{"total":21,"rows":[{"id":"2603.06003","title":"EvoESAP: Non-Uniform Expert Pruning for Sparse MoE","created":"2026-03-06","cited_by_count":9,"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":"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":"2603.02217","title":"Is Retraining-Free Enough? The Necessity of Router Calibration for Efficient MoE Compression","created":"2026-02-10","cited_by_count":4,"area":"moe"},{"id":"2606.01062","title":"DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts","created":"2026-05-31","cited_by_count":3,"area":"moe"},{"id":"2606.09886","title":"SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs","created":"2026-06-03","cited_by_count":3,"area":"moe"},{"id":"2605.06665","title":"UniPool: A Globally Shared Expert Pool for Mixture-of-Experts","created":"2026-05-07","cited_by_count":1,"area":"moe"},{"id":"2605.28207","title":"Pruning and Distilling Mixture-of-Experts into Dense Language Models","created":"2026-05-27","cited_by_count":1,"area":"moe"},{"id":"2605.29350","title":"ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression","created":"2026-05-28","cited_by_count":1,"area":"moe"},{"id":"2607.01444","title":"On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain","created":"2026-07-01","cited_by_count":1,"area":"moe"},{"id":"2606.09885","title":"TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts","created":"2026-06-03","cited_by_count":0,"area":"moe"},{"id":"2606.18304","title":"Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression","created":"2026-06-16","cited_by_count":0,"area":"moe"},{"id":"2606.27866","title":"FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models","created":"2026-06-26","cited_by_count":0,"area":"moe"},{"id":"2608.07890","title":"Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models","created":"2026-08-08","cited_by_count":0,"area":"moe"},{"id":"2609.06076","title":"Beyond Retraining-Free MoE Compression: A Cost-Normalized Study of Post-Compression Adjustment","created":"2026-09-05","cited_by_count":0,"area":"moe"},{"id":"2609.08663","title":"MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models","created":"2026-09-08","cited_by_count":0,"area":"moe"},{"id":"2609.25809","title":"You Only Need 2/3 of the Chosen Experts: An Empirical Study of Dynamic Expert Pruning in Fine-Grained MoE LLMs","created":"2026-09-22","cited_by_count":0,"area":"moe"},{"id":"2609.31631","title":"OMP-MoE: Efficient Expert Pruning for Mixture-of-Experts LLMs via Orthogonal Matching Pursuit","created":"2026-09-09","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":"2610.13982","title":"PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression","created":"2026-09-23","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}