Our work is connected to several themes in the literature, which we describe next.

Is the whole more than the sum of its parts?

We empirically find that the discrepancy of predictions between the student and a stronger teacher may tend to be fairly severer.

Or fix us, which requires us to believe AI can be a therapist despite the evidence to the contrary? Or should it inform us with cold, to-the-point responses that may leave users bored and less likely to stay engaged? It's safe to say the company has failed to pick a lane. Back in April, it reversed a design update after people complained ChatGPT had turned into a suck-up, showering them with glib compliments. GPT-5, released on August 7, was meant to be a bit colder.

Specifically, a random path is sampled during each inference procedure so that the network itself can be treated as an ensemble of a wide range of different models.

It has been observed that visual classification models often rely mostly on spurious cues such as the image background, which hurts their robustness to distribution changes. To alleviate this shortcoming, we propose to monitor the model's relevancy signal and direct the model to base its prediction on the foreground object.