{"total":54,"rows":[{"id":"2412.15115","title":"Qwen2.5 Technical Report","created":"2024-12-19","cited_by_count":4942,"area":"moe"},{"id":"2409.02060","title":"OLMoE: Open Mixture-of-Experts Language Models","created":"2024-09-03","cited_by_count":301,"area":"moe"},{"id":"2411.02265","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":"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":"2502.19811","title":"Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts","created":"2025-02-27","cited_by_count":49,"area":"moe"},{"id":"2412.14219","title":"A Survey on Inference Optimization Techniques for Mixture of Experts Models","created":"2024-12-18","cited_by_count":47,"area":"moe"},{"id":"2502.05370","title":"Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading","created":"2025-02-07","cited_by_count":24,"area":"moe"},{"id":"2502.19261","title":"Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization","created":"2025-02-26","cited_by_count":24,"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":"2410.10626","title":"Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts","created":"2024-10-14","cited_by_count":17,"area":"moe"},{"id":"2411.18797","title":"SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?","created":"2024-11-27","cited_by_count":14,"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":"2504.18598","title":"BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts","created":"2025-04-24","cited_by_count":14,"area":"moe"},{"id":"2506.20675","title":"Utility-Driven Speculative Decoding for Mixture-of-Experts","created":"2025-06-17","cited_by_count":13,"area":"moe"},{"id":"2411.06681","title":"WDMoE: Wireless Distributed Mixture of Experts for Large Language Models","created":"2024-11-11","cited_by_count":12,"area":"moe"},{"id":"2508.12461","title":"Is GPT-OSS Good? A Comprehensive Evaluation of OpenAI's Latest Open Source Models","created":"2025-08-17","cited_by_count":11,"area":"moe"},{"id":"2503.01359","title":"DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models","created":"2025-03-03","cited_by_count":10,"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":"2505.05950","title":"FloE: On-the-Fly MoE Inference on Memory-constrained GPU","created":"2025-05-09","cited_by_count":9,"area":"moe"},{"id":"2506.18945","title":"Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models","created":"2025-06-23","cited_by_count":9,"area":"moe"},{"id":"2412.16971","title":"Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models","created":"2024-12-22","cited_by_count":7,"area":"moe"},{"id":"2502.12947","title":"Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models","created":"2025-02-18","cited_by_count":6,"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":"2503.05029","title":"Continual Pre-training of MoEs: How robust is your router?","created":"2025-03-06","cited_by_count":5,"area":"moe"},{"id":"2510.04476","title":"Compressed Convolutional Attention: Efficient Attention in a Compressed Latent Space","created":"2025-10-06","cited_by_count":5,"area":"moe"},{"id":"2505.13345","title":"Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference","created":"2025-05-19","cited_by_count":4,"area":"moe"},{"id":"2505.24205","title":"On the Expressive Power of Mixture-of-Experts for Structured Complex Tasks","created":"2025-05-30","cited_by_count":4,"area":"moe"},{"id":"2511.10676","title":"Pre-Attention Expert Prediction and Prefetching for Mixture-of-Experts Large Language Models","created":"2025-11-10","cited_by_count":4,"area":"moe"},{"id":"2604.03592","title":"Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation","created":"2026-04-04","cited_by_count":4,"area":"moe"},{"id":"2503.00245","title":"CoSMoEs: Compact Sparse Mixture of Experts","created":"2025-02-28","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":"2509.10436","title":"RefactorCoderQA: Benchmarking LLMs for Multi-Domain Coding Question Solutions in Cloud and Edge Deployment","created":"2025-09-12","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":"2512.16248","title":"Sigma-MoE-Tiny Technical Report","created":"2025-12-18","cited_by_count":2,"area":"moe"},{"id":"2602.08019","title":"The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications","created":"2026-02-08","cited_by_count":2,"area":"moe"},{"id":"2602.11192","title":"MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models","created":"2026-01-30","cited_by_count":2,"area":"moe"},{"id":"2502.02040","title":"M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference","created":"2025-02-04","cited_by_count":1,"area":"moe"},{"id":"2509.18137","title":"LoRALib: A Standardized Benchmark for Evaluating LoRA-MoE Methods","created":"2025-09-14","cited_by_count":1,"area":"moe"},{"id":"2512.16473","title":"Efficient CPU-GPU Collaborative Inference for MoE-based LLMs on Memory-Limited Systems","created":"2025-12-18","cited_by_count":1,"area":"moe"},{"id":"2601.01260","title":"MambaFormer: Token-Level Guided Routing Mixture-of-Experts for Accurate and Efficient Clinical Assistance","created":"2026-01-03","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":"2603.06350","title":"MoEless: Efficient MoE LLM Serving via Serverless Computing","created":"2026-03-06","cited_by_count":1,"area":"moe"},{"id":"2605.21427","title":"PALS: Power-Aware LLM Serving for Mixture-of-Experts Models","created":"2026-05-20","cited_by_count":1,"area":"moe"},{"id":"2603.27624","title":"Expert Streaming: Accelerating Low-Batch MoE Inference via Multi-chiplet Architecture and Dynamic Expert Trajectory Scheduling","created":"2026-03-29","cited_by_count":0,"area":"moe"},{"id":"2604.10496","title":"CodeQuant: Unified Clustering and Quantization for Enhanced Outlier Smoothing in Low-Precision Mixture-of-Experts","created":"2026-04-12","cited_by_count":0,"area":"moe"},{"id":"2606.27866","title":"FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models","created":"2026-06-26","cited_by_count":0,"area":"moe"},{"id":"2607.20443","title":"GLAN-QnA-KR: A Seedless Taxonomy-Driven Korean Instruction Corpus","created":"2026-05-12","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":"2608.15787","title":"Routing Divergence Is Not Evidence of Behavioral Influence in Same-Weight MoE Self-Distillation","created":"2026-08-16","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":"2608.25276","title":"Groundhog Bit-Flip Attack: Seeding Infinite Generation Loops in Mixture-of-Experts LLMs through Bit Flips","created":"2026-08-26","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":"2609.33614","title":"EAT: Expert Account Tracker for Efficient MoE Inference","created":"2026-09-27","cited_by_count":0,"area":"moe"}],"next_cursor":null}