Decision models

A new class of AI models called decision models has emerged since the last Radar. TypeSafe AI released Jev, which it calls a "system one" model, built to make fast, structured decisions. Many other models have followed, including Laya, Nimble and Kev. In contrast to deliberative reasoning models, decision models output predefined structured values instead of generated text. Rather than using reinforcement learning from human feedback (RLHF) to optimize for human preferences, Jev uses reinforcement learning from calibrated decisions (RLCD) to optimize for well-calibrated decisions, where predicted probabilities reflect how often an outcome actually occurs. Their smaller, structured outputs enable faster and cheaper processing. Decision models receive a state and a set of questions. Every question asked against the state must map to one of three types: Noul for yes/no questions that return the probability of a "yes" answer, Choice for categorized multiple-choice options or Score for evaluations on an ordered list of levels or a numerical scale. Decision model use cases include Jev-as-a-judge as an alternative to LLM-as-a-judge, model and tool routing, policy gates and bulk labeling. We recommend teams avoid overenthusiastic LLM use and define evaluation criteria to determine whether a decision model can replace an LLM for classification use cases.