Core Viewpoint - The MoE (Mixture of Experts) model is fundamentally a sparse attention mechanism aimed at improving computational efficiency, rather than a model where each expert corresponds to a specific scenario [1][2]. Group 1: Scene Limitations - Having multiple MoE sub-models does not mean they can only handle specific scenes; it is impractical to train separate models for each scenario under the one model paradigm [1]. - If models are divided by scene, it does not represent a true MoE structure [1]. Group 2: Uniform Distribution - If only one type of scenario is run, a significant portion of the model's parameters may remain unused, leading to inefficiencies [2]. - It is more effective to distribute tasks evenly among experts rather than assigning specific experts to specific tasks, as low-usage experts may not justify their inclusion [2]. Group 3: Multiple Experts Activation - The MoE model can activate multiple experts simultaneously, allowing for a more even distribution of computational resources and addressing more complex problems effectively [2]. - The essence of the MoE model lies in the fact that only a small number of parameters significantly influence the output, making it a sparse model that enhances computational efficiency [2]. Group 4: Understanding the Model - Describing different experts as being suited for specific scenarios is a simplification that aids understanding, but it does not reflect the intentional design of the model [3].