LatentVerse：理解多模态潜在表示中共享与模态特定信息的框架
LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations
- Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）
- Massachusetts Institute of Technology（麻省理工学院）
- The Schmidt Center, Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所施密特中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
LatentVerse是一个结合可视化平台与命令行界面的表示分析框架，通过分解共享与模态特定组件，实现多模态潜在表示的质量评估，提升可理解性与可复现性。
AI中文摘要：
潜在嵌入已成为现代机器学习中的核心数据抽象，尤其是在生物医学领域，基础模型越来越多地被用于编码多模态数据，如临床文本、医学图像、组学数据和生理信号。然而，这些表示的有效性和价值取决于对其质量、结构及所编码信息的理解。现有的表示评估分析工作流仍然分散在自定义脚本和孤立指标中，最重要的是缺乏多模态分析，这限制了可访问性和可复现性。我们提出了LatentVerse，一个表示分析资源，它结合了基于Web的可视化分析平台，用于可访问的、报告驱动的探索，以及命令行界面，用于可扩展的技术工作流。LatentVerse统一了各种表示质量指标的诊断，并通过将嵌入分解为共享和模态特定组件，扩展到多模态设置。我们通过受控的单模态和多模态模拟、对真实生物医学嵌入的发现导向分析以及跨不同用例的用户研究来评估LatentVerse。通过支持对潜在空间的全面且可解释的评估，LatentVerse使基础模型表示在生物医学和数据科学应用中更加可理解。
英文摘要：
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.
