InfoEdit: Probing Global Layout Reasoning in Infographic Editing

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 the 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.

Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Software Engineering (cs.SE)

arXiv:2609.33286 [cs.CV]

(or arXiv:2609.33286v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.33286 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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