# Foundations and Recent Trends in Multimodal Mobile Agents: A Survey

arXiv: https://arxiv.org/abs/2411.02006 · HTML: https://mlsanity.com/paper/2411.02006/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 69 of its 118 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 30 of 69)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://mlsanity.com/paper/2407.18901/) (2024)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [OS-ATLAS: A Foundation Action Model for Generalist GUI Agents](https://mlsanity.com/paper/2410.23218/) (2024)
- [A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis](https://mlsanity.com/paper/2307.12856/) (2023)
- [Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception](https://mlsanity.com/paper/2401.16158/) (2024)
- [Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration](https://mlsanity.com/paper/1802.08802/) (2018)
- [DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning](https://mlsanity.com/paper/2406.11896/) (2024)
- [On the Effects of Data Scale on UI Control Agents](https://mlsanity.com/paper/2406.03679/) (2024)
- [GUIOdyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices](https://mlsanity.com/paper/2406.08451/) (2024)
- [Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration](https://mlsanity.com/paper/2406.01014/) (2024)
- [You Only Look at Screens: Multimodal Chain-of-Action Agents](https://mlsanity.com/paper/2309.11436/) (2023)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [Mapping Natural Language Instructions to Mobile UI Action Sequences](https://mlsanity.com/paper/2005.03776/) (2020)
- [Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs](https://mlsanity.com/paper/2404.05719/) (2024)

## Built on by (most cited first, 30 of 31)

- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Agentic Web: Weaving the Next Web with AI Agents](https://mlsanity.com/paper/2507.21206/) (2025)
- [OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use](https://mlsanity.com/paper/2508.04482/) (2025)
- [A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions](https://mlsanity.com/paper/2501.16150/) (2025)
- [LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects](https://mlsanity.com/paper/2504.19838/) (2025)
- [The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News](https://mlsanity.com/paper/2505.08532/) (2025)
- [PresentAgent: Multimodal Agent for Presentation Video Generation](https://mlsanity.com/paper/2507.04036/) (2025)
- [From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents](https://mlsanity.com/paper/2505.12981/) (2025)
- [EcoAgent: An Efficient Device-Cloud Collaborative Multi-Agent Framework for Mobile Automation](https://mlsanity.com/paper/2505.05440/) (2025)
- [FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks](https://mlsanity.com/paper/2505.19662/) (2025)
- [Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2601.00388/) (2026)
- [Generalization in Online Reinforcement Learning for Mobile Agents](https://mlsanity.com/paper/2603.07432/) (2026)
- [CORE: Reducing UI Exposure in Mobile Agents via Collaboration Between Cloud and Local LLMs](https://mlsanity.com/paper/2510.15455/) (2025)
- [LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark](https://mlsanity.com/paper/2504.13805/) (2025)
- [Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents](https://mlsanity.com/paper/2510.07809/) (2025)
- [ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical Forgetting](https://mlsanity.com/paper/2605.03804/) (2026)
- [Automotive-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems](https://mlsanity.com/paper/2509.21143/) (2025)
- [Do Multi-Agents Dream of Electric Screens? Achieving Perfect Accuracy on AndroidWorld Through Task Decomposition](https://mlsanity.com/paper/2602.07787/) (2026)
- [ScreenExplorer: Training a Vision-Language Model for Diverse Exploration in Open GUI World](https://mlsanity.com/paper/2505.19095/) (2025)
- [ReInAgent: A Context-Aware GUI Agent Enabling Human-in-the-Loop Mobile Task Navigation](https://mlsanity.com/paper/2510.07988/) (2025)
- [DigiData: Training and Evaluating General-Purpose Mobile Control Agents](https://mlsanity.com/paper/2511.07413/) (2025)
- [GhostUI: Unveiling Hidden Interactions in Mobile UI](https://mlsanity.com/paper/2601.19258/) (2026)
- [UIPress: Bringing Optical Token Compression to UI-to-Code Generation](https://mlsanity.com/paper/2604.09442/) (2026)
- [AndroidReality: How Far Are Mobile Agents from the Real World?](https://mlsanity.com/paper/2608.07775/) (2026)
- [Software Engineering for and with GUI Agent](https://mlsanity.com/paper/2608.09278/) (2026)
- [Automated Trajectory Evaluation for Mobile Agents via Step-Level Consequence Reasoning and Aggregation](https://mlsanity.com/paper/2608.20797/) (2026)
- [MobileRAG: Enhancing Mobile Agent with Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.03891/) (2025)
- [Do LLMs Need to See Everything? A Benchmark and Study of Failures in LLM-driven Smartphone Automation using Screentext vs. Screenshots](https://mlsanity.com/paper/2604.17817/) (2026)
- [Explicit Representation Alignment for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2606.09148/) (2026)

## Abstract (arXiv)

Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and process multimodal data have grown. This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction. Recent evaluation benchmarks have been developed better to capture the static and interactive environments of mobile tasks, offering more accurate assessments of agents' performance. We then categorize these advancements into two main approaches: prompt-based methods, which utilize large language models (LLMs) for instruction-based task execution, and training-based methods, which fine-tune multimodal models for mobile-specific applications. Additionally, we explore complementary technologies that augment agent performance. By discussing key challenges and outlining future research directions, this survey offers valuable insights for advancing mobile agent technologies. A comprehensive resource list is available at https://github.com/aialt/awesome-mobile-agents