{"total":46,"rows":[{"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":"2403.19887","title":"Jamba: A Hybrid Transformer-Mamba Language Model","created":"2024-03-28","cited_by_count":401,"area":"moe"},{"id":"2409.02060","title":"OLMoE: Open Mixture-of-Experts Language Models","created":"2024-09-03","cited_by_count":301,"area":"moe"},{"id":"2411.01433","title":"HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference","created":"2024-11-03","cited_by_count":55,"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":"2406.05955","title":"Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters","created":"2024-06-10","cited_by_count":38,"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":"2405.17741","title":"LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design","created":"2024-05-28","cited_by_count":18,"area":"moe"},{"id":"2410.10456","title":"Ada-K Routing: Boosting the Efficiency of MoE-based LLMs","created":"2024-10-14","cited_by_count":16,"area":"moe"},{"id":"2411.02433","title":"SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models","created":"2024-11-01","cited_by_count":16,"area":"moe"},{"id":"2605.23872","title":"Training-Free Looped Transformers","created":"2026-05-22","cited_by_count":15,"area":"moe"},{"id":"2501.10375","title":"DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference","created":"2024-12-16","cited_by_count":14,"area":"moe"},{"id":"2402.12399","title":"Turn Waste into Worth: Rectifying Top-$k$ Router of MoE","created":"2024-02-17","cited_by_count":11,"area":"moe"},{"id":"2407.11030","title":"DLO: Dynamic Layer Operation for Efficient Vertical Scaling of LLMs","created":"2024-07-03","cited_by_count":10,"area":"moe"},{"id":"2501.15316","title":"ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning","created":"2025-01-25","cited_by_count":10,"area":"moe"},{"id":"2510.00844","title":"Learning Compact Representations of LLM Abilities via Item Response Theory","created":"2025-10-01","cited_by_count":10,"area":"moe"},{"id":"2509.08486","title":"Too Helpful, Too Harmless, Too Honest or Just Right?","created":"2025-09-10","cited_by_count":9,"area":"moe"},{"id":"2406.11353","title":"$\\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts","created":"2024-06-17","cited_by_count":7,"area":"moe"},{"id":"2503.00634","title":"Efficiently Editing Mixture-of-Experts Models with Compressed Experts","created":"2025-03-01","cited_by_count":6,"area":"moe"},{"id":"2606.05688","title":"Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models","created":"2026-06-04","cited_by_count":6,"area":"moe"},{"id":"2602.14159","title":"Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization","created":"2026-02-15","cited_by_count":5,"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":"2408.17280","title":"Flexible and Effective Mixing of Large Language Models into a Mixture of Domain Experts","created":"2024-08-30","cited_by_count":2,"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":"2512.04476","title":"Context-Aware Mixture-of-Experts Inference on CXL-Enabled GPU-NDP Systems","created":"2025-12-04","cited_by_count":2,"area":"moe"},{"id":"2603.11873","title":"AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization","created":"2026-03-12","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":"2607.20426","title":"Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations","created":"2026-05-08","cited_by_count":2,"area":"moe"},{"id":"2401.15713","title":"Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings","created":"2024-01-28","cited_by_count":1,"area":"moe"},{"id":"2512.03927","title":"OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference","created":"2025-12-03","cited_by_count":1,"area":"moe"},{"id":"2602.07381","title":"When the Model Said 'No Comment', We Knew Helpfulness Was Dead, Honesty Was Alive, and Safety Was Terrified","created":"2026-02-07","cited_by_count":1,"area":"moe"},{"id":"2603.27141","title":"Routing Sensitivity Without Controllability: A Diagnostic Study of Fairness in MoE Language Models","created":"2026-03-28","cited_by_count":1,"area":"moe"},{"id":"2605.13997","title":"HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts","created":"2026-05-13","cited_by_count":1,"area":"moe"},{"id":"2505.06481","title":"QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration","created":"2025-05-10","cited_by_count":0,"area":"moe"},{"id":"2604.14246","title":"Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations","created":"2026-04-15","cited_by_count":0,"area":"moe"},{"id":"2605.08575","title":"Uncovering Intra-expert Activation Sparsity for Efficient Mixture-of-Expert Model Execution","created":"2026-05-09","cited_by_count":0,"area":"moe"},{"id":"2605.09516","title":"Mixture of Layers with Hybrid Attention","created":"2026-05-10","cited_by_count":0,"area":"moe"},{"id":"2605.31268","title":"Mellum2 Technical Report","created":"2026-05-29","cited_by_count":0,"area":"moe"},{"id":"2608.11688","title":"APEX: Adaptive Expert Prefetching for Memory-Efficient Edge MoE Inference","created":"2026-08-12","cited_by_count":0,"area":"moe"},{"id":"2608.15687","title":"THESIS-MoE: Trainable Hierarchical Extraction and SteerIng of Sycophancy in Mixture-of-Experts","created":"2026-08-16","cited_by_count":0,"area":"moe"},{"id":"2608.17687","title":"Mixture-of-Expert Blocks Contain Strong Hallucination Detection Signals","created":"2026-08-18","cited_by_count":0,"area":"moe"},{"id":"2608.21236","title":"RARE: Decoupling Representation Steering from Expert Routing in Mixture-of-Experts Language Models","created":"2026-08-21","cited_by_count":0,"area":"moe"},{"id":"2609.02293","title":"SEAL: Reinforcing Global Safety in Mixture-of-Experts through Shared Expert ALignment","created":"2026-09-02","cited_by_count":0,"area":"moe"},{"id":"2609.06076","title":"Beyond Retraining-Free MoE Compression: A Cost-Normalized Study of Post-Compression Adjustment","created":"2026-09-05","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":"2610.20353","title":"Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization","created":"2026-10-14","cited_by_count":0,"area":"moe"}],"next_cursor":null}