Multi-turn Drift in AI Systems as Sequential Coherence Degradation using Coherence Intelligence Architecture

This paper presents a case study examining response drift in AI systems across multi-turn interactions using Coherence Intelligence Architecture (CIA). It analyzes how systems that initially produce coherent and aligned responses begin to lose alignment over sequential exchanges, even when the underlying model and intent remain consistent. The case demonstrates that drift is not caused by isolated errors, but emerges from the accumulation of context over time. As interaction progresses, changes in signal transmission, relational pathways, and alignment stability lead to gradual variation in response clarity and focus. The analysis applies CIA to identify how substrate engagement, field conditions, topology of internal pathways, coherence stability, and response behavior evolve across sequential interactions. It shows that drift is a layered, time-dependent phenomenon rather than a single-point failure. This paper applies CIA as a diagnostic framework to a temporal scenario, illustrating how system behavior changes with accumulated context without introducing new concepts or extending the architecture.