AI systems often perform well on data distributions similar to those encountered during training, but their performance can deteriorate significantly in novel or previously unseen environments. This limitation indicates that current AI systems have relatively weak adaptability. Humans, in contrast, can rapidly adapt to unfamiliar situations by combining prior knowledge with newly acquired information. I therefore believe that test-time adaptation is essential for enabling AI systems to generalize effectively and respond flexibly to changing environments.