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language:
- zh
- en
license: cc-by-nc-4.0
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
- image-text-to-text
tags:
- gui-agent
- benchmark
- multimodal
pretty_name: UniGUI-Bench
configs:
- config_name: element_grounding
data_files:
- split: test
path: data/process_eval/element_grounding.json
field: examples
- config_name: action_understanding
data_files:
- split: test
path: data/process_eval/action_understanding.json
field: examples
- config_name: page_understanding
data_files:
- split: test
path: data/process_eval/page_understanding.json
field: examples
- config_name: action_anticipation
data_files:
- split: test
path: data/process_eval/action_anticipation.json
field: examples
- config_name: intent_reasoning
data_files:
- split: test
path: data/process_eval/intent_reasoning.json
field: examples
- config_name: action_execution
data_files:
- split: test
path: data/process_eval/action_execution.json
field: examples
- config_name: dynamic_adaptation
data_files:
- split: test
path: data/process_eval/dynamic_adaptation.json
field: examples
- config_name: task_planning
data_files:
- split: test
path: data/process_eval/task_planning.json
field: examples
- config_name: task_progress_awareness
data_files:
- split: test
path: data/process_eval/task_progress_awareness.json
field: examples
- config_name: task_completion_detection
data_files:
- split: test
path: data/process_eval/task_completion_detection.json
field: examples
- config_name: intern_state_memory
data_files:
- split: test
path: data/process_eval/intern_state_memory.json
field: examples
---
# UniGUI-Bench
A comprehensive benchmark for evaluating GUI agents across 11 capability dimensions.
## Dataset Structure
### System Evaluation
- **trajectory**: 1,200 complete GUI task trajectories with step-by-step screenshots
- **gui_system**: 7,108 individual step evaluation samples
### Process Evaluation (11 Abilities, 200 samples each)
| Ability | Description |
|---------|-------------|
| element_grounding | Locating UI elements given instructions |
| action_understanding | Understanding what action was performed |
| page_understanding | Comprehending page content and layout |
| action_anticipation | Predicting the next action |
| intent_reasoning | Inferring user intent from actions |
| action_execution | Executing the correct action |
| dynamic_adaptation | Adapting to changing interfaces |
| task_planning | Planning multi-step tasks |
| task_progress_awareness | Tracking task completion progress |
| task_completion_detection | Detecting when a task is done |
| intern_state_memory | Remembering internal states |
## File Structure
```
├── data/
│ ├── system_eval/
│ │ ├── trajectory.json
│ │ └── gui_system.json
│ └── process_eval/
│ ├── element_grounding.json
│ ├── action_understanding.json
│ └── ... (11 ability files)
├── screenshots/
│ ├── tasks/{task_id}/step_XX.png
│ └── abilities/{ability_name}/
└── manifest.json
```
## Usage
```python
from datasets import load_dataset
# Load a specific ability
ds = load_dataset("AGI-Eval/UniGUI-Bench", "element_grounding")
# Access samples
for sample in ds["test"]:
print(sample["input"])
print(sample["target"])
print(sample["extra_data"])
```
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