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Here, we demonstrate the need for and propose a longitudinal modeling and evaluation paradigm that consequently updates four parts of the NLP pipeline: (1) evaluation splits aligned to generalization over people ($\\textit{cross-sectional}$) and/or time ($\\textit{prospective}$); (2) accuracy metrics separating between-person differences from within-person dynamics; (3) sequence inputs to incorporate history by default; and (4) model internals that support different $\\textit{coarseness}$ of latent state over histories (pooled summaries, explicit dynamics, or interaction-based models). 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