Jev 三模型分工：前沿模型提概念、Jev 量化、CatBoost 学策略
TypeSafe 葡萄酒实验展示「发现—测量—学习—执行」四段式架构：Jev 只负责把概念转成概率与不确定性，预测交给 CatBoost，不靠单个大模型包办。
THE BEST JEV SYSTEM MAY USE THREE MODELS AND JEV IS NOT THE SMARTEST ONE TypeSafe's wine experiment shows a stack that looks nothing like the usual “one giant agent” architecture First, a frontier LLM proposes concepts that may matter inside messy text Second, Jev measures those concepts across every row as probabilities, choices, expected scores and uncertainty Third, CatBoost learns which features actually predict the label The final policy comes from your data. Jev does not need to write the answer. The frontier model does not need to reread every record. The classical model does not need to understand language Each component does the job it is structurally good at That matters because most “AI architecture” today means forcing one model to perceive, reason, decide, explain and act inside one prompt This stack breaks the job apart: discover measure learn enforce The future may not belong to the model with the biggest context window It may belong to the system that gives every model less work
这条是引用着另一条写的：x.com/fluixoo/status/2103123493567025321