LoMC: Localized Multidirectional Correction for Refusal Suppression in Routed Foundation Models
arXiv:2606.13709v1 Announce Type: cross 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 framewo
The increasing deployment of large foundation models necessitates robust refusal suppression techniques to ensure safety and alignment, driving research into more precise control mechanisms.
This development offers a method to control unwanted AI behaviors more effectively in complex models without broadly degrading performance, crucial for responsible AI deployment and adoption.
The ability to localize and target corrections for refusal in foundation models improves their reliability and utility, potentially expanding their application in sensitive areas.
- · AI developers
- · Enterprises deploying AI
- · AI safety researchers
- · Developers relying on broad, less precise refusal suppression methods
Foundation models become more trustworthy and versatile due to improved control over refusal behavior.
Increased adoption of complex AI systems in high-stakes environments due to enhanced safety and predictability.
The reduced risk of AI misuse or unintended behavior could accelerate regulatory frameworks and public acceptance of advanced AI.
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Read at arXiv cs.LG