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This paper identifies a capacity-induced failure mode in physics-informed neural networks (PINNs) where overparameterized networks develop functional modularity that hinders convergence, and proposes Modular-Sparsity Synchronization (ModSync), a framework that penalizes task-exclusive connections to maintain cross-objective interaction and achieve state-of-the-art accuracy.