FRA-δ? Method is a way to train a model to independently track early changes in the architecture of decision-making. The idea is that the model should notice the moment when its reasoning begins to change form — when a new frame appears, a hidden premise emerges, tension between options grows, an unexplained residual remains, or the reasoning shifts into an overly general formula. At that moment, the model should not silently fix the new conclusion as a finished answer. It should mark the shift and trigger a check: saw a shift — do not fix it silently The method is useful because it can work as an additional layer on top of an existing model: through logging, a lightweight classifier head, an inference-time check, or embedding analysis. This means it does not require full retraining from scratch. The goal of FRA-δ? is to make early changes in reasoning visible before they turn into stable errors inside confident answers.
AdmailFRA (Tue,) studied this question.