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

Abstract:We study controlled post-training refusal suppression in routed MoE and hybrid-MoE foundation models, aiming to increase non-refusal target-response behavior while preserving general capability under a compact intervention footprint. Existing broad direction-based edits can perturb general-purpose computation, whereas support-only expert edits often lack sufficient capacity to correct heterogeneous refusal representations. To address this limitation, we introduce Localized Multidirectional Correction (LoMC), a support-gated intervention framework that follows a support-then-correction execution order: it first identifies a compact edit support, then aggregates prototype correction directions into layer-wise correction directions, and finally applies rank-one layer-wise correction only within the selected support. By using the edit support as a structural gating constraint, LoMC increases correction capacity without expanding the intervention scope. Experiments on text-only and multimodal safety benchmarks across four routed backbones show that LoMC substantially improves non-refusal target-response behavior while maintaining general capability under a compact intervention footprint.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2606.13709 [stat.ML]

(or arXiv:2606.13709v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2606.13709 Focus to learn more arXiv-issued DOI via DataCite

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