The paper introduces a generalized multimodal foundation model that can handle arbitrary combinations of modalities and prediction tasks. It trains on large-scale synthetic multimodal datasets with diverse causal structures to learn transferable multimodal correlations. Experiments on 18 real-world datasets across 12 modalities and 11 tasks show competitive performance compared to specialized models without task-specific adaptation.
By Huizi Cui, Zongbo Han, Chenggong Ding, Naichuan Xiao, Jialong Yang, Jingdong Chen, Guangyu Wang, Qinghua Hu, Changqing Zhang
arXiv:2607. 05019v1 Announce Type: new Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks.
By Ilya Burenko, Dmitry Vetrov
The paper introduces Fusion Anything Model (FAM), a foundation model designed for generalized multimodal data fusion that can handle arbitrary modality combinations and prediction tasks. FAM is trained on large-scale synthetic multimodal datasets generated via Structural Multimodal Causal Models (SMCMs), enabling it to encode transferable multimodal correlations. Experiments on 18 real-world datasets across 12 modalities and 11 tasks show that FAM performs competitively with specialized models without requiring task-specific adaptation.
By Huizi Cui, Zongbo Han, Chenggong Ding, Naichuan Xiao, Jialong Yang, Jingdong Chen, Guangyu Wang, Qinghua Hu, Changqing Zhang
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.
By Yu Chang, Anzhe Cheng, Chenwei Wu, Zhuoran Wang, Jiahao Chen, Tamoghna Chattopadhyay, Sophia I. Thomopoulos, Paul M. Thompson, Liyue Shen, Paul Bogdan
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
The paper presents a newly curated, multi-center, multi-modal, and longitudinal lung cancer dataset comprising 1,365 patients with whole-slide images, CT scans, PET scans, structured clinical data, transcriptomics, and follow-up information. The dataset features substantial, non-uniform missingness across modalities, making it ideal for evaluating robust multi-modal fusion strategies. Benchmarks on 12‑month overall survival, disease‑specific survival, and longitudinal hazard prediction demonstrate that integrating complementary modalities consistently outperforms uni-modal approaches, even under severe missing data.
By Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago, Catarina Barata
arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.
By Jingyu Hu, Giuseppe Tripodi, Reed Naidoo, Sarah F. McGough, Tapabrata Chakraborti
Cross-modal alignment (CA) and cross-modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic understanding of when each succeeds, when each fails, and when cross-modal training helps at all -- a gap that leaves practitioners, especially in scientific domains like biomedicine or astrophysics, with heterogeneous instruments and multiple levels of organization and measurement, unable to diagnose why standard methods underperform the best single modality. We develop a unified linear framework that addresses both questions.
arXiv:2606. 11190v1 Announce Type: new Abstract: Cross-modal alignment (CA) and cross-modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic understanding of when each succeeds, when each fails, and when cross-modal training helps at all -- a gap that leaves practitioners, especially in scientific domains like biomedicine or astrophysics, with heterogeneous instruments and multiple levels of organization and measurement, unable to diagnose why standard methods underperform the best single modality.
By Ilay Kamai, Hugues Van Assel, Aviv Regev, Hagai B. Perets, Randall Balestriero
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.
By Hui Wang, Tianyu Ren, Joseph Butler, Christopher Baker, Karen Rafferty, Simon McDade
arXiv:2607. 20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis.
By Mohammad Raahemi, Ali Sekhavati, Alireza Maleki, Hamid Nasiri
The paper introduces a framework to tackle modality imbalance in multimodal learning by focusing on sample-level variations. It defines a Modality Gap metric to measure prediction discrepancies, models the resulting bimodal distribution with a Gaussian Mixture Model, and uses Bayesian probabilities for soft separation of balanced and imbalanced samples. A two‑stage training process—Warm‑up and Adaptive Training—reallocates loss weights based on the GMM, strengthening alignment for imbalanced samples while favoring fusion for balanced ones, and shows superior performance over existing baselines.
By Zhiwen Yu, Zhaocheng Liu, Xiaoqing Liu, Huanqiang Zeng, C. L. Philip Chen