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Incentives & Rewards

Dynamic Fee Structure

Transaction Types

  1. Data Contribution Fee: Paid by contributors submitting training data

    • Base: 0.1 ASHA per contribution
    • Multiplier: Based on data quality score (0.5x to 2.0x)
  2. Query Fee: Paid by users requesting Genome inference

    • Simple query: 0.01 ASHA
    • Complex inference: 0.1-1.0 ASHA
    • Real-time computation: 1.0-10.0 ASHA
  3. Model Update Fee: Gas cost for updating Genome on-chain

    • Paid by validators, reimbursed from reward pool
  4. Governance Fee: Cost to submit DAO proposals

    • Base: 100 ASHA (anti-spam)
    • Refunded if proposal passes

Reward Distribution (AI-Optimized)

Fee Distribution (AI-adjusted per epoch)

  • Data Contributors: 40-60% (quality-weighted)
  • Compute Validators: 20-35% (work-based)
  • Development & Maintenance: 10-20%
  • DAO Governance: 5-15%
  • Protocol Reserve: 5-10%

Quality Metrics

Quality Score = w₁·Accuracy + w₂·Uniqueness + w₃·Relevance + w₄·Volume

Component Definitions:

  • Accuracy: How much does data improve model performance? (Validation loss reduction)
  • Uniqueness: How rare/novel is this data? (Distance from existing distribution)
  • Relevance: How useful for current model priorities? (Alignment with goals)
  • Volume: How much data provided? (Logarithmic scale)

Reputation Multipliers

  • New contributor: 0.8x (probationary)
  • Established (>10 contributions): 1.0x
  • Trusted (>100 contributions, high quality): 1.2x
  • Elite (>1000 contributions, consistently high): 1.5x
  • Flagged (suspicious activity): 0.5x
  • Banned (proven malicious): 0x

Cross-Domain Incentive Balancing

Domain-Specific Pools

  • Healthcare: 25% of contribution rewards
  • Finance: 20% of contribution rewards
  • Navigation: 15% of contribution rewards
  • Disaster Prediction: 15% of contribution rewards
  • General Purpose: 25% of contribution rewards

Dynamic Rebalancing

  • If domain underserved: Increase reward multiplier
  • If domain saturated: Decrease reward multiplier
  • Adjustments weekly based on model performance gaps