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Multi-task scientific foundation models often mistake parameter preservation for true representation reuse, failing to outperform specialized single-task operators across most evaluated physics regimes.
Uniform sampling fails to adequately cover the vulnerability landscape of neural operators, as optimal training data is architecture-dependent, necessitating targeted data generation strategies.
Forget retraining monolithic models: SCNO unlocks zero-forgetting modular expansion for PDE solvers by composing spiking neural operator blocks, outperforming traditional methods with fewer parameters.
