Does predictive expert caching in SMoE multimodal models improve robustness to cross-modal distribution shifts

Abstract

Abstract: Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning architectures that generalize to the complex distributional shifts naturally occurring in the real world. Here, we develop GraphMETRO, a Graph Neural Network architecture that models natural diversity and captures complex distributional shifts. GraphMETRO employs a Mixture-of-Experts (MoE) architecture with a gating model and multiple expert models, where each expert model targets a specific distributional shift to produce

Research Question

Does predictive expert caching in SMoE multimodal models improve robustness to cross-modal distribution shifts on MMMU, measured by accuracy drop and throughput consistency compared to dense baselines?

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