{"total":32,"rows":[{"id":"2402.08562","title":"Higher Layers Need More LoRA Experts","created":"2024-02-13","cited_by_count":102,"area":"moe"},{"id":"2405.14297","title":"Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models","created":"2024-05-23","cited_by_count":56,"area":"moe"},{"id":"2407.00945","title":"Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs","created":"2024-07-01","cited_by_count":53,"area":"moe"},{"id":"2603.03276","title":"Beyond Language Modeling: An Exploration of Multimodal Pretraining","created":"2026-03-03","cited_by_count":37,"area":"moe"},{"id":"2510.08396","title":"FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts","created":"2025-10-09","cited_by_count":35,"area":"moe"},{"id":"2311.02684","title":"Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE","created":"2023-11-05","cited_by_count":25,"area":"moe"},{"id":"2410.10054","title":"AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality","created":"2024-10-14","cited_by_count":20,"area":"moe"},{"id":"2405.17741","title":"LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design","created":"2024-05-28","cited_by_count":18,"area":"moe"},{"id":"2408.03511","title":"MoExtend: Tuning New Experts for Modality and Task Extension","created":"2024-08-07","cited_by_count":12,"area":"moe"},{"id":"2505.06685","title":"Emotion-Qwen: A Unified Framework for Emotion and Vision Understanding","created":"2025-05-10","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":"2502.15828","title":"A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models","created":"2025-02-20","cited_by_count":9,"area":"moe"},{"id":"2407.21571","title":"PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learning","created":"2024-07-31","cited_by_count":8,"area":"moe"},{"id":"2507.06567","title":"SlimCaching: Edge Caching of Mixture-of-Experts for Distributed Inference","created":"2025-07-09","cited_by_count":7,"area":"moe"},{"id":"2411.00918","title":"LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models","created":"2024-11-01","cited_by_count":6,"area":"moe"},{"id":"2506.14646","title":"GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors","created":"2025-06-17","cited_by_count":6,"area":"moe"},{"id":"2509.02753","title":"LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference","created":"2025-09-02","cited_by_count":6,"area":"moe"},{"id":"2509.02512","title":"MoPEQ: Mixture of Mixed Precision Quantized Experts","created":"2025-09-02","cited_by_count":5,"area":"moe"},{"id":"2603.12378","title":"NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation","created":"2026-03-12","cited_by_count":4,"area":"moe"},{"id":"2505.18451","title":"$\\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts","created":"2025-05-24","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":"2607.00162","title":"FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts","created":"2026-06-30","cited_by_count":3,"area":"moe"},{"id":"2502.03884","title":"Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning","created":"2025-02-06","cited_by_count":2,"area":"moe"},{"id":"2601.06356","title":"Monkey Jump : MoE-Style PEFT for Efficient Multi-Task Learning","created":"2026-01-09","cited_by_count":2,"area":"moe"},{"id":"2603.11873","title":"AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization","created":"2026-03-12","cited_by_count":2,"area":"moe"},{"id":"2604.16930","title":"CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering","created":"2026-04-18","cited_by_count":1,"area":"moe"},{"id":"2604.26340","title":"Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning","created":"2026-04-29","cited_by_count":1,"area":"moe"},{"id":"2604.02338","title":"LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning","created":"2026-02-01","cited_by_count":0,"area":"moe"},{"id":"2605.25952","title":"VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding","created":"2026-05-25","cited_by_count":0,"area":"moe"},{"id":"2609.05770","title":"RAPTOR: Role-Aware Private Training for Mixture-of-Experts","created":"2026-09-04","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"},{"id":"2610.19033","title":"How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts","created":"2026-10-13","cited_by_count":0,"area":"moe"}],"next_cursor":null}