{"total":48,"rows":[{"id":"2604.15804","title":"Qwen3.5-Omni Technical Report","created":"2026-04-17","cited_by_count":174,"area":"moe"},{"id":"2509.23951","title":"HunyuanImage 3.0 Technical Report","created":"2025-09-28","cited_by_count":149,"area":"moe"},{"id":"2305.14705","title":"Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models","created":"2023-05-24","cited_by_count":65,"area":"moe"},{"id":"2405.14297","title":"Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models","created":"2024-05-23","cited_by_count":56,"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":"2509.03498","title":"OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation","created":"2025-09-03","cited_by_count":50,"area":"moe"},{"id":"2502.19811","title":"Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts","created":"2025-02-27","cited_by_count":49,"area":"moe"},{"id":"2409.03277","title":"ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding","created":"2024-09-05","cited_by_count":46,"area":"moe"},{"id":"2312.01381","title":"Language-driven All-in-one Adverse Weather Removal","created":"2023-12-03","cited_by_count":36,"area":"moe"},{"id":"2311.02684","title":"Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE","created":"2023-11-05","cited_by_count":25,"area":"moe"},{"id":"2403.11808","title":"Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation","created":"2024-03-18","cited_by_count":25,"area":"moe"},{"id":"2409.19291","title":"CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling","created":"2024-09-28","cited_by_count":23,"area":"moe"},{"id":"2511.12609","title":"Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data","created":"2025-11-16","cited_by_count":21,"area":"moe"},{"id":"2508.02317","title":"VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo","created":"2025-08-04","cited_by_count":20,"area":"moe"},{"id":"2410.15385","title":"LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration","created":"2024-10-20","cited_by_count":18,"area":"moe"},{"id":"2507.23279","title":"Unveiling Super Experts in Mixture-of-Experts Large Language Models","created":"2025-07-31","cited_by_count":17,"area":"moe"},{"id":"2508.01699","title":"TimeExpert: An Expert-Guided Video LLM for Video Temporal Grounding","created":"2025-08-03","cited_by_count":14,"area":"moe"},{"id":"2409.05929","title":"M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture","created":"2024-09-09","cited_by_count":13,"area":"moe"},{"id":"2505.14336","title":"Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach","created":"2025-05-20","cited_by_count":10,"area":"moe"},{"id":"2410.14574","title":"MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts","created":"2024-10-18","cited_by_count":9,"area":"moe"},{"id":"2502.15828","title":"A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models","created":"2025-02-20","cited_by_count":9,"area":"moe"},{"id":"2509.23638","title":"LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers","created":"2025-09-28","cited_by_count":8,"area":"moe"},{"id":"2512.18425","title":"MoE Pathfinder: Trajectory-driven Expert Pruning","created":"2025-12-20","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":"2502.03044","title":"RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts","created":"2025-02-05","cited_by_count":6,"area":"moe"},{"id":"2505.20001","title":"NEXT: Multi-Grained Mixture of Experts via Text-Modulation for Multi-Modal Object Re-Identification","created":"2025-05-26","cited_by_count":6,"area":"moe"},{"id":"2509.25913","title":"Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel","created":"2025-09-30","cited_by_count":6,"area":"moe"},{"id":"2511.01463","title":"HMVLM: Human Motion-Vision-Lanuage Model via MoE LoRA","created":"2025-11-03","cited_by_count":4,"area":"moe"},{"id":"2602.21693","title":"TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts","created":"2026-02-25","cited_by_count":4,"area":"moe"},{"id":"2503.22517","title":"Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities","created":"2025-03-28","cited_by_count":3,"area":"moe"},{"id":"2507.09955","title":"DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models","created":"2025-07-14","cited_by_count":3,"area":"moe"},{"id":"2510.16411","title":"Modeling Expert Interactions in Sparse Mixture of Experts via Graph Structures","created":"2025-10-18","cited_by_count":3,"area":"moe"},{"id":"2605.20247","title":"CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning","created":"2026-05-18","cited_by_count":2,"area":"moe"},{"id":"2408.15305","title":"Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis","created":"2024-08-27","cited_by_count":1,"area":"moe"},{"id":"2411.01288","title":"Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts","created":"2024-11-02","cited_by_count":1,"area":"moe"},{"id":"2601.21547","title":"Multi-Modal Time Series Prediction via Mixture of Modulated Experts","created":"2026-01-29","cited_by_count":1,"area":"moe"},{"id":"2602.01468","title":"A Statistical Theory of Gated Attention through the Lens of Hierarchical Mixture of Experts","created":"2026-02-01","cited_by_count":1,"area":"moe"},{"id":"2607.14041","title":"Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study","created":"2026-07-15","cited_by_count":1,"area":"moe"},{"id":"2410.04511","title":"Realizing Video Summarization from the Path of Language-based Semantic Understanding","created":"2024-10-06","cited_by_count":0,"area":"moe"},{"id":"2506.20986","title":"EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning","created":"2025-06-26","cited_by_count":0,"area":"moe"},{"id":"2602.12546","title":"Decoder-only Conformer with Modality-aware Sparse Mixtures of Experts for ASR","created":"2026-02-13","cited_by_count":0,"area":"moe"},{"id":"2604.15090","title":"Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID","created":"2026-04-16","cited_by_count":0,"area":"moe"},{"id":"2607.00293","title":"Rosetta: Composable Native Multimodal Pretraining","created":"2026-07-01","cited_by_count":0,"area":"moe"},{"id":"2607.08215","title":"On the Limitations of Non-GPU AI Accelerators for Large-Model Inference: A Field Study of MoE and Multimodal Serving on Huawei Ascend","created":"2026-07-09","cited_by_count":0,"area":"moe"},{"id":"2608.24763","title":"MoTE: Mixture of Task Experts for Multi-Task Video Understanding","created":"2026-08-25","cited_by_count":0,"area":"moe"},{"id":"2608.25773","title":"Drift-Aware Multimodal User Representation Learning via Multi-Scale Temporal Modeling and Sparse Mixture-of-Experts","created":"2026-08-26","cited_by_count":0,"area":"moe"},{"id":"2609.38823","title":"DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference","created":"2026-09-30","cited_by_count":0,"area":"moe"},{"id":"2610.01921","title":"Cross-Lingual Alignment for Decoder-Only Models using MoE Routers","created":"2026-10-01","cited_by_count":0,"area":"moe"}],"next_cursor":null}