A Self-Reflective Cognitive Architecture for Human–AI Systems

This work presents a conceptual framework and position paper for self-reflective cognitive architectures in human–AI systems. Rather than proposing a new algorithm or model, the paper articulates the structural conditions under which cognitive coherence, interpretability, and decision stability can be maintained as system complexity exceeds human-scale comprehension. The framework challenges prevailing assumptions in AI development by arguing that cognition cannot be preloaded through data or optimization alone, but must emerge through structured interaction, feedback, and recursive self-correction. The paper introduces a developmental and relational view of cognition, integrating self-reflection, weighted decision dynamics, and interpretive stability across human–AI collaboration. This preprint is intended as a foundational contribution and an opening position for further research, not as a finalized solution.