Continual Lifelong Learning in Foundation Models: Mitigating Catastrophic Forgetting, Model Editing, and Dynamic Modular Expansion

Abstract—Modern foundation models, particularly autoregressive Large Language Models (LLMs) and Vision-Language Models (VLMs), operate predominantly under a static pre-train-then-freeze deployment paradigm. However, real-world enterprise deployments demand continuous, lifelong adaptation to non-stationary data streams: ingesting emerging factual knowledge, correcting obsolete or legally sensitive associations (machine unlearning), and acquiring novel domain-specific capabilities without retraining models from scratch. Under standard sequential fine-tuning, deep neural networks suffer from Catastrophic Forgetting: new gradient updates catastrophically overwrite previously consolidated orthogonal representations, degrading baseline general reasoning and linguistic fluency. Resolving this challenge requires confronting the classical Stability-Plasticity Dilemma: how can a neural architecture remain sufficiently plastic to assimilate non-stationary novel knowledge while remaining structurally stable to preserve historical competence? This paper delivers an exhaustive, mathematically formal technical investigation and empirical benchmark of Continual Lifelong Learning and Model Editing in modern foundation models. We formalize catastrophic forgetting dynamics using Fisher Information matrix projections and Elastic Weight Consolidation (EWC), model rank-one associative memory editing via ROME and MEMIT across multi-layer perceptron (MLP) weight spaces, and analyze dynamic modular expansion architectures (e.g., continual Mixture-of-Experts and modular adapter routing). Furthermore, we construct an enterprise lifelong learning testbed evaluating open foundation models (Llama-3-8B, Mistral-7B) across 5,000 sequential factual updates, longitudinal clinical guideline shifts, and temporal financial market regimes. Our empirical findings demonstrate that localized rank-one memory editing achieves a 96.4% factual edit success rate while bounding collateral locality damage to under 1.8%, outperforming standard parameter-efficient fine-tuning (LoRA) by 41.2% in retaining upstream historical benchmarks. Concurrently, dynamic modular routing preserves 98.6% of pre-trained reasoning capabilities over extended continual learning horizons. Finally, we formulate open challenges in lifelong compositional safety alignment, scalability bounds of memory editing, and neuro-symbolic continual knowledge graphs.