arXiv AI
What a World Model Represents Is Three Questions
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arXiv AI
Sep 10
Learning Counterfactual World Models for Embodied Reasoning under Partial Observability
arXiv:2609.05834v1 Announce Type: new Abstract: World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the...
arXiv Machine Learning
Sep 1
The Intervention Gap in Latent World Models
The paper introduces the concept of intervention fidelity in latent world models, measuring whether a model’s open‑loop transitions align with actual environment interventions. Experiments on TD‑MPC2, Cheetah, and DreamerV3 show that high reward fit does not guarantee fidelity, and that self‑supervised models can outperform task‑anchored ones in preserving intervention effects. The authors propose a capture‑gated audit to localize failures and argue that fidelity must be directly audited on the model’s native interface.
arXiv Computer Vision
Sep 22
Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies
The paper proposes a method to distill world‑model representations into compact Vision‑Language‑Action (VLA) policies. By adding a single feature‑alignment term during VLA training, a frozen world model’s internal features are cached and the student policy learns to match them, eliminating the need for a generative future‑rolling component. The resulting lightweight policy runs in 32 ms on an RTX 5090, achieving high performance on LIBERO and RoboCasa‑GR1, and transfers effectively to real robotic hardware.
arXiv Machine Learning
Jul 30
What Can Latent World Models Know? Physical Parameter Identifiability in Multimodal Predictive Representations
arXiv:2607. 27017v1 Announce Type: new Abstract: A central premise of latent world models is that predicting the future forces a representation to internalize the physics of its environment.
arXiv Machine Learning
Jun 8
Predictive Statistics Shape Emergent World Representations of Grid Walkers
arXiv:2603. 16689v2 Announce Type: replace Abstract: Next-token predictors often appear to develop internal representations of the latent world and its rules.
arXiv AI
Jul 9
The Rank-One Corner: How Much Value Equivalence Does a Task Need from a World Model?
arXiv:2607. 06640v1 Announce Type: cross Abstract: A learned world model is usually judged by how faithfully it reconstructs its observations or predicts reward, as though quality were something the model simply has or lacks.