{"total":16,"rows":[{"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":"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":"2505.00315","title":"Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing","created":"2025-05-01","cited_by_count":14,"area":"moe"},{"id":"2506.21103","title":"Learning to Skip the Middle Layers of Transformers","created":"2025-06-26","cited_by_count":10,"area":"moe"},{"id":"2505.07260","title":"UMoE: Unifying Attention and FFN with Shared Experts","created":"2025-05-12","cited_by_count":6,"area":"moe"},{"id":"2511.06494","title":"Route Experts by Sequence, not by Token","created":"2025-11-09","cited_by_count":6,"area":"moe"},{"id":"2502.00281","title":"Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self-Attention: A Mixture-of-Experts Perspective","created":"2025-02-01","cited_by_count":4,"area":"moe"},{"id":"2510.00570","title":"Adaptive Shared Experts with LoRA-Based Mixture of Experts for Multi-Task Learning","created":"2025-10-01","cited_by_count":3,"area":"moe"},{"id":"2606.20945","title":"Grouped Query Experts: Mixture-of-Experts on GQA Self-Attention","created":"2026-06-18","cited_by_count":2,"area":"moe"},{"id":"2506.13541","title":"Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization","created":"2025-06-16","cited_by_count":1,"area":"moe"},{"id":"2601.15370","title":"Improving MoE Compute Efficiency by Composing Weight and Data Sparsity","created":"2026-01-21","cited_by_count":1,"area":"moe"},{"id":"2504.08989","title":"RouterKT: Mixture-of-Experts for Knowledge Tracing","created":"2025-04-11","cited_by_count":0,"area":"moe"},{"id":"2601.08631","title":"M$^2$FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting","created":"2026-01-13","cited_by_count":0,"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":"2609.39137","title":"ID Balancing: Stable Training of Extremely Sparse MoE via PID-Based Load Control","created":"2026-09-30","cited_by_count":0,"area":"moe"},{"id":"2610.07207","title":"Distributionally Robust Mixture-of-Experts Training","created":"2026-10-05","cited_by_count":0,"area":"moe"}],"next_cursor":null}