shared-experts

This paper proposes UniF-MoE, a unified framework for token-adaptive Mixture-of-Experts computation that first shares reusable computation across experts and then routes the remaining residual demand, improving performance while reducing activated computation, latency, and memory on DomainBed and GLUE benchmarks.