MASS: Multiplayer World Models with Authoritative Shared State
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
Record details
Published: 6 August 2026
Source: arXiv cs.HC
Category: Research
Topics: Environment
Retrieved: 7 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Design and Evaluation of a Touchscreen-Based Teleoperation Interface for Robotic Manipulators
arXiv cs.HC · 6 August 2026
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
arXiv cs.AI · 6 August 2026
Divergent Perceptuomotor Recalibration in Virtual Reality and Video-Passthrough Mixed Reality on the Same Head-Mounted Display
arXiv cs.HC · 6 August 2026
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
arXiv cs.AI · 6 August 2026
Does Latent Context Help? A Controlled Evaluation of Inverse Reinforcement Learning in Arctic Shipping
arXiv cs.AI · 6 August 2026
The Digital Evolution of the Medical Black Bag: Environmental Scan With Trend Analysis and Horizon Scanning
JMIR (Journal of Medical Internet Research) · 6 August 2026
How to cite this record
ethics.ai (6 August 2026), “MASS: Multiplayer World Models with Authoritative Shared State,” evidence record 17354, https://ethics.ai/record/17354 (originally published by arXiv cs.HC).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.