# How the Arena Works

1. #### Recording

Users launch the Dojo and begin interacting with a target application. This could be a browser-based SaaS tool, a desktop program, or a game.<br>

The platform captures:

* Cursor movement and click sequences
* Keyboard inputs and field interactions
* UI layouts and screen transitions
* Timing, pauses, corrections, and multi-step workflows

2. #### Structuring

Once the demo is complete, the raw interaction data is parsed and mapped into a structured format that agents can interpret. This includes:

* Action timelines (e.g., "click this", "type that")
* Decision branches (e.g., conditional steps)
* Interface object recognition (e.g., buttons, fields, menus)

This structure forms the basis of a Behavior Tree — a modular, composable logic graph that the agent can use to reason through the task.

3. #### Generalization

The agent’s training model uses multiple demonstrations — from different users, applications, or contexts — to generalize beyond one-off examples. Over time, this builds resilience to layout changes, dynamic content, or slightly different input patterns.

The more diverse the demos, the more robust the agent becomes.

4. #### Validation

Community validators or automated test harnesses evaluate the trained agent's performance:

* Can it complete the task without intervention?
* How does it handle edge cases or interface changes?
* Does it execute efficiently and accurately?

Validations contribute to agent rankings and help determine token rewards for contributors.

5. #### Reward

Contributors receive $SOL based on:

* Quality and clarity of the demonstration
* Reusability and generalizability of the behavior
* Reward pool set by Dojo creator

This reward loop creates a powerful incentive to share real, high-quality digital workflows.


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```

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