Random-vector controls provide a reference for interpreting activation-steering effects, but a single realization hides variation in that reference. We examine this variation and two separate implementation checks. In a released activation-addition (ActAdd) configuration on GPT-2-XL, we reproduce the steering arm over 57 prompts with a maximum absolute error of 1.74 × 10⁻⁶. At coefficient 10, 9 of 20 fresh random-control realizations have greater mean normalized Kullback–Leibler (KL) divergence than the steering arm, and 19 have lower means than the stored random realization. In a separate four-model study over 96 prompts, changing batch size or attention implementation changes the greedy next token for at most 5 and 6 prompts, respectively; an identical rerun changes none. Finally, an erroneous left-padding index in our own analysis pipeline, shared by the intervention and readout, overstates the maximum greedy-token change rate in 10 of 12 model-dose settings, with a median relative distortion of 46.8%. These findings support reporting realization-level variation, measuring configuration sensitivity, and validating intervention and readout indices. The results concern distributional change in the evaluated configurations, not semantic steering success.