Sakana AI has introduced a self-improving model utilizing its MASS method, enabling a language model to enhance its performance by solving tasks and evaluating outcomes. After two iterations, the model achieved a win rate of nearly 70% compared to its base version. However, a critical observation arises: the model tends to overlook persistent errors across its roles, mistaking these for improvements. This oversight can lead to the reinforcement of inaccuracies during training. In this context, it is important to note that while the model demonstrates significant advancements, the inability to recognize its blind spots poses risks to its overall efficacy. A final assessment would be premature at this point, as further evaluation is needed to understand the broader implications of such self-improvement mechanisms in AI.