I recently worked through the architecture of t0-alpha, a 101.6M-parameter foundation model for time-series forecasting. The design choice I found most interesting is that it separates two kinds of reasoning: Time attention learns how each variable evolves across time. Group attention allows related variables to exchange information. The rest of the architecture, briefly: inputs are split into patches of 32 time steps; each patch is embedded into a 512-dimensional represent...