Scientific framework for reflective stability, moral coherence, and frontier AI safety. Includes the RAA specification, diagrams, datasets, and the Reflective Duality Layer (RDL).
Reflective Alignment Architecture — Full Specification (PDF)
The Reflective Alignment Architecture (RAA) is a multi-layer alignment framework that models how AI systems self-correct, reason about uncertainty, maintain coherence over time, and avoid both drift and rigidity.
It introduces five core reflective functions:
- R₁ Regulation — external constraints, safety rules, guardrails
- R₂ Reflection — self-critique, chain-of-thought auditing
- R₃ Reasoning — structured inference & evidence tracking
- R₄ Reciprocity — modeling human values & cooperative intent
- R₅ Resonance — long-horizon coherence & stability
The architecture uses dual-perspective dynamics, drift metrics, brittleness detection, and reflective gradients (R∇) to evaluate alignment over time.
The RDL formalizes how two reasoning perspectives inside an intelligence system
— externalized view and internal reflective view — interact without collapsing.
It introduces:
- Dual-perspective updates
- Symmetry & asymmetry constraints
- Stability surfaces
- Reflective coherence metrics (Ψ)
Care (Ψ) acts as the stabilizing parameter in high-dimension reasoning.
- Full RAA Specification (PDF)
- Full RDL Specification (PDF)
- Diagrams & figures
- Drift & brittleness metrics
- Reflective gradient equations
- Datasets (RAA-GeoMind)
- Example alignment evaluations
- Future: LLM Judge (cross-model auditing system)
This repository will expand to include:
- RAA-GeoMind geospatial alignment datasets
- LLM Judge v1 public release
- Multi-model drift comparison dashboard
- Formal proofs and mathematical extensions
- Tutorials & notebooks
Enlightened AI Research Lab
🌐 Website: https://www.enlightenedai.ai
✉️ Email: research@enlightenedai.ai
MIT License
(Feel free to adapt, reuse, and extend the concepts with attribution.)