Generative World Simulation with DAO and JING

XGEN Team, Guohao Cai, Haoxuan Che, Frank Fan, Chenran Huang +14 more
10/14/2026
cs.CV

No confident code implementation found for this paper.

Abstract

We present a Generative World Simulation framework that couples persistent shared world state with continuous first-person audiovisual generation. The framework integrates DAO, a computable world engine, and JING, an Egocentric Interactive Experience Model, to support sustained interaction for humans and agents. DAO maintains the states of characters, objects, and spaces and their relationships, advancing the world according to interaction rules and world time while preserving the consequences of past actions. From this evolving state, DAO derives observer-specific conditions for JING, which combines these conditions with character, object, and scene references, observation history, and current actions to generate each observer's audiovisual experience. Users can explore environments, manipulate objects, and communicate with characters through viewpoint control and text instructions, with their actions shaping both the shared state and subsequent experiences. By connecting world-state evolution with experience generation in a continuous interaction loop, the framework offers an approach to building interactive worlds that retain their history, evolve through action, and persist beyond any individual observer.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{team2026generative,
  title  = {Generative World Simulation with DAO and JING},
  author = {XGEN Team and Guohao Cai and Haoxuan Che and Frank Fan and Chenran Huang and Junjia Huang and Jiayi Jiang and Yanheng Li and Leon Liu and Shaoteng Liu and Xi Liu and Feng Nie and Zhefan Rao and An Wang and Hanwen Wang and Pengxiang Yan and Jieming Yu and Qian Zha and Wentao Zheng},
  year   = {2026},
  eprint = {2610.19726},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.19726v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2610.19726.svg)](https://codeofpaper.com/paper/2610.19726)

Discussion