AppAgent: Multimodal Agents as Smartphone Users
On the map: LLM agents and tool use → GUI and mobile agents
Abstract (arXiv)
Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.
Key facts. Cited by 415 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 27 of its 53 references that are in the map. First posted on arXiv 21 Dec 2023.
Builds on
27 of 53 references.
Closest ancestors · 2
Same direction, newest first
- 2023CogAgent: A Visual Language Model for GUI Agents14 Dec
- 2023You Only Look at Screens: Multimodal Chain-of-Action Agents20 Sep
Other references · 25
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- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 3,080
- 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,061
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
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- 2023Auto-GPT for Online Decision Making: Benchmarks and Additional Opinionscited by 255
- 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 241
- 2023JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Modelscited by 171
- 2023Multimodal Web Navigation with Instruction-Finetuned Foundation Modelscited by 160
- 2023OpenAgents: An Open Platform for Language Agents in the Wildcited by 137
- 2023GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigationcited by 137
- 2023Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planningcited by 60
- 2023StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Datacited by 33
- 2023Never-ending Learning of User Interfacescited by 9
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Citing papers per year: 2024: 107; 2025: 155; 2026: 153.
415 papers.
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