Hi! I’m a master’s student at MIT. I am affiliated with the Institute for Data, Systems, and Society (IDSS), the Center for Computational Science and Engineering (CCSE), and the Laboratory for Information and Decision Systems (LIDS). I am advised by Michiel Bakker. Before MIT, I received my B.S. in Mathematics and Physics from Tsinghua University in 2025.
My research draws on ideas from mathematical logic, cognitive science, and statistical physics to study how intelligence arises. I’m currently exploring this through self-referential AI and multi-agent systems.
Feel free to reach out at xycao [at] mit [dot] edu.
Hofstadter’s strange loops argue that a mind is what happens when a system models itself. Schmidhuber’s Gödel Machine gave this intuition a computational form. I see the same principle at work in self-reward, self-play, self-distillation, and recursive self-improvement. They look like separate techniques. I think they share a root: a model that reasons over its own outputs to reshape itself.
My starting point is Anderson’s “more is different”: at scale, new phenomena emerge that you can’t predict from the parts. Wolfram’s A New Kind of Science makes the same case computationally. Simple rules, iterated, produce genuinely complex behavior. I want to study this in multi-agent AI, where agent swarms, social intelligence, and game theory give rise to collective phenomena that no single agent could produce.
