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Introduces VisEditBench, a benchmark of 1,395 human-annotated visualization code-editing tasks for vision-language models, covering feedback-guided repair and reference-guided restyling. Evaluates 20 VLMs and proposes VisEditAgent, a render-grounded editing framework that improves pass rates from 55.75% to 67.99%.