Method Drift›Mixture-of-experts routing
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SD-MoE
SD-MoE: Spectral Decomposition for Effective Expert SpecializationMixture-of-experts routing · first seen Feb 13, 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current frontier — recent, not yet superseded in the knowledge base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Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- PARAMΔ Integration into Upcycled MoEA Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$Δ$ Integration into Upcycled MoEMay 18, 2026
- MEMIT-like framework for MoEScalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured UpdatesMay 15, 2026
- May 11, 2026
- May 8, 2026
- Apr 28, 2026
- CoGR-MoECoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question AnsweringApr 18, 2026
- Apr 2, 2026
- On Token's DilemmaOn Token's Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language ModelsMar 29, 2026
- Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for MLIPsScaling Machine Learning Interatomic Potentials with Mixtures of ExpertsMar 9, 2026
- Mar 5, 2026
- Feb 13, 2026
- Multiscale Interaction Mixture of Experts (MI-MoE)Topology-Aware Multiscale Mixture of Experts for Efficient Molecular Property PredictionJan 19, 2026