Nullify achieves training-free LLM unlearning via null-space activation steering, raising TOFU forget10 FQ to 0.58 while holding MU at 0.62, matching Retrain
Nullify introduces a training-free, weight-preserving inference-time activation steering method for LLM unlearning: it builds a forget direction from IDK-contrastive activations and confines every correction to the subspace orthogonal to retained-knowledge activations via a null-space projector, matching or surpassing baselines in forget quality on TOFU and MUSE while keeping model utility near-lossless at a small fraction of their compute.