This paper introduces Recursive Language Models (RLMs), a novel inference strategy designed to overcome the limitations of context windows and the performance degradation of standard large language models. Unlike traditional approaches that feed long prompts directly into a neural network, an RLM treats the input as an external environment within a Python REPL. This allows the model to use code to programmatically examine, decompose, and filter massive datasets that would otherwise exceed its memory capacity. By recursively calling itself on smaller, manageable snippets of the prompt, the system can handle inputs up to two orders of magnitude larger than standard limits. Experimental results using frontier models like GPT-5 show that RLMs significantly outperform existing methods on complex, information-dense tasks while maintaining comparable costs. Ultimately, this framework provides a scalable way for AI to process millions of tokens without losing the fine-grained reasoning capabilities required for deep research and data aggregation.

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