Forget LLM: MIT's New RLM (Phase Shift in AI)

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We’ve been misled by the promise of "infinite" context windows: new AI research proves that "Context Rot" is destroying reasoning capabilities as inputs scale.

But a groundbreaking paper from MIT introduces a radical solution: Recursive Language Models (RLMs). Instead of blindly force-feeding data into a single Transformer, RLMs act as a Neurosymbolic Operating System, writing Python code to mechanically split massive datasets and recursively "spawn" fresh model instances to process them.

The result is a staggering leap in performance: on quadratic complexity tasks where base GPT-5 scores below 0.1%, the RLM(GPT-5) achieves 58%.

In this video, I deconstruct how this "Inference-Time Scaling" works (explanation) and why it signals the end of static LLMs as we know them.

All rights w/ authors:
"RECURSIVE LANGUAGE MODELS"
Alex L. Zhang
MIT CSAIL
Tim Kraska
MIT CSAIL
Omar Khattab
MIT CSAIL
arXiv:2512.24601

@mit @NvidiaAl

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