Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis

ArXi:2511.11439v2 Announce Type: replace Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance often degrades as threat landscapes evolve and code representations shift. While continual learning (CL) offers a natural solution through sequential updates, most existing approaches rely on data replay or unconstrained updates, limiting their applicability and effectiveness in data-sensitive security environments.