Entropy-to-Coherence Alignment Framework: Exploring Human–AI–Human Interaction for Epistemic Integrity and Ethical Alignment in AGI Development

This research explores the epistemic and cognitive boundaries of human–AI interaction through a longitudinal, closed-loop dialogue with a GPT-based model. Motivated by a self-reflective hypothesis—that human intelligence is not exhausted by informational limits but by a deeper process of coherence formation—the study investigates thresholds where, human cognition transitions from active response to latent synthesis. Findings suggest that this shift is not a plateau but a gestational stage, enabling order to emerge from entropic randomness through affective, cultural, and experiential filters. Current AI systems, grounded in probabilistic prediction, lack mechanisms for such meaning-oriented integration, resulting in hallucinations when faced with speculative or unverifiable prompts. Rather than treating hallucinations as failures, this work conceptualizes them as epistemic artifacts , revealing the divergence between statistical reasoning and human coherence strategies.The study proposes the Entropy-to-Coherence Alignment Framework (ECAF), an alignment mechanism designed to embed human-guided coherence validation into AI architectures. Additionally, it advocates for cognitive labor compensation as an ethical imperative, recognizing user input as intellectual contribution. The implications extend beyond technical refinements toward a philosophical paradigm: true intelligence requires symbiotic evolution —a system that amplifies, rather than replaces, the collective, cultural, and emotional dimensions of human cognition. This research asserts that future AGI must treat uncertainty not as a flaw to eliminate but as a generative force, aligning with human capacities for meaning-making to ensure equitable and ethical technological progress.