@inproceedings{zhao-etal-2026-mas,
title = "{MAS}-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile {GUI} Agents",
author = "Zhao, Pengxiang and
Liu, Guangyi and
Liang, Yaozhen and
He, Weiqing and
Lu, Zhengxi and
Wang, WenHao and
Huang, Yuehao and
Chai, Yuxiang and
Kang, Zhaolu and
Guo, Yaxuan and
Wang, Hao and
Zhang, Kexin and
Liu, Liang and
Liu, Yong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.316/",
pages = "6960--6985",
ISBN = "979-8-89176-390-6",
abstract = "Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for MLLM-based mobile automation. However, systematic evaluation of GUI{--}shortcut hybrid agents remains largely underexplored. To bridge this gap, we introduce **MAS-Bench**, a benchmark that pioneers the evaluation of GUI-shortcut hybrid agents with a specific focus on the mobile domain. Beyond merely using predefined shortcuts, MAS-Bench assesses an agent{'}s capability to *autonomously generate* shortcuts by discovering and creating reusable, low-cost workflows. It features 139 complex tasks across 11 real-world applications, a knowledge base of 88 predefined shortcuts (APIs, deep-links, RPA scripts), and 9 evaluation metrics. Experiments demonstrate that hybrid agents achieve up to 68.3{\%} success rate and 39{\%} greater execution efficiency than GUI-only counterparts. Furthermore, our evaluation framework effectively reveals the quality gap between predefined and agent-generated shortcuts, validating its capability to assess shortcut generation methods. MAS-Bench addresses the lack of systematic benchmarks for GUI-shortcut hybrid mobile agents, providing a foundational platform for future advancements in creating more efficient and robust intelligent agents."
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<abstract>Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for MLLM-based mobile automation. However, systematic evaluation of GUI–shortcut hybrid agents remains largely underexplored. To bridge this gap, we introduce **MAS-Bench**, a benchmark that pioneers the evaluation of GUI-shortcut hybrid agents with a specific focus on the mobile domain. Beyond merely using predefined shortcuts, MAS-Bench assesses an agent’s capability to *autonomously generate* shortcuts by discovering and creating reusable, low-cost workflows. It features 139 complex tasks across 11 real-world applications, a knowledge base of 88 predefined shortcuts (APIs, deep-links, RPA scripts), and 9 evaluation metrics. Experiments demonstrate that hybrid agents achieve up to 68.3% success rate and 39% greater execution efficiency than GUI-only counterparts. Furthermore, our evaluation framework effectively reveals the quality gap between predefined and agent-generated shortcuts, validating its capability to assess shortcut generation methods. MAS-Bench addresses the lack of systematic benchmarks for GUI-shortcut hybrid mobile agents, providing a foundational platform for future advancements in creating more efficient and robust intelligent agents.</abstract>
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%0 Conference Proceedings
%T MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents
%A Zhao, Pengxiang
%A Liu, Guangyi
%A Liang, Yaozhen
%A He, Weiqing
%A Lu, Zhengxi
%A Wang, WenHao
%A Huang, Yuehao
%A Chai, Yuxiang
%A Kang, Zhaolu
%A Guo, Yaxuan
%A Wang, Hao
%A Zhang, Kexin
%A Liu, Liang
%A Liu, Yong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhao-etal-2026-mas
%X Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for MLLM-based mobile automation. However, systematic evaluation of GUI–shortcut hybrid agents remains largely underexplored. To bridge this gap, we introduce **MAS-Bench**, a benchmark that pioneers the evaluation of GUI-shortcut hybrid agents with a specific focus on the mobile domain. Beyond merely using predefined shortcuts, MAS-Bench assesses an agent’s capability to *autonomously generate* shortcuts by discovering and creating reusable, low-cost workflows. It features 139 complex tasks across 11 real-world applications, a knowledge base of 88 predefined shortcuts (APIs, deep-links, RPA scripts), and 9 evaluation metrics. Experiments demonstrate that hybrid agents achieve up to 68.3% success rate and 39% greater execution efficiency than GUI-only counterparts. Furthermore, our evaluation framework effectively reveals the quality gap between predefined and agent-generated shortcuts, validating its capability to assess shortcut generation methods. MAS-Bench addresses the lack of systematic benchmarks for GUI-shortcut hybrid mobile agents, providing a foundational platform for future advancements in creating more efficient and robust intelligent agents.
%U https://aclanthology.org/2026.acl-long.316/
%P 6960-6985
Markdown (Informal)
[MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents](https://aclanthology.org/2026.acl-long.316/) (Zhao et al., ACL 2026)
ACL
- Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, and Yong Liu. 2026. MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6960–6985, San Diego, California, United States. Association for Computational Linguistics.