You can add new knowledge to any LLM without touching its weights by training a separate memory model that retrieves and augments the LLM's responses—making it practical for real-world applications needing frequent updates.
MeMo introduces a modular memory model that stores new knowledge separately from a frozen LLM, enabling efficient updates without retraining. It works with any LLM (open or proprietary), handles complex document relationships, and maintains constant retrieval cost regardless of corpus size.