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This article focuses on the evolution of Large Language Models into Recursive Language Models (RLMs) and introduces new architectures like Context Language Models (CLMs) that manage their own context autonomously. It highlights research from MIT's Alex Zhang regarding self-improving agents, GPU kernel optimization, and the shift from primitive systems to more capable reasoning frameworks. These developments represent significant advancements in model architecture and autonomous task execution within the AI field.
Sursă care acoperăLatent Space
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Academia is for Ambition — Alex Zhang, MIT

Latent Space88 min de cititArticolul și perspectiva sunt în engleză.

We catch up with RLM first author Alex Zhang, MIT PhD, on Jev, PhD masxing, and the future of harnesses.

Last call for regular tickets for AI Engineer NYC! As an exclusive for Latent Space subscribers, the first 30 of you can take a 30% off code if it helps - for new tickets only, no refunds! See you in 2 weeks! While we tend to cover industry on the pod, every so often we celebrate a clearly emerging superstar PhD. In 2024 we featured Shunyu Yao, who went on to build Operator at OpenAI and is now Chief AI Scientist of Tencent. In 2025 we featured Jack Morris, who went on to cofound Engram at $600m and is now a leading voice on continual learning. This year we are proud to feature the work of Alex Zhang of MIT. From GPU kernels and KernelBench to Recursive Language Models, Mismanaged Geniuses, and massive multi-agent swarms, Alex Zhang is exploring how much capability we’re leaving on the table by wrapping increasingly powerful models in primitive systems. Much like the switch in 2025 from language models to reasoning models, we think 2026 will be all about the switch to Recursive Language Models (RLMs). It turns out that models can be far more powerful if you allow them to treat *their own prompts*…

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