arXiv — NLP / Computation & Language · · 3 min read

VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing

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Computer Science > Computation and Language

arXiv:2605.15677 (cs)
[Submitted on 15 May 2026]

Title:VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing

View a PDF of the paper titled VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing, by Xiaoyan Su and 10 other authors
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Abstract:Despite the rapid advancements in Vision-Language Models (VLMs), a critical gap remains in their ability to handle structured, controllable diagrammatic tasks essential for professional workflows. Existing methods predominantly rely on pixel-based synthesis, which operates in probabilistic pixel spaces and is inherently limited in editability and fidelity. Instead, we propose a new Diagram-as-Code paradigm with symbolic logic that leverages mxGraph Extensible Markup Language (XML) for precise diagram generation and editing. We present VCG-Bench, a unified benchmark for visual-centric \texttt{mxGraph} tasks. VCG-Bench comprises: (1) a taxonomized dataset of 1,449 diverse diagrams spanning 6 domains and 15 sub-domains, (2) a paradigm definition that integrates Generation (Vision-to-Code) and Editability (Code-to-Code), (3) a Tailored Evaluation Protocol employing multi-dimensional metrics such as \texttt{mxGraph} Execution Success Rate, Style Consistency Score (SCS), etc. Experimental results highlight the challenges faced by current State-of-the-Art (SOTA) VLMs in structured fidelity and instruction compliance, reflecting their vision and reasoning capabilities.
Comments: Accepted by ICML2026, 37 pages, 10 figures
Subjects: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.15677 [cs.CL]
  (or arXiv:2605.15677v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2605.15677
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Peijie Dong [view email]
[v1] Fri, 15 May 2026 06:59:28 UTC (19,227 KB)
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