# AppAgent v2: Advanced Agent for Flexible Mobile Interactions

arXiv: https://arxiv.org/abs/2408.11824 · HTML: https://mlsanity.com/paper/2408.11824/

## Key facts

- Cited by 68 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 25 of its 40 references that are in the map.
- First posted on arXiv 5 Aug 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, 25 of 25)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action](https://mlsanity.com/paper/2303.11381/) (2023)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception](https://mlsanity.com/paper/2401.16158/) (2024)
- [AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head](https://mlsanity.com/paper/2304.12995/) (2023)
- [GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction](https://mlsanity.com/paper/2305.18752/) (2023)
- [Real-time Scene Text Detection with Differentiable Binarization](https://mlsanity.com/paper/1911.08947/) (2019)
- [LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents](https://mlsanity.com/paper/2311.05437/) (2023)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [SmartPlay: A Benchmark for LLMs as Intelligent Agents](https://mlsanity.com/paper/2310.01557/) (2023)
- [Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions](https://mlsanity.com/paper/2309.11382/) (2023)
- [AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn](https://mlsanity.com/paper/2306.08640/) (2023)
- [On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving](https://mlsanity.com/paper/2311.05332/) (2023)
- [Octopus: Embodied Vision-Language Programmer from Environmental Feedback](https://mlsanity.com/paper/2310.08588/) (2023)
- [MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception](https://mlsanity.com/paper/2312.07472/) (2023)
- [ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System](https://mlsanity.com/paper/2304.14407/) (2023)
- [DroidBot-GPT: GPT-powered UI Automation for Android](https://mlsanity.com/paper/2304.07061/) (2023)
- [StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data](https://mlsanity.com/paper/2308.10253/) (2023)

## Built on by (most cited first, 30 of 68)

- [Mobile-Agent-v3: Fundamental Agents for GUI Automation](https://mlsanity.com/paper/2508.15144/) (2025)
- [UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning](https://mlsanity.com/paper/2503.21620/) (2025)
- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks](https://mlsanity.com/paper/2501.11733/) (2025)
- [Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning](https://mlsanity.com/paper/2505.12370/) (2025)
- [A Survey on (M)LLM-Based GUI Agents](https://mlsanity.com/paper/2504.13865/) (2025)
- [Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms](https://mlsanity.com/paper/2410.18967/) (2024)
- [AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.01391/) (2025)
- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [Advancing Mobile GUI Agents: A Verifier-Driven Approach to Practical Deployment](https://mlsanity.com/paper/2503.15937/) (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)
- [ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models](https://mlsanity.com/paper/2505.16517/) (2025)
- [Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation](https://mlsanity.com/paper/2506.04614/) (2025)
- [GUI-Reflection: Empowering Multimodal GUI Models with Self-Reflection Behavior](https://mlsanity.com/paper/2506.08012/) (2025)
- [Falcon-UI: Understanding GUI Before Following User Instructions](https://mlsanity.com/paper/2412.09362/) (2024)
- [Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models](https://mlsanity.com/paper/2505.15406/) (2025)
- [MobileWorldBench: Towards Semantic World Modeling For Mobile Agents](https://mlsanity.com/paper/2512.14014/) (2025)
- [Mobile-Bench-v2: A More Realistic and Comprehensive Benchmark for VLM-based Mobile Agents](https://mlsanity.com/paper/2505.11891/) (2025)
- [Uncertainty-Aware GUI Agent: Adaptive Perception through Component Recommendation and Human-in-the-Loop Refinement](https://mlsanity.com/paper/2508.04025/) (2025)
- [HALO: Hierarchical Autonomous Logic-Oriented Orchestration for Multi-Agent LLM Systems](https://mlsanity.com/paper/2505.13516/) (2025)
- [Chain-of-Memory: Enhancing GUI Agents for Cross-Application Navigation](https://mlsanity.com/paper/2506.18158/) (2025)
- [Agent-SAMA: State-Aware Mobile Assistant](https://mlsanity.com/paper/2505.23596/) (2025)
- [Atomic-to-Compositional Generalization for Mobile Agents with A New Benchmark and Scheduling System](https://mlsanity.com/paper/2506.08972/) (2025)
- [From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents](https://mlsanity.com/paper/2505.12981/) (2025)
- [MobileIPL: Enhancing Mobile Agents Thinking Process via Iterative Preference Learning](https://mlsanity.com/paper/2505.12299/) (2025)
- [MAGNET: Towards Adaptive GUI Agents with Memory-Driven Knowledge Evolution](https://mlsanity.com/paper/2601.19199/) (2026)
- [GEM: Gaussian Embedding Modeling for Out-of-Distribution Detection in GUI Agents](https://mlsanity.com/paper/2505.12842/) (2025)
- [Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents](https://mlsanity.com/paper/2508.19493/) (2025)

## Abstract (arXiv)

With the advancement of Multimodal Large Language Models (MLLM), LLM-driven visual agents are increasingly impacting software interfaces, particularly those with graphical user interfaces. This work introduces a novel LLM-based multimodal agent framework for mobile devices. This framework, capable of navigating mobile devices, emulates human-like interactions. Our agent constructs a flexible action space that enhances adaptability across various applications including parser, text and vision descriptions. The agent operates through two main phases: exploration and deployment. During the exploration phase, functionalities of user interface elements are documented either through agent-driven or manual explorations into a customized structured knowledge base. In the deployment phase, RAG technology enables efficient retrieval and update from this knowledge base, thereby empowering the agent to perform tasks effectively and accurately. This includes performing complex, multi-step operations across various applications, thereby demonstrating the framework's adaptability and precision in handling customized task workflows. Our experimental results across various benchmarks demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios. Our code will be open source soon.