ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing

• URL: http://arxiv.org/abs/2601.21694v1
• Date: Thu, 29 Jan 2026 13:29:27 GMT
• Title: ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing
• Authors: Shuo Li, Jiajun Sun, Zhekai Wang, Xiaoran Fan, Hui Li, Dingwen Yang, Zhiheng Xi, Yijun Wang, Zifei Shan, Tao Gui, Qi Zhang, Xuanjing Huang,
• Abstract summary: ChartE$3$ is an End-to-End Chart Editing benchmark.<n>It directly evaluates models without relying on intermediate natural language programs or code-level supervision.<n>It contains over 1,200 high-quality samples constructed via a well-designed data pipeline with human curation.
• Score: 64.65742943745866
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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