Powered by

# Technical Architecture Overview

The KCG+CAG ecosystem is composed of several interconnected layers and actors, forming a decentralized, efficient, and verifiable knowledge reasoning pipeline optimized for Tiny LLMs.

# Components Overview

# Knowledge Cache Graph (KCG)

  • Immutable Knowledge Layer: Stored on Arweave.
  • Stores:
    • Distilled knowledge entries (facts, QA pairs, reasoning chains).
    • Semantic relations and ontologies.
    • KV-cache entries for fast retrieval (keyed by embeddings, queries, or topics).
    • Proof-of-knowledge and validator signatures.
  • Public and verifiable: Accessible by any Gateway, agent, or user.

# Cache-Augmented Generation (CAG) Layer

  • Reasoning Layer on top of KCG and KV-caches.
  • Enables Tiny LLMs to perform fast retrieval and reasoning over verified knowledge without retraining.
  • Utilizes Selective Contextual Reasoning (SCR) pipelines:
    • Semantic retrieval.
    • Filtering and confirmation.
    • Context-enriched generation.

# Distillation on Demand (DoD) Agents

  • Specialized agents or Tiny LLMs acting as initiators of DoD queries.
  • Responsible for detecting outdated knowledge, gaps, or novel queries.
  • Orchestrate SCR reasoning pipelines.
  • Propose distilled knowledge entries to Gateways for validation.

# Gateways

  • Federated nodes acting as intermediaries between DoD agents and KCG.
  • Responsible for:
    • Knowledge validation and packaging.
    • Recording entries into KCG.
    • Managing off-chain semantic indexes and vector stores for ultra-fast retrieval.
    • Serving enriched knowledge and SCR-ready prompts to Tiny LLMs and agents.
  • Gateways control write-access to KCG and enforce quality standards.

# Validator Nodes

  • Participate in consensus voting, knowledge verification, and proof-of-knowledge processes.
  • Ensure correctness, prevent spam, and provide auditability.
  • Optionally contribute to off-chain indexing or SCR pre-computation services.

# Hybrid KV-Cache Architecture

  • On-device Tiny LLM KV-cache: Fast, personalized cache of frequently used knowledge.
  • Gateway KV-cache: High-performance, shared caching of validated knowledge and reasoning shortcuts.
  • Public KV-layer in KCG: Immutable, community-validated cache ensuring knowledge persistence and transparency.

This modular, multi-layered architecture ensures:

  • Tiny LLMs can reason, learn, and update dynamically without retraining.
  • Gateways and validators enforce knowledge quality and validation.
  • Caching at multiple layers reduces inference costs, latency, and dependence on Big LLMs.