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This paper introduces MM-Spectrum, a sparse Mixture-of-Experts framework designed to enhance molecular structure inference from multimodal spectroscopic data. By addressing the challenges of multimodal imbalance through a modality-aware routing mechanism and heterogeneous expert capacities, MM-Spectrum effectively extracts both unique and synergistic information from diverse spectral inputs. The method demonstrates significant performance improvements across various settings, including full-modality and missing-modality scenarios, as validated by comprehensive ablation studies and interpretability analyses.
MM-Spectrum achieves substantial performance gains in molecular structure elucidation by intelligently routing multimodal data, revealing the power of tailored expert mechanisms in handling spectral heterogeneity.
Inferring molecular structures from multimodal spectroscopic measurements requires integrating complementary yet highly heterogeneous signals. However, the common paradigm of directly concatenating multispectral sequences can exhibit anomalous performance degradation, primarily due to pronounced heterogeneity and the resulting multimodal imbalance across modalities. As a remedy, we propose MM-Spectrum, a sparse Mixture-of-Experts framework tailored for multimodal multispectral spectra-to-structure elucidation. To better match the information characteristics under multispectral imbalance, MM-Spectrum introduces an explicit modality-aware routing mechanism that exposes spectral identity to the router in addition to token content representations. Moreover, it incorporates shared and interaction experts, together with heterogeneous expert capacities, to extract multispectral modality-unique and cross-modal synergistic information while suppressing noise-induced interference. Across full-modality, bimodal, and missing-modality settings on molecular structural elucidation, MM-Spectrum achieves consistent and substantial improvements, supported by ablation studies and interpretability analyses.
