The paper introduces Ban&Pick, a post‑training, plug‑and‑play routing strategy for Sparse Mixture‑of‑Experts large language models. It identifies and reinforces a small group of highly influential experts while dynamically pruning redundant ones, leading to accuracy gains across math, code, and reasoning benchmarks. Experiments on DeepSeek and Qwen3 show notable performance improvements and a 1.25× inference speedup without retraining or architectural changes.
By Yuanteng Chen, Peisong Wang, Yuantian Shao, Nanxin Zeng, Chang Xu, Jian Cheng
The paper introduces InnerExpert, a method that uses Mixture-of-Experts (MoE) internal signals—such as router entropy, expert disagreement, and usage patterns—to detect hallucinations at the token level in large language models. By combining these MoE-specific signals with standard transformer features into compact per-token vectors, InnerExpert trains a lightweight detector using an LLM-as-a-judge pipeline, enabling continuous updates without manual labeling. Experiments across five datasets and two MoE architectures show that InnerExpert outperforms existing methods, achieving up to 0.91 answer-level and 0.76 token-level AUROC with only a single forward pass.
arXiv:2411. 00918v5 Announce Type: replace-cross Abstract: Mixture of experts (MoE) architectures have become a cornerstone for scaling up and are a key component in most large language models such as GPT-OSS, DeepSeek-V3, Llama-4, and Gemini-2.
By Nam V. Nguyen, Thong T. Doan, Luong Tran, Van Nguyen, Quang Pham
The paper discusses how modern mixture-of-experts (MoE) models combine fine‑grained expert segmentation with shared experts, noting that shared experts can reduce routing exploration and limit the benefits of fine‑grained segmentation. This creates an exploration dilemma that hampers the full expressiveness of the model. The authors propose MirrorMoE, which adaptively suppresses unnecessary experts to shrink the exploration space, and report consistent performance gains across different model scales.
By Bingcong Li, Hao Ma, Michael Muehlebach, Niao He, Yi Ma
DivMoE is a new framework that enables fine‑grained mixture‑of‑experts (MoE) upcycling from pre‑trained dense language models while avoiding the structural collapse that plagues naive approaches. It achieves this by initializing experts from domain‑adapted continual pre‑training and enforcing diversity‑constrained routing that forces each token to activate experts from distinct domain groups. Across two base models and 15 benchmarks, DivMoE consistently outperforms six upcycling baselines and improves over the dense base model on every benchmark after Stage 2 continual pre‑training, ultimately matching a larger 16B‑parameter MoE model with only 12B parameters.
By Yuxuan Lou, Kai Yang, Geng Zhang, Yong Liu, Yang You
The paper introduces two token-error supervision methods for sparse mixture-of-experts (MoE) large language models, aligning routing affinities with token-level cross-entropy loss. The first method predicts an error score per expert and uses it to adjust affinities before top‑K selection, while the second directly aligns router affinities to the model’s objective without extra heads or inference changes. Experiments on Granite and ARC‑Challenge show accuracy gains of about 2.3–2.94 percentage points over a parameter‑matched baseline, preserving the native sparse execution budget.
By Yury Nahshan, Nati Daniel, Jacob Goldberger, Yoli Shavit
arXiv:2606. 06098v1 Announce Type: cross Abstract: Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialized tasks via domain-expert LLMs.
By Eros Fan\`i, O\u{g}uzhan Ersoy
The paper presents a statistical framework for Mixture-of-Experts (MoE) models, treating them as localized aggregation systems. It derives oracle risk bounds that separate approximation, expert‑learning, and router‑estimation errors for both dense and sparse routing with evolving experts. The authors also analyze how sparse Top‑K routing balances computational cost with performance, interpret gating geometrically, and explain how shared experts can capture common predictive structure while allowing routed experts to focus on local residuals.
By Siyuan He, Bokai Yang, Jie Hu, Ziwen Gao, Yuhong Yang
arXiv:2608. 10392v1 Announce Type: new Abstract: Mixture-of-experts (MoE) models have recently moved beyond routing a fixed number of complete experts.
By Gongli Zhang, Zhulin Liu, C. L. Philip Chen
The paper introduces InnerExpert, a method that uses Mixture-of-Experts (MoE) architecture signals—such as router entropy, expert disagreement, and usage patterns—to detect hallucinations at the token level in Large Language Models. By combining these MoE-specific signals with standard transformer features into compact per-token vectors, InnerExpert trains a lightweight detector using an LLM-as-a-judge pipeline, enabling continuous updates without manual labeling. Experiments across five datasets and two MoE architectures show that InnerExpert outperforms existing methods, achieving up to 0.91 answer-level and 0.76 token-level AUROC with only a single forward pass.
By Joao Fonseca, Rodrigo Rodrigues, Paolo Romano
MASKerade is a new dense‑to‑MoE training method that learns experts as sparse subnetworks of a frozen pretrained feed‑forward network (FFN) using binary masks. A token‑level router selects which masked FFNs to execute, and both the router and mask scores are jointly optimized while the underlying FFN weights remain unchanged. In experiments on five vision‑language benchmarks with Qwen and Gemma backbones, a configuration of four 2:4 experts with top‑2 routing achieved the best performance among compared baselines, demonstrating that mask learning over frozen weights is a practical alternative for constructing token‑routed MoE experts.
By Mingyuan Zhang, Yue Bai, Zhongruo Wang, Yupin Huang, Yiyang Huang, Hailing Wang, Huimin Zeng, Yun Fu
arXiv:2601. 05106v5 Announce Type: replace Abstract: Large language models (LLMs) exhibit strengths across diverse domains.
By Nuoya Xiong, Yuhang Zhou, Hanqing Zeng, Zhaorun Chen, Furong Huang, Shuchao Bi, Lizhu Zhang, Zhuokai Zhao