EngramEdit proposes a conditional memory architecture where input n‑grams retrieve learned embeddings, expanding LLM capacity with minimal extra computation. By decoupling factual knowledge storage from the Transformer’s general‑purpose computation, it enables factual updates while keeping the backbone fixed. This approach offers a scalable path to knowledge‑editable LLMs.