发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Seq-Flow提出一种条件流模型，通过自展开训练控制误差累积，实现少步采样下的高效概率预测，在束流溢出任务中CRPS降低65%。
AI 中文摘要
许多科学预测任务需要在新的观测数据到达时更新未来轨迹的分布。传统的扩散模型和流模型从高斯噪声生成每次预测，通常以大量采样步骤为代价。热启动方法通过重用先前的预测来降低这一成本，但其模型并未针对预测更新本身进行训练，这可能在少步采样下损害预测质量。在本工作中，我们提出了Seq-Flow，一种条件流模型，其常微分方程将样本从先前的预测分布传输到更新后的分布。由于连续的预测往往差异不大，这种传输从一个信息丰富的分布开始，可以在少量流评估下产生准确的更新。递归重用也带来了挑战：一次预测中的误差会成为后续流初始状态的误差。我们通过自展开训练来解决这一问题，其中模型的移动平均副本生成预测，用于初始化后续的训练更新。与自强制方法（重用生成的输出作为条件上下文）不同，Seq-Flow将生成的输出重用为下一个流的源。在粒子加速器束流溢出预测实验中，Seq-Flow在少NFE采样预算下将CRPS降低了65%，同时在流体动力学预测任务上与强基线保持竞争力。尽管仅在最多四次更新的自展开上训练，Seq-Flow在超过400次连续更新中仍保持准确。我们的代码可在该https URL获取。
英文摘要
Many scientific forecasting tasks require updating a distribution over future trajectories as new observations arrive. Conventional diffusion and flow models generate each forecast from Gaussian noise, often at the cost of many sampling steps. Warm-start methods reuse earlier predictions to reduce this cost, but their models are not trained to perform the forecast update itself, which can compromise quality under few-step sampling. In this work, we introduce Seq-Flow, a conditional flow model whose ODE transports samples from the previous forecast distribution to the updated one. Because successive forecasts often differ only modestly, this transport starts from an informative distribution and can produce accurate updates with few flow evaluations. Recursive reuse also creates a challenge: errors in one forecast become errors in the initial states of subsequent flows. We address this with self-rollout training, in which a moving average copy of the model generates forecasts that initialize later training updates. Unlike self-forcing methods, which reuse generated outputs as conditioning context, Seq-Flow reuses them as the source of the next flow. Experiments On particle-accelerator beam spill forecasting show Seq-Flow reduces CRPS by 65% under a few-NFE sampling budget, while remaining competitive with strong baselines on fluid-dynamics forecasting tasks. Although trained on self-rollouts of at most four updates, Seq-Flow remains accurate over more than 400 consecutive updates. Our code is available at https://github.com/Graph-COM/Seq-Flow.
