ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows
Qiushi Sun, Zhoumianze Liu, Chang Ma, Zichen Ding, Fangzhi Xu, Zhangyue Yin, Haiteng Zhao, Zhenyu Wu, Kanzhi Cheng, Zhaoyang Liu, Jianing Wang, Qintong Li, Xiangru Tang, Tianbao Xie, Xiachong Feng, Xiang Li, Ben Kao, Wenhai Wang, Biqing Qi, Lingpeng Kong, Zhiyong Wu
2025-05-28
Summary
This paper talks about ScienceBoard, a new platform that lets researchers test how well AI agents, which use large language models, can handle real scientific work that involves both text and images.
What's the problem?
The problem is that while AI agents are getting better at understanding language and images, it's not clear how well they can actually perform in real scientific workflows, which are often complex and require handling different types of information and tasks together. Without a good way to test these skills, it's hard to know how useful these AI agents really are for science.
What's the solution?
The authors created ScienceBoard, which acts like a realistic lab environment where AI agents can be given scientific tasks to solve. This setup helps measure their strengths and weaknesses in handling complicated, real-world scientific problems that involve multiple steps and different types of data.
Why it matters?
This is important because it gives the scientific community a clear way to see what current AI agents can and can't do, helping guide future improvements and making it easier to build AI systems that can actually help scientists with real research.
Abstract
ScienceBoard provides a realistic scientific workflow environment and benchmark to evaluate the performance of LLM-based agents, demonstrating their current limitations in complex scientific tasks.