Emu3.5: Native Multimodal Models are World Learners
Authors: name: Yufeng Cui, name: Honghao Chen, name: Haoge Deng, name: Xu Huang, name: Xinghang Li, name: Jirong Liu, name: Yang Liu, name: Zhuoyan Luo, name: Jinsheng Wang, name: Wenxuan Wang, name: Yueze Wang, name: Chengyuan Wang, name: Fan Zhang, name: Yingli Zhao, name: Ting Pan, name: Xianduo Li, name: Zecheng Hao, name: Wenxuan Ma, name: Zhuo Chen, name: Yulong Ao, name: Tiejun Huang, name: Zhongyuan Wang, name: Xinlong Wang
Organization: Beijing Academy of Artificial Intelligence

We introduce Emu3.5, a large-scale multimodal world model that natively
predicts the next state across vision and language. Emu3.5 is pre-trained
end-to-end with a unified next-token prediction objective on a corpus of
vision-language interleaved data containing over 10 trillion tokens, primarily
derived from sequential frames and transcripts of internet videos. The model
naturally accepts interleaved vision-language inputs and generates interleaved
vision-language outputs. Emu3.5 is further post-trained with large-scale
reinforcement learning to enhance multimodal reasoning and generation. To
improve inference efficiency, we propose Discrete Diffusion Adaptation (DiDA),
which converts token-by-token decoding into bidirectional parallel prediction,
accelerating per-image inference by about 20x without sacrificing performance.
Emu3.5 exhibits strong native multimodal capabilities, including long-horizon
vision-language generation, any-to-image (X2I) generation, and complex
text-rich image generation. It also exhibits generalizable world-modeling
abilities, enabling spatiotemporally consistent world exploration and
open-world embodied manipulation across diverse scenarios and tasks. For
comparison, Emu3.5 achieves performance comparable to Gemini 2.5 Flash Image
(Nano Banana) on image generation and editing tasks and demonstrates superior
results on a suite of interleaved generation tasks. We open-source Emu3.5 at
https://github.com/baaivision/Emu3.5 to support community research.