{"total":30,"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":"2510.13999","title":"REAP the Experts: Why Pruning Prevails for One-Shot MoE compression","created":"2025-10-15","cited_by_count":44,"area":"moe"},{"id":"2504.06792","title":"Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations","created":"2025-04-09","cited_by_count":16,"area":"moe"},{"id":"2511.14102","title":"MoE-SpeQ: Speculative Quantized Decoding with Proactive Expert Prefetching and Offloading for Mixture-of-Experts","created":"2025-11-18","cited_by_count":11,"area":"moe"},{"id":"2506.13329","title":"EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization","created":"2025-06-16","cited_by_count":10,"area":"moe"},{"id":"2603.06003","title":"EvoESAP: Non-Uniform Expert Pruning for Sparse MoE","created":"2026-03-06","cited_by_count":9,"area":"moe"},{"id":"2505.16056","title":"Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models","created":"2025-05-21","cited_by_count":8,"area":"moe"},{"id":"2511.02237","title":"Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining","created":"2025-11-04","cited_by_count":7,"area":"moe"},{"id":"2603.05806","title":"MoE Lens -- An Expert Is All You Need","created":"2026-03-06","cited_by_count":6,"area":"moe"},{"id":"2606.15716","title":"How to Score Experts for One-Shot MoE Expert Pruning: A Unified Formulation and Selection Principle","created":"2026-06-14","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":"2605.00342","title":"Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding","created":"2026-05-01","cited_by_count":4,"area":"moe"},{"id":"2607.12696","title":"Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts","created":"2026-07-14","cited_by_count":3,"area":"moe"},{"id":"2607.17154","title":"OrderMoE: An expert similarity driven distributed edge MoE inference","created":"2026-07-19","cited_by_count":3,"area":"moe"},{"id":"2602.08019","title":"The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications","created":"2026-02-08","cited_by_count":2,"area":"moe"},{"id":"2512.03927","title":"OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference","created":"2025-12-03","cited_by_count":1,"area":"moe"},{"id":"2606.29982","title":"Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference","created":"2026-06-29","cited_by_count":1,"area":"moe"},{"id":"2608.18261","title":"Cacheable by Design? Training Mixture-of-Experts Routers for Locality Against the Edge Memory-Bandwidth Wall: A Pre-Registered Negative Result with a Systems Measurement Study","created":"2026-08-18","cited_by_count":1,"area":"moe"},{"id":"2505.06481","title":"QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration","created":"2025-05-10","cited_by_count":0,"area":"moe"},{"id":"2602.05711","title":"OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale","created":"2026-02-05","cited_by_count":0,"area":"moe"},{"id":"2605.11537","title":"Fast MoE Inference via Predictive Prefetching and Expert Replication","created":"2026-05-12","cited_by_count":0,"area":"moe"},{"id":"2605.17889","title":"CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution","created":"2026-05-18","cited_by_count":0,"area":"moe"},{"id":"2607.11444","title":"UMoE:Unlocking Every Expert in Domain-Specific Training","created":"2026-07-13","cited_by_count":0,"area":"moe"},{"id":"2607.20981","title":"Beyond Independent Optimization: Compression, MoE Routing, and Quantization Interactions in Multimodal Edge Intelligence","created":"2026-07-23","cited_by_count":0,"area":"moe"},{"id":"2607.22577","title":"cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs","created":"2026-06-06","cited_by_count":0,"area":"moe"},{"id":"2608.08650","title":"The Evolution of Mixture-of-Experts Architectures in Large Language Models: Routing, Topology, Load Balancing, and Expert Parallelism","created":"2026-08-09","cited_by_count":0,"area":"moe"},{"id":"2609.12978","title":"SeqMoE: Toward Full-Load Performance via Predictive and Graph-Compatible MoE Offloading","created":"2026-09-11","cited_by_count":0,"area":"moe"},{"id":"2609.18842","title":"Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data","created":"2026-09-16","cited_by_count":0,"area":"moe"},{"id":"2610.00558","title":"Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization","created":"2026-09-30","cited_by_count":0,"area":"moe"},{"id":"2610.16484","title":"SLEM: Spectrum-Aware Latent Expert Merging","created":"2026-10-12","cited_by_count":0,"area":"moe"}],"next_cursor":null}