# Safety, Toxicity and Hallucination Control

Behavior control in large language models often costs general capability; CANS instead assigns each feed-forward network neuron a scaling coefficient that accounts both for its contribution to the target behavior and for its disruption of general outputs. Target behavior is proxied by the log-probability of target tokens in model responses, capability preservation by output-distribution KL divergence on general-purpose text, and local approximations of the two yield a closed-form solution with no iterative training and no added inference-time computation. On Qwen3-4B and Llama models, CANS matches or beats SFT, CAA and neuron-level baselines on safety, toxicity and hallucination control while better preserving MMLU, BBH and IFEval performance.

Behavior control in large language models often costs general capability; CANS instead assigns each feed-forward network neuron a scaling coefficient that accounts both for its contribution to the target behavior and for its disruption of general outputs. Target behavior is proxied by the log-probability of target tokens in model responses, capability preservation by output-distribution KL divergence on general-purpose text, and local approximations of the two yield a closed-form solution with no iterative training and no added inference-time computation. On Qwen3-4B and Llama models, CANS matches or beats SFT, CAA and neuron-level baselines on safety, toxicity and hallucination control while better preserving MMLU, BBH and IFEval performance.