Currently the FST suggester (really an FSA) quantizes weights into buckets (e.g. single byte) and puts them in front of the word.
This makes it fast, but you lose granularity in your suggestions.
Lately the question was raised, if you build lucene's FST with positiveintoutputs, does it behave the same as a tropical semiring wFST?
In other words, after completing the word, we instead traverse min(output) at each node to find the 'shortest path' to the
best suggestion (with the highest score).
This means we wouldnt need to quantize weights at all and it might make some operations (e.g. adding fuzzy matching etc) a lot easier.