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

arXiv: https://arxiv.org/abs/2607.15686 · HTML: https://mlsanity.com/paper/2607.15686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 32 of its 96 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 30 of 32)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
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- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://mlsanity.com/paper/2501.17811/) (2025)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation](https://mlsanity.com/paper/2404.14396/) (2024)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [AlphaFold Meets Flow Matching for Generating Protein Ensembles](https://mlsanity.com/paper/2402.04845/) (2024)
- [EigenFold: Generative Protein Structure Prediction with Diffusion Models](https://mlsanity.com/paper/2304.02198/) (2023)
- [PiFlow: Principle-Aware Scientific Discovery with Multi-Agent Collaboration](https://mlsanity.com/paper/2505.15047/) (2025)
- [Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation](https://mlsanity.com/paper/2305.12347/) (2023)
- [Investigating Text Insulation and Attention Mechanisms for Complex Visual Text Generation](https://mlsanity.com/paper/2503.23461/) (2025)
- [Uni-Mol2: Exploring Molecular Pretraining Model at Scale](https://mlsanity.com/paper/2406.14969/) (2024)
- [Chem-R: Learning to Reason as a Chemist](https://mlsanity.com/paper/2510.16880/) (2025)
- [SimpleFold: Folding Proteins is Simpler than You Think](https://mlsanity.com/paper/2509.18480/) (2025)
- [Reasoning-Enhanced Large Language Models for Molecular Property Prediction](https://mlsanity.com/paper/2510.10248/) (2025)
- [Proteo-R1: Reasoning Foundation Models for De Novo Protein Design](https://mlsanity.com/paper/2605.02937/) (2026)
- [DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models](https://mlsanity.com/paper/2507.06853/) (2025)
- [S1-VL: Scientific Multimodal Reasoning Model with Thinking-with-Images](https://mlsanity.com/paper/2604.21409/) (2026)
- [Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration](https://mlsanity.com/paper/2510.21358/) (2025)
- [Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation](https://mlsanity.com/paper/2405.20668/) (2024)
- [BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models](https://mlsanity.com/paper/2505.01912/) (2025)
- [Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences](https://mlsanity.com/paper/2606.16905/) (2026)
- [TechImage-Bench: Rubric-Based Evaluation for Technical Image Generation](https://mlsanity.com/paper/2512.12220/) (2025)
- [Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation](https://mlsanity.com/paper/2512.21643/) (2025)

## Abstract (arXiv)

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.