One potential limitation of the confidence-based approach in the lightweight inference scheme is the reliance on predefined thresholds for determining when to stop the inference process at each layer. If the thresholds are not appropriately set or if the data distribution varies significantly, it may lead to suboptimal performance. To address this limitation, adaptive thresholding techniques can be implemented. These techniques dynamically adjust the confidence thresholds based on the characteristics of the input data, ensuring that the inference process is optimized for different scenarios. Additionally, incorporating uncertainty estimation methods can provide a more nuanced understanding of the model's confidence levels and improve decision-making in the inference process.