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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d5a610721942adb0a0524b47746b5e6b4a8104c5254edc54f5b43a8ab1959216 | [
"arxiv",
"semantic_scholar"
] | AW-MoE: All-Weather Mixture of Experts for Robust Multi-Modal 3D Object Detection | Robust 3D object detection under adverse weather conditions is crucial for autonomous driving. However, most existing methods simply combine all weather samples for training while overlooking data distribution discrepancies across different weather scenarios, leading to performance conflicts. To address this issue, we ... | [
"Hongwei Lin",
"Xun Huang",
"Chenglu Wen",
"Cheng Wang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-17T00:00:00 | https://arxiv.org/abs/2603.16261 | https://arxiv.org/pdf/2603.16261v1 | 2603.16261 | 10.48550/arXiv.2603.16261 | 1 | 0 | true | https://github.com/windlinsherlock/AW-MoE | arXiv.org | 0.7898 |
c3ec1871e482b75e9e21ecdacbcd120285394b8336817fa5153dfc3534331ea8 | [
"arxiv",
"semantic_scholar"
] | Knowledge Localization in Mixture-of-Experts LLMs Using Cross-Lingual Inconsistency | Modern LLMs continue to exhibit significant variance in behavior across languages, such as being able to recall factual information in some languages but not others. While typically studied as a problem to be mitigated, in this work, we propose leveraging this cross-lingual inconsistency as a tool for interpretability ... | [
"Lucas Bandarkar",
"Alan Ansell",
"Trevor Cohn"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-17T00:00:00 | https://arxiv.org/abs/2603.17102 | https://arxiv.org/pdf/2603.17102v1 | 2603.17102 | 10.48550/arXiv.2603.17102 | 3 | 1 | false | null | arXiv.org | 0.511 |
a46dfd39c829b4c978a1de6e3466e79636bfc759d4dddaeedc331a25666ba955 | [
"arxiv",
"semantic_scholar"
] | MoE-ACT: Scaling Multi-Task Bimanual Manipulation with Sparse Language-Conditioned Mixture-of-Experts Transformers | The ability of robots to handle multiple tasks under a unified policy is critical for deploying embodied intelligence in real-world household and industrial applications. However, out-of-distribution variation across tasks often causes severe task interference and negative transfer when training general robotic policie... | [
"Kangjun Guo",
"Haichao Liu",
"Yanji Sun",
"Ruhan Zhao",
"Jinni Zhou",
"Jun Ma"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2026-03-16T00:00:00 | https://arxiv.org/abs/2603.15265 | https://arxiv.org/pdf/2603.15265v1 | 2603.15265 | 10.48550/arXiv.2603.15265 | 1 | 0 | true | null | arXiv.org | 0.788 |
943a64d8001b65702238b54306a6c7b8e336656930e75411ac05ae1dc913342c | [
"arxiv",
"semantic_scholar"
] | Bridging Local and Global Knowledge: Cascaded Mixture-of-Experts Learning for Near-Shortest Path Routing | While deep learning models that leverage local features have demonstrated significant potential for near-optimal routing in dense Euclidean graphs, they struggle to generalize well in sparse networks where topological irregularities require broader structural awareness. To address this limitation, we train a Cascaded M... | [
"Yung-Fu Chen",
"Anish Arora"
] | [
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2026-03-16T00:00:00 | https://arxiv.org/abs/2603.15541 | https://arxiv.org/pdf/2603.15541v1 | 2603.15541 | 10.48550/arXiv.2603.15541 | 0 | 0 | false | null | arXiv.org | 0.5099 |
bf0f16d8e660f4559def9b285d5c616764e7222fa47ee96e3ccdf52716efda04 | [
"arxiv",
"semantic_scholar"
] | Task-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers | Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patt... | [
"Mynampati Sri Ranganadha Avinash"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-11T00:00:00 | https://arxiv.org/abs/2603.11114 | https://arxiv.org/pdf/2603.11114v1 | 2603.11114 | 10.48550/arXiv.2603.11114 | 0 | 0 | false | null | arXiv.org | 0.5042 |
23a28d13f48b04f0a383a3b8fab9edbfad6cd4900012e024d222abffee93f757 | [
"arxiv",
"semantic_scholar"
] | Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers | Foundation models are increasingly being deployed in contexts where understanding the uncertainty of their outputs is critical to ensuring responsible deployment. While Bayesian methods offer a principled approach to uncertainty quantification, their computational overhead renders their use impractical for training or ... | [
"Albus Yizhuo Li",
"Matthew Wicker"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-03-10T00:00:00 | https://arxiv.org/abs/2603.09453 | https://arxiv.org/pdf/2603.09453v3 | 2603.09453 | 10.48550/arXiv.2603.09453 | 2 | 1 | false | null | arXiv.org | 0.503 |
e1f0fe191047317dca18254b9917abfe82eaff9ca8123b38100dbfa97f705e2d | [
"arxiv",
"semantic_scholar"
] | LAR-MoE: Latent-Aligned Routing for Mixture of Experts in Robotic Imitation Learning | Imitation learning enables robots to acquire manipulation skills from demonstrations, yet deploying a policy across tasks with heterogeneous dynamics remains challenging, as models tend to average over distinct behavioral modes present in the demonstrations. Mixture-of-Experts (MoE) architectures address this by activa... | [
"Ariel Rodriguez",
"Chenpan Li",
"Lorenzo Mazza",
"Rayan Younis",
"Ortrun Hellig",
"Sebastian Bodenstedt",
"Martin Wagner",
"Stefanie Speidel"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.08476 | https://arxiv.org/pdf/2603.08476v1 | 2603.08476 | 10.48550/arXiv.2603.08476 | 2 | 0 | false | null | arXiv.org | 0.5019 |
0100340ad49fd23fe58b9e0a96b3229672973e90dda2f753b21b72f518da7a91 | [
"arxiv",
"semantic_scholar"
] | Scaling Machine Learning Interatomic Potentials with Mixtures of Experts | Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we systematically develop Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for MLIPs and analyze the effects of rout... | [
"Yuzhi Liu",
"Duo Zhang",
"Anyang Peng",
"Weinan E",
"Linfeng Zhang",
"Han Wang"
] | [
"physics.chem-ph",
"cs.LG",
"physics.comp-ph"
] | [
"Computer Science",
"Physics"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.07977 | https://arxiv.org/pdf/2603.07977v2 | 2603.07977 | 10.48550/arXiv.2603.07977 | 2 | 0 | false | null | arXiv.org | 0.5019 |
abaf72d9dcb8237f170e98af3b8e9308c2e965d7c8234d464348ebbfec8e115b | [
"arxiv",
"semantic_scholar"
] | Speculating Experts Accelerates Inference for Mixture-of-Experts | Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token compute. However, in memory-constrained inference settings, expert weights must be offloaded to CPU, creating a performance bottleneck from... | [
"Vivan Madan",
"Prajwal Singhania",
"Abhinav Bhatele",
"Tom Goldstein",
"Ashwinee Panda"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-09T00:00:00 | https://arxiv.org/abs/2603.19289 | https://arxiv.org/pdf/2603.19289v1 | 2603.19289 | 10.48550/arXiv.2603.19289 | 0 | 0 | true | https://github.com/axonn-ai/yalis/tree/offload_prefetch | arXiv.org | 0.7756 |
4b6300ee0dc9a714d53be5a8f22959b02d75ae8892ff1d79c5d22ccf884d7aa8 | [
"arxiv",
"semantic_scholar"
] | MoE Lens -- An Expert Is All You Need | Mixture of Experts (MoE) models enable parameter-efficient scaling through sparse expert activations, yet optimizing their inference and memory costs remains challenging due to limited understanding of their specialization behavior. We present a systematic analysis of expert specialization in MoEs through two complemen... | [
"Marmik Chaudhari",
"Idhant Gulati",
"Nishkal Hundia",
"Pranav Karra",
"Shivam Raval"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-06T00:00:00 | https://arxiv.org/abs/2603.05806 | https://arxiv.org/pdf/2603.05806v1 | 2603.05806 | 10.48550/arXiv.2603.05806 | 11 | 2 | false | null | arXiv.org | 0.4984 |
4b15603ac4292096a7583ef0684facc063bbba16a4b514e3cb3761923f6341d0 | [
"arxiv",
"semantic_scholar"
] | Sparse Crosscoders for diffing MoEs and Dense models | Mixture of Experts (MoE) achieve parameter-efficient scaling through sparse expert routing, yet their internal representations remain poorly understood compared to dense models. We present a systematic comparison of MoE and dense model internals using crosscoders, a variant of sparse autoencoders, that jointly models m... | [
"Marmik Chaudhari",
"Nishkal Hundia",
"Idhant Gulati"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-06T00:00:00 | https://arxiv.org/abs/2603.05805 | https://arxiv.org/pdf/2603.05805v1 | 2603.05805 | 10.48550/arXiv.2603.05805 | 0 | 0 | false | null | arXiv.org | 0.4984 |
14f31e1bf75fbd2fe91977feed130dc9682a85a38cb99a957c43c695db983471 | [
"arxiv",
"semantic_scholar"
] | MoEless: Efficient MoE LLM Serving via Serverless Computing | Large Language Models (LLMs) have become a cornerstone of AI, driving progress across diverse domains such as content creation, search and recommendation systems, and AI-assisted workflows. To alleviate extreme training costs and advancing model scales, Mixture-of-Experts (MoE) has become a popular backbone for modern ... | [
"Hanfei Yu",
"Bei Ouyang",
"Shwai He",
"Ang Li",
"Hao Wang"
] | [
"cs.DC",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-06T00:00:00 | https://arxiv.org/abs/2603.06350 | https://arxiv.org/pdf/2603.06350v1 | 2603.06350 | 10.48550/arXiv.2603.06350 | 0 | 0 | true | null | arXiv.org | 0.7703 |
5ec00039916aa4614b68a0a56d271285b5ab6d5c39cde89c38bb74fa8ca1b317 | [
"arxiv",
"semantic_scholar"
] | Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation | Mixture-of-Experts (MoE) decouples model capacity from per-token computation, yet their scalability remains limited by the physical dimensions of depth and width. To overcome this, we propose Mixture of Universal Experts (MOUE),a MoE generalization introducing a novel scaling dimension: Virtual Width. In general, MoUE ... | [
"Yilong Chen",
"Naibin Gu",
"Junyuan Shang",
"Zhenyu Zhang",
"Yuchen Feng",
"Jiawei Sheng",
"Tingwen Liu",
"Shuohuan Wang",
"Yu Sun",
"Hua Wu",
"Haifeng Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-05T00:00:00 | https://arxiv.org/abs/2603.04971 | https://arxiv.org/pdf/2603.04971v1 | 2603.04971 | 10.48550/arXiv.2603.04971 | 3 | 1 | false | null | arXiv.org | 0.4973 |
54fb8a3bff1281d74187205935a92da17aaa4c4cbb204292856aa1c682d3b505 | [
"arxiv",
"semantic_scholar"
] | ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model | Electrocardiography (ECG) analysis is crucial for cardiac diagnosis, yet existing foundation models often fail to capture the periodicity and diverse features required for varied clinical tasks. We propose ECG-MoE, a hybrid architecture that integrates multi-model temporal features with a cardiac period-aware expert mo... | [
"Yuhao Xu",
"Xiaoda Wang",
"Yi Wu",
"Wei Jin",
"Xiao Hu",
"Carl Yang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-03-04T00:00:00 | https://arxiv.org/abs/2603.04589 | https://arxiv.org/pdf/2603.04589v1 | 2603.04589 | 10.48550/arXiv.2603.04589 | 4 | 0 | false | null | arXiv.org | 0.4961 |
6aa880401d0e7ff960d95a3c16529fe191cbbfb9c62d35556712869aa302f50d | [
"arxiv",
"semantic_scholar"
] | CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging | Large language models (LLMs) achieve remarkable performance on diverse downstream and domain-specific tasks via parameter-efficient fine-tuning (PEFT). However, existing PEFT methods, particularly MoE-LoRA architectures, suffer from limited parameter efficiency and coarse-grained adaptation due to the proliferation of ... | [
"Jie Cao",
"Zhenxuan Fan",
"Zhuonan Wang",
"Tianwei Lin",
"Ziyuan Zhao",
"Rolan Yan",
"Wenqiao Zhang",
"Feifei Shao",
"Hongwei Wang",
"Jun Xiao",
"Siliang Tang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-28T00:00:00 | https://arxiv.org/abs/2603.00573 | https://arxiv.org/pdf/2603.00573v2 | 2603.00573 | 10.48550/arXiv.2603.00573 | 2 | 0 | false | null | arXiv.org | 0.4916 |
c6782d8c86d1ce181bdd4433644e93baeaea622c64803a9e6e3a89dacc07b1c8 | [
"arxiv",
"semantic_scholar"
] | Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation | Multimodal recommendation enhances ranking by integrating user-item interactions with item content, which is particularly effective under sparse feedback and long-tail distributions. However, multimodal signals are inherently heterogeneous and can conflict in specific contexts, making effective fusion both crucial and ... | [
"Ji Dai",
"Quan Fang",
"Dengsheng Cai"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-02-24T00:00:00 | https://arxiv.org/abs/2602.20723 | https://arxiv.org/pdf/2602.20723v2 | 2602.20723 | 10.48550/arXiv.2602.20723 | 0 | 0 | false | null | arXiv.org | 0.487 |
8d4e0d766aa7178ee3264d47cca931984e5394f867867e3616b7c70b6b3895e8 | [
"arxiv",
"semantic_scholar"
] | A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs | Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently, delivering strong accuracy under fixed compute budgets. However, SMoE models often suffer from severe load imbalance across experts, where a small subset of experts receives most tokens while others are unde... | [
"Zijie Liu",
"Jie Peng",
"Jinhao Duan",
"Zirui Liu",
"Kaixiong Zhou",
"Mingfu Liang",
"Luke Simon",
"Xi Liu",
"Zhaozhuo Xu",
"Tianlong Chen"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-23T00:00:00 | https://arxiv.org/abs/2602.19938 | https://arxiv.org/pdf/2602.19938v1 | 2602.19938 | 10.48550/arXiv.2602.19938 | 0 | 0 | false | null | arXiv.org | 0.4858 |
0af64980ae04fd1447fefb5fae02a9d597350b18863cee21b23ccbf401a0e7c3 | [
"arxiv",
"semantic_scholar"
] | Grouter: Decoupling Routing from Representation for Accelerated MoE Training | Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads to sluggish convergence and training insta... | [
"Yuqi Xu",
"Rizhen Hu",
"Zihan Liu",
"Mou Sun",
"Kun Yuan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-22T00:00:00 | https://arxiv.org/abs/2603.06626 | https://arxiv.org/pdf/2603.06626v2 | 2603.06626 | 10.48550/arXiv.2603.06626 | 6 | 0 | true | https://github.com/JimmyAwoe/Grouter | arXiv.org | 0.7491 |
52b1b3ad5eb18edf5f47250fbebd6a09e8a5ecd0bbf3edf9d5616a3544fa68e2 | [
"arxiv",
"semantic_scholar"
] | Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection | Mixture-of-Experts (MoE) offers flexible graph reasoning by combining multiple views of a graph through a learned router. We investigate routing-aware explanations for MoE graph models in malware detection using control flow graphs (CFGs). Our architecture builds diversity at two levels. At the node level, each layer c... | [
"Hossein Shokouhinejad",
"Roozbeh Razavi-Far",
"Griffin Higgins",
"Ali. A Ghorbani"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-22T00:00:00 | https://arxiv.org/abs/2602.19025 | https://arxiv.org/pdf/2602.19025v1 | 2602.19025 | 10.48550/arXiv.2602.19025 | 0 | 0 | false | null | arXiv.org | 0.4847 |
0cc3f2c90ec6f0e37139eea420a6776034cd71e82a5590278a8b89f3c812c940 | [
"arxiv",
"semantic_scholar"
] | Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds | Mixture-of-Experts models rely on learned routers to assign tokens to experts, yet standard softmax gating provides no principled mechanism to control the tradeoff between sparsity and utilization. We propose Grassmannian MoE (GrMoE), a routing framework that operates on the Grassmannian manifold of subspaces, where ga... | [
"Ibne Farabi Shihab",
"Sanjeda Akter",
"Anuj Sharma"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-19T00:00:00 | https://arxiv.org/abs/2602.17798 | https://arxiv.org/pdf/2602.17798v1 | 2602.17798 | 10.48550/arXiv.2602.17798 | 1 | 0 | false | null | arXiv.org | 0.4813 |
89825c64e037aa9c0e06a6cc615ab129a6ad01aa1d1ee180d5936b305e4572e0 | [
"arxiv",
"semantic_scholar"
] | Phase-Aware Mixture of Experts for Agentic Reinforcement Learning | Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing \emph{simplicity bias} where simple tasks occupy most parameters and dominate gradient updates, leaving insufficient capacity for complex ta... | [
"Shengtian Yang",
"Yu Li",
"Shuo He",
"Yewen Li",
"Qingpeng Cai",
"Peng Jiang",
"Lei Feng"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-02-19T00:00:00 | https://arxiv.org/abs/2602.17038 | https://arxiv.org/pdf/2602.17038v3 | 2602.17038 | 10.48550/arXiv.2602.17038 | 1 | 0 | false | null | arXiv.org | 0.4813 |
f8028e71a8ea62bb19e7f8fc6c259b427149351e6c3097da0bd5378774eca8b0 | [
"arxiv",
"semantic_scholar"
] | MoE-Spec: Expert Budgeting for Efficient Speculative Decoding | Speculative decoding accelerates Large Language Model (LLM) inference by verifying multiple drafted tokens in parallel. However, for Mixture-of-Experts (MoE) models, this parallelism introduces a severe bottleneck: large draft trees activate many unique experts, significantly increasing memory pressure and diminishing ... | [
"Bradley McDanel",
"Steven Li",
"Sruthikesh Surineni",
"Harshit Khaitan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-17T00:00:00 | https://arxiv.org/abs/2602.16052 | https://arxiv.org/pdf/2602.16052v1 | 2602.16052 | 10.48550/arXiv.2602.16052 | 3 | 0 | false | null | arXiv.org | 0.479 |
ffd4c7f8a6af305c960bcd8fb055bb357281c2546865e7a3e06c5fe258f73f4e | [
"arxiv",
"semantic_scholar"
] | DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices | Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these models require vast and diverse training data. Federated learning (FL) addresses this challenge by leveraging private data from heterogeneous... | [
"Songyuan Li",
"Jia Hu",
"Ahmed M. Abdelmoniem",
"Geyong Min",
"Haojun Huang",
"Jiwei Huang"
] | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2026-02-15T00:00:00 | https://arxiv.org/abs/2602.14301 | https://arxiv.org/pdf/2602.14301v1 | 2602.14301 | 10.48550/arXiv.2602.14301 | 0 | 0 | false | null | arXiv.org | 0.4767 |
a4033669b6fb0622d5a64aecb51d83cca0d243fe9d89ab1f32ccb3921bb41b36 | [
"arxiv",
"semantic_scholar"
] | Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization | Sparse Mixture-of-Experts (MoE) models scale Transformers efficiently but suffer from expert overlap -- redundant representations across experts and routing ambiguity, resulting in severely underutilized model capacity. While architectural solutions like DeepSeekMoE promote specialization, they require substantial stru... | [
"Rizhen Hu",
"Yuan Cao",
"Boao Kong",
"Mou Sun",
"Kun Yuan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-15T00:00:00 | https://arxiv.org/abs/2602.14159 | https://arxiv.org/pdf/2602.14159v1 | 2602.14159 | 10.48550/arXiv.2602.14159 | 4 | 0 | false | null | arXiv.org | 0.4767 |
2b826d46e8df83d3bf0af41fa3cf437711e8ec7fd2a8dc1a33ebabf4b98d0add | [
"arxiv",
"semantic_scholar"
] | Decoder-only Conformer with Modality-aware Sparse Mixtures of Experts for ASR | We present a decoder-only Conformer for automatic speech recognition (ASR) that processes speech and text in a single stack without external speech encoders or pretrained large language models (LLM). The model uses a modality-aware sparse mixture of experts (MoE): disjoint expert pools for speech and text with hard rou... | [
"Jaeyoung Lee",
"Masato Mimura"
] | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.SD"
] | [
"Computer Science",
"Engineering"
] | 2026-02-13T00:00:00 | https://arxiv.org/abs/2602.12546 | https://arxiv.org/pdf/2602.12546v1 | 2602.12546 | 10.48550/arXiv.2602.12546 | 0 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.4744 |
9154470e2461ebc8c3ece58e9f6853e46ae6f663ddce4579433295fc9cbf351c | [
"arxiv",
"semantic_scholar"
] | SD-MoE: Spectral Decomposition for Effective Expert Specialization | Mixture-of-Experts (MoE) architectures scale Large Language Models via expert specialization induced by conditional computation. In practice, however, expert specialization often fails: some experts become functionally similar, while others functioning as de facto shared experts, limiting the effective capacity and mod... | [
"Ruijun Huang",
"Fang Dong",
"Xin Zhang",
"Hengjie Cao",
"Zhendong Huang",
"Anrui Chen",
"Jixian Zhou",
"Mengyi Chen",
"Yifeng Yang",
"Mingzhi Dong",
"Yujiang Wang",
"Jinlong Hou",
"Qin Lv",
"Robert P. Dick",
"Yuan Cheng",
"Fan Yang",
"Tun Lu",
"Chun Zhang",
"Li Shang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-13T00:00:00 | https://arxiv.org/abs/2602.12556 | https://arxiv.org/pdf/2602.12556v1 | 2602.12556 | 10.48550/arXiv.2602.12556 | 1 | 0 | false | null | arXiv.org | 0.4744 |
20aed9382dfe0e743c409c346f4acbc869d5e9215670840d7e51b1309b860ec9 | [
"arxiv",
"semantic_scholar"
] | LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training | Expert parallelism is vital for effectively training Mixture-of-Experts (MoE) models, enabling different devices to host distinct experts, with each device processing different input data. However, during expert parallel training, dynamic routing results in significant load imbalance among experts: a handful of overloa... | [
"Xinyi Liu",
"Yujie Wang",
"Fangcheng Fu",
"Xuefeng Xiao",
"Huixia Li",
"Jiashi Li",
"Bin Cui"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2026-02-12T00:00:00 | https://arxiv.org/abs/2602.11686 | https://arxiv.org/pdf/2602.11686v1 | 2602.11686 | 10.1145/3779212.3790180 | 4 | 1 | true | https://github.com/PKU-DAIR/Hetu-Galvatron/tree/laer-moe | International Conference on Architectural Support for Programming Languages and Operating Systems | 0.7314 |
36c398ec794c67f78fd809c50f190fdbff0ebf64e88abda8ae46e6792ab7c8dc | [
"arxiv",
"semantic_scholar"
] | MoEEdit: Efficient and Routing-Stable Knowledge Editing for Mixture-of-Experts LLMs | Knowledge editing (KE) enables precise modifications to factual content in large language models (LLMs). Existing KE methods are largely designed for dense architectures, limiting their applicability to the increasingly prevalent sparse Mixture-of-Experts (MoE) models that underpin modern scalable LLMs. Although MoEs o... | [
"Yupu Gu",
"Rongzhe Wei",
"Andy Zhu",
"Pan Li"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-11T00:00:00 | https://arxiv.org/abs/2602.10965 | https://arxiv.org/pdf/2602.10965v1 | 2602.10965 | 10.48550/arXiv.2602.10965 | 3 | 2 | false | null | arXiv.org | 0.4721 |
1fb6d6c909f8ebc78dfe7c6fe2fe1cc0ee3274c077c15d1bec8d2377266074c1 | [
"arxiv",
"semantic_scholar"
] | Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density | Mixture-of-Experts (MoE) based Large Language Models (LLMs) have achieved superior performance, yet the massive memory overhead caused by storing multiple expert networks severely hinders their practical deployment. Singular Value Decomposition (SVD)-based compression has emerged as a promising post-training technique;... | [
"Zhendong Mi",
"Yixiao Chen",
"Pu Zhao",
"Xiaodong Yu",
"Hao Wang",
"Yanzhi Wang",
"Shaoyi Huang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-10T00:00:00 | https://arxiv.org/abs/2602.09316 | https://arxiv.org/pdf/2602.09316v2 | 2602.09316 | 10.48550/arXiv.2602.09316 | 2 | 0 | false | null | arXiv.org | 0.4709 |
2e7b0ef1600044e1f60a406991c40b6268f88b835954e40978969c56885ad0ba | [
"arxiv",
"semantic_scholar"
] | Revealing the Challenges of Attention-FFN Disaggregation for Modern MoE Models and Hardware Systems | Deploying large-scale MoE models presents challenges in memory capacity and bandwidth for expert activation. While Attention-FFN Disaggregation (AFD) has emerged as a potential architecture to decouple compute and memory resources, its performance boundaries compared to standard large-scale Expert Parallelism (EP) rema... | [
"Guowei Liu",
"Hongming Li",
"Yaning Guo",
"Yongxi Lyu",
"Mo Zhou",
"Yi Liu",
"Zhaogeng Li",
"Yanpeng Wang"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2026-02-10T00:00:00 | https://arxiv.org/abs/2602.09721 | https://arxiv.org/pdf/2602.09721v1 | 2602.09721 | 10.48550/arXiv.2602.09721 | 1 | 0 | false | null | arXiv.org | 0.4709 |
3012d2da9a1a77cef08711b197e58f965c8fbadfa74428e61f819b454d015430 | [
"arxiv",
"semantic_scholar"
] | Expert Divergence Learning for MoE-based Language Models | The Mixture-of-Experts (MoE) architecture is a powerful technique for scaling language models, yet it often suffers from expert homogenization, where experts learn redundant functionalities, thereby limiting MoE's full potential. To address this, we introduce Expert Divergence Learning, a novel pre-training strategy th... | [
"Jiaang Li",
"Haibin Chen",
"Langming Liu",
"Yujin Yuan",
"Yadao Wang",
"Yizhen Zhang",
"Chengting Yu",
"Xin Tong",
"Weidong Zhang",
"Shilei Liu",
"Wenbo Su",
"Bo Zheng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-10T00:00:00 | https://arxiv.org/abs/2603.00054 | https://arxiv.org/pdf/2603.00054v1 | 2603.00054 | 10.48550/arXiv.2603.00054 | 1 | 0 | false | null | arXiv.org | 0.4709 |
6101522e7988761580a40fde26fdccbc22a9107b2f1f89287f20c8985258497b | [
"arxiv",
"semantic_scholar"
] | Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs | By introducing routers to selectively activate experts in Transformer layers, the mixture-of-experts (MoE) architecture significantly reduces computational costs in large language models (LLMs) while maintaining competitive performance, especially for models with massive parameters. However, prior work has largely focu... | [
"Yukun Jiang",
"Hai Huang",
"Mingjie Li",
"Yage Zhang",
"Michael Backes",
"Yang Zhang"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.08621 | https://arxiv.org/pdf/2602.08621v1 | 2602.08621 | 10.48550/arXiv.2602.08621 | 8 | 1 | true | https://github.com/TrustAIRLab/UnsafeMoE | arXiv.org | 0.726 |
60caa7ff888c963b5cb9940667dd86305a49741634dea6d384d35c6274e9c897 | [
"arxiv",
"semantic_scholar"
] | DirMoE: Dirichlet-routed Mixture of Experts | Mixture-of-Experts (MoE) models have demonstrated exceptional performance in large-scale language models. Existing routers typically rely on non-differentiable Top-$k$+Softmax, limiting their performance and scalability. We argue that two distinct decisions, which experts to activate and how to distribute expert contri... | [
"Amirhossein Vahidi",
"Hesam Asadollahzadeh",
"Navid Akhavan Attar",
"Marie Moullet",
"Kevin Ly",
"Xingyi Yang",
"Mohammad Lotfollahi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.09001 | https://arxiv.org/pdf/2602.09001v1 | 2602.09001 | 10.48550/arXiv.2602.09001 | 1 | 0 | false | null | arXiv.org | 0.4698 |
7db41f588e40cc557fcdb29074932c6f148f31369063514015e78c0a230fd06a | [
"arxiv",
"semantic_scholar"
] | TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration | Diffusion large language models (dLLMs) have recently gained significant attention due to their inherent support for parallel decoding. Building on this paradigm, Mixture-of-Experts (MoE) dLLMs with autoregressive (AR) initialization have further demonstrated strong performance competitive with mainstream AR models. Ho... | [
"Linye Wei",
"Zixiang Luo",
"Pingzhi Tang",
"Meng Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.08404 | https://arxiv.org/pdf/2602.08404v2 | 2602.08404 | 10.48550/arXiv.2602.08404 | 5 | 1 | true | https://github.com/PKU-SEC-Lab/TEAM-MoE-dLLM | arXiv.org | 0.726 |
d977098d946caca03317032d4a8c37c79394480d07f46da2e4698dcfef4d32f1 | [
"arxiv",
"semantic_scholar"
] | Large Language Lobotomy: Jailbreaking Mixture-of-Experts via Expert Silencing | The rapid adoption of Mixture-of-Experts (MoE) architectures marks a major shift in the deployment of Large Language Models (LLMs). MoE LLMs improve scaling efficiency by activating only a small subset of parameters per token, but their routing structure introduces new safety attack surfaces. We find that safety-critic... | [
"Jona te Lintelo",
"Lichao Wu",
"Stjepan Picek"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.08741 | https://arxiv.org/pdf/2602.08741v1 | 2602.08741 | 10.48550/arXiv.2602.08741 | 2 | 1 | true | null | arXiv.org | 0.726 |
76e4678e878ebc9a1f93c1baa7465caf8f45c6d9ed480d1206c326446f340cc4 | [
"arxiv",
"semantic_scholar"
] | The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications | The sparse Mixture of Experts(MoE) architecture has evolved as a powerful approach for scaling deep learning models to more parameters with comparable computation cost. As an important branch of large language model(LLM), MoE model only activate a subset of experts based on a routing network. This sparse conditional co... | [
"Dong Pan",
"Bingtao Li",
"Yongsheng Zheng",
"Jiren Ma",
"Victor Fei"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-08T00:00:00 | https://arxiv.org/abs/2602.08019 | https://arxiv.org/pdf/2602.08019v1 | 2602.08019 | 10.54097/bvpfjj49 | 0 | 0 | false | null | Journal of Computer Science and Artificial Intelligence 5 (2025) 25-41 | 0.4686 |
684827ebfe9b201184a816182932dbea455e7ef5b18573427a6476e863f1f97b | [
"arxiv",
"semantic_scholar"
] | SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models | Mixture-of-Experts (MoE) architectures employ sparse activation to deliver faster training and inference with higher accuracy than dense LLMs. However, in production serving, MoE models require batch inference to optimize hardware efficiency, which may cause excessive expert activation and thus slow the memory-bound de... | [
"Juntong Wu",
"Jialiang Cheng",
"Fuyu Lv",
"Ou Dan",
"Li Yuan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-07T00:00:00 | https://arxiv.org/abs/2602.07616 | https://arxiv.org/pdf/2602.07616v1 | 2602.07616 | 10.48550/arXiv.2602.07616 | 1 | 0 | true | https://github.com/JL-Cheng/SERE | arXiv.org | 0.7225 |
e37b1309e72586ee98e4ea2aeb8f671b8267a0e7fc932c34f5ba3d4f13dde1f0 | [
"arxiv",
"semantic_scholar"
] | MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models | Mixture-of-Experts (MoE) models scale large language models efficiently by sparsely activating experts, but once an expert is selected, it is executed fully. Hence, the trade-off between accuracy and computation in an MoE model typically exhibits large discontinuities. We propose Mixture of Slimmable Experts (MoSE), an... | [
"Nurbek Tastan",
"Stefanos Laskaridis",
"Karthik Nandakumar",
"Samuel Horvath"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.06154 | https://arxiv.org/pdf/2602.06154v2 | 2602.06154 | 10.48550/arXiv.2602.06154 | 1 | 0 | true | https://github.com/tnurbek/mose | arXiv.org | 0.719 |
0787bcd3bf50d6f582a4f2c6ab48f81099f6ae88e89ea2d151f6d48efcf24f49 | [
"arxiv",
"semantic_scholar"
] | OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale | Mixture-of-Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency. However, existing MoE designs face an inherent trade-off between the granularity of expert specialization and hardware execution efficiency. We propose OmniMoE, a system-algorithm co-designed framework that pu... | [
"Jingze Shi",
"Zhangyang Peng",
"Yizhang Zhu",
"Yifan Wu",
"Guang Liu",
"Yuyu Luo"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.05711 | https://arxiv.org/pdf/2602.05711v1 | 2602.05711 | 10.48550/arXiv.2602.05711 | 0 | 0 | true | https://github.com/flash-algo/omni-moe | arXiv.org | 0.719 |
aece6c0cfe8674dbc23c441fd17884797dc83655b8233e0881aa0af0df7e2fdc | [
"arxiv",
"semantic_scholar"
] | RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models | Mixture-of-Experts (MoE) language models introduce unique challenges for safety alignment due to their sparse routing mechanisms, which can enable degenerate optimization behaviors under standard full-parameter fine-tuning. In our preliminary experiments, we observe that naively applying full-parameter safety fine-tuni... | [
"Jiacheng Liang",
"Yuhui Wang",
"Tanqiu Jiang",
"Ting Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2026-02-04T00:00:00 | https://arxiv.org/abs/2602.04448 | https://arxiv.org/pdf/2602.04448v2 | 2602.04448 | 10.48550/arXiv.2602.04448 | 1 | 0 | false | null | arXiv.org | 0.4641 |
f449c475e42011401863c89cda0acda8ac033b189edb381f7c539e0c06df8832 | [
"arxiv",
"semantic_scholar"
] | Expert Selections In MoE Models Reveal (Almost) As Much As Text | We present a text-reconstruction attack on mixture-of-experts (MoE) language models that recovers tokens from expert selections alone. In MoE models, each token is routed to a subset of expert subnetworks; we show these routing decisions leak substantially more information than previously understood. Prior work using l... | [
"Amir Nuriyev",
"Gabriel Kulp"
] | [
"cs.CL",
"cs.CR"
] | [
"Computer Science"
] | 2026-02-04T00:00:00 | https://arxiv.org/abs/2602.04105 | https://arxiv.org/pdf/2602.04105v3 | 2602.04105 | 10.48550/arXiv.2602.04105 | 3 | 1 | false | null | arXiv.org | 0.4641 |
a7387bd0d832dc92b6f08c7eab49fcfe50a4131b15e0a8adf40bfd38d158975c | [
"arxiv",
"semantic_scholar"
] | DALI: A Workload-Aware Offloading Framework for Efficient MoE Inference on Local PCs | Mixture of Experts (MoE) architectures significantly enhance the capacity of LLMs without proportional increases in computation, but at the cost of a vast parameter size. Offloading MoE expert parameters to host memory and leveraging both CPU and GPU computation has recently emerged as a promising direction to support ... | [
"Zeyu Zhu",
"Gang Li",
"Peisong Wang",
"Zitao Mo",
"Minnan Pei",
"Zhuoran Song",
"Xiaoyao Liang",
"Jian Cheng"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2026-02-03T00:00:00 | https://arxiv.org/abs/2602.03495 | https://arxiv.org/pdf/2602.03495v1 | 2602.03495 | 10.48550/arXiv.2602.03495 | 0 | 0 | false | null | arXiv.org | 0.4629 |
7072c3a0e8235fd9d9e83d75449d82d257ed84695edcb558b43eed02004e2fc7 | [
"arxiv",
"semantic_scholar"
] | Certain Head, Uncertain Tail: Expert-Sample for Test-Time Scaling in Fine-Grained MoE | Test-time scaling improves LLM performance by generating multiple candidate solutions, yet token-level sampling requires temperature tuning that trades off diversity against stability. Fine-grained MoE, featuring hundreds of well-trained experts per layer and multi-expert activation per token, offers an unexplored alte... | [
"Yuanteng Chen",
"Peisong Wang",
"Nanxin Zeng",
"Yuantian Shao",
"Shuang Qiu",
"Gang Li",
"Jing Liu",
"Jian Cheng"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-02T00:00:00 | https://arxiv.org/abs/2602.02443 | https://arxiv.org/pdf/2602.02443v2 | 2602.02443 | 10.48550/arXiv.2602.02443 | 1 | 0 | false | null | arXiv.org | 0.4618 |
22f189e56be4b9d48a6cbdc6b44c485b6ae1c85229f52d687186bdcfc828a6d7 | [
"arxiv",
"semantic_scholar"
] | Dynamic Expert Sharing: Decoupling Memory from Parallelism in Mixture-of-Experts Diffusion LLMs | Among parallel decoding paradigms, diffusion large language models (dLLMs) have emerged as a promising candidate that balances generation quality and throughput. However, their integration with Mixture-of-Experts (MoE) architectures is constrained by an expert explosion: as the number of tokens generated in parallel in... | [
"Hao Mark Chen",
"Zhiwen Mo",
"Royson Lee",
"Qianzhou Wang",
"Da Li",
"Shell Xu Hu",
"Wayne Luk",
"Timothy Hospedales",
"Hongxiang Fan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-31T00:00:00 | https://arxiv.org/abs/2602.00879 | https://arxiv.org/pdf/2602.00879v1 | 2602.00879 | 10.48550/arXiv.2602.00879 | 1 | 1 | false | null | arXiv.org | 0.4595 |
16a7898c1008a48fa4b56c0a8280ec753948998f80765541b523c44d5443c1ee | [
"arxiv",
"semantic_scholar"
] | L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts | Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central role in determining expert specialization and overall model performance. However, many modern MoE systems still adopt linear routers in raw high-dimensional representation spaces... | [
"Minghao Yang",
"Ren Togo",
"Guang Li",
"Takahiro Ogawa",
"Miki Haseyama"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.21349 | https://arxiv.org/pdf/2601.21349v2 | 2601.21349 | 10.48550/arXiv.2601.21349 | 0 | 0 | false | null | arXiv.org | 0.4572 |
bbe46306bd85ad4b4ee4bf447514b2e8f0a0e9577759177f95cbb88a9d7683c7 | [
"arxiv",
"semantic_scholar"
] | BrainStack: Neuro-MoE with Functionally Guided Expert Routing for EEG-Based Language Decoding | Decoding linguistic information from electroencephalography (EEG) remains challenging due to the brain's distributed and nonlinear organization. We present BrainStack, a functionally guided neuro-mixture-of-experts (Neuro-MoE) framework that models the brain's modular functional architecture through anatomically partit... | [
"Ziyi Zhao",
"Jinzhao Zhou",
"Xiaowei Jiang",
"Beining Cao",
"Wenhao Ma",
"Yang Shen",
"Ren Li",
"Yu-Kai Wang",
"Chin-teng Lin"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.21148 | https://arxiv.org/pdf/2601.21148v1 | 2601.21148 | 10.48550/arXiv.2601.21148 | 0 | 0 | false | null | arXiv.org | 0.4572 |
0f8d0c64456ade09e8b431c207c62401c24df950d886db75485cb262cefa66c8 | [
"arxiv",
"semantic_scholar"
] | Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers | Transformer-based models have recently made significant advances in accurate time-series forecasting, but even these architectures struggle to scale efficiently while capturing long-term temporal dynamics. Mixture-of-Experts (MoE) layers are a proven solution to scaling problems in natural language processing. However,... | [
"Evandro S. Ortigossa",
"Eran Segal"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.21641 | https://arxiv.org/pdf/2601.21641v2 | 2601.21641 | 10.48550/arXiv.2601.21641 | 2 | 0 | false | null | arXiv.org | 0.4572 |
f54a138161fccab0d8f3dbcc8c3d5c4763f02c9d444e40798a750445e8705dfe | [
"arxiv",
"semantic_scholar"
] | ShardMemo: Masked MoE Routing for Sharded Agentic LLM Memory | Agentic large language model (LLM) systems rely on external memory for long-horizon state and concurrent multi-agent execution, but centralized indexes and heuristic partitions become bottlenecks as memory volume and parallel access grow. We present ShardMemo, a budgeted tiered memory service with Tier A per-agent work... | [
"Yang Zhao",
"Chengxiao Dai",
"Yue Xiu",
"Mengying Kou",
"Yuliang Zheng",
"Dusit Niyato"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.21545 | https://arxiv.org/pdf/2601.21545v1 | 2601.21545 | 10.48550/arXiv.2601.21545 | 0 | 0 | false | null | arXiv.org | 0.4572 |
5b9301d4ceb134c4f1c2f3c609e384ff79cbd7732b3bccc3d9498d01e918c44c | [
"arxiv",
"semantic_scholar"
] | Dynamic Multi-Expert Projectors with Stabilized Routing for Multilingual Speech Recognition | Recent advances in LLM-based ASR connect frozen speech encoders with Large Language Models (LLMs) via lightweight projectors. While effective in monolingual settings, a single projector struggles to capture the diverse acoustic-to-semantic mappings required for multilingual ASR. To address this, we propose SMEAR-MoE, a... | [
"Isha Pandey",
"Ashish Mittal",
"Vartul Bahuguna",
"Ganesh Ramakrishnan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-01-27T00:00:00 | https://arxiv.org/abs/2601.19451 | https://arxiv.org/pdf/2601.19451v1 | 2601.19451 | 10.48550/arXiv.2601.19451 | 1 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.4549 |
779454640ef85adfd6089ba2b7e2352f9cfebf42d4473e58d691729b7d996ed9 | [
"arxiv",
"semantic_scholar"
] | $\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts | The Mixture of Experts (MoE) selects a few feed-forward networks (FFNs) per token, achieving an effective trade-off between computational cost and performance. In conventional MoE, each expert is treated as entirely independent, and experts are combined in a discrete space. As a result, when the number of experts incre... | [
"Shota Takashiro",
"Takeshi Kojima",
"Shohei Taniguchi",
"Yusuke Iwasawa",
"Yutaka Matsuo"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-01-25T00:00:00 | https://arxiv.org/abs/2601.17680 | https://arxiv.org/pdf/2601.17680v1 | 2601.17680 | 10.48550/arXiv.2601.17680 | 0 | 0 | false | null | arXiv.org | 0.4526 |
388ef9690bb443c6cac313ba44813aad4dd1684fafbb9c15d15846575b1ed745 | [
"arxiv",
"semantic_scholar"
] | Split-on-Share: Mixture of Sparse Experts for Task-Agnostic Continual Learning | Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capa... | [
"Fatema Siddika",
"Md Anwar Hossen",
"Tanwi Mallick",
"Ali Jannesari"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-24T00:00:00 | https://arxiv.org/abs/2601.17616 | https://arxiv.org/pdf/2601.17616v2 | 2601.17616 | 10.48550/arXiv.2601.17616 | 0 | 0 | false | null | arXiv.org | 0.4515 |
c68300df7d856da59b5d9c50d52ce3e0d5d84025c170ed9902ac83e80f3a3a80 | [
"arxiv",
"semantic_scholar"
] | Mixture-of-Experts Models in Vision: Routing, Optimization, and Generalization | Mixture-of-Experts (MoE) architectures enable conditional computation by routing inputs to multiple expert subnetworks and are often motivated as a mechanism for scaling large language models. In this project, we instead study MoE behavior in an image classification setting, focusing on predictive performance, expert u... | [
"Adam Rokah",
"Daniel Veress",
"Caleb Caulk",
"Sourav Sharan"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2026-01-21T00:00:00 | https://arxiv.org/abs/2601.15021 | https://arxiv.org/pdf/2601.15021v1 | 2601.15021 | 10.48550/arXiv.2601.15021 | 0 | 0 | true | https://github.com/moe-project-uu/mixture-of-experts-project | arXiv.org | 0.6924 |
1e9b467e183ce8590e7f6265dbe578374760c88ef85f3ecb097a50ae6685088f | [
"arxiv",
"semantic_scholar"
] | Understanding Multilingualism in Mixture-of-Experts LLMs: Routing Mechanism, Expert Specialization, and Layerwise Steering | Mixture-of-Experts (MoE) architectures have shown strong multilingual capabilities, yet the internal mechanisms underlying performance gains and cross-language differences remain insufficiently understood. In this work, we conduct a systematic analysis of MoE models, examining routing behavior and expert specialization... | [
"Yuxin Chen",
"Zhengzhou Cai",
"Xiangtian Ji",
"Weixiang Zhao",
"An Zhang",
"Xiang Wang",
"Tat-Seng Chua"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-01-20T00:00:00 | https://arxiv.org/abs/2601.14050 | https://arxiv.org/pdf/2601.14050v1 | 2601.14050 | 10.48550/arXiv.2601.14050 | 5 | 0 | true | https://github.com/conctsai/Multilingualism-in-Mixture-of-Experts-LLMs | arXiv.org | 0.6906 |
4a633722ab848010d8eddea89ed516e0ae257ab21164a41f334c5ccd38fb2c20 | [
"arxiv",
"semantic_scholar"
] | EMoE: Eigenbasis-Guided Routing for Mixture-of-Experts | The relentless scaling of deep learning models has led to unsustainable computational demands, positioning Mixture-of-Experts (MoE) architectures as a promising path towards greater efficiency. However, MoE models are plagued by two fundamental challenges: 1) a load imbalance problem known as the``rich get richer" phen... | [
"Anzhe Cheng",
"Shukai Duan",
"Shixuan Li",
"Chenzhong Yin",
"Mingxi Cheng",
"Shahin Nazarian",
"Paul Thompson",
"Paul Bogdan"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2026-01-17T00:00:00 | https://arxiv.org/abs/2601.12137 | https://arxiv.org/pdf/2601.12137v1 | 2601.12137 | 10.48550/arXiv.2601.12137 | 2 | 0 | true | https://github.com/Belis0811/EMoE | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.6853 |
1f6ee9eeb79bac95fe2c8633686439b3fe4c4f13ff649bee7d5fa3de9bc75cf6 | [
"arxiv",
"semantic_scholar"
] | MoST: Mixing Speech and Text with Modality-Aware Mixture of Experts | We present MoST (Mixture of Speech and Text), a novel multimodal large language model that seamlessly integrates speech and text processing through our proposed Modality-Aware Mixture of Experts (MAMoE) architecture. While current multimodal models typically process diverse modality representations with identical param... | [
"Yuxuan Lou",
"Kai Yang",
"Yang You"
] | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SD"
] | [
"Computer Science"
] | 2026-01-15T00:00:00 | https://arxiv.org/abs/2601.10272 | https://arxiv.org/pdf/2601.10272v1 | 2601.10272 | 10.48550/arXiv.2601.10272 | 1 | 0 | true | https://github.com/NUS-HPC-AI-Lab/MoST | arXiv.org | 0.6818 |
fe13e99e36545b2df993d7bb01ea5b52cbdcceb71c6bb4c3b916acb1bb30027f | [
"arxiv",
"semantic_scholar"
] | Horseshoe Mixtures-of-Experts (HS-MoE) | Horseshoe mixtures-of-experts (HS-MoE) models provide a Bayesian framework for sparse expert selection in mixture-of-experts architectures. We combine the horseshoe prior's adaptive global-local shrinkage with input-dependent gating, yielding data-adaptive sparsity in expert usage. Our primary methodological contributi... | [
"Nick Polson",
"Vadim Sokolov"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2026-01-14T00:00:00 | https://arxiv.org/abs/2601.09043 | https://arxiv.org/pdf/2601.09043v1 | 2601.09043 | 10.48550/arXiv.2601.09043 | 0 | 0 | false | null | arXiv.org | 0.44 |
17f5d046f53c6df60b02b0ffd68fbc13e0a676372a171e85deddd52ab67ee18a | [
"arxiv",
"semantic_scholar"
] | TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts | Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a prom... | [
"Yu Xu",
"Hongbin Yan",
"Juan Cao",
"Yiji Cheng",
"Tiankai Hang",
"Runze He",
"Zijin Yin",
"Shiyi Zhang",
"Yuxin Zhang",
"Jintao Li",
"Chunyu Wang",
"Qinglin Lu",
"Tong-Yee Lee",
"Fan Tang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-12T00:00:00 | https://arxiv.org/abs/2601.08881 | https://arxiv.org/pdf/2601.08881v2 | 2601.08881 | 10.48550/arXiv.2601.08881 | 6 | 0 | false | null | arXiv.org | 0.4377 |
4df1244db8a0122524ad699eb57bc3ed7ace11d10ad3e529ee38d18abfbbfb74 | [
"arxiv",
"semantic_scholar"
] | MoE-DisCo:Low Economy Cost Training Mixture-of-Experts Models | Training large-scale Mixture-of-Experts (MoE) models typically requires high-memory, high-bandwidth GPUs (e.g., A100), and their high cost has become a major barrier to large-model training. In contrast, affordable hardware is low-cost but constrained by memory capacity and bandwidth, making it unsuitable for direct LL... | [
"Xin Ye",
"Daning Cheng",
"Boyang Zhang",
"Yunquan Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-11T00:00:00 | https://arxiv.org/abs/2601.06857 | https://arxiv.org/pdf/2601.06857v1 | 2601.06857 | 10.48550/arXiv.2601.06857 | 1 | 0 | false | null | arXiv.org | 0.4366 |
dbbb0d1c7a8cd5e006ab422b342f0ee25ef5c466bfcb5fc06b43e97c9ad4b6b4 | [
"arxiv",
"semantic_scholar"
] | Spectral Manifold Regularization for Stable and Modular Routing in Deep MoE Architectures | Mixture of Experts (MoE) architectures enable efficient scaling of neural networks but suffer from expert collapse, where routing converges to a few dominant experts. This reduces model capacity and causes catastrophic interference during adaptation. We propose the Spectrally-Regularized Mixture of Experts (SR-MoE), wh... | [
"Ibrahim Delibasoglu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-07T00:00:00 | https://arxiv.org/abs/2601.03889 | https://arxiv.org/pdf/2601.03889v1 | 2601.03889 | 10.48550/arXiv.2601.03889 | 1 | 0 | false | null | arXiv.org | 0.432 |
e741de7adc9cf1ffd1ce278f7d31e6d9107befa4ac8dd31a0352a5162858de1f | [
"arxiv",
"semantic_scholar"
] | Routing by Analogy: kNN-Augmented Expert Assignment for Mixture-of-Experts | Mixture-of-Experts (MoE) architectures scale large language models efficiently by employing a parametric ``router'' to dispatch tokens to a sparse subset of experts. Typically, this router is trained once and then frozen, rendering routing decisions brittle under distribution shifts. We address this limitation by intro... | [
"Boxuan Lyu",
"Soichiro Murakami",
"Hidetaka Kamigaito",
"Peinan Zhang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-05T00:00:00 | https://arxiv.org/abs/2601.02144 | https://arxiv.org/pdf/2601.02144v2 | 2601.02144 | 10.48550/arXiv.2601.02144 | 0 | 0 | false | null | arXiv.org | 0.4297 |
cac6ebee88d7f3efa51f26446aae913568067d99fb4b1b453755de172daa39d7 | [
"arxiv",
"semantic_scholar"
] | MambaFormer: Token-Level Guided Routing Mixture-of-Experts for Accurate and Efficient Clinical Assistance | The deployment of large language models (LLMs) in real-world clinical applications is constrained by the fundamental trade-off between computational cost and the efficiency of linear-time models. To address this, we propose an LLM-based MambaFormer hybrid Mixture-of-Experts (MoE) framework for efficient medical questio... | [
"Hamad Khan",
"Saddam Hussain Khan"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-01-03T00:00:00 | https://arxiv.org/abs/2601.01260 | https://arxiv.org/pdf/2601.01260v1 | 2601.01260 | 10.48550/arXiv.2601.01260 | 1 | 0 | false | null | arXiv.org | 0.4274 |
1230e5d4bb15257effde3798385f7823755c1c82a72fa9012a509716055fcb86 | [
"arxiv",
"semantic_scholar"
] | HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts | While federated learning (FL) enables fine-tuning of large language models (LLMs) without compromising data privacy, the substantial size of an LLM renders on-device training impractical for resource-constrained clients, such as mobile devices. Thus, Mixture-of-Experts (MoE) models have emerged as a computation-efficie... | [
"Zihan Fang",
"Zheng Lin",
"Senkang Hu",
"Yanan Ma",
"Yihang Tao",
"Yiqin Deng",
"Xianhao Chen",
"Yuguang Fang"
] | [
"cs.LG",
"cs.AI",
"cs.NI"
] | [
"Computer Science"
] | 2026-01-02T00:00:00 | https://arxiv.org/abs/2601.00583 | https://arxiv.org/pdf/2601.00583v1 | 2601.00583 | 10.48550/arXiv.2601.00583 | 9 | 0 | false | null | arXiv.org | 0.4263 |
6401f98dfcb0f4abf7d136339cbcd8e00bed90606d5f420e1c3ba3cc6b279407 | [
"arxiv",
"semantic_scholar"
] | FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing | Mixture-of-Experts (MoE) models enable scalable neural networks through conditional computation, offering enhanced effectiveness and efficiency for next-generation wireless communications. However, deploying MoE with federated learning (FL) over wireless and IoT edge networks faces two critical challenges: 1) resource-... | [
"Boyang Zhang",
"Xiaobing Chen",
"Songyang Zhang",
"Shuai Zhang",
"Xiangwei Zhou",
"Jian Zhang",
"Mingxuan Sun"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-12-28T00:00:00 | https://arxiv.org/abs/2512.23070 | https://arxiv.org/pdf/2512.23070v2 | 2512.23070 | 10.48550/arXiv.2512.23070 | 1 | 0 | false | null | arXiv.org | 0.4205 |
9b1bb34ffe1bdd6a02ba7b6629865ee10596a4804d3517ea32ed10545c620fce | [
"arxiv",
"semantic_scholar"
] | Text-Routed Sparse Mixture-of-Experts Model with Explanation and Temporal Alignment for Multi-Modal Sentiment Analysis | Human-interaction-involved applications underscore the need for Multi-modal Sentiment Analysis (MSA). Although many approaches have been proposed to address the subtle emotions in different modalities, the power of explanations and temporal alignments is still underexplored. Thus, this paper proposes the Text-routed sp... | [
"Dongning Rao",
"Yunbiao Zeng",
"Zhihua Jiang",
"Jujian Lv"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-12-28T00:00:00 | https://arxiv.org/abs/2512.22741 | https://arxiv.org/pdf/2512.22741v1 | 2512.22741 | 10.48550/arXiv.2512.22741 | 0 | 0 | false | null | arXiv.org | 0.4205 |
dfcd97d4cfd46aa071450c96d6d51cc6fab58b715790856086319ab493078c03 | [
"arxiv",
"semantic_scholar"
] | Hybrid Quantum-Classical Mixture of Experts: Unlocking Topological Advantage via Interference-Based Routing | The Mixture-of-Experts (MoE) architecture has emerged as a powerful paradigm for scaling deep learning models, yet it is fundamentally limited by challenges such as expert imbalance and the computational complexity of classical routing mechanisms. This paper investigates the potential of Quantum Machine Learning (QML) ... | [
"Reda Heddad",
"Lamiae Bouanane"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-12-25T00:00:00 | https://arxiv.org/abs/2512.22296 | https://arxiv.org/pdf/2512.22296v1 | 2512.22296 | 10.48550/arXiv.2512.22296 | 0 | 0 | true | https://github.com/RH2004/QMOE | arXiv.org | 0.6446 |
c6654c2b0914954b53a1d886d831eccbf8d75b06db012c8dc0e60601a4064f2d | [
"arxiv",
"semantic_scholar"
] | Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism | The mixture-of-experts (MoE) architecture scales model size with sublinear computational increase but suffers from memory-intensive inference due to KV caches and sparse expert activation. Recent disaggregated expert parallelism (DEP) distributes attention and experts to dedicated GPU groups but lacks support for share... | [
"Xinglin Pan",
"Shaohuai Shi",
"Wenxiang Lin",
"Yuxin Wang",
"Zhenheng Tang",
"Wei Wang",
"Xiaowen Chu"
] | [
"cs.DC",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-25T00:00:00 | https://arxiv.org/abs/2512.21487 | https://arxiv.org/pdf/2512.21487v1 | 2512.21487 | 10.48550/arXiv.2512.21487 | 2 | 0 | false | null | arXiv.org | 0.4171 |
3023f80e009287d851b250be96d9c16ae2ba218b8fc62400bdd96d54591ab238 | [
"arxiv",
"semantic_scholar"
] | MoE-DiffuSeq: Enhancing Long-Document Diffusion Models with Sparse Attention and Mixture of Experts | We propose \textbf{MoE-DiffuSeq}, a diffusion-based framework for efficient long-form text generation that integrates sparse attention with a Mixture-of-Experts (MoE) architecture. Existing sequence diffusion models suffer from prohibitive computational and memory costs when scaling to long documents, largely due to de... | [
"Alexandros Christoforos",
"Chadbourne Davis"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-12-23T00:00:00 | https://arxiv.org/abs/2512.20604 | https://arxiv.org/pdf/2512.20604v2 | 2512.20604 | 10.48550/arXiv.2512.20604 | 1 | 0 | false | null | arXiv.org | 0.4148 |
16d417c1ee13fdeba3ccf3a93950d9983f92e5e8334805d7bca977060dbd6585 | [
"arxiv",
"semantic_scholar"
] | How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts | Finding the optimal configuration of Sparse Mixture-ofExperts (SMoE) that maximizes semantic differentiation among experts is essential for exploiting the full potential of MoE architectures. However, existing SMoE frameworks either heavily rely on hyperparameter tuning or overlook the importance of diversifying semant... | [
"Sumin Park",
"Noseong Park"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-21T00:00:00 | https://arxiv.org/abs/2512.19765 | https://arxiv.org/pdf/2512.19765v1 | 2512.19765 | 10.48550/arXiv.2512.19765 | 0 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.4125 |
09cdadce27619dd6ee8ccadbe388198877ef454c248dcfb03fc4b5c390151476 | [
"arxiv",
"semantic_scholar"
] | Secret mixtures of experts inside your LLM | Despite being one of the earliest neural network layers, the Multilayer Perceptron (MLP) is arguably one of the least understood parts of the transformer architecture due to its dense computation and lack of easy visualization. This paper seeks to understand the MLP layers in dense LLM models by hypothesizing that thes... | [
"Enric Boix-Adsera"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-12-20T00:00:00 | https://arxiv.org/abs/2512.18452 | https://arxiv.org/pdf/2512.18452v1 | 2512.18452 | 10.48550/arXiv.2512.18452 | 0 | 0 | false | null | arXiv.org | 0.4114 |
7ddffe35f92b69a66cea873119849100e8a0a6fbedc9385bf1c570b2a1bc9993 | [
"arxiv",
"semantic_scholar"
] | MoE Pathfinder: Trajectory-driven Expert Pruning | Mixture-of-experts (MoE) architectures used in large language models (LLMs) achieve state-of-the-art performance across diverse tasks yet face practical challenges such as deployment complexity and low activation efficiency. Expert pruning has thus emerged as a promising solution to reduce computational overhead and si... | [
"Xican Yang",
"Yuanhe Tian",
"Yan Song"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-12-20T00:00:00 | https://arxiv.org/abs/2512.18425 | https://arxiv.org/pdf/2512.18425v1 | 2512.18425 | 10.48550/arXiv.2512.18425 | 3 | 0 | false | null | arXiv.org | 0.4114 |
3c01cfba72f29b4b35903cb2a0a49c04903aaa98d824c34d8846d248b196f7f3 | [
"arxiv",
"semantic_scholar"
] | Janus: Disaggregating Attention and Experts for Scalable MoE Inference | Serving large Mixture-of-Experts (MoE) models is challenging because of their large memory footprints, heterogeneous resource demands, and highly dynamic inference workloads. Most existing MoE inference systems deploy the entire model as a monolithic unit, forcing attention and MoE layers to share the same resource con... | [
"Zhexiang Zhang",
"Ye Wang",
"Yumiao Zhao",
"Jiayu Xiao",
"Qianjing Yang",
"Xiangyu Wang",
"Jingzhe Jiang",
"Qizhen Weng",
"Ruichuan Chen",
"Shaohuai Shi",
"Adel N. Toosi",
"Yin Chen",
"Minchen Yu"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-12-15T00:00:00 | https://arxiv.org/abs/2512.13525 | https://arxiv.org/pdf/2512.13525v3 | 2512.13525 | 10.48550/arXiv.2512.13525 | 4 | 0 | false | null | arXiv.org | 0.4056 |
ace7e9297e5cc28c866ad86f9709a6ec76f2ef420d4813e716721c089e252336 | [
"arxiv",
"semantic_scholar"
] | Efficient MoE Serving in the Memory-Bound Regime: Balance Activated Experts, Not Tokens | Expert Parallelism (EP) permits Mixture of Experts (MoE) models to scale beyond a single GPU. To address load imbalance across GPUs in EP, existing approaches aim to balance the number of tokens each GPU processes. Surprisingly, we find that this objective degrades performance rather than improving it when processing i... | [
"Yanpeng Yu",
"Haiyue Ma",
"Krish Agarwal",
"Nicolai Oswald",
"Qijing Huang",
"Hugo Linsenmaier",
"Chunhui Mei",
"Ritchie Zhao",
"Ritika Borkar",
"Bita Darvish Rouhani",
"David Nellans",
"Ronny Krashinsky",
"Anurag Khandelwal"
] | [
"cs.DC",
"cs.AR"
] | [
"Computer Science"
] | 2025-12-10T00:00:00 | https://arxiv.org/abs/2512.09277 | https://arxiv.org/pdf/2512.09277v1 | 2512.09277 | 10.48550/arXiv.2512.09277 | 2 | 0 | false | null | arXiv.org | 0.3999 |
7732d4b8b3df31a35e92a6c4fb130f5b7f0611abfad2f274df4d5cf42e530ce7 | [
"arxiv",
"semantic_scholar"
] | Each Prompt Matters: Scaling Reinforcement Learning Without Wasting Rollouts on Hundred-Billion-Scale MoE | We present CompassMax-V3-Thinking, a hundred-billion-scale MoE reasoning model trained with a new RL framework built on one principle: each prompt must matter. Scaling RL to this size exposes critical inefficiencies-zero-variance prompts that waste rollouts, unstable importance sampling over long horizons, advantage in... | [
"Anxiang Zeng",
"Haibo Zhang",
"Hailing Zhang",
"Kaixiang Mo",
"Liang Yao",
"Ling Hu",
"Long Zhang",
"Shuman Liu",
"Shuyi Xie",
"Yanshi Li",
"Yizhang Chen",
"Yuepeng Sheng",
"Yuwei Huang",
"Zhaochen Xu",
"Zhiqiang Zhou",
"Ziqin Liew"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-12-08T00:00:00 | https://arxiv.org/abs/2512.07710 | https://arxiv.org/pdf/2512.07710v1 | 2512.07710 | 10.48550/arXiv.2512.07710 | 1 | 0 | false | null | arXiv.org | 0.3976 |
931b940202eb676b0b18bf356c422479d8f0dfd2492f65f289effd7b39985506 | [
"arxiv",
"semantic_scholar"
] | Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks | The sparse activation mechanism of mixture of experts (MoE) model empowers edge intelligence with enhanced training efficiency and reduced computational resource consumption. However, traditional token routing in distributed MoE training faces significant challenges in resource-constrained edge networks characterized b... | [
"Long Shi",
"Bingyan Ou",
"Kang Wei",
"Weihao Zhu",
"Zhe Wang",
"Zhiyong Chen"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-12-07T00:00:00 | https://arxiv.org/abs/2512.06784 | https://arxiv.org/pdf/2512.06784v3 | 2512.06784 | 10.48550/arXiv.2512.06784 | 2 | 0 | false | null | IEEE Transactions on Vehicular Technology | 0.3965 |
42d19b42514972a00fa86949fa099b03acadfa34c3560766855670384a939504 | [
"arxiv",
"semantic_scholar"
] | Statistic-Augmented, Decoupled MoE Routing and Aggregating in Autonomous Driving | Autonomous driving (AD) scenarios are inherently complex and diverse, posing significant challenges for a single deep learning model to effectively cover all possible conditions, such as varying weather, traffic densities, and road types. Large Model (LM)-Driven Mixture of Experts (MoE) paradigm offers a promising solu... | [
"Wei-Bin Kou",
"Guangxu Zhu",
"Jingreng Lei",
"Chen Zhang",
"Yik-Chung Wu",
"Jianping Wang"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-12-07T00:00:00 | https://arxiv.org/abs/2512.06664 | https://arxiv.org/pdf/2512.06664v1 | 2512.06664 | 10.48550/arXiv.2512.06664 | 0 | 0 | false | null | arXiv.org | 0.3965 |
a9f36bdb1d4cfa210dd4df13f946e841fa50c1c1ef69b6da3a4abfdc2e40dc5a | [
"arxiv",
"semantic_scholar"
] | OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference | Mixture-of-Experts (MoE), while offering significant advantages as a Large Language Model (LLM) architecture, faces substantial challenges when deployed on low-cost edge devices with tight memory constraints. Expert offloading mitigates this issue by storing expert parameters in CPU memory and caching a subset of popul... | [
"Liujianfu Wang",
"Yuyang Du",
"Yuchen Pan",
"Soung Chang Liew",
"Jiacheng Liu",
"Kexin Chen"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2025-12-03T00:00:00 | https://arxiv.org/abs/2512.03927 | https://arxiv.org/pdf/2512.03927v1 | 2512.03927 | 10.48550/arXiv.2512.03927 | 0 | 0 | false | null | arXiv.org | 0.3919 |
fc3d88b3aa1b79c80ba43ffcc0df462de85266d6e2e95830ecbdf7c225c5600c | [
"arxiv",
"semantic_scholar"
] | A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models | In large-scale AI training, Sparse Mixture-of-Experts (s-MoE) layers enable scaling by activating only a small subset of experts per token. An operational challenge in this design is load balancing: routing tokens to minimize the number of idle experts, which is important for the efficient utilization of costly GPUs an... | [
"X. Y. Han",
"Yuan Zhong"
] | [
"math.OC",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2025-12-03T00:00:00 | https://arxiv.org/abs/2512.03915 | https://arxiv.org/pdf/2512.03915v3 | 2512.03915 | 10.48550/arXiv.2512.03915 | 1 | 0 | false | null | arXiv.org | 0.3919 |
6a258fcdaaaa9aebceb18605f4754bffb6c061c0b583ff44e9ddead2028437fd | [
"arxiv",
"semantic_scholar"
] | Exploiting the Experts: Unauthorized Compression in MoE-LLMs | Mixture-of-Experts (MoE) architectures are increasingly adopted in large language models (LLMs) for their scalability and efficiency. However, their modular structure introduces a unique vulnerability: adversaries can attempt to compress or repurpose models by pruning experts and cheaply fine-tuning the remainder, effe... | [
"Pinaki Prasad Guha Neogi",
"Ahmad Mohammadshirazi",
"Dheeraj Kulshrestha",
"Rajiv Ramnath"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-22T00:00:00 | https://arxiv.org/abs/2511.19480 | https://arxiv.org/pdf/2511.19480v1 | 2511.19480 | 10.48550/arXiv.2511.19480 | 1 | 0 | false | null | arXiv.org | 0.3793 |
099ecc68acddd44a69c80f723c4998e52e73c574ec28e8410fb24567aedf51dd | [
"arxiv",
"semantic_scholar"
] | Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference | Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying MoE models on a single, memory-limited GPU remains difficult because expert weights dominate the HBM footprint. Existing expert offloading and prefetching systems reduce the reside... | [
"Kexin Chu",
"Dawei Xiang",
"Zixu Shen",
"Yiwei Yang",
"Zecheng Liu",
"Wei Zhang"
] | [
"cs.PF",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-11-19T00:00:00 | https://arxiv.org/abs/2511.15015 | https://arxiv.org/pdf/2511.15015v3 | 2511.15015 | 10.48550/arXiv.2511.15015 | 1 | 0 | false | null | arXiv.org | 0.3758 |
16420223fb875466df3924b1d6c115ca1527bb818e901a088e7fc754977eefaf | [
"arxiv",
"semantic_scholar"
] | MoE-SpeQ: Speculative Quantized Decoding with Proactive Expert Prefetching and Offloading for Mixture-of-Experts | The immense memory requirements of state-of-the-art Mixture-of-Experts (MoE) models present a significant challenge for inference, often exceeding the capacity of a single accelerator. While offloading experts to host memory is a common solution, it introduces a severe I/O bottleneck over the PCIe bus, as the data-depe... | [
"Wenfeng Wang",
"Jiacheng Liu",
"Xiaofeng Hou",
"Xinfeng Xia",
"Peng Tang",
"Mingxuan Zhang",
"Chao Li",
"Minyi Guo"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-11-18T00:00:00 | https://arxiv.org/abs/2511.14102 | https://arxiv.org/pdf/2511.14102v1 | 2511.14102 | 10.48550/arXiv.2511.14102 | 3 | 0 | false | null | arXiv.org | 0.3747 |
b04dd1cb338035b28c2ef9c426691fed4c8186e58de0584182c5caa9436f8dc3 | [
"arxiv",
"semantic_scholar"
] | Orchestrating Heterogeneous Experts: A Scalable MoE Framework with Anisotropy-Preserving Fusion | In cross-border e-commerce, search relevance modeling faces the dual challenge of extreme linguistic diversity and fine-grained semantic nuances. Existing approaches typically rely on scaling up a single monolithic Large Language Model (LLM). However, our empirical analysis reveals that single models suffer from uneven... | [
"Ye Liu",
"Xu Chen",
"Wuji Chen",
"Mang Li"
] | [
"cs.IR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-11-18T00:00:00 | https://arxiv.org/abs/2602.00003 | https://arxiv.org/pdf/2602.00003v2 | 2602.00003 | 10.48550/arXiv.2602.00003 | 0 | 0 | true | null | null | 0.4428 |
fa3499c61e180b0cfbf393d18ccbda6cafb3678af006963e24d05b888da91ffc | [
"arxiv",
"semantic_scholar"
] | YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection | This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Precision (mAP) and Average Recall (AR) compared to a single YOLOv9-T model. | [
"Ori Meiraz",
"Sharon Shalev",
"Avishai Weizman"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-11-17T00:00:00 | https://arxiv.org/abs/2511.13344 | https://arxiv.org/pdf/2511.13344v4 | 2511.13344 | 10.48550/arXiv.2511.13344 | 1 | 0 | false | null | arXiv.org | 0.3735 |
2344e6adea3b65e525574797d2910aedbd1c55cf494b0855dc8e89dbde8d1a72 | [
"arxiv",
"semantic_scholar"
] | Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data | We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal understanding, reasoning, and generating. Based on the dense LLM, we build Uni-MoE-2.0-Omni from scratch through three core contributions: d... | [
"Yunxin Li",
"Xinyu Chen",
"Shenyuan Jiang",
"Haoyuan Shi",
"Zhenyu Liu",
"Xuanyu Zhang",
"Nanhao Deng",
"Zhenran Xu",
"Yicheng Ma",
"Meishan Zhang",
"Baotian Hu",
"Min Zhang"
] | [
"cs.CL",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-11-16T00:00:00 | https://arxiv.org/abs/2511.12609 | https://arxiv.org/pdf/2511.12609v2 | 2511.12609 | 10.48550/arXiv.2511.12609 | 16 | 1 | true | https://github.com/HITsz-TMG/Uni-MoE | arXiv.org | 0.5755 |
6485d4fccd3870f6b77a4862a3fc46460656be6469fb736510be8f1d14c45fd4 | [
"arxiv",
"semantic_scholar"
] | SAC-MoE: Reinforcement Learning with Mixture-of-Experts for Control of Hybrid Dynamical Systems with Uncertainty | Hybrid dynamical systems result from the interaction of continuous-variable dynamics with discrete events and encompass various systems such as legged robots, vehicles and aircrafts. Challenges arise when the system's modes are characterized by unobservable (latent) parameters and the events that cause system dynamics ... | [
"Leroy D'Souza",
"Akash Karthikeyan",
"Yash Vardhan Pant",
"Sebastian Fischmeister"
] | [
"cs.RO",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2025-11-15T00:00:00 | https://arxiv.org/abs/2511.12361 | https://arxiv.org/pdf/2511.12361v1 | 2511.12361 | 10.48550/arXiv.2511.12361 | 1 | 0 | false | null | arXiv.org | 0.3713 |
99de0ddc4e855f1b80148a763ddc9ba2d36f1c12f302e77c4927e242f03afaa8 | [
"arxiv",
"semantic_scholar"
] | ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization | Mixture-of-Experts (MoE) architectures expand model capacity by sparsely activating experts but face two core challenges: misalignment between router logits and each expert's internal structure leads to unstable routing and expert underutilization, and load imbalances create straggler bottlenecks. Standard solutions, s... | [
"Anzhe Cheng",
"Shukai Duan",
"Shixuan Li",
"Chenzhong Yin",
"Mingxi Cheng",
"Heng Ping",
"Tamoghna Chattopadhyay",
"Sophia I Thomopoulos",
"Shahin Nazarian",
"Paul Thompson",
"Paul Bogdan"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-11-14T00:00:00 | https://arxiv.org/abs/2511.10971 | https://arxiv.org/pdf/2511.10971v2 | 2511.10971 | 10.48550/arXiv.2511.10971 | 4 | 0 | false | null | arXiv.org | 0.3701 |
14ff08dceb360fea2d99af8aa5c1b61fe6e27848acc6d4a7db0b0b4b6546a8e3 | [
"arxiv",
"semantic_scholar"
] | Selective Sinkhorn Routing for Improved Sparse Mixture of Experts | Sparse Mixture-of-Experts (SMoE) models are scalable and computationally efficient, enabling large increases in model capacity with limited inference overhead. Existing SMoE methods often depend on auxiliary objectives, such as load-balancing loss and z-loss, or additional trainable components such as noisy gating. Whi... | [
"Duc Anh Nguyen",
"Huu Binh Ta",
"Nhuan Le Duc",
"Tan Minh Nguyen",
"Toan Tran"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-12T00:00:00 | https://arxiv.org/abs/2511.08972 | https://arxiv.org/pdf/2511.08972v2 | 2511.08972 | 10.48550/arXiv.2511.08972 | 0 | 0 | false | null | arXiv.org | 0.3678 |
d4196b1583d555be7e55d4a4ccdac140b71419be07d969a9ad8293c8be3b46ad | [
"arxiv",
"semantic_scholar"
] | Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs | Sparse Mixture-of-Experts (MoE) have been widely adopted in recent large language models since it can efficiently scale up the model capability without increasing the inference cost. However, evaluations on broad downstream tasks reveal a consistent suboptimality of the routers in existing MoE LLMs, which results in a ... | [
"Zhongyang Li",
"Ziyue Li",
"Tianyi Zhou"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-10T00:00:00 | https://arxiv.org/abs/2511.07419 | https://arxiv.org/pdf/2511.07419v2 | 2511.07419 | 10.48550/arXiv.2511.07419 | 1 | 1 | false | null | arXiv.org | 0.3655 |
a0c9b39f2b7bfef42feb98fd79457ee6fc1b978f037ac0806b700b76b682956a | [
"arxiv",
"semantic_scholar"
] | One Router to Route Them All: Homogeneous Expert Routing for Heterogeneous Graph Transformers | A common practice in heterogeneous graph neural networks (HGNNs) is to condition parameters on node/edge types, assuming types reflect semantic roles. However, this can cause overreliance on surface-level labels and impede cross-type knowledge transfer. We explore integrating Mixture-of-Experts (MoE) into HGNNs--a dire... | [
"Georgiy Shakirov",
"Albert Arakelov"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-10T00:00:00 | https://arxiv.org/abs/2511.07603 | https://arxiv.org/pdf/2511.07603v2 | 2511.07603 | 10.48550/arXiv.2511.07603 | 0 | 0 | false | null | arXiv.org | 0.3655 |
153109bdb7c31da34c653bdb6361d7320c022a3dddba4cb7e020d603b93286b4 | [
"arxiv",
"semantic_scholar"
] | Route Experts by Sequence, not by Token | Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed number of experts to all tokens, ignoring their varying complexity. Prior adaptive routing methods introduce additional modules and hyperparame... | [
"Tiansheng Wen",
"Yifei Wang",
"Aosong Feng",
"Long Ma",
"Xinyang Liu",
"Yifan Wang",
"Lixuan Guo",
"Bo Chen",
"Stefanie Jegelka",
"Chenyu You"
] | [
"cs.LG",
"cs.AI",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2025-11-09T00:00:00 | https://arxiv.org/abs/2511.06494 | https://arxiv.org/pdf/2511.06494v2 | 2511.06494 | 10.48550/arXiv.2511.06494 | 2 | 0 | true | https://github.com/Y-Research-SBU/SeqTopK | arXiv.org | 0.5631 |
a1767610f9530aa739c3e30d09644cbae641460bcd894c781b3097743d06b0d4 | [
"arxiv",
"semantic_scholar"
] | PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference | Mixture-of-Experts (MoE) models have shown strong potential in scaling language models efficiently by activating only a small subset of experts per input. However, their widespread deployment remains limited due to the high memory overhead associated with storing all expert parameters, particularly as the number of exp... | [
"Yushu Zhao",
"Zheng Wang",
"Minjia Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-06T00:00:00 | https://arxiv.org/abs/2511.04805 | https://arxiv.org/pdf/2511.04805v1 | 2511.04805 | 10.48550/arXiv.2511.04805 | 4 | 1 | false | null | arXiv.org | 0.3609 |
b07c6a5f8c0f15a8d3749076fad6b9d77ebc5aefd112cd27f987fdb5a8522ba2 | [
"arxiv",
"semantic_scholar"
] | Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining | An increasing number of LLMs employ Mixture-of-Experts (MoE) architectures where the feed-forward layer is replaced by a pool of experts and each token only activates a small subset of them. During autoregressive generation, these models often enter a memory-bound regime even for moderate batch sizes because the averag... | [
"Costin-Andrei Oncescu",
"Qingyang Wu",
"Wai Tong Chung",
"Robert Wu",
"Bryan Gopal",
"Junxiong Wang",
"Tri Dao",
"Ben Athiwaratkun"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-04T00:00:00 | https://arxiv.org/abs/2511.02237 | https://arxiv.org/pdf/2511.02237v1 | 2511.02237 | 10.48550/arXiv.2511.02237 | 3 | 1 | false | null | arXiv.org | 0.3586 |
04fb65c141d981f69671f96f47d9f767667c35ae872dcc68574515535ae0d069 | [
"arxiv",
"semantic_scholar"
] | CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing | Private large language model (LLM) inference based on cryptographic primitives offers a promising path towards privacy-preserving deep learning. However, existing frameworks only support dense LLMs like LLaMA-1 and struggle to scale to mixture-of-experts (MoE) architectures. The key challenge comes from securely evalua... | [
"Yifan Zhou",
"Tianshi Xu",
"Jue Hong",
"Ye Wu",
"Meng Li"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2025-11-03T00:00:00 | https://arxiv.org/abs/2511.01197 | https://arxiv.org/pdf/2511.01197v3 | 2511.01197 | 10.48550/arXiv.2511.01197 | 2 | 0 | true | https://github.com/PKU-SEC-Lab/CryptoMoE | arXiv.org | 0.5525 |
fa8c683ea312d5ff3518197b16240b02db46d57fa958b35c945a03f6f2715825 | [
"arxiv",
"semantic_scholar"
] | ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference | The expansion of large language models is increasingly limited by the constrained memory capacity of modern GPUs. To mitigate this, Mixture-of-Experts (MoE) architectures activate only a small portion of parameters during inference, significantly lowering both memory demand and computational overhead. However, conventi... | [
"Zixu Shen",
"Kexin Chu",
"Yifan Zhang",
"Dawei Xiang",
"Runxin Wu",
"Wei Zhang"
] | [
"cs.DC",
"cs.AI",
"cs.PF"
] | [
"Computer Science"
] | 2025-10-30T00:00:00 | https://arxiv.org/abs/2510.26730 | https://arxiv.org/pdf/2510.26730v1 | 2510.26730 | 10.48550/arXiv.2510.26730 | 3 | 0 | false | null | arXiv.org | 0.3529 |
49f2fcad29ebe4a9e8ff55b43ae51f6205a7ce60e42e0e9f75ea3d7466434d97 | [
"arxiv",
"semantic_scholar"
] | Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance | Mixture-of-Experts (MoE) has emerged as a powerful paradigm for scaling model capacity while preserving computational efficiency. Despite its notable success in large language models (LLMs), existing attempts to apply MoE to Diffusion Transformers (DiTs) have yielded limited gains. We attribute this gap to fundamental ... | [
"Yujie Wei",
"Shiwei Zhang",
"Hangjie Yuan",
"Yujin Han",
"Zhekai Chen",
"Jiayu Wang",
"Difan Zou",
"Xihui Liu",
"Yingya Zhang",
"Yu Liu",
"Hongming Shan"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-10-28T00:00:00 | https://arxiv.org/abs/2510.24711 | https://arxiv.org/pdf/2510.24711v2 | 2510.24711 | 10.48550/arXiv.2510.24711 | 16 | 2 | true | https://github.com/ali-vilab/ProMoE | arXiv.org | 0.5419 |
931a2a5dd445188983c1e9800df854cd09a854647a660f8c0a878f7d23b3e11c | [
"arxiv",
"semantic_scholar"
] | MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting | Recent advances in dynamic scene reconstruction have significantly benefited from 3D Gaussian Splatting, yet existing methods show inconsistent performance across diverse scenes, indicating no single approach effectively handles all dynamic challenges. To overcome these limitations, we propose Mixture of Experts for Dy... | [
"In-Hwan Jin",
"Hyeongju Mun",
"Joonsoo Kim",
"Kugjin Yun",
"Kyeongbo Kong"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.19210 | https://arxiv.org/pdf/2510.19210v2 | 2510.19210 | 10.48550/arXiv.2510.19210 | 1 | 0 | false | null | arXiv.org | 0.3438 |
cdb11ca280f47f85907cd0d9ff2b124ab9f20e26b1a2ea9531caaedfb5fad71f | [
"arxiv",
"semantic_scholar"
] | MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs | Mixture-of-Experts (MoE) models, the state-of-the-art in large-scale AI, achieve high quality by sparsely activating parameters. However, their reliance on routing between a few monolithic experts via a top-k mechanism creates a "quality cliff", offering only a few coarse-grained operating points. This inflexibility fo... | [
"Xinfeng Xia",
"Jiacheng Liu",
"Xiaofeng Hou",
"Peng Tang",
"Mingxuan Zhang",
"Wenfeng Wang",
"Chao Li"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.19366 | https://arxiv.org/pdf/2510.19366v1 | 2510.19366 | 10.48550/arXiv.2510.19366 | 1 | 0 | false | null | arXiv.org | 0.3438 |
af317171f31a701a31a7feb1cb798a10e005882cdb769546c0298617d98a88ff | [
"arxiv",
"semantic_scholar"
] | ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts | Mixture-of-Experts (MoE) architectures have emerged as a promising approach to scale Large Language Models (LLMs). MoE boosts the efficiency by activating a subset of experts per token. Recent works show that fine-grained experts substantially enriches the combinatorial flexibility of active experts and enhances model ... | [
"Zheyue Tan",
"Zhiyuan Li",
"Tao Yuan",
"Dong Zhou",
"Weilin Liu",
"Yueqing Zhuang",
"Yadong Li",
"Guowei Niu",
"Cheng Qin",
"Zhuyu Yao",
"Congyi Liu",
"Haiyang Xu",
"Boxun Li",
"Guohao Dai",
"Bo Zhao",
"Yu Wang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-20T00:00:00 | https://arxiv.org/abs/2510.17483 | https://arxiv.org/pdf/2510.17483v1 | 2510.17483 | 10.48550/arXiv.2510.17483 | 3 | 0 | false | null | arXiv.org | 0.3415 |
b7a0950ed6e315642fe010f1a5099a99552cd3d4f94bd846b3d093276279a44a | [
"arxiv",
"semantic_scholar"
] | L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts | The Mixture of Experts (MoE) architecture enables the scaling of Large Language Models (LLMs) to trillions of parameters by activating a sparse subset of weights for each input, maintaining constant computational cost during inference. Concurrently, Low-Rank Adaptation (LoRA) has emerged as a dominant technique for par... | [
"Shihao Ji",
"Zihui Song"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-19T00:00:00 | https://arxiv.org/abs/2510.17898 | https://arxiv.org/pdf/2510.17898v2 | 2510.17898 | 10.48550/arXiv.2510.17898 | 2 | 0 | false | null | arXiv.org | 0.3403 |
480373c7fff3e3edf3a34ad8de55b0c904b647809359a23cfbc572bd745ebd68 | [
"arxiv",
"semantic_scholar"
] | Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures | Knowledge Distillation (KD) accelerates training of large language models (LLMs) but poses intellectual property protection and LLM diversity risks. Existing KD detection methods based on self-identity or output similarity can be easily evaded through prompt engineering. We present a KD detection framework effective in... | [
"Pingzhi Li",
"Morris Yu-Chao Huang",
"Zhen Tan",
"Qingquan Song",
"Jie Peng",
"Kai Zou",
"Yu Cheng",
"Kaidi Xu",
"Tianlong Chen"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-10-19T00:00:00 | https://arxiv.org/abs/2510.16968 | https://arxiv.org/pdf/2510.16968v1 | 2510.16968 | 10.48550/arXiv.2510.16968 | 0 | 0 | true | https://github.com/unites-lab/shadow-moe | arXiv.org | 0.5259 |
e5da4ed5302283dbc91c0c7f4886d9d62b16c257ec299bab8bf7af43993aeda1 | [
"arxiv",
"semantic_scholar"
] | Input Domain Aware MoE: Decoupling Routing Decisions from Task Optimization in Mixture of Experts | Sparse Mixture of Experts (sMoE) has become a pivotal approach for scaling large vision-language models, offering substantial capacity while maintaining computational efficiency through dynamic, sparse activation of experts. However, existing routing mechanisms, typically based on similarity scoring, struggle to effect... | [
"Yongxiang Hua",
"Haoyu Cao",
"Zhou Tao",
"Bocheng Li",
"Zihao Wu",
"Chaohu Liu",
"Linli Xu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-18T00:00:00 | https://arxiv.org/abs/2510.16448 | https://arxiv.org/pdf/2510.16448v1 | 2510.16448 | 10.1145/3746027.3755754 | 2 | 0 | false | null | ACM Multimedia | 0.3392 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.