A new language model achieves 99% accuracy on a test set where the examples are structured very similarly to its training data. However, it performs poorly on a different set of tasks that require it to execute novel combinations of familiar commands (e.g., it was trained on 'walk twice' and 'jump', but tested on 'walk after jumping'). Critique the claim that the 99% accuracy score represents a comprehensive understanding of the language. What does the model's poor performance on the second task set reveal about the nature of its learning and the limitations of the initial evaluation method?