3 papers
cs.CL2026
Thoth: Mid-Training Bridges LLMs to Time Series Understanding
Jiafeng Lin, Yuxuan Wang, Jialong Wu +3
Large Language Models (LLMs) have demonstrated remarkable success in general-purpose reasoning. However, they still struggle to understand and reason about time series data, which…
cs.LG2026
TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
Jiafeng Lin, Yuxuan Wang, Huakun Luo +2
Multimodal time series forecasting has garnered significant attention for its potential to provide more accurate predictions than traditional single-modality models by leveraging r…
cs.LG2025
Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries
Huakun Luo, Haixu Wu, Hang Zhou +4
Although deep models have been widely explored in solving partial differential equations (PDEs), previous works are primarily limited to data only with up to tens of thousands of m…