让模型从科学图表中提出有深度的疑问，提升科研理解能力。

Discourse comprehension in complex documents often involves continuously posing and resolving Questions Under Discussion (QUDs). While QUD frameworks have so far focused on text, scientific literature is inherently multimodal: figures convey discourse goals distinct from their textual counterparts, thus invoking implicit questions that the surrounding text answers. In scientific discovery, knowing the right questions to ask is as important as knowing how to answer them, yet this capability remains largely absent in current models. In this work, we extend QUD to multimodal discourse in scientific literature, targeting questions evoked by figures that are (1) inquisitive, i.e., not resolved in the prior context; (2) salient, i.e., relevant to the paper's research claims and addressed later in the paper; (3) grounded in visual insights. To benchmark model capability to generate such questions, we introduce MQUD, a dataset of 1,250 figure-evoked questions from 56 scientific papers, including 708 questions annotated by the original authors of these papers. Our experiments show that open-source VLMs such as Qwen 3.5 predominantly ask questions that can be answered by the figure alone. Fine-tuning on MQUD teaches the model to ask scientifically relevant, inquisitive questions that target the role a figure plays in the paper's argument.