Am I the only one struggling to parse the distinction System One (the system/harness?) and Jev (the model?)?
Introducing System One Models and Jev
331–340 of 512 posts
Re: Introducing System One Models and Jev
#332Earlier quoted context omitted.
if it puts a high confidence value on a wrong answer, thats still hallucinating, no? llm hallucinations are high probability tokens that are incorrect vs the real world
Correct, they have not made a universal all-knowing omniscient oracle, which is what would be required for "can't hallucinate".
Not to be tooo pedantic, but a bot that assigned 0 confidence to everything wouldn’t hallucinate.
A calculator either gets the right answer or doesn’t answer.
It wouldn’t have to be all knowing as long as it knew perfectly what it doesn’t know
Re: Introducing System One Models and Jev
#333As 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?
Re: Introducing System One Models and Jev
#334Earlier quoted context omitted.
Yeah that's precisely correct. For e.g. classification tasks, even in 2026 people are doing things like hallucinating deliberately, and then matching the hallucinations to embeddings - https://softwaredoug.com/blog/2026/08/10/hypothetical-classi ... With TypeSafe it just picks the class (actually probabilities across classes), reliably every single time.
404?
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Re: Introducing System One Models and Jev
#335Earlier quoted context omitted.
if it puts a high confidence value on a wrong answer, thats still hallucinating, no? llm hallucinations are high probability tokens that are incorrect vs the real world
Correct, they have not made a universal all-knowing omniscient oracle, which is what would be required for "can't hallucinate".
That seems like a weird standard.
I would be happy enough with: only produces what it can verify with sources.
If you eg try to remember a court case (ie produce the reference via LLM token generation only), it's easy enough to check with your data whether it really exists. Similar for following links and other references.
If your data or sources are wrong, obviously your report about them will be wrong. But I wouldn't call that a hallucination.
Re: Introducing System One Models and Jev
#336I 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...
Re: Introducing System One Models and Jev
#337Cool I guess. Definitely not worth the 1000+ points though.
Re: Introducing System One Models and Jev
#338This, 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.y…
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Re: Introducing System One Models and Jev
#339After 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 th…
Curious what your use case is if not confidential.
Not confidential, but not super relevant, as this is something I have learned the hard way over the past year across various projects.
A lot of people have become prompt maximalists, asking for complex multi-part solutions or dynamic workflows in a single prompt. You can get this to work sort of reliably with frontier models, but without much confidence or clarity where things might break in practice. My goal is to strip out as much determinism as possible from prompts so the LLM only needs to handle a narrow, well-informed decision, like "Pick one of these three things" and build around the answer. Sometimes you need to fill out a whole JSON payload and LLMs really actually suck at manipulating and adhering to JSON. They do ok now because labs have put in a ton of effort on making harnesses play nice with structured data. But it comes at a high token and context cost because under the hood I suspect the model is churning invalid text repeatedly until it gets around to passing some internal validation.
Re: Introducing System One Models and Jev
#340I 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/a…
LLM seems to have become synonymous with Generative Transformer architecture.
While this model may share much with GPT-style models on the encoder side, it clearly has a different decoder architecture. So is a high-parameter count language model an LLM even when it doesn't have a GPT-style decoder? The definitions are in flux.