{"total":29,"rows":[{"id":"2309.05444","title":"Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning","created":"2023-09-11","cited_by_count":150,"area":"moe"},{"id":"2407.04153","title":"Mixture of A Million Experts","created":"2024-07-04","cited_by_count":72,"area":"moe"},{"id":"2407.09590","title":"Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts","created":"2024-07-12","cited_by_count":37,"area":"moe"},{"id":"2311.09179","title":"SiRA: Sparse Mixture of Low Rank Adaptation","created":"2023-11-15","cited_by_count":32,"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":"2403.11808","title":"Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation","created":"2024-03-18","cited_by_count":25,"area":"moe"},{"id":"2412.05552","title":"SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts","created":"2024-12-07","cited_by_count":16,"area":"moe"},{"id":"2507.11181","title":"Mixture of Experts in Large Language Models","created":"2025-07-15","cited_by_count":15,"area":"moe"},{"id":"2406.20030","title":"LEMoE: Advanced Mixture of Experts Adaptor for Lifelong Model Editing of Large Language Models","created":"2024-06-28","cited_by_count":14,"area":"moe"},{"id":"2503.23100","title":"MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models","created":"2025-03-29","cited_by_count":10,"area":"moe"},{"id":"2509.16882","title":"Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation","created":"2025-09-21","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":"2402.15082","title":"PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning","created":"2024-02-23","cited_by_count":7,"area":"moe"},{"id":"2504.21190","title":"TT-LoRA MoE: Unifying Parameter-Efficient Fine-Tuning and Sparse Mixture-of-Experts","created":"2025-04-29","cited_by_count":6,"area":"moe"},{"id":"2402.00828","title":"Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of Adapters","created":"2024-02-01","cited_by_count":5,"area":"moe"},{"id":"2406.04984","title":"MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter","created":"2024-06-07","cited_by_count":5,"area":"moe"},{"id":"2412.11864","title":"Investigating Mixture of Experts in Dense Retrieval","created":"2024-12-16","cited_by_count":4,"area":"moe"},{"id":"2603.00573","title":"CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging","created":"2026-02-28","cited_by_count":4,"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":"2506.05928","title":"MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models","created":"2025-06-06","cited_by_count":3,"area":"moe"},{"id":"2509.10513","title":"Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning","created":"2025-09-03","cited_by_count":3,"area":"moe"},{"id":"2510.15683","title":"Mixture of Experts Approaches in Dense Retrieval Tasks","created":"2025-10-17","cited_by_count":2,"area":"moe"},{"id":"2602.06862","title":"Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing","created":"2026-02-06","cited_by_count":1,"area":"moe"},{"id":"2605.25565","title":"RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism","created":"2026-05-25","cited_by_count":1,"area":"moe"},{"id":"2602.08818","title":"FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models","created":"2026-02-09","cited_by_count":0,"area":"moe"},{"id":"2604.01762","title":"FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models","created":"2026-04-02","cited_by_count":0,"area":"moe"},{"id":"2608.29790","title":"HiVe: Beyond Static Prompts for Multitask Learning via Hierarchy-based Vertical Mixture-of-Experts","created":"2026-08-30","cited_by_count":0,"area":"moe"},{"id":"2609.39445","title":"Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models","created":"2026-09-30","cited_by_count":0,"area":"moe"}],"next_cursor":null}