mlsanityData to 7 Oct 2026

Enginuity: Building an Open Multi-Domain Dataset of Complex Engineering Diagrams

arXiv 2601.13299 ↗First posted 19 Jan 2026cs.CVcited by 1

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Abstract (arXiv)

We propose Enginuity - the first open, large-scale, multi-domain engineering diagram dataset with comprehensive structural annotations designed for automated diagram parsing. By capturing hierarchical component relationships, connections, and semantic elements across diverse engineering domains, our proposed dataset would enable multimodal large language models to address critical downstream tasks including structured diagram parsing, cross-modal information retrieval, and AI-assisted engineering simulation. Enginuity would be transformative for AI for Scientific Discovery by enabling artificial intelligence systems to comprehend and manipulate the visual-structural knowledge embedded in engineering diagrams, breaking down a fundamental barrier that currently prevents AI from fully participating in scientific workflows where diagram interpretation, technical drawing analysis, and visual reasoning are essential for hypothesis generation, experimental design, and discovery.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 19 Jan 2026.

Builds on

5 of 13 references.

References · 5

  1. 2016A Diagram Is Worth A Dozen Imagescited by 793
  2. 2019Object Detection in 20 Years: A Surveycited by 528
  3. 2024Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extractioncited by 18
  4. 2024From Engineering Diagrams to Graphs: Digitizing P&IDs with Transformerscited by 5
  5. 2023A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawingscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2025Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation
  2. 2026DiagramNet: An End-to-End Recognition Framework and Dataset for Non-Standard System-Level Diagrams
  3. 2025CircuitSense: A Hierarchical MLLM Benchmark Bridging Visual Comprehension and Symbolic Reasoning in Engineering Design Process
  4. 2026Knossos and Ariadne: Benchmarking and Learning Complete Diagram Topology Extraction with Vision-Language Models
  5. 2023MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities
  6. 2026DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings
  7. 2026Beyond Pixels: Vector-to-Graph Transformation for Reliable Schematic Auditing
  8. 2026Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams