MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE Framework
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-Sp
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- PossiblePossibly related (embedding) · 50%Vibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set - Nature →
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“Fuzzy title match (0.73): “MM-Spectrum: Multimodal Multi-spectral Molecular Structural ” ≈ “stabilityai/stable-diffusion-3.5-large””
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- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “MM-Spectrum: Multimodal Multi-spectral Molecular Structural ” ≈ “CompVis/stable-diffusion-v1-4””
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“Fuzzy title match (0.73): “MM-Spectrum: Multimodal Multi-spectral Molecular Structural ” ≈ “DLR-RM/stable-baselines3””
- LinkedLinked via arxiv author · 85%Hai-tao Yu →
“MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE Framework”