This work introduces a conceptual and methodological framework for studying alignment in conversational AI at the level of sustained human–model interaction. While most alignment research evaluates behavior at training time or through isolated prompts, this paper focuses on how behavioral constraints persist, drift, and recover across extended interaction. We define a behavioral constraint profile (BCP) as an explicit specification of values, reasoning norms, tone boundaries, and collaboration rules that guide model behavior during interaction. Building on this construct, we propose the Joint Adaptive Alignment System (JAAS), an interaction protocol through which users and models iteratively establish, reinforce, and restore these constraints without modifying model weights. The paper outlines a pilot study design and introduces measurable signals including drift frequency, correction latency, constraint recall, and self-correction across multi-session interactions. Rather than presenting empirical results, this work formalizes a testable framework intended for downstream validation. JAAS is positioned as a complementary approach to existing alignment methods, enabling longitudinal evaluation of behavioral stability across users, contexts, and tasks.
James Bridges (Fri,) studied this question.