40 - Jason Gross on Compact Proofs and Interpretability

How do we figure out whether interpretability is doing its job? One way is to see if it helps us prove things about models that we care about knowing. In this episode, I speak with Jason Gross about his agenda to benchmark interpretability in this way, and his exploration of the intersection of proofs and modern machine learning.

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Transcript: https://axrp.net/episode/2025/03/28/episode-40-jason-gross-compact-proofs-interpretability.html

Topics we discuss, and timestamps:

0:00:40 - Why compact proofs

0:07:25 - Compact Proofs of Model Performance via Mechanistic Interpretability

0:14:19 - What compact proofs look like

0:32:43 - Structureless noise, and why proofs

0:48:23 - What we've learned about compact proofs in general

0:59:02 - Generalizing 'symmetry'

1:11:24 - Grading mechanistic interpretability