CoEvoST: A Dual Evolution Agent for Continual Spatiotemporal Forecasting

Spatiotemporal forecasting systems operate on evolving streams with changing data distributions, graph structures, observed node sets, and external conditions, requiring continual predictor adaptation. Existing continual spatiotemporal forecasting methods primarily focus on how to adapt the predictor through specialized mechanisms, while higher-level decisions about whether, where, on what data, and by which mechanism to adapt are typically prescribed rather than learned from evolving states. This raises two challenges: forming an appropriate adaptation plan from heterogeneous diagnostic evidence, and improving adaptation decisions from limited execution feedback. We therefore propose CoEvoST, a dual evolution framework that couples fast predictor adaptation with slower evolution of an LLM adaptation agent. For evidence-to-action reasoning, CoEvoST starts from compact diagnostic summaries and performs multi-round evidence retrieval and plan refinement, producing an adaptation plan that specifies whether, where, on what data, and by which mechanism to update the predictor with sufficient diagnostic support. For outcome-to-policy learning, CoEvoST executes candidate plans from the same predictor checkpoint and constructs near-optimal preference sets from their realized outcomes, accumulating them as experience for online field-weighted DPO to improve the agent's decision policy. Experiments across four spatiotemporal streams demonstrate that CoEvoST improves forecasting performance over strong fixed and continual baselines while adapting its decisions to changing operating conditions.