Now this is how[0] we get some of the most magical Star Trek technology that eludes us to this day, such as automatic doors. Because if you notice, they work much, much better than real-life ones, because they seem to be doing something like this:
if(within 10 meters of door then) {
if(Jev(
[A] Intends to go through, expects doors to open
[B] Approaches with no intent to pass
[C] Passing by, loiters, or otherwise
[D] Other
) == most definitely A) {
// open doors, +/- identity/security/interlocks check
} else {
// ignore
}
}
Keywords: ambient awareness, understanding of intent.
Most interactive tech on Star Trek is like this - from phasers to consoles to communicators to voice interactions with the ship's computer. The computer seems to be aware of the user and surrounding, and actively infers intent from context, to DWIM ("do what I mean") and when they mean it, instead of doing dumb things[1] on simple triggers.
--
[0] - The direction, not final implementation - surely we can work out how to do it more efficiently than wrapping around final stage of LLM. But the point is, multimodal.
[1] - Obviously it's a fictional show, but in this, both Watsonian and Doylist explanations align near-perfectly: this is/portrays advanced technology, that Just Works and doesn't do stupid shit. Same intent recognition algorithm is there - fictionally in the computer, in reality in the minds of on-set technicians.
> Most interactive tech on Star Trek is like this - from phasers to consoles to communicators to voice interactions with the ship's computer.
Almost like the Star Trek mechanisms can infer perfect intent.
Like there’s a hidden script or something.
More seriously, I think there’s real value in an automatic door that behaves consistently rather than one that tries to infer messy human intent. Real life isn’t a TV show and there’s both ambiguity in how people behave and how they even intend to behave. It’s mostly not hard to understand how a proximity sensor door will function. Using a black-box classifier to improve that won’t necessarily make people like it more. And calling up to the cloud for every sensor event, ignoring privacy issues, adds weird latency and a huge failure mode during data center outages.
Nice! I would love to use it for images as well.
Then again is using Grammar-Based Decoding with a json response not the same? Is Jev just that with nice caching?
Because then I have been using that already…
Presumably this is much less good than Jev, because the normal LLM models have been trained with RLHF and to be agents. Especially on a large model, I'd expect it to decide in an earlier layer.
I'd hope whatever Jev's Reinforcement Learning for Calibrated Decisions (RLCD) does is better at training the models to give accurate probabilities in the weights.
Jev doesn't support images, so it's hard to compare this directly. But in general this approach beats Jev in its own benchmarks for accuracy and speed and is about the same price.
Jev is rumored to be a 30B model, and it's input price is MUCH cheaper than similarly sized models. The maker is also heavily focused on having a profitable product, so it's unlikely to be subsidizing the cost, especially since they say they have more demand than what they can serve.
Exactly, imo it’s not even that cheap if you look into perspective and consider the fact that providers could subsidize the cost of cached input tokens to virtually zero if they would allow for a more flexible API (e.g. tree of message blocks instead of chain). Most of the cost is the infrastructure around keeping KV caches, estimating their lifetimes, etc. When mist people just want to run one context block with multiple subsequent variants of a second block in parallel. I still stand by my statement.
That's an interesting point, if you send a batch with a shared prefix you basically only end up paying for the sequence length difference effectively.
There is still some minor memory bandwidth issue on outputting more tokens, but the truth is that if you process e.g. 16 messages at once you wont end up being much slower than Jev even though you have to perform several autoregressive passes.
They can't overturn the economics of attention by restricting themselves to a single token output.
Sure they are no longer memory bandwidth bound thanks to that but someone could add a similar projector to a conventional model, train with a Jev style dataset and call it a day.
Whatever they are doing on inputs must either mean they intentionally chose a Mamba successor or they suffer from the same compute costs as everyone else.
Most interactive tech on Star Trek is like this - from phasers to consoles to communicators to voice interactions with the ship's computer. The computer seems to be aware of the user and surrounding, and actively infers intent from context, to DWIM ("do what I mean") and when they mean it, instead of doing dumb things[1] on simple triggers.
--
[0] - The direction, not final implementation - surely we can work out how to do it more efficiently than wrapping around final stage of LLM. But the point is, multimodal.
[1] - Obviously it's a fictional show, but in this, both Watsonian and Doylist explanations align near-perfectly: this is/portrays advanced technology, that Just Works and doesn't do stupid shit. Same intent recognition algorithm is there - fictionally in the computer, in reality in the minds of on-set technicians.
We’ve had the tool to do this since mid 2025, V-JEPA2 [1], Yann Lecun’s last work at Meta.
It runs at several FPS on a macbook and can even be trained locally. Chaining it with Jev for decision-making would probably work great!
[1] https://ai.meta.com/research/vjepa/
Almost like the Star Trek mechanisms can infer perfect intent.
Like there’s a hidden script or something.
More seriously, I think there’s real value in an automatic door that behaves consistently rather than one that tries to infer messy human intent. Real life isn’t a TV show and there’s both ambiguity in how people behave and how they even intend to behave. It’s mostly not hard to understand how a proximity sensor door will function. Using a black-box classifier to improve that won’t necessarily make people like it more. And calling up to the cloud for every sensor event, ignoring privacy issues, adds weird latency and a huge failure mode during data center outages.
I'd hope whatever Jev's Reinforcement Learning for Calibrated Decisions (RLCD) does is better at training the models to give accurate probabilities in the weights.
https://github.com/Mushroom-Systems/lichen
[B] Not a hotdog
https://github.com/Mushroom-Systems/lichen
https://openrouter.ai/google/gemma-4-26b-a4b-it
There is still some minor memory bandwidth issue on outputting more tokens, but the truth is that if you process e.g. 16 messages at once you wont end up being much slower than Jev even though you have to perform several autoregressive passes.
Sure they are no longer memory bandwidth bound thanks to that but someone could add a similar projector to a conventional model, train with a Jev style dataset and call it a day.
Whatever they are doing on inputs must either mean they intentionally chose a Mamba successor or they suffer from the same compute costs as everyone else.