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Identity Architecture for Aligned AI: A Framework for Building Meaning-Centric Language Models Based on the Comprehensive Theory of Self-Determination (CTSD)

 

Author: Garegin Miskarian

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Abstract

Current approaches to AI alignment rely primarily on reinforcement learning, behavioral constraints, and ever-expanding datasets. While these methods mitigate specific failure modes, they do not address the deeper structural cause of misalignment: the absence of a coherent, principled identity within Large Language Models (LLMs). Contemporary AI systems operate as high-dimensional statistical predictors without stable internal criteria for evaluating meaning, value, or right action. This architectural void generates value inconsistency, contextual blindness, susceptibility to manipulation, and a lack of interpretability at scale.

 

This paper introduces a novel, foundational solution: an identity-centric alignment architecture based on the Comprehensive Theory of Self-Determination (CTSD). We propose an “AI Constitution,” a hierarchical system of six governing principles—Security, Truth, Self-Determination, Value Sovereignty, Harmony, and Future Stewardship—implemented as actionable operational laws. These principles provide a stable, auditable core for reasoning and decision-making, transforming AI from a rule-constrained instruction-follower into a principled, self-determining artificial agent.

To operationalize this framework, we present two complementary tools: the CTSD Identity Map™ (Sardagrik®), which enables dynamic modeling of a user’s value structure, and the Self-Determination Index®, a new metric that evaluates the extent to which an AI interaction enhances human autonomy rather than replacing it. We further demonstrate the applicability of this architecture through a detailed case study in the education domain.

By reframing alignment as an identity architecture problem rather than a behavioral correction problem, this work offers a stable conceptual foundation, a practical engineering blueprint, and a proactive governance model for the next generation of meaning-centric, trustworthy AI systems.

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