AI Pulls an All-Nighter—and Remembers It

Today’s AI forgets faster than your boss after a long weekend. But MIT’s new SEAL model just gave LLMs the gift of memory, and a dangerous taste for self-improvement.

Modern LLMs can write, code, and joke, but they can’t remember. That changes with MIT’s SEAL (Self-Adapting Language Models), a method that allows AI to learn continuously by generating its own training data and updating itself. Imagine a chatbot that not only responds to you but remembers what matters, and gets better at it over time.

SEAL works by having a model reflect on new inputs, generate new passages (like human notes), and fold those into its own weights. It’s been tested on Meta’s Llama and Alibaba’s Qwen, and showed continued learning on both text and abstract reasoning benchmarks like ARC.

For now, it’s not infinite learning. Issues like “catastrophic forgetting” and computational cost remain but it’s a breakthrough in self-directed learning.

MIT’s Pulkit Agrawal sees SEAL as a step toward more personalized, resilient AI:

• SEAL lets models update themselves with synthetic data • Models improve on-the-fly through reinforcement learning • Tested successfully on Llama, Qwen, and ARC benchmarks

The ability to reflect, remember, and refine isn’t just human anymore. So, what kind of intelligence are we really training, and can we trust it to evolve?

Read the full article on Wired.

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