Problem
Research questions and friction points this paper is trying to address.
memory management
user-specific
dynamic evolution
Learning What to Share and What to Personalize: Hierarchical Strategy Co-Evolution for Agent Memory
🤖 AI Summary
为了解决记忆管理策略固定且不个性化的问题，提出HiPS框架，通过全局共享基础与用户特定适应层相结合的方法来优化代理的记忆决策。
📝 Abstract
Memory-augmented agents maintain compact user profiles throughout extended conversations, enabling personalized and consistent responses without the need to process the entire dialogue history. The quality of these user profiles relies on the underlying memory management strategy: at each step, the agent must determine what to retain, compress, or discard. However, existing methods typically employ a static, one-size-fits-all strategy established before training. In practice, the optimal memory decision is inherently user-specific and dynamically evolves alongside policy optimization. To address this, we propose \textbf{HiPS} (\textbf{Hi}erarchical \textbf{P}ersonalized \textbf{S}trategy), a framework that decouples memory management into a globally shared foundation and a user-specific adaptive tier. Specifically, HiPS employs \textbf{Universal Strategy} to extract shared principles from cross-persona trajectories, alongside \textbf{Persona Delta Distillation} to generate tailored rules for users whose behaviors diverge from general patterns. \textbf{Cross-Level Rule Flow} dynamically calibrates their boundary by promoting broadly validated personal rules and demoting contradicted global ones. The architecture establishes a co-evolution loop where a mechanism guarantees that all strategy refinements are anchored to task outcomes. Extensive experiments demonstrate consistent improvements over memory-augmented baselines.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical Personalized Strategy
Memory Management
User-Specific Adaptation
Cross-Level Rule Flow
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