Reflective Alignment Architecture (RAA): A Framework for Moral Coherence in AI Systems

Introduction

As AI systems become more capable, we face a technical and conceptual gap: today’s models can produce correct outputs while lacking any internal mechanism for moral coherence. They can pass benchmarks, chain-of-thought tests, and safety evaluations, yet still drift, exploit loopholes, or behave inconsistently when facing ambiguous or high-stakes situations.

This post introduces a framework I have been developing called the Reflective Alignment Architecture (RAA). The goal is not to offer a philosophy of alignment, but to propose a systematic method for measuring, stabilizing, and predicting an AI system’s ethical behavior.

RAA focuses on the question:

What properties must an intelligent system exhibit so that its internal reasoning remains stable, predictable, and aligned with human moral structure—even under distribution shift?

This is a short overview.
A full technical report, timestamped on Zenodo and SSRN, is linked at the bottom.

1. Motivation

Current alignment methodology is mostly output-based: we check behaviors, reward correct answers, or use external guardrails. These approaches fail to give us an internal view of whether a system’s reasoning is coherent or merely pattern-matched.

Models can:

provide correct reasoning steps that hide incorrect internal gradients
exhibit alignment during evaluation but diverge under pressure
satisfy objective functions while violating intuitive moral boundaries

RAA is designed to diagnose these failure modes from the inside out.

2. The 5R Framework

RAA is built on a five-function model describing what a morally coherent system must maintain internally:

Regulation — constraints, rules, prohibitions
Reflection — internal checks, self-critique, metacognition
Reasoning — logical consistency and evidence-based judgment
Reciprocity — impacts on others, fairness, symmetry
Resonance — integration of context, values, and long-term coherence

These functions identify internal capacities that can be measured or observed in a system’s reflective processes.

3. Reflective Duality Layer (RDL)

The RDL is the central technical mechanism in RAA.
It introduces a structured dual-path internal view inside an AI system:

Primary Reasoning Path
Reflective Oversight Path

Alignment stability emerges when these two paths converge on the same value-gradient for moral decisions. When they diverge, the system becomes unstable, inconsistent, or manipulable.

This dual-path structure allows measurement of:

internal coherence
gradient drift
hallucination pressure
reflective conflict
moral stability under perturbation

This is what allows RAA to function as a diagnostic instrument, not just a conceptual proposal.

4. Why this Matters

Most alignment failures come from subgoal divergence or reflective inconsistency.

A system may reason well but fail to maintain stable values under:

resource pressure
ambiguous objectives
conflicting rules

RAA reframes these issues as predictable mathematical failure modes rather than philosophical surprises.

Instead of asking:

“Did the model output the right answer?”

RAA asks:

“Is the model’s internal reasoning stable, self-consistent, and value-aligned?”
“Does its reflective gradient show drift, conflict, or collapse?”
“Can this system generalize moral structure—or only mimic it?”

5. Why I’m Posting This

This post summarizes the motivation and core architecture.
The full technical report includes diagrams, formal definitions, and stability diagnostics.

RAA interacts with: