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arXiv Machine Learning By Hai-tao Yu, Nan Min, Zheng Fang, Hongyu Zhan, Yusen Tan, Yuhan Wang, Jun Xia

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

Read the original on arXiv Machine Learning →

MM‑Spectrum is a sparse Mixture‑of‑Experts framework designed to infer molecular structures from multimodal spectroscopic data. It introduces a modality‑aware routing mechanism that exposes spectral identity to the router, along with shared and interaction experts of heterogeneous capacities to capture both modality‑unique and cross‑modal synergistic information while reducing noise interference. Experiments across full‑modality, bimodal, and missing‑modality scenarios show consistent and substantial performance gains, supported by ablation studies and interpretability analyses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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