Jev-DLC：从类型化反馈中做决策级持续学习
Jev-DLC: Decision-Level Continual Learning from Typed Feedback
一篇讲个性化持续学习的新论文，用户的每次采纳或撤销都能变成可复用的决策信号，还附带 TCPA-Bench 基准，做个性化系统可以看看。
arXiv 论文提出 Jev-DLC，将用户在 Jev 风格类型化界面中的采纳或撤销反馈，转化为对重复出现的动作身份及其与被否决策之间有向关系的证据。方法在冻结的评分器之上用两个有界通道保存这些信号，不保留原始交互历史，不增加评分器调用，也不做运行时语言模型反向传播。作者同时发布 TCPA-Bench，其中 TCPA-Repeat 子集只在修正影响后续决策时才计分。Jev-DLC 在 TCPA-Repeat 上达到 44.83% 的 user-macro 准确率，比 Recent-4 历史回放高 2.43 个百分点；在至少有两次历史反馈的深度上达到 56.07%，超出该深度最强对比方法 7.54 个百分点。
Jev-DLC: Decision-Level Continual Learning from Typed Feedback
Individual preferences often become visible only after deployment, when a user accepts one decision or revokes it in favor of another. Jev-style typed interfaces make this feedback explicit by identifying the selected action and, under revocation, the decision it replaces. We study how these consequences persist across requests as Decision-Level Continual Learning from Typed Feedback (Jev-DLC). Jev-DLC treats each correction as both evidence about a recurring action identity and a directed relation between the preferred and rejected choices. It preserves these signals in complementary bounded channels above a frozen scorer and combines their scores to shape the next decision. The method retains no raw interaction history, adds no scorer call, and performs no runtime language-model backward pass. We also introduce TCPA-Bench, a chronological suite that credits a correction only through its effect on later decisions. On TCPA-Repeat, Jev-DLC reaches 44.83% user-macro accuracy overall, 2.43 points above Recent-4 history replay. After at least two earlier feedback events, it reaches 56.07%, a 7.54-point margin over the strongest reported comparison at this depth. These results show how explicit typed corrections can become compact causal state for continual personalization.
