Jev 不是分类器，而是状态到概率化行动决策
作者列举 Jev 的真实用法：邮件是否需今日处理、agent 危险操作审批门、游戏与机器人控制循环，比 LLM 更快更便宜。
Think less "classification" and more "state -> calibrated probablistic decision to act upon". E.g. you can use other classifiers to classify emails as urgent or imporant or spam etc. But Jev can look at more than email body (user state, thread age, calendar, company state etc) and then give you answers to things like "does it require an action today and is delaying until tomorrow risky", "does it need a reply from the user or not" etc. Other good examples are agent safety gates for command/action approvals (it can evaluate risky actions, does it need confirmation, is it consistent with original requset, is it reversible etc), games/robot/automation control loops and many more. Many of those class of questions can also be done by LLMs btw but jev is faster and cheaper than them there, and many of those can also be done with other classifiers by training/finetuning them but jev scales better by being general purpose.