While SQUEEZEATTENTION offers significant benefits in optimizing the KV-cache allocation based on layer-wise importance, there are potential drawbacks and limitations to consider. One limitation is the reliance on cosine similarity as a metric to quantify layer importance, which may not capture all nuances of layer contributions accurately. Future work could explore more sophisticated metrics or combine multiple indicators to provide a more comprehensive assessment of layer importance. Another limitation is the clustering of layers into predefined groups, which may not always align perfectly with the actual importance distribution. To address this, adaptive clustering algorithms or continuous adjustment of budget allocations based on real-time performance feedback could be implemented. Additionally, the algorithm's dependency on a hyperparameter (p) for budget reallocation could introduce sensitivity to its value selection, requiring further optimization or automated tuning methods. Overall, addressing these limitations through advanced metrics, dynamic clustering techniques, and hyperparameter optimization can enhance the effectiveness and robustness of the SQUEEZEATTENTION approach.