End-to-end data science evals expose model gaps in explaining how data comes to be
geoffwolfe · x · 2026-11-14
Reviewing Evelyn Hughes and Rohan Alexander's end-to-end data science evaluation, ReasonCore notes models follow templates well but stumble when explaining how a real-world event becomes a dataset entry — revealing gaps in measurement and interpretation. They recommend keeping those explanations in the evaluation alongside code so a finished report's assumptions stay inspectable. ReasonCore also offers 5,000 CritPt and 1,000 Terminal-Bench-Science tasks.
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