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arXiv · 2505.18451

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts

Toshiaki Koike-Akino·Jing Liu·Ye Wang
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Abstract

To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these rely on calibration data, domain shift may arise for unknown downstream tasks. With a computationally efficient calibration, activation-aware pruning can be executed for every prompt adaptively, yet achieving reduced complexity at inference. We formulate it as a mixture of micro-experts, called $\mu$-MoE. Several experiments demonstrate that $\mu$-MoE can dynamically adapt to task/prompt-dependent structured sparsity on the fly.

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Toshiaki Koike-Akino, Jing Liu, Ye Wang. 2025-05-24. $\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts. https://arxiv.org/abs/2505.18451

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