MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE Framework

2026-08-27

AI Summary

本文提出MM-Spectrum，通过多模态感知路由机制和混合专家框架解决多光谱分子结构解析中的信号异质性和不平衡问题。

Abstract

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.

Problem

• multimodal spectroscopic measurements
• heterogeneous signals
• performance degradation
• multispectral imbalance

Innovation

• sparse Mixture-of-Experts
• modality-aware routing mechanism
• shared and interaction experts
• multimodal multispectral spectra-to-structure elucidation