Recursive Language Models (RLMs) are emerging as a solution to managing long contexts in large language models (LLMs). Traditional LLMs struggle with context rot, where performance declines as context length increases. RLMs address this by allowing models to manage their own context actively. Instead of summarizing information—leading to potential loss—RLMs delegate context handling to Python scripts and sub-LLMs. This approach aligns with current trends in AI development, emphasizing efficiency and effectiveness in managing extensive data without overwhelming the main model. The RLM operates through a persistent Python REPL, which allows it to inspect and transform input data without directly loading everything into its context. This design keeps the LLM lean and prevents context rot. It can also search and filter data programmatically, optimizing the processing of large datasets. The RLM is structured to handle tasks that typically require significant context, such as analyzing PDFs or large datasets. This architecture enables the model to perform complex tasks while maintaining clarity and coherence in its outputs. Prime Intellect is currently experimenting with the RLM, comparing its performance against standard LLMs and RLMs with specific environmental tips. The experiments focus on understanding how well RLMs can handle long-horizon tasks compared to traditional models. The results show that RLMs not only manage context more effectively but can also utilize tools and libraries in a way that minimizes token usage by offloading work to sub-LLMs. The experimental setup sets a timeout of 120 seconds for REPL calls, balancing performance and responsiveness in real-world applications.