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MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents

arXiv AI/CL/LG（论文）T2
01:0807/29, 01:08 AMPaperRead the original ↗Export MD ↓

Score

74
Information gain8 ×0.3

首次将Shapley值用于LLM记忆效用评估，并在系统层面提供可视化对比，信息增量高。

Content quality8 ×0.2

方法阐述清晰，系统设计完整，实验对比充分，但仅有一个应用场景。

Practical value9 ×0.2

直接针对当前Agent记忆管理的痛点，提供可落地的开源思路和交互工具，实用价值高。

Timeliness8 ×0.15

发表于arXiv 2025年7月，紧跟Agent长期记忆研究热点，时效性良好。

Source reliability8 ×0.15

来自arXiv预印本，虽未经同行评审，但方法完整且代码可能开源，可信度较高。

Base 82 × T2 Source weight ×0.9 = 74

Curation line 70Selected

Why selected

Novel methodology (Shapley value for memory evaluation), complete system design with interactive dashboard, yet validated on only one application; generalizability remains to be seen.

AI commentary

「This paper is highly relevant for teams and researchers working on LLM-based agents—it directly addresses the core question of 'what to keep and what to discard' in long-term memory management. The introduction of Shapley value mitigates both information loss from forgetting and storage bloat. The interactive dashboard design makes debugging intuitive, especially for product engineers who need to continuously optimize memory strategies.」

Quick overview

MemLens introduces a value-aware memory management approach that treats heterogeneous interaction records as first-class data objects, employing Shapley-style evaluation for memory utility, hierarchical visualizations of storage structures, and comparative analysis of strategies (response quality, retrieval latency, token consumption). The system provides an end-to-end interactive dashboard, enabling researchers and developers to intuitively understand and optimize LLM-based agents' long-term memory, enhancing personalized reasoning and knowledge reuse. Experiments on a study-copilot application validate its effectiveness in reducing redundancy and improving response quality.

  • Proposes memory records as first-class data objects with Shapley-style utility evaluation for value-aware storage.
  • Builds an end-to-end interactive analytics dashboard supporting memory value visualization, hierarchical structure browsing, and strategy comparison.
  • Validates system advantages in reducing redundant records, improving response quality and retrieval efficiency on a study-copilot application.

Full text

This is an AI-generated Chinese digest; use "View original" for the source text.

Current memory systems for LLM-based agents typically store heterogeneous user-LLM interaction records in a coarse-grained, utility-agnostic manner, leading to persistent accumulation of redundant and low-impact records. MemLens treats memory records as first-class data objects and introduces Shapley-style evaluation to assess each record's marginal contribution to final responses, enabling value-aware storage. The system offers an end-to-end interactive analytics dashboard where users can inspect memory value distributions, hierarchical structures, and compare different storage strategies (e.g., full storage, time decay, value threshold) in terms of response quality, retrieval latency, and token consumption. This design not only improves memory system efficiency and interpretability but also provides researchers and developers with a visual tool for debugging and optimization.

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