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This renders data-driven models unreliable under distribution shift. We propose \\textbf{SL-BiLEM} (Structured Learnable Behavior-in-the-Loop Epidemic Model), leveraging physical constraints as regularization for robust extrapolation. The framework decomposes effective transmission as $\\beta_{\\text{eff}}(t,g) = \\beta_0(g) \\times m_{\\text{policy}}(t) \\times m_{\\text{media}}(t) \\times m_{\\text{comp}}(t,g)$, where monotonicity, smoothness, and bounded-jump constraints on the learned compliance function maintain predictive validity under novel policy regimes. Beyond forecasting, SL-BiLEM enables counterfactual analysis for intervention decision support. 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