| title | S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation |
|---|---|
| authors | Jiahao Zhao, Junyi Liu, Lifeng Xu, Nan Xu, Qingli Wang, Qingxiao Li, Tianle Chen, Xiaoyu Wu, Yawen Zheng, Zikai Wang, Guanming Liu, Hequn Zhou, Jingyi Wang, Jingyuan Shu, Keqi Wang, Li He, Songyang Diao, Wenhui Xu, Xinyu Ren, Yaqin Fan, Yujin Zhou, Zhanao Yao |
| date | 2026-07-17 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.15686 |
| upvotes | 10 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We present S1-Omni, a unified multimodal reasoning model for scientific understanding, prediction, and generation. AI for Science (AI4S) has advanced significantly through domain-specific models, tool-augmented LLMs, and scientific language models. However, model capabilities remain highly fragmented, limiting the joint modeling of heterogeneous data, scientific laws, and expert knowledge. S1-Omni addresses this gap by consolidating these capabilities into a single, coherent scientific reasoning model. The architecture of S1-Omni is built upon three core components: unified representation of scientific data, natural-world knowledge alignment, and decoding for domain-specific tasks. First, S1-Omni maps natural-language instructions and scientific objects, including CIF, SMILES, protein sequences, spectra, and scientific images, into a shared representation space. Second, it incorporates scientific laws and expert knowledge into data construction and training, enabling the model to reason from scientific evidence. Third, it performs task-specific decoding to support a broad range of applications, including property prediction, spectrum-to-molecular generation, protein site and structure prediction, and scientific image generation and editing. S1-Omni is trained on S1-Omni-Corpus, which covers 200 scientific tasks and contains millions of reasoning samples, and is evaluated on over 60 scientific benchmarks. It outperforms GPT-5.5 and Gemini-3.1-Pro on most benchmarks and matches or surpasses domain-specific models on several benchmarks. Overall, S1-Omni provides a practical path toward unified scientific modeling. |
cite
@misc{zhao2026s,
title={S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation},
author={Jiahao Zhao and Junyi Liu and Lifeng Xu and Nan Xu and Qingli Wang and Qingxiao Li and Tianle Chen and Xiaoyu Wu and Yawen Zheng and Zikai Wang and Guanming Liu and Hequn Zhou and Jingyi Wang and Jingyuan Shu and Keqi Wang and Li He and Songyang Diao and Wenhui Xu and Xinyu Ren and Yaqin Fan and Yujin Zhou and Zhanao Yao},
year={2026},
eprint={2607.15686},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.15686}
}