How does the latency-accuracy trade-off of Qwen3's dynamic expert specialization compare to fixed routing in M

Abstract

Abstract: Mixture-of-Experts (MoE) networks promise favorable accuracy-compute trade-offs, yet practical vision deployments are hindered by expert collapse and limited end-to-end efficiency gains. We study when sparse top-\$k\$ routing with hard capacity constraints helps in vision classification, evaluated under multi-seed protocols on four benchmarks (CIFAR-10/100, Tiny-ImageNet, ImageNet-1K). We observe a emph\compute-leverage pattern\: positive accuracy gaps require a substantial fraction \$ρ\$ of total FLOPs to be routed; at ImageNet scale this is necessary but not sufficient, as multi-expert routing

Research Question

How does the latency-accuracy trade-off of Qwen3's dynamic expert specialization compare to fixed routing in MoE models on multi-step reasoning tasks across varying batch sizes on the GQA benchmark?

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