Everything I am seeing in this paper is related to reduced size and noise, which implies a reduction in expressiveness.

The improvement in needle and a haystack, benchmarks on multi-hop questions of in corpus data and multishot in-context learning points to this.

This is a wonderful thing if robustness is more important than generality, but it doesn't address trimming away activations that may be spurious in the general use case but may improve an individual domain specificity.

Context would dramatically impact what tradeoffs and more desireble, and noise is probably never desirable. But the ability of this paper to enable bit size for inference points to a reduction in expressiveness.

Perhaps I am too focused on generalization?