# ConsistWorld: Evidence Routing for Consistent Multi-Agent World Models

arXiv: https://arxiv.org/abs/2609.22641 · HTML: https://mlsanity.com/paper/2609.22641/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Video-generation world models and simulators, one of 11 directions in World models.
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 18 Sep 2026.

## Placed in

- World models / [Video-generation world models and simulators](https://mlsanity.com/wm/video-generation-world-models-and-simulators/)

## Builds on (most cited first, 14 of 14)

- [RELIC: Interactive Video World Model with Long-Horizon Memory](https://mlsanity.com/paper/2512.04040/) (2025)
- [Infinite Worlds with Versatile Interactions](https://mlsanity.com/paper/2607.07534/) (2026)
- [Solaris: Building a Multiplayer Video World Model in Minecraft](https://mlsanity.com/paper/2602.22208/) (2026)
- [MosaicMem: Hybrid Spatial Memory for Controllable Video World Models](https://mlsanity.com/paper/2603.17117/) (2026)
- [MultiWorld: Scalable Multi-Agent Multi-View Video World Models](https://mlsanity.com/paper/2604.18564/) (2026)
- [MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines](https://mlsanity.com/paper/2603.06679/) (2026)
- [WorldKV: Efficient World Memory with World Retrieval and Compression](https://mlsanity.com/paper/2605.22718/) (2026)
- [Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players](https://mlsanity.com/paper/2605.28816/) (2026)
- [Addressable Memory for Video World Models](https://mlsanity.com/paper/2608.07408/) (2026)
- [Prisma-World: Camera-Controllable Multi-Agent Video World Model](https://mlsanity.com/paper/2606.09507/) (2026)
- [MASS: Multiplayer World Models with Authoritative Shared State](https://mlsanity.com/paper/2608.06257/) (2026)
- [MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data](https://mlsanity.com/paper/2606.02753/) (2026)
- [Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers](https://mlsanity.com/paper/2607.21594/) (2026)
- [IC-World: In-Context Generation for Shared World Modeling](https://mlsanity.com/paper/2512.02793/) (2025)

## Built on by (most cited first, 3 of 3)

- [Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction](https://mlsanity.com/paper/2610.12299/) (2026)
- [Esports World Model Dataset](https://mlsanity.com/paper/2610.16166/) (2026)
- [PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents](https://mlsanity.com/paper/2610.20240/) (2026)

## Abstract (arXiv)

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.