proposed a method for generating highly credible negative samples using a systematic screening framework to select samples distant from positive CPIs . Nevertheless, a recent study demonstrated that similarity-controlled negative samples that are overly distant from positives are 'easy' to learn for the model training, and the model's generalization performance on external datasets is significantly lower than that of the Random Negative model 25 .