MIT CSAIL researchers built Retrieval Language Models that store full documents outside the model’s context window and let the AI query them via code, slicing, and parallel sub-instances. This approach handles inputs up to 10 million tokens, doubles benchmark performance, and matches or beats the cost of massive-context calls.

• MIT CSAIL's RLM approach stores full documents outside the context window and lets the AI query them via code (grep/slice) plus parallel sub-instances, instead of stuffing everything into context.
• It scales to inputs up to 10 million tokens, while standard LLMs like GPT-5 solve fewer than 10% of problems beyond 75,000 tokens.
• RLMs beat RAG methods by double-digit percentage gains at equal or lower cost per query.
• The code is open source and designed as a drop-in replacement for existing API calls, suggesting bigger context windows are a dead-end strategy compared to teaching models where to look.