{"total":109,"rows":[{"id":"2409.02060","title":"OLMoE: Open Mixture-of-Experts Language Models","created":"2024-09-03","cited_by_count":301,"area":"moe"},{"id":"2410.05993","title":"Aria: An Open Multimodal Native Mixture-of-Experts Model","created":"2024-10-08","cited_by_count":138,"area":"moe"},{"id":"2407.04153","title":"Mixture of A Million Experts","created":"2024-07-04","cited_by_count":72,"area":"moe"},{"id":"2405.01029","title":"MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts","created":"2024-05-02","cited_by_count":70,"area":"moe"},{"id":"2405.16039","title":"MoEUT: Mixture-of-Experts Universal Transformers","created":"2024-05-25","cited_by_count":59,"area":"moe"},{"id":"2412.14711","title":"ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing","created":"2024-12-19","cited_by_count":54,"area":"moe"},{"id":"2501.12370","title":"Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models","created":"2025-01-21","cited_by_count":50,"area":"moe"},{"id":"2410.07524","title":"Upcycling Large Language Models into Mixture of Experts","created":"2024-10-10","cited_by_count":49,"area":"moe"},{"id":"2507.17702","title":"Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models","created":"2025-07-23","cited_by_count":45,"area":"moe"},{"id":"2502.07972","title":"Training Sparse Mixture Of Experts Text Embedding Models","created":"2025-02-11","cited_by_count":32,"area":"moe"},{"id":"2408.11304","title":"FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts","created":"2024-08-21","cited_by_count":27,"area":"moe"},{"id":"2410.19034","title":"Mixture of Parrots: Experts improve memorization more than reasoning","created":"2024-10-24","cited_by_count":21,"area":"moe"},{"id":"2512.14080","title":"SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations","created":"2025-12-16","cited_by_count":21,"area":"moe"},{"id":"2411.12364","title":"Ultra-Sparse Memory Network","created":"2024-11-19","cited_by_count":19,"area":"moe"},{"id":"2603.07685","title":"Scalable Training of Mixture-of-Experts Models with Megatron Core","created":"2026-03-08","cited_by_count":19,"area":"moe"},{"id":"2507.00029","title":"LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing","created":"2025-06-17","cited_by_count":18,"area":"moe"},{"id":"2508.01625","title":"EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models","created":"2025-08-03","cited_by_count":18,"area":"moe"},{"id":"2609.01343","title":"SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers","created":"2026-09-01","cited_by_count":18,"area":"moe"},{"id":"2504.14960","title":"MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core","created":"2025-04-21","cited_by_count":17,"area":"moe"},{"id":"2408.08274","title":"BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts","created":"2024-08-15","cited_by_count":16,"area":"moe"},{"id":"2403.08245","title":"Scattered Mixture-of-Experts Implementation","created":"2024-03-13","cited_by_count":15,"area":"moe"},{"id":"2409.06669","title":"DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models","created":"2024-09-10","cited_by_count":15,"area":"moe"},{"id":"2412.00099","title":"Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference","created":"2024-11-27","cited_by_count":14,"area":"moe"},{"id":"2502.03009","title":"Scaling Laws for Upcycling Mixture-of-Experts Language Models","created":"2025-02-05","cited_by_count":13,"area":"moe"},{"id":"2601.20205","title":"Hyperparameter Transfer with Mixture-of-Expert Layers","created":"2026-01-28","cited_by_count":13,"area":"moe"},{"id":"2509.16882","title":"Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation","created":"2025-09-21","cited_by_count":10,"area":"moe"},{"id":"2509.23678","title":"Towards a Comprehensive Scaling Law of Mixture-of-Experts","created":"2025-09-28","cited_by_count":10,"area":"moe"},{"id":"2510.05245","title":"Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving","created":"2025-10-06","cited_by_count":10,"area":"moe"},{"id":"2510.17483","title":"ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts","created":"2025-10-20","cited_by_count":10,"area":"moe"},{"id":"2406.08155","title":"QuantMoE-Bench: Examining Post-Training Quantization for Mixture-of-Experts","created":"2024-06-12","cited_by_count":9,"area":"moe"},{"id":"2505.06839","title":"The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts","created":"2025-05-11","cited_by_count":9,"area":"moe"},{"id":"2603.04971","title":"Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation","created":"2026-03-05","cited_by_count":8,"area":"moe"},{"id":"2510.05781","title":"Mixture of Neuron Experts","created":"2025-10-07","cited_by_count":7,"area":"moe"},{"id":"2410.12247","title":"EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference","created":"2024-10-16","cited_by_count":6,"area":"moe"},{"id":"2505.07260","title":"UMoE: Unifying Attention and FFN with Shared Experts","created":"2025-05-12","cited_by_count":6,"area":"moe"},{"id":"2507.08771","title":"BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity","created":"2025-07-11","cited_by_count":6,"area":"moe"},{"id":"2511.06494","title":"Route Experts by Sequence, not by Token","created":"2025-11-09","cited_by_count":6,"area":"moe"},{"id":"2503.05029","title":"Continual Pre-training of MoEs: How robust is your router?","created":"2025-03-06","cited_by_count":5,"area":"moe"},{"id":"2506.21035","title":"Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts","created":"2025-06-26","cited_by_count":5,"area":"moe"},{"id":"2508.18756","title":"UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning","created":"2025-08-26","cited_by_count":5,"area":"moe"},{"id":"2603.06626","title":"Grouter: Decoupling Routing from Representation for Accelerated MoE Training","created":"2026-02-22","cited_by_count":5,"area":"moe"},{"id":"2408.04278","title":"LaDiMo: Layer-wise Distillation Inspired MoEfier","created":"2024-08-08","cited_by_count":4,"area":"moe"},{"id":"2510.04136","title":"MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition","created":"2025-10-05","cited_by_count":4,"area":"moe"},{"id":"2602.09721","title":"Revealing the Challenges of Attention-FFN Disaggregation for Modern MoE Models and Hardware Systems","created":"2026-02-10","cited_by_count":4,"area":"moe"},{"id":"2606.17952","title":"SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs","created":"2026-06-16","cited_by_count":4,"area":"moe"},{"id":"2412.15411","title":"Sparse Checkpointing for Fast and Reliable MoE Training","created":"2024-12-19","cited_by_count":3,"area":"moe"},{"id":"2504.06426","title":"S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning","created":"2025-04-08","cited_by_count":3,"area":"moe"},{"id":"2508.09752","title":"$\\mu$-Parametrization for Mixture of Experts","created":"2025-08-13","cited_by_count":3,"area":"moe"},{"id":"2510.00570","title":"Adaptive Shared Experts with LoRA-Based Mixture of Experts for Multi-Task Learning","created":"2025-10-01","cited_by_count":3,"area":"moe"},{"id":"2512.13996","title":"DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training","created":"2025-12-16","cited_by_count":3,"area":"moe"},{"id":"2602.00800","title":"JTok: On Token Embedding as another Axis of Scaling Law via Joint Token Self-modulation","created":"2026-01-31","cited_by_count":3,"area":"moe"},{"id":"2603.11535","title":"Expert Threshold Routing for Autoregressive Language Modeling with Dynamic Computation Allocation and Load Balancing","created":"2026-03-12","cited_by_count":3,"area":"moe"},{"id":"2605.27358","title":"MobileMoE: Scaling On-Device Mixture of Experts","created":"2026-05-26","cited_by_count":3,"area":"moe"},{"id":"2506.02890","title":"Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights","created":"2025-06-03","cited_by_count":2,"area":"moe"},{"id":"2507.03526","title":"Decoupled Relative Learning Rate Schedules","created":"2025-07-04","cited_by_count":2,"area":"moe"},{"id":"2507.19142","title":"A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration","created":"2025-07-25","cited_by_count":2,"area":"moe"},{"id":"2510.01185","title":"Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEs","created":"2025-10-01","cited_by_count":2,"area":"moe"},{"id":"2512.18452","title":"Secret mixtures of experts inside your LLM","created":"2025-12-20","cited_by_count":2,"area":"moe"},{"id":"2601.21461","title":"L$^3$: Large Lookup Layers","created":"2026-01-29","cited_by_count":2,"area":"moe"},{"id":"2602.17798","title":"Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds","created":"2026-02-19","cited_by_count":2,"area":"moe"},{"id":"2603.21862","title":"Holistic Scaling Laws for Optimal Mixture-of-Experts Architecture Optimization","created":"2026-03-23","cited_by_count":2,"area":"moe"},{"id":"2605.11689","title":"Slicing and Dicing: Configuring Optimal Mixtures of Experts","created":"2026-05-12","cited_by_count":2,"area":"moe"},{"id":"2503.09304","title":"Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference","created":"2025-03-12","cited_by_count":1,"area":"moe"},{"id":"2503.22996","title":"Rethinking Sparse Mixture of Experts from a Unified Perspective","created":"2025-03-29","cited_by_count":1,"area":"moe"},{"id":"2508.18376","title":"DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction","created":"2025-08-25","cited_by_count":1,"area":"moe"},{"id":"2510.12357","title":"MoBiLE: Efficient Mixture-of-Experts Inference on Consumer GPU with Mixture of Big Little Experts","created":"2025-10-14","cited_by_count":1,"area":"moe"},{"id":"2601.11616","title":"Mixture-of-Experts as Soft Clustering: A Dual Jacobian-PCA Spectral Geometry Perspective","created":"2026-01-09","cited_by_count":1,"area":"moe"},{"id":"2601.17680","title":"$\\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts","created":"2026-01-25","cited_by_count":1,"area":"moe"},{"id":"2604.07030","title":"MoE Routing Testbed: Studying Expert Specialization and Routing Behavior at Small Scale","created":"2026-04-08","cited_by_count":1,"area":"moe"},{"id":"2604.14419","title":"Equifinality in Mixture of Experts: Routing Topology Does Not Determine Language Modeling Quality","created":"2026-04-15","cited_by_count":1,"area":"moe"},{"id":"2604.18473","title":"Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts","created":"2026-04-20","cited_by_count":1,"area":"moe"},{"id":"2604.19835","title":"Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts","created":"2026-04-21","cited_by_count":1,"area":"moe"},{"id":"2605.06665","title":"UniPool: A Globally Shared Expert Pool for Mixture-of-Experts","created":"2026-05-07","cited_by_count":1,"area":"moe"},{"id":"2605.13769","title":"Dense vs Sparse Pretraining at Tiny Scale: Active-Parameter vs Total-Parameter Matching","created":"2026-05-13","cited_by_count":1,"area":"moe"},{"id":"2607.09424","title":"A Sovereign, Open-Source Foundation Model for German and English","created":"2026-07-10","cited_by_count":1,"area":"moe"},{"id":"2607.20145","title":"SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD","created":"2026-07-22","cited_by_count":1,"area":"moe"},{"id":"2607.28308","title":"Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing","created":"2026-07-30","cited_by_count":1,"area":"moe"},{"id":"2609.35751","title":"How to Loop MoE: Flatten the Experts, Untie the Attention","created":"2026-09-28","cited_by_count":1,"area":"moe"},{"id":"2609.40316","title":"Scaling Laws for Looped Mixture of Experts","created":"2026-09-30","cited_by_count":1,"area":"moe"},{"id":"2505.23184","title":"Two Is Better Than One: Rotations Scale LoRAs","created":"2025-05-29","cited_by_count":0,"area":"moe"},{"id":"2508.06617","title":"Generalizing Scaling Laws for Dense and Sparse Large Language Models","created":"2025-08-08","cited_by_count":0,"area":"moe"},{"id":"2601.08215","title":"Towards Principled Design of Mixture-of-Experts Language Models under Memory and Inference Constraints","created":"2026-01-13","cited_by_count":0,"area":"moe"},{"id":"2602.14301","title":"DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices","created":"2026-02-15","cited_by_count":0,"area":"moe"},{"id":"2603.10379","title":"Optimal Expert-Attention Allocation in Mixture-of-Experts: A Scalable Law for Dynamic Model Design","created":"2026-03-11","cited_by_count":0,"area":"moe"},{"id":"2603.13364","title":"FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach","created":"2026-03-09","cited_by_count":0,"area":"moe"},{"id":"2604.09175","title":"Generalization and Scaling Laws for Mixture-of-Experts Transformers","created":"2026-04-10","cited_by_count":0,"area":"moe"},{"id":"2604.18842","title":"Multi-Domain Learning with Global Expert Mapping","created":"2026-04-20","cited_by_count":0,"area":"moe"},{"id":"2605.03348","title":"Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts","created":"2026-05-05","cited_by_count":0,"area":"moe"},{"id":"2605.04952","title":"Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts","created":"2026-05-06","cited_by_count":0,"area":"moe"},{"id":"2605.06206","title":"Federation of Experts: Communication Efficient Distributed Inference for Large Language Models","created":"2026-05-07","cited_by_count":0,"area":"moe"},{"id":"2605.10933","title":"DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices","created":"2026-05-11","cited_by_count":0,"area":"moe"},{"id":"2605.14200","title":"How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization","created":"2026-05-13","cited_by_count":0,"area":"moe"},{"id":"2605.16349","title":"Geometric Asymmetry in MoE Specialization: Functional Decorrelation and Representational Overlap","created":"2026-05-08","cited_by_count":0,"area":"moe"},{"id":"2605.27722","title":"NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling","created":"2026-05-26","cited_by_count":0,"area":"moe"},{"id":"2605.31268","title":"Mellum2 Technical Report","created":"2026-05-29","cited_by_count":0,"area":"moe"},{"id":"2606.07414","title":"Sparsely gated tiny linear experts","created":"2026-06-05","cited_by_count":0,"area":"moe"},{"id":"2606.19025","title":"FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs","created":"2026-06-17","cited_by_count":0,"area":"moe"},{"id":"2608.10605","title":"Compute-Optimal Is Not Cluster-Optimal: Systems-Aware Scaling for Sparse Mixture-of-Experts","created":"2026-08-11","cited_by_count":0,"area":"moe"},{"id":"2608.20061","title":"Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts","created":"2026-08-20","cited_by_count":0,"area":"moe"},{"id":"2609.18842","title":"Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data","created":"2026-09-16","cited_by_count":0,"area":"moe"}],"next_cursor":"100"}