@inproceedings{liu-etal-2026-infiguiagent,
title = "{I}nfi{GUIA}gent: A Multimodal Generalist {GUI} Agent with Native Reasoning and Reflection",
author = "Liu, Yuhang and
Li, Pengxiang and
Wei, Zishu and
Xie, Congkai and
Hu, Xueyu and
Xu, Xinchen and
Zhang, Shengyu and
Han, Xiaotian and
Yang, Hongxia and
Wu, Fei",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.45/",
pages = "1035--1051",
ISBN = "979-8-89176-380-7",
abstract = "Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers and mobile phones. However, existing agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. We introduce InfiGUIAgent, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline. Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills using synthesized data to enable native reasoning abilities of the agents. InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks."
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<abstract>Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers and mobile phones. However, existing agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. We introduce InfiGUIAgent, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline. Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills using synthesized data to enable native reasoning abilities of the agents. InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks.</abstract>
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%0 Conference Proceedings
%T InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection
%A Liu, Yuhang
%A Li, Pengxiang
%A Wei, Zishu
%A Xie, Congkai
%A Hu, Xueyu
%A Xu, Xinchen
%A Zhang, Shengyu
%A Han, Xiaotian
%A Yang, Hongxia
%A Wu, Fei
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F liu-etal-2026-infiguiagent
%X Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers and mobile phones. However, existing agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. We introduce InfiGUIAgent, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline. Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills using synthesized data to enable native reasoning abilities of the agents. InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks.
%U https://aclanthology.org/2026.eacl-long.45/
%P 1035-1051
Markdown (Informal)
[InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection](https://aclanthology.org/2026.eacl-long.45/) (Liu et al., EACL 2026)
ACL
- Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, and Fei Wu. 2026. InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1035–1051, Rabat, Morocco. Association for Computational Linguistics.