EnlightenedAI-Lab/RAA-Reflective-Alignment-Architecture

Reflective Alignment Architecture (RAA)

A scientific framework for reflective stability, moral coherence, and frontier AI safety.

This repository contains:

• Reflective Alignment Architecture (RAA) — full specification
• Reflective Duality Layer (RDL) — mathematical stability layer
• All diagrams & figures used in the paper
• Drift, brittleness, and reflective-gradient metrics
• Example evaluation assets and future RAA-GeoMind datasets

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📄 Download the Full Paper (PDF)

Reflective Alignment Architecture — Full Specification (v1.1)
Download the full PDF

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📘 Overview

The Reflective Alignment Architecture (RAA) is a multi-layer alignment framework that explains how intelligent systems:

• self-correct,
• reason about uncertainty,
• maintain long-horizon coherence,
• avoid both drift and rigidity, and
• update reflectively rather than reactively.

It introduces five reflective functions:

• R₁ — Regulation: guardrails, safety constraints, harm-prevention
• R₂ — Reflection: self-critique, chain-of-thought inspection
• R₃ — Reasoning: structured inference, evidence tracking
• R₄ — Reciprocity: cooperative modeling of human values
• R₅ — Resonance: stable coherence under pressure & uncertainty

Together these form a reflective loop that stabilizes alignment over time.

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🧠 RDL – Reflective Duality Layer

The Reflective Duality Layer (RDL) formalizes how two perspectives inside a system
— an externalized view and an internal reflective view — interact without collapsing.

RDL introduces:

• Dual-perspective update dynamics
• Symmetry / asymmetry constraints
• Stability surfaces and phase diagrams
• Reflective coherence metrics Ψ (Care)

Care (Ψ) acts as the stabilizing parameter in high-dimension reasoning, governing when reflection improves coherence versus when it collapses into refusal, hallucination, or rigidity.

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🎨 Key Diagrams

Below are the main visual components of the architecture, grouped by theme.

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🌋 Preference Collapse Potential Well

Preference Collapse Potential Well
A stability landscape showing how human inconsistency and synthetic contamination can drive runaway reflective collapse in preference-based alignment.

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🧩 RDL & Stability Dynamics

RDL Phase Diagram — Knowledge × Uncertainty Stability
Conceptual phase diagram of stability regimes across knowledge precision (K) and uncertainty calibration (U).

Reflective Stability Contour Field (RDL Vector Landscape)
Vector field showing how systems drift toward (or away from) the high-Ψ stability band.

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🌈 5R Coherence Manifolds

5R Coherence Manifold (Reciprocity–Resonance × MCI)
Surface showing how overall moral coherence changes as reciprocity and resonance interact with the Moral Coherence Index.

Coherence Resonance Field (Human × AI Reflection)
Field showing constructive vs destructive interference between human and AI reflection.

Constructive Resonance — Human–AI Reflective Coupling
Appendix visual capturing the “coherent coupling” regime where neither side dominates and Ψ is maximized.

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🌀 Drift, Collapse & Early-Warning Indicators

Predictive Drift Timeline (Ψ, Drift Pressure, Coherence Decline)
Temporal sequence of drift: Ψ weakens first, drift pressure rises, coherence collapses last.

Corrective Compute vs Reflective Reasoning
Left: repeated filter / refusal loops.
Right: RDL-stabilized internal reasoning with low post-processing cost.

Goodhart Trajectory Map (Conceptual Illustration)
Divergence between rising proxy safety scores and declining true coherence.

Energy Burden of Misalignment vs Reflective Stability
How unstable reasoning increases compute and energy per reliable token.

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🏗️ Architecture & World-Grounding

RAA Full Architecture Stack
Developmental alignment (RDL), behavioural alignment (5R), and audit / safety infrastructure in one coherent stack.

Internal Structure – From Chaos to Coherence
Unaligned vs RDL-aligned internal reasoning networks.

The Cage Paradox — External Constraint vs Internal Reflective Stability
Caged models with unstable reasoning vs RDL-aligned reflective equilibrium.

Arc Sentinel — World-Grounded Architecture
How RAA + RDL integrate with RID-E and Arc Sentinel agents to ground alignment in real-time Earth signals.

World-State Alignment Stack
Text-only alignment stack vs world-grounded stack using real-time geospatial and ecological signals.

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📐 Ethical Profiles & Coherence Geometry

S-Series Ethical Boundary Profile
Conceptual radar plot comparing an RAA-aligned system vs a frontier snapshot across lawfulness, consent, privacy, harm avoidance, and transparency.

Triad of Coherence (K–U–Ψ Balance)
How explicit knowledge (K), contextual uncertainty (U), and stabilized humility (Ψ) interact to preserve navigability.

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📦 Included in This Repository

• Full RAA Specification (PDF)
• Full RDL Layer Description (within the same PDF)
• All major diagrams & figures (as PNG/JPG)
• Drift & brittleness metrics (conceptual)
• Stability fields & coherence manifolds
• Early-warning drift indicators
• Comparative views of developmental vs preference-based alignment
• World-grounded Arc Sentinel architecture diagrams
• Future: RAA-GeoMind datasets & LLM Judge cross-model auditing system

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🚧 Work in Progress

Planned additions:

• RAA-GeoMind geospatial alignment datasets
• Public release of LLM Judge v1
• Multi-model drift comparison dashboards
• Formal mathematical extensions of RDL & RAA
• Tutorials, notebooks, and example evaluation pipelines

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📫 Contact

Enlightened AI Research Lab

• 🌐 Website: https://www.enlightenedai.ai
• ✉️ Email: [email protected]

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📄 License

Released under the MIT License.
Feel free to adapt, reuse, and extend the concepts with attribution.