The aspect that I like the most is the typesafe API that introduces new probabilistic concepts that are more sound than json schema and constrained decoding with quasi-confidence scores. Developers were asking LLMs to also emit confidences which made absolutely no sense whatsoever.
Name Entity Recognition (NER) is one example.
So many of them... https://huggingface.co/models?language=ner&sort=trending
Also used to block SSN and CC #s from logs, etc... as small and fast enough to do it. You don't want to call OpenAI GPT-6 and ask it to return your text with the SSN blanked out. I am sure people do though... (SSN is a bit simple, but all kinds of PPI in one model is more likely).
The nice thing about Jev is that people started taking about models that are not LLM text streams again.
This is also bogus unless you are talking about Bayesian inference. No classifier can output CI for a single point estimate. In every ML theory textbooks worth their $, it's always stressed not to treat these sigmoid'ed or softmax'ed numbers as probabilities or confidence scores, there is no such thing as CI for point estimate.
This looks pretty decent: https://www.aidancooper.co.uk/constrained-decoding/
No comment on this model itself, might be over fitted to the Jev benchmarks just to beat it.