The concept of complementary learning in neuroscience has a significant influence on the design of machine learning models like ComS2T by providing insights into how different regions in the brain work together to consolidate historical memory and assimilate new knowledge. In neuroscience, it is observed that the neocortex gradually acquires structured and well-learned historical knowledge, while the hippocampus efficiently learns specific instance-level skills. This insight is translated into ComS2T by disentangling the neural architecture into stable neocortex and dynamic hippocampus structures. The stable neocortex retains invariant relations across environments, while the dynamic hippocampus adapts to new data distributions through prompts. This division allows for efficient model evolution and adaptation to changing data scenarios.