@inproceedings{yang-etal-2026-infoedit,
title = "{I}nfo{E}dit: Probing Global Layout Reasoning in Infographic Editing",
author = "Yang, Cheng and
Shi, Chufan and
Wang, Huijuan and
Shui, Bo and
Wu, Yaokang and
Tao, Muzi and
Yan, Yibo and
Ma, Xuezhe and
Berg-Kirkpatrick, Taylor",
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.2513/",
pages = "57130--57168",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, infographics encode information through logical relations; editing one element often requires surrounding elements to be adapted. We refer to this global layout reasoning capability as reflow. Existing image-editing benchmarks neither provide a dedicated setting for structured visual content nor evaluate the reflow capability. We introduce InfoEdit, a novel benchmark of 1,000 infographics across eight logical-relation families, paired with 4,000 editing instructions across four editing tasks, and a reflow-aware evaluation protocol. Across eight frontier editors, only GPT-Image-2 clears 60{\%} average success rate; most models fall below 7{\%}, and no editor exceeds 36{\%} on Swap-Block task even with perfect target localization. We further show that code-level editing can match the strongest pixel-level editor, revealing complementary strengths across tasks. InfoEdit identifies reflow as a central challenge in structured visual content editing and provides a diagnostic benchmark to facilitate future progress."
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<abstract>Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, infographics encode information through logical relations; editing one element often requires surrounding elements to be adapted. We refer to this global layout reasoning capability as reflow. Existing image-editing benchmarks neither provide a dedicated setting for structured visual content nor evaluate the reflow capability. We introduce InfoEdit, a novel benchmark of 1,000 infographics across eight logical-relation families, paired with 4,000 editing instructions across four editing tasks, and a reflow-aware evaluation protocol. Across eight frontier editors, only GPT-Image-2 clears 60% average success rate; most models fall below 7%, and no editor exceeds 36% on Swap-Block task even with perfect target localization. We further show that code-level editing can match the strongest pixel-level editor, revealing complementary strengths across tasks. InfoEdit identifies reflow as a central challenge in structured visual content editing and provides a diagnostic benchmark to facilitate future progress.</abstract>
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%0 Conference Proceedings
%T InfoEdit: Probing Global Layout Reasoning in Infographic Editing
%A Yang, Cheng
%A Shi, Chufan
%A Wang, Huijuan
%A Shui, Bo
%A Wu, Yaokang
%A Tao, Muzi
%A Yan, Yibo
%A Ma, Xuezhe
%A Berg-Kirkpatrick, Taylor
%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 yang-etal-2026-infoedit
%X Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, infographics encode information through logical relations; editing one element often requires surrounding elements to be adapted. We refer to this global layout reasoning capability as reflow. Existing image-editing benchmarks neither provide a dedicated setting for structured visual content nor evaluate the reflow capability. We introduce InfoEdit, a novel benchmark of 1,000 infographics across eight logical-relation families, paired with 4,000 editing instructions across four editing tasks, and a reflow-aware evaluation protocol. Across eight frontier editors, only GPT-Image-2 clears 60% average success rate; most models fall below 7%, and no editor exceeds 36% on Swap-Block task even with perfect target localization. We further show that code-level editing can match the strongest pixel-level editor, revealing complementary strengths across tasks. InfoEdit identifies reflow as a central challenge in structured visual content editing and provides a diagnostic benchmark to facilitate future progress.
%U https://aclanthology.org/2026.emnlp-main.2513/
%P 57130-57168
Markdown (Informal)
[InfoEdit: Probing Global Layout Reasoning in Infographic Editing](https://aclanthology.org/2026.emnlp-main.2513/) (Yang et al., EMNLP 2026)
ACL
- Cheng Yang, Chufan Shi, Huijuan Wang, Bo Shui, Yaokang Wu, Muzi Tao, Yibo Yan, Xuezhe Ma, and Taylor Berg-Kirkpatrick. 2026. InfoEdit: Probing Global Layout Reasoning in Infographic Editing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57130–57168, Budapest, Hungary. Association for Computational Linguistics.