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Is this fundamentally different from other text-based LLMs, or is it the same except with special reinforcement learning a safe guards around generating valid types?

Surely it’s still generating some kind unstructured data internally? For example, what if I told it to generate a short story, but the short story is output as a JSON string?

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An LLM takes (text in) -> (text out).

Jev takes (…questions in) -> (…probabilities out)

So Jev won’t write a story or emit arbitrary structured data. But if you ask it the right questions, it can make near-instant “decisions” against those questions, with accuracy and world knowledge on par with LLMs. The economic advantage is that it’s parallelizable and can give back up to 255 answers at once, in milliseconds.

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