这篇论文提出了MemLens，能让LLM智能体更聪明地管理记忆，通过价值感知和交互分析来避免冗余记录。对智能体长期记忆感兴趣的话，值得一读。
MemLens是一种为LLM智能体设计的价值感知内存管理系统，采用Shapley式评估对记忆记录进行价值排序。系统提供交互式分析仪表板，支持用户检查记忆价值、可视化分层记忆结构。通过研究助手应用，用户可比较不同记忆策略在响应质量、检索延迟和token消耗方面的表现。该系统旨在实现高效、可解释的长期记忆管理。
MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents
Recently, memory management has become a key infrastructure for LLM-based agents, as it directly affects long-horizon reasoning, personalized responses, and knowledge reuse. However, existing LLM memory systems typically adopt a coarse-grained (utility-agnostic) manner that treats heterogeneous user-LLM interaction records uniformly, leading to redundant and low-impact records persisting in the memory repository. To address this challenge, we present MemLens, a value-aware memory management system that takes memory records as first-class data objects. MemLens provides an end-to-end interactive analytics dashboard that exposes the complete memory lifecycle, including Shapley-style memory evaluation, value-aware storage, and memory-assisted response. Through a study-copilot application, the system enables users to inspect memory values, visualize hierarchical memory structures, and compare various memory management strategies in terms of response quality, retrieval latency, and token consumption. Therefore, our MemLens can serve as an efficient, interpretable, and personalized long-term memory management system for LLM-based agents.