MIT新论文：2026推理模型过时了，“套娃模型”当立

Core Viewpoint - The article discusses the emergence of a new paradigm in language models called the "Recursive Language Model" (RLM), which significantly improves the handling of long texts and reduces costs compared to traditional models like GPT-5 [3][5][23]. Group 1: RLM Overview - The RLM introduces a novel approach by storing text in a code environment and allowing the model to write programs that recursively call itself to process the text [5][9]. - This method decouples the length of input data from the model's context window size, enabling the processing of text limited only by physical memory rather than the constraints of the Transformer architecture [10][12]. Group 2: Performance Metrics - RLM has demonstrated the ability to effectively handle up to 10 million tokens, surpassing the context window of leading models like GPT-5 by two orders of magnitude [23]. - In various benchmark tests, RLM outperformed traditional models in complex tasks, achieving F1 scores of 58.00% and 23.11% in OOLONG and OOLONG-Pairs tests, respectively, while traditional models scored below 0.1% [27]. Group 3: Cost Efficiency - RLM's approach allows for selective reading of relevant text segments, leading to a significant reduction in operational costs. For instance, the average cost for RLM in the BrowseComp-Plus benchmark was only $0.99, compared to $1.50 to $2.75 for GPT-5 [29][31]. - This cost efficiency indicates that RLM can maintain performance while controlling inference costs, making it a viable option for large-scale applications involving long texts [32].