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.
arXiv:2610.32657v1 Announce Type: new Abstract: Decision Transformer (DT) has demonstrated strong performance in various offline reinforcement learning (RL) tasks by conditioning action generation on...
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.
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.
arXiv:2610.19364v1 Announce Type: new Abstract: Dynamic graph learning models how entities and their relations evolve from historical observations to predict future graph states. Existing methods do...
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.
arXiv:2608. 07158v1 Announce Type: new Abstract: Temporal graph learning has become essential for analyzing real-world systems whose interactions continuously evolve over time, including financial transaction networks, communication systems, and online social platforms.
arXiv:2608. 06765v1 Announce Type: new Abstract: Continuous-time dynamic graph models predict future links by compressing past interactions into neural states.
arXiv:2602.11965v2 Announce Type: replace Abstract: Temporal domain generalization (TDG) learns from a sequence of observed domains to predict models for unseen future domains. Existing parameter-dyn...
The paper investigates how encouraging action predictability in latent world models can preserve intrinsic dynamics, which are crucial for planning. By deriving a theoretical lower bound and conducting empirical tests, the authors show that action predictability prevents the encoder from merging distinct action outcomes, even when visual appearance varies. Using a method called Concat IDM, they achieve a substantial increase in planning success on the Push‑T benchmark, from 9.1% to 77.2%, demonstrating the practical benefit of this inductive bias.
arXiv:2504. 07337v2 Announce Type: replace Abstract: Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs.
arXiv:2607. 27891v1 Announce Type: cross Abstract: Temporal graph learning is commonly organized around the evolution of node states or the encoding of interaction histories.