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TimeOmni-VL: Unified Models for Time Series Understanding and Generation
arXiv:2602.17149v2 Announce Type: replace Abstract: Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generation models often rely on superficial pattern matching, while understanding-oriented models struggle with high-fidelity numerical output. Although unified multimodal models (UMMs) have bridged this gap in vision, their potential for time series remains untapped.