Unified multimodal models target joint understanding, reasoning, and generation, but current image editing benchmarks are largely confined to natural images and shallow commonsense reasoning, offering limited assessment of this capability under structured, domain-specific constraints. In this work, we introduce GRADE, the first benchmark to assess discipline-informed knowledge and reasoning in image editing. GRADE comprises 520 carefully curated samples across 10 academic domains, spanning from natural science to social science. To support rigorous evaluation, we propose a multi-dimensional evaluation protocol that jointly assesses Discipline Reasoning, Visual Consistency, and Logical Readability. Extensive experiments on 20 state-of-the-art open-source and closed-source models reveal substantial limitations in current models under implicit, knowledge-intensive editing settings, leading to large performance gaps. Beyond quantitative scores, we conduct rigorous analyses and ablations to expose model shortcomings and identify the constraints within disciplinary editing. Together, GRADE pinpoints key directions for the future development of unified multimodal models, advancing the research on discipline-informed image editing and reasoning. Our benchmark and evaluation code are publicly released.

翻译：统一多模态模型旨在实现联合理解、推理与生成，但当前的图像编辑基准大多局限于自然图像和浅层常识推理，难以评估其在结构化、领域特定约束下的能力。本工作提出了首个用于评估图像编辑中学科知识与推理能力的基准——GRADE。该基准涵盖从自然科学到社会科学的10个学科领域，包含520个精心构建的样本。为支持严谨评估，我们提出一个多维度评估框架，从学科推理、视觉一致性和逻辑可读性三个维度进行综合评价。通过对20个前沿开源与闭源模型的大规模实验，我们发现当前模型在隐含、知识密集的编辑场景中存在显著局限，导致性能差距巨大。除量化评分外，我们通过系统分析与消融实验揭示了模型的不足，并明确了学科编辑任务的内在约束。GRADE为统一多模态模型的未来发展指明了关键方向，推动了学科知识驱动的图像编辑与推理研究。本基准及相关评估代码已公开发布。