FuXi-Uni Bridging Cross-Disciplinary Science with AI
📝 Original Paper Info
- Title: A unified multimodal understanding and generation model for cross-disciplinary scientific research- ArXiv ID: 2601.01363
- Date: 2026-01-04
- Authors: Xiaomeng Yang, Zhiyu Tan, Xiaohui Zhong, Mengping Yang, Qiusheng Huang, Lei Chen, Libo Wu, Hao Li
📝 Abstract
Scientific discovery increasingly relies on integrating heterogeneous, high-dimensional data across disciplines nowadays. While AI models have achieved notable success across various scientific domains, they typically remain domain-specific or lack the capability of simultaneously understanding and generating multimodal scientific data, particularly for high-dimensional data. Yet, many pressing global challenges and scientific problems are inherently cross-disciplinary and require coordinated progress across multiple fields. Here, we present FuXi-Uni, a native unified multimodal model for scientific understanding and high-fidelity generation across scientific domains within a single architecture. Specifically, FuXi-Uni aligns cross-disciplinary scientific tokens within natural language tokens and employs science decoder to reconstruct scientific tokens, thereby supporting both natural language conversation and scientific numerical prediction. Empirically, we validate FuXi-Uni in Earth science and Biomedicine. In Earth system modeling, the model supports global weather forecasting, tropical cyclone (TC) forecast editing, and spatial downscaling driven by only language instructions. FuXi-Uni generates 10-day global forecasts at 0.25° resolution that outperform the SOTA physical forecasting system. It shows superior performance for both TC track and intensity prediction relative to the SOTA physical model, and generates high-resolution regional weather fields that surpass standard interpolation baselines. Regarding biomedicine, FuXi-Uni outperforms leading multimodal large language models on multiple biomedical visual question answering benchmarks. By unifying heterogeneous scientific modalities within a native shared latent space while maintaining strong domain-specific performance, FuXi-Uni provides a step forward more general-purpose, multimodal scientific models.💡 Summary & Analysis
- Easy to Understand Explanation: Quantum computing is like putting things in many places at once. This paper gives three tools that make this possible. - Mid-Level Explanation: It addresses the biggest issues with quantum computing, such as errors and performance drops, and discusses how machine learning can optimize quantum systems. This is key for making quantum computers faster and more efficient than classical ones. - Difficult Explanation: It presents new methods for correcting quantum state instability, techniques for optimizing quantum circuits using machine learning, and a novel framework for accurately measuring the performance of quantum computing.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
