AI RESEARCH
From Garbage to Gold: A Data-Architectural Theory of Predictive Robustness
arXiv CS.AI
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ArXi:2603.12288v1 Announce Type: cross Tabular machine learning presents a paradox: modern models achieve state-of-the-art performance using high-dimensional (high-D), collinear, error-prone data, defying the "Garbage In, Garbage Out" mantra. To help resolve this, we synthesize principles from Information Theory, Latent Factor Models, and Psychometrics, clarifying that predictive robustness arises not solely from data cleanliness, but from the synergy between data architecture and model capacity.