Scientists' First Exam: Probing Cognitive Abilities of MLLM via
  Perception, Understanding, and Reasoning
Authors: name: Yuhao Zhou, name: Yiheng Wang, name: Xuming He, name: Ruoyao Xiao, name: Zhiwei Li, name: Qiantai Feng, name: Zijie Guo, name: Yuejin Yang, name: Hao Wu, name: Wenxuan Huang, name: Jiaqi Wei, name: Dan Si, name: Xiuqi Yao, name: Jia Bu, name: Haiwen Huang, name: Tianfan Fu, name: Shixiang Tang, name: Ben Fei, name: Dongzhan Zhou, name: Fenghua Ling, name: Yan Lu, name: Siqi Sun, name: Chenhui Li, name: Guanjie Zheng, name: Jiancheng Lv, name: Wenlong Zhang, name: Lei Bai

Scientific discoveries increasingly rely on complex multimodal reasoning
based on information-intensive scientific data and domain-specific expertise.
Empowered by expert-level scientific benchmarks, scientific Multimodal Large
Language Models (MLLMs) hold the potential to significantly enhance this
discovery process in realistic workflows. However, current scientific
benchmarks mostly focus on evaluating the knowledge understanding capabilities
of MLLMs, leading to an inadequate assessment of their perception and reasoning
abilities. To address this gap, we present the Scientists' First Exam (SFE)
benchmark, designed to evaluate the scientific cognitive capacities of MLLMs
through three interconnected levels: scientific signal perception, scientific
attribute understanding, scientific comparative reasoning. Specifically, SFE
comprises 830 expert-verified VQA pairs across three question types, spanning
66 multimodal tasks across five high-value disciplines. Extensive experiments
reveal that current state-of-the-art GPT-o3 and InternVL-3 achieve only 34.08%
and 26.52% on SFE, highlighting significant room for MLLMs to improve in
scientific realms. We hope the insights obtained in SFE will facilitate further
developments in AI-enhanced scientific discoveries.