{"total":56,"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":"2502.17298","title":"Delta Decompression for MoE-based LLMs Compression","created":"2025-02-24","cited_by_count":33,"area":"moe"},{"id":"2506.23266","title":"Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging","created":"2025-06-29","cited_by_count":28,"area":"moe"},{"id":"2509.10377","title":"Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs","created":"2025-09-12","cited_by_count":25,"area":"moe"},{"id":"2505.19645","title":"MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE","created":"2025-05-26","cited_by_count":19,"area":"moe"},{"id":"2508.01625","title":"EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models","created":"2025-08-03","cited_by_count":18,"area":"moe"},{"id":"2507.23279","title":"Unveiling Super Experts in Mixture-of-Experts Large Language Models","created":"2025-07-31","cited_by_count":17,"area":"moe"},{"id":"2411.08982","title":"Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection","created":"2024-11-13","cited_by_count":16,"area":"moe"},{"id":"2601.18089","title":"LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts","created":"2026-01-26","cited_by_count":14,"area":"moe"},{"id":"2507.00390","title":"MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE","created":"2025-07-01","cited_by_count":13,"area":"moe"},{"id":"2602.16052","title":"MoE-Spec: Expert Budgeting for Efficient Speculative Decoding","created":"2026-02-17","cited_by_count":12,"area":"moe"},{"id":"2510.14436","title":"MergeMoE: Efficient Compression of MoE Models via Expert Output Merging","created":"2025-10-16","cited_by_count":10,"area":"moe"},{"id":"2604.04356","title":"REAM: Merging Improves Pruning of Experts in LLMs","created":"2026-04-06","cited_by_count":10,"area":"moe"},{"id":"2505.03531","title":"Faster MoE LLM Inference for Extremely Large Models","created":"2025-05-06","cited_by_count":9,"area":"moe"},{"id":"2508.05257","title":"MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs","created":"2025-08-07","cited_by_count":9,"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":"2508.02322","title":"CAMERA: Multi-Matrix Joint Compression for MoE Models via Micro-Expert Redundancy Analysis","created":"2025-08-04","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":"2602.09316","title":"Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density","created":"2026-02-10","cited_by_count":7,"area":"moe"},{"id":"2503.00634","title":"Efficiently Editing Mixture-of-Experts Models with Compressed Experts","created":"2025-03-01","cited_by_count":6,"area":"moe"},{"id":"2509.02753","title":"LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference","created":"2025-09-02","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":"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":"2503.22517","title":"Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities","created":"2025-03-28","cited_by_count":3,"area":"moe"},{"id":"2509.22299","title":"HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space","created":"2025-09-26","cited_by_count":3,"area":"moe"},{"id":"2509.25689","title":"Collaborative Compression for Large-Scale MoE Deployment on Edge","created":"2025-09-30","cited_by_count":3,"area":"moe"},{"id":"2604.20156","title":"Temporally Extended Mixture-of-Experts Models","created":"2026-04-22","cited_by_count":3,"area":"moe"},{"id":"2603.12645","title":"LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing","created":"2026-03-13","cited_by_count":2,"area":"moe"},{"id":"2604.06515","title":"Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees","created":"2026-04-07","cited_by_count":2,"area":"moe"},{"id":"2606.05538","title":"Less is MoE: Trimming Experts in Domain-Specialist Language Models","created":"2026-06-04","cited_by_count":2,"area":"moe"},{"id":"2606.10703","title":"From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models","created":"2026-06-09","cited_by_count":2,"area":"moe"},{"id":"2607.08601","title":"It Takes a MAESTRO To Prune Bad Experts","created":"2026-07-09","cited_by_count":2,"area":"moe"},{"id":"2511.19480","title":"Exploiting the Experts: Unauthorized Compression in MoE-LLMs","created":"2025-11-22","cited_by_count":1,"area":"moe"},{"id":"2604.10603","title":"MoEITS: A Green AI approach for simplifying MoE-LLMs","created":"2026-04-12","cited_by_count":1,"area":"moe"},{"id":"2605.13997","title":"HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts","created":"2026-05-13","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":"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":"2608.15299","title":"MAPLE: MoE Adaptive Plug-and-play Layer-wise Expert allocation","created":"2026-08-15","cited_by_count":1,"area":"moe"},{"id":"2601.14327","title":"Yuan3.0 Ultra: A Trillion-Parameter Enterprise-Oriented MoE LLM","created":"2026-01-20","cited_by_count":0,"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":"2608.05303","title":"EdgeXpert: An Edge Device for Memory-Efficient LLM Inference with Mixture-of-Experts and Speculative Decoding","created":"2026-08-05","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":"2608.10823","title":"MoE Proxy Models for Low-Cost Failure Reproduction and Diagnosis in LLM RL Post-Training","created":"2026-08-11","cited_by_count":0,"area":"moe"},{"id":"2609.01024","title":"PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition","created":"2026-09-01","cited_by_count":0,"area":"moe"},{"id":"2609.05228","title":"ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs","created":"2026-09-04","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":"2609.30465","title":"RAZOR: Pruning Replaceable Experts in LLMs","created":"2026-09-24","cited_by_count":0,"area":"moe"},{"id":"2609.33614","title":"EAT: Expert Account Tracker for Efficient MoE Inference","created":"2026-09-27","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":"2609.38823","title":"DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference","created":"2026-09-30","cited_by_count":0,"area":"moe"},{"id":"2610.00367","title":"MoRA: MoE Pruning via Router Bias Learning and Expert Approximation","created":"2026-09-30","cited_by_count":0,"area":"moe"},{"id":"2610.09342","title":"Shared Low-rank Basis Factorization for Data-free Mixture-of-Experts Compression","created":"2026-10-07","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":"2610.15578","title":"Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning","created":"2026-10-11","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}