MaineCoon: Pursuing A Real-Time Audio-Visual Social World Model

Lichen Bai; Tianhao Zhang; Shitong Shao; Dingwei Tan; Qiyu Zhong; Zhengpeng Xie; Haopeng Li; Qinghao Huang; Dandan Shen; Tengjiao Ji; Wei Wang; Peicheng Wu; Yuxuan Zhao; Xiangyu Zhu; Welly Luo; Shurui Yang; Zeke Xie

title

authors

Lichen Bai, Tianhao Zhang, Shitong Shao, Dingwei Tan, Qiyu Zhong, Zhengpeng Xie, Haopeng Li, Qinghao Huang, Dandan Shen, Tengjiao Ji, Wei Wang, Peicheng Wu, Yuxuan Zhao, Xiangyu Zhu, Welly Luo, Shurui Yang, Zeke Xie

date

2026-06-16

venue

arXiv (preprint, not peer reviewed)

arXiv:2606.17800

affiliation

catnip

upvotes

12 (Hugging Face Daily Papers, 2026-11-01)

links

abs pdf hf code

primer

World models

abstract

As an increasing majority of global video content is consumed on social platforms for interactive social purposes, video generation models built for social worlds are important but largely overlooked by previous studies. In this work, we define the position of social world models and build a prototype model as the first step towards this goal. While previous world models successfully simulate physical environments or gaming world exploration, they remain fundamentally detached from human-centric social dynamics. To bridge this gap as the first step to social world models, we present MaineCoon, the first real-time audio-visual autoregressive model that has 22B parameters and is capable of real-time streaming generation and sub-second interaction, with a record-breaking frame rate of up to 47.5 FPS, on a single GPU. To the best of our knowledge, MaineCoon is also the first real-time audio-visual generation model specifically optimized for social-interactive applications. To enable efficient and stable training, we introduce several novel techniques into MaineCoon, including self-resampling, cross-modal representation alignment, domain-aware preference optimization, and reinforced online-policy distillation (ROPD). We also design the first agentic streaming inference framework that supports thousand-second-scale or even longer generation while mitigating drift with agentic cache management and prompt planing. These innovations significantly accelerate training while optimizing real-time inference performance. We believe this work not only sets a new state-of-the-art (SOTA) performance benchmark for high-quality, low-latency, and long-horizon audio-visual autoregressive models, but also points out the paradigm shift desired for next-generation AI-native social platforms.

@misc{bai2026mainecoon,
  title={MaineCoon: Pursuing A Real-Time Audio-Visual Social World Model},
  author={Lichen Bai and Tianhao Zhang and Shitong Shao and Dingwei Tan and Qiyu Zhong and Zhengpeng Xie and Haopeng Li and Qinghao Huang and Dandan Shen and Tengjiao Ji and Wei Wang and Peicheng Wu and Yuxuan Zhao and Xiangyu Zhu and Welly Luo and Shurui Yang and Zeke Xie},
  year={2026},
  eprint={2606.17800},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2606.17800}
}