{"total":49,"rows":[{"id":"2606.19348","title":"DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence","created":"2026-04-26","cited_by_count":1040,"area":"moe"},{"id":"2503.07137","title":"A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications","created":"2025-03-10","cited_by_count":91,"area":"moe"},{"id":"2510.13999","title":"REAP the Experts: Why Pruning Prevails for One-Shot MoE compression","created":"2025-10-15","cited_by_count":44,"area":"moe"},{"id":"2603.07685","title":"Scalable Training of Mixture-of-Experts Models with Megatron Core","created":"2026-03-08","cited_by_count":19,"area":"moe"},{"id":"2607.07675","title":"Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence","created":"2026-07-08","cited_by_count":15,"area":"moe"},{"id":"2605.18106","title":"Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers","created":"2026-05-18","cited_by_count":11,"area":"moe"},{"id":"2601.17111","title":"Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts","created":"2026-01-23","cited_by_count":9,"area":"moe"},{"id":"2511.10054","title":"BuddyMoE: Exploiting Expert Redundancy to Accelerate Memory-Constrained Mixture-of-Experts Inference","created":"2025-11-13","cited_by_count":5,"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":"2511.13760","title":"MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm","created":"2025-11-14","cited_by_count":4,"area":"moe"},{"id":"2602.00509","title":"PROBE: Co-Balancing Computation and Communication in MoE Inference via Real-Time Predictive Prefetching","created":"2026-01-31","cited_by_count":4,"area":"moe"},{"id":"2606.16825","title":"Tying the Loop -- Tied Expert Layers in Mixture-of-Experts Language Models","created":"2026-06-15","cited_by_count":4,"area":"moe"},{"id":"2505.23926","title":"Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation","created":"2025-05-29","cited_by_count":3,"area":"moe"},{"id":"2509.25934","title":"UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression","created":"2025-09-30","cited_by_count":3,"area":"moe"},{"id":"2501.16103","title":"Static Batching of Irregular Workloads on GPUs: Framework and Application to Efficient MoE Model Inference","created":"2025-01-27","cited_by_count":2,"area":"moe"},{"id":"2510.07205","title":"Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts","created":"2025-10-08","cited_by_count":2,"area":"moe"},{"id":"2512.18674","title":"Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing","created":"2025-12-21","cited_by_count":2,"area":"moe"},{"id":"2601.01310","title":"Making MoE-based LLM Inference Resilient with Tarragon","created":"2026-01-04","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":"2602.17798","title":"Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds","created":"2026-02-19","cited_by_count":2,"area":"moe"},{"id":"2603.28768","title":"CRAFT: Fine-Grained Cost-Aware Expert Replication For Efficient Mixture-of-Experts Serving","created":"2026-01-12","cited_by_count":2,"area":"moe"},{"id":"2604.00801","title":"Routing-Free Mixture-of-Experts","created":"2026-04-01","cited_by_count":2,"area":"moe"},{"id":"2604.18788","title":"Efficient Mixture-of-Experts LLM Inference with Apple Silicon NPUs","created":"2026-04-20","cited_by_count":2,"area":"moe"},{"id":"2605.10670","title":"Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference","created":"2026-05-11","cited_by_count":2,"area":"moe"},{"id":"2605.11689","title":"Slicing and Dicing: Configuring Optimal Mixtures of Experts","created":"2026-05-12","cited_by_count":2,"area":"moe"},{"id":"2606.10493","title":"Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design","created":"2026-06-09","cited_by_count":2,"area":"moe"},{"id":"2606.15177","title":"Coordinated Scheduling for MoE LLM Serving","created":"2026-06-13","cited_by_count":2,"area":"moe"},{"id":"2509.23830","title":"Bayesian Mixture-of-Experts: Towards Making LLMs Know What They Don't Know","created":"2025-09-28","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":"2604.03044","title":"JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency","created":"2026-04-03","cited_by_count":1,"area":"moe"},{"id":"2604.20551","title":"On Bayesian Softmax-Gated Mixture-of-Experts Models","created":"2026-04-22","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":"2502.17187","title":"Evaluating Expert Contributions in a MoE LLM for Quiz-Based Tasks","created":"2025-02-24","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":"2605.11005","title":"DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism","created":"2026-05-10","cited_by_count":0,"area":"moe"},{"id":"2608.09119","title":"Motif 3: Technical Report","created":"2026-08-10","cited_by_count":0,"area":"moe"},{"id":"2608.10605","title":"Compute-Optimal Is Not Cluster-Optimal: Systems-Aware Scaling for Sparse Mixture-of-Experts","created":"2026-08-11","cited_by_count":0,"area":"moe"},{"id":"2608.10823","title":"MoE Proxy Models for Low-Cost Failure Reproduction and Diagnosis in LLM RL Post-Training","created":"2026-08-11","cited_by_count":0,"area":"moe"},{"id":"2608.11623","title":"FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting","created":"2026-08-12","cited_by_count":0,"area":"moe"},{"id":"2609.03501","title":"Towards a Statistical Understanding of Mixture-of-Experts","created":"2026-09-03","cited_by_count":0,"area":"moe"},{"id":"2609.32581","title":"HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion","created":"2026-09-26","cited_by_count":0,"area":"moe"},{"id":"2609.33385","title":"OLED-MoE: Accelerating MoE-Based dLLM Inference via Inter-Iteration Locality-Aware Expert Offloading","created":"2026-09-27","cited_by_count":0,"area":"moe"},{"id":"2609.36301","title":"MoRE: Scaling mixture of experts with hardware-aware low-rank routing","created":"2026-09-28","cited_by_count":0,"area":"moe"},{"id":"2610.06677","title":"How Sparse Probability Maps Shape Mixture-of-Experts Routing","created":"2026-10-05","cited_by_count":0,"area":"moe"},{"id":"2610.06725","title":"BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models","created":"2026-10-05","cited_by_count":0,"area":"moe"},{"id":"2610.07333","title":"Memory-Efficient Expert Routing for Distributed MoE Training","created":"2026-10-05","cited_by_count":0,"area":"moe"},{"id":"2610.09342","title":"Shared Low-rank Basis Factorization for Data-free Mixture-of-Experts Compression","created":"2026-10-07","cited_by_count":0,"area":"moe"},{"id":"2610.09372","title":"Expert Coupling in MoE Pretraining: Reducing All-to-All Overhead with Correlated Placement and Token Shuffling","created":"2026-10-07","cited_by_count":0,"area":"moe"},{"id":"2610.11775","title":"RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing","created":"2026-10-08","cited_by_count":0,"area":"moe"}],"next_cursor":null}