arXiv Machine Learning
Jul 28
Neural operator discovery from heterogeneous trajectories
arXiv:2607. 23337v1 Announce Type: new Abstract: Neural operators provide data-driven mappings for modeling dynamical systems.
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.
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arXiv Machine Learning
Jun 30
J-LAW: Joint Localization and Actionable World Modeling via Coupled Latent Factor Graphs
arXiv:2606. 28712v1 Announce Type: cross Abstract: Classical SLAM estimates metric poses and a geometric map but produces no actionable predictive model for planning.
arXiv AI
Jun 9
FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
arXiv:2606. 09311v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods like the Cross-Entropy Method (CEM).
arXiv AI
Jun 19
Sensorimotor World Models: Perception for Action via Inverse Dynamics
arXiv:2606. 20104v1 Announce Type: cross Abstract: Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions.
arXiv AI
Jul 1
Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
arXiv:2606. 31232v1 Announce Type: new Abstract: Learning visual world models for planning requires compact latent dynamics that remain sensitive to actions, yet reconstruction-free joint-embedding objectives can collapse to action-insensitive representations.
arXiv AI
22h ago
Towards Zero-Shot Task Transfer with Neurosymbolic World Models
arXiv:2608. 17959v1 Announce Type: new Abstract: State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the structure of the underlying environment.
arXiv Machine Learning
Jul 7
Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling
arXiv:2607. 04409v1 Announce Type: new Abstract: Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making.
arXiv AI
Jul 21
SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning
arXiv:2607. 17973v1 Announce Type: new Abstract: Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and evaluate candidate action sequences.
arXiv Machine Learning
Jul 27
On the Identifiability of Controlled World Models
arXiv:2607. 22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control.
arXiv AI
Jun 17
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
arXiv:2603. 22281v2 Announce Type: replace-cross Abstract: Recent progress in latent world models (e.
arXiv Machine Learning
Aug 4
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
arXiv:2608. 00491v1 Announce Type: new Abstract: Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data.
arXiv Machine Learning
Jul 14
A Control Theory of Predictability in Latent World Models
arXiv:2607. 10362v1 Announce Type: new Abstract: Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward.