Large Language and Vision Models (LLMs and LVMs) have revolutionized text and image modeling, enabling a wide range of applications with both limited data and expert supervision. Time series foundation models (TSFMs) [8, 7, 1, 19, 21, 6, 4, 12] promise to bring similar transformative advancements to modeling time series. However, most of these models, barring MOIRAI [21] and TTMs [6], can only model short univariate time series, limiting their widespread use in applications such as healthcare where long and multivariate time series are common. Most of these approaches downsample long time series to handle extended context lengths and model different channels independently to manage multivariate inputs, which limits their ability to capture potentially informative high-frequency and intra-variate dependencies. A straightforward solution to modeling both long and multivariate inputs is to re-design and pre-train TSFMs with longer context lengths and to concatenate multiple channels sequentially [21]. However, this naive solution drastically increases computational complexity as Transformer-based foundation models [8, 7, 1, 19, 21, 4, 12] are constrained by context-dependent memory, due to their quadratic complexity in the length of the input.