Skip to content
English

Switch theme

arXiv 2609.37533 · cs.CL · 2026-09-29

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

Arseny Ivanov, Alexander Kolesov, Alexander Korotin, Ivan Oseledets, Mikhail Goncharov

Affiliation unverified

In latest · #255 Added on 2026-10-21

One-line takeaway

E-MoE improves non-factorised diffusion language models with enhanced mixture-of-experts, raising few-step quality without posterior collapse.

E-MoE 用增强的混合专家改进非分解式扩散语言模型,提升少步解码质量并避免后验坍塌。

68HF upvotes
10GitHub stars
0Citations (Semantic Scholar)
50.0Buzz score

Read on arXiv PDF Hugging Face discussion Code

About this data

Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-22.

The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.

Data updated: Latest AI papers(UTC+8)