Scope Before Update: Graph-Guided Paired Supervision for Entity Unlearning

Large language models can reproduce sensitive or copyrighted information, motivating machine unlearning to remove selected knowledge while preserving other capabilities. Existing pipelines commonly require users to supply a forget set, which can re-expose sensitive content and complicate request auditing. We study a user-minimized interface in which the requester identifies a target entity and the service constructs the supervision needed for the update. This shifts the challenge to recovering useful target associations and separating behavior to change from nearby knowledge to preserve. We introduce AERIS (Association-guided Entity Removal with Integrated Supervision), which organizes repeatedly elicited target-model associations into a weighted local graph and converts its paths into paired forget and neighbor data. The constructed supervision requires no original training passages and can be reused with standard unlearning objectives. Experiments on RWKU and TOFU show stronger forgetting than direct self-generation across the evaluated update objectives. With forget data fixed, graph-local neighbor supervision improves nearby knowledge preservation over Wikipedia retain data under both regularized objectives. On eight shared RWKU targets, AERIS reduces Forget-All by 20.2% relative to DirectQA under GA+GD.