ArXiv

MASS: Multiplayer World Models with Authoritative Shared State

2026-08-06 · 16:47 UTC ·Ziqi Cai, Siqi Yang, Yimu Wang... ·1 min read
Authors
Ziqi Cai, Siqi Yang, Yimu Wang...
Categories
cs.CV, cs.HC
arXiv
https://arxiv.org/abs/2608.06257v1
PDF
https://arxiv.org/pdf/2608.06257v1

Brief

MAS introduces an authoritative shared typed state advanced by a learned Logic Engine (no hand-written transitions) and a Rendering Engine that produces consistent per-camera views on demand. On a matched multiplayer Snake benchmark, MAS achieves higher state accuracy and lower cross-view inconsistency than state-of-the-art multi-view baselines, scaling to 1,024 concurrent players over 10,000 recurrent steps.

Why it matters

MAS disentangles world dynamics and view rendering by maintaining an authoritative typed global state advanced by a learned Logic Engine (the sole recurrent memory; no hand-written transition function) and a separate learned Rendering Engine that generates consistent per-camera views on demand.

Key details

  • On a matched multiplayer Snake benchmark, MAS achieves superior state accuracy and lower cross-view inconsistency than state-of-the-art multi-view baselines and scales to 1,024 concurrent players for 10,000 recurrent steps.
  • Paper by Ziqi Cai, Siqi Yang, Yimu Wang, et al.; arXiv:2608.06257v1 (published 2026-08-06), PDF available at the provided arXiv link.
Source evidence

Abstract

Current video world models struggle in multiplayer environments because they entangle world state with view-dependent visual latents, leading to redundant compute, view inconsistencies, and poor scalability. We propose MAS (Multiplayer world models with Authoritative Shared State) to resolve this limitation. Inspired by multiplayer game architectures, MAS disentangles world dynamics and view rendering. A learned Logic Engine advances a global, authoritative typed state from joint actions without any hand-written transition function, acting as the sole recurrent memory and synchronization reference. From this shared state, a learned Rendering Engine generates independent and consistent views for any requested camera on demand. This explicit disentangling allows MAS to achieve superior state accuracy and lower cross-view inconsistency compared to state-of-the-art multi-view baselines on a matched multiplayer Snake benchmark. It advances predicted worlds with 1,024 concurrent players for 10,000 recurrent steps. Our results show that explicit, authoritative state modeling provides a practical foundation for scalable and consistent multi-agent world simulation.