We plotted consistency versus accuracy for models finetuned with and without KL divergence loss (Figure ). In both cases, there was minimal correlation between false consistency and accuracy, confirming their orthogonality. Linear regression of the data yielded slopes of -0.29 (with KL divergence loss) and 0.08 (without KL divergence loss), further demonstrating that improvements in accuracy do not directly lead to better consistency. These findings highlight the need for distinct strategies to enhance both metrics independently.