Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits. MeMo, a framework from researchers at multiple universities, encodes new knowledge into a dedicated smaller memory model that operates separately from the main LLM. The modular architecture works with both open- and closed-source models and sidesteps the complexity of RAG pipelines and full model retraining. Experiments show that MeMo handles complex queries reliably even when retrieval pipelines are noisy. It avoids the catastrophic forgetting associated with direct fine-tuning and provides a cost-effective pathway for continuous knowledge updates. The challenge of updating LLM memory Large language models are frozen after training and their internal knowledge remains static until they undergo subsequent, computationally massive updates. Comparison of different LLM memory frameworks (source: arXiv) Currently, developers rely on three main approaches to integrate external knowledge into an LLM, each with distinct drawbacks: Non-parametric methods, such as retrieval-augmented generation (RAG) and in-context learning, retrieve relevant documents from an external …