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VLMs forget visual reasoning skills in continual learning because today's methods over-protect the language model while neglecting the vision encoder.
Injecting physics-based priors derived from MLLMs at decoding time significantly boosts weather forecasting accuracy and stability, even in long autoregressive rollouts.
