arXiv AI By Hantong Feng, Duxin Chen, Wenwu Yu

From Complex Histories to Simple Predictors: Predictive Dimension in Dynamic Graph Learning

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arXiv Machine Learning
Jun 26

A Generalization Theory for JEPA-Based World Models

arXiv:2606. 27014v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have recently emerged as a promising paradigm for world modeling by learning predictive dynamics in a latent space rather than generating future observations at the input level.

By Jingyi Cui, Qi Zhang, Hongwei Wen, Yisen Wang
arXiv Machine Learning
Jul 17

What Do Temporal Graph Learning Models Learn?

arXiv:2510. 09416v4 Announce Type: replace Abstract: Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models.

By Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier
arXiv Machine Learning
Oct 3

Learning Commute-Time-Preserving World Models for Planning

The paper introduces Commute-Time-Preserving World Models (CTWMs), which learn latent representations that reflect commute-times in an environment by using a latent displacement predictor and a log-determinant regularizer. This approach addresses the issue that existing self-supervised methods degrade the necessary eigenvalue-dependent scaling for accurate commute-time representation. In experiments, CTWMs outperform the task-agnostic baseline LeWM on several continuous goal-reaching benchmarks while using only half the parameters.

By Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch, Friedemann Zenke
arXiv Machine Learning
Aug 27

Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models

The paper introduces Regularized Latent Dynamics Prediction (RLDP), a method that adds orthogonality regularization to self‑supervised next‑state prediction in latent space. RLDP maintains feature diversity, matching or surpassing complex representation learning approaches for zero‑shot reinforcement learning. It also performs robustly in low‑coverage data settings where prior methods fail.

By Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal, Amy Zhang, Scott Niekum, Martha White