To extend the SEER-MoE framework to handle dynamic changes in the input distribution during inference, adaptive expert allocation strategies can be implemented. One approach could involve incorporating a feedback loop mechanism that continuously monitors the input data distribution and adjusts the expert allocation accordingly. This feedback loop could be based on real-time metrics such as expert activation counts, softmax probabilities, or even external signals related to the input data characteristics. By dynamically updating the expert allocation based on the evolving input distribution, the SEER-MoE model can adapt to changing patterns and optimize resource utilization for improved efficiency during inference.