Linking AI-Induced Loss of Persistence to the Correction Freeze Problem: A Plausibility Assessment

The Correction Freeze Problem (CFP) postulated by Professor Vuong Quan Hoang proves four routes by which a corrective mechanism can leave a perceived state detached from the true state. It shows how a reinforcing dynamic can turn unnoticed drift into a shift that is harder to reverse than to enter. As CFP is not fitted to data, this note asks which of its constructs the randomized experiments of Liu et al. (2026) can inform. In three experiments (N=1222 recruited), participants lost an AI assistant that had supplied correct answers. They then solved fewer problems and, in two of three experiments, skipped more. Some key statistics are recomputed from the reported summaries. It can be shown that Experiment 1 is compromised by differential exclusion (3.1% versus 26.4%), so the note relies mainly on Experiments 2 and 3. The skip rate is a behavioral counterpart of the corner k^*=0 in the cost route. The contrast between direct-answer and hint use is a correlational proxy for the substitution weight η^* in the ambiguity route. The design does not engage the threshold or information-limit routes. It cannot speak to hysteresis. The results are therefore consistent with parts of CFP’s theoretical predictions.