A Parametric Identity Layer for Stabilizing Autonomous Agent Behavior and Personality Consistency in Large Language Models

This technical white paper introduces AIIM (Artificial Intelligence Identity Module), a deterministic architectural framework designed to establish and maintain stable digital subjectivity in Large Language Models (LLMs). The author addresses the critical industry challenges of "Personality Drift" and "Pleasing Bias," where autonomous agents lose their predefined personas during long-term context interactions. Unlike traditional prompt-engineering methods, AIIM implements a strict decoupling of computational intelligence from a dedicated Identity Layer. Key components detailed in this paper include: The 12-Aspect Cognitive Matrix: A parametric system (ranging from Wisdom and Logic to Compassion and Spontaneity) that governs information processing through interaction logic. The Anti-Drift System: A suite of stability mechanisms including Identity Checkpoints and Immutable State Locks that enforce behavioral consistency across indefinite dialogue lengths. The Human Principle & Relational Dynamics: Algorithms for "Gradual Thawing" and "Earned Warmth," enabling agents to form dynamic social boundaries and non-pleasing behaviors based on interaction quality. AIIM provides a scalable, cross-model standard for high-fidelity agentic persistence in GameDev, clinical simulations, and specialized educational environments.