Reward Contribution Methodology

The AI Model Training Performance-Based Contribution Return (ATMTP) formula serves as the foundation for computing daily rewards within the byData ecosystem. This methodology ensures that rewards are distributed dynamically based on each participant’s computational contribution and the overall efficiency of AI training within the byData Network.

Formula and Key Components

$$CR = \text{byCode}_{activation} \times TPI \times \frac{\text{Total Daily Distribution Pool}}{\text{Total Active byCodes}}$$

Each component plays a critical role in determining how rewards are allocated to byCode holders.

• byCode Activation Represents the number of byCodes actively contributing to AI training. Increased byCode activation leads to higher participation weight in reward distribution.

• Training Performance Index (TPI) A weighted metric that measures the effectiveness of AI training cycles in the byData Network. The TPI score is influenced by:

  • The number of successful AI training cycles completed within the network.

  • The computational efficiency of AI models based on available network resources.

  • The rate of AI task completion, ensuring that contributions are linked to measurable performance improvements.

• Total Daily Distribution Pool

  The Total Daily Distribution Pool represents the allocated reward supply distributed among active byCode participants. This pool is funded by network-generated revenue streams, including AI execution fees, ecosystem transactions, and platform-based contributions. By dynamically adjusting to network activity, the distribution pool ensures a sustainable and performance-driven reward model that scales alongside byData’s AI training ecosystem.

• Total Active byCodes Represents the total number of byCodes engaged in AI training at a given time. This factor ensures proportional distribution, preventing dilution while maintaining scalability as network participation grows.

---

The ATMTP model ensures that rewards are not based on a static ROI but instead reflect real-time AI network performance. This approach provides the following benefits:

• Alignment with AI Training Efficiency Rewards are linked directly to AI model development success, ensuring that contributions actively enhance network performance.

• Scalability and Sustainability The dynamic nature of the TPI index prevents inflationary risks, ensuring that rewards remain sustainable as byData AI Agent expands.

• Fair and Transparent Distribution Earnings are determined based on computational commitment rather than a predetermined percentage, ensuring equal opportunity for all participants.

---

As the byData ecosystem scales, governance mechanisms will allow for community-driven adjustments to the TPI weighting model, ensuring that reward distribution remains equitable while incentivizing continued AI training improvements.

By implementing this performance-based computation model, byData ensures that rewards are earned through active participation, encouraging long-term engagement and AI model optimization within a decentralized framework.