![[2605.30876] dMoE: dLLMs with Learnable Block Experts](/static/img/curated/desks/afbytes-tech.w400.webp)
[2605.30876] dMoE: dLLMs with Learnable Block Experts
Abstract page for arXiv paper 2605.30876: dMoE: dLLMs with Learnable Block Experts
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Mixture of Experts
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[2605.30992] Eigenvectors of Experts are Training-free Non-collapsing Routers
Abstract page for arXiv paper 2605.30992: Eigenvectors of Experts are Training-free Non-collapsing Routers
[2605.31010] MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
Abstract page for arXiv paper 2605.31010: MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
[2605.31463] PithTrain: A Compact and Agent-Native MoE Training System
Abstract page for arXiv paper 2605.31463: PithTrain: A Compact and Agent-Native MoE Training System
[2605.29714] Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation
Abstract page for arXiv paper 2605.29714: Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation
The MoE-ification of the Open Model Ecosystem, and What It Means for Your Inference Bill
What Mixture of Experts (MoE) models mean for your GPU costs, your serving stack, and your deployment strategy in 2026.