Method Drift›Mixture-of-experts routing
Tracked
null experts within token-choice MoE
Improving MoE Compute Efficiency by Composing Weight and Data SparsityMixture-of-experts routing · first seen Jan 21, 2026
current frontier — recent, not yet superseded in the knowledge base
0 papers critique it · 0 beat it on benchmarks
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- MetaMoEMetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts UnificationMay 14, 2026
- Apr 20, 2026
- BERT-MoE FrameworkAspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User ReviewsFeb 13, 2026
- null experts within token-choice MoEImproving MoE Compute Efficiency by Composing Weight and Data SparsityJan 21, 2026
- MixtureKitMixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts ModelsDec 13, 2025
- ERMoEERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable SpecializationNov 14, 2025
- Dirichlet-Prior Shaping Loss (DPSL)Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEsOct 1, 2025
- Symphony-MoESymphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-ExpertsSep 23, 2025