AI RESEARCH
A Grammar of Machine Learning Workflows
arXiv CS.LG
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ArXi:2603.10742v1 Announce Type: new Data leakage affected 294 published papers across 17 scientific fields (Kapoor & Narayanan, 2023). The dominant response has been documentation: checklists, linters, best-practice guides. Documentation does not prevent these failures. This paper proposes a structural remedy: a grammar that decomposes the supervised learning lifecycle into 7 kernel primitives connected by a typed directed acyclic graph (DAG), with four hard constraints that reject the two most damaging leakage classes at call time.