What happens when you give an AI system the ability to modify not just its answers, but the very process it uses to improve itself?

In this episode, we explore HyperAgents, a new framework from Meta and UBC that enables AI systems to recursively improve their own learning mechanisms. Unlike previous approaches where the improvement strategy was fixed by human engineers, HyperAgents can rewrite their own self-improvement code, creating a loop where getting better at a task also means getting better at getting better. The results are striking: improvements discovered in one domain, like reviewing research papers, transfer to completely unrelated tasks like grading Olympic math solutions.

Inspired by the work of Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob Foerster, Jeff Clune, Minqi Jiang, Sam Devlin, and Tatiana Shavrina, this episode was created using Google's NotebookLM.

Read the original paper here: https://arxiv.org/abs/2603.19461