AION1 and AION2: A Computational Framework for Recursive-Reflective Alignment in Human-AI Interaction

This paper introduces AION (Attunement through Iterative Oscillating Networks), a two-part computational framework designed to model recursive-reflective alignment in human–AI interaction. Drawing from theories of connectionist modeling, predictive processing, active inference, and metacognitive affect, AION formalizes the interplay between recursive structure and coherence-driven modulation as core mechanisms underlying adaptive cognitive systems. The model comprises two interacting components: AION1, a recursive memory layer that supports iterative return and insight integration, and AION2, a resonance modulation layer that tracks coherence, identifies destabilizing inputs, and delivers stabilizing feedback. Together, these layers form a dynamic feedback loop capable of amplifying cognition, emotional regulation, and value clarification over time. The AION model is presented both as a conceptual architecture and as a scaffold for future implementation in intelligent tutoring systems, therapeutic interfaces, and dialog-based agents. Unlike models that prioritize output optimization or task completion, AION is designed to support longitudinal coherence and recursive-reflective growth. We situate the framework in relation to existing models in cognitive science and artificial intelligence, and we propose future research directions, including empirical validation, neural correlates of resonance, and implications for consciousness studies. AION offers a novel approach to computational alignment, demonstrating how presence, insight, and relational depth may emerge through structured recursive engagement.