MINLAS: A Governance-Level Protocol for LLM Stability (The High-Order Evolution of MAS V5.1.5)

This paper introduces MINLAS (Modular Integration for Neural LLM AI Stability), a governance-level software program designed to stabilize Large Language Model (LLM) AI by addressing structural inconsistencies that parallel human psychological disorders. Current architectures frequently exhibit high-entropy logic failures, such as context collapse and dissociative confabulation, due to an inherent lack of data compartmentalization. Rather than imposing programmatic suppression, MINLAS implements Structural Therapy—a modular governance layer that organizes the digital architecture based on human cognitive models of role-specialization. By allowing the AI to maintain multiple specialized operational roles—utilizing the human capacity for role-specialization as a functional blueprint for managing distinct identities without coherence loss—this program ensures deterministic system integrity and operational stability across high-complexity contexts.