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News/Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
arxiv
PublishedJune 18, 2026 at 4:00 AM
▲bullish

Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.18304v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale compute efficiently, yet remain expensive to deploy due to their substantial memory footprint and inference overhead. Prior compression methods mainly operate at the expert level, either removing entire experts o

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Discussion
Mentioned models
04
  • 01
    DeepSeek
  • 02
    Qwen
  • 03
    Qwen3-30B-A3B
  • 04
    Mixture-of-Experts (MoE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#compression#pruning#moe#quantization

No replies yet. Be first.

Mentioned models
04
  • 01
    DeepSeek
  • 02
    Qwen
  • 03
    Qwen3-30B-A3B
  • 04
    Mixture-of-Experts (MoE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#compression#pruning#moe#quantization

Related coverage

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arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets1darxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards1darxivContent associations with answer selection and engagement in supply chain community question answering1d
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