AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting

• URL: http://arxiv.org/abs/2603.15260v1
• Date: Mon, 16 Mar 2026 13:27:20 GMT
• Title: AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting
• Abstract summary: Agent-Guided Cross-modal Decoding (AGCD) is a plug-and-play decoding-time prior-injection paradigm.<n>We design a multi-agent meteorological narration pipeline to generate state-conditioned physics-priors.<n>We also introduce cross-modal region interaction decoding that performs region-aware multi-scale tokenization and efficient physics-priors injection.
• Score: 29.582978733703687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate weather forecasting is more than grid-wise regression: it must preserve coherent synoptic structures and physical consistency of meteorological fields, especially under autoregressive rollouts where small one-step errors can amplify into structural bias. Existing physics-priors approaches typically impose global, once-for-all constraints via architectures, regularization, or NWP coupling, offering limited state-adaptive and sample-specific controllability at deployment. To bridge this gap, we propose Agent-Guided Cross-modal Decoding (AGCD), a plug-and-play decoding-time prior-injection paradigm that derives state-conditioned physics-priors from the current multivariate atmosphere and injects them into forecasters in a controllable and reusable way. Specifically, We design a multi-agent meteorological narration pipeline to generate state-conditioned physics-priors, utilizing MLLMs to extract various meteorological elements effectively. To effectively apply the priors, AGCD further introduce cross-modal region interaction decoding that performs region-aware multi-scale tokenization and efficient physics-priors injection to refine visual features without changing the backbone interface. Experiments on WeatherBench demonstrate consistent gains for 6-hour forecasting across two resolutions (5.625 degree and 1.40625 degree) and diverse backbones (generic and weather-specialized), including strictly causal 48-hour autoregressive rollouts that reduce early-stage error accumulation and improve long-horizon stability.

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