GenExam: A Multidisciplinary Text-to-Image Exam
Authors: name: Zhaokai Wang, name: Penghao Yin, name: Xiangyu Zhao, name: Changyao Tian, name: Yu Qiao, name: Wenhai Wang, name: Jifeng Dai, name: Gen Luo

Exams are a fundamental test of expert-level intelligence and require
integrated understanding, reasoning, and generation. Existing exam-style
benchmarks mainly focus on understanding and reasoning tasks, and current
generation benchmarks emphasize the illustration of world knowledge and visual
concepts, neglecting the evaluation of rigorous drawing exams. We introduce
GenExam, the first benchmark for multidisciplinary text-to-image exams,
featuring 1,000 samples across 10 subjects with exam-style prompts organized
under a four-level taxonomy. Each problem is equipped with ground-truth images
and fine-grained scoring points to enable a precise evaluation of semantic
correctness and visual plausibility. Experiments show that even
state-of-the-art models such as GPT-Image-1 and Gemini-2.5-Flash-Image achieve
less than 15% strict scores, and most models yield almost 0%, suggesting the
great challenge of our benchmark. By framing image generation as an exam,
GenExam offers a rigorous assessment of models' ability to integrate knowledge,
reasoning, and generation, providing insights on the path to general AGI.