CleanScore: Black-Box Benchmark Audits with Negative Controls and Sensitivity Bounds

Abstract:Public benchmark scores may reflect skill, prior exposure to the questions, or both, and for most models the training data are unknown. We present CleanScore, a black-box audit using scored outputs only. Each benchmark question becomes a parent item with one public form and two independently written fresh forms preserving its numbers, facts and answer. The audit reports an interval for the public-form advantage rather than a verdict, and a private negative-control bank with an explicit transport radius separates exposure from ordinary form mismatch. A registered controlled-exposure experiment detects planted exposure and stays quiet under fresh-form exposure. A registered audit of five open models on 200 GSM8K and 200 ARC-Challenge items finds no exposure-consistent advantage, bounding surface-form inflation below five points. Registered positive controls then bound what such a null can mean. Leaking an item raises accuracy on paraphrases the model never saw almost as much as on the leaked wording, leaving 52% to 110% of the effect invisible to a paraphrase audit. On ARC a planted 49-point advantage shows an observable gap of -0.020, and about 20 points survive rewriting stem and options, across four training seeds. A surface-form null bounds far less than the phrase contamination audit implies.

43 pages, 15 figures, 21 tables. Item bank, code and pre-registrations released

Machine Learning (cs.LG); Methodology (stat.ME)

I.2.7; I.2.6; G.3

arXiv:2609.22183 [cs.LG]

(or arXiv:2609.22183v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22183 Focus to learn more arXiv-issued DOI via DataCite

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