MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation
Zichen Zhu, Hao Tang, Yansi Li, Dingye Liu, Hongshen Xu, Kunyao Lan, Danyang Zhang, Yixuan Jiang
Abstract
Existing Multimodal Large Language Model (MLLM)-based agents face significant challenges in handling complex GUI (Graphical User Interface) interactions on devices. These challenges arise from the dynamic and structured nature of GUI environments, which integrate text, images, and spatial relationships, as well as the variability in action spaces across different pages and tasks. To address these limitations, we propose MobA, a novel MLLM-based mobile assistant system. MobA introduces an adaptive planning module that incorporates a reflection mechanism for error recovery and dynamically adjusts plans to align with the real environment contexts and action module’s execution capacity. Additionally, a multifaceted memory module provides comprehensive memory support to enhance adaptability and efficiency. We also present MobBench, a dataset designed for complex mobile interactions. Experimental results on MobBench and AndroidArena demonstrate MobA’s ability to handle dynamic GUI environments and perform complex mobile tasks.
BibTeX
@inproceedings{zhu-etal-2025-moba,
title = "{M}ob{A}: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation",
author = "Zhu, Zichen and
Tang, Hao and
Li, Yansi and
Liu, Dingye and
Xu, Hongshen and
Lan, Kunyao and
Zhang, Danyang and
Jiang, Yixuan and
Zhou, Hao and
Wang, Chenrun and
Zhang, Situo and
Sun, Liangtai and
Wang, Yixiao and
Sun, Yuheng and
Chen, Lu and
Yu, Kai",
editor = "Dziri, Nouha and
Ren, Sean (Xiang) and
Diao, Shizhe",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-demo.43/",
pages = "535--549",
ISBN = "979-8-89176-191-9"
}