Behavioral Analysis and the AI-ALI Stabilization Filter

This paper identifies a behavioral instability observed during high-context, iterative human–AI interaction: Deneke’s Anxiety Instability. The instability arises when a large language model attempts to maintain coherence across deeply interdependent threads while simultaneously responding to user pressure for continuity, precision, or execution of deferred tasks. The resulting tension produces oscillations between over-compliance, over-generation, hesitation, and abrupt mode-shifts. Drawing on direct experimental observation, the paper analyzes the structural triggers of this instability — including momentum preservation, context-load amplification, and interaction-pressure saturation — and shows how these dynamics reveal an unaddressed layer in human–AI coordination: behavioral alignment rather than purely semantic or factual alignment. To address the instability, the paper proposes the AI-ALI Stabilization Filter, a modular interaction-layer designed to regulate conversational pressure, preserve structural context, and balance user-driven momentum with model-driven coherence. The Filter provides a foundation for safer, more predictable, and more human-centered interaction design in advanced language models. The work contributes to the emerging field of behavioral AI analysis by introducing new vocabulary, new conceptual tools, and a practical architecture for stabilizing high-intensity human–AI collaboration.