MIT Just KILLED the Transformer! RLM: The Secret to 1 Million Token Infinite Context

Is the era of the standard Large Language Model already over? MIT researchers have just unveiled a "phase shift" in artificial intelligence called the Recursive Language Model (RLM). This groundbreaking inference methodology aims to solve the biggest flaw in current AI: "Context Rot".
While models like GBD5 boast large context windows, their reasoning capabilities actually degrade drastically as information density increases. In fact, tests show that GBD5 starts to fail at just 16,000 tokens and drops to near-zero performance on complex tasks by 33,000 tokens,. MIT’s solution? Stop feeding massive prompts directly into the transformer and start using a "neuro-symbolic exoskeleton",.
In this video, we explore:
• The Death of RAG: Why traditional Retrieval-Augmented Generation is "lossy" and probabilistic, while RLM is deterministic and exhaustive,.
• The Python Solution: How the AI now acts as an architect, writing its own Python code to decompose, chunk, and recursively call sub-instances of itself to process data,.
• 1 Million Token Reasoning: How RLM achieves a performance jump from 0.04% to 58% on high-complexity tasks that would normally crash a standard GBD5,.
• Infinite Context: Why moving memory from neural weights to an external environment effectively gives AI an infinite context window,.