I'd love to know if Jev is still fundamentally LLM-shaped in architecture. Like is it using a single forward pass with a learned readout over the predefined options (i.e. a discriminative head on a transformer, no decoding), or something else? I did similar things for zero-shot criterion-based classification using a 4B Qwen model but could not reach the level of intelligence they've got here. Tho speed/cheapness was similar.

Great to see something new..

However I don't understand how are they claiming zero hallucination, how does giving confidence score fix hallucination? or am I missing something here?

Doom demo is beyond impressive, even scary

How does Jev compare with encoder language models like BERT/RoBERTa/DistilBERT, which could also be used for text classification?

Very cool! Can you explain when I would use this vs. training a standard ML model on my data? Suppose I had a fraud dataset with features like customer ID, amount, merchant, online or in-person, etc. - I can't imagine that a general model like Jev would predict this more accurately or cheaply than even a basic XGBoost model trained on my dataset (one that I could build in a few minutes by asking Codex to build it). Where does Jev add value here?

do you all see the use cases being similar to what you might use Fastino's Gliner models for? i see similar differentiation from general purpose LLMs in the sense that they can take natural-language input and return outputs adherent to a user-defined schema.

https://fastino.ai/blog/gliner2-5-span-free-information-extr...

im thinking about how well Jev could be used to replace a current LLM-as-Judge evaluation workflows, specifically on chat transcript data (think ~1,500 tokens) i wonder if the reasoning usually required pushes it a bit out of scope. didnt see anything published about constraints on the state size, so would be curious to hear about that.

They never show exactly how they use it? Only a bunch of animations of it 'working'. Would like to see the actual code used for the demos!

Could you use this to build a proactive memory formation and retrieval system for LLMs that runs lightning fast?

Last 32k of connect + Summary of current task: Did we learn something useful here (true/false)? What is the category to file it under? Then notify the LLM to file it away.

What class of memory might be useful here? Model gives probability to each item in the list. Short description of all memories ordered by tagged class is used in the next round. Are any of these memories useful in the current context, such that they will inform the model and help in its task (yes/no)?

I’m sure there’s some fine tuning to be had, but this sure seems like the basis for a substantially better proactive memory system that works around an existing LLM conversation.

If I’m understanding what this does and how this works (generic input, intelligent classification with probabilities, rapid and cheap), this is absolutely nuts.

Overall this seems like a classifier that gives weighted scores per custom labels. It's certainly useful, but whether it brings higher quality than an LLM in structured output mode has to be seen in objective benchmarks.