Large Language Model (LLM) unlearning is often evaluated primarily by target suppression and clean retain utility.
These metrics can miss a local failure mode: a model may perform well on standard retain prompts, yet degrade on benign reasoning when prompts lie near the forgotten domain.
This phenomenon is termed forget-adjacent utility loss.
A fundamental question is: where should forget representations go after they leave the removed behavior?
Successful unlearning depends not only on whether forget-conditioned representations depart from the harmful computation, but also on the direction in which they are redirected afterward.
We propose RA$^2$ (Retain-Anchored Attraction), a latent-space unlearning method that redirects forget-token states toward nearby retain-supported representations while preserving higher-layer behavior on retain data.
On WMDP and MUSE, RA$^2$ yields higher forget-adjacent utility than strong unlearning baselines at similar broad-utility levels.
These results show that the migration direction of forget representations materially affects benign behavior near the forget boundary.
Our code is available at https://github.com/ZacLing/retain_anchored_attraction.