{"total":15,"rows":[{"id":"2412.14219","title":"A Survey on Inference Optimization Techniques for Mixture of Experts Models","created":"2024-12-18","cited_by_count":47,"area":"moe"},{"id":"2502.05370","title":"Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading","created":"2025-02-07","cited_by_count":24,"area":"moe"},{"id":"2509.23638","title":"LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers","created":"2025-09-28","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":"2602.07265","title":"XShare: Collaborative in-Batch Expert Sharing for Faster MoE Inference","created":"2026-02-06","cited_by_count":7,"area":"moe"},{"id":"2510.05497","title":"Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference","created":"2025-10-07","cited_by_count":6,"area":"moe"},{"id":"2602.07616","title":"SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models","created":"2026-02-07","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":"2512.18674","title":"Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing","created":"2025-12-21","cited_by_count":2,"area":"moe"},{"id":"2608.24938","title":"ExFold: Unified Expert Folding for Training-Free MoE Prefill-Decode Acceleration","created":"2026-08-24","cited_by_count":2,"area":"moe"},{"id":"2508.09229","title":"Cluster Topology-Driven Placement of Experts Reduces Network Traffic in MoE Inference","created":"2025-08-12","cited_by_count":1,"area":"moe"},{"id":"2603.27624","title":"Expert Streaming: Accelerating Low-Batch MoE Inference via Multi-chiplet Architecture and Dynamic Expert Trajectory Scheduling","created":"2026-03-29","cited_by_count":0,"area":"moe"},{"id":"2607.05116","title":"Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference","created":"2026-07-06","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"},{"id":"2609.36222","title":"BASE: Batch-Aware Selection of Experts Using Predicted Removal Error for Efficient MoE Decoding","created":"2026-09-28","cited_by_count":0,"area":"moe"}],"next_cursor":null}