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We found an approach to get Jev-like properties from standard LLMs like GLM-5.3-Flash.
The core idea is to craft the input prompt so that the first output token answers the question. This makes it possible to get a decision with a single forward pass.
In the blog post, we describe the approach in detail for GLM-5.3-Flash and vLLM. We benchmark this setup against Jev and Laya. We find that our setup is on-par with Jev in terms of accuracy and speed and that it substantially outperforms Laya.
Still, in terms of costs per decision, Jev is several x better than our setup. In turn, our setup supports vision inputs.
Discussion (8 Comments)Read Original on HackerNews
I took a random book excerpt with 23,000 words (±30k input tokens) and used it as context. Jev still responds in 800ms. Other estimates put it between 50-100,000 tok/s prefill, which is obviously not possible with normal LLMs. Not even Cerebras is this fast.
i have tested jev for my use cases and its horrendously wrong, but then the follow up from jev's team is "oh, you need to boil the question down further". it's a spiral of how much do you wanna dumb down the ask so that it answers it correctly. i'll pass for now.
also, 30k input tokens is a lot.
Fyi, I haven't tested this yet.
But it is likely more than just a fine tune + novel training. At the very least the LM head is swapped out for a classifier one and then or also idk, bidirectional attention for the encoding pass I'm out of my depth at this point and will stop guessing. The training is probably where they have the biggest moat though, not that it's necessarily huge.
I have a project that fits jev as advertised almost comically well and I've been playing with it, and the various hacks and open versions. Jev doesn't necessarily perform better overall but it is quite different. It's sensitive to prompt phrasing in ways the others aren't, it's easy to generate questions where all the other models cluster in confidence but jev is an outlier. Not necessarily more correct, but it does feel like it's getting its answers in a different way.
I'm guessing just as much as anyone else but I've been spending a ton of time on this the last couple weeks, it landed right when I was most ready to dig into it.