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Learning Personalized Memory Gating from Delayed One-Sided Feedback

Paper recorded by Signals 4 on 2026-10-16 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.LG · 机器学习 · first seen 2026-10-19

Abstract

Long-term memory enables language-model agents to personalize future interactions, but a finite memory bank cannot preserve every extracted fact. Existing memory gating policies are typically shared across users and therefore fail to account for user-specific patterns of future reuse. In this paper, we study memory gating at the fact level. We first propose a retrieval-based labeling strategy that

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#254 most recent of 749 cs.LG papers we have recorded · ↑ newer: CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcem · ↓ older: Explainable Machine Learning Analysis of Temperature Effects on Mechan
Cite this page: Learning Personalized Memory Gating from Delayed One-Sided Feedback: the #254 most recent of 749 cs.LG papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-personalized-memory-gating-from-delayed-one-sided-feedback.html
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