When they say "can't hallucinate" they mean they produce a confidence value for every result, so you could see for example it has 0.1 confidence, and you can disregard the result - that'd be different from hallucinating where it believes it's correct

Has LLM become so synonymous with Generative Transformer that other high-parameter count models that interpret language need a different name?

For all we know this might be a non-language-generative transformer e.g. a transformer where the decoder produces confidence scores rather than language. Please provide more likely architectures if you know them, I'm genuinely curious.

I think the meaning of can't hallucinate in this model is that the type won't be hallucinated.

So if the generated schema is for a tool call for calculator, then the numbers will be valid numbers for sure (and not random words).

To me, it looks similar to BNF schema already introduced and implemented few years ago: generally speaking - it limits the next token that is allowed to be generated, probs are drawn from a subset tokens.

(tbh, I'm not sure why it didn't pick up as a more standard interface to LLMs, as it made a lot of sense back then, and now.)

I don’t think it’s misleading if you compare on the use cases they suggested. It’s faster and cheaper (no idea if higher quality), so it’s immediately interesting for certain things.

And if you buy their RLCD claims, this might be even better than huge models that know a bunch of irrelevant things.

I'm biased but I wouldn't call it misleading - generating text is super awesome and flexible, (we describe that in the blog post - and I personally use string models all the time) but it's true you pay a high tax for autoregressive generation

> Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value.

that is likely true of all ML! perhaps we could debate semantics, but I don't think it's fair to say a random forest "hallucinates" in the way LLMs do

> Assuming there's no funny business, the Doom demo is cool.

The Doom demo seems very funny business. They're not feeding it video, they're feeding it a text description of what's going on in the game. It's not reading pixel data.

I think LLMs would play a lot better with that input too but Jev does seem to have a huge speed advantage; I don't know if the other models could do that in real-time.

> Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring,

My first thought was that it would be ideal for robotics? As in control of limbs, general planning, route finding, etc.

> but it's nothing like the code generating models we're all using today for code and automation.

Is this true? Code is structured output. At the very least it seems like a question of degree rather than kind.

While the LLMs we're using today are limited to sequenced text, it seems that a model like Jev could excel at coding on a more structural level (factoring, controls) by working within the constraints of an actual language specification and supplemental domain model. I don't know, though -- maybe that's too deep and complex.

I feel like the power of the approach presented here is that it gives a model a proper "language" to describe computations directly vs moving tape silliness.

I foresee this to be the path moving forward - giving AI models understanding of the computation directly(as well as compositional rules) This feels like a short path towards total software in many areas.

Agreed. It's a wildly dishonest presentation of their product from many perspectives, which is a shame because it might actually have some good use cases.

The comparison between LLM speed and Jev speed is misleading, because they're using autoregression to generate all of the type names, all of the schema, etc. A closer comparison would be if the LLM was purely outputting the raw numbers. Even then, comparisons to LLMs are pointless because you could train a transformer on the same sort of task that Jev is doing and get even better performance yet again, and a smaller model. I suspect this is some form of stripped down diffusion language model.

You really have to do a lot of hand holding here, and map out your problem space manually, and very carefully, to get any sort of accuracy. For example:

> Keep each Score to one dimension. If a description says “punctual and smart and experienced”, the question is measuring three things, and an input that is high on one and low on another can’t be placed. Confidence drops and the score means less. Split it into one Score per thing and combine them in code

If you don't perfectly represent the distributions of possible answers then you'll likely get garbage results. As far as probabilistic state machines are concerned, I'd say creating the distributions of possible answers, and their hierarchy, is the actual hard part.

One of their examples is:

- "state": "I have asked three times now. Can I please just talk to a real person?"

- "Is the customer asking for a human agent?"

Imagine the users request is: "I want your human agent to call me tomorrow at 5pm."

Human conversation is fuzzy, getting useful reliable results out of this is going to be a challenge. Of course, you could add follow up checks like: "Do they want that now, or later?" -> if later -> "Do they want that tomorrow, or the day after?" and so on... But now you're building an LLM out of if statements. I am skeptical of whether this model has much utility for fluid language interpretation - I suspect it'll only be useful for scenarios where you've tightly constrained the answer space but want to use fuzzy language to describe it. Like:

- Question to human: "Would you like a support agent RIGHT NOW?"

- Their response: Yes | Yeah | Mhmm | ye sure (any possible yes signal)

Model input: "Did they ask for a support agent?"

Still... a tiny LLM could accomplish this sort of thing without problem. And that doesn't stop someone from saying: "No, not right now. But tomorrow." - and the tomorrow would get missed. I think this is why people haven't really tried this approach much already.

Also their Doom demo is on structured state, not on images. Meaning, the enemies must be being served to the model as coordinates (or the exact angle of projectiles that hit the player), otherwise it'd have to scan every pixel of the 360 degrees to know whether an enemy is in front of the crosshair or not. You can see from the map below that it's also choosing travel checkpoints/destinations through walls. So they've severely cooked this to make it look far more capable than it is in practice, and any speed advantage that is offered here is not factoring in the shortcuts it is taking, the training on the map, and the fact that it can cheat because the structured state it is using is not bound by obstructions.

Here is their docs by the way: https://docs.typesafe.ai/ - so you can understand how it works.

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