{"total":14,"rows":[{"id":"2502.07972","title":"Training Sparse Mixture Of Experts Text Embedding Models","created":"2025-02-11","cited_by_count":32,"area":"moe"},{"id":"2411.15708","title":"LLaMA-MoE v2: Exploring Sparsity of LLaMA from Perspective of Mixture-of-Experts with Post-Training","created":"2024-11-24","cited_by_count":26,"area":"moe"},{"id":"2508.19594","title":"Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs","created":"2025-08-27","cited_by_count":12,"area":"moe"},{"id":"2506.09440","title":"GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture","created":"2025-06-11","cited_by_count":8,"area":"moe"},{"id":"2601.03425","title":"The Illusion of Specialization: Unveiling the Domain-Invariant \"Standing Committee\" in Mixture-of-Experts Models","created":"2026-01-06","cited_by_count":6,"area":"moe"},{"id":"2510.02345","title":"Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression","created":"2025-09-27","cited_by_count":3,"area":"moe"},{"id":"2505.14173","title":"THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation","created":"2025-05-20","cited_by_count":2,"area":"moe"},{"id":"2604.17837","title":"Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs","created":"2026-04-20","cited_by_count":2,"area":"moe"},{"id":"2503.22996","title":"Rethinking Sparse Mixture of Experts from a Unified Perspective","created":"2025-03-29","cited_by_count":1,"area":"moe"},{"id":"2605.30992","title":"Eigenvectors of Experts are Training-free Non-collapsing Routers","created":"2026-05-29","cited_by_count":1,"area":"moe"},{"id":"2608.06396","title":"TEXAS: Task-Expert-Aware Supervision for Downstream Mixture-of-Experts LLM Adaptation","created":"2026-07-31","cited_by_count":0,"area":"moe"},{"id":"2609.08663","title":"MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models","created":"2026-09-08","cited_by_count":0,"area":"moe"},{"id":"2609.13486","title":"Mixture-of-Experts Language Models Can Be Strong and Efficient Retrievers","created":"2026-09-11","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}