用合成数据训练模型直接预测多变量概率分布，无需调参即可精准建模不确定性。
JointFM-0.1: A Foundation Model for Multi-Target Joint Distributional Prediction
- 通过生成无限合成SDE数据，训练通用模型直接预测联合分布
- 零样本下比最强基线能量损失降低21.1%
- 适合需要快速、无校准地建模多变量不确定性的场景
尽管人工智能发展迅速，随机微分方程（SDEs）仍是建模不确定系统的核心方法。但实际应用中存在建模风险高、校准脆弱、高保真模拟计算成本大的问题。本文提出JointFM，一种颠覆传统范式的基础模型：不将SDE拟合到数据，而是采样无限流的合成SDE，训练通用模型直接预测未来联合概率分布。该方法使JointFM成为首个用于耦合时间序列分布预测的基础模型，无需任务特定校准或微调。在纯零样本设置下，其相对于最强基线在恢复未见合成SDE生成的最优联合分布时，能量损失降低了21.1%。
原文摘要 · Abstract (English)
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 21.1% relative to the strongest baseline when recovering oracle joint distributions generated by unseen synthetic SDEs.
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