3 papers
cs.AI2026
Deconstructing Pre-training: Knowledge Attribution Analysis in MoE and Dense Models
Bo Wang, Junzhuo Li, Hong Chen +3
Mixture-of-Experts (MoE) architectures decouple model capacity from per-token computation, enabling scaling beyond the computational limits imposed by dense scaling laws. Yet how M…
cs.LG2025
Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation
Junzhuo Li, Bo Wang, Xiuze Zhou +1
Mixture-of-Experts (MoE) models offer immense capacity via sparsely gated expert subnetworks, yet adapting them to multiple domains without catastrophic forgetting remains an open…
cs.CL2025
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis
Junzhuo Li, Bo Wang, Xiuze Zhou +3
The interpretability of Mixture-of-Experts (MoE) models, especially those with heterogeneous designs, remains underexplored. Existing attribution methods for dense models fail to c…