---
id: 20260626-T0-09
title: "TrustMem：赋予LLM智能体可信赖的长期记忆整合能力"
title_en: "TrustMem: Enabling Trustworthy Memory Consolidation for LLM Agents"
url: https://ai.daily.yangsir.net/daily/20260626-T0-09
issue_date: 2026-06-26
publish_date: 2026-06-25T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2606.25161
---

# TrustMem：赋予LLM智能体可信赖的长期记忆整合能力

为了解决大模型智能体在长周期交互中记忆不可靠的问题，新论文提出了TrustMem框架。现有的智能体虽然能通过读写操作更新外部记忆，但常面临信息过时或冲突的挑战。TrustMem通过学习可信赖的记忆整合机制，优化了记忆的存储与检索策略，确保智能体在有限上下文窗口外仍能提供个性化且准确的辅助。这一进展提升了AI智能体在长期任务中的稳定性。

## English Version

**TrustMem: Enabling Trustworthy Memory Consolidation for LLM Agents**

To solve unreliable memory in LLM agents during extended interactions, new research proposes TrustMem. While existing agents update external memory via write and delete operations, they often struggle with outdated or conflicting information. TrustMem learns a trustworthy memory consolidation mechanism to optimize storage and retrieval, ensuring agents provide accurate, personalized assistance beyond finite context windows.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2606.25161)

**详情页**：https://ai.daily.yangsir.net/daily/20260626-T0-09

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