The recent advancements in model merging and multi-task learning have significantly enhanced the robustness and performance of integrated models. A notable trend is the focus on addressing security vulnerabilities, such as backdoor attacks, during the merging process. This is achieved through innovative techniques that balance task-specific knowledge preservation with security measures, ensuring that merged models are not only efficient but also secure. Additionally, there is a growing emphasis on mitigating representation bias and task conflict, which are critical for improving the generalization capabilities of merged models. Techniques like deep representation surgery and self-positioning in model merging are emerging as effective solutions to these challenges, offering substantial performance gains across various tasks. Furthermore, the integration of meta-learning strategies with heterogeneous task management is proving to be a powerful approach for enhancing the adaptability and performance of models in diverse real-world scenarios. These developments collectively push the boundaries of what is possible in multi-task learning and model merging, paving the way for more sophisticated and reliable AI systems.