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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
afd1624e4774072deb075cc400fdb6950ff9a25dc9fc83129f3197937f0c5677 | [
"arxiv",
"semantic_scholar"
] | Modeling Expert Interactions in Sparse Mixture of Experts via Graph Structures | Sparse Mixture of Experts (SMoE) has emerged as a promising solution to achieving unparalleled scalability in deep learning by decoupling model parameter count from computational cost. By activating only a small subset of parameters per sample, SMoE enables significant growth in model capacity while maintaining efficie... | [
"Minh-Khoi Nguyen-Nhat",
"Rachel S. Y. Teo",
"Laziz Abdullaev",
"Maurice Mok",
"Viet-Hoang Tran",
"Tan Minh Nguyen"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-18T00:00:00 | https://arxiv.org/abs/2510.16411 | https://arxiv.org/pdf/2510.16411v1 | 2510.16411 | 10.48550/arXiv.2510.16411 | 1 | 0 | false | null | arXiv.org | 0.3392 |
cde562fbb4c913e83504097dee86bfe2f258961deefa653e83036aab4f1f1071 | [
"arxiv",
"semantic_scholar"
] | Expert Merging in Sparse Mixture of Experts with Nash Bargaining | Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert parameters, but often lack a principled weighting mechanism. In this work, we reinterpret expert merging through the lens of game theory, revealing cooperative and competiti... | [
"Dung V. Nguyen",
"Anh T. Nguyen",
"Minh H. Nguyen",
"Luc Q. Nguyen",
"Shiqi Jiang",
"Ethan Fetaya",
"Linh Duy Tran",
"Gal Chechik",
"Tan M. Nguyen"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-10-17T00:00:00 | https://arxiv.org/abs/2510.16138 | https://arxiv.org/pdf/2510.16138v2 | 2510.16138 | 10.48550/arXiv.2510.16138 | 3 | 0 | true | https://github.com/anh147/NAMEx | arXiv.org | 0.5224 |
05660eff6de2c9d1022649de1aedea9fc777c909b82276c47308aadd230c987a | [
"arxiv",
"semantic_scholar"
] | Mixture of Experts Approaches in Dense Retrieval Tasks | Dense Retrieval Models (DRMs) are a prominent development in Information Retrieval (IR). A key challenge with these neural Transformer-based models is that they often struggle to generalize beyond the specific tasks and domains they were trained on. To address this challenge, prior research in IR incorporated the Mixtu... | [
"Effrosyni Sokli",
"Pranav Kasela",
"Georgios Peikos",
"Gabriella Pasi"
] | [
"cs.IR",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-17T00:00:00 | https://arxiv.org/abs/2510.15683 | https://arxiv.org/pdf/2510.15683v1 | 2510.15683 | 10.48550/arXiv.2510.15683 | 0 | 0 | true | https://github.com/FaySokli/SB-MoE | arXiv.org | 0.5224 |
92703cef23ddf92818fca6d1d473a9ca865418cf3457290d9074bb3cf417349e | [
"arxiv",
"semantic_scholar"
] | MergeMoE: Efficient Compression of MoE Models via Expert Output Merging | The Mixture-of-Experts (MoE) technique has proven to be a promising solution to efficiently scale the model size, which has been widely applied in recent LLM advancements. However, the substantial memory overhead of MoE models has made their compression an important research direction. In this work, we provide a theore... | [
"Ruijie Miao",
"Yilun Yao",
"Zihan Wang",
"Zhiming Wang",
"Bairen Yi",
"LingJun Liu",
"Yikai Zhao",
"Tong Yang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-16T00:00:00 | https://arxiv.org/abs/2510.14436 | https://arxiv.org/pdf/2510.14436v1 | 2510.14436 | 10.48550/arXiv.2510.14436 | 7 | 1 | false | null | arXiv.org | 0.3369 |
c72d3ddfe8a61de0449ab6bea395f7716355f88d72566c412238b836ab89b609 | [
"arxiv",
"semantic_scholar"
] | Who Speaks for the Trigger? Dynamic Expert Routing in Backdoored Mixture-of-Experts Transformers | Large language models (LLMs) with Mixture-of-Experts (MoE) architectures achieve impressive performance and efficiency by dynamically routing inputs to specialized subnetworks, known as experts. However, this sparse routing mechanism inherently exhibits task preferences due to expert specialization, introducing a new a... | [
"Xin Zhao",
"Xiaojun Chen",
"Bingshan Liu",
"Haoyu Gao",
"Zhendong Zhao",
"Yilong Chen"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2025-10-15T00:00:00 | https://arxiv.org/abs/2510.13462 | https://arxiv.org/pdf/2510.13462v1 | 2510.13462 | 10.48550/arXiv.2510.13462 | 0 | 0 | false | null | arXiv.org | 0.3357 |
dc267a9d5e013280fe9a65b49b1d3d0b177002756c3a81c86298d6b9e5f775fb | [
"arxiv",
"semantic_scholar"
] | MoBiLE: Efficient Mixture-of-Experts Inference on Consumer GPU with Mixture of Big Little Experts | Mixture-of-Experts (MoE) models have recently demonstrated exceptional performance across a diverse range of applications. The principle of sparse activation in MoE models facilitates an offloading strategy, wherein active experts are maintained in GPU HBM, while inactive experts are stored in CPU DRAM. The efficacy of... | [
"Yushu Zhao",
"Yubin Qin",
"Yang Wang",
"Xiaolong Yang",
"Huiming Han",
"Shaojun Wei",
"Yang Hu",
"Shouyi Yin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-14T00:00:00 | https://arxiv.org/abs/2510.12357 | https://arxiv.org/pdf/2510.12357v1 | 2510.12357 | 10.1109/ASP-DAC66049.2026.11420472 | 1 | 0 | false | null | Asia and South Pacific Design Automation Conference | 0.3346 |
914ec280ff9dbb3558c419c79e7768ff8d5f158b49e911353325643514a416ed | [
"arxiv",
"semantic_scholar"
] | MC#: Mixture Compressor for Mixture-of-Experts Large Models | Mixture-of-Experts (MoE) effectively scales large language models (LLMs) and vision-language models (VLMs) by increasing capacity through sparse activation. However, preloading all experts into memory and activating multiple experts per input introduces significant computational and memory overhead, making the expert m... | [
"Wei Huang",
"Yue Liao",
"Yukang Chen",
"Jianhui Liu",
"Haoru Tan",
"Si Liu",
"Shiming Zhang",
"Shuicheng Yan",
"Xiaojuan Qi"
] | [
"cs.LG",
"cs.AI"
] | [
"Medicine",
"Computer Science"
] | 2025-10-13T00:00:00 | https://arxiv.org/abs/2510.10962 | https://arxiv.org/pdf/2510.10962v1 | 2510.10962 | 10.1109/TPAMI.2026.3664873 | 1 | 0 | false | null | IEEE Transactions on Pattern Analysis and Machine Intelligence | 0.3334 |
2a530942eb5597836afd7e677be65dbd213603f7d792e0e4d9d0dc7dfba89836 | [
"arxiv",
"semantic_scholar"
] | Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts | Mixture-of-Experts (MoE) architectures have emerged as a cornerstone of modern AI systems. In particular, MoEs route inputs dynamically to specialized experts whose outputs are aggregated through weighted summation. Despite their widespread application, theoretical understanding of MoE training dynamics remains limited... | [
"Fangshuo Liao",
"Anastasios Kyrillidis"
] | [
"cs.LG",
"math.OC"
] | [
"Computer Science",
"Mathematics"
] | 2025-10-08T00:00:00 | https://arxiv.org/abs/2510.07205 | https://arxiv.org/pdf/2510.07205v1 | 2510.07205 | 10.48550/arXiv.2510.07205 | 1 | 0 | false | null | arXiv.org | 0.3277 |
e41b4339a83e0da574a8282f9f186fc0b30560298ecaac8f5cb7d353c5e6a163 | [
"arxiv",
"semantic_scholar"
] | Mixture of Neuron Experts | In this work, we first explore whether the parameters activated by the MoE layer remain highly sparse at inference. We perform a sparsification study on several representative MoE models. For each expert, we rank parameters by the magnitude of their activations from the gate projection and progressively prune the activ... | [
"Runxi Cheng",
"Yuchen Guan",
"Yucheng Ding",
"Qingguo Hu",
"Yongxian Wei",
"Chun Yuan",
"Yelong Shen",
"Weizhu Chen",
"Yeyun Gong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-07T00:00:00 | https://arxiv.org/abs/2510.05781 | https://arxiv.org/pdf/2510.05781v1 | 2510.05781 | 10.48550/arXiv.2510.05781 | 4 | 1 | false | null | arXiv.org | 0.3266 |
a7d127629ac9ceaef5112ee129a0a425bf236aaafe5988f12d01945a8876f4ad | [
"arxiv",
"semantic_scholar"
] | Multilingual Routing in Mixture-of-Experts | Mixture-of-Experts (MoE) architectures have become the key to scaling modern LLMs, yet little is understood about how their sparse routing dynamics respond to multilingual data. In this work, we analyze expert routing patterns using parallel multilingual datasets and present highly interpretable layer-wise phenomena. W... | [
"Lucas Bandarkar",
"Chenyuan Yang",
"Mohsen Fayyaz",
"Junlin Hu",
"Nanyun Peng"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-06T00:00:00 | https://arxiv.org/abs/2510.04694 | https://arxiv.org/pdf/2510.04694v2 | 2510.04694 | 10.48550/arXiv.2510.04694 | 18 | 2 | false | null | arXiv.org | 0.3254 |
6bd1c7b707409327165bcab6cfef094c3a72698f71b214e6ccc75f97db098d73 | [
"arxiv",
"semantic_scholar"
] | Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEs | Upcycling pre-trained dense models into sparse Mixture-of-Experts (MoEs) efficiently increases model capacity but often suffers from poor expert specialization due to naive weight replication. Our analysis reveals that upcycled MoEs, even with conventional regularization, exhibit low-confidence, weakly differentiated r... | [
"Leyla Mirvakhabova",
"Babak Ehteshami Bejnordi",
"Gaurav Kumar",
"Hanxue Liang",
"Wanru Zhao",
"Paul Whatmough"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-01T00:00:00 | https://arxiv.org/abs/2510.01185 | https://arxiv.org/pdf/2510.01185v1 | 2510.01185 | 10.48550/arXiv.2510.01185 | 1 | 0 | false | null | arXiv.org | 0.3197 |
7306b0a046a460995257046c7e55f4a79e71d73011ed82a760636fd96379c4de | [
"arxiv",
"semantic_scholar"
] | Adaptive Shared Experts with LoRA-Based Mixture of Experts for Multi-Task Learning | Mixture-of-Experts (MoE) has emerged as a powerful framework for multi-task learning (MTL). However, existing MoE-MTL methods often rely on single-task pretrained backbones and suffer from redundant adaptation and inefficient knowledge sharing during the transition from single-task to multi-task learning (STL to MTL). ... | [
"Minghao Yang",
"Ren Togo",
"Guang Li",
"Takahiro Ogawa",
"Miki Haseyama"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-01T00:00:00 | https://arxiv.org/abs/2510.00570 | https://arxiv.org/pdf/2510.00570v1 | 2510.00570 | 10.48550/arXiv.2510.00570 | 2 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.3197 |
ef63e89fc84f1d521c062ef328712cd9007508578cb91933ffadc9b63da846aa | [
"arxiv",
"semantic_scholar"
] | LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts | Recent studies have shown that combining parameter-efficient fine-tuning (PEFT) with mixture-of-experts (MoE) is an effective strategy for adapting large language models (LLMs) to the downstream tasks. However, most existing approaches rely on conventional TopK routing, which requires careful hyperparameter tuning and ... | [
"Yuan Zhuang",
"Yi Shen",
"Yuexin Bian",
"Qing Su",
"Shihao Ji",
"Yuanyuan Shi",
"Fei Miao"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-30T00:00:00 | https://arxiv.org/abs/2509.25684 | https://arxiv.org/pdf/2509.25684v2 | 2509.25684 | 10.48550/arXiv.2509.25684 | 5 | 0 | false | null | arXiv.org | 0.3185 |
9e476f63edd212a312f81d2e1e48c4a09e2bc452d2428f461f1ffb40454c9d51 | [
"arxiv",
"semantic_scholar"
] | Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRI | Multiparametric 3D brain MRI (mpMRI) is central to neuroradiology, but producing tumor location, appearance, size, and involvement of critical structures for neurosurgical planning remains challenging. We introduce mpLLM, a multimodal LLM for visual question answering (VQA) on mpMRI that produces clinically interpretab... | [
"Arvind Murari Vepa",
"Yannan Yu",
"Jingru Gan",
"Anthony Cuturrufo",
"Michael F. Romano",
"Weikai Li",
"Fabien Scalzo",
"Wei Wang",
"Yizhou Sun"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2025-09-30T00:00:00 | https://arxiv.org/abs/2509.25889 | https://arxiv.org/pdf/2509.25889v3 | 2509.25889 | null | 0 | 0 | true | https://github.com/arvindmvepa/mpllm | null | 0.3765 |
8deb5a6f0f24ff37abffa7cf8cb7f64eb7d938cfa5049b48065142d50bf85970 | [
"arxiv",
"semantic_scholar"
] | From Score Distributions to Balance: Plug-and-Play Mixture-of-Experts Routing | Mixture-of-Experts (MoE) models can scale parameter capacity by routing each token to a subset of experts through a learned gate function. While conditional routing reduces training costs, it shifts the burden on inference memory: expert parameters and activations consume memory, limiting the number of experts per devi... | [
"Rana Shahout",
"Colin Cai",
"Yilun Du",
"Minlan Yu",
"Michael Mitzenmacher"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2025-09-29T00:00:00 | https://arxiv.org/abs/2510.03293 | https://arxiv.org/pdf/2510.03293v1 | 2510.03293 | 10.48550/arXiv.2510.03293 | 4 | 1 | false | null | arXiv.org | 0.3174 |
3d3d87286eda6f5d0875c605528e9a0a95f2f8ed54d7759186674d83e6e93e8d | [
"arxiv",
"semantic_scholar"
] | GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference | Sparse Mixture of Experts (SMoE) enables scalable parameter growth in large language models (LLMs) by selectively activating a subset of experts, and its large parameter count necessitates distributed deployment for inference. However, distributed inference faces a critical dilemma: although communication overhead cons... | [
"Yu Han",
"Lehan Pan",
"Jie Peng",
"Ziyang Tao",
"Hanqi Zhu",
"Wuyang Zhang",
"Yanyong Zhang"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-09-29T00:00:00 | https://arxiv.org/abs/2509.25041 | https://arxiv.org/pdf/2509.25041v4 | 2509.25041 | 10.48550/arXiv.2509.25041 | 9 | 0 | false | null | arXiv.org | 0.3174 |
190d4cbd0318ad77662df7b8bf792df9db6114899783f00da39c5b48da0002de | [
"arxiv",
"semantic_scholar"
] | One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning | Prompt-based methods have recently gained prominence in Continual Learning (CL) due to their strong performance and memory efficiency. A prevalent strategy in this paradigm assigns a dedicated subset of prompts to each task, which, while effective, incurs substantial computational overhead and causes memory requirement... | [
"Minh Le",
"Bao-Ngoc Dao",
"Huy Nguyen",
"Quyen Tran",
"Anh Nguyen",
"Nhat Ho"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-29T00:00:00 | https://arxiv.org/abs/2509.24483 | https://arxiv.org/pdf/2509.24483v3 | 2509.24483 | 10.48550/arXiv.2509.24483 | 1 | 0 | false | null | arXiv.org | 0.3174 |
4c0d37c5c89339e581988e4c66a24f546eaa122ecbde9867d136e10298219561 | [
"arxiv",
"semantic_scholar"
] | LLaDA-MoE: A Sparse MoE Diffusion Language Model | We introduce LLaDA-MoE, a large language diffusion model with the Mixture-of-Experts (MoE) architecture, trained from scratch on approximately 20T tokens. LLaDA-MoE achieves competitive performance with significantly reduced computational overhead by maintaining a 7B-parameter capacity while activating only 1.4B parame... | [
"Fengqi Zhu",
"Zebin You",
"Yipeng Xing",
"Zenan Huang",
"Lin Liu",
"Yihong Zhuang",
"Guoshan Lu",
"Kangyu Wang",
"Xudong Wang",
"Lanning Wei",
"Hongrui Guo",
"Jiaqi Hu",
"Wentao Ye",
"Tieyuan Chen",
"Chenchen Li",
"Chengfu Tang",
"Haibo Feng",
"Jun Hu",
"Jun Zhou",
"Xiaolu Zha... | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-29T00:00:00 | https://arxiv.org/abs/2509.24389 | https://arxiv.org/pdf/2509.24389v1 | 2509.24389 | 10.48550/arXiv.2509.24389 | 38 | 3 | false | null | arXiv.org | 0.3978 |
caebc1b6e088880ff69cb1442f4b80d837003118ee3e6f316be0f1482bc927c8 | [
"arxiv",
"semantic_scholar"
] | Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training | Device-cloud collaboration holds promise for deploying large language models (LLMs), leveraging lightweight on-device models for efficiency while relying on powerful cloud models for superior reasoning. A central challenge in this setting is determining, for each incoming query, whether it should be processed locally o... | [
"Wenzhi Fang",
"Dong-Jun Han",
"Liangqi Yuan",
"Evan Chen",
"Christopher Brinton"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-28T00:00:00 | https://arxiv.org/abs/2509.24050 | https://arxiv.org/pdf/2509.24050v4 | 2509.24050 | null | 2 | 0 | false | null | null | 0.2012 |
f1cb3de4b603c06a4a203cddf781ba656b19d43d0238be4d6a4419a5d4653f8b | [
"arxiv",
"semantic_scholar"
] | Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression | Mixture-of-Experts (MoE) Large Language Models (LLMs) face a trilemma of load imbalance, parameter redundancy, and communication overhead. We introduce a unified framework based on dynamic expert clustering and structured compression to address these issues cohesively. Our method employs an online clustering procedure ... | [
"Peijun Zhu",
"Ning Yang",
"Baoliang Tian",
"Jiayu Wei",
"Weihao Zhang",
"Haijun Zhang",
"Pin Lv"
] | [
"cs.CL",
"cs.AI",
"cs.DC",
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2025-09-27T00:00:00 | https://arxiv.org/abs/2510.02345 | https://arxiv.org/pdf/2510.02345v3 | 2510.02345 | 10.48550/arXiv.2510.02345 | 2 | 0 | true | https://github.com/szdtzpj/Breaking_the_moe_trilemma | arXiv.org | 0.487 |
7bfe590415727f390e1af8c4c16da7663c8818002440d4b4e45cb407cc316506 | [
"arxiv",
"semantic_scholar"
] | Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts | Mixture-of-Experts (MoE) models typically fix the number of activated experts $k$ at both training and inference. However, real-world deployments often face heterogeneous hardware, fluctuating workloads, and diverse quality-latency requirements, while training separate models for each scenario is costly. Considering th... | [
"Naibin Gu",
"Zhenyu Zhang",
"Yuchen Feng",
"Yilong Chen",
"Peng Fu",
"Zheng Lin",
"Shuohuan Wang",
"Yu Sun",
"Hua Wu",
"Weiping Wang",
"Haifeng Wang"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2509.21892 | https://arxiv.org/pdf/2509.21892v2 | 2509.21892 | 10.48550/arXiv.2509.21892 | 8 | 0 | false | null | arXiv.org | 0.314 |
33f1eb9d9208b84f61e8fe6c06a4fba952e3d84783b21a4907570daa8abfd70d | [
"arxiv",
"semantic_scholar"
] | Defending MoE LLMs against Harmful Fine-Tuning via Safety Routing Alignment | Recent large language models (LLMs) have increasingly adopted the Mixture-of-Experts (MoE) architecture for efficiency. MoE-based LLMs heavily depend on a superficial safety mechanism in which harmful inputs are routed safety-critical experts. However, our analysis reveals that routing decisions for harmful inputs drif... | [
"Jaehan Kim",
"Minkyoo Song",
"Seungwon Shin",
"Sooel Son"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2509.22745 | https://arxiv.org/pdf/2509.22745v2 | 2509.22745 | 10.48550/arXiv.2509.22745 | 8 | 1 | true | null | arXiv.org | 0.4852 |
bdd0d701020c0d7ead0f0118c47d1168829419e0a3f9c969cc6d32764c4c5ed4 | [
"arxiv",
"semantic_scholar"
] | Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time | Test-Time Scaling (TTS) enhances the reasoning ability of large language models (LLMs) by allocating additional computation during inference. However, existing approaches primarily rely on output-level sampling while overlooking the role of model architecture. In mainstream Mixture-of-Experts (MoE) LLMs, we observe tha... | [
"Yixuan Han",
"Fan Ma",
"Ruijie Quan",
"Yi Yang"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2509.22572 | https://arxiv.org/pdf/2509.22572v1 | 2509.22572 | 10.48550/arXiv.2509.22572 | 0 | 0 | false | null | arXiv.org | 0.314 |
9d2d43618fdaed60a29ed7c9bee403a7f777028b884d1275471f3443edab2099 | [
"arxiv",
"semantic_scholar"
] | Faster, Smaller, and Smarter: Task-Aware Expert Merging for Online MoE Inference | Sparse Mixture of Experts (SMoE) has become a preferred architecture for scaling Transformer capacity without increasing computational cost, as it activates only a small subset of experts for each input. However, deploying such an approach for \textit{online inference} remains challenging due to the large size of a ful... | [
"Ziyi Han",
"Xutong Liu",
"Ruiting Zhou",
"Xiangxiang Dai",
"John C. S. Lui"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-24T00:00:00 | https://arxiv.org/abs/2509.19781 | https://arxiv.org/pdf/2509.19781v2 | 2509.19781 | 10.48550/arXiv.2509.19781 | 1 | 0 | false | null | arXiv.org | 0.3117 |
f33389032cb9bbdf15e5d037727dcb410c140647fafa44f8cd6199e2908d58f5 | [
"arxiv",
"semantic_scholar"
] | Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts | Mixture-of-Experts (MoE) models enable scalable performance by activating large parameter sets sparsely, minimizing computational overhead. To mitigate the prohibitive cost of training MoEs from scratch, recent work employs upcycling, reusing a single pre-trained dense model by replicating its feed-forward network (FFN... | [
"Qi Wang",
"Hanyang Peng",
"Yue Yu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-23T00:00:00 | https://arxiv.org/abs/2509.18542 | https://arxiv.org/pdf/2509.18542v2 | 2509.18542 | 10.48550/arXiv.2509.18542 | 1 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.3105 |
aa66404c7953d86b31a00188d01c5434d1d1433cfcc7e3a275c9bb8131e934cd | [
"arxiv",
"semantic_scholar"
] | TrueMoE: Dual-Routing Mixture of Discriminative Experts for Synthetic Image Detection | The rapid progress of generative models has made synthetic image detection an increasingly critical task. Most existing approaches attempt to construct a single, universal discriminative space to separate real from fake content. However, such unified spaces tend to be complex and brittle, often struggling to generalize... | [
"Laixin Zhang",
"Shuaibo Li",
"Wei Ma",
"Hongbin Zha"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-09-19T00:00:00 | https://arxiv.org/abs/2509.15741 | https://arxiv.org/pdf/2509.15741v1 | 2509.15741 | 10.48550/arXiv.2509.15741 | 1 | 0 | false | null | arXiv.org | 0.3059 |
405ea56e6594c687fc8c59a3c79b15f625b487fc33c13090161ac5650c25efb5 | [
"arxiv",
"semantic_scholar"
] | Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation | Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification. This paper introduces Semi-MOE, to the best of our knowledge, t... | [
"Nguyen Lan Vi Vu",
"Thanh-Huy Nguyen",
"Thien Nguyen",
"Daisuke Kihara",
"Tianyang Wang",
"Xingjian Li",
"Min Xu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-09-17T00:00:00 | https://arxiv.org/abs/2509.13834 | https://arxiv.org/pdf/2509.13834v1 | 2509.13834 | 10.48550/arXiv.2509.13834 | 5 | 0 | true | https://github.com/vnlvi2k3/Semi-MoE | arXiv.org | 0.4693 |
b50471c90aea8c3e103f00ae4afdb2e692c6d590239d269431ea6dbad65d5a1e | [
"arxiv",
"semantic_scholar"
] | Exploring Expert Specialization through Unsupervised Training in Sparse Mixture of Experts | Understanding the internal organization of neural networks remains a fundamental challenge in deep learning interpretability. We address this challenge by exploring a novel Sparse Mixture of Experts Variational Autoencoder (SMoE-VAE) architecture. We test our model on the QuickDraw dataset, comparing unsupervised exper... | [
"Strahinja Nikolic",
"Ilker Oguz",
"Demetri Psaltis"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-12T00:00:00 | https://arxiv.org/abs/2509.10025 | https://arxiv.org/pdf/2509.10025v1 | 2509.10025 | 10.48550/arXiv.2509.10025 | 4 | 1 | false | null | arXiv.org | 0.2979 |
3b764df325e774fe747b07ec546f62eee147f760105e1bb7ff5757d71487770c | [
"arxiv",
"semantic_scholar"
] | Cosine-Similarity Routing with Semantic Anchors for Interpretable Mixture-of-Experts Language Models | Mixture-of-Experts (MoE) models improve efficiency through sparse activation, but their learned gating functions provide limited insight into routing decisions. This work introduces the Semantic Resonance Architecture (SRA), which routes tokens to experts via cosine similarity between token representations and learnabl... | [
"Ivan Ternovtsii",
"Yurii Bilak"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-12T00:00:00 | https://arxiv.org/abs/2509.14255 | https://arxiv.org/pdf/2509.14255v2 | 2509.14255 | null | 0 | 0 | true | https://github.com/ITernovtsii/semantic-resonance | null | 0.3521 |
2b29dd056f7ecbd8ccd59426a901abfabb5ba9d056c9c5c1f7daed1d3894cc90 | [
"arxiv",
"semantic_scholar"
] | Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs | Sparse Mixture-of-Experts (SMoE) architectures are widely used in large language models (LLMs) due to their computational efficiency. However, though only a few experts are activated for each token, SMoE still requires loading all expert parameters, leading to high memory usage and challenges in deployment. Previous wo... | [
"Yixiao Zhou",
"Ziyu Zhao",
"Dongzhou Cheng",
"zhiliang wu",
"Jie Gui",
"Yi Yang",
"Fei Wu",
"Yu Cheng",
"Hehe Fan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-09-12T00:00:00 | https://arxiv.org/abs/2509.10377 | https://arxiv.org/pdf/2509.10377v1 | 2509.10377 | 10.48550/arXiv.2509.10377 | 27 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3618 |
fcbdd91a6677dcd2f2f6ea96d7da3fbcb39869ffa16b5770bf06f426e552bf39 | [
"arxiv",
"semantic_scholar"
] | Steering MoE LLMs via Expert (De)Activation | Mixture-of-Experts (MoE) in Large Language Models (LLMs) routes each token through a subset of specialized Feed-Forward Networks (FFN), known as experts. We present SteerMoE, a framework to steer MoE models by detecting and controlling behavior-associated experts. We detect key experts by comparing how often they activ... | [
"Mohsen Fayyaz",
"Ali Modarressi",
"Hanieh Deilamsalehy",
"Franck Dernoncourt",
"Ryan Rossi",
"Trung Bui",
"Hinrich Schütze",
"Nanyun Peng"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-11T00:00:00 | https://arxiv.org/abs/2509.09660 | https://arxiv.org/pdf/2509.09660v2 | 2509.09660 | 10.48550/arXiv.2509.09660 | 21 | 4 | true | https://github.com/adobe-research/SteerMoE | arXiv.org | 0.4586 |
55ee14951b682ee0f028cf50fbef7a5bf025bf4498d457901de300ab6cf8432f | [
"arxiv",
"semantic_scholar"
] | HD-MoE: Hybrid and Dynamic Parallelism for Mixture-of-Expert LLMs with 3D Near-Memory Processing | Large Language Models (LLMs) with Mixture-of-Expert (MoE) architectures achieve superior model performance with reduced computation costs, but at the cost of high memory capacity and bandwidth requirements. Near-Memory Processing (NMP) accelerators that stack memory directly on the compute through hybrid bonding have d... | [
"Haochen Huang",
"Shuzhang Zhong",
"Zhe Zhang",
"Shuangchen Li",
"Dimin Niu",
"Hongzhong Zheng",
"Runsheng Wang",
"Meng Li"
] | [
"cs.PF"
] | [
"Computer Science"
] | 2025-09-11T00:00:00 | https://arxiv.org/abs/2509.09420 | https://arxiv.org/pdf/2509.09420v1 | 2509.09420 | 10.1109/ICCAD66269.2025.11240984 | 4 | 0 | false | null | null | 0.1889 |
e5bc229e021cf5da0bdd1d8f2c5056b05532bb035d3720a98d871a178fd5caf4 | [
"arxiv",
"semantic_scholar"
] | MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? | With the widespread application of Mixture of Experts (MoE) reasoning models in the field of LLM learning, efficiently serving MoE models under limited GPU memory constraints has emerged as a significant challenge. Offloading the non-activated experts to main memory has been identified as an efficient approach to addre... | [
"Songkai Ma",
"Zhaorui Zhang",
"Sheng Di",
"Benben Liu",
"Xiaodong Yu",
"Xiaoyi Lu",
"Dan Wang"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-09-09T00:00:00 | https://arxiv.org/abs/2509.07727 | https://arxiv.org/pdf/2509.07727v1 | 2509.07727 | 10.48550/arXiv.2509.07727 | 3 | 0 | false | null | arXiv.org | 0.2945 |
1b6ca26112f56919089aa8763b66fd1adfa0f8ba8d469d9728945312c54565fc | [
"arxiv",
"semantic_scholar"
] | DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance | Large Language Models (LLMs) are increasingly deployed as Internet/Web services (LLM-as-a-Service) with strict latency Service-Level Objectives (SLOs) under tight GPU memory budgets. Mixture-of-Experts (MoE) models improve quality and throughput via sparse expert activation, but serving them efficiently is challenging ... | [
"Yuning Zhang",
"Grant Pinkert",
"Nan Yang",
"Yanli Li",
"Dong Yuan"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-09-09T00:00:00 | https://arxiv.org/abs/2509.07379 | https://arxiv.org/pdf/2509.07379v2 | 2509.07379 | null | 1 | 0 | false | null | null | 0.1874 |
bfe5ce1d1eaf5cf2442350686951e952c86952bff6dce48650c67b1cf94f9204 | [
"arxiv",
"semantic_scholar"
] | Ban&Pick: Ehancing Performance and Efficiency of MoE-LLMs via Smarter Routing | Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs) efficiently. Recent fine-grained MoE designs introduce hundreds of experts per layer, with multiple experts activated per token, enabling stronger specialization. However, during pre-training, routers are optimized ma... | [
"Yuanteng Chen",
"Peisong Wang",
"Yuantian Shao",
"Nanxin Zeng",
"Chang Xu",
"Jian Cheng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-08T00:00:00 | https://arxiv.org/abs/2509.06346 | https://arxiv.org/pdf/2509.06346v2 | 2509.06346 | 10.48550/arXiv.2509.06346 | 0 | 0 | false | null | arXiv.org | 0.2933 |
8cb1af491c54d2df9e237d053d443c229858b8f6ffa135cc268226ecd6eb6a3c | [
"arxiv",
"semantic_scholar"
] | Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers | Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging and often requires resource-intensive countermeasures. We explore the use of sparse mixture-of-experts (MoE) layers to improve robustness by replacing selected residual blocks or convolutional layers, thereby increasing mo... | [
"Svetlana Pavlitska",
"Haixi Fan",
"Konstantin Ditschuneit",
"J. Marius Zöllner"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-05T00:00:00 | https://arxiv.org/abs/2509.05086 | https://arxiv.org/pdf/2509.05086v1 | 2509.05086 | 10.1109/ICCVW69036.2025.00032 | 3 | 0 | true | https://github.com/KASTEL-MobilityLab/robust-sparse-moes | null | 0.3426 |
32827409f2ffae9010d3b25e615f7bf888691801016463500e48b5d5575916e7 | [
"arxiv",
"semantic_scholar"
] | Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation | Estimating accurate and well-calibrated predictive uncertainty is important for enhancing the reliability of computer vision models, especially in safety-critical applications like traffic scene perception. While ensemble methods are commonly used to quantify uncertainty by combining multiple models, a mixture of exper... | [
"Svetlana Pavlitska",
"Beyza Keskin",
"Alwin Faßbender",
"Christian Hubschneider",
"J. Marius Zöllner"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-05T00:00:00 | https://arxiv.org/abs/2509.04816 | https://arxiv.org/pdf/2509.04816v1 | 2509.04816 | 10.1109/ICCVW69036.2025.00038 | 5 | 0 | true | https://github.com/KASTEL-MobilityLab/mixtures-of-experts/ | null | 0.3426 |
2746d45bd43354c84d81501fa0fe35c7237b7fc8d78aa30221d02490e2509950 | [
"arxiv",
"semantic_scholar"
] | Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning | A sparse Mixture-of-Experts (MoE) architecture has emerged as a highly scalable solution by conditionally activating sub-modules without a proportional increase in computational costs. However, improving expert specialization to enhance performance and generalization remains a challenge for MoE, especially in instructi... | [
"Sugyeong Eo",
"Jungjun Lee",
"Chanjun Park",
"Heuiseok Lim"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-03T00:00:00 | https://arxiv.org/abs/2509.10513 | https://arxiv.org/pdf/2509.10513v1 | 2509.10513 | 10.48550/arXiv.2509.10513 | 3 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2876 |
5c9179b3d5330344223584866b2e8371e7e3d01d00dd9164c3dfec7a00c66c72 | [
"arxiv",
"semantic_scholar"
] | LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference | Mixture-of-Experts (MoE) models scale efficiently by activating only a subset of experts per token, offering a computationally sparse alternative to dense architectures. While prior post-training optimizations, such as inter- and intra-expert pruning, reduce memory usage they provide limited gains in inference-time com... | [
"Krishna Teja Chitty-Venkata",
"Sandeep Madireddy",
"Murali Emani",
"Venkatram Vishwanath"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-09-02T00:00:00 | https://arxiv.org/abs/2509.02753 | https://arxiv.org/pdf/2509.02753v1 | 2509.02753 | 10.48550/arXiv.2509.02753 | 2 | 0 | false | null | arXiv.org | 0.2865 |
15d6a2f927d4c2ebd145bd467769306cfdc6d0ccd385a856fcf02f73aae45288 | [
"arxiv",
"semantic_scholar"
] | Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding | Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been proposed to address the GPU memory limitations of MoE inference. However, due to the I/O bottleneck and sparse computation of MoE models, exis... | [
"Zhibin Wang",
"Zhonghui Zhang",
"Yuhang Zhou",
"Zibo Wang",
"Mo Zhou",
"Peng Jiang",
"Weilin Cai",
"Chengying Huan",
"Rong Gu",
"Sheng Zhong",
"Chen Tian"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-08-29T00:00:00 | https://arxiv.org/abs/2508.21706 | https://arxiv.org/pdf/2508.21706v2 | 2508.21706 | 10.48550/arXiv.2508.21706 | 2 | 0 | false | null | arXiv.org | 0.2819 |
2bf68e3f3728a591967d3b99de57a2198e82171e4af2a530d8516d56ff915306 | [
"arxiv",
"semantic_scholar"
] | MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction | Healthcare systems generate diverse multimodal data, including Electronic Health Records (EHR), clinical notes, and medical images. Effectively leveraging this data for clinical prediction is challenging, particularly as real-world samples often present with varied or incomplete modalities. Existing approaches typicall... | [
"Xiaoyang Wang",
"Christopher C. Yang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-29T00:00:00 | https://arxiv.org/abs/2508.21793 | https://arxiv.org/pdf/2508.21793v1 | 2508.21793 | 10.1145/3765612.3767207 | 5 | 1 | false | null | ACM International Conference on Bioinformatics, Computational Biology and Biomedicine | 0.2819 |
ccbc1f55ab26e4d8de4a8f8b67fa862992c38766901ed104e0f0eb1a6b986cd5 | [
"arxiv",
"semantic_scholar"
] | FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge | As FMs drive progress toward Artificial General Intelligence (AGI), fine-tuning them under privacy and resource constraints has become increasingly critical particularly when highquality training data resides on distributed edge devices. Federated Learning (FL) offers a compelling solution through Federated Fine-Tuning... | [
"Gang Hu",
"Yinglei Teng",
"Pengfei Wu",
"Nan Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-26T00:00:00 | https://arxiv.org/abs/2508.18663 | https://arxiv.org/pdf/2508.18663v1 | 2508.18663 | 10.48550/arXiv.2508.18663 | 6 | 1 | false | null | arXiv.org | 0.2784 |
f8a0763ba0ad62d6a83070c9411524d4d968714a521db849fd586b8ef8a51fff | [
"arxiv",
"semantic_scholar"
] | DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction | Mixture of Experts (MoE) has become a mainstream architecture for building Large Language Models (LLMs) by reducing per-token computation while enabling model scaling. It can be viewed as partitioning a large Feed-Forward Network (FFN) at the tensor level into fine-grained sub-FFNs, or experts, and activating only a sp... | [
"Weilin Cai",
"Le Qin",
"Shwai He",
"Junwei Cui",
"Ang Li",
"Jiayi Huang"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-08-25T00:00:00 | https://arxiv.org/abs/2508.18376 | https://arxiv.org/pdf/2508.18376v1 | 2508.18376 | 10.48550/arXiv.2508.18376 | 0 | 0 | false | null | arXiv.org | 0.2773 |
a7be5241cfe765939d030a842075d3d40770121f1f808883fc77bcb8bfe4664a | [
"arxiv",
"semantic_scholar"
] | MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models | Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the addition... | [
"Krishna Teja Chitty-Venkata",
"Sylvia Howland",
"Golara Azar",
"Daria Soboleva",
"Natalia Vassilieva",
"Siddhisanket Raskar",
"Murali Emani",
"Venkatram Vishwanath"
] | [
"cs.LG",
"cs.PF"
] | [
"Computer Science"
] | 2025-08-24T00:00:00 | https://arxiv.org/abs/2508.17467 | https://arxiv.org/pdf/2508.17467v1 | 2508.17467 | 10.1145/3731599.3767706 | 7 | 0 | false | null | null | 0.2258 |
db724565b6206a474fe9b243d00aa03111359e97c715b0f3df37e7d427c1c35a | [
"arxiv",
"semantic_scholar"
] | Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation | Large Language Models (LLMs) have shown potential in automatic bundle generation but suffer from prohibitive computational costs. Although knowledge distillation offers a pathway to more efficient student models, our preliminary study reveals that naively integrating diverse types of distilled knowledge from teacher LL... | [
"Kaidong Feng",
"Zhu Sun",
"Hui Fang",
"Jie Yang",
"Wenyuan Liu",
"Yew-Soon Ong"
] | [
"cs.CL",
"cs.IR"
] | [
"Computer Science"
] | 2025-08-24T00:00:00 | https://arxiv.org/abs/2508.17250 | https://arxiv.org/pdf/2508.17250v1 | 2508.17250 | 10.48550/arXiv.2508.17250 | 2 | 0 | false | null | arXiv.org | 0.2761 |
7d7cdb270a4f403a8610cb3912726e5cb4665b507d870b24a3fa9befcb807af1 | [
"arxiv",
"semantic_scholar"
] | MoE-Beyond: Learning-Based Expert Activation Prediction on Edge Devices | The deployment of large-scale Mixture-of-Experts (MoE) models on edge devices presents significant challenges due to memory constraints. While MoE architectures enable efficient utilization of computational resources by activating only a subset of experts per inference, they require careful memory management to operate... | [
"Nishant Gavhane",
"Arush Mehrotra",
"Rohit Chawla",
"Peter Proenca"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-08-23T00:00:00 | https://arxiv.org/abs/2508.17137 | https://arxiv.org/pdf/2508.17137v1 | 2508.17137 | 10.48550/arXiv.2508.17137 | 1 | 0 | false | null | arXiv.org | 0.275 |
d9e6788376eaa6cdfc39762bc126ed164763c0ffd85c2551e94a8ac845489f41 | [
"arxiv",
"semantic_scholar"
] | MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts | Despite LLMs' excellent code creation capabilities, multilingual code generation remains extremely challenging. To address this, we intent to improve the multi-programming-lingual (MultiPL) performance of the base LLMs while retaining the most popular ones using restricted computational resources. We consider MultiPL t... | [
"Qing Wang",
"Xue Han",
"Jiahui Wang",
"Lehao Xing",
"Qian Hu",
"Lianlian Zhang",
"Chao Deng",
"Junlan Feng"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-22T00:00:00 | https://arxiv.org/abs/2508.19268 | https://arxiv.org/pdf/2508.19268v2 | 2508.19268 | 10.48550/arXiv.2508.19268 | 3 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2739 |
6c0dec220b933f81c418bba3429e7cf9402f6f3aed989bb9804c32af63be7416 | [
"arxiv",
"semantic_scholar"
] | Anchor-MoE: A Mean-Anchored Mixture of Experts For Probabilistic Regression | Regression under uncertainty is fundamental across science and engineering. We present an Anchored Mixture of Experts (Anchor-MoE), a model that handles both probabilistic and point regression. For simplicity, we use a tuned gradient-boosting model to furnish the anchor mean; however, any off-the-shelf point regressor ... | [
"Baozhuo Su",
"Zhengxian Qu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-08-22T00:00:00 | https://arxiv.org/abs/2508.16802 | https://arxiv.org/pdf/2508.16802v1 | 2508.16802 | 10.48550/arXiv.2508.16802 | 0 | 0 | true | https://github.com/BaozhuoSU/Probabilistic_Regression | arXiv.org | 0.4232 |
6fd5e8a8a126c4205de32dc3a43644fb28a1bd7d0d3451f465a4d80ec96ed567 | [
"arxiv",
"semantic_scholar"
] | Maximum Score Routing For Mixture-of-Experts | Routing networks in sparsely activated mixture-of-experts (MoE) dynamically allocate input tokens to top-k experts through differentiable sparse transformations, enabling scalable model capacity while preserving computational efficiency. Traditional MoE networks impose an expert capacity constraint to ensure GPU-friend... | [
"Bowen Dong",
"Yilong Fan",
"Yutao Sun",
"Zhenyu Li",
"Tengyu Pan",
"Xun Zhou",
"Jianyong Wang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-08-18T00:00:00 | https://arxiv.org/abs/2508.12801 | https://arxiv.org/pdf/2508.12801v1 | 2508.12801 | 10.18653/v1/2025.findings-acl.653 | 5 | 0 | true | https://github.com/dongbw18/MaxScore.git}{MaxScore}$ | Annual Meeting of the Association for Computational Linguistics | 0.4161 |
e23d49ca00075e75a929aa309a74cbc840aaf918bc961c2dc006bdfe72bf6d22 | [
"arxiv",
"semantic_scholar"
] | X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms | Emerging expert-specialized Mixture-of-Experts (MoE) architectures, such as DeepSeek-MoE, deliver strong model quality through fine-grained expert segmentation and large top-k routing. However, their scalability is limited by substantial activation memory overhead and costly all-to-all communication. Furthermore, curre... | [
"Yueming Yuan",
"Ahan Gupta",
"Jianping Li",
"Sajal Dash",
"Feiyi Wang",
"Minjia Zhang"
] | [
"cs.LG",
"cs.CL",
"cs.DC"
] | [
"Computer Science"
] | 2025-08-18T00:00:00 | https://arxiv.org/abs/2508.13337 | https://arxiv.org/pdf/2508.13337v1 | 2508.13337 | 10.1145/3712285.3759886 | 2 | 0 | true | https://github.com/Supercomputing-System-AI-Lab/X-MoE | International Conference on Software Composition | 0.4161 |
43f1f6f9624816450eaeef4ea26ba228ad76ba7a8776fb2ccb74b7b42a69ef0b | [
"arxiv",
"semantic_scholar"
] | HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap | The sparsely activated mixture-of-experts (MoE) transformer has become a common architecture for large language models (LLMs) due to its sparsity, which requires fewer computational demands while easily scaling the model size. In MoE models, each MoE layer requires to dynamically choose tokens to activate particular ex... | [
"Wenxiang Lin",
"Xinglin Pan",
"Lin Zhang",
"Shaohuai Shi",
"Xuan Wang",
"Xiaowen Chu"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-08-13T00:00:00 | https://arxiv.org/abs/2508.09591 | https://arxiv.org/pdf/2508.09591v1 | 2508.09591 | 10.48550/arXiv.2508.09591 | 2 | 0 | false | null | arXiv.org | 0.2635 |
a53393b02c06a95e03f886eb7649d570e276105dfbd5647150c72f824bb5811c | [
"arxiv",
"semantic_scholar"
] | MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models | Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across multi-modal tasks by scaling model size and training data. However, these dense LVLMs incur significant computational costs and motivate the exploration of sparse Mixture of Experts (MoE) architectures. While MoE improve parameter effi... | [
"Dianyi Wang",
"Siyuan Wang",
"Zejun Li",
"Yikun Wang",
"Yitong Li",
"Duyu Tang",
"Xiaoyu Shen",
"Xuanjing Huang",
"Zhongyu Wei"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-08-13T00:00:00 | https://arxiv.org/abs/2508.09779 | https://arxiv.org/pdf/2508.09779v2 | 2508.09779 | 10.48550/arXiv.2508.09779 | 5 | 0 | true | https://github.com/AlenjandroWang/MoIIE | arXiv.org | 0.4073 |
cacd80aeb54359b740d084aefddf7814c9ea9b30466b73fb9f593822c2c46a26 | [
"arxiv",
"semantic_scholar"
] | Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts | The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse parameter activation. However, traditional MoE architecture uses homogeneous experts of a uniform size, activating a fixed number of parameters ... | [
"Haoyuan Wu",
"Haoxing Chen",
"Xiaodong Chen",
"Zhanchao Zhou",
"Tieyuan Chen",
"Yihong Zhuang",
"Guoshan Lu",
"Zenan Huang",
"Junbo Zhao",
"Lin Liu",
"Zhenzhong Lan",
"Bei Yu",
"Jianguo Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-08-11T00:00:00 | https://arxiv.org/abs/2508.07785 | https://arxiv.org/pdf/2508.07785v1 | 2508.07785 | 10.48550/arXiv.2508.07785 | 8 | 0 | true | null | arXiv.org | 0.4037 |
576b60e01c00309afb1372004dffa02485557b2cee45e851a0c125b3e81f843b | [
"arxiv",
"semantic_scholar"
] | CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving | Bird's Eye View (BEV) perception systems based on multi-sensor feature fusion have become a fundamental cornerstone for end-to-end autonomous driving. However, existing multi-modal BEV methods commonly suffer from limited input adaptability, constrained modeling capacity, and suboptimal generalization. To address these... | [
"Qi Xiang",
"Kunsong Shi",
"Zhigui Lin",
"Lei He"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-08-11T00:00:00 | https://arxiv.org/abs/2508.07838 | https://arxiv.org/pdf/2508.07838v1 | 2508.07838 | 10.48550/arXiv.2508.07838 | 4 | 0 | false | null | arXiv.org | 0.2612 |
d3cd8fd2425ac79b41ce6ed439368c4552e2d6d4a87c7c64556d472a0efeeba3 | [
"arxiv",
"semantic_scholar"
] | Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts | Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing users' latest preferences is challenging, as interactions reflecting recent interests are limited and new items often lack sufficient feedbac... | [
"Yunke Qu",
"Liang Qu",
"Tong Chen",
"Quoc Viet Hung Nguyen",
"Hongzhi Yin"
] | [
"cs.IR"
] | [
"Computer Science"
] | 2025-08-08T00:00:00 | https://arxiv.org/abs/2508.05993 | https://arxiv.org/pdf/2508.05993v3 | 2508.05993 | 10.1145/3746252.3761390 | 6 | 0 | true | https://github.com/qykcq/Efficient-Multimodal-Streaming-Recommendation-via-Expandable-Side-Mixture-of-Experts | International Conference on Information and Knowledge Management | 0.3984 |
81818ae372c0059e5e2e99e6a4ca37ad0ff3c0f9ca9c49d8b33da88017b2de2d | [
"arxiv",
"semantic_scholar"
] | MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs | The Mixture-of-Experts (MoE) architecture has become a predominant paradigm for scaling large language models (LLMs). Despite offering strong performance and computational efficiency, large MoE-based LLMs like DeepSeek-V3-0324 and Kimi-K2-Instruct present serious challenges due to substantial memory requirements in dep... | [
"Xiaodong Chen",
"Mingming Ha",
"Zhenzhong Lan",
"Jing Zhang",
"Jianguo Li"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-08-07T00:00:00 | https://arxiv.org/abs/2508.05257 | https://arxiv.org/pdf/2508.05257v1 | 2508.05257 | 10.48550/arXiv.2508.05257 | 5 | 2 | false | null | arXiv.org | 0.2567 |
6f68b5a2916a75fea3a2a24d7c564ab45af8baf77a2eec80300c0fa893ef9ce0 | [
"arxiv",
"semantic_scholar"
] | Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules | Mixture-of-Experts (MoE) benefits from a dynamic routing mechanism among their specialized experts, which existing Parameter- Efficient Fine-Tuning (PEFT) strategies fail to leverage. This motivates us to investigate whether adaptation modules themselves should incorporate routing mechanisms to align with MoE's multi-e... | [
"Yilun Liu",
"Yunpu Ma",
"Yuetian Lu",
"Shuo Chen",
"Zifeng Ding",
"Volker Tresp"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-08-04T00:00:00 | https://arxiv.org/abs/2508.02587 | https://arxiv.org/pdf/2508.02587v1 | 2508.02587 | 10.48550/arXiv.2508.02587 | 0 | 0 | false | null | Conference of the European Chapter of the Association for Computational Linguistics | 0.2532 |
c34691f02cbdff0ef09dc7c09d0c5ba8af2de845da0feaaf0d66443ea5ac90b7 | [
"arxiv",
"semantic_scholar"
] | EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models | Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs. However, it is hindered by two critical challenges: (1) substantial GPU memory consumption to load all experts; (2) low activated parameters cannot be equivalently translated into inference acceleration effects. In this work, we propose EAC-... | [
"Yuanteng Chen",
"Yuantian Shao",
"Peisong Wang",
"Jian Cheng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-03T00:00:00 | https://arxiv.org/abs/2508.01625 | https://arxiv.org/pdf/2508.01625v1 | 2508.01625 | 10.18653/v1/2025.acl-long.633 | 14 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.294 |
0b21d163f7dbdfd1b80a778cf4fda4124d774b8a5076ae7fad5a23fa9ff39607 | [
"arxiv",
"semantic_scholar"
] | RouteMark: A Fingerprint for Intellectual Property Attribution in Routing-based Model Merging | Model merging via Mixture-of-Experts (MoE) has emerged as a scalable solution for consolidating multiple task-specific models into a unified sparse architecture, where each expert is derived from a model fine-tuned on a distinct task. While effective for multi-task integration, this paradigm introduces a critical yet u... | [
"Xin He",
"Junxi Shen",
"Zhenheng Tang",
"Xiaowen Chu",
"Bo Li",
"Ivor W. Tsang",
"Yew-Soon Ong"
] | [
"cs.CR",
"cs.AI",
"cs.ET",
"cs.LG"
] | [
"Computer Science"
] | 2025-08-03T00:00:00 | https://arxiv.org/abs/2508.01784 | https://arxiv.org/pdf/2508.01784v1 | 2508.01784 | 10.48550/arXiv.2508.01784 | 4 | 1 | false | null | arXiv.org | 0.2521 |
4d451a988ba5947b17dfb01408edda247f8adbe3a6da490f998af5a137368d36 | [
"arxiv",
"semantic_scholar"
] | A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration | Conventional large language models (LLMs) are equipped with dozens of GB to TB of model parameters, making inference highly energy-intensive and costly as all the weights need to be loaded to onboard processing elements during computation. Recently, the Mixture-of-Experts (MoE) architecture has emerged as an efficient ... | [
"Wei-Hsing Huang",
"Janak Sharda",
"Cheng-Jhih Shih",
"Yuyao Kong",
"Faaiq Waqar",
"Pin-Jun Chen",
" Yingyan",
" Lin",
"Shimeng Yu"
] | [
"cs.AR"
] | [
"Computer Science"
] | 2025-07-25T00:00:00 | https://arxiv.org/abs/2507.19142 | https://arxiv.org/pdf/2507.19142v1 | 2507.19142 | 10.48550/arXiv.2507.19142 | 1 | 0 | false | null | arXiv.org | 0.2418 |
8f4a1c3f547790d836d0f9a0573c10755bc11f55bf596054b7ad1a800f3f7ee4 | [
"arxiv",
"semantic_scholar"
] | Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series | Multiband astronomical time series exhibit heterogeneous variability patterns, sampling cadences, and signal characteristics across bands. Standard transformers apply shared parameters to all bands, potentially limiting their ability to model this rich structure. In this work, we introduce Astro-MoE, a foundational tra... | [
"Martina Cádiz-Leyton",
"Guillermo Cabrera-Vives",
"Pavlos Protopapas",
"Daniel Moreno-Cartagena",
"Ignacio Becker"
] | [
"astro-ph.IM"
] | [
"Physics"
] | 2025-07-16T00:00:00 | https://arxiv.org/abs/2507.12611 | https://arxiv.org/pdf/2507.12611v1 | 2507.12611 | null | 0 | 0 | false | null | null | 0.1473 |
561763d7db457c6b87ccfb2ec92db8ffae43c6f1bbd9e59e660364652c7ebfeb | [
"arxiv",
"semantic_scholar"
] | Mixture of Raytraced Experts | We introduce a Mixture of Raytraced Experts, a stacked Mixture of Experts (MoE) architecture which can dynamically select sequences of experts, producing computational graphs of variable width and depth. Existing MoE architectures generally require a fixed amount of computation for a given sample. Our approach, in cont... | [
"Andrea Perin",
"Giacomo Lagomarsini",
"Claudio Gallicchio",
"Giuseppe Nuti"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-16T00:00:00 | https://arxiv.org/abs/2507.12419 | https://arxiv.org/pdf/2507.12419v1 | 2507.12419 | 10.48550/arXiv.2507.12419 | 0 | 0 | true | https://github.com/nutig/RayTracing | arXiv.org | 0.3577 |
9e452af04149c17fb2edf43217870e77e4f1b29193cdfe55d4a9ad65d5b36461 | [
"arxiv",
"semantic_scholar"
] | Mixture of Experts in Large Language Models | This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performance while maintaining minimal computational overhead. Through a systematic analysis spanning theoretical foundations, core architectural desig... | [
"Danyang Zhang",
"Junhao Song",
"Ziqian Bi",
"Xinyuan Song",
"Yingfang Yuan",
"Tianyang Wang",
"Joe Yeong",
"Junfeng Hao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-15T00:00:00 | https://arxiv.org/abs/2507.11181 | https://arxiv.org/pdf/2507.11181v2 | 2507.11181 | 10.48550/arXiv.2507.11181 | 17 | 0 | false | null | arXiv.org | 0.3138 |
90931df041fe069ee89a106bcba0bbc51e51c6d2465381f2246a9a5c0518b80c | [
"arxiv",
"semantic_scholar"
] | Omni-Router: Sharing Routing Decisions in Sparse Mixture-of-Experts for Speech Recognition | Mixture-of-experts (MoE) architectures have expanded from language modeling to automatic speech recognition (ASR). Traditional MoE methods, such as the Switch Transformer, route experts independently within each layer. Our analysis reveals that routers in most layers make expert choices that are not strongly correlated... | [
"Zijin Gu",
"Tatiana Likhomanenko",
"Navdeep Jaitly"
] | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2025-07-08T00:00:00 | https://arxiv.org/abs/2507.05724 | https://arxiv.org/pdf/2507.05724v3 | 2507.05724 | 10.1109/ASRU65441.2025.11434645 | 5 | 0 | false | null | Automatic Speech Recognition & Understanding | 0.2223 |
7b9dc73071cc766c5efb917c0483d7f3bf791822016a02b89b1a7755227dc26f | [
"arxiv",
"semantic_scholar"
] | Neural Inhibition Improves Dynamic Routing and Mixture of Experts | To be effective, efficient, and diverse, deep learning models need to dynamically choose its architecture based on signals from a population of neurons. We hypothesize dynamic routing models can be improved with neural inhibition in those neural populations. This means signals commonly shared among the various modes of... | [
"Will Y. Zou",
"Jennifer Y. Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-03T00:00:00 | https://arxiv.org/abs/2507.03221 | https://arxiv.org/pdf/2507.03221v1 | 2507.03221 | 10.48550/arXiv.2507.03221 | 0 | 0 | false | null | arXiv.org | 0.2166 |
7f759f14df81d99c9f15c9070ed72eafc402ff157161665a867af981c4ff0572 | [
"arxiv",
"semantic_scholar"
] | Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging | Mixture of Experts (MoE) LLMs face significant obstacles due to their massive parameter scale, which imposes memory, storage, and deployment challenges. Although recent expert merging methods promise greater efficiency by consolidating multiple experts, they are fundamentally hindered by parameter conflicts arising fro... | [
"Lujun Li",
"Zhu Qiyuan",
"Jiacheng Wang",
"Wei Li",
"Hao Gu",
"Sirui Han",
"Yike Guo"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-06-29T00:00:00 | https://arxiv.org/abs/2506.23266 | https://arxiv.org/pdf/2506.23266v1 | 2506.23266 | 10.48550/arXiv.2506.23266 | 20 | 3 | true | https://github.com/lliai/MoERazor | arXiv.org | 0.3306 |
b06deff662ae80a82faf5acbc179dbc02078ea76f74e4a8ad11be5a9d3a76b3c | [
"arxiv",
"semantic_scholar"
] | Latent Prototype Routing: Achieving Near-Perfect Load Balancing in Mixture-of-Experts | Mixture-of-Experts (MoE) architectures have emerged as a key strategy for scaling large language models (LLMs) efficiently. However, current MoE systems suffer from severe load imbalance, where only a small subset of experts is consistently activated during training and inference, leading to significant underutilizatio... | [
"Jiajie Yang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-06-26T00:00:00 | https://arxiv.org/abs/2506.21328 | https://arxiv.org/pdf/2506.21328v1 | 2506.21328 | 10.48550/arXiv.2506.21328 | 3 | 1 | true | null | arXiv.org | 0.3223 |
3358b6341012f9f51d0327dc3c65043c732884cc77987e744e268ef0ee5fb680 | [
"arxiv",
"semantic_scholar"
] | Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts | Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based Mixture-of-Experts (MoE) methods expand capacity by adding isolated new experts while freezing old ones, but still suffer from redundancy, interference, routing ambiguity, a... | [
"Haodong Lu",
"Chongyang Zhao",
"Minhui Xue",
"Lina Yao",
"Kristen Moore",
"Dong Gong"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-06-26T00:00:00 | https://arxiv.org/abs/2506.21035 | https://arxiv.org/pdf/2506.21035v6 | 2506.21035 | null | 1 | 0 | false | null | null | 0.1327 |
6bef6539037940b89a3ed683fc5d05f74085caa76e2b384fd833ceff4fe26e17 | [
"arxiv"
] | SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation | The Mixture of Experts (MoE) architecture has emerged as a powerful paradigm for scaling large language models (LLMs) while maintaining inference efficiency. However, their enormous memory requirements make them prohibitively expensive to fine-tune or deploy in resource-constrained environments. To address this challen... | [
"Zichong Li",
"Chen Liang",
"Zixuan Zhang",
"Ilgee Hong",
"Young Jin Kim",
"Weizhu Chen",
"Tuo Zhao"
] | [
"cs.LG",
"cs.CL"
] | [] | 2025-06-23T00:00:00 | https://arxiv.org/abs/2506.18349 | https://arxiv.org/pdf/2506.18349v1 | 2506.18349 | null | 0 | 0 | false | null | null | 0.1305 |
70975b8c57bd95b226981727407b06668d67bc171c6a48aca965f3802035deb4 | [
"arxiv",
"semantic_scholar"
] | Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection | Linear State Space Models (SSMs) offer remarkable performance gains in efficient sequence modeling, with constant inference-time computation and memory complexity. Recent advances, such as Mamba, further enhance SSMs with input-dependent gating and hardware-aware implementations, positioning them as strong alternatives... | [
"Zheng Zhan",
"Liliang Ren",
"Shuohang Wang",
"Liyuan Liu",
"Yang Liu",
"Yeyun Gong",
"Yanzhi Wang",
"Yelong Shen"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-22T00:00:00 | https://arxiv.org/abs/2506.18145 | https://arxiv.org/pdf/2506.18145v1 | 2506.18145 | 10.48550/arXiv.2506.18145 | 2 | 0 | false | null | arXiv.org | 0.204 |
bb5bb3d3619fa535c65634c6e918f6a2ea2ea5fd0428a383f25cbc5d1e76dd21 | [
"arxiv",
"semantic_scholar"
] | SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs via Stable Safety-critical Expert Identification | Large language models with Mixture-of-Experts (MoE) architectures achieve efficiency and scalability, yet their routing mechanisms introduce safety alignment challenges insufficiently addressed by techniques developed for dense models. In this work, the MoE-specific safety risk of positional vulnerability-that safety-a... | [
"Zhenglin Lai",
"Mengyao Liao",
"Bingzhe Wu",
"Dong Xu",
"Zebin Zhao",
"Zhihang Yuan",
"Chao Fan",
"Jianqiang Li"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2025-06-20T00:00:00 | https://arxiv.org/abs/2506.17368 | https://arxiv.org/pdf/2506.17368v2 | 2506.17368 | 10.48550/arXiv.2506.17368 | 15 | 8 | false | null | arXiv.org | 0.4771 |
0d907cc216d8f42f4201f8761a51c6a63d67b81474867f20cc48449a8cb75844 | [
"arxiv",
"semantic_scholar"
] | GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors | Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs. However, their performance is limited by the small number of trainable parameters. Recent work combines LoRA with the Mixture-of-Experts (MoE),... | [
"Hengyuan Zhang",
"Xinrong Chen",
"Yingmin Qiu",
"Xiao Liang",
"Ziyue Li",
"Guanyu Wang",
"Weiping Li",
"Tong Mo",
"Hayden Kwok-Hay So",
"Ngai Wong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.14646 | https://arxiv.org/pdf/2506.14646v2 | 2506.14646 | 10.48550/arXiv.2506.14646 | 5 | 0 | true | https://github.com/Liar406/Gui-LoMo.git | arXiv.org | 0.3064 |
ecc4958fb51fcf1e958702d726b0cc8d15004f602491b0fe8c5244cd696a82fc | [
"arxiv",
"semantic_scholar"
] | EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization | Mixture-of-Experts (MoE) models enable scalable computation and performance in large-scale deep learning but face quantization challenges due to sparse expert activation and dynamic routing. Existing post-training quantization (PTQ) methods fail to address activation outliers, routing instability, and sparse expert cal... | [
"Zhongqian Fu",
"Tianyi Zhao",
"Ning Ding",
"Xianzhi Yu",
"Xiaosong Li",
"Yehui Tang",
"Yunhe Wang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-06-16T00:00:00 | https://arxiv.org/abs/2506.13329 | https://arxiv.org/pdf/2506.13329v3 | 2506.13329 | 10.48550/arXiv.2506.13329 | 5 | 1 | true | https://github.com/darren-fzq1/EAQuant | arXiv.org | 0.3046 |
d5c66741fb8caad467dbdbd21b80574faf1b2918595ecbb5db4567b1ddaea712 | [
"arxiv",
"semantic_scholar"
] | Load Balancing Mixture of Experts with Similarity Preserving Routers | Sparse Mixture of Experts (MoE) models offer a scalable and efficient architecture for training large neural networks by activating only a subset of parameters ("experts") for each input. A learned router computes a distribution over these experts, and assigns input tokens to a small subset. However, without auxiliary ... | [
"Nabil Omi",
"Siddhartha Sen",
"Ali Farhadi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-06-16T00:00:00 | https://arxiv.org/abs/2506.14038 | https://arxiv.org/pdf/2506.14038v2 | 2506.14038 | 10.48550/arXiv.2506.14038 | 20 | 1 | false | null | arXiv.org | 0.3306 |
28ff1d0cc25efc98fd2f31075c3b19b391cfac6a310c2d1cd5c538dfb3b44dff | [
"arxiv",
"semantic_scholar"
] | Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts | We present Sparse Interpolated Mixture-of-Experts (SIMoE) instruction-tuning, an end-to-end algorithm designed to fine-tune a dense pre-trained Large Language Model (LLM) into a MoE-style model that possesses capabilities in multiple specialized domains. During instruction-tuning, SIMoE automatically identifies multipl... | [
"Shengzhuang Chen",
"Ying Wei",
"Jonathan Richard Schwarz"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-06-14T00:00:00 | https://arxiv.org/abs/2506.12597 | https://arxiv.org/pdf/2506.12597v1 | 2506.12597 | 10.48550/arXiv.2506.12597 | 2 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1948 |
16742791c22dc07462091a4c64de8d0cf91d9849504166466f70a626693f2598 | [
"arxiv",
"semantic_scholar"
] | DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts | Large language models (LLMs) with the Mixture-of-Experts (MoE) architecture achieve high cost-efficiency by selectively activating a subset of the parameters. Despite the inference efficiency of MoE LLMs, the training of extensive experts from scratch incurs substantial overhead, whereas reconstructing a dense LLM into... | [
"Yuchen Feng",
"Bowen Shen",
"Naibin Gu",
"Jiaxuan Zhao",
"Peng Fu",
"Zheng Lin",
"Weiping Wang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-06-11T00:00:00 | https://arxiv.org/abs/2506.09351 | https://arxiv.org/pdf/2506.09351v1 | 2506.09351 | 10.48550/arXiv.2506.09351 | 7 | 0 | true | null | Annual Meeting of the Association for Computational Linguistics | 0.2957 |
34994366161d794a934641ae79e71d97d44a71b86871674dbd71095e2ec4199e | [
"arxiv",
"semantic_scholar"
] | MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing | In multi-GPU Mixture-of-Experts (MoE) network, experts are distributed across different GPUs, which creates load imbalance as each expert processes different number of tokens. Recent works improve MoE inference load balance by dynamically duplicating popular experts to more GPUs to process excessive tokens, which requi... | [
"Haiyue Ma",
"Zhixu Du",
"Yiran Chen"
] | [
"cs.LG",
"cs.AR"
] | [
"Computer Science"
] | 2025-06-09T00:00:00 | https://arxiv.org/abs/2506.07366 | https://arxiv.org/pdf/2506.07366v1 | 2506.07366 | 10.48550/arXiv.2506.07366 | 3 | 1 | false | null | arXiv.org | 0.1891 |
3d5be2f18e2ba2a66f0b85097901fd0fd031325484b458c188038183bee1ff68 | [
"arxiv",
"semantic_scholar"
] | SMAR: Soft Modality-Aware Routing Strategy for MoE-based Multimodal Large Language Models Preserving Language Capabilities | Mixture of Experts (MoE) architectures have become a key approach for scaling large language models, with growing interest in extending them to multimodal tasks. Existing methods to build multimodal MoE models either incur high training costs or suffer from degraded language capabilities when adapting pretrained models... | [
"Guoyang Xia",
"Yifeng Ding",
"Fengfa Li",
"Lei Ren",
"Wei Chen",
"Fangxiang Feng",
"Xiaojie Wang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-06T00:00:00 | https://arxiv.org/abs/2506.06406 | https://arxiv.org/pdf/2506.06406v2 | 2506.06406 | 10.48550/arXiv.2506.06406 | 3 | 0 | false | null | arXiv.org | 0.1856 |
254cda689695d6c290a2db92e579a416a30d299d659c60a8bc046326c978e5f6 | [
"arxiv",
"semantic_scholar"
] | PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs | Mixture-of-Experts (MoE) has been gaining popularity due to its successful adaptation to large language models (LLMs). In this work, we introduce Privacy-preserving Collaborative Mixture-of-Experts (PC-MoE), which leverages the sparsity of the MoE architecture for memory-efficient decentralized collaborative LLM traini... | [
"Ze Yu Zhang",
"Bolin Ding",
"Bryan Kian Hsiang Low"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-06-03T00:00:00 | https://arxiv.org/abs/2506.02965 | https://arxiv.org/pdf/2506.02965v2 | 2506.02965 | 10.1007/s10994-025-06901-2 | 0 | 0 | false | null | Machine-mediated learning | 0.1822 |
133c8b83d21acdfc50d881dcddf2a16956e0e6c16ab080da4c4164de2e878836 | [
"arxiv",
"semantic_scholar"
] | EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models | Recent advancements have shown that the Mixture of Experts (MoE) approach significantly enhances the capacity of large language models (LLMs) and improves performance on downstream tasks. Building on these promising results, multi-modal large language models (MLLMs) have increasingly adopted MoE techniques. However, ex... | [
"Linglin Jing",
"Yuting Gao",
"Zhigang Wang",
"Wang Lan",
"Yiwen Tang",
"Wenhai Wang",
"Kaipeng Zhang",
"Qingpei Guo"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.23830 | https://arxiv.org/pdf/2505.23830v1 | 2505.23830 | 10.48550/arXiv.2505.23830 | 4 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.1753 |
efe0fba3bf46dbe23b276390f98d3ab08490eb826cb09d11305c979648104bdd | [
"arxiv",
"semantic_scholar"
] | Advancing Expert Specialization for Better MoE | Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing, which hinders expert specialization and degrades o... | [
"Hongcan Guo",
"Haolang Lu",
"Guoshun Nan",
"Bolun Chu",
"Jialin Zhuang",
"Yuan Yang",
"Wenhao Che",
"Xinye Cao",
"Sicong Leng",
"Qimei Cui",
"Xudong Jiang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.22323 | https://arxiv.org/pdf/2505.22323v5 | 2505.22323 | 10.48550/arXiv.2505.22323 | 33 | 4 | false | null | arXiv.org | 0.3829 |
52f4e94628ca52cdc18e6fd6231248b42f4edddfd6c29eed9c278c1a6e185080 | [
"arxiv",
"semantic_scholar"
] | Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts | Continually expanding new languages for existing large language models (LLMs) is a promising yet challenging approach to building powerful multilingual LLMs. The biggest challenge is to make the model continuously learn new languages while preserving the proficient ability of old languages. To achieve this, recent work... | [
"Xue Zhang",
"Yunlong Liang",
"Fandong Meng",
"Songming Zhang",
"Yufeng Chen",
"Jinan Xu",
"Jie Zhou"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.22582 | https://arxiv.org/pdf/2505.22582v1 | 2505.22582 | 10.48550/arXiv.2505.22582 | 13 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2865 |
ef75723a797c024eb7f3baf12df51ad90bff17ebd314a62e9e7eeb5e316586bf | [
"arxiv",
"semantic_scholar"
] | Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity | The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because only a small fraction of parameters are activated for each input token. However, it is commonly observed that some experts are activated far m... | [
"Yehui Tang",
"Xiaosong Li",
"Fangcheng Liu",
"Wei Guo",
"Hang Zhou",
"Yaoyuan Wang",
"Kai Han",
"Xianzhi Yu",
"Jinpeng Li",
"Hui Zang",
"Fei Mi",
"Xiaojun Meng",
"Zhicheng Liu",
"Hanting Chen",
"Binfan Zheng",
"Can Chen",
"Youliang Yan",
"Ruiming Tang",
"Peifeng Qin",
"Xinghao... | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-27T00:00:00 | https://arxiv.org/abs/2505.21411 | https://arxiv.org/pdf/2505.21411v2 | 2505.21411 | 10.48550/arXiv.2505.21411 | 16 | 3 | true | null | arXiv.org | 0.3076 |
2f5ad05c41b713f4bf8dcc6b3520f43f12cf016e8e89ba5a12e3ff5db0932376 | [
"arxiv",
"semantic_scholar"
] | Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts | Recent advancements in multimodal large language models (MLLMs) have demonstrated considerable potential for comprehensive 3D scene understanding. However, existing approaches typically utilize only one or a limited subset of 3D modalities, resulting in incomplete representations of 3D scenes and reduced interpretive a... | [
"Yue Zhang",
"Yingzhao Jian",
"Hehe Fan",
"Yi Yang",
"Roger Zimmermann"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-05-27T00:00:00 | https://arxiv.org/abs/2505.21079 | https://arxiv.org/pdf/2505.21079v1 | 2505.21079 | 10.48550/arXiv.2505.21079 | 3 | 0 | false | null | arXiv.org | 0.1742 |
95d8394f44a9518a2b18dc2b861c93a623aae02a9ce233f011e11b1983e482a8 | [
"arxiv",
"semantic_scholar"
] | FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models | Recent large language models such as Gemini-1.5, DeepSeek-V3, and Llama-4 increasingly adopt Mixture-of-Experts (MoE) architectures, which offer strong efficiency-performance trade-offs by activating only a fraction of the model per token. Yet academic researchers still lack a fully open, end-to-end MoE platform for in... | [
"Hao Kang",
"Zichun Yu",
"Chenyan Xiong"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-26T00:00:00 | https://arxiv.org/abs/2505.20225 | https://arxiv.org/pdf/2505.20225v1 | 2505.20225 | 10.48550/arXiv.2505.20225 | 3 | 0 | true | https://github.com/cmu-flame/FLAME-MoE | arXiv.org | 0.2674 |
afc6cdfb6273e683d3d25a94edcba4c5a9032e9423a79a92272725f4589d4a90 | [
"arxiv",
"semantic_scholar"
] | Guiding the Experts: Semantic Priors for Efficient and Focused MoE Routing | Mixture-of-Experts (MoE) models have emerged as a promising direction for scaling vision architectures efficiently. Among them, Soft MoE improves training stability by assigning each token to all experts via continuous dispatch weights. However, current designs overlook the semantic structure which is implicitly encode... | [
"Chengxi Min",
"Wei Wang",
"Yahui Liu",
"Weixin Ye",
"Enver Sangineto",
"Qi Wang",
"Yao Zhao"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-05-24T00:00:00 | https://arxiv.org/abs/2505.18586 | https://arxiv.org/pdf/2505.18586v1 | 2505.18586 | 10.48550/arXiv.2505.18586 | 1 | 0 | false | null | arXiv.org | 0.1707 |
1ca5f0f0d3da940a24065d0c53de9d53316538767e4d9ef9289eeb860ea3db14 | [
"arxiv",
"semantic_scholar"
] | $μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts | To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these rely on calibration data, domain shift may arise for unknown downstream tasks. With a computationally efficient calibration, activation-aware pruning ... | [
"Toshiaki Koike-Akino",
"Jing Liu",
"Ye Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-05-24T00:00:00 | https://arxiv.org/abs/2505.18451 | https://arxiv.org/pdf/2505.18451v1 | 2505.18451 | 10.48550/arXiv.2505.18451 | 3 | 0 | false | null | arXiv.org | 0.1707 |
49eda568f4355086ac1c9df76e3fed369b34ef50e87ea506eea52e69fa3a25af | [
"arxiv",
"semantic_scholar"
] | CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning | In parameter-efficient fine-tuning, mixture-of-experts (MoE), which involves specializing functionalities into different experts and sparsely activating them appropriately, has been widely adopted as a promising approach to trade-off between model capacity and computation overhead. However, current MoE variants fall sh... | [
"Jinyuan Feng",
"Chaopeng Wei",
"Tenghai Qiu",
"Tianyi Hu",
"Zhiqiang Pu"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-05-23T00:00:00 | https://arxiv.org/abs/2505.17553 | https://arxiv.org/pdf/2505.17553v2 | 2505.17553 | 10.48550/arXiv.2505.17553 | 2 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1696 |
cc6e8d4212cd1dd0c60401e14027458d29acf384350ea7b2a9370bbe9701495f | [
"arxiv",
"semantic_scholar"
] | Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models | Mixture-of-Experts (MoE) enables efficient scaling of large language models (LLMs) with sparsely activated experts during inference. To effectively deploy large MoE models on memory-constrained devices, many systems introduce *expert offloading* that caches a subset of experts in fast memory, leaving others on slow mem... | [
"Jingcong Liang",
"Siyuan Wang",
"Miren Tian",
"Yitong Li",
"Duyu Tang",
"Zhongyu Wei"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-21T00:00:00 | https://arxiv.org/abs/2505.16056 | https://arxiv.org/pdf/2505.16056v4 | 2505.16056 | 10.48550/arXiv.2505.16056 | 4 | 0 | true | https://github.com/ljcleo/moe-lrc | arXiv.org | 0.2585 |
3b4f9c0f6faef23099891e07a0634baf7a676d48e2535afec63a54f35d08a8a4 | [
"arxiv",
"semantic_scholar"
] | MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning | Class-incremental learning (CIL) requires deep learning models to continuously acquire new knowledge from streaming data while preserving previously learned information. Recently, CIL based on pre-trained models (PTMs) has achieved remarkable success. However, prompt-based approaches suffer from prompt overwriting, whi... | [
"Linjie Li",
"Zhenyu Wu",
"Yang Ji"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-05-21T00:00:00 | https://arxiv.org/abs/2506.11038 | https://arxiv.org/pdf/2506.11038v1 | 2506.11038 | 10.1016/j.knosys.2025.113795 | 3 | 0 | true | https://github.com/Franklilinjie/MoTE | Knowledge-Based Systems | 0.2585 |
f02597040f3f6a8d34a96ee4385a8e3f316a41cdf0d985023200b80fd60027c1 | [
"arxiv",
"semantic_scholar"
] | THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation | The sparse Mixture-of-Experts (MoE) has achieved significant progress for neural machine translation (NMT). However, there exist two limitations in current MoE solutions which may lead to sub-optimal performance: 1) they directly use the task knowledge of NMT into MoE (\emph{e.g.}, domain/linguistics-specific knowledge... | [
"Yunlong Liang",
"Fandong Meng",
"Jie Zhou"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-20T00:00:00 | https://arxiv.org/abs/2505.14173 | https://arxiv.org/pdf/2505.14173v1 | 2505.14173 | 10.48550/arXiv.2505.14173 | 1 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1661 |
7f6262269245f0d07b1a685080557ac95b1353418925233d6aec3c72d11b3ee0 | [
"arxiv",
"semantic_scholar"
] | MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems | The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently, but it depends on heterogeneous compute and memory resources. These factors jointly affect system Cost, Accuracy, and Performance (CAP), making trade-offs inevitable. Existing benchmarks often ... | [
"Yinsicheng Jiang",
"Yao Fu",
"Yeqi Huang",
"Ping Nie",
"Zhan Lu",
"Leyang Xue",
"Congjie He",
"Man-Kit Sit",
"Jilong Xue",
"Li Dong",
"Ziming Miao",
"Dayou Du",
"Tairan Xu",
"Kai Zou",
"Edoardo Ponti",
"Luo Mai"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-05-16T00:00:00 | https://arxiv.org/abs/2505.11415 | https://arxiv.org/pdf/2505.11415v2 | 2505.11415 | 10.48550/arXiv.2505.11415 | 5 | 0 | false | null | arXiv.org | 0.1945 |
357454536d4c796eee8bb7f6bc1ea619ec57ff993dfef8eb47aeabf77adcf209 | [
"arxiv",
"semantic_scholar"
] | MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production | We present MegaScale-MoE, a production system tailored for the efficient training of large-scale mixture-of-experts (MoE) models. MoE emerges as a promising architecture to scale large language models (LLMs) to unprecedented sizes, thereby enhancing model performance. However, existing MoE training systems experience a... | [
"Chao Jin",
"Ziheng Jiang",
"Zhihao Bai",
"Zheng Zhong",
"Juncai Liu",
"Xiang Li",
"Ningxin Zheng",
"Xi Wang",
"Cong Xie",
"Qi Huang",
"Wen Heng",
"Yiyuan Ma",
"Wenlei Bao",
"Size Zheng",
"Yanghua Peng",
"Haibin Lin",
"Xuanzhe Liu",
"Xin Jin",
"Xin Liu"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-05-16T00:00:00 | https://arxiv.org/abs/2505.11432 | https://arxiv.org/pdf/2505.11432v3 | 2505.11432 | 10.48550/arXiv.2505.11432 | 25 | 1 | false | null | European Conference on Computer Systems | 0.3537 |
481906f3c7f5be018872996a1626f0dc3202145618ae021f1a184a1ce3b3506f | [
"arxiv",
"semantic_scholar"
] | PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning | Parameter-efficient fine-tuning (PEFT) methods have shown promise in adapting large language models, yet existing approaches exhibit counter-intuitive phenomena: integrating router into prompt tuning (PT) increases training efficiency yet does not improve performance universally; parameter reduction through matrix deco... | [
"Zongqian Li",
"Yixuan Su",
"Nigel Collier"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-14T00:00:00 | https://arxiv.org/abs/2505.09519 | https://arxiv.org/pdf/2505.09519v1 | 2505.09519 | 10.48550/arXiv.2505.09519 | 5 | 0 | false | null | arXiv.org | 0.1945 |
de12de7aeb43f6e33fdb6e1f5ebc017a0bf8872e6c6d73d27af8b84574ec116f | [
"arxiv",
"semantic_scholar"
] | PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts | Large language models (LLMs) hosted on cloud servers alleviate the computational and storage burdens on local devices but raise privacy concerns due to sensitive data transmission and require substantial communication bandwidth, which is challenging in constrained environments. In contrast, small language models (SLMs)... | [
"Yang Su",
"Na Yan",
"Yansha Deng",
"Robert Schober"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-13T00:00:00 | https://arxiv.org/abs/2505.08719 | https://arxiv.org/pdf/2505.08719v1 | 2505.08719 | 10.48550/arXiv.2505.08719 | 2 | 0 | false | null | arXiv.org | 0.1581 |
ebdf9e6b4e572b0c2a2488ec9b0a794e7da546ddf1e791858fde484d02b4e668 | [
"arxiv",
"semantic_scholar"
] | Faster MoE LLM Inference for Extremely Large Models | Sparse Mixture of Experts (MoE) large language models (LLMs) are gradually becoming the mainstream approach for ultra-large-scale models. Existing optimization efforts for MoE models have focused primarily on coarse-grained MoE architectures. With the emergence of DeepSeek Models, fine-grained MoE models are gaining po... | [
"Haoqi Yang",
"Luohe Shi",
"Qiwei Li",
"Zuchao Li",
"Ping Wang",
"Bo Du",
"Mengjia Shen",
"Hai Zhao"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-06T00:00:00 | https://arxiv.org/abs/2505.03531 | https://arxiv.org/pdf/2505.03531v1 | 2505.03531 | 10.48550/arXiv.2505.03531 | 6 | 0 | false | null | arXiv.org | 0.2113 |
865fc294938d2a135ccef74d6a83d905234c1b0a8b636627e104cf8a0bf6c1b8 | [
"arxiv",
"semantic_scholar"
] | BadPatches: Routing-aware Backdoor Attacks on Vision Mixture of Experts | Mixture of Experts (MoE) architectures have gained popularity for reducing computational costs in deep neural networks by activating only a subset of parameters during inference. While this efficiency makes MoE attractive for vision tasks, the patch-based processing in vision models introduces new methods for adversari... | [
"Cedric Chan",
"Jona te Lintelo",
"Stjepan Picek"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2025-05-03T00:00:00 | https://arxiv.org/abs/2505.01811 | https://arxiv.org/pdf/2505.01811v3 | 2505.01811 | null | 0 | 0 | false | null | null | 0.0933 |
8b8a43d8b3be39541d857a30645bde8fbe8f19fd2bca5c29339ea5f94777de23 | [
"arxiv",
"semantic_scholar"
] | MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance | Mixture-of-Experts (MoE) large language models (LLMs), which leverage dynamic routing and sparse activation to enhance efficiency and scalability, have achieved higher performance while reducing computational costs. However, these models face significant memory overheads, limiting their practical deployment and broader... | [
"Xing Hu",
"Zhixuan Chen",
"Dawei Yang",
"Zukang Xu",
"Chen Xu",
"Zhihang Yuan",
"Sifan Zhou",
"Jiangyong Yu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-02T00:00:00 | https://arxiv.org/abs/2505.03804 | https://arxiv.org/pdf/2505.03804v1 | 2505.03804 | 10.48550/arXiv.2505.03804 | 27 | 4 | false | null | International Conference on Machine Learning | 0.3618 |
49d84ea516a58a2027c12fd553db197cd551e197155be319c39e951a8572ef28 | [
"arxiv",
"semantic_scholar"
] | Improving Routing in Sparse Mixture of Experts with Graph of Tokens | Sparse Mixture of Experts (SMoE) has emerged as a key to achieving unprecedented scalability in deep learning. By activating only a small subset of parameters per sample, SMoE achieves an exponential increase in parameter counts while maintaining a constant computational overhead. However, SMoE models are susceptible t... | [
"Tam Nguyen",
"Ngoc N. Tran",
"Khai Nguyen",
"Richard G. Baraniuk"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-05-01T00:00:00 | https://arxiv.org/abs/2505.00792 | https://arxiv.org/pdf/2505.00792v1 | 2505.00792 | 10.48550/arXiv.2505.00792 | 5 | 1 | false | null | arXiv.org | 0.1945 |
5e30efb942712f326734ad86b70b49da828429274c808faaa835bf7957ce2a95 | [
"arxiv",
"semantic_scholar"
] | Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing | Recent advances in large language models highlighted the excessive quadratic cost of self-attention. Despite the significant research efforts, subquadratic attention methods still suffer from inferior performance in practice. We hypothesize that dynamic, learned content-based sparsity can lead to more efficient attenti... | [
"Piotr Piękos",
"Róbert Csordás",
"Jürgen Schmidhuber"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-05-01T00:00:00 | https://arxiv.org/abs/2505.00315 | https://arxiv.org/pdf/2505.00315v1 | 2505.00315 | 10.48550/arXiv.2505.00315 | 10 | 0 | false | null | arXiv.org | 0.2603 |
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