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

Paper recorded by Signals 4 on 2026-10-22 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23

Category: cs.AI · 人工智能 · first seen 2026-10-23

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). Je

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#198 most recent of 960 cs.AI papers we have recorded · ↑ newer: Beyond Causal Masks: Auditing and Localizing Hidden Future-Token Leaka · ↓ older: The Geometry of Metaphor: Probing and Steering Metaphor Representation
Cite this page: Jev-DLC: Decision-Level Continual Learning from Typed Feedback: the #198 most recent of 960 cs.AI papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/jev-dlc-decision-level-continual-learning-from-typed-feedback.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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