Cover Image

MoE Pathfinder: Trajectory-driven Expert Pruning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Xican, Tian, Yuanhe, Song, Yan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Frustratingly Easy Task-aware Pruning for Large Language Models
by: Tian, Yuanhe, et al.
Published: (2025)

MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE
by: Zhang, Geng, et al.
Published: (2025)

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
by: Liu, Zongfang, et al.
Published: (2026)

$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)

MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
by: Yang, Cheng, et al.
Published: (2024)

STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning
by: Lee, Jaeseong, et al.
Published: (2024)

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
by: Lasby, Mike, et al.
Published: (2025)

MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)

Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)

PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
by: Su, Yang, et al.
Published: (2025)

AIMER: Calibration-Free Task-Agnostic MoE Pruning
by: Liu, Zongfang, et al.
Published: (2026)

Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning
by: Li, Weihang, et al.
Published: (2026)

MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
by: Xue, Leyang, et al.
Published: (2024)

MoE Lens -- An Expert Is All You Need
by: Chaudhari, Marmik, et al.
Published: (2026)

FT-MoE: Sustainable-learning Mixture of Experts for Fault-Tolerant Computing
by: Xiao, Wenjing, et al.
Published: (2025)

MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)

MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
by: Xie, Yanyue, et al.
Published: (2024)

$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)

MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
by: Ma, Haiyue, et al.
Published: (2025)

MergeMoE: Efficient Compression of MoE Models via Expert Output Merging
by: Miao, Ruijie, et al.
Published: (2025)

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
by: Deng, Jianing, et al.
Published: (2026)

DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
by: Liu, Qingxiu, et al.
Published: (2026)

MoE-Spec: Expert Budgeting for Efficient Speculative Decoding
by: McDanel, Bradley, et al.
Published: (2026)

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning
by: Cao, Mingyu, et al.
Published: (2024)

SD-MoE: Spectral Decomposition for Effective Expert Specialization
by: Huang, Ruijun, et al.
Published: (2026)

Exploiting the Experts: Unauthorized Compression in MoE-LLMs
by: Neogi, Pinaki Prasad Guha, et al.
Published: (2025)

Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations
by: Hu, Wentao, et al.
Published: (2026)

Leave It to the Experts: Detecting Knowledge Distillation via MoE Expert Signatures
by: Li, Pingzhi, et al.
Published: (2025)

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)

MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
by: Xia, Xinfeng, et al.
Published: (2025)

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)

Fast MoE Inference via Predictive Prefetching and Expert Replication
by: Jyothish, Ankit, et al.
Published: (2026)

Expert Routing for Communication-Efficient MoE via Finite Expert Banks
by: Salehi, Mohammad Reza Deylam, et al.
Published: (2026)

SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)

CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
by: Son, Muyoung, et al.
Published: (2026)

Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)

Steering MoE LLMs via Expert (De)Activation
by: Fayyaz, Mohsen, et al.
Published: (2025)

SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)

Expert Divergence Learning for MoE-based Language Models
by: Li, Jiaang, et al.
Published: (2026)