Shuaike Shen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shuaike Shen has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Ben Gao
Shanghai Artificial Intelligence Laboratory, Shanghai, China
1 shared publication - Biqing Qi
Shanghai Artificial Intelligence Laboratory, Shanghai, China
1 shared publication - Daolang Wang
North University of China, ShanXi, CHINA
1 shared publication - Dongzhan Zhou
Shanghai AI Laboratory, China
1 shared publication - Jiaqing Xie
Shanghai Artificial Intelligence Laboratory, Shanghai, China
1 shared publication - Jun Jiang
University of Science and Technology of China
1 shared publication
Latest Publications
- Open Access
- 8 August 2026
- Published by Association for Computing Machinery (ACM)
- p. 12655-12666
SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce SpectrumWorld, a unified infrastructure for AI-driven spectroscopy. SpectrumWorld consists of SpectrumLab, a pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy; SpectrumAnnotator, an annotation and curation module that generates high-quality benchmarks from limited seed data; and SpectrumVQA, a multi-layered benchmark suite covering 14 spectroscopic tasks and over 10 spectrum types, featuring spectra curated from over 1.2 million distinct chemical substances. Thorough empirical studies on SpectrumVQA with 23 cutting-edge multimodal LLMs reveal critical limitations of current approaches. We hope SpectrumWorld will serve as a crucial foundation for future advancements in deep learning-driven spectroscopy. Code is released at https://github.com/InternScience/SpectrumLab.
- 8 August 2026
- Published by Association for Computing Machinery (ACM)
- p. 12655-12666
Open AccessSpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce SpectrumWorld, a unified infrastructure for AI-driven spectroscopy. SpectrumWorld consists of SpectrumLab, a pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy; SpectrumAnnotator, an annotation and curation module that generates high-quality benchmarks from limited seed data; and SpectrumVQA, a multi-layered benchmark suite covering 14 spectroscopic tasks and over 10 spectrum types, featuring spectra curated from over 1.2 million distinct chemical substances. Thorough empirical studies on SpectrumVQA with 23 cutting-edge multimodal LLMs reveal critical limitations of current approaches. We hope SpectrumWorld will serve as a crucial foundation for future advancements in deep learning-driven spectroscopy. Code is released at https://github.com/InternScience/SpectrumLab.