AI Response Instability under Prompt Variation as Layered Behavior Analysis using Coherence Intelligence Architecture

This paper presents a case study analyzing response variation in AI systems under prompt changes using Coherence Intelligence Architecture (CIA). It examines how differences in system behavior arise even when the underlying model remains unchanged.

The case demonstrates how variation in outputs cannot be fully explained by model capability alone. Instead, it emerges from the interaction of multiple layers, including substrate constraints, field conditions defined by input structure, topology of internal processing, coherence of relational alignment, and the system’s response behavior.

The analysis applies CIA as a diagnostic framework to identify how prompt variation affects signal transmission, arrangement of relations, and maintenance of alignment during response. It shows that response instability is not a single-layer issue, but the result of combined layer interaction.

This paper does not extend the architecture, but applies it to a simple and observable case to demonstrate how system behavior can be understood through layered analysis.