WSU researchers propose data contract to fix mislabeled manufacturing experiments

A data framework inspired by Washington State University's additive manufacturing research aims to prevent experiment selectors from mislabeling untested or interrupted machine runs as failed outcomes. The concept draws from a WSU-highlighted study on GRCop-42 alloy, published in an AAAI paper on March 14, 2026, which used surrogate models to select small test batches validated through directed energy deposition. The proposed contract separates three distinct record types — candidate configurations, selection decisions, and observations — ensuring that a missing result is not treated as a negative training example. It also distinguishes between execution state (whether a physical test completed) and assessment state (what a completed test actually established), preventing ambiguous outcomes from corrupting model training data. Additionally, the framework preserves historical evidence cutoffs so that later-arriving results cannot retroactively alter the information a model had when it made earlier selections.