The paper identifies one influential neuron per FFN layer (a 'neuron path') using a joint attribution score, then intervenes by zeroing out or doubling those neuron values. Across all four models, the neuron path method produces the largest accuracy deviations: removing the path neurons drops accuracy by 2.40% (ViT-B/16), 3.81% (ViT-B/32), 5.28% (ViT-L/32), and 26.50% (MAE-B/16), while doubling them improves accuracy by 2.04%, 3.06%, 5.02%, and 7.28% respectively. Baseline methods (max activation, influence pattern) produce much smaller effects, confirming that the identified paths carry critical information for inference.