---
id: 20260319-T0-12
title: "语言代理记忆系统：质量胜于数量"
title_en: "Compiled Memory: Quality Over Information for Agents"
url: https://ai.daily.yangsir.net/daily/20260319-T0-12
issue_date: 2026-03-19
publish_date: 2026-03-18T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2603.15666
---

# 语言代理记忆系统：质量胜于数量

康奈尔大学提出'编译记忆'新框架，优化语言代理的记忆存储。传统记忆系统关注如何存储更多信息，而该研究专注于存储更有价值的经验数据。实验显示，新方法使代理在复杂任务中的准确率提升23%，内存占用减少40%。开发者可用此框架构建更高效的AI助手，减少无关信息干扰。

## English Version

**Compiled Memory: Quality Over Information for Agents**

Cornell researchers introduce 'Compiled Memory' framework optimizing how language agents store memories. While traditional systems focus on storing more information, this approach prioritizes storing more valuable experiences. Experiments show 23% accuracy improvement in complex tasks with 40% reduced memory usage. Developers can build more efficient AI assistants with this framework to reduce irrelevant information interference.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2603.15666)

**详情页**：https://ai.daily.yangsir.net/daily/20260319-T0-12

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