LoMC: Localized Multidirectional Correction for Refusal Suppression in Routed Foundation Models

We study whether localized post-training weight edits can suppress refusal in safety-aligned routed models without substantially degrading general capabilities. Sparse routing offers localized editing sites, but single-direction estimation may miss diverse refusal patterns, whereas broad edits risk degrading general capabilities. We introduce Localized Multidirectional Correction (LoMC), which separates edit-support selection where to edit from correction-direction estimation (how to correct). Stage I ranks expert output projections by weight alignment with the harmful–benign mean activation difference, then uses validation under capability and edit-budget constraints to fix a sparse set of expert projection matrices as the routed edit support. This limits the scope of routed weight changes to reduce the risk of capability degradation. With this support fixed, Stage II restarts from the original checkpoint and constructs harmful-prompt activation prototypes, each defining a candidate direction relative to the benign activation mean. It selects four prototype-derived directions, favoring alignment with the harmful–benign mean-difference direction while discouraging redundancy, then aggregates them with this base direction. The aggregation incorporates diverse activation patterns into one correction direction per edited layer, with each selected matrix receiving an update of rank at most one. This design combines richer direction estimation with localized weight changes to strengthen refusal suppression while limiting capability degradation. Across four routed vision-language models and four safety benchmarks, LoMC achieves 96.33% mean HarmBench-judged target compliance, exceeding the best baseline in each setting by over 12.72% on average while maintaining near-original average scores on four general capability benchmarks.