Emu3.5: Native Multimodal Models are World Learners
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.
690414cc2c556835fa67efb5
Yufeng Cui
claimed_verified
2025-10-31T14:27:32.859Z
648683de623b5f050213f2be
https://cdn-avatars.huggingface.co/v1/production/uploads/648683de623b5f050213f2be/0NHJpfa5WqLuXndV7M0H9.jpeg
Yufeng Cui
YufengCui
user
YufengCui
690414cc2c556835fa67efb6
Honghao Chen
690414cc2c556835fa67efb7
Haoge Deng
claimed_verified
2025-11-03T20:56:40.412Z
64dd9404f2c8f66d52604fb3
/avatars/1d7fec043e0848a145e4dcaf8973a575.svg
Haoge Deng
Bitterdhg
user
Bitterdhg
690414cc2c556835fa67efb8
Xu Huang
claimed_verified
2025-11-03T20:56:34.451Z
64560c21babbbbd3486df362
/avatars/69a37786be8d5ec964d25a565afa3103.svg
Xu Huang
Savoia
user
Savoia
690414cc2c556835fa67efb9
Xinghang Li
690414cc2c556835fa67efba
Jirong Liu
690414cc2c556835fa67efbb
Yang Liu
690414cc2c556835fa67efbc
Zhuoyan Luo
690414cc2c556835fa67efbd
Jinsheng Wang
690414cc2c556835fa67efbe
Wenxuan Wang
claimed_verified
2025-10-31T14:27:30.411Z
656428b5462e5ebcbf537d4e
https://cdn-avatars.huggingface.co/v1/production/uploads/656428b5462e5ebcbf537d4e/WkX8Z_OJPOGAcuKq3aPXL.jpeg
Wenxuan Wang
Rookielion
user
Rookielion
690414cc2c556835fa67efbf
Yueze Wang
claimed_verified
2025-11-03T20:56:36.538Z
6458b59c7a7e192202df8fa0
/avatars/33ee716477e5686da8723d01e199cd27.svg
Yueze Wang
yzwang
user
yzwang
690414cc2c556835fa67efc0
Chengyuan Wang
admin_assigned
2025-11-05T09:30:33.851Z
64255c5a175bd29522825e1f
/avatars/4334dbfb99a967a39d6bef67d4792172.svg
Chengyuan Wang
aikx
user
aikx
690414cc2c556835fa67efc1
Fan Zhang
claimed_verified
2025-11-03T20:56:42.605Z
640ed40dc025ddf618950af7
/avatars/a99abd4346fa649ad4144f284ebcc972.svg
Fan Zhang
ryanzhangfan
user
ryanzhangfan
690414cc2c556835fa67efc2
Yingli Zhao
690414cc2c556835fa67efc3
Ting Pan
claimed_verified
2025-10-31T14:27:37.508Z
6565bc5ee5aac326bfc98e39
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vIfHy9Y1yAK6A96UCHNBH.jpeg
Ting Pan
PhyscalX
user
PhyscalX
690414cc2c556835fa67efc4
Xianduo Li
690414cc2c556835fa67efc5
Zecheng Hao
690414cc2c556835fa67efc6
Wenxuan Ma
690414cc2c556835fa67efc7
Zhuo Chen
690414cc2c556835fa67efc8
Yulong Ao
690414cc2c556835fa67efc9
Tiejun Huang
690414cc2c556835fa67efca
Zhongyuan Wang
690414cc2c556835fa67efcb
Xinlong Wang
claimed_verified
2025-10-31T14:27:35.127Z
63ca558304c979828311c5a5
/avatars/2a439d79fba2f987cabe780d10c94d25.svg
Xinlong Wang
xinlongwang
user
xinlongwang
Emu3.5: Native Multimodal Models are World Learners
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.
Emu3.5, a large-scale multimodal world model, predicts next states in vision and language, enhanced with reinforcement learning and Discrete Diffusion Adaptation for efficient inference, achieving strong performance in various multimodal tasks.
multimodal world model next-token prediction vision-language interleaved data Discrete Diffusion Adaptation bidirectional parallel prediction long-horizon vision-language generation any-to-image generation complex text-rich image generation spatiotemporally consistent world exploration open-world embodied manipulation