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Jev-DLC: Decision-Level Continual Learning from Typed Feedback

Yangyang Feng, Jianle Gan, Zhuoyan Feng, Chengwei Qin · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31521 · open-access PDF

Korean

사용자가 한 결정을 받아들이거나 다른 결정을 철회할 때 개별적인 선호도는 종종 배포 후에만 눈에 띄게됩니다. Jev 스타일의 인쇄 인터페이스는 선택한 행동을 식별하고, 철회하는 경우는 그가 대체하는 결정을 식별함으로써 이러한 피드백을 명시적으로 만듭니다. 우리는 이러한 결과가 결정 수준의 타이핑된 피드백(Jev-DLC)으로서의 결정을 통해 어떻게 지속되는지 연구합니다. Jev-DLC는 각 수정을 반복적인 행동 정체성과 선호된 선택과 거부된 선택 사이의 방향적인 관계에 대한 증거로 취급합니다.

Machine translation (Meta M2M100); the English original follows.

Abstract

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

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