“PIRAMID: Progress and Plans” by Lauren Greenspan, Ari Brill, TomCarlson, Andrew Mack, Nischal Mainali, Jennifer Lin, Lucas Teixeira, Dmitry Vaintrob

In a recent post, we presented PIRAMID, its leadership and research pillars, and a plan for how they fit together. In this post, we sketch a team-by-team account of progress and targets for the next 6–12 months. We include results to date as evidence of viability: we’ve been a small team, with much of the past year spent building behind the scenes, and we aim to greatly accelerate our progress over the coming year as we expand our efforts and our teams. Like any fundamental scientific effort, none of this is set in stone. We expect some of these bets to need revision and are confident in our ability to reassess and change course as new evidence comes to light. If you’re interested in collaborating or supporting our work as we expand, please get in touch. Advancements in Learning Theory  We aim to formulate statistical and mesoscopic theories of feature learning and generalization which provide a level of description between microscopic parameter-level dynamics and macroscopic performance metrics. Our work so far has treated statistical physics (e.g., mean field methods) as a candidate language for statistically describing learned structure, and covariance measures between neurons or weights as candidate [...] ---Outline:(00:59) Advancements in Learning Theory(09:46) Interpretability Applications(12:01) Bottom-Up Methods for Scale-Aware Feature Discovery(19:31) Top-Down Hierarchical Architectures: Scaling Sparse Transformers(23:35) Data Models and Validation Methods(29:36) Coming Soon --- First published: July 27th, 2026 Source: https://www.lesswrong.com/posts/T2REsZneix3bmAKtL/piramid-progress-and-plans --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.