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Comment by thomasikzelf | Hacker News Reader

What kind of decisions though? jev does not output joint angles, and if it did it also needs to know how. It also does not take in images.

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As a proof of concept I've been working on a Jev style vision model that can rapidly answer boolean and choice questions based on images (See [0] but not the point of this comment). One of the reasons I wanted to make this concept is because I was also thinking about exactly what the parent comment suggests. I just haven't gotten around to getting (3d printing) a robot arm yet :)

[0]: https://huggingface.co/MeerDevelopment/Qevi-2B

Nice work! Vision on the qwen models works very good. Jev is a really neat idea. I tried replicating jev style with qwen models using max 1 token output, which works great. How does this compare to that in terms of speed?

The problem is that with max 1 token it is not 100% guaranteed that it will be a yes/no or a score. This model (and engine) solves that by taking the probability of the yes/no token. For 1 question it is faster but not a whole lot, the power comes from being able to ask more then 1 questions for merely a few ms extra per question.

E.g. 1 question would be 300ms but 30 questions would be 360ms total. Most of the time comes from the image decoding.

hot dog or not hot dog!?

I've seen on X that someone made a self driving car driven by Jev. I didn't check in details what it was about. I suppose you could give it some finite set of answers. Just wondering if someone tried it.

With a car I can imagine asking questions like "am I on the left side of the lane", "am I closer then 10 meter to the next car" which feel like questions the current models could answer. I see models understanding the calibration of the robot. Maybe you could have a jev style model make decisions like "move left", "activate gripper" and do the inverse kinematics in a different system.

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