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Hanzo AI Architecture & Framework

Hanzo AI Team
Created: 2024-12-20

HIP-0: Hanzo AI Architecture & Framework

Abstract

This document outlines the Hanzo AI architecture, development framework, and the Hanzo Improvement Proposal (HIP) process. It serves as the foundational reference for understanding Hanzo's AI infrastructure, multimodal capabilities, and community governance model.

Hanzo AI Overview

Evolution: From Web2.0 to Blockchain AI

Hanzo began as hanzo.ai in the Web2.0 era, pioneering AI infrastructure and services. We've now evolved to hanzo.network, launching initially as L2 on Lux Network with a clear path to sovereign L1 status, bringing our AI expertise to the blockchain ecosystem.

Timeline:

  • Web2.0 Era (hanzo.ai): AI infrastructure, enterprise services, and foundational model development
  • Blockchain Launch (hanzo.network): Initial L2 deployment on Lux Network
  • Sovereignty Upgrade: Forthcoming transition to full sovereign L1
  • Current Architecture: Hybrid web2/web3 with progressive decentralization

Current Architecture

Hanzo operates as an L2 on Lux Network with a sovereign L1 upgrade path:

Phase 1 (Live Now):

  • L2 EVM chain deployed on Lux Network
  • AI infrastructure and $AI tokenomics active
  • Integration with Lux infrastructure services

Phase 2 (Next):

  • HMM chain launch with native DEX functionality
  • Sovereign L1 upgrade maintaining Lux integration

Key Components:

  1. Blockchain: L2 on Lux Network → Sovereign L1 (next)
  2. HMM DEX: Native exchange for AI compute resources
  3. AI Models: Multimodal (text/vision/audio/3D)
  4. Ownership: Per-user model forks as assets
  5. Security: Quantum-safe via Lux Q-Chain rollups
  6. Token: $AI for governance, compute, training

Lux Infrastructure (see Lux LIPs):

  • Wallet: Multi-sig via Lux Safe (LIP-1)
  • Exchange: $AI trading on lux.exchange (LIP-2)
  • Bridge: Cross-chain via Lux Bridge (LIP-3)
  • Identity: SSO via Lux ID (LIP-4)
  • Consensus: Quasar photonic selection (LIP-5)

Technical Architecture

Current Status: L2 EVM chain on Lux Network (Live)
Next Phase: HMM chain launch with sovereign L1 upgrade
Consensus: Proof of Compute (PoC) - miners provide compute
Block Time: 2 seconds
Finality: Instant (single-slot)
Validators: Compute providers with GPU resources
Native Token: $AI
Quantum Safety: Lux Network Q-Chain quantum rollups
Compute DEX: HMM (Hanzo Market Maker) for AI resources

Upgrade Path:
  Phase 1 (Live): L2 EVM chain on Lux
  Phase 2 (Next): HMM chain + Sovereign L1

Proof of Compute (PoC) Consensus

Hanzo uses Proof of Compute where miners provide AI compute resources to secure the network:

PoC Protocol:
  - Compute Mining: Miners provide GPU/TPU resources for AI tasks
  - Work Validation: Cryptographic proof of computation performed
  - Rewards: $AI tokens for validated compute contributions
  - Quality Metrics: Performance-based reward adjustments
  - Resource Types:
    - Model training compute
    - Inference serving
    - Fine-tuning operations
    - Embedding generation

Quantum Safety via Q-Chain

Hanzo achieves full quantum safety through Lux Network's Q-Chain quantum rollups:

Q-Chain Integration:
  - Quantum Rollups: All transactions quantum-resistant
  - PQC Algorithms: NIST-approved ML-KEM/ML-DSA
  - Cross-Chain Security: Quantum safety across EVM and HMM chains
  - Future-Proof: Ready for quantum computing era
  - Zero-Knowledge: Optional ZK proofs for privacy

HMM Native DEX

The HMM (Hanzo Market Maker) is our native decentralized exchange specifically designed for AI compute resources:

HMM Features:
  - Compute Resource Trading: Buy/sell GPU time, model inference, training slots
  - Dynamic Pricing: Market-based pricing for AI compute
  - Resource Pools: Liquidity pools for different compute types
  - Instant Settlement: Sub-second compute allocation
  - Quality Metrics: Performance-based pricing adjustments
  - Cross-Chain Bridge: Access compute from Lux, Ethereum, and other chains
  - Quantum-Safe: All transactions protected by Q-Chain

Architecture Components

Core Infrastructure

hanzo/
├── llm/              # LLM gateway and routing
├── agent/            # Agent SDK and orchestration
├── mcp/              # Model Context Protocol
├── jin/              # Multimodal framework
├── search/           # AI-powered search
├── platform/         # PaaS infrastructure
└── node/             # Hanzo Node with PQC

Hanzo Multimodal Models (HMMs)

Hanzo's proprietary multimodal AI models supporting:

  • Text: Natural language understanding and generation
  • Vision: Image understanding and generation
  • Audio: Speech recognition and synthesis
  • 3D: Spatial understanding and generation
  • Cross-modal: Unified representations across modalities

Agent Framework

  • Autonomous Agents: Self-directed task completion
  • Tool Use: Integration with external tools and APIs
  • Memory Systems: Long-term and working memory
  • Planning: Multi-step reasoning and execution
  • Collaboration: Multi-agent coordination

Security Infrastructure

  • Post-Quantum Cryptography: NIST-compliant ML-KEM/ML-DSA
  • TEE Integration: Secure enclaves for sensitive operations
  • Privacy Tiers: Adaptive security levels
  • Key Management: Hanzo KBS for secure key handling

HIP Process

Proposal Lifecycle

  1. Idea: Community discussion and refinement
  2. Draft: Formal proposal creation
  3. Review: Technical and community review
  4. Last Call: Final review period (14 days)
  5. Final: Accepted and ready for implementation
  6. Superseded: Replaced by newer proposal

Proposal Types

  • Standards Track: Technical specifications
  • Meta: Process and governance
  • Informational: Best practices and guidelines

Numbering Convention

  • 0-99: Core infrastructure and governance
  • 100-199: AI models and architectures
  • 200-299: Agent frameworks
  • 300-399: Tools and integrations
  • 400-499: Security and privacy
  • 500+: Application standards

Development Principles

AI-First Design

  • Every component designed for AI workloads
  • Optimized for inference and training
  • Multimodal by default

Scalability

  • Horizontal scaling for inference
  • Distributed training support
  • Edge to cloud deployment

Interoperability

  • Open standards (MCP, OpenAI API)
  • Multiple model provider support
  • Cross-platform compatibility

Security

  • Quantum-resistant by design
  • Defense in depth
  • Privacy-preserving computation

Community Governance

Decision Making

  • Rough consensus model
  • Technical merit primary consideration
  • Community input via forums and discussions

Roles

  • Authors: Propose and maintain HIPs
  • Editors: Review and merge proposals
  • Implementers: Build HIP specifications
  • Community: Provide feedback and consensus

Communication Channels

  • GitHub: Code and proposals
  • Forum: Long-form discussions
  • Discord: Real-time chat
  • Twitter: Announcements

Implementation Requirements

For HIP Authors

  1. Clear problem statement
  2. Detailed specification
  3. Security considerations
  4. Test cases
  5. Reference implementation (when applicable)

For Implementers

  1. Follow HIP specifications exactly
  2. Include comprehensive tests
  3. Document deviations
  4. Provide migration guides

Future Direction

Short-term (Q1-Q2 2025)

  • HMM v1.0 release
  • Agent framework standardization
  • MCP full integration

Medium-term (Q3-Q4 2025)

  • Distributed inference protocol
  • Federated learning support
  • Advanced multimodal capabilities

Long-term (2026+)

  • AGI research initiatives
  • Neuromorphic computing
  • Quantum AI algorithms

References

  1. Hanzo AI Documentation
  2. Hanzo Node Repository
  3. Model Context Protocol
  4. NIST PQC Standards

Copyright

Copyright and related rights waived via CC0.

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