The article discusses a new method called predecessor combination search (PCS) that improves the accuracy of neural networks by adjusting their output probabilities. This method helps make sure that the networks give reliable results, especially in critical situations. By analyzing different parts of the network and using PCS, the researchers were able to enhance the network's performance on various datasets and architectures. This approach also made the network more robust when faced with changes in the data it was trained on.