@inproceedings{li-etal-2026-whats,
title = "What{'}s Missing in Screen-to-Action? Towards a {UI}-in-the-Loop Paradigm for Multimodal {GUI} Reasoning",
author = "Li, Songze and
Guo, Xiaoke and
Liu, Tianqi and
Yi, Biao and
Gong, Zhaoyan and
Liu, Zhiqiang and
Chen, Huajun and
Zhang, Wen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.876/",
pages = "17674--17690",
ISBN = "979-8-89176-395-1",
abstract = "Existing Graphical User Interface (GUI) reasoning tasks remain challenging, particularly in UI understanding. Current methods typically rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure. To enhance the understanding and interaction with UIs, we propose an innovative GUI reasoning paradigm called ***UI-in-the-Loop*** (UILoop). Our approach treats the GUI reasoning task as a cyclic ***Screen-UI elements-Action*** process. By enabling Multimodal Large Language Models (MLLMs) to explicitly learn the localization, semantic functions, and practical usage of key UI elements, UILoop achieves precise element discovery and performs interpretable reasoning. Furthermore, we introduce a more challenging ***UI Comprehension*** task centered on UI elements with three evaluation metrics. Correspondingly, we contribute a benchmark of 26K samples (UI Comprehension-Bench) to comprehensively evaluate existing methods' mastery of UI elements. Extensive experiments demonstrate that UILoop achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks."
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<abstract>Existing Graphical User Interface (GUI) reasoning tasks remain challenging, particularly in UI understanding. Current methods typically rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure. To enhance the understanding and interaction with UIs, we propose an innovative GUI reasoning paradigm called ***UI-in-the-Loop*** (UILoop). Our approach treats the GUI reasoning task as a cyclic ***Screen-UI elements-Action*** process. By enabling Multimodal Large Language Models (MLLMs) to explicitly learn the localization, semantic functions, and practical usage of key UI elements, UILoop achieves precise element discovery and performs interpretable reasoning. Furthermore, we introduce a more challenging ***UI Comprehension*** task centered on UI elements with three evaluation metrics. Correspondingly, we contribute a benchmark of 26K samples (UI Comprehension-Bench) to comprehensively evaluate existing methods’ mastery of UI elements. Extensive experiments demonstrate that UILoop achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks.</abstract>
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%0 Conference Proceedings
%T What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning
%A Li, Songze
%A Guo, Xiaoke
%A Liu, Tianqi
%A Yi, Biao
%A Gong, Zhaoyan
%A Liu, Zhiqiang
%A Chen, Huajun
%A Zhang, Wen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-whats
%X Existing Graphical User Interface (GUI) reasoning tasks remain challenging, particularly in UI understanding. Current methods typically rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure. To enhance the understanding and interaction with UIs, we propose an innovative GUI reasoning paradigm called ***UI-in-the-Loop*** (UILoop). Our approach treats the GUI reasoning task as a cyclic ***Screen-UI elements-Action*** process. By enabling Multimodal Large Language Models (MLLMs) to explicitly learn the localization, semantic functions, and practical usage of key UI elements, UILoop achieves precise element discovery and performs interpretable reasoning. Furthermore, we introduce a more challenging ***UI Comprehension*** task centered on UI elements with three evaluation metrics. Correspondingly, we contribute a benchmark of 26K samples (UI Comprehension-Bench) to comprehensively evaluate existing methods’ mastery of UI elements. Extensive experiments demonstrate that UILoop achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks.
%U https://aclanthology.org/2026.findings-acl.876/
%P 17674-17690
Markdown (Informal)
[What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning](https://aclanthology.org/2026.findings-acl.876/) (Li et al., Findings 2026)
ACL
- Songze Li, Xiaoke Guo, Tianqi Liu, Biao Yi, Zhaoyan Gong, Zhiqiang Liu, Huajun Chen, and Wen Zhang. 2026. What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17674–17690, San Diego, California, United States. Association for Computational Linguistics.