Rethinking Multimodal Fusion for Time Series: Auxiliary Modalities Need Constrai

arXiv:2604.05364v1 Announce Type: new Abstract: We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally,

arXiv:2606.14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns. Inspired by Retrieval-Augmented Generation (RAG), recent

arXiv:2606.15038v1 Announce Type: new Abstract: Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and

arXiv:2609.06188v1 Announce Type: new Abstract: Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions

arXiv:2603.18712v1 Announce Type: new Abstract: The task of multi-channel time series forecasting is ubiquitous in numerous fields such as finance, supply chain management, and

arXiv:2603.18462v1 Announce Type: new Abstract: In the era of large-scale pre-trained models, effectively adapting general knowledge to specific affective computing tasks remains

arXiv:2610.24395v1 Announce Type: new Abstract: In multimodal learning, modality imbalance often lets one modality dominate optimization and keeps the model from fully exploiting