iAgent whitepaper
iAgent whitepaper
  • Introduction
  • Philosophy
    • Problem & Solution
    • Vision & Mission
  • Product and Ecosystem
    • Visual Learning Model (VLM)
    • ERC-AI Agent Standard
    • AI Agent Marketplace
    • Data Collection & Labeling
    • iAgent Dev Hub
  • Visual Learning Model Explained
  • iAgent Protocol Explained
  • Data Contribution Explained
  • Protocol Governance
    • Governance Framework
    • Governance Core
    • Privacy and Security
    • Terms & Conditions
  • Protocol Nodes
    • Node Deployment
    • Video Tutorial
    • FAQs
    • Key Aspects
    • Consensus Algorithms
    • Reward Calculation
    • How to KYC
    • Purchase and Sale Agreement
  • $AGNT Tokenomics
    • AGNT Contract
    • AGNT Vesting
    • AGNT Emission
    • AGNT Solo Staking
    • AGNT Liquid Staking
    • AGNT Governance
  • Roadmap
  • Conclusion
  • Official Links
Powered by GitBook
On this page
  1. Product and Ecosystem

Visual Learning Model (VLM)

PreviousProduct and EcosystemNextERC-AI Agent Standard

Last updated 2 months ago

The Adaptive AI Training framework, powered by Visual Learning Models (VLMs), allows AI agents to evolve dynamically by analyzing real-world gameplay footage. This revolutionizes AI by enabling agents to adapt, strategize, and optimize their behavior in real-time.

How It Works:

  • Real-Time Gameplay Learning: AI models process live and recorded gameplay data to understand mechanics and player actions.

  • Continuous Evolution: Reinforcement learning fine-tunes AI behavior based on in-game interactions.

  • On-Chain Validation: AI improvements and training data are recorded transparently, ensuring verifiability.

  • Scalability Through DePIN: AI training is distributed across 3,500+ decentralized compute nodes for efficiency and cost reduction.

AI Agents for Gaming – Intelligent Digital Assistants

AI agents trained via VLM go beyond traditional NPCs, offering:

  • Personalized Game Assistants: AI-powered coaching and real-time strategy insights.

  • Predictive Analytics: AI-driven market trend predictions and player behavior modeling.

  • Anti-Cheat Systems: AI models detect cheating patterns, maintaining fair play.

  • Game Moderation & Safety: NLP-powered AI filters harmful content and ensures a safer multiplayer experience and much more.

Why It’s Revolutionary:

  • Transforms gaming AI from scripted NPCs to dynamic, evolving entities.

  • Enhances player experiences through real-time AI adaptation.

  • Brings AI to Web3 gaming with verifiable, on-chain model evolution.

ComputeHub
Page cover image