andy12_

We are 8 years into Sanchez's government. Ridiculous scapegoat.

They are very bad capitalists then, because ADIF high speed lost 200 million dollars last year.

By the way, don't act as if the Japanese high speed network is not the best on the planet. It's much more liberalized than the Spanish one.

> worth maintaining publicly via an actual government agency rather than a government-owned for-profit corporation required to compete in a market in which it is at the inherent disadvantage of public scrutiny.

The rail infrastructure is already publicly maintained via a government agency, ADIF. ADIF is not a goverment-owned for-profit corporation, its primary mandate is public service, not profit maximization, and ADIF doesn't compete with anyone, it's a state monopoly.

Neither of those words are very specific, and everyone in the LLM space knows that "reasoning" tokens are just tokens that don't appear in the answer shown to the user.

It's a special case for an LLM, and you can use an LLM with structure output to get similar results, but you can engineer specifically for that case to get better results per dollar for it. That's why there is little reason to adapt GPT 5.6 Sol or wathever for this task; it can already do it (at a high cost). For OpenAI to compete with Jev they have to maintain another line of models, something like "GPT-5.6-instant-decision", that is small, fast and cheap, in the scale of GPT-5 nano.

Note that I don't think OpenAI is incapable of doing it, but I just don't think they will bother with it.

I find it unlikely. OpenAI is all in training models with reasoning with RL, and Jev-like models are the total opposite. They are made to not reason at all to be fast. If you want to add reasoning on top, you might as well use a conventional LLM because you lose the price and speed benefits when you output auto-regressive tokens. I don't think OpenAI will even bother with this.

> My main assumption is that Jev is using something quite close to a conventional large language model. As evidence of this, Latent Space reports that many of the early clones are indeed LLM-based.

Not proof that this is the case with Jev though. It might use non causal text encoder for the state, which could make sense given that it's very good for its price.

I hope so. And I would really like to try an actual Jev open source model. But it will make it more difficult to market it when someone releases something like that because of so many of these "open source Jev-like model".

All the people that are just writing an Jev-like API on top of a normal LLM are missing the point. What makes Jev special is the training data; it's how it's trained. The architecture is probably nothing special. Just a text encoder with parallel prediction branches.

I have tried many of these open-source Jev-like models on some linguistic tasks and they are so bad compared to Jev.

I think the demo is this way simply because looking at the LLM find wacky ways of implementing features without breaking the law is fun and drives the point across. In practice I imagine you would write something like "If you don't see a clear way of implementing a feature without breaking the law, ask me for directions" in AGENTS.md

If the LLM changes the laws to bypass them that's on you. The whole point of this is that you don't have to manually review most code written; only the laws. If the LLM changes the laws and you ignore it that's a you problem.