One potential limitation of the layer traversing approach is the increased computational overhead associated with multiple forward propagations, which could lead to longer inference times. This can be addressed by optimizing the implementation of the layer traversing technique, such as using efficient attention mechanisms like FlashAttention2, which can reduce the computational burden. Another drawback is the risk of overfitting, as the model may become too reliant on retracing steps rather than learning to generalize from the data. To mitigate this, regularization techniques and dropout can be employed during training to ensure that the model maintains its ability to generalize. Additionally, the complexity of managing the mixing operations between traversed layers could lead to difficulties in model interpretability. To address this, developing visualization tools that elucidate the decision-making process of the model during layer traversing could enhance understanding and trust in the model's outputs.