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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.21694 |
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| _version_ | 1866908797789798400 |
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| author | Li, Shuo Sun, Jiajun Wang, Zhekai Fan, Xiaoran Li, Hui Yang, Dingwen Xi, Zhiheng Wang, Yijun Shan, Zifei Gui, Tao Zhang, Qi Huang, Xuanjing |
| author_facet | Li, Shuo Sun, Jiajun Wang, Zhekai Fan, Xiaoran Li, Hui Yang, Dingwen Xi, Zhiheng Wang, Yijun Shan, Zifei Gui, Tao Zhang, Qi Huang, Xuanjing |
| contents | Charts are a fundamental visualization format for structured data analysis. Enabling end-to-end chart editing according to user intent is of great practical value, yet remains challenging due to the need for both fine-grained control and global structural consistency. Most existing approaches adopt pipeline-based designs, where natural language or code serves as an intermediate representation, limiting their ability to faithfully execute complex edits. We introduce ChartE$^{3}$, an End-to-End Chart Editing benchmark that directly evaluates models without relying on intermediate natural language programs or code-level supervision. ChartE$^{3}$ focuses on two complementary editing dimensions: local editing, which involves fine-grained appearance changes such as font or color adjustments, and global editing, which requires holistic, data-centric transformations including data filtering and trend line addition. ChartE$^{3}$ contains over 1,200 high-quality samples constructed via a well-designed data pipeline with human curation. Each sample is provided as a triplet of a chart image, its underlying code, and a multimodal editing instruction, enabling evaluation from both objective and subjective perspectives. Extensive benchmarking of state-of-the-art multimodal large language models reveals substantial performance gaps, particularly on global editing tasks, highlighting critical limitations in current end-to-end chart editing capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21694 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing Li, Shuo Sun, Jiajun Wang, Zhekai Fan, Xiaoran Li, Hui Yang, Dingwen Xi, Zhiheng Wang, Yijun Shan, Zifei Gui, Tao Zhang, Qi Huang, Xuanjing Computer Vision and Pattern Recognition Charts are a fundamental visualization format for structured data analysis. Enabling end-to-end chart editing according to user intent is of great practical value, yet remains challenging due to the need for both fine-grained control and global structural consistency. Most existing approaches adopt pipeline-based designs, where natural language or code serves as an intermediate representation, limiting their ability to faithfully execute complex edits. We introduce ChartE$^{3}$, an End-to-End Chart Editing benchmark that directly evaluates models without relying on intermediate natural language programs or code-level supervision. ChartE$^{3}$ focuses on two complementary editing dimensions: local editing, which involves fine-grained appearance changes such as font or color adjustments, and global editing, which requires holistic, data-centric transformations including data filtering and trend line addition. ChartE$^{3}$ contains over 1,200 high-quality samples constructed via a well-designed data pipeline with human curation. Each sample is provided as a triplet of a chart image, its underlying code, and a multimodal editing instruction, enabling evaluation from both objective and subjective perspectives. Extensive benchmarking of state-of-the-art multimodal large language models reveals substantial performance gaps, particularly on global editing tasks, highlighting critical limitations in current end-to-end chart editing capabilities. |
| title | ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.21694 |