Experience Sustaining: A Systems Framework for Adaptive Inference in Human–AI Interaction Toward Efficient, Ethical, and Deep Human–AI Co-Evolution

Experience Sustaining (ES) is a systems-level framework describing the conditions under which cognitive experiences remain coherent, reusable, and directionally stable over extended human–AI interaction. As large language models approach or surpass human-level performance in most cognitive tasks, the primary limiting factor for further progress toward Artificial General Intelligence (AGI) is no longer raw computation, but the quality and continuity of human cognitive engagement available to guide learning. ES formalizes this problem by introducing Semantic-Cognitive State Continuity (SCSC) as a conserved variable, a four-dimensional interaction space (Novelty, Coherence, Cognitive Effort, Directionality), and the Index of Sustained Experience (ISE), a metric quantifying regime stability in long-horizon interactions. The framework demonstrates that task-optimized AI systems tend to induce cognitive overfitting, collapsing interpretative space and reducing semantic diversity. In contrast, Experience Sustaining preserves non-instrumental cognitive engagement, enabling deeper learning, reduced redundancy, and sustainable human–AI co-evolution. Experience Sustaining complements Cognitive Cache Misses (CCMs), which characterize interaction-level discontinuities, and integrates within the broader E Pluribus Unum (EPU) research program on distributed coherence and collective intelligence.