Neural proxies are a constant-time approximator for anything.
There's still difficulty in finding exactly where the proxy should go, i.e. what step can be approximated without losing fidelity in the output. Apparently, you also need to select auxiliary features to guide training. But if you figure those out, you can replace hours of computation with milliseconds, accurate to the limits of human perception.
Aren't they a faster, lower fidelity model, like all models?