@inproceedings{xie-etal-2026-secagent,
title = "{S}ec{A}gent: Efficient Mobile {GUI} Agent with Semantic Context",
author = "Xie, Yiping and
Chensong and
Xing, Jingxuan and
Jiang, Wei and
Zhu, Zekun and
Wang, Yingyao and
Bu, Pi and
Song, Jun and
Jiang, Yuning and
Zheng, Bo",
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.1141/",
pages = "26159--26174",
ISBN = "979-8-89176-459-0",
abstract = "Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, existing approaches face two critical limitations: the scarcity of high-quality multilingual datasets, particularly for non-English ecosystems, and inefficient history representation methods. To address these challenges, we present SecAgent, an efficient mobile GUI agent at 3B scale. We first construct a human-verified Chinese mobile GUI dataset with 18k grounding samples and 121k navigation steps across 44 applications, along with a Chinese navigation benchmark featuring multi-choice action annotations. Building upon this dataset, we propose a semantic context mechanism that distills history screenshots and actions into concise, natural language summaries, significantly reducing computational costs while preserving task-relevant information. Through supervised and reinforcement fine-tuning, SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks. Our dataset and benchmark are available at https://huggingface.co/datasets/alibabagroup/CMGUI."
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<abstract>Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, existing approaches face two critical limitations: the scarcity of high-quality multilingual datasets, particularly for non-English ecosystems, and inefficient history representation methods. To address these challenges, we present SecAgent, an efficient mobile GUI agent at 3B scale. We first construct a human-verified Chinese mobile GUI dataset with 18k grounding samples and 121k navigation steps across 44 applications, along with a Chinese navigation benchmark featuring multi-choice action annotations. Building upon this dataset, we propose a semantic context mechanism that distills history screenshots and actions into concise, natural language summaries, significantly reducing computational costs while preserving task-relevant information. Through supervised and reinforcement fine-tuning, SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks. Our dataset and benchmark are available at https://huggingface.co/datasets/alibabagroup/CMGUI.</abstract>
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%0 Conference Proceedings
%T SecAgent: Efficient Mobile GUI Agent with Semantic Context
%A Xie, Yiping
%A Xing, Jingxuan
%A Jiang, Wei
%A Zhu, Zekun
%A Wang, Yingyao
%A Bu, Pi
%A Song, Jun
%A Jiang, Yuning
%A Zheng, Bo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Chensong
%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 xie-etal-2026-secagent
%X Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, existing approaches face two critical limitations: the scarcity of high-quality multilingual datasets, particularly for non-English ecosystems, and inefficient history representation methods. To address these challenges, we present SecAgent, an efficient mobile GUI agent at 3B scale. We first construct a human-verified Chinese mobile GUI dataset with 18k grounding samples and 121k navigation steps across 44 applications, along with a Chinese navigation benchmark featuring multi-choice action annotations. Building upon this dataset, we propose a semantic context mechanism that distills history screenshots and actions into concise, natural language summaries, significantly reducing computational costs while preserving task-relevant information. Through supervised and reinforcement fine-tuning, SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks. Our dataset and benchmark are available at https://huggingface.co/datasets/alibabagroup/CMGUI.
%U https://aclanthology.org/2026.emnlp-main.1141/
%P 26159-26174
Markdown (Informal)
[SecAgent: Efficient Mobile GUI Agent with Semantic Context](https://aclanthology.org/2026.emnlp-main.1141/) (Xie et al., EMNLP 2026)
ACL
- Yiping Xie, Chensong, Jingxuan Xing, Wei Jiang, Zekun Zhu, Yingyao Wang, Pi Bu, Jun Song, Yuning Jiang, and Bo Zheng. 2026. SecAgent: Efficient Mobile GUI Agent with Semantic Context. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26159–26174, Budapest, Hungary. Association for Computational Linguistics.