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This paper introduces VisEditBench, a benchmark consisting of 1,395 tasks designed to evaluate the ability of vision-language models (VLMs) to edit visualization code based on multimodal feedback. The study reveals that while state-of-the-art models like Claude-4.6-Sonnet achieve a pass rate of 74.46%, most open-source models struggle with performance, particularly in tasks requiring visually grounded style adaptation. To address these challenges, the authors propose VisEditAgent, a render-grounded editing framework that enhances performance through iterative feedback, raising the overall pass rate significantly from 55.75% to 67.99%.
Visualization code editing is a tough nut to crack, with leading models only achieving a 74.46% pass rate on complex multimodal tasks.
Vision-language models (VLMs) have shown strong capabilities in generating visualization code from textual or visual specifications. However, real-world visualization authoring is inherently iterative: users frequently revise existing visualizations to repair flawed charts or adapt them to desired styles. Existing benchmarks primarily evaluate generation from scratch, leaving visualization code editing from multimodal feedback largely unexplored. We introduce VisEditBench, a benchmark of 1,395 human-annotated visualization code-editing tasks grounded in realistic visualization workflows and failure cases. VisEditBench covers two practical settings: feedback-guided repair, where models revise visualization code using buggy or marked charts together with textual feedback, and reference-guided restyling, where models modify code to match a target chart image. Evaluating 20 state-of-the-art VLMs reveals that visualization code editing remains challenging: Claude-4.6-Sonnet achieves the best overall pass rate of 74.46%, while most open-source models remain below 50%. Performance is particularly weak on visually grounded style adaptation, where Claude-4.6-Sonnet achieves only 55.71%. To establish a strong baseline, we further propose VisEditAgent, a render-grounded editing framework that iteratively generates, executes, validates, and refines candidate edits. Built on GPT-4o, VisEditAgent improves overall pass rate from 55.75% to 67.99%, demonstrating the importance of render-grounded feedback for faithful visualization editing. We will release VisEditBench at https://github.com/vis-nlp/VisEditBench.
