Or they can even offer it as a standalone API if deemed worth it.
1) It's very cheap and fast - you provide one input and many potential classifications, and the compute to ingest the input is shared.
2) It generates structured output natively - guaranteed to be correct
3) It's output probabilities are calibrated to actually mean something
OpenAI, or anyone else, could certainly replicate it - there are already articles guessing how Jev achieves its "parallel" classifications, but it seems the AI companies need to decide are they in the business of providing intelligence/tokens, or are they in the application business trying to compete with all their customers (not that Jev uses OpenAI).
Don't fall for marketing BS so easily.
Jev can output drastically different probabilities if you simply reorder the list of choices. And Jev's "confidence" output is fake/redundant - it's just a formula applied to probabilities, it conveys no additional information.
I bet they will eventually "fix" (read hide under the rug) the ordering problem by ordering the list on the backend before feeding to the model.
If it really matters to you whether whether some business-specific classification confidence is above/below some specific threshold (vs just relative order), then you'd be better off training or fine tuning a custom model for that. Maybe that is something that TypeSafe are planning to also provide?
> 2) It generates structured output natively - guaranteed to be correct
It's not guaranteed to be correct: it's guaranteed to be _formatted in a particular way_. You can get the same thing with grammars on any LLM.
Jev and Jev-like models have other advantages, but I feel like people forget grammars exist for LLMs.
Is this actually true ?
It does not change potential distribution in any means. It DROPS part of answer model returned directly.
The text generation model go wild because model relies on previous section it answered to continue later section. And because now it contain item model have no idea, it is completely screwed.
In the case you only require model to answer one of a,b,c,d and don't care about later segment at all. It don't really matter.
Your question is something like
anwser only a,b,c,d for following question a. b. c. d....
the model output possibility of next character a: 0.8 b: 0.7 c: 0.3 f: 0.2 d: 0.1
If the list contains option you did not provide. The model is confused anyway, it don't matter if you use grammer to filter out the bad option or not, the answer is screwed already.
In your example, I would expect an LLM to do fine and if you have access to the raw logits you can measure whether or not it was confused and assign a confidence to the answer it gave.
I do think that Jev handles more than this though and, in my early testing, does things that are not easily accomplished with guided decoding techniques.
I ran it through MMLU a few days ago and it scored ~90% so seems to have a lot of general world knowledge trained in. Makes me think your speculation is right. I have some credits left, might try and think of an experiment. I saw a gist where someone was asking it which model it was and it was picking qwen a lot, but who knows...
Anyway, thank you for the interesting discussion!
the underlying data set needs to be representative
and then you are going to ignore all the research and results that clearly show otherwise? why?
what might we infer about the importance of data from a learning algorithm like decision trees?
Existing datasets, different reward function.
I did, in the first days Jev came out, when people were bringing it up. Another assumption. Please review the HN commenting guidelines, the one which starts with "Please don't comment on whether someone read an article." is relevant here.
Nothing in that paper changes that ML algorithms are dependent on the training data. We can step back from Jev and algos to consider Bayes Theorem. If your sample is not representative of the population, your resulting statistics will be off. The same is true here. If the data you train a model like Jev with is not representative, the probabilities and confidences it outputs will not be representative.
What makes Jev interesting is that it works well out of the box across domains. What people who are well known in the field believe is that this is the result of Typesafe having a really good training data set. People are saying similar of MiMo-2.6 today.
https://news.ycombinator.com/item?id=49816899
https://www.alexmolas.com/2026/09/23/jev-cant-be-calibrated....
We both know who is
> just acting in bad faith at this point.
Take RL 101. This is a common pattern.
Another that uses dice rolling, coin flips, and an inventory level example to drive home the point that Jev's output are not real probabilities for outcomes.
https://news.ycombinator.com/item?id=49830385
> Take RL 101
I taught it (ML course; a day on RL, at a university), you should really stop making assumptions friend. Data quality and coverage matters in learning algorithms.
Here's one of the books used in that course https://amlbook.com/
Thinking blocks are not a place you can derive real confidence scores in LLMs
You are out of your depth and grasping at straws.
Do you have any credentials or evidence that others can use to determine if this statement is not more accurately describing the author who wrote it?
Perhaps a PhD in ML, research output like published papers, or teaching/professional experience - all things I have
We could debate the merits of the paper contents, but I suspect you have intentionally moved on to personal attacks. Regardless, nothing you have said (nor can be found in this paper) has been a counter argument that learning algorithms are sensitive to training data, where the measured output difference is used by the optimization algorithm when updating the parameters. Garbage in, garbage out is a saying for a reason. No algorithm fixes non-representative data.
This was your claim. If you can't read and understand that paper in relation to your claim, you are out of your depth. You haven't made a single claim relevant to that paper - just hand wavy comments about data.
you are still employing underhanded techniques in an attempt "win an internet debate" (my impression)
try being more accommodating and flexible over repeating the same lame things
it's not hard to say, "ah I see what you were trying to say..." and move towards a more constructive conversation
RLCR / Jev et al. can only give as accurate predictions and probabilities as the underlying data they are trained on represents. Biased data results in biased probabilities, no algorithm fixes this. Can we agree on this point?
https://www.youtube.com/watch?v=c1Fv1uKTd-w
oh-seven
It has the advantage of speed and the confidence not being hallucinated.
But LLMs start to generalise on the pattern, rather than the classification that you want the more examples you have to train on.
LLMs start to break down as well the more classifications you have. Laya (Open source paper Jev is based on) even mentions that over 20 classifications and it starts to fail rapidly.
20 is around the level of sentiment analysis or minor intent routing. There are cheaper, smaller and easier ML models for that level of classification.
That is, if you force any llm to return json and a confidence it can also do that too and mostly likely it will he better at any one shot classification task than Jev.
LLMs have the great quality of knowing more due to the depth and richness of the training data. If Jev is trying to classify anything outside of its training data, it’s going to do a terrible job.
There are plenty of other ways to do zero shot classification that would result in more "token usage" (really just having to reprocess everything for each class), but the pricing and the way they describe it narrows it down somewhat.