LatentVerse is a new framework that provides a web-based visual analytics platform and a command-line interface for analyzing multimodal latent representations. It unifies diagnostics for representation quality metrics and extends analysis to multimodal settings by decomposing embeddings into shared and modality-specific components. The authors evaluate the tool through simulations, real biomedical data analyses, and a user study, demonstrating its utility for interpretable evaluation of foundation model representations.
arXiv:2606. 12362v1 Announce Type: cross Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made.
arXiv:2606. 06285v1 Announce Type: new Abstract: Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks.
arXiv:2602. 00462v4 Announce Type: replace-cross Abstract: Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM.
MolEmb is a lightweight framework that adapts multimodal large language models (MLLMs) to serve as general molecular embedding models. By aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective, MolEmb produces embeddings conditioned on both a molecular profile and a natural‑language semantic context. The model performs competitively on molecular property prediction and enables cross‑modal molecule‑text retrieval, while the newly introduced MolCAR benchmark demonstrates that context‑aware molecular embedding is largely a data property of the supervision.
Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding. However, gene expression data acquisition remains constrained by high costs and privacy concerns, limiting its use in multimodal research and AI-driven applications.
arXiv:2607. 07673v1 Announce Type: cross Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams.
Case2Flow is a new task that retrieves the most relevant guideline flowchart for a given patient case from a collection of medical guideline documents. The authors created FlowAtlas, a curated corpus of 202 flowcharts extracted from 2,080 guidelines, and a pipeline that generates 1,911 aligned case‑flowchart pairs. Their evaluation shows that existing multimodal retrieval methods often overrely on keywords and spurious token‑patch matches, and they propose CRISP, a training‑free scoring method that improves Recall@1 by up to 18.71 percentage points and gains preliminary feasibility evidence from a blinded physician assessment.
arXiv:2607. 21941v1 Announce Type: new Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their reactions.
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv:2607. 21343v1 Announce Type: cross Abstract: Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding.
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
arXiv:2510. 09764v2 Announce Type: replace Abstract: Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide complementary perspectives on interconnected physiological processes.