AI for science is about making problems verifiable

Sun, 18 Oct 2026 08:55:51 -0700

I can't remember the last time an LLM failed to solve a verifiable problem for me.¹ A verifiable problem is one where the model can get its own feedback: whether the code compiles, whether the tests pass, whether the number goes up, or whether a proof of a very hard problem is valid. One method of post-training LLMs is reinforcement learning with verifiable rewards, which tunes the models to be good at solving these closed-loop problems. These kinds of techniques have led to breakthroughs like solving a Millennium Prize problem by showing that the Navier-Stokes equations can develop a singularity in finite time, backed by an enormous Lean-verified proof.

Why Jupyter Notebooks Won't Replace Academic Papers

Sun, 29 Jul 2018 00:00:00 -0400

Recently there has been some buzz around Jupyter Notebooks in science, especially in light of the LIGO team sharing their detection and analysis of gravitational waves in Jupyter Notebooks. Others have claimed that Jupyter Notebooks will render traditional academic journal articles obsolete. The notebook or literate programming format improves on the reproducibility and disseminating of scientific work, however several key factors limit the notebook as a way to communicate science. In the article, I'll touch on these issues and explain why I believe that the current model of sharing computational science is here to stay.

Science on bocklund.io

AI for science is about making problems verifiable

Why Jupyter Notebooks Won't Replace Academic Papers