mlsanityData to 14 Oct 2026

ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams

arXiv 2607.24707 ↗First posted 27 Jul 2026cs.AI cs.CV cs.DBcited by 0

On the map: Multimodal LLMs / vision-language models → Chart understanding and anomaly detection

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 10 of its 48 references that are in the map. First posted on arXiv 27 Jul 2026.

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  1. 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding6 Jul

Other references · 9

  1. 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 706
  2. 2021IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoningcited by 267
  3. 2024OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotationscited by 150
  4. 2025MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsingcited by 116
  5. 2022VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformerscited by 53
  6. 2024Do Vision-Language Models Really Understand Visual Language?cited by 32
  7. 2025Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMscited by 15
  8. 2022PG-Schema: Schemas for Property Graphscited by 13
  9. 2020QueryVis: Logic-based diagrams help users understand complicated SQL queries fastercited by 4

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