{"total":66,"rows":[{"id":"2201.05596","title":"DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale","created":"2022-01-14","cited_by_count":404,"area":"moe"},{"id":"2406.16554","title":"LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training","created":"2024-06-24","cited_by_count":123,"area":"moe"},{"id":"2407.04153","title":"Mixture of A Million Experts","created":"2024-07-04","cited_by_count":72,"area":"moe"},{"id":"2405.16039","title":"MoEUT: Mixture-of-Experts Universal Transformers","created":"2024-05-25","cited_by_count":59,"area":"moe"},{"id":"2404.07413","title":"JetMoE: Reaching Llama2 Performance with 0.1M Dollars","created":"2024-04-11","cited_by_count":57,"area":"moe"},{"id":"2410.07348","title":"MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts","created":"2024-10-09","cited_by_count":56,"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":"2410.11842","title":"MoH: Multi-Head Attention as Mixture-of-Head Attention","created":"2024-10-15","cited_by_count":44,"area":"moe"},{"id":"2402.01771","title":"BlackMamba: Mixture of Experts for State-Space Models","created":"2024-02-01","cited_by_count":37,"area":"moe"},{"id":"2312.07987","title":"SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention","created":"2023-12-13","cited_by_count":34,"area":"moe"},{"id":"2601.08393","title":"Controlled LLM Training on Spectral Sphere","created":"2026-01-13","cited_by_count":31,"area":"moe"},{"id":"2502.06643","title":"MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing","created":"2025-02-10","cited_by_count":26,"area":"moe"},{"id":"2503.07639","title":"Mixture of Experts Made Intrinsically Interpretable","created":"2025-03-05","cited_by_count":24,"area":"moe"},{"id":"2408.06793","title":"Layerwise Recurrent Router for Mixture-of-Experts","created":"2024-08-13","cited_by_count":21,"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":"2605.23872","title":"Training-Free Looped Transformers","created":"2026-05-22","cited_by_count":15,"area":"moe"},{"id":"2505.00315","title":"Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing","created":"2025-05-01","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":"2503.15798","title":"Mixture of Lookup Experts","created":"2025-03-20","cited_by_count":12,"area":"moe"},{"id":"2504.12463","title":"Dense Backpropagation Improves Training for Sparse Mixture-of-Experts","created":"2025-04-16","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":"2407.09816","title":"MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-Experts","created":"2024-07-13","cited_by_count":9,"area":"moe"},{"id":"2405.15052","title":"Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training","created":"2024-05-23","cited_by_count":8,"area":"moe"},{"id":"2602.11184","title":"KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models","created":"2026-01-30","cited_by_count":8,"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":"2411.00918","title":"LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models","created":"2024-11-01","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":"2409.12210","title":"Mixture of Diverse Size Experts","created":"2024-09-18","cited_by_count":5,"area":"moe"},{"id":"2410.16077","title":"CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts","created":"2024-10-21","cited_by_count":5,"area":"moe"},{"id":"2503.11144","title":"MoLEx: Mixture of Layer Experts for Finetuning with Sparse Upcycling","created":"2025-03-14","cited_by_count":5,"area":"moe"},{"id":"2506.05767","title":"dots.llm1 Technical Report","created":"2025-06-06","cited_by_count":5,"area":"moe"},{"id":"2605.20948","title":"Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory","created":"2026-05-20","cited_by_count":5,"area":"moe"},{"id":"2506.18145","title":"Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection","created":"2025-06-22","cited_by_count":4,"area":"moe"},{"id":"2603.18297","title":"Path-Constrained Mixture-of-Experts","created":"2026-03-18","cited_by_count":4,"area":"moe"},{"id":"2604.08133","title":"Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference","created":"2026-04-09","cited_by_count":4,"area":"moe"},{"id":"2605.24956","title":"NITP: Next Implicit Token Prediction for LLM Pre-training","created":"2026-05-24","cited_by_count":4,"area":"moe"},{"id":"2508.12801","title":"Maximum Score Routing For Mixture-of-Experts","created":"2025-08-18","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":"2602.06154","title":"MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models","created":"2026-02-05","cited_by_count":3,"area":"moe"},{"id":"2604.00421","title":"Self-Routing: Parameter-Free Expert Routing from Hidden States","created":"2026-04-01","cited_by_count":3,"area":"moe"},{"id":"2606.01062","title":"DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts","created":"2026-05-31","cited_by_count":3,"area":"moe"},{"id":"2411.08968","title":"Sparse Upcycling: Inference Inefficient Finetuning","created":"2024-11-13","cited_by_count":2,"area":"moe"},{"id":"2504.04342","title":"Pruning Laws for Large Language Models","created":"2025-04-06","cited_by_count":2,"area":"moe"},{"id":"2604.06798","title":"MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization","created":"2026-04-08","cited_by_count":2,"area":"moe"},{"id":"2604.23108","title":"Mixture of Heterogeneous Grouped Experts for Language Modeling","created":"2026-04-25","cited_by_count":2,"area":"moe"},{"id":"2607.08601","title":"It Takes a MAESTRO To Prune Bad Experts","created":"2026-07-09","cited_by_count":2,"area":"moe"},{"id":"2403.02436","title":"How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers","created":"2024-03-04","cited_by_count":1,"area":"moe"},{"id":"2502.12455","title":"DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs","created":"2025-02-18","cited_by_count":1,"area":"moe"},{"id":"2602.09001","title":"DirMoE: Dirichlet-routed Mixture of Experts","created":"2026-02-09","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":"2605.23893","title":"Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models","created":"2026-05-22","cited_by_count":1,"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":"2606.08814","title":"STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning","created":"2026-06-07","cited_by_count":0,"area":"moe"},{"id":"2606.12397","title":"Redesign Mixture-of-Experts Routers with Manifold Power Iteration","created":"2026-06-10","cited_by_count":0,"area":"moe"},{"id":"2607.06601","title":"TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation","created":"2026-07-07","cited_by_count":0,"area":"moe"},{"id":"2609.01100","title":"Beyond Magnitude: Contrastive Routing for Modular Mixture-of-Experts","created":"2026-09-01","cited_by_count":0,"area":"moe"},{"id":"2609.06551","title":"EStream: Fast and Memory-Efficient MoE Prefill through Expert Virtualization on Mobile NPUs","created":"2026-09-06","cited_by_count":0,"area":"moe"},{"id":"2609.15126","title":"MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup","created":"2026-09-14","cited_by_count":0,"area":"moe"},{"id":"2609.18131","title":"Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing","created":"2026-09-16","cited_by_count":0,"area":"moe"},{"id":"2609.22471","title":"Efficient Mixture-of-Experts with Speculative Decoding via Expert Coactivation","created":"2026-09-18","cited_by_count":0,"area":"moe"},{"id":"2609.34212","title":"X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths","created":"2026-09-28","cited_by_count":0,"area":"moe"},{"id":"2610.11575","title":"Smoothing the Top-k Exposure Boundary for Sparse Mixture-of-Experts","created":"2026-10-08","cited_by_count":0,"area":"moe"},{"id":"2610.19975","title":"LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation","created":"2026-10-14","cited_by_count":0,"area":"moe"}],"next_cursor":null}