MIT Just Broke AI: Recursive Language Models Change Everything

For two years the entire AI industry has been obsessed with context windows — 200k, 1M, 10M tokens. But MIT just dropped a disruptive research paper introducing Recursive Language Models (RLMs) that flip everything we thought we knew about large language models.

Instead of passively consuming long text, an RLM can actively probe, slice, filter, break down tasks, call itself recursively, and verify its own output. This transforms context from input into environment, and turns LLMs from passive text generators into agentic problem solvers.

In this breakdown we explore:

✅ Why scaling context windows is hitting a wall
✅ Why RAG (retrieval augmented generation) is not enough
✅ How RLMs outperform standard LLMs on reasoning tasks
✅ The “Oolong” benchmark breakthrough (0.04% → 58% success)
✅ How neurosymbolic reasoning changes the future

And more importantly — how this changes the next phase of AI deployment, agents, tool-use, inference-time scaling, and autonomous systems.

If you're serious about AI, ML, Robotics, Agents, or LLM engineering — this is one of the most important shifts since Transformers.

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