Generalizing GNNs with Tokenized Mixture of Experts

Abstract

Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations. We show that static inference induces a fundamental tradeoff: improving stability requires reducing reliance on shift-sensitive features, leaving an irreducible worst-case generalization floor. Instance-conditional routing can break this ceiling, but is fragile because shifts can mislead routing and perturbations can make routing fluctuate. We capture these effects via two decompositions separating coverage vs selection, and base sens

Research Question

How robust is SMoES to cross-domain generalization and modality imbalance in multimodal benchmarks like MMBench and SEED-Bench, compared to hard-routing MoE baselines, under controlled perturbations to image-text alignment?

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