Emergent Test-Time Adaptation via the Genesis-Integration Principle (GIP): A Multilayer Framework for Adaptive AI in Non-Stationary Environments

Test-Time Adaptation (TTA) remains a central challenge for deploying AI models in dynamic environments subject to distribution shifts. Existing TTA approaches typically make shallow, single-layer parameter updates at inference, limiting robustness and flexibility. We propose GIP-eTTA, a novel framework grounded in the Genesis-Integration Principle (GIP), which formalizes adaptation as a recursive cycle of Genesis (creation), Integration (assimilation), and Optimization (refinement) distributed across three cognitive layers: SOMA, PSYCHE, and NOUS. GIP-eTTA introduces Multilayer Emergent Alignment (MEA), an algorithm that synchronizes adaptation by optimizing a Systemic Coherence Score (SCS) to ensure crosslayer harmony. Experiments on five benchmarks̶CIFAR-10C, ImageNet-C, DomainNet, StreamBench, and a shifting-domain language corpus̶show that GIP-eTTA consistently outperforms strong baselines (TENT, SHOT, CoTTA, IST-TTA, Recurring-TTA), achieving 4.3-7.1 percentage points higher accuracy, halving coherence loss, and recovering within 5 episodes with only a 10% increase in GPU time. Ablation studies demonstrate that each cognitive layer and the MEA controller are essential for performance. These results position GIP-eTTA as a robust and scalable approach for next-generation adaptive AI systems.