---
title: "MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.25992v1"
permalink: "https://feed.astralor.com/items/itm_f6bd977e16fa6e30d48a5397"
published: "2026-07-28T17:08:56.000Z"
score: 74
channel: "ai"
tags: ["LLM agent","MemLens","Shapley Value","Interactive Analytics","Memory Management"]
---

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

## 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.

## At a glance

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.

## Key points

- 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

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.

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