SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Siqi Chen, Xinyu Dong, Haolei Xu, Xingyu Wu, Fei Tang, Hang Zhang, Yuchen Yan, Linjuan Wu, Wenqi Zhang, Guiyang Hou, Yongliang Shen, Weiming Lu, Yueting Zhuang

TL;DR
SVGenius is a comprehensive benchmark for evaluating large language models on SVG understanding, editing, and generation tasks, revealing current limitations and guiding future improvements in vector graphics AI.
Contribution
It introduces the first systematic evaluation framework for SVG processing, covering real-world data, complexity stratification, and diverse models, with extensive metrics and analysis.
Findings
Proprietary models outperform open-source ones.
Performance drops with increasing task complexity.
Reasoning training improves capabilities more than scaling.
Abstract
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…
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Taxonomy
TopicsNatural Language Processing Techniques
