S1-Omni：面向科学理解、预测与生成的统一多模态推理模型

摘要

我们提出了S1-Omni，一个面向科学理解、预测与生成的统一多模态推理模型。AI for Science (AI4S) 已通过领域专用模型、工具增强的大语言模型及科学语言模型取得了显著进展。然而，模型能力仍高度碎片化，限制了异构数据、科学规律与专家知识的联合建模。S1-Omni通过将这些能力整合为一个统一的科学推理模型来弥补这一不足。S1-Omni的架构基于三个核心组件：科学数据的统一表示、自然世界知识对齐以及面向特定领域任务的解码。首先，S1-Omni将自然语言指令与科学对象（包括CIF、SMILES、蛋白质序列、光谱及科学图像）映射至共享表示空间。其次，它将科学规律与专家知识融入数据构建与训练过程，使模型能够基于科学证据进行推理。第三，它执行任务特定的解码，以支持广泛的应用场景，包括性质预测、光谱到分子生成、蛋白质位点与结构预测，以及科学图像生成与编辑。S1-Omni基于包含200个科学任务、数百万条推理样本的S1-Omni-Corpus语料库进行训练，并在超过60项科学基准上进行评估。在大多数基准上，它优于GPT-5.5与Gemini-3.1-Pro，在多项基准上达到或超越领域专用模型水平。总体而言，S1-Omni为统一的科学建模提供了一条可行路径。

English

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.