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.
@article{arxiv.2506.10521,
title = {Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning},
author = {Yuhao Zhou and Yiheng Wang and Xuming He and Ao Shen and Ruoyao Xiao and Zhiwei Li and Qiantai Feng and Zijie Guo and Yuejin Yang and Hao Wu and Wenxuan Huang and Jiaqi Wei and Dan Si and Xiuqi Yao and Jia Bu and Haiwen Huang and Manning Wang and Tianfan Fu and Shixiang Tang and Ben Fei and Dongzhan Zhou and Fenghua Ling and Yan Lu and Siqi Sun and Chenhui Li and Guanjie Zheng and Jiancheng Lv and Wenlong Zhang and Lei Bai},
journal= {arXiv preprint arXiv:2506.10521},
year = {2025}
}