> Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle.

Oh, I have one of those use cases, matching people in genealogy trees. You can ask all sorts of questions: do the names match? Do they match within some edit distance? Do they match according to soundex/ metaphone rules (which are themselves a ginormous set of rules for letters and letter combinations which may or may not result in the same sounds, hand-coded as a huge if tree by a linguist not a programmer)? What about their relatives, do they match by the same rules? Should we incorporate domain knowledge about local naming customs? Etc etc.

I pointed a coding agent to this problem, and it aggressively started coming up with complex scoring rules and testing them against real datasets. Which led to sort-of acceptable results, but it still missed lots of cases which were obvious to a human, and had false positives which were obvious to a human. Which I could trade off, and slightly improve, with more back and forth with the coding agent.

Pointing a good LLM to all the information about two people, would of course give great results. Maybe even better than human judgment. But I can't do that for 100000^2 people, it would be too expensive in all sorts of ways. I need a fast, reliable scorer. I could maybe train an embedding, but that would be a huge job and where would I get the quality data?

First, congrats to the team on launching something genuinely interesting and new.

Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that.

This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do.

Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring, but it's nothing like the code generating models we're all using today for code and automation.

Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. You can enforce structured output from an LLM too, with an appropriate harness, etc.

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

Wasn't really till seeing this home assistant demo they have (https://www.loom.com/share/18c4dbcf8db546dfb2d7f2ef018e78e4) that the value really clicked for me.

Seems really cool.

This, combined with contracts, could make a lot of things so much fun now!

For those who don't know (which is probably everyone but me), I ported the design-by-contract pattern in Python and combined it with LLMs. This was early 2025. I originally wrote about it here: https://leoveanu.com/2025-03-01-dbc/ . Contracts are a core feature of SymbolicAI ever since. The community seems to have loved it too (https://news.ycombinator.com/item?id=44399234).

I think I'm starting to glimpse the implications and it's gonna change agentic workloads if it holds up to scrutiny. It's too early for me to tell anything other than jot down some rough thoughts.

In short, you get blazingly fast semantic branching you can use in control flows. For contracts, I can now directly take the data model that you have to design and convert it into Jev's expected format. Or I can use Jev for semantic branching in postconditions.

If my understanding is correct, that should be doable, but I need to think more about it. It could be that with Jev I can finally “compile contracts” and better chain them into workflows, which is something I always wanted but didn't know how to do properly.

Eager to test. On the waiting list.

This is a very promising idea - a model that takes arbitrary text input (which can be a complex json), plus a set of questions (yes/no, multiple-choice, or score) and quickly (milliseconds) and cheaply ($0.042/MTok) answers those questions.

Unfortunately, none of this is explained in the announcement, but the documentation [0] is pretty good.

[0]: https://docs.typesafe.ai/concepts/how-to-build-with-system-o...

One application that sounds pretty interesting would be the creation of wikidata pages for anything. Plug a topic/word/concept/historical event in, take a bunch of wikidata properties, rephrase them as questions with the choices being the existing property values. Then feed it to LLMs or something. Does that make them more reliable? Probably not.

Cool approach, i think less latency and cost is the way to go.

Here's how this would have likely been made.

- Tiny transformer or equivalent model (maybe a few bn or so?), explaining latency and cost

- Questions are sent in parallel to multiple copies of it (I'm sure they're edge located)

- The model is post-trained for calibration in a wide variety of data (the recipe is relatively simple, and likely targeted on distillation of logprobs / confidence of a bigger model)

Notice how cost is ONLY for input tokens as output is merely numbers (few tokens) because input could be huge (questions and options).

At 0.042-per-million price they have, Astra estimates the model to be 3bn parameters.

One could replicate this by post training Qwen 3.5 2Bn. I expect people to do so soon!

It seems like the docs[0] are a better explanation? The comparison to llm tokens is kinda confusing.

It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence).

Edit: On the AI primer page, it looks like they do the RLCD on a pre-trained base model?

[0]:https://docs.typesafe.ai/concepts/system-one

Looking at the example Jev use cases, it almost feels like Jev's incredible cost/task can make it competitive as a generalized "poor man's ranking" algorithm that can be useful for lean startups or any fast paced development org.

I need to rank 1000 articles and pick the 5 most relevant for the user? Jev.

I need to audit and strip out content because my user is affected by regional privacy laws (without hallucinating)? Jev.

I need to surface the 3 funniest media comments that match the user's sense of humour? Jev.

I am not an expert in this domain but as an engineer-turned-researcher, this looks a lot like GliNER with a fitting harness.

This is something I focus on in a bunch of my experiments - how to get immense value out of tiny models (<1b params). There are lots of different architectures out there and there is so much to optimize if you know what you are asking and have a grammar to constrain with.

Great to see this and I hope this is a lot on top of what is already openly available.

So in theory you could feed it incomplete text, and then ask it for the probabilities of what the next character could be?

After much fumbling around with prompts and evals, this is exactly how I am using LLMs in production, to narrowly make choices and return structured data. Any deterministic work gets pulled out of the prompt and my goal is to narrow the model output to be as clearly defined and as minimal as possible.

Jev's focus on structured I/O and confidence scores are game changing. If this does at all what it claims, I think this is going to quickly become the new standard approach for agentic systems.

Side note: it took me more time than I would like to admit to realize that Diogo Almeida isn’t a satirical version of the name Dario Amodei

I would love for things like this to be accessible via hubs like open router or AWS bedrock. It's hard to justify adding new model vendors directly with all the heightened concerns about privacy and security, but if bold new capabilities are added to a centralized already-vendor like AWS, technical people can adopt them without going through a whole compliance/purchasing/vendor review process. And an extra middleman tax is well worth it when the cost savings of the model itself can be one-two orders of magnitude.

This sounds good but so far all claims just sound like marketing terms. I'd love to see real proof. e.g. "RLCD" and "parallel sampling" have nothing to back it up.

also "70-500ms vs 3-329 seconds" are apples-to-oranges unless the LLM baseline is doing comparable work (e.g., long chain-of-thought). If Jev is skipping generation entirely for a narrow structured task, of course it's faster.

Nonetheless i want this to be true, so I'm looking forward to Jev

Edit: I really have to say that I like their manifesto https://typesafe.ai/manifesto

I'm trying to parse it down to what we had before vs what is new here.

We already had encoder models that skipped text generation for giving us a numerical output that could be computed as a probability. we also got no hallucinations and faster inference for free there. So we already had

1. "unstructured state in, probabilistic decisions out" 2. "orders of magnitude faster and more efficient"

What was hard there was to train the model head without ML expertise, and considerable amount of data.

This seems like this is a democratization of those encoders? The addition over existing encoders seems to be coming from being able to specify the output shape (up to a cardinality of 255). It is unclear to me if this is possible using Jev without additional labels for fine-tuning.

If so, that is still very impressive, but I think the faster inference and 0 hallucinations might come for free, from it not being generative.

I could see this being fantastic for classification tasks. Last year I shifted from using LLMs for bulk data classification tasks (1M transcripts) to generating embeddings and categorizing based on cosine similarity. It saved a ton of costs and time, but wasn't as accurate as LLMs. This seems like it can give me Terra-level classification ability with the cost/speed I need.

How does Jev compare with encoder language models like BERT/RoBERTa/DistilBERT, which could also be used for text classification?

I think this is a great direction -- for some kinds of users. And this makes me wonder if the 'vs' framing is misleading.

Yes, I think it's a mistake that many organizations are cramming LLMs inside of automated pipelines where the extreme generality/flexibility of the model is at odds with the fact that you're using it for a very specific task that gets repeated over and over, and needs a very specific structured output to be successful. But specifying your task carefully (as well as deciding what counts as your input state representation etc) seems like a form of programming. Something (a person or a model working in a relatively unrestricted way) will need to produce a configuration/specification for this system.

So rather than Jev vs Claude I imagine that using Claude/ChatGPT/whatever interactively to define / refine your Jev config which then runs in prod might be the happy combination?

I think what this shows is how important branding and comms are. They've captured imaginations with their demos and nomenclature, despite the arguably non-novel architecture. One forward pass, read the embedding space, train some regressors on predicate structure, [??]

> Input tokens: $0.042 / MTok ($42 per billion tokens).

> Output tokens: FREE (too cheap to meter).

Insane. The video demos are really compelling, in particular the speed.

> Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle. The surrounding code constrains their freedom, making them easier to compose into reliable systems.

I buy this vision. A lot of LLM integration I see these days is ultimately exactly this. OpenAI-style structured outputs works decently but this would be a great improvement in cost, latency.

I am confused why they say it is not an LLM and then in the documentation it is shown as being an LLM derivative. The documentation makes it sound like they're taking a pretrained LLM and then giving it their unique post-training. How is that not an LLM?

FAQ: Is Jev just a smaller LLM?

Jev is neither small nor an LLM, hence being off the intelligence Pareto curve.

Image in documentation: https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-prime...

There's a whole lot of information on this page that doesn't tell me anything about what this actually is. Can anyone spell out what the architecture is here?

They claim it's not an LLM, which I read as "not an auto-regressive token generator". I assume they are still using a transformer, otherwise they would be talking about the thing that's not a transformer, instead of all the fluff on the linked page. But they emphasize parallel generation, so is it like a text diffusion model?

Always exciting to see people working on novel models, rather than the Nth version of the same slightly tweaked LLM.

I'm very curious how much ressources are needed to run such a model. This could be a complete game changer for local applications.

This is a good product but the naming/branding is pretty unfortunate.

Typesafe.AI sounds like some typescript/structured output type of tool…

What even is “system one” ?

IMO the product/tech is really there, just needs better communication.

Can I put it as Air Traffic Controller? With similar error rates as humans?

That would be the litmus test.

"Does not hallucinate" is not the same as "is never wrong".

So the ATC test could be the benchmark.

https://x.com/completeskeptic/status/2099925682726002904?s=4...

The doom demo is quite cool

Wow, this is really cool. If this holds up to scrutiny, and has a decent context window (+16k), it suddenly changes our project's status from "cool concept, too slow and expensive to release" to "doable", just like that.

Just joined the waitlist, excited to try it out!

This has the potential to be huge for computer use.

OpenAI has been teasing how fast computer use is with their models running on Cerebras chips but the difference here is a burning hole in your pocket.

This is actually pretty cool. I think the undertalked about part of this for TypeSafe is that they can always "extract"/distill the frontier of this type of task from the newest LLMs for cheap. Jev seems seems to be GPT-6-Astra/Fable 5.1 but I imagine a bunch of training data is from earlier models?

Then, you can serve it faster/cheaper than the frontier LLMs. It's basically distilling a small but extremely common use-case from LLMs and serving it. Then RLCD comes into play to update weights when a new model comes out, etc.

Any thoughts on what the next potential "cheap" win to be distilled from frontier LLMs is? I'm going to need to play around with this.

It could be used for coding if you gave it an AST.

If you work at TypeSafe please try this.

Side note: This is probably how LLMs would perform with better encoders and next-latent prediction, so eventually those will beat this architecture out. Still amazing though.

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?

do you all see the use cases being similar to what you might use Fastino's Gliner models for? i see similar differentiation from general purpose LLMs in the sense that they can take natural-language input and return outputs adherent to a user-defined schema.

https://fastino.ai/blog/gliner2-5-span-free-information-extr...

im thinking about how well Jev could be used to replace a current LLM-as-Judge evaluation workflows, specifically on chat transcript data (think ~1,500 tokens) i wonder if the reasoning usually required pushes it a bit out of scope. didnt see anything published about constraints on the state size, so would be curious to hear about that.

I'd love to know if Jev is still fundamentally LLM-shaped in architecture. Like is it using a single forward pass with a learned readout over the predefined options (i.e. a discriminative head on a transformer, no decoding), or something else? I did similar things for zero-shot criterion-based classification using a 4B Qwen model but could not reach the level of intelligence they've got here. Tho speed/cheapness was similar.

Overall this seems like a classifier that gives weighted scores per custom labels. It's certainly useful, but whether it brings higher quality than an LLM in structured output mode has to be seen in objective benchmarks.

Great to see something new..

However I don't understand how are they claiming zero hallucination, how does giving confidence score fix hallucination? or am I missing something here?

We've already started using it for some pretty powerful decision tree stuff. We're just scratching the surface. We shipped an extension for Swamp[1] a few minutes ago and the combination is great!

The one downside is that the context window is very small (32k.) So some initial ideas we had for initial evaluation of code reviews won't fit yet in the window.

1: https://swamp-club.com/extensions/@swamp/typesafe-ai

Very cool! Can you explain when I would use this vs. training a standard ML model on my data? Suppose I had a fraud dataset with features like customer ID, amount, merchant, online or in-person, etc. - I can't imagine that a general model like Jev would predict this more accurately or cheaply than even a basic XGBoost model trained on my dataset (one that I could build in a few minutes by asking Codex to build it). Where does Jev add value here?

Could you use this to build a proactive memory formation and retrieval system for LLMs that runs lightning fast?

Last 32k of connect + Summary of current task: Did we learn something useful here (true/false)? What is the category to file it under? Then notify the LLM to file it away.

What class of memory might be useful here? Model gives probability to each item in the list. Short description of all memories ordered by tagged class is used in the next round. Are any of these memories useful in the current context, such that they will inform the model and help in its task (yes/no)?

I’m sure there’s some fine tuning to be had, but this sure seems like the basis for a substantially better proactive memory system that works around an existing LLM conversation.

If I’m understanding what this does and how this works (generic input, intelligent classification with probabilities, rapid and cheap), this is absolutely nuts.

I'm trying to understand what difference does this make over LLMs.

LLMs are universal simulators, their latents model the world. So I bet if you compare their logprobs with probabilities output by this model, it will be highly correlated.

Someone should do this quick experiment. I bet there won't be enough of a meaningful difference.

Doom demo is beyond impressive, even scary

This is too much for me. ML playing doom was a thing since before LLMs, decisions tree were always insanely and no one ever used then anyway, i can't see anything new in this yet everyone is treating this as a revolution. This technology was always there and quite easily accessible all along.

They never show exactly how they use it? Only a bunch of animations of it 'working'. Would like to see the actual code used for the demos!

This is best thing to use for decision making, evaluation, classification. If I'm not wrong.

Seems like LLM can do everything Jev can do (just structured outputs?) but Jev is highly optimized and purpose built for it and thus way faster and cheaper. Is that a fair description?

As a zero-shot classifier, I expect that effectiveness is dependent on the data trained upon.

Jev input … > Unstructured data (e.g. text) with an emphasis on structured program state.

What pre-training data/model is Jev based on? Surely result effectiveness is dependent (outside of one’s own input as “state”) on that?

> AI Map Reduce over Big Data

> Search for relevant information over giant corpuses

Do you mean as an alternative to embeddings?

> [others] Output tokens: ~5x more expensive than input tokens.

> [them] Output tokens: FREE (too cheap to meter).

I'm very confused by this.

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...

The Doom demo looks impressive but was it a fine-tuned model? It's the difference between a cool demo and revolutionary tech.

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

Super cool! Instantly joined the waitlist.

It might be boring, but I can see exactly how I could use this right now to improve my agentic rag.[0] In two months I am supposed to deal with a giant corpus, while still maintaining responsive chat UX. I have been working my butt off to make our first big client happy. This could really help solve the chunk ranking problem.

[0] assuming the policies are compatible with sensitive production workloads, some time in the near future.

Looks promising. I'm building an AI video editor and multi tool calls take >30s using Gemini. This would be a a game changer if Jev can take that down to single digits at p95.

I was thinking about something similar (maybe) - generally speaking, embeddings for LLMs tend to learn real world concepts - things like 'fruit' or 'France' or 'city' as directions in embeddings.

But in things like programming, most concepts are abstract - 'if hungry eat an apple' in programming terms would look like

'if hunger > 50 {apples--; hunger-=30;}'

and compilers work with 'concept erasure' - to them, tokens (which are like llm tokens) look like

'if var1 > 50 {var2--;var1-=30}'.

They don't care about how these things map to real concepts. So all the embedding directions used to encode real-world concepts are just noise to LLMs when programming. This greatly reduces dimensionality and training costs. So does a token representation tuned for programming constructs, rather than natural language would probably have a more efficient encoding.

I can see the value in this but looks like there's going to be trouble in communicating the difference between this and a regular LLM, and also proving the potential cost savings in using this to replace existing systems that are using LLMs with frameworks like langgraph, as this can't be a drop in replacement and would require a significant amount of re-architecting/reengineering of systems to get the type system to work

If we could come up with a system to classify the probabilities across a large number of candidate words (or components thereof) then this could actually be good at producing text, one element at a time. We could call these elements 'tokens' and picking the right one could be called something like 'decoding'. Crazy idea but hear me out...

On a more serious note, it will be fascinating to see how this different spin on modelling inference will create new paradigms or slot into existing ones.

This is quite the paradigm shift. Can't wait to get my hands on it.

While I understand that accelerating development isn't necessarily the target for this, and it's not at all intended to generate code the way many of us are...

I think this could be pretty decent in CI? There's a lot of "flakes" I've mediated that this could have handled much more efficiently. Maybe observability as well, triggering elevated logging and other initial measures?

This will be insane for tool usage, and probably where the major economics for day-to-day usage will be.

The goal is going to be to use llms to distill operations down to some dsl, and pass it into something like Jev.

Hm, would be good to understand the architecture better. Is this answering just from a world model informed prior? How informed is it by the information in the prompt? I can't see this maintaining calibration across all domains and all types of structured output.

Is there anything published on how it maintains calibration? Or when you say "outputs calibrated probabilities" you mean "as calibrated as frontier LLM models, just cheaper" - which is a different claim; as LLM's aren't particularly well calibrated

I think I missed why is this faster? What I’m reading here is it’s similar to constrained decoding but I’m not seeing the explanation of why it’s able to get those results.

Hasn't there been a lot talk about Astra's opaque reasoning capabilities (being able to think through complex questions without using a chain of thought)?

Given that, can't you just replicate Jev by telling Astra "here is the question, you must make a multiple choice decision / output a score between 1-10, please answer directly in a single word, no reasoning allowed"?

(Edit: Ok, Jev is much cheaper in input tokens so these two aren't directly comparable at all)

insane doom demo i wonder what the limits of its intelligence are? i'm guessing it's not great at reasoning tasks, it seems breaking down the problem helps significantly, but how much does a problem need to be broken down for reliable performance? also this would be huge if it could run locally but it seems like there's no intention to do that at the moment

This is amazing. I really could use this.

I like the idea of System one models but all LLMs so far work as system 1 thinking because humans generate speech subconsciously with system 1.

System 2 thinking requires consciousness which AI does not have, so even reasoning models are still system 1 thinking as system 1 in humans has reasoning with heuristics.

Its limited but most people navigate the world with it completely, so it's enough for AI.

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.

A question I have, with the type { output: string }, would the model not become a LLM? And if it does, shouldn’t it cost the same as a LLM for output?

This is basically a zero-shot classifier that can accept raw text (or structured text) as an input, and is able to classify that text as accurately (they claim) as a frontier-level LLM. I have workflows this would be useful for, looking forward to it showing up on OpenRouter.

You know you're too old when you see the company name and think! Oh I wonder what Martin Odeskey , Jonas Bonér and co are up to. Wait, didn't they become lightbend... Altho this comment takes away from what these guys are doing which legitimately sounds interesting.

This makes me think of Expressions of Change [1], a project that aimed to make updates to a program a first-class primitive in a programming language. A model like this can't output code directly, but perhaps it would be well suited to select from the small set of discrete operations on code envisioned by the EoC author?

[1]: www.expressionsofchange.org

A few questions:

1. Do you provide any kind of largest common subtree caching for cheaper input?

2. Have you tried auto-generating Lisp programs structurally?

3. Have you tried augmenting a Lisp language with a `choice` function that makes choices given a prompt, the environment, and the continuation stack?

So is it a structured data-based language model? Or is there a model and a harness? Hopefully they’ll open up and explain more.

Huh this looks fantastic. The Doom demo really sold for me that this could be a great tool for accelerating QA at my gamedev studio. Signed up for early access.

Funny how the authors are asserting that "doing the right task > data > compute > algorithms" while simultaneously releasing AI model for calibrated decision making, which if they work, would mean that "compute > doing the right task"

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?

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.

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

This sort of stuff almost sends shivers down my spine, it's like i'm looking 5 years into the future.

I am positive I know exactly how this works, I made something similar a few months back. But the problem is without generation you are extremely limited in the use cases. And while the model can't hallucinate, it can still be wrong. It just can't make up data.

Very cool. LLMs have been borderline unusable as functions for the longest time, very excited for this direction.

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

> 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.

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?

Is this a Markov/Diffusion model with some sort of external Engram memory? If so, this could be extremely interesting.

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

a) Is this available on Bedrock? b) Does it support structured outputs? c) What about trying it out?

Seems like "some" of LLMs tasks are now Jev tasks.

If it is so cheap, why such a limited release?

So... a classifier model?

If I understand correctly, it can play chess and rubic cube better than LLM ? ( may be go too ? )

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

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.

If it work as good as they say it does, confidence score + really fast response when you want very fast response, basically.. To me it is a crime against humanity to not open source it. Just get the money from cloud inference and cloud agentic sessions or whatever but open source it. This tech, a good harness, a good model provider, and you have basically a AGI building machine.

I like it. What is it?

The whole page reads like it was vibe-written by an AI. If I'd built something as disruptive as this claims to be, I'd have spent at least fifteen minutes writing the announcement myself. Every time I see 'we' in an announcement like this, I picture one guy alone in his basement.

This looks and feels a lot like productionized conformal prediction

Can this be used in conjunction with a text-generating LLM for better quality code generation?

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

> Extraordinary claims require extraordinary evidence so see below for the receipts.

Yes, that’s the kind of attitude I want to see in these model releases

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.

Would it be fair to say that this is tailored for tool-selection subagents?

Can HN have a tag for open-weight vs closed-source models please? The progress is nice, but if it is not released at least in papers or open-weight? These are just ads?

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

Okay, so it doesn't output text, that much is understood. What are the inputs like? I'm assuming maybe a text input? maybe an AST definition? Really hard to tell how this works at all from the demos, especially since we can't really try it out.

Any relation / inspiration to GLiClass?

Woof, that page is hard to read. I don't understand what they've done to the way text is rendering but it's not great for my eyes.

Why did they pick the name System One? It's not really explained what "System One tasks" and "System One shaped queries" are. Things that need a fast response?

Does this imply it's a very small model? I couldn't find anything about the model itself.

The eval is baffling me

> we assume there is a correct compute graph (a “workflow” represented in code) and use the predictions of the largest, smartest, and most expensive external models as reference probabilities. ... Rephrased: every model gets the same workflow. We test how they compare to the average of the smartest models (in this case, Astra and Fable).

They assume there is a correct graph, but they don't compare to that, they compare to the average of the smarts models? So the smartest models are getting it wrong but you compare that anyway as a benchmark? So the outcome is "how much of a Fable am I getting" etc. Why not compare the actually correct thing?

But then even on this hand constructed eval, the first plot is showing Jev at less than Sonnet 5 accuracy. It is barely better than Luna. There are two Opus 5's and two Sonnet 5's without explanation. What is the plot showing?

I gave up.

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.

Is there a downloadable technical report somewhere?

Can this be used in practice to write code?

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 potentially huge and can crash the Big Two's stock prices or block their IPOs completely.

Am I the only one struggling to parse the distinction System One (the system/harness?) and Jev (the model?)?

I could put this to use today.

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

this might finally be smart enough and fast enough for jarvis. hard to feel like iron man when your assistant takes 8 seconds to decide to pause your music

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

um what is going on with the outfit changes in the launch video...

https://x.com/CompleteSkeptic/status/2099925682726002904

Is there a bottleneck which would hinder putting this architecture in charge of a humanoid? Would it be able to operate continuously, for example in conjunction with an LLM for long-term reasoning? Doom seemingly works extremely well.

oooooh it can play Doom!

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

Parallel inference where you don't want a subagent seems niche. But there is a lot of random things where businesses ultimately want some kind of score instead of generating something.

I think the interesting thing would be seeing if prompt injections still work with this kind of model.

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 :|

Looks like a great model for NLP.

my sons name is also Jev

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

"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.

> 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...

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

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.

Finally a fast solution to isOdd / isEven :) /s

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

"is this the real thing or is just fantasy"

This makes me kind of nervous for the whole AI thing now. Are people gonna lose their jobs, etc.? so much of the economy is now built on top of LLMs.

Cool I guess. Definitely not worth the 1000+ points though.