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.
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:2604. 02330v2 Announce Type: replace-cross Abstract: Recent advances in video diffusion have enabled the development of "world models" capable of simulating interactive environments.
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.
TAPESTRY repurposes a pretrained video diffusion transformer into a robotic policy by treating robot actions as single-patch tokens in the model’s latent space, eliminating the need for video generation at inference. The lightweight linear projection maps each action timestep to the VAE patch dimensions, and the shared backbone is trained with three diffusion objectives—video diffusion, joint video+action diffusion, and action-only diffusion—to learn dynamics priors and action denoising. At test time, the model produces smooth action chunks in a single DDIM step, achieving strong performance on simulation and real‑robot benchmarks with significantly lower inference cost than comparable joint video–action policies.
arXiv:2508.13009v5 Announce Type: replace Abstract: Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynami...
The paper introduces CoDeR, a new paradigm for world modeling that explicitly builds an executable world using code rather than relying solely on visual observations. CoDeR translates high‑level concepts into structured world rules, executable dynamics, and perceptual observations through five complementary roles, enabling long‑term memory, open‑ended interactions, autonomous world evolution, and persistent multi‑agent dynamics. Experiments show that this framework extends the capabilities of existing world models and achieves state‑of‑the‑art performance across multiple evaluation settings.
arXiv:2607. 05352v1 Announce Type: cross Abstract: We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions.
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...
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.
arXiv:2610.28554v1 Announce Type: new Abstract: Despite the success of video world models, extending them to multiple agents is difficult because it requires generating several agent-specific video s...
We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios.