Can Multimodal Foundation Models Understand Schematic Diagrams? An
  Empirical Study on Information-Seeking QA over Scientific Papers
Authors: name: Yilun Zhao, name: Chengye Wang, name: Chuhan Li, name: Arman Cohan
Organization: Yale NLP Lab

This paper introduces MISS-QA, the first benchmark specifically designed to
evaluate the ability of models to interpret schematic diagrams within
scientific literature. MISS-QA comprises 1,500 expert-annotated examples over
465 scientific papers. In this benchmark, models are tasked with interpreting
schematic diagrams that illustrate research overviews and answering
corresponding information-seeking questions based on the broader context of the
paper. We assess the performance of 18 frontier multimodal foundation models,
including o4-mini, Gemini-2.5-Flash, and Qwen2.5-VL. We reveal a significant
performance gap between these models and human experts on MISS-QA. Our analysis
of model performance on unanswerable questions and our detailed error analysis
further highlight the strengths and limitations of current models, offering key
insights to enhance models in comprehending multimodal scientific literature.