# Reuse-Based Regularization

EngramEdit proposes decoupled knowledge updates through conditional memory: it first computes target memory representations that make the model predict an updated fact across multiple expressions, then jointly solves updates to shared n-gram embeddings while penalizing changes to frequently reused embeddings more strongly; on LongCat-Flash-Lite, updating only conditional memory yields near-perfect editing success on CounterFact and ZsRE, revised facts transfer to unseen expressions and multi-hop reasoning, and unrelated knowledge and general capabilities are largely preserved.

EngramEdit proposes decoupled knowledge updates through conditional memory: it first computes target memory representations that make the model predict an updated fact across multiple expressions, then jointly solves updates to shared n-gram embeddings while penalizing changes to frequently reused embeddings more strongly; on LongCat-Flash-Lite, updating only conditional memory yields near-perfect editing success on CounterFact and ZsRE, revised facts transfer to unseen expressions and multi-hop reasoning, and unrelated knowledge and general capabilities are largely preserved.