---
id: 20260319-T0-16
title: "递归语言模型：自反思程序提升长文本处理"
title_en: "Recursive Models Improve Long Context via Self-Reflection"
url: https://ai.daily.yangsir.net/daily/20260319-T0-16
issue_date: 2026-03-19
publish_date: 2026-03-18T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.15653
---

# 递归语言模型：自反思程序提升长文本处理

MIT提出递归语言模型新方法，通过自反思程序处理长文本上下文。现有模型在长文本中经常丢失信息，新方法在100k tokens长文档中的事实准确率提升32%。研究团队开源了代码实现，开发者可用此技术构建更好的长文本处理系统，如法律文档分析或学术论文理解。

## English Version

**Recursive Models Improve Long Context via Self-Reflection**

MIT researchers propose recursive language models using self-reflection for long context processing. Existing models often lose information in long texts, while the new method improves fact accuracy by 32% on 100k token documents. The team has open-sourced the code implementation. Developers can use this technology to build better long-text processing systems for legal document analysis or academic paper understanding.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.15653)

**详情页**：https://ai.daily.yangsir.net/daily/20260319-T0-16

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