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)
    • 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. AI Learning & Quality Control

Data Collection Standards

SageUnion is committed to maintaining a high standard of information quality within its ecosystem. To achieve this, the platform enforces clear and transparent data collection standards:

  • Relevance: Submitted data must directly respond to the weekly question and contribute meaningful insights.

  • Accuracy: Information must be fact-based, verifiable, and free from intentional misinformation.

  • Originality: Responses must reflect the contributor’s own understanding, analysis, or research, avoiding plagiarism or AI-generated spam.

  • Clarity: Submissions should be well-structured and easily understandable by other users and the AI evaluation engine.

  • Language Compliance: The platform supports multilingual submissions but may initially require responses in specific languages to ensure evaluation accuracy.

Before AI evaluation, all user-submitted content undergoes a preprocessing phase to remove spam, irrelevant content, and low-effort responses that do not meet these standards.

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Last updated 1 month ago