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Here are my thoughts on those aspects.

The approach to data decay I took in RougeDC was to have an "index" dimension which continually counted up. This is kind of mean/nasty to the kd-tree performance, but as far as KNN search I think it's a very natural way to model decay.

Regarding varied depths, I'm pretty sure the depth of VCS segmentation is extremely analogous to the number of KNN points used and how they are weighted. The way to match that aspect of VCS systems is to mix the result of varied numbers of points in varied weightings. Since processing the same points multiple times is redundant it simplifies to the following: The way to get the same effect as varied depth VCS, is to work on how your weighting of KNN points rolls off, and use plenty of KNN points so it rolls off properly before the limit on number of points is reached.

I don't know if you were referring to this Voidious, but with regards to having many stat buffers as some like DrussGT do, my experience is you get the same effect by performing antialiasing and interpolation. This implies to me that the primary cause of "many stat buffers" being effective for traditional VCS is that it acts as a sort of accidental stochastic antialiasing. A KNN approach implicitly needs no antialiasing/interpolation, so that aspect of VCS setups does not need to be arranged.