arXiv:2606. 02753v1 Announce Type: cross Abstract: Video world models are a foundational generative technology for embodied AI and the Metaverse, yet existing approaches are inherently limited to a single agent observing from a single perspective.
arXiv:2603. 02697v2 Announce Type: replace-cross Abstract: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction.
arXiv:2610.07031v1 Announce Type: new Abstract: Recent video world models have witnessed the paradigm shift from single-agent to multi-agent involvements, which can reveal more complicated dynamics a...
World Observer introduces a joint actor‑observer framework for persistent world modeling, decoupling the agent’s view from panoramic observers that track regions outside the actor’s field of view. By warping from a shared panoramic source and using an Observer Sink for high‑resolution references, the method maintains accurate object states and dynamics even when they leave and re‑enter the actor’s view. The approach is evaluated with new world‑space metrics and a benchmark across real and synthetic scenes, showing improved out‑of‑view dynamics while preserving visual fidelity, camera control, and 3D adherence.
Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings.
The paper introduces Multi-agent Egocentric World Model (ME-World), a framework that generates synchronized first-person video streams for multiple agents interacting in a shared environment. It addresses fine-grained embodied actions by ensuring cross-view action consistency, shared-environment consistency, and consistent state updates across agents. Experiments on real and synthetic data demonstrate that ME-World outperforms existing methods in shared-world consistency, action control, identity preservation, and video quality.
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:2608.08600v4 Announce Type: replace-cross Abstract: World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent en...
arXiv:2606. 17536v1 Announce Type: cross Abstract: Generative world models for autonomous driving face two unresolved tensions: heterogeneous control injection, where free-form language, HD-maps, trajectories, and camera poses reside in incompatible representational spaces, and post-hoc cross-view fusion, where per-camera latents fail to encode global 3-D geometry.
MVAgent is a multi‑agent pipeline for multi‑shot video generation that ensures consistent character appearance, stable spatial layout, and continuous character state across shots. The system uses typed conditioning inputs: a Spatial Grounding agent samples camera views, an Observer records shot endings into a continuity memory, a Transition agent builds action and spatial references for subsequent shots, and an Orchestrator composes these inputs into generator requests. Trained with agentic reinforcement learning (Trunk‑GDPO) while keeping the generator and judges frozen, MVAgent achieves the highest cross‑shot consistency and narrative‑planning quality on ViMax‑Bench and is preferred over the strongest agentic baseline in human evaluation.
arXiv:2608. 08600v1 Announce Type: cross Abstract: World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environments introduces a fundamental scalability challenge.
Oneira is an interactive video world model that links open-ended generation with persistent interaction. It uses a coding agent to manage an extensible world state, allowing newly generated or discovered entities to become actionable and ensuring that changes made by an agent are retained across video segments. Experiments demonstrate that Oneira supports consistent interaction with new objects while preserving prior interaction effects over long horizons.