arXiv:2602.04521v2 Announce Type: replace Abstract: Modern deployments require LLMs to enforce safety policies at scale, yet many controls rely on inference-time interventions that add recurring compute cost and serving complexity. Activation steering is widely used, but it requires runtime hooks and scales cost with the number of generations; conditional variants improve selectivity by gating when steering is applied but still retain an inference-time control path. We ask whether selective refusal can be moved entirely offline: can a mechanistic understanding of category-specific refusal be d
The increasing deployment of LLMs at scale necessitates more efficient safety mechanisms due to the high computational cost and complexity of current inference-time interventions.
This research could significantly reduce the recurring compute cost and serving complexity associated with enforcing safety policies in large language models, making their deployment more sustainable and scalable.
Safety policy enforcement in LLMs could shift from expensive real-time interventions to more efficient offline mechanisms, baked into the model architecture itself.
- · LLM deployers
- · AI infrastructure providers
- · AI safety researchers
- · Cloud computing providers
- · Companies relying solely on inference-time intervention solutions
- · Hardware providers optimized only for inference compute peaks
Reduced operational costs for large-scale AI deployments enforcing ethical guidelines.
Faster iteration cycles and broader adoption of AI models in sensitive applications due to baked-in safety.
Enhanced trust in AI systems leading to wider societal integration and regulatory frameworks that prioritize pre-computed safety.
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Read at arXiv cs.CL