We explore language models that recursively call themselves or other LLMs before providing a final answer. Our goal is to enable the processing of essentially unbounded input context length and output length and to mitigate degradation “context rot”.

We propose Recursive Language Models, or RLMs, a general inference strategy where language models can decompose and recursively interact with their input context as a variable. We design a specific instantiation of this where GPT-5 or GPT-5-mini is queried in a Python REPL environment that stores the user’s prompt in a variable. — Read More