Mixture-of-experts routing
BTM
Superseded baseline#99 of 1,370 most-superseded
Superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 1 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites BTM as a baseline.
BTM ensembling demonstrates strong performance; however, the inference cost increases linearly with the number of experts relative to a single dense model.
What to use instead
Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- 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