AdaMem: Learning What to Remember for Personalized Long-Horizon LLM Agents

• URL: http://arxiv.org/abs/2606.21144v1
• Date: Fri, 19 Jun 2026 06:35:52 GMT
• Title: AdaMem: Learning What to Remember for Personalized Long-Horizon LLM Agents
• Abstract summary: We argue that what is worth remembering is role-dependent, and propose textbfAdaMem (Adaptive Memory)<n>AdaMem improves QA accuracy by up to textbf+9.0% over the uniform Mem0 baseline while shrinking memory volume by textbf9%.
• Score: 79.03892269184145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-term memory systems for Large Language Model (LLM) agents typically try to \emph{remember everything}, extracting memories uniformly to retain as many facts as possible. In production, however, inference cost and finite context budgets make this untenable: beyond consolidating raw dialogue into memory, an agent must exert \emph{write control}, efficiently keeping only the information each user actually cares about. Otherwise, long-horizon personalized interactions suffer \emph{memory bloat}, where irrelevant trivia crowds out useful information and steadily erodes question-answering (QA) accuracy. We argue that what is worth remembering is role-dependent, and propose \textbf{AdaMem} (Adaptive Memory), a method that \emph{learns what to remember} for each user from feedback. AdaMem maintains a structured, role-specific Memory Policy and refines it from weekly QA feedback through a lightweight, patch-style self-reflection step with failure rollback. To study this setting, we build \textbf{AdaMem-Bench}, a benchmark that simulates weeks of interaction with week-by-week QA. Across two extraction models and two feedback modes, AdaMem improves QA accuracy by up to \textbf{+9.0\%} over the uniform Mem0 baseline while shrinking memory volume by \textbf{9\%}.

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