That is a very interesting post. My intuition is that it is precisely <i>because</i> there is generalization across models that it is an effect of overfitting. This is a different kind of generalization than the never-before-seen-data generalization that, when high, means no overfitting. If the underlying structural correlations in models result in similar fragile decision boundaries when trained on the same data, that does suggest overfitting to me.<p>The example with softmax regression is extremely interesting and I have to think about it more, but I don&#x27;t think it quite represents what I was saying about plain OLS in my comment. With even basic logistic regression, you get the logistic function sort of &quot;smearing&quot; perturbations from the decision boundary, and so an effect that only occurs in a single direction even may be large enough to cause the logistic function to &quot;amplify&quot; it. Reasoning about that same possibility in a super high-dimensional softmax scenario seems very hard and it&#x27;s at least not obvious to me that the softmax functions wouldn&#x27;t similarly &quot;amplify&quot; small differences, leading to places where the decision boundary could be fragile in this overfitting sense.