提出通用纠错框架，解决多系统耦合预测中误差累积问题。
PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
- 分离物理模拟与纠错过程，用修正代理主动抵消系统性偏差。
- 在300天全球海气耦合预测中，误差降低28%，优于现有模型。
- 适用于气候、气象等复杂耦合系统，提升长期预测稳定性。
耦合时空预测对多个相互作用的动力系统未来演化预测至关重要，如气候建模。然而，现有方法严重受限于误差累积的顽固瓶颈。在耦合系统中，各子系统模拟器的误差会相互传播并放大，我们称之为互惠误差放大，导致长期预测迅速崩溃。为此，我们提出通用框架PnP-Corrector（即插即用纠错器）。其核心思想是将物理模拟与误差纠正过程解耦：冻结预训练的物理模拟引擎，仅训练一个纠正代理来主动对抗耦合系统中产生的系统性偏差。此外，我们设计了高效预测模型架构DSLCast作为该框架的骨干。大量实验表明，该方法显著提升了耦合预测系统的长期稳定性和准确性。例如，在具有挑战性的300天全球海气耦合预测任务中，我们的框架使基线模型的预测误差降低28%，并在多个关键指标上超越现有最先进模型。
原文摘要 · Abstract (English)
Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models. However, existing methods are severely constrained by the persistent bottleneck of compounding errors. In coupled systems, errors from each subsystem simulator propagate and amplify one another, a phenomenon we term Reciprocal Error Amplification, leading to a rapid collapse of long-range predictions. To address this challenge, we propose a universal framework called PnP-Corrector (Plug-and-Play Corrector). The core idea of our framework is to decouple the physical simulation from the error correction process: it freezes pre-trained physics simulation engines and exclusively trains a correction agent to proactively counteract the systematic biases emerging from the coupled system. Furthermore, we design an efficient predictive model architecture, DSLCast, to serve as the backbone of this framework. Extensive experiments demonstrate that our method significantly enhances the long-term stability and accuracy of coupled forecasting systems. For instance, in the challenging task of a 300-day global ocean-atmosphere coupled forecast, our PnP-Corrector framework reduces the prediction error of the baseline model by 28% and surpasses state-of-the-art models on several key metrics.
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