LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations

Abstract:Latent embeddings have become a central data abstraction in modern machine learning, especially in biomedicine, where foundation models are increasingly used to encode multimodal data like clinical text, medical images, omics, and physiological signals. However, the utility and value of these representations depends on understanding their quality, structure, and the information they encode. Existing analysis workflows for evaluating representations remain fragmented across custom scripts, isolated metrics, and most importantly lack multimodal analysis, limiting accessibility and reproducibility. We present LatentVerse, a representation analysis resource that combines a web-based visual analytics platform for accessible, report-driven exploration with a command-line interface for scalable technical workflows. LatentVerse unifies diagnostics for various representation quality metrics and extends to multimodal settings by decomposing embeddings into shared and modality-specific components. We evaluate LatentVerse through controlled unimodal and multimodal simulations, discovery-oriented analyses on real biomedical embeddings, and a user study across diverse use cases. By supporting thorough and interpretable evaluation of latent spaces, LatentVerse makes foundation model representations more understandable in biomedical and data science applications.

27 pages, 7 figures, 2 tables. Code: this https URL

Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)

arXiv:2609.12364 [cs.LG]

(or arXiv:2609.12364v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.12364 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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