title: SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
summary: Large Language Models (LLMs) and Multimodal LLMs have shown promising
  capabilities for SVG processing, yet existing benchmarks suffer from limited
  real-world coverage, lack of complexity stratification, and fragmented
  evaluation paradigms. We introduce SVGenius, a comprehensive benchmark
  comprising 2,377 queries across three progressive dimensions: understanding,
  editing, and generation. Built on real-world data from 24 application domains
  with systematic complexity stratification, SVGenius evaluates models through 8
  task categories and 18 metrics. We assess 22 mainstream models spanning
  different scales, architectures, training paradigms, and accessibility levels.
  Our analysis reveals that while proprietary models significantly outperform
  open-source counterparts, all models exhibit systematic performance degradation
  with increasing complexity, indicating fundamental limitations in current
  approaches; however, reasoning-enhanced training proves more effective than
  pure scaling for overcoming these limitations, though style transfer remains
  the most challenging capability across all model types. SVGenius establishes
  the first systematic evaluation framework for SVG processing, providing crucial
  insights for developing more capable vector graphics models and advancing
  automated graphic design applications. Appendix and supplementary materials
  (including all data and code) are available at
  https://zju-real.github.io/SVGenius.
paper_authors._id: 683fb0a7be8421eda3152283
paper_authors.name: Siqi Chen
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paper_authors.fullname: chensiqi
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paper_authors.user: xiaoooobai
paper_authors._id: 683fb0a7be8421eda3152284
paper_authors.name: Xinyu Dong
paper_authors._id: 683fb0a7be8421eda3152285
paper_authors.name: Haolei Xu
paper_authors.status: claimed_verified
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paper_authors.fullname: xhl
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paper_authors.user: zjuxhl
paper_authors._id: 683fb0a7be8421eda3152286
paper_authors.name: Xingyu Wu
paper_authors._id: 683fb0a7be8421eda3152287
paper_authors.name: Fei Tang
paper_authors._id: 683fb0a7be8421eda3152288
paper_authors.name: Hang Zhang
paper_authors._id: 683fb0a7be8421eda3152289
paper_authors.name: Yuchen Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:56:47.244Z
paper_authors._id: 64098738342c26884c792c93
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paper_authors.fullname: Yuchen Yan
paper_authors.type: user
paper_authors.user: yanyc
paper_authors._id: 683fb0a7be8421eda315228a
paper_authors.name: Linjuan Wu
paper_authors._id: 683fb0a7be8421eda315228b
paper_authors.name: Wenqi Zhang
paper_authors._id: 683fb0a7be8421eda315228c
paper_authors.name: Guiyang Hou
paper_authors._id: 683fb0a7be8421eda315228d
paper_authors.name: Yongliang Shen
paper_authors._id: 683fb0a7be8421eda315228e
paper_authors.name: Weiming Lu
paper_authors._id: 683fb0a7be8421eda315228f
paper_authors.name: Yueting Zhuang
paper_title: SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
paper_summary: Large Language Models (LLMs) and Multimodal LLMs have shown promising
  capabilities for SVG processing, yet existing benchmarks suffer from limited
  real-world coverage, lack of complexity stratification, and fragmented
  evaluation paradigms. We introduce SVGenius, a comprehensive benchmark
  comprising 2,377 queries across three progressive dimensions: understanding,
  editing, and generation. Built on real-world data from 24 application domains
  with systematic complexity stratification, SVGenius evaluates models through 8
  task categories and 18 metrics. We assess 22 mainstream models spanning
  different scales, architectures, training paradigms, and accessibility levels.
  Our analysis reveals that while proprietary models significantly outperform
  open-source counterparts, all models exhibit systematic performance degradation
  with increasing complexity, indicating fundamental limitations in current
  approaches; however, reasoning-enhanced training proves more effective than
  pure scaling for overcoming these limitations, though style transfer remains
  the most challenging capability across all model types. SVGenius establishes
  the first systematic evaluation framework for SVG processing, providing crucial
  insights for developing more capable vector graphics models and advancing
  automated graphic design applications. Appendix and supplementary materials
  (including all data and code) are available at
  https://zju-real.github.io/SVGenius.
paper_ai_summary: SVGenius evaluates Large Language Models and Multimodal LLMs for SVG processing using a comprehensive benchmark across three dimensions: understanding, editing, and generation, revealing insights into model capabilities and limitations.
paper_ai_keywords: Large Language Models Multimodal LLMs SVG processing SVGenius complexity stratification reasoning-enhanced training style transfer