I linked to the tweet that has the video because if you are not signed in you cannot see the whole thread of tweets.

I can see the individual tweets in the browser while not signed in though.

The doom demo is also in the article, for anyone that doesn't want to go to X.com. :)

Link to a raw MP4 of the video, from the parent article: https://framerusercontent.com/assets/rlL7ImEbISFoYt3IJEHHfvj...

It's in the parent article under a section named "Doom" in case that asset URL ever changes.

i think its just telling the model that it cant output a shoot action

It wasn't until the demo videos that I realized the post wasn't satirical.

I feel like should be Cario Amodei. The D to C flip a rotation of the M to W flip

Wild that it doesn't generate text. I wonder how its technology compares to Tesla's FSD stack.

Signed up for the beta! :) would love to put this through some real-world shootouts against traditional LLMs to see where this type of model really excels.

I’m guessing it might be able to replace maybe 40-70% of LLM calls for a given pipeline depending on the business task, cutting the API costs on those calls by an order of magnitude.

https://typesafe.ai/blog/antibenchmaxxing

But also effectively this is a classification model. It excels at specific certain types of workloads, and obviously will fail at others. Not really sure how one benchmarks this tbf. I can see their argument on why this requires a novel specific eval for whatever your usecase is. A consistent "global" benchmark might be hard to do

Looks like a great model for NLP.

The whole video seemed generated to me

definitely not AI-generated - this is my real wardrobe

we also thought the voice at the end was AI-ish, but apparently that's a real voice actor but slightly sped up

Pretty sure that's an intended joke.

Reminds me of this: https://www.reddit.com/r/ITcrowd/comments/tg05j1/i_cant_beli...

Cool project!

That's what I was insinuating through "better encoder"; the model creating more efficient representations of ASTs using something like JEPA

Yeah it would be quite trivial to try and implement an auto regressive AST generator for STLC with Jev provided that you had bounded variable names and integers.

As you said, if it worked, they would have demoed it haha

I could see Jev being great at finding key symbols in codebase before a code generation/code review task. I sent you guys an email (to hello@) about using Jev in Code Review for www.ellipsis.dev.

There was no "AI Winter"

I could put this to use today.

I think we'll see a bunch of different architectures over the next five years.

The doom demo shows the program state / query.

Super intrigued by this - large scale automation using LLMs is quite annoying due to deprecation cycles of models from frontier labs and cost of running your own being prohibitive when you have a blend of them.

The thing is, in this climate it's hard to believe such tech will remain secret for long.

So, assuming this is not vaporware, this would raise the tide for everyone because it shows what's possible.

You can set privacy requirements and define an allow list. To me the main value prop is that I get one bill for all models and can quickly try new models without signing up anywhere or changing my code. Oh! Also you can pass an array of models and if the first provider is down it automatically falls through to the next provider. More useful than it should be...

I always setup guard rails so that only zdr providers are used.

They don't like adding stealth startups :(

They have various benchmarks, e.g. how much time it takes them to do wikipedia page -> page games. Jev seems to take the same or fewer hops but in ~10x less time and for ~10x less money.

It's totally reasonable to compare against LLMs doing chain of thought if it gets comparable performance.

It's not an LLM though it's a frontier model on structured data

so what? put an LLM on Cerebras and get its responses faster, and put Jev on Cerebras and gets its responses even faster

love that you love the manifesto! letting the first batches off the waitlist now, but we do have some early users describing their experience (https://x.com/danshipper/status/2099947471518474522)

they’re talking about two totally different things, right?

it's our output tokens that are free (under the system one / jev column)

What is it about the rendering of this page that is so... off? It almost looks like the entire thing is a <canvas> element.

edit: looks like a framer export where there is a text stroke being applied :|

Bingo. It's a Psychology term for the part of our brain that reacts instinctively rather than thoughtfully and logically

If you zoom in (especially on the large title), you'll see that the text is a semi-transparent gray with a black internal outline. It seems like all the typography is SVG-rendered. Actually insane. I've never seen this before. Not even the most vibeslopped websites have that.

We deliberately chose not to publish performance against public benchmarks. In fact, we plan to only have one-off evals when we make product updates.

lol, I bet they would publish them if their score on those benchmarks were good.

"is this the real thing or is just fantasy"

yup just joined the waiting list with a very similar use case in mind

it is just a model, no harness yet ;)

it is a structured data model, but technically not a language model (it doesn't generate language)

If you provide it an AST of the english language, yes.

you could, but it the model is not optimized for text

this is complex, but generating text is highly complicated and requires mode dropping to make long cohesive text

I think the joke here is getting missed

It looks like a specialized encoder-only(-ish) transformer with scalar and ordinal output heads. Acausal in effect, maybe? Probably not even autoregressive?

I'd use this as a tool an LLM can use for specialized tasks. It's not AI in itself.

If this is true means, AI Stock bubble burst. (For good)

1. I am extremely on the same page 2. I do think that subconscious is not only much smarter than we give it credit for, but also much more robust than the "jagged frontier" of current LLMs

(shilling my blog post on that jaggedness: https://www.completeskeptic.com/p/lies-damned-lies-and-bench...)

Hi - first congratulations, System One looks really promising.

The Doom demo really help me, at least, to understand how System One differs from LLMs. However the first demo (Side-by-side demonstration) - I'm struggling to understand what is going on here!

very accurate!

the one nuance I'd get into is I'd call it "zero-shot" over "instruction-tuned" (the latter often implies a particular distribution), but very safe for sharing

architecture is close to the chest for now, but we have talked about writing a paper

I don't want to shill my blog too much, but I will say data is probably far most interesting than architecture: https://www.completeskeptic.com/p/the-bitterest-lesson

I assume this isn't really for consumers/individuals currently? Kinda feels like an improved magic 8 ball.

I can't really intuit how I should think about when the model will be accurate. Is there somewhere to read more about that? I assume customers would just have some tests or talk to you.

If this is true, then AI Stock Bubble burst (for Good)

Is the tradeoff of the parallel output that we don't get arbitrary string generation? like output # of tokens is fixed ahead of time?

Either way, really cool and impressive.

Outputs

LLMS > Strings / generated text. Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be parsed + validated. There is also always some risk that the AI goes off the rails.

Jev > Type-safe structured values. Possible outputs and structure are defined in advance. The model never makes type errors. All answers are accompanied with calibrated probabilities and confidence scores.

I mean, this isn't even remotely comparable to LLMs so why compare? Also, why are they bringing up AGI given there approach is so restrictive that what they're building literally cannot have the creativity required for AGI? The video is 100% marketing slop...

The bulk of the application of LLMs is that they generate reasonably reliable text which doesn't need to be defined in advanced. I'm sure there is a niche for this and congrats to the team, but please let's not hype this as if it's the next big thing in AI...

we have played with this! the fascinating thing we've found so far is that adversarial examples for our model are quite different from that of LLMs so that they work even better together

So an encoder-only model with a classifier trained on the heads or something? DeepSeek recently switched to an encoder-decoder architecture in an attempt to get the best of both worlds (fast prefill while preserving generation capability), I wonder if that might be the future?

That was the first thing that come into my head. OK I can train very simple model, that can generate json's for specific tasks, so what? How we can be sure that this "limited use cases" not just overfitting for particular outputs (or even distillation?)

Except this, this thing looks like revolution.

oooooh it can play Doom!

forget LLM benchmaxxing sidequests, I'm sold on the real benchmark

This puts the human even more out of the loop I'll guess?

Funny how it can do everything but not chat. Sort of how when I was a kid I thought of a medicine that could cure any disease except the common cold.

this is interesting, so not an LLM but can be used in these use cases that LLM's have been shoehorned into

https://docs.typesafe.ai/concepts/use-case-map

exactly right!

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.

that's right, but because these models are probabilistic, it's also possible to be confidently wrong (and all future models will be smarter still and still have that possibility)

Technically speaking when you send the prefix “The capital of France is “ into an LLM it will also produce probabilities across its whole vocabulary.

From a quick look at this it looks like it could easily generate natural language text by following a structured representation like UMR (Uniform Meaning Representation) or the similar representation the Abstract-Wikipedia folks will be working on for generic encyclopedic text (which will be heavily informed by Universal Dependencies). These are basically linguistically principled and frame-based counterparts to a programming language AST, that can be then converted to natural language (in a broadly language-independent way, to the extent that semantics and pragmatics make that feasible) via some sort of NLG rendering.

(To be clear, this one raw model does not support outputing a full AST directly - it wants to output "choice" among fixed options, "score" on a sliding scale, or a true/false answer (all of these with confidence scores attached), so building the AST/structure would be a code-driven (or even perhaps outside LLM-driven in some more challenging cases) multi-step affair where the model would essentially be playing a "game" of building the structured output step by step and getting a revised partial state back. But one could expect this to lead to interesting results.)

Just to be sure that I understand, you're saying that your model "can't hallucinate" because it only outputs a single thing, right? In this way, an LLM can't hallucinate either if I prompt it to do a classification task with a discrete set of possible outputs, right? (Assuming I reject non-conforming output. Actually, maybe what you're saying is that your system can't output non-conforming output?)

I'm biased but I wouldn't call it misleading

- @CompleteSkeptic

Very strange.

Hallucinations were defined in the context of text generation models so your question does not really make sense.

IMO your system can make mistakes that are similar in spirit to hallucination (i.e. answering with a false answer instead of abstaining to answer).

What was misleading was the original title:

"Jev: New frontier model 40-400x cheaper and 20-200x faster"

I'm not the gatekeeper of who gets to call themselves a frontier model, but I don't think most people would count Jev in that group. It sounds false.

If their specific claims hold up, then it would make more sense to say something like:

"Advanced the speed/cost frontier for structured decisions"

So... a classifier model?

Will need hands on to truly tell, but the doom demo seems very promising. If it can play that with text descriptions of where stuff is by distance and degrees in a 3D context then many GUI automation tasks should be easily doable

Can this be used in practice to write code?

What's the difference compared to just taking an embedding and feed forward a simple net trained for the task?

we hope so! the bigger hope is to not just eat LLM market share, but to allow for people to use AI much more in the inner loop of software

Congrats on the release!

Finetuning a language model for decision classification (with probabilities) is already well-understood. What specifically changes in the training objective with RLCD? Are its benefits isolated from Jev’s new architecture/parallelism?

Interesting concept, I can't see a reason to use a generalist classifier over an api rather then just training my own? If it was open weights I would probably mess around with it.

thanks a ton!

constrained decoding (OpenAI-style structured outputs) make models dumber unfortunately - the short+dense version is that simply masking logits is insufficient because if ever a model was assigning probability to an invalid token, the model is by definition confused. you'd be better off erroring IMO

If I’m understanding correctly, this will work well for self driving cars?

I’m not understanding what this is. It’s a faster cheaper LLM?

Congrats on the launch! What's different between Jev and Microsoft's Guidance package? https://github.com/guidance-ai/guidance Is it a diffusion generator under the hood?

Is there a downloadable technical report somewhere?

From the person in the video regarding issues with benchmarks in general, and for LLMs. Also their approach. Good article.

https://substack.com/home/post/p-215252866

We had early access and found it to be pretty useful. Having a second form of verification, where you can ask multiple questions (in the form of Nouls) raised our confidence in the outputs of other models. [0] IMHO This type of model works incredibly well in concert with LLMs, not as a replacement.

[0] https://goodstartlabs.com/research/verification-is-the-bottl...

Current models go beyond the simple embedding because you start to encode groups of concepts in the context-aware part of the model (attention heads or any other method). So it is never simply words/tokens in isolation anymore.

inputs are structured program state. there is an example at around second 30 of the doom demo

(though ideally everyone gets off the waitlist and can try it out for themselves )

I mean, it's a structured output model that (apparently) can't hallucinate. I don't mind the name.

I don’t see this as an option in their website

You could theoretically ask “what is the next appropriate character?” and add the entire ascii charset but i doubt it’d work well and you’d be implementing autoregressive churn across network latency…

strings (and all sequential data structures) are not allowed at all - this is how we make sure all outputs can be computed in parallel (thus no output token cost)

"While Jev gives up string generation, it’s optimized for structured outputs and can’t hallucinate"

Ouh! Any open weights models that can do this yet?? If not, how much longer? I have a Mac Studio coming soon.

what is the…epistemic status, for lack of a better way to put it, of the probabilities? what do they mean? what (probabilistic) guarantees do we have about, say, the responses to

- is the capital of france paris?

- it is august. is it raining in paris?

(forgive the examples; they're probably not semantically the sort of thing jev is trained to work on. but i figure the point translates to various kinds of questions that come up in "inner loop of agentic pid controller" contexts)

a normal text-generating model if asked to produce a number will also do that just fine. i assume in jev's case it was actually rled to essentially learn to express priors over things using its implicit world model, which definitely ought to help, but can we say more?

The technology and the results are very handwavy. What is RLCD exactly ? What are scores on benchmarks compared to LLMs ?

This website does not inspire confidence at all, it all sounds like a marketing piece. I wish it was true, some kind of text-prompted classifier with LLM performance would be cool, but I can't trust it with what we are given.

Haven't seen any docs or so. Is this actually a general model, or does it need training on the the data set it answers? Finding it suspicious you never see some kind of prompt.

Edit: never mind, found https://docs.typesafe.ai/introduction/quickstart by now

Doom demo is beyond impressive, even scary