XAI.Expert

CDC / DCG Explainable & Computable Framework

This demo shows how to use CDC / DCG domain context graphs for explainable and computable analysis. Choose a mode from the left to try the demos.

CDC Cognitive Loop

Concept · Domain · Context

  • Identify key concepts and relations inside a specific Domain
  • Chain facts, intents and strategies through the CDC loop to form panoramic reasoning
  • Support cross-domain transfer while keeping semantic traceability

Domain Context Graph (DCG)

Domain Context Graph

  • Functor mappings across Domains guide reasoning into new contexts
  • Nodes represent concepts, edges record relation types and verification evidence
  • Creates navigable knowledge paths for replay and reuse

Explainability

Explainability Protocol

  • All conclusions carry source references and verification status
  • Phased outputs: problem decomposition → evidence aggregation → CDC synthesis
  • Can be mapped directly to organizational processes or expert review norms

Computability

Computability

  • CDC/DCG structures can be programmatically parsed and replayed
  • Support turning analysis into decision trees, vector search or automation
  • Compatible with multimodal inputs: text, regulations, metrics

Usage Flow

  1. 1Pick a mode from the left navigation (Legal / Medical / Learning / Search)
  2. 2Describe your question or research topic in the mode's input box
  3. 3The system runs CDC prompts and toolsets to perform zero-hallucination analysis
  4. 4Result pages stream raw CDC outputs for traceability and review

Note: Example content is for research/demo use and does not constitute real medical or legal advice.