想测VLM能不能看懂ER图？这个新基准用2960张图挨个打分，弱实体和N元关系掉得厉害，读论文就能知道差距在哪儿。
ERUnderstand是首个大规模评估视觉语言模型（VLM）对实体关系图（ERD）结构化理解能力的基准，包含2960张来自教育、真实世界和合成数据源的图。测试发现常见元素F1>0.74，但弱实体F1低至0.28，多值属性仅0.14，N元关系仅0.07。使用推理增强的模型整体提升15%-25%，但仍受语言先验和复杂度影响。基准、数据、工具和生成代码已开源。
ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering. We introduce ERUnderstand, the first large-scale benchmark for structured understanding of ER diagrams, comprising 2,960 diagrams collected from curated educational sources, real-world schemas, and synthetically generated examples spanning diverse domains, notations, complexity levels, and Extended Entity-Relationship (EER) constructs. Each diagram is paired with a standardized machine-readable representation for fine-grained evaluation of schema elements. Evaluating state-of-the-art Vision-Language Models (VLMs), we find that while common ERD elements are recovered reliably (F1 > 0.74), performance drops sharply on weak entities (as low as 0.28 F1), multivalued attributes (0.14 F1), and N-ary relationships (0.07 F1). Reasoning-augmented models improve overall performance by 15-25% but remain sensitive to linguistic priors and increasing diagram complexity. ERUnderstand provides a standardized benchmark for evaluating multimodal understanding of conceptual database schemas. The benchmark, dataset, evaluation toolkit, and generation code are publicly available at https://github.com/salinaria/ERUnderstand.