TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts (2026.findings-acl)
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| Challenge: | Existing compression approaches remove entire experts, disrupting routing topology and harming performance, or rely on unstructured weight pruning with limited practical efficiency. |
| Approach: | They propose a structured **T**rapezoidal **E**xpert **N**euron **P**running framework that uses a trapezoidal pattern to identify and retain important experts while applying expert neuron pruning (ENP) to less important experts. |
| Outcome: | The proposed framework outperforms the full-parameter model by 10% on code generation tasks under a sparse activation of experts and a 40% routing sparsity. |
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