🪧Demonstrations

The core of the Training Gym is the demonstration system, where users record themselves completing tasks so that AI can learn from real human interactions. Every demonstration is evaluated using a grading system that determines reward payouts and AI training effectiveness.

This ensures that only high-quality demonstrations improve AI models, while contributors are fairly compensated in $VIRAL based on their performance.

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How Demonstrations Work?

1. Users Record Demonstrations

• Contributors perform a task on their computer while the system records every action.

• The demonstration captures clicks, keystrokes, UI navigation, and task execution.

• The AI observes and processes how humans complete tasks.

📌 Example: A user records a demonstration of sending a Solana transaction using Phantom Wallet, navigating through wallet settings, entering recipient addresses, and confirming fees.

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2. Processing & Structuring Data for AI Training

• After recording, users process their demonstration to structure it into clear, repeatable steps AI can learn from.

• AI models analyze workflow sequences, decision-making logic, and UI interactions, allowing them to mimic human behavior efficiently.

• Contributors can review their submission to ensure it’s accurate and useful.

📌 Example: The system learns to break the Solana transaction prompt into structured steps like “Open Phantom Wallet,” “Enter Recipient Address,” “Review Gas Fees,” and “Confirm Transaction.”

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3. Submission & Quality Review

• Once processed, users upload their demonstration for AI training.

• Each submission is evaluated by ViralMind’s data quality agent, which grades it based on clarity, accuracy, and effectiveness.

• The higher the quality, the better the AI learns and the greater the reward for the contributor.

📌 Example: A well-structured Solana transaction demo receives an 85% quality rating, qualifying for near-max rewards.

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Grading System: How Demonstrations Are Scored

Each uploaded demonstration is scored by an AI-powered data quality agent. The grading process evaluates the submission across multiple dimensions:

🔹 1. Clarity & Step-by-Step Execution (40%)

• Are the actions performed in a clear, structured, and repeatable way?

• Does the demonstration include all necessary steps without skipping any?

• Is the recording free of unnecessary delays or misclicks?

📌 Example: A contributor records a clear, step-by-step demonstration of sending crypto without extra delays → High Score

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🔹 2. Accuracy & Task Completion (30%)

• Did the user correctly complete the task from start to finish?

• Is the workflow accurate and applicable to real-world use?

• Are errors corrected quickly without affecting the AI’s ability to learn?

📌 Example: A user enters a wrong wallet address but fixes it immediately and completes the transaction successfully → Good Score

📌 Example: A user submits a demonstration with missing steps, like forgetting to confirm a transaction → Low Score

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🔹 3. AI Training Usefulness (20%)

• Is this demonstration generalizable so AI can apply it to different cases?

• Does it help the AI recognize patterns in human decision-making?

• Is it a new, valuable contribution, or a duplicate of an existing submission?

📌 Example: A unique demonstration of interacting with a complex UI workflow → High Score

📌 Example: A duplicate of an existing task without meaningful variation → Low Score

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🔹 4. Efficiency & Flow (10%)

• Was the demonstration efficiently completed without unnecessary delays?

• Did the user execute the task smoothly without excessive hesitations?

• Was the workflow consistent and optimized for AI learning?

📌 Example: A user executes a workflow quickly and effectively without mistakes → High Score

📌 Example: A user takes too long or has inconsistent actions, making it hard for AI to learn → Low Score

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Reward Payouts Based on Grading

The higher the quality of a demonstration, the higher the payout.

Quality Score	Reward Payout	Notes
90-100%	100% payout	Perfect execution, maximally useful AI training
80-89%	85% payout	High quality, minor inefficiencies or small errors
70-79%	70% payout	Good submission, may need slight improvements
50-69%	50% payout	Basic level, needs optimization
Below 50%	No payout	Poor quality, refunded to training pool

📌 Example:

A demonstration worth $0.20 per submission gets graded at 85%.

The worker receives $0.17, and $0.03 is refunded to the training pool.

📌 Example:

A low-quality demonstration graded at 40% receives no reward, and the full $0.20 is refunded to the pool for future high-quality submissions.

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Dynamic Reward Pool Efficiency

• Unused funds from low-quality submissions are returned to the Gym’s training pool, ensuring that AI only learns from high-quality data.

• This incentivizes contributors to submit clear, structured, and useful demonstrations, maximizing AI performance.

• Gym owners can adjust reward structures, increasing payouts to attract better contributors.

📌 Example: A Gym not receiving enough high-quality submissions increases its bid from $0.20 per demo to $0.30, bringing in more skilled trainers.