Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks

• URL: http://arxiv.org/abs/2501.11733v2
• Date: Tue, 28 Jan 2025 16:58:02 GMT
• Title: Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks
• Authors: Zhenhailong Wang, Haiyang Xu, Junyang Wang, Xi Zhang, Ming Yan, Ji Zhang, Fei Huang, Heng Ji,
• Abstract summary: Mobile-Agent-E is a hierarchical multi-agent framework capable of self-evolution through past experience. Mobile-Agent-E achieves a 22% absolute improvement over previous state-of-the-art approaches.
• Score: 85.48034185086169
• License:
• Abstract: Smartphones have become indispensable in modern life, yet navigating complex tasks on mobile devices often remains frustrating. Recent advancements in large multimodal model (LMM)-based mobile agents have demonstrated the ability to perceive and act in mobile environments. However, current approaches face significant limitations: they fall short in addressing real-world human needs, struggle with reasoning-intensive and long-horizon tasks, and lack mechanisms to learn and improve from prior experiences. To overcome these challenges, we introduce Mobile-Agent-E, a hierarchical multi-agent framework capable of self-evolution through past experience. By hierarchical, we mean an explicit separation of high-level planning and low-level action execution. The framework comprises a Manager, responsible for devising overall plans by breaking down complex tasks into subgoals, and four subordinate agents--Perceptor, Operator, Action Reflector, and Notetaker--which handle fine-grained visual perception, immediate action execution, error verification, and information aggregation, respectively. Mobile-Agent-E also features a novel self-evolution module which maintains a persistent long-term memory comprising Tips and Shortcuts. Tips are general guidance and lessons learned from prior tasks on how to effectively interact with the environment. Shortcuts are reusable, executable sequences of atomic operations tailored for specific subroutines. The inclusion of Tips and Shortcuts facilitates continuous refinement in performance and efficiency. Alongside this framework, we introduce Mobile-Eval-E, a new benchmark featuring complex mobile tasks requiring long-horizon, multi-app interactions. Empirical results show that Mobile-Agent-E achieves a 22% absolute improvement over previous state-of-the-art approaches across three foundation model backbones. Project page: https://x-plug.github.io/MobileAgent.

Related papers

• Foundations and Recent Trends in Multimodal Mobile Agents: A Survey [57.677161006710065]
  Mobile agents are essential for automating tasks in complex and dynamic mobile environments. Recent advancements enhance real-time adaptability and multimodal interaction. We categorize these advancements into two main approaches: prompt-based methods and training-based methods.
  arXiv  Detail & Related papers  (2024-11-04T11:50:58Z)
• SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation [89.24729958546168]
  We present SPA-Bench, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents. SPA-Bench offers three key contributions: A diverse set of tasks covering system and third-party apps in both English and Chinese, focusing on features commonly used in daily routines. A novel evaluation pipeline that automatically assesses agent performance across multiple dimensions, encompassing seven metrics related to task completion and resource consumption.
  arXiv  Detail & Related papers  (2024-10-19T17:28:48Z)
• MobA: A Two-Level Agent System for Efficient Mobile Task Automation [22.844404052755294]
  MobA is a novel Mobile phone Agent powered by multimodal large language models. The high-level Global Agent (GA) is responsible for understanding user commands, tracking history memories, and planning tasks. The low-level Local Agent (LA) predicts detailed actions in the form of function calls, guided by sub-tasks and memory from the GA.
  arXiv  Detail & Related papers  (2024-10-17T16:53:50Z)
• AppAgent v2: Advanced Agent for Flexible Mobile Interactions [46.789563920416626]
  This work introduces a novel LLM-based multimodal agent framework for mobile devices. Our agent constructs a flexible action space that enhances adaptability across various applications. Our results demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-05T06:31:39Z)
• MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices [17.702068044185086]
  This paper introduces MobileExperts, which for the first time introduces tool formulation and multi-agent collaboration. We develop a dual-layer planning mechanism to establish coordinate collaboration among experts. Experimental results demonstrate that MobileExperts performs better on all intelligence levels and achieves 22% reduction in reasoning costs.
  arXiv  Detail & Related papers  (2024-07-04T13:12:19Z)
• Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration [52.25473993987409]
  We propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. We show that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture.
  arXiv  Detail & Related papers  (2024-06-03T05:50:00Z)
• Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception [52.5831204440714]
  We introduce Mobile-Agent, an autonomous multi-modal mobile device agent. Mobile-Agent first leverages visual perception tools to accurately identify and locate both the visual and textual elements within the app's front-end interface. It then autonomously plans and decomposes the complex operation task, and navigates the mobile Apps through operations step by step.
  arXiv  Detail & Related papers  (2024-01-29T13:46:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.