---
id: 20261003-T0-06
title: "CAVE-Mem：记忆搜索引入边界感知经验校验，减少错误复用"
title_en: "CAVE-Mem Adds Boundary-Aware Validation to Memory Search for LLM Agents"
url: https://ai.daily.yangsir.net/daily/20261003-T0-06
issue_date: 2026-10-03
publish_date: 2026-10-02T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2610.00238
---

# CAVE-Mem：记忆搜索引入边界感知经验校验，减少错误复用

针对长期记忆智能体依赖迭代搜索和可复用经验回答个人历史、事实与叙事类问题的场景，现有经验记忆系统主要优化相关性，容易把不适用或错误的经验直接复用。CAVE-Mem 提出边界感知的经验校验机制，在检索到过往经验后先判断其适用边界，再决定是否用于当前问题，从而降低错误迁移。该方法适用于大规模个人记忆库中的问答任务，让智能体在长程对话中更可靠地调用历史经验。

## English Version

**CAVE-Mem Adds Boundary-Aware Validation to Memory Search for LLM Agents**

CAVE-Mem introduces boundary-aware experience validation for long-term memory agents that rely on iterative search and reusable experience over large personal, factual, or narrative histories. Current experience-memory systems mainly optimize relevance, which can lead to reusing inapplicable or incorrect experience. CAVE-Mem validates the applicability boundary of retrieved experience before reuse, reducing erroneous transfer. The approach targets question answering over large personal memory stores, making agent memory recall more reliable in long conversations.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2610.00238)

**详情页**：https://ai.daily.yangsir.net/daily/20261003-T0-06

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