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

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

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 reproducibilit

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