A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation

Fuente: arXiv
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Autori principali: Chai, Yuxiang, Tang, Shunye, Xiao, Han, Lin, Weifeng, Li, Hanhao, Zhang, Jiayu, Liu, Liang, Zhao, Pengxiang, Liu, Guangyi, Wang, Guozhi, Ren, Shuai, Han, Rongduo, Zhang, Haining, Huang, Siyuan, Li, Hongsheng
Natura: Preprint
Pubblicazione: 2025
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author Chai, Yuxiang
Tang, Shunye
Xiao, Han
Lin, Weifeng
Li, Hanhao
Zhang, Jiayu
Liu, Liang
Zhao, Pengxiang
Liu, Guangyi
Wang, Guozhi
Ren, Shuai
Han, Rongduo
Zhang, Haining
Huang, Siyuan
Li, Hongsheng
author_facet Chai, Yuxiang
Tang, Shunye
Xiao, Han
Lin, Weifeng
Li, Hanhao
Zhang, Jiayu
Liu, Liang
Zhao, Pengxiang
Liu, Guangyi
Wang, Guozhi
Ren, Shuai
Han, Rongduo
Zhang, Haining
Huang, Siyuan
Li, Hongsheng
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2501_01149
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation
Chai, Yuxiang
Tang, Shunye
Xiao, Han
Lin, Weifeng
Li, Hanhao
Zhang, Jiayu
Liu, Liang
Zhao, Pengxiang
Liu, Guangyi
Wang, Guozhi
Ren, Shuai
Han, Rongduo
Zhang, Haining
Huang, Siyuan
Li, Hongsheng
Artificial Intelligence
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.
title A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation
topic Artificial Intelligence
url https://arxiv.org/abs/2501.01149