# Workflow Overview

The KCG+CAG system introduces an optimized workflow that allows Tiny LLMs to acquire, reason over, and integrate fresh knowledge without the need for retraining. The process leverages multi-layer caching, Selective Contextual Reasoning (SCR), and Distillation on Demand (DoD) to ensure knowledge freshness, verifiability, and efficiency.

# Full Knowledge Lifecycle Workflow

# 1. DoD Request Trigger

• A Tiny LLM or DoD Agent identifies a knowledge gap, outdated fact, or complex reasoning need.
• It initiates a DoD query, requesting knowledge retrieval, validation, or distillation.

# 2. SCR Reasoning Pipeline via Gateway

• The DoD Agent or Tiny LLM triggers the Selective Contextual Reasoning (SCR) pipeline via the Gateway:
  • Semantic retrieval from the KCG KV-layer and Gateway off-chain index.
  • Filtering and confirmation step performed locally by the agent or Tiny LLM.
  • Contextual reasoning using enriched prompt with retrieved knowledge.
• If sufficient reasoning is possible using the local or Gateway caches, the process completes without invoking Big LLMs.

# 3. Fallback to Big LLM (if necessary)

• For novel, ambiguous, or high-confidence-required queries, the DoD Agent escalates the request to selected Big LLM APIs (GPT, Claude, Gemini, etc.).
• Multiple models may be queried and compared.

# 4. Knowledge Distillation Proposal

• Based on SCR or Big LLM outputs, the DoD Agent synthesizes a distilled knowledge proposal.
• The proposal includes:
  • Summary.
  • Sources and evidence.
  • Semantic relations and context.
  • Optional embeddings for KV-caching.

# 5. Gateway Validation and Recording

• The proposal is submitted to the Gateway.
• The Gateway performs validation steps:
  • Verifies data integrity and formatting.
  • Checks evidence, references, and originality.
  • Optionally invokes Validator consensus for high-value knowledge.
• If validated, the Gateway records the entry into the KCG (Arweave).
• A corresponding KV entry is updated in the public KCG KV-layer for efficient future retrieval.

# 6. Confirmation and Reward Distribution

• The DoD Agent receives the finalized KCG TXID as confirmation.
• Validators and Gateways receive incentives for their role.
• A portion of tokens from the DoD query are burned to maintain deflationary tokenomics.

# Key Optimizations in the Workflow

• SCR-first by default reduces calls to Big LLM by up to 80%.
• Multi-layer caching ensures fastest possible retrieval for repeated or similar queries.
• Federated Gateway layer enables scaling and redundancy of knowledge retrieval and validation services.
• Immutable KCG layer ensures global, shared, and verifiable knowledge memory for all participants.

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