# Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction

arXiv: https://arxiv.org/abs/2305.08144 · HTML: https://mlsanity.com/paper/2305.08144/

## Key facts

- Cited by 36 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 12 of its 50 references that are in the map.
- First posted on arXiv 14 May 2023.

## Placed in

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

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration](https://mlsanity.com/paper/1802.08802/) (2018)
- [Mapping Natural Language Instructions to Mobile UI Action Sequences](https://mlsanity.com/paper/2005.03776/) (2020)
- [AndroidEnv: A Reinforcement Learning Platform for Android](https://mlsanity.com/paper/2105.13231/) (2021)
- [A data-driven approach for learning to control computers](https://mlsanity.com/paper/2202.08137/) (2022)
- [META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI](https://mlsanity.com/paper/2205.11029/) (2022)
- [Enabling Conversational Interaction with Mobile UI using Large Language Models](https://mlsanity.com/paper/2209.08655/) (2022)
- [AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning](https://mlsanity.com/paper/2106.00133/) (2021)

## Built on by (most cited first, 30 of 36)

- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [Evaluation and Benchmarking of LLM Agents: A Survey](https://mlsanity.com/paper/2507.21504/) (2025)
- [Large Language Models for Planning: A Comprehensive and Systematic Survey](https://mlsanity.com/paper/2505.19683/) (2025)
- [A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions](https://mlsanity.com/paper/2501.16150/) (2025)
- [ProgRM: Build Better GUI Agents with Progress Rewards](https://mlsanity.com/paper/2505.18121/) (2025)
- [SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents](https://mlsanity.com/paper/2507.00841/) (2025)
- [MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation](https://mlsanity.com/paper/2410.13757/) (2024)
- [Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents](https://mlsanity.com/paper/2505.12632/) (2025)
- [Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey](https://mlsanity.com/paper/2506.11102/) (2025)
- [UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience](https://mlsanity.com/paper/2603.24533/) (2026)
- [ColorBench: Benchmarking Mobile Agents with Graph-Structured Framework for Complex Long-Horizon Tasks](https://mlsanity.com/paper/2510.14621/) (2025)
- [MobiAgent: A Systematic Framework for Customizable Mobile Agents](https://mlsanity.com/paper/2509.00531/) (2025)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [AmbiBench: Benchmarking Mobile GUI Agents Beyond One-Shot Instructions in the Wild](https://mlsanity.com/paper/2602.11750/) (2026)
- [TextAtari: 100K Frames Game Playing with Language Agents](https://mlsanity.com/paper/2506.04098/) (2025)
- [InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2508.19679/) (2025)
- [MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment](https://mlsanity.com/paper/2601.20335/) (2026)
- [SecAgent: Efficient Mobile GUI Agent with Semantic Context](https://mlsanity.com/paper/2603.08533/) (2026)
- [GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation](https://mlsanity.com/paper/2603.26266/) (2026)
- [How Benchmarks Mis-Score Computer-Use Agents](https://mlsanity.com/paper/2607.28367/) (2026)
- [Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance](https://mlsanity.com/paper/2508.01057/) (2025)
- [MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents](https://mlsanity.com/paper/2512.12634/) (2025)
- [Interactive Evaluation Requires a Design Science](https://mlsanity.com/paper/2605.17829/) (2026)
- [Automotive-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems](https://mlsanity.com/paper/2509.21143/) (2025)
- [EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies](https://mlsanity.com/paper/2602.09514/) (2026)
- [ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis](https://mlsanity.com/paper/2605.25160/) (2026)
- [Instructional Text Across Disciplines: A Survey of Representations, Downstream Tasks, and Open Challenges Toward Capable AI Agents](https://mlsanity.com/paper/2410.18529/) (2024)

## Abstract (arXiv)

The Graphical User Interface (GUI) is pivotal for human interaction with the digital world, enabling efficient device control and the completion of complex tasks. Recent progress in Large Language Models (LLMs) and Vision Language Models (VLMs) offers the chance to create advanced GUI agents. To ensure their effectiveness, there's a pressing need for qualified benchmarks that provide trustworthy and reproducible evaluations -- a challenge current benchmarks often fail to address. To tackle this issue, we introduce Mobile-Env, a comprehensive toolkit tailored for creating GUI benchmarks in the Android mobile environment. Mobile-Env offers an isolated and controllable setting for reliable evaluations, and accommodates intermediate instructions and rewards to reflect real-world usage more naturally. Utilizing Mobile-Env, we collect an open-world task set across various real-world apps and a fixed world set, WikiHow, which captures a significant amount of dynamic online contents for fully controllable and reproducible evaluation. We conduct comprehensive evaluations of LLM agents using these benchmarks. Our findings reveal that even advanced models (e.g., GPT-4V and LLaMA-3) struggle with tasks that are relatively simple for humans. This highlights a crucial gap in current models and underscores the importance of developing more capable foundation models and more effective GUI agent frameworks.