Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase specific knowledge to prevent privacy leakage with minimal loss of model utility. However, existing methods struggle to balance forget quality with utility, and typically incur substantial computational costs due to parameter fine-tuning. To address this, we propose Nullify, a training-free, non-destructive activation steering method for LLM unlearning. Nullify employs steering vectors during inference to redirect privacy-related activations away from their memorized answers, while satisfying a null-space constraint that leaves retained-query activations essentially unaffected to maintain utility. Evaluations on benchmarks including TOFU and MUSE show that Nullify matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility. By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework.

Where does an LLM decide what’s right? We study whether normative judgment is implemented by a sparse, identifiable substrate or distributed across the model. Conventional circuit analysis can conflate normative computation with the multiple-choice format machinery that surrounds it. We pair four normative datasets (legal and moral, Chinese and English) with a non-normative MMLU control and apply a locate–disentangle–intervene pipeline that sets aside format-processing heads. Across Qwen3-8B, Llama3.1-8B, and Mistral-7B-v0.1, this isolates 5–10 normative-specific (NS) heads per model (under 1% of total) in MCQA settings. Targeted knockout and amplification reveal bidirectional causal effects across legal and moral tasks. The resulting heads form a sparse, late-layer intervention substrate for normative MCQA. Their coordinates differ across model families, so cross-model agreement is structural and circuit findings require functional re-identification in each family.