@inproceedings{chai-etal-2026-a3,
title = "A3: Android Agent Arena for Mobile {GUI} Agents with Essential-State Procedural Evaluation",
author = "Chai, Yuxiang and
Tang, Shunye and
Xiao, Han and
Lin, Weifeng and
Li, Hanhao and
Zhang, Jiayu and
Liu, Liang and
Zhao, Pengxiang and
Liu, Guangyi and
Wang, Guozhi and
Ren, Shuai and
Han, Rongduo and
Zhang, Haining and
Huang, Siyuan and
Li, Hongsheng",
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.184/",
pages = "3774--3789",
ISBN = "979-8-89176-395-1",
abstract = "The advancement of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has catalyzed the development of mobile graphic user interface (GUI) AI agents, which is designed to autonomously perform tasks on mobile devices. However, a significant gap persists in mobile GUI agent evaluation, where existing benchmarks predominantly rely on either static frame assessments such as AndroidControl or offline static apps such as AndroidWorld and thus fail to capture agent performance in dynamic, real-world online mobile apps. To address this gap, we present Android Agent Arena (A3), a novel ``essential-state'' based procedural evaluation system for mobile GUI agents. A3 introduces a benchmark of 100 tasks derived from 20 widely-used, dynamic online apps across 20 categories from the Google Play Store, ensuring evaluation comprehension. A3 also presents a novel ``essential-state'' based procedural evaluation method that leverages MLLMs as reward models to progressively verify task completion and process achievement. This evaluation approach address the limitations of traditional function based evaluation methods on online dynamic apps. Furthermore, A3 includes a toolkit to streamline Android device interaction, reset online environment and apps and facilitate data collection from both human and agent demonstrations. The complete A3 system, including the benchmark and tools, will be publicly released to provide a robust foundation for future research and development in mobile GUI agents."
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<abstract>The advancement of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has catalyzed the development of mobile graphic user interface (GUI) AI agents, which is designed to autonomously perform tasks on mobile devices. However, a significant gap persists in mobile GUI agent evaluation, where existing benchmarks predominantly rely on either static frame assessments such as AndroidControl or offline static apps such as AndroidWorld and thus fail to capture agent performance in dynamic, real-world online mobile apps. To address this gap, we present Android Agent Arena (A3), a novel “essential-state” based procedural evaluation system for mobile GUI agents. A3 introduces a benchmark of 100 tasks derived from 20 widely-used, dynamic online apps across 20 categories from the Google Play Store, ensuring evaluation comprehension. A3 also presents a novel “essential-state” based procedural evaluation method that leverages MLLMs as reward models to progressively verify task completion and process achievement. This evaluation approach address the limitations of traditional function based evaluation methods on online dynamic apps. Furthermore, A3 includes a toolkit to streamline Android device interaction, reset online environment and apps and facilitate data collection from both human and agent demonstrations. The complete A3 system, including the benchmark and tools, will be publicly released to provide a robust foundation for future research and development in mobile GUI agents.</abstract>
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%0 Conference Proceedings
%T A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation
%A Chai, Yuxiang
%A Tang, Shunye
%A Xiao, Han
%A Lin, Weifeng
%A Li, Hanhao
%A Zhang, Jiayu
%A Liu, Liang
%A Zhao, Pengxiang
%A Liu, Guangyi
%A Wang, Guozhi
%A Ren, Shuai
%A Han, Rongduo
%A Zhang, Haining
%A Huang, Siyuan
%A Li, Hongsheng
%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 chai-etal-2026-a3
%X The advancement of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has catalyzed the development of mobile graphic user interface (GUI) AI agents, which is designed to autonomously perform tasks on mobile devices. However, a significant gap persists in mobile GUI agent evaluation, where existing benchmarks predominantly rely on either static frame assessments such as AndroidControl or offline static apps such as AndroidWorld and thus fail to capture agent performance in dynamic, real-world online mobile apps. To address this gap, we present Android Agent Arena (A3), a novel “essential-state” based procedural evaluation system for mobile GUI agents. A3 introduces a benchmark of 100 tasks derived from 20 widely-used, dynamic online apps across 20 categories from the Google Play Store, ensuring evaluation comprehension. A3 also presents a novel “essential-state” based procedural evaluation method that leverages MLLMs as reward models to progressively verify task completion and process achievement. This evaluation approach address the limitations of traditional function based evaluation methods on online dynamic apps. Furthermore, A3 includes a toolkit to streamline Android device interaction, reset online environment and apps and facilitate data collection from both human and agent demonstrations. The complete A3 system, including the benchmark and tools, will be publicly released to provide a robust foundation for future research and development in mobile GUI agents.
%U https://aclanthology.org/2026.findings-acl.184/
%P 3774-3789
Markdown (Informal)
[A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation](https://aclanthology.org/2026.findings-acl.184/) (Chai et al., Findings 2026)
ACL
- Yuxiang Chai, Shunye Tang, Han Xiao, Weifeng Lin, Hanhao Li, Jiayu Zhang, Liang Liu, Pengxiang Zhao, Guangyi Liu, Guozhi Wang, Shuai Ren, Rongduo Han, Haining Zhang, Siyuan Huang, and Hongsheng Li. 2026. A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3774–3789, San Diego, California, United States. Association for Computational Linguistics.