Persistent Integrative Learning Framework: A Retrovirus-Inspired Approach to Mitigating Catastrophic Forgetting in Multi-Domain Artificial Intelligence

This paper presents the Progressive Integrative Lifelong Learning Framework (PILF), a novel approach inspired by biological sciences for Continual Learning (CL), which leverages retroviral DNA integration mechanisms to combat catastrophic forgetting in neural networks, Large Language Models (LLMs), and Vision-Language Models (VLMs). PILF employs core knowledge manifolds to assimilate persistent features via Lipschitz-2 mappings, proximal quadratic smoothing to mitigate parameter perturbations, and causal interventions to eliminate spurious correlations. Theoretical contributions include sublinear forgetting bounds of O(1/√T), convergence rates, information-theoretic generalization bounds, and PAC-Bayesian guarantees under non-convex losses. Experimental evaluations across benchmarks such as Split-MNIST, Permuted MNIST, Split-CIFAR-10, DomainBed, MemoryBench, and TiC-LM demonstrate PILF's superiority over state-of-the-art methods like EWC, GEM, MoRAL, SAPT, InsCL, and Self-Synthesized Rehearsal by 15% to 25% in average accuracy, with backward transfer less than 2%. Ablation studies validate the key components, while AI-guided gates (XAI) enhance interpretability, providing a flexible and scalable solution for multi-domain artificial intelligence in the era of foundation models.