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This arXiv paper identifies 'geometry–physics confounding' as a unified failure mechanism for learning PDEs across varying domains, and proposes a de-confounding framework using geometry-induced coefficient fields and geometry-complete candidate libraries that dramatically improves operator learning and equation discovery accuracy.