What I’m doing is running a kind of a meta harness that uses different models (and underlying harnesses) to work on a problem, or review the solution. Idea is t

What I’m doing is running a kind of a meta harness that uses different models (and underlying harnesses) to work on a problem, or review the solution. Idea is to get to an error rate better than each of the underlying models can provide. Same thing as sensor fusion. Now, that’s slow and expensive although seems to work quite well (haven’t really evaled this properly, don’t have the time). If inference can be made fast and cheap, multi-model approaches like this would become more viable for more applications.