Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts

• URL: http://arxiv.org/abs/2605.04952v1
• Date: Wed, 06 May 2026 14:15:10 GMT
• Title: Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts
• Abstract summary: Mixture-of-experts (MoE) models enable scalable transformer architectures by activating only a subset of experts per token.<n>Recent evidence suggests that performance improves with increasingly granular experts, i.e., many small experts instead of a few large ones.<n>We introduce adaptive inverted-index routing for MoE (AIR-MoE), an inverted-index-inspired routing architecture based on vector quantization (VQ)
• Score: 49.09151538536423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixture-of-experts (MoE) models enable scalable transformer architectures by activating only a subset of experts per token. Recent evidence suggests that performance improves with increasingly granular experts, i.e., many small experts instead of a few large ones. However, this regime substantially increases routing cost, which can dominate computation. We introduce adaptive inverted-index routing for MoE (AIR-MoE), an inverted-index-inspired routing architecture based on vector quantization (VQ). In a first stage, AIR-MoE performs coarse shortlisting by assigning tokens to VQ codewords to construct a candidate set of experts. In a second stage, fine scoring computes exact routing scores restricted to this shortlist. This two-stage procedure approximates true top-k routing while avoiding full expert scoring and, in contrast to prior work, imposing no structural constraints on expert parameters. AIR-MoE serves as a drop-in replacement for standard routers and requires no modifications to the model architecture or loss function. We further provide a lower bound on the mass recall achieved by AIR-MoE that yields insights into its inner workings. Empirically, we demonstrate that AIR-MoE achieves improved performance compared to existing routing approaches in granular MoE settings.

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