Sparse Mixture-of-Experts (SMoE) models represent a significant advancement in large language model (LLM) development through their efficient parameter utilization. These models achieve substantial performance improvements at reduced inference costs. However, the deployment of SMoE models faces constraints from extensive memory requirements of expert components in resource-limited environments. To address these limitations, this paper introduces Hierarchical Clustering for Sparsely activated Mixture of Experts (HC-SMoE), a task-agnostic expert merging framework for parameter reduction without retraining. HC-SMoE introduces a novel hierarchical clustering approach based on expert outputs to ensure merging robustness independent of routing decisions. The proposed output-based clustering method enables effective capture of functional relationships between experts for largescale architectures. We provide theoretical analysis and comprehensive evaluations across multiple zero-shot language tasks to demonstrate HC-SMoE’s effectiveness in state-of-the-art models including Qwen and Mixtral. The experimental results validate HC-SMoE’s superior performance and practical applicability for real-world deployments. Our implementation is available at https://github.com/wazenmai/HC-SMoE.

Projection Mapping, the superimposing of virtual images upon actual objects, is already extensively used in performance arts. Applications of it are already quite mature, therefore, here we wish to achieve the opposite, or specifically speaking, the superimposing of actual objects into virtual images. This method of reverse superimposition is called "camera mapping." Through cameras, camera mapping captures actual objects, and introduces them into a virtual world. Then using superimposition, this allows for actual objects to be rendered as virtual objects. However, the actual objects here must have refined shapes so that they may be superimposed back into the camera. Through the proliferation of 3D printing, virtual 3D models in computers can be created in reality, thereby providing a framework for the limits and demands of "camera mapping." The new media artwork Digital Buddha combines 3D Printing and camera mapping. This work was created by 3-D deformable modeling through a computer, then transforming the model into a sculpture using 3D printing, and then remapping the materially produced sculpture back into the camera. Finally, it uses the already known algorithm to convert the model back into that of the original non-deformed sculpture. From this creation project, in the real world, audiences will see a deformed, abstract sculpture; and in the virtual world, through camera mapping, they will see a concrete sculpture (Buddha). In its representation, this piece of work pays homage to the work TV Buddha produced by video art master Nam June Paik. Using the influence television possesses over people, this work extends into the most important concepts of the digital era, "coding" and "decoding," simultaneously addressing the shock and insecurity people in the digital era feel toward images.