在人工智能日新月异的时代，如何让大模型更高效地处理长文本成为了研究者们关注的焦点。最近，麻省理工学院计算机科学与人工智能实验室（MIT CSAIL）提出了一种革命性的递归语言模型（Recursive Language Model，RLM），旨在解决大模型在处理超长文本时所面临的上下文腐烂问题。这项研究的核心理念是：不对模型的架构进行改动，依然能够让像GPT-5和Qwen-3这样的顶尖模型处理多达千 ...

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MIT’s Recursive Language Models rethink AI memory by treating documents like searchable environments, enabling models to ...

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研究人员提出的递归语言模型（Recursive Language Models, RLMs）通过引入Python交互环境，让模型学会写代码去分块读取、检索并递归调用自身处理信息，成功在千万级Token的超长文本任务中实现了性能与成本的双重突破。

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这个批评有一定道理。从本质上看，RLM确实是把上下文问题转化为搜索问题，而非真正的压缩或记忆。它依赖模型生成可靠的检索代码，而当前模型在这方面并不完美——可能写出糟糕的正则、陷入无限递归、或者遗漏关键片段。