{"total":33,"rows":[{"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":"2511.04805","title":"PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference","created":"2025-11-06","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":"2512.18425","title":"MoE Pathfinder: Trajectory-driven Expert Pruning","created":"2025-12-20","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":"2603.18492","title":"AIMER: Calibration-Free Task-Agnostic MoE Expert Pruning","created":"2026-03-19","cited_by_count":5,"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":"2605.18643","title":"Post-Trained MoE Can Skip Half Experts via Self-Distillation","created":"2026-05-18","cited_by_count":4,"area":"moe"},{"id":"2505.13052","title":"Model Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures","created":"2025-05-19","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":"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":"2603.12645","title":"LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing","created":"2026-03-13","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":"2607.08601","title":"It Takes a MAESTRO To Prune Bad Experts","created":"2026-07-09","cited_by_count":2,"area":"moe"},{"id":"2608.08627","title":"UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models","created":"2026-08-09","cited_by_count":2,"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":"2605.14438","title":"BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE","created":"2026-05-14","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":"2608.07814","title":"Shape Mutating Expert Compression:LorExperts and BTExperts","created":"2026-08-07","cited_by_count":0,"area":"moe"},{"id":"2608.26650","title":"Meta-Learning Where to Allocate Experts: Task-Conditioned Layer-Wise Compression for MoEs","created":"2026-08-27","cited_by_count":0,"area":"moe"},{"id":"2609.00575","title":"Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs","created":"2026-09-01","cited_by_count":0,"area":"moe"},{"id":"2609.04453","title":"When Load-Balancing Goes Too Far: Expert Pruning in Over-Dispersed Mixture-of-Experts Models","created":"2026-09-03","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":"2609.31631","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":"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":"2610.00367","title":"MoRA: MoE Pruning via Router Bias Learning and Expert Approximation","created":"2026-09-30","cited_by_count":0,"area":"moe"},{"id":"2610.03265","title":"SPEAR: A Spectral-Disentangled MoE Neural Operator with Knowledge-Guided Expert Aggregation for Large-Scale PDE Pretraining","created":"2026-10-02","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}