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S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

Research Multimodal & Generative

Merged summary

TL;DR - S1-Omni is a unified multimodal model that handles diverse scientific data and tasks within one reasoning framework. It aims to replace fragmented domain-specific systems while achieving strong results across scientific benchmarks.

  • Maps text, molecular formats, proteins, spectra, and images into a shared representation space.
  • Integrates scientific laws and expert knowledge during data construction and training.
  • Supports prediction, molecular generation, protein modeling, and scientific image generation and editing.
  • Trained on millions of reasoning samples across 200 tasks and evaluated on more than 60 benchmarks.

Sources (1)

S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

arXiv cs.AI 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 2026-07-17 arXiv:2607.15686

TL;DR - S1-Omni is a unified multimodal model that handles diverse scientific data and tasks within one reasoning framework. It aims to replace fragmented domain-specific systems while achieving strong results across scientific benchmarks.

  • Maps text, molecular formats, proteins, spectra, and images into a shared representation space.
  • Integrates scientific laws and expert knowledge during data construction and training.
  • Supports prediction, molecular generation, protein modeling, and scientific image generation and editing.
  • Trained on millions of reasoning samples across 200 tasks and evaluated on more than 60 benchmarks.
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