@inproceedings{rahman-etal-2026-viseditbench,
title = "{V}is{E}dit{B}ench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?",
author = "Rahman, Mizanur and
Azimlu, Arshia and
Rahman, Shadikur and
Laskar, Md Tahmid Rahman and
Bhuiyan, Amran and
Joty, Shafiq and
Prince, Enamul Hoque",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1038/",
pages = "23828--23846",
ISBN = "979-8-89176-459-0",
abstract = "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 24 state-of-the-art VLMs reveals that visualization code editing remains challenging: Claude Opus 4.6 achieves the best overall pass rate of 76.0{\%}, 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 {\ensuremath{<}}redacted{\ensuremath{>}}."
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<abstract>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 24 state-of-the-art VLMs reveals that visualization code editing remains challenging: Claude Opus 4.6 achieves the best overall pass rate of 76.0%, 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 \ensuremath<redacted\ensuremath>.</abstract>
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%0 Conference Proceedings
%T VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?
%A Rahman, Mizanur
%A Azimlu, Arshia
%A Rahman, Shadikur
%A Laskar, Md Tahmid Rahman
%A Bhuiyan, Amran
%A Joty, Shafiq
%A Prince, Enamul Hoque
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F rahman-etal-2026-viseditbench
%X 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 24 state-of-the-art VLMs reveals that visualization code editing remains challenging: Claude Opus 4.6 achieves the best overall pass rate of 76.0%, 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 \ensuremath<redacted\ensuremath>.
%U https://aclanthology.org/2026.emnlp-main.1038/
%P 23828-23846
Markdown (Informal)
[VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback?](https://aclanthology.org/2026.emnlp-main.1038/) (Rahman et al., EMNLP 2026)
ACL