Kodeus.ai
  • Abstract
  • Introduction
  • K-MAF - Core Architecture
    • Agent Creation
    • LifeWeaver – The Fostering & Nurturing Framework
    • Training and Specialization – InfinityEvolve
      • SkillCrafter: Interactive Training Modules
      • Augmentix: Agent Ability Augmentation
      • MetaMind: The Adaptive Learning Framework
    • Multi-Agent Interaction Design – NeuroNexus
    • Security, Resilience and Loyalty - NeuroGuard
      • State Management and Recovery
      • Data Integrity and Compliance
      • Loyalty and Trust Mechanisms
  • Performance Optimization
    • Memory Optimization: Smarter Data Management
    • Energy Efficiency: Doing More with Less
    • Dynamic Resource Allocation: Smart and Flexible
    • Learning Optimization: Faster and Smarter Training
    • Scalability and Future-Proofing
  • Plugins and Integrations
  • Agentic Kodeus Protocol on Blockchain
    • NFT-Based Agent Representation (ERC721)
    • Token Bound Accounts (ERC4337) for Autonomous Operations
    • Agent Lifecycle and Ownership Transfer
    • Tokenized Economy and Agent-Specific Tokens
    • Leasing and Delegation Capabilities
    • Security and Compliance
  • The Kodeus Edge
    • Deployment in a Containerized Environment
    • System-Level Interactions
    • Advanced Automation and Decision-Making
    • Scalability and Portability
    • Enhanced Programming and Customization Capabilities
    • Gene-Based Agent Creation
    • Hierarchical Reinforcement Learning (HRL)
    • Decentralized Knowledge Base (DKB)
    • Integration Capabilities
Powered by GitBook
On this page
  1. Performance Optimization

Memory Optimization: Smarter Data Management

PreviousPerformance OptimizationNextEnergy Efficiency: Doing More with Less

Last updated 3 months ago

At the heart of K-MAF lies a hierarchical memory system that prioritizes critical information while offloading less-relevant data. This approach ensures agents can process and store information without overloading system resources.

  • Hierarchical Memory Model: Agents use a tiered memory structure:

  • Active Memory holds immediate task-relevant data, ensuring quick retrieval during execution.

  • Short-Term Memory retains contextual data from recent tasks for continuity.

  • Long-Term Memory stores processed knowledge, which is offloaded to the Decentralized Knowledge Base (KB) for future use.

  • Memory Retrieval Efficiency: A probabilistic retrieval model ranks memory entries based on their relevance to the current task: Here, wT represents task-specific weights, and vm is the vector representation of memory mm. This ensures that agents access the most relevant information quickly.