Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
arXiv preprint arXiv:2506.21611 , year=
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PIQL integrates privileged information to accelerate convergence, lower loss, and improve generalization in tabular foundation models.
A semi-synthetic augmentation creates the CAF-7M dataset and demonstrates that improved context data enables multimodal models to outperform unimodal baselines in context-aided forecasting.
citing papers explorer
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Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
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Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning
PIQL integrates privileged information to accelerate convergence, lower loss, and improve generalization in tabular foundation models.
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Overcoming the Modality Gap in Context-Aided Forecasting
A semi-synthetic augmentation creates the CAF-7M dataset and demonstrates that improved context data enables multimodal models to outperform unimodal baselines in context-aided forecasting.