What's Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning
Organizations: Zhejiang University · ZJU-Ant Group Joint Lab of Knowledge Graph
Abstract
Existing Graphical User Interface (GUI) reasoning tasks remain challenging, particularly in UI understanding. Current methods typically rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure. To enhance the understanding and interaction with UIs, we propose an innovative GUI reasoning paradigm called UI-in-the-Loop (UILoop). Our approach treats the GUI reasoning task as a cyclic Screen-UI elements-Action process. By enabling Multimodal Large Language Models (MLLMs) to explicitly learn the localization, semantic functions, and practical usage of key UI elements, UILoop achieves precise element discovery and performs interpretable reasoning. Furthermore, we introduce a more challenging UI Comprehension task centered on UI elements with three evaluation metrics. Correspondingly, we contribute a benchmark of 26K samples (UI Comprehension-Bench) to comprehensively evaluate existing methods' mastery of UI elements. Extensive experiments demonstrate that UILoop achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks.
Figures & tables
| Datasets | # Episodes | # Unique Instructions | Annotation | ||||||
| Screen Desc. | Key UI Element | Action Coord | Action Desc. | Action Think | |||||
| Loc. | Lin. | Lev. | |||||||
| AITW | 715142 | 30378 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| Android Control | 15283 | 15283 | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ | ✗ |
| MMBench-GUI | 8123 | 8123 | ✗ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ |
| ScreenSpot-Pro | 1581 | 1581 | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Methods | ScreenSpot-Pro | AndroidControl-High | ||||||||||||||||
| Dev | Creative | CAD | Sci. | Office | OS | Overall | Type | SR | GR | |||||||||
| Text | Icon | Text | Icon | Text | Icon | Text | Icon | Text | Icon | Text | Icon | Text | Icon | Avg. | ||||
| Zero-Shot Models | ||||||||||||||||||
| Claude-CU | 22.0 | 3.9 | 25.9 | 3.4 | 14.5 | 3.7 | 33.9 | 15.8 | 30.1 | 16.3 | 11.0 | 4.5 | 23.4 | 7.1 | 17.1 | 63.7 | 12.5 | - |
| GPT-4o | 1.3 | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 2.1 | 0.0 | 1.1 | 0.0 | 0.0 | 0.0 | 1.3 | 0.0 | 0.8 | 63.1 | 21.2 | 30.9 |
| Qwen2.5-VL-3B | 16.2 | 1.4 | 23.3 | 1.4 | 10.2 | 4.7 | 38.2 | 6.4 | 24.3 | 3.8 | 15.0 | 1.1 | 21.2 | 3.1 | 12.2 | 47.8 | 38.9 | 46.5 |
| Methods | Android Control-High | Impact | ||
| Type | SR | GR | Avg. Ratio | |
| GPT-4o-mini | ||||
| base | 68.1 | 20.9 | 6.9 | - |
| w/ UI info. | 69.9 | 51.4 | 62.9 | +29.4 |
| w/ false UI info. | 67.2 | 18.4 | 5.8 | -1.5 |
| Qwen2.5-VL-3B-Instruct | ||||
| Methods | UI Comprehension-Bench | |||
| Loc. | Lin. | Lev. | Overall | |
| Zero-shot Models | ||||
| GPT-4o | 22.5 | 30.7 | 11.8 | 0.8 |
| Qwen2.5-VL-3B-Instruct | 48.7 | 9.5 | 36.6 | 1.7 |
| Qwen2.5-VL-7B-Instruct | 46.8 | 27.5 | 29.1 | 3.7 |
| Screen-to-Action Training Models | ||||
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
| Datasets | # Episodes | # Unique Instructions | Annotation | ||||||
| Screen Desc. | Key UI Element | Action Coord | Action Desc. | Action Think | |||||
| Loc. | Lin. | Lev. | |||||||
| PixelHelp | 187 | 187 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| MoTIF | 4707 | 270 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| UGIF | 523 | 420 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| Meta-GUI | 4684 | 1125 | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✗ |
GUI Grounding
Graphical user interface (GUI) grounding maps instructions to the screen elements or locations an agent should interact with.
See all 57 papers