Jev模型的技术基础：RLCD校准决策系统

Jev模型的核心创新不在于传统语言模型架构，而是建立在RLCD（校准、多路决策的约束模型）基础之上1。该系统通过将奖励模型从隐藏组件转变为产品接口，实现了更加透明和可控的决策流程1。

RLCD采用schema-conditioned Plackett-Luce目标函数作为其数学基础1。这一方法引入了成对偏好奖励模型（PPRM），标志着从标量奖励向相对偏好评估的转变1。Plackett-Luce模型将传统的二元Bradley-Terry模型扩展到多路选择场景，使系统能够处理复杂的多选决策1。为了确保决策的可靠性，系统采用Brier评分进行校准，将模型的置信度量化为成本代价1。

在实现层面，Jev的决策头通过缩放内积公式计算候选效用（u = (q·k)/√r，其中r=512）1。为支持高效的并行推理，系统使用序列打包和树注意力掩码技术，防止跨问题和跨候选的数据污染1。Jev提供三个核心原语供用户调用：Choice返回所选选项、完整概率分布和置信度；Score沿用户定义的级别返回评分；Noul返回命题为真的概率1。

The Jev model operates on a fundamentally different architecture than conventional language models, built upon RLCD—a schema-conditioned Plackett-Luce objective function designed for calibrated, multi-way decision-making 1. Rather than treating reward modeling as an internal hidden component, Jev exposes it as a product interface, enabling users to interact directly with probabilistic decision outputs 1.

At the core of this system lies a transformation from scalar rewards to relative preferences through PPRM (pairwise preference reward models) 1. The Plackett-Luce framework extends the binary Bradley-Terry model to handle multi-way selections, while Brier scoring provides calibration by quantifying confidence as a measurable cost 1. Jev's decision head computes candidate utility using a scaled inner product formula—u = (q·k)/√r, where r equals 512—enabling efficient evaluation of multiple options simultaneously 1.

The system supports parallel inference through sequence packing and tree attention masks, which prevent data contamination across different questions and candidates 1. Jev provides three core primitives for users: Choice returns selected options along with complete probability distributions and confidence scores; Score returns calibrated ratings along user-defined scales; and Noul computes the probability that propositions are true 1.