Thoth: Mid-Training Bridges LLMs to Time Series Understanding
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 limits their effectiveness in decision-making scenarios that depend on temporal dynamics. In this paper, we propose Thoth, the first family of mid-trained LLMs with general-purpose time series understanding capabilities. As a pivotal intermediate stage, mid-training achieves task- and domain-agnostic alignment between time series and natural language, for which we construct Book-of-Thoth, a high-quality, time-series-centric mid-training corpus. Book-of-Thoth enables both time-series-to-text and text-to-time-seri...
| Authors | Jiafeng Lin, Yuxuan Wang, Jialong Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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