The architecture of a copolymer determines how the material behaves. Designing a polymer for a target property, therefore, should begin with designing for a target architecture. Yet no method predicts the architecture a given monomer pair will react to under given conditions. Even though reactivity ratios have been known since Mayo and Lewis to depend on solvent, temperature, and polymerization mechanism, every predictive approach, from the Q–e scheme of Alfrey and Price to recent machine-learning models, takes monomer structure alone as input. Measurements for thousands of monomer pairs exist but are scattered across eight decades of literature, much of it predating machine-readable publishing and surviving only as scanned page images. We address this gap with PolyCARP, a classifier that predicts copolymer architecture from monomer pair and reaction conditions before any synthesis, trained on a database we extracted from the literature. A vision–language-model pipeline parses 1,206 publications, yielding 3,791 copolymerizations annotated with reactivity ratios, solvent, temperature, and polymerization mechanism. This, to our knowledge, is the first dataset at this scale to record reaction conditions per entry. For any monomer pair and set of conditions, PolyCARP returns an architecture class and the nearest experimental analogues in the database. We validate the tool against a literature case study, where it captures solvent-driven architectural transitions, and against three prospective laboratory copolymerizations. The database, model, and code are openly released—enabling the community to extend both data and models, and to make more of polymer chemistry predictable.