{"total":19,"rows":[{"id":"2402.01739","title":"OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models","created":"2024-01-29","cited_by_count":187,"area":"moe"},{"id":"2209.01667","title":"A Review of Sparse Expert Models in Deep Learning","created":"2022-09-04","cited_by_count":148,"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":"2501.12370","title":"Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models","created":"2025-01-21","cited_by_count":50,"area":"moe"},{"id":"2505.22323","title":"Advancing Expert Specialization for Better MoE","created":"2025-05-28","cited_by_count":39,"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":"2609.01343","title":"SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers","created":"2026-09-01","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":"2405.13053","title":"MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models","created":"2024-05-19","cited_by_count":16,"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":"2508.05257","title":"MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs","created":"2025-08-07","cited_by_count":9,"area":"moe"},{"id":"2410.00938","title":"MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards","created":"2024-10-01","cited_by_count":7,"area":"moe"},{"id":"2504.07964","title":"C3PO: Critical-Layer, Core-Expert, Collaborative Pathway Optimization for Test-Time Expert Re-Mixing","created":"2025-04-10","cited_by_count":7,"area":"moe"},{"id":"2508.12851","title":"Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement","created":"2025-08-18","cited_by_count":7,"area":"moe"},{"id":"2511.07419","title":"Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs","created":"2025-11-10","cited_by_count":7,"area":"moe"},{"id":"2411.08968","title":"Sparse Upcycling: Inference Inefficient Finetuning","created":"2024-11-13","cited_by_count":2,"area":"moe"},{"id":"2510.11001","title":"DND: Boosting Large Language Models with Dynamic Nested Depth","created":"2025-10-13","cited_by_count":1,"area":"moe"},{"id":"2609.15126","title":"MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup","created":"2026-09-14","cited_by_count":0,"area":"moe"},{"id":"2610.02123","title":"Harnessing Domain Specialists in Multimodal Mixture-of-Experts for Efficient Adaptation","created":"2026-10-01","cited_by_count":0,"area":"moe"}],"next_cursor":null}