个性化再存储：面向长时程智能体的个性化记忆基准测试与学习
Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents
AI总结：
针对现有基于大语言模型的记忆系统采用通用静态策略忽略用户间存储上下文差异的问题，提出首个个性化记忆基准PerMemBench和会话级存储门控框架，验证个性化能显著提升记忆保留但精确门控仍是关键挑战。
摘要：
现有的基于大语言模型（LLM）的记忆系统采用通用、静态的策略，忽略了一个基本现实：不同用户值得存储在记忆中的上下文是不同的。这种错位将有限的记忆预算浪费在短暂交互上，同时未能为长时程任务保留关键上下文。为解决这一差距，我们研究了一个未被充分探索的问题：基于LLM的记忆系统能否学习个性化的记忆策略？我们引入了PerMemBench，这是首个用于评估个性化记忆系统的基准，具有跨多年、多领域、多样化用户角色的交互历史。我们进一步提出了记忆个性化的首个实证研究，提出了会话级存储门控，这是一个轻量级框架，可选择性地绕过短暂会话的记忆操作。我们的研究证实，在完美门控下，个性化能带来显著的保留增益，但同时也揭示出精确门控仍然是一个开放且关键的挑战。
英文摘要：
Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are different across users. This misalignment wastes limited memory budget on transient interactions while failing to preserve critical context for long horizon tasks. To address this gap, we investigate an underexplored question: can LLM based memory systems learn personalized memory policies? We introduce PerMemBench, the first benchmark for evaluating personalized memory systems, featuring multi year, multi domain interaction histories across diverse user personas. We further present the first empirical study of memory personalization, proposing session level storage gating, a lightweight framework that selectively bypasses memory operations for transient sessions. Our study confirms that personalization yields substantial retention gains under perfect gating, yet reveals that accurate gating remains an open and critical challenge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
