[2603.20266] JointFM-0.1: A Foundation Model for Multi-Target Joint Distributional Prediction

Computer Science > Machine Learning arXiv:2603.20266 (cs) [Submitted on 14 Mar 2026] Title:JointFM-0.1: A Foundation Model for Multi-Target Joint Distributional Prediction Authors:Stefan Hackmann View a PDF of the paper titled JointFM-0.1: A Foundation Model for Multi-Target Joint Distributional Prediction, by Stefan Hackmann View PDF HTML (experimental) Abstract:Despite the rapid advancements in Artificial Intelligence (AI), Stochastic Differential Equations (SDEs) remain the gold-standard formalism for modeling systems under uncertainty. However, applying SDEs in practice is fraught with challenges: modeling risk is high, calibration is often brittle, and high-fidelity simulations are computationally expensive. This technical report introduces JointFM, a foundation model that inverts this paradigm. Instead of fitting SDEs to data, we sample an infinite stream of synthetic SDEs to train a generic model to predict future joint probability distributions directly. This approach establishes JointFM as the first foundation model for distributional predictions of coupled time series - requiring no task-specific calibration or finetuning. Despite operating in a purely zero-shot setting, JointFM reduces the energy loss by 14.2% relative to the strongest baseline when recovering oracle joint distributions generated by unseen synthetic SDEs. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.20266 [cs.LG]   (or arXiv:2603.20266v1 [cs.LG] for thi...