links 7/24/26: https://roamresearch.com/#/app/srcpublic/page/07-24-2026

• • https://en.wikipedia.org/wiki/Jacob_Tsimerman I was in college when he was in grad school. Fields Medalist! Pivoting to AI safety!
  • papers on statistical mechanics in relation to AI:
    • https://arxiv.org/abs/2409.17858 uses a reproducing kernel Hilbert space! everything old is new again. (haven't read all of it)
    • https://journals.aps.org/prx/abstract/10.1103/PhysRevX.14.031001 How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Problem. in the genre of paper "why do these things start working so well when they get big"
    • https://arxiv.org/abs/2603.11331 the empirical case: when you model neural nets with a spin-glass model that gives you a discrete number of basins in the loss landscape, you get scaling laws for susceptibility to prompt injection that match a few empirical data points
    • https://en.wikipedia.org/wiki/Ising_model one of many stat-mech models, maybe the easiest to understand
    • https://www.lesswrong.com/posts/siu22scEfuKxpSgfK/a-tale-of-three-theories-sparsity-frustration-and "frustration" (like magnetic poles that don't "want" to be close) is a good model for superposition of features in a too-small space, polysemanticity. motivation for Why Stat Mech.
    • https://en.wikipedia.org/wiki/Dynamical_mean-field_theory this one i'm a long way from understanding
    • https://www.lesswrong.com/s/3fknGqkujhGrnRodA/p/74wSgnCKPHAuqExe7 to-read
  • https://arxiv.org/pdf/2504.07912 RL seems to select for outputs that look like a particular source of training data (if the training data comes from a mixture of sources)