As the scale of Large Language Models (LLMs) continues to grow rapidly, the cost of training and inference has significantly increased, limiting their application in resource-constrained scenarios. To address this challenge, model pruning has been widely used to reduce computational complexity. Among various pruning strategies, block-wise pruning has gained popularity due to its ability to accelerate computation by removing entire blocks of parameters. However, existing methods often rely on hard labels from calibration datasets and neglect the cumulative effects of pruning on subsequent blocks. To address this, we propose two complementary techniques: the Logit Disruption Score (LDS), a novel block importance criterion that measures the impact of pruning by comparing the cosine similarity between the logits of the original and pruned models, focusing on the most informative logit dimensions to better preserve the model’s core capabilities; and Activation Statistics Correction (ASC), an affine transformation mechanism that aligns the mean and variance of activations in the pruned model with those of the original model, effectively mitigating the distribution shift caused by block removal and improving the information flow in subsequent blocks. Experiments across multiple datasets show that our approach reduces reliance on calibration data and improves generalization, achieving competitive results with existing methods.