id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b2d211d97213b0fc16bb66c6b596f75a6106139ca5d4f2a89039bd82bee32db6 | [
"arxiv"
] | SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs | Sparse Mixture-of-Experts (MoE) architectures enable scaling LLM parameters under a fixed inference budget by activating only a small subset of experts via top-$k$ routing. While this preserves causality and suits autoregressive language models, the discrete top-$k$ operator is not differentiable, forcing a fixed numbe... | [
"Mikołaj Zasada",
"Łukasz Struski",
"Jacek Tabor",
"Marcin Kurdziel"
] | [
"cs.LG",
"cs.AI"
] | [] | 2026-06-16T00:00:00 | https://arxiv.org/abs/2606.17952 | https://arxiv.org/pdf/2606.17952v1 | 2606.17952 | null | 0 | 0 | false | null | null | 0.35 |
09ea161a7d80f04e610defecd5c107e48b875ee666fd57935e11aea866e9c4a3 | [
"arxiv",
"semantic_scholar"
] | Tying the Loop -- Tied Expert Layers in Mixture-of-Experts Language Models | Mixture-of-Experts (MoE) architectures efficiently scale Large Language Models (LLMs) by activating only a small fraction of their experts per token, yet the full parameter count - dominated by the expert parameters - must be held in training and inference memory. To address this, we introduce Expert Tying, an architec... | [
"Martin Jaggi"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-06-15T00:00:00 | https://arxiv.org/abs/2606.16825 | https://arxiv.org/pdf/2606.16825v1 | 2606.16825 | null | 0 | 0 | true | https://github.com/epfml/looped-moe | null | 0.65 |
60430c52e8dd2ef3e39ce542fed4ea86469a732dcf5c9a28db75e4262c25810e | [
"arxiv"
] | MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs | Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compression essential. Among PTQ methods, expert-level mixed-precision quantization has proven effective for MoE-LLMs, yet suffers notable degradation on MoE-MLLMs due to two overl... | [
"Yuanteng Chen",
"Peisong Wang",
"Zhilei Liu",
"Nanxin Zeng",
"Yuantian Shao",
"Shiqiang Lang",
"Tao Liu",
"Chuangyi Li",
"Qinghao Hu",
"Gang Li",
"Jing Liu",
"Jian Cheng"
] | [
"cs.LG",
"cs.AI"
] | [] | 2026-06-15T00:00:00 | https://arxiv.org/abs/2606.17118 | https://arxiv.org/pdf/2606.17118v1 | 2606.17118 | null | 0 | 0 | false | null | null | 0.35 |
0760d97bc48847ffd1e2eab76dfe12982f9d27e2aa4c0388b4ec18aa3a8f94ba | [
"arxiv",
"semantic_scholar"
] | DynFS-MoE: Dynamic Functional-Structural Mixture-of-Experts for Post-Traumatic Epilepsy Diagnosis | Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI), yet early identification remains challenging due to the complex structural and functional alterations it induces in the brain. To address this, we propose a dynamic multimodal Mixture-of-Experts (MoE) framework that integrates funct... | [
"Jun-En Ding",
"Spencer Chen",
"Henry Noren",
"Daniel Valdivia",
"Christine Yohn",
"Suhina Patel",
"Taylor Zink",
"Hai Sun",
"Feng Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-06-15T00:00:00 | https://arxiv.org/abs/2606.16203 | https://arxiv.org/pdf/2606.16203v2 | 2606.16203 | null | 0 | 0 | false | null | null | 0.35 |
001bf195bfd8bc85e85ca1044ced24297d8a8a020106dcd182dd56bbfc3d1a8c | [
"arxiv",
"semantic_scholar"
] | How to Score Experts for One-Shot MoE Expert Pruning: A Unified Formulation and Selection Principle | Mixture-of-Experts (MoE) language models reduce per-token computation through sparse expert activation, yet deployment still requires storing the full expert pool, making one-shot expert pruning a practical approach for reducing memory usage. Although effective, existing criteria are largely heuristic, and no single cr... | [
"Zongfang Liu",
"Jinghui Zhang",
"Zijian Ma",
"Guangyi Chen",
"Xin Yuan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-14T00:00:00 | https://arxiv.org/abs/2606.15716 | https://arxiv.org/pdf/2606.15716v1 | 2606.15716 | null | 0 | 0 | false | null | null | 0.35 |
f7a30d41c0220f3e6c8c60ac612d4f5bc13f75c2419c990182ced4cf44089f4c | [
"arxiv",
"semantic_scholar"
] | A Spatio-Temporal Expert Prefetching Framework for Efficient MoE-based LLM Inference | Mixture-of-Experts (MoE) based large language models (LLMs), such as Qwen and DeepSeek, have recently emerged as an effective approach to improving model capacity without proportionally increasing computational cost. By replacing the conventional feed-forward network in dense LLMs with a set of experts and activating o... | [
"Yingnan Zhao",
"Razvan Bunescu",
"Ahmed Louri",
"Avinash Karanth",
"Ke Wang"
] | [
"cs.AR",
"cs.LG"
] | [
"Computer Science"
] | 2026-06-13T00:00:00 | https://arxiv.org/abs/2606.15453 | https://arxiv.org/pdf/2606.15453v1 | 2606.15453 | null | 0 | 0 | false | null | null | 0.35 |
ef799393c45af71a9b784532b7b076d17c68b78a4b5d0df38e555133a08eff3e | [
"arxiv",
"semantic_scholar"
] | A theoretical model for task routing in mixture-of-expert transformers | Mixture-of-experts (MoE) layers enable the scaling of transformer models while keeping the inference compute fixed. While task-expert specialization has been observed in empirical studies of frontier MoE transformer models, existing theoretical work analyzes this using continuous mixture models that cannot be used to m... | [
"Vinoth Nandakumar",
"Yongli Xiang",
"Yunzhi Yao",
"Peike Li",
"Tongliang Liu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-12T00:00:00 | https://arxiv.org/abs/2606.14398 | https://arxiv.org/pdf/2606.14398v2 | 2606.14398 | null | 0 | 0 | false | null | null | 0.35 |
aeea47930489c3c29a15b5d032ebbd01137ca01018d6230a4d3ceabe6832ac05 | [
"arxiv",
"semantic_scholar"
] | Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models | Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored. Unlike dense models, MoE architectures employ a router at each layer to assign each token to a sparse subset of experts. In this work, we observe that forget data often... | [
"Jingyi Xie",
"Yijun Lin",
"Yinjiang Xiong",
"Zhikun Zhang",
"Sai Li"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-06-09T00:00:00 | https://arxiv.org/abs/2606.10338 | https://arxiv.org/pdf/2606.10338v1 | 2606.10338 | null | 0 | 0 | false | null | null | 0.35 |
dba21527b12bb79fd47787f44818b7bea7a1972ff2267ec146dc4221afc9f563 | [
"arxiv",
"semantic_scholar"
] | STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning | Mixture-of-Experts (MoE) scales model capacity efficiently by selectively routing inputs to a specialized subset of experts. However, input-expert specialization, the core motivation of MoE, critically depends on whether the router is actually aware of input structure. In practice, MoE routing is typically implemented ... | [
"Sumin Park",
"Noseong Park"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-06-07T00:00:00 | https://arxiv.org/abs/2606.08814 | https://arxiv.org/pdf/2606.08814v1 | 2606.08814 | null | 0 | 0 | false | null | null | 0.35 |
545471aea2dd74925794f33cf51f3913eeb80c642134aa3bb45f59ff1b2584ab | [
"arxiv",
"semantic_scholar"
] | Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling | This paper reports on training a hundred-billion-parameter sparse mixture of experts on a single eight-GPU node, end to end. LightningLM 0.1V is a recurrence-backbone language model family grown in four stages from a small dense seed, through a 5B and a 9B mixture of experts, to a 120B model with 460 routed experts und... | [
"Rohan Shravan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-05T00:00:00 | https://arxiv.org/abs/2606.07404 | https://arxiv.org/pdf/2606.07404v1 | 2606.07404 | null | 0 | 0 | true | https://github.com/The-School-of-AI/LLM | null | 0.65 |
5ea3182923a447700ff7de9a75dc9d912a92dbc16a5d4fdefccb458d42d9e167 | [
"arxiv",
"semantic_scholar"
] | YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition | Large language models (LLMs) drive significant financial innovations, yet their high-concurrency deployment is severely bottlenecked by KV cache memory overhead, which inflates infrastructure costs and throttles scalability. To address this, we propose YouZhi-LLM, a highly efficient financial LLM empowered by a compreh... | [
" PSBC LLM Team",
" Huawei LLM Team",
"Ruihan Long",
"Junjie Wu",
"Tianan Zhang",
"Duo Zhang",
"Yaozong Wu",
"Jinbin Fu",
"Chang Liu",
"Zhentao Tang",
"Wenshuang Yang",
"Xin Wang",
"Zhihao Song",
"Ning Huang",
"Wenjing Xu",
"Shuai Zong",
"Shupei Sun",
"Sen Wang",
"Jing Hu",
"Bi... | [
"cs.CL"
] | [
"Computer Science"
] | 2026-06-04T00:00:00 | https://arxiv.org/abs/2606.05868 | https://arxiv.org/pdf/2606.05868v1 | 2606.05868 | null | 0 | 0 | false | null | null | 0.35 |
51c0fafe4237deb7e37e634c4b1eb03507a834d97ea6de053d7c8a30353a889a | [
"arxiv",
"semantic_scholar"
] | Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models | Mixture-of-Experts (MoE) models scale foundation models efficiently by activating only a subset of experts for each token, but their large number of expert parameters still makes quantization essential for practical deployment. Unlike dense models, however, MoE models are sensitive to routing instability: small quantiz... | [
"Hancheol Park",
"Geonho Lee",
"Tairen Piao",
"Tae-Ho Kim"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-06-04T00:00:00 | https://arxiv.org/abs/2606.05688 | https://arxiv.org/pdf/2606.05688v1 | 2606.05688 | null | 0 | 0 | false | null | null | 0.35 |
d98765a0cc7fec86538d4fb4aba9d678b35fe350ba967ff7df21501a168e1d55 | [
"arxiv",
"semantic_scholar"
] | Less is MoE: Trimming Experts in Domain-Specialist Language Models | Mixture-of-Experts (MoE) models achieve strong performance through conditional computation, but their large parameter footprint poses deployment challenges. Prior MoE compression approaches catastrophically fail when evaluated on general-purpose benchmarks beyond commonsense reasoning. We trace this failure to the gran... | [
"Haoze He",
"Xinkai Zou",
"Xuan Jiang",
"Xingyuan Ding",
"Ao Qu",
"Juncheng Billy Li",
"Heather Miller"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-06-04T00:00:00 | https://arxiv.org/abs/2606.05538 | https://arxiv.org/pdf/2606.05538v1 | 2606.05538 | null | 0 | 0 | false | null | null | 0.35 |
ff4d98f6b0b06cd072a826b0088f9a2be1ac76f474e701014d7d0e6caf8817ff | [
"arxiv",
"semantic_scholar"
] | SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs | Sparse Mixture-of-Experts (MoE) large language models achieve strong quality with low per-token compute, yet their deployment is often limited by the memory wall: the full expert pool must remain resident to support token-dependent routing. Expert pruning is a direct remedy, but prior criteria typically score experts i... | [
"Yuhao Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-06-03T00:00:00 | https://arxiv.org/abs/2606.09886 | https://arxiv.org/pdf/2606.09886v1 | 2606.09886 | null | 0 | 0 | true | https://github.com/Alizen-1009/Shapley-Moe | null | 0.65 |
07e219c4a0b5e8637c1a78a853167272aff18638a7b9f17ce412b5cfbeeeafa9 | [
"arxiv",
"semantic_scholar"
] | Expert-Aware Causal Tracing of Factual Recall in Sparse MoE Language Models | Causal tracing of factual recall has been studied predominantly in dense transformer language models, where interventions localize information flow to layers or feed-forward modules. Sparse mixture-of-experts (MoE) language models introduce a sharper question: when a factual prediction is mediated by a routed MoE block... | [
"Yuetian Lu",
"Ali Modarressi",
"Yihong Liu",
"Hinrich Schütze"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-06-02T00:00:00 | https://arxiv.org/abs/2606.03780 | https://arxiv.org/pdf/2606.03780v1 | 2606.03780 | null | 0 | 0 | false | null | null | 0.35 |
ffa7dd593613a170a8aefdf8639b244173ec134c9309d4131f852ba6e5d3818e | [
"arxiv",
"semantic_scholar"
] | Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling | Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with human values. However, most existing approaches assume a universal reward function, neglecting the diversity and heterogeneity of human preferences. To address this limitati... | [
"Yifan Wang",
"Jinyi Mu",
"Mayank Jobanputra",
"Yu Wang",
"Ji-Ung Lee",
"Soyoung Oh",
"Isabel Valera",
"Vera Demberg"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-06-02T00:00:00 | https://arxiv.org/abs/2606.04284 | https://arxiv.org/pdf/2606.04284v1 | 2606.04284 | null | 0 | 0 | false | null | null | 0.35 |
94fad39b46a45fc010ee9ac33de2fc46ee763585ba9ed646e199cbca786238f8 | [
"arxiv",
"semantic_scholar"
] | When Model Merging Breaks Routing: Training-Free Calibration for MoE | Model merging has emerged as a cost-effective approach for consolidating the capabilities of multiple LLMs without retraining. However, existing merging techniques, largely based on linear parameter arithmetic or optimization, struggle when applied to Mixture-of-Experts (MoE) architectures. We identify a critical failu... | [
"Canbin Huang",
"Tianyuan Shi",
"Xiaojun Quan",
"Jingang Wang",
"Jianfei Zhang",
"Qifan Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-06-02T00:00:00 | https://arxiv.org/abs/2606.03391 | https://arxiv.org/pdf/2606.03391v1 | 2606.03391 | null | 0 | 0 | true | https://github.com/huangcb01/HARC | null | 0.65 |
84a56ce3f3c4253499659b6510485f8b267ea5ca1eb3e8bd49c5ee207fb2c611 | [
"arxiv",
"semantic_scholar"
] | ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts | Mixture-of-Experts (MoE) models scale by activating only a small subset of experts per token. However, training such models remains challenging because top-$k$ routing is discrete and non-differentiable, requiring gradient estimators for expert selection whose design remains a central open problem. We introduce ProbMoE... | [
"Heng Zhao",
"Zilei Shao",
"Guy Van den Broeck",
"Zhe Zeng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-06-01T00:00:00 | https://arxiv.org/abs/2606.01509 | https://arxiv.org/pdf/2606.01509v1 | 2606.01509 | null | 0 | 0 | false | null | null | 0.35 |
27ad4d392659179ee22e669923b194321238275ce5ff05d1c3f0ecddffe1e993 | [
"arxiv",
"semantic_scholar"
] | DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts | Mixture-of-Experts (MoE) models have become a leading approach for decoupling parameter count from computational cost in large language models, yet effectively scaling MoE performance remains a challenge. Prior work shows that fine-grained experts enlarge the space of expert combinations and improve flexibility, but th... | [
"Jiarui Feng",
"Hanqing Zeng",
"Karish Grover",
"Ruizhong Qiu",
"Yinglong Xia",
"Qiang Zhang",
"Qifan Wang",
"Ren Chen",
"Dongqi Fu",
"Jiayi Liu",
"Zhoukai Zhao",
"Xiangjun Fan",
"Benyu Zhang",
"Yixin Chen"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-31T00:00:00 | https://arxiv.org/abs/2606.01062 | https://arxiv.org/pdf/2606.01062v1 | 2606.01062 | null | 1 | 0 | false | null | null | 0.35 |
ea8b3767e1b2bfc8d1f5ccdccda8c68c9dfc5ae03e3f5ed4cb22d65f47ee6fa2 | [
"arxiv",
"semantic_scholar"
] | PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning | Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale. However, reinforcement learning (RL) on MoE-based LLMs often suffers from training instability. A root cause is router drift, i.e., expert activations can change drastically across model updates and differ between disaggregated r... | [
"Daize Dong",
"Junlin Chen",
"Haolong Jia",
"Jiang Liu",
"Jiawei Wu",
"Huanwei Di",
"Jialian Wu",
"Zhengzhong Liu",
"Zicheng Liu",
"Emad Barsoum",
"Dimitris N. Metaxas",
"Hongyi Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-29T00:00:00 | https://arxiv.org/abs/2606.00395 | https://arxiv.org/pdf/2606.00395v2 | 2606.00395 | null | 0 | 0 | false | null | null | 0.35 |
a8d3312b5c7c7d574e91e4e921cbaea3ebbcfb099210631933c18bf8e7f457ab | [
"arxiv",
"semantic_scholar"
] | Understanding Safety-Sensitive Expert Behavior in Mixture-of-Experts LLMs | Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization remains underexplored. A common intuition is that safety behavior may be controlled by routing harmful requests to distinct refusal-oriented experts. In this work, we provi... | [
"Zhibo Zhang",
"Yuxi Li",
"Zhen Ouyang",
"Ling Shi",
"Kailong Wang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.29708 | https://arxiv.org/pdf/2605.29708v1 | 2605.29708 | null | 0 | 0 | false | null | null | 0.35 |
a5831e669d725d1e4b2635777049fc108b3f0888bc1894c190c828f40ed697b8 | [
"arxiv",
"semantic_scholar"
] | Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation | Mixture-of-Experts (MoE) models are widely used to scale language models, yet their expert routing behavior and adaptation in a multilingual setting remain underexplored. In this work, we study multilingual routing dynamics during continual pre-training of an English-centric MoE model on a multilingual corpus, analyzin... | [
"Aditi Khandelwal",
"Marius Mosbach",
"Verna Dankers",
"Siva Reddy",
"Golnoosh Farnadi"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.29714 | https://arxiv.org/pdf/2605.29714v1 | 2605.29714 | null | 0 | 0 | true | https://github.com/aditi184/moe-routing-adaptation | null | 0.65 |
ea437a6b57a0a04601aa92b74f7282904035a423378aebc18bb6ea2e6025f335 | [
"arxiv",
"semantic_scholar"
] | ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression | Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intensive. Existing post-training compression methods mainly shrink this cost by pruning experts or merging their weights. We formulate post-training MoE compression as exper... | [
"Yilun Yao",
"Jiaming Pan",
"Elsie Dai",
"Peizhuang Cong",
"Yaoming Li",
"Tong Yang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.29350 | https://arxiv.org/pdf/2605.29350v1 | 2605.29350 | null | 0 | 0 | false | null | null | 0.35 |
bb990fba3c9f75cb10fb9cff57b6e223123b7e0bec5772da42cf4b9e791470bd | [
"arxiv",
"semantic_scholar"
] | Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models | Mixture-of-Experts (MoE) models have emerged as a dominant paradigm for efficient LLM scaling, yet adapting them to non-English downstream tasks remains challenging. Existing fine-tuning approaches treat MoE models as monolithic learners, ignoring the heterogeneous routing structure that develops during pretraining. We... | [
"Guanzhi Deng",
"Kuan Wu",
"Haibo Wang",
"Shing Yin Wong",
"Sichun Luo",
"Linqi Song"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-27T00:00:00 | https://arxiv.org/abs/2605.28306 | https://arxiv.org/pdf/2605.28306v1 | 2605.28306 | null | 0 | 0 | false | null | null | 0.35 |
b932e82ed1d79b177eee5497205ae1d57a047b5427c0a0d9220ebbea8ca2a6e5 | [
"arxiv",
"semantic_scholar"
] | FPMoE: A Sparse Mixture-of-Experts Approach to Functional Code Generation | Despite rapid progress in LLM-based code generation, existing models are predominantly trained on imperative languages, leaving functional programming languages (FPLs) such as Haskell, OCaml, and Scala chronically underexplored, with even frontier models performing substantially worse on FPLs. Fine-tuning is a natural ... | [
"Loc Pham",
"Lang Hong Nguyet Anh",
"Thanh Le-Cong"
] | [
"cs.PL",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-27T00:00:00 | https://arxiv.org/abs/2605.27849 | https://arxiv.org/pdf/2605.27849v1 | 2605.27849 | null | 0 | 0 | true | null | null | 0.65 |
94f6014df324797f71eeeefb71f1626b7bdc1e2ad7a7b57ffe80fb74d93f3baf | [
"arxiv",
"semantic_scholar"
] | ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference | Fine-grained Mixture-of-Experts (MoE) models sparsely activate only a subset of experts per token, reducing activated computation while maintaining high model capacity. However, in memory-constrained inference scenarios, only a small set of experts can be cached. Experts not in the cache must be fetched from slow exter... | [
"Xiongwei Zhu",
"Xiaojian Liao",
"Tianyang Jiang",
"Yusen Zhang",
"Liang Wang",
"Limin Xiao"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2026-05-26T00:00:00 | https://arxiv.org/abs/2605.27081 | https://arxiv.org/pdf/2605.27081v1 | 2605.27081 | null | 0 | 0 | true | https://github.com/BUAA-OSCAR/ReMoE | null | 0.65 |
8922eb922492a3eaa9cfc80b9951558467d90f2d451467f8cded4d4aa4e68a59 | [
"arxiv",
"semantic_scholar"
] | BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma | Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic factors; thus, similar survival outcomes may reflect fundamentally different underlying biological processes. Prognostic modeling in HCC is informed by rich multimodal inf... | [
"Junlin Yang",
"Tian Yu",
"Nicha C. Dvornek",
"Yuexi Du",
"Peiyu Duan",
"Annabella Shewarega",
"Lawrence H. Staib",
"James S. Duncan",
"Julius Chapiro"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-25T00:00:00 | https://arxiv.org/abs/2605.26376 | https://arxiv.org/pdf/2605.26376v1 | 2605.26376 | null | 0 | 0 | true | https://github.com/jy-639/BioFact-MoE | null | 0.65 |
01766860289bf7462da1adc8c4096eca2fb38bf947e91d55fa8beee387e6a605 | [
"arxiv",
"semantic_scholar"
] | RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism | While Large Language Models (LLMs) are commonly fine-tuned to handle domain-specific tasks before being applied to vertical applications, adapting them to complex scenarios with diverse specialized knowledge remains challenging. Meanwhile, Mixture-of-Experts (MoE) architecture has risen as a crucial paradigm for traini... | [
"Mengyang Sun",
"Maochuan Dou",
"Tao Feng",
"Dan Zhang",
"Yihao Wang",
"Junpeng Liu",
"Yifan Zhu",
"Jie Tang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-25T00:00:00 | https://arxiv.org/abs/2605.25565 | https://arxiv.org/pdf/2605.25565v1 | 2605.25565 | null | 0 | 0 | false | null | null | 0.35 |
40184c69a06ed460ef6db4dc81c9c355c502f5d8f843034a42073362e46abad2 | [
"arxiv",
"semantic_scholar"
] | RouteScan: A Non-Intrusive Approach to Auditing MoE LLMs Safety via Expert Routing Telemetry | Mixture-of-Experts (MoE) architectures have become an increasingly important paradigm for scaling Large Language Models (LLMs). As MoE models are increasingly deployed in real-world services, safety auditing becomes necessary to verify whether these models produce or facilitate harmful behaviors during operation. Howev... | [
"Bo Lv",
"Zhiheng Xu",
"KeDong Xiu",
"Ruyi Ding",
"Tianhang Zheng",
"Zhibo Wang",
"Kui Ren"
] | [
"cs.CR",
"cs.AR",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-24T00:00:00 | https://arxiv.org/abs/2605.24817 | https://arxiv.org/pdf/2605.24817v1 | 2605.24817 | null | 0 | 0 | true | null | null | 0.65 |
e4b2536b0fe4f721202b1ac28b188887b92fcec60eec13ffeac945e8035a6138 | [
"arxiv",
"semantic_scholar"
] | AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting | Time series forecasting models are increasingly scaled through large Transformer backbones, yet most existing approaches process all series through a shared dense computation path despite substantial heterogeneity in temporal structure. Mixture-of-Experts (MoE) offers a natural alternative by enabling conditional compu... | [
"Rui Wang",
"Renhao Xue",
"Ray Razi",
"Huan Song",
"Hannah R. Marlowe"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-24T00:00:00 | https://arxiv.org/abs/2605.25166 | https://arxiv.org/pdf/2605.25166v1 | 2605.25166 | null | 0 | 0 | false | null | null | 0.35 |
9fb1f5cdf7e5fa9789b3b962a72a70295676685c01d248a432e1fe4e1b426229 | [
"arxiv",
"semantic_scholar"
] | Safety-Oriented Routing Analysis of Mixtral MoE Under Benign and Harmful Prompts | Sparse mixture-of-experts (MoE) language models activate only a small subset of parameters for each token, making router behavior a central part of model computation. This paper studies routing behavior of Mixtral 8x7B-Instruct under benign and harmful prompts using two complementary signals: activation-based routing s... | [
"Md Nurul Absar Siddiky"
] | [
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2026-05-22T00:00:00 | https://arxiv.org/abs/2605.24270 | https://arxiv.org/pdf/2605.24270v1 | 2605.24270 | null | 0 | 0 | false | null | null | 0.35 |
5c1fa6f5cd729e4de740c84a6528488c846d8af0e5aa39e6696bd1094bc2b8e6 | [
"arxiv",
"semantic_scholar"
] | GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs | Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters. Mixed-precision quantization mitigates this cost by allocating expert-wise bit-widths based on their importance, approaching the accuracy-memory Pareto frontier and enab... | [
"Jianing Deng",
"Song Wang",
"Dongwei Wang",
"Zijie Liu",
"Tianlong Chen",
"Huanrui Yang",
"Jingtong Hu"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-21T00:00:00 | https://arxiv.org/abs/2605.23078 | https://arxiv.org/pdf/2605.23078v1 | 2605.23078 | null | 0 | 0 | true | https://github.com/jndeng/GEMQ | null | 0.65 |
4e5862621c61c300f9ab95b4f8fa94ef1ce14225651c8649ef5568fb11cbfc53 | [
"arxiv",
"semantic_scholar"
] | Beyond Routing: Characterising Expert Tuning and Representation in Vision Mixture-of-Experts | Mixture-of-Experts (MoE) models are often interpreted by analysing which categories are routed to which experts. However, routing alone does not reveal what each expert actually encodes. We train sparsely-gated convolutional MoE models with a contrastive objective on natural images and characterise expert specialisatio... | [
"Gene Tangtartharakul",
"Katherine R. Storrs"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.20610 | https://arxiv.org/pdf/2605.20610v1 | 2605.20610 | null | 0 | 0 | false | null | null | 0.35 |
c08824c4c5c184721917a400dc5a9eb4e992dd4df7274f6748aad169dd56983a | [
"arxiv",
"semantic_scholar"
] | Task-Routed Mixture-of-Experts with Cognitive Appraisal for Implicit Sentiment Analysis | Implicit sentiment analysis is challenging because sentiment toward an aspect is often inferred from events rather than expressed through explicit opinion words. Existing models typically learn from the final polarity label, which provides limited guidance for reasoning about sentiment from the context. Motivated by co... | [
"Yaping Chai",
"Haoran Xie",
"Joe S. Qin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.20916 | https://arxiv.org/pdf/2605.20916v1 | 2605.20916 | null | 0 | 0 | true | https://github.com/yaping166/TRMoE-ISA | null | 0.65 |
227f742ce30306c31364321e7f9b321d3fc52f5124666dbfaa0e69a91e0c4219 | [
"arxiv",
"semantic_scholar"
] | GEM: GPU-Variability-Aware Expert to GPU Mapping for MoE Systems | Mixture-of-Expert (MoE) models enable efficient inference by employing smaller experts and activating only a subset of them per token. MoE serving engines distribute experts across multiple GPUs and route tokens to appropriate GPUs at inference time based on experts activated. They process tokens in lock-step fashion, ... | [
"Sourish Wawdhane",
"Avinash Kumar",
"Poulami Das"
] | [
"cs.DC",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.19945 | https://arxiv.org/pdf/2605.19945v1 | 2605.19945 | null | 0 | 0 | false | null | null | 0.35 |
dad487bf361cd50b76dc26f154f08f386fcd2fa8687ecb069b8b7c00adc80424 | [
"arxiv",
"semantic_scholar"
] | CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning | Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs). Although Mixture-of-Experts (MoE) architectures offer an efficient path to scaling, existing LoRA-based MoE continual learning methods still face a fundamental trade-off: they eithe... | [
"Yang Liu",
"Toan Nguyen",
"Flora D. Salim"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.20247 | https://arxiv.org/pdf/2605.20247v1 | 2605.20247 | null | 0 | 0 | false | null | null | 0.35 |
70007cbe46cb60004f951353a022582aa2407d27647592c0e1a2e8f864162cde | [
"arxiv",
"semantic_scholar"
] | Post-Trained MoE Can Skip Half Experts via Self-Distillation | Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its dynamic variant further reduces computation by adjusting the activated experts in an input-dependent manner. Existing dynamic MoE methods usually rely on pre-training from scratch or task-specific adaptation, leaving t... | [
"Xingtai Lv",
"Li Sheng",
"Kaiyan Zhang",
"Yichen You",
"Siyan Gao",
"Xueheng Luo",
"Yuxin Zuo",
"Yuchen Fan",
"Junlin Yang",
"Ganqu Cui",
"Bingning Wang",
"Fan Yang",
"Youbang Sun",
"Ning Ding",
"Bowen Zhou"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.18643 | https://arxiv.org/pdf/2605.18643v2 | 2605.18643 | null | 0 | 0 | false | null | null | 0.35 |
da51de550d416c9c388c92e7cd00169479fad6d651d3fc9185ff0029a63770e9 | [
"arxiv",
"semantic_scholar"
] | CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution | The Mixture-of-Experts (MoE) architecture improves computational efficiency via sparse expert activation, but throughput-oriented inference faces substantial GPU memory pressure due to a significant parameter size and intermediate data. Prior works attempt to mitigate this using expert offloading with micro-batching or... | [
"Muyoung Son",
"Yi Chen",
"Seungjae Yoo",
"Soongyu Choi",
"Joo-Young Kim"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.17889 | https://arxiv.org/pdf/2605.17889v2 | 2605.17889 | 10.1145/3770743.3804296 | 0 | 0 | false | null | null | 0.35 |
4c8039055e72df82136c1b13514d5cf1bfe2e310acd1ae152938e49722960cf7 | [
"arxiv",
"semantic_scholar"
] | Stable Routing for Mixture-of-Experts in Class-Incremental Learning | Class-incremental learning (CIL) requires models to learn new classes sequentially while preserving prior knowledge. Recently, approaches that combine pre-trained models with mixture-of-experts (MoE) have received increasing attention in CIL: they typically expand experts during learning and employ a router to assign w... | [
"Zirui Guo",
"Quan Cheng",
"Da-Wei Zhou",
"Lijun Zhang"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17571 | https://arxiv.org/pdf/2605.17571v1 | 2605.17571 | null | 0 | 0 | false | null | null | 0.35 |
1a128d3435cc8c20b2102a526e0fdd9f27106e523f2de09de19370cc4d21c2e5 | [
"arxiv",
"semantic_scholar"
] | Mixture of Experts for Low-Resource LLMs | Mixture-of-Experts (MoE) architectures enable efficient model scaling, yet expert routing behavior across underrepresented languages remains poorly understood. We analyze routing dynamics in two architecturally distinct MoE models -- a pure Transformer (Qwen3-30B-A3B) and a hybrid Mamba-Transformer (Nemotron-3-Nano-30B... | [
"Ori Bar Joseph",
"Smadar Arvatz",
"Noam Kayzer",
"Dan Revital",
"Sarel Weinberger"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.17598 | https://arxiv.org/pdf/2605.17598v1 | 2605.17598 | null | 0 | 0 | false | null | null | 0.35 |
c4fa328f487b6388fb835929bfcd9985a0e1eb046dc49085a0df37beba1a0925 | [
"arxiv",
"semantic_scholar"
] | When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing | Mixture-of-Experts (MoE) networks promise favorable accuracy-compute trade-offs, yet practical vision deployments are hindered by expert collapse and limited end-to-end efficiency gains. We study when sparse top-$k$ routing with hard capacity constraints helps in vision classification, evaluated under multi-seed protoc... | [
"Libo Sun",
"Po-wei Harn",
"Peixiong He",
"Xiao Qin"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-15T00:00:00 | https://arxiv.org/abs/2605.15484 | https://arxiv.org/pdf/2605.15484v1 | 2605.15484 | null | 0 | 0 | true | https://github.com/libophd/sparse-moe-vision-rho | null | 0.65 |
13cbd8f25fffdf1d761bad64c2d409be957da44ae36702503e6f85f3ccef5ccd | [
"arxiv",
"semantic_scholar"
] | Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates | Knowledge editing (KE) provides a lightweight alternative to repeated fine-tuning of LLMs. However, most existing KE methods target dense feed-forward layers, while modern LLMs increasingly adopt Mixture-of-Experts (MoE) architectures for their superior memory footprint and inference efficiency. This mismatch leaves a ... | [
"Roman Maksimov",
"Vladimir Aletov",
"Dmitry Bylinkin",
"Daniil Medyakov",
"Vladimir Solodkin",
"Aleksandr Beznosikov"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-15T00:00:00 | https://arxiv.org/abs/2605.16686 | https://arxiv.org/pdf/2605.16686v1 | 2605.16686 | null | 0 | 0 | false | null | null | 0.35 |
ff481e8d16ed8916ca1a6a7f9c311f5a43c80c1a8d1d876f8d51d0256bd657da | [
"arxiv",
"semantic_scholar"
] | BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE | Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K routing strategy, leading to redundant computation and suboptimal inference latency. Existing acceleration methods either require costly re... | [
"Juntong Wu",
"Jialiang Cheng",
"Qishen Yin",
"Yue Dai",
"Yuliang Yan",
"Fuyu Lv",
"Ou Dan",
"Li Yuan"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14438 | https://arxiv.org/pdf/2605.14438v1 | 2605.14438 | null | 0 | 0 | false | null | null | 0.35 |
22e44138f60f689ab135f54fedccf041a78538f1ce546105b0d75c3ad5d18b48 | [
"arxiv",
"semantic_scholar"
] | RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression | Vector quantization is a fundamental tool for compressing high-dimensional embeddings, yet existing multi-codebook methods rely on static codebooks that limit expressiveness under heterogeneous data geometry. While recent dynamic quantizers like QINCo adapt codebooks to individual inputs and improve expressiveness, the... | [
"Zhengjia Zhong",
"Shuyan Ke",
"Zaizhou Lin",
"Jiaqi Song",
"Hongyi Lan",
"Hui Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.14359 | https://arxiv.org/pdf/2605.14359v1 | 2605.14359 | null | 0 | 0 | true | https://github.com/KDEGroup/RQ-MoE | null | 0.65 |
fb668949e35e901f640695146e3ed2504eb19bacc7ffc3005cf0a06d42017960 | [
"arxiv",
"semantic_scholar"
] | Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing | Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of disparate partial differential equation (PDE) regimes can induce gradient conflict, unstable optimization, and plasticity loss in dense neural operators. In particular, br... | [
"Ellwil Sharma",
"Arastu Sharma"
] | [
"cs.LG",
"cs.AI",
"physics.comp-ph"
] | [
"Computer Science",
"Physics"
] | 2026-05-14T00:00:00 | https://arxiv.org/abs/2605.15179 | https://arxiv.org/pdf/2605.15179v1 | 2605.15179 | null | 0 | 0 | false | null | null | 0.35 |
a6770ec8df8337956d27e296bb66489720f2535c356bab623db4fdbb4bbf49b8 | [
"arxiv",
"semantic_scholar"
] | HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts | Sparse Mixture-of-Experts (MoE) layers route tokens through a handful of experts, and learning-free compression of these layers reduces inference cost without retraining. A subtle obstruction blocks every existing compressor in this family: three experts can each be pairwise compatible yet form an irreducible cycle whe... | [
"Tao Zhong",
"Dongzhe Zheng",
"Christine Allen-Blanchette"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13997 | https://arxiv.org/pdf/2605.13997v1 | 2605.13997 | null | 1 | 0 | false | null | null | 0.35 |
ca9d22c67454bb9e3ad117448f5d725bb9a7f6b5e76e6fe6753af11d9c73b748 | [
"arxiv",
"semantic_scholar"
] | ROMER: Expert Replacement and Router Calibration for Robust MoE LLMs on Analog Compute-in-Memory Systems | Large language models (LLMs) with mixture-of-experts (MoE) architectures achieve remarkable scalability by sparsely activating a subset of experts per token, yet their frequent expert switching creates memory bandwidth bottlenecks that compute-in-memory (CIM) architectures are well-suited to mitigate. However, analog C... | [
"Wenyong Zhou",
"Yuannuo Feng",
"Yizhe Chen",
"Taiqiang Wu",
"Wendong Xu",
"Wenbo Qi",
"Zhengwu Liu",
"Wang Kang",
"Ngai Wong"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11800 | https://arxiv.org/pdf/2605.11800v1 | 2605.11800 | null | 0 | 0 | false | null | null | 0.35 |
2649365b9ef754a8eded64ee29fef529cb948f7fa086ca1c8b4872c31eeb11f0 | [
"arxiv",
"semantic_scholar"
] | Sparse Mixture-of-Experts Routing in Visual Diffusion Transformers:Diagnosis, Boundary Calibration and Evolutionary Roadmap from Routing Collapse to Selective Deadlock | This paper systematically diagnoses the training failure modes of Token-Choice sparse Mixture-of-Experts (MoE) on video Diffusion Transformers. Starting from a pretrained dense model of about 5 billion parameters, we convert it into an MoE architecture following three laws: routed experts exactly clone the original FFN... | [
"Haiying Sha"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.19378 | https://arxiv.org/pdf/2605.19378v1 | 2605.19378 | null | 0 | 0 | false | null | null | 0.35 |
6b1c74f8d098790e24800236e07e6dc62c4f7a2c3c649473e66cc15b77ffdb04 | [
"arxiv",
"semantic_scholar"
] | Fast MoE Inference via Predictive Prefetching and Expert Replication | The Mixture of Experts (MoE) architecture has become a fundamental building block in state-of-the-art large language models (LLMs), improving domain-specific expertise in LLMs and scaling model capacity without proportionally increasing their computational overhead. However, MoE inference often suffers from suboptimal ... | [
"Ankit Jyothish",
"Ali Jannesari",
"Aishwarya Sarkar",
"Joseph Zuber"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.11537 | https://arxiv.org/pdf/2605.11537v1 | 2605.11537 | null | 0 | 0 | false | null | null | 0.35 |
1fad0cb93aaa669ab230021fef265de93672f8b0a0afdef8d6262fe91d8051cf | [
"arxiv",
"semantic_scholar"
] | Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts | Sparse Mixture-of-Experts (SMoE) models enable scaling language models efficiently, but training them remains challenging, as routing can collapse onto few experts and auxiliary load-balancing losses can reduce specialization. Motivated by these hurdles, we study how routing decisions in SMoEs are formed mechanisticall... | [
"Sagi Ahrac",
"Noya Hochwald",
"Mor Geva"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.12476 | https://arxiv.org/pdf/2605.12476v1 | 2605.12476 | null | 0 | 0 | false | null | null | 0.35 |
8d9b1dd13670e546e863d6e0f40204db7e5c9633e3b2671484dc53a2537d4059 | [
"arxiv",
"semantic_scholar"
] | DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices | While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates significant storage and memory-access bottlenecks, which hinder efficient end-side deployment that simultaneously requires high performance, low computational cost, and small... | [
"Chenyang Song",
"Weilin Zhao",
"Xu Han",
"Chaojun Xiao",
"Yingfa Chen",
"Zhiyuan Liu"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-11T00:00:00 | https://arxiv.org/abs/2605.10933 | https://arxiv.org/pdf/2605.10933v3 | 2605.10933 | null | 0 | 0 | true | https://github.com/thunlp/DECO | null | 0.65 |
d10b208fcadce025522f523023fd498a10c0a51a05cdf48242fd8efd07cd05ed | [
"arxiv",
"semantic_scholar"
] | ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning | Load imbalance is a long-standing challenge in Mixture-of-Experts (MoE) training and is exacerbated in reinforcement learning (RL) for LLMs, where hot experts can shift frequently across micro-batches. Existing MoE training systems rely on historical loads to predict future expert demand, making them less effective und... | [
"Chao Jin",
"Xinming Wei",
"Yinmin Zhong",
"Chengxu Yang",
"Bingyang Wu",
"Ruidong Zhu",
"Zili Zhang",
"Yuliang Liu",
"Xin Jin"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-09T00:00:00 | https://arxiv.org/abs/2605.08639 | https://arxiv.org/pdf/2605.08639v1 | 2605.08639 | null | 1 | 0 | false | null | null | 0.35 |
1fc1067bcee9a2f92d682f5284e06b1aa6237f8e1d6cb66e4ca222724a6c1627 | [
"arxiv",
"semantic_scholar"
] | SDG-MoE: Signed Debate Graph Mixture-of-Experts | Sparse MoE models achieve a good balance between capacity and compute by routing each token to a small subset of experts. However, in most MoE architectures, once a token is routed, the selected experts process it independently and their outputs are combined via a weighted sum. This leaves open whether enabling communi... | [
"Stepan Kulibaba",
"Kirill Labzin",
"Artem Dzhalilov",
"Roman Pakhomov",
"Oleg Svidchenko",
"Alexander Gasnikov",
"Aleksei Shpilman"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.08322 | https://arxiv.org/pdf/2605.08322v2 | 2605.08322 | null | 0 | 0 | false | null | null | 0.35 |
a633d22994699e0be8dab405d558f163fdd5c9b9ab4f2785c337994bdc6b87f1 | [
"arxiv",
"semantic_scholar"
] | When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models | Mixture-of-Experts (MoE) language models route each token to a small subset of experts, but whether the routes selected by a trained top-$k$ router are good ones is rarely evaluated directly. Holding the model fixed, we compare each standard route against sampled equal-compute alternatives for the same token and score ... | [
"Youngsik Yoon",
"Siwei Wang",
"Wei Chen",
"Jungseul Ok"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.07260 | https://arxiv.org/pdf/2605.07260v1 | 2605.07260 | null | 0 | 0 | false | null | null | 0.35 |
f481399e160c099ead5bb8154483676e307c4fcd79045bc7c46274fbda829724 | [
"arxiv",
"semantic_scholar"
] | Hierarchical Mixture-of-Experts with Two-Stage Optimization | Sparse Mixture-of-Experts (MoE) models scale capacity by routing each token to a small subset of experts. However, their routers exhibit a fundamental trade-off: strong load balancing can suppress expert specialization, while aggressive diversity often causes routing collapse. We propose Hi-MoE, a grouped MoE framework... | [
"Gleb Molodtsov",
"Alexander Miasnikov",
"Aleksandr Beznosikov"
] | [
"cs.LG",
"cs.AI",
"math.OC"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.08292 | https://arxiv.org/pdf/2605.08292v1 | 2605.08292 | null | 2 | 0 | false | null | null | 0.35 |
1a97c99a67d652e5ee5a3f234f74d12fe527edd4454a4e3b997aa540a99c406f | [
"arxiv",
"semantic_scholar"
] | Expert Routing for Communication-Efficient MoE via Finite Expert Banks | Resource-efficient machine learning increasingly uses sparse Mixture-of-Experts (MoE) architectures, where the gate acts as both a learning component and a routing interface controlling computation, communication, and accuracy. Motivated by finite-rate interpretations of MoE gating, we treat the gate as a stochastic ch... | [
"Mohammad Reza Deylam Salehi",
"Ali Khalesi"
] | [
"cs.LG",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.05278 | https://arxiv.org/pdf/2605.05278v1 | 2605.05278 | null | 1 | 0 | false | null | null | 0.35 |
9a4cde77ec3dd676758cdd0d50b62bbb05d28df120647d91081b91c81773855a | [
"arxiv",
"semantic_scholar"
] | GEM: Graph-Enhanced Mixture-of-Experts with ReAct Agents for Dialogue State Tracking | Dialogue State Tracking (DST) requires precise extraction of structured information from multi-domain conversations, a task where Large Language Models (LLMs) struggle despite their impressive general capabilities. We present GEM (Graph-Enhanced Mixture-of-Experts), a novel framework that combines language models and g... | [
"Ziqi Zhu",
"Adithya Suresh",
"Tomal Deb",
"Iman Abbasnejad"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04449 | https://arxiv.org/pdf/2605.04449v1 | 2605.04449 | null | 0 | 0 | false | null | null | 0.35 |
8430e6b515734c28e56ee4aa7f5017caeff983fc15780479b33a60c4974f63ff | [
"arxiv",
"semantic_scholar"
] | Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts | Mixture-of-experts (MoE) models enable scalable transformer architectures by activating only a subset of experts per token. Recent evidence suggests that performance improves with increasingly granular experts, i.e., many small experts instead of a few large ones. However, this regime substantially increases routing co... | [
"Klaus-Rudolf Kladny",
"Maximilian Mordig",
"Bernhard Schölkopf",
"Michael Muehlebach"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04952 | https://arxiv.org/pdf/2605.04952v1 | 2605.04952 | null | 0 | 0 | false | null | null | 0.35 |
4a789bbc361b9a23dc2c111f7aab7771a2a8b3dfd6d497c7cd89572a24c2d920 | [
"arxiv",
"semantic_scholar"
] | Misrouter: Exploiting Routing Mechanisms for Input-Only Attacks on Mixture-of-Experts LLMs | Mixture-of-Experts (MoE) architectures have emerged as a leading paradigm for scaling large language models through sparse, routing-based computation. However, this design introduces a new attack surface: the routing mechanism that determines which experts process each input. Prior work shows that manipulating routing ... | [
"Zekun Fei",
"Zihao Wang",
"Weijie Liu",
"Ruiqi He",
"Jianing Geng",
"Zheli Liu",
"XiaoFeng Wang"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04446 | https://arxiv.org/pdf/2605.04446v1 | 2605.04446 | null | 1 | 0 | true | null | null | 0.65 |
d0fbb94fe1b345c19327ec4cb7bf5e19fec57a13b879ce35a83e0b053420dd30 | [
"arxiv",
"semantic_scholar"
] | Soft-to-Hard Routing in Sparse Mixture-of-Experts Models | Softmax routing approaches hard top-1 routing as the temperature tends to zero, but the limiting passage is singular at router ties. This paper develops a boundary-layer calculus for this soft-to-hard limit in population squared-loss mixture-of-experts regression. For a router with logits $a_k(x;φ)$, the relevant local... | [
"Reza Rastegar"
] | [
"cs.LG",
"cs.AI",
"math.PR"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-04T00:00:00 | https://arxiv.org/abs/2605.02124 | https://arxiv.org/pdf/2605.02124v2 | 2605.02124 | null | 1 | 0 | false | null | null | 0.35 |
3f90697cba1df1cf4315203cd881232aa35e1d68d1cea139ba7729df7b039269 | [
"arxiv",
"semantic_scholar"
] | RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs | Safety alignment is critical for the responsible deployment of large language models (LLMs). As Mixture-of-Experts (MoE) architectures are increasingly adopted to scale model capacity, understanding their safety robustness becomes essential. Existing adversarial attacks, however, have notable limitations. Prompt-based ... | [
"Zhiyuan Xu",
"Joseph Gardiner",
"Sana Belguith",
"Lichao Wu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-01T00:00:00 | https://arxiv.org/abs/2605.02946 | https://arxiv.org/pdf/2605.02946v1 | 2605.02946 | null | 2 | 0 | false | null | null | 0.35 |
8c1937e90e48523a24100793ad084d7331784fc48de9bb09da6a08281f384202 | [
"arxiv",
"semantic_scholar"
] | Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling | We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only around 5\% of the total parameters are activated per input token. This extreme sparsity, combined with upcycling from dense models, enables efficient pre-training on 5... | [
"Fan Jiang",
"Yu Zhao",
"Chenyang Lyu",
"Tianqi Shi",
"Yichao Du",
"Feihu Jiang",
"Longyue Wang",
"Weihua Luo"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-28T00:00:00 | https://arxiv.org/abs/2604.25578 | https://arxiv.org/pdf/2604.25578v1 | 2604.25578 | 10.48550/arXiv.2604.25578 | 0 | 0 | false | null | arXiv.org | 0.55 |
2bcd8c7bb424f1f5ac37005fa451537977c5e42d962cf5d6a4cf577909ace47f | [
"arxiv",
"semantic_scholar"
] | SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs | Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert routing remains under-explored. Existing routing strategies are either hand-crafted or modality-agnostic, relying on idealized priors that ignore the layer-dependent mo... | [
"Zi-Hao Bo",
"Yaqian Li",
"Anzhou Hou",
"Rinyoichi Takezoe",
"Ertao Zhao",
"Tianxiang Pan",
"Jiale Yan",
"Mo Guang",
"Kaiwen Long"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-27T00:00:00 | https://arxiv.org/abs/2604.23996 | https://arxiv.org/pdf/2604.23996v1 | 2604.23996 | 10.48550/arXiv.2604.23996 | 0 | 0 | false | null | arXiv.org | 0.55 |
819d2eb3f0cc7702fb53daebe933e4d2df11701be6e3d8b852cd64f6db70677c | [
"arxiv",
"semantic_scholar"
] | Mixture of Heterogeneous Grouped Experts for Language Modeling | Large Language Models (LLMs) based on Mixture-of-Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficiently. However, standard MoEs enforce uniform expert sizes,creating a rigidity that fails to align computational costs with varying token-level complexity. While heterogeneo... | [
"Zhicheng Ma",
"Xiang Liu",
"Zhaoxiang Liu",
"Ning Wang",
"Yi Shen",
"Kai Wang",
"Shuming Shi",
"Shiguo Lian"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-25T00:00:00 | https://arxiv.org/abs/2604.23108 | https://arxiv.org/pdf/2604.23108v2 | 2604.23108 | 10.48550/arXiv.2604.23108 | 0 | 0 | true | https://github.com/UnicomAI/MoHGE | arXiv.org | 0.85 |
566f4f5f44350f4e0e6a5acac83ecd1af628a006d254a6ee196e364f2df5bbc8 | [
"arxiv",
"semantic_scholar"
] | Teacher-Guided Routing for Sparse Vision Mixture-of-Experts | Recent progress in deep learning has been driven by increasingly large-scale models, but the resulting computational cost has become a critical bottleneck. Sparse Mixture of Experts (MoE) offers an effective solution by activating only a small subset of experts for each input, achieving high scalability without sacrifi... | [
"Masahiro Kada",
"Ryota Yoshihashi",
"Satoshi Ikehata",
"Rei Kawakami",
"Ikuro Sato"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-23T00:00:00 | https://arxiv.org/abs/2604.21330 | https://arxiv.org/pdf/2604.21330v1 | 2604.21330 | 10.48550/arXiv.2604.21330 | 0 | 0 | false | null | arXiv.org | 0.55 |
55caf3cf72eeaa5ce78d52e1fbae8cb056bc4095a00fa07f0c9a64fb97f87395 | [
"arxiv",
"semantic_scholar"
] | Rethinking Cross-Domain Evaluation for Face Forgery Detection with Semantic Fine-grained Alignment and Mixture-of-Experts | Nowadays, visual data forgery detection plays an increasingly important role in social and economic security with the rapid development of generative models. Existing face forgery detectors still can't achieve satisfactory performance because of poor generalization ability across datasets. The key factor that led to th... | [
"Yuhan Luo",
"Tao Chen",
"Decheng Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-23T00:00:00 | https://arxiv.org/abs/2604.21478 | https://arxiv.org/pdf/2604.21478v1 | 2604.21478 | 10.48550/arXiv.2604.21478 | 0 | 0 | true | https://github.com/Yuhan-Luo/Semantic-Fine-grained-Alignment-and-Mixture-of-Experts | arXiv.org | 0.85 |
4bf5ad019c0d6007cf73a483f2544581c4a7d6e6b9926707837a1b09660fe97b | [
"arxiv",
"semantic_scholar"
] | Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts | Mixture-of-Experts (MoE) has become the dominant architecture for scaling large language models: frontier models routinely decouple total parameters from per-token computation through sparse expert routing. Scaling laws show that under fixed active computation, model quality scales predictably with total parameters, an... | [
"Chaitanya Dwivedi",
"Binxuan Huang",
"Himanshu Gupta",
"Pratik Jayarao",
"Neeraj Varshney",
"Bing Yin"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-21T00:00:00 | https://arxiv.org/abs/2604.19835 | https://arxiv.org/pdf/2604.19835v2 | 2604.19835 | 10.48550/arXiv.2604.19835 | 0 | 0 | false | null | arXiv.org | 0.55 |
01530af61de47c1c6ddb2d6dd4cf2f476c0ea5088b48a9cb811976e63ee8151d | [
"arxiv",
"semantic_scholar"
] | Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs | An LLM's residual stream is both state and instruction: it encodes the current context and determines the next transformation. We introduce a parameter-free decomposition for Mixture-of-Experts models that splits each layer's hidden state into a control signal that causally drives routing and an orthogonal content chan... | [
"Charles Ye",
"Bo Yuan",
"Lee Sharkey"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.17837 | https://arxiv.org/pdf/2604.17837v1 | 2604.17837 | 10.48550/arXiv.2604.17837 | 0 | 0 | false | null | arXiv.org | 0.55 |
d97b7228a5e6972e30bf0371705bce4431a378635a81fcd6177d9e358b73dc45 | [
"arxiv",
"semantic_scholar"
] | MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression | KV cache memory is the dominant bottleneck for long-context LLM inference. Existing compression methods each act on a single axis of the four-dimensional KV tensor -- token eviction (sequence), quantization (precision), low-rank projection (head dimension), or cross-layer sharing -- but apply the same recipe to every l... | [
"Libo Sun",
"Peixiong He",
"Po-Wei Harn",
"Xiao Qin"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.17695 | https://arxiv.org/pdf/2604.17695v1 | 2604.17695 | 10.48550/arXiv.2604.17695 | 0 | 0 | false | null | arXiv.org | 0.55 |
d954a955521897820e5aa9035a3c5ef113ed0907200869317db0fbad6bbf5c28 | [
"arxiv",
"semantic_scholar"
] | Efficient Mixture-of-Experts LLM Inference with Apple Silicon NPUs | Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inference efficiency via sparse activation but are challenging for NPUs in three ways: expert routing is unpredictable and introduces dynamic tensor shapes that conflict with ... | [
"Afsara Benazir",
"Felix Xiaozhu Lin"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.18788 | https://arxiv.org/pdf/2604.18788v1 | 2604.18788 | 10.48550/arXiv.2604.18788 | 0 | 0 | false | null | arXiv.org | 0.55 |
1f15bb1f449f9f066a6756ebc68206b172abc53e40840f9528eb595eb9f71c86 | [
"arxiv",
"semantic_scholar"
] | CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering | Visual Question Answering (VQA) requires models to identify the correct answer options based on both visual and textual evidence. Recent Mixture-of-Experts (MoE) methods improve option reasoning by grouping similar concepts or routing based on examples. However, unstable routing can lead to inconsistent expert selectio... | [
"Xiyin Zeng",
"Yi Lu",
"Hao Wang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-18T00:00:00 | https://arxiv.org/abs/2604.16930 | https://arxiv.org/pdf/2604.16930v1 | 2604.16930 | 10.48550/arXiv.2604.16930 | 0 | 0 | false | null | arXiv.org | 0.5477 |
c12ba085126bb19f4fab5642a6d2bede59b56436b172294c944991f85470b666 | [
"arxiv",
"semantic_scholar"
] | Geometric Routing Enables Causal Expert Control in Mixture of Experts | Sparse Mixture-of-Experts (MoE) models scale parameters while fixing active computation per token, but the specialization of individual experts remains opaque. In a companion paper we showed that routing topology is quality-neutral: five structurally different configurations converge to statistically equivalent languag... | [
"Ivan Ternovtsii",
"Yurii Bilak"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-15T00:00:00 | https://arxiv.org/abs/2604.14434 | https://arxiv.org/pdf/2604.14434v1 | 2604.14434 | 10.48550/arXiv.2604.14434 | 2 | 0 | false | null | arXiv.org | 0.5443 |
5f3fc99c6e87648ba63eb1ea1054f6a70cb1a86a5fe5b10668d54916d3aed793 | [
"arxiv",
"semantic_scholar"
] | Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation | Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost and are widely used in transformer architectures, where they typically replace feed-forward network blocks. In contrast, integrating sparse MoE layers into convolutional ... | [
"Svetlana Pavlitska",
"Haixi Fan",
"Konstantin Ditschuneit",
"J. Marius Zöllner"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-15T00:00:00 | https://arxiv.org/abs/2604.13761 | https://arxiv.org/pdf/2604.13761v1 | 2604.13761 | 10.48550/arXiv.2604.13761 | 1 | 0 | true | https://github.com/KASTEL-MobilityLab/moe-layers/ | arXiv.org | 0.8411 |
b0219f75b9bef0ac1fd423254e916728187e7f45f7bcd7b76eb1ceb2b6692b32 | [
"arxiv",
"semantic_scholar"
] | Equifinality in Mixture of Experts: Routing Topology Does Not Determine Language Modeling Quality | Sparse Mixture-of-Experts (MoE) architectures employ increasingly sophisticated routing mechanisms -- learned routers, multi-hop trajectories, token-dependent gating. We ask: does routing topology actually determine language modeling quality? We build a geometric MoE (ST-MoE) using cosine-similarity routing against lea... | [
"Ivan Ternovtsii",
"Yurii Bilak"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-15T00:00:00 | https://arxiv.org/abs/2604.14419 | https://arxiv.org/pdf/2604.14419v1 | 2604.14419 | 10.48550/arXiv.2604.14419 | 2 | 0 | false | null | arXiv.org | 0.5443 |
f0f54cf02d286315396a5f7841e83c8918ad678054028476027e8ef005312bb7 | [
"arxiv",
"semantic_scholar"
] | Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations | Sparse Mixture-of-Experts (MoE) models have achieved remarkable scalability, yet they remain vulnerable to hallucinations, particularly when processing long-tail knowledge. We identify that this fragility stems from static Top-$k$ routing: routers tend to favor high-frequency patterns over rare factual associations. Co... | [
"Wentao Hu",
"Yanbo Zhai",
"Xiaohui Hu",
"Mingkuan Zhao",
"Shanhong yu",
"Xue Liu",
"Kaidong Yu",
"Shuangyong Song",
"Xuelong Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-15T00:00:00 | https://arxiv.org/abs/2604.14246 | https://arxiv.org/pdf/2604.14246v2 | 2604.14246 | 10.48550/arXiv.2604.14246 | 0 | 0 | false | null | arXiv.org | 0.5443 |
cc887166da6f4fabc95e4d1944081bca5adde00f1ddbeaff02947254849188cb | [
"arxiv",
"semantic_scholar"
] | Nucleus-Image: Sparse MoE for Image Generation | We present Nucleus-Image, a text-to-image generation model that establishes a new Pareto frontier in quality-versus-efficiency by matching or exceeding leading models on GenEval, DPG-Bench, and OneIG-Bench while activating only approximately 2B parameters per forward pass. Nucleus-Image employs a sparse mixture-of-expe... | [
"Chandan Akiti",
"Ajay Modukuri",
"Murali Nandan Nagarapu",
"Gunavardhan Akiti",
"Haozhe Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-14T00:00:00 | https://arxiv.org/abs/2604.12163 | https://arxiv.org/pdf/2604.12163v1 | 2604.12163 | 10.48550/arXiv.2604.12163 | 2 | 0 | true | null | arXiv.org | 0.8394 |
19216a298011a671051d513fc9249a307f483ffe3ddfa83e6cd2b125a13ac9b9 | [
"arxiv",
"semantic_scholar"
] | The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise | Mixture of Experts (MoEs) are now ubiquitous in large language models, yet the mechanisms behind their "expert specialization" remain poorly understood. We show that, since MoE routers are linear maps, hidden state similarity is both necessary and sufficient to explain expert usage similarity, and specialization is the... | [
"Xi Wang",
"Soufiane Hayou",
"Eric Nalisnick"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-10T00:00:00 | https://arxiv.org/abs/2604.09780 | https://arxiv.org/pdf/2604.09780v1 | 2604.09780 | 10.48550/arXiv.2604.09780 | 4 | 1 | false | null | arXiv.org | 0.5385 |
219ab6374d4916c7e505d4a442b1d9194b034d9b45dbe0e21202df45f44a28bc | [
"arxiv",
"semantic_scholar"
] | Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts | Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking: models accurately perceive image content yet fail in subsequent reasoning, while correctly solving identical problems presented as pure tex... | [
"Haolei Xu",
"Haiwen Hong",
"Hongxing Li",
"Rui Zhou",
"Yang Zhang",
"Longtao Huang",
"Hui Xue",
"Yongliang Shen",
"Weiming Lu",
"Yueting Zhuang"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-09T00:00:00 | https://arxiv.org/abs/2604.08541 | https://arxiv.org/pdf/2604.08541v1 | 2604.08541 | 10.48550/arXiv.2604.08541 | 2 | 0 | false | null | arXiv.org | 0.5374 |
bcaca74006d18d962c840c438ad987027885ca125da1bca37a25c44c29b0b3f1 | [
"arxiv",
"semantic_scholar"
] | Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference | Mixture-of-Experts (MoE) has become a dominant architecture for scaling large language models due to their sparse activation mechanism. However, the substantial number of expert activations creates a critical latency bottleneck during inference, especially in resource-constrained deployment scenarios. Existing approach... | [
"Baihui Liu",
"Kaiyuan Tian",
"Wei Wang",
"Zhaoning Zhang",
"Linbo Qiao",
"Dongsheng Li"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-09T00:00:00 | https://arxiv.org/abs/2604.08133 | https://arxiv.org/pdf/2604.08133v1 | 2604.08133 | 10.48550/arXiv.2604.08133 | 0 | 0 | false | null | arXiv.org | 0.5374 |
760850ffc8ebc3ec9dcf72c5f256543f82110a0e11e15cfd6ec875c25db04962 | [
"arxiv",
"semantic_scholar"
] | MoE Routing Testbed: Studying Expert Specialization and Routing Behavior at Small Scale | Sparse Mixture-of-Experts (MoE) architectures are increasingly popular for frontier large language models (LLM) but they introduce training challenges due to routing complexity. Fully leveraging parameters of an MoE model requires all experts to be well-trained and to specialize in non-redundant ways. Assessing this, h... | [
"Tobias Falke",
"Nicolas Anastassacos",
"Samson Tan",
"Chankrisna Richy Meas",
"Chandana Satya Prakash",
"Nitesh Sekhar",
"M Saiful Bari",
"Krishna Kompella",
"Gamaleldin F. Elsayed"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.07030 | https://arxiv.org/pdf/2604.07030v1 | 2604.07030 | 10.48550/arXiv.2604.07030 | 0 | 0 | false | null | arXiv.org | 0.5363 |
17a3008c95aaa5ef05bc9849d9b88037f9e3cdc5efb3c45430990406554f06fa | [
"arxiv",
"semantic_scholar"
] | Region-Graph Optimal Transport Routing for Mixture-of-Experts Whole-Slide Image Classification | Multiple Instance Learning (MIL) is the dominant framework for gigapixel whole-slide image (WSI) classification in computational pathology. However, current MIL aggregators route all instances through a shared pathway, constraining their capacity to specialise across the pathological heterogeneity inherent in each slid... | [
"Xin Tian",
"Jiuliu Lu",
"Ephraim Tsalik",
"Bart Wanders",
"Colleen Knoth",
"Julian Knight"
] | [
"cs.CV",
"cs.AI",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.07298 | https://arxiv.org/pdf/2604.07298v1 | 2604.07298 | 10.48550/arXiv.2604.07298 | 0 | 0 | false | null | arXiv.org | 0.5363 |
6cbfcfa948788f6c102de75255ea891114756250d835c043a13f3614ea31947e | [
"arxiv",
"semantic_scholar"
] | Does a Global Perspective Help Prune Sparse MoEs Elegantly? | Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. Sparse Mixture-of-Experts (MoEs) offer a promising alternative by activating only a subset of experts per forward pass, improving efficiency without sacrificing performan... | [
"Zeliang Zhang",
"Nikhil Ghosh",
"Jiani Liu",
"Bin Yu",
"Xiaodong Liu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.06542 | https://arxiv.org/pdf/2604.06542v1 | 2604.06542 | 10.48550/arXiv.2604.06542 | 0 | 0 | false | null | arXiv.org | 0.5363 |
37b3ae67c869171ae3fedaa6cd198cf44c35a23518bbf8c04a0566fb99b8f2b4 | [
"arxiv",
"semantic_scholar"
] | Cross-Platform Fused MoE Dispatch in Triton: Portable Expert Routing Without CUDA | Mixture-of-Experts (MoE) architectures power the majority of frontier large language models, but their inference is bottlenecked by irregular memory access patterns and expert routing overhead. Existing optimized MoE kernels (Megablocks, Tutel, FasterMoE) are implemented in CUDA and locked to NVIDIA hardware. We presen... | [
"Subhadip Mitra"
] | [
"cs.DC",
"cs.ET",
"cs.PF"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2605.23911 | https://arxiv.org/pdf/2605.23911v1 | 2605.23911 | null | 0 | 0 | true | https://github.com/bassrehab/triton-kernels | null | 0.6324 |
0e7e5d481f91939e921cd9aafb0daf1676d464af31136a40495e8e91cafdc7a4 | [
"arxiv",
"semantic_scholar"
] | Do Domain-specific Experts exist in MoE-based LLMs? | In the era of Large Language Models (LLMs), the Mixture of Experts (MoE) architecture has emerged as an effective approach for training extremely large models with improved computational efficiency. This success builds upon extensive prior research aimed at enhancing expert specialization in MoE-based LLMs. However, th... | [
"Giang Do",
"Hung Le",
"Truyen Tran"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.05267 | https://arxiv.org/pdf/2604.05267v1 | 2604.05267 | 10.48550/arXiv.2604.05267 | 0 | 0 | true | https://github.com/giangdip2410/Domain-specific-Experts | arXiv.org | 0.827 |
99c67625df6867a7c38f19fa64c500d57e625dbe523bcb56c049dec8248e4854 | [
"arxiv",
"semantic_scholar"
] | QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis | Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, multimodal inputs are often compromised by dynamic noise or modality missingness. Existing methods typically treat these imperfections as discrete cases or assume fixed corrupt... | [
"Yitong Zhu",
"Yuxuan Jiang",
"Guanxuan Jiang",
"Bojing Hou",
"Peng Yuan Zhou",
"Ge Lin Kan",
"Yuyang Wang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.05704 | https://arxiv.org/pdf/2604.05704v2 | 2604.05704 | 10.48550/arXiv.2604.05704 | 0 | 0 | false | null | arXiv.org | 0.5351 |
08d93cdbbaa584f81f3ef8db0bc1f1dad5b9a94dacec6141ca75291d35b5f7a8 | [
"arxiv",
"semantic_scholar"
] | HI-MoE: Hierarchical Instance-Conditioned Mixture-of-Experts for Object Detection | Mixture-of-Experts (MoE) architectures enable conditional computation by activating only a subset of model parameters for each input. Although sparse routing has been highly effective in language models and has also shown promise in vision, most vision MoE methods operate at the image or patch level. This granularity i... | [
"Vadim Vashkelis",
"Natalia Trukhina"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-06T00:00:00 | https://arxiv.org/abs/2604.04908 | https://arxiv.org/pdf/2604.04908v1 | 2604.04908 | 10.48550/arXiv.2604.04908 | 2 | 0 | false | null | arXiv.org | 0.534 |
4ed89d2d1ab879e6a0e4f4d9e4b00f8c2c37e8027b8c4ba572297b012bbc5f24 | [
"arxiv",
"semantic_scholar"
] | Three Phases of Expert Routing: How Load Balance Evolves During Mixture-of-Experts Training | We model Mixture-of-Experts (MoE) token routing as a congestion game with a single effective parameter, the congestion coefficient gamma_eff, that quantifies the balance-quality tradeoff. Tracking gamma_eff across training checkpoints of two open-source MoE models, OLMoE-1B-7B (20 checkpoints, with dense sampling in th... | [
"Charafeddine Mouzouni"
] | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2026-04-05T00:00:00 | https://arxiv.org/abs/2604.04230 | https://arxiv.org/pdf/2604.04230v1 | 2604.04230 | 10.48550/arXiv.2604.04230 | 2 | 0 | true | null | arXiv.org | 0.8234 |
e39376cc2cf6add9fd4a108cdd97193c20af219bca92df878c46ae5b056ee2bf | [
"arxiv",
"semantic_scholar"
] | Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation | Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps remain poorly understood. In this work, we conduct a systematic analysis of expert routing patterns in MoE models, revealing a phenomenon we term Language Routing Isolation, in which... | [
"Kening Zheng",
"Wei-Chieh Huang",
"Jiahao Huo",
"Zhonghao Li",
"Henry Peng Zou",
"Yibo Yan",
"Xin Zou",
"Jungang Li",
"Junzhuo Li",
"Hanrong Zhang",
"Xuming Hu",
"Philip S. Yu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-04T00:00:00 | https://arxiv.org/abs/2604.03592 | https://arxiv.org/pdf/2604.03592v1 | 2604.03592 | 10.48550/arXiv.2604.03592 | 2 | 2 | false | null | arXiv.org | 0.5317 |
a62dab8eb8e64f4c5e996644409c8b60bd38b04c9876d49cf6b00644f12629f7 | [
"arxiv",
"semantic_scholar"
] | The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level | Mixture-of-Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of parameters per token. While MoE architectures are primarily adopted for computational efficiency, it remains an open question whether their sparsity makes them inherently easier t... | [
"Jeremy Herbst",
"Stefan Wermter",
"Jae Hee Lee"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-02T00:00:00 | https://arxiv.org/abs/2604.02178 | https://arxiv.org/pdf/2604.02178v2 | 2604.02178 | 10.48550/arXiv.2604.02178 | 5 | 0 | true | https://github.com/jerryy33/MoE_analysis | arXiv.org | 0.8181 |
1d2e9986934d368ad386fc2e878f262e2c5616acbfca731dceb5fe1e507ad859 | [
"arxiv",
"semantic_scholar"
] | Routing-Free Mixture-of-Experts | Standard Mixture-of-Experts (MoE) models rely on centralized routing mechanisms that introduce rigid inductive biases. We propose Routing-Free MoE which eliminates any hard-coded centralized designs including external routers, Softmax, Top-K and load balancing, instead encapsulating all activation functionalities withi... | [
"Yilun Liu",
"Jinru Han",
"Sikuan Yan",
"Volker Tresp",
"Yunpu Ma"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.00801 | https://arxiv.org/pdf/2604.00801v1 | 2604.00801 | 10.48550/arXiv.2604.00801 | 0 | 0 | true | https://github.com/liuyilun2000/RoutingFreeMoE/tree/release | arXiv.org | 0.8164 |
edaf6c3f5f0ae614f6f446e377f12a44acff8afe7b6bf0e568b41ba819bd19cc | [
"arxiv",
"semantic_scholar"
] | Self-Routing: Parameter-Free Expert Routing from Hidden States | Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely on a learned router to map hidden states to expert assignments. In this work, we ask whether a dedicated learned router is strictly necessary in the MoE settings we study. We propose Self-R... | [
"Jama Hussein Mohamud",
"Drew Wagner",
"Mirco Ravanelli"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.00421 | https://arxiv.org/pdf/2604.00421v1 | 2604.00421 | 10.48550/arXiv.2604.00421 | 1 | 0 | false | null | arXiv.org | 0.5282 |
89337f2757e2c199bdaa57890555145ca8fbdba1f0e42d3e9604905d40e4450f | [
"arxiv",
"semantic_scholar"
] | Routing Sensitivity Without Controllability: A Diagnostic Study of Fairness in MoE Language Models | Mixture-of-Experts (MoE) language models are universally sensitive to demographic content at the routing level, yet exploiting this sensitivity for fairness control is structurally limited. We introduce Fairness-Aware Routing Equilibrium (FARE), a diagnostic framework designed to probe the limits of routing-level stere... | [
"Junhyeok Lee",
"Kyu Sung Choi"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-28T00:00:00 | https://arxiv.org/abs/2603.27141 | https://arxiv.org/pdf/2603.27141v1 | 2603.27141 | 10.48550/arXiv.2603.27141 | 0 | 0 | false | null | arXiv.org | 0.5236 |
8bcb27a67eed753bc3eb3f9de560c4155357e455dea1771114e21dbd073179b4 | [
"arxiv",
"semantic_scholar"
] | MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models | Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures by sparsely activating a subset of parameters for each token while preserving high model capacity. This paradigm has recently been extended to Vision-Language Models (VLMs), enabling scalable ... | [
"Dohwan Ko",
"Jinyoung Park",
"Seoung Choi",
"Sanghyeok Lee",
"Seohyun Lee",
"Hyunwoo J. Kim"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-03-26T00:00:00 | https://arxiv.org/abs/2603.24984 | https://arxiv.org/pdf/2603.24984v2 | 2603.24984 | 10.48550/arXiv.2603.24984 | 1 | 0 | false | null | arXiv.org | 0.5214 |
fb6ad4ba67ce67709099724f7a4736b35c6ab6ec1a64c0b4b69c2d2cb7208247 | [
"arxiv",
"semantic_scholar"
] | MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting | Precipitation forecasting remains a persistent challenge in tropical regions like Vietnam, where complex topography and convective instability often limit the accuracy of Numerical Weather Prediction (NWP) models. While data-driven post-processing is widely used to mitigate these biases, most existing frameworks rely o... | [
"Huyen Ngoc Tran",
"Dung Trung Tran",
"Hong Nguyen",
"Xuan Vu Phan",
"Nam-Phong Nguyen"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-26T00:00:00 | https://arxiv.org/abs/2603.25046 | https://arxiv.org/pdf/2603.25046v1 | 2603.25046 | 10.48550/arXiv.2603.25046 | 0 | 0 | false | null | arXiv.org | 0.5214 |
bcf6a3e028a27d0e6c9e7c0b3c711b4019234fcd93e71ef99136128515d40236 | [
"arxiv",
"semantic_scholar"
] | MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning | Standard LoRA fine-tuning of Mixture-of-Experts (MoE) models applies adapters to every expert, yet our profiling shows that per-layer expert routing is highly skewed: a small subset of experts handles most tokens in each layer, while many others are rarely activated ("cold"). We propose MoE-Sieve, a simple routing-guid... | [
"Andrea Manzoni"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2603.24044 | https://arxiv.org/pdf/2603.24044v1 | 2603.24044 | 10.48550/arXiv.2603.24044 | 0 | 0 | false | null | arXiv.org | 0.5202 |
9b7caf8cf273d1086eb81812e9c0cd1eb2a523dbd49b487ba37502019c883ab7 | [
"arxiv",
"semantic_scholar"
] | SiftMoE: Similarity-Aware Energy-Efficient Expert Selection for Wireless Distributed MoE Inference | Mixture-of-Experts (MoE) architectures leverage sparse activation to enhance the scalability of large language models (LLMs), making them suitable for deployment in resource-constrained edge networks. However, the sheer number of experts often exceeds the memory capacity of individual edge nodes, necessitating wireless... | [
"Qian Chen",
"Xianhao Chen",
"Kaibin Huang"
] | [
"cs.IT",
"cs.NI"
] | [
"Computer Science",
"Mathematics"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2603.23888 | https://arxiv.org/pdf/2603.23888v1 | 2603.23888 | 10.48550/arXiv.2603.23888 | 2 | 0 | false | null | arXiv.org | 0.5202 |
184cb8a2cfcc3629fd3cdbf524eea69b8b4bb7fad1d4eff7095a5288de5fa696 | [
"arxiv",
"semantic_scholar"
] | Mixture of Experts with Soft Nearest Neighbor Loss: Resolving Expert Collapse via Representation Disentanglement | The Mixture-of-Experts (MoE) model uses a set of expert networks that specialize on subsets of a dataset under the supervision of a gating network. A common issue in MoE architectures is ``expert collapse'' where overlapping class boundaries in the raw input feature space cause multiple experts to learn redundant repre... | [
"Abien Fred Agarap",
"Arnulfo P. Azcarraga"
] | [
"cs.NE",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-20T00:00:00 | https://arxiv.org/abs/2603.26734 | https://arxiv.org/pdf/2603.26734v1 | 2603.26734 | 10.48550/arXiv.2603.26734 | 0 | 0 | false | null | arXiv.org | 0.5145 |
2bafc89e70d327ebae6bd46cce96bc73e1cc5b86ca09c6bfd3703d252c892d1e | [
"arxiv",
"semantic_scholar"
] | AIMER: Calibration-Free Task-Agnostic MoE Expert Pruning | Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token computation, yet deployment still requires storing the full expert pool, making expert pruning important for reducing memory and serving overhead. Existing task-agnostic expert-pruning methods are typically calibration-d... | [
"Zongfang Liu",
"Guangyi Chen",
"Shengkun Tang",
"Yifan Shen",
"Huan Wang",
"Xin Yuan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-19T00:00:00 | https://arxiv.org/abs/2603.18492 | https://arxiv.org/pdf/2603.18492v3 | 2603.18492 | 10.48550/arXiv.2603.18492 | 2 | 0 | false | null | arXiv.org | 0.5133 |
cf85a98e79ed94640c116e0e63bc4074d1caec760b23e668784adceaed77185b | [
"arxiv",
"semantic_scholar"
] | ATG-MoE: Autoregressive trajectory generation with mixture-of-experts for assembly skill learning | Flexible manufacturing requires robot systems that can adapt to constantly changing tasks, objects, and environments. However, traditional robot programming is labor-intensive and inflexible, while existing learning-based assembly methods often suffer from weak positional generalization, complex multi-stage designs, an... | [
"Weihang Huang",
"Chaoran Zhang",
"Xiaoxin Deng",
"Hao Zhou",
"Zhaobo Xu",
"Shubo Cui",
"Long Zeng"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2026-03-19T00:00:00 | https://arxiv.org/abs/2603.19029 | https://arxiv.org/pdf/2603.19029v1 | 2603.19029 | 10.48550/arXiv.2603.19029 | 0 | 0 | false | null | arXiv.org | 0.5133 |
edecef9494ffc7ff8a6f1b3e9f2a2e42b0cbd6fdf33f919d51a66512ad4b170e | [
"arxiv",
"semantic_scholar"
] | Path-Constrained Mixture-of-Experts | Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of \emph{expert paths} -- the sequence of expert selections a token makes across all layers. This perspective reveals that, despite $N^L$ possible p... | [
"Zijin Gu",
"Tatiana Likhomanenko",
"Vimal Thilak",
"Jason Ramapuram",
"Navdeep Jaitly"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-18T00:00:00 | https://arxiv.org/abs/2603.18297 | https://arxiv.org/pdf/2603.18297v2 | 2603.18297 | 10.48550/arXiv.2603.18297 | 1 | 0 | false | null | arXiv.org | 0.5122 |
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