Quoting from the below link:
https://archerhume.com/posts/jevs-architecture-unmasked
"An ordinary LLM generates “90% confident” as text; its probability of producing those words does not establish a 90% probability of being right. Yet we build fraud screening, moderation, routing and risk assessment around precisely this pattern: paying for token-by-token generation, then treating an unvalidated confidence claim as a probability our software can act on."
That's extremely useful, and is something I have wished larger platforms provided. Thanks chants!
Quote:
Originally Posted by chants
Jev, released by TypeSafe AI in September 2026, is a "System One" decision model designed to return structured judgments rather than generate text. Unlike normal large language models (LLMs) which act as open-ended text generators, Jev acts as a classification and scoring engine built specifically to integrate with software logic.
The cost is way lower, it does not hallucinate, it is not auto regressive or token by token but gives back a predefined schema.
Looks extremely powerful and useful in a reversing workflow. Also hiding intent is far easier as it doesnt have all those intermediate thinking and output tokens to hit a filter. Looks important to the future of agentic reversing.
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