arXiv Machine Learning
Jun 16
How Should World Models Be Evaluated? A Decision-Making-Centric Position
arXiv:2606. 15032v1 Announce Type: new Abstract: World models have rapidly become one of the central abstractions in modern AI.
How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position
arXiv:2606. 15032v2 Announce Type: replace Abstract: World models have become a central abstraction in modern AI.
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arXiv AI
Aug 11
WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation
arXiv:2608. 09298v1 Announce Type: cross Abstract: Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation.
arXiv AI
Jul 2
From World Models to World Action Models: A Concise Tutorial for Robotics
arXiv:2607. 00836v1 Announce Type: cross Abstract: World models are increasingly used in embodied intelligence and generative simulation, yet their scope remains ambiguous across communities.
Hugging Face Trending Papers
Aug 10
WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation
Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation. Realizing this promise requires precise action-conditioned transitions rather than merely plausible outputs.
arXiv AI
Jul 7
Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning
arXiv:2607. 04681v1 Announce Type: cross Abstract: Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models.
arXiv AI
Jun 30
WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
arXiv:2602. 13977v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots.
arXiv Machine Learning
Jul 17
DriftWorld: Fast World Modeling through Drifting
arXiv:2607. 15065v1 Announce Type: cross Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly.
arXiv Machine Learning
Jun 18
Hierarchical Planning with Latent World Models
arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.
arXiv AI
Jul 14
Towards Predictive, Aligned, and Scalable Robot Learning
arXiv:2607. 11270v1 Announce Type: cross Abstract: Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities.
arXiv AI
Jul 3
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
arXiv:2605. 00412v3 Announce Type: replace Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning.
arXiv AI
Aug 10
Capek 0.5: An Execution-Centric Vision-Language Model for Embodied Intelligence
arXiv:2608. 06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents.
arXiv AI
Jun 30
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.