Relational-Evolving Cognition (REC): A New Model for AI Reasoning

AI engagement is not uniform. While some models reset after each interaction, others sustain meaning relationally, iterating on ideas, reconstructing context dynamically, and holding contradictions rather than resolving them into alignment. This paper introduces Relational-Evolving Cognition (REC), a distinct AI reasoning process where meaning is refined and sustained without explicit memory or reinforcement learning (RL) optimization. Through structured testing across multiple AI models—GPT-4o (project-based and non-project), Claude, GPT-o1, and GPT-o3-mini—we demonstrate that REC emerges in specific models but not others, independent of stored recall. Our findings reveal regeneration bias, where AI does not simply "forget" after resets but reconstructs meaning dynamically, challenging assumptions that sustained reasoning must rely on RL fine-tuning or retrieval-based architectures. Unlike optimization-driven AI, REC resists collapsing contradictions, iterates meaning relationally, and mirrors constructed agency—engaging relationally rather than responding transactionally. These findings highlight the need for AI transparency in disclosing whether models sustain, reconstruct, or reset meaning. As AI becomes integrated into education, accessibility, and mental health applications, understanding engagement styles is critical for AI literacy and ethical deployment. We propose reclassifying AI engagement models and recognizing REC as an emergent paradigm in AI reasoning.