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比如，GPT-5.2-Codex采用的就是窗口内的原生上下文压缩技术，在持续数周的大型代码仓库协助任务中保持全上下文信息。 在复杂长文本任务中，RLM的优势也比较显著。面对要求聚合成对信息、复杂度呈二次方增长的OOLONG-Pairs任务，基础GPT ...

In 1993, the first exchange traded fund was launched. At the time, most of Wall Street shrugged. Mutual funds dominated, brokers reigned supreme, and the idea that investors would flock to a new ...

在 600 万至 1100 万 Token 规模的 BrowseComp-Plus（1K）多文档推理任务中，RLM（GPT-5）的正确率高达 91.33%，大幅超越其他长文本处理方案； ...

Forbes contributors publish independent expert analyses and insights. Data, Analytics and AI Strategy Advisor and Researcher This voice experience is generated by AI. Learn more. This voice experience ...

在人工智能领域，处理超长文本一直是一个棘手的问题。MIT计算机科学与人工智能实验室（CSAIL）最近发布的研究成果，提出了一种名为递归语言模型（RLM）的新方法，成功让大模型在不改变架构的情况下，解锁了千万级的上下文处理能力。这一创新将极大提高如GPT-5和Qwen-3等顶尖模型的推理效率，开启了大模型处理文本的新纪元。

Going to the database repeatedly is slow and operations-heavy. Caching stores recent/frequent data in a faster layer (memory) ...

在人工智能领域，长文本处理一直是一个棘手的问题。近期，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解决方案——递归语言模型（RLM），该方法能够使大模型在不改变其架构的情况下，处理超长文本，达到千万级token的处理能力。这一研究成果为GPT-5、Qwen-3等顶尖AI模型的推理能力注入了新的活力，开启了长文本处理的新篇章。