arXiv Computer Vision
Counterfactual World Models via Digital Twin-conditioned Video Diffusion
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EvoWorld: Towards Out-of-Sight World Evolution in Generative Video Models
EvoWorld introduces a framework that couples a vision‑language model with a segment‑autoregressive video renderer to enable generative video models to reason about world evolution beyond visual fidelity. The system maintains an evolving account of the world, infers event consequences, and renders causally plausible outcomes even when events occur out of sight. The accompanying EvoWorld‑Data and EvoWorld‑Bench datasets provide training and evaluation for event realization, causal reactions, and revisit consistency.
arXiv Machine Learning
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StateCraft: Augmenting Video World Models with Abstract-State Reasoning
StateCraft introduces a new architecture that separates a persistent, updatable abstract state from the visual state in video world models. By pairing a language model with a video diffusion model, it tracks entity changes over time and conditions visual generation on this abstract state. The approach is evaluated on generative interfaces and a new benchmark, showing improved generation quality, memory usage, and controllability, while enabling direct editing of unseen states and extending to spatial 3D environments.
arXiv Machine Learning
Aug 11
Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Models
arXiv:2608. 08982v1 Announce Type: new Abstract: Interactive video world models generate rollouts autoregressively under an action stream, yet they are trained and evaluated almost exclusively on factual prediction.
arXiv Machine Learning
Jun 24
Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models
arXiv:2606. 24152v1 Announce Type: cross Abstract: Existing literature claims that video generation essentially is world modelling.
arXiv AI
Jun 2
StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement
arXiv:2606. 00267v1 Announce Type: cross Abstract: Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot actions.
arXiv AI
Oct 27
Video World Models Need Predictable Latents
arXiv:2610.34663v1 Announce Type: cross Abstract: Video world models trained on large-scale robot data promise a safe and scalable approach for learning and evaluating policies that act in the physic...