arXiv:2609.10540v1 Announce Type: new Abstract: Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent w...
arXiv:2608.29910v1 Announce Type: new Abstract: Interactive world models extend video generation from offline clip synthesis toward persistent simulation of interactive virtual worlds, enabling appli...
BoxWorld introduces a novel approach to real‑time interactive video worlds by decoupling interaction reasoning from generative rendering. A coding agent builds a Box World Engine that applies user‑defined rules to determine interaction outcomes and exposes object states via a lightweight interface of layout boxes, persistent attributes, and dynamic status variables. A separate Box‑State Generative Renderer then autoregressively renders these evolving states into video, enabling online control over object motion, state switches, and conditional events. The system employs a three‑stage training strategy—learning object grounding from open‑domain videos, state transitions from gameplay videos, and rollout distillation on self‑generated histories—to handle real‑time state updates while maintaining visual coherence. BoxWorld outperforms end‑to‑end world models and remains competitive across multiple public benchmarks, with a new PairedStateBench benchmark for fine‑grained interaction control.
arXiv:2603. 03482v2 Announce Type: replace-cross Abstract: Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities.
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.
Valerant is a training‑free framework that turns a pretrained action‑conditioned world model into a world action model (WAM) capable of exploring and building 3D game maps. By combining predictive visual rollouts with SLAM‑based spatial reconstruction and exploration‑driven action selection, it converts a single image into a persistent, navigable 3D geometry. This approach extends WAM‑based interaction beyond 2D visual simulation and reduces manual effort in 3D game‑map creation.
Video world models enable interactive exploration of dynamic environments, yet struggle to respect prior observations over long horizons and across viewpoints. We present WorldCrafter, a video world m...
AgentGarten is a framework that links simulators and game engines with a shared neural renderer to create real‑time interactive virtual worlds. It uses a pretrained video model adapted to geometry conditions, refined with Adversarial Forcing, to produce realistic visual observations while maintaining consistent state and rules. The system enables agents to learn efficiently—achieving comparable performance in only four training rounds versus millions in traditional reinforcement learning—by distilling experience into playbooks that successive agents can inherit and improve upon.
arXiv:2610.20240v1 Announce Type: new Abstract: A multiplayer world model must predict how the independent actions of several players change a shared environment, including changes outside a player's...
WorldMind is a decoupled framework for state-aware NPC behavior in game world models, separating interactive world modeling into four layers: Understanding, Decision, Control, and Generation. It constructs a compact state from generated frames, reasons over it to plan NPC actions, translates actions into temporally aligned conditions, and synthesizes visual outcomes. Experiments on the newly introduced BOSS-140K dataset show that WorldMind achieves more tactically appropriate and coherent NPC behavior than baseline models in about 70% of pairwise comparisons.
arXiv:2608. 14530v1 Announce Type: cross Abstract: Interactive game world models typically autoregress visual observations directly in pixel or latent space, forcing structured properties such as pose, geometry, and occlusion to be implicitly maintained by the same generative sequence.
WorldCrafter is a video world model that introduces a camera‑queryable implicit 3D‑aware memory to improve long‑horizon consistency and viewpoint control. The model compresses multi‑view evidence into a limited token budget shaped by the requested viewpoint, integrating historical observations via a memory encoder and pose‑conditioned readout before denoising. Experiments on static and dynamic scenes demonstrate significant gains in consistency and camera‑control accuracy while maintaining visual quality during minute‑scale exploration.