TypeSafe AI Launches Jev, a Model Built for Structured Decisions Over Text Generation

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TypeSafe AI has released Jev, its first public 'System One' model, designed to make fast, structured decisions that software can consume directly rather than generating natural language responses. The model is inspired by Daniel Kahneman's System 1 and System 2 thinking framework, positioning Jev as a quick, intuitive decision-maker suited for backend applications. Instead of parsing generated text into booleans or scores, developers define typed questions and receive structured outputs such as yes/no answers, categorical choices, or scored evaluations in parallel. Jev uses a new architecture combining a parallel sampler and a training method called Reinforcement Learning for Calibrated Decisions (RLCD) to deliver calibrated probabilities alongside each decision. The model targets use cases like routing support tickets, flagging sensitive prompts, approving agent actions, and triaging transactions where structured outputs matter more than human-readable explanations.

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