Winterhalder

diffusion safety

arXiv AI

4d ago

Stochastic World Models for Verifying Vision-Based Neural Feedback Systems

arXiv:2609.38120v1 Announce Type: new Abstract: Verifying a vision-based neural feedback system requires a model of the observations its controller acts upon. Such a model must capture the variation...

By I. Samuel Akinwande, Mykel J. Kochenderfer, Clark Barrett

diffusion benchmarks safety

arXiv Machine Learning

Jul 21

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data

arXiv:2607. 16251v1 Announce Type: new Abstract: Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time.

By Yutong Feng, Shiyuan Piao, Yutong Xia, Xu Liu, Wenqi Fan, Fugee Tsung, See-Kiong Ng, Yuxuan Liang

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