{"total":15,"rows":[{"id":"2509.17863","title":"Expert-as-a-Service: Towards Efficient, Scalable, and Robust Large-scale MoE Serving","created":"2025-09-22","cited_by_count":13,"area":"moe"},{"id":"2410.12247","title":"EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference","created":"2024-10-16","cited_by_count":6,"area":"moe"},{"id":"2603.09983","title":"MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios","created":"2026-02-12","cited_by_count":5,"area":"moe"},{"id":"2504.09345","title":"MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints","created":"2025-04-12","cited_by_count":3,"area":"moe"},{"id":"2607.00466","title":"ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving","created":"2026-07-01","cited_by_count":3,"area":"moe"},{"id":"2605.02960","title":"MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving","created":"2026-05-03","cited_by_count":2,"area":"moe"},{"id":"2604.03044","title":"JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency","created":"2026-04-03","cited_by_count":1,"area":"moe"},{"id":"2607.18002","title":"ExpertPlex: A High-Goodput Disaggregated Serving System for MoE LLMs with Adaptive Persistent Kernels","created":"2026-07-20","cited_by_count":1,"area":"moe"},{"id":"2510.20111","title":"AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training","created":"2025-10-23","cited_by_count":0,"area":"moe"},{"id":"2604.02715","title":"FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving","created":"2026-04-03","cited_by_count":0,"area":"moe"},{"id":"2608.04502","title":"AFD-Ledger: Deployment Provisioning for Attention--FFN Disaggregation","created":"2026-08-05","cited_by_count":0,"area":"moe"},{"id":"2609.13537","title":"Dynamic HBM Repartitioning for Multi-Turn MoE Serving","created":"2026-09-11","cited_by_count":0,"area":"moe"},{"id":"2609.14237","title":"OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving","created":"2026-09-13","cited_by_count":0,"area":"moe"},{"id":"2609.14507","title":"Physically Partitioned KVCache Format for CPU--GPU Load Balancing in MoE Inference","created":"2026-09-13","cited_by_count":0,"area":"moe"},{"id":"2609.34117","title":"SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving","created":"2026-09-28","cited_by_count":0,"area":"moe"}],"next_cursor":null}