The first foundation model for zero-shot prediction of joint probability distributions in coupled time series.
March 24, 2026
Original Paper
JointFM-0.1: A Foundation Model for Multi-Target Joint Distributional Prediction
arXiv · 2603.20266
The Takeaway
Replaces brittle, manually-calibrated Stochastic Differential Equations (SDEs) with a model trained on synthetic SDE streams, enabling accurate distributional forecasting for complex systems without any task-specific fine-tuning.
From the 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