Core Insights - The article discusses a new method called Recursive Language Model (RLM) developed by MIT CSAIL for processing long texts, addressing the issue of context decay in large models [1][5][11] - RLM allows top models like GPT-5 and Qwen-3 to handle super long texts with millions of tokens without modifying their architecture [2][23] Summary by Sections Context Decay Issue - Large models struggle with context decay, where the performance declines as the text length increases, leading to a loss of memory for earlier information [5][6] - Current mainstream solutions include context compression, retrieval-augmented generation (RAG), and architectural optimizations [7][10] RLM Methodology - RLM outsources context processing to an interactive Python environment, enabling models to programmatically break down tasks and process them as needed [4][13][15] - The model initiates a Python REPL environment, storing long prompts as string variables and performing operations like keyword filtering and logical decomposition [14] Performance Metrics - RLM has demonstrated the ability to effectively handle over 10 million tokens, significantly surpassing the native context window of models like GPT-5 [16] - In complex long text tasks, RLM showed substantial improvements, achieving F1 scores of 58.00% and 23.11% for GPT-5 and Qwen-3, respectively, in the OOLONG-Pairs task [16] - For the BrowseComp-Plus multi-document reasoning task, RLM (GPT-5) achieved a correct rate of 91.33%, outperforming other long text processing methods [16] Cost Efficiency - RLM's cost at the 50th percentile is competitive with other long text processing solutions, indicating a favorable cost-performance ratio in most regular task scenarios [19] - However, at the 95th percentile, RLM's costs can spike due to its dynamic reasoning process, which increases API call frequency based on task complexity [20][21]