ConsistWorld: Evidence Routing for Consistent Multi-Agent World Models

2026-09-18

AI Summary

本文提出ConsistWorld，通过证据路由方法解决多智能体世界模型中跨视角和时间的一致性问题。

Abstract

Autoregressive video world models enable temporally coherent generation for a single observer. Extending them to multiple agents requires consistency across independently controlled views and temporal gaps under causal streaming. We present ConsistWorld, a multi-agent world model that generates camera-controlled video streams of a static scene from one shared image. We formulate consistency as routing evidence from committed multi-agent history and concurrently generated peer views to the tokens being generated. Pose Conditioned Memory Retrieval selects relevant historical observations from all agents, recovering evidence beyond the recent context window. Visibility-Gated Peer Sharing regulates current peer information according to estimated historical coverage and current-view overlap. Together, they determine which historical observations enter the context and where concurrent peer information contributes, supporting long-term recall and coordinated exploration. Both mechanisms use camera geometry and maintain a bounded active context for a fixed agent count and retrieval budget. Experiments on evidence sharing cases and video length and agent number generalizations show that ConsistWorld achieves a strong cross-time and cross-agent consistency while preserving competitive generation quality.

Problem

• multi-agent world models
• consistency
• temporal coherence
• causal streaming
• evidence routing

Innovation

• Pose Conditioned Memory Retrieval
• Visibility-Gated Peer Sharing
• Multi-Agent World Models