S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation
Authors: name: Jiahao Zhao, name: Junyi Liu, name: Lifeng Xu, name: Nan Xu, name: Qingli Wang, name: Qingxiao Li, name: Tianle Chen, name: Xiaoyu Wu, name: Yawen Zheng, name: Zikai Wang, name: Guanming Liu, name: Hequn Zhou, name: Jingyi Wang, name: Jingyuan Shu, name: Keqi Wang, name: Li He, name: Songyang Diao, name: Wenhui Xu, name: Xinyu Ren, name: Yaqin Fan, name: Yujin Zhou, name: Zhanao Yao

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.