{"total":126,"rows":[{"id":"2401.04088","title":"Mixtral of Experts","created":"2024-01-08","cited_by_count":1823,"area":"moe"},{"id":"2405.04434","title":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","created":"2024-05-07","cited_by_count":1135,"area":"moe"},{"id":"2606.19348","title":"DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence","created":"2026-04-26","cited_by_count":1040,"area":"moe"},{"id":"2401.06066","title":"DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models","created":"2024-01-11","cited_by_count":867,"area":"moe"},{"id":"2502.16982","title":"Muon is Scalable for LLM Training","created":"2025-02-24","cited_by_count":428,"area":"moe"},{"id":"2406.11931","title":"DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence","created":"2024-06-17","cited_by_count":331,"area":"moe"},{"id":"2409.02060","title":"OLMoE: Open Mixture-of-Experts Language Models","created":"2024-09-03","cited_by_count":301,"area":"moe"},{"id":"2407.06204","title":"A Survey on Mixture of Experts in Large Language Models","created":"2024-06-26","cited_by_count":237,"area":"moe"},{"id":"2601.02780","title":"MiMo-V2-Flash Technical Report","created":"2026-01-06","cited_by_count":191,"area":"moe"},{"id":"2402.14800","title":"Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models","created":"2024-02-22","cited_by_count":146,"area":"moe"},{"id":"2411.02265","title":"Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent","created":"2024-11-04","cited_by_count":90,"area":"moe"},{"id":"2601.07372","title":"Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models","created":"2026-01-12","cited_by_count":86,"area":"moe"},{"id":"2510.11370","title":"Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers","created":"2025-10-13","cited_by_count":72,"area":"moe"},{"id":"2406.06563","title":"Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models","created":"2024-06-03","cited_by_count":59,"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":"2410.06270","title":"Mixture Compressor for Mixture-of-Experts LLMs Gains More","created":"2024-10-08","cited_by_count":41,"area":"moe"},{"id":"2410.08589","title":"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering","created":"2024-10-11","cited_by_count":35,"area":"moe"},{"id":"2608.30320","title":"On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability","created":"2026-08-31","cited_by_count":35,"area":"moe"},{"id":"2511.00279","title":"LongCat-Flash-Omni Technical Report","created":"2025-10-31","cited_by_count":26,"area":"moe"},{"id":"2406.12034","title":"Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts","created":"2024-06-17","cited_by_count":23,"area":"moe"},{"id":"2401.02731","title":"Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks","created":"2024-01-05","cited_by_count":22,"area":"moe"},{"id":"2503.05139","title":"Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs","created":"2025-03-07","cited_by_count":22,"area":"moe"},{"id":"2407.01906","title":"Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models","created":"2024-07-02","cited_by_count":21,"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":"2505.21411","title":"Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity","created":"2025-05-27","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":"2409.12136","title":"GRIN: GRadient-INformed MoE","created":"2024-09-18","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":"2507.07024","title":"FlexOlmo: Open Language Models for Flexible Data Use","created":"2025-07-09","cited_by_count":15,"area":"moe"},{"id":"2601.18699","title":"Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning","created":"2026-01-26","cited_by_count":14,"area":"moe"},{"id":"2503.17793","title":"Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM","created":"2025-03-22","cited_by_count":13,"area":"moe"},{"id":"2509.25041","title":"GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference","created":"2025-09-29","cited_by_count":13,"area":"moe"},{"id":"2505.11415","title":"MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems","created":"2025-05-16","cited_by_count":11,"area":"moe"},{"id":"2511.19822","title":"Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models","created":"2025-11-25","cited_by_count":11,"area":"moe"},{"id":"2512.23447","title":"Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss","created":"2025-12-29","cited_by_count":11,"area":"moe"},{"id":"2602.04705","title":"ERNIE 5.0 Technical Report","created":"2026-02-04","cited_by_count":11,"area":"moe"},{"id":"2602.10693","title":"VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training","created":"2026-02-11","cited_by_count":11,"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":"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":"2602.03560","title":"HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing","created":"2026-02-03","cited_by_count":10,"area":"moe"},{"id":"2606.04438","title":"LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling","created":"2026-06-03","cited_by_count":10,"area":"moe"},{"id":"2507.19017","title":"MindSpeed RL: Distributed Dataflow for Scalable and Efficient RL Training on Ascend NPU Cluster","created":"2025-07-25","cited_by_count":9,"area":"moe"},{"id":"2508.05257","title":"MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs","created":"2025-08-07","cited_by_count":9,"area":"moe"},{"id":"2508.07785","title":"Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts","created":"2025-08-11","cited_by_count":9,"area":"moe"},{"id":"2603.06003","title":"EvoESAP: Non-Uniform Expert Pruning for Sparse MoE","created":"2026-03-06","cited_by_count":9,"area":"moe"},{"id":"2604.08541","title":"Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts","created":"2026-04-09","cited_by_count":9,"area":"moe"},{"id":"2605.07260","title":"When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models","created":"2026-05-08","cited_by_count":9,"area":"moe"},{"id":"2506.09440","title":"GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture","created":"2025-06-11","cited_by_count":8,"area":"moe"},{"id":"2409.18412","title":"SciDFM: A Large Language Model with Mixture-of-Experts for Science","created":"2024-09-27","cited_by_count":7,"area":"moe"},{"id":"2511.02237","title":"Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining","created":"2025-11-04","cited_by_count":7,"area":"moe"},{"id":"2605.23078","title":"GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs","created":"2026-05-21","cited_by_count":7,"area":"moe"},{"id":"2606.04980","title":"AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization","created":"2026-06-03","cited_by_count":7,"area":"moe"},{"id":"2601.03425","title":"The Illusion of Specialization: Unveiling the Domain-Invariant \"Standing Committee\" in Mixture-of-Experts Models","created":"2026-01-06","cited_by_count":6,"area":"moe"},{"id":"2606.15716","title":"How to Score Experts for One-Shot MoE Expert Pruning: A Unified Formulation and Selection Principle","created":"2026-06-14","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":"2506.05767","title":"dots.llm1 Technical Report","created":"2025-06-06","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":"2602.01203","title":"Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse","created":"2026-02-01","cited_by_count":5,"area":"moe"},{"id":"2603.18492","title":"AIMER: Calibration-Free Task-Agnostic MoE Expert Pruning","created":"2026-03-19","cited_by_count":5,"area":"moe"},{"id":"2606.26560","title":"Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention","created":"2026-06-25","cited_by_count":5,"area":"moe"},{"id":"2507.18671","title":"Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling","created":"2025-07-24","cited_by_count":4,"area":"moe"},{"id":"2603.02217","title":"Is Retraining-Free Enough? The Necessity of Router Calibration for Efficient MoE Compression","created":"2026-02-10","cited_by_count":4,"area":"moe"},{"id":"2510.14853","title":"Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models","created":"2025-10-16","cited_by_count":3,"area":"moe"},{"id":"2601.21420","title":"ConceptMoE: Adaptive Token-to-Concept Compression for Implicit Compute Allocation","created":"2026-01-29","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":"2602.07616","title":"SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models","created":"2026-02-07","cited_by_count":3,"area":"moe"},{"id":"2604.20156","title":"Temporally Extended Mixture-of-Experts Models","created":"2026-04-22","cited_by_count":3,"area":"moe"},{"id":"2604.25578","title":"Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling","created":"2026-04-28","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":"2607.00162","title":"FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts","created":"2026-06-30","cited_by_count":3,"area":"moe"},{"id":"2607.00466","title":"ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving","created":"2026-07-01","cited_by_count":3,"area":"moe"},{"id":"2607.12696","title":"Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts","created":"2026-07-14","cited_by_count":3,"area":"moe"},{"id":"2609.04895","title":"Cache-Aware Joint Router Adaptation for Memory-Efficient MoE Inference","created":"2026-09-04","cited_by_count":3,"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":"2512.16248","title":"Sigma-MoE-Tiny Technical Report","created":"2025-12-18","cited_by_count":2,"area":"moe"},{"id":"2512.21911","title":"Accelerate Speculative Decoding with Sparse Computation in Verification","created":"2025-12-26","cited_by_count":2,"area":"moe"},{"id":"2602.11215","title":"Charting Empirical Laws for LLM Fine-Tuning in Scientific Multi-Discipline Learning","created":"2026-02-11","cited_by_count":2,"area":"moe"},{"id":"2604.17695","title":"MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression","created":"2026-04-20","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":"2604.23150","title":"Scaling Multi-Node Mixture-of-Experts Inference Using Expert Activation Patterns","created":"2026-04-25","cited_by_count":2,"area":"moe"},{"id":"2606.05538","title":"Less is MoE: Trimming Experts in Domain-Specialist Language Models","created":"2026-06-04","cited_by_count":2,"area":"moe"},{"id":"2607.01789","title":"EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning","created":"2026-07-02","cited_by_count":2,"area":"moe"},{"id":"2607.26052","title":"Spend Experts Where You Are Unsure: Confidence-Adaptive Routing for Mixture-of-Experts LoRA","created":"2026-07-28","cited_by_count":2,"area":"moe"},{"id":"2608.24938","title":"ExFold: Unified Expert Folding for Training-Free MoE Prefill-Decode Acceleration","created":"2026-08-24","cited_by_count":2,"area":"moe"},{"id":"2510.11001","title":"DND: Boosting Large Language Models with Dynamic Nested Depth","created":"2025-10-13","cited_by_count":1,"area":"moe"},{"id":"2604.03044","title":"JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency","created":"2026-04-03","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":"2605.14438","title":"BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE","created":"2026-05-14","cited_by_count":1,"area":"moe"},{"id":"2606.00395","title":"PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning","created":"2026-05-29","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":"2609.25655","title":"From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs","created":"2026-09-22","cited_by_count":1,"area":"moe"},{"id":"2412.07067","title":"MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems","created":"2024-12-10","cited_by_count":0,"area":"moe"},{"id":"2509.10530","title":"Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts","created":"2025-09-05","cited_by_count":0,"area":"moe"},{"id":"2602.05711","title":"OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale","created":"2026-02-05","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":"2605.07731","title":"Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs","created":"2026-05-08","cited_by_count":0,"area":"moe"},{"id":"2605.25698","title":"How Should LLMs Consume High-Quality Data? Optimal Data Scheduling via Quality-Aware Functional Scaling Laws","created":"2026-05-25","cited_by_count":0,"area":"moe"},{"id":"2605.26842","title":"MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training","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":"2606.00079","title":"BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization","created":"2026-05-22","cited_by_count":0,"area":"moe"}],"next_cursor":"100"}