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LearnGUI: A Unified Demonstration Benchmark for Mobile GUI Agents

The LearnAct Framework and LearnGUI Benchmark focus on addressing the long-tail challenges in mobile GUI agent performance through demonstration-based learning.

📄 Paper | 💻 Code | 🌐 Project Page

Overview

LearnGUI is the first comprehensive dataset specifically designed for studying demonstration-based learning in mobile GUI agents. It comprises 2,353 instructions across 73 applications with an average of 13.2 steps per task, featuring high-quality human demonstrations for both offline and online evaluation scenarios.

🌟 Key Features

  • Unified Benchmark Framework: Provides standardized metrics and evaluation protocols for demonstration-based learning in mobile GUI agents
  • Dual Evaluation Modes: Supports both offline (2,252 tasks) and online (101 tasks) evaluation scenarios to assess agent performance
  • Rich Few-shot Learning Support: Includes k-shot combinations (k=1,2,3) for each task with varying similarity profiles
  • Multi-dimensional Similarity Metrics: Quantifies demonstration relevance across instruction, UI, and action dimensions
  • Diverse Real-world Coverage: Spans 73 mobile applications with 2,353 naturally varied tasks reflecting real-world usage patterns
  • Expert-annotated Trajectories: Contains high-quality human demonstrations with detailed step-by-step action sequences and element annotations

📊 Dataset Structure and Statistics

The dataset is organized into three main splits:

Dataset Statistics

Split K-shot Tasks Apps Step actions Avg InsSim Avg UISim Avg ActSim UISHActSH UISHActSL UISLActSH UISLActSL
Offline-Train 1-shot 2,001 44 26,184 0.845 0.901 0.858 364 400 403 834
Offline-Train 2-shot 2,001 44 26,184 0.818 0.898 0.845 216 360 358 1,067
Offline-Train 3-shot 2,001 44 26,184 0.798 0.895 0.836 152 346 310 1,193
Offline-Test 1-shot 251 9 3,469 0.798 0.868 0.867 37 49 56 109
Offline-Test 2-shot 251 9 3,469 0.767 0.855 0.853 15 42 55 139
Offline-Test 3-shot 251 9 3,469 0.745 0.847 0.847 10 36 49 156
Online-Test 1-shot 101 20 1,423 - - - - - - -

Each task in LearnGUI contains:

  • High-level instruction
  • Low-level action sequences
  • Screenshot of each step
  • UI element details
  • Ground truth action labels
  • Demonstration pairings with varying similarity profiles

📁 Directory Structure 

LearnGUI/
├── offline/                            # Offline evaluation dataset
│   ├── screenshot.zip                  # Screenshot archives (multi-part)
│   ├── screenshot.z01-z05              # Screenshot archive parts
│   ├── element_anno.zip                # Element annotations
│   ├── instruction_anno.zip            # Instruction annotations
│   ├── task_spilit.json                # Task splitting information
│   └── low_level_instructions.json     # Detailed step-by-step instructions
│
└── online/                             # Online evaluation dataset
    ├── low_level_instructions/         # JSON files with step instructions for each task
    │   ├── AudioRecorderRecordAudio.json
    │   ├── BrowserDraw.json
    │   ├── SimpleCalendarAddOneEvent.json
    │   └── ... (98 more task instruction files)
    └── raw_data/                       # Raw data for each online task
        ├── AudioRecorderRecordAudio/
        ├── BrowserDraw/
        ├── SimpleCalendarAddOneEvent/
        └── ... (98 more task data directories)

🔍 Comparison with Existing Datasets

LearnGUI offers several advantages over existing GUI datasets:

Dataset # Inst. # Apps # Step Env. HL LL GT FS
PixelHelp 187 4 4.2 ❌ ✅ ❌ ✅ ❌
MoTIF 276 125 4.5 ❌ ✅ ✅ ✅ ❌
UIBert 16,660 - 1 ❌ ❌ ✅ ✅ ❌
UGIF 523 12 6.3 ❌ ✅ ✅ ✅ ❌
AITW 30,378 357 6.5 ❌ ✅ ❌ ✅ ❌
AITZ 2,504 70 7.5 ❌ ✅ ✅ ✅ ❌
AndroidControl 15,283 833 4.8 ❌ ✅ ✅ ✅ ❌
AMEX 2,946 110 12.8 ❌ ✅ ❌ ✅ ❌
MobileAgentBench 100 10 - ❌ ✅ ❌ ❌ ❌
AppAgent 50 10 - ❌ ✅ ❌ ❌ ❌
LlamaTouch 496 57 7.01 ✅ ✅ ❌ ✅ ❌
AndroidWorld 116 20 - ✅ ✅ ❌ ❌ ❌
AndroidLab 138 9 8.5 ✅ ✅ ❌ ❌ ❌
LearnGUI (Ours) 2,353 73 13.2 ✅ ✅ ✅ ✅ ✅

Note: # Inst. (number of instructions), # Apps (number of applications), # Step (average steps per task), Env. (supports environment interactions), HL (has high-level instructions), LL (has low-level instructions), GT (provides ground truth trajectories), FS (supports few-shot learning).

📄 License

This dataset is licensed under Apache License 2.0.