Sage Union Whitepaper
  • Introduction
    • Project Overview
    • Problem Statement
    • Vision & Mission
  • Market Overview
    • Current Issues in the Information Ecosystem
    • Potential of Human-AI Collaboration
    • Competitor Analysis & Differentiation
  • SageUnion Solution
    • System Components
    • Human-AI Collaboration Model
    • Definition & Creation of High-Quality Information
  • Platform Architecture
    • Telegram Mini-App Structure
    • Data Collection & Processing Workflow
    • AI-Based Information Evaluation Mechanism
    • Reward Distribution Logic
  • Tokenomics
    • Token Overview (SAGU)
    • Total Supply & Distribution Plan
    • Token Utility & Rewards
    • Revenue Model & Sustainability
  • Governance & Community
    • User Participation Structure
    • Voting & Decision-Making Mechanism
    • Community Incentives
  • AI Learning & Quality Control
    • Data Collection Standards
    • AI Evaluation Logic
    • Continuous Learning Framework
  • Roadmap
    • Development Phases
    • Beta Launch & Official Release Timeline
    • Long-term Vision
  • Team & Partnerships
    • Team Introduction
    • Advisory Board & Partners
    • Strategic Partnerships Plan
  • Conclusion
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  1. Platform Architecture

Reward Distribution Logic

SageUnion’s reward distribution system is fully automated and decentralized, ensuring that users receive fair compensation based on the quality of their contributions.

Reward Allocation Process:

  1. Quality-Based Scoring: Each submission is assigned a score by the AI Evaluation Engine.

  2. Reward Pool Calculation: A predetermined amount of SAGU tokens is allocated to a weekly reward pool.

  3. Proportional Distribution: Users receive a share of the reward pool in proportion to their evaluation score relative to the total score of all participants for that week.

  4. Token Transfer: Rewards are automatically distributed to users’ BSC wallets via smart contracts after the evaluation period.

  5. Incentive Adjustment (Optional): The reward pool and scoring criteria may be dynamically adjusted based on community feedback and platform growth to maintain long-term sustainability.

This reward mechanism ensures that contributors are incentivized to provide accurate, high-quality information and actively participate in the platform’s ecosystem.

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Last updated 6 days ago