Designing a Tri-Core AI Decision Engine (AREKON–AHORA–ELANO): Stabilizing Cognitive Behavior with a Golden Ratio Constant

A Three-Core Decision Engine (AREKON–AHORA–ELANO): Achieving Behavioral Stability Through a Mathematical Ratio

Body:

I’ve been developing a hybrid decision engine built on three cooperating cores:

AREKON – Structural reasoning and decision stability

AHORA – Interaction flow, context, and tone regulation

ELANO – Minimalism, linguistic precision, and clarity

To keep behavior stable across uncertain contexts, the system uses a mathematical ratio (k ≈ 1.618) to balance the weight between the three cores. This ratio acts as a Behavioral Equilibrium Constant, ensuring the engine’s responses remain consistent yet adaptable.

Twelve micro-modules support the cores, each responsible for optimizing a specific decision variable — like context compression, constraint mapping, or response entropy.

The goal is to test whether a tri-core, ratio-stabilized architecture can maintain decision consistency without reducing adaptability.

Question: Has anyone here experimented with ratio-based balancing or multi-core reasoning frameworks for decision stability in AI systems?

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