MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%

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.