@rohanpaul_ai: Great idea for self-evolving AI scientists from this new MIT paper. Tries to make an AI scientist notice when its curre…

Great idea for self-evolving AI scientists from this new MIT paper.

Tries to make an AI scientist notice when its current way of thinking is too small, then add new scientific concepts instead of merely searching harder.

The problem is that most AI science systems still search inside a fixed setup, even when real science sometimes needs new kinds of variables, tools, tests, or claims.

The paper’s core idea is to make every data point, model, tool output, failure, and claim a typed artifact, where typed means the system records what kind of thing it is and how it was produced.

Then the system can tell the difference between retrieval, which adds known things, search, which explores a fixed setup, and discovery, which changes the setup itself.

So novelty AI scientists is not defined by surprise, fluency, or benchmark gain, but by what could not be expressed inside the previous schema.

A serious attempt to formalize something most AI systems still fake: the difference between finding an answer inside a language and earning the right to change the language.

arxiv. org/abs/2606.01444

Title: “Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic AI”

Markus J. Buehler (@ProfBuehlerMIT): We’ve made a breakthrough in self-evolving AI scientists moving from “search” to “principled discovery”: Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for