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This work formalizes influence as a counterfactual estimand, distinguishes specification mismatch across estimands from approximation error in estimating a fixed estimand, and organizes representative estimators by their implied specifications.
A training-free defense that first identifies safety-specific neurons through per-neuron hypothesis tests under false-discovery-rate control together with a utility-specificity filter, and reduces the average attack success rate to at most 2.0% while incurring a utility drop of only 0.3% on MT-Bench, the smallest among...
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