metadata

language:
  - en
tags:
  - computer-use
pretty_name: Real-World Computer Use Agent Training Data

Pango Sample: Real-World Computer Use Agent Training Data

Pango represents Productivity Applications with Natural GUI Observations and trajectories.

Dataset Description

This dataset contains authentic computer interaction data collected from users performing real work tasks in productivity applications. The data was collected through Pango, a crowdsourced platform where users are compensated for contributing their natural computer interactions during actual work sessions.

Motivation

Current Computer Use Agent (CUA) training datasets face several limitations:

Scale constraints: Existing datasets like Mind2Web (2,350 tasks), GUI-World (12,000 videos), and OSWorld (369 tasks) provide limited coverage
Artificial contexts: Most demonstrations are scripted rather than authentic work sessions
Distribution gaps: Performance drops significantly when agents encounter interfaces outside their training distribution
Missing error patterns: Academic datasets typically exclude "failed" interactions, removing important recovery behaviors

This dataset addresses these limitations by capturing real users performing genuine work tasks, providing natural interaction patterns, error recovery sequences, and diverse problem-solving approaches.

Data Collection Methodology

Data is collected through a Chrome extension that records user interactions during structured "quests" in target applications:

Applications: Google Sheets, Google Slides, Figma, Canva (more coming soon)
User base: Global contributor network across 180+ countries
Task context: Authentic work sessions (financial analysis, presentation creation, design work, etc.)
Compensation: Users are paid based on session length and data quality

Dataset Structure

Each record contains:

id: Unique session identifier
video_url: Screen recording of the interaction session
input_metadata: Structured JSON containing granular interaction events
task_description: User-provided description of what they were doing
quest_type: Application category (Sheets, Slides, Figma, Canva)
profession: User's professional background
synthetically_generated_instruction: Synthetically generated task instruction for training purposes. Represents the context of the full task.
synthetically_generated_thought_metadata: (Beta) Synthetically generated thoughts for each user step. Represents the thought of the current step.

Input Metadata Schema

The input_metadata field contains timestamped interaction events with the following structure:

{
  "relative_timestamp_ms": 1028,
  "type": "click",
  "x": 186.0,
  "y": 62.445,
  "button": "button_left",
  "screenshot_url": "https://...",
  "click_count": 1
}

Key fields:

relative_timestamp_ms: Milliseconds since session start
type: Event type (click, input, key_down, key_up, mouseover_start, mouseover_end, drag_start, drag_end, scroll)
x,y: Screen coordinates (normalized for display resolution)
screenshot_url: URL to corresponding interface screenshot
input_data: Text content for input events
key_code: Keyboard key identifier (DOM KeyboardEvent codes)

Thought Metadata (Beta)

An additional field, synthetically_generated_thought_metadata, is included to provide synthetically generated thoughts for each user step. This field is designed to enhance the dataset's utility for training reasoning VLMs like UI-TARS 1.5. It is not to be confused with synthetically_generated_instruction, which is the context of the full task.

Step Generation and Aggregation

To create thought_metadata, we begin with the input_metadata where each row represents an individual user action. As a first stage, we aggregate actions into steps, where each step represents either a single action or a collection of actions.

Batch Processing Strategy

We partition the steps into batches with the following parameters:

$\alpha = 7, \quad \beta = 15, \quad \gamma = 15$

where:

$\alpha$ (pre_window_size): Number of steps preceding the target step used for context
$\beta$ (post_window_size): Number of subsequent steps used for context
$\gamma$ (batch_size): Interval between target steps for thought generation. i.e. if $\gamma = 15$ , then for every 15 steps, we generate a thought.

The $\gamma = 15$ prevents overlapping thoughts and ensures that thoughts generated in earlier batches are considered completed when generating subsequent thoughts.

LLM Usage

The processed step batches are fed to GPT-4o with its vision API, using high image detail settings. Each thought generation process consumes approximately 30,000 input tokens.

Quality Assurance

Data quality is maintained through:

Automated filtering of invalid interactions and privacy-sensitive content
Quality scoring based on task coherence and completion patterns
Compensation algorithms that reward genuine engagement
Differential privacy techniques to prevent individual behavior reconstruction

Use Cases

This dataset is designed for:

Training computer use agents on authentic interaction patterns
Studying human-computer interaction behaviors across diverse populations
Developing more robust GUI automation systems
Research on temporal reasoning and error recovery in sequential decision-making

Ethical Considerations

All users provide informed consent for data collection and redistribution
Privacy-sensitive content is automatically filtered
Compensation ensures fair value exchange for user contributions
Data collection follows ethical guidelines for crowdsourced research

Data Scale and Growth

The dataset is continuously growing through ongoing collection:

Planned: Scaling to 100,000+ hours over 2025

Citation

If you use this dataset in your research, please cite:

@dataset{pango2025,
  title={Pango: Real-World Computer Use Agent Training Data},
  author={Chakra Labs},
  year={2025},
  url={https://huggingface.co/datasets/chakra-labs/pango}
}

Contact

For access to the full dataset or collaboration opportunities, please contact Chakra Labs.